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		<title>Publicationlist</title>
		<link>https://www.4dnanoscope.de/2020/10/24/publicationlist/</link>
		
		<dc:creator><![CDATA[Sandra Jeleazcov]]></dc:creator>
		<pubDate>Sat, 24 Oct 2020 07:12:12 +0000</pubDate>
				<category><![CDATA[Allgemein]]></category>
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					<description><![CDATA[Pecherstorfer C, Simon D, Unbehend S, Ellmann H, Englbrecht M, Hartmann F, Figueiredo C, Hueber A, Haschka J, Kocijan R, Kleyer A, Schett G, Rech J, Bayat S. A Detailed Analysis of the Association between Urate Deposition and Erosions and Osteophytes in Gout. ACR Open Rheumatol. 2020 Sep 21. Online ahead of print. doi: 10.1002/acr2.11172 &#8230; <p class="link-more"><a href="https://www.4dnanoscope.de/2020/10/24/publicationlist/" class="more-link">Continue reading<span class="screen-reader-text"> "Publicationlist"</span></a></p>]]></description>
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		<title>New Article in Nature Machine Intelligence accepted</title>
		<link>https://www.4dnanoscope.de/2019/05/20/hallo-welt/</link>
		
		<dc:creator><![CDATA[Sandra Jeleazcov]]></dc:creator>
		<pubDate>Mon, 20 May 2019 14:06:51 +0000</pubDate>
				<category><![CDATA[Allgemein]]></category>
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					<description><![CDATA[We have good news: Our fundamental article on &#8220;Known Operator Learning&#8221; was just published in Nature Machine Intelligence: https://www.nature.com/articles/s42256-019-0077-5 We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation &#8230; <p class="link-more"><a href="https://www.4dnanoscope.de/2019/05/20/hallo-welt/" class="more-link">Continue reading<span class="screen-reader-text"> "New Article in Nature Machine Intelligence accepted"</span></a></p>]]></description>
										<content:encoded><![CDATA[<p>We have good news: Our fundamental article on &ldquo;Known Operator Learning&rdquo; was just published in Nature Machine Intelligence: https://www.nature.com/articles/s42256&#x2d;019&#x2d;0077&#x2d;5 We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the&#8230;</p>
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