October 2025 arXiv papers — page 140

Axel Janson, Joakim Andén

We propose a Bayesian approach to the problem of multi-reference alignment -- the recovery of signals from noisy, randomly shifted observations. While existing frequentist methods accurately recover the signal at arbitrarily low signal-to-noise ratios, they require a large number of samples to do so. In contrast, our proposed method leverages diffusion model

Rafał Kucharski, Anastasia Psarou, Natello Descormier

User Equilibrium is the standard representation of the so-called routing game in which drivers adjust their route choices to arrive at their destinations as fast as possible. Asking whether this Equilibrium is strong or not was meaningless for human drivers who did not form coalitions due to technical and behavioral constraints. This is no longer the case fo

Maximilian Mauel, Manuel Hinz, Patrick Seifner, David Berghaus

Ordinary differential equations (ODEs) describe dynamical systems evolving deterministically in continuous time. Accurate data-driven modeling of systems as ODEs, a central problem across the natural sciences, remains challenging, especially if the data is sparse or noisy. We introduce FIM-ODE (Foundation Inference Model for ODEs), a pretrained neural model

Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao

We present a precise measurement of the westward, rigidity-dependent shift of the Moon's shadow using three and a half years of cosmic-ray data collected by the Kilometer Square Array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO). These measurements enable us to calibrate the detector energy response in the range 20-260 TeV, with results

Abdelali Arous, Hamza Haif, Arman Farhang, Huseyin Arslan

The emergence of alternative multiplexing domains to the time-frequency domains, e.g., the delay-Doppler and chirp domains, offers a promising approach for addressing the challenges posed by complex propagation environments and next-generation applications. Unlike the time and frequency domains, these domains offer unique channel representations which provid

Richard J. Mathar

Time-dependent phase screens in ground-based astronomy are typically simulated in the so-called frozen-screen approximation by establishing a static phase screen on a large pupil and dragging an aperture equivalent to the size of the actual input pupil across this oversized phase screen. The speed of this motion sweeping through the large phase screen is equ

Gessica Alecci, Michele Graffeo, Alexander Stokes

The aim of these notes is to present an accessible overview of some topics in classical algebraic geometry which have applications to aspects of discrete integrable systems. Precisely, we focus on surface theory on the algebraic geometry side, which is applied to differential and discrete Painlev\'e equations on the integrable systems side. Along the way we

Yanlin Jiang, Yuchen Liu, Mingren Liu

Zero-shot denoisers address the dataset dependency of deep-learning-based denoisers, enabling the denoising of unseen single images. Nonetheless, existing zero-shot methods suffer from long training times and rely on the assumption of noise independence and a zero-mean property, limiting their effectiveness in real-world denoising scenarios where noise chara

Lexi N. Gault, Liese van Zee, Elizabeth A. K. Adams, James M. Wells

Stellar feedback drives winds and outflows critical to the baryon cycles of low-mass galaxies whose shallow gravitational potential wells make them particularly susceptible to mass and metal loss through outflows. However, spatially resolved observations of stellar-feedback-driven outflows are limited due to their low-surface brightness and transient nature.

Richard von Eckardstein

The GEFF - the Gradient Expansion Formalism Factory - is a new Python package designed to study gauge-field production during inflation. The package provides a framework to implement and use the gradient expansion formalism (GEF), a numerical technique devised to study the nonlinear dynamics associated with inflationary gauge-field generation. The GEF has al

Chaoxu Pang, Yixuan Cao, Ping Luo

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities under the widely adopted SFT+RLVR paradigm, which first performs Supervised Fine-Tuning (SFT) on human-annotated reasoning trajectories (rationales) to establish initial reasoning behaviors, then applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize the model

Meihui Zhang, Liming Wang, Chi Zhang, Zhaojing Luo

A growing trend in modern data analysis is the integration of data management with learning, guided by accuracy, latency, and cost requirements. In practice, applications draw data of different formats from many sources. In the meanwhile, the objectives and budgets change over time. Existing systems handle these applications across databases, analysis librar

Ziao Yang, Longbo Huang, Hongfu Liu

Data-centric learning emphasizes curating high-quality training samples to boost performance rather than designing new architectures. A central problem is to estimate the influence of training sample efficiently. Prior studies largely focus on static influence measured on a converged model, overlooking how data valuation dynamically changes during optimizati

Martin Bullinger, Adam Dunajski, Edith Elkind, Matan Gilboa

We study stability in additively separable hedonic games when coalition sizes have to respect fixed size bounds. We consider four classic notions of stability based on single-agent deviations, namely, Nash stability, individual stability, contractual Nash stability, and contractual individual stability. For each stability notion, we consider two variants: in

David Berghaus, Patrick Seifner, Kostadin Cvejoski, Ramses J. Sanchez

Many scientific fields, from medicine to seismology, rely on analyzing sequences of events over time to understand complex systems. Traditionally, machine learning models must be built and trained from scratch for each new dataset, which is a slow and costly process. We introduce a new approach: a single, powerful model that learns the underlying patterns of

Anand Srinivasan, Jean-Jacques Slotine

Entropy-regularized optimal transport, which has strong links to the Schr\"odinger bridge problem in statistical mechanics, enjoys a variety of applications from trajectory inference to generative modeling. A major driver of renewed interest in this problem is the recent development of fast matrix-scaling algorithms\textemdash known as iterative proportional

Arip Asadulaev, Fakhri Karray, Martin Takac

Offline reinforcement learning (RL) learns exclusively from static datasets, without further interaction with the environment. In practice, such datasets vary widely in quality, often mixing expert, suboptimal, and even random trajectories. The choice of algorithm therefore depends on dataset fidelity. Behavior cloning can suffice on high-quality data, where

Nzubechukwu C. Ohalete, Kevin B. Gittner, Lauren M. Matheny

Large Language Models (LLMs) are highly sensitive to prompt design, and making optimized prompting techniques is crucial for generating consistent, high-quality outputs. In this study, we introduce COSTAR-A, a novel prompt engineering framework that enhances the existing COSTAR method, which stands for Context, Objective, Style, Tone, Audience, and Response,

Yuxiang Zhang, Jiangming Shu, Ye Ma, Xueyuan Lin

Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent's reasoning state, leading to suboptimal decisions. We propose Memory-as-Action (MemAct), a framework that

A. De Rújula

The Cannon-Ball model of Gamma-Ray Bursts and their afterglows--described in the text and in innumerable previous occasions--is extremely successful and predictive. In a few intrinsically bright GRBs, gamma-rays with energies in the TeV range have been observed. The CB model, I argue, has no difficulty in describing the origin and approximate properties of t

Guangming Sheng, Yuxuan Tong, Borui Wan, Wang Zhang

Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performance. However, the scalability of existing RL frameworks is limited, as extreme long-tail skewness in RL trajectory generation causes severe GPU underutilization. Current asynchronous

Lamsahel Noureddine, Abdeladim El Akri, Ahmed Ratnani

This work addresses the Galerkin isogeometric discretization of the one-dimensional Laplace eigenvalue problem subject to homogeneous Dirichlet boundary conditions on a bounded interval. We employ GLT theory to analyze the behavior of the eigenfrequencies when a reparametrization is applied to the computational domain. Under suitable assumptions on the repar

Friedemann Brock, Francesco Chiacchio

We study the following class of Steklov eigenvalue problems: \[ \nabla \cdot \bigl( w \nabla u \bigr) = 0 \quad \text{in } \Omega, \qquad \frac{\partial u}{\partial \nu} = \gamma v u \quad \text{on } \partial \Omega, \] where $w$ and $v$ are prescribed positive radial functions, $\Omega$ is a Lipschitz domain in $\mathbb{R}^N$ with $N \geq 2$ and $\nu$ denot

Ajith Anil Meera, Abian Torres, Pablo Lanillos

Prehistoric humans invented stone tools for specialized tasks by not just maximizing the tool's immediate goal-completion accuracy, but also increasing their confidence in the tool for later use under similar settings. This factor contributed to the increased robustness of the tool, i.e., the least performance deviations under environmental uncertainties. Ho

Gunwoo Kim, Taejune Park, Jinwoo Kim

In modern containerized cloud environments, the adoption of RDMA (Remote Direct Memory Access) has expanded to reduce CPU overhead and enable high-performance data exchange. Achieving this requires strong performance isolation to ensure that one container's RDMA workload does not degrade the performance of others, thereby maintaining critical security assura

Patricia Marques, Andreas Wichert, Duarte Magano, Bruno Coutinho

Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein interaction (PPI) networks offers a promising avenue for their detection [ 1, 2], but scaling to large network datasets is computationally demanding. Quantum computing offers compact repre

Alper Çakan, Vipul Goyal, Fuyuki Kitagawa, Ryo Nishimaki

Unclonable cryptography leverages the quantum no-cloning principle to copy-protect cryptographic functionalities. While most existing works address the basic single-copy security, the stronger notion of multi-copy security remains largely unexplored. We introduce a generic compiler that upgrades collusion-resistant unclonable primitives to achieve multi-copy

Peter Doyle, Richard Evan Schwartz

A paper torus is a piecewise linear isometric embedding of a flat torus into $\R^3$. Following up on the $8$-vertex paper tori discovered by the second author, we prove universality and collapsibility results about these objects. One corollary is that any flat torus without reflection symmetry is realized as an $8$-vertex paper torus. Another corollary is th

Iñaki Lacunza, Javier Garcia Gilabert, Francesca De Luca Fornaciari, Javier Aula-Blasco

We present ACADATA, a high-quality parallel dataset for academic translation, that consists of two subsets: ACAD-TRAIN, which contains approximately 1.5 million author-generated paragraph pairs across 96 language directions and ACAD-BENCH, a curated evaluation set of almost 6,000 translations covering 12 directions. To validate its utility, we fine-tune two

Alexander C. Mayer, Rüdiger Pakmor, Thorsten Naab, Oliver Zier

We present an implementation of radiative transfer with flux-limited diffusion (FLD) for the moving-mesh code {\small AREPO} and use the method in a physical model for the formation of protostars with non-ideal radiation-magnetohydrodynamics (RMHD). We follow previous work in splitting the additional terms to the hydrodynamical equations arising from the inc

Sepehr Assadi, Soheil Behnezhad, Sayan Bhattacharya, Martín Costa

Vizing's theorem states that any $n$-vertex $m$-edge graph of maximum degree $\Delta$ can be edge colored using at most $\Delta + 1$ different colors. Vizing's original proof is easily translated into a deterministic $O(mn)$ time algorithm. This deterministic time bound was subsequently improved to $\tilde O(m \sqrt n)$ time, independently by [Arjomandi, 198

Manuel Hinz, Maximilian Mauel, Patrick Seifner, David Berghaus

High-dimensional recordings of dynamical processes are often characterized by a much smaller set of effective variables, evolving on low-dimensional manifolds. Identifying these latent dynamics requires solving two intertwined problems: discovering appropriate coarse-grained variables and simultaneously fitting the governing equations. Most machine learning

Davide Greco, Konrad Rawlik

Large Language Models are increasingly popular in genomics due to their potential to decode complex biological sequences. Hence, researchers require a standardized benchmark to evaluate DNA Language Models (DNA LMs) capabilities. However, evaluating DNA LMs is a complex task that intersects genomic's domain-specific challenges and machine learning methodolog

Muhammad Ashfaq, Ahmed R. Sadik, Teerath Das, Muhammad Waseem

Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems (CSs)--is crucial for adaptability. Despite growing interest, the literature lacks a cohesive synthesis of runtime composit

Israel Mason-Williams, Gabryel Mason-Williams, Helen Yannakoudakis

Knowledge distillation is considered a compression mechanism when judged on the resulting student's accuracy and loss, yet its functional impact is poorly understood. We quantify the compression capacity of knowledge distillation and the resulting knowledge transfer from a functional perspective, decoupling compression from architectural reduction to provide

Jordan Teeter, Topojit Debnath, Harshil Goyal, Md Sabbir Hossen Bijoy

We report that MoX3 (X = I, Br) are rare van der Waals materials that exhibit signatures of both quantum spin chains with a spin singlet ground state and classical Neel order. Bulk single crystals grown by chemical vapor transport exhibit classical antiferromagnetic ground states with a transition temperature of ~40 K as revealed by susceptibility and specif

Juan Pablo Aguilera, Thibaut Kouptchinsky

We prove that Arithmetical Comprehension is equivalent to the determinacy of all clopen integer games in which each player has at most two moves per turn.

Lukas Pries, Markus Ryll

In the evolving landscape of high-speed agile quadrotor flight, achieving precise trajectory tracking at the platform's operational limits is paramount. Controllers must handle actuator constraints, exhibit robustness to disturbances, and remain computationally efficient for safety-critical applications. In this work, we present a novel neural-augmented feed

Amir Sabzalipour, Mohammad Shafiei, Milorad V. Milošević

Magnetic topological insulators (MTIs) are among the prominent platforms for the next generation of high-speed and low-power spintronic devices. However, unlike their non-magnetic counterparts, where the surface spin-momentum locking prevents electrons from being scattered by non-magnetic impurities and results in a dissipationless electronic flow, magnetic

David G. J. Heesterbeek, Max H. C. van Riel, Ray S. S. Sheombarsing, Tristan van Leeuwen

As disease often alters the structural properties of soft tissue, noninvasive elastography techniques have emerged to quantitatively assess in vivo mechanical properties. Magnetic Resonance Elastography (MRE) based on dynamic deformations is the standard technique for imaging mechanical properties, but the viscoelastic nature of soft tissue makes the results

Siyuan Li, Aodu Wulianghai, Xi Lin, Guangyan Li

With the increasing integration of large language models (LLMs) into open-domain writing, detecting machine-generated text has become a critical task for ensuring content authenticity and trust. Existing approaches rely on statistical discrepancies or model-specific heuristics to distinguish between LLM-generated and human-written text. However, these method

Robert Cardona, Julian Chaidez, Francisco Torres de Lizaur

The helicity, or asymptotic linking number, is a functional of exact volume-preserving vector fields on 3-manifolds, invariant under volume-preserving transformations. It is known to exhibit remarkable uniqueness properties: many invariant functionals reduce to functions of helicity. We examine how severely this uniqueness can fail. On integral homology sphe

Runting Li, Shijie Lian, Hua Li, Yutong Li

Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable

D. K. Kozyreva, V. V. Ryzhikov

Generic extensions of aperiodic probability space automorphisms are recurrent.

Giacomo Bertollo, Naz Bodemir, Jonah Burgess

Analyzing 500 CTF participants, this paper shows that while participants readily bypassed simple AI guardrails using common techniques, layered multi-step defenses still posed significant challenges, offering concrete insights for building safer AI systems.

Qihang Zhao, Zhongbo Sun, Xiaoyang Zheng, Xian Guo

With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becoming a longstanding issue. Existing methods align items' side content with collaborative information to transfer collaborative signals from hi

Chao Chen, Zhixin Ma, Yongqi Li, Yupeng Hu

Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoning to the integration of visual information, enabling the thought process to be conveyed through both images and text. Despite its effectiveness, current multimodal reasoning metho

Minghuan Zeng, Ling Qin, Shiping Feng, Dong-Hui Xu

Spin current generated electrically is among the core phenomena of spintronics for driving high-performance spin device applications. Here, on the basis of systematic investigations for the hole doped single-layer Haldane-Hubbard(HH) model, we propose a new bilayer HH model to realize the compensated odd-parity spin splitting and the $T$-even spin Hall condu

Marina Dolfin, Leone Leonida

This essay provides a critical overview of the mathematical kinetic theory of active particles, which is used to model and study collective systems consisting of interacting living entities, such as those involved in behavior and evolution. The main objective is to study the interactions of large systems of living entities mathematically. More specifically,

Masoud Makrehchi

Artificial Intelligence (AI) has emerged as both a continuation of historical technological revolutions and a potential rupture with them. This paper argues that AI must be viewed simultaneously through three lenses: \textit{risk}, where it resembles nuclear technology in its irreversible and global externalities; \textit{transformation}, where it parallels

Loïc Albert, Sabrina R. Poulsen, Érika Le Bourdais, John H. Debes

The MIRI Excesses Around Degenerates Survey is a Cycle 2 James Webb Space Telescope (JWST) Survey program designed to image nearby white dwarfs in the mid-IR with the MIRI imaging mode. Only a handful of white dwarfs have previously been observed beyond 8~\micron. This survey gathered observations for 56 white dwarfs within 25~pc at 10 and 15~\micron, probin

Balázs Gerencsér, Viktor Harangi

Determining the asymptotic independence ratio of random regular graphs is a key challenge in the area of sparse random graphs. Due to the interpolation method, we have very good upper bounds at our disposal, which are actually known to be sharp for sufficiently large degrees. However, we are still in need of good explicit lower bounds for specific degrees. T

Shuyi Yan

A common step in algorithms related to shortest paths in undirected graphs is that, we select a subset of vertices as centers, then grow a ball around each vertex until a center is reached. We want the balls to be as small as possible. A randomized algorithm can uniformly sample $r$ centers to achieve the optimal (expected) ball size of $\Theta(n/r)$. A folk

Ilya Baldin, Michael Goodrich, Vardan Gyurjyan, Graham Heyes

Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define and implement an edge to compute cluster computational load balancing acceleration architecture. The ESnet-JLab FPGA Accelerated Transport (EJFAT) architecture focuses on FPGA acceleration to address compression, fragmentation, UDP packet destin

Kevin Kuo, Chhavi Yadav, Virginia Smith

Cross-silo federated learning (FL) is a promising approach to enable cross-organization collaboration in machine learning model development without directly sharing private data. Despite growing organizational interest driven by data protection regulations such as GDPR and HIPAA, the adoption of cross-silo FL remains limited in practice. In this paper, we co

Abel Schootemeijer, Ylva Götberg, Norbert Langer, Giacomo Bortolini

Stellar wind mass loss is often assumed to depend on their metallicity $Z$. Therefore, evolutionary models of massive stars at lower $Z$ are able to retain more of their H-rich layers and evolve into brighter cool supergiants (cool SGs; $T_\mathrm{eff} < 7$ kK). Surprisingly, in galaxies in the range $0.2 \lesssim Z / Z_\odot \lesssim 1.5$ previous studies d

Ian G. Moss

This paper aims to gather together some of the basic ideas behind the theory of false vacuum decay in quantum Ising models, focusing on the application of spin chains as analogue systems to false vacuum decay in elementary particle theory. Elementary results on quantum Ising models are reformulated and extended to more closely resemble the theory of false va

Thiago Brevidelli

Recently, Korkmaz established the lower bound of $3g - 2$ for the dimension of a faithful representation of the mapping class group of an orientable surface of genus $g \ge 3$. We raise this bound to $4g - 3$ in the setting of surfaces of genus $g \ge 7$. A new ingredient is a finer study of the commutation relations in $\operatorname{PMod}(\Sigma)$. We use

Shi-Ting Ni, Kairong Fang, Yuyang Wang, Pan Hui

Virtual Reality (VR) is a promising tool for interview training, yet the psychological dynamics of group interviews, such as social comparison, remain underexplored. We investigate this phenomenon by developing an immersive VR group interview system and conducting an eye-tracking study with 73 participants. We manipulated peer performance using ambiguous beh

Hadi Nemati, Álvaro Ortega, Pedro Sánchez-Martín, Lukas Sigrist

In the transition toward a sustainable power system, renewable-based Virtual Power Plants (RVPPs) have emerged as a promising solution to the challenges of integrating renewable energy sources into electricity markets. Their viability, however, depends on effective market participation strategies and the ability to manage uncertainties while leveraging flexi

Bryan Eikema, Evgenia Ilia, José G. C. de Souza, Chrysoula Zerva

Large language models (LLMs) often miscommunicate their uncertainty: repeated queries can produce divergent answers, yet generated responses are typically unhedged or hedged in ways that do not reflect this variability. This conveys unfaithful information about the uncertain state of the LLMs' knowledge, creating a faithfulness gap that affects even strong L

Jiachen Lei, Keli Liu, Julius Berner, Haiming Yu

Pixel-space generative models are often more difficult to train and generally underperform compared to their latent-space counterparts, leaving a persistent performance and efficiency gap. In this paper, we introduce a novel two-stage training framework that closes this gap for pixel-space diffusion and consistency models. In the first stage, we pre-train en

Shiyang Hu, Shijie Tan, Dan Li, Lina Zhang

This paper presents OCTOPUS, a relativistic ray-tracing algorithm developed within a Fortran-based, OpenMP-accelerated framework, designed for asymptotically flat, spherically symmetric curved spacetimes. The code efficiently and accurately computes key relativistic features -- including the black hole event horizon, photon rings, critical curves, and innerm

Caroline Ferguson, Yan Li, Yi Zhang, Xueju Wang

Organoids offer a promising alternative in biomedical research and clinical medicine, with better feature recapitulation than 2D cultures. They also have more consistent responses with clinical results when compared to animal models. However, major challenges exist in the longevity of culture, the reproducibility of organoid properties, and the development o

J. Woodfield, A. Lobbe

Convenient, easy to implement stochastic integration methods are developed on the basis of abstract one-step deterministic order $p$ integration techniques. The abstraction as an arbitrary one step map allows the inspection of easy to implement stochastic exponential time differencing Runge-Kutta (SETDRK), stochastic integrating factor Runge-Kutta (SIFRK) an

Mark Koch, Alan Lawrence, Kartik Singhal, Seyon Sivarajah

We present ongoing work on Guppy, a domain-specific language embedded in Python that allows users to write high-level hybrid quantum programs with complex control flow in Pythonic syntax, aiming to run them on actual quantum hardware.

Yasaman Haghighi, Bastien van Delft, Mariam Hassan, Alexandre Alahi

We propose LayerSync, a domain-agnostic approach for improving the generation quality and the training efficiency of diffusion models. Prior studies have highlighted the connection between the quality of generation and the representations learned by diffusion models, showing that external guidance on model intermediate representations accelerates training. W

Manuel Dizenhaus, Franco De Simone, German A. Patterson

We investigate the influence of an anonymous leader on a collective of self-propelled robots using Kilobot experiments and numerical simulations. A single leader alternated deterministically between clockwise and counterclockwise motion, while the other robots followed a stochastic majority rule. Although the leader does not change global order, it induces c

Simon Ravé, Jean-Christophe Lombardo, Pejman Rasti, Alexis Joly

We present a zero-shot segmentation approach for agricultural imagery that leverages Plantnet, a large-scale plant classification model, in conjunction with its DinoV2 backbone and the Segment Anything Model (SAM). Rather than collecting and annotating new datasets, our method exploits Plantnet's specialized plant representations to identify plant regions an

Yuki Matsubara

We study the moduli space of parabolic connections of rank two on the complex projective line $\mathbb{P}^1$ minus five points with fixed spectral data. This paper aims to compute the cohomology of the structure sheaf and a certain vector bundle on this space. We use this computation to extend the results of Arinkin, which proved a specific Geometric Langlan

Hao Lin, Wenling Zhou

A $3$-uniform hypergraph (or $3$-graph) $H=(V,E)$ is $(d,\mu,1)$-\emph{dense} if for any subsets $X,Y,Z\subseteq V$, the number of triples $(x,y,z)\in X\times Y\times Z$ such that $\{x,y,z\}$ is an edge of $H$ is at least $d|X||Y||Z|-\mu |V|^3$. The \emph{$k$-star} $S_k$ is the $3$-graph with a center vertex and $k$ distinct leaf vertices, whose edge set con

Isabella Khan

This paper proves a Koszul duality result between weighted $\mathcal{A}_{\infty}$-algebras constructed in the author's previous work. In the process, we construct a new box tensor product for weighted $\mathcal{A}_{\infty}$ bimodules, and verify a correspondence between weighted $\mathcal{A}_{\infty}$-algebra maps and a particular class of $\mathcal{A}_{\inf

Herng Yi Cheng

The Brown Representability Theorem implies that cohomology operations can be represented by continuous maps between Eilenberg-Maclane spaces. These Eilenberg-Maclane spaces have explicit geometric models as spaces of cycles on round spheres and spaces of relative cycles on unit disks, due to the Almgren Isomorphism Theorem. A. Nabutovsky asked what maps betw

Quang Nguyen, Tri Le, Baoru Huang, Minh Nhat Vu

Learning human motion based on a time-dependent input signal presents a challenging yet impactful task with various applications. The goal of this task is to generate or estimate human movement that consistently reflects the temporal patterns of conditioning inputs. Existing methods typically rely on cross-attention mechanisms to fuse the condition with moti

Kishore C. Patra, Ryan J. Foley, Nicholas Earl, Kyle W. Davis

We present JWST/NIRSpec and NIRCam observations of the first optically selected off-nuclear tidal disruption event (TDE), AT 2024tvd, along with Keck/KCWI integral field unit spectroscopy. The spectra show broad H and He emission lines that are characteristic of a TDE. Stellar kinematics show smooth host-galaxy morphology and ordered bulge rotation, with no

Wojciech Sadowski, Christin Velten, Maximilian Brömmer, Hakan Demir

The present study focuses on the gas flow through an experiment-scale modular packed bed reactor consisting of square bars, arranged in layers. Each layer is rotated by $30^\circ$ resulting in a complex shape of the void spaces between the bars. Particle Image Velocimetry measurement results inside and on top of the studied system are presented for particle-

Emiliano Liwski

We study a class of determinantal ideals arising from conditional independence (CI) statements with hidden variables. Such CI statements translate into determinantal conditions on a matrix whose entries represent the probabilities of events involving the observed random variables. Our main objective is to determine the irreducible components of the correspon

Volker Tresp, Hang Li, Federico Harjes, Yunpu Ma

Although quantum systems are generally described by quantum state vectors, we show that in certain cases their measurement processes can be reformulated as probabilistic equations expressed in terms of probabilistic state vectors. These probabilistic representations can, in turn, be approximated by the neural network dynamics of the Tensor Brain (TB) model.

Dilek Söylemez, Mehmet Ünver

Approximation theory has long been concerned with the development of positive linear operators that effectively approximate classes of functions. Among the most well-known results in this area are Korovkin-type approximation theorems, which provide simple and elegant criteria for convergence by testing only on a small set of functions. Motivated by these cla

Zi-Xia Song, Thomas Tibbetts

A dominating $K_t$ minor in a graph $G$ is a sequence $(T_1,\dots,T_t)$ of pairwise disjoint non-empty connected subgraphs of $G$, such that for $1 \leq i<j\leq t$, every vertex in $T_j$ has a neighbor in $T_i$. Replacing ``every vertex in $T_j$'' by ``some vertex in $T_j$'' retrieves the standard definition of a $K_t$ minor. The strengthened notion was intr

Maja H. Kirkeby, Timmie Lagermann

Sustainability reporting for web-based services often relies on simplified end-user energy models that assume constant laptop power during browser interactions. Energy models such as Digst and DIMPACT apply fixed power values (15-22W), yet the validity of this approach for realistic browsing remains underexplored. We empirically evaluate constant-power assum

Tianhao Li, Tingfa Xu, Ying Wang, Haolin Qin

Drone-based multi-object tracking is essential yet highly challenging due to small targets, severe occlusions, and cluttered backgrounds. Existing RGB-based tracking algorithms heavily depend on spatial appearance cues such as color and texture, which often degrade in aerial views, compromising reliability. Multispectral imagery, capturing pixel-level spectr

Michael Scully, Zi-Xia Song

Hadwiger's Conjecture from 1943 states that every graph with chromatic number $t$ contains a $K_t$ minor. Illingworth and Wood [arXiv:2405.14299] introduced the concept of a ``dominating $K_t$ minor'' and asked whether every graph with chromatic number $t$ contains a dominating $K_t$ minor. This question is a substantial strengthening of Hadwiger's Conjectur

Jingcong Liang, Shijun Wan, Xuehai Wu, Yitong Li

Large Reasoning Models (LRMs) have demonstrated impressive performance on complex tasks, including logical puzzle games that require deriving solutions satisfying all constraints. However, whether they can flexibly apply appropriate rules to varying conditions, particularly when faced with non-canonical game variants, remains an open question. Existing corpo

A. Inventar, S. Gabici, E. Peretti

Young massive stellar clusters (YMSCs) have recently regained interest as PeVatron candidates, potentially accounting for the cosmic-ray (CR) knee as alternatives to isolated supernova remnants (SNRs). LHAASO's unique capability to detect photons above 0.1 PeV, hence tracing multi-PeV CRs, can provide critical constraints on galactic acceleration models when

Andreas Radler, Vincent Seyfried, Johannes Brandstetter, Thomas Lichtenegger

Neural surrogates have shown great potential in simulating dynamical systems, while offering real-time capabilities. We envision Neural Twins as a progression of neural surrogates, aiming to create digital replicas of real systems. A neural twin consumes measurements at test time to update its state, thereby enabling context-specific decision-making. We argu

R. Terra, A. V. Giannini, F. S. Navarra

This work investigates the possibility of accessing the initial geometric shape of the proton in proton-proton and proton-nucleus collisions at the LHC. In particular, we look for manifestations of the configuration in which the proton is made of three quarks linked by a Y-shape gluon string, called baryon junction. This initial state spatial configuration h

Olesya Razuvayevskaya, Adel Tayebi, Ulrikke Dybdal Sørensen, Kalina Bontcheva

This study presents the first large-scale quantitative analysis of the efficiency of X's Community Notes, a crowdsourced moderation system for identifying and contextualising potentially misleading content. Drawing on over 1.8 million notes, we examine three key dimensions of crowdsourced moderation: participation inequality, consensus formation, and timelin

Chandrodoy Chattopadhyay, Josh Ott, Thomas Schaefer, Vladimir V. Skokov

We study heat conduction and momentum transport in the context of stochastic fluid dynamics. We consider a fluid described by model H in the classification of Hohenberg and Halperin. We study both non-critical and critical fluids, and we investigate transport properties in two as well as three dimensions. Our results are based on numerical simulations of mod

V. O. Gotovtsev, I. V. Dyakonov, O. V. Borzenkova, K. A. Taratorin

In the field of quantum technology, single photons have emerged as a pivotal resource, prompting the development of heralded single photon sources (HSPS) with enhanced generation probability. The majority of such sources are based on spontaneous parametric down-conversion (SPDC), but they exhibit a low single photon generation probability. The multiplexing p

Andries Rosseau, Raphaël Avalos, Ann Nowé

The competitive and cooperative forces of natural selection have driven the evolution of intelligence for millions of years, culminating in nature's vast biodiversity and the complexity of human minds. Inspired by this process, we propose a novel multi-agent reinforcement learning framework where each agent is assigned a genotype and where reward functions a

Karina Hudson, Davide Costa, Davide Degli Esposti, Lucas E. A. Stehouwer

Constricting transport through a one-dimensional quantum point contact in the quantum Hall regime enables gate-tunable selection of the edge modes propagating between voltage probe electrodes. Here we investigate the quantum Hall effect in a quantum point contact fabricated on low disorder strained germanium quantum wells. For increasing magnetic field, we o

Finn A. Roper, Yan-Chuan Cai, John A. Peacock

We detect the kinetic Sunyaev-Zeldovich imprint of peculiar motions of galaxy groups and clusters, using the photometric DESI Legacy Survey together with cosmic microwave background (CMB) maps from the Atacama Cosmology Telescope (ACT). We develop a comprehensive forward model based on the AbacusSummit cosmological simulations: mock galaxy group catalogues a

Nicolas El Maalouly, Kostas Lakis

The Exact Matching (EM) problem asks whether there exists a perfect matching which uses a prescribed number of red edges in a red/blue edge-colored graph. While there exists a randomized polynomial-time algorithm for the problem, only some special cases admit a deterministic one so far, making it a natural candidate for testing the P=RP hypothesis. A polynom

Jose Beltrán Jiménez, David Figueruelo, David F. Mota, Hans A. Winther

Cosmological models where dark matter interacts with dark energy via a pure momentum transfer and with no energy exchange (i.e. elastic) provide compelling scenarios for addressing the apparent lack of structures at low redshift. In particular, it has been shown that measurements of $S_8$ may show a statistically significant preference for the presence of el

Andrey Nesterov

An asymptotic small parameter expansion of a single Cauchy problem is constructed for a singularly perturbed system of hyperbolic equations describing vibrations of two rigidly connected strings. Equations (such as generalized Korteweg-de Vriesequations) and initial conditions for the terms of the asymptotic expansion of the solution are determined, and unde

Jieming Ke, Jimin Wang, Ji-Feng Zhang

This paper focuses on the privacy-preserving distributed estimation problem with a limited data rate, where the observations are the sensitive information. Specifically, a binary-valued quantizer-based privacy-preserving distributed estimation algorithm is developed, which improves the algorithm's privacy-preserving capability and simultaneously reduces the

Stella Frank, Emily Allaway

While Vision Language Models (VLMs) learn conceptual representations, in the form of generalized knowledge, during training, they are typically used to analyze individual instances. When evaluation instances are atypical, this paradigm results in tension between two priors in the model. The first is a pragmatic prior that the textual and visual input are bot

Madi Matymov, Ba-Hien Tran, Maurizio Filippone

An open problem in Machine Learning is how to avoid models to exploit spurious correlations in the data; a famous example is the background-label shortcut in the Waterbirds dataset. A common remedy is to train a model across multiple environments; in the Waterbirds dataset, this corresponds to training by randomizing the background. However, selecting the ri

Hashini Gunatilake, John Grundy, Rashina Hoda, Ingo Mueller

Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scal