new papers and logos

Author: nhanvtran

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 	#portfolio .box
 	{
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images/darpa.jpg

@@ -59,31 +59,31 @@ <h2>Learn more!</h2>
 			</span>
 		</div>
 		<div class="column1">
-			<a href="#" class="image image-full"><img src="images/hls4ml.png" alt="" /></a>
-			<div class="box">
+			<a href="#" class="image image-full"><img src="images/hls4ml.png" width=282 alt="" /></a>
+			<div class="box" height=300>
 				<p>hls4ml: an open-source code framework for translating machine learning algorithms directly into FPGA firmware</p>
 				<a href="https://fastmachinelearning.org/hls4ml/" class="button">hls4ml home</a>
 			</div>
 		</div>			
 		<div class="column2">
-			<a href="#" class="image image-full"><img src="images/xilinx.png" alt="" /></a>
-			<div class="box">
+			<a href="#" class="image image-full"><img src="images/xilinx.png" width=282 alt="" /></a>
+			<div class="box" height=300>
 				<p>A story from Xilinx on how we use high level synthesis to find the best events at the Large Hadron Collider</p>
 				<a href="https://www.xilinx.com/publications/powered-by-xilinx/cerncasestudy-final.pdf" class="button">Visit Xilinx</a>
 			</div>
 		</div>			
 		<div class="column3">
-			<a href="#" class="image image-full"><img src="images/azure.png" alt="" /></a>
-			<div class="box">
+			<a href="#" class="image image-full"><img src="images/azure.png" width=282 alt="" /></a>
+			<div class="box" height=300>
 				<p>Microsoft Azure collaboration deploying FPGAs to accelerate ML to prototype computing solutions for future big science experiments</p>
 				<a href="https://customers.microsoft.com/en-us/story/724137-fermilab-led-team-tests-azure-ai-for-particle-physics-data-challenge" class="button">Visit Microsoft</a>
 				<a href="https://news.fnal.gov/2019/08/a-glimpse-into-the-future-accelerated-computing-for-accelerated-particles/" class="button">Fermi News</a>
 				<a href="http://news.mit.edu/2019/boosting-computing-power-for-future-particle-physics-mit-lns-0819" class="button"Visit>MIT News</a>
 			</div>
 		</div>			
 		<div class="column4">
-			<a href="#" class="image image-full"><img src="images/zenuity.png" alt="" /></a>
-			<div class="box">
+			<a href="#" class="image image-full"><img src="images/zenuity.png" width=282 alt="" /></a>
+			<div class="box" height=300>
 				<p>Zenuity has become the first automotive to team up with CERN to develop ML for autonomous drive cars</p>
 				<a href="https://www.prnewswire.co.uk/news-releases/zenuity-and-cern-team-up-on-fast-machine-learning-for-autonomous-driving-855081160.html" class="button">See the press</a>
 			</div>
@@ -131,15 +131,14 @@ <h2>Contact</h2>
 	<div id="contact" class="container">
 		<div class="major">
 			<h2>Support</h2>
-			<span class="byline">We are graciously funded by these organizations</span>
+			<span class="byline">We are graciously funded by these organizations</span> <br>
+			<a href="#"><img src="images/erc.png" width="120px" alt="" /></a>
+			<a href="#"><img src="images/doe.png" width="200px" alt="" /></a>
+			<a href="#"><img src="images/nsf.png" width="120px" alt="" /></a>
+			<a href="#"><img src="images/i2.png" width="180px" alt="" /></a>
+			<a href="#"><img src="images/iris-hep.png" width="180px" alt="" /></a>
+			<a href="#"><img src="images/darpa.jpg" width="180px" alt="" /></a>
 		</div>
-		<ul class="contact">
-			<li><a href="#" class="icon icon-twitter"><span>Twitter</span></a></li>
-			<li><a href="#" class="icon icon-facebook"><span></span></a></li>
-			<li><a href="#" class="icon icon-dribbble"><span>Pinterest</span></a></li>
-			<li><a href="#" class="icon icon-tumblr"><span>Google+</span></a></li>
-			<li><a href="#" class="icon icon-rss"><span>Pinterest</span></a></li>
-		</ul>
 	</div>
 </div>
 <div id="copyright" class="container">

images/doe.png

@@ -48,6 +48,9 @@ <h2>Projects</h2>
 		<h3> Papers </h3>
 		<p style="line-height:1.3">
 			<ul>
+				<li> Fast inference of Boosted Decision Trees in FPGAs for particle physics. <a href="https://arxiv.org/abs/2002.02534">arXiv:2002.02534 [physics.comp-ph]</a>, February 2020. </li>
+				<li> Accelerated Machine Learning as a Service for Particle Physics Computing. <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
+				<li> Low-latency machine learning inference on FPGAs. <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_74.pdf">NeurIPS ML4PS Workshop, 2019</a>. </li>				
 				<li> Fast inference of deep neural networks in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/13/07/P07027">JINST 13, P07027 (2018)</a> </li>
 				<li> FPGA-accelerated machine learning inference as a service for particle physics computing, <a href="https://doi.org/10.1007/s41781-019-0027-2">Comput Softw Big Sci (2019) 3: 13</a> </li>
 			</ul>