Tootfinder

4 people convicted of conspiracy in US trial tied to 2021 assassination of Haiti's president (Politico)
https://www.politico.com/news/2026/05/08/4-people-convicted-of-conspiracy-in-us-trial-tied-to-2021-assassination-of-haitis-president-00913186
http://www.memeorandum.com/260508/p95#a260508p95

Siri (Beta) is really interesting.
On one hand, I don't have to trust any new parties with my data. Apple already has it since they have my mobile devices, so if they were going to maliciously steal (e.g.) my email contents, they could already do that. This opens the door to a whole bunch of LLM data based interrogation that I wouldn't trust with other providers.
…on the other hand, the "on-device only" Siri falls over immediately when disabling the internet c…

Seit 2021 hat Charlotte die Jugendarbeit beim HSK Lister Turm komplett neu belebt.
Ob Training, Turniere oder einfach ein offenes Ohr: Charlotte ist immer da. Sie organisiert, motiviert, begleitet – und schafft einen Raum, in dem sich Kinder und Jugendliche entfalten können.

Eine mehr als würdige Kandidatin für den Goldenen Chesso!
Bewerbungsfrist für dieses Jahr ist der Montag, 27. April 2026.
Alle Informationen findest du in unserem Artikel:

Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift
Bong Gyun Shin, Chan Sik Lee, Hyesun Suh
https://arxiv.org/abs/2605.21507 https://arxiv.org/pdf/2605.21507 https://arxiv.org/html/2605.21507
arXiv:2605.21507v1 Announce Type: new
Abstract: Atmospheric visibility is a critical variable for transportation safety and air quality management, however, accurate prediction remains challenging due to the complex interactions between meteorological conditions and air pollutants, as well as the rarity of low-visibility events. This study introduces a machine learning framework to nowcast visibility in six major South Korean cities. To handle the imbalance in the 2018-2020 training data, we applied the Synthetic Minority Over-sampling Technique with Nominal and Continuous (SMOTENC) and Conditional Tabular Generative Adversarial Network (CTGAN). An ensemble approach combining machine learning and deep learning models was then used and evaluated on a 2021 test dataset. The results revealed a marked decline in predictive performance in the test set compared to the cross-validation phase. This degradation was attributed to a distributional shift between training and testing periods, which was quantitatively confirmed by measuring the Wasserstein distance of the most influential feature identified by SHAP analysis. In general, this study presents a methodology that aims to simultaneously address the dual challenges of data imbalance and temporal distributional shifts, and emphasizes the necessity of accounting for evolving external environmental factors when implementing nowcasting models on time-series data.
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