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Rasmus Broberg AndersenMost recent work<br> Sun cloudy 1-6, 2025<br> Beauty at my buss ride to Denmark<br> <br> <br> <br> <a href="https://pixelfed.social/discover/tags/photography?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#photography</a> <a href="https://pixelfed.social/discover/tags/clouds?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#clouds</a> <a href="https://pixelfed.social/discover/tags/sun?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#sun</a> <a href="https://pixelfed.social/discover/tags/raw?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#raw</a> <a href="https://pixelfed.social/discover/tags/aesthetics?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#aesthetics</a> <a href="https://pixelfed.social/discover/tags/grey?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#grey</a> <a href="https://pixelfed.social/discover/tags/gray?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#gray</a> <a href="https://pixelfed.social/discover/tags/grå?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#grå</a> <a href="https://pixelfed.social/discover/tags/dag?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#dag</a> <a href="https://pixelfed.social/discover/tags/kunst?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#kunst</a> <a href="https://pixelfed.social/discover/tags/art?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#art</a> <a href="https://pixelfed.social/discover/tags/buss?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#buss</a> <a href="https://pixelfed.social/discover/tags/on?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#on</a> <a href="https://pixelfed.social/discover/tags/the?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#the</a> <a href="https://pixelfed.social/discover/tags/road?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#road</a> <a href="https://pixelfed.social/discover/tags/artist?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#artist</a> <a href="https://pixelfed.social/discover/tags/kunstner?src=hash" class="u-url hashtag" rel="nofollow noopener noreferrer" target="_blank">#kunstner</a>
Alex Gordienko<p>Check this out!</p><p><a href="https://techhub.social/tags/DAGMonitor" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DAGMonitor</span></a> is an iOS app for data engineers that helps monitor and control DAGs on your server. With help of the app, data engineers can access server jobs, monitor task instances, rerun them and read through task logs. Simply specify the username and password in the app settings and connect to your company's network (if your server is available only from the corporate WiFi or VPN).</p><p>FEATURES AVAILABLE IN VERSION 1.0:<br>• Connect to server via API (simple user/password authentication supported)<br>• List DAGs and get info about paused ones<br>• Access DAG runs and monitor statuses<br>• List task instances for DAG run<br>• Clear status for a task instance<br>• Access task instance logs</p><p><a href="https://apps.apple.com/me/app/dag-monitor/id6474609156" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">apps.apple.com/me/app/dag-moni</span><span class="invisible">tor/id6474609156</span></a></p><p><a href="https://techhub.social/tags/data" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>data</span></a> <a href="https://techhub.social/tags/iOS" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>iOS</span></a> <a href="https://techhub.social/tags/dag" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dag</span></a> <a href="https://techhub.social/tags/airflow" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>airflow</span></a> <a href="https://techhub.social/tags/etl" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>etl</span></a> <a href="https://techhub.social/tags/logs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>logs</span></a> <a href="https://techhub.social/tags/dataengineering" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>dataengineering</span></a></p>
Joseph A di Paolantonio<p>I also need to be researching and writing more about multi-modal <a href="https://mastodon.social/tags/causal" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>causal</span></a> <a href="https://mastodon.social/tags/DigitalTwins" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DigitalTwins</span></a> in these contexts, especially with the advent of liquid neural networks <a href="https://mastodon.social/tags/LNN" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LNN</span></a> within the realm of truly <a href="https://mastodon.social/tags/Bayesian" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bayesian</span></a> neural networks using empirical Bayes and weighted Bayesian variables for a priori similarity of engineering and technical parameters expressed as directed acyclic graphs <a href="https://mastodon.social/tags/DAG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DAG</span></a> within the digital twins of the <a href="https://mastodon.social/tags/SensAE" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SensAE</span></a> and interactions/dependencies of neighboring sensor analytics ecosystems</p>
Christos Argyropoulos MD, PhD<p>While waiting for a meeting to start, I decided to revisit one of the classics <br><a href="https://mstdn.science/tags/graphicalmodels" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>graphicalmodels</span></a> <a href="https://mstdn.science/tags/probabilityandstatistics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilityandstatistics</span></a> <a href="https://mstdn.science/tags/probabilitytheory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probabilitytheory</span></a> <a href="https://mstdn.science/tags/probability" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>probability</span></a> <a href="https://mstdn.science/tags/DAG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DAG</span></a></p>
Uwe Remer<p>Introduction:</p><p>Hi, I’m a political science postdoc researcher at the Computational Social Science Lab <span class="h-card"><a href="https://xn--baw-joa.social/@Uni_Stuttgart" class="u-url mention" rel="nofollow noopener noreferrer" target="_blank">@<span>Uni_Stuttgart</span></a></span><br> <br>My roots are research on attitudes, <a href="https://fediscience.org/tags/participation" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>participation</span></a>, and <a href="https://fediscience.org/tags/voting" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>voting</span></a> behavior. My dissertation on participatory and <a href="https://fediscience.org/tags/deliberative" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>deliberative</span></a> <a href="https://fediscience.org/tags/democracy" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>democracy</span></a> on local level brought me to python, webscraping and machine learning. <a href="https://fediscience.org/tags/AI" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>AI</span></a> </p><p>I consider causal reasoning to be the most important part of statistical modeling. <a href="https://fediscience.org/tags/DAG" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DAG</span></a> <a href="https://fediscience.org/tags/DGP" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DGP</span></a> </p><p>What else to say? I love teaching <a href="https://fediscience.org/tags/rstats" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>rstats</span></a> the <a href="https://fediscience.org/tags/baseR" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>baseR</span></a> way!</p>