Tootfinder

Analysis: of the 8,808 global data centers in October 2025, ~7,000 are in areas outside the optimal 18C to 27C temperature range; 600 are in areas above 27C (Rest of World)
https://restofworld.org/2025/data-center-heat-map/

{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats

Analysis: New Data Suggests Russia Is Sustaining Mi-8 Output Despite Wartime Losses: https://benborges.xyz/2025/12/11/analysis-new-data-suggests-russia.html

The Kash bureau doesn’t seem to be doing so well. This is, what, the 4th or 5th high-profile case where they’ve “caught” the wrong person, or announced that someone was in custody when that wasn’t true…
https://www.reuters.com/world/us/providenc<…

As Cyber Threats Escalate, the National Vulnerability Database Is Falling Behind
The National Institute of Standards and Technology (NIST) is struggling.
It faces a growing backlog to process data in its vulnerability repository, which publicly shares information assessing and detailing mitigation solutions against new cyber exploits.
With nearly 1,800 new reported vulnerabilities sitting in a queue for analysis this year, delays in processing leave the United States increa…

📈 #LogsQL query language provides fast full-text search, advanced analytics, and data extraction/transformation at query time. Can be combined with Unix tools like grep, less, sort, and jq for log analysis.
🎯 Optimized for high cardinality fields like trace_id, user_id, and ip addresses. Supports logs with hundreds of fields (wide events), multitenancy, out-of-order ingestion, live taili…

"Did the Upper Great Highway closure make Sunset neighborhood streets less safe? Supervisor Alan Wong claimed it did at a January 8, 2026 press conference, citing a simple year-over-year map comparison of crash data. But my analysis, using the same DataSF crash data with rigorous statistical controls, finds no evidence to support that claim, and if anything, the data suggest the opposite."

Your data, their rules
https://blog.42futures.com/p/your-data-their-rules

An analysis of 47,000 publicly shared ChatGPT conversations: ~10% related to emotional or mental health, ChatGPT exhibits a "default to yes" behavior, and more (Washington Post)
https://www.washingtonpost.com/technology/2025/11/12/how-people-use-ch…

Joint neutrino oscillation analysis from the T2K and NOvA experiments: #neutrinos may hold the keys to why we exist: https://www.eurekalert.org/news-releases/1103650 - MSU scientists help merge data from two neutrino experiments to offer most precise look yet at elusive particles.

A template for data analysis projects structured as R packages (or not) https://github.com/Pakillo/template by @…

Between #Matlab and #Python, which one would you recommend to learn, for a student who wants to learn programming (from scratch) to do data analysis? And why?
I am conflicted because I think Matlab is maybe slightly more straightforward to learn, but Python should be more useful in the long …

If there is any truth in these allegations, we really have to worry about that is going on in both AGS and GSOC.
The initial story was bad enough: the Irish police service being so incompetent that their statistics on homicides were wildly incorrect, and the whistleblower getting penalised; but this is batshit.

I rewrote a data analysis pipeline, moving it from #python to #julialang . I am now in love with the threading support in Julia.
The task is very parallelizable but each thread needs random read access to a tens-of-GB dataset. In Python (with multiprocessing, shared stores, etc) data bookkeeping was a nightmar…

The Tow Center releases a tracker that monitors lawsuits, deals, grants, and other developments between news publishers and AI companies (Klaudia Jaźwińska/Columbia Journalism Review)
https://www.cjr.org/analysis/lawsuit-license-openai-micros…

🚜 California farmland doused with 2.5 million pounds of PFAS pesticides each year, analysis finds
https://www.thenewlede.org/2025/11/california-farmland-doused-with-2-5-million-pounds-of-pfas-pesticides-each-y…

98 % des arbres fruitiers et oliviers de Gaza ont été détruits. 90 % des serres sont endommagées et 75 % détruites, selon une analyse des images satellitaires.
https://www.zmescience.com/science/news-sc

@…

Data Science is different things to different people so the list of things you need to know is hard to pin down.
It's often understood to go beyond data analysis.

In addition to Python programming and statistics you need to know at least:
- data manipulation tools including SQL, dataframes (polars, pandas)
- fund…

@…

Data Science is different things to different people so the list of things you need to know is hard to pin down.
It's often understood to go beyond data analysis.

In addition to Python programming and statistics you need to know at least:
- data manipulation tools including SQL, dataframes (polars, pandas)
- fund…

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Optimal control of Volterra integral diffusions and application to contract theory
Dylan Possama\"i, Mehdi Talbi
https://arxiv.org/abs/2511.09701 https://mastoxiv.page/@arXiv_mathPR_bot/115547093766733637
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
- Sample Complexity of Quadratically Regularized Optimal Transport
Alberto Gonz\'alez-Sanz, Eustasio del Barrio, Marcel Nutz
https://arxiv.org/abs/2511.09807 https://mastoxiv.page/@arXiv_mathST_bot/115546975796760368
- On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Struct...
Arthur Castello Branco de Oliveira, Dhruv Jatkar, Eduardo Sontag
https://arxiv.org/abs/2511.09810 https://mastoxiv.page/@arXiv_csLG_bot/115547543989283588
- Implicit Multiple Tensor Decomposition
Kunjing Yang, Libin Zheng, Minru Bai
https://arxiv.org/abs/2511.09916 https://mastoxiv.page/@arXiv_mathNA_bot/115547169767663335
- Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies
Takehiro Tottori, Tetsuya J. Kobayashi
https://arxiv.org/abs/2511.10184 https://mastoxiv.page/@arXiv_physicsbioph_bot/115546979073652600
- Zeroes and Extrema of Functions via Random Measures
Athanasios Christou Micheas
https://arxiv.org/abs/2511.10293 https://mastoxiv.page/@arXiv_statME_bot/115547493525198835
- Operator Models for Continuous-Time Offline Reinforcement Learning
Nicolas Hoischen, Petar Bevanda, Max Beier, Stefan Sosnowski, Boris Houska, Sandra Hirche
https://arxiv.org/abs/2511.10383 https://mastoxiv.page/@arXiv_statML_bot/115547254989932993
- On topological properties of closed attractors
Wouter Jongeneel
https://arxiv.org/abs/2511.10429 https://mastoxiv.page/@arXiv_mathDS_bot/115547276594491411
- Learning parameter-dependent shear viscosity from data, with application to sea and land ice
Gonzalo G. de Diego, Georg Stadler
https://arxiv.org/abs/2511.10452 https://mastoxiv.page/@arXiv_mathNA_bot/115547323782478749
- Formal Verification of Control Lyapunov-Barrier Functions for Safe Stabilization with Bounded Con...
Jun Liu
https://arxiv.org/abs/2511.10510 https://mastoxiv.page/@arXiv_eessSY_bot/115547429321496393
- Direction-of-Arrival and Noise Covariance Matrix joint estimation for beamforming
Vitor Gelsleichter Probst Curtarelli
https://arxiv.org/abs/2511.10639 https://mastoxiv.page/@arXiv_eessAS_bot/115547188796143762
toXiv_bot_toot

End-of-Year Threat Intelligence Sightings Forecast
This report presents an analysis of Threat Intelligence (TI) Sightings aggregated from several key data sources, including social platforms, code repositories, and specialized TI feeds. The primary objective is to visually track historical trends per source and provide a short-term adaptive forecast for a defined period (in days).
#opensource

The problem to me is though, she could have easily denounced the #Trump administration when they used her song, especially when she is someone who is notorious for suing people using her songs without authorization. The silence here is quite deafening.
https://

Modeling, Segmenting and Statistics of Transient Spindles via Two-Dimensional Ornstein-Uhlenbeck Dynamics
C. Sun, D. Fettahoglu, D. Holcman
https://arxiv.org/abs/2512.10844 https://arxiv.org/pdf/2512.10844 https://arxiv.org/html/2512.10844
arXiv:2512.10844v1 Announce Type: new
Abstract: We develop here a stochastic framework for modeling and segmenting transient spindle- like oscillatory bursts in electroencephalogram (EEG) signals. At the modeling level, individ- ual spindles are represented as path realizations of a two-dimensional Ornstein{Uhlenbeck (OU) process with a stable focus, providing a low-dimensional stochastic dynamical sys- tem whose trajectories reproduce key morphological features of spindles, including their characteristic rise{decay amplitude envelopes. On the signal processing side, we propose a segmentation procedure based on Empirical Mode Decomposition (EMD) combined with the detection of a central extremum, which isolates single spindle events and yields a collection of oscillatory atoms. This construction enables a systematic statistical analysis of spindle features: we derive empirical laws for the distributions of amplitudes, inter-spindle intervals, and rise/decay durations, and show that these exhibit exponential tails consistent with the underlying OU dynamics. We further extend the model to a pair of weakly coupled OU processes with distinct natural frequencies, generating a stochastic mixture of slow, fast, and mixed spindles in random temporal order. The resulting framework provides a data- driven framework for the analysis of transient oscillations in EEG and, more generally, in nonstationary time series.
toXiv_bot_toot

“This seems the best bang for your buck; it’s less per year than private school.”, said the future mother.
UK IVF couples use legal loophole to rank embryos based on potential IQ, height and health
https://www.theguar…

#ShamelesslyStolenFromBlueSky

🍔 Thermal, Mechanical, And Material Stresses Grow With Die Stacking
https://semiengineering.com/thermal-mechanical-and-material-stresses-grow-with-die-stacking/

Revealed: Europe’s water reserves drying up due to climate breakdown.
“When we compare the total terrestrial water storage data with climate datasets, the trends broadly correlate,” said Mohammad Shamsudduha, professor of water crisis and risk reduction at UCL.

An analysis of 30 US data center proposals in 14 states: in most cases, local officials signed NDAs and worked with apparent shell companies to hide details (Natalie Kainz/NBC News)
https://www.nbcnews.com/tech/tech-news/dat

@… harmonic analysis of metrics data I love it

Most Cambodia & Laos tree cover loss in 2024 happened inside protected areas https://news.mongabay.com/short-article/2025/10/most-cambodia-laos-tree-cover-loss-in-2024-happened-inside-protected-areas/
…

ICE shift in tactics leads to soaring number of unjustifiable arrests
Government data shows that more than 60 percent of the people detained in at-large arrests since June did NOT have criminal convictions or pending charges.
-- even as authorities insist that immigration officers are focusing on violent criminals whom they describe as “the worst of the worst.”

Beyond Revenue and Welfare: Counterfactual Analysis of Spectrum Auctions with Application to Canada's 3800MHz Allocation
Sara Jalili Shani, Kris Joseph, Michael B. McNally, James R. Wright
https://arxiv.org/abs/2512.08106 https://arxiv.org/pdf/2512.08106 https://arxiv.org/html/2512.08106
arXiv:2512.08106v1 Announce Type: new
Abstract: Spectrum auctions are the primary mechanism through which governments allocate scarce radio frequencies, with outcomes that shape competition, coverage, and innovation in telecommunications markets. While traditional models of spectrum auctions often rely on strong equilibrium assumptions, we take a more parsimonious approach by modeling bidders as myopic and straightforward: in each round, firms simply demand the bundle that maximizes their utility given current prices. Despite its simplicity, this model proves effective in predicting the outcomes of Canada's 2023 auction of 3800 MHz spectrum licenses. Using detailed round-by-round bidding data, we estimate bidders' valuations through a linear programming framework and validate that our model reproduces key features of the observed allocation and price evolution. We then use these estimated valuations to simulate a counterfactual auction under an alternative mechanism that incentivizes deployment in rural and remote regions, aligning with one of the key objectives set out in the Canadian Telecommunications Act. The results show that the proposed mechanism substantially improves population coverage in underserved areas. These findings demonstrate that a behavioral model with minimal assumptions is sufficient to generate reliable counterfactual predictions, making it a practical tool for policymakers to evaluate how alternative auction designs may influence future outcomes. In particular, our study demonstrates a method for counterfactual mechanism design, providing a framework to evaluate how alternative auction rules could advance policy goals such as equitable deployment across Canada.
toXiv_bot_toot

@… following up on our chat at #SDSL2025, I finally found some time to see how a #QGIS Processing Algorithm Provider plugin can be unit tested. Here's what I've …

"This paper presents a comprehensive scientometric analysis of the long-term impact of [event] on the nation scientific development."
*oh, interesting!*
"Using Scopus-indexed data..."
*closes tab*

Teaching students simple #WebScraping was always quite rewarding. It opens up numerous relevant, real-world data sources that are the foundation for any further analysis. Things already got more complicated with dynamic content loading, but now bot-exclusion-mechanisms make it almost impossible in many cases. Is web scraping for the

RE: https://mastodon.social/@cheeaun/115415146417702654
After looking at this, got curious to know the limits in most servers.
So I did a little data analysis. Servers list from @…

Google adds Gemini's Deep Search to Google Finance, which will also have prediction market data from Kalshi and Polymarket for event analysis, first in the US (Aamir Siddiqui/Android Authority)
https://www.androidauthority.com/google-finance…

Inside the deportation machine (giftlink)
https://www.nytimes.com/interactive/2025/12/22/us/trump-immigration-deportation-network-ice-arrests.html?unlocked_art…

🕶️ Community Analysis of Social Virtual Reality Based on Large-Scale Log Data of a Commercial Metaverse Platform
#vr

Hi #Linux team - any recommendations for a work desktop computer with the following requirements:

• can do basic research stuff (reading, writing) and also a little bit of basic data analysis (with python or Matlab)
• would run a distribution like #ZorinOS or Mint
• with a Max…

Dataminr to acquire cybersecurity firm ThreatConnect for $290 million
https://cyberscoop.com/dataminr-threatconnect-acquisition/

Geospatial Reasoning fuses weather, satellite and population
data with Gemini AI for risk analysis
It runs in Google's Trusted Tester program for early access
(That ain't you)
https://www.testingcatalog.com/google-laun…

An analysis of AI training datasets, compiled by The Atlantic, shows AI models were trained on hundreds of thousands of YouTube videos from news publishers (Andrew Deck/Nieman Lab)
https://www.niemanlab.org/2025/10/hundred…

Claude skills are a big deal™️
Thanks to skills, you can reduce your multi-agent setup to a single agent with skills, greatly reducing complexity and increasing speed of execution.
In fact, if in the past you could have a number of agents each specialized in, for example, data analysis, getting data from a particular set of websites, making that data available in a dashboard, etc., with skills you can substitute all these agents with skills. (1/2)

DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
Najla Abassi, Lea Schwarz, Edoardo Filippi, Federico Marini
https://arxiv.org/abs/2512.05731

#nytimes #report #usapol
"Inside the Deportation Machine"
-
"At least 32 people have died in ICE custody since Mr. Trump took office, more than the number in Mr. Biden’s entire…

Kayak: in principle, an application that may be well-served by "old" rules-based AI. Its function is supposed to be deterministic, needing more data ingestion & analysis than humans can tolerate.
But if they're calling it "AI" today, I'm sure it's a LLM/neural net gadget which will hallucinate flights & fares. Because we're playing out the theory that the #XRisk

Regional temperature records broken across the world in 2025 #environment

There is enough data to start publishing reports of my statistical analysis of the Italian Volleyball Serie A1 championship.
https://davideaversa.it/experiment/volley/seriea1w2025.html

An analysis of Crunchbase and PitchBook data: in 2025 so far, 80 tech startups reached $1B valuations, many of them focused on AI with exceptions like Kalshi (Dominic-Madori Davis/TechCrunch)
https://techcrunch.com/2025/12/01/at-least-36-new-tech-…

For decades, scientists have been intrigued by a strange twist in the Moon’s history.
Toward its last stages of formation, the lunar mantle likely flipped:
Minerals that had formed at the top sank to its bottom, in a process called lunar mantle overturn.
The idea emerged from simulations based on the analysis of lunar rocks brought back by the Apollo missions,
but a new study published in Nature Geoscience offers the first evidence supporting this theory.
Fou…

This is a good start but the subway should curve south down 19th Ave, meet up with Daly City BART and continue on the BART tracks down to Millbrae. That part is essential; a branch to Outer Richmond could be added later as a nice-to-have.
https://musubi3.github.io/sfmta-geary-subway…

You Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
toXiv_bot_toot

An analysis of Waymo's data covering ~100M driverless miles across four US cities: Waymo cars have far lower crash rates per million miles than human drivers (Jonathan Slotkin/New York Times)
https://www.nytimes.com/2025/12/02/o…

Claude skills are a big deal™️
Thanks to skills, you can reduce your multi-agent setup to a single agent with skills, greatly reducing complexity and increasing speed of execution.
In fact, if in the past you could have a number of agents each specialized in, for example, data analysis, getting data from a particular set of websites, making that data available in a dashboard, etc., with skills you can substitute all these agents with skills. (1/2)

Analysis: since 2023, data center power demands have delayed 15 coal plants' retirements; the Trump administration has ordered two power plants to remain open (Ariel Wittenberg/Politico)
https://www.politico.com/news/2025/11/27/a

"UK’s largest proposed datacentre ‘understating planned water use’"
#UK #UnitedKingdom #Water #Technology

👨🏿‍🌾 Traces of old farm chemicals contaminate water across the US
#chemicals

A look at Meta's 2GW Hyperion data center in Louisiana, with the first phase opening in 2028; an analysis shows sales tax breaks on GPUs could total $3.3B (Jon Keegan/Sherwood News)
https://sherwood.news/tech/hyperion/

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
Gon\c{c}alo Faria, Noah A. Smith
https://arxiv.org/abs/2504.03790 https://mastoxiv.page/@arXiv_csCL_bot/114301112970577326
- A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair, Morten M{\o}rup
https://arxiv.org/abs/2504.12392 https://mastoxiv.page/@arXiv_statME_bot/114357826909813483
- The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
Michael L. Wells, Kamel Lahouel, Bruno Jedynak
https://arxiv.org/abs/2505.11622 https://mastoxiv.page/@arXiv_statML_bot/114539065460187982
- BOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Kingsley Yeon, Promit Ghosal, Mihai Anitescu
https://arxiv.org/abs/2505.12289 https://mastoxiv.page/@arXiv_mathNA_bot/114539035462135281
- Clustering and Pruning in Causal Data Fusion
Otto Tabell, Santtu Tikka, Juha Karvanen
https://arxiv.org/abs/2505.15215 https://mastoxiv.page/@arXiv_statML_bot/114550346291754635
- On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of ph...
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi, Alison L. Marsden
https://arxiv.org/abs/2506.11683 https://mastoxiv.page/@arXiv_statML_bot/114692410563481289
- Beyond Force Metrics: Pre-Training MLFFs for Stable MD Simulations
Maheshwari, Tang, Ock, Kolluru, Farimani, Kitchin
https://arxiv.org/abs/2506.14850 https://mastoxiv.page/@arXiv_physicschemph_bot/114709402590755731
- Quantifying Uncertainty in the Presence of Distribution Shifts
Yuli Slavutsky, David M. Blei
https://arxiv.org/abs/2506.18283 https://mastoxiv.page/@arXiv_statML_bot/114738165218533987
- ZKPROV: A Zero-Knowledge Approach to Dataset Provenance for Large Language Models
Mina Namazi, Alexander Nemecek, Erman Ayday
https://arxiv.org/abs/2506.20915 https://mastoxiv.page/@arXiv_csCR_bot/114754394485208892
- SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
Zhao, Huang, Xue, Kong, Liu, Tang, Beers, Ting, Luo
https://arxiv.org/abs/2507.01939 https://mastoxiv.page/@arXiv_astrophIM_bot/114788369702591337
- Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based I...
Ko Watanabe, Stanislav Frolov, Aya Hassan, David Dembinsky, Adriano Lucieri, Andreas Dengel
https://arxiv.org/abs/2507.17860 https://mastoxiv.page/@arXiv_csCV_bot/114912976717523345
- PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501 https://mastoxiv.page/@arXiv_csAI_bot/115032101532614110
- Unified Acoustic Representations for Screening Neurological and Respiratory Pathologies from Voice
Ran Piao, Yuan Lu, Hareld Kemps, Tong Xia, Aaqib Saeed
https://arxiv.org/abs/2508.20717 https://mastoxiv.page/@arXiv_csSD_bot/115111255835875066
- Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, et al.
https://arxiv.org/abs/2509.11512 https://mastoxiv.page/@arXiv_csDC_bot/115213444524490263
- MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair
Ali Reza Ibrahimzada, Brandon Paulsen, Reyhaneh Jabbarvand, Joey Dodds, Daniel Kroening
https://arxiv.org/abs/2509.16187 https://mastoxiv.page/@arXiv_csSE_bot/115247172280557686
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation ...
Adam Lahouari, Jutta Rogal, Mark E. Tuckerman
https://arxiv.org/abs/2509.21647 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115286737423175311
- Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference
Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma
https://arxiv.org/abs/2509.21791 https://mastoxiv.page/@arXiv_csCL_bot/115287166674809413
- The Generation Phases of Flow Matching: a Denoising Perspective
Anne Gagneux, S\'egol\`ene Martin, R\'emi Gribonval, Mathurin Massias
https://arxiv.org/abs/2510.24830 https://mastoxiv.page/@arXiv_csCV_bot/115462527449411627
- Data-driven uncertainty-aware seakeeping prediction of the Delft 372 catamaran using ensemble Han...
Giorgio Palma, Andrea Serani, Matteo Diez
https://arxiv.org/abs/2511.04461 https://mastoxiv.page/@arXiv_eessSY_bot/115507785247809767
- Generalized infinite dimensional Alpha-Procrustes based geometries
Salvish Goomanee, Andi Han, Pratik Jawanpuria, Bamdev Mishra
https://arxiv.org/abs/2511.09801 https://mastoxiv.page/@arXiv_statML_bot/115547135711272091
toXiv_bot_toot

Analysis: Oracle has moved $66B of debt for building AI data centers off its balance sheet using SPVs; Meta has moved $30B, xAI moved $20B, and CoreWeave $2.6B (Tabby Kinder/Financial Times)
https://www.ft.com/content/0ae9d6cd-6b94-4e22-a559-f047734bef83

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
https://arxiv.org/abs/2512.17341 https://mastoxiv.page/@arXiv_statML_bot/115762312049963700
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
https://arxiv.org/abs/2512.17381 https://mastoxiv.page/@arXiv_csNI_bot/115762180316858485
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
https://arxiv.org/abs/2512.17419 https://mastoxiv.page/@arXiv_csSE_bot/115762487015279852
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
https://arxiv.org/abs/2512.17426 https://mastoxiv.page/@arXiv_statML_bot/115762346108219997
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
https://arxiv.org/abs/2512.17450 https://mastoxiv.page/@arXiv_csCV_bot/115762717053353674
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
https://arxiv.org/abs/2512.17460 https://mastoxiv.page/@arXiv_csSE_bot/115762500123147574
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
https://arxiv.org/abs/2512.17462 https://mastoxiv.page/@arXiv_csIR_bot/115762430673347625
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
https://arxiv.org/abs/2512.17466 https://mastoxiv.page/@arXiv_eessSY_bot/115762336277179643
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
https://arxiv.org/abs/2512.17470 https://mastoxiv.page/@arXiv_csAI_bot/115762556506696539
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
https://arxiv.org/abs/2512.17473 https://mastoxiv.page/@arXiv_eessSP_bot/115762580078964235
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
https://arxiv.org/abs/2512.17488 https://mastoxiv.page/@arXiv_csCV_bot/115762726884307901
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
https://arxiv.org/abs/2512.17515 https://mastoxiv.page/@arXiv_eessIV_bot/115762459510336799
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
https://arxiv.org/abs/2512.17517 https://mastoxiv.page/@arXiv_csCV_bot/115762741957639051
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
https://arxiv.org/abs/2512.17534 https://mastoxiv.page/@arXiv_physicsfludyn_bot/115762391350754768
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
https://arxiv.org/abs/2512.17562 https://mastoxiv.page/@arXiv_csSD_bot/115762423443170715
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
https://arxiv.org/abs/2512.17574 https://mastoxiv.page/@arXiv_csDC_bot/115762425409322293
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
https://arxiv.org/abs/2512.17585 https://mastoxiv.page/@arXiv_eessIV_bot/115762479150695610
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
https://arxiv.org/abs/2512.17594 https://mastoxiv.page/@arXiv_csCR_bot/115762509298207765
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
https://arxiv.org/abs/2512.17630 https://mastoxiv.page/@arXiv_csCL_bot/115762575512981257
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
https://arxiv.org/abs/2512.17659 https://mastoxiv.page/@arXiv_statML_bot/115762554519447500
toXiv_bot_toot

Threat intel company Dataminr plans to acquire cybersecurity threat intel provider ThreatConnect for $290M; Dataminr raised $85M in convertible funding in March (Greg Otto/CyberScoop)
https://cyberscoop.com/dataminr-threatconnect-acquisition/

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
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