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Montag: Rückkehr der Vorratsdatenspeicherung, Deutschlandfonds für Tech-Boost
Gesetzentwurf in ministerieller Abstimmung Milliarden-Boost für Big-Tech Warnung von US-Firmen vor Visa-Prüfung Avatar-Spiel unterschätzt Trump pro Mond

NASA is quietly ending financial support for independent planetary science advisory groups, according to a letter posted to the agency’s website on January 16.
The affected groups have historically offered feedback to the space agency on science efforts
-- ranging from the exploration of Mars and ocean worlds to the storage of extraterrestrial samples, and more.
The decision has taken many in the scientific community by surprise, says Jack Kiraly,
director of governme…

NASA quietly ends financial support for planetary science groups
The U.S. space agency will quit funding several independent science advisory groups this year

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- The Diffusion Duality
Sahoo, Deschenaux, Gokaslan, Wang, Chiu, Kuleshov
https://arxiv.org/abs/2506.10892 https://mastoxiv.page/@arXiv_csLG_bot/114675526577078472
- Multimodal Representation Learning and Fusion
Jin, Ge, Xie, Luo, Song, Bi, Liang, Guan, Yeong, Song, Hao
https://arxiv.org/abs/2506.20494 https://mastoxiv.page/@arXiv_csLG_bot/114749113025183688
- The kernel of graph indices for vector search
Mariano Tepper, Ted Willke
https://arxiv.org/abs/2506.20584 https://mastoxiv.page/@arXiv_csLG_bot/114749118923266356
- OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen, Xiao-Yong Wei
https://arxiv.org/abs/2506.22376 https://mastoxiv.page/@arXiv_csLG_bot/114771735361664528
- Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods
Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal
https://arxiv.org/abs/2507.18242 https://mastoxiv.page/@arXiv_csLG_bot/114913322736512937
- MolMark: Safeguarding Molecular Structures through Learnable Atom-Level Watermarking
Runwen Hu, Peilin Chen, Keyan Ding, Shiqi Wang
https://arxiv.org/abs/2508.17702 https://mastoxiv.page/@arXiv_csLG_bot/115095014405732247
- Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Protot...
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang, Anuj Sharma, Juan Pablo Mu\~noz, Ali Jannesari
https://arxiv.org/abs/2508.19009 https://mastoxiv.page/@arXiv_csLG_bot/115100269482762688
- STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems
Gary Simethy, Daniel Ortiz-Arroyo, Petar Durdevic
https://arxiv.org/abs/2508.19011 https://mastoxiv.page/@arXiv_csLG_bot/115100270137397046
- EEGDM: Learning EEG Representation with Latent Diffusion Model
Shaocong Wang, Tong Liu, Yihan Li, Ming Li, Kairui Wen, Pei Yang, Wenqi Ji, Minjing Yu, Yong-Jin Liu
https://arxiv.org/abs/2508.20705 https://mastoxiv.page/@arXiv_csLG_bot/115111565155687451
- Data-Free Continual Learning of Server Models in Model-Heterogeneous Cloud-Device Collaboration
Xiao Zhang, Zengzhe Chen, Yuan Yuan, Yifei Zou, Fuzhen Zhuang, Wenyu Jiao, Yuke Wang, Dongxiao Yu
https://arxiv.org/abs/2509.25977 https://mastoxiv.page/@arXiv_csLG_bot/115298721327100391
- Fine-Tuning Masked Diffusion for Provable Self-Correction
Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham Kakade, Sitan Chen
https://arxiv.org/abs/2510.01384 https://mastoxiv.page/@arXiv_csLG_bot/115309690976554356
- A Generic Machine Learning Framework for Radio Frequency Fingerprinting
Alex Hiles, Bashar I. Ahmad
https://arxiv.org/abs/2510.09775 https://mastoxiv.page/@arXiv_csLG_bot/115372387779061015
- ASecond-Order SpikingSSM for Wearables
Kartikay Agrawal, Abhijeet Vikram, Vedant Sharma, Vaishnavi Nagabhushana, Ayon Borthakur
https://arxiv.org/abs/2510.14386 https://mastoxiv.page/@arXiv_csLG_bot/115389079527543821
- Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning
Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji
https://arxiv.org/abs/2510.16882 https://mastoxiv.page/@arXiv_csLG_bot/115412243355962887
- Seeing Structural Failure Before it Happens: An Image-Based Physics-Informed Neural Network (PINN...
Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan, Shams Ul Arifeen
https://arxiv.org/abs/2510.23117 https://mastoxiv.page/@arXiv_csLG_bot/115451891042176876
- Training Deep Physics-Informed Kolmogorov-Arnold Networks
Spyros Rigas, Fotios Anagnostopoulos, Michalis Papachristou, Georgios Alexandridis
https://arxiv.org/abs/2510.23501 https://mastoxiv.page/@arXiv_csLG_bot/115451942159737549
- Semi-Supervised Preference Optimization with Limited Feedback
Seonggyun Lee, Sungjun Lim, Seojin Park, Soeun Cheon, Kyungwoo Song
https://arxiv.org/abs/2511.00040 https://mastoxiv.page/@arXiv_csLG_bot/115490555013124989
- Towards Causal Market Simulators
Dennis Thumm, Luis Ontaneda Mijares
https://arxiv.org/abs/2511.04469 https://mastoxiv.page/@arXiv_csLG_bot/115507943827841017
- Incremental Generation is Necessary and Sufficient for Universality in Flow-Based Modelling
Hossein Rouhvarzi, Anastasis Kratsios
https://arxiv.org/abs/2511.09902 https://mastoxiv.page/@arXiv_csLG_bot/115547587245365920
- Optimizing Mixture of Block Attention
Guangxuan Xiao, Junxian Guo, Kasra Mazaheri, Song Han
https://arxiv.org/abs/2511.11571 https://mastoxiv.page/@arXiv_csLG_bot/115564541392410174
- Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole, Charles Britton, Ming Yin, Jan Wolber, Ke Li
https://arxiv.org/abs/2511.12817 https://mastoxiv.page/@arXiv_csLG_bot/115570877730326947
toXiv_bot_toot

🇺🇦 #NowPlaying on KEXP's #MiddayShow
The Radio Dept.:
🎵 Heaven's on Fire
#TheRadioDept
https://deadhorsebeats.bandcamp.com/track/the-radio-dept-heavens-on-fire-dhb-bootleg
https://open.spotify.com/track/6aayVb6veJ48dn4RqeWQN5

Psychose bei Absetzen der Pille? Kann es offenbar geben.
Der populärwissenschaftliche Schweizer Psychologiepodcast „Dingue“ berichtet von einer Frau, die über lange Zeit eine Pille genommen hatte, die den Zyklus komplett unterdrückt. Als sie nach Eintritt der Menopause die Pille absetzte, kam es zu einer Psychose und sie wurde in eine Psychiatrie eingewiesen. 1/2

Don't miss today's Metacurity for a concise round-up of the most critical infosec developments you should know, including
--UK's NCSC warns of Russian-aligned hacktivist groups,
--UK and China enter a forum to discuss cyberattacks,
--Makina Finance lost $4.2m in an exploit,
--Ingram Micro report ransomware attack affecting 42k,
--Minnesota DHS breach affected 304k,
--SK Telecom appeals $91m fine,
--NexShield malvertising campaign crashes b…

UK's NCSC warns of Russian-aligned hacktivist groups
UK and China enter a forum to discuss cyberattacks, Makina Finance lost $4.2m in an exploit, Ingram Micro report ransomware attack affecting 42k, Minnesota DHS breach affected 304k, SK Telecom appeals $91m fine, NexShield malvertising campaign crashes browsers, much more

US Defense Secretary Pete Hegseth appears to have little patience for questions that do not conform to his preferred style of declaring unsubstantiated victories,
whether against South Americans or in the Middle East.
In a charged press conference on March 13, Hegseth did more than attack journalists for questioning his unverified claims about the course of the war in the Middle East.
He singled out CNN, introducing a troubling dimension to the conversation.
“The soo…

Trump and Hegseth Cannot Define the Truth of the US-Israeli War on Iran
US Defense Secretary Pete Hegseth appears to have little patience for questions that do not conform to his preferred style of declaring unsubstantiated

Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
Luca Miglior, Matteo Tolloso, Alessio Gravina, Davide Bacciu
https://arxiv.org/abs/2512.17762 https://arxiv.org/pdf/2512.17762 https://arxiv.org/html/2512.17762
arXiv:2512.17762v1 Announce Type: new
Abstract: Effectively capturing long-range interactions remains a fundamental yet unresolved challenge in graph neural network (GNN) research, critical for applications across diverse fields of science. To systematically address this, we introduce ECHO (Evaluating Communication over long HOps), a novel benchmark specifically designed to rigorously assess the capabilities of GNNs in handling very long-range graph propagation. ECHO includes three synthetic graph tasks, namely single-source shortest paths, node eccentricity, and graph diameter, each constructed over diverse and structurally challenging topologies intentionally designed to introduce significant information bottlenecks. ECHO also includes two real-world datasets, ECHO-Charge and ECHO-Energy, which define chemically grounded benchmarks for predicting atomic partial charges and molecular total energies, respectively, with reference computations obtained at the density functional theory (DFT) level. Both tasks inherently depend on capturing complex long-range molecular interactions. Our extensive benchmarking of popular GNN architectures reveals clear performance gaps, emphasizing the difficulty of true long-range propagation and highlighting design choices capable of overcoming inherent limitations. ECHO thereby sets a new standard for evaluating long-range information propagation, also providing a compelling example for its need in AI for science.
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