Tootfinder

The Guardian partners with Cambridge University to launch Secure Messaging in its app, protecting sources by making messages indistinguishable from other data (Katharine Viner/The Guardian)
https://www.theguardian.com/membership/20…

The Guardian partners with Cambridge University to launch Secure Messaging in its app, protecting sources by making messages indistinguishable from other data (Katharine Viner/The Guardian)
https://www.theguardian.com/membership/20…

Dygtige skribenter har en fantastisk forståelse for sprogets nuancer, helt ned (op?) på et fŸlelsesmæssigt niveau. Jeg har lige nydt Kathryn Schulz’ diskussion af fŸlelsen i ordet “og” (dvs. “and” fordi hun taler om engelsk, men pointerne er de samme for dansk) i Ezra Kleins podcast.
Man kunne tale om en tankens grammatik, en formning af vores tanker. Og “og” er vidunderlig fordi denne konjunktion kan sammensætte både det kongruente og det inkongruente, og samtidig peger ordet ind i f…

Our Lives Are an Endless Series of 'And'
Podcastepisode · The Ezra Klein Show · 30.05.2025 · 1 t. 6 min.

This was foreseeable. Want to go on vacation on the state's dime? Pull your kids out of school and get vouchers.
Reminds me of the time I saw my sister-in-law tell her first grader to read a book for a treat. That was her idea of teaching her kid how to read. Both she and my brother benefited from special education throughout their schooling. The state was fine with them homeschooling. I believe they were eventually convinced by her parents to send the kids back to school. My pare…

Auch dieses Jahr streamen wir alle Runden der #dem25 live über https://twitch.tv/schachjugend
Jede Runde wird euch Katharina Reinecke durch den Stream begleiten. Dabei erfährt Sie Unterstützung von wec…

carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng, Tim Ruhkopf, Leona Hennig, Soham Basu, Neeratyoy Mallik, Edward Bergman, Deyao Chen, Fran\c{c}ois Cl\'ement, Matthias Feurer, Katharina Eggensperger, Frank Hutter, Carola Doerr, Marius Lindauer
https://

carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks
Hyperparameter Optimization (HPO) is crucial to develop well-performing machine learning models. In order to ease prototyping and benchmarking of HPO methods, we propose carps, a benchmark framework for Comprehensive Automated Research Performance Studies allowing to evaluate N optimizers on M benchmark tasks. In this first release of carps, we focus on the four most important types of HPO task types: blackbox, multi-fidelity, multi-objective and multi-fidelity-multi-objective. With 3 336 tasks…

"Es ist schwer, heute gegen Faschismus zu kämpfen. Weil man gegen eine Wand aus Ignoranz, Opportunismus, Gewohnheit und wohlgemeinter Naivität läuft. Zitate von Rechtsradikalen? Geben wir als Medienhäuser wieder, man muss ja objektiv berichten über das, was ist. Und dann müssen wir sie zwecks Ausgewogenheit in Talkshows einladen. Verbotsverfahren prüfen? Nein, wir müssen den Gegner politisch stellen."
@…

Kettering Health confirms attack by Interlock ransomware group as health record system is restored https://therecord.media/kettering-health-ohio-interlock-ransomware

Ramanujan's partition generating functions modulo $\ell$
Kathrin Bringmann, William Craig, Ken Ono
https://arxiv.org/abs/2506.06101 https://

Ramanujan's partition generating functions modulo $\ell$
For the partition function $p(n)$, Ramanujan proved the striking identities $$ P_5(q):=\sum_{n\geq 0} p(5n+4)q^n =5\prod_{n\geq 1} \frac{\left(q^5;q^5\right)_{\infty}^5}{(q;q)_{\infty}^6}, $$ $$ P_7(q):=\sum_{n\geq 0} p(7n+5)q^n =7\prod_{n\geq 1}\frac{\left(q^7;q^7\right)_{\infty}^3}{(q;q)_{\infty}^4}+49q \prod_{n\geq 1}\frac{\left(q^7;q^7\right)_{\infty}^7}{(q;q)_{\infty}^8}, $$ where $(q;q)_{\infty}:=\prod_{n\geq 1}(1-q^n).$ As these identities imply his celebrated congruences modulo 5 …

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Md Aminul Islam, Kathryn Vasilaky, Elena Zheleva
https://arxiv.org/abs/2506.06989

Correcting for Position Bias in Learning to Rank: A Control Function Approach
Implicit feedback data, such as user clicks, is commonly used in learning-to-rank (LTR) systems because it is easy to collect and it often reflects user preferences. However, this data is prone to various biases, and training an LTR system directly on biased data can result in suboptimal ranking performance. One of the most prominent and well-studied biases in implicit feedback data is position bias, which occurs because users are more likely to interact with higher-ranked documents regardless …