Tootfinder

A huge impact 4.3 billion years ago partially melted the Moon's mantle & made it lopsided
https://skywriter.blue/@coreyspowell.bsky.social/3meoshhxwic2u

On 3-Connected Planar Graphs with Unique Orientable Circuit Double Covers
Meike Wei{\ss}, Reymond Akpanya, Alice C. Niemeyer
https://arxiv.org/abs/2601.10171 https://

@…
Good question! The numbers are from a production server with NVMe storage.
Breakdown:
- 19k files (15 GB): ~8-9 sec
- 13k files (53 GB): ~13-14 sec
- Total: 32k files (68 GB): ~21 sec
That's ~3.2 GB/s throughput - achievable with:
1. NVMe SSDs (3-7 GB/s sequential read)
2. Linux page cache on subseque…

The CfP for the 3rd International Workshop of Semantic Digital Humanities is out!
submission deadline for papers & panels: March 3, 2026
webpage: #semanticweb

Say that the event is taking place in Switzerland without saying that the event is taking place in Switzerland

@… for clarity: I don't see pkgbase as strange, or dangerous.
It's sometimes misunderstood. Sometimes the big picture is not seen.
The 15.0 errata page, for example. The recent statement about major upgrades from 14.3 is wrong:
"systems installed with pkgbase(7) must backup and reinstall"
– that's unneces…

The 3-symmetric Pseudolinear Crossing Number of $K_{33}$
V\'ictor H. G\'omez Mart\'inez, C\'esar Hern\'andez-V\'elez, Jes\'us Lea\~nos
https://arxiv.org/abs/2601.09689

#PiDay
ln( 640320^3 744 )/(163)^0.5 = 3.141592653589793238462643383279...
till that last 9 it works, but then it continues with
72661...
rather than 50288... as π does. Nice hoax by Martin Gardner in 1975 [1]

Submit Your Song to the Second General Strike Song Contest - Labor Heritage Foundation https://laborheritage.org/content.aspx?page_id=5&club_id=533040&item_id=132879&action=view&al=y&actr=3

Submit Your Song to the Second General Strike Song Contest - Labor Heritage Foundation
Songwriters, musicians, and activists are invited to submit entries to the second General Strike Song Contest, a project to create new songs and chants that can be quickly learned and sung by large groups at rallies, picket lines, and demonstrations. Last year’s winning entry was “Hold Fast! Hol

This weekend I worked on my custom /blogroll page
it has 3 input types :
1. you construct the collection by adding blogs manually
2. you import an OPML with your collection
3. you connect your /microsub existing collection to feed the blogroll on the frontend.
https://rmendes.net/notes/2026/02/08/3

Ricardo Mendes
8 February 2026 indiekit plugins blogroll opml This weekend I worked on my custom /blogroll page it has 3 input types : you construct the collection by adding blogs manually you import an OPML with yo...

As @…'s CI is seeing regressions around #IPv6, I'm exploring @…, which uses a Woodpecker fork called Crow. Among many other ni…

Cylinder type and $p$-divisible sets in $\mathbb{F}_p^3$
Gergely Kiss, \'Ad\'am Mark\'o, Zolt\'an L\'or\'ant Nagy, G\'abor Somlai
https://arxiv.org/abs/2601.09910

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Diffusion Modulation via Environment Mechanism Modeling for Planning
Hanping Zhang, Yuhong Guo
https://arxiv.org/abs/2602.20422 https://mastoxiv.page/@arXiv_csAI_bot/116130110576555049
- Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt, Suyash Gupta
https://arxiv.org/abs/2602.20450 https://mastoxiv.page/@arXiv_csDC_bot/116130191233002036
- Prior-Agnostic Incentive-Compatible Exploration
Ramya Ramalingam, Osbert Bastani, Aaron Roth
https://arxiv.org/abs/2602.20465 https://mastoxiv.page/@arXiv_csGT_bot/116130245628406144
- PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Mohammed Rakib, Luke Vaughan, Shivang Patel, Flera Rizatdinova, Alexander Khanov, Atriya Sen
https://arxiv.org/abs/2602.20475 https://mastoxiv.page/@arXiv_hepex_bot/116130242350426528
- ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory
Zhong, Faisal, Fran\c{c}a, Leesatapornwongsa, Szekeres, Rong, Nath
https://arxiv.org/abs/2602.20502 https://mastoxiv.page/@arXiv_csAI_bot/116130180718734838
- Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Rakshit Trivedi, Kartik Sharma, David C Parkes
https://arxiv.org/abs/2602.20517 https://mastoxiv.page/@arXiv_csAI_bot/116130223344095649
- Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Lovelace, Belardi, Zalouk, Polavaram, Kundurthy, Weinberger
https://arxiv.org/abs/2602.20528 https://mastoxiv.page/@arXiv_csCL_bot/116130628998822849
- Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ T...
Yanming Lai, Defeng Sun
https://arxiv.org/abs/2602.20555 https://mastoxiv.page/@arXiv_statML_bot/116130512372759166
- Personal Information Parroting in Language Models
Nishant Subramani, Kshitish Ghate, Mona Diab
https://arxiv.org/abs/2602.20580 https://mastoxiv.page/@arXiv_csCL_bot/116130630309564204
- Characterizing Online and Private Learnability under Distributional Constraints via Generalized S...
Mo\"ise Blanchard, Abhishek Shetty, Alexander Rakhlin
https://arxiv.org/abs/2602.20585 https://mastoxiv.page/@arXiv_statML_bot/116130525452248337
- Amortized Bayesian inference for actigraph time sheet data from mobile devices
Daniel Zhou, Sudipto Banerjee
https://arxiv.org/abs/2602.20611 https://mastoxiv.page/@arXiv_statML_bot/116130543144314661
- Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
Xueqiang Lv, Shizhou Zhang, Yinghui Xing, Di Xu, Peng Wang, Yanning Zhang
https://arxiv.org/abs/2602.20616 https://mastoxiv.page/@arXiv_csCV_bot/116130795466851481
- On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
Boao Kong, Hengrui Zhang, Kun Yuan
https://arxiv.org/abs/2602.20646 https://mastoxiv.page/@arXiv_mathOC_bot/116130476952419594
- DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Feng, Reich, Beaglehole, Luo, Park, Yoo, Huang, Mao, Boz, Kim
https://arxiv.org/abs/2602.20652 https://mastoxiv.page/@arXiv_statML_bot/116130551664144143
- Vision-Language Models for Ergonomic Assessment of Manual Lifting Tasks: Estimating Horizontal an...
Mohammad Sadra Rajabi, Aanuoluwapo Ojelade, Sunwook Kim, Maury A. Nussbaum
https://arxiv.org/abs/2602.20658 https://mastoxiv.page/@arXiv_csCV_bot/116130809228818544
- F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performanc...
Xuran Ma, et al.
https://arxiv.org/abs/2602.20712 https://mastoxiv.page/@arXiv_astrophIM_bot/116130530693731576
- Communication-Inspired Tokenization for Structured Image Representations
Davtyan, Sahin, Haghighi, Stapf, Acuaviva, Alahi, Favaro
https://arxiv.org/abs/2602.20731 https://mastoxiv.page/@arXiv_csCV_bot/116130824303022936
- SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, et al.
https://arxiv.org/abs/2602.20751 https://mastoxiv.page/@arXiv_csCL_bot/116130739757479992
- Assessing the Impact of Speaker Identity in Speech Spoofing Detection
Anh-Tuan Dao, Driss Matrouf, Nicholas Evans
https://arxiv.org/abs/2602.20805 https://mastoxiv.page/@arXiv_csSD_bot/116130218074059060
- Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
Sayantan Dasgupta, Trevor Cohn, Timothy Baldwin
https://arxiv.org/abs/2602.20816 https://mastoxiv.page/@arXiv_csCL_bot/116130753521420972
- DRESS: A Continuous Framework for Structural Graph Refinement
Eduar Castrillo Velilla
https://arxiv.org/abs/2602.20833 https://mastoxiv.page/@arXiv_csDS_bot/116130545112457981
toXiv_bot_toot

[2026-02-13 Fri (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Heute vor 65 Jahren: Am 14. März 1961 stürzte ein B-52 Bomber der USAF in der Nähe von Yuba City, Kalifornien, mit zwei Atombomben ab, die keine nukleare Explosion auslösten.

Space Complexity Dichotomies for Subgraph Finding Problems in the Streaming Model
Yu-Sheng Shih, Meng-Tsung Tsai, Yen-Chu Tsai, Ying-Sian Wu
https://arxiv.org/abs/2602.08002 https://arxiv.org/pdf/2602.08002 https://arxiv.org/html/2602.08002
arXiv:2602.08002v1 Announce Type: new
Abstract: We study the space complexity of four variants of the standard subgraph finding problem in the streaming model. Specifically, given an $n$-vertex input graph and a fixed-size pattern graph, we consider two settings: undirected simple graphs, denoted by $G$ and $H$, and oriented graphs, denoted by $\vec{G}$ and $\vec{H}$. Depending on the setting, the task is to decide whether $G$ contains $H$ as a subgraph or as an induced subgraph, or whether $\vec{G}$ contains $\vec{H}$ as a subgraph or as an induced subgraph. Let Sub$(H)$, IndSub$(H)$, Sub$(\vec{H})$, and IndSub$(\vec{H})$ denote these four variants, respectively.
An oriented graph is well-oriented if it admits a bipartition in which every arc is oriented from one part to the other, and a vertex is non-well-oriented if both its in-degree and out-degree are non-zero. For each variant, we obtain a complete dichotomy theorem, briefly summarized as follows.
(1) Sub$(H)$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if $H$ is bipartite.
(2) IndSub$(H)$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if $H \in \{P_3, P_4, co\mbox{-}P_3\}$.
(3) Sub$(\vec{H})$ can be solved by a single-pass $n^{2-\Omega(1)}$-space algorithm if and only if every connected component of $\vec H$ is either a well-oriented bipartite graph or a tree containing at most one non-well-oriented vertex.
(4) IndSub$(\vec{H})$ can be solved by an $\tilde{O}(1)$-pass $n^{2-\Omega(1)}$-space algorithm if and only if the underlying undirected simple graph $H$ is a $co\mbox{-}P_3$.
toXiv_bot_toot

Chiral states induced by symmetry-breaking in $\alpha-T_3$ lattices: Magnetic field effect
J. P. G. Nascimento, J. M. Pereira Jr., R. N. Costa Filho, F. M. Peeters, M. M. Freire, W. P. Lima, D. R. da Costa
https://arxiv.org/abs/2602.10288

I realize that in 2026, saying the WaPo op-ed page has gotten dumber than a bag of bricks is not saying something novel, but "Your salted caramel mocha latte is destroying society", invoking Edmund Burke, might set a benchmark for the year even though we're only 3 months in.

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Farabi, Pinto, Lu, Ramos-Maqueda, Das, Deeb, Sautmann
https://arxiv.org/abs/2602.18431 https://mastoxiv.page/@arXiv_csCY_bot/116119352329590193
- Benchmarking Distilled Language Models: Performance and Efficiency in Resource-Constrained Settings
Sachin Gopal Wani, Eric Page, Ajay Dholakia, David Ellison
https://arxiv.org/abs/2602.20164 https://mastoxiv.page/@arXiv_csCL_bot/116130101399805837
- VISION-ICE: Video-based Interpretation and Spatial Identification of Arrhythmia Origins via Neura...
Dorsa EPMoghaddam, Feng Gao, Drew Bernard, Kavya Sinha, Mehdi Razavi, Behnaam Aazhang
https://arxiv.org/abs/2602.20165 https://mastoxiv.page/@arXiv_csCV_bot/116130222034322594
- Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Un...
KMA Solaiman, Joshua Sebastian, Karma Tobden
https://arxiv.org/abs/2602.20168 https://mastoxiv.page/@arXiv_csCY_bot/116130239074411770
- Cross-Chirality Generalization by Axial Vectors for Hetero-Chiral Protein-Peptide Interaction Design
Yang, Tian, Jia, Zhang, Zheng, Wang, Su, He, Liu, Lan
https://arxiv.org/abs/2602.20176 https://mastoxiv.page/@arXiv_qbioBM_bot/116130281674122586
- Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic St...
Aniruddha Bora, Isabel K. Alvarez, Julie Chalfant, Chryssostomos Chryssostomidis
https://arxiv.org/abs/2602.20177 https://mastoxiv.page/@arXiv_csNE_bot/116130397676559696
- Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis
Yongwei Yi, Xinping Yi, Wenjin Wang, Xiao Li, Shi Jin
https://arxiv.org/abs/2602.20178 https://mastoxiv.page/@arXiv_eessSP_bot/116130257424413457
- OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Mohammadmahdi Vahediahmar, Matthew A. McDonald, Feng Liu
https://arxiv.org/abs/2602.20195 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/116130271189617558
- KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction
Soumik Deb Niloy, Md. Fahmid-Ul-Alam Juboraj, Swakkhar Shatabda
https://arxiv.org/abs/2602.20198 https://mastoxiv.page/@arXiv_qbioQM_bot/116130341315320687
- Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control
Shaorong Chen, Jingbo Zhou, Jun Xia
https://arxiv.org/abs/2602.20209 https://mastoxiv.page/@arXiv_qbioQM_bot/116130374083646541
- The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA
Zien Ma, S. M. Shermer, Oktay Karaku\c{s}, Frank C. Langbein
https://arxiv.org/abs/2602.20289 https://mastoxiv.page/@arXiv_eessSP_bot/116130267228834775
- Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Appro...
Haochen Zhang, Zhong Zheng, Lingzhou Xue
https://arxiv.org/abs/2602.20297 https://mastoxiv.page/@arXiv_statML_bot/116130255458256497
- Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Eva...
Joyanta Jyoti Mondal
https://arxiv.org/abs/2602.20303 https://mastoxiv.page/@arXiv_csAI_bot/116130097466859145
- An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes ...
Shyr, Hu, Tinker, Cassini, Byram, Hamid, Fabbri, Wright, Peterson, Bastarache, Xu
https://arxiv.org/abs/2602.20324 https://mastoxiv.page/@arXiv_csAI_bot/116130100089848459
- Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Th...
Jingcheng Yang, Tianhu Xiong, Shengyi Qian, Klara Nahrstedt, Mingyuan Wu
https://arxiv.org/abs/2602.20330 https://mastoxiv.page/@arXiv_csCV_bot/116130463214879334
- No One Size Fits All: QueryBandits for Hallucination Mitigation
Nicole Cho, William Watson, Alec Koppel, Sumitra Ganesh, Manuela Veloso
https://arxiv.org/abs/2602.20332 https://mastoxiv.page/@arXiv_csCL_bot/116130370809116915
- Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS
Mohanad Obeed, Ming Jian
https://arxiv.org/abs/2602.20361 https://mastoxiv.page/@arXiv_csIT_bot/116130289537785136
- Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Joel Persson, Jurri\"en Bakker, Dennis Bohle, Stefan Feuerriegel, Florian von Wangenheim
https://arxiv.org/abs/2602.20383 https://mastoxiv.page/@arXiv_statME_bot/116130509065601748
- Selecting Optimal Variable Order in Autoregressive Ising Models
Shiba Biswal, Marc Vuffray, Andrey Y. Lokhov
https://arxiv.org/abs/2602.20394 https://mastoxiv.page/@arXiv_statML_bot/116130299369541741
toXiv_bot_toot

[2026-03-11 Wed (UTC), 3 new articles found for math.CA Classical Analysis and ODEs]
toXiv_bot_toot

[2026-03-13 Fri (UTC), 3 new articles found for q-bio.GN Genomics]
toXiv_bot_toot

Assessing the Impact of Fitting Methodology at aN$^3$LO with FPPDF: an Open Source Tool for Extracting Parton Distribution Functions in the Hessian Approach
J. M. Cruz-Martinez, T. Giani, L. A. Harland-Lang
https://arxiv.org/abs/2602.07118

AgentCgroup: Understanding and Controlling OS Resources of AI Agents
Yusheng Zheng, Jiakun Fan, Quanzhi Fu, Yiwei Yang, Wei Zhang, Andi Quinn
https://arxiv.org/abs/2602.09345 https://arxiv.org/pdf/2602.09345 https://arxiv.org/html/2602.09345
arXiv:2602.09345v1 Announce Type: new
Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations. We present a systematic characterization of OS-level resource dynamics in sandboxed AI coding agents, analyzing 144 software engineering tasks from the SWE-rebench benchmark across two LLM models. Our measurements reveal that (1) OS-level execution (tool calls, container and agent initialization) accounts for 56-74% of end-to-end task latency; (2) memory, not CPU, is the concurrency bottleneck; (3) memory spikes are tool-call-driven with a up to 15.4x peak-to-average ratio; and (4) resource demands are highly unpredictable across tasks, runs, and models. Comparing these characteristics against serverless, microservice, and batch workloads, we identify three mismatches in existing resource controls: a granularity mismatch (container-level policies vs. tool-call-level dynamics), a responsiveness mismatch (user-space reaction vs. sub-second unpredictable bursts), and an adaptability mismatch (history-based prediction vs. non-deterministic stateful execution). We propose AgentCgroup , an eBPF-based resource controller that addresses these mismatches through hierarchical cgroup structures aligned with tool-call boundaries, in-kernel enforcement via sched_ext and memcg_bpf_ops, and runtime-adaptive policies driven by in-kernel monitoring. Preliminary evaluation demonstrates improved multi-tenant isolation and reduced resource waste.
toXiv_bot_toot

Batteries fully charged. Flashlights are ready. UPS for router and mesh system ready, only thing connected to it is the router. UPS behind TV is ready, but, that is only for the TV. Third UPS is beside me here, fully charged and ready to go. Last UPS is connected to the upstairs mesh device. Turned up the thermostat so when (not if) we lose power the house will stay warm enough for a few hours.
Still checking out the battery backup systems, but have to save enough to buy one (acc…

[2026-03-13 Fri (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-02-09 Mon (UTC), 3 new articles found for math.AC Commutative Algebra]
toXiv_bot_toot

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Functional Continuous Decomposition
Teymur Aghayev
https://arxiv.org/abs/2602.20857 https://mastoxiv.page/@arXiv_eessSP_bot/116130499236089653
- SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language Models
Xie, Zhang, Shan, Zhu, Tang, Wei, Song, Wan, Song
https://arxiv.org/abs/2602.20901 https://mastoxiv.page/@arXiv_csCV_bot/116130845273808954
- Some Simple Economics of AGI
Christian Catalini, Xiang Hui, Jane Wu
https://arxiv.org/abs/2602.20946 https://mastoxiv.page/@arXiv_econGN_bot/116130470423837005
- Multimodal MRI Report Findings Supervised Brain Lesion Segmentation with Substructures
Yubin Ge, Yongsong Huang, Xiaofeng Liu
https://arxiv.org/abs/2602.20994 https://mastoxiv.page/@arXiv_eessIV_bot/116130212832138624
- MIP Candy: A Modular PyTorch Framework for Medical Image Processing
Tianhao Fu, Yucheng Chen
https://arxiv.org/abs/2602.21033 https://mastoxiv.page/@arXiv_csCV_bot/116130864279556063
- Empirically Calibrated Conditional Independence Tests
Milleno Pan, Antoine de Mathelin, Wesley Tansey
https://arxiv.org/abs/2602.21036 https://mastoxiv.page/@arXiv_statME_bot/116130690605113562
- Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet
https://arxiv.org/abs/2602.21039 https://mastoxiv.page/@arXiv_statML_bot/116130572661848449
- Position-Aware Sequential Attention for Accurate Next Item Recommendations
Timur Nabiev, Evgeny Frolov
https://arxiv.org/abs/2602.21052 https://mastoxiv.page/@arXiv_csIR_bot/116130263323086316
- Motivation is Something You Need
Mehdi Acheli, Walid Gaaloul
https://arxiv.org/abs/2602.21064 https://mastoxiv.page/@arXiv_csAI_bot/116130680774678580
- An Enhanced Projection Pursuit Tree Classifier with Visual Methods for Assessing Algorithmic Impr...
Natalia da Silva, Dianne Cook, Eun-Kyung Lee
https://arxiv.org/abs/2602.21130 https://mastoxiv.page/@arXiv_statML_bot/116130610674573081
- Complexity of Classical Acceleration for $\ell_1$-Regularized PageRank
Kimon Fountoulakis, David Mart\'inez-Rubio
https://arxiv.org/abs/2602.21138 https://mastoxiv.page/@arXiv_mathOC_bot/116130547076073836
- LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis
Jiang, Yang, Nath, Parida, Kulkarni, Xu, Xu, Anwar, Roth, Linguraru
https://arxiv.org/abs/2602.21142 https://mastoxiv.page/@arXiv_csCV_bot/116130871488694585
- A Benchmark for Deep Information Synthesis
Debjit Paul, et al.
https://arxiv.org/abs/2602.21143 https://mastoxiv.page/@arXiv_csAI_bot/116130692571594706
- Scaling State-Space Models on Multiple GPUs with Tensor Parallelism
Anurag Dutt, Nimit Shah, Hazem Masarani, Anshul Gandhi
https://arxiv.org/abs/2602.21144 https://mastoxiv.page/@arXiv_csDC_bot/116130520888343997
- Not Just How Much, But Where: Decomposing Epistemic Uncertainty into Per-Class Contributions
Mame Diarra Toure, David A. Stephens
https://arxiv.org/abs/2602.21160 https://mastoxiv.page/@arXiv_statML_bot/116130618512594211
- Aletheia tackles FirstProof autonomously
Tony Feng, et al.
https://arxiv.org/abs/2602.21201 https://mastoxiv.page/@arXiv_csAI_bot/116130705679345625
- Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
Abdulaziz Almuzairee, Henrik I. Christensen
https://arxiv.org/abs/2602.21203 https://mastoxiv.page/@arXiv_csRO_bot/116130765974498223
toXiv_bot_toot

[2026-02-09 Mon (UTC), 3 new articles found for physics.ins-det Instrumentation and Detectors]
toXiv_bot_toot

Happy to contribute to #Cilium (#documentation).
Good tools deserve good docs. ✨
https://github.com/cilium/cilium/pull/

docs: add policy language chapter headline by orangecms · Pull Request #44204 · cilium/cilium
Up until now, the page title had been 'Layer 3 Examples', which is a section headline and confusing, since it is among other examples. Please ensure your pull request adheres to the followi...

"Your login failed. We will not tell you why, or what field is in error. But if you submit bad data 3 times, we'll block you for the next 30 minutes so you can randomly guess again."
Seriously??? This was already understood to be fire-worthy bad design in the bloody 1990s! HOW does such a page still exist? Even on a government site, this is shockingly incompetent.

So Bad Bunny
isn't a good "role model"
but Kid Rock is?❓
[Verse 3: Kid Rock & Joe-C]
On my cell phone I'm paid, G,
can't call me, just page me
👉Young ladies, young ladies,
I like 'em underage, see
Some say that's statutory
💥(But I say it's mandatory)

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/6]:
- Towards Scalable Oversight via Partitioned Human Supervision
Ren Yin, Takashi Ishida, Masashi Sugiyama
https://arxiv.org/abs/2510.22500 https://mastoxiv.page/@arXiv_csLG_bot/115451787490434401
- ContextPilot: Fast Long-Context Inference via Context Reuse
Yinsicheng Jiang, Yeqi Huang, Liang Cheng, Cheng Deng, Xuan Sun, Luo Mai
https://arxiv.org/abs/2511.03475 https://mastoxiv.page/@arXiv_csLG_bot/115502245581974540
- Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning
Nabil Belacel, Mohamed Rachid Boulassel
https://arxiv.org/abs/2601.11283 https://mastoxiv.page/@arXiv_csLG_bot/115921183182326799
- PhysE-Inv: A Physics-Encoded Inverse Modeling approach for Arctic Snow Depth Prediction
Akila Sampath, Vandana Janeja, Jianwu Wang
https://arxiv.org/abs/2601.17074
- SAGE-5GC: Security-Aware Guidelines for Evaluating Anomaly Detection in the 5G Core Network
Cristian Manca, Christian Scano, Giorgio Piras, Fabio Brau, Maura Pintor, Battista Biggio
https://arxiv.org/abs/2602.03596
- LORE: Jointly Learning the Intrinsic Dimensionality and Relative Similarity Structure From Ordina...
Anand, Helbling, Davenport, Berman, Alagapan, Rozell
https://arxiv.org/abs/2602.04192
- Towards Robust Scaling Laws for Optimizers
Alexandra Volkova, Mher Safaryan, Christoph H. Lampert, Dan Alistarh
https://arxiv.org/abs/2602.07712 https://mastoxiv.page/@arXiv_csLG_bot/116046369672796465
- Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs
Sagnik Mukherjee, Lifan Yuan, Pavan Jayasinha, Dilek Hakkani-T\"ur, Hao Peng
https://arxiv.org/abs/2602.07729 https://mastoxiv.page/@arXiv_csLG_bot/116046377539155485
- AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine L...
Yuzhu Cai, Zexi Liu, Xinyu Zhu, Cheng Wang, Siheng Chen
https://arxiv.org/abs/2602.07906 https://mastoxiv.page/@arXiv_csLG_bot/116046423413650658
- VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training
Guobin Shen, Chenxiao Zhao, Xiang Cheng, Lei Huang, Xing Yu
https://arxiv.org/abs/2602.10693 https://mastoxiv.page/@arXiv_csLG_bot/116057229834947730
- KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Zukang Xu, Zhixiong Zhao, Xing Hu, Zhixuan Chen, Dawei Yang
https://arxiv.org/abs/2602.11184 https://mastoxiv.page/@arXiv_csLG_bot/116062537528208461
- MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Correia, Ferreira, Martins, Bento, Guerreiro, Pereira, Gomes, Bono, Ferreira, Bizarro
https://arxiv.org/abs/2602.11776 https://mastoxiv.page/@arXiv_csLG_bot/116062952355379801
- Pawsterior: Variational Flow Matching for Structured Simulation-Based Inference
Jorge Carrasco-Pollo, Floor Eijkelboom, Jan-Willem van de Meent
https://arxiv.org/abs/2602.13813 https://mastoxiv.page/@arXiv_csLG_bot/116085828112928218
- Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misa...
Hong Li, Zhen Zhou, Honggang Zhang, Yuping Luo, Xinyue Wang, Han Gong, Zhiyuan Liu
https://arxiv.org/abs/2602.14462 https://mastoxiv.page/@arXiv_csLG_bot/116085997857526328
- Divine Benevolence is an $x^2$: GLUs scale asymptotically faster than MLPs
Alejandro Francisco Queiruga
https://arxiv.org/abs/2602.14495 https://mastoxiv.page/@arXiv_csLG_bot/116086011618741857
- \"UberWeb: Insights from Multilingual Curation for a 20-Trillion-Token Dataset
DatologyAI, et al.
https://arxiv.org/abs/2602.15210 https://mastoxiv.page/@arXiv_csLG_bot/116090912256712568
- GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, et al.
https://arxiv.org/abs/2602.15763 https://mastoxiv.page/@arXiv_csLG_bot/116091080686771018
- Anatomy of Capability Emergence: Scale-Invariant Representation Collapse and Top-Down Reorganizat...
Jayadev Billa
https://arxiv.org/abs/2602.15997 https://mastoxiv.page/@arXiv_csLG_bot/116096541546306333
- AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
KC Santosh, Srikanth Baride, Rodrigue Rizk
https://arxiv.org/abs/2602.16042 https://mastoxiv.page/@arXiv_csLG_bot/116096581524696028
- Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng, Li Zhang, Haolin Ye, Ziyu Zhao, Yuhe Jiang, Tara Saba, Xinyu Wang, Xujie Si
https://arxiv.org/abs/2602.16947 https://mastoxiv.page/@arXiv_csLG_bot/116102426238903124
toXiv_bot_toot

OFERA: Blendshape-driven 3D Gaussian Control for Occluded Facial Expression to Realistic Avatars in VR
Seokhwan Yang, Boram Yoon, Seoyoung Kang, Hail Song, Woontack Woo
https://arxiv.org/abs/2602.01748 https://arxiv.org/pdf/2602.01748 https://arxiv.org/html/2602.01748
arXiv:2602.01748v1 Announce Type: new
Abstract: We propose OFERA, a novel framework for real-time expression control of photorealistic Gaussian head avatars for VR headset users. Existing approaches attempt to recover occluded facial expressions using additional sensors or internal cameras, but sensor-based methods increase device weight and discomfort, while camera-based methods raise privacy concerns and suffer from limited access to raw data. To overcome these limitations, we leverage the blendshape signals provided by commercial VR headsets as expression inputs. Our framework consists of three key components: (1) Blendshape Distribution Alignment (BDA), which applies linear regression to align the headset-provided blendshape distribution to a canonical input space; (2) an Expression Parameter Mapper (EPM) that maps the aligned blendshape signals into an expression parameter space for controlling Gaussian head avatars; and (3) a Mapper-integrated Avatar (MiA) that incorporates EPM into the avatar learning process to ensure distributional consistency. Furthermore, OFERA establishes an end-to-end pipeline that senses and maps expressions, updates Gaussian avatars, and renders them in real-time within VR environments. We show that EPM outperforms existing mapping methods on quantitative metrics, and we demonstrate through a user study that the full OFERA framework enhances expression fidelity while preserving avatar realism. By enabling real-time and photorealistic avatar expression control, OFERA significantly improves telepresence in VR communication. A project page is available at https://ysshwan147.github.io/projects/ofera/.
toXiv_bot_toot

[2026-02-10 Tue (UTC), 3 new articles found for cs.OS Operating Systems]
toXiv_bot_toot

[2026-01-12 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Texans hit offseason with familiar feeling after another divisional round loss https://www.espn.com/nfl/story/_/page/Houston-Texans/houston-texans-offseason-familiar-place-divisional-round-loss-cj-stroud

A Faster Directed Single-Source Shortest Path Algorithm
Ran Duan, Xiao Mao, Xinkai Shu, Longhui Yin
https://arxiv.org/abs/2602.07868 https://arxiv.org/pdf/2602.07868 https://arxiv.org/html/2602.07868
arXiv:2602.07868v1 Announce Type: new
Abstract: This paper presents a new deterministic algorithm for single-source shortest paths (SSSP) on real non-negative edge-weighted directed graphs, with running time $O(m\sqrt{\log n} \sqrt{mn\log n\log \log n})$, which is $O(m\sqrt{\log n\log \log n})$ for sparse graphs. This improves the recent breakthrough result of $O(m\log^{2/3} n)$ time for directed SSSP algorithm [Duan, Mao, Mao, Shu, Yin 2025].
toXiv_bot_toot

Quadric surfaces of revolution in the 3-sphere as Weingarten surfaces
Ildefonso Castro, Daniel L\'opez-L\'opez
https://arxiv.org/abs/2602.21785 https://arxiv.org/pdf/2602.21785 https://arxiv.org/html/2602.21785
arXiv:2602.21785v1 Announce Type: new
Abstract: The study of quadric surfaces of revolution is a cornerstone of classical Euclidean geometry, but its extension to the three-dimensional sphere $\mathbb{S}^3$ has not been sufficiently explored. This article addresses this important gap by providing a rigorous classification and characterization of non-degenerate quadric surfaces of revolution in $\mathbb{S}^3$, namely spherical ellipsoids, hyperboloids and paraboloids, generated by the rotation of spherical conics around a geodesic axis containing their foci or is orthogonal to them.
Using the concept of spherical angular momentum as a prominent geometric invariant, we discover that these surfaces constitute a remarkable class of Weingarten surfaces and prove that they are uniquely characterised by a specific cubic functional relation between their principal curvatures. This result not only provides a unified description of spherical quadric surfaces of revolution, but also highlights a profound geometric universality, reflecting exactly the same cubic Weingarten relations observed in their Euclidean and Lorentzian counterparts.
toXiv_bot_toot

[2026-03-03 Tue (UTC), 3 new articles found for math.CV Complex Variables]
toXiv_bot_toot

Replaced article(s) found for math.AC. https://arxiv.org/list/math.AC/new
[1/1]:
- A topological approach to key polynomials
Enric Nart, Josnei Novacoski, Giulio Peruginelli
https://arxiv.org/abs/2404.08357 https://mastoxiv.page/@arXiv_mathAC_bot/112273919597079364
- Local cohomology with support in Schubert varieties
Michael Perlman
https://arxiv.org/abs/2405.02142 https://mastoxiv.page/@arXiv_mathAG_bot/112392829352971463
- Retrieving biparameter persistence modules from monoparameter ones: a characterization of hook-de...
Isabella Mastroianni, Marco Guerra, Ulderico Fugacci, Emanuela De Negri
https://arxiv.org/abs/2506.14678 https://mastoxiv.page/@test_3/114703138072612318
toXiv_bot_toot

High-Performance KV$_3$Sb$_5$/WSe$_2$ van der Waals Photodetectors
Yang Yang, Shaofeng Rao, Yuxuan Hou, Jiabo Liu, Deng Hu, Yunfei Guo, Jianzhou Zhao, Hechen Ren, Zhiwei Wang, Fan Yang
https://arxiv.org/abs/2512.24229

I of course goofed and left out the explanation of Lorentz Contraction from the obvious place in Part 3 where I should have talked about it.
So I've slotted it in here:
https://wrog.dreamwidth.org/71271.html#hrel3_lorentz
along with a new diagram to make super…

[2026-02-02 Mon (UTC), 3 new articles found for physics.bio-ph Biological Physics]
toXiv_bot_toot

[2026-02-12 Thu (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-02-09 Mon (UTC), 3 new articles found for physics.ins-det Instrumentation and Detectors]
toXiv_bot_toot

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach
Xinyu Guan, Shaohua Zhang
https://arxiv.org/abs/2512.16927 https://mastoxiv.page/@arXiv_csDS_bot/115762062326187898
- SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
Shravan Chaudhari, Rahul Thomas Jacob, Mononito Goswami, Jiajun Cao, Shihab Rashid, Christian Bock
https://arxiv.org/abs/2512.16956 https://mastoxiv.page/@arXiv_csSE_bot/115762248476963893
- MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
Saksham Sahai Srivastava, Haoyu He
https://arxiv.org/abs/2512.16962 https://mastoxiv.page/@arXiv_csCR_bot/115762140339109012
- Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Clas...
Faisal Ahmed
https://arxiv.org/abs/2512.16964 https://mastoxiv.page/@arXiv_eessIV_bot/115762196702065869
- Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, et al.
https://arxiv.org/abs/2512.16969 https://mastoxiv.page/@arXiv_csAI_bot/115762050529328276
- PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Kamer Ali Yuksel
https://arxiv.org/abs/2512.16970 https://mastoxiv.page/@arXiv_csAI_bot/115762054461584205
- A Women's Health Benchmark for Large Language Models
Elisabeth Gruber, et al.
https://arxiv.org/abs/2512.17028 https://mastoxiv.page/@arXiv_csCL_bot/115762049873946945
- Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
https://arxiv.org/abs/2512.17075 https://mastoxiv.page/@arXiv_csCL_bot/115762077400293945
- Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
https://arxiv.org/abs/2512.17127 https://mastoxiv.page/@arXiv_statML_bot/115762251753966702
- Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
Huixin Zhan
https://arxiv.org/abs/2512.17146 https://mastoxiv.page/@arXiv_csCR_bot/115762318582013305
- Application of machine learning to predict food processing level using Open Food Facts
Arora, Chauhan, Rana, Aditya, Bhagat, Kumar, Kumar, Semar, Singh, Bagler
https://arxiv.org/abs/2512.17169 https://mastoxiv.page/@arXiv_qbioBM_bot/115762302873829397
- Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
Sandeep Neela
https://arxiv.org/abs/2512.17185 https://mastoxiv.page/@arXiv_qfinRM_bot/115762275982224870
- Do Foundational Audio Encoders Understand Music Structure?
Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji
https://arxiv.org/abs/2512.17209 https://mastoxiv.page/@arXiv_csSD_bot/115762341541572505
- CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency
Xiao Liang, Yuxuan An, Di Wang, Jiawei Hu, Zhicheng Jiao, Bin Jing, Quan Wang
https://arxiv.org/abs/2512.17213 https://mastoxiv.page/@arXiv_csCV_bot/115762574180736975
- Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Fun...
Deriyan Senjaya, Stephen Ekaputra Limantoro
https://arxiv.org/abs/2512.17245 https://mastoxiv.page/@arXiv_condmatmtrlsci_bot/115762447037143855
- AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
Madhava Gaikwad
https://arxiv.org/abs/2512.17251 https://mastoxiv.page/@arXiv_csCR_bot/115762396593872943
- Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi, Peizhao Zhou, Yuan Wang, Tong Li, Zheli Liu
https://arxiv.org/abs/2512.17254 https://mastoxiv.page/@arXiv_csCR_bot/115762402470985707
- Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling A...
Abhivansh Gupta
https://arxiv.org/abs/2512.17259 https://mastoxiv.page/@arXiv_csMA_bot/115762225538364939
- Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
Saeed Ebrahimi, Weijie Jiang, Jaewon Yang, Olafur Gudmundsson, Yucheng Tu, Huizhong Duan
https://arxiv.org/abs/2512.17277 https://mastoxiv.page/@arXiv_csIR_bot/115762214396869930
- LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection
Ioannis Stylianou, Achintya kr. Sarkar, Nauman Dawalatabad, James Glass, Zheng-Hua Tan
https://arxiv.org/abs/2512.17281 https://mastoxiv.page/@arXiv_csSD_bot/115762361858560703
- Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose
https://arxiv.org/abs/2512.17340 https://mastoxiv.page/@arXiv_statME_bot/115762446402738033
toXiv_bot_toot

Conical Magnetic Structure and Atomic Displacements in Chiral Helimagnet Yb(Ni,Cu)$_3$Al$_9$ in Magnetic Fields along the Helical $c$ Axis
Takeshi Matsumura, Mitsuru Tsukagoshi, Shota Nakamura, Shigeo Ohara
https://arxiv.org/abs/2601.23033

[2026-02-02 Mon (UTC), 3 new articles found for q-fin.GN General Finance]
toXiv_bot_toot

Fork, Explore, Commit: OS Primitives for Agentic Exploration
Cong Wang, Yusheng Zheng
https://arxiv.org/abs/2602.08199 https://arxiv.org/pdf/2602.08199 https://arxiv.org/html/2602.08199
arXiv:2602.08199v1 Announce Type: new
Abstract: AI agents increasingly perform agentic exploration: pursuing multiple solution paths in parallel and committing only the successful one. Because each exploration path may modify files and spawn processes, agents require isolated environments with atomic commit and rollback semantics for both filesystem state and process state. We introduce the branch context, a new OS abstraction that provides: (1) copy-on-write state isolation with independent filesystem views and process groups, (2) a structured lifecycle of fork, explore, and commit/abort, (3) first-commit-wins resolution that automatically invalidates sibling branches, and (4) nestable contexts for hierarchical exploration. We realize branch contexts in Linux through two complementary components. First, BranchFS is a FUSE-based filesystem that gives each branch context an isolated copy-on-write workspace, with O(1) creation, atomic commit to the parent, and automatic sibling invalidation, all without root privileges. BranchFS is open sourced in https://github.com/multikernel/branchfs. Second, branch() is a proposed Linux syscall that spawns processes into branch contexts with reliable termination, kernel-enforced sibling isolation, and first-commit-wins coordination. Preliminary evaluation of BranchFS shows sub-350 us branch creation independent of base filesystem size, and modification-proportional commit overhead (under 1 ms for small changes).
toXiv_bot_toot

Mirra Andreeva, the 18-year-old who surprised many by winning this event last year,
torched Solana Sierra of Argentina in 50 minutes
winning 6-0, 6-0.
She won 54 of the 75 total points.
Andreeva advances to play either Leylah Fernandez or Katerina Siniakova in the third round Monday.
Other top 10 women required a little more time on court Saturday.
No. 2 Iga Swiatek only had room for one bagel but then battled past Kayla Day 6-0, 7-6(2).
No. 3 Elena …

Submodular Maximization over a Matroid $k$-Intersection: Multiplicative Improvement over Greedy
Moran Feldman, Justin Ward
https://arxiv.org/abs/2602.08473 https://arxiv.org/pdf/2602.08473 https://arxiv.org/html/2602.08473
arXiv:2602.08473v1 Announce Type: new
Abstract: We study the problem of maximizing a non-negative monotone submodular objective $f$ subject to the intersection of $k$ arbitrary matroid constraints. The natural greedy algorithm guarantees $(k 1)$-approximation for this problem, and the state-of-the-art algorithm only improves this approximation ratio to $k$. We give a $\frac{2k\ln2}{1 \ln2} O(\sqrt{k})<0.819k O(\sqrt{k})$ approximation for this problem. Our result is the first multiplicative improvement over the approximation ratio of the greedy algorithm for general $k$. We further show that our algorithm can be used to obtain roughly the same approximation ratio also for the more general problem in which the objective is not guaranteed to be monotone (the sublinear term in the approximation ratio becomes $O(k^{2/3})$ rather than $O(\sqrt{k})$ in this case).
All of our results hold also when the $k$-matroid intersection constraint is replaced with a more general matroid $k$-parity constraint. Furthermore, unlike the case in many of the previous works, our algorithms run in time that is independent of $k$ and polynomial in the size of the ground set. Our algorithms are based on a hybrid greedy local search approach recently introduced by Singer and Thiery (STOC 2025) for the weighted matroid $k$-intersection problem, which is a special case of the problem we consider. Leveraging their approach in the submodular setting requires several non-trivial insights and algorithmic modifications since the marginals of a submodular function $f$, which correspond to the weights in the weighted case, are not independent of the algorithm's internal randomness. In the special weighted case studied by Singer and Thiery, our algorithms reduce to a variant of their algorithm with an improved approximation ratio of $k\ln2 1-\ln2<0.694k 0.307$, compared to an approximation ratio of $\frac{k 1}{2\ln2}\approx0.722k 0.722$ guaranteed by Singer and Thiery.
toXiv_bot_toot

[2026-03-10 Tue (UTC), 3 new articles found for q-bio.GN Genomics]
toXiv_bot_toot

[2026-02-24 Tue (UTC), 3 new articles found for physics.acc-ph Accelerator Physics]
toXiv_bot_toot

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

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
toXiv_bot_toot

M 3.8 #earthquake, southern Illinois in the Illinois basin - Ozark dome region. Jan 20, 2026; 8.8 km depth. Scroll down the page for a general tectonic overview by #USGS.

[2026-02-03 Tue (UTC), 3 new articles found for cs.GR Graphics]
toXiv_bot_toot

Online Algorithm for Fractional Matchings with Edge Arrivals in Graphs of Maximum Degree Three
Kanstantsin Pashkovich, Thomas Snow
https://arxiv.org/abs/2602.07355 https://arxiv.org/pdf/2602.07355 https://arxiv.org/html/2602.07355
arXiv:2602.07355v1 Announce Type: new
Abstract: We study online algorithms for maximum cardinality matchings with edge arrivals in graphs of low degree. Buchbinder, Segev, and Tkach showed that no online algorithm for maximum cardinality fractional matchings can achieve a competitive ratio larger than $4/(9-\sqrt 5)\approx 0.5914$ even for graphs of maximum degree three. The negative result of Buchbinder et al. holds even when the graph is bipartite and edges are revealed according to vertex arrivals, i.e. once a vertex arrives, all edges are revealed that include the newly arrived vertex and one of the previously arrived vertices. In this work, we complement the negative result of Buchbinder et al. by providing an online algorithm for maximum cardinality fractional matchings with a competitive ratio at least $4/(9-\sqrt 5)\approx 0.5914$ for graphs of maximum degree three. We also demonstrate that no online algorithm for maximum cardinality integral matchings can have the competitive guarantee $0.5807$, establishing a gap between integral and fractional matchings for graphs of maximum degree three. Note that the work of Buchbinder et al. shows that for graphs of maximum degree two, there is no such gap between fractional and integral matchings, because for both of them the best achievable competitive ratio is $2/3$. Also, our results demonstrate that for graphs of maximum degree three best possible competitive ratios for fractional matchings are the same in the vertex arrival and in the edge arrival models.
toXiv_bot_toot

CAGE: An Internal Source Scanning Cryostat for HPGe Characterization
G. Othman, C. Wiseman, T. H. Burritt, J. A. Detwiler, M. P. Held, R. Henning, T. Mathew, D. Peterson, W. Pettus, G. Song, T. D. Van Wechel
https://arxiv.org/abs/2602.06289 https://arxiv.org/pdf/2602.06289 https://arxiv.org/html/2602.06289
arXiv:2602.06289v1 Announce Type: new
Abstract: The success of current and future-generation neutrinoless double beta decay experiments relies on the ability to eliminate or reduce extraneous backgrounds. In addition to constructing experiments using radiopure materials and handling in underground laboratories, it is necessary to understand and reduce known backgrounds in data analysis. The Large Enriched Germanium Experiment for Neutrinoless double beta Decay is searching for this decay using 76Ge-enriched high-purity germanium detectors submerged in an active liquid argon veto. A significant background in LEGEND is surface events from shallowly-impinging radiation on detector surfaces. In this paper we introduce the Collimated Alphas, Gammas, and Electrons (CAGE) scanning system, an internal-source scanning vacuum cryostat, designed to perform studies of surface events on sensitive surfaces of HPGe in a surface-lab. CAGE features a collimated radionuclide source inside a movable infrared shield that is able to perform precision scans of detector surfaces by utilizing three independent motor stages for source positioning. This allows detailed studies of pulse shapes as a function of source position and incident angle, where defining features can be extracted and exploited for removing surface backgrounds in data analysis in LEGEND. In this paper, we describe CAGE and demonstrate its performance with a commissioning run with 241Am. The commissioning run was completed with the source at normal incidence, and we estimate a beam spot precision of 3.1 mm, which includes positioning uncertainties and the beam-spot size. Using the 59.5 keV gamma population from 241Am, we show that low-energy photon events near the passivated surface feature risetimes that increase with radial distance from the detector center. We suggest a specific metric that can be used to discriminate low-energy gamma backgrounds in LEGEND with similar characteristics.
toXiv_bot_toot

Magnetic equations on the Heisenberg group: symmetries, solutions and the inverse problem of the calculus of variations
Gabriela Ovando, Mauro Subils
https://arxiv.org/abs/2602.21187 https://arxiv.org/pdf/2602.21187 https://arxiv.org/html/2602.21187
arXiv:2602.21187v1 Announce Type: new
Abstract: The Heisenberg Lie group $H_3$ is modeled on the differentiable structure of $\mathbb{R}^3$ but equipped with another non-commutative product operation. By fixing the usual metric on the Heisenberg Lie group, this work provides a comprehensive overview of the behavior of magnetic geodesics for any invariant Lorentz force. After writing the magnetic equations, we found symmetries that enable the explicit computation of the magnetic trajectories for any homogeneous exact and non-exact magnetic form. Finally we show that these magnetic trajectories are solutions of a variational problem: we present explicit examples of Lagrangians.
toXiv_bot_toot

[2026-01-09 Fri (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Towards Efficient Data Structures for Approximate Search with Range Queries
Ladan Kian, Dariusz R. Kowalski
https://arxiv.org/abs/2602.06860 https://arxiv.org/pdf/2602.06860 https://arxiv.org/html/2602.06860
arXiv:2602.06860v1 Announce Type: new
Abstract: Range queries are simple and popular types of queries used in data retrieval. However, extracting exact and complete information using range queries is costly. As a remedy, some previous work proposed a faster principle, {\em approximate} search with range queries, also called single range cover (SRC) search. It can, however, produce some false positives. In this work we introduce a new SRC search structure, a $c$-DAG (Directed Acyclic Graph), which provably decreases the average number of false positives by logarithmic factor while keeping asymptotically same time and memory complexities as a classic tree structure. A $c$-DAG is a tunable augmentation of the 1D-Tree with denser overlapping branches ($c \geq 3$ children per node). We perform a competitive analysis of a $c$-DAG with respect to 1D-Tree and derive an additive constant time overhead and a multiplicative logarithmic improvement of the false positives ratio, on average. We also provide a generic framework to extend our results to empirical distributions of queries, and demonstrate its effectiveness for Gowalla dataset. Finally, we quantify and discuss security and privacy aspects of SRC search on $c$-DAG vs 1D-Tree, mainly mitigation of structural leakage, which makes $c$-DAG a good data structure candidate for deployment in privacy-preserving systems (e.g., searchable encryption) and multimedia retrieval.
toXiv_bot_toot

There's a tool named #WinSlop that gets rid of any and all unpleasantries #microslop seems to shovel upon us. Alas, there is a notice on the github page of the project that the domain winslop.com is not owned or operated by the creator of WinSlop.

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/3]:
- Fraud detection in credit card transactions using Quantum-Assisted Restricted Boltzmann Machines
Jo\~ao Marcos Cavalcanti de Albuquerque Neto, Gustavo Castro do Amaral, Guilherme Penello Tempor\~ao
https://arxiv.org/abs/2512.17660 https://mastoxiv.page/@arXiv_quantph_bot/115762703945731580
- Vidarc: Embodied Video Diffusion Model for Closed-loop Control
Feng, Xiang, Mao, Tan, Zhang, Huang, Zheng, Liu, Su, Zhu
https://arxiv.org/abs/2512.17661 https://mastoxiv.page/@arXiv_csRO_bot/115762650859932523
- Imputation Uncertainty in Interpretable Machine Learning Methods
Pegah Golchian, Marvin N. Wright
https://arxiv.org/abs/2512.17689 https://mastoxiv.page/@arXiv_statML_bot/115762577479255577
- Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo ...
Shengdu Chai, Chen Lin, Xinyang Dong, Yuqiang Li, Wanli Ouyang, Lei Wang, X. C. Xie
https://arxiv.org/abs/2512.17703 https://mastoxiv.page/@arXiv_condmatstrel_bot/115762481116668454
- Breast Cancer Neoadjuvant Chemotherapy Treatment Response Prediction Using Aligned Longitudinal M...
Rahul Ravi, Ruizhe Li, Tarek Abdelfatah, Stephen Chan, Xin Chen
https://arxiv.org/abs/2512.17759 https://mastoxiv.page/@arXiv_eessIV_bot/115762481771898369
- MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Im...
Roy, Kirchhoff, Ulrich, Rokuss, Wald, Isensee, Maier-Hein
https://arxiv.org/abs/2512.17774 https://mastoxiv.page/@arXiv_eessIV_bot/115762492258209812
- Domain-Aware Quantum Circuit for QML
Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz, Jr
https://arxiv.org/abs/2512.17800 https://mastoxiv.page/@arXiv_quantph_bot/115762723607200478
- Visually Prompted Benchmarks Are Surprisingly Fragile
Feng, Lian, Dunlap, Shu, Wang, Wang, Darrell, Suhr, Kanazawa
https://arxiv.org/abs/2512.17875 https://mastoxiv.page/@arXiv_csCV_bot/115762781936221554
- Learning vertical coordinates via automatic differentiation of a dynamical core
Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo, Alejandro Di Luca, Alex Bihlo
https://arxiv.org/abs/2512.17877 https://mastoxiv.page/@arXiv_physicsaoph_bot/115762405092703069
- RadarGen: Automotive Radar Point Cloud Generation from Cameras
Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang, Or Litany
https://arxiv.org/abs/2512.17897 https://mastoxiv.page/@arXiv_csCV_bot/115762783246540528
- Distributionally Robust Imitation Learning: Layered Control Architecture for Certifiable Autonomy
Gahlawat, Aboudonia, Banik, Hovakimyan, Matni, Ames, Zardini, Speranzon
https://arxiv.org/abs/2512.17899 https://mastoxiv.page/@arXiv_eessSY_bot/115762532257741954
- Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting
Ananta R. Bhattarai, Helge Rhodin
https://arxiv.org/abs/2512.17908 https://mastoxiv.page/@arXiv_csCV_bot/115762785868778349
toXiv_bot_toot

[2026-02-03 Tue (UTC), 3 new articles found for math.AC Commutative Algebra]
toXiv_bot_toot

[2026-01-23 Fri (UTC), 3 new articles found for cond-mat.dis-nn Disordered Systems and Neural Networks]
toXiv_bot_toot

Features of the van der Waals Interaction on the Cesium $6S_{1/2} \rightarrow 7P_{3/2}$ Transition in an Optical Nanocell
Armen Sargsyan, Anahit Gogyan, David Sarkisyan
https://arxiv.org/abs/2601.04661

Superconducting Accelerator Magnets
Stephane Sanfilippo
https://arxiv.org/abs/2602.19830 https://arxiv.org/pdf/2602.19830 https://arxiv.org/html/2602.19830
arXiv:2602.19830v1 Announce Type: new
Abstract: This course introduces key aspects of superconducting magnet technology in accelerators: basic principles, superconducting materials (NbTi, Nb$_3$Sn, ReBCO), wire and cable architectures, and fabrication methods. Compared to copper or permanent magnets, superconducting systems require cryogenics and complex protection schemes but enable superior performance. Core challenges - like flux pinning, magnetization effects, quench behavior, mechanical forces interception, power tests and magnetic measurements - are addressed through examples of magnets from PSI and CERN.
toXiv_bot_toot

[2025-12-17 Wed (UTC), 3 new articles found for physics.geo-ph Geophysics]
toXiv_bot_toot

EAG-PT: Emission-Aware Gaussians and Path Tracing for Indoor Scene Reconstruction and Editing
Xijie Yang, Mulin Yu, Changjian Jiang, Kerui Ren, Tao Lu, Jiangmiao Pang, Dahua Lin, Bo Dai, Linning Xu
https://arxiv.org/abs/2601.23065 https://arxiv.org/pdf/2601.23065 https://arxiv.org/html/2601.23065
arXiv:2601.23065v1 Announce Type: new
Abstract: Recent reconstruction methods based on radiance field such as NeRF and 3DGS reproduce indoor scenes with high visual fidelity, but break down under scene editing due to baked illumination and the lack of explicit light transport. In contrast, physically based inverse rendering relies on mesh representations and path tracing, which enforce correct light transport but place strong requirements on geometric fidelity, becoming a practical bottleneck for real indoor scenes. In this work, we propose Emission-Aware Gaussians and Path Tracing (EAG-PT), aiming for physically based light transport with a unified 2D Gaussian representation. Our design is based on three cores: (1) using 2D Gaussians as a unified scene representation and transport-friendly geometry proxy that avoids reconstructed mesh, (2) explicitly separating emissive and non-emissive components during reconstruction for further scene editing, and (3) decoupling reconstruction from final rendering by using efficient single-bounce optimization and high-quality multi-bounce path tracing after scene editing. Experiments on synthetic and real indoor scenes show that EAG-PT produces more natural and physically consistent renders after editing than radiant scene reconstructions, while preserving finer geometric detail and avoiding mesh-induced artifacts compared to mesh-based inverse path tracing. These results suggest promising directions for future use in interior design, XR content creation, and embodied AI.
toXiv_bot_toot

L1-2-type surfaces in 3-dimensional De Sitter and anti De Sitter spaces
S. Carolina Garc\'ia-Mart\'inez, Pascual Lucas, H. Fabi\'an Ram\'irez-Ospina
https://arxiv.org/abs/2601.18019

Judge Aileen Cannon permanently bared DoJ from releasing Jack Smith's report on Trump documents
Advocacy groups will likely try to consolidate all the issues into one appellate case.
So far, they’ve collectively raised 1st Amendment and common law right of access claims, as well as Freedom of Information Act-related claims.
@weareoversight.bsky.social

Electron capture induced fragmentation of CO$_2^{3 }$: Influence of projectile charge on sequential and concerted break-up pathways
Akash Srivastav, Sumit Srivastav, Bhas Bapat
https://arxiv.org/abs/2601.03711

PCIe400 generic readout board qualification test
Kevin Arnaud, Antoine Back, Daniel Charlet, Gabriel Degret, Luigi Del Buono, Paolo Durante, Amaury Hervo, Fr\'ed\'eric Hachon, Xavier Lafay, Julien Langou\"et, Renaud Le Gac, Jea-Luc Meunier, Jean-Marc Nappa, Costy Nassif Mattar, Christophe Renard, Guillaume Vouters
https://arxiv.org/abs/2602.01422 https://arxiv.org/pdf/2602.01422 https://arxiv.org/html/2602.01422
arXiv:2602.01422v1 Announce Type: new
Abstract: The PCIe400 is a generic board for high-throughput data acquisition systems in high energy physics experiments. Its purpose is to interface up to 48 bidirectional links, supporting custom protocols at 1 to 26 Gbit/s, to modern commercial back-end links providing 400 Gbit/s bandwidth. It also targets clock distribution with phase determinism below 10 ps peak-to-peak. It has been designed for LHCb LS3 enhancement upgrade with experimental features to prepare LHCb Upgrade II, foreseeing an aggregated throughput of 200 Tbit/s. However, its versatility allows it to be used in several experimental environments. The board embeds Altera's flagship Agilex 7 M-series FPGA with a PCIe Gen 5 interface and an experimental QSFP112 serial interface. We present the results of qualification tests performed on prototype boards and the challenges encountered to meet specifications. Section 1 describes board-level validation, including power-up behavior and peripheral access. Section 2 focuses on high-bandwidth interface qualification through BER measurements. Finally, Section 3 investigates phase determinism in Agilex transceivers, a key requirement for precise clock distribution.
toXiv_bot_toot

Curvature and Lagrangian submanifolds of nearly K\"ahler $\mathbb{C}P^3$
Micha\"el Liefsoens, Joeri Van der Veken
https://arxiv.org/abs/2601.18504 https://

#Ally43 : #OnRecrute en #Auvergne #HauteLoire
L’association

Bears QB Caleb Williams 'excited for the moment' ahead of Saturday's Week 16 rematch vs. Packers https://www.nfl.com/news/bears-qb-caleb-williams-excited-for-the-moment-ahead-of-saturday-s-week-16-rematch-vs-packers…

[2026-01-06 Tue (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Minnesota’s coalition is demanding:
1: ICE must leave Minnesota now.
2: Renee Good’s killer, Jonathan Ross, must be held legally accountable.
3: No additional federal funding for ICE in the upcoming budget.
“These are moral common sense for a state that values truth, freedom, and life.”
https://

Hardness and Tractability of T_{h 1}-Free Edge Deletion
Ajinkya Gaikwad, Soumen Maity, Leeja R
https://arxiv.org/abs/2602.00644 https://arxiv.org/pdf/2602.00644 https://arxiv.org/html/2602.00644
arXiv:2602.00644v1 Announce Type: new
Abstract: We study the parameterized complexity of the T(h 1)-Free Edge Deletion problem. Given a graph G and integers k and h, the task is to delete at most k edges so that every connected component of the resulting graph has size at most h. The problem is NP-complete for every fixed h at least 3, while it is solvable in polynomial time for h at most 2.
Recent work showed strong hardness barriers: the problem is W[1]-hard when parameterized by the solution size together with the size of a feedback edge set, ruling out fixed-parameter tractability for many classical structural parameters. We significantly strengthen these negative results by proving W[1]-hardness when parameterized by the vertex deletion distance to a disjoint union of paths, the vertex deletion distance to a disjoint union of stars, or the twin cover number. These results unify and extend known hardness results for treewidth, pathwidth, and feedback vertex set, and show that several restrictive parameters, including treedepth, cluster vertex deletion number, and modular width, do not yield fixed-parameter tractability when h is unbounded.
On the positive side, we identify parameterizations that restore tractability. We show that the problem is fixed-parameter tractable when parameterized by cluster vertex deletion together with h, and also when parameterized by neighborhood diversity together with h via an integer linear programming formulation. We further present a fixed-parameter tractable bicriteria approximation algorithm parameterized by k. Finally, we show that the problem admits fixed-parameter tractable algorithms on split graphs and interval graphs, and we establish hardness for a directed generalization even on directed acyclic graphs.
toXiv_bot_toot

LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction
Xinhui Liu, Can Wang, Lei Liu, Zhenghao Chen, Wei Jiang, Wei Wang, Dong Xu
https://arxiv.org/abs/2601.18475 https://arxiv.org/pdf/2601.18475 https://arxiv.org/html/2601.18475
arXiv:2601.18475v1 Announce Type: new
Abstract: Free-Viewpoint Video (FVV) reconstruction enables photorealistic and interactive 3D scene visualization; however, real-time streaming is often bottlenecked by sparse-view inputs, prohibitive training costs, and bandwidth constraints. While recent 3D Gaussian Splatting (3DGS) has advanced FVV due to its superior rendering speed, Streaming Free-Viewpoint Video (SFVV) introduces additional demands for rapid optimization, high-fidelity reconstruction under sparse constraints, and minimal storage footprints. To bridge this gap, we propose StreamLoD-GS, an LoD-based Gaussian Splatting framework designed specifically for SFVV. Our approach integrates three core innovations: 1) an Anchor- and Octree-based LoD-structured 3DGS with a hierarchical Gaussian dropout technique to ensure efficient and stable optimization while maintaining high-quality rendering; 2) a GMM-based motion partitioning mechanism that separates dynamic and static content, refining dynamic regions while preserving background stability; and 3) a quantized residual refinement framework that significantly reduces storage requirements without compromising visual fidelity. Extensive experiments demonstrate that StreamLoD-GS achieves competitive or state-of-the-art performance in terms of quality, efficiency, and storage.
toXiv_bot_toot

[2026-03-05 Thu (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

#forgejoAneksajo #gitAnnex #dataLad crowd:
Anyone else running into this experience-crippling #forgejo

bug: extremely slow feed load times with many pages
### Can you reproduce the bug on the Forgejo test instance? No ### Description My forgejo instance has really long load times. Loading the Homepage takes about 700ms at best and more than 3 seconds at worst. Page: 704ms Template: 111ms ### Forgejo Version 12.0.0+gitea-1.22.0 ### How are yo...

Proc3D: Procedural 3D Generation and Parametric Editing of 3D Shapes with Large Language Models
Fadlullah Raji, Stefano Petrangeli, Matheus Gadelha, Yu Shen, Uttaran Bhattacharya, Gang Wu
https://arxiv.org/abs/2601.12234 https://arxiv.org/pdf/2601.12234 https://arxiv.org/html/2601.12234
arXiv:2601.12234v1 Announce Type: new
Abstract: Generating 3D models has traditionally been a complex task requiring specialized expertise. While recent advances in generative AI have sought to automate this process, existing methods produce non-editable representation, such as meshes or point clouds, limiting their adaptability for iterative design. In this paper, we introduce Proc3D, a system designed to generate editable 3D models while enabling real-time modifications. At its core, Proc3D introduces procedural compact graph (PCG), a graph representation of 3D models, that encodes the algorithmic rules and structures necessary for generating the model. This representation exposes key parameters, allowing intuitive manual adjustments via sliders and checkboxes, as well as real-time, automated modifications through natural language prompts using Large Language Models (LLMs). We demonstrate Proc3D's capabilities using two generative approaches: GPT-4o with in-context learning (ICL) and a fine-tuned LLAMA-3 model. Experimental results show that Proc3D outperforms existing methods in editing efficiency, achieving more than 400x speedup over conventional approaches that require full regeneration for each modification. Additionally, Proc3D improves ULIP scores by 28%, a metric that evaluates the alignment between generated 3D models and text prompts. By enabling text-aligned 3D model generation along with precise, real-time parametric edits, Proc3D facilitates highly accurate text-based image editing applications.
toXiv_bot_toot

Low energy elastic scattering of H, D and T on $^{3}$He and $^{4}$He
B. J. P. Jones
https://arxiv.org/abs/2601.22360 https://arxiv.org/pdf/2601.22360

In #klexadventskalender gehts heute ganz himmlisch zu, neben Sonne und Mond (nur konsequent nachdem am 3.12. die Sterne dran waren) habe ich saumässig himmlische Engel und Bengel gemalt - Einzelstücke, jedes nur 1x verfügbar - auf #fediArt #mastoArt #creativeToots #artForSale #miniaturepainting #pigs #wingedpigs

Rethink Efficiency Side of Neural Combinatorial Solver: An Offline and Self-Play Paradigm
Zhenxing Xu, Zeyuan Ma, Weidong Bao, Hui Yan, Yan Zheng, Ji Wang
https://arxiv.org/abs/2602.20730 https://arxiv.org/pdf/2602.20730 https://arxiv.org/html/2602.20730
arXiv:2602.20730v1 Announce Type: new
Abstract: We propose ECO, a versatile learning paradigm that enables efficient offline self-play for Neural Combinatorial Optimization (NCO). ECO addresses key limitations in the field through: 1) Paradigm Shift: Moving beyond inefficient online paradigms, we introduce a two-phase offline paradigm consisting of supervised warm-up and iterative Direct Preference Optimization (DPO); 2) Architecture Shift: We deliberately design a Mamba-based architecture to further enhance the efficiency in the offline paradigm; and 3) Progressive Bootstrapping: To stabilize training, we employ a heuristic-based bootstrapping mechanism that ensures continuous policy improvement during training. Comparison results on TSP and CVRP highlight that ECO performs competitively with up-to-date baselines, with significant advantage on the efficiency side in terms of memory utilization and training throughput. We provide further in-depth analysis on the efficiency, throughput and memory usage of ECO. Ablation studies show rationale behind our designs.
toXiv_bot_toot

Atomic clock frequency ratios with fractional uncertainty $\leq 3.2 \times 10^{-18}$
Alexander Aeppli, Willa J. Arthur-Dworschack, Kyle Beloy, Caitlin M. Berry, Tobias Bothwell, Angela Folz, Tara M. Fortier, Tanner Grogan, Youssef S. Hassan, Zoey Z. Hu, David B. Hume, Benjamin D. Hunt, Kyungtae Kim, Amanda Koepke, Dahyeon Lee, David R. Leibrandt, Ben Lewis, Andrew D. Ludlow, Mason C. Marshall, Nicholas V. Nardelli, Harikesh Ranganath, Daniel A. Rodriguez Castillo, Jeffrey A. Sherman, J…

WeirNet: A Large-Scale 3D CFD Benchmark for Geometric Surrogate Modeling of Piano Key Weirs
Lisa L\"uddecke, Michael Hohmann, Sebastian Eilermann, Jan Tillmann-Mumm, Pezhman Pourabdollah, Mario Oertel, Oliver Niggemann
https://arxiv.org/abs/2602.20714 https://arxiv.org/pdf/2602.20714 https://arxiv.org/html/2602.20714
arXiv:2602.20714v1 Announce Type: new
Abstract: Reliable prediction of hydraulic performance is challenging for Piano Key Weir (PKW) design because discharge capacity depends on three-dimensional geometry and operating conditions. Surrogate models can accelerate hydraulic-structure design, but progress is limited by scarce large, well-documented datasets that jointly capture geometric variation, operating conditions, and functional performance. This study presents WeirNet, a large 3D CFD benchmark dataset for geometric surrogate modeling of PKWs. WeirNet contains 3,794 parametric, feasibility-constrained rectangular and trapezoidal PKW geometries, each scheduled at 19 discharge conditions using a consistent free-surface OpenFOAM workflow, resulting in 71,387 completed simulations that form the benchmark and with complete discharge coefficient labels. The dataset is released as multiple modalities compact parametric descriptors, watertight surface meshes and high-resolution point clouds together with standardized tasks and in-distribution and out-of-distribution splits. Representative surrogate families are benchmarked for discharge coefficient prediction. Tree-based regressors on parametric descriptors achieve the best overall accuracy, while point- and mesh-based models remain competitive and offer parameterization-agnostic inference. All surrogates evaluate in milliseconds per sample, providing orders-of-magnitude speedups over CFD runtimes. Out-of-distribution results identify geometry shift as the dominant failure mode compared to unseen discharge values, and data-efficiency experiments show diminishing returns beyond roughly 60% of the training data. By publicly releasing the dataset together with simulation setups and evaluation pipelines, WeirNet establishes a reproducible framework for data-driven hydraulic modeling and enables faster exploration of PKW designs during the early stages of hydraulic planning.
toXiv_bot_toot

Regret-Guided Search Control for Efficient Learning in AlphaZero
Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu
https://arxiv.org/abs/2602.20809 https://arxiv.org/pdf/2602.20809 https://arxiv.org/html/2602.20809
arXiv:2602.20809v1 Announce Type: new
Abstract: Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but it treats all states equally, regardless of their learning potential. We propose Regret-Guided Search Control (RGSC), which extends AlphaZero with a regret network that learns to identify high-regret states, where the agent's evaluation diverges most from the actual outcome. These states are collected from both self-play trajectories and MCTS nodes, stored in a prioritized regret buffer, and reused as new starting positions. Across 9x9 Go, 10x10 Othello, and 11x11 Hex, RGSC outperforms AlphaZero and Go-Exploit by an average of 77 and 89 Elo, respectively. When training on a well-trained 9x9 Go model, RGSC further improves the win rate against KataGo from 69.3% to 78.2%, while both baselines show no improvement. These results demonstrate that RGSC provides an effective mechanism for search control, improving both efficiency and robustness of AlphaZero training. Our code is available at https://rlg.iis.sinica.edu.tw/papers/rgsc.
toXiv_bot_toot

[2026-01-28 Wed (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-02-24 Tue (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

[2026-02-23 Mon (UTC), 3 new articles found for physics.atom-ph Atomic Physics]
toXiv_bot_toot

Precision continuous-wave laser measurement of the $\text{1}^\text{3}\text{S}_\text{1} \to \text{2}^\text{3}\text{S}_\text{1}$ interval in positronium
Lucas de Sousa Borges, Edward Thorpe-Woods, Evans Javary, Paolo Crivelli
https://arxiv.org/abs/2512.16018

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Spin-orbital entanglement in Cr$^{3 }$-doped glasses
J. S. Robles-P\'aez, A. T. Sarre\~no-Santos, V. Garc\'ia-Rojas, J. F. P\'erez-Torres