May 2025 arXiv papers — page 122

Cezary Turski, Maria Lisa Brozzetti, Gergely Dálya, Michele Punturo

Gravitational waves (GWs) offer a novel avenue for probing the Universe. One of their exciting applications is the independent measurement of the Hubble constant, $H_0$, using dark standard sirens, which combine GW signals with galaxy catalogues considering that GW events are hosted by galaxies. However, due to the limited reach of telescopes, galaxy catalog

Lorena Garcia-Foncillas Macias, Aaron Kujawa, Aya Elshalakany, Jonathan Shapey

Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplete defacing or degradation of brain tissue regions. We present a robust, generalisable defacing pipeline for high-resolution MRI that integrates atlas-based registration with brain

Vinkle Srivastav, Juliette Puel, Jonathan Vappou, Elijah Van Houten

Transcranial focused ultrasound (tFUS) is an emerging modality for non-invasive brain stimulation and therapeutic intervention, offering millimeter-scale spatial precision and the ability to target deep brain structures. However, the heterogeneous and anisotropic nature of the human skull introduces significant distortions to the propagating ultrasound wavef

Somdeb Lahiri

We provide an axiomatic characterization of lexicographic preferences over the set of all random availability functions using two assumptions. The first assumption is strong monotonicity, which in our framework is equivalent to the strong dominance property in microeconomics. The second assumption is independence of worse alternatives and we show that a weak

Jiaan Wang, Fandong Meng, Jie Zhou

In recent years, the emergence of large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, has shown impressive capabilities in complex problems, e.g., mathematics and coding. Some pioneering studies attempt to bring the success of LRMs in neural machine translation (MT). They try to build LRMs with deep reasoning MT ability via reinforcement learni

Wojciech Ozga, Guerney D. H. Hunt, Michael V. Le, Lennard Gäher

Confidential computing has proven its value in cloud environments, but its potential for securing edge and high-end embedded systems (e.g., automotive controllers, cryptographic accelerators, telco infrastructure) remains largely unexplored. We present ACE, an open-source, royalty-free virtualization-based confidential computing system for RISC-V targeting t

Xuanjun Chen, I-Ming Lin, Lin Zhang, Jiawei Du

Recent advances in neural audio codec-based speech generation (CoSG) models have produced remarkably realistic audio deepfakes. We refer to deepfake speech generated by CoSG systems as codec-based deepfake, or CodecFake. Although existing anti-spoofing research on CodecFake predominantly focuses on verifying the authenticity of audio samples, almost no atten

Zhaohui Wu, Hao Peng, Xiaoming Zeng, Zhaoli Li

We present the first experimental realization of a four-dimensional (4D) plasma hologram capable of recording and reconstructing the full spatiotemporal information of intense laser pulses. The holographic encoding is achieved through the interference of a long object pulse and a counter-propagating short reference pulse, generating an ionized plasma grating

Dominik Wagner, Ilja Baumann, Natalie Engert, Seanie Lee

In this work, we present our submission to the Speech Accessibility Project challenge for dysarthric speech recognition. We integrate parameter-efficient fine-tuning with latent audio representations to improve an encoder-decoder ASR system. Synthetic training data is generated by fine-tuning Parler-TTS to mimic dysarthric speech, using LLM-generated prompts

Yang Gao, Qi-Zheng Ji, Chao-Bo Liu, Qi Xiao

The negatively charged nitrogen-vacancy center is a leading quantum platform due to its excellent spin coherence and stable interactions. Understanding its ultrafast dynamics is crucial for quantum applications but presents significant challenges for both experimental characterization and atomic-scale modeling. Here, we employ real-time time-dependent densit

Douglas Orr, Luka Ribar, Carlo Luschi

Weight quantisation is an essential technique for enabling efficient training and deployment of modern deep learning models. However, the recipe book of quantisation formats is large and formats are often chosen empirically. In this paper, we propose a framework for systematic design and analysis of quantisation formats. By connecting the question of format

Nils Bosbach, Rebecca Pelke, Niko Zurstraßen, Jan Henrik Weinstock

The increasing complexity of hardware and software requires advanced development and test methodologies for modern systems on chips. This paper presents a novel approach to ARM-on-ARM virtualization within SystemC-based simulators using Linux's KVM to achieve high-performance simulation. By running target software natively on ARM-based hosts with hardware-ba

Chengxun Yuan, E. A. Bogdanov, A. A. Kudryavtsev, Jingfeng Yao

This paper proposes a novel approach for creating an inverse electron distribution function (EDF). Based on the obtained criteria for the formation of an inverse EDF in a non-uniform plasma, studies are conducted in low- and medium-pressure glow discharges with flat and hollow cathodes. The results of the numerical modeling and theoretical analysis are used

Gaurav Bhandari, S. D. Pathak

In this paper, we investigate the Stark effect in the hydrogen atom under an external electric field, incorporating relativistic generalized uncertainty principle (RGUP) corrections within Minkowskian spacetime and calculate the upper bound on $\beta$ the RGUP parameter. Employing RGUP algebra and the Stetsko-Tkachuk approximation, we derive modifications to

Shriton Hembrom, Roshni Bhaumik, Sourav Dutta, Subenoy Chakraborty

The paper deals with a cosmological model containing two scalar fields which can be considered as an extension of the Brans-Dicke scalar field model. Due to highly coupled non linear field equations, Noether Symmetry analysis has been imposed and as a result the Lagrangian as well as the field equations become much simple in form to have the classical soluti

Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang

Many-to-many summarization (M2MS) aims to process documents in any language and generate the corresponding summaries also in any language. Recently, large language models (LLMs) have shown strong multi-lingual abilities, giving them the potential to perform M2MS in real applications. This work presents a systematic empirical study on LLMs' M2MS ability. Spec

Tai Nguyen, Phong Le, Carola Doerr, Nguyen Dang

It is well known that evolutionary algorithms can benefit from dynamic choices of the key parameters that control their behavior, to adjust their search strategy to the different stages of the optimization process. A prominent example where dynamic parameter choices have shown a provable super-constant speed-up is the $(1+(\lambda,\lambda))$ Genetic Algorith

Liangxuan Wu, Chao Wang, Tianming Liu, Yanjie Zhao

The growing adoption of large language models (LLMs) has led to a new paradigm in mobile computing--LLM-powered mobile AI agents--capable of decomposing and automating complex tasks directly on smartphones. However, the security implications of these agents remain largely unexplored. In this paper, we present the first comprehensive security analysis of mobi

Ophir Uziel, Efi Fogel, Dan Halperin, Sivan Toledo

For three decades, carrier-phase observations have been used to obtain the most accurate location estimates using global navigation satellite systems (GNSS). These estimates are computed by minimizing a nonlinear mixed-integer least-squares problem. Existing algorithms linearize the problem, orthogonally project it to eliminate real variables, and then solve

Hanteng Wang, Xingyu Li, Chengshu Li

The Kibble-Zurek (KZ) mechanism has been extensively studied in various second-order phase transitions, yet the case of tricriticality-the point where second-order phase transition lines terminate-remains experimentally elusive. Here, we theoretically propose probing KZ scaling at tricritical points using Rydberg atom arrays arranged as two- and three-leg la

Yinzhe Wu, Jiahao Huang, Fanwen Wang, Mengze Gao

Diffusion MRI (dMRI) is essential for studying brain microstructure, but high-resolution imaging remains challenging due to the inherent trade-offs between acquisition time and signal-to-noise ratio (SNR). Conventional methods often optimize only the diffusion-weighted images (DWIs) without considering their relationship with the non-diffusion-weighted (b=0)

Komeil Nosrati, Juri Belikov, Aleksei Tepljakov, Eduard Petlenkov

In model predictive control (MPC), the choice of cost-weighting matrices and designing the Hessian matrix directly affects the trade-off between rapid state regulation and minimizing the control effort. However, traditional MPC in quadratic programming relies on fixed design matrices across the entire horizon, which can lead to suboptimal performance. This s

Theresa Csar, Martin Lackner, Reinhard Pichler

The Schulze method is a voting rule widely used in practice and enjoys many positive axiomatic properties. While it is computable in polynomial time, its straight-forward implementation does not scale well for large elections. In this paper, we develop a highly optimised algorithm for computing the Schulze method with Pregel, a framework for massively parall

Mariia Anapolska, Dario van den Boom, Christina Büsing, Timo Gersing

Algorithms for computing fractional solutions to the quickest transshipment problem have been significantly improved since Hoppe and Tardos first solved the problem in strongly polynomial time. For integral solutions, runtime improvements are limited to general progress on submodular function minimization, which is an integral part of Hoppe and Tardos' algor

Mahta Fetrat Qharabagh, Zahra Dehghanian, Hamid R. Rabiee

Homograph disambiguation remains a significant challenge in grapheme-to-phoneme (G2P) conversion, especially for low-resource languages. This challenge is twofold: (1) creating balanced and comprehensive homograph datasets is labor-intensive and costly, and (2) specific disambiguation strategies introduce additional latency, making them unsuitable for real-t

Carlos Aguilera-Ventura, Xinghan Liu, Emiliano Lorini, Dmitry Rozplokhas

We present a computationally grounded semantics for counterfactual conditionals in which i) the state in a model is decomposed into two elements: a propositional valuation and a causal base in propositional form that represents the causal information available at the state; and ii) the comparative similarity relation between states is computed from the state

Daphne Aurouet, Valentin Patilea

We propose a new approach for estimating the finite dimensional transition matrix of a Markov chain using a large number of independent sample paths observed at random times. The sample paths may be observed as few as two times, and the transitions are allowed to depend on covariates. Simple and easy to update kernel estimates are proposed, and their uniform

Robin Jegan, Andreas Henrich

The continued rise of neural networks and large language models in the more recent past has altered the natural language processing landscape, enabling new approaches towards typical language tasks and achieving mainstream success. Despite the huge success of large language models, many disadvantages still remain and through this work we assess the state of

Yingzhi Wang, Anas Alhmoud, Saad Alsahly, Muhammad Alqurishi

OpenAI's Whisper has achieved significant success in Automatic Speech Recognition. However, it has consistently been found to exhibit hallucination issues, particularly in non-speech segments, which limits its broader application in complex industrial settings. In this paper, we introduce a novel method to reduce Whisper's hallucination on non-speech segment

Claudio Correia, Guilherme Santos, Luis Rodrigues

Anonymous authentication is a technique that allows to combine access control with privacy preservation. Typically, clients use different pseudonyms for each access, hindering providers from correlating their activities. To perform the revocation of pseudonyms in a privacy preserving manner is notoriously challenging. When multiple pseudonyms are revoked tog

Akhila Henry, Nithin Nagaraj

This study presents novel Augmented Regression Models using Neurochaos Learning (NL), where Tracemean features derived from the Neurochaos Learning framework are integrated with traditional regression algorithms : Linear Regression, Ridge Regression, Lasso Regression, and Support Vector Regression (SVR). Our approach was evaluated using ten diverse real-life

Zihan Xiong, Xiaohua Wu, Lei Chen, Fangqi Lou

Advances in computer vision and deep learning have blurred the line between deepfakes and authentic media, undermining multimedia credibility through audio-visual forgery. Current multimodal detection methods remain limited by unbalanced learning between modalities. To tackle this issue, we propose an Audio-Visual Joint Learning Method (MACB-DF) to better mi

Jonathan DeMont, Alon E. Faraggi, Mark Goodsell, Marco Guzzi

With the SNOWMASS 2021 process in the US and the on--going European Strategy Report 2025, the field of elementary particle physics is undergoing detailed community evaluation, and the experimental particle physics program, which requires substantial public investment, is under scrutiny. We offer an assessment of the current experimental particle physics prio

Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne, Narumi Tokunaga

Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mention identification, often fail to capture complex concepts not explicitly stated in the text. To overcome this limitation, we introduce MA-COI

Yoav Ger, Omri Barak

Recurrent neural networks (RNNs) trained on neuroscience-inspired tasks offer powerful models of brain computation. However, typical training paradigms rely on open-loop, supervised settings, whereas real-world learning unfolds in closed-loop environments. Here, we develop a mathematical theory describing the learning dynamics of linear RNNs trained in close

Maksim I. Ivanov, Olga E. Mendybaeva, Yuri E. Karyakin, Igor N. Glukhikh

This article explores the use of artificial intelligence for the diagnosis of pathologies of the temporomandibular joint (TMJ), in particular, for the segmentation of the articular disc on MRI images. The relevance of the work is due to the high prevalence of TMJ pathologies, as well as the need to improve the accuracy and speed of diagnosis in medical insti

Alexander P. Fellows, Louis Lehmann, Álvaro Díaz Duque, Martin Wolf

The unique structural properties of interfacial water are at the heart of a vast range of important processes in electrochemistry, climate science, and biophysics. At interfaces, water molecules exhibit preferential orientations and an altered intermolecular H-bond connectivity. Characterising this layer-dependent anisotropic structure for such a thin molecu

Yannick Kuhn, Bhawna Rana, Micha Philipp, Christina Schmitt

Interdisciplinary collaboration in battery science is required for rapid evaluation of better compositions and materials. However, diverging domain vocabulary and non-compatible experimental results slow down cooperation. We critically assess the current state-of-the-art and develop a structured data management and interpretation system to make data curation

Rupam Samanta

In this thesis, we focus on the fluctuations and correlations of the collective observables such as the mean transverse momentum per particle ($[p_T]$) and harmonic flow coefficients ($v_n$) of particles produced in the ultrarelativistic heavy-ion collisions at RHIC and the LHC. Specifically, we show that the fluctuations of harmonic flow can be probed by th

Chengping He, Mingrui Jiang, Keyi Shan, Szu-Hao Yang

Brain-inspired computing aims to mimic cognitive functions like associative memory, the ability to recall complete patterns from partial cues. Memristor technology offers promising hardware for such neuromorphic systems due to its potential for efficient in-memory analog computing. Hopfield Neural Networks (HNNs) are a classic model for associative memory, b

Wolfgang Treimer, Frank Haußer, Ingeborg Beckers, Martin Suda

A neutron Laue crystal interferometer has been reported by Saranac et al . to demonstrate neutron holography of a spiral phase plate. Using its two coherent beams as the object and reference beams, the resulting interference pattern was interpreted as a hologram. This interference pattern was then reported to reconstruct neutron beams with various intrinsic

David Immel, Matous Mrovec, Ralf Drautz, Godehard Sutmann

We perform nanoindentation simulations for both the prototypical face-centered cubic metal copper and the body-centered cubic metal tungsten with a new adaptive-precision description of interaction potentials including different accuracy and computational costs: We combine both a computationally efficient embedded atom method (EAM) potential and a precise bu

Oier Mentxaka, Natalia Díaz-Rodríguez, Mark Coeckelbergh, Marcos López de Prado

Artificial Intelligence (AI) poses both significant risks and valuable opportunities for democratic governance. This paper introduces a dual taxonomy to evaluate AI's complex relationship with democracy: the AI Risks to Democracy (AIRD) taxonomy, which identifies how AI can undermine core democratic principles such as autonomy, fairness, and trust; and the A

Roop Kumar Mech, Alessandra Canetta, Yubin Huang, Sergio Gonzalez-Munoz

Two-dimensional (2D) materials have attracted significant interest due to their tunable physical properties when stacked into homo- and hetero-structures. Twisting adjacent layers introduces moir\'{e} patterns that strongly influence the material electronic and thermal behavior. In twisted graphene systems, the twist angle critically alters phonon transport,

L. B. Avila, A. Cantudo, M. A. Villena, D. Maldonado

This work presents a comprehensive analysis of the variability and reliability of the resistive switching (RS) behavior in Prussian Blue (a mixed-valence iron(III/II) hexacyanoferrate compound) thin films, used as the active layer. These films are fabricated through a simple and scalable electrochemical process, and exhibit robust bipolar resistive switching

Hyeon Hwang, Seokjoo Go, Guhwan Kim, Hong-Seok Kim

Thin-film lithium niobate (TFLN) has emerged as a powerful platform for integrated nonlinear and quantum photonics, owing to its strong optical nonlinearities, wide transparency window, and electro- and piezo-optic properties. However, conventional traveling-wave resonators, such as micro-rings, disks, and racetracks, suffer from curvature-dependent group di

Vorapong Suppakitpaisarn, Donlapark Ponnoprat, Nicha Hirankarn, Quentin Hillebrand

The problem of counting subgraphs or graphlets under local differential privacy is an important challenge that has attracted significant attention from researchers. However, much of the existing work focuses on small graphlets like triangles or $k$-stars. In this paper, we propose a non-interactive, locally differentially private algorithm capable of countin

Pal Jedlovszky, Christoph Dellago, Marcello Sega

The discovery of high-density liquid (HDL) and low-density liquid (LDL) water has been a major success of molecular simulations, yet extending this analysis to interfacial water is challenging due to conventional order parameters assuming local homogeneity. This limitation previously prevented resolving the composition of the surface layer of the liquid/vapo

Chuanxing Geng, Qifei Li, Xinrui Wang, Dong Liang

Using unlabeled wild data containing both in-distribution (ID) and out-of-distribution (OOD) data to improve the safety and reliability of models has recently received increasing attention. Existing methods either design customized losses for labeled ID and unlabeled wild data then perform joint optimization, or first filter out OOD data from the latter then

Xuerui Su, Liya Guo, Yue Wang, Yi Zhu

Inference scaling further accelerates Large Language Models (LLMs) toward Artificial General Intelligence (AGI), with large-scale Reinforcement Learning (RL) to unleash long Chain-of-Thought reasoning. Most contemporary reasoning approaches usually rely on handcrafted rule-based reward functions. However, the tarde-offs of exploration and exploitation in RL

Daehee Kim, Deokhyung Kang, Jonghwi Kim, Sangwon Ryu

Legal Passage Retrieval (LPR) systems are crucial as they help practitioners save time when drafting legal arguments. However, it remains an underexplored avenue. One primary reason is the significant vocabulary mismatch between the query and the target passage. To address this, we propose a simple yet effective method, the Generative query REwriter (GuRE).

Adil Belhaj, Abderrahim Bouhouch

In this paper, we reconsider the study of five-dimensional supersymmetric black branes in the context of the M-theory compactification on a special Calabi-Yau manifold called tetra-quadric, being realized as complete intersections of homogenous polynomials in the projective space $ \mathbb{CP}^{1}\times\mathbb{CP}^{1}\times\mathbb{CP}^{1}\times\mathbb{CP}^{1

Aymeric Capitaine, Maxime Haddouche, Eric Moulines, Michael I. Jordan

Decision-focused learning (DFL) is an increasingly popular paradigm for training predictive models whose outputs are used in decision-making tasks. Instead of merely optimizing for predictive accuracy, DFL trains models to directly minimize the loss associated with downstream decisions. However, existing studies focus solely on scenarios where a fixed batch

Xiaohui Wang, Peng Ye, Chenyu Huang, Shenghe Zheng

With the rise of the fine-tuned-pretrained paradigm, storing numerous fine-tuned models for multi-tasking creates significant storage overhead. Delta compression alleviates this by storing only the pretrained model and the highly compressed delta weights (the differences between fine-tuned and pretrained model weights). However, existing methods fail to main

Sa'ar Zehavi

We present a practical, unconditional algorithm for determining the $S$-integral points on any elliptic moduli problem $\mathcal{Y}/\mathbb{Z}[1/S]$ -- that is, on any geometrically connected curve carrying a non-isotrivial elliptic fibration $\mathcal{E} \to \mathcal{Y}$. The associated map $\Phi_M\colon \mathcal{Y} \to \mathcal{M}_{1,1}$ (the modular perio

Bo Ai, Yunlong Lu, Yuguang Fang, Dusit Niyato

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of 5G has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet.

Alix Violet Freckelton, Annelies Mortier, Megan Bedell, Sam Morrell

As the fields of stellar and exoplanetary study grow and revolutionary new detection instruments are created, it is imperative that a homogeneous, precise source of stellar parameters is available. This first work of the gr8stars collaboration presents the all-sky magnitude limited sample of 5645 bright FGKM dwarfs, along with homogeneously derived spectrosc

Jan Hagnberger, Daniel Musekamp, Mathias Niepert

Solving time-dependent Partial Differential Equations (PDEs) using a densely discretized spatial domain is a fundamental problem in various scientific and engineering disciplines, including modeling climate phenomena and fluid dynamics. However, performing these computations directly in the physical space often incurs significant computational costs. To addr

Krzysztof Rogowski, Marcin Dziubiński

We study the problem of design of strategyproof in expectation (SP) mechanisms for facility location on a cycle, with the objective of minimizing the sum of costs of $n$ agents. We show that there exists an SP mechanism that attains an approximation ratio of $7/4$ with respect to the sum of costs of the agents, thus improving the best known upper bound of $2

Abhiram Menon

We derive a unified closed-form expression for the Frame-Stewart algorithm in the multi-peg Tower of Hanoi: M(p,n) = 2^(i(p,n)+1)*n - sum_{k=0}^{i(p,n)} 2^k * C(p+k-2, k), where i(p,n) = min{ j >= 0 : n <= C(p-1+j, j+1) }. and prove it satisfies the Frame-Stewart recurrence for all (p,n) via double induction using discrete slope analysis with simplex boundar

James Rowbottom, Stefania Fresca, Pietro Lio, Carola-Bibiane Schönlieb

Operator learning is a rapidly growing field that aims to approximate nonlinear operators related to partial differential equations (PDEs) using neural operators. These rely on discretization of input and output functions and are, usually, expensive to train for large-scale problems at high-resolution. Motivated by this, we present a Multi-Level Monte Carlo

Souvik Roy, Santanu K. Maiti, David Laroze

In this work, we investigate the magnetic response by examining flux-driven circular currents in a Su-Schrieffer-Heeger (SSH) tight-binding (TB) ring threaded by an Aharonov-Bohm (AB) flux, $\phi$. We consider both non-interacting and interacting electrons, where site energies are modulated by a slowly varying cosine form. Repulsive electron-electron interac

Uri Dalal, Meirav Segal, Zvika Ben-Haim, Dan Lahav

Large language models (LLMs) achieve impressive abilities in numerous domains, but exhibit inconsistent performance in response to minor input changes. Rather than view this as a drawback, in this paper we introduce a novel method for leveraging models' inconsistency to boost Pass@k performance. Specifically, we present a "Variator" agent that generates k va

Diksha Gupta, Konijeti Sreenadh

This paper explores the existence of solutions to a class of nonlinear elliptic equations involving a mixed local-nonlocal operator of the form $-\Delta_{\mathbb{B}^N} + (-\Delta_{\mathbb{B}^N})^s$, with $0 < s < 1$, set in the hyperbolic space $\mathbb{B}^N$. By employing variational methods, we address both subcritical and critical nonlinearities, establis

Di You, Daniel Siromani, Pier Luigi Dragotti

There is a growing interest in the use of latent diffusion models (LDMs) for image restoration (IR) tasks due to their ability to model effectively the distribution of natural images. While significant progress has been made, there are still key challenges that need to be addressed. First, many approaches depend on a predefined degradation operator, making t

Haodi Hu, Yue Wu, Feifei Qian, Daniel Seita

Legged robots have the potential to leverage obstacles to climb steep sand slopes. However, efficiently repositioning these obstacles to desired locations is challenging. Here we present DiffusiveGRAIN, a learning-based method that enables a multi-legged robot to strategically induce localized sand avalanches during locomotion and indirectly manipulate obsta

Judith Ludwig, Christian Merten

In this article we describe the formalisation of the Bruhat-Tits tree - an important tool in modern number theory - in the Lean Theorem Prover. Motivated by the goal of connecting to ongoing research, we apply our formalisation to verify a result about harmonic cochains on the tree.

P. Zucca, P. Zhang, K. Kozarev, M. Nedal

Shocks in the solar corona can accelerate electrons that in turn generate radio emission known as type II radio bursts. The characteristics and morphology of these radio bursts in the dynamic spectrum reflect the evolution of the shock itself, together with the properties of the local corona where it propagates. In this work, we study the evolution of a comp

Bernd Hofmann, Stefan Kindermann

In this article, concepts of well- and ill-posedness for linear operators in Hilbert and Banach spaces are discussed. While these concepts are well understood in Hilbert spaces, this is not the case in Banach spaces, as there are several competing definitions, related to the occurrence of uncomplemented subspaces. We provide an overview of the various defini

Takasugu Shigenobu, Naoyuki Kamiyama

An integer linear system is a set of inequalities with integer constraints. The solution graph of an integer linear system is an undirected graph defined on the set of feasible solutions to the integer linear system. In this graph, a pair of feasible solutions is connected by an edge if the Hamming distance between them is one. In this paper, we consider a c

Zhihe Yang, Xufang Luo, Zilong Wang, Dongqi Han

Reinforcement learning (RL) has become a cornerstone for enhancing the reasoning capabilities of large language models (LLMs), with recent innovations such as Group Relative Policy Optimization (GRPO) demonstrating exceptional effectiveness. In this study, we identify a critical yet underexplored issue in RL training: low-probability tokens disproportionatel

Trever Schirmer, Natalie Carl, Nils Höller, Tobias Pfandzelter

Serverless Function-as-a-Service (FaaS) is a popular cloud paradigm to quickly and cheaply implement complex applications. Because the function instances cloud providers start to execute user code run on shared infrastructure, their performance can vary. From a user perspective, slower instances not only take longer to complete, but also increase cost due to

Sung-Soo Byun, Peter J. Forrester

In the present context, superintegrability is a property of certain probability density functions coming from matrix models, which relates to the average over a distinguished basis of symmetric functions, typically the Jack or Macdonald polynomials. It states that the average can be computed according a certain combination of those same polynomials, now spec

Han Deng, Yuan Meng, Shixiang Tang, Wanli Ouyang

Competitive programming benchmarks are widely used in scenarios such as programming contests and large language model assessments. However, the growing presence of duplicate or highly similar problems raises concerns not only about competition fairness, but also about the validity of competitive programming as a benchmark for model evaluation. In this paper,

Shishen Lin

Learning in games is a fundamental problem in machine learning and artificial intelligence, with numerous applications~\citep{silver2016mastering,schrittwieser2020mastering}. This work investigates two-player zero-sum matrix games with an unknown payoff matrix and bandit feedback, where each player observes their actions and the corresponding noisy payoff. P

Vladimir A. Tolstykh

We obtain a number of analogues of the classical results of the 1960s on the general linear groups $\mathrm{GL}_n(\mathbf Z)$ and special linear groups $\mathrm{SL}_n(\mathbf Z)$ for the automorphism group $\Gamma_A=\mathrm{Aut}(A)$ of an infinitely generated free abelian group $A.$ In particular, we obtain a description of normal generators of the group $\m

Pedro M. P. Curvo

As AI systems increasingly assume roles where trust and alignment with human values are essential, understanding when and why they engage in deception has become a critical research priority. We introduce The Traitors, a multi-agent simulation framework inspired by social deduction games, designed to probe deception, trust formation, and strategic communicat

Vladimir A. Tolstykh

We prove that the outer automorphism group of a free group of countably infinite rank is complete.

Yingxiang Hu, Mohammad N. Ivaki

In this paper, we solve the even capillary $L_p$-Minkowski problem for the range $-n < p < 1$ and $\theta \in (0,\frac{\pi}{2})$. Our approach is based on an iterative scheme that builds on the solution to the capillary Minkowski problem (i.e., the case $p = 1$) and leverages the monotonicity of a class of functionals under a family of capillary curvature im

Paul Van Eecke, Katrien Beuls

We present PyFCG, an open source software library that ports Fluid Construction Grammar (FCG) to the Python programming language. PyFCG enables its users to seamlessly integrate FCG functionality into Python programs, and to use FCG in combination with other libraries within Python's rich ecosystem. Apart from a general description of the library, this paper

Eilon Vaknin Laufer, Boaz Nadler

Recovering a low rank matrix from a subset of its entries, some of which may be corrupted, is known as the robust matrix completion (RMC) problem. Existing RMC methods have several limitations: they require a relatively large number of observed entries; they may fail under overparametrization, when their assumed rank is higher than the correct one; and many

Vladimir A. Tolstykh

We prove that the outer automorphism group $\mathrm{Out}(N)$ of an infinitely generated free nilpotent group $N$ of class two is complete.

Guoxuan Mao, Ting Cao, Ziyang Li, Yuan Dong

Semantic segmentation stands as a pivotal research focus in computer vision. In the context of industrial image inspection, conventional semantic segmentation models fail to maintain the segmentation consistency of fixed components across varying contextual environments due to a lack of perception of object contours. Given the real-time constraints and limit

Shengsheng Lin, Haojun Chen, Haijie Wu, Chunyun Qiu

Sufficiently modeling the correlations among variables (aka channels) is crucial for achieving accurate multivariate time series forecasting (MTSF). In this paper, we propose a novel technique called Temporal Query (TQ) to more effectively capture multivariate correlations, thereby improving model performance in MTSF tasks. Technically, the TQ technique empl

Gui-Jun Ding, Stephen F. King, Jun-Nan Lu, Ming-Hua Weng

We systematically develop the weighton mechanism for natural quark and charged lepton mass hierarchies in the framework of modular symmetry with a single modulus field $\tau$. The weighton $\phi$ is defined as a complete singlet with unit modular weight, leading to fermion mass suppression by powers of $\tilde{\phi}$, which is the vacuum expectation value of

Rene Marczinzik, Daniel Owens

We give the first example of a non-trivial cluster tilting module in a local finite dimensional algebra. To do this, we give an explicit calculation of the corresponding higher Auslander algebra by quiver and relations using the GAP-package QPA. We discuss related problems and conjectures for local finite-dimensional algebras.

E V Charan Reddy, Abhijit Chatterjee

Copper-based catalysts play a crucial role in industrial oxidation reactions. Although many theoretical studies consider copper to be metallic, it is well established that copper readily oxides at ambient conditions, forming a passivating oxide layer. Experimental investigations spanning two decades have shown that in addition to the anticipated step-oxide f

Tom George Grigg, Mason Burlage, Oliver Brook Scott, Adam Taouil

Exhaustive virtual screening is highly informative but often intractable against the expensive objective functions involved in modern drug discovery. This problem is exacerbated in combinatorial contexts such as multi-vector expansion, where molecular spaces can quickly become ultra-large. Here, we introduce Scalable Active Learning via Synthon Acquisition (

Kazuki Adachi, Shin&#39;ya Yamaguchi, Tomoki Hamagami

Pre-trained vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated a remarkable generalizability, enabling a wide range of applications, including zero-shot classification. However, vision-language models still struggle to handle distribution shifts, where input samples have large gaps from training ones. We found t

Simone Alberto Peirone, Francesca Pistilli, Giuseppe Averta

Human activities are particularly complex and variable, and this makes challenging for deep learning models to reason about them. However, we note that such variability does have an underlying structure, composed of a hierarchy of patterns of related actions. We argue that such structure can emerge naturally from unscripted videos of human activities, and ca

Le Cheng, Peican Zhu, Yangming Guo, Chao Gao

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of

Alberto Fernández-Hernández, Jose I. Mestre, Manuel F. Dolz, Jose Duato

Initialization plays a critical role in Deep Neural Network training, directly influencing convergence, stability, and generalization. Common approaches such as Glorot and He initializations rely on randomness, which can produce uneven weight distributions across layer connections. In this paper, we introduce the Sinusoidal initialization, a novel determinis

Xiao Wang, Yu Jin, Lan Chen, Bo Jiang

Event-based Vision Sensors (EVS) have demonstrated significant advantages over traditional RGB frame-based cameras in low-light conditions, high-speed motion capture, and low latency. Consequently, object detection based on EVS has attracted increasing attention from researchers. Current event stream object detection algorithms are typically built upon Convo

Christian Kerskens

When substrate-constrained covariance flow on the Bures--Wasserstein manifold reaches the Williamson boundary, single-mode compression saturates and further admissible covariance evolution is forced into the cross-mode complement. This paper derives how that substrate boundary transition becomes experimentally visible in an embedded spin probe in the living

Kun Cheng

A well-known result of Chv\'{a}tal and Erd\H{o}s from 1972 states that a graph with connectivity not less than its independence number plus one is hamiltonian-connected. A graph $G$ is called an $[s,t]$-graph if any induced subgraph of $G$ of order $s$ has size at least $t.$ We prove that every $k$-connected $[k+1,2]$-graph is hamiltonian-connected except $k

Zhiwei Yang, Zeyang Fan, Yihang Lai, Qi Chen

MZI-based block optical neural networks (BONNs), which can achieve large-scale network models, have increasingly drawn attentions. However, the robustness of the current training algorithm is not high enough. Moreover, large-scale BONNs usually contain numerous trainable parameters, resulting in expensive computation and power consumption. In this article, b

Francesca Calore, Christopher Eckner

These lecture notes provide an overview of high-energy astrophysical processes involving axions, axion-like particles (ALPs), and other weakly interacting slim particles (WISPs) focusing on their potential observational signatures in astrophysical environments. After introducing key concepts in high-energy astrophysics, we present the fundamental properties

Hilde I. Hummel, Arwin Gansekoele, Sandjai Bhulai, Rob van der Mei

The increasing level of sound pollution in marine environments poses an increased threat to ocean health, making it crucial to monitor underwater noise. By monitoring this noise, the sources responsible for this pollution can be mapped. Monitoring is performed by passively listening to these sounds. This generates a large amount of data records, capturing a

Shiao Wang, Xiao Wang, Liye Jin, Bo Jiang

Existing tracking algorithms typically rely on low-frame-rate RGB cameras coupled with computationally intensive deep neural network architectures to achieve effective tracking. However, such frame-based methods inherently face challenges in achieving low-latency performance and often fail in resource-constrained environments. Visual object tracking using bi

Hao Fang, Kai Huang, Hao Ye, Chongtao Guo

The pursuit of rate maximization in wireless communication frequently encounters substantial challenges associated with user fairness. This paper addresses these challenges by exploring a novel power allocation approach for delay optimization, utilizing graph neural networks (GNNs)-based reinforcement learning (RL) in device-to-device (D2D) communication. Th