pdf_metadata.json

{
  "Read/A synthetic human 3D in vitro lymphoid model.pdf": {
    "pdf_title": "A synthetic human 3D in vitro lymphoid model enhancing B-cell survival and functional differentiation",
    "pdf_author": "Maaike V.J. Braham",
    "arxiv": "",
    "doi": "10.1016/j.isci.2022.105741",
    "first_lines": [
      "iScience",
      "ll",
      "OPEN ACCESS",
      "Article",
      "A synthetic human 3D in vitro lymphoid model",
      "enhancing B-cell survival and functional",
      "differentiation",
      "Maaike V.J.",
      "Braham, Rob S.",
      "van Binnendijk,",
      "Anne-Marie M.",
      "Buisman, Reina E.",
      "Mebius, Jelle de",
      "Wit, Cécile A.C.M.",
      "van Els"
    ]
  },
  "Read/A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI.pdf": {
    "pdf_title": "A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI",
    "pdf_author": "Junhao Chen",
    "arxiv": "",
    "doi": "10.1186/s12880-023-01163-7",
    "first_lines": [
      "(2023) 23:200",
      "Chen et al. BMC Medical Imaging",
      "https://doi.org/10.1186/s12880-023-01163-7",
      "BMC Medical Imaging",
      "Open Access",
      "RESEARCH",
      "A transfer learning nomogram for predicting",
      "prostate cancer and benign conditions on MRI",
      "Junhao Chen1,2†, Bao Feng2,3†, Maoqing Hu2, Feidong Huang4, Yehang Chen3, Xilun Ma5 and",
      "Wansheng Long1,2,6*",
      "Abstract",
      "Background Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The",
      "present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa",
      "and benign conditions (BCs) on magnetic resonance imaging (MRI).",
      "Methods In this retrospective study, a total of 709 patients with pathologically confirmed PCa and BCs from two"
    ]
  },
  "Read/AFNO_Adaptive Fourier Neural Operator_Effective token mixer for Transformers_Guibas 2022.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2111.13587v2",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2022",
      "A DAPTIVE F OURIER N EURAL O PERATORS : E FFICIENT",
      "T OKEN M IXERS FOR T RANSFORMERS",
      "arXiv:2111.13587v2 [cs.CV] 27 Mar 2022",
      "John Guibas3∗ , Morteza Mardani1∗ , Zongyi Li1,2 , Andrew Tao1 ,",
      "Anima Aanandkumar1,2 , Bryan Catanzaro1",
      "NVIDIA1 , California Institute of Technology2 , Stanford University3",
      "jtguibas@stanford.edu,",
      "{mmardani,zongyil,atao,bcatanzaro,aanandkumar}@nvidia.com",
      "A BSTRACT",
      "Vision transformers have delivered tremendous success in representation learning.",
      "This is primarily due to effective token mixing through self-attention. However,",
      "this scales quadratically with the number of pixels, which becomes infeasible for",
      "high-resolution inputs. To cope with this challenge, we propose Adaptive Fourier",
      "Neural Operator (AFNO) as an efficient token mixer that learns to mix in the"
    ]
  },
  "Read/Alignment and translation model_Dzmitry Bahdanau_2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1409.0473v7",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2015",
      "N EURAL M ACHINE T RANSLATION",
      "BY J OINTLY L EARNING TO A LIGN AND T RANSLATE",
      "Dzmitry Bahdanau",
      "Jacobs University Bremen, Germany",
      "arXiv:1409.0473v7 [cs.CL] 19 May 2016",
      "KyungHyun Cho",
      "Yoshua Bengio∗",
      "Université de Montréal",
      "A BSTRACT",
      "Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine",
      "translation aims at building a single neural network that can be jointly tuned to",
      "maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders and encode",
      "a source sentence into a fixed-length vector from which a decoder generates a",
      "translation. In this paper, we conjecture that the use of a fixed-length vector is a"
    ]
  },
  "Read/Amyloidogenic Nanoplaques.pdf": {
    "pdf_title": "Keywords:",
    "pdf_author": "Creator:         DVIPSONE 2.2.2  http://www.YandY.com",
    "arxiv": "",
    "doi": "10.3233/JAD-200237",
    "first_lines": [
      "Journal of Alzheimer’s Disease 77 (2020) 831–842",
      "DOI 10.3233/JAD-200237",
      "IOS Press",
      "831",
      "Amyloidogenic Nanoplaques in",
      "Cerebrospinal Fluid: Relationship",
      "to Amyloid Brain Uptake and Clinical",
      "Alzheimer’s Disease in a Memory",
      "Clinic Cohort",
      "Mari Aksnesa,b,∗ , Ebba Gløersen Müllerb,c , Ann Tiimand , Trine Holt Edwinb,e,f ,",
      "Lars Tereniusd , Mona-Elisabeth Revheimb,c , Vladana Vukojevićd , Nenad Bogdanovića,b,g",
      "and Anne-Brita Knapskoge",
      "a Department of Geriatric Medicine, University of Oslo, Oslo, Norway",
      "b Institute of Clinical Medicine, University of Oslo, Oslo, Norway",
      "c Department of Nuclear Medicine, Division of Radiology and Nuclear Medicine, Oslo University Hospital,"
    ]
  },
  "Read/An Enhanced Conditional Variational Autoencoder-Based_Normative model for neuroimaging analysis_Mai Ho 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Phuong Ho",
    "arxiv": "",
    "doi": "10.1101/2025.01.05.631276",
    "first_lines": [
      "bioRxiv preprint doi: https://doi.org/10.1101/2025.01.05.631276; this version posted January 5, 2025. The copyright holder for this preprint",
      "(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is",
      "made available under aCC-BY-NC-ND 4.0 International license.",
      "An Enhanced Conditional Variational Autoencoder-Based",
      "Normative Model for Neuroimaging Analysis",
      "Mai P. Ho1, Yang Song, PhD3, Perminder S. Sachdev, MD, PhD1,2, Jiyang Jiang, PhD1, Wei Wen,",
      "PhD1,2",
      "*Corresponding authors: Mai Ho, mai.ho@unsw.edu.au, Wei Wen, w.wen@unsw.edu.au",
      "Affiliations:",
      "1. Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, University of New South",
      "Wales (UNSW), Kensington, New South Wales 2052, Australia",
      "2. Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New",
      "South Wales 2031, Australia",
      "3. School of Computer Science and Engineering, University of New South Wales (UNSW),",
      "Kensington, New South Wales 2052, Australia"
    ]
  },
  "Read/An overview of deep learning in medical imaging focusing on.pdf": {
    "pdf_title": "An overview of deep learning in medical imaging focusing on MRI",
    "pdf_author": "Alexander Selvikvåg Lundervold",
    "arxiv": "",
    "doi": "10.1016/j.zemedi.2018.11.002",
    "first_lines": [
      "REVIEW",
      "An overview of deep learning in medical imaging focusing on",
      "MRI",
      "Alexander Selvikvåg Lundervold a,b,∗ , Arvid Lundervold a,c,d",
      "a",
      "Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway",
      "Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Norway",
      "c",
      "Neuroinformatics and Image Analysis Laboratory, Department of Biomedicine, University of Bergen, Norway",
      "d",
      "Department of Health and Functioning, Western Norway University of Applied Sciences, Norway",
      "b",
      "Received 2 October 2018; accepted 21 November 2018",
      "Abstract",
      "What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine"
    ]
  },
  "Read/AnoDDPM_Anomaly_Detection_With_Denoising_Diffusion_Probabilistic_Models_Using_Simplex_Julian Wyatt_2022.pdf": {
    "pdf_title": "AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise",
    "pdf_author": "Julian Wyatt; Adam Leach; Sebastian M. Schmon; Chris G. Willcocks",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "The model is trained solely on normal (healthy) data to reconstruct what's considered standard normal version of data. At inference, it is fed an anomalous",
      "image and then we measure the difference between the healthy version generated by the model and the real anomalous original input. It is based",
      "on how much the input deviates from what's considered normal by the model.",
      "The problem is, the input is compared with a fictive version of itself generated by the model, and thus is based solely on the ability of the model to",
      "capture important features of a standard healthy dataset, not on its discriminative ability.",
      "The distance between the fictive version and the original data contains both the deviation of the outlier from the standard and the uncertainty about the model's",
      "ability to faithfully reconstruct a healthy original input.",
      "This might be effective for obvious deviation, but might fail to detect legion that lies close to normal tissue?",
      "AnoDDPM: Anomaly Detection with Denoising Diffusion",
      "Probabilistic Models using Simplex Noise",
      "Julian Wyatt1",
      "Adam Leach1",
      "Sebastian M. Schmon2",
      "Chris G. Willcocks1",
      "1"
    ]
  },
  "Read/Ardekani,B.A.,Bachman,A.H.,Figarsky,K.,andSidtis,J.J.(2014).Corpus callosum shape changes in early Alzheimer’s disease an MRI study using the OASISbraindatabase.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1007/s00429-013-0503-0",
    "first_lines": [
      "See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/234142326",
      "Corpus callosum shape changes in early Alzheimer's disease: An MRI study",
      "using the OASIS brain database",
      "Article in Brain Structure and Function · January 2013",
      "DOI: 10.1007/s00429-013-0503-0 · Source: PubMed",
      "CITATIONS",
      "READS",
      "71",
      "317",
      "4 authors, including:",
      "John J Sidtis",
      "Nathan Kline Institute",
      "358 PUBLICATIONS 11,275 CITATIONS",
      "SEE PROFILE",
      "All content following this page was uploaded by John J Sidtis on 27 May 2016."
    ]
  },
  "Read/Attention Is All You Need.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "1706.03762v7",
    "doi": "",
    "first_lines": [
      "Provided proper attribution is provided, Google hereby grants permission to",
      "reproduce the tables and figures in this paper solely for use in journalistic or",
      "scholarly works.",
      "arXiv:1706.03762v7 [cs.CL] 2 Aug 2023",
      "Attention Is All You Need",
      "Ashish Vaswani∗",
      "Google Brain",
      "avaswani@google.com",
      "Llion Jones∗",
      "Google Research",
      "llion@google.com",
      "Noam Shazeer∗",
      "Google Brain",
      "noam@google.com",
      "Niki Parmar∗"
    ]
  },
  "Read/Attention-based Neural Machine Translation_ThangLuong2015.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1508.04025v5",
    "doi": "",
    "first_lines": [
      "Effective Approaches to Attention-based Neural Machine Translation",
      "Minh-Thang Luong",
      "Hieu Pham",
      "Christopher D. Manning",
      "Computer Science Department, Stanford University, Stanford, CA 94305",
      "{lmthang,hyhieu,manning}@stanford.edu",
      "arXiv:1508.04025v5 [cs.CL] 20 Sep 2015",
      "Abstract",
      "An attentional mechanism has lately been",
      "used to improve neural machine translation (NMT) by selectively focusing on",
      "parts of the source sentence during translation. However, there has been little",
      "work exploring useful architectures for",
      "attention-based NMT. This paper examines two simple and effective classes of attentional mechanism: a global approach",
      "which always attends to all source words",
      "and a local one that only looks at a subset"
    ]
  },
  "Read/Automatic Chemical Design_Gomez-Bombarelli 2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1610.02415v3",
    "doi": "",
    "first_lines": [
      "Automatic Chemical Design Using a Data-Driven",
      "arXiv:1610.02415v3 [cs.LG] 5 Dec 2017",
      "Continuous Representation of Molecules",
      "Rafael Gómez-Bombarelli,†,# Jennifer N. Wei,‡,# David Duvenaud,¶,# José",
      "Miguel Hernández-Lobato,§,# Benjamín Sánchez-Lengeling,‡ Dennis Sheberla,‡",
      "Jorge Aguilera-Iparraguirre,† Timothy D. Hirzel,† Ryan P. Adams,k and Alán",
      "Aspuru-Guzik∗,‡,⊥",
      "†Kyulux North America Inc.",
      "‡Department of Chemistry and Chemical Biology, Harvard University, Cambridge MA 02138,",
      "USA",
      "¶Department of Computer Science, University of Toronto",
      "§Department of Engineering, University of Cambridge Trumpington Street, Cambridge CB2",
      "1PZ, UK",
      "kGoogle Brain and Princeton University",
      "⊥Canadian Institute for Advanced Research (CIFAR), Biologically-Inspired Solar Energy"
    ]
  },
  "Read/Automatic Differentiation Variational Inference_DustinTran2016.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1603.00788v1",
    "doi": "",
    "first_lines": [
      "Automatic Differentiation Variational Inference",
      "Alp Kucukelbir",
      "Data Science Institute, Department of Computer Science",
      "Columbia University",
      "arXiv:1603.00788v1 [stat.ML] 2 Mar 2016",
      "Dustin Tran",
      "Department of Computer Science",
      "Columbia University",
      "Rajesh Ranganath",
      "Department of Computer Science",
      "Princeton University",
      "Andrew Gelman",
      "Data Science Institute, Departments of Political Science and Statistics",
      "Columbia University",
      "David M. Blei"
    ]
  },
  "Read/Bayesian Data Analysis_AndrewGelman.pdf": {
    "pdf_title": "",
    "pdf_author": "Andrew Gelman",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "This electronic edition is for non-commercial purposes only.",
      "Bayesian Data Analysis",
      "Third edition",
      "(with errors fixed as of 20 February 2025)",
      "Andrew Gelman",
      "Columbia University",
      "John B. Carlin",
      "University of Melbourne",
      "Hal S. Stern",
      "University of California, Irvine",
      "David B. Dunson",
      "Duke University",
      "Aki Vehtari",
      "Aalto University",
      "Donald B. Rubin"
    ]
  },
  "Read/Brain Tumor Segmentation with Deep Neural Networks.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1505.03540v3",
    "doi": "",
    "first_lines": [
      "Brain Tumor Segmentation with Deep Neural NetworksI",
      "Mohammad Havaeia,1 , Axel Davyb , David Warde-Farleyc , Antoine Biardc,d , Aaron Courvillec , Yoshua Bengioc , Chris Palc,e ,",
      "Pierre-Marc Jodoina , Hugo Larochellea,f",
      "a Université de Sherbrooke, Sherbrooke, Qc, Canada",
      "b École Normale supérieure, Paris, France",
      "c Université de Montréal, Montréal, Canada",
      "d École polytechnique, Palaiseau, France",
      "e École Polytechnique de Montréal , Canada",
      "arXiv:1505.03540v3 [cs.CV] 20 May 2016",
      "f Twitter, USA",
      "Abstract",
      "In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The",
      "proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these",
      "tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our",
      "exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we"
    ]
  },
  "Read/Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations_Seedat 2022.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2206.08311v1",
    "doi": "",
    "first_lines": [
      "Continuous-Time Modeling of Counterfactual Outcomes Using Neural",
      "Controlled Differential Equations",
      "Nabeel Seedat * 1 Fergus Imrie * 2 Alexis Bellot 3 Zhaozhi Qian 1 Mihaela van der Schaar 1 2 4",
      "arXiv:2206.08311v1 [cs.LG] 16 Jun 2022",
      "Abstract",
      "Estimating counterfactual outcomes over time has",
      "the potential to unlock personalized healthcare",
      "by assisting decision-makers to answer “what-if”",
      "questions. Existing causal inference approaches",
      "typically consider regular, discrete-time intervals",
      "between observations and treatment decisions and",
      "hence are unable to naturally model irregularly",
      "sampled data, which is the common setting in",
      "practice. To handle arbitrary observation patterns,",
      "we interpret the data as samples from an underlying continuous-time process and propose to model"
    ]
  },
  "Read/DENSITY ESTIMATION USING REAL NVP_Dinh & Dickstein 2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1605.08803v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2017",
      "D ENSITY ESTIMATION USING R EAL NVP",
      "Laurent Dinh∗",
      "Montreal Institute for Learning Algorithms",
      "University of Montreal",
      "Montreal, QC H3T1J4",
      "arXiv:1605.08803v3 [cs.LG] 27 Feb 2017",
      "Jascha Sohl-Dickstein",
      "Google Brain",
      "Samy Bengio",
      "Google Brain",
      "A BSTRACT",
      "Unsupervised learning of probabilistic models is a central yet challenging problem",
      "in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space",
      "of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful, stably invertible, and learnable transformations, resulting"
    ]
  },
  "Read/Decoding the complexity of Alzheimer's disease _ ScienceDaily.pdf": {
    "pdf_title": "Decoding the complexity of Alzheimer's disease | ScienceDaily",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "7/2/24, 3:25 AM",
      "Decoding the complexity of Alzheimer's disease | ScienceDaily",
      "Skip to main content",
      "(/)",
      "Your source for the latest research news",
      "New! Sign up for our free email newsletter (https://sciencedaily.substack.com/#).",
      "Science News",
      "from research organizations",
      "Decoding the complexity of Alzheimer's",
      "disease",
      "Date:",
      "September 28, 2023",
      "Source:",
      "Massachusetts Institute of Technology",
      "Summary:"
    ]
  },
  "Read/Deep Langevin Ensemble_Wild 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2305.15027v2",
    "doi": "",
    "first_lines": [
      "Provide theoretical prove for convergence by sampling with Deep Repulsive Langevin Ensemble (section 4.3), whilst other deep ensemble methods and MCMC fail to guarantee",
      "convergence for non convex problem space.",
      "A Rigorous Link between Deep Ensembles and",
      "(Variational) Bayesian Methods",
      "arXiv:2305.15027v2 [stat.ML] 22 Oct 2023",
      "Veit D. Wild∗",
      "University of Oxford",
      "Sahra Ghalebikesabi",
      "University of Oxford",
      "Dino Sejdinovic",
      "University of Adelaide",
      "Jeremias Knoblauch",
      "University College London",
      "Abstract",
      "We establish the first mathematically rigorous link between Bayesian, variational"
    ]
  },
  "Read/Deep Learning Applications in Magnetic Resonance Imaging.pdf": {
    "pdf_title": "Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?",
    "pdf_author": "Sebastian Gassenmaier, Thomas Küstner, Dominik Nickel, Judith Herrmann, Rüdiger Hoffmann, Haidara Almansour, Saif Afat, Konstantin Nikolaou and Ahmed E. Othman",
    "arxiv": "",
    "doi": "10.3390/diagnostics11122181",
    "first_lines": [
      "diagnostics",
      "Review",
      "Deep Learning Applications in Magnetic Resonance Imaging:",
      "Has the Future Become Present?",
      "Sebastian Gassenmaier 1 , Thomas Küstner 2 , Dominik Nickel 3 , Judith Herrmann 1 , Rüdiger Hoffmann 1 ,",
      "Haidara Almansour 1 , Saif Afat 1 , Konstantin Nikolaou 1 and Ahmed E. Othman 1,4, *",
      "1",
      "2",
      "3",
      "4",
      "*",
      "\u0001\u0002\u0003\u0001\u0004\u0005\u0006\u0007\b\u0001",
      "\u0001\u0002\u0003\u0004\u0005\u0006\u0007",
      "Citation: Gassenmaier, S.;",
      "Küstner, T.; Nickel, D.; Herrmann, J.;"
    ]
  },
  "Read/Deep Learning Enables Automatic Detection and Segmentation.pdf": {
    "pdf_title": "Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multisequence MRI",
    "pdf_author": "Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Daniel Rubin, Greg Zaharchuk",
    "arxiv": "",
    "doi": "10.1002/jmri.26766",
    "first_lines": [
      "HHS Public Access",
      "Author manuscript",
      "Author Manuscript",
      "J Magn Reson Imaging. Author manuscript; available in PMC 2020 May 05.",
      "Published in final edited form as:",
      "J Magn Reson Imaging. 2020 January ; 51(1): 175–182. doi:10.1002/jmri.26766.",
      "Deep Learning Enables Automatic Detection and Segmentation",
      "of Brain Metastases on Multisequence MRI",
      "Endre Grøvik, PhD1,2, Darvin Yi, MS3, Michael Iv, MD1, Elizabeth Tong, MD1, Daniel Rubin,",
      "PhD#3, Greg Zaharchuk, MD, PhD#1,*",
      "1Department of Radiology, Stanford University, Stanford, California, USA",
      "Author Manuscript",
      "2Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway",
      "3Department of Biomedical Data Science, Stanford University, Stanford, California, USA",
      "# These authors contributed equally to this work."
    ]
  },
  "Read/Deep Learning in MR Image Processing.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.13104/imri.2019.23.2.81",
    "first_lines": [
      "pISSN 2384-1095",
      "eISSN 2384-1109",
      "iMRI 2019;23:81-99",
      "https://doi.org/10.13104/imri.2019.23.2.81",
      "Deep Learning in MR Image Processing",
      "Doohee Lee1, Jingu Lee1, Jingyu Ko1, Jaeyeon Yoon1, Kanghyun Ryu2,",
      "Yoonho Nam3",
      "1",
      "Laboratory for Imaging Science and Technology, Department of Electrical and Computer",
      "Engineering, Institute of Engineering Research, Seoul National University, Seoul, Korea",
      "2",
      "Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea",
      "3",
      "Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of",
      "Korea, Seoul, Korea"
    ]
  },
  "Read/Deep Residual Learning for Image Recognition.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1512.03385v1",
    "doi": "",
    "first_lines": [
      "Deep Residual Learning for Image Recognition",
      "Kaiming He",
      "Xiangyu Zhang",
      "Shaoqing Ren",
      "Microsoft Research",
      "Jian Sun",
      "Deeper neural networks are more difficult to train. We",
      "present a residual learning framework to ease the training",
      "of networks that are substantially deeper than those used",
      "previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual",
      "networks are easier to optimize, and can gain accuracy from",
      "considerably increased depth. On the ImageNet dataset we",
      "evaluate residual nets with a depth of up to 152 layers—8×",
      "deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error",
      "on the ImageNet test set. This result won the 1st place on the"
    ]
  },
  "Read/Deep learning  for the classification of psychiatric disorders using neuroimaging data_Meta review_Quaak 2021.pdf": {
    "pdf_title": "Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis",
    "pdf_author": "Mirjam Quaak",
    "arxiv": "",
    "doi": "10.1016/j.nicl.2021.102584",
    "first_lines": [
      "NeuroImage: Clinical 30 (2021) 102584",
      "Contents lists available at ScienceDirect",
      "NeuroImage: Clinical",
      "journal homepage: www.elsevier.com/locate/ynicl",
      "Deep learning applications for the classification of psychiatric disorders",
      "using neuroimaging data: Systematic review and meta-analysis",
      "Mirjam Quaak, Laurens van de Mortel, Rajat Mani Thomas, Guido van Wingen *",
      "Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands",
      "A R T I C L E I N F O",
      "A B S T R A C T",
      "Keywords:",
      "Deep learning",
      "Machine learning",
      "Psychiatry",
      "Neuroimaging"
    ]
  },
  "Read/Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cance.pdf": {
    "pdf_title": "Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images",
    "pdf_author": "Oscar J. Pellicer-Valero",
    "arxiv": "",
    "doi": "10.1038/s41598-022-06730-6",
    "first_lines": [
      "www.nature.com/scientificreports",
      "OPEN",
      "Deep learning for fully",
      "automatic detection,",
      "segmentation, and Gleason grade",
      "estimation of prostate cancer",
      "in multiparametric magnetic",
      "resonance images",
      "Oscar J. Pellicer‑Valero1*, José L. Marenco Jiménez2, Victor Gonzalez‑Perez3,",
      "Juan Luis Casanova Ramón‑Borja2, Isabel Martín García4, María Barrios Benito4,",
      "Paula Pelechano Gómez4, José Rubio‑Briones2, María José Rupérez5 &",
      "José D. Martín‑Guerrero1",
      "Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a",
      "profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still",
      "complex even for experts. This paper proposes a fully automatic system based on Deep Learning that"
    ]
  },
  "Read/Deep learning of joint myelin and T1w MRI features in normal-appearing.pdf": {
    "pdf_title": "Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls",
    "pdf_author": "Youngjin Yoo",
    "arxiv": "",
    "doi": "10.1016/j.nicl.2017.10.015",
    "first_lines": [
      "NeuroImage: Clinical 17 (2018) 169–178",
      "Contents lists available at ScienceDirect",
      "NeuroImage: Clinical",
      "journal homepage: www.elsevier.com/locate/ynicl",
      "Deep learning of joint myelin and T1w MRI features in normal-appearing",
      "brain tissue to distinguish between multiple sclerosis patients and healthy",
      "controls",
      "T",
      "Youngjin Yooa,b,e,*, Lisa Y.W. Tangc,e, Tom Broscha,b,e, David K.B. Lic,e, Shannon Kolindc,d,e,g,",
      "Irene Vavasourc, Alexander Rauscherf, Alex L. MacKayc,g, Anthony Traboulseed,e,",
      "Roger C. Tamb,c,e",
      "a",
      "Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada",
      "Biomedical Engineering Program, University of British Columbia, Vancouver, BC, Canada",
      "Department of Radiology, University of British Columbia, Vancouver, BC, Canada"
    ]
  },
  "Read/DeepSeek-V3 Technical Report.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2412.19437v2",
    "doi": "",
    "first_lines": [
      "DeepSeek-V3 Technical Report",
      "DeepSeek-AI",
      "research@deepseek.com",
      "We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total",
      "parameters with 37B activated for each token. To achieve efficient inference and cost-effective",
      "training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers",
      "an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training",
      "objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and",
      "high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to",
      "fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms",
      "other open-source models and achieves performance comparable to leading closed-source",
      "models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours",
      "for its full training. In addition, its training process is remarkably stable. Throughout the entire",
      "training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.",
      "The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3."
    ]
  },
  "Read/DeepSeek_R1.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via",
      "Reinforcement Learning",
      "DeepSeek-AI",
      "research@deepseek.com",
      "Abstract",
      "We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.",
      "DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities.",
      "Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing",
      "reasoning behaviors. However, it encounters challenges such as poor readability, and language",
      "mixing. To address these issues and further enhance reasoning performance, we introduce",
      "DeepSeek-R1, which incorporates multi-stage training and cold-start data before RL. DeepSeekR1 achieves performance comparable to OpenAI-o1-1217 on reasoning tasks. To support the",
      "research community, we open-source DeepSeek-R1-Zero, DeepSeek-R1, and six dense models",
      "(1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama.",
      "DeepSeek-R1",
      "100"
    ]
  },
  "Read/Denoising diffusion model_JonHo_2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2006.11239v2",
    "doi": "",
    "first_lines": [
      "Denoising Diffusion Probabilistic Models",
      "arXiv:2006.11239v2 [cs.LG] 16 Dec 2020",
      "Jonathan Ho",
      "UC Berkeley",
      "jonathanho@berkeley.edu",
      "Ajay Jain",
      "UC Berkeley",
      "ajayj@berkeley.edu",
      "Pieter Abbeel",
      "UC Berkeley",
      "pabbeel@cs.berkeley.edu",
      "Abstract",
      "We present high quality image synthesis results using diffusion probabilistic models,",
      "a class of latent variable models inspired by considerations from nonequilibrium",
      "thermodynamics. Our best results are obtained by training on a weighted variational"
    ]
  },
  "Read/Density estimation by dual ascent of log likelihood (invertible flows)_Tabak 2010.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "c 2010 International Press",
      "COMMUN. MATH. SCI.",
      "Vol. 8, No. 1, pp. 217–233",
      "DENSITY ESTIMATION BY DUAL ASCENT OF THE",
      "LOG-LIKELIHOOD∗",
      "ESTEBAN G. TABAK† AND ERIC VANDEN-EIJNDEN‡",
      "Dedicated to the sixtieth birthday of Professor Andrew Majda",
      "Abstract. A methodology is developed to assign, from an observed sample, a joint-probability",
      "distribution to a set of continuous variables. The algorithm proposed performs this assignment by",
      "mapping the original variables onto a jointly-Gaussian set. The map is built iteratively, ascending",
      "the log-likelihood of the observations, through a series of steps that move the marginal distributions",
      "along a random set of orthogonal directions towards normality.",
      "Key words. Density estimation, machine learning, maximum likelihood.",
      "AMS subject classifications. 34A50, 65C30, 65L20, 60H35.",
      "1. Introduction and problem setting"
    ]
  },
  "Read/Diffusion models_Hollistic Review_YANG 2024.pdf": {
    "pdf_title": "Diffusion Models: A Comprehensive Survey of Methods and Applications",
    "pdf_author": "",
    "arxiv": "2209.00796v14",
    "doi": "",
    "first_lines": [
      "arXiv:2209.00796v14 [cs.LG] 2 Dec 2024",
      "Diffusion Models: A Comprehensive Survey of Methods and Applications",
      "LING YANG, Peking University, China",
      "ZHILONG ZHANG∗ , Peking University, China",
      "YANG SONG, OpenAI, USA",
      "SHENDA HONG, Peking University, China",
      "RUNSHENG XU, University of California, Los Angeles, USA",
      "YUE ZHAO, Carnegie Mellon University, USA",
      "WENTAO ZHANG, Peking University, China",
      "BIN CUI, Peking University, China",
      "MING-HSUAN YANG† , University of California at Merced, USA",
      "Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many",
      "applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly",
      "expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood",
      "estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative"
    ]
  },
  "Read/Eric Kandel - Functional Prions in the Brain.pdf": {
    "pdf_title": "untitled",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1101/cshperspect.a023671",
    "first_lines": [
      "Functional Prions in the Brain",
      "Joseph B. Rayman1 and Eric R. Kandel1,2,3,4,5",
      "1",
      "Department of Neuroscience, College of Physicians and Surgeons of Columbia University, New York,",
      "New York 10032",
      "2",
      "Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York,",
      "New York 10032",
      "3",
      "Howard Hughes Medical Institute, Chevy Chase, Maryland 20815-6789",
      "4",
      "Zuckerman Mind Brain Behavior Institute, New York, New York 10027",
      "5",
      "Kavli Institute for Brain Science, New York, New York 10032",
      "Correspondence: erk5@columbia.edu"
    ]
  },
  "Read/Estimating Epistemic Uncertainty in Diffusion Models_Berry 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2406.18580v1",
    "doi": "",
    "first_lines": [
      "Shedding Light on Large Generative Networks:",
      "Estimating Epistemic Uncertainty in Diffusion Models",
      "Lucas Berry1,2",
      "Axel Brando3",
      "David Meger1,2",
      "1",
      "School of Computer Science, McGill University, Montreal, Quebec, Canada",
      "Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada",
      "3",
      "Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS), Spain",
      "arXiv:2406.18580v1 [cs.CV] 5 Jun 2024",
      "2",
      "Abstract",
      "Generative diffusion models, notable for their large",
      "parameter count (exceeding 100 million) and operation within high-dimensional image spaces,"
    ]
  },
  "Read/Eur J Immunol - 2006 - Sioud - Single‐stranded small interfering RNA are more immunostimulatory than their double‐stranded.pdf": {
    "pdf_title": "Single‐stranded small interfering RNA are more immunostimulatory than their double‐stranded counterparts: A central role for 2′‐hydroxyl uridines in immune responses",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1002/eji.200535708",
    "first_lines": [
      "1222",
      "Mouldy Sioud",
      "Eur. J. Immunol. 2006. 36: 1222–1230",
      "Innate immunity",
      "Single-stranded small interfering RNA are more",
      "immunostimulatory than their double-stranded",
      "counterparts: A central role for 20-hydroxyl uridines in",
      "immune responses",
      "Mouldy Sioud",
      "The Norwegian Radium Hospital, Department of Immunology, Molecular Medicine",
      "Group Montebello, Oslo, Norway",
      "It has recently become apparent that certain small interfering RNA (siRNA) sequences",
      "stimulate the innate immunity through endosomal Toll-like receptors (TLR),",
      "particularly TLR7 and TLR8. However, it remains unclear whether siRNA duplexes",
      "act as specific ligands for these receptors. To address this question and to overcome the"
    ]
  },
  "Read/Foundation of diffusion model_Deep unsupervised learning using Nonequilibrium Thermodynamics_Dickstein_Weiss_2015.pdf": {
    "pdf_title": "Deep Unsupervised Learning using Nonequilibrium Thermodynamics",
    "pdf_author": "Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli",
    "arxiv": "1503.03585v8",
    "doi": "",
    "first_lines": [
      "arXiv:1503.03585v8 [cs.LG] 18 Nov 2015",
      "Deep Unsupervised Learning using",
      "Nonequilibrium Thermodynamics",
      "Jascha Sohl-Dickstein",
      "Stanford University",
      "JASCHA @ STANFORD . EDU",
      "Eric A. Weiss",
      "University of California, Berkeley",
      "EAWEISS @ BERKELEY. EDU",
      "Niru Maheswaranathan",
      "Stanford University",
      "NIRUM @ STANFORD . EDU",
      "Surya Ganguli",
      "Stanford University",
      "SGANGULI @ STANFORD . EDU"
    ]
  },
  "Read/Gaussian Processes for Machine Learning_Rasmussen_Williams.pdf": {
    "pdf_title": "Gaussian Processes for Machine Learning",
    "pdf_author": "Carl Edward Rasmussen and Christopher K. I. Williams",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006,",
      "ISBN 026218253X. c 2006 Massachusetts Institute of Technology. www.GaussianProcess.org/gpml",
      "Gaussian Processes for Machine Learning",
      "C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006,",
      "ISBN 026218253X. c 2006 Massachusetts Institute of Technology. www.GaussianProcess.org/gpml",
      "Adaptive Computation and Machine Learning",
      "Thomas Dietterich, Editor",
      "Christopher Bishop, David Heckerman, Michael Jordan, and Michael Kearns, Associate Editors",
      "Bioinformatics: The Machine Learning Approach,",
      "Pierre Baldi and Søren Brunak",
      "Reinforcement Learning: An Introduction,",
      "Richard S. Sutton and Andrew G. Barto",
      "Graphical Models for Machine Learning and Digital Communication,",
      "Brendan J. Frey",
      "Learning in Graphical Models,"
    ]
  },
  "Read/Gaussian process-based prediction and detection of gray matter abnormalities_Alzheimer_Ziegler 2014.pdf": {
    "pdf_title": "Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1016/j.neuroimage.2014.04.018",
    "first_lines": [
      "NeuroImage 97 (2014) 333–348",
      "Contents lists available at ScienceDirect",
      "NeuroImage",
      "journal homepage: www.elsevier.com/locate/ynimg",
      "Individualized Gaussian process-based prediction and detection of local",
      "and global gray matter abnormalities in elderly subjects",
      "G. Ziegler a,b,⁎, G.R. Ridgway a, R. Dahnke b, C. Gaser b,c, for The Alzheimer's Disease Neuroimaging Initiative 1",
      "a",
      "b",
      "c",
      "Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK",
      "Department of Psychiatry, Jena University Hospital, Jena, Germany",
      "Department of Neurology, Jena University Hospital, Jena, Germany",
      "a r t i c l e",
      "i n f o"
    ]
  },
  "Read/Human microglial state dynamics in AD progression.pdf": {
    "pdf_title": "Human microglial state dynamics in Alzheimer's disease progression",
    "pdf_author": "Na Sun",
    "arxiv": "",
    "doi": "10.1016/j.cell.2023.08.037",
    "first_lines": [
      "Article",
      "Human microglial state dynamics in Alzheimer’s",
      "disease progression",
      "Graphical abstract",
      "Authors",
      "Na Sun, Matheus B. Victor,",
      "Yongjin P. Park, ..., David A. Bennett,",
      "Li-Huei Tsai, Manolis Kellis",
      "Correspondence",
      "lhtsai@mit.edu (L.-H.T.),",
      "manoli@mit.edu (M.K.)",
      "In brief",
      "Highlights",
      "d",
      "Single-nucleus transcriptomes and epigenomes of human"
    ]
  },
  "Read/ImageNet_G Hinton.pdf": {
    "pdf_title": "ImageNet Classification with Deep Convolutional Neural Networks",
    "pdf_author": "Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "ImageNet Classification with Deep Convolutional",
      "Neural Networks",
      "Alex Krizhevsky",
      "University of Toronto",
      "kriz@cs.utoronto.ca",
      "Ilya Sutskever",
      "University of Toronto",
      "ilya@cs.utoronto.ca",
      "Geoffrey E. Hinton",
      "University of Toronto",
      "hinton@cs.utoronto.ca",
      "Abstract",
      "We trained a large, deep convolutional neural network to classify the 1.2 million",
      "high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%",
      "and 17.0% which is considerably better than the previous state-of-the-art. The"
    ]
  },
  "Read/Inkjet-printed unclonable quantum dot fluorescent anti-counterfeiting label.pdf": {
    "pdf_title": "Inkjet-printed unclonable quantum dot fluorescent anti-counterfeiting labels with artificial intelligence authentication",
    "pdf_author": "Yang Liu",
    "arxiv": "",
    "doi": "10.1038/s41467-019-10406-7",
    "first_lines": [
      "ARTICLE",
      "https://doi.org/10.1038/s41467-019-10406-7",
      "OPEN",
      "Inkjet-printed unclonable quantum dot fluorescent",
      "anti-counterfeiting labels with artificial intelligence",
      "authentication",
      "1234567890():,;",
      "Yang Liu1, Fei Han2, Fushan Li1, Yan Zhao1, Maosheng Chen1, Zhongwei Xu1, Xin Zheng1, Hailong Hu 1,",
      "Jianmin Yao1, Tailiang Guo1, Wanzhen Lin2, Yuanhui Zheng 2, Baogui You3, Pai Liu3, Yang Li3 & Lei Qian4",
      "An ideal anti-counterfeiting technique has to be inexpensive, mass-producible, nondestructive, unclonable and convenient for authentication. Although many anti-counterfeiting",
      "technologies have been developed, very few of them fulfill all the above requirements. Here",
      "we report a non-destructive, inkjet-printable, artificial intelligence (AI)-decodable and",
      "unclonable security label. The stochastic pinning points at the three-phase contact line of the",
      "ink droplets is crucial for the successful inkjet printing of the unclonable security labels. Upon",
      "the solvent evaporation, the three-phase contact lines are pinned around the pinning points,"
    ]
  },
  "Read/Latent diffusion model_Robin_2022.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2112.10752v2",
    "doi": "",
    "first_lines": [
      "High-Resolution Image Synthesis with Latent Diffusion Models",
      "Robin Rombach1 *",
      "1",
      "Andreas Blattmann1 ∗",
      "Dominik Lorenz1",
      "Björn Ommer1",
      "Patrick Esser",
      "Ludwig Maximilian University of Munich & IWR, Heidelberg University, Germany",
      "Runway ML",
      "https://github.com/CompVis/latent-diffusion",
      "arXiv:2112.10752v2 [cs.CV] 13 Apr 2022",
      "Abstract",
      "Input",
      "By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion",
      "models (DMs) achieve state-of-the-art synthesis results on"
    ]
  },
  "Read/Longterm durability of immune response mRNA COvid vaccines.pdf": {
    "pdf_title": "Long-term predictions of humoral immunity after two doses of BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex",
    "pdf_author": "Chapin S. Korosec1,2,*, Suzan Farhang-Sardroodi1,2, David W. Dick1,2, Samaneh Gholami1,2, Mohammad Sajjad Ghaemi3, Iain R. Moyles2, Morgan Craig4, Hsu Kiang Ooi3, and Jane M. Heffernan1,2,, , , 10 1. Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, ON M3J 1P3, Canada, 2. Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, ON M3J 1P3, Canada, 3. Digital Technologies Research Centre, National Research Council Canada, Toronto, ON C1A 4P3, Canada, 4. Sainte-Justine University Hospital Research Centre and Department of Mathematics and Statistics, Université de Montréal, Montreal, QC H3T 1J4, Canada, correspondence: *chapinSkorosec@gmail.com,  jmheffer@yorku.ca ;",
    "arxiv": "",
    "doi": "10.1101/2021.10.13.21264957",
    "first_lines": [
      "medRxiv preprint doi: https://doi.org/10.1101/2021.10.13.21264957; this version posted October 18, 2021. The copyright holder for this preprint",
      "(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.",
      "It is made available under a CC-BY-NC-ND 4.0 International license .",
      "Long-term predictions of humoral immunity after two",
      "doses of BNT162b2 and mRNA-1273 vaccines based on",
      "dosage, age and sex",
      "Chapin S. Korosec1,2,∗ , Suzan Farhang-Sardroodi1,2 , David W. Dick1,2 ,",
      "Samaneh Gholami1,2 , Mohammad Sajjad Ghaemi3 , Iain R. Moyles2 ,",
      "Morgan Craig4 , Hsu Kiang Ooi3 , and Jane M. Heffernan1,2,ψ",
      "1. Modelling Infection and Immunity Lab, Mathematics Statistics, York",
      "University, Toronto, ON M3J 1P3, Canada",
      "2. Centre for Disease Modelling (CDM), Mathematics Statistics, York",
      "University, Toronto, ON M3J 1P3, Canada",
      "3. Digital Technologies Research Centre, National Research Council",
      "Canada, Toronto, ON C1A 4P3, Canada"
    ]
  },
  "Read/Mitochondrial links between brain aging and Alzheimer’s disease.pdf": {
    "pdf_title": "Mitochondrial links between brain aging and Alzheimer’s disease",
    "pdf_author": "Heather M. Wilkins",
    "arxiv": "",
    "doi": "10.1186/s40035-021-00261-2",
    "first_lines": [
      "Wilkins and Swerdlow Transl Neurodegener",
      "https://doi.org/10.1186/s40035-021-00261-2",
      "(2021) 10:33",
      "Open Access",
      "REVIEW",
      "Mitochondrial links between brain aging",
      "and Alzheimer’s disease",
      "Heather M. Wilkins1,2,3 and Russell H. Swerdlow1,2,3,4*",
      "Abstract",
      "Advancing age is a major risk factor for Alzheimer’s disease (AD). This raises the question of whether AD biology",
      "mechanistically diverges from aging biology or alternatively represents exaggerated aging. Correlative and modeling",
      "studies can inform this question, but without a firm grasp of what drives aging and AD it is difficult to definitively",
      "resolve this quandary. This review speculates over the relevance of a particular hallmark of aging, mitochondrial",
      "function, to AD, and further provides background information that is pertinent to and provides perspective on this",
      "speculation."
    ]
  },
  "Read/Neural Controlled Differential Equations for Time Series_Kidger 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2005.08926v2",
    "doi": "",
    "first_lines": [
      "arXiv:2005.08926v2 [cs.LG] 5 Nov 2020",
      "Neural Controlled Differential Equations for",
      "Irregular Time Series",
      "Patrick Kidger",
      "James Morrill",
      "James Foster",
      "Terry Lyons",
      "Mathematical Institute, University of Oxford",
      "The Alan Turing Institute, British Library",
      "{kidger, morrill, foster, tlyons}@maths.ox.ac.uk",
      "Abstract",
      "Neural ordinary differential equations are an attractive option for modelling",
      "temporal dynamics. However, a fundamental issue is that the solution to an",
      "ordinary differential equation is determined by its initial condition, and there",
      "is no mechanism for adjusting the trajectory based on subsequent observations."
    ]
  },
  "Read/Neural Radiance Field in 3D Vision_Review_Gao 2025.pdf": {
    "pdf_title": "NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)",
    "pdf_author": "Kyle Gao; Yina Gao; Hongjie He; Dening Lu; Linlin Xu; Jonathan Li",
    "arxiv": "2210.00379v6",
    "doi": "",
    "first_lines": [
      "1",
      "NeRF: Neural Radiance Field in 3D Vision: A",
      "Comprehensive Review",
      "arXiv:2210.00379v6 [cs.CV] 20 Jun 2025",
      "Kyle (Yilin) Gao, Member, IEEE, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, Member, IEEE, Jonathan",
      "Li, Fellow, IEEE",
      "Abstract—In March 2020, Neural Radiance Field (NeRF)",
      "revolutionized Computer Vision, allowing for implicit, neural",
      "network-based scene representation and novel view synthesis.",
      "NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented",
      "reality, and more. In August 2023, Gaussian Splatting, a direct",
      "competitor to the NeRF-based framework, was proposed, gaining",
      "tremendous momentum and overtaking NeRF-based research in",
      "terms of interest as the dominant framework for novel view",
      "synthesis. We present a comprehensive survey of NeRF papers"
    ]
  },
  "Read/Neural radiance field in medical imaging_Challenges_Wang 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2402.17797v3",
    "doi": "",
    "first_lines": [
      "NEURAL RADIANCE FIELDS IN MEDICAL IMAGING: CHALLENGES AND NEXT STEPS",
      "Xin Wang 1,⋆ , Shu Hu 2 , Heng Fan 3 , Hongtu Zhu 4 , Xin Li 1",
      "arXiv:2402.17797v3 [eess.IV] 21 Mar 2024",
      "1",
      "University at Albany, State University of New York {xwang56,xli48}@albany.edu",
      "2",
      "Purdue University hu968@purdue.edu",
      "3",
      "University of North Texas heng.fan@unt.edu",
      "4",
      "University of North Carolina at Chapel Hill htzhu@email.unc.edu",
      "ABSTRACT",
      "Neural Radiance Fields (NeRF), as a pioneering technique in",
      "computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical",
      "applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary"
    ]
  },
  "Read/Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty modeling_Berry 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2302.01312v3",
    "doi": "",
    "first_lines": [
      "Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty",
      "Modeling",
      "Lucas Berry,1 David Meger 1",
      "arXiv:2302.01312v3 [cs.LG] 3 Oct 2023",
      "1",
      "McGill University, Centre for Intelligent Machines, Montreal, Canada",
      "lucas.berry@mail.mcgill.ca",
      "Abstract",
      "In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to",
      "capture complicated aleatoric distributions. To this end, we",
      "propose an ensemble of Normalizing Flows (NF), which are",
      "state-of-the-art in modeling aleatoric uncertainty. The ensembles are created via sets of fixed dropout masks, making them",
      "less expensive than creating separate NF models. We demonstrate how to leverage the unique structure of NFs, base distributions, to estimate aleatoric uncertainty without relying on",
      "samples, provide a comprehensive set of baselines, and derive",
      "unbiased estimates for differential entropy. The methods were"
    ]
  },
  "Read/Normative methods.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Age",
      "Age",
      "Reward-related brain",
      "activity derived from",
      "task fMRI",
      "Cortical Thickness",
      "Cortical thickness",
      "Alpha band brain activity",
      "derived from electroencephalogaphy",
      "Brain volume",
      "Age",
      "Task-related fMRI data",
      "Functional connectivity",
      "measures derived from",
      "resting fMRI"
    ]
  },
  "Read/Normative model to detect schizophrenia_longitudinal MRI_ Aaron Bloch 2015.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1016/j.biopsych.2014.02.010",
    "first_lines": [
      "HHS Public Access",
      "Author manuscript",
      "Author Manuscript",
      "Biol Psychiatry. Author manuscript; available in PMC 2015 September 15.",
      "Published in final edited form as:",
      "Biol Psychiatry. 2014 September 15; 76(6): 438–446. doi:10.1016/j.biopsych.2014.02.010.",
      "aalexanderbloch@gmail.com",
      "Abnormal cortical growth in schizophrenia targets normative",
      "modules of synchronized development",
      "Aaron F. Alexander-Bloch1,2,3, Philip T. Reiss4,5, Judith Rapoport1, Harry McAdams1, Jay",
      "N. Giedd1, Ed T. Bullmore2,6,7,*, and Nitin Gogtay1,*",
      "1Child Psychiatry Branch, National Institute of Mental Health, Bethesda, MD",
      "Author Manuscript",
      "2Brain Mapping Unit, Behavioural & Clinical Neuroscience Institute, University of Cambridge,",
      "Cambridge, UK"
    ]
  },
  "Read/Normative model to map the heterogeneity in ADHD_Wolfers 2019.pdf": {
    "pdf_title": "S0033291719000084jra 314..323",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Psychological Medicine",
      "cambridge.org/psm",
      "Original Article",
      "*equal contributions",
      "Cite this article: Wolfers T, Beckmann CF,",
      "Hoogman M, Buitelaar JK, Franke B, Marquand",
      "AF (2020). Individual differences v. the average",
      "patient: mapping the heterogeneity in ADHD",
      "using normative models. Psychological",
      "Medicine 50, 314–323. https://doi.org/10.1017/",
      "S0033291719000084",
      "Received: 28 July 2018",
      "Revised: 4 January 2019",
      "Accepted: 8 January 2019",
      "First published online: 14 February 2019"
    ]
  },
  "Read/Normative modeling on Alzheimer_Adversarial autoencoders_Walter 2020.pdf": {
    "pdf_title": "Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer’s disease",
    "pdf_author": "Walter H. L. Pinaya; Cristina Scarpazza; Rafael Garcia-Dias; Sandra Vieira; Lea Baecker; Pedro F. da Costa; Alberto Redolfi; Giovanni B. Frisoni; Michela Pievani; Vince D. Calhoun; João R. Sato; Andrea Mechelli; the Alzheimer’s Disease Neuroimaging Initiative; the Australian Imaging Biomarkers and Lifestyle flagship study of ageing;",
    "arxiv": "",
    "doi": "10.1101/2020.02.10.931824",
    "first_lines": [
      "bioRxiv preprint doi: https://doi.org/10.1101/2020.02.10.931824; this version posted February 11, 2020. The copyright holder for this",
      "preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in",
      "perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.",
      "Normative modelling using deep autoencoders: a multi-cohort",
      "study on mild cognitive impairment and Alzheimer’s disease",
      "Walter H. L. Pinayaa,b,∗, Cristina Scarpazzaa,c , Rafael Garcia-Diasa , Sandra Vieiraa , Lea",
      "Baeckera , Pedro F. da Costad,e , Alberto Redolfif , Giovanni B. Frisonig,h , Michela Pievanig ,",
      "Vince D. Calhouni,j , João R. Satob , Andrea Mechellia , the Alzheimer’s Disease",
      "Neuroimaging Initiative1 , the Australian Imaging Biomarkers and Lifestyle flagship study",
      "of ageing2",
      "a",
      "Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College",
      "London, London, UK.",
      "b",
      "Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil."
    ]
  },
  "Read/Normative models_Review_Marquand 2019.pdf": {
    "pdf_title": "Conceptualizing mental disorders as deviations from normative functioning",
    "pdf_author": "Andre F. Marquand",
    "arxiv": "",
    "doi": "10.1038/s41380-019-0441-1",
    "first_lines": [
      "Molecular Psychiatry (2019) 24:1415–1424",
      "https://doi.org/10.1038/s41380-019-0441-1",
      "REVIEW ARTICLE",
      "Conceptualizing mental disorders as deviations from",
      "normative functioning",
      "This article has been corrected since Advance Online Publication and a correction is also",
      "printed in this issue",
      "Andre F. Marquand1,2,3 Seyed Mostafa Kia",
      "Christian F. Beckmann1,2,5",
      "●",
      "1,2",
      "●",
      "Mariam Zabihi1,2 Thomas Wolfers1,2 Jan K. Buitelaar",
      "●",
      "●"
    ]
  },
  "Read/POISONPROMPT_BACKDOOR ATTACK.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2310.12439v2",
    "doi": "",
    "first_lines": [
      "P OISON P ROMPT: BACKDOOR ATTACK ON PROMPT-BASED LARGE LANGUAGE MODELS",
      "Hongwei Yao1",
      "Jian Lou2",
      "Zhan Qin1 ∗",
      "1",
      "arXiv:2310.12439v2 [cs.CL] 18 Dec 2023",
      "2",
      "Zhejiang University, Hangzhou, China",
      "ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China",
      "ABSTRACT",
      "Prompts have significantly improved the performance of pretrained Large Language Models (LLMs) on various downstream tasks recently, making them increasingly indispensable for a diverse range of LLM application scenarios. However, the backdoor vulnerability, a serious security threat that",
      "can maliciously alter the victim model’s normal predictions,",
      "has not been sufficiently explored for prompt-based LLMs. In",
      "this paper, we present P OISON P ROMPT, a novel backdoor attack capable of successfully compromising both hard and soft",
      "prompt-based LLMs. We evaluate the effectiveness, fidelity,"
    ]
  },
  "Read/PRusiner - NEURODEGENERATIVE DISEASES AND PRIONS.pdf": {
    "pdf_title": "Neurodegenerative Diseases and Prions",
    "pdf_author": "Prusiner",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "The Ne w E n g l a nd Jo u r n a l o f Me d ic i ne",
      "Special Article",
      "SHATTUCK LECTURE — NEURODEGENERATIVE DISEASES AND PRIONS",
      "STANLEY B. PRUSINER, M.D.",
      "T",
      "WENTY-FIVE years ago, little was known",
      "about the causes of neurodegenerative diseases. Now, however, it is clear that they result from abnormalities in the processing of",
      "proteins. In each of these diseases, defective processing causes the accumulation of one or more specific",
      "neuronal proteins.",
      "Of all the laboratory research on neurodegenerative diseases, the studies that led to the discovery of",
      "prions have yielded the most unexpected findings. The",
      "idea that a protein can act as an infectious pathogen",
      "and cause degeneration of the central nervous system",
      "was accepted only after a long and arduous battle.1",
      "The concept of prions not only has provided an explanation of how a disease can be both infectious and"
    ]
  },
  "Read/Performers_Fast Attention Via positive Orthogonal Random Features_Choromansky_2022.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2009.14794v4",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2021",
      "R ETHINKING ATTENTION WITH P ERFORMERS",
      "Krzysztof Choromanski∗1 , Valerii Likhosherstov∗2 , David Dohan∗1 , Xingyou Song∗1",
      "Andreea Gane∗1 , Tamas Sarlos∗1 , Peter Hawkins∗1 , Jared Davis∗3 , Afroz Mohiuddin1",
      "Lukasz Kaiser1 , David Belanger1 , Lucy Colwell1,2 , Adrian Weller2,4",
      "1",
      "Google 2 University of Cambridge 3 DeepMind 4 Alan Turing Institute",
      "arXiv:2009.14794v4 [cs.LG] 19 Nov 2022",
      "A BSTRACT",
      "We introduce Performers, Transformer architectures which can estimate regular",
      "(softmax) full-rank-attention Transformers with provable accuracy, but using only",
      "linear (as opposed to quadratic) space and time complexity, without relying on",
      "any priors such as sparsity or low-rankness. To approximate softmax attentionkernels, Performers use a novel Fast Attention Via positive Orthogonal Random",
      "features approach (FAVOR+), which may be of independent interest for scalable",
      "kernel methods. FAVOR+ can also be used to efficiently model kernelizable"
    ]
  },
  "Read/Prompt-Specific Poisoning Attacks.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2310.13828v3",
    "doi": "",
    "first_lines": [
      "Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models",
      "arXiv:2310.13828v3 [cs.CR] 29 Apr 2024",
      "Shawn Shan, Wenxin Ding, Josephine Passananti, Stanley Wu, Haitao Zheng, Ben Y. Zhao",
      "Department of Computer Science, University of Chicago",
      "{shawnshan, wenxind, josephinep, stanleywu, htzheng, ravenben}@cs.uchicago.edu",
      "Abstract—Trained on billions of images, diffusion-based textto-image models seem impervious to traditional data poisoning",
      "attacks, which typically require poison samples approaching",
      "20% of the training set. In this paper, we show that stateof-the-art text-to-image generative models are in fact highly",
      "vulnerable to poisoning attacks. Our work is driven by two key",
      "insights. First, while diffusion models are trained on billions",
      "of samples, the number of training samples associated with",
      "a specific concept or prompt is generally on the order of",
      "thousands. This suggests that these models will be vulnerable to",
      "prompt-specific poisoning attacks that corrupt a model’s ability",
      "to respond to specific targeted prompts. Second, poison samples"
    ]
  },
  "Read/Prusiner - Biology and Genetics of Prions Causing Neurodegeneration.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1146/annurev-genet-110711-155524",
    "first_lines": [
      "NIH Public Access",
      "Author Manuscript",
      "Annu Rev Genet. Author manuscript; available in PMC 2014 May 05.",
      "NIH-PA Author Manuscript",
      "Published in final edited form as:",
      "Annu Rev Genet. 2013 ; 47: 601–623. doi:10.1146/annurev-genet-110711-155524.",
      "Biology and Genetics of Prions Causing Neurodegeneration",
      "Stanley B. Prusiner",
      "Institute for Neurodegenerative Diseases and Department of Neurology, University of California,",
      "San Francisco, California",
      "Stanley B. Prusiner: stanley@ind.ucsf.edu",
      "Abstract",
      "NIH-PA Author Manuscript",
      "Prions are proteins that acquire alternative conformations that become self-propagating.",
      "Transformation of proteins into prions is generally accompanied by an increase in β-sheet"
    ]
  },
  "Read/Reversible Residual Network_Backprop without storing activations_Aidan Gomez 2017.pdf": {
    "pdf_title": "The Reversible Residual Network: Backpropagation Without Storing Activations",
    "pdf_author": "Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "The Reversible Residual Network:",
      "Backpropagation Without Storing Activations",
      "Aidan N. Gomez∗ 1 , Mengye Ren∗ 1,2,3 , Raquel Urtasun1,2,3 , Roger B. Grosse1,2",
      "University of Toronto1",
      "Vector Institute for Artificial Intelligence2",
      "Uber Advanced Technologies Group3",
      "{aidan, mren, urtasun, rgrosse}@cs.toronto.edu",
      "Abstract",
      "Deep residual networks (ResNets) have significantly pushed forward the state-ofthe-art on image classification, increasing in performance as networks grow both",
      "deeper and wider. However, memory consumption becomes a bottleneck, as one",
      "needs to store the activations in order to calculate gradients using backpropagation. We present the Reversible Residual Network (RevNet), a variant of ResNets",
      "where each layer’s activations can be reconstructed exactly from the next layer’s.",
      "Therefore, the activations for most layers need not be stored in memory during",
      "backpropagation. We demonstrate the effectiveness of RevNets on CIFAR-10,",
      "CIFAR-100, and ImageNet, establishing nearly identical classification accuracy"
    ]
  },
  "Read/Role of B cells as antigen presenting cells.pdf": {
    "pdf_title": "Role of B cells as antigen presenting cells",
    "pdf_author": "Ichwaku Rastogi",
    "arxiv": "",
    "doi": "10.3389/fimmu.2022.954936",
    "first_lines": [
      "TYPE Review",
      "PUBLISHED 08 September 2022",
      "DOI 10.3389/fimmu.2022.954936",
      "OPEN ACCESS",
      "Role of B cells as antigen",
      "presenting cells",
      "EDITED BY",
      "William L. Redmond,",
      "Earle A. Chiles Research Institute,",
      "United States",
      "REVIEWED BY",
      "Wenxia Song,",
      "University of Maryland, College Park,",
      "United States",
      "Wataru Ise,"
    ]
  },
  "Read/STRUCTURED ATTENTION NETWORKS.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1702.00887v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2017",
      "S TRUCTURED ATTENTION N ETWORKS",
      "Yoon Kim∗",
      "Carl Denton∗",
      "Luong Hoang",
      "Alexander M. Rush",
      "{yoonkim@seas,carldenton@college,lhoang@g,srush@seas}.harvard.edu",
      "School of Engineering and Applied Sciences",
      "Harvard University",
      "Cambridge, MA 02138, USA",
      "arXiv:1702.00887v3 [cs.CL] 16 Feb 2017",
      "A BSTRACT",
      "Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we",
      "may want to model richer structural dependencies without abandoning end-to-end",
      "training. In this work, we experiment with incorporating richer structural distributions, encoded using graphical models, within deep networks. We show that"
    ]
  },
  "Read/Scientist AI_Yoshua Bengio 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Yoshua Bengio",
    "arxiv": "2502.15657v2",
    "doi": "",
    "first_lines": [
      "Superintelligent Agents Pose Catastrophic Risks:",
      "Can Scientist AI Offer a Safer Path?",
      "Yoshua Bengio∗1,2 , Michael Cohen3 , Damiano Fornasiere1 , Joumana Ghosn1 , Pietro Greiner1 , Matt",
      "MacDermott4,1 , Sören Mindermann1 , Adam Oberman1,5 , Jesse Richardson1 , Oliver Richardson1,2 ,",
      "Marc-Antoine Rondeau1 , Pierre-Luc St-Charles1 , David Williams-King1",
      "arXiv:2502.15657v2 [cs.AI] 24 Feb 2025",
      "1",
      "Mila — Quebec AI Institute",
      "2",
      "Université de Montréal",
      "3",
      "University of California, Berkeley",
      "4",
      "Imperial College London",
      "5"
    ]
  },
  "Read/Searching for prostate cancer by fully automated magnetic resonance imaging classification_ deep learning versus non-deep learning.pdf": {
    "pdf_title": "Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning",
    "pdf_author": "Xinggang Wang",
    "arxiv": "",
    "doi": "10.1038/s41598-017-15720-y",
    "first_lines": [
      "www.nature.com/scientificreports",
      "OPEN",
      "Received: 20 October 2016",
      "Accepted: 31 October 2017",
      "Published: xx xx xxxx",
      "Searching for prostate cancer",
      "by fully automated magnetic",
      "resonance imaging classification:",
      "deep learning versus non-deep",
      "learning",
      "Xinggang Wang1,2, Wei Yang3, Jeffrey Weinreb4, Juan Han5, Qiubai Li6, Xiangchuang Kong7,",
      "Yongluan Yan2, Zan Ke1, Bo Luo8, Tao Liu8 & Liang Wang1,9",
      "Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic",
      "mummy imaging. PCa detection is critical to personalized medicine and varies considerably under",
      "an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the"
    ]
  },
  "Read/Sparse identification of nonlinear dynamical systems_Brunton 2016.pdf": {
    "pdf_title": "Discovering governing equations from data by sparse identification of nonlinear dynamical systems",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1073/pnas.1517384113",
    "first_lines": [
      "Discovering governing equations from data by sparse",
      "identification of nonlinear dynamical systems",
      "Steven L. Bruntona,1, Joshua L. Proctorb, and J. Nathan Kutzc",
      "a",
      "Department of Mechanical Engineering, University of Washington, Seattle, WA 98195; bInstitute for Disease Modeling, Bellevue, WA 98005;",
      "and cDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195",
      "Edited by William Bialek, Princeton University, Princeton, NJ, and approved March 1, 2016 (received for review August 31, 2015)",
      "Downloaded from https://www.pnas.org by 46.183.109.246 on July 8, 2025 from IP address 46.183.109.246.",
      "Extracting governing equations from data is a central challenge in",
      "many diverse areas of science and engineering. Data are abundant",
      "whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few",
      "examples. In this work, we combine sparsity-promoting techniques",
      "and machine learning with nonlinear dynamical systems to discover",
      "governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few",
      "important terms that govern the dynamics, so that the equations are"
    ]
  },
  "Read/Staging Alzheimer’s Disease Risk by Sequencing Brain Function and Structure, Cerebrospinal Fluid,and Cognition Biomarkers.pdf": {
    "pdf_title": "Keywords:",
    "pdf_author": "Creator:         DVIPSONE 2.2.2  http://www.YandY.com",
    "arxiv": "",
    "doi": "10.3233/JAD-160537",
    "first_lines": [
      "983",
      "Journal of Alzheimer’s Disease 54 (2016) 983–993",
      "DOI 10.3233/JAD-160537",
      "IOS Press",
      "Staging Alzheimer’s Disease Risk",
      "by Sequencing Brain Function",
      "and Structure, Cerebrospinal Fluid,",
      "and Cognition Biomarkers",
      "Guangyu Chena,1 , Hao Shua,b,1 , Gang Chena , B. Douglas Warda , Piero G. Antuonoc ,",
      "Zhijun Zhangb , Shi-Jiang Lia,∗ and Alzheimer’s Disease Neuroimaging Initiative2",
      "a Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA",
      "b Department of Neurology, Affiliated ZhongDa Hospital, Neuropsychiatric Institute and Medical School of",
      "Southeast University, Nanjing, Jiangsu, China",
      "c Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA",
      "Accepted 22 June 2016"
    ]
  },
  "Read/Stochastic Variational Inference_Hoffman2013.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1206.7051v3",
    "doi": "",
    "first_lines": [
      "Journal of Machine Learning Research (2013, in press)",
      "Submitted ; Published",
      "Stochastic Variational Inference",
      "Matt Hoffman",
      "Adobe Research",
      "MATHOFFM @ ADOBE . COM",
      "arXiv:1206.7051v3 [stat.ML] 22 Apr 2013",
      "David M. Blei",
      "Department of Computer Science",
      "Princeton University",
      "BLEI @ CS . PRINCETON . EDU",
      "Chong Wang",
      "Machine Learning Department",
      "Carnegie Mellon University",
      "CHONGW @ CS . CMU . EDU"
    ]
  },
  "Read/Stochastic differential VAE to model patient trajectory_Hess 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2310.17463v2",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2024",
      "BAYESIAN N EURAL C ONTROLLED D IFFERENTIAL",
      "E QUATIONS FOR T REATMENT E FFECT E STIMATION",
      "arXiv:2310.17463v2 [cs.LG] 3 Apr 2024",
      "Konstantin Hess, Valentyn Melnychuk, Dennis Frauen & Stefan Feuerriegel",
      "Munich Center for Machine Learning",
      "LMU Munich",
      "{k.hess,melnychuk,frauen,feuerriegel}@lmu.de",
      "A BSTRACT",
      "Treatment effect estimation in continuous time is crucial for personalized",
      "medicine. However, existing methods for this task are limited to point estimates of",
      "the potential outcomes, whereas uncertainty estimates have been ignored. Needless to say, uncertainty quantification is crucial for reliable decision-making in",
      "medical applications. To fill this gap, we propose a novel Bayesian neural controlled differential equation (BNCDE) for treatment effect estimation in continuous time. In our BNCDE, the time dimension is modeled through a coupled",
      "system of neural controlled differential equations and neural stochastic differential equations, where the neural stochastic differential equations allow for tractable",
      "variational Bayesian inference. Thereby, for an assigned sequence of treatments,"
    ]
  },
  "Read/SuperARC_Benchmark framework for superintelligence_Espinosa 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2503.16743v3",
    "doi": "",
    "first_lines": [
      "SuperARC: An Agnostic Test for Narrow,",
      "General, and Super Intelligence Based On the",
      "Principles of Causal Recursive Compression and",
      "Algorithmic Probability",
      "Alberto Hernández-Espinosa1 , Luan Ozelim1,2 , Felipe S.",
      "Abrahão1,2,3,4 , and Hector Zenil∗1,2,5,6",
      "arXiv:2503.16743v3 [cs.AI] 22 Apr 2025",
      "1",
      "Oxford Immune Algorithmics, Oxford University Innovation & London",
      "Institute for Healthcare Engineering, U.K.",
      "2",
      "Algorithmic Dynamics Lab, Center of Molecular Medicine, Karolinska",
      "Institute & King’s College London, U.K.",
      "3",
      "Centre for Logic, Epistemology and the History of Science, University of"
    ]
  },
  "Read/Tutorial on variational autoencoders_CarlDoersch.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "1606.05908v3",
    "doi": "",
    "first_lines": [
      "Tutorial on Variational Autoencoders",
      "C ARL D OERSCH",
      "Carnegie Mellon / UC Berkeley",
      "arXiv:1606.05908v3 [stat.ML] 3 Jan 2021",
      "August 16, 2016, with very minor revisions on January 3, 2021",
      "Abstract",
      "In just three years, Variational Autoencoders (VAEs) have emerged",
      "as one of the most popular approaches to unsupervised learning of",
      "complicated distributions. VAEs are appealing because they are built",
      "on top of standard function approximators (neural networks), and",
      "can be trained with stochastic gradient descent. VAEs have already",
      "shown promise in generating many kinds of complicated data, including handwritten digits [1, 2], faces [1, 3, 4], house numbers [5, 6],",
      "CIFAR images [6], physical models of scenes [4], segmentation [7], and",
      "predicting the future from static images [8]. This tutorial introduces the",
      "intuitions behind VAEs, explains the mathematics behind them, and"
    ]
  },
  "Read/Uncertainty Quantification for Machine Learning in Healthcare_Survey_Lopez 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2505.02874v1",
    "doi": "",
    "first_lines": [
      "Proceedings of Machine Learning Research 287:1–46, 2025",
      "Conference on Health, Inference, and Learning (CHIL) 2025",
      "arXiv:2505.02874v1 [cs.LG] 4 May 2025",
      "Uncertainty Quantification for Machine Learning in Healthcare:",
      "A Survey",
      "L. Julián Lechuga López 1,2",
      "Shaza Elsharief 2",
      "Dhiyaa Al Jorf 2",
      "Firas Darwish 2",
      "Congbo Ma 2",
      "Farah E. Shamout 1,2",
      "leopoldo.lechuga@nyu.edu",
      "se1525@nyu.edu",
      "da2863@nyu.edu",
      "fbd2014@nyu.edu"
    ]
  },
  "Read/Uncovering Insurance Fraud Conspiracy with Network Learning.pdf": {
    "pdf_title": "Uncovering Insurance Fraud Conspiracy with Network Learning",
    "pdf_author": "Chen Liang, Ziqi Liu, Bin Liu, Jun Zhou, Xiaolong Li, Shuang Yang, and Yuan Qi",
    "arxiv": "2002.12789v1",
    "doi": "10.1145/3331184.3331372",
    "first_lines": [
      "arXiv:2002.12789v1 [cs.CR] 27 Feb 2020",
      "Uncovering Insurance Fraud Conspiracy with Network Learning",
      "Chen Liang",
      "Ziqi Liu",
      "Bin Liu",
      "Ant Financial",
      "Hangzhou, China",
      "lc155190@antfin.com",
      "Ant Financial",
      "Hangzhou, China",
      "ziqiliu@antfin.com",
      "Ant Financial",
      "Hangzhou, China",
      "lb88701@alibaba-inc.com",
      "Jun Zhou"
    ]
  },
  "Read/Unsupervised Brain Anomaly Detection and Segmentation_Walter Hugo 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2102.11650v1",
    "doi": "",
    "first_lines": [
      "Proceedings of Machine Learning Research – Under Review:1–22, 2021",
      "Full Paper – MIDL 2021 submission",
      "arXiv:2102.11650v1 [eess.IV] 23 Feb 2021",
      "Unsupervised Brain Anomaly Detection and Segmentation",
      "with Transformers",
      "Walter Hugo Lopez Pinaya 1",
      "Petru-Daniel Tudosiu 1",
      "Robert Gray 2",
      "Geraint Rees 3",
      "Parashkev Nachev2",
      "Sébastien Ourselin 1",
      "M. Jorge Cardoso1",
      "walter.diaz sanz@kcl.ac.uk",
      "petru.tudosiu@kcl.ac.uk",
      "r.gray@ucl.ac.uk"
    ]
  },
  "Read/VQ_VAE_van den Oord_2018.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1711.00937v2",
    "doi": "",
    "first_lines": [
      "Neural Discrete Representation Learning",
      "arXiv:1711.00937v2 [cs.LG] 30 May 2018",
      "Aaron van den Oord",
      "DeepMind",
      "avdnoord@google.com",
      "Oriol Vinyals",
      "DeepMind",
      "vinyals@google.com",
      "Koray Kavukcuoglu",
      "DeepMind",
      "korayk@google.com",
      "Abstract",
      "Learning useful representations without supervision remains a key challenge in",
      "machine learning. In this paper, we propose a simple yet powerful generative",
      "model that learns such discrete representations. Our model, the Vector QuantisedVariational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the"
    ]
  },
  "Read/Variational autoencoders_Kingma_2019.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1906.02691v3",
    "doi": "10.1561/XXXXXXXXX",
    "first_lines": [
      "arXiv:1906.02691v3 [cs.LG] 11 Dec 2019",
      "Foundations and Trends R in Machine Learning",
      "An Introduction to",
      "Variational Autoencoders",
      "Suggested Citation: Diederik P. Kingma and Max Welling (2019), “An Introduction to",
      "Variational Autoencoders”, Foundations and Trends R in Machine Learning: Vol. xx, No.",
      "xx, pp 1–18. DOI: 10.1561/XXXXXXXXX.",
      "Diederik P. Kingma",
      "Google",
      "durk@google.com",
      "Max Welling",
      "Universiteit van Amsterdam, Qualcomm",
      "mwelling@qti.qualcomm.com",
      "This article may be used only for the purpose of research, teaching,",
      "and/or private study. Commercial use or systematic downloading"
    ]
  },
  "Read/Vision Transformer_2019.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2010.11929v2",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2021",
      "A N I MAGE IS W ORTH 16 X 16 W ORDS :",
      "T RANSFORMERS FOR I MAGE R ECOGNITION AT S CALE",
      "arXiv:2010.11929v2 [cs.CV] 3 Jun 2021",
      "Alexey Dosovitskiy∗,† , Lucas Beyer∗ , Alexander Kolesnikov∗ , Dirk Weissenborn∗ ,",
      "Xiaohua Zhai∗ , Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer,",
      "Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby∗,†",
      "∗",
      "equal technical contribution, † equal advising",
      "Google Research, Brain Team",
      "{adosovitskiy, neilhoulsby}@google.com",
      "A BSTRACT",
      "While the Transformer architecture has become the de-facto standard for natural",
      "language processing tasks, its applications to computer vision remain limited. In",
      "vision, attention is either applied in conjunction with convolutional networks, or"
    ]
  },
  "Read/YOUR CLASSIFIER IS SECRETLY AN ENERGY BASED_Grathwohl_2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1912.03263v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2020",
      "YOUR CLASSIFIER IS SECRETLY AN ENERGY BASED",
      "MODEL AND YOU SHOULD TREAT IT LIKE ONE",
      "Kuan-Chieh Wang∗& Jörn-Henrik Jacobsen∗",
      "University of Toronto & Vector Institute",
      "wangkua1@cs.toronto.edu",
      "j.jacobsen@vectorinstitute.ai",
      "arXiv:1912.03263v3 [cs.LG] 15 Sep 2020",
      "Will Grathwohl",
      "University of Toronto & Vector Institute",
      "Google Research",
      "wgrathwohl@cs.toronto.edu",
      "David Duvenaud",
      "University of Toronto & Vector Institute",
      "duvenaud@cs.toronto.edu"
    ]
  },
  "Read/ZERO-SHOT IMAGE RESTORATION USING denoising diffusion null space model_Wang 2022.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2212.00490v2",
    "doi": "",
    "first_lines": [
      "Z ERO -S HOT I MAGE R ESTORATION U SING",
      "D ENOISING D IFFUSION N ULL -S PACE M ODEL",
      "Yinhuai Wang1∗ , Jiwen Yu1∗ , Jian Zhang1,2",
      "1",
      "Peking University Shenzhen Graduate School, 2 Peng Cheng Laboratory",
      "{yinhuai; yujiwen}@stu.pku.edu.cn, zhangjian.sz@pku.edu.cn",
      "arXiv:2212.00490v2 [cs.CV] 7 Dec 2022",
      "A BSTRACT",
      "Most existing Image Restoration (IR) models are task-specific, which can not be",
      "generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for",
      "arbitrary linear IR problems, including but not limited to image super-resolution,",
      "colorization, inpainting, compressed sensing, and deblurring. DDNM only needs",
      "a pre-trained off-the-shelf diffusion model as the generative prior, without any extra training or network modifications. By refining only the null-space contents",
      "during the reverse diffusion process, we can yield diverse results satisfying both",
      "data consistency and realness. We further propose an enhanced and robust version,"
    ]
  },
  "Read/automatic chain of thoughts.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2210.03493v1",
    "doi": "",
    "first_lines": [
      "AUTOMATIC C HAIN OF T HOUGHT P ROMPTING",
      "IN L ARGE L ANGUAGE M ODELS",
      "arXiv:2210.03493v1 [cs.CL] 7 Oct 2022",
      "Zhuosheng Zhang†∗, Aston Zhang‡ , Mu Li‡ , Alex Smola‡",
      "†",
      "Shanghai Jiao Tong University, ‡ Amazon Web Services",
      "A BSTRACT",
      "Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning",
      "steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting.",
      "CoT prompting has two major paradigms. One leverages a simple prompt like “Let’s think step by",
      "step” to facilitate step-by-step thinking before answering a question. The other uses a few manual",
      "demonstrations one by one, each composed of a question and a reasoning chain that leads to an",
      "answer. The superior performance of the second paradigm hinges on the hand-crafting of task-specific",
      "demonstrations one by one. We show that such manual efforts may be eliminated by leveraging",
      "LLMs with the “Let’s think step by step” prompt to generate reasoning chains for demonstrations one"
    ]
  },
  "Read/brain data augmentation.pdf": {
    "pdf_title": "Data Augmentation for Brain-Tumor Segmentation: A Review",
    "pdf_author": "Jakub Nalepa",
    "arxiv": "",
    "doi": "10.3389/fncom.2019.00083",
    "first_lines": [
      "REVIEW",
      "published: 11 December 2019",
      "doi: 10.3389/fncom.2019.00083",
      "Data Augmentation for Brain-Tumor",
      "Segmentation: A Review",
      "Jakub Nalepa 1,2*, Michal Marcinkiewicz 3 and Michal Kawulok 2",
      "1",
      "Future Processing, Gliwice, Poland, 2 Silesian University of Technology, Gliwice, Poland, 3 Netguru, Poznan, Poland",
      "Data augmentation is a popular technique which helps improve generalization capabilities",
      "of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal",
      "role in scenarios in which the amount of high-quality ground-truth data is limited, and",
      "acquiring new examples is costly and time-consuming. This is a very common problem in",
      "medical image analysis, especially tumor delineation. In this paper, we review the current",
      "advances in data-augmentation techniques applied to magnetic resonance images",
      "of brain tumors. To better understand the practical aspects of such algorithms, we"
    ]
  },
  "Read/mRNA targeting immune cell.pdf": {
    "pdf_title": "Creator:         Arbortext Advanced Print Publisher 10.0.1306/W Unicode",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.3389/fimmu.2023.1294929",
    "first_lines": [
      "TYPE Review",
      "PUBLISHED 27 November 2023",
      "DOI 10.3389/fimmu.2023.1294929",
      "OPEN ACCESS",
      "EDITED BY",
      "Dan Peer,",
      "Tel Aviv University, Israel",
      "REVIEWED BY",
      "Lukas Heger,",
      "University Hospital Erlangen, Germany",
      "Uri Elia,",
      "Israel Institute for Biological Research",
      "(IIBR), Israel",
      "Straight to the point: targeted",
      "mRNA-delivery to immune cells"
    ]
  },
  "Read/xue-et-al-2017-thioflavin-t-as-an-amyloid-dye-fibril-quantification-optimal-concentration-and-effect-on-aggregation.pdf": {
    "pdf_title": "Thioflavin T as an amyloid dye: fibril quantification, optimal concentration and effect on aggregation",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "10.1098/rsos.160696",
    "first_lines": [
      "rsos.royalsocietypublishing.org",
      "Downloaded from https://royalsocietypublishing.org/ on 19 June 2024",
      "Research",
      "Cite this article: Xue C, Lin TY, Chang D, Guo",
      "Z. 2017 Thioflavin T as an amyloid dye: fibril",
      "quantification, optimal concentration and",
      "effect on aggregation. R. Soc. open sci.",
      "4: 160696.",
      "http://dx.doi.org/10.1098/rsos.160696",
      "Received: 21 September 2016",
      "Accepted: 28 November 2016",
      "Subject Category:",
      "Biochemistry and biophysics",
      "Subject Areas:",
      "biochemistry"
    ]
  },
  "To read/A Baseline for Detecting Misclassified and Out-of-Distribution_Hendrycks 2017.pdf": {
    "pdf_title": "A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks",
    "pdf_author": "Dan Hendrycks and Kevin Gimpel",
    "arxiv": "1610.02136v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2017",
      "A BASELINE FOR D ETECTING M ISCLASSIFIED AND",
      "O UT- OF -D ISTRIBUTION E XAMPLES",
      "IN N EURAL N ETWORKS",
      "arXiv:1610.02136v3 [cs.NE] 3 Oct 2018",
      "Dan Hendrycks∗",
      "University of California, Berkeley",
      "hendrycks@berkeley.edu",
      "Kevin Gimpel",
      "Toyota Technological Institute at Chicago",
      "kgimpel@ttic.edu",
      "A BSTRACT",
      "We consider the two related problems of detecting if an example is misclassified or",
      "out-of-distribution. We present a simple baseline that utilizes probabilities from",
      "softmax distributions. Correctly classified examples tend to have greater maximum softmax probabilities than erroneously classified and out-of-distribution examples, allowing for their detection. We assess performance by defining several tasks in computer vision, natural language processing, and automatic speech"
    ]
  },
  "To read/A Deep Learning Approach for Brain Tumor Classification.pdf": {
    "pdf_title": "A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network",
    "pdf_author": "Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez and David González-Ortega",
    "arxiv": "",
    "doi": "10.3390/healthcare9020153",
    "first_lines": [
      "healthcare",
      "Article",
      "A Deep Learning Approach for Brain Tumor Classification",
      "and Segmentation Using a Multiscale Convolutional",
      "Neural Network",
      "Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela",
      "and David González-Ortega *",
      ", Míriam Antón-Rodríguez",
      "Department of Signal Theory, Communications and Telematics Engineering, Telecommunications Engineering",
      "School, University of Valladolid, 47011 Valladolid, Spain; pacper@tel.uva.es (F.J.D.-P.);",
      "marmar@tel.uva.es (M.M.-Z.); mirant@tel.uva.es (M.A.-R.)",
      "* Correspondence: davgon@tel.uva.es; Tel.: +34-983-423-000 (ext. 5552)",
      "\u0001\u0002\u0003\u0001\u0004\u0005\u0006\u0007\b\u0001",
      "\u0001\u0002\u0003\u0004\u0005\u0006\u0007",
      "Citation: Díaz-Pernas, F.J.;"
    ]
  },
  "To read/A Simple Baseline for Bayesian Uncertainty_Maddox 2019.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "1902.02476v2",
    "doi": "",
    "first_lines": [
      "A Simple Baseline for Bayesian Uncertainty",
      "in Deep Learning",
      "arXiv:1902.02476v2 [cs.LG] 31 Dec 2019",
      "Wesley J. Maddox∗1 Timur Garipov∗2 Pavel Izmailov∗1",
      "Dmitry Vetrov2,3 Andrew Gordon Wilson1",
      "1",
      "New York University",
      "Samsung AI Center Moscow",
      "3",
      "Samsung-HSE Laboratory, National Research University Higher School of Economics",
      "2",
      "Abstract",
      "We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose",
      "approach for uncertainty representation and calibration in deep learning. Stochastic",
      "Weight Averaging (SWA), which computes the first moment of stochastic gradient"
    ]
  },
  "To read/A Survey on Graph Neural Networks for Time Series_Forecasting, classification, imputation, anomaly detection_Jin 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2307.03759v3",
    "doi": "",
    "first_lines": [
      "JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021",
      "1",
      "A Survey on Graph Neural Networks for Time",
      "Series: Forecasting, Classification, Imputation,",
      "and Anomaly Detection",
      "arXiv:2307.03759v3 [cs.LG] 9 Aug 2024",
      "Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Fellow, IEEE,",
      "Geoffrey I. Webb, Fellow, IEEE, Irwin King, Fellow, IEEE, Shirui Pan, Senior Member, IEEE",
      "Abstract—Time series are the primary data type used to record dynamic system measurements and generated in great volume by",
      "both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of",
      "information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in",
      "GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships,",
      "which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of",
      "graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification,",
      "anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance"
    ]
  },
  "To read/A review on learning to solve combinatorial optimisation problems _Cong Zhang 2023.pdf": {
    "pdf_title": "A review on learning to solve combinatorial optimisation problems in manufacturing",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1049/cim2.12072",
    "first_lines": [
      "Received: 5 July 2022",
      "- Revised: 9 November 2022",
      "Accepted: 15 December 2022",
      "DOI: 10.1049/cim2.12072",
      "REVIEW",
      "-",
      "IET Collaborative Intelligent Manufacturing",
      "A review on learning to solve combinatorial optimisation",
      "problems in manufacturing",
      "Cong Zhang1",
      "| Yaoxin Wu1 | Yining Ma2",
      "Zhiguang Cao5 | Jie Zhang1",
      "| Wen Song3 | Zhang Le4 |",
      "1",
      "School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore"
    ]
  },
  "To read/AI pharmaco.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.3389/fphar.2024.1330855",
    "first_lines": [
      "TYPE Original Research",
      "PUBLISHED 16 February 2024",
      "DOI 10.3389/fphar.2024.1330855",
      "OPEN ACCESS",
      "EDITED BY",
      "Maria Grazia Morgese,",
      "University of Foggia, Italy",
      "REVIEWED BY",
      "Natalícia De Jesus Antunes,",
      "State University of Campinas, Brazil",
      "Ajay Vikram Singh,",
      "Federal Institute for Risk Assessment (BfR),",
      "Germany",
      "*CORRESPONDENCE",
      "Bo Liu,"
    ]
  },
  "To read/AUTOMATIC CHAIN OF THOUGHT PROMPTING_ZHANG 2022..pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2210.03493v1",
    "doi": "",
    "first_lines": [
      "AUTOMATIC C HAIN OF T HOUGHT P ROMPTING",
      "IN L ARGE L ANGUAGE M ODELS",
      "arXiv:2210.03493v1 [cs.CL] 7 Oct 2022",
      "Zhuosheng Zhang†∗, Aston Zhang‡ , Mu Li‡ , Alex Smola‡",
      "†",
      "Shanghai Jiao Tong University, ‡ Amazon Web Services",
      "A BSTRACT",
      "Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning",
      "steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting.",
      "CoT prompting has two major paradigms. One leverages a simple prompt like “Let’s think step by",
      "step” to facilitate step-by-step thinking before answering a question. The other uses a few manual",
      "demonstrations one by one, each composed of a question and a reasoning chain that leads to an",
      "answer. The superior performance of the second paradigm hinges on the hand-crafting of task-specific",
      "demonstrations one by one. We show that such manual efforts may be eliminated by leveraging",
      "LLMs with the “Let’s think step by step” prompt to generate reasoning chains for demonstrations one"
    ]
  },
  "To read/Active learning_Aggarwal 2014.pdf": {
    "pdf_title": "Active Learning: A Survey",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Chapter 22",
      "Active Learning: A Survey",
      "Charu C. Aggarwal",
      "IBM T. J. Watson Research Center",
      "Yorktown Heights, NY",
      "charu@us.ibm.com",
      "Xiangnan Kong",
      "University of Illinois at Chicago",
      "Chicago, IL",
      "xkong4@uic.edu",
      "Quanquan Gu",
      "University of Illinois at Urbana-Champaign",
      "Urbana, IL",
      "qgu3@illinois.edu",
      "Jiawei Han"
    ]
  },
  "To read/Acute stroke detection segmentation_Review_Wang 2024.pdf": {
    "pdf_title": "Preprint - 10.2196/preprints.59711",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.2196/preprints.59711",
    "first_lines": [
      "JMIR Preprints",
      "Wang et al",
      "Artificial Intelligence for Diagnosing Acute Stroke: A",
      "25-Year Retrospective",
      "Zhaoxin Wang, Wenwen Yang, Zhengyu Li, Ze Rong, Xing Wang, Jincong Han,",
      "Lei Ma",
      "Submitted to: Journal of Medical Internet Research",
      "on: April 20, 2024",
      "Disclaimer: © The authors. All rights reserved. This is a privileged document currently under peer-review/community",
      "review. Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for",
      "review purposes only. While the final peer-reviewed paper may be licensed under a CC BY license on publication, at this",
      "stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.",
      "https://preprints.jmir.org/preprint/59711",
      "[unpublished, non-peer-reviewed preprint]",
      "JMIR Preprints"
    ]
  },
  "To read/Aligning Books and Movies.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1506.06724v1",
    "doi": "",
    "first_lines": [
      "Aligning Books and Movies: Towards Story-like Visual Explanations by",
      "Watching Movies and Reading Books",
      "Yukun Zhu ∗,1 Ryan Kiros*,1 Richard Zemel1 Ruslan Salakhutdinov1",
      "Raquel Urtasun1 Antonio Torralba2 Sanja Fidler1",
      "1",
      "University of Toronto 2 Massachusetts Institute of Technology",
      "arXiv:1506.06724v1 [cs.CV] 22 Jun 2015",
      "{yukun,rkiros,zemel,rsalakhu,urtasun,fidler}@cs.toronto.edu, torralba@csail.mit.edu",
      "Abstract",
      "Books are a rich source of both fine-grained information,",
      "how a character, an object or a scene looks like, as well as",
      "high-level semantics, what someone is thinking, feeling and",
      "how these states evolve through a story. This paper aims to",
      "align books to their movie releases in order to provide rich",
      "descriptive explanations for visual content that go semantically far beyond the captions available in current datasets."
    ]
  },
  "To read/AlphaFold 3.pdf": {
    "pdf_title": "Accurate structure prediction of biomolecular interactions with AlphaFold 3",
    "pdf_author": "Josh Abramson",
    "arxiv": "",
    "doi": "10.1038/s41586-024-07487-w",
    "first_lines": [
      "Article",
      "Accurate structure prediction of",
      "biomolecular interactions with AlphaFold 3",
      "https://doi.org/10.1038/s41586-024-07487-w",
      "Received: 19 December 2023",
      "Accepted: 29 April 2024",
      "Published online: 8 May 2024",
      "Open access",
      "Check for updates",
      "Josh Abramson1,7, Jonas Adler1,7, Jack Dunger1,7, Richard Evans1,7, Tim Green1,7,",
      "Alexander Pritzel1,7, Olaf Ronneberger1,7, Lindsay Willmore1,7, Andrew J. Ballard1,",
      "Joshua Bambrick2, Sebastian W. Bodenstein1, David A. Evans1, Chia-Chun Hung2,",
      "Michael O’Neill1, David Reiman1, Kathryn Tunyasuvunakool1, Zachary Wu1, Akvilė Žemgulytė1,",
      "Eirini Arvaniti3, Charles Beattie3, Ottavia Bertolli3, Alex Bridgland3, Alexey Cherepanov4,",
      "Miles Congreve4, Alexander I. Cowen-Rivers3, Andrew Cowie3, Michael Figurnov3,"
    ]
  },
  "To read/AlphaFold_Jumper2021.pdf": {
    "pdf_title": "Highly accurate protein structure prediction with AlphaFold",
    "pdf_author": "John Jumper",
    "arxiv": "",
    "doi": "10.1038/s41586-021-03819-2",
    "first_lines": [
      "Article",
      "Highly accurate protein structure prediction",
      "with AlphaFold",
      "https://doi.org/10.1038/s41586-021-03819-2",
      "Received: 11 May 2021",
      "Accepted: 12 July 2021",
      "Published online: 15 July 2021",
      "Open access",
      "Check for updates",
      "John Jumper1,4 ✉, Richard Evans1,4, Alexander Pritzel1,4, Tim Green1,4, Michael Figurnov1,4,",
      "Olaf Ronneberger1,4, Kathryn Tunyasuvunakool1,4, Russ Bates1,4, Augustin Žídek1,4,",
      "Anna Potapenko1,4, Alex Bridgland1,4, Clemens Meyer1,4, Simon A. A. Kohl1,4,",
      "Andrew J. Ballard1,4, Andrew Cowie1,4, Bernardino Romera-Paredes1,4, Stanislav Nikolov1,4,",
      "Rishub Jain1,4, Jonas Adler1, Trevor Back1, Stig Petersen1, David Reiman1, Ellen Clancy1,",
      "Michal Zielinski1, Martin Steinegger2,3, Michalina Pacholska1, Tamas Berghammer1,"
    ]
  },
  "To read/Amortized Variational Inference: When and Why_Margossian 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2307.11018v4",
    "doi": "",
    "first_lines": [
      "Amortized Variational Inference: When and Why?",
      "Charles C. Margossian1",
      "David M. Blei2",
      "1",
      "arXiv:2307.11018v4 [stat.ML] 23 May 2024",
      "2",
      "1",
      "Center for Computational Mathematics , Flatiron Institute , New York, New York, USA",
      "Department of Computer Science and Statistics, Columbia University, New York, New York, USA",
      "Abstract",
      "Q and finds the member of the family that minimizes the",
      "Kullback-Leibler (KL) divergence",
      "In a probabilistic latent variable model, factorized",
      "(or mean-field) variational inference (F-VI) fits",
      "a separate parametric distribution for each latent"
    ]
  },
  "To read/Amyloidogenic Nanoplaques.pdf": {
    "pdf_title": "Keywords:",
    "pdf_author": "Creator:         DVIPSONE 2.2.2  http://www.YandY.com",
    "arxiv": "",
    "doi": "10.3233/JAD-200237",
    "first_lines": [
      "Journal of Alzheimer’s Disease 77 (2020) 831–842",
      "DOI 10.3233/JAD-200237",
      "IOS Press",
      "831",
      "Amyloidogenic Nanoplaques in",
      "Cerebrospinal Fluid: Relationship",
      "to Amyloid Brain Uptake and Clinical",
      "Alzheimer’s Disease in a Memory",
      "Clinic Cohort",
      "Mari Aksnesa,b,∗ , Ebba Gløersen Müllerb,c , Ann Tiimand , Trine Holt Edwinb,e,f ,",
      "Lars Tereniusd , Mona-Elisabeth Revheimb,c , Vladana Vukojevićd , Nenad Bogdanovića,b,g",
      "and Anne-Brita Knapskoge",
      "a Department of Geriatric Medicine, University of Oslo, Oslo, Norway",
      "b Institute of Clinical Medicine, University of Oslo, Oslo, Norway",
      "c Department of Nuclear Medicine, Division of Radiology and Nuclear Medicine, Oslo University Hospital,"
    ]
  },
  "To read/An introduction to Topological Data Analysis_Chazal 2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1710.04019v1",
    "doi": "",
    "first_lines": [
      "An introduction to Topological Data Analysis: fundamental",
      "and practical aspects for data scientists",
      "Frédéric Chazal and Bertrand Michel",
      "October 12, 2017",
      "arXiv:1710.04019v1 [math.ST] 11 Oct 2017",
      "Abstract",
      "Topological Data Analysis (tda) is a recent and fast growing field providing a set of",
      "new topological and geometric tools to infer relevant features for possibly complex data.",
      "This paper is a brief introduction, through a few selected topics, to basic fundamental and",
      "practical aspects of tda for non experts.",
      "1",
      "Introduction and motivation",
      "Topological Data Analysis (tda) is a recent field that emerged from various works in applied",
      "(algebraic) topology and computational geometry during the first decade of the century. Although one can trace back geometric approaches for data analysis quite far in the past, tda",
      "really started as a field with the pioneering works of Edelsbrunner et al. (2002) and Zomorodian"
    ]
  },
  "To read/Ant Colony Sampling with GFlowNets for Combinatorial optimization_KIm Minsu 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2403.07041v4",
    "doi": "",
    "first_lines": [
      "arXiv:2403.07041v4 [cs.LG] 28 Feb 2025",
      "Ant Colony Sampling with GFlowNets for",
      "Combinatorial Optimization",
      "Minsu Kim∗",
      "Mila, Université de Montréal, KAIST",
      "Sanghyeok Choi∗",
      "KAIST",
      "Hyeonah Kim†",
      "Mila, Université de Montréal",
      "Jiwoo Son",
      "Omelet",
      "Jinkyoo Park",
      "KAIST, Omelet",
      "Yoshua Bengio",
      "Mila, Université de Montréal"
    ]
  },
  "To read/Ardekani,B.A.,Bachman,A.H.,Figarsky,K.,andSidtis,J.J.(2014).Corpus callosum shape changes in early Alzheimer’s disease an MRI study using the OASISbraindatabase.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1007/s00429-013-0503-0",
    "first_lines": [
      "See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/234142326",
      "Corpus callosum shape changes in early Alzheimer's disease: An MRI study",
      "using the OASIS brain database",
      "Article in Brain Structure and Function · January 2013",
      "DOI: 10.1007/s00429-013-0503-0 · Source: PubMed",
      "CITATIONS",
      "READS",
      "71",
      "317",
      "4 authors, including:",
      "John J Sidtis",
      "Nathan Kline Institute",
      "358 PUBLICATIONS 11,275 CITATIONS",
      "SEE PROFILE",
      "All content following this page was uploaded by John J Sidtis on 27 May 2016."
    ]
  },
  "To read/Artificial Intelligence for Diagnosing Acute Stroke_Review_Wang 2024.pdf": {
    "pdf_title": "Preprint - 10.2196/preprints.59711",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.2196/preprints.59711",
    "first_lines": [
      "JMIR Preprints",
      "Wang et al",
      "Artificial Intelligence for Diagnosing Acute Stroke: A",
      "25-Year Retrospective",
      "Zhaoxin Wang, Wenwen Yang, Zhengyu Li, Ze Rong, Xing Wang, Jincong Han,",
      "Lei Ma",
      "Submitted to: Journal of Medical Internet Research",
      "on: April 20, 2024",
      "Disclaimer: © The authors. All rights reserved. This is a privileged document currently under peer-review/community",
      "review. Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for",
      "review purposes only. While the final peer-reviewed paper may be licensed under a CC BY license on publication, at this",
      "stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.",
      "https://preprints.jmir.org/preprint/59711",
      "[unpublished, non-peer-reviewed preprint]",
      "JMIR Preprints"
    ]
  },
  "To read/Artificial Intelligence in Stroke Imaging_ A Comprehensive Review_Koska 2023.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.5152/eurasianjmed.2023.23347",
    "first_lines": [
      "Review",
      "Eurasian J Med 2023; 55(Suppl. Issue: 1): S91-S97",
      "Central Nervous System",
      "Artificial Intelligence in Stroke Imaging: A Comprehensive Review",
      "Ilker Ozgur Koska1,2 , Alper Selver3,4",
      "ABSTRACT",
      "The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are",
      "important components of these chronic conditions. Stroke is the acute cessation of blood in the brain,",
      "which can lead to rapid tissue loss. Therefore, fast, accurate, and reliable automatic methods are required",
      "to facilitate stroke management. The performance of artificial intelligence (AI) methods is increasing in all",
      "domains. Vision tasks, including natural images and medical images, are particularly benefiting from the skills",
      "of AI models. The AI methods that can be applied to stroke imaging have a broad range, including classical",
      "machine learning tools such as support vector machines, random forests, logistic regression, and linear discriminant analysis, as well as deep learning models, such as convolutional neural networks, recurrent neural",
      "networks, autoencoders, and U-Net. Both tools can be applied to various aspects of stroke management,",
      "including time-to-event onset determination, stroke confirmation, large vessel occlusion detection, diffusion"
    ]
  },
  "To read/Artificial Intelligence in Stroke Imaging_A Comprehensive Review_Koska 2023.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.5152/eurasianjmed.2023.23347",
    "first_lines": [
      "Review",
      "Eurasian J Med 2023; 55(Suppl. Issue: 1): S91-S97",
      "Central Nervous System",
      "Artificial Intelligence in Stroke Imaging: A Comprehensive Review",
      "Ilker Ozgur Koska1,2 , Alper Selver3,4",
      "ABSTRACT",
      "The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are",
      "important components of these chronic conditions. Stroke is the acute cessation of blood in the brain,",
      "which can lead to rapid tissue loss. Therefore, fast, accurate, and reliable automatic methods are required",
      "to facilitate stroke management. The performance of artificial intelligence (AI) methods is increasing in all",
      "domains. Vision tasks, including natural images and medical images, are particularly benefiting from the skills",
      "of AI models. The AI methods that can be applied to stroke imaging have a broad range, including classical",
      "machine learning tools such as support vector machines, random forests, logistic regression, and linear discriminant analysis, as well as deep learning models, such as convolutional neural networks, recurrent neural",
      "networks, autoencoders, and U-Net. Both tools can be applied to various aspects of stroke management,",
      "including time-to-event onset determination, stroke confirmation, large vessel occlusion detection, diffusion"
    ]
  },
  "To read/Automatic Brain Tumor Detection and Segmentation.pdf": {
    "pdf_title": "Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks",
    "pdf_author": "Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo",
    "arxiv": "1705.03820v3",
    "doi": "",
    "first_lines": [
      "MIUA2017, 069, v3: ’Automatic Brain Tumor Detection and Segmentation Using U-Net . . .",
      "Automatic Brain Tumor Detection and Segmentation",
      "Using U-Net Based Fully Convolutional Networks",
      "Hao Dong †1, Guang Yang †2,3, Fangde Liu †1, Yuanhan Mo 1, Yike Guo ‡1",
      "1",
      "Data Science Institute, Imperial College London, SW7 2AZ, London, UK",
      "Neurosciences Research Centre, Molecular and Clinical Sciences Institute,",
      "St. George’s, University of London, London SW17 0RE, UK",
      "3",
      "National Heart and Lung Institute, Imperial College London, SW7 2AZ, London, UK",
      "2",
      "arXiv:1705.03820v3 [cs.CV] 3 Jun 2017",
      "†",
      "Co-first authors contributed equally to this study.",
      "‡"
    ]
  },
  "To read/Automatic Segmentation in Acute Ischemic Stroke_Prognosis on stroke volumes and outcome_Kelvin Wong 2022.pdf": {
    "pdf_title": "Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1161/STROKEAHA.121.037982",
    "first_lines": [
      "Stroke",
      "manual ground truth segmentation + data augmentation (rotation, reflection)",
      "CLINICAL AND POPULATION SCIENCES",
      "Automatic Segmentation in Acute Ischemic",
      "Stroke: Prognostic Significance of Topological",
      "Stroke Volumes on Stroke Outcome",
      "Kelvin K. Wong , PhD*; Jonathon S. Cummock , BS*; Guihua Li , MD*; Rahul Ghosh , MS; Pingyi Xu , MD, PhD;",
      "John J. Volpi, MD; Stephen T.C. Wong , PhD",
      "BACKGROUND: Stroke infarct volume predicts patient disability and has utility for clinical trial outcomes. Accurate infarct volume",
      "measurement requires manual segmentation of stroke boundaries in diffusion-weighted magnetic resonance imaging",
      "scans which is time-consuming and subject to variability. Automatic infarct segmentation should be robust to rotation and",
      "reflection; however, prior work has not encoded this property into deep learning architecture. Here, we use rotation-reflection",
      "equivariance and train a deep learning model to segment stroke volumes in a large cohort of well-characterized patients with",
      "acute ischemic stroke in different vascular territories.",
      "Downloaded from http://ahajournals.org by on January 5, 2026"
    ]
  },
  "To read/Automatic brain ischemic stroke segmentation with deep learning_A review_Abbasi 2023.pdf": {
    "pdf_title": "Automatic brain ischemic stroke segmentation with deep learning: A review",
    "pdf_author": "Hossein Abbasi",
    "arxiv": "",
    "doi": "10.1016/j.neuri.2023.100145",
    "first_lines": [
      "Neuroscience Informatics 3 (2023) 100145",
      "Contents lists available at ScienceDirect",
      "Neuroscience Informatics",
      "journal homepage: www.elsevier.com/locate/neuri",
      "Review",
      "Automatic brain ischemic stroke segmentation with deep learning:",
      "A review",
      "Hossein Abbasi a,∗ , Maysam Orouskhani b , Samaneh Asgari c , Sara Shomal Zadeh d",
      "a",
      "Islamic Azad University, South Tehran Branch, Tehran, Iran",
      "Department of Radiology, University of Washington, USA",
      "c",
      "Islamic Azad University, Karaj Branch, Karaj, Iran",
      "d",
      "Department of Civil and Environmental Engineering, Lamar University, USA"
    ]
  },
  "To read/Automatically Parcellating the Human Cerebral Cortex_Fischl_2004.pdf": {
    "pdf_title": "bhg087.fm",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1093/cercor/bhg087",
    "first_lines": [
      "Automatically Parcellating the Human",
      "Cerebral Cortex",
      "Bruce Fischl1,2, André van der Kouwe1, Christophe",
      "Destrieux3,4, Eric Halgren1, Florent Ségonne2, David H. Salat1,",
      "Evelina Busa1, Larry J. Seidman5,6,7, Jill Goldstein5, David",
      "Kennedy2,8, Verne Caviness7, Nikos Makris8, Bruce Rosen1 and",
      "Anders M. Dale1,2",
      "1MGH/MIT/Harvard Medical School Athinoula A. Martinos",
      "Center for Biomedical Imaging, Department of Radiology,",
      "MGH, 13th Street, Charlestown, MA 02129, USA, 2Artificial",
      "Intelligence Laboratory, MIT, 13th Street, Charlestown, MA",
      "02129, USA, 3Laboratoire d’Anatomie, Faculté de Médecine/",
      "CHRU, Tours, France, 4INSERM U316, Tours, France, 5Harvard",
      "Department of Psychiatry at Massachusetts Mental Health",
      "Center, 13th Street, Charlestown, MA 02129, USA, 6Harvard"
    ]
  },
  "To read/Biodistribution_of_gold_nanoparticles_synthesized_.pdf": {
    "pdf_title": "Biodistribution of gold nanoparticles synthesized by γ-irradiation after intravenous administration in mice",
    "pdf_author": "Quang Luan Le",
    "arxiv": "",
    "doi": "10.1088/2043-6262/5/2/025009",
    "first_lines": [
      "See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/262113070",
      "Biodistribution of gold nanoparticles synthesized by γ-irradiation after",
      "intravenous administration in mice",
      "Article in Advances in Natural Sciences Nanoscience and Nanotechnology · May 2014",
      "DOI: 10.1088/2043-6262/5/2/025009",
      "CITATIONS",
      "READS",
      "5",
      "62",
      "5 authors, including:",
      "Luan Le Quang",
      "Phuong do",
      "Biotechnology Center of Ho Chi Minh City",
      "Foreign Trade University",
      "49 PUBLICATIONS 543 CITATIONS"
    ]
  },
  "To read/Biological Sequence Design with GFlowNets_Jain 2022.pdf": {
    "pdf_title": "Subject:         Proceedings of the International Conference on Machine Learning 2022",
    "pdf_author": "Anonymous Submission",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Biological Sequence Design with GFlowNets",
      "Moksh Jain 1 2 Emmanuel Bengio 1 3 Alex-Hernandez Garcia 1 2 Jarrid Rector-Brooks 1 2",
      "Bonaventure F. P. Dossou 1 2 4 Chanakya Ekbote 1 Jie Fu 1 2 Tianyu Zhang 1 2 Michael Kilgour 5",
      "Dinghuai Zhang 1 2 Lena Simine 3 Payel Das 6 Yoshua Bengio 1 2 7",
      "Abstract",
      "Design of de novo biological sequences with desired properties, like protein and DNA sequences,",
      "often involves an active loop with several rounds",
      "of molecule ideation and expensive wet-lab evaluations. These experiments can consist of multiple",
      "stages, with increasing levels of precision and",
      "cost of evaluation, where candidates are filtered.",
      "This makes the diversity of proposed candidates",
      "a key consideration in the ideation phase. In this",
      "work, we propose an active learning algorithm",
      "leveraging epistemic uncertainty estimation and",
      "the recently proposed GFlowNets as a generator"
    ]
  },
  "To read/Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems_Kalapaaking 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2304.13360v1",
    "doi": "",
    "first_lines": [
      "1",
      "Blockchain-based Federated Learning with SMPC",
      "Model Verification Against Poisoning Attack for",
      "Healthcare Systems",
      "arXiv:2304.13360v1 [cs.CR] 26 Apr 2023",
      "Aditya Pribadi Kalapaaking, Ibrahim Khalil, Xun Yi",
      "Abstract—Due to the rising awareness of privacy and security",
      "in machine learning applications, federated learning (FL) has",
      "received widespread attention and applied to several areas, e.g.,",
      "intelligence healthcare systems, IoT-based industries, and smart",
      "cities. FL enables clients to train a global model collaboratively",
      "without accessing their local training data. However, the current",
      "FL schemes are vulnerable to adversarial attacks. Its architecture",
      "makes detecting and defending against malicious model updates",
      "difficult. In addition, most recent studies to detect FL from"
    ]
  },
  "To read/Blockchain-empowered Federated Learning_Review_Cai 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2403.00873v2",
    "doi": "",
    "first_lines": [
      "arXiv:2403.00873v2 [cs.CR] 5 Jul 2024",
      "B LOCKCHAIN - EMPOWERED F EDERATED L EARNING : B ENEFITS ,",
      "C HALLENGES , AND S OLUTIONS",
      "Zeju Cai, Jianguo Chen, Yuting Fan, Zibin Zheng",
      "School of Software Engineering",
      "Sun Yat-sen University",
      "Zhuhai, Guangdong, P.R China",
      "Keqin Li",
      "Department of Computer Science",
      "State University of New York",
      "New Paltz, NY, USA",
      "A BSTRACT",
      "Federated learning (FL) is a distributed machine learning approach that protects user data privacy",
      "by training models locally on clients and aggregating them on a parameter server. While effective at",
      "preserving privacy, FL systems face limitations such as single points of failure, lack of incentives,"
    ]
  },
  "To read/Brain Morphology Normative modelling platform for abnormality and centile estimation_Brain Monocle_Bethany Little 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2406.01107v4",
    "doi": "",
    "first_lines": [
      "Brain Morphology Normative modelling platform for",
      "arXiv:2406.01107v4 [q-bio.NC] 23 Apr 2025",
      "abnormality and Centile estimation: Brain MoNoCle",
      "Bethany Little1,2 , Nida Alyas1 , Alexander Surtees3 ,",
      "Gavin P Winston4,5 , John S Duncan4 , David A Cousins2,6 , John-Paul Taylor2 ,",
      "Peter Taylor1,2,4 , Karoline Leiberg1 ‡, Yujiang Wang1,2,4∗‡",
      "April 25, 2025",
      "1. CNNP Lab (www.cnnp-lab.com), School of Computing, Newcastle University; Newcastle",
      "upon Tyne, United Kingdom.",
      "2. Faculty of Medical Sciences, Newcastle University; Newcastle upon Tyne, United Kingdom.",
      "3. Research Software Engineers, Newcastle University; Newcastle-upon-Tyne, United Kingdom.",
      "4. UCL Queen Square Institute of Neurology; Queen Square, London, United Kingdom.",
      "5. Department of Medicine (Division of Neurology), Queen’s University; Kingston, Canada.",
      "6. Cumbria, Northumberland Tyne and Wear NHS Foundation Trust; Newcastle upon Tyne,",
      "United Kingdom."
    ]
  },
  "To read/CPT Pharmacom   Syst Pharma - 2019 - Fidler - Nonlinear Mixed‐Effects Model Development and Simulation Using nlmixr and.pdf": {
    "pdf_title": "Nonlinear Mixed‐Effects Model Development and Simulation Using nlmixr and Related R Open‐Source Packages",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1002/psp4.12445",
    "first_lines": [
      "Citation: CPT Pharmacometrics Syst. Pharmacol. (2019) 8, 621–633; doi:10.1002/psp4.12445",
      "TUTORIAL",
      "Nonlinear Mixed-Effects Model Development and",
      "Simulation Using nlmixr and Related R Open-Source",
      "Packages",
      "Matthew Fidler1,*, Justin J. Wilkins2, Richard Hooijmaijers3, Teun M. Post3, Rik Schoemaker2, Mirjam N. Trame4, Yuan Xiong5 and",
      "Wenping Wang6",
      "nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint",
      "PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional",
      "compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe",
      "that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and",
      "Phoenix NLME.",
      "This tutorial explains the motivation behind the development of an open-source model development tool in R (R",
      "Foundation for Statistical Computing, Vienna Austria) and",
      "demonstrates model building principles over a series of"
    ]
  },
  "To read/CdTe_and_CdSe_quantum_dots_synthesis_characterizat.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "10.1088/2043-6262/3/4/043001",
    "first_lines": [
      "Home",
      "Search",
      "Collections",
      "Journals",
      "About",
      "Contact us",
      "My IOPscience",
      "CdTe and CdSe quantum dots: synthesis, characterizations and applications in agriculture",
      "This content has been downloaded from IOPscience. Please scroll down to see the full text.",
      "2012 Adv. Nat. Sci: Nanosci. Nanotechnol. 3 043001",
      "(http://iopscience.iop.org/2043-6262/3/4/043001)",
      "View the table of contents for this issue, or go to the journal homepage for more",
      "Download details:",
      "IP Address: 113.98.123.3",
      "This content was downloaded on 28/09/2013 at 23:14"
    ]
  },
  "To read/Cerebrospinal fluid biomarker supported diagnosis.pdf": {
    "pdf_title": "OP-BRAI120155 3051..3061",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1093/brain/aws238",
    "first_lines": [
      "doi:10.1093/brain/aws238",
      "Brain 2012: 135; 3051–3061",
      "| 3051",
      "BRAIN",
      "A JOURNAL OF NEUROLOGY",
      "Cerebrospinal fluid biomarker supported diagnosis",
      "of Creutzfeldt–Jakob disease and rapid dementias:",
      "a longitudinal multicentre study over 10 years",
      "Katharina Stoeck,1 Pascual Sanchez-Juan,2 Joanna Gawinecka,1 Alison Green,3 Anna Ladogana,4",
      "Maurizio Pocchiari,4 Raquel Sanchez-Valle,5 Eva Mitrova,6 Theodor Sklaviadis,7 Jerzy Kulczycki,8",
      "Dana Slivarichova,6 Albert Saiz,5 Miguel Calero,9 Richard Knight,3 Adriano Aguzzi,10",
      "Jean-Louis Laplanche,11 Katell Peoc’h,11 Gabi Schelzke,1 Andre Karch,1 Cornelia M. van Duijn12",
      "and Inga Zerr1",
      "1 Department of Neurology, National Reference Centre for Transmissible Spongiform Encephalopathies, Georg-August-University, 37075",
      "Göttingen, Germany"
    ]
  },
  "To read/Chain-of-Thought Prompting Elicits Reasoning_WEi 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2201.11903v6",
    "doi": "",
    "first_lines": [
      "Chain-of-Thought Prompting Elicits Reasoning",
      "in Large Language Models",
      "Jason Wei",
      "arXiv:2201.11903v6 [cs.CL] 10 Jan 2023",
      "Brian Ichter",
      "Xuezhi Wang",
      "Fei Xia",
      "Dale Schuurmans",
      "Ed H. Chi",
      "Quoc V. Le",
      "Maarten Bosma",
      "Denny Zhou",
      "Google Research, Brain Team",
      "{jasonwei,dennyzhou}@google.com",
      "Abstract"
    ]
  },
  "To read/Chameleon_ Mixed-Modal Early-Fusion Foundation_Meta_2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2405.09818v2",
    "doi": "",
    "first_lines": [
      "Chameleon: Mixed-Modal Early-Fusion Foundation",
      "Models",
      "Chameleon Team1,∗",
      "1",
      "arXiv:2405.09818v2 [cs.CL] 21 Mar 2025",
      "∗",
      "FAIR at Meta",
      "See Contributions section for full author list.",
      "We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training",
      "approach from inception, an alignment recipe, and an architectural parameterization tailored for the",
      "early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range",
      "of tasks, including visual question answering, image captioning, text generation, image generation, and",
      "long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including",
      "state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while",
      "being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image"
    ]
  },
  "To read/Cheetah_Secure distributed inference framework_Zhicong 2022.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference",
      "(Full Version)",
      "Zhicong Huang",
      "Alibaba Group",
      "Wen-jie Lu∗",
      "Alibaba Group",
      "Cheng Hong",
      "Alibaba Group",
      "Abstract",
      "and fulfill both parties’ requirements: The participant(s) could",
      "learn the inference result F(x) but nothing else beyond what",
      "can be derived from F(x). A possible application is privacypreserving face recognition, where the server could identify",
      "criminals from photos without viewing the photo contents.",
      "Unfortunately, there still exist performance gaps between",
      "the current 2PC-NN inference systems and real-world applications. Because of large computation and communication overhead, those systems have been limited to small datasets (such"
    ]
  },
  "To read/Cold Diffusion_Diffusion without noise_Bansal 2022.pdf": {
    "pdf_title": "Overleaf Example",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2208.09392v1",
    "doi": "",
    "first_lines": [
      "Arpit Bansal1 Eitan Borgnia∗1 Hong-Min Chu∗1 Jie S. Li1",
      "Hamid Kazemi1 Furong Huang1 Micah Goldblum2",
      "Jonas Geiping1 Tom Goldstein1",
      "1",
      "University of Maryland 2 New York University",
      "Abstract",
      "Standard diffusion models involve an image transform – adding Gaussian noise –",
      "and an image restoration operator that inverts this degradation. We observe that the",
      "generative behavior of diffusion models is not strongly dependent on the choice",
      "of image degradation, and in fact an entire family of generative models can be",
      "constructed by varying this choice. Even when using completely deterministic",
      "degradations (e.g., blur, masking, and more), the training and test-time update",
      "rules that underlie diffusion models can be easily generalized to create generative",
      "models. The success of these fully deterministic models calls into question the",
      "community’s understanding of diffusion models, which relies on noise in either"
    ]
  },
  "To read/Comorbidity Analysis with Bayesian nonparametric_Ruiz 2014.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Journal of Machine Learning Research 15 (2014) 1215-1247",
      "Submitted 1/13; Revised 12/13; Published 4/14",
      "Bayesian Nonparametric Comorbidity Analysis of",
      "Psychiatric Disorders",
      "Francisco J. R. Ruiz∗",
      "Isabel Valera∗",
      "franrruiz@tsc.uc3m.es",
      "ivalera@tsc.uc3m.es",
      "Department of Signal Processing and Communications",
      "University Carlos III in Madrid",
      "Avda. de la Universidad, 30",
      "28911 Leganés (Madrid, Spain)",
      "Carlos Blanco",
      "cblanco@nyspi.columbia.edu",
      "Department of Psychiatry, New York State Psychiatric Institute"
    ]
  },
  "To read/Computational Approaches for Stratifying psychiatric disorders_Marquand 2016.pdf": {
    "pdf_title": "Beyond Lumping and Splitting_ A Review of Computational Approaches for Stratifying Psychiatric Disorders",
    "pdf_author": "Andre F. Marquand",
    "arxiv": "",
    "doi": "10.1016/j.bpsc.2016.04.002",
    "first_lines": [
      "Biological",
      "Psychiatry:",
      "CNNI",
      "Review",
      "Beyond Lumping and Splitting: A Review of",
      "Computational Approaches for Stratifying",
      "Psychiatric Disorders",
      "Andre F. Marquand, Thomas Wolfers, Maarten Mennes, Jan Buitelaar, and",
      "Christian F. Beckmann",
      "ABSTRACT",
      "Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease",
      "course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and",
      "developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders,",
      "aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed",
      "interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the"
    ]
  },
  "To read/Conceptualizing mental disorders as deviations from normative functioning_Marquand 2019.pdf": {
    "pdf_title": "Conceptualizing mental disorders as deviations from normative functioning",
    "pdf_author": "Andre F. Marquand",
    "arxiv": "",
    "doi": "10.1038/s41380-019-0441-1",
    "first_lines": [
      "Molecular Psychiatry (2019) 24:1415–1424",
      "https://doi.org/10.1038/s41380-019-0441-1",
      "REVIEW ARTICLE",
      "Conceptualizing mental disorders as deviations from",
      "normative functioning",
      "This article has been corrected since Advance Online Publication and a correction is also",
      "printed in this issue",
      "Andre F. Marquand1,2,3 Seyed Mostafa Kia",
      "Christian F. Beckmann1,2,5",
      "●",
      "1,2",
      "●",
      "Mariam Zabihi1,2 Thomas Wolfers1,2 Jan K. Buitelaar",
      "●",
      "●"
    ]
  },
  "To read/Conformal Prediction_Angelopoulos 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2107.07511v6",
    "doi": "",
    "first_lines": [
      "A Gentle Introduction to Conformal Prediction and",
      "Distribution-Free Uncertainty Quantification",
      "Anastasios N. Angelopoulos and Stephen Bates",
      "arXiv:2107.07511v6 [cs.LG] 7 Dec 2022",
      "December 8, 2022",
      "Abstract",
      "Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction (a.k.a. conformal inference) is a user-friendly paradigm for creating statistically rigorous uncertainty",
      "sets/intervals for the predictions of such models. Critically, the sets are valid in a distribution-free sense:",
      "they possess explicit, non-asymptotic guarantees even without distributional assumptions or model assumptions. One can use conformal prediction with any pre-trained model, such as a neural network, to",
      "produce sets that are guaranteed to contain the ground truth with a user-specified probability, such as",
      "90%. It is easy-to-understand, easy-to-use, and general, applying naturally to problems arising in the",
      "fields of computer vision, natural language processing, deep reinforcement learning, and so on.",
      "This hands-on introduction is aimed to provide the reader a working understanding of conformal",
      "prediction and related distribution-free uncertainty quantification techniques with one self-contained",
      "document. We lead the reader through practical theory for and examples of conformal prediction and"
    ]
  },
  "To read/Constraint Active Search for Multiobjective design_Malkomes 2021.pdf": {
    "pdf_title": "Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design",
    "pdf_author": "Gustavo Malkomes, Bolong Cheng, Eric Hans Lee, Michael McCourt",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Beyond the Pareto Efficient Frontier: Constraint Active Search for",
      "Multiobjective Experimental Design",
      "Gustavo Malkomes 1 Bolong Cheng 1 Eric Hans Lee 1 Michael McCourt 1",
      "Abstract",
      "Many problems in engineering design and simulation require balancing competing objectives under",
      "the presence of uncertainty. Sample-efficient multiobjective optimization methods focus on the objective function values in metric space and ignore",
      "the sampling behavior of the design configurations in parameter space. Consequently, they may",
      "provide little actionable insight on how to choose",
      "designs in the presence of metric uncertainty or",
      "limited precision when implementing a chosen",
      "design. We propose a new formulation that accounts for the importance of the parameter space",
      "and is thus more suitable for multiobjective design problems; instead of searching for the Paretoefficient frontier, we solicit the desired minimum",
      "performance thresholds on all objectives to define",
      "regions of satisfaction. We introduce an active",
      "search algorithm called Expected Coverage Improvement (ECI) to efficiently discover the region"
    ]
  },
  "To read/Cortical Surface-Based Analysis (FreeSurfer pipeline)_Dale 1998_1.pdf": {
    "pdf_title": "Cortical Surface-Based Analysis",
    "pdf_author": "Dale, A. M., et al",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "NeuroImage 9, 179–194 (1999)",
      "Article ID nimg.1998.0395, available online at http://www.idealibrary.com on",
      "Cortical Surface-Based Analysis",
      "I. Segmentation and Surface Reconstruction",
      "Anders M. Dale,*,1 Bruce Fischl,* and Martin I. Sereno†",
      "*Nuclear Magnetic Resonance Center, Massachusetts General Hosp/Harvard Medical School, Building 149, 13th Street,",
      "Charlestown, Massachusetts 02129; and †Department of Cognitive Science, University of California at San Diego, Mail Code 0515,",
      "9500 Gilman Drive, La Jolla, California 92093-0515",
      "Received May 22, 1998",
      "Several properties of the cerebral cortex, including",
      "its columnar and laminar organization, as well as the",
      "topographic organization of cortical areas, can only be",
      "properly understood in the context of the intrinsic",
      "two-dimensional structure of the cortical surface. In",
      "order to study such cortical properties in humans, it is"
    ]
  },
  "To read/Cortical Surface-Based Analysis (FreeSurfer pipeline)_Dale 1998_2.pdf": {
    "pdf_title": "Cortical Surface-Based Analysis",
    "pdf_author": "Fischl, B., et al",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "NeuroImage 9, 195–207 (1999)",
      "Article ID nimg.1998.0396, available online at http://www.idealibrary.com on",
      "Cortical Surface-Based Analysis",
      "II: Inflation, Flattening, and a Surface-Based Coordinate System",
      "Bruce Fischl,* Martin I. Sereno,† and Anders M. Dale*,1",
      "*Nuclear Magnetic Resonance Center, Massachusetts General Hosp/Harvard Medical School, Building 149, 13th Street,",
      "Charlestown, Massachusetts 02129; and †Department of Cognitive Science, University of California at San Diego,",
      "Mailcode 0515, 9500 Gilman Drive, La Jolla, California 92093-0515",
      "Received May 27, 1998",
      "The surface of the human cerebral cortex is a highly",
      "folded sheet with the majority of its surface area",
      "buried within folds. As such, it is a difficult domain for",
      "computational as well as visualization purposes. We",
      "have therefore designed a set of procedures for modifying the representation of the cortical surface to (i)",
      "inflate it so that activity buried inside sulci may be"
    ]
  },
  "To read/Cortical complexity estimation using fractal dimension_A systematic review_Maregalli 2022.pdf": {
    "pdf_title": "Cortical complexity estimation using fractal dimension: A systematic review of the literature on clinical and nonclinical samples",
    "pdf_author": "Valentina Meregalli, Francesco Alberti, Christopher R. Madan, Paolo Meneguzzo, Alessandro Miola, Nicolò Trevisan, Fabio Sambataro, Angela Favaro, Enrico Collantoni",
    "arxiv": "",
    "doi": "10.1111/ejn.15631",
    "first_lines": [
      "Received: 2 December 2021",
      "Revised: 27 January 2022",
      "Accepted: 20 February 2022",
      "DOI: 10.1111/ejn.15631",
      "REVIEW ARTICLE",
      "Cortical complexity estimation using fractal dimension: A",
      "systematic review of the literature on clinical and",
      "nonclinical samples",
      "Valentina Meregalli1,2",
      "Paolo Meneguzzo1",
      "|",
      "Fabio Sambataro1,2",
      "| Francesco Alberti1",
      "| Christopher R. Madan3",
      "Alessandro Miola1,2"
    ]
  },
  "To read/Cross-table Pretraining for Tabular Transformers.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "XTab: Cross-table Pretraining for Tabular Transformers",
      "Bingzhao Zhu 1 2 * Xingjian Shi 3 † Nick Erickson 4 Mu Li 3 † George Karypis 4 Mahsa Shoaran 1",
      "Abstract",
      "tokens, tables are highly data-specific. Tabular data can",
      "vary in the number and types of columns. This makes it",
      "difficult for tabular deep learning models to transfer the",
      "knowledge learned from one table to another, leading to",
      "poor generalization abilities. Therefore, self-supervised",
      "learning for tabular data (He et al., 2022; Devlin et al., 2018),",
      "particularly one that is able to bootstrap the learning on new",
      "tables, is still an open problem.",
      "The success of self-supervised learning in computer vision and natural language processing has",
      "motivated pretraining methods on tabular data.",
      "However, most existing tabular self-supervised",
      "learning models fail to leverage information"
    ]
  },
  "To read/DIMON_Learning Solution Operators of Partial Differential Equations_Diffeomorphic_Yin_Brody_2024.pdf": {
    "pdf_title": "DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains",
    "pdf_author": "Minglang Yina; Nicolas Charonb; Ryan Brodya,e; Lu Luc; Natalia Trayanovaa,d,e; Mauro Maggionid,f;",
    "arxiv": "2402.07250v1",
    "doi": "",
    "first_lines": [
      "DIMON: Learning Solution Operators of Partial Differential Equations on a",
      "Diffeomorphic Family of Domains",
      "Minglang Yina , Nicolas Charonb , Ryan Brodya,e , Lu Luc , Natalia Trayanovaa,d,e∗, Mauro Maggionid,f∗",
      "a Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD",
      "b Department of Mathematics, University of Houston, Houston, TX",
      "c Department of Statistics and Data Science, Yale University, New Haven, CT",
      "d Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD",
      "e School of Medicine, Johns Hopkins University, Baltimore, MD",
      "arXiv:2402.07250v1 [cs.LG] 11 Feb 2024",
      "f Department of Mathematics, Johns Hopkins University, Baltimore, MD",
      "Abstract",
      "The solution of a PDE over varying initial/boundary conditions on multiple domains is needed in a wide",
      "variety of applications, but it is computationally expensive if the solution is computed de novo whenever",
      "the initial/boundary conditions of the domain change. We introduce a general operator learning framework,",
      "called DIffeomorphic Mapping Operator learNing (DIMON) to learn approximate PDE solutions over a"
    ]
  },
  "To read/Data Driven Science Engineering_Brunton.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Data Driven Science & Engineering",
      "Machine Learning, Dynamical Systems, and Control",
      "Steven L. Brunton",
      "Department of Mechanical Engineering",
      "University of Washington",
      "J. Nathan Kutz",
      "Department of Applied Mathematics",
      "University of Washington",
      "ii",
      "Copyright © 2017 Brunton & Kutz. All Rights Reserved."
    ]
  },
  "To read/DeCaf_Iterative Collaborative Processing over the Edge.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "DeCaf: Iterative Collaborative Processing over the Edge",
      "Dhruv Kumar 0 , Aravind Alagiri Ramkumar 0 , Rohit Sindhu 0 , Abhishek Chandra",
      "University of Minnesota, Twin Cities",
      "Abstract",
      "The increase in privacy concerns among the users has led to",
      "edge based analytics applications such as federated learning",
      "which trains machine learning models in an iterative and collaborative fashion on the edge devices without sending the",
      "raw private data to the central cloud. In this paper, we propose a system for enabling iterative collaborative processing",
      "(ICP) in resource constrained edge environments. We first",
      "identify the unique systems challenges posed by ICP, which",
      "are not addressed by the existing distributed machine learning",
      "frameworks such as the parameter server. We then propose the",
      "system components necessary for ICP to work well in highly",
      "distributed edge environments. Based on this, we propose a",
      "system design for enabling such applications over the edge."
    ]
  },
  "To read/Deep Anomaly Detection with Outlier_Hendrycks 2019.pdf": {
    "pdf_title": "Deep Anomaly Detection with Outlier Exposure",
    "pdf_author": "Dan Hendrycks and Mantas Mazeika and Thomas Dietterich",
    "arxiv": "1812.04606v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2019",
      "D EEP A NOMALY D ETECTION WITH",
      "O UTLIER E XPOSURE",
      "Dan Hendrycks",
      "University of California, Berkeley",
      "hendrycks@berkeley.edu",
      "Mantas Mazeika",
      "University of Chicago",
      "mantas@ttic.edu",
      "Thomas Dietterich",
      "Oregon State University",
      "tgd@oregonstate.edu",
      "arXiv:1812.04606v3 [cs.LG] 28 Jan 2019",
      "A BSTRACT",
      "It is important to detect anomalous inputs when deploying machine learning"
    ]
  },
  "To read/Deep Learning-Based Desikan-Killiany Parcellation of the Brain using Diffusion MRI_Sadegheih 2025.pdf": {
    "pdf_title": "Deep Learning-Based Desikan-Killiany Parcellation of the Brain Using Diffusion MRI",
    "pdf_author": "Yousef Sadegheih; Dorit Merhof",
    "arxiv": "2508.07815v1",
    "doi": "",
    "first_lines": [
      "Deep Learning-Based Desikan-Killiany Parcellation of the",
      "arXiv:2508.07815v1 [eess.IV] 11 Aug 2025",
      "Brain Using Diffusion MRI",
      "Yousef Sadegheih,1 Dorit Merhof,1,2∗",
      "1 Faculty of Informatics and Data Science, University of Regensburg, Regensburg, 93053, Germany",
      "2 Fraunhofer Institute for Digital Medicine MEVIS, Bremen 28359, Germany",
      "∗ Correspondence: dorit.merhof@ur.de",
      "August 12, 2025",
      "Keywords: Deep Learning, Parcellation and Diffusion MRI",
      "Abstract:",
      "Accurate brain parcellation in diffusion MRI (dMRI) space is essential for ad-",
      "vanced neuroimaging analyses. However, most existing approaches rely on anatomical MRI",
      "for segmentation and inter-modality registration, a process that can introduce errors and",
      "limit the versatility of the technique. In this study, we present a novel deep learning-based",
      "framework for direct parcellation based on the Desikan-Killiany (DK) atlas using only diffusion MRI data. Our method utilizes a hierarchical, two-stage segmentation network: the"
    ]
  },
  "To read/Deep learning frameworks for MRI-based diagnosis of neurological disorders_Meta review_Ali 2025.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1007/s10462-025-11146-5",
    "first_lines": [
      "Artificial Intelligence Review (2025) 58:171",
      "https://doi.org/10.1007/s10462-025-11146-5",
      "Deep learning frameworks for MRI-based diagnosis of",
      "neurological disorders: a systematic review and metaanalysis",
      "Syed Saad Azhar Ali1,2 · Khuhed Memon3 · Norashikin Yahya3 · Shujaat Khan4,5",
      "Accepted: 11 February 2025 / Published online: 17 March 2025",
      "© The Author(s) 2025",
      "Abstract",
      "The automatic diagnosis of neurological disorders using Magnetic Resonance Imaging",
      "(MRI) is a widely researched problem. MRI is a non-invasive and highly informative",
      "imaging modality, which is one of the most widely accepted and used neuroimaging modalities for visualizing the human brain. The advent of tremendous processing capabilities,",
      "multi-modal data, and deep-learning techniques has enabled researchers to develop intelligent, sufficiently accurate classification methods. A comprehensive literature review has",
      "revealed extensive research on the automatic diagnosis of neurological disorders. However, despite numerous studies, a systematically developed framework is lacking, that",
      "relies on a sufficiently robust dataset or ensures reliable accuracy. To date, no consolidated",
      "framework has been established to classify multiple diseases and their subtypes effectively"
    ]
  },
  "To read/Deep normative modelling_multi-modal neuroimaging data to detect early onset Alzheimer disease_Aguila 2025.pdf": {
    "pdf_title": "Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data",
    "pdf_author": "Ana Lawry Aguila",
    "arxiv": "",
    "doi": "10.1186/s13195-025-01753-3",
    "first_lines": [
      "Lawry Aguila et al.",
      "Alzheimer’s Research & Therapy",
      "(2025) 17:107",
      "https://doi.org/10.1186/s13195-025-01753-3",
      "Alzheimer’s",
      "Research & Therapy",
      "Open Access",
      "RESEARCH",
      "Deep normative modelling reveals insights",
      "into early-stage Alzheimer’s disease using multimodal neuroimaging data",
      "Ana Lawry Aguila1,6*, Luigi Lorenzini2,3, Mohammed Janahi1,4, Frederik Barkhof1,2,5 and Andre Altmann1,6*",
      "Abstract",
      "Background Exploring the early stages of Alzheimer’s disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for affected individuals and their families. However, the study of the early",
      "stages of AD involves analysing heterogeneous disease cohorts which may present challenges for some modelling",
      "techniques. This heterogeneity stems from the diverse nature of AD itself, as well as the inclusion of undiagnosed"
    ]
  },
  "To read/DeepSeek LLM.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2401.02954v1",
    "doi": "",
    "first_lines": [
      "arXiv:2401.02954v1 [cs.CL] 5 Jan 2024",
      "DeepSeek LLM",
      "Scaling Open-Source Language Models with Longtermism",
      "Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng,",
      "Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, Huazuo Gao, Kaige Gao, Wenjun Gao,",
      "Ruiqi Ge, Kang Guan, Daya Guo, Jianzhong Guo, Guangbo Hao, Zhewen Hao, Ying He,",
      "Wenjie Hu, Panpan Huang, Erhang Li, Guowei Li, Jiashi Li, Yao Li, Y.K. Li, Wenfeng Liang,",
      "Fangyun Lin, A.X. Liu, Bo Liu, Wen Liu, Xiaodong Liu, Xin Liu, Yiyuan Liu, Haoyu Lu,",
      "Shanghao Lu, Fuli Luo, Shirong Ma, Xiaotao Nie, Tian Pei, Yishi Piao, Junjie Qiu, Hui Qu,",
      "Tongzheng Ren, Zehui Ren, Chong Ruan, Zhangli Sha, Zhihong Shao, Junxiao Song,",
      "Xuecheng Su, Jingxiang Sun, Yaofeng Sun, Minghui Tang, Bingxuan Wang, Peiyi Wang,",
      "Shiyu Wang, Yaohui Wang, Yongji Wang, Tong Wu, Y. Wu, Xin Xie, Zhenda Xie, Ziwei Xie,",
      "Yiliang Xiong, Hanwei Xu, R.X. Xu, Yanhong Xu, Dejian Yang, Yuxiang You, Shuiping Yu,",
      "Xingkai Yu, B. Zhang, Haowei Zhang, Lecong Zhang, Liyue Zhang, Mingchuan Zhang,",
      "Minghua Zhang, Wentao Zhang, Yichao Zhang, Chenggang Zhao, Yao Zhao,"
    ]
  },
  "To read/DeepSeekLLM.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2401.02954v1",
    "doi": "",
    "first_lines": [
      "arXiv:2401.02954v1 [cs.CL] 5 Jan 2024",
      "DeepSeek LLM",
      "Scaling Open-Source Language Models with Longtermism",
      "Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng,",
      "Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, Huazuo Gao, Kaige Gao, Wenjun Gao,",
      "Ruiqi Ge, Kang Guan, Daya Guo, Jianzhong Guo, Guangbo Hao, Zhewen Hao, Ying He,",
      "Wenjie Hu, Panpan Huang, Erhang Li, Guowei Li, Jiashi Li, Yao Li, Y.K. Li, Wenfeng Liang,",
      "Fangyun Lin, A.X. Liu, Bo Liu, Wen Liu, Xiaodong Liu, Xin Liu, Yiyuan Liu, Haoyu Lu,",
      "Shanghao Lu, Fuli Luo, Shirong Ma, Xiaotao Nie, Tian Pei, Yishi Piao, Junjie Qiu, Hui Qu,",
      "Tongzheng Ren, Zehui Ren, Chong Ruan, Zhangli Sha, Zhihong Shao, Junxiao Song,",
      "Xuecheng Su, Jingxiang Sun, Yaofeng Sun, Minghui Tang, Bingxuan Wang, Peiyi Wang,",
      "Shiyu Wang, Yaohui Wang, Yongji Wang, Tong Wu, Y. Wu, Xin Xie, Zhenda Xie, Ziwei Xie,",
      "Yiliang Xiong, Hanwei Xu, R.X. Xu, Yanhong Xu, Dejian Yang, Yuxiang You, Shuiping Yu,",
      "Xingkai Yu, B. Zhang, Haowei Zhang, Lecong Zhang, Liyue Zhang, Mingchuan Zhang,",
      "Minghua Zhang, Wentao Zhang, Yichao Zhang, Chenggang Zhao, Yao Zhao,"
    ]
  },
  "To read/Denoising diffusion implicit model_DDIM_Song 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2010.02502v4",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2021",
      "D ENOISING D IFFUSION I MPLICIT M ODELS",
      "Jiaming Song, Chenlin Meng & Stefano Ermon",
      "Stanford University",
      "{tsong,chenlin,ermon}@cs.stanford.edu",
      "arXiv:2010.02502v4 [cs.LG] 5 Oct 2022",
      "A BSTRACT",
      "Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a",
      "Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient",
      "class of iterative implicit probabilistic models with the same training procedure as",
      "DDPMs. In DDPMs, the generative process is defined as the reverse of a particular",
      "Markovian diffusion process. We generalize DDPMs via a class of non-Markovian",
      "diffusion processes that lead to the same training objective. These non-Markovian",
      "processes can correspond to generative processes that are deterministic, giving rise",
      "to implicit models that produce high quality samples much faster. We empirically"
    ]
  },
  "To read/Design-Bench_Benchmarks for Data-Driven Offline Model-Based Optimization_Trabucco 2022.pdf": {
    "pdf_title": "Subject:         Proceedings of the International Conference on Machine Learning 2022",
    "pdf_author": "Anonymous Submission",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization",
      "Brandon Trabucco * 1 Xinyang Geng * 2 Aviral Kumar 2 Sergey Levine 2",
      "Abstract",
      "Black-box model-based optimization (MBO)",
      "problems, where the goal is to find a design input that maximizes an unknown objective function, are ubiquitous in a wide range of domains,",
      "such as the design of proteins, DNA sequences,",
      "aircraft, and robots. Solving model-based optimization problems typically requires actively",
      "querying the unknown objective function on design proposals, which means physically building the candidate molecule, aircraft, or robot,",
      "testing it, and storing the result. This process",
      "can be expensive and time consuming, and one",
      "might instead prefer to optimize for the best design using only the data one already has. This",
      "setting—called offline MBO—poses substantial",
      "and different algorithmic challenges than more",
      "commonly studied online techniques. A number of recent works have demonstrated success",
      "with offline MBO for high-dimensional optimization problems using high-capacity deep neural networks. However, the lack of standardized benchmarks in this emerging field is making progress"
    ]
  },
  "To read/Diffusion Models Beat GANs on Image Synthesis (Guided diffusion)_Dhariwa 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2105.05233v4",
    "doi": "",
    "first_lines": [
      "Diffusion Models Beat GANs on Image Synthesis",
      "arXiv:2105.05233v4 [cs.LG] 1 Jun 2021",
      "Prafulla Dhariwal∗",
      "OpenAI",
      "prafulla@openai.com",
      "Alex Nichol∗",
      "OpenAI",
      "alex@openai.com",
      "Abstract",
      "We show that diffusion models can achieve image sample quality superior to the",
      "current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For",
      "conditional image synthesis, we further improve sample quality with classifier guidance: a simple, compute-efficient method for trading off diversity for fidelity using",
      "gradients from a classifier. We achieve an FID of 2.97 on ImageNet 128×128,",
      "4.59 on ImageNet 256×256, and 7.72 on ImageNet 512×512, and we match",
      "BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution. Finally, we find that classifier guidance"
    ]
  },
  "To read/Diffusion Normalizing Flow_ZHANG 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2110.07579v1",
    "doi": "",
    "first_lines": [
      "Diffusion Normalizing Flow",
      "arXiv:2110.07579v1 [cs.LG] 14 Oct 2021",
      "Qinsheng Zhang",
      "Georgia Institute of Technology",
      "qzhang419@gatech.edu",
      "Yongxin Chen",
      "Georgia Institute of Technology",
      "yongchen@gatech.edu",
      "Abstract",
      "We present a novel generative modeling method called diffusion normalizing flow",
      "based on stochastic differential equations (SDEs). The algorithm consists of two",
      "neural SDEs: a forward SDE that gradually adds noise to the data to transform the",
      "data into Gaussian random noise, and a backward SDE that gradually removes the",
      "noise to sample from the data distribution. By jointly training the two neural SDEs",
      "to minimize a common cost function that quantifies the difference between the"
    ]
  },
  "To read/Do deep generative models know what they don’t know_Nalisnick 2019.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1810.09136v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2019",
      "D O D EEP G ENERATIVE M ODELS K NOW",
      "W HAT T HEY D ON ’ T K NOW ?",
      "Eric Nalisnick∗,† Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan∗",
      "DeepMind",
      "arXiv:1810.09136v3 [stat.ML] 24 Feb 2019",
      "A BSTRACT",
      "A neural network deployed in the wild may be asked to make predictions for inputs",
      "that were drawn from a different distribution than that of the training data. A",
      "plethora of work has demonstrated that it is easy to find or synthesize inputs for",
      "which a neural network is highly confident yet wrong. Generative models are",
      "widely viewed to be robust to such mistaken confidence as modeling the density of",
      "the input features can be used to detect novel, out-of-distribution inputs. In this",
      "paper we challenge this assumption. We find that the density learned by flow-based",
      "models, VAEs, and PixelCNNs cannot distinguish images of common objects"
    ]
  },
  "To read/Domain Generalization for Medical image analysis_ Yoon 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2310.08598v2",
    "doi": "",
    "first_lines": [
      "1",
      "Domain Generalization",
      "for Medical Image Analysis: A Survey",
      "arXiv:2310.08598v2 [eess.IV] 15 Feb 2024",
      "Jee Seok Yoon, Kwanseok Oh, Yooseung Shin, Maciej A. Mazurowski, and Heung-Il Suk, Senior",
      "Member, IEEE",
      "Abstract— Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis,",
      "prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances.",
      "However, deploying DL models for MedIA in real-world situations remains challenging due to their failure to generalize across the",
      "distributional gap between training and testing samples — a problem known as domain shift. Researchers have dedicated their",
      "efforts to developing various DL methods to adapt and perform robustly on unknown and out-of-distribution data distributions. This",
      "paper comprehensively reviews domain generalization studies specifically tailored for MedIA. We provide a holistic view of how",
      "domain generalization techniques interact within the broader MedIA system, going beyond methodologies to consider the operational",
      "implications on the entire MedIA workflow. Specifically, we categorize domain generalization methods into data-level, feature-level,",
      "model-level, and analysis-level methods. We show how those methods can be used in various stages of the MedIA workflow with DL"
    ]
  },
  "To read/EfficientNet.pdf": {
    "pdf_title": "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
    "pdf_author": "Mingxing Tan, Quoc V. Le",
    "arxiv": "1905.11946v5",
    "doi": "",
    "first_lines": [
      "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks",
      "Mingxing Tan 1 Quoc V. Le 1",
      "Abstract",
      "EfficientNet-B7",
      "84",
      "To go even further, we use neural architecture search to design a new baseline network",
      "and scale it up to obtain a family of models,",
      "called EfficientNets, which achieve much",
      "better accuracy and efficiency than previous",
      "ConvNets. In particular, our EfficientNet-B7",
      "achieves state-of-the-art 84.3% top-1 accuracy",
      "on ImageNet, while being 8.4x smaller and",
      "6.1x faster on inference than the best existing",
      "ConvNet. Our EfficientNets also transfer well and",
      "achieve state-of-the-art accuracy on CIFAR-100"
    ]
  },
  "To read/Elastic Processing of Analytical Query workloads on IaaS Clouds_Herald Kllapi 2015.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "1501.01070v1",
    "doi": "",
    "first_lines": [
      "Elastic Processing of Analytical Query Workloads",
      "on IaaS Clouds",
      "arXiv:1501.01070v1 [cs.DB] 6 Jan 2015",
      "Herald Kllapi∗",
      "Panos Sakkos−",
      "Alex Delis∗+",
      "Dimitrios Gunopulos∗",
      "Yannis Ioannidis∗+",
      "*MaDgIK Lab, Dept. of Informatics and Telecommunications,",
      "University of Athens, Athens 15784, Greece.",
      "email: {herald, ad, dg, yannis}@di.uoa.gr",
      "-Microsoft, Norway, email: panoss@microsoft.com, work done at University of Athens",
      "+“Athena” Research Center, Athens 15125, Greece",
      "ABSTRACT",
      "Result"
    ]
  },
  "To read/Equalities and Inequalities_Thermodynamics at nanoscale_Jarzynski_2011.pdf": {
    "pdf_title": "Equalities and Inequalities: Irreversibility and the Second Law of Thermodynamics at the Nanoscale",
    "pdf_author": "Christopher Jarzynski",
    "arxiv": "",
    "doi": "10.1146/annurev-conmatphys-062910-140506",
    "first_lines": [
      "Annu. Rev. Condens. Matter Phys. 2011.2:329-351. Downloaded from www.annualreviews.org",
      "by University of Maryland - College Park on 03/02/11. For personal use only.",
      "Equalities and Inequalities:",
      "Irreversibility and the Second",
      "Law of Thermodynamics at",
      "the Nanoscale",
      "Christopher Jarzynski",
      "Department of Chemistry and Biochemistry and Institute for Physical Science and",
      "Technology, University of Maryland, College Park, Maryland 20742;",
      "email: cjarzyns@umd.edu",
      "Annu. Rev. Condens. Matter Phys. 2011. 2:329–51",
      "Keywords",
      "First published online as a Review in Advance on",
      "November 22, 2010",
      "second law of thermodynamics, fluctuation theorems, nonequilibrium"
    ]
  },
  "To read/Estimation of Non-Normalized Statistical Models by Score Matching_Hyvarinen_2005.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Journal of Machine Learning Research 6 (2005) 695–709",
      "Submitted 11/04; Revised 3/05; Published 4/05",
      "Estimation of Non-Normalized Statistical Models",
      "by Score Matching",
      "Aapo Hyvärinen",
      "aapo.hyvarinen@helsinki.fi",
      "Helsinki Institute for Information Technology (BRU)",
      "Department of Computer Science",
      "FIN-00014 University of Helsinki, Finland",
      "Editor: Peter Dayan",
      "Abstract",
      "One often wants to estimate statistical models where the probability density function is",
      "known only up to a multiplicative normalization constant. Typically, one then has to resort",
      "to Markov Chain Monte Carlo methods, or approximations of the normalization constant.",
      "Here, we propose that such models can be estimated by minimizing the expected squared"
    ]
  },
  "To read/Eur J Immunol - 2006 - Sioud - Single‐stranded small interfering RNA are more immunostimulatory than their double‐stranded.pdf": {
    "pdf_title": "Single‐stranded small interfering RNA are more immunostimulatory than their double‐stranded counterparts: A central role for 2′‐hydroxyl uridines in immune responses",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1002/eji.200535708",
    "first_lines": [
      "1222",
      "Mouldy Sioud",
      "Eur. J. Immunol. 2006. 36: 1222–1230",
      "Innate immunity",
      "Single-stranded small interfering RNA are more",
      "immunostimulatory than their double-stranded",
      "counterparts: A central role for 20-hydroxyl uridines in",
      "immune responses",
      "Mouldy Sioud",
      "The Norwegian Radium Hospital, Department of Immunology, Molecular Medicine",
      "Group Montebello, Oslo, Norway",
      "It has recently become apparent that certain small interfering RNA (siRNA) sequences",
      "stimulate the innate immunity through endosomal Toll-like receptors (TLR),",
      "particularly TLR7 and TLR8. However, it remains unclear whether siRNA duplexes",
      "act as specific ligands for these receptors. To address this question and to overcome the"
    ]
  },
  "To read/Exoshuffle_An Extensible Shuffle Architecture_Luan 2023.pdf": {
    "pdf_title": "Exoshuffle: An Extensible Shuffle Architecture",
    "pdf_author": "",
    "arxiv": "2203.05072v5",
    "doi": "10.1145/3603269.3604848",
    "first_lines": [
      "arXiv:2203.05072v5 [cs.DC] 18 Aug 2023",
      "Exoshuffle: An Extensible Shuffle Architecture",
      "Frank Sifei Luan",
      "Stephanie Wang",
      "Samyukta Yagati",
      "UC Berkeley",
      "Berkeley, CA, USA",
      "UC Berkeley and Anyscale",
      "Berkeley, CA, USA",
      "UC Berkeley",
      "Berkeley, CA, USA",
      "Sean Kim",
      "Kenneth Lien",
      "Isaac Ong",
      "UC Berkeley"
    ]
  },
  "To read/Exploring Large Action Sets with Hyperspherical Embeddings_Benada 2025.pdf": {
    "pdf_title": "Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling",
    "pdf_author": "Walid Bendada; Guillaume Salha-Galvan; Romain Hennequin; Théo Bontempelli; Thomas Bouabça; Tristan Cazenave",
    "arxiv": "2507.00518v1",
    "doi": "",
    "first_lines": [
      "Exploring Large Action Sets with Hyperspherical Embeddings",
      "using von Mises-Fisher Sampling",
      "Walid Bendada 1 2 Guillaume Salha-Galvan 3 Romain Hennequin 1",
      "Théo Bontempelli 1 Thomas Bouabça 1 Tristan Cazenave 2",
      "arXiv:2507.00518v1 [cs.LG] 1 Jul 2025",
      "Abstract",
      "2002; Slivkins et al., 2019; Sutton & Barto, 2018; Tang et al.,",
      "2017). However, as the set of actions to explore grows larger,",
      "the exploration process becomes increasingly challenging.",
      "Indeed, large action sets can lead to higher computational",
      "costs, longer learning times, and the risk of inadequate exploration and suboptimal policy development (Amin et al.,",
      "2021; Chen et al., 2021; Dulac-Arnold et al., 2015; Lillicrap",
      "et al., 2016; Sutton & Barto, 2018; Tomasi et al., 2023).",
      "This paper introduces von Mises-Fisher exploration (vMF-exp), a scalable method for exploring large action sets in reinforcement learning",
      "problems where hyperspherical embedding vectors represent these actions. vMF-exp involves"
    ]
  },
  "To read/FADL_Federated-Autonomous Deep Learning for distributed eletronic health record_Liu 2018.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1811.11400v2",
    "doi": "",
    "first_lines": [
      "FADL:Federated-Autonomous Deep Learning for",
      "Distributed Electronic Health Record",
      "arXiv:1811.11400v2 [cs.CY] 3 Dec 2018",
      "Dianbo Liu ,Timothy Miller, Raheel Sayeed and Kenneth D. Mandl∗",
      "Computational Health Informatics Program",
      "Boston Children’s Hospital and Harvard Medical School ,One Autumn Street Boston, MA USA, 02215",
      "Abstract",
      "Electronic health record (EHR) data is collected by individual institutions and often",
      "stored across locations in silos. Getting access to these data is difficult and slow",
      "due to security, privacy, regulatory, and operational issues. We show, using ICU",
      "data from 58 different hospitals, that machine learning models to predict patient",
      "mortality can be trained efficiently without moving health data out of their silos",
      "using a distributed machine learning strategy. We propose a new method, called",
      "Federated-Autonomous Deep Learning (FADL) that trains part of the model using",
      "all data sources in a distributed manner and other parts using data from specific"
    ]
  },
  "To read/FL-SMPC_ A robust framework for privacy-preserving federated learning_Dib 2025.pdf": {
    "pdf_title": "FL-SMPC++: A robust framework for privacy-preserving federated learning",
    "pdf_author": "Omar Dib",
    "arxiv": "",
    "doi": "10.1016/j.rineng.2025.107380",
    "first_lines": [
      "Results in Engineering 28 (2025) 107380",
      "Contents lists available at ScienceDirect",
      "Results in Engineering",
      "journal homepage: www.sciencedirect.com/journal/results-in-engineering",
      "Research paper",
      "FL-SMPC++: A robust framework for privacy-preserving federated learning",
      "Omar Dib a,b,c,∗ , Shiyun Li a,b,c, , Zhengkun Li a,b,c,d, , Rouwaida Abdallah e, ,",
      "El-hacen Diallo f ,",
      "a",
      "Department of Computer Science, Wenzhou-Kean University, 88 Daxue Rd, Ouhai, Wenzhou 325060, Zhejiang, China",
      "Department of Computer Science, Kean University, 1000 Morris Avenue, Union 07083, NJ, USA",
      "c",
      "Computer Science and Artificial Intelligence Center, Wenzhou-Kean University, 88 Daxue Rd, Ouhai, Wenzhou 325060, Zhejiang, China",
      "d",
      "Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines L2S 3A1, Ontario, Canada"
    ]
  },
  "To read/FastSurferLIT_Lesion inpainting tool for whole- brain MRI segmentation_Pollak 2025.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1162/imag_a_00446",
    "first_lines": [
      "Research Article",
      "FastSurfer-­LIT: Lesion inpainting tool for whole-­brain MRI segmentation",
      "with tumors, cavities, and abnormalities",
      "Clemens Pollaka, David Küglera, Tobias Bauerb,c, Theodor Rüberb,c,d, Martin Reutera,e,f",
      "AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany",
      "a",
      "Department of Neuroradiology, Bonn University Hospital, Bonn, Germany",
      "b",
      "Department of Epileptology, Bonn University Hospital, Bonn, Germany",
      "c",
      "Center for Medical Data Usability and Translation, University of Bonn, Bonn, Germany",
      "d",
      "A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States",
      "e",
      "Department of Radiology, Harvard Medical School, Boston, MA, United States"
    ]
  },
  "To read/FastSurfer_A fast and accurate deep learning based neuroimaging pipeline_Henschel 2020.pdf": {
    "pdf_title": "FastSurfer - A fast and accurate deep learning based neuroimaging pipeline",
    "pdf_author": "Leonie Henschel",
    "arxiv": "",
    "doi": "10.1016/j.neuroimage.2020.117012",
    "first_lines": [
      "NeuroImage 219 (2020) 117012",
      "Contents lists available at ScienceDirect",
      "NeuroImage",
      "journal homepage: www.elsevier.com/locate/neuroimage",
      "FastSurfer - A fast and accurate deep learning based neuroimaging pipeline",
      "Leonie Henschel a, Sailesh Conjeti a, Santiago Estrada a, Kersten Diers a, Bruce Fischl b, c, d,",
      "Martin Reuter a, b, c, *, 1",
      "a",
      "German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany",
      "A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston MA, USA",
      "c",
      "Department of Radiology, Harvard Medical School, Boston, MA, USA",
      "d",
      "Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA",
      "b"
    ]
  },
  "To read/FedML_A Research Library and Benchmark for federated learning_He 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2007.13518v4",
    "doi": "",
    "first_lines": [
      "arXiv:2007.13518v4 [cs.LG] 8 Nov 2020",
      "FedML: A Research Library and Benchmark for",
      "Federated Machine Learning",
      "Chaoyang He∗,",
      "Songze Li,",
      "Jinhyun So,",
      "Xiao Zeng,",
      "Mi Zhang",
      "USC",
      "Stanford",
      "USC",
      "MSU",
      "MSU",
      "Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu",
      "UW-Madison"
    ]
  },
  "To read/Federated Learning for Smart Healthcare_ Survey_Dinh 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1811.11400v2",
    "doi": "",
    "first_lines": [
      "FADL:Federated-Autonomous Deep Learning for",
      "Distributed Electronic Health Record",
      "arXiv:1811.11400v2 [cs.CY] 3 Dec 2018",
      "Dianbo Liu ,Timothy Miller, Raheel Sayeed and Kenneth D. Mandl∗",
      "Computational Health Informatics Program",
      "Boston Children’s Hospital and Harvard Medical School ,One Autumn Street Boston, MA USA, 02215",
      "Abstract",
      "Electronic health record (EHR) data is collected by individual institutions and often",
      "stored across locations in silos. Getting access to these data is difficult and slow",
      "due to security, privacy, regulatory, and operational issues. We show, using ICU",
      "data from 58 different hospitals, that machine learning models to predict patient",
      "mortality can be trained efficiently without moving health data out of their silos",
      "using a distributed machine learning strategy. We propose a new method, called",
      "Federated-Autonomous Deep Learning (FADL) that trains part of the model using",
      "all data sources in a distributed manner and other parts using data from specific"
    ]
  },
  "To read/Federated machine learning in healthcare_Teo 2024.pdf": {
    "pdf_title": "Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture",
    "pdf_author": "Zhen Ling Teo",
    "arxiv": "",
    "doi": "10.1016/j.xcrm.2024.101419",
    "first_lines": [
      "Article",
      "Federated machine learning in healthcare: A",
      "systematic review on clinical applications and",
      "technical architecture",
      "Graphical abstract",
      "Authors",
      "Zhen Ling Teo, Liyuan Jin, Siqi Li, ...,",
      "Yong Liu, Rick Siow Mong Goh,",
      "Daniel Shu Wei Ting",
      "Correspondence",
      "daniel.ting@duke-nus.edu.sg",
      "In brief",
      "Teo et al. provides a comprehensive",
      "systematic review of federated learning",
      "(FL) in health and includes 612 articles. FL"
    ]
  },
  "To read/Flow Network based Generative Models for iterative diverse candidate generation_E Bangio 2021.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Flow Network based Generative Models for",
      "Non-Iterative Diverse Candidate Generation",
      "Emmanuel Bengio1,2 , Moksh Jain1,5 , Maksym Korablyov1",
      "Doina Precup1,2,4 , Yoshua Bengio1,3",
      "1",
      "2",
      "Mila, McGill University, 3 Université de Montréal, 4 DeepMind, 5 Microsoft",
      "Abstract",
      "This paper is about the problem of learning a stochastic policy for generating",
      "an object (like a molecular graph) from a sequence of actions, such that the",
      "probability of generating an object is proportional to a given positive reward for",
      "that object. Whereas standard return maximization tends to converge to a single",
      "return-maximizing sequence, there are cases where we would like to sample a",
      "diverse set of high-return solutions. These arise, for example, in black-box function",
      "optimization when few rounds are possible, each with large batches of queries,"
    ]
  },
  "To read/Fpocket: An open source platform for ligand pocket detection.pdf": {
    "pdf_title": "Author:",
    "pdf_author": "Creator:         FrameMaker 7.1",
    "arxiv": "",
    "doi": "10.1186/1471-2105-10-168",
    "first_lines": [
      "BMC Bioinformatics",
      "BioMed Central",
      "Software",
      "Open Access",
      "Fpocket: An open source platform for ligand pocket detection",
      "Vincent Le Guilloux†1, Peter Schmidtke†2 and Pierre Tuffery*3,4",
      "Address: 1ICOA – Institut de chimie organique et analytique – UMR CNRS 6005, Div. of chemoinformatics and molecular modeling, University",
      "of Orléans, Orléans, France, 2Dpto Fisicoquimica, Fac Farmacia, Univ Barcelona, Barcelona, Spain, 3Molécules Therapeutiques in silico, INSERM,",
      "UMR-S 973, University Paris Diderot – Paris 7, Paris, France and 4Ressource Parisienne en Bioinformatique Structurale, University Paris-Diderot,",
      "Paris, France",
      "Email: Vincent Le Guilloux - vincent.le-guilloux@univ-orleans.fr; Peter Schmidtke - pschmidtke@mmb.pcb.ub.es;",
      "Pierre Tuffery* - pierre.tuffery@univ-paris-diderot.fr",
      "* Corresponding author †Equal contributors",
      "Published: 2 June 2009",
      "BMC Bioinformatics 2009, 10:168"
    ]
  },
  "To read/Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity_Franca 2017.pdf": {
    "pdf_title": "Fractal and Multifractal Properties of Electrographic Recordings of Human Brain Activity: Toward Its Use as a Signal Feature for Machine Learning in Clinical Applications",
    "pdf_author": "Lucas G. Souza França and Yujiang Wang",
    "arxiv": "",
    "doi": "10.3389/fphys.2018.01767",
    "first_lines": [
      "METHODS",
      "published: 10 December 2018",
      "doi: 10.3389/fphys.2018.01767",
      "Fractal and Multifractal Properties of",
      "Electrographic Recordings of Human",
      "Brain Activity: Toward Its Use as a",
      "Signal Feature for Machine Learning",
      "in Clinical Applications",
      "Lucas G. Souza França 1*, José G. Vivas Miranda 2 , Marco Leite 1 , Niraj K. Sharma 1 ,",
      "Matthew C. Walker 1 , Louis Lemieux 1 and Yujiang Wang 1,3,4*",
      "1",
      "Edited by:",
      "Paul Bogdan,",
      "University of Southern California,",
      "United States"
    ]
  },
  "To read/GFlowNets for AI-driven scientific discovery_Jain 2023.pdf": {
    "pdf_title": "GFlowNets for AI-driven scientific discovery",
    "pdf_author": "Moksh Jain",
    "arxiv": "",
    "doi": "10.1039/d3dd00002h",
    "first_lines": [
      "Digital",
      "Discovery",
      "View Article Online",
      "Open Access Article. Published on 05 April 2023. Downloaded on 9/4/2025 2:29:24 PM.",
      "This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.",
      "PERSPECTIVE",
      "View Journal | View Issue",
      "GFlowNets for AI-driven scientific discovery",
      "Cite this: Digital Discovery, 2023, 2, 557",
      "Moksh Jain,*ab Tristan Deleu,ab Jason Hartford,ab Cheng-Hao Liu,cb Alex HernandezGarciaab and Yoshua Bengio abd",
      "Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global",
      "pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on",
      "trial and error and even serendipity to a large extent, the last few decades have seen a surge of datadriven scientific discoveries. However, in order to truly leverage large-scale data sets and highthroughput experimental setups, machine learning methods will need to be further improved and better",
      "integrated in the scientific discovery pipeline. A key challenge for current machine learning methods in",
      "this context is the efficient exploration of very large search spaces, which requires techniques for"
    ]
  },
  "To read/Gaussian process-based prediction and detection of gray matter abnormalities_Alzheimer_Ziegler 2014.pdf.pdf": {
    "pdf_title": "Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1016/j.neuroimage.2014.04.018",
    "first_lines": [
      "NeuroImage 97 (2014) 333–348",
      "Contents lists available at ScienceDirect",
      "NeuroImage",
      "journal homepage: www.elsevier.com/locate/ynimg",
      "Individualized Gaussian process-based prediction and detection of local",
      "and global gray matter abnormalities in elderly subjects",
      "G. Ziegler a,b,⁎, G.R. Ridgway a, R. Dahnke b, C. Gaser b,c, for The Alzheimer's Disease Neuroimaging Initiative 1",
      "a",
      "b",
      "c",
      "Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK",
      "Department of Psychiatry, Jena University Hospital, Jena, Germany",
      "Department of Neurology, Jena University Hospital, Jena, Germany",
      "a r t i c l e",
      "i n f o"
    ]
  },
  "To read/Geometry in Medical Imaging_DICOM and NIFTI.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Geometry in Medical Imaging: DICOM and",
      "NIfTI formats",
      "David Atkinson [orcid.org/0000-0003-1124-6666]",
      "April 2022",
      "1",
      "Introduction",
      "The spatial relation between the pixels in a medical image and their real-world",
      "position is important for clinical image display, surgery planning, image fusion",
      "and comparison of images acquired with different pixel sizes, orientations, scanners or time points.",
      "The correct manipulation of this ‘geometry’ or ‘spatial referencing’ can be",
      "challenging. This paper aims to provide a clear description of the link between",
      "the data in the computer, array indexing and the 3D location where the image",
      "data was acquired.",
      "Coordinate systems link coordinates to spatial locations. A scanner coordinate system is usually fixed relative to the room, whereas a patient coordinate",
      "system is fixed relative to the patient. The DICOM standard [1] uses an ‘LPH+’"
    ]
  },
  "To read/Glaze_Protecting Artists from Style Mimicry.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2302.04222v5",
    "doi": "",
    "first_lines": [
      "Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models",
      "arXiv:2302.04222v5 [cs.CR] 3 Aug 2023",
      "Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, Ben Y. Zhao",
      "Department of Computer Science, University of Chicago",
      "{shawnshan, jennacryan, ewillson, htzheng, ranahanocka, ravenben}@cs.uchicago.edu",
      "Abstract",
      "Recent text-to-image diffusion models such as MidJourney",
      "and Stable Diffusion threaten to displace many in the professional artist community. In particular, models can learn to",
      "mimic the artistic style of specific artists after “fine-tuning”",
      "on samples of their art. In this paper, we describe the design,",
      "implementation and evaluation of Glaze, a tool that enables",
      "artists to apply “style cloaks” to their art before sharing online. These cloaks apply barely perceptible perturbations to",
      "images, and when used as training data, mislead generative",
      "models that try to mimic a specific artist. In coordination with",
      "the professional artist community, we deploy user studies to"
    ]
  },
  "To read/Graph Neural Networks: A Bibliometric Mapping_da Silva 2024.pdf": {
    "pdf_title": "Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications",
    "pdf_author": "Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga, Fabio Batista Mota, Victor Maricato and Luiz Anastacio Alves",
    "arxiv": "",
    "doi": "10.3390/info15100626",
    "first_lines": [
      "information",
      "Article",
      "Graph Neural Networks: A Bibliometric Mapping of the",
      "Research Landscape and Applications",
      "Annielle Mendes Brito da Silva 1,2,† , Natiele Carla da Silva Ferreira 1 , Luiza Amara Maciel Braga 1,† ,",
      "Fabio Batista Mota 1 , Victor Maricato 3 and Luiz Anastacio Alves 1, *",
      "1",
      "2",
      "3",
      "*",
      "†",
      "Citation: da Silva, A.M.B.;",
      "Ferreira, N.C.d.S.; Braga, L.A.M.;",
      "Mota, F.B.; Maricato, V.; Alves, L.A.",
      "Graph Neural Networks:"
    ]
  },
  "To read/Graph neural networks for single-cell omics data_Li 2025.pdf": {
    "pdf_title": "Graph neural networks for single-cell omics data: a review of approaches and applications",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "10.1093/bib/bbaf109",
    "first_lines": [
      "Briefings in Bioinformatics, 2025, 26(2), bbaf109",
      "https://doi.org/10.1093/bib/bbaf109",
      "Review",
      "Graph neural networks for single-cell omics data:",
      "a review of approaches and applications",
      "Sijie Li",
      "‡ , Heyang Hua",
      "‡ , Shengquan Chen",
      "*",
      "School of Mathematical Sciences and The Key Laboratory of Pure Mathematics and Combinatorics, Ministry of Education (LPMC), Nankai University, No. 94 Weijin",
      "Road, Nankai District, Tianjin 300071, China",
      "‡ Sijie Li and Heyang Hua contributed equally to this work.",
      "Abstract",
      "Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various",
      "omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced"
    ]
  },
  "To read/Graph neural networks_A review of methods and applications_Zhou 2020.pdf": {
    "pdf_title": "Graph neural networks: A review of methods and applications",
    "pdf_author": "Jie Zhou",
    "arxiv": "",
    "doi": "10.1016/j.aiopen.2021.01.001",
    "first_lines": [
      "i An update to this article is included at the end",
      "AI Open 1 (2020) 57–81",
      "Contents lists available at ScienceDirect",
      "AI Open",
      "journal homepage: www.keaipublishing.com/en/journals/ai-open",
      "Graph neural networks: A review of methods and applications",
      "Jie Zhou a, 1, Ganqu Cui a, 1, Shengding Hu a, Zhengyan Zhang a, Cheng Yang b, Zhiyuan Liu a, *,",
      "Lifeng Wang c, Changcheng Li c, Maosong Sun a",
      "a",
      "Department of Computer Science and Technology, Tsinghua University, Beijing, China",
      "School of Computer Science, Beijing University of Posts and Telecommunications, China",
      "c",
      "Tencent Incorporation, Shenzhen, China",
      "b",
      "A R T I C L E I N F O"
    ]
  },
  "To read/Group-derived and individual disconnection in stroke prognosis_Deep graph learning_Bey 2025.pdf": {
    "pdf_title": "Group-derived and individual disconnection in stroke: recovery prediction and deep graph learning",
    "pdf_author": "Patrik Bey",
    "arxiv": "",
    "doi": "10.1101/2025.07.02.25330753",
    "first_lines": [
      "medRxiv preprint doi: https://doi.org/10.1101/2025.07.02.25330753; this version posted July 3, 2025. The copyright holder for this preprint",
      "(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.",
      "It is made available under a CC-BY 4.0 International license .",
      "Group-derived and individual disconnection in stroke:",
      "recovery prediction and deep graph learning",
      "Patrik Bey1,2,3, Kiret Dhindsa2,3, Torsten Rackoll4,5,9, Jan Feldheim6, Marlene Bönstrup6,",
      "Götz Thomalla6, Robert Schulz6, Bastian Cheng6, Christian Gerloff6, Matthias Endres4,7,8,9,10,",
      "Alexander Heinrich Nave2,3,4,7,8, and Petra Ritter2,3,11,12,13",
      "Author affiliations:",
      "1 High Dimensional Neurology Group, UCL Queen Square Institute of Neurology,",
      "University College London, Russell Square House, Bloomsbury, London, UK",
      "2 Berlin Institute of Health at Charité, Charitéplatz 1, 10117 Berlin, Berlin, Germany",
      "3 Department of Neurology with Experimental Neurology, Brain Simulation Section, Charité",
      "– Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Berlin, Germany",
      "4 Center for Stroke Research Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany"
    ]
  },
  "To read/HOW POWERFUL ARE GRAPH NEURAL NETWORKS_XU 2019.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1810.00826v3",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2019",
      "H OW P OWERFUL ARE G RAPH N EURAL N ETWORKS ?",
      "Keyulu Xu ∗ †",
      "MIT",
      "keyulu@mit.edu",
      "Weihua Hu ∗ ‡",
      "Stanford University",
      "weihuahu@stanford.edu",
      "Jure Leskovec",
      "Stanford University",
      "jure@cs.stanford.edu",
      "arXiv:1810.00826v3 [cs.LG] 22 Feb 2019",
      "Stefanie Jegelka",
      "MIT",
      "stefje@mit.edu"
    ]
  },
  "To read/How Good is the Bayes Posterior in Deep Neural Networks Really_Wenzel 2020.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "2002.02405v2",
    "doi": "",
    "first_lines": [
      "How Good is the Bayes Posterior in Deep Neural Networks Really?",
      "4+",
      "Florian Wenzel * 1 Kevin Roth * + 2 Bastiaan S. Veeling * + 3 1 Jakub Światkowski",
      "˛",
      "Linh Tran 5 +",
      "6+",
      "1",
      "1",
      "1",
      "Stephan Mandt",
      "Jasper Snoek Tim Salimans Rodolphe Jenatton Sebastian Nowozin 7 +",
      "arXiv:2002.02405v2 [stat.ML] 2 Jul 2020",
      "During the past five years the Bayesian deep learning community has developed increasingly accurate and efficient approximate inference procedures that allow for Bayesian inference in deep",
      "neural networks. However, despite this algorithmic progress and the promise of improved",
      "uncertainty quantification and sample efficiency"
    ]
  },
  "To read/HyperCluster_Decentralized Large Language Model Inference over Peer-to-Peer_Jain 2026.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "HyperCluster: Decentralized Large Language Model Inference over Peer-to-Peer",
      "Wireless Networks",
      "Vyoman Jain, Samarth P, Akepati Ramya Sri, Sanjiv Raghunandan, Richa Sharma",
      "Department of Computer Science",
      "PES University",
      "Bengaluru, India",
      "{PES2UG22CS672, PES2UG22CS495, PES2UG22CS045, PES2UG22CS504}@pesu.pes.edu",
      "richasharma@pes.edu",
      "Abstract",
      "The substantial memory and computational requirements of",
      "large language models (LLMs) hinder their deployment on",
      "individual resource-constrained wireless devices. This paper",
      "introduces HyperCluster, a framework for fully decentralized",
      "collaborative inference over wireless networks. Our system",
      "presents two core innovations: first, a ring-based pipelined inference protocol where nodes deterministically self-organize"
    ]
  },
  "To read/IPLS _A Framework for Decentralized Federated Learning_Pampas 2021.pdf": {
    "pdf_title": "IPLS : A Framework for Decentralized Federated Learning",
    "pdf_author": "",
    "arxiv": "2101.01901v1",
    "doi": "10.1145/nnnnnnn.nnnnnnn",
    "first_lines": [
      "IPLS : A Framework for Decentralized",
      "Federated Learning",
      "Christodoulos Pappas",
      "University Of Thessaly",
      "chrpappas@uth.gr",
      "arXiv:2101.01901v1 [cs.NI] 6 Jan 2021",
      "Spyros Lalis",
      "University of Thessaly",
      "lalis@uth.gr",
      "ABSTRACT",
      "The proliferation of resourceful mobile devices that store rich,",
      "multidimensional and privacy-sensitive user data motivate",
      "the design of federated learning (FL), a machine-learning",
      "(ML) paradigm that enables mobile devices to produce an",
      "ML model without sharing their data. However, the majority"
    ]
  },
  "To read/Image Super-Resolution Using CNN_Dong 2015.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1501.00092v3",
    "doi": "",
    "first_lines": [
      "1",
      "Image Super-Resolution Using Deep",
      "Convolutional Networks",
      "arXiv:1501.00092v3 [cs.CV] 31 Jul 2015",
      "Chao Dong, Chen Change Loy, Member, IEEE, Kaiming He, Member, IEEE,",
      "and Xiaoou Tang, Fellow, IEEE",
      "Abstract—We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end",
      "mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes",
      "the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR",
      "methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately,",
      "our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality,",
      "and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve tradeoffs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show",
      "better overall reconstruction quality.",
      "Index Terms—Super-resolution, deep convolutional neural networks, sparse coding",
      "F"
    ]
  },
  "To read/Implicit Neural Representations with Periodic Activation functions_SIREN_Sitzmann 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "2006.09661v1",
    "doi": "",
    "first_lines": [
      "Implicit Neural Representations with Periodic",
      "Activation Functions",
      "arXiv:2006.09661v1 [cs.CV] 17 Jun 2020",
      "Vincent Sitzmann∗",
      "sitzmann@cs.stanford.edu",
      "Julien N. P. Martel∗",
      "jnmartel@stanford.edu",
      "David B. Lindell",
      "lindell@stanford.edu",
      "Alexander W. Bergman",
      "awb@stanford.edu",
      "Gordon Wetzstein",
      "gordon.wetzstein@stanford.edu",
      "Stanford University",
      "vsitzmann.github.io/siren/"
    ]
  },
  "To read/Independent components of human brain morphology_Yujiang Wang_Bethany Little 2021.pdf": {
    "pdf_title": "Independent components of human brain morphology",
    "pdf_author": "Yujiang Wang",
    "arxiv": "",
    "doi": "10.1016/j.neuroimage.2020.117546",
    "first_lines": [
      "NeuroImage 226 (2021) 117546",
      "Contents lists available at ScienceDirect",
      "NeuroImage",
      "journal homepage: www.elsevier.com/locate/neuroimage",
      "the study combines linear regression, PCA with classic statistical tests.",
      "The model for universal scaling law from the original study (Wang, 2016) is also linear regression.",
      "Independent components of human brain morphology",
      "Yujiang Wang a,b,c,∗, Karoline Leiberg a, Tobias Ludwig d, Bethany Little a,b, Joe H Necus a,b,",
      "Gavin Winston c,f,g, Sjoerd B Vos c,e,g, Jane de Tisi c, John S Duncan c,g, Peter N Taylor a,b,c,1,",
      "Bruno Mota h,1",
      "a",
      "CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK",
      "Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK",
      "c",
      "UCL Queen Square Institute of Neurology, London, UK"
    ]
  },
  "To read/Interactive segmentation of medical images through fully convolutional neural networks_Sakinis 2019.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "1903.08205v1",
    "doi": "",
    "first_lines": [
      "1",
      "Interactive segmentation of medical images through",
      "fully convolutional neural networks",
      "Abstract—Image segmentation plays an essential role in",
      "medicine for both diagnostic and interventional tasks. Segmentation approaches are either manual, semi-automated or fullyautomated. Manual segmentation offers full control over the",
      "quality of the results, but is tedious, time consuming and prone to",
      "operator bias. Fully automated methods require no human effort,",
      "but often deliver sub-optimal results without providing users",
      "with the means to make corrections. Semi-automated approaches",
      "keep users in control of the results by providing means for",
      "interaction, but the main challenge is to offer a good tradeoff between precision and required interaction. In this paper we",
      "present a deep learning (DL) based semi-automated segmentation",
      "approach that aims to be a ”smart” interactive tool for region",
      "of interest delineation in medical images. We demonstrate its use",
      "for segmenting multiple organs on computed tomography (CT)"
    ]
  },
  "To read/Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl_Cranmer 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2305.01582v3",
    "doi": "",
    "first_lines": [
      "PySR & SymbolicRegression.jl",
      "dev",
      "github.com/MilesCranmer/pysr_paper",
      "Interpretable Machine Learning for Science",
      "with PySR and SymbolicRegression.jl",
      "arXiv:2305.01582v3 [astro-ph.IM] 5 May 2023",
      "Miles Cranmer1,2",
      "1",
      "Princeton University, Princeton, NJ, USA",
      "2",
      "Flatiron Institute, New York, NY, USA",
      "May 2, 2023",
      "PySR3 is an open-source library for practical symbolic regression, a type of machine learning",
      "which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed backend, a flexible search algorithm, and interfaces with several deep learning packages.",
      "PySR’s internal search algorithm is a multi-population evolutionary algorithm, which consists"
    ]
  },
  "To read/Invertible Diffusion Models for Compressed Sensing_Chen 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2403.17006v2",
    "doi": "",
    "first_lines": [
      "1",
      "Invertible Diffusion Models for Compressed",
      "Sensing",
      "arXiv:2403.17006v2 [cs.CV] 3 Feb 2025",
      "Bin Chen, Zhenyu Zhang, Weiqi Li, Chen Zhao, Jiwen Yu, Shijie Zhao, Jie Chen, and Jian Zhang",
      "Abstract—While deep neural networks (NN) significantly advance image compressed sensing (CS) by improving reconstruction",
      "quality, the necessity of training current CS NNs from scratch constrains their effectiveness and hampers rapid deployment. Although",
      "recent methods utilize pre-trained diffusion models for image reconstruction, they struggle with slow inference and restricted",
      "adaptability to CS. To tackle these challenges, this paper proposes Invertible Diffusion Models (IDM), a novel efficient, end-to-end",
      "diffusion-based CS method. IDM repurposes a large-scale diffusion sampling process as a reconstruction model, and fine-tunes it",
      "end-to-end to recover original images directly from CS measurements, moving beyond the traditional paradigm of one-step noise",
      "estimation learning. To enable such memory-intensive end-to-end fine-tuning, we propose a novel two-level invertible design to",
      "transform both (1) multi-step sampling process and (2) noise estimation U-Net in each step into invertible networks. As a result, most",
      "intermediate features are cleared during training to reduce up to 93.8% GPU memory. In addition, we develop a set of lightweight",
      "modules to inject measurements into noise estimator to further facilitate reconstruction. Experiments demonstrate that IDM outperforms"
    ]
  },
  "To read/Inverting Gradients_How easy is it to break privacy in federated learning_Geiping 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "2003.14053v2",
    "doi": "",
    "first_lines": [
      "Inverting Gradients - How easy is it to break privacy",
      "in federated learning?",
      "arXiv:2003.14053v2 [cs.CV] 11 Sep 2020",
      "Jonas Geiping∗",
      "Hartmut Bauermeister ∗",
      "Hannah Dröge ∗",
      "Michael Moeller",
      "Dep. of Electrical Engineering and Computer Science",
      "University of Siegen",
      "{jonas.geiping, hartmut.bauermeister, hannah.droege,",
      "michael.moeller }@uni-siegen.de",
      "Abstract",
      "The idea of federated learning is to collaboratively train a neural network on a",
      "server. Each user receives the current weights of the network and in turns sends",
      "parameter updates (gradients) based on local data. This protocol has been designed"
    ]
  },
  "To read/Large Language Models are Zero-Shot Reasoners_Kojima 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2205.11916v4",
    "doi": "",
    "first_lines": [
      "Large Language Models are Zero-Shot Reasoners",
      "Takeshi Kojima",
      "The University of Tokyo",
      "t.kojima@weblab.t.u-tokyo.ac.jp",
      "arXiv:2205.11916v4 [cs.CL] 29 Jan 2023",
      "Machel Reid",
      "Google Research∗",
      "Yutaka Matsuo",
      "The University of Tokyo",
      "Shixiang Shane Gu",
      "Google Research, Brain Team",
      "Yusuke Iwasawa",
      "The University of Tokyo",
      "Abstract",
      "Pretrained large language models (LLMs) are widely used in many sub-fields of"
    ]
  },
  "To read/Large scale image recognition deep CNN.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1409.1556v6",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2015",
      "V ERY D EEP C ONVOLUTIONAL N ETWORKS",
      "FOR L ARGE -S CALE I MAGE R ECOGNITION",
      "arXiv:1409.1556v6 [cs.CV] 10 Apr 2015",
      "Karen Simonyan∗ & Andrew Zisserman+",
      "Visual Geometry Group, Department of Engineering Science, University of Oxford",
      "{karen,az}@robots.ox.ac.uk",
      "A BSTRACT",
      "In this work we investigate the effect of the convolutional network depth on its",
      "accuracy in the large-scale image recognition setting. Our main contribution is",
      "a thorough evaluation of networks of increasing depth using an architecture with",
      "very small (3 × 3) convolution filters, which shows that a significant improvement",
      "on the prior-art configurations can be achieved by pushing the depth to 16–19",
      "weight layers. These findings were the basis of our ImageNet Challenge 2014",
      "submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations"
    ]
  },
  "To read/Large-scale de novo DNA synthesis: technologies and applications.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1038/nmeth.2918",
    "first_lines": [
      "Focus on Synthetic Biology\b",
      "review",
      "Large-scale de novo DNA synthesis:",
      "technologies and applications",
      "npg",
      "© 2014 Nature America, Inc. All rights reserved.",
      "Sriram Kosuri1 & George M Church2,3",
      "For over 60 years, the synthetic production of new DNA sequences has helped",
      "researchers understand and engineer biology. Here we summarize methods and caveats",
      "for the de novo synthesis of DNA, with particular emphasis on recent technologies",
      "that allow for large-scale and low-cost production. In addition, we discuss emerging",
      "applications enabled by large-scale de novo DNA constructs, as well as the challenges",
      "and opportunities that lie ahead.",
      "DNA is the predominant information carrier for life.",
      "The development of synthetic techniques to construct"
    ]
  },
  "To read/LeAPP_A lesion-aware automated processing framework_Bey 2023.pdf": {
    "pdf_title": "A lesion‐aware automated processing framework for clinical stroke magnetic resonance imaging",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1002/hbm.26701",
    "first_lines": [
      "Received: 5 December 2023",
      "Revised: 11 April 2024",
      "Accepted: 16 April 2024",
      "DOI: 10.1002/hbm.26701",
      "RESEARCH ARTICLE",
      "A lesion-aware automated processing framework for clinical",
      "stroke magnetic resonance imaging",
      "Patrik Bey 1,2",
      "|",
      "Kiret Dhindsa 1,2",
      "6",
      "| Amrit Kashyap 1",
      "6",
      "|",
      "Michael Schirner 1,2,3,4,5"
    ]
  },
  "To read/Learning How Federated Learning Learns_A predictive approach_Teixeira 2025.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Learning How Federated Learning Learns: A",
      "Predictive Modeling Approach",
      "Rafael Teixeira∗† , Mário Antunes∗† , Diogo Gomes∗† and Rui L. Aguiar∗†",
      "∗ Instituto de Telecomunicações, Universidade de Aveiro",
      "Email: {rafaelgteixeira,mario.antunes,dgomes,ruilaa}@av.it.pt",
      "† Departamento de Eletrónica, Telecomunicações e Informática , Universidade de Aveiro",
      "Email: {rafaelgteixeira,mario.antunes,dgomes,ruilaa}@ua.pt",
      "Abstract—As the development of 6G networks accelerates,",
      "integrating Artificial Intelligence (AI) becomes essential for",
      "achieving ultra-fast data rates, low latency, and intelligent",
      "resource management. However, implementing AI at the edge",
      "introduces significant challenges, particularly under privacypreserving frameworks like Federated Learning (FL). While",
      "FL mitigates data privacy concerns by keeping data on user",
      "devices, it also introduces high computational demands on the",
      "user side and complicates model training due to the unbalanced"
    ]
  },
  "To read/Learning data driven discretizations for partial differential equations_Sinai 2019.pdf": {
    "pdf_title": "Learning data-driven discretizations for partial differential equations",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "10.1073/pnas.1814058116",
    "first_lines": [
      "Learning data-driven discretizations for partial",
      "differential equations",
      "Yohai Bar-Sinaia,1,2 , Stephan Hoyerb,1,2 , Jason Hickeyb , and Michael P. Brennera,b",
      "a",
      "School of Engineering and Applied Sciences, Harvard University, Cambridge MA 02138; and b Google Research, Mountain View, CA 94043",
      "Edited by John B. Bell, Lawrence Berkeley National Laboratory, Berkeley, CA, and approved June 21, 2019 (received for review August 14, 2018)",
      "The numerical solution of partial differential equations (PDEs)",
      "is challenging because of the need to resolve spatiotemporal",
      "features over wide length- and timescales. Often, it is computationally intractable to resolve the finest features in the solution.",
      "The only recourse is to use approximate coarse-grained representations, which aim to accurately represent long-wavelength",
      "dynamics while properly accounting for unresolved small-scale",
      "physics. Deriving such coarse-grained equations is notoriously",
      "difficult and often ad hoc. Here we introduce data-driven",
      "discretization, a method for learning optimized approximations",
      "to PDEs based on actual solutions to the known underlying"
    ]
  },
  "To read/Linking dynamical systems with human neuroimaging_John 2022.pdf.pdf": {
    "pdf_title": "nwn00230 960..979",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1162/netn_a_00230",
    "first_lines": [
      "FOCUS FEATURE:",
      "Connectivity, Cognition, and Consciousness",
      "It’s about time: Linking dynamical systems",
      "with human neuroimaging to",
      "understand the brain",
      "Yohan J. John1, Kayle S. Sawyer2,3,4,5, Karthik Srinivasan6, Eli J. Müller7,",
      "Brandon R. Munn7, and James M. Shine7",
      "1",
      "2",
      "Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA",
      "Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA",
      "3",
      "Department of Radiology, Massachusetts General Hospital, Boston, MA, USA",
      "4",
      "Boston VA Healthcare System, Boston, MA, USA"
    ]
  },
  "To read/Local cortical surface complexity maps from spherical harmonic reconstructions_Yotter 2011.pdf": {
    "pdf_title": "Local cortical surface complexity maps from spherical harmonic reconstructions",
    "pdf_author": "Rachel A. Yotter",
    "arxiv": "",
    "doi": "10.1016/j.neuroimage.2011.02.007",
    "first_lines": [
      "NeuroImage 56 (2011) 961–973",
      "Contents lists available at ScienceDirect",
      "NeuroImage",
      "j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g",
      "Local cortical surface complexity maps from spherical harmonic reconstructions☆",
      "Rachel A. Yotter a,⁎, Igor Nenadic a, Gabriel Ziegler a, Paul M. Thompson b, Christian Gaser a",
      "a",
      "b",
      "Department of Psychiatry, Friedrich-Schiller University, D-07743 Jena, Germany",
      "Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90095, USA",
      "a r t i c l e",
      "i n f o",
      "Article history:",
      "Received 8 October 2010",
      "Revised 1 February 2011"
    ]
  },
  "To read/Machine Learning Approaches to Predict Symptoms in People With Cancer_Review_Zeinali 2024.pdf": {
    "pdf_title": "Untitled",
    "pdf_author": "Unknown",
    "arxiv": "",
    "doi": "10.2196/52322",
    "first_lines": [
      "JMIR CANCER",
      "Zeinali et al",
      "Review",
      "Machine Learning Approaches to Predict Symptoms in People",
      "With Cancer: Systematic Review",
      "Nahid Zeinali1, MS; Nayung Youn2, RN, MSN; Alaa Albashayreh2, RN, MSHI, PhD; Weiguo Fan3, PhD; Stéphanie",
      "Gilbertson White2, PhD",
      "1",
      "Department of Computer Science and Informatics, University of Iowa, Iowa City, IA, United States",
      "2",
      "College of Nursing, University of Iowa, Iowa City, IA, United States",
      "3",
      "Department of Business Analytics, University of Iowa, Iowa City, IA, United States",
      "Corresponding Author:",
      "Stéphanie Gilbertson White, PhD"
    ]
  },
  "To read/Membership Inference Attacks against Machine Learning Models_Shokri 2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1610.05820v2",
    "doi": "",
    "first_lines": [
      "Membership Inference Attacks Against",
      "Machine Learning Models",
      "arXiv:1610.05820v2 [cs.CR] 31 Mar 2017",
      "Reza Shokri",
      "Cornell Tech",
      "shokri@cornell.edu",
      "Marco Stronati∗",
      "INRIA",
      "marco@stronati.org",
      "Abstract—We quantitatively investigate how machine learning",
      "models leak information about the individual data records on",
      "which they were trained. We focus on the basic membership",
      "inference attack: given a data record and black-box access to",
      "a model, determine if the record was in the model’s training",
      "dataset. To perform membership inference against a target model,"
    ]
  },
  "To read/Meta learning with synthetic medical data_Oh 2024.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Uncertainty-aware meta-weighted optimization",
      "framework for domain-generalized medical image",
      "segmentation",
      "Seok-Hwan Oh1⋆ , Guil Jung1⋆ , Sang-Yun Kim1 , Myeong-Gee Kim2 ,",
      "Young-Min Kim1 , Hyeon-Jik Lee1 , Hyuk-Sool Kwon3 , and Hyeon-Min Bae1",
      "Electrical Engineering Department, KAIST, 34141 Daejeon, South Korea",
      "joseph9337@kaist.ac.kr",
      "2",
      "Barreleye Inc., 06211 Seoul, South Korea",
      "3",
      "Department of Emergency Medicine, SNUBH, 13620 Seong-nam, South Korea",
      "1",
      "Abstract. Accurate segmentation of echocardiograph images is essential for the diagnosis of cardiovascular diseases. Recent advances in deep",
      "learning have opened a possibility for automated cardiac image segmentation. However, the data-driven echocardiography segmentation schemes",
      "suffer from domain shift problems, since the ultrasonic image characteristics are largely affected by measurement conditions determined by device"
    ]
  },
  "To read/MobileNetV2.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1801.04381v4",
    "doi": "",
    "first_lines": [
      "MobileNetV2: Inverted Residuals and Linear Bottlenecks",
      "arXiv:1801.04381v4 [cs.CV] 21 Mar 2019",
      "Mark Sandler",
      "Andrew Howard",
      "Menglong Zhu Andrey Zhmoginov Liang-Chieh Chen",
      "Google Inc.",
      "{sandler, howarda, menglong, azhmogin, lcchen}@google.com",
      "Abstract",
      "In this paper we describe a new mobile architecture,",
      "MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model",
      "sizes. We also describe efficient ways of applying these",
      "mobile models to object detection in a novel framework",
      "we call SSDLite. Additionally, we demonstrate how",
      "to build mobile semantic segmentation models through",
      "a reduced form of DeepLabv3 which we call Mobile"
    ]
  },
  "To read/MobileNets.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1704.04861v1",
    "doi": "",
    "first_lines": [
      "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision",
      "Applications",
      "arXiv:1704.04861v1 [cs.CV] 17 Apr 2017",
      "Andrew G. Howard",
      "Menglong Zhu",
      "Bo Chen",
      "Dmitry Kalenichenko",
      "Weijun Wang",
      "Tobias Weyand",
      "Marco Andreetto",
      "Hartwig Adam",
      "Google Inc.",
      "{howarda,menglong,bochen,dkalenichenko,weijunw,weyand,anm,hadam}@google.com",
      "Abstract",
      "models. Section 3 describes the MobileNet architecture and"
    ]
  },
  "To read/Multiscale cortical morphometry_ regional and scale dependent variations across the lifespan_Leiberg & Little 2024.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2311.13501v4",
    "doi": "",
    "first_lines": [
      "Multiscale cortical morphometry reveals pronounced",
      "arXiv:2311.13501v4 [q-bio.NC] 6 Nov 2024",
      "regional and scale-dependent variations across the lifespan",
      "Karoline Leiberg1 , Timo Blattner2 , Bethany Little1 ,",
      "Victor B.B. Mello2 , Fernanda H.P. de Moraes3,4 , Christian Rummel2 ,",
      "Peter N. Taylor1,5,6 , Bruno Mota3 , and Yujiang Wang1,5,6∗",
      "November 8, 2024",
      "1. CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems",
      "Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom",
      "2. Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic",
      "and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital,",
      "Bern, Switzerland",
      "3. metaBIO Lab, Instituto de Fı́sica, Universidade Federal do Rio de Janeiro (UFRJ), Rio de",
      "Janeiro, Brazil",
      "4. Brain Connectivity Unit, D’Or Institute of Research and Education (IDOR), Rio de Janeiro,"
    ]
  },
  "To read/NIPS-2016-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings-Paper.pdf": {
    "pdf_title": "Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings",
    "pdf_author": "Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, Adam T. Kalai",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Man is to Computer Programmer as Woman is to",
      "Homemaker? Debiasing Word Embeddings",
      "Tolga Bolukbasi1 , Kai-Wei Chang2 , James Zou2 , Venkatesh Saligrama1,2 , Adam Kalai2",
      "1",
      "Boston University, 8 Saint Mary’s Street, Boston, MA",
      "Microsoft Research New England, 1 Memorial Drive, Cambridge, MA",
      "tolgab@bu.edu, kw@kwchang.net, jamesyzou@gmail.com, srv@bu.edu, adam.kalai@microsoft.com",
      "2",
      "Abstract",
      "The blind application of machine learning runs the risk of amplifying biases present",
      "in data. Such a danger is facing us with word embedding, a popular framework to",
      "represent text data as vectors which has been used in many machine learning and",
      "natural language processing tasks. We show that even word embeddings trained on",
      "Google News articles exhibit female/male gender stereotypes to a disturbing extent.",
      "This raises concerns because their widespread use, as we describe, often tends to"
    ]
  },
  "To read/Neural Radiance Fields for View Synthesis_Mildenhall 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "2003.08934v2",
    "doi": "",
    "first_lines": [
      "arXiv:2003.08934v2 [cs.CV] 3 Aug 2020",
      "NeRF: Representing Scenes as",
      "Neural Radiance Fields for View Synthesis",
      "Ben Mildenhall1? Pratul P. Srinivasan1? Matthew Tancik1?",
      "Jonathan T. Barron2 Ravi Ramamoorthi3 Ren Ng1",
      "1",
      "UC Berkeley",
      "2",
      "Google Research",
      "3",
      "UC San Diego",
      "Abstract. We present a method that achieves state-of-the-art results",
      "for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input",
      "views. Our algorithm represents a scene using a fully-connected (nonconvolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x, y, z) and viewing direction (θ, φ)) and whose",
      "output is the volume density and view-dependent emitted radiance at"
    ]
  },
  "To read/Neural combinatiorial optimization with RL_Bello 2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1611.09940v3",
    "doi": "",
    "first_lines": [
      "Under review as a conference paper at ICLR 2017",
      "N EURAL C OMBINATORIAL O PTIMIZATION",
      "WITH R EINFORCEMENT L EARNING",
      "arXiv:1611.09940v3 [cs.AI] 12 Jan 2017",
      "Irwan Bello∗, Hieu Pham∗, Quoc V. Le, Mohammad Norouzi, Samy Bengio",
      "Google Brain",
      "{ibello,hyhieu,qvl,mnorouzi,bengio}@google.com",
      "A BSTRACT",
      "This paper presents a framework to tackle combinatorial optimization problems",
      "using neural networks and reinforcement learning. We focus on the traveling",
      "salesman problem (TSP) and train a recurrent neural network that, given a set",
      "of city coordinates, predicts a distribution over different city permutations. Using",
      "negative tour length as the reward signal, we optimize the parameters of the recurrent neural network using a policy gradient method. We compare learning the",
      "network parameters on a set of training graphs against learning them on individual test graphs. Despite the computational expense, without much engineering and",
      "heuristic designing, Neural Combinatorial Optimization achieves close to optimal"
    ]
  },
  "To read/Neural style transfer.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1508.06576v2",
    "doi": "",
    "first_lines": [
      "A Neural Algorithm of Artistic Style",
      "arXiv:1508.06576v2 [cs.CV] 2 Sep 2015",
      "Leon A. Gatys,1,2,3∗ Alexander S. Ecker,1,2,4,5 Matthias Bethge1,2,4",
      "1",
      "Werner Reichardt Centre for Integrative Neuroscience",
      "and Institute of Theoretical Physics, University of Tübingen, Germany",
      "2",
      "Bernstein Center for Computational Neuroscience, Tübingen, Germany",
      "3",
      "Graduate School for Neural Information Processing, Tübingen, Germany",
      "4",
      "Max Planck Institute for Biological Cybernetics, Tübingen, Germany",
      "5",
      "Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA",
      "∗"
    ]
  },
  "To read/Neuro-evolutionary evidence for a universal fractal primate brain shape_Yujiang Wang 2024.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.7554/eLife.92080",
    "first_lines": [
      "RESEARCH ARTICLE",
      "Neuro-­evolutionary evidence for a",
      "universal fractal primate brain shape",
      "Yujiang Wang1,2,3*, Karoline Leiberg1, Nathan Kindred2, Christopher R Madan4,",
      "Colline Poirier2, Christopher I Petkov2,5, Peter Neal Taylor1,2,3, Bruno Mota6",
      "1",
      "CNNP Lab (https://www.​cnnp-­​lab.​com), School of Computing, Newcastle University,",
      "Newcastle upon Tyne, United Kingdom; 2Faculty of Medical Sciences, Newcastle",
      "University, Newcastle upon Tyne, United Kingdom; 3UCL Institute of Neurology,",
      "Queen Square, London, United Kingdom; 4School of Psychology, University of",
      "Nottingham, Nottingham, United Kingdom; 5Department of Neurosurgery, University",
      "of Iowa, Des Moines, United States; 6metaBIO Lab, Instituto de Física, Universidade",
      "Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil",
      "*For correspondence:",
      "yujiang.wang@newcastle.ac.uk"
    ]
  },
  "To read/Normative modelling_Scalable Multi-Task Gaussian Processes_Kia 2018.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1806.01047v2",
    "doi": "",
    "first_lines": [
      "Normative Modeling of Neuroimaging Data",
      "using Scalable Multi-Task Gaussian Processes",
      "Seyed Mostafa Kia1,2 and Andre Marquand1,2",
      "arXiv:1806.01047v2 [stat.ML] 5 Jun 2018",
      "1",
      "Department of Cognitive Neuroscience, Radboud University Medical Centre,",
      "Nijmegen, The Netherlands",
      "2",
      "Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition",
      "and Behaviour, Radboud University, Nijmegen, The Netherlands",
      "{s.kia,a.marquand}@donders.ru.nl",
      "Abstract. Normative modeling has recently been proposed as an alternative for the case-control approach in modeling heterogeneity within clinical",
      "cohorts. Normative modeling is based on single-output Gaussian process",
      "regression that provides coherent estimates of uncertainty required by",
      "the method but does not consider spatial covariance structure. Here, we"
    ]
  },
  "To read/Open Source Pharmacokinetic Pharmacodynamic.pdf": {
    "pdf_title": "Open Source Pharmacokinetic/Pharmacodynamic Framework: Tutorial on the BioGears Engine",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1002/psp4.12371",
    "first_lines": [
      "Citation: CPT Pharmacometrics Syst. Pharmacol. (2019) 8, 12–25; doi:10.1002/psp4.12371",
      "TUTORIAL",
      "Open Source Pharmacokinetic/Pharmacodynamic",
      "Framework: Tutorial on the BioGears Engine",
      "Matthew McDaniel1, Jennifer Carter1, Jonathan M Keller2, Steven A. White1 and Austin Baird1,*",
      "BioGears is an open-­source, lumped parameter, full-­body human physiology engine. Its purpose is to provide realistic and",
      "comprehensive simulations for medical training, research, and education. BioGears incorporates a physiologically based",
      "pharmacokinetic/pharmacodynamic (PK/PD) model that is designed to be applicable to a diversity of drug classes and patients and is extensible to future drugs. In addition, BioGears also supports drug interactions with various patient insults and",
      "interventions allowing for a realistic research framework and accurate dose-­patient responses. This tutorial will demonstrate how the generic BioGears PK/PD model can be extended to a new substance for prediction of drug administration",
      "outcomes.",
      "The development of increasingly accurate physiological",
      "modeling and simulation tools represents a critical oppor­",
      "tunity for advancement in the medical research and train­",
      "ing fields. Training and educational applications require",
      "physiology models capable of characterizing, among other"
    ]
  },
  "To read/PINN_A Review_Ren 2025.pdf": {
    "pdf_title": "Physics-Informed Neural Networks: A Review of Methodological Evolution, Theoretical Foundations, and Interdisciplinary Frontiers Toward Next-Generation Scientific Computing",
    "pdf_author": "Zhiyuan Ren, Shijie Zhou, Dong Liu and Qihe Liu",
    "arxiv": "",
    "doi": "10.3390/app15148092",
    "first_lines": [
      "applied",
      "sciences",
      "Review",
      "Physics-Informed Neural Networks: A Review of",
      "Methodological Evolution, Theoretical Foundations,",
      "and Interdisciplinary Frontiers Toward Next-Generation",
      "Scientific Computing",
      "Zhiyuan Ren * , Shijie Zhou *, Dong Liu and Qihe Liu",
      "School of Information and Software Engineering, University of Electronic Science and Technology of China,",
      "Chengdu 610054, China; liudong@uestc.edu.cn (D.L.); qiheliu@uestc.edu.cn (Q.L.)",
      "* Correspondence: renzhiyuan@uestc.edu.cn (Z.R.); sjzhou@uestc.edu.cn (S.Z.)",
      "Abstract",
      "Academic Editor: Douglas",
      "O’Shaughnessy",
      "Received: 6 June 2025"
    ]
  },
  "To read/Pattern recognition for neuroimaging-based psychiatric diagnostics_Review_Wolfers 2015.pdf": {
    "pdf_title": "From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics",
    "pdf_author": "Thomas Wolfers",
    "arxiv": "",
    "doi": "10.1016/j.neubiorev.2015.08.001",
    "first_lines": [
      "Neuroscience and Biobehavioral Reviews 57 (2015) 328–349",
      "Contents lists available at ScienceDirect",
      "Neuroscience and Biobehavioral Reviews",
      "journal homepage: www.elsevier.com/locate/neubiorev",
      "Review",
      "From estimating activation locality to predicting disorder: A review of",
      "pattern recognition for neuroimaging-based psychiatric diagnostics",
      "Thomas Wolfers a,b,∗ , Jan K. Buitelaar c,d , Christian F. Beckmann b,c,e , Barbara Franke a,f ,",
      "Andre F. Marquand b,g",
      "a",
      "Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, PO Box 9101, 6500 HB",
      "Nijmegen, The Netherlands",
      "b",
      "Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, PO Box 9101, 6500 HB Nijmegen,",
      "The Netherlands"
    ]
  },
  "To read/Physics-Informed Machine learning in biomedical engineering_Ahmadi 2025.pdf.pdf": {
    "pdf_title": "Physics-Informed Machine Learning in Biomedical Science and Engineering",
    "pdf_author": "Nazanin Ahmadi; Qianying Cao; Jay D. Humphrey; George Em Karniadakis",
    "arxiv": "2510.05433v1",
    "doi": "10.1146/((please",
    "first_lines": [
      "Physics-Informed Machine",
      "Learning in Biomedical",
      "Science and Engineering",
      "arXiv:2510.05433v1 [cs.LG] 6 Oct 2025",
      "Nazanin Ahmadi1 , Qianying Cao2 , Jay D.",
      "Humphrey3 , and George Em Karniadakis2,*",
      "1",
      "Center for Biomedical Engineering, Brown University, Providence, RI 02912,",
      "USA; nazanin ahmadi daryakenari@brown.edu",
      "2",
      "Division of Applied Mathematics, Brown University, Providence, RI 02912,",
      "USA; qianying cao@brown.edu, george karniadakis@brown.edu",
      "3",
      "Department of Biomedical Engineering, Yale University, New Haven, CT 06520,",
      "USA; jay.humphrey@yale.edu"
    ]
  },
  "To read/Physics-Informed Neural Networks and Neural Operators for Parametric PDEs_ Zhang 2025.pdf": {
    "pdf_title": "Physics-Informed Neural Networks and Neural Operators for Parametric PDEs: A Human-AI Collaborative Analysis",
    "pdf_author": "Zhuo Zhang; Xiong Xiong; Sen Zhang; Yuan Zhao; Xi Yang",
    "arxiv": "2511.04576v1",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICAIS 2025",
      "arXiv:2511.04576v1 [stat.ML] 6 Nov 2025",
      "Physics-Informed Neural Networks and Neural Operators",
      "for Parametric PDEs: A Human-AI Collaborative Analysis∗",
      "Zhuo Zhang",
      "College of Computer Science and Technology",
      "National University of Defense Technology",
      "Changsha, Hunan, China",
      "zhangzhuo@nudt.edu.cn",
      "Xiong Xiong",
      "School of Mathematics and Statistics",
      "Northwestern Polytechnical University",
      "Xi’an, Shaanxi, China",
      "xiongxiongwpu@mail.nwpu.edu.cn",
      "Sen Zhang"
    ]
  },
  "To read/Posterior Network_Uncertainty estimation without OOD samples_Charpentier 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2006.09239v2",
    "doi": "",
    "first_lines": [
      "Posterior Network: Uncertainty Estimation without",
      "OOD Samples via Density-Based Pseudo-Counts",
      "arXiv:2006.09239v2 [cs.LG] 22 Oct 2020",
      "Bertrand Charpentier, Daniel Zügner, Stephan Günnemann",
      "Technical University of Munich, Germany",
      "{charpent, zuegnerd, guennemann}@in.tum.de",
      "Abstract",
      "Accurate estimation of aleatoric and epistemic uncertainty is crucial to build",
      "safe and reliable systems. Traditional approaches, such as dropout and ensemble",
      "methods, estimate uncertainty by sampling probability predictions from different",
      "submodels, which leads to slow uncertainty estimation at inference time. Recent",
      "works address this drawback by directly predicting parameters of prior distributions",
      "over the probability predictions with a neural network. While this approach has",
      "demonstrated accurate uncertainty estimation, it requires defining arbitrary target",
      "parameters for in-distribution data and makes the unrealistic assumption that outof-distribution (OOD) data is known at training time."
    ]
  },
  "To read/Practical Secure Aggregation for Federated Learning on User-Held Data_Bonawitz 2016.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1611.04482v1",
    "doi": "",
    "first_lines": [
      "arXiv:1611.04482v1 [cs.CR] 14 Nov 2016",
      "Practical Secure Aggregation for",
      "Federated Learning on User-Held Data",
      "Keith Bonawitz* , Vladimir Ivanov* , Ben Kreuter* , Antonio Marcedone†* ,",
      "H. Brendan McMahan* , Sarvar Patel* , Daniel Ramage* , Aaron Segal* , and Karn Seth*",
      "*",
      "{bonawitz,vlivan,benkreuter,mcmahan,sarvar,dramage,asegal,karn}@google.com",
      "Google, Mountain View, California 94043",
      "†",
      "marcedone@cs.cornell.edu",
      "Cornell University, Ithaca, New York 14853",
      "1",
      "Introduction",
      "Secure Aggregation is a class of Secure Multi-Party Computation algorithms wherein a group of",
      "mutually distrustful parties u ∈ P"
    ]
  },
  "To read/Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research_Eitel 2023.pdf": {
    "pdf_title": "Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research",
    "pdf_author": "Fabian Eitel; Marc-André Schulz; Moritz Seiler; Henrik Walter; Kerstin Ritter;",
    "arxiv": "2301.08525v1",
    "doi": "",
    "first_lines": [
      "Promises and pitfalls of deep neural networks in",
      "neuroimaging-based psychiatric research",
      "arXiv:2301.08525v1 [cs.LG] 20 Jan 2023",
      "Fabian Eitela,∗, Marc-André Schulza,∗, Moritz Seilera,∗, Henrik Waltera ,",
      "Kerstin Rittera,1",
      "a",
      "Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin,",
      "Humboldt-Universität zu Berlin, and Berlin Institute of Health; Department of",
      "Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience; 10117",
      "Berlin, Germany",
      "Abstract",
      "By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first",
      "give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as",
      "modelling domain-specific priors. After reviewing recent applications within",
      "neuroimaging-based psychiatric research, such as the diagnosis of psychiatric"
    ]
  },
  "To read/QuickNAT_CNN for brain segmentation_Roy 2019.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "1801.04161v2",
    "doi": "",
    "first_lines": [
      "QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of",
      "Neuroanatomy",
      "Abhijit Guha Roya,b∗, Sailesh Conjetib,c , Nassir Navabb,d , Christian Wachingera",
      "a Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, LMU München, Germany",
      "b Computer Aided Medical Procedures, Department of Informatics, Technical University of Munich, Germany",
      "c German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany",
      "arXiv:1801.04161v2 [cs.CV] 24 Nov 2018",
      "d Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA.",
      "Abstract",
      "Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological",
      "analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition.",
      "We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in",
      "20 seconds. To enable training of the complex network with millions of learnable parameters using limited annotated",
      "data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the",
      "pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are"
    ]
  },
  "To read/REVERSE-TIME DIFFUSION EQUATION MODELS_Anderson 1982.pdf": {
    "pdf_title": "PII: 0304-4149(82)90051-5",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Stochastic Processes and their Applications 12 (1982) 313-326",
      "North-HoUand Publishing Company",
      "313",
      "REVERSE-TIME DIFFUSION EQUATION MODELS*",
      "Brian D.O. ANDERSON**",
      "Department of Electrical Engineering, The University of Newcastle, N.S. W. 2308, Australia",
      "Received 13 November 1979",
      "Revised 12 May 1980",
      "Reverse-time stochastic diffusion equation models are defined and it is shown how most",
      "processes defined via a forward-time or conventional diffusion equation model have an associated",
      "reverse-time model.",
      "Stochastic equations",
      "reverse-time equations",
      "Fokker-Pianrk equations",
      "diffusi~;m equations"
    ]
  },
  "To read/RNAInter v4.0 - RNA interactome repository.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         XMLPublish v.3.0 Copyright 1991-2017 Aptara Inc.",
    "arxiv": "",
    "doi": "10.1093/nar/gkab997",
    "first_lines": [
      "D326–D332 Nucleic Acids Research, 2022, Vol. 50, Database issue",
      "https://doi.org/10.1093/nar/gkab997",
      "Published online 30 October 2021",
      "RNAInter v4.0: RNA interactome repository with",
      "redefined confidence scoring system and improved",
      "accessibility",
      "Juanjuan Kang1,2,† , Qiang Tang3,4,† , Jun He5,† , Le Li6 , Nianling Yang5 , Shuiyan Yu5 ,",
      "Mengyao Wang5 , Yuchen Zhang5 , Jiahao Lin2 , Tianyu Cui2 , Yongfei Hu2 , Puwen Tan2 ,",
      "Jun Cheng1,2 , Hailong Zheng2 , Dong Wang 2,* , Xi Su1,* , Wei Chen 3,4,* and Yan Huang2,7,8,*",
      "1",
      "Received September 15, 2021; Revised October 07, 2021; Editorial Decision October 08, 2021; Accepted October 09, 2021",
      "ABSTRACT",
      "Establishing an RNA-associated interaction repository facilitates the system-level understanding of",
      "RNA functions. However, as these interactions are",
      "distributed throughout various resources, an essential prerequisite for effectively applying these data"
    ]
  },
  "To read/Raidionics_Brain and lesion segmentation_Bouget 2023.pdf": {
    "pdf_title": "Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting",
    "pdf_author": "David Bouget",
    "arxiv": "",
    "doi": "10.1038/s41598-023-42048-7",
    "first_lines": [
      "www.nature.com/scientificreports",
      "OPEN",
      "Raidionics: an open software",
      "for pre‑ and postoperative",
      "central nervous system tumor",
      "segmentation and standardized",
      "reporting",
      "David Bouget 1, Demah Alsinan 1, Valeria Gaitan 1, Ragnhild Holden Helland 1,2,",
      "André Pedersen 1, Ole Solheim 3,4 & Ingerid Reinertsen 1,2*",
      "For patients suffering from central nervous system tumors, prognosis estimation, treatment",
      "decisions, and postoperative assessments are made from the analysis of a set of magnetic resonance",
      "(MR) scans. Currently, the lack of open tools for standardized and automatic tumor segmentation",
      "and generation of clinical reports, incorporating relevant tumor characteristics, leads to potential",
      "risks from inherent decisions’ subjectivity. To tackle this problem, the proposed Raidionics opensource software has been developed, offering both a user-friendly graphical user interface and stable",
      "processing backend. The software includes preoperative segmentation models for each of the most"
    ]
  },
  "To read/Raidionics_Brain and lesion segmentation_v1_Bouget 2021.pdf": {
    "pdf_title": "Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task",
    "pdf_author": "Firstname Lastname, Firstname Lastname and Firstname Lastname",
    "arxiv": "",
    "doi": "10.3390/cancers13184674",
    "first_lines": [
      "cancers",
      "Article",
      "Glioblastoma Surgery Imaging–Reporting and Data System:",
      "Validation and Performance of the Automated",
      "Segmentation Task",
      "David Bouget 1, * , Roelant S. Eijgelaar 2,3 , André Pedersen 1 , Ivar Kommers 2,3 , Hilko Ardon 4 ,",
      "Frederik Barkhof 5,6 , Lorenzo Bello 7 , Mitchel S. Berger 8 , Marco Conti Nibali 7 , Julia Furtner 9 ,",
      "Even Hovig Fyllingen 10,11 , Shawn Hervey-Jumper 8 , Albert J. S. Idema 12 , Barbara Kiesel 13 , Alfred Kloet 14 ,",
      "Emmanuel Mandonnet 15 , Domenique M. J. Müller 2,3 , Pierre A. Robe 16 , Marco Rossi 7 , Lisa M. Sagberg 17 ,",
      "Tommaso Sciortino 7 , Wimar A. Van den Brink 18 , Michiel Wagemakers 19 , Georg Widhalm 13 ,",
      "Marnix G. Witte 20 , Aeilko H. Zwinderman 21 , Ingerid Reinertsen 1,19 , Philip C. De Witt Hamer 2,3",
      "and Ole Solheim 21,22",
      "1",
      "2",
      "\u0001\u0002\u0003\u0001\u0004\u0005\u0006\u0007\b\u0001"
    ]
  },
  "To read/Reinforcement Learning and Dynamic Programming.pdf": {
    "pdf_title": "Reinforcement Learning and Dynamic Programming",
    "pdf_author": "Andrew G. Barto",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Copyright © IFAC Man-Machine Systems,",
      "Cambridge, Massachusetts, USA, 1995",
      "REINFORCEMENT LEARNING AND",
      "DYNAMIC PROGRAMMING",
      "Andrew G. Barto·",
      "·University of Massachusetts, Department of Computer Science, Amherst, MA 01003, USA",
      "Abstract. Reinforcement learning refers to a class of learning tasks and algoritluns based on experimental psychology's principle of reinforcement . Recent research uses the framework of stochastic",
      "optimal control to model problems in which a learning agent has to incrementally approximate an",
      "optimal control rule, or policy, often starting with incomplete information about the dynamics of its",
      "environment . Although these problems have been studied intensively for many years, the methods",
      "being developed by reinforcement learning researchers are adding some novel elements to classical",
      "dynamic programming solution methods. This article provides a brief account of these methods,",
      "explains what is novel about them, and suggests what their advantages might be over classical",
      "applications of dynamic programming to large-scale stochastic optimal control problems.",
      "Key Words. Adaptive control, dynamic programming, function approximation, large scale systems,"
    ]
  },
  "To read/SAMSEG_Longitudinal whole-brain and white matter lesion segmentation_Cerri 2023.pdf": {
    "pdf_title": "An open-source tool for longitudinal whole-brain and white matter lesion segmentation",
    "pdf_author": "Stefano Cerri",
    "arxiv": "",
    "doi": "10.1016/j.nicl.2023.103354",
    "first_lines": [
      "NeuroImage: Clinical 38 (2023) 103354",
      "Contents lists available at ScienceDirect",
      "NeuroImage: Clinical",
      "journal homepage: www.elsevier.com/locate/ynicl",
      "An open-source tool for longitudinal whole-brain and white matter",
      "lesion segmentation",
      "Stefano Cerri a, *, Douglas N. Greve a, c, Andrew Hoopes a, Henrik Lundell b,",
      "Hartwig R. Siebner b, d, e, Mark Mühlau f, Koen Van Leemput a, g",
      "a",
      "Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA",
      "Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark",
      "Department of Radiology, Harvard Medical School, USA",
      "d",
      "Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark",
      "e"
    ]
  },
  "To read/SMPC-Based Federated Learning for 6G-Enabled Internet of Medical Things_Kalapaaking 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2304.13352v1",
    "doi": "",
    "first_lines": [
      "1",
      "SMPC-based Federated Learning for 6G enabled",
      "Internet of Medical Things",
      "arXiv:2304.13352v1 [cs.CR] 26 Apr 2023",
      "Aditya Pribadi Kalapaaking, Veronika Stephanie, Ibrahim Khalil, Mohammed Atiquzzaman, Xun Yi, Mahathir",
      "Almashor",
      "Abstract—Rapidly developing intelligent healthcare systems",
      "are underpinned by Sixth Generation (6G) connectivity, ubiquitous Internet of Things (IoT), and Deep Learning (DL) techniques. This portends a future where 6G powers the Internet of",
      "Medical Things (IoMT) with seamless, large-scale, and real-time",
      "connectivity amongst entities. This article proposes a Convolutional Neural Network (CNN) based Federated Learning framework that combines Secure Multi-Party Computation (SMPC)",
      "based aggregation and Encrypted Inference methods, all within",
      "the context of 6G and IoMT. We consider multiple hospitals with",
      "clusters of mixed IoMT and edge devices that encrypt locally",
      "trained models. Subsequently, each hospital sends the encrypted",
      "local models for SMPC-based encrypted aggregation in the cloud,"
    ]
  },
  "To read/SRATTA_Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning_Marchand 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2306.07644v1",
    "doi": "",
    "first_lines": [
      "SRATTA: S AMPLE R E -ATT RIBUTION ATTACK OF S ECURE",
      "AGGREGATION IN F EDERATED L EARNING .",
      "arXiv:2306.07644v1 [cs.LG] 13 Jun 2023",
      "P REPRINT - ACCEPTED TO ICML2023",
      "Tanguy Marchand",
      "Owkin Inc., New York, USA.",
      "tanguy.marchand@owkin.com",
      "Régis Loeb ˚",
      "Owkin Inc., New York, USA.",
      "Jean Ogier du Terrail *",
      "Owkin Inc., New York, USA.",
      "Ulysse Marteau-Ferey *",
      "Owkin Inc., New York, USA.",
      "Arthur Pignet *",
      "Owkin Inc., New York, USA."
    ]
  },
  "To read/Score-Based Generative Modeling through Stochastic Differential Equations_Song 2020.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2011.13456v2",
    "doi": "",
    "first_lines": [
      "Published as a conference paper at ICLR 2021",
      "S CORE -BASED G ENERATIVE M ODELING",
      "S TOCHASTIC D IFFERENTIAL E QUATIONS",
      "arXiv:2011.13456v2 [cs.LG] 10 Feb 2021",
      "Yang Song˚",
      "Stanford University",
      "yangsong@cs.stanford.edu",
      "Abhishek Kumar",
      "Google Brain",
      "abhishk@google.com",
      "Jascha Sohl-Dickstein",
      "Google Brain",
      "jaschasd@google.com",
      "Stefano Ermon",
      "Stanford University"
    ]
  },
  "To read/Secure Aggregation for Federated Learning_Review_Zhang 2025.pdf": {
    "pdf_title": "A Review of Research on Secure Aggregation for Federated Learning",
    "pdf_author": "Zhang, X.; Luo, Y.; Li, T.",
    "arxiv": "",
    "doi": "10.3390/fi17070308",
    "first_lines": [
      "Review",
      "A Review of Research on Secure Aggregation for",
      "Federated Learning",
      "Xing Zhang 1 , Yuexiang Luo 1 and Tianning Li 2, *",
      "1",
      "2",
      "*",
      "School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China;",
      "xingzhang@ujs.edu.cn (X.Z.); lyx769550400@gmail.com (Y.L.)",
      "School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China",
      "Correspondence: ltn@ujs.edu.cn",
      "Abstract",
      "Federated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning",
      "and the growing importance of privacy protection, federated learning methods that can",
      "securely aggregate models have received widespread attention. Federated learning enables"
    ]
  },
  "To read/Solving PDES on unknown manifolds with machine Learning_Liang 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "S OLVING PDE S ON U NKNOWN M ANIFOLDS WITH M ACHINE",
      "L EARNING",
      "A P REPRINT",
      "Senwei Liang",
      "Department of Mathematics, Purdue University, IN 47907, USA",
      "liang339@purdue.edu",
      "Shixiao W. Jiang",
      "Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, 201210, China",
      "jiangshx@shanghaitech.edu.cn",
      "John Harlim",
      "Department of Mathematics, Department of Meteorology and Atmospheric Science,",
      "Institute for Computational and Data Sciences",
      "The Pennsylvania State University, University Park, PA 16802, USA",
      "jharlim@psu.edu",
      "Haizhao Yang"
    ]
  },
  "To read/Statistical Challenges in Bigdata neuroimaging_Smith 2017.pdf": {
    "pdf_title": "Statistical Challenges in ``Big Data'' Human Neuroimaging",
    "pdf_author": "Stephen M. Smith",
    "arxiv": "",
    "doi": "10.1016/j.neuron.2017.12.018",
    "first_lines": [
      "Neuron",
      "NeuroView",
      "Statistical Challenges",
      "in ‘‘Big Data’’ Human Neuroimaging",
      "Stephen M. Smith1,* and Thomas E. Nichols1,2,3",
      "1Wellcome Trust Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK",
      "2Big Data Institute, University of Oxford, Oxford, UK",
      "3Department of Statistics, University of Warwick, Coventry, UK",
      "*Correspondence: steve@fmrib.ox.ac.uk",
      "https://doi.org/10.1016/j.neuron.2017.12.018",
      "Smith and Nichols discuss ‘‘big data’’ human neuroimaging studies, with very large subject numbers and",
      "amounts of data. These studies provide great opportunities for making new discoveries about the brain",
      "but raise many new analytical challenges and interpretational risks.",
      "Introduction",
      "Over the last 30 years, structural and functional brain imaging has become a powerful and widespread tool for clinical and"
    ]
  },
  "To read/Stochastic Gradient Descent as Approximate Bayesian Inference_StephanMandt2017.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Journal of Machine Learning Research 18 (2017) 1-35",
      "Submitted 4/17; Revised 10/17; Published 12/17",
      "Stochastic Gradient Descent as Approximate Bayesian Inference",
      "Stephan Mandt",
      "STEPHAN . MANDT @ GMAIL . COM",
      "Data Science Institute",
      "Department of Computer Science",
      "Columbia University",
      "New York, NY 10025, USA",
      "Matthew D. Hoffman",
      "MATHOFFM @ ADOBE . COM",
      "Adobe Research",
      "Adobe Systems Incorporated",
      "601 Townsend Street",
      "San Francisco, CA 94103, USA"
    ]
  },
  "To read/Straggler-Robust Distributed Optimization in Parameter server networks_Atallah 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2108.09173v1",
    "doi": "",
    "first_lines": [
      "1",
      "Straggler-Robust Distributed Optimization in",
      "Parameter-Server Networks",
      "Elie Atallah, Nazanin Rahnavard Senior Member, IEEE, and Chinwendu Enyioha",
      "arXiv:2108.09173v1 [math.OC] 20 Aug 2021",
      "Department of Electrical and Computer Engineering",
      "University of Central Florida, Orlando, FL",
      "Emails: {elieatallah@knights., nazanin@eecs., cenyioha@}ucf.edu",
      "Abstract",
      "Optimization in distributed networks plays a central role in almost all distributed machine learning problems. In",
      "principle, the use of distributed task allocation has reduced the computational time, allowing better response rates and",
      "higher data reliability. However, for these computational algorithms to run effectively in complex distributed systems,",
      "the algorithms ought to compensate for communication asynchrony, network node failures and delays known as",
      "stragglers. These issues can change the effective connection topology of the network, which may vary over time, thus",
      "hindering the optimization process. In this paper, we propose a new distributed unconstrained optimization algorithm"
    ]
  },
  "To read/Survey on Graph Neural Networks_Vretos 2024.pdf": {
    "pdf_title": "Survey on Graph Neural Networks",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1109/ACCESS.2024.3456913",
    "first_lines": [
      "This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and",
      "content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3456913",
      "Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.",
      "Digital Object Identifier 10.1109/ACCESS.2024.0429000",
      "Survey on Graph Neural Networks",
      "G. GKARMPOUNIS, C. VRANIS, N. VRETOS, (Member, IEEE), P. DARAS, (Senior Member, IEEE)",
      "Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece",
      "Corresponding author: G.Gkarmpounis (e-mail: ggkarmpounis@iti.gr).",
      "This work was supported by the European Commission (CONNECTOR, Customs Extended Interoperable Common Information Sharing",
      "Environment) under Grant 101121271.",
      "ABSTRACT Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graphstructured data. Their ability to capture complex relationships and dependencies within graph structures,",
      "allows them to have a great number of applications in various domains, including social network analysis,",
      "recommendation systems and drug discovery. The aim of this work is to provide a detailed overview of",
      "the models in Graph Neural Networks and propose a new taxonomy of GNNs, including Deep Generative",
      "Models for graphs as a distinct category. The works included were selected based on their impact, with recent"
    ]
  },
  "To read/SynthSeg_Segmentation of brain MRI scans on synthesis images_Billot 2023 .pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "2107.09559v4",
    "doi": "",
    "first_lines": [
      "Medical Image Analysis (2023)",
      "Contents lists available at ScienceDirect",
      "Medical Image Analysis",
      "journal homepage: www.elsevier.com/locate/media",
      "arXiv:2107.09559v4 [eess.IV] 28 Feb 2023",
      "SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without",
      "retraining",
      "Benjamin Billota,∗, Douglas N. Greveb , Oula Puontic , Axel Thielscherc,d , Koen Van Leemputb,d , Bruce Fischlb,e,f , Adrian V.",
      "Dalcab,e , Juan Eugenio Iglesiasa,b,e , for the ADNI1",
      "a Centre for Medical Image Computing, University College London, UK",
      "b Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA",
      "c Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Denmark",
      "d Department of Health Technology, Technical University of Denmark",
      "e Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA",
      "f Program in Health Sciences and Technology, Massachusetts Institute of Technology, USA"
    ]
  },
  "To read/The Virtual Brain (TVB EBRAINS)_Alzheimer disease simulation_Stefanovski 2019.pdf": {
    "pdf_title": "Linking Molecular Pathways and Large-Scale Computational Modeling to Assess Candidate Disease Mechanisms and Pharmacodynamics in Alzheimer's Disease",
    "pdf_author": "Leon Stefanovski",
    "arxiv": "",
    "doi": "10.3389/fncom.2019.00054",
    "first_lines": [
      "ORIGINAL RESEARCH",
      "published: 13 August 2019",
      "doi: 10.3389/fncom.2019.00054",
      "Linking Molecular Pathways and",
      "Large-Scale Computational Modeling",
      "to Assess Candidate Disease",
      "Mechanisms and Pharmacodynamics",
      "in Alzheimer’s Disease",
      "Edited by:",
      "Anke Meyer-Baese,",
      "Florida State University, United States",
      "Reviewed by:",
      "Li Su,",
      "University of Cambridge,",
      "United Kingdom"
    ]
  },
  "To read/Topological data analysis and biology, molecule_Amezquida 2020.pdf": {
    "pdf_title": "The shape of things to come: Topological Data Analysis and biology, from molecules to organisms",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1002/dvdy.175",
    "first_lines": [
      "Received: 8 February 2020",
      "Revised: 29 March 2020",
      "Accepted: 29 March 2020",
      "DOI: 10.1002/dvdy.175",
      "REVIEW",
      "The shape of things to come: Topological data analysis and",
      "biology, from molecules to organisms",
      "Erik J. Amézquita1",
      "Elizabeth Munch1,3",
      "| Michelle Y. Quigley2",
      "| Daniel H. Chitwood1,2",
      "| Tim Ophelders1",
      "|",
      "1",
      "Department of Computational"
    ]
  },
  "To read/Trajectory balance_GFlowNets_Malkin 2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2201.13259v3",
    "doi": "",
    "first_lines": [
      "Trajectory balance:",
      "Improved credit assignment in GFlowNets",
      "arXiv:2201.13259v3 [cs.LG] 4 Oct 2023",
      "Nikolay Malkin",
      "Mila, Université de Montréal",
      "Montréal, Québec, Canada",
      "Moksh Jain",
      "Mila, Université de Montréal",
      "Montréal, Québec, Canada",
      "Emmanuel Bengio",
      "Mila, McGill University, Recursion",
      "Montréal, Québec, Canada",
      "Chen Sun",
      "Mila, Université de Montréal",
      "Montréal, Québec, Canada"
    ]
  },
  "To read/TransMorph_ Transformer for unsupervised medical image registration_Junyu Chen 2022.pdf": {
    "pdf_title": "TransMorph: Transformer for unsupervised medical image registration",
    "pdf_author": "Junyu Chen; Eric C. Frey; Yufan He; William P. Segars; Ye Li; Yong Du;",
    "arxiv": "2111.10480v6",
    "doi": "",
    "first_lines": [
      "Contents lists available at ScienceDirect",
      "Medical Image Analysis",
      "journal homepage: www.elsevier.com/locate/media",
      "TransMorph: Transformer for unsupervised medical image registration",
      "arXiv:2111.10480v6 [eess.IV] 15 Oct 2022",
      "Junyu Chena,b,∗, Eric C. Freya,b , Yufan Hee , William P. Segarsc , Ye Lid , Yong Dua",
      "a Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutes, Baltimore, MD, USA",
      "b Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA",
      "c Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA",
      "d Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA",
      "e NVIDIA Corporation, Bethesda, MD, USA",
      "ARTICLE INFO",
      "ABSTRACT",
      "Article history:",
      "Received xx xx 20xx"
    ]
  },
  "To read/Turbo-Aggregate_Breaking the Quadratic Aggregation Barrier in Secure Federated learning_So 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2002.04156v3",
    "doi": "",
    "first_lines": [
      "1",
      "Turbo-Aggregate: Breaking the Quadratic",
      "Aggregation Barrier in Secure Federated Learning",
      "arXiv:2002.04156v3 [cs.LG] 20 Feb 2021",
      "Jinhyun So, Başak Güler, Member, IEEE",
      "Abstract—Federated learning is a distributed framework for",
      "training machine learning models over the data residing at",
      "mobile devices, while protecting the privacy of individual users.",
      "A major bottleneck in scaling federated learning to a large",
      "number of users is the overhead of secure model aggregation",
      "across many users. In particular, the overhead of the state-of-theart protocols for secure model aggregation grows quadratically",
      "with the number of users. In this paper, we propose the first",
      "secure aggregation framework, named Turbo-Aggregate, that in",
      "a network with 𝑁 users achieves a secure aggregation overhead of",
      "𝑂 (𝑁 log 𝑁), as opposed to 𝑂 (𝑁 2 ), while tolerating up to a user"
    ]
  },
  "To read/Uncertainty Baselines_google 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2106.04015v3",
    "doi": "",
    "first_lines": [
      "Uncertainty Baselines",
      "arXiv:2106.04015v3 [cs.LG] 5 Jan 2022",
      "Uncertainty Baselines:",
      "Benchmarks for Uncertainty & Robustness in Deep Learning",
      "znado@google.com∗",
      "neil.band@cs.ox.ac.uk†",
      "markcollier@google.com∗",
      "josipd@google.com∗",
      "dusenberrymw@google.com∗",
      "sebastian.farquhar@cs.ox.ac.uk†",
      "qixuan.feng1@gmail.com†",
      "angelos.filos@cs.ox.ac.uk†",
      "mh740@cam.ac.uk‡",
      "rjenatton@google.com∗",
      "ghassen@google.com∗"
    ]
  },
  "To read/Uncertainty-aware meta-weighted optimization framework for domain-generalized medical image segmentation_Oh 2024.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Uncertainty-aware meta-weighted optimization",
      "framework for domain-generalized medical image",
      "segmentation",
      "Seok-Hwan Oh1⋆ , Guil Jung1⋆ , Sang-Yun Kim1 , Myeong-Gee Kim2 ,",
      "Young-Min Kim1 , Hyeon-Jik Lee1 , Hyuk-Sool Kwon3 , and Hyeon-Min Bae1",
      "Electrical Engineering Department, KAIST, 34141 Daejeon, South Korea",
      "joseph9337@kaist.ac.kr",
      "2",
      "Barreleye Inc., 06211 Seoul, South Korea",
      "3",
      "Department of Emergency Medicine, SNUBH, 13620 Seong-nam, South Korea",
      "1",
      "Abstract. Accurate segmentation of echocardiograph images is essential for the diagnosis of cardiovascular diseases. Recent advances in deep",
      "learning have opened a possibility for automated cardiac image segmentation. However, the data-driven echocardiography segmentation schemes",
      "suffer from domain shift problems, since the ultrasonic image characteristics are largely affected by measurement conditions determined by device"
    ]
  },
  "To read/Universality in human cortical folding in health and disease_Wang 2016.pdf": {
    "pdf_title": "Universality in human cortical folding in health and disease",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.1073/pnas.1610175113",
    "first_lines": [
      "Fractal dimension is computed by box counting method, applied on brain MRI at different scales.",
      "Universality in human cortical folding in health",
      "and disease",
      "Yujiang Wanga,1, Joe Necusb, Marcus Kaisera,b, and Bruno Motac",
      "a",
      "Interdisciplinary Computing and Complex BioSystems, School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom;",
      "Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, United Kingdom; and cInstituto de Física, Universidade Federal do Rio de",
      "Janeiro, RJ 21941-909, Rio de Janeiro, Brazil",
      "b",
      "Downloaded from https://www.pnas.org by 45.84.136.224 on October 16, 2025 from IP address 45.84.136.224.",
      "Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved September 13, 2016 (received for review June 23, 2016)",
      "The folding of the cortex in mammalian brains across species has",
      "recently been shown to follow a universal scaling law that can be",
      "derived from a simple physics model. However, it was yet to be",
      "determined whether this law also applies to the morphological"
    ]
  },
  "To read/Universality in human cortical folding in health and disease_Wang 2016_appendix.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Text S1:",
      "Primary measures of the healthy cohorts",
      "Fig. S1.1, S1.2, and S1.3 show the MRI derived measures of average cortical thickness, total",
      "grey matter surface area, and exposed grey matter surface area, respectively. As in the main",
      "text, we show each hemisphere as a separate data point. We show the data separately for",
      "both genders together (first column in each figure), females (second column in each figure),",
      "and males (third column in each figure). All measures are also shown dependent on age, to",
      "ensure that any systematic changes between the datasets are not due to age or gender effects.",
      "Systematic and significant changes in offset can be seen between all three healthy cohorts",
      "(NKI, OASIS, HCP) in their average cortical thickness.",
      "Fig. S1.1: Average cortical thickness for all three health cohorts shown over age for both genders, females, and males,",
      "respectively.",
      "No systematic changes in offset can be seen visually between all three healthy cohorts (NKI,",
      "OASIS, HCP) in their total grey matter surface area, although statistically there may be a",
      "marginally significant difference in offset."
    ]
  },
  "To read/Unsupervised Brain Anomaly Detection and Segmentation_Walter Hugo 2021.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref",
    "arxiv": "2102.11650v1",
    "doi": "",
    "first_lines": [
      "Proceedings of Machine Learning Research – Under Review:1–22, 2021",
      "Full Paper – MIDL 2021 submission",
      "arXiv:2102.11650v1 [eess.IV] 23 Feb 2021",
      "Unsupervised Brain Anomaly Detection and Segmentation",
      "with Transformers",
      "Walter Hugo Lopez Pinaya 1",
      "Petru-Daniel Tudosiu 1",
      "Robert Gray 2",
      "Geraint Rees 3",
      "Parashkev Nachev2",
      "Sébastien Ourselin 1",
      "M. Jorge Cardoso1",
      "walter.diaz sanz@kcl.ac.uk",
      "petru.tudosiu@kcl.ac.uk",
      "r.gray@ucl.ac.uk"
    ]
  },
  "To read/Unsupervised Lesion Detection in Brain CT_Bayesian CNN_Pawlowsky_2018.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Unsupervised Lesion Detection in Brain CT",
      "using Bayesian Convolutional Autoencoders",
      "Nick Pawlowski1 , Matthew C.H. Lee1,2 , Martin Rajchl1 , Steven McDonagh1 , Enzo Ferrante3 ,",
      "Konstantinos Kamnitsas1 , Sam Cooke4 , Susan Stevenson4 , Aneesh Khetani4 , Tom Newman4 ,",
      "Fred Zeiler4 , Richard Digby4 , Jonathan P. Coles4 , Daniel Rueckert1 , David K. Menon4 ,",
      "Virginia F.J. Newcombe4* , and Ben Glocker1*",
      "1",
      "Biomedical Image Analysis Group, Imperial College London, UK",
      "2",
      "HeartFlow, California, CA 94063, USA",
      "3",
      "Universidad Nacional del Litoral / CONICET, Argentina",
      "4",
      "Division of Anaesthesia, Department of Medicine, University of Cambridge, UK",
      "*"
    ]
  },
  "To read/Using Physics-Informed Neural Networks (PINNs) for Tumor Cell Growth Modeling_Rodrigues_2024.pdf": {
    "pdf_title": "Using Physics-Informed Neural Networks (PINNs) for Tumor Cell Growth Modeling",
    "pdf_author": "José Alberto Rodrigues",
    "arxiv": "",
    "doi": "10.3390/math12081195",
    "first_lines": [
      "mathematics",
      "Article",
      "Using Physics-Informed Neural Networks (PINNs) for Tumor",
      "Cell Growth Modeling",
      "José Alberto Rodrigues",
      "CIMA and Department of Mathematics of ISEL—Higher Institute of Engineering of Lisbon, Rua Conselheiro",
      "Emídio Navarro, 1, 1959-007 Lisbon, Portugal; jose.rodrigues@isel.pt",
      "Abstract: This paper presents a comprehensive investigation into the applicability and performance",
      "of two prominent growth models, namely, the Verhulst model and the Montroll model, in the",
      "context of modeling tumor cell growth dynamics. Leveraging the power of Physics-Informed",
      "Neural Networks (PINNs), we aim to assess and compare the predictive capabilities of these models",
      "against experimental data obtained from the growth patterns of tumor cells. We employed a dataset",
      "comprising detailed measurements of tumor cell growth to train and evaluate the Verhulst and",
      "Montroll models. By integrating PINNs, we not only account for experimental noise but also embed",
      "physical insights into the learning process, enabling the models to capture the underlying mechanisms"
    ]
  },
  "To read/WADO protocol.pdf": {
    "pdf_title": "DICOM PS 3.18 2011 - Web Access to DICOM Persistent Objects (WADO)",
    "pdf_author": "DICOM Standards Committee",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "PS 3.18-2011",
      "Digital Imaging and Communications in Medicine (DICOM)",
      "Part 18: Web Access to DICOM Persistent Objects (WADO)",
      "Published by",
      "National Electrical Manufacturers Association",
      "1300 N. 17th Street",
      "Rosslyn, Virginia 22209 USA",
      "© Copyright 2011 by the National Electrical Manufacturers Association. All rights including translation into",
      "other languages, reserved under the Universal Copyright Convention, the Berne Convention for the",
      "Protection of Literacy and Artistic Works, and the International and Pan American Copyright Conventions.",
      "- Standard -",
      "PS 3.18-2011",
      "Page 2",
      "NOTICE AND DISCLAIMER",
      "The information in this publication was considered technically sound by the consensus of persons"
    ]
  },
  "To read/WAIC_Generative Ensemble for robust anomaly detection_Choi & Jang 2019.pdf": {
    "pdf_title": "WAIC, but Why?  Generative Ensembles for Robust Anomaly Detection",
    "pdf_author": "Hyunsun Choi, Eric Jang, Alexander A. Alemi",
    "arxiv": "1810.01392v4",
    "doi": "",
    "first_lines": [
      "WAIC, but Why?",
      "Generative Ensembles for Robust Anomaly Detection",
      "arXiv:1810.01392v4 [stat.ML] 23 May 2019",
      "Hyunsun Choi * Eric Jang * 1 Alexander A. Alemi 1",
      "Abstract",
      "Machine learning models encounter Out-ofDistribution (OoD) errors when the data seen at",
      "test time are generated from a different stochastic",
      "generator than the one used to generate the training data. One proposal to scale OoD detection",
      "to high-dimensional data is to learn a tractable",
      "likelihood approximation of the training distribution, and use it to reject unlikely inputs. However,",
      "likelihood models on natural data are themselves",
      "susceptible to OoD errors, and even assign large",
      "likelihoods to samples from other datasets. To",
      "mitigate this problem, we propose Generative Ensembles, which robustify density-based OoD detection by way of estimating epistemic uncertainty",
      "of the likelihood model. We present a puzzling"
    ]
  },
  "To read/Whole Brain Segmentation_Fischl_2000.pdf": {
    "pdf_title": "PII: S0896-6273(02)00569-X",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "Neuron, Vol. 33, 341–355, January 31, 2002, Copyright 2002 by Cell Press",
      "Whole Brain Segmentation:",
      "Neurotechnique",
      "Automated Labeling of Neuroanatomical",
      "Structures in the Human Brain",
      "Bruce Fischl,1 David H. Salat,1 Evelina Busa,1",
      "Marilyn Albert,2,3 Megan Dieterich,5",
      "Christian Haselgrove,5Andre van der Kouwe,1",
      "Ron Killiany,4 David Kennedy,5 Shuna Klaveness,5",
      "Albert Montillo,6 Nikos Makris,5 Bruce Rosen,1",
      "and Anders M. Dale1,7",
      "1",
      "Massachusetts General Hospital",
      "Nuclear Magnetic Resonance Center",
      "Rm. 2328, Building 149"
    ]
  },
  "To read/XTab_Cross-table Pretraining for Tabular Transformers_BIngZhao Zhu_2023.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Bingzhao Zhu, Xingjian Shi, Nick Erickson, Mu Li, George Karypis, Mahsa Shoaran",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "XTab: Cross-table Pretraining for Tabular Transformers",
      "Bingzhao Zhu 1 2 * Xingjian Shi 3 † Nick Erickson 4 Mu Li 3 † George Karypis 4 Mahsa Shoaran 1",
      "Abstract",
      "tokens, tables are highly data-specific. Tabular data can",
      "vary in the number and types of columns. This makes it",
      "difficult for tabular deep learning models to transfer the",
      "knowledge learned from one table to another, leading to",
      "poor generalization abilities. Therefore, self-supervised",
      "learning for tabular data (He et al., 2022; Devlin et al., 2018),",
      "particularly one that is able to bootstrap the learning on new",
      "tables, is still an open problem.",
      "The success of self-supervised learning in computer vision and natural language processing has",
      "motivated pretraining methods on tabular data.",
      "However, most existing tabular self-supervised",
      "learning models fail to leverage information"
    ]
  },
  "To read/YOLO.pdf": {
    "pdf_title": "Subject:",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "1506.02640v5",
    "doi": "",
    "first_lines": [
      "You Only Look Once:",
      "Unified, Real-Time Object Detection",
      "Joseph Redmon∗ , Santosh Divvala∗† , Ross Girshick¶ , Ali Farhadi∗†",
      "University of Washington∗ , Allen Institute for AI† , Facebook AI Research¶",
      "http://pjreddie.com/yolo/",
      "arXiv:1506.02640v5 [cs.CV] 9 May 2016",
      "Abstract",
      "Person: 0.64",
      "Horse: 0.28",
      "We present YOLO, a new approach to object detection.",
      "Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and",
      "associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from",
      "full images in one evaluation. Since the whole detection",
      "pipeline is a single network, it can be optimized end-to-end",
      "directly on detection performance."
    ]
  },
  "To read/genomic variant identification generative AI.pdf": {
    "pdf_title": "A GPU-accelerated compute framework for pathogen genomic variant identification to aid genomic epidemiology of infectious disease: a malaria case study",
    "pdf_author": "Creator:         LaTeX with hyperref package",
    "arxiv": "",
    "doi": "10.1093/bib/bbac314",
    "first_lines": [
      "Briefings in Bioinformatics, 2022, 23(5), 1–11",
      "https://doi.org/10.1093/bib/bbac314",
      "Case Study",
      "A GPU-accelerated compute framework for pathogen",
      "genomic variant identification to aid genomic",
      "epidemiology of infectious disease: a malaria case study",
      "Giovanna Carpi",
      ", Lev Gorenstein",
      ", Timothy T. Harkins, Mehrzad Samadi and Pankaj Vats",
      "Abstract",
      "As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission",
      "patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to",
      "scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated,",
      "scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of",
      "its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes. We demonstrate superior"
    ]
  },
  "To read/graph neural network applications in mechanics-related domains_Zhao 2024.pdf": {
    "pdf_title": "",
    "pdf_author": "ZHAO, Yingxue",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "A review of graph neural network applications in mechanics-related domains",
      "Yingxue Zhao 1, Haoran Li 1, Haosu Zhou 1, Hamid Reza Attar 1, Tobias Pfaff 2, Nan Li 1*",
      "1 Dyson School of Design Engineering, Imperial College London, London, UK",
      "2 Google DeepMind, London, UK",
      "∗ Corresponding author. E-mail address: n.li09@imperial.ac.uk (N. Li)",
      "Abstract",
      "Mechanics-related problems often present unique challenges in achieving accurate geometric and physical representations,",
      "particularly for non-uniform structures. Graph neural networks (GNNs) have emerged as a promising tool to tackle these",
      "challenges by adeptly learning from graph data with irregular underlying structures. Consequently, recent years have",
      "witnessed a surge in complex mechanics-related applications inspired by the advancements of GNNs. Despite this process,",
      "there is a notable absence of a systematic review addressing the recent advancement of GNNs in solving mechanicsrelated problems. To bridge this gap, this review article aims to provide an in-depth overview of the GNN applications in",
      "mechanics-related domains while identifying key challenges and outlining potential future research directions. In this",
      "review article, we begin by introducing the fundamental algorithms of GNNs that are widely employed in mechanicsrelated applications. We provide a concise explanation of their underlying principles to establish a solid understanding",
      "that will serve as a basis for exploring the applications of GNNs in mechanics-related domains. The scope of this paper",
      "is intended to cover the categorisation of literature into solid mechanics, fluid mechanics, and interdisciplinary mechanicsrelated domains, providing a comprehensive summary of graph representation methodologies, GNN architectures, and"
    ]
  },
  "To read/mRNA targeting immune cell.pdf": {
    "pdf_title": "Creator:         Arbortext Advanced Print Publisher 10.0.1306/W Unicode",
    "pdf_author": "",
    "arxiv": "",
    "doi": "10.3389/fimmu.2023.1294929",
    "first_lines": [
      "TYPE Review",
      "PUBLISHED 27 November 2023",
      "DOI 10.3389/fimmu.2023.1294929",
      "OPEN ACCESS",
      "EDITED BY",
      "Dan Peer,",
      "Tel Aviv University, Israel",
      "REVIEWED BY",
      "Lukas Heger,",
      "University Hospital Erlangen, Germany",
      "Uri Elia,",
      "Israel Institute for Biological Research",
      "(IIBR), Israel",
      "Straight to the point: targeted",
      "mRNA-delivery to immune cells"
    ]
  },
  "To read/reinforcement learning.pdf": {
    "pdf_title": "",
    "pdf_author": "",
    "arxiv": "",
    "doi": "",
    "first_lines": [
      "ii",
      "Adaptive Computation and Machine Learning",
      "Francis Bach",
      "A complete list of books published in the Adaptive Computation and Machine Learning",
      "series appears at the back of this book.",
      "The cover design is based on the trajectories of a simulated bicycle controlled by a",
      "reinforcement learning system developed by Jette Randløv."
    ]
  },
  "To read/scapGNN_ graph neural network for active pathway and gene module inference_Han 2023.pdf": {
    "pdf_title": "scapGNN: A graph neural network–based framework for active pathway and gene module inference from single-cell multi-omics data",
    "pdf_author": "Xudong Han, Bing Wang, Chenghao Situ, Yaling Qi, Hui Zhu, Yan Li, Xuejiang Guo",
    "arxiv": "",
    "doi": "10.1371/journal.pbio.3002369",
    "first_lines": [
      "METHODS AND RESOURCES",
      "scapGNN: A graph neural network–based",
      "framework for active pathway and gene",
      "module inference from single-cell multi-omics",
      "data",
      "Xudong Han1,2, Bing Wang1,2, Chenghao Situ2, Yaling Qi2, Hui Zhu2*, Yan Li3*,",
      "Xuejiang Guo ID1,2*",
      "a1111111111",
      "a1111111111",
      "a1111111111",
      "a1111111111",
      "a1111111111",
      "1 State Key Laboratory of Reproductive Medicine and Offspring Health, School of Medicine, Southeast",
      "University, Nanjing, China, 2 Department of Histology and Embryology, State Key Laboratory of",
      "Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China, 3 Department of"
    ]
  }
}