Tootfinder

Replaced article(s) found for math.AP. https://arxiv.org/list/math.AP/new
[1/1]:
- Strong relaxation limit and uniform time asymptotics of the Jin-Xin model in the $L^{p}$ framework
Timoth\'ee Crin-Barat, Ling-Yun Shou, Jianzhong Zhang
https://arxiv.org/abs/2311.04105 https://mastoxiv.page/@arXiv_mathAP_bot/111373086323167893
- Long time regularity for 3d gravity waves with vorticity
Daniel Ginsberg, Fabio Pusateri
https://arxiv.org/abs/2401.10096 https://mastoxiv.page/@arXiv_mathAP_bot/111780770295022186
- Wasserstein Gradient Flows of MMD Functionals with Distance Kernel and Cauchy Problems on Quantil...
Richard Duong, Viktor Stein, Robert Beinert, Johannes Hertrich, Gabriele Steidl
https://arxiv.org/abs/2408.07498 https://mastoxiv.page/@arXiv_mathAP_bot/112964949403866709
- Regularity of solutions for degenerate or singular fully nonlinear integro-differential equations
Jiangwen Wang, Feida Jiang
https://arxiv.org/abs/2408.14779 https://mastoxiv.page/@arXiv_mathAP_bot/113038547448492855
- An overview of the stability of Sobolev inequalities on Riemannian manifolds with Ricci lower bounds
Francesco Nobili
https://arxiv.org/abs/2412.05935 https://mastoxiv.page/@arXiv_mathAP_bot/113627373212995916
- The $\mathcal{M}$-Operator and Uniqueness of Nonlinear Kinetic Equations
Ricardo Alonso, Maria Pia Gualdani, Weiran Sun
https://arxiv.org/abs/2506.20775 https://mastoxiv.page/@arXiv_mathAP_bot/114754297465809405
- Magnetic Stabilization of Compressible Flows: Global Existence in 3D Inviscid Non-Isentropic MHD ...
Jiahong Wu, Fuyi Xu, Xiaoping Zhai
https://arxiv.org/abs/2507.00888 https://mastoxiv.page/@arXiv_mathAP_bot/114782973460202498
- Free boundary regularity for a tumor growth model with obstacle
Giulia Bevilacqua, Matteo Carducci
https://arxiv.org/abs/2507.02837 https://mastoxiv.page/@arXiv_mathAP_bot/114794384026764859
- Three bound states with prescribed angular momentum to the cubic-quintic NLS equations in $\mathb...
Shuai Yao, Juntao Sun
https://arxiv.org/abs/2507.04057 https://mastoxiv.page/@arXiv_mathAP_bot/114817504459971991
- Some remarks on a mathematical model for water flow in porous media with competition between tran...
Judita Runczikov\'a, Jan Chleboun, Chiara Gavioli, Pavel Krej\v{c}\'i
https://arxiv.org/abs/2405.10751 https://mastoxiv.page/@arXiv_mathNA_bot/112472226685837805
- SDEs with critical general distributional drifts: sharp solvability and blow ups
D. Kinzebulatov, R. Vafadar
https://arxiv.org/abs/2506.09244 https://mastoxiv.page/@arXiv_mathPR_bot/114669448852836191
- One sided orthogonal polynomials and a pointwise convergence result for $SU(2)$-valued nonlinear ...
Michel Alexis, Gevorg Mnatsakanyan, Christoph Thiele
https://arxiv.org/abs/2507.05124 https://mastoxiv.page/@arXiv_mathCA_bot/114817025355217864
toXiv_bot_toot

YouTube will remove its Trending page and Trending Now list in favor of category-specific charts, says it better aligns with the way content is discovered now (Aisha Malik/TechCrunch)
https://techcrunch.com/2025/07/10/youtube-is…

YouTube is getting rid of its Trending page and Trending Now list | TechCrunch
YouTube is replacing these Trending lists with category-specific charts, the company announced on Thursday.

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, Kedong Wang, Lucen Zhong, Mingdao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifan An, Yilin Niu, Yuanhao Wen, Yushi Bai, Zhengxiao Du, Zihan Wang, Zilin Zhu, Bohan Zhang, Bosi Wen, Bowen Wu, Bowen Xu, Can Huang, Casey Zhao, Changpeng Cai, Chao Yu, Chen Li, Chendi …

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64…

A constructive characterization of uniformly 4-connected graphs
Xiang Chen, Shuai Kou, Chengfu Qin, Liqiong Xu, Weihua Yang
https://arxiv.org/abs/2507.07656

A constructive characterization of uniformly 4-connected graphs
A constructive characterization of the class of uniformly $4$-connected graphs is presented. The characterization is based on the application of graph operations to appropriate vertex and edge sets in uniformly $4$-connected graphs, that is, any uniformly $4$-connected graph can be obtained from $C_5^2$ or $C_6^2$ by a number of $Δ_1^+$ or $Δ_2^+$-operations to quasi $4$-compatible sets.

YouTube will remove its Trending page and Trending Now list in favor of category-specific charts, says it better aligns with the way content is discovered now (Aisha Malik/TechCrunch)
https://techcrunch.com/2025/07/10/youtube-is-getting-ri…

YouTube is getting rid of its Trending page and Trending Now list | TechCrunch
YouTube is replacing these Trending lists with category-specific charts, the company announced on Thursday.

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Smart Fast Finish: Preventing Overdelivery via Daily Budget Pacing at DoorDash
Rohan Garg (Dianxia), Yongjin Xiao (Dianxia), Jason (Dianxia), Yang, Mandar Rahurkar

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- Research on Conversational Recommender System Considering Consumer Types
Yaying Luo, Hui Fang, Zhu Sun

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Tora2: Motion and Appearance Customized Diffusion Transformer for Multi-Entity Video Generation
Zhenghao Zhang, Junchao Liao, Xiangyu Meng, Long Qin, Weizhi Wang

$AdS_3 \times S^3$ Virasoro-Shapiro amplitude with KK modes
Hongliang Jiang, De-liang Zhong
https://arxiv.org/abs/2508.06039 https://arxiv.org/pdf/2508.060…

$AdS_3 \times S^3$ Virasoro-Shapiro amplitude with KK modes
We study the first curvature correction to the string amplitude of four Kaluza--Klein (KK) modes on $AdS_3 \times S^3 \times M_4$, with $M_4=K3$ or $T^4$, in type IIB string theory, which is holographically dual to the four--point correlator $\langle \mathcal{O}_{p_1} \mathcal{O}_{p_2} \mathcal{O}_{p_3} \mathcal{O}_{p_4} \rangle$ of certain half--BPS operators in the boundary D1--D5 CFT. The result takes the form of an integral over the Riemann sphere, analogous to the flat-space Virasoro--Shap…

$\pi_1$-injective bounding and application to 3- and 4-manifolds
Jianfeng Lin, Zhongzi Wang
https://arxiv.org/abs/2506.08847 https://…

$π_1$-injective bounding and application to 3- and 4-manifolds
Suppose a closed oriented $n$-manifold $M$ bounds an oriented $(n+1)$-manifold.It is known that $M$ $π_1$-injectively bounds an oriented $(n+1)$-manifold $W$. We prove that $π_1(W)$ can be residually finite if $π_1(M)$ is, and $π_1(W)$ can be finite if $π_1(M)$ is. In particular, each closed 3-manifold $M$ $π_1$-injectively bounds a 4-manifold with residually finite $π_1$, and bounds a 4-manifold with finite $π_1$ if $π_1(M)$ is finite. Applications to 3- and 4-manifolds are given: (…

In situ impedance spectroscopy tests of Li$_{4-x}$Ge$_{1-x}$P$_x$O$_4$ as potential solid state electrolyte for Micro Li ion Batteries
Mohammadhossein Montazerian, Kyle J. Stephens, Vladimir Roddatis, Christof Vockenhuber, Arnold M\"uller, Anders J. Barlow, Thomas Lippert, Nick A. Shepelin, Daniele Pergolesi
https://arxiv.o…

In situ impedance spectroscopy tests of Li$_{4-x}$Ge$_{1-x}$P$_x$O$_4$ as potential solid state electrolyte for Micro Li ion Batteries
Lithium-ion batteries employing solid-state electrolytes (SSEs) are emerging as a safer and more compact alternative to conventional batteries using liquid electrolytes, especially for miniaturized energy storage systems. However, the industry-standard SSE, LiPON, imposes limitations due to its incompatibility with high-temperature processing. In this study, we investigate Li$_{4-x}$Ge$_{1-x}$P$_x$O$_4$ (LGPO), a LISICON-type oxide, as a promising alternative thin-film SSE. LGPO thin films are …

📰 Einem US-amerikanischen Konzern Geld aus der Tasche zu leiern, um damit Hosting-Rechnungen für Gratis-Angebote zu bezahlen, kann man machen. Aber schön ist es nicht. Ich würde meine Amazon-Partnerlinks gern loswerden.
Bitte unterstütze meine Arbeit mit einer Steady-Mitgliedschaft: https://steady.page/klimacrew

KlimaCrew
Ich informiere über Zukunftsthemen, um Menschen vor Risiken zu warnen und Fortschritte sichtbar zu machen.

St. Joseph’s Hospital: Stop Animal Experiments & Rehome Survivors! #AnimalRights

TAKE ACTION: Dogs Brutalized & Killed in Secret Lab at St. Joseph’s Hospital
Whistleblowers have exposed heartbreaking cruelty at St. Joseph’s Hospital, where dogs are deliberately given heart attacks, killed, & discarded. Survivors deserve love, not a death sentence—demand an end to this suffering 💔

Inverse Seesaw Model in Non-holomorphic Modular $A_4$ Flavor Symmetry
Xianshuo Zhang, Yakefu Reyimuaji
https://arxiv.org/abs/2507.06945 https://

Inverse Seesaw Model in Non-holomorphic Modular $A_4$ Flavor Symmetry
We investigate an inverse seesaw model of neutrino masses based on non-holomorphic modular $A_4$ symmetry, by extending the framework of modular-invariant flavor models beyond the conventional holomorphic paradigm. After establishing the general theoretical framework, we analyze three concrete model realizations distinguished by their $A_4$ representation assignments and modular weight configurations for the matter fields. Focusing on these three specific realizations, we perform a comprehensiv…

CRACI: A Cloud-Native Reference Architecture for the Industrial Compute Continuum
Hai Dinh-Tuan
https://arxiv.org/abs/2509.07498 https://arxiv.org/pdf/2509…

CRACI: A Cloud-Native Reference Architecture for the Industrial Compute Continuum
The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 exposes the limitations of traditional, hierarchical architectures like ISA-95 and RAMI 4.0. Their inherent rigidity, data silos, and lack of support for cloud-native technologies impair the development of scalable and interoperable industrial systems. This paper addresses this issue by introducing CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum. Among other features,…

Wow, I guess irony’s not dead.
Source: https://www.theguardian.com/us-news/live/2025/sep/10/donald-trump-jeffrey-epstein-tariffs-immigration-crime-us-politics-live-news-updates?page=with:block-68c1d48b8f084454b70cbf90#block-68c1d48b8f084454b70cbf90

[2025-06-11 Wed (UTC), 4 new articles found for cs.LO Logic in Computer Science]
toXiv_bot_toot

Solving Convex-Concave Problems with $\tilde{\mathcal{O}}(\epsilon^{-4/7})$ Second-Order Oracle Complexity
Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang
https://arxiv.org/abs/2506.08362

Solving Convex-Concave Problems with $\tilde{\mathcal{O}}(ε^{-4/7})$ Second-Order Oracle Complexity
Previous algorithms can solve convex-concave minimax problems $\min_{x \in \mathcal{X}} \max_{y \in \mathcal{Y}} f(x,y)$ with $\mathcal{O}(ε^{-2/3})$ second-order oracle calls using Newton-type methods. This result has been speculated to be optimal because the upper bound is achieved by a natural generalization of the optimal first-order method. In this work, we show an improved upper bound of $\tilde{\mathcal{O}}(ε^{-4/7})$ by generalizing the optimal second-order method for convex optimizat…

[2025-07-10 Thu (UTC), 4 new articles found for cs.PL Programming Languages]
toXiv_bot_toot

On the Markoff spectrum on the 6th Hecke group
Byungchul Cha, Dong Han Kim, Deokwon Sim
https://arxiv.org/abs/2506.08358 https://arxi…

On the Markoff spectrum on the 6th Hecke group
The discrete part of the Markoff spectrum on the 6th Hecke group $\mathbf H_6$ was known by A. Schmidt. In this paper, we study the Markoff and Lagrange spectra on $\mathbf H_6$ after the smallest accumulation point $4/\sqrt{3}$. We show that both spectra below $4/\sqrt{3} + ε$ have positive Hausdorff dimension for any positive $ε$. We also find maximal gaps and an isolated point in the spectra.

[2025-09-11 Thu (UTC), 4 new articles found for stat.ML Machine Learning]
toXiv_bot_toot

ROS Help Desk: GenAI Powered, User-Centric Framework for ROS Error Diagnosis and Debugging
Kavindie Katuwandeniya, Samith Rajapaksha Jayasekara Widhanapathirana
https://arxiv.org/abs/2507.07846

ROS Help Desk: GenAI Powered, User-Centric Framework for ROS Error Diagnosis and Debugging
As the robotics systems increasingly integrate into daily life, from smart home assistants to the new-wave of industrial automation systems (Industry 4.0), there's an increasing need to bridge the gap between complex robotic systems and everyday users. The Robot Operating System (ROS) is a flexible framework often utilised in writing robot software, providing tools and libraries for building complex robotic systems. However, ROS's distributed architecture and technical messaging system create b…

Existence and nonexistence of commutativity gadgets for entangled CSPs
Eric Culf, Josse van Dobben de Bruyn, Matthijs Vernooij, Peter Zeman
https://arxiv.org/abs/2509.07835 http…

Existence and nonexistence of commutativity gadgets for entangled CSPs
Commutativity gadgets allow NP-hardness proofs for classical constraint satisfaction problems (CSPs) to be carried over to undecidability proofs for the corresponding entangled CSPs. This has been done, for instance, for NP-complete boolean CSPs and 3-colouring in the work of Culf and Mastel. For many CSPs over larger alphabets, including $k$-colouring when $k \geq 4$, it is not known whether or not commutativity gadgets exist, or if the entangled CSP is decidable. In this paper, we study commu…

There will come a day when my dad passes away, and eventually, a day when we all do. That thought makes me sad, but I realize there’s no use worrying about the future. Instead, I want to focus on living fully in the present moment.
Life is truly worth livin, and If you want to learn more about the challenges I have faced, including struggles with mental health and identity, you can read my personal story here:

Motion of spinning particles around a quantum-corrected black hole without Cauchy horizons
Jiawei Chen, Jinsong Yang
https://arxiv.org/abs/2509.07682 https://

Motion of spinning particles around a quantum-corrected black hole without Cauchy horizons
In this paper, we investigate the motion of spinning particles around a covariant quantum-corrected black hole without a Cauchy horizon within the framework of effective quantum gravity, and examine the influence of quantum gravitational effects on the motion of these spinning particles. First, we employ the Mathisson-Papapetrou-Dixon equations to derive the 4-momentum and 4-velocity of spinning particles, and introduce the effective potential for radial motion using the components of the 4-mom…

Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework
Satyapriya Krishna, Ninareh Mehrabi, Abhinav Mohanty, Matteo Memelli, Vincent Ponzo, Payal Motwani, Rahul Gupta
https://arxiv.org/abs/2507.06260

Evaluating the Critical Risks of Amazon's Nova Premier under the Frontier Model Safety Framework
Nova Premier is Amazon's most capable multimodal foundation model and teacher for model distillation. It processes text, images, and video with a one-million-token context window, enabling analysis of large codebases, 400-page documents, and 90-minute videos in a single prompt. We present the first comprehensive evaluation of Nova Premier's critical risk profile under the Frontier Model Safety Framework. Evaluations target three high-risk domains -- Chemical, Biological, Radiological & Nuclear …

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/4]:
- No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Huma...
Kyra Wilson, Mattea Sim, Anna-Maria Gueorguieva, Aylin Caliskan

Optimal decomposition of $K_{18}$ and $K_{19}$ into $K_3$ and $K_4$
Petr Kov\'a\v{r}, Yifan Zhang
https://arxiv.org/abs/2507.06745 https://

Optimal decomposition of $K_{18}$ and $K_{19}$ into $K_3$ and $K_4$
This article explores a new way to obtain the optimal decomposition of a complete graph of order 18 and 19 into cliques of order 3 and 4.

Transition from weak to strong coupling in thermal gauge theories: Lessons from ${\cal N}=4$ SYM Theory
Berndt M\"uller
https://arxiv.org/abs/2507.06845

Transition from weak to strong coupling in thermal gauge theories: Lessons from ${\cal N}=4$ SYM Theory
We investigate the transition between weak and strong coupling in thermal ${\cal N}=4$ supersymmetric Yang-Mills (SYM) theory as a function of 't Hooft coupling $λ$ for several quantities of phenomenological interest for which next-to-leading order calculations are available in both regimes. We use Padé approximants to interpolate between the weak and strong coupling expansions and determine the location and width of the transition between the asymptotic regimes.

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- ER-LoRA: Effective-Rank Guided Adaptation for Weather-Generalized Depth Estimation
Weilong Yan, Xin Zhang, Robby T. Tan

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- SCIZOR: A Self-Supervised Approach to Data Curation for Large-Scale Imitation Learning
Yu Zhang, Yuqi Xie, Huihan Liu, Rutav Shah, Michael Wan, Linxi Fan, Yuke Zhu

Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies
Binghan Wu, Shoufeng Wang, Yunxin Liu, Ya-Qin Zhang, Joseph Sifakis, Ye Ouyang
https://arxiv.org/abs/2509.08312

Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies
The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achieve genuine cognitive capabilities--fulfilling TM Forum's vision of self-configuring, self-healing, and self-optimizing systems that deliver zero-wait, zero-touch, and zero-fault services. This work bridges the gap between architectural theory and operational reality by implementing Joseph Sifakis's AN Agent reference …

Potential of the reaction $e^ e^-\to p\bar{p}\pi^0$ for constructing higher $\rho$-meson spectroscopy above 2.4 GeV
Dan Guo, Jun-Zhang Wang, Qin-Song Zhou
https://arxiv.org/abs/2509.07821

Potential of the reaction $e^+e^-\to p\bar{p}π^0$ for constructing higher $ρ$-meson spectroscopy above 2.4 GeV
The spectrum of light unflavored vector mesons above 2.4 GeV has not yet been firmly established experimentally. In this work, we demonstrate that the process $e^+e^- \to p\bar{p}π$ serves as a particularly promising channel as a probe for higher isovector $ρ$ excitations. In contrast to typical electron-positron annihilation processes into purely light-meson final states, the reaction $e^+e^- \to p\bar{p}π$ benefits from a relatively low continuum background, on the order of $(50\sim100)~\t…

Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs
Petr Novak, Stefan Biffl, Marek Obitko, Petr Kadera
https://arxiv.org/abs/2508.06278

Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs
Contemporary industrial cyber-physical production systems (CPPS) composed of robotic workcells face significant challenges in the analysis of undesired conditions due to the flexibility of Industry 4.0 that disrupts traditional quality assurance mechanisms. This paper presents a novel industry-oriented semantic model called Product-Process-Resource Asset Knowledge Graph (PPR-AKG), which is designed to analyze and mitigate undesired conditions in flexible CPPS. Built on top of the well-proven Pr…

Cavity-based optical switching via phase modulation in warm rubidium vapor
Georgia Booton, Tabijah Wasawo, William O. C. Davis, Cameron McGarry, K. R. Rusimova, Alex O. C. Davis, Josh Nunn, Peter J. Mosley
https://arxiv.org/abs/2508.06255

Cavity-based optical switching via phase modulation in warm rubidium vapor
Optical switching remains a key outstanding challenge for scalable fault-tolerant photonic quantum computing due to the trade-off between speed, bandwidth, and loss. Scalable quantum photonics demands all three, to enable high computational clock rates and resource efficient scaling to large systems. We present a cavity-based optical switch that overcomes this limitation, demonstrating 22 ns rise time, insertion loss of 2.4 dB, and 17.5 dB extinction ratio. All-optical control is achieved via p…

Vectorised Hashing Based on Bernstein-Rabin-Winograd Polynomials over Prime Order Fields
Kaushik Nath, Palash Sarkar
https://arxiv.org/abs/2507.06490 https…

Vectorised Hashing Based on Bernstein-Rabin-Winograd Polynomials over Prime Order Fields
We introduce the new AXU hash function decBRWHash, which is parameterised by the positive integer $c$ and is based on Bernstein-Rabin-Winograd (BRW) polynomials. Choosing $c>1$ gives a hash function which can be implemented using $c$-way single instruction multiple data (SIMD) instructions. We report a set of very comprehensive hand optimised assembly implementations of 4-decBRWHash using avx2 SIMD instructions available on modern Intel processors. For comparison, we also report similar careful…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- BranchGRPO: Stable and Efficient GRPO with Structured Branching in Diffusion Models
Yuming Li, Yikai Wang, Yuying Zhu, Zhongyu Zhao, Ming Lu, Qi She, Shanghang Zhang

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification
Patrick Wienholt, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/4]:
- GRADA: Graph-based Reranking against Adversarial Documents Attack
Zheng, Gema, Hong, He, Minervini, Sun, Xu

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/6]:
- Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Ol...
van Dijk, Kuiper, Ahmed, Levebvre, Johnson, Duin, Mooijaart, Spruit

Tilings of the sphere by congruent pentagons V: Edge combination $a^{4}b$ with rational angles
Jinjin Liang, Yixi Liao, Wenchuan Hu, Erxiao Wang
https://arxiv.org/abs/2507.07038

Tilings of the sphere by congruent pentagons V: Edge combination $a^{4}b$ with rational angles
We classify edge-to-edge tilings of the sphere by congruent pentagons with the edge combination $a^4b$ and with rational angles in degree: they are a one-parameter family of symmetric $a^4b$-pentagonal subdivisions of the tetrahedron with $12$ tiles; a sequence of unique symmetric $a^4b$-pentagons admitting a symmetric $3$-layer earth map tiling by $4m$ tiles for any $m\ge4$, among which each odd $m$ case admits two standard flip modifications; and a unique non-symmetric and degenerate $a^4b$-p…

Resonant leptogenesis in inverse see-saw framework with modular $S_4$ symmetry
Abhishek, V. Suryanarayana Mummidi
https://arxiv.org/abs/2507.06610 https://…

Resonant leptogenesis in inverse see-saw framework with modular $S_4$ symmetry
This work introduces a model for lepton mass generation and flavor mixing, realized through a (2,3) inverse seesaw structure within a modular \( S_4 \) symmetry framework. The model employs modular forms to construct the lepton Yukawa couplings, thereby significantly simplifying the model by reducing its complexity. A detailed numerical analysis demonstrates consistency with current neutrino oscillation data, yielding constrained predictions for the mixing angles and CP-violating phases. The Di…

The Geometry of Conformal Quivers
Canberk \c{S}anl{\i}
https://arxiv.org/abs/2509.07838 https://arxiv.org/pdf/2509.07838

The Geometry of Conformal Quivers
We study the geometry of the gauged quiver quantum mechanics realizing $D(2,1;0)$ superconformal symmetry. These models arise as effective descriptions of multi-centered D-brane systems in type II Calabi-Yau compactifications, in the $AdS_2$ scaling limit, and they describe the near-horizon microscopics of multi-centered BPS black holes in the four dimensional $\mathcal{N}=2$ supergravity. We work in the gauged sigma model formulation with off-shell $(4,4,0)$ multiplets, which allows us to intr…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Sequentially Auditing Differential Privacy
Tom\'as Gonz\'alez, Mateo Dulce-Rubio, Aaditya Ramdas, M\'onica Ribero

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- CTourLLM: Enhancing LLMs with Chinese Tourism Knowledge
Qikai Wei, Mingzhi Yang, Jinqiang Wang, Wenwei Mao, Jiabo Xu, Huansheng Ning

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/5]:
- From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in...
Amirabbas Hojjati, Lu Li, Ibrahim Hameed, Anis Yazidi, Pedro G. Lind, Rabindra Khadka

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/4]:
- Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases
Bufan Gao, Elisa Kreiss

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation
David Egea, Barproda Halder, Sanghamitra Dutta

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psy...
Gustavo Assun\c{c}\~ao, Miguel Castelo-Branco, Paulo Menezes

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- ReCamMaster: Camera-Controlled Generative Rendering from A Single Video
Bai, Xia, Fu, Wang, Mu, Cao, Liu, Hu, Bai, Wan, Zhang

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/4]:
- FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain
Suifeng Zhao, Zhuoran Jin, Sujian Li, Jun Gao

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/6]:
- Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax opti...
William Kengne, Modou Wade

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/6]:
- Mitigating Attention Localization in Small Scale: Self-Attention Refinement via One-step Belief P...
Nakyung Lee, Yeongoon Kim, Minhae Oh, Suhwan Kim, Jin Woo Koo, Hyewon Jo, Jungwoo Lee

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Hespi: A pipeline for automatically detecting information from hebarium specimen sheets
Robert Turnbull, Emily Fitzgerald, Karen Thompson, Joanne L. Birch

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/4]:
- UPLex: Fine-Grained Personality Control in Large Language Models via Unsupervised Lexical Modulation
Li, Liu, Wu, Dou, Wang, Lv, Wang, Zheng, Huang

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach
Timo Fudala, Vasileios Tsouvalas, Nirvana Meratnia

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Bayesian Multi-Scale Neural Network for Crowd Counting
Abhinav Sagar

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/6]:
- A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Langu...
Miao Zhang, Zhenlong Fang, Tianyi Wang, Qian Zhang, Shuai Lu, Junfeng Jiao, Tianyu Shi

MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
Xixi Wu, Yanchao Tan, Nan Hou, Ruiyang Zhang, Hong Cheng
https://arxiv.org/abs/2509.07666 htt…

MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
Document Understanding is a foundational AI capability with broad applications, and Document Question Answering (DocQA) is a key evaluation task. Traditional methods convert the document into text for processing by Large Language Models (LLMs), but this process strips away critical multi-modal information like figures. While Large Vision-Language Models (LVLMs) address this limitation, their constrained input size makes multi-page document comprehension infeasible. Retrieval-augmented generatio…

Evaluating Large Multimodal Models for Nutrition Analysis: A Benchmark Enriched with Contextual Metadata
Bruce Coburn, Jiangpeng He, Megan E. Rollo, Satvinder S. Dhaliwal, Deborah A. Kerr, Fengqing Zhu
https://arxiv.org/abs/2507.07048

Evaluating Large Multimodal Models for Nutrition Analysis: A Benchmark Enriched with Contextual Metadata
Large Multimodal Models (LMMs) are increasingly applied to meal images for nutrition analysis. However, existing work primarily evaluates proprietary models, such as GPT-4. This leaves the broad range of LLMs underexplored. Additionally, the influence of integrating contextual metadata and its interaction with various reasoning modifiers remains largely uncharted. This work investigates how interpreting contextual metadata derived from GPS coordinates (converted to location/venue type), timesta…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Charles Hong, Brendan Roberts, Huijae An, Alex Um, Advay Ratan, Yakun Sophia Shao

Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Yuxing Liu, Yuze Ge, Rui Pan, An Kang, Tong Zhang
https://arxiv.org/abs/2509.07972 https://

Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
Learning rate warmup is a popular and practical technique in training large-scale deep neural networks. Despite the huge success in practice, the theoretical advantages of this strategy of gradually increasing the learning rate at the beginning of the training process have not been fully understood. To resolve this gap between theory and practice, we first propose a novel family of generalized smoothness assumptions, and validate its applicability both theoretically and empirically. Under the n…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[5/5]:
- Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applicat...
Yuan Zhang, Xinfeng Zhang, Xiaoming Qi, Xinyu Wu, Feng Chen, Guanyu Yang, Huazhu Fu

Customizing the Inductive Biases of Softmax Attention using Structured Matrices
Yilun Kuang, Noah Amsel, Sanae Lotfi, Shikai Qiu, Andres Potapczynski, Andrew Gordon Wilson
https://arxiv.org/abs/2509.07963

Customizing the Inductive Biases of Softmax Attention using Structured Matrices
The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product of each pair. While the low-dimensional projection improves efficiency, it causes information loss for certain tasks that have intrinsically high-dimensional inputs. Additionally, attention uses the same scoring function for all input pairs, without imposing a distance-dependent compute bias for neighboring tokens in the sequence. In this work, we …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- CoreMark: Toward Robust and Universal Text Watermarking Technique
Jiale Meng, Yiming Li, Zheming Lu, Zewei He, Hao Luo, Tianwei Zhang

ACE and Diverse Generalization via Selective Disagreement
Oliver Daniels, Stuart Armstrong, Alexandre Maranh\~ao, Mahirah Fairuz Rahman, Benjamin M. Marlin, Rebecca Gorman
https://arxiv.org/abs/2509.07955

ACE and Diverse Generalization via Selective Disagreement
Deep neural networks are notoriously sensitive to spurious correlations - where a model learns a shortcut that fails out-of-distribution. Existing work on spurious correlations has often focused on incomplete correlations,leveraging access to labeled instances that break the correlation. But in cases where the spurious correlations are complete, the correct generalization is fundamentally \textit{underspecified}. To resolve this underspecification, we propose learning a set of concepts that are…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
Bessie Dominguez-Dager, Felix Escalona, Francisco Gomez-Donoso, Miguel Cazorla

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Kasra Borazjani, Naji Khosravan, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour
https://arxiv.org/abs/2509.07946

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges
Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/5]:
- Representation-Centric Survey of Skeletal Action Recognition and the ANUBIS Benchmark
Liu, Yang, Perera, Ji, Kim, Xu, Wang, Anwar, Gedeon, Wang, Qin

One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
Yuan Pu, Yazhe Niu, Jia Tang, Junyu Xiong, Shuai Hu, Hongsheng Li
https://arxiv.org/abs/2509.07945

One Model for All Tasks: Leveraging Efficient World Models in Multi-Task Planning
In heterogeneous multi-task learning, tasks not only exhibit diverse observation and action spaces but also vary substantially in intrinsic difficulty. While conventional multi-task world models like UniZero excel in single-task settings, we find that when handling large-scale heterogeneous environments, gradient conflicts and the loss of model plasticity often constrain their sample and computational efficiency. In this work, we address these challenges from two perspectives: the single learni…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Signal-Based Malware Classification Using 1D CNNs
Jack Wilkie, Hanan Hindy, Ivan Andonovic, Christos Tachtatzis, Robert Atkinson

Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
https://

Uncovering Scaling Laws for Large Language Models via Inverse Problems
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can …

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/2]:
- LocoMamba: Vision-Driven Locomotion via End-to-End Deep Reinforcement Learning with Mamba
Yinuo Wang, Gavin Tao

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Hamid Ahmad, Heiko Paulheim, Rita T. Sousa
https://arxiv.org/abs/2509.07905 https://

Bio-KGvec2go: Serving up-to-date Dynamic Biomedical Knowledge Graph Embeddings
Knowledge graphs and ontologies represent entities and their relationships in a structured way, having gained significance in the development of modern AI applications. Integrating these semantic resources with machine learning models often relies on knowledge graph embedding models to transform graph data into numerical representations. Therefore, pre-trained models for popular knowledge graphs and ontologies are increasingly valuable, as they spare the need to retrain models for different tas…

A Modular Algorithm for Non-Stationary Online Convex-Concave Optimization
Qing-xin Meng, Xia Lei, Jian-wei Liu
https://arxiv.org/abs/2509.07901 https://arx…

A Modular Algorithm for Non-Stationary Online Convex-Concave Optimization
This paper investigates the problem of Online Convex-Concave Optimization, which extends Online Convex Optimization to two-player time-varying convex-concave games. The goal is to minimize the dynamic duality gap (D-DGap), a critical performance measure that evaluates players' strategies against arbitrary comparator sequences. Existing algorithms fail to deliver optimal performance, particularly in stationary or predictable environments. To address this, we propose a novel modular algorithm wit…

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings
Philipp Lepold, Jonas Leichtle, Tobias R\"oddiger, Michael Beigl
https://arxiv.org/abs/2509.07896

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings
Automatic sleep staging typically relies on gold-standard EEG setups, which are accurate but obtrusive and impractical for everyday use outside sleep laboratories. This limits applicability in real-world settings, such as home environments, where continuous, long-term monitoring is needed. Detecting sleep onset is particularly relevant, enabling consumer applications (e.g. automatically pausing media playback when the user falls asleep). Recent research has shown correlations between in-ear EEG…

A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Katherine Berry, Liang Cheng
https://arxiv.org/abs/2509.07887 https://

A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literature across several categories of drug discovery research. This paper covers the research categories comprehensively with recent papers, namely molecular property prediction, including drug-target binding affinity prediction, drug-drug inter…

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Yajun Yu, Steve Jiang, Robert Timmerman, Hao Peng
https://arxiv.org/abs/2509.07872

Leveraging Support Vector Regression for Outcome Prediction in Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy
Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel treatment that delivers radiation in pulses of protracted intervals. Accurate prediction of gross tumor volume (GTV) changes through regression models has substantial prognostic value. This study aims to develop a multi-omics based support vector regression (SVR) model for predicting GTV change. A retrospective cohort of 39 patients with 69 brain metastases was analyzed, based on radiomics (MRI images) and do…

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Antoine de Mathelin, Christopher Tosh, Wesley Tansey
https://arxiv.org/abs/2509.07850 https://

Addressing the Cold-Start Problem for Personalized Combination Drug Screening
Personalizing combination therapies in oncology requires navigating an immense space of possible drug and dose combinations, a task that remains largely infeasible through exhaustive experimentation. Recent developments in patient-derived models have enabled high-throughput ex vivo screening, but the number of feasible experiments is limited. Further, a tight therapeutic window makes gathering molecular profiling information (e.g. RNA-seq) impractical as a means of guiding drug response predict…

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Mohammad Zana Majidi, Sajjad Karimi, Teng Wang, Robert Kluger, Reginald Souleyrette
https://arxiv.org/abs/2509.07845

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Predicting injuries and fatalities in traffic crashes plays a critical role in enhancing road safety, improving emergency response, and guiding public health interventions. This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity. Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed t…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
Jonathan Teagan
https://arxiv.org/abs/2509.07813 https://arxiv.org/pdf/2509…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning …

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
Songkai Ma, Zhaorui Zhang, Sheng Di, Benben Liu, Xiaodong Yu, Xiaoyi Lu, Dan Wang
https://arxiv.org/abs/2509.07727

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
With the widespread application of Mixture of Experts (MoE) reasoning models in the field of LLM learning, efficiently serving MoE models under limited GPU memory constraints has emerged as a significant challenge. Offloading the non-activated experts to main memory has been identified as an efficient approach to address such a problem, while it brings the challenges of transferring the expert between the GPU memory and main memory. We need to explore an efficient approach to compress the exper…

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Shusen Ma, Tianhao Zhang, Qijiu Xia, Yun-Bo Zhao
https://arxiv.org/abs/2509.07725

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Multivariate time series forecasting (MTSF) often faces challenges from missing variables, which hinder conventional spatial-temporal graph neural networks in modeling inter-variable correlations. While GinAR addresses variable missing using attention-based imputation and adaptive graph learning for the first time, it lacks interpretability and fails to capture more latent temporal patterns due to its simple recursive units (RUs). To overcome these limitations, we propose the Interpretable Bidi…

FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings
Pierre Lambert, Edouard Couplet, Michel Verleysen, John Aldo Lee
https://arxiv.org/abs/2509.07681

FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings
Neighbour embeddings (NE) allow the representation of high dimensional datasets into lower dimensional spaces and are often used in data visualisation. In practice, accelerated approximations are employed to handle very large datasets. Accelerating NE is challenging, and two main directions have been explored: very coarse approximations based on negative sampling (as in UMAP) achieve high effective speed but may lack quality in the extracted structures; less coarse approximations, as used in FI…

Graph-based Integrated Gradients for Explaining Graph Neural Networks
Lachlan Simpson, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew
https://arxiv.org/abs/2509.07648

Graph-based Integrated Gradients for Explaining Graph Neural Networks
Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to graphs. In this work, we introduce graph-based integrated gradients (GB-IG); an extension of IG to graphs. We demonstrate on four synthetic datasets that GB-IG accurately identifies crucial structural components of the graph used in classification tasks. We further demonstrate on t…

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
Ali Nawaz, Amir Ahmad, Shehroz S. Khan
https://arxiv.org/abs/2509.07605 …

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare. While numerous studies have explored rebalancing techniques to address this issue, less attention has been given to evaluating the performance of binary classifiers under imbalance when no such techniques are applied. Therefore, the goal of this study is to assess the performance of binary classifiers…

K2-Think: A Parameter-Efficient Reasoning System
Zhoujun Cheng, Richard Fan, Shibo Hao, Taylor W. Killian, Haonan Li, Suqi Sun, Hector Ren, Alexander Moreno, Daqian Zhang, Tianjun Zhong, Yuxin Xiong, Yuanzhe Hu, Yutao Xie, Xudong Han, Yuqi Wang, Varad Pimpalkhute, Yonghao Zhuang, Aaryamonvikram Singh, Xuezhi Liang, Anze Xie, Jianshu She, Desai Fan, Chengqian Gao, Liqun Ma, Mikhail Yurochkin, John Maggs, Xuezhe Ma, Guowei He, Zhiting Hu, Zhengzhong Liu, Eric P. Xing

K2-Think: A Parameter-Efficient Reasoning System
K2-Think is a reasoning system that achieves state-of-the-art performance with a 32B parameter model, matching or surpassing much larger models like GPT-OSS 120B and DeepSeek v3.1. Built on the Qwen2.5 base model, our system shows that smaller models can compete at the highest levels by combining advanced post-training and test-time computation techniques. The approach is based on six key technical pillars: Long Chain-of-thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewa…

Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards
Mehdi Bejani, Marco Mauri, Daniele Acconcia, Simone Todaro, Stefano Mariani
https://arxiv.org/abs/2509.07603

Transformer-Based Approach to Optimal Sensor Placement for Structural Health Monitoring of Probe Cards
This paper presents an innovative Transformer-based deep learning strategy for optimizing the placement of sensors aiming at structural health monitoring of semiconductor probe cards. Failures in probe cards, including substrate cracks and loosened screws, would critically affect semiconductor manufacturing yield and reliability. Some failure modes could be detected by equipping a probe card with adequate sensors. Frequency response functions from simulated failure scenarios are adopted within …

Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
Liya Gaynutdinova, Martin Do\v{s}k\'a\v{r}, Ond\v{r}ej Roko\v{s}, Ivana Pultarov\'a
https://arxiv.org/abs/2509.07579

Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks
Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs) relevant to multiscale modeling, but they often fail when applied to materials with discontinuous coefficients, such as media with piecewise constant properties. This paper introduces a dual formulation for the PINN framework to improve the reliability of the homogenization of periodic thermo-conductive composites, for both strong and variational (weak) formulations. The dual approach fa…

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
Zakeria Sharif Ali
https://arxiv.org/abs/2509.07569 https://arxiv.org/pdf/2509.07569

uGMM-NN: Univariate Gaussian Mixture Model Neural Network
This paper introduces the Univariate Gaussian Mixture Model Neural Network (uGMM-NN), a novel neural architecture that embeds probabilistic reasoning directly into the computational units of deep networks. Unlike traditional neurons, which apply weighted sums followed by fixed nonlinearities, each uGMM-NN node parameterizes its activations as a univariate Gaussian mixture, with learnable means, variances, and mixing coefficients. This design enables richer representations by capturing multimoda…

$\Delta L$ Normalization: Rethink Loss Aggregation in RLVR
Zhiyuan He, Xufang Luo, Yike Zhang, Yuqing Yang, Lili Qiu
https://arxiv.org/abs/2509.07558 https://

$ΔL$ Normalization: Rethink Loss Aggregation in RLVR
We propose $ΔL$ Normalization, a simple yet effective loss aggregation method tailored to the characteristic of dynamic generation lengths in Reinforcement Learning with Verifiable Rewards (RLVR). Recently, RLVR has demonstrated strong potential in improving the reasoning capabilities of large language models (LLMs), but a major challenge lies in the large variability of response lengths during training, which leads to high gradient variance and unstable optimization. Although previous methods…

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Jad Yehya, Mansour Benbakoura, C\'edric Allain, Beno\^it Malezieux, Matthieu Kowalski, Thomas Moreau
https://arxiv.org/abs/2509.07523

RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare-event Detection
Identifying recurring patterns and rare events in large-scale signals is a fundamental challenge in fields such as astronomy, physical simulations, and biomedical science. Convolutional Dictionary Learning (CDL) offers a powerful framework for modeling local structures in signals, but its use for detecting rare or anomalous events remains largely unexplored. In particular, CDL faces two key challenges in this setting: high computational cost and sensitivity to artifacts and outliers. In this pa…

Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Tom\'as Arias-Vergara, Andreas Maier, Siming Bayer
https://arxiv.org/abs/2509.07515

Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate change, securing water resources and supply has emerged as an urgent global issue with extensive socioeconomic ramifications. Hourly consumption data from end-users have yielded substantial insights for projecting demand across regions characterized by diverse consumption patterns. Nevertheless, the prediction of water…

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
Antoine Ledent, Petr Kasalick\'y, Rodrigo Alves, Hady W. Lauw
https://arxiv.org/abs/2509.07499

Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks
We introduce a new convolutional AutoEncoder architecture for user modelling and recommendation tasks with several improvements over the state of the art. Firstly, our model has the flexibility to learn a set of associations and combinations between different interaction types in a way that carries over to each user and item. Secondly, our model is able to learn jointly from both the explicit ratings and the implicit information in the sampling pattern (which we refer to as `implicit feedback')…

The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
Long Li, Jiaran Hao, Jason Klein Liu, Zhijian Zhou, Xiaoyu Tan, Wei Chu, Zhe Wang, Shirui Pan, Chao Qu, Yuan Qi
https://arxiv.org/abs/2509.07430

The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pass@k) despite improvements in single-attempt accuracy (Pass@1). This is often accompanied by catastrophic forgetting, where models lose previously acquired skills. While various methods have been proposed, the choice and function of the divergence term have been surprisingly unexamined as a proactive solution. We argu…

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
Arslan Majal, Aamir Hussain Chughtai, Muhammad Tahir
https://arxiv.org/abs/2509.07415 http…

EMORF-II: Adaptive EM-based Outlier-Robust Filtering with Correlated Measurement Noise
We present a learning-based outlier-robust filter for a general setup where the measurement noise can be correlated. Since it is an enhanced version of EM-based outlier robust filter (EMORF), we call it as EMORF-II. As it is equipped with an additional powerful feature to learn the outlier characteristics during inference along with outlier-detection, EMORF-II has improved outlier-mitigation capability. Numerical experiments confirm performance gains as compared to the state-of-the-art methods …

Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
Gyuyeon Na, Minjung Park, Hyeonjeong Cha, Soyoun Kim, Sunyoung Moon, Sua Lee, Jaeyoung Choi, Hyemin Lee, Sangmi Chai
https://arxiv.org/abs/2509.07392

Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines.

EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
Qasim Zia, Avais Jan, Zafar Iqbal, Muhammad Mumtaz Ali, Mukarram Ali, Murray Patterson
https://arxiv.org/abs/2509.07388

EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized card…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Qihu Xie, Yuan Li, Yi Kang
https://arxiv.org/abs/2509.07373 https…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Implicit neural representations have recently been extended to represent convolutional neural network weights via neural representation for neural networks, offering promising parameter compression benefits. However, standard multi-layer perceptrons used in neural representation for neural networks exhibit a pronounced spectral bias, hampering their ability to reconstruct high-frequency details effectively. In this paper, we propose SBS, a parameter-efficient enhancement to neural representatio…

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Yuxuan Bai, Yuxuan Sun, Tan Chen, Wei Chen, Sheng Zhou, Zhisheng Niu
https://arxiv.org/abs/2509.07342

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a tempor…