Tootfinder

RE: #redshift measured spectroscopically hailed in this press release as well as in https://esawebb.org/news/weic2603/ and https://www.esa.int/Science_Exploration/Space_Science/Webb/Webb_gazes_further_back_in_time has been published in May 2025 as https://arxiv.org/abs/2505.11263 (appearing on ArXiv counts as a publication in today's academic landscape) and made some headlines then, including by yours truly: https://skyweek.wordpress.com/2025/05/18/resiliences-mond-punch-fur-alle-pslv-versagt/ ("Neuer spektroskopischer Rotverschiebungs-Rekord: z=14.44").

Mit dem heutigen #mdRzA — übrigens bei laut Wetterbericht "anhaltendem Schneeregen", aber da war leider nur "anhaltender Regen" — habe ich 700km beim #Frostpendeln erreicht. Deutlich mehr als meine letztjährige Frostpendeln-Gesamtstrecke von knapp 550km insgesamt. Vielleicht schaffe…

Die monatliche #Energieerzeugung meiner #Kernfusionskollektoranlage im Vergleich zu Prognosedaten aus einer Simulation.
Gesamtleistung: 480 Wp
Ausrichtung: West
Anstellwinkel: 58 Grad
👉 So berechnest Du die Jahresertragsprognose:

Beim Lesen dieses Kommentarthreads habe ich mich gerade ein wenig in meinen Mund erbrochen.
https://www.linkedin.com/feed/update/urn:li:activity:7442852180…

»ADINT—#Überwachung'sfirmen können Menschen mit „anonymen“ Werbe-IDs ausspionieren:
Meist im Verborgenen bereiten Unternehmen #Daten aus der #Online-

ADINT: Überwachungsfirmen können Menschen mit „anonymen“ Werbe-IDs ausspionieren
Meist im Verborgenen bereiten Unternehmen Daten aus der Online-Werbung für Geheimdienste auf. Manche prahlen damit, praktisch jedes Handy verfolgen zu können. Eine Recherche von Le Monde gewährt seltene Einblicke in eine Branche, die auch europäische Sicherheitsbehörden umwirbt.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
https://arxiv.org/abs/2505.19698 https://mastoxiv.page/@arXiv_csLG_bot/114578810521008766
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
https://arxiv.org/abs/2506.08660 https://mastoxiv.page/@arXiv_csLG_bot/114664238967892509
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
https://arxiv.org/abs/2506.10167 https://mastoxiv.page/@arXiv_csLG_bot/114675175949432731
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
https://arxiv.org/abs/2506.10914 https://mastoxiv.page/@arXiv_csLG_bot/114675529854402158
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
https://arxiv.org/abs/2506.18481 https://mastoxiv.page/@arXiv_csLG_bot/114738421450807709
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
https://arxiv.org/abs/2506.21220 https://mastoxiv.page/@arXiv_csLG_bot/114754764750730849
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
https://arxiv.org/abs/2507.04448 https://mastoxiv.page/@arXiv_csLG_bot/114818005803079705
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
https://arxiv.org/abs/2508.01115 https://mastoxiv.page/@arXiv_csLG_bot/114975999992144374
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
https://arxiv.org/abs/2508.13904 https://mastoxiv.page/@arXiv_csLG_bot/115060568241390847
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
https://arxiv.org/abs/2508.16815 https://mastoxiv.page/@arXiv_csLG_bot/115094785677272005
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
https://arxiv.org/abs/2508.21785 https://mastoxiv.page/@arXiv_csLG_bot/115128450608548173
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
https://arxiv.org/abs/2509.15796 https://mastoxiv.page/@arXiv_csLG_bot/115247429156900905
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
https://arxiv.org/abs/2509.19975 https://mastoxiv.page/@arXiv_csLG_bot/115264498084813952
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
https://arxiv.org/abs/2509.21895 https://mastoxiv.page/@arXiv_csLG_bot/115287261047939306
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
https://arxiv.org/abs/2509.22566 https://mastoxiv.page/@arXiv_csLG_bot/115287379672141023
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
https://arxiv.org/abs/2509.23115 https://mastoxiv.page/@arXiv_csLG_bot/115293273559547106
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
https://arxiv.org/abs/2509.25424 https://mastoxiv.page/@arXiv_csLG_bot/115298579764580635
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
https://arxiv.org/abs/2509.26626 https://mastoxiv.page/@arXiv_csLG_bot/115298789487177431
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
https://arxiv.org/abs/2510.12402 https://mastoxiv.page/@arXiv_csLG_bot/115377759317818093
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
https://arxiv.org/abs/2510.15425 https://mastoxiv.page/@arXiv_csLG_bot/115405933861293858
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
https://arxiv.org/abs/2510.18114 https://mastoxiv.page/@arXiv_csLG_bot/115417332500265972
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
https://arxiv.org/abs/2510.22293 https://mastoxiv.page/@arXiv_csLG_bot/115451746201804373
toXiv_bot_toot

OpenAI plans to retire several models from ChatGPT on February 13, including GPT‑4o, GPT‑4.1, and o4-mini, saying only 0.1% of users still choose GPT-4o (Ashley Capoot/CNBC)
https://www.cnbc.com/2026/01/29/openai-will-retire-gpt-4o-from-chatgpt-next-mon…

OpenAI will retire several models, including GPT-4o, from ChatGPT next month
GPT‑4o is beloved by some users because of its warm conversational style

AI Gave Investors a Glimpse of the Future This Month.
And Then They Sold Their Stocks.
“The main story is still tech and AI uncertainty,” said Ross Mayfield, investment strategist at Baird.
“It is making investors question the fundamental underpinning of the profitability of a lot of industries.” 
Those worries have triggered several nauseating stock swings in recent weeks, some with relatively innocuous catalysts
—an incremental update to a particular AI tool, …

AI Gave Investors a Glimpse of the Future This Month. And They Sold Their Stocks. — The Wall Street Journal
The S&P 500 and Nasdaq composite fell in February, dragged down by firms whose businesses might be disrupted

Die Solarfalle der Katherine Reiche
Heizung, PV und Gas: Katherine Reiche steht in der Kritik, wird als Gas-Lobbyistin bezeichnet. Doch ihr könnte Historisches gelingen, meint Redakteur Jan Mahn.
https://www.

Appfigures: Sora's app downloads dropped 32% month-over-month in December and 45% in January to 1.2M; consumer spending on the app fell 32% MoM as of January (Sarah Perez/TechCrunch)
https://techcrunch.com/2026/01/29/openais-sora-app-is-struggli…

OpenAI's Sora app is struggling after its stellar launch | TechCrunch
New data suggests the app is seeing declines in both app downloads and consumer spending, as the early hype about the AI video social network wears off.