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<!DOCTYPE HTML>
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<h1 id="logo"><a href="#">Negar Foroutan</a></h1>
<p>PhD student at <a href="https://www.epfl.ch/schools/ic/"><i>EPFL</i></a></p>
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<li><a href="#one" class="active">About</a></li>
<li><a href="#two">Education</a></li>
<li><a href="#three">Publications</a></li>
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<h3>Welcome to My Homepage :-)</h3>
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<p>My name is Negar Foroutan (Persian: نگار فروتن), and I am from Iran. Currently I am a PhD student at the School of Computer and Communication Sciences <a href="http://ic.epfl.ch/"><i>(IC)</i></a>, <a href=https://www.epfl.ch/en><i>EPFL</i></a>,
and a doctoral research assistant at <a href="https://www.epfl.ch/labs/lsir/"><i>LSIR</i></a> and <a href="https://nlp.epfl.ch"><i>NLP</i></a> labs under supervision of <a href="https://people.epfl.ch/karl.aberer?lang=en"><i>Karl Aberer</i></a> and <a href="https://atcbosselut.github.io"><i>Antoine Bosselut</i></a>. <br/>Currently, I'm a research intern at Google.
<br/><br/>
I've got my Master degree in Artificial Intelligence at the Department of CSE and IT of <a href=http://shirazu.ac.ir/en><i>Shiraz University</i></a>, where I also received my B.Sc. degree in Software Engineering. During my master studies, I was working in the area of social network analysis, and in my thesis, I investigated the problem of inferring the structure and dynamics of networks using information diffusion.
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After finishing my master studies, and for three months, I was a research intern at the Max Planck Institute for Software Systems <a href=https://www.mpi-sws.org/>(MPI-SWS)</a> in Germany, under the supervision of <a href="https://people.mpi-sws.org/~manuelgr/">Manuel Gomez Rodriguez</a>. I've also had a chance to spend three months in Switzerland as an intern at the Data Analytics Laboratory, ETH Zurich, under the supervision of <a href="http://www.carsten-eickhoff.com">Carsten Eickoff</a>.
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I also spent a year working as a research assistant at the Machine Learning and Optimization laboratory (<a href="https://mlo.epfl.ch">MLO</a>) at EPFL, under the supervision of <a href="https://people.epfl.ch/martin.jaggi">Martin Jaggi</a>.<br>
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<b style="color:#008055">Research Interests:</b> My research interests lie broadly in the fields of natural language processing (NLP) and machine learning.
I am particularly interested in multilingual NLP and cross-lingual transfer, focusing on low-resource scenarios.</p>
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<h3>Education</h3>
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<li> PhD in Computer & Communication Sciences<i style="float: right">2019-2025 (Expected)</i><br><a href=https://www.epfl.ch/en><i>Swiss Federal Institue of Technology in Lausanne (EPFL)</i></a>, Lausanne, Switzerland</li><br>
<li> M.Sc. in Computer Engineering (Artificial Intelligence)<i style="float: right">2013-2016</i><br><a href=http://shirazu.ac.ir/en><i>Shiraz University</i></a>, Shiraz, Iran <i style="float: right">Supervisor: <a href="http://shirazu.ac.ir/faculty/home/ali/en">Dr. Ali Hamzeh</a></i>
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<li><b style="color:#008055">Thesis:</b> Inferring Social Networks Structure Using Information Diffusion
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<li> B.Sc. in Computer Engineering (Software Engineering)<i style="float: right">2009-2013</i><br><a href=http://shirazu.ac.ir/en><i>Shiraz University</i></a>, Shiraz, Iran</li>
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<h3>Publications</h3>
<p><ul>
<li>A. Romaneo, <b><u>N. Foroutan</u></b>, Anna Sotnikova, et al., "INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge," <i>ICLR</i>, 2025. (<a href="https://arxiv.org/abs/2411.19799">More Information</a>) </li><br>
<li> B. Borges*, <b><u>N. Foroutan*</u></b>, D. Bayazit*, A. Sotnikova*, et. al., "Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants," <i>PNAS 2024 </i>. (<a href="https://www.pnas.org/doi/10.1073/pnas.2414955121">More Information</a>) </li><br>
<li>D. Bayazit, <b><u>N. Foroutan</u></b>, Z. Chen, G. Weiss, and A. Bosselut, "Discovering Knowledge-Critical Subnetworks in Pretrained Language Models," <i>EMNLP</i>, 2024. (<a href="https://arxiv.org/abs/2310.03084">More Information</a>) </li><br>
<li>C. Fierro, <b><u>N. Foroutan</u></b>, D. Elliott, and A. Søgaard, "How Do Multilingual Models Remember? Investigating Multilingual Factual Recall Mechanisms," <i>arXiv</i>, 2024. (<a href="https://arxiv.org/pdf/2410.14387">More Information</a>) </li><br>
<li><b><u>N. Foroutan</u></b>, M. Banaei, Karl Aberer, and A. Bosselut, "Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention," <i>EMNLP 2023 - Findings</i>. (<a href="http://arxiv.org/abs/2310.15258">More Information</a>) </li><br>
<li>Y. Karoui, R. Lebret, <b><u>N. Foroutan</u></b>, and K. Aberer, "Stop Pre-Training: Adapt Visual-Language Models to Unseen Languages," <i>ACL</i>, 2023. (<a href="https://arxiv.org/abs/2306.16774">More Information</a>)</li><br>
<li><b><u>N. Foroutan</u></b>, M. Banaei, R. Lebret, A. Bosselut, and K. Aberer, "Discovering Language-neutral Sub-networks in Multilingual Language Models," <i>EMNLP</i>, 2022. (<a href="https://arxiv.org/abs/2205.12672"> More Information</a>)</li><br>
<li><b><u>N. Foroutan</u></b>, A. Romanou, S. Massonnet, R. Lebret, and K. Aberer, "Multilingual Text Summarization on Financial Documents," <i>Proceedings of the 4th Financial Narrative Processing Workshop@ LREC</i>, 2022. (<a href="https://aclanthology.org/2022.fnp-1.7.pdf"> More Information</a>)</li><br>
<li><b><u>N. Foroutan </u></b> and M. Jaggi, "Sparse Communication for Training Deep Networks," <i>Workshop on Beyond first-order methods in ML systems at ICML</i>, 2020. (<a href="https://arxiv.org/pdf/2009.09271.pdf">More Information</a>)</li><br>
<li><b><u>N. Foroutan</u></b> and A. Hamzeh, "Discovering the Hidden Structure of a Social Network: A Semi Supervised Approach," <i>IEEE Transactions on Computational Social Systems</i>, pp. 14-25. 2017. (<a href = "https://doi.org/10.1109/TCSS.2017.2668843"> More Information </a>)</li><br>
<li><b><u>N. Foroutan Eghlidi</u></b>, Jannick Griner, Nicolas Mesot, Leandro von Werra and Carsten Eickhoff, "ETH Zurich at TREC Precision Medicine 2017," <i>Proceedings of the 26th Text Retrieval Conference (TREC)</i>, 2017.</li><br>
<li><b><u>N. Foroutan Eghlidi</u></b>, A. Afshar, B. Ashenagar and A. Hamzeh, "A lightweight method to investigate unknown social network structure." <i>Computer and Knowledge Engineering (ICCKE), 2015 5th International Conference on</i>, pp. 262-267. IEEE, 2015. (<a href= "http://dx.doi.org/10.1109/ICCKE.2015.7365838" >More Information</a>)</li><br>
<li> B. Ashenagar, <b><u>N. Foroutan Eghlidi</u></b>, A. Afshar and A. Hamzeh, "Team formation in social networks based on local distance metric." <i>Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on</i>, pp. 946-952. IEEE, 2015. (<a href="http://dx.doi.org/10.1109/FSKD.2015.7382071">More Information</a>)</li><br>
<li>B. Ashenagar, A. Hamzeh, <b><u>N. Foroutan Eghlidi</u></b> and A. Afshar, "A fast approach for multi-objective team formation in social networks." <i>Information and Knowledge Technology (IKT), 2015 7th Conference on</i>, pp. 1-6. IEEE, 2015. (<a href="http://dx.doi.org/10.1109/IKT.2015.7288755">More Information</a>)</li><br>
<li>A. Afshar, B. Ashenagar. <b><u>N. Foroutan Eghlidi</u></b>, Mansour Zolghadri Jahromi and Ali Hamzeh, "Using local utility maximization to detect social networks communities." <i>Computer Science and Software Engineering (CSSE), 2015 International Symposium on</i>, pp. 1-8. IEEE, 2015. (<a href="http://dx.doi.org/10.1109/CSICSSE.2015.7369236"> More Information</a>)</li>
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<h3>Projects</h3>
<p>More information about my projects: <a href="https://ir.linkedin.com/in/negarforoutan">LinkedIn</a> <br><ul>
<li><b>Discovering Language-neutral Sub-networks in Multilingual Language Models</b><span style="float: right"><i>2022</i></span><br>
In this project, we investigate the effect of language-neutral parameters on the cross-lingual transfer performance of multilingual language models.
We conceptualize language neutrality of multilingual models as a function of the overlap between language-encoding sub-networks of these models.
We employ the lottery ticket hypothesis to discover sub-networks that are individually optimized for various languages and tasks.
Our evaluation across three distinct tasks and eleven typologically-diverse languages demonstrates that sub-networks for different languages are topologically similar, making them effective initializations for cross-lingual transfer with limited performance degradation.</li>
<br>
<li><b>Cross-lingual Representation Learning for Low-Resource Languages</b><span style="float: right"><i>March 2020 - June 2020</i></span><br>
In this project, our goal is to transfer a pre-trained monolingual model in a source language to a target language by zero-shot learning.
Unlabelled data was used to train token embeddings for the target language, and labelled data from the source language was used to fine-tune the model for the down-stream task.
In our experiments, we use the German language as the source and Swiss German as the target language.
We evaluate the obtained model using sentiment analysis as our evaluation down-stream task.</li>
<br>
<li><b>News Article Contextual Embedding</b><span style="float: right"><i>September 2020 - December 2020</i></span><br>
In this project, we propose a method for news article embedding such that the new embedding space can categorize articles based on the events they cover.
We use BERT embedding as a basis and add a feed-forward neural network on top of it. We also take advantage of triplet loss in the training process.
Experimental results show the proposed method outperforms the baselines in terms of adjusted mutual information and adjusted rand index scores.</li>
<br>
<li><b>Art Creation Using Generative Adversarial Networks (GANs)</b><span style="float: right"><i>October 2020 - December 2020</i></span><br>
In this project, we used BigGAN with inversion technique and progressive-growing GAN networks to generate images of Mesopotamian artifacts as well as Assyrian and (Neo-) Sumerian artifacts which evolved from the Mesopotamian region.</li>
<br>
<li><b>mlbench: Distributed Machine Learning Benchmark</b><span style="float: right"><i>September 2018 - September 2019</i></span><br>
The goal of this project is to provide a collection of reference implementations of distributed machine learning algorithms for different frameworks and system platforms. Currently, we are working on supervised machine learning algorithms, such as deep learning tasks and linear models. We provide a set of defined tasks and related datasets to have a fair and precise comparison of all algorithms, frameworks, and hardware platforms.</li>
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<li><b>TREC 2017 Precision Medicine track</b><span style="float: right"><i>August 2017</i></span><br>
The goal of this project was to retrieve biomedical articles and clinical trials addressing appropriate treatments for a given patient. We began by performing literal query term matching, taking into account the likelihood of document relevance in terms of cancer types, genetic variants, and demographics. Next, in a subsequent phase, we re-ranked the most relevant results based on a range of deep neural gene embeddings that project literal genetic expressions into a semantics-preserving vector space. We used feed-forward networks trained on PubMed and NCBI information but also relying on generative adversarial methods to determine the likelihood of co-occurrence of various mutations within the same patient/article. Experimental results show that even without existing expert labels, the proposed method can introduce marginal improvements over competitive TF-IDF baselines. I was involved in this project during my internship at ETH Zurich.</li>
<br>
<li><b>Citation Network Analysis</b><span style="float: right"><i>March 2017 - Present</i></span><br>
The goal of the project is to gain a better understanding of what happening in citation networks. Indeed, we want to quantify the value of a set of published papers and model the knowledge diffusionin a citation network. First, we assume that writing a paper is an event which is generated by acounting process. Then, it is supposed that there is an infinite number of latent of cluster each event belongs and each cluster has a characteristic vocabulary and cited authors. Given a citation network contains a set of papers, and by using the words in the papers and the authors they cite, we want to infer the latent clusters. These clusters will reveal something meaningful about research trends, cited authors, citation process, etc. This project is a collaboration work that I was involved during my stay at the <a href="https://www.mpi-sws.org/">MPI-SWS</a>.</li>
<br>
<li><b>Inferring Social Networks Structure (M.Sc. Thesis)</b> <span style="float: right"><i>2014-2016</i></span><br>Diffusion of information, spread of rumors, ideas and diseases are assumed to be stochastic processes that occur over the edges of some network structure. Many times, the underlying network structures are unobserved and we only observe the infection times of nodes. In some cases, such networks are also dynamic and change over time. In my thesis, I investigated the problem of inferring the structure and dynamics of networks using information diffusion. First, I modeled the diffusion network as a Markov Decision Process (MDP). Then, as Reinforcement Learning (RL) is one the methods to solve finite MDPs, I used Q-learning, which is a kind of RL, to solve this MDP.</li>
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<li><b>Face Identification and Tracking from a Video Source</b> <i style="float: right">December 2016</i><br>
The goal of this project is to identify and to track faces in a live video stream. Here, we have to train a model for each face we want to identify. To do this, we use different feature extraction and selection methods to find features which best explain the faces. Also, some normalization techniques are applied to normalize a face for position and illumination to reduce the variance caused by these.</li>
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<li><b>Predicting Excitement for DonorsChoose.org <a href="https://www.kaggle.com/c/kdd-cup-2014-predicting-excitement-at-donors-choose">KDDCup2014</a></b> <i style="float: right">July 2014</i><br>
The task of KDD Cup 2014 was predicting the excitement of available projects on <a href="DonorsChoose.org">DonorsChoose.org</a>. We employed some data mining approaches in order to extract relevant and discriminative features and we studied some sampling methods to solve imbalanced data problem. We predicted project's excitement using some models based on Random Forest, Stacking, and Naive Bayes. Also, we were at <u><b>Top 25%</b></u> of participants in this competition.<br> </li>
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<li><b>Perception of Emotion in Facial Expressions</b> <i style="float: right">February 2014</i><br>
In this project, we want to design a system to classify facial expressions of emotion. After employing some image processing techniques to extract initial features we used epsilon-SVR and kernel methods to map features into another space to find more discriminative and shape-related features. We model emotion detection using an SVM classifier that calculated the probability for each emotion category.</li>
<br>
<li><b>Information Retrieval System</b> <i style="float: right">July 2014</i><br>
The goal of the project is to Implement a vector-space information retrieval system. By knowing the set of relevant and non-relevant documents for a given query we tried to find optimal separator and improve it's performance by maximizing F-measure.</li>
<br>
<li><b>Malware Detection</b> <i style="float: right">February 2014</i><br>
In this project we implemented a system to distinguish malicious from benign binary files. Extracting the n-gram of API Call sequences as our dataset, we employed two different methods to solve this problem: 1. "XCS" which is a reinforcement learning-based Genetic algorithm, and 2. N-gram based feature weighting using Genetic Algorithm.</li>
<br>
<li><b>Fuzzy Rule-Based Classification System</b> <i style="float: right">February 2014</i><br>
The goal of this project is to learn rule weights in fuzzy rule-based classification systems. After constructing an initial rule-base for the problem, we applied an algorithm based on hill-climbing search method in which sequentially the solution improved by finding a neighbor solution that is better than the current one. This approach is fast, efficient, and tries to keep much fewer rules than the initial one.</li>
</ul></p>
<!-- <div class="features">
<article>
<a href="#" class="image"><img src="images/pic01.jpg" alt="" /></a>
<div class="inner">
<h4>Possibly broke spacetime</h4>
<p>Integer eu ante ornare amet commetus vestibulum blandit integer in curae ac faucibus integer adipiscing ornare amet.</p>
</div>
</article>
<article>
<a href="#" class="image"><img src="images/pic02.jpg" alt="" /></a>
<div class="inner">
<h4>Terraformed a small moon</h4>
<p>Integer eu ante ornare amet commetus vestibulum blandit integer in curae ac faucibus integer adipiscing ornare amet.</p>
</div>
</article>
<article>
<a href="#" class="image"><img src="images/pic03.jpg" alt="" /></a>
<div class="inner">
<h4>Snapped dark matter in the wild</h4>
<p>Integer eu ante ornare amet commetus vestibulum blandit integer in curae ac faucibus integer adipiscing ornare amet.</p>
</div>
</article>
</div> -->
</div>
</section>
<!-- Five -->
<section id="five">
<div class="container">
<h3>Contact Me</h3>
The best way to contact me is via email, but you can find me on LinkedIn as well.<br><br>
<p>
<a href="mailto:negar.foroutan@epfl.ch" class="icon fa-envelope"><span class="label">Email</span></a> Email: negar [dot] foroutan [at] epfl [dot] ch<br>
<a href="https://ir.linkedin.com/in/negarforoutan" class="icon fa-linkedin"><span class="label">LinkedIn</span></a> LinkedIn: <a href="https://ir.linkedin.com/in/negarforoutan">negarforoutan</a><br>
<a href="https://github.com/negar-foroutan" class="icon fa-github"><span class="label">GitHub</span></a> GitHub: <a href="https://github.com/negar-foroutan">negar-foroutan</a><br>
<a href="https://twitter.com/negarforoutan" class="icon fa-twitter"><span class="label">Twitter</span></a> Twitter: <a href="https://twitter.com/negarforoutan">negarforoutan</a><br>
<a href="https://scholar.google.com/citations?user=jHeHoScAAAAJ&hl=en" class="icon fa-graduation-cap"><span class="label">Google Scholar</span></a> <a href="https://scholar.google.com/citations?user=jHeHoScAAAAJ&hl=en">Google Scholar</a><br>
</p>
</div>
</section>
<!-- Five -->
<!--
<section id="five">
<div class="container">
<h3>Elements</h3>
<section>
<h4>Text</h4>
<p>This is <b>bold</b> and this is <strong>strong</strong>. This is <i>italic</i> and this is <em>emphasized</em>.
This is <sup>superscript</sup> text and this is <sub>subscript</sub> text.
This is <u>underlined</u> and this is code: <code>for (;;) { ... }</code>. Finally, <a href="#">this is a link</a>.</p>
<hr />
<header>
<h4>Heading with a Subtitle</h4>
<p>Lorem ipsum dolor sit amet nullam id egestas urna aliquam</p>
</header>
<p>Nunc lacinia ante nunc ac lobortis. Interdum adipiscing gravida odio porttitor sem non mi integer non faucibus ornare mi ut ante amet placerat aliquet. Volutpat eu sed ante lacinia sapien lorem accumsan varius montes viverra nibh in adipiscing blandit tempus accumsan.</p>
<header>
<h5>Heading with a Subtitle</h5>
<p>Lorem ipsum dolor sit amet nullam id egestas urna aliquam</p>
</header>
<p>Nunc lacinia ante nunc ac lobortis. Interdum adipiscing gravida odio porttitor sem non mi integer non faucibus ornare mi ut ante amet placerat aliquet. Volutpat eu sed ante lacinia sapien lorem accumsan varius montes viverra nibh in adipiscing blandit tempus accumsan.</p>
<hr />
<h2>Heading Level 2</h2>
<h3>Heading Level 3</h3>
<h4>Heading Level 4</h4>
<h5>Heading Level 5</h5>
<h6>Heading Level 6</h6>
<hr />
<h5>Blockquote</h5>
<blockquote>Fringilla nisl. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan faucibus. Vestibulum ante ipsum primis in faucibus lorem ipsum dolor sit amet nullam adipiscing eu felis.</blockquote>
<h5>Preformatted</h5>
<pre><code>i = 0;
while (!deck.isInOrder()) {
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';</code></pre>
</section>
<section>
<h4>Lists</h4>
<div class="row">
<div class="6u 12u(xsmall)">
<h5>Unordered</h5>
<ul>
<li>Dolor pulvinar etiam magna etiam.</li>
<li>Sagittis adipiscing lorem eleifend.</li>
<li>Felis enim feugiat dolore viverra.</li>
</ul>
<h5>Alternate</h5>
<ul class="alt">
<li>Dolor pulvinar etiam magna etiam.</li>
<li>Sagittis adipiscing lorem eleifend.</li>
<li>Felis enim feugiat dolore viverra.</li>
</ul>
</div>
<div class="6u 12u(xsmall)">
<h5>Ordered</h5>
<ol>
<li>Dolor pulvinar etiam magna etiam.</li>
<li>Etiam vel felis at lorem sed viverra.</li>
<li>Felis enim feugiat dolore viverra.</li>
<li>Dolor pulvinar etiam magna etiam.</li>
<li>Etiam vel felis at lorem sed viverra.</li>
<li>Felis enim feugiat dolore viverra.</li>
</ol>
<h5>Icons</h5>
<ul class="icons">
<li><a href="#" class="icon fa-twitter"><span class="label">Twitter</span></a></li>
<li><a href="#" class="icon fa-facebook"><span class="label">Facebook</span></a></li>
<li><a href="#" class="icon fa-instagram"><span class="label">Instagram</span></a></li>
<li><a href="#" class="icon fa-github"><span class="label">Github</span></a></li>
<li><a href="#" class="icon fa-dribbble"><span class="label">Dribbble</span></a></li>
<li><a href="#" class="icon fa-tumblr"><span class="label">Tumblr</span></a></li>
</ul>
</div>
</div>
<h5>Actions</h5>
<ul class="actions">
<li><a href="#" class="button special">Default</a></li>
<li><a href="#" class="button">Default</a></li>
<li><a href="#" class="button alt">Default</a></li>
</ul>
<ul class="actions small">
<li><a href="#" class="button special small">Small</a></li>
<li><a href="#" class="button small">Small</a></li>
<li><a href="#" class="button alt small">Small</a></li>
</ul>
<div class="row">
<div class="3u 6u(medium) 12u$(xsmall)">
<ul class="actions vertical">
<li><a href="#" class="button special">Default</a></li>
<li><a href="#" class="button">Default</a></li>
<li><a href="#" class="button alt">Default</a></li>
</ul>
</div>
<div class="3u 6u$(medium) 12u$(xsmall)">
<ul class="actions vertical small">
<li><a href="#" class="button special small">Small</a></li>
<li><a href="#" class="button small">Small</a></li>
<li><a href="#" class="button alt small">Small</a></li>
</ul>
</div>
<div class="3u 6u(medium) 12u$(xsmall)">
<ul class="actions vertical">
<li><a href="#" class="button special fit">Default</a></li>
<li><a href="#" class="button fit">Default</a></li>
<li><a href="#" class="button alt fit">Default</a></li>
</ul>
</div>
<div class="3u 6u$(medium) 12u$(xsmall)">
<ul class="actions vertical small">
<li><a href="#" class="button special small fit">Small</a></li>
<li><a href="#" class="button small fit">Small</a></li>
<li><a href="#" class="button alt small fit">Small</a></li>
</ul>
</div>
</div>
</section>
<section>
<h4>Table</h4>
<h5>Default</h5>
<div class="table-wrapper">
<table>
<thead>
<tr>
<th>Name</th>
<th>Description</th>
<th>Price</th>
</tr>
</thead>
<tbody>
<tr>
<td>Item One</td>
<td>Ante turpis integer aliquet porttitor.</td>
<td>29.99</td>
</tr>
<tr>
<td>Item Two</td>
<td>Vis ac commodo adipiscing arcu aliquet.</td>
<td>19.99</td>
</tr>
<tr>
<td>Item Three</td>
<td> Morbi faucibus arcu accumsan lorem.</td>
<td>29.99</td>
</tr>
<tr>
<td>Item Four</td>
<td>Vitae integer tempus condimentum.</td>
<td>19.99</td>
</tr>
<tr>
<td>Item Five</td>
<td>Ante turpis integer aliquet porttitor.</td>
<td>29.99</td>
</tr>
</tbody>
<tfoot>
<tr>
<td colspan="2"></td>
<td>100.00</td>
</tr>
</tfoot>
</table>
</div>
<h5>Alternate</h5>
<div class="table-wrapper">
<table class="alt">
<thead>
<tr>
<th>Name</th>
<th>Description</th>
<th>Price</th>
</tr>
</thead>
<tbody>
<tr>
<td>Item One</td>
<td>Ante turpis integer aliquet porttitor.</td>
<td>29.99</td>
</tr>
<tr>
<td>Item Two</td>
<td>Vis ac commodo adipiscing arcu aliquet.</td>
<td>19.99</td>
</tr>
<tr>
<td>Item Three</td>
<td> Morbi faucibus arcu accumsan lorem.</td>
<td>29.99</td>
</tr>
<tr>
<td>Item Four</td>
<td>Vitae integer tempus condimentum.</td>
<td>19.99</td>
</tr>
<tr>
<td>Item Five</td>
<td>Ante turpis integer aliquet porttitor.</td>
<td>29.99</td>
</tr>
</tbody>
<tfoot>
<tr>
<td colspan="2"></td>
<td>100.00</td>
</tr>
</tfoot>
</table>
</div>
</section>
<section>
<h4>Buttons</h4>
<ul class="actions">
<li><a href="#" class="button special">Special</a></li>
<li><a href="#" class="button">Default</a></li>
<li><a href="#" class="button alt">Alternate</a></li>
</ul>
<ul class="actions">
<li><a href="#" class="button special big">Big</a></li>
<li><a href="#" class="button">Default</a></li>
<li><a href="#" class="button alt small">Small</a></li>
</ul>
<ul class="actions fit">
<li><a href="#" class="button special fit">Fit</a></li>
<li><a href="#" class="button fit">Fit</a></li>
<li><a href="#" class="button alt fit">Fit</a></li>
</ul>
<ul class="actions fit small">
<li><a href="#" class="button special fit small">Fit + Small</a></li>
<li><a href="#" class="button fit small">Fit + Small</a></li>
<li><a href="#" class="button alt fit small">Fit + Small</a></li>
</ul>
<ul class="actions">
<li><a href="#" class="button special icon fa-download">Icon</a></li>
<li><a href="#" class="button icon fa-download">Icon</a></li>
<li><a href="#" class="button alt icon fa-check">Icon</a></li>
</ul>
<ul class="actions">
<li><span class="button special disabled">Special</span></li>
<li><span class="button disabled">Default</span></li>
<li><span class="button alt disabled">Alternate</span></li>
</ul>
</section>
<section>
<h4>Form</h4>
<form method="post" action="#">
<div class="row uniform">
<div class="6u 12u(xsmall)">
<input type="text" name="demo-name" id="demo-name" value="" placeholder="Name" />
</div>
<div class="6u 12u(xsmall)">
<input type="email" name="demo-email" id="demo-email" value="" placeholder="Email" />
</div>
</div>
<div class="row uniform">
<div class="12u">
<div class="select-wrapper">
<select name="demo-category" id="demo-category">
<option value="">- Category -</option>
<option value="1">Manufacturing</option>
<option value="1">Shipping</option>
<option value="1">Administration</option>
<option value="1">Human Resources</option>
</select>
</div>
</div>
</div>
<div class="row uniform">
<div class="4u 12u(medium)">
<input type="radio" id="demo-priority-low" name="demo-priority" checked>
<label for="demo-priority-low">Low Priority</label>
</div>
<div class="4u 12u(medium)">
<input type="radio" id="demo-priority-normal" name="demo-priority">
<label for="demo-priority-normal">Normal Priority</label>
</div>
<div class="4u 12u(medium)">
<input type="radio" id="demo-priority-high" name="demo-priority">
<label for="demo-priority-high">High Priority</label>
</div>
</div>
<div class="row uniform">
<div class="6u 12u(medium)">
<input type="checkbox" id="demo-copy" name="demo-copy">
<label for="demo-copy">Email me a copy of this message</label>
</div>
<div class="6u 12u(medium)">
<input type="checkbox" id="demo-human" name="demo-human" checked>
<label for="demo-human">I am a human and not a robot</label>
</div>
</div>
<div class="row uniform">
<div class="12u">
<textarea name="demo-message" id="demo-message" placeholder="Enter your message" rows="6"></textarea>
</div>
</div>
<div class="row uniform">
<div class="12u">
<ul class="actions">
<li><input type="submit" value="Send Message" /></li>
<li><input type="reset" value="Reset" class="alt" /></li>
</ul>
</div>
</div>
</form>
</section>
<section>
<h4>Image</h4>
<h5>Fit</h5>
<span class="image fit"><img src="images/banner.jpg" alt="" /></span>
<div class="box alt">
<div class="row 50% uniform">
<div class="4u"><span class="image fit"><img src="images/pic01.jpg" alt="" /></span></div>
<div class="4u"><span class="image fit"><img src="images/pic02.jpg" alt="" /></span></div>
<div class="4u"><span class="image fit"><img src="images/pic03.jpg" alt="" /></span></div>
</div>
<div class="row 50% uniform">
<div class="4u"><span class="image fit"><img src="images/pic02.jpg" alt="" /></span></div>
<div class="4u"><span class="image fit"><img src="images/pic03.jpg" alt="" /></span></div>
<div class="4u"><span class="image fit"><img src="images/pic01.jpg" alt="" /></span></div>
</div>
<div class="row 50% uniform">
<div class="4u"><span class="image fit"><img src="images/pic03.jpg" alt="" /></span></div>
<div class="4u"><span class="image fit"><img src="images/pic01.jpg" alt="" /></span></div>
<div class="4u"><span class="image fit"><img src="images/pic02.jpg" alt="" /></span></div>
</div>
</div>
<h5>Left & Right</h5>
<p><span class="image left"><img src="images/avatar.jpg" alt="" /></span>Fringilla nisl. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent.</p>
<p><span class="image right"><img src="images/avatar.jpg" alt="" /></span>Fringilla nisl. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent. Donec accumsan interdum nisi, quis tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent tincidunt felis sagittis eget. tempus euismod. Vestibulum ante ipsum primis in faucibus vestibulum. Blandit adipiscing eu felis iaculis volutpat ac adipiscing accumsan eu faucibus. Integer ac pellentesque praesent.</p>
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