October 2025 arXiv papers — page 180

Peter Sin

A family of oriented, normal, nonabelian Cayley graphs is presented, whose continuous-time quantum walks exhibit uniform mixing.

Aofan Liu, Haoxuan Li, Bin Wang, Ao Yang

Code generation models based on large language models (LLMs) have gained wide adoption, but challenges remain in ensuring safety, accuracy, and controllability, especially for complex tasks. Existing methods often lack dynamic integration of external tools, transparent reasoning, and user control over safety. To address these issues, we propose a controllabl

Sebastian Schuster, Jessica Santiago, Justin Feng, Matt Visser

Limits on the dark matter fraction of small mass primordial black holes from Hawking radiation are predominantly derived from the assumption of a Schwarzschild black hole evaporating. However, astrophysical black holes are usually much more realistically modelled by the rotating Kerr black hole solution. Meanwhile, electromagnetically charged black holes are

Fernanda Famá, Roberto Pereira, Charalampos Kalalas, Paolo Dini

While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored. The substantial computational demands required for training conventional CL frameworks pose a set of challenges, particularly in terms of energy consumption, data availability, an

Hanke Xie, Dake Guo, Chengyou Wang, Yue Li

Recent advances in text-to-speech (TTS) synthesis, particularly those leveraging large language models (LLMs), have significantly improved expressiveness and naturalness. However, generating human-like, interactive dialogue speech remains challenging. Current systems face limitations due to the scarcity of dual-track data and difficulties in achieving natura

Ioana Marinescu, Kyunghyun Cho, Eric Karl Oermann

In-context learning (ICL) is the ability of a large language model (LLM) to learn a new task from a few demonstrations presented as part of the context. Past studies have attributed a large portion of the success of ICL to the way these in-context demonstrations are represented, particularly to how labels are represented in classification tasks. On the other

Cedric Wind, Chris Nill, Julia Gamper, Samuel Germer

Mechanical systems provide a unique test bed for studying quantum phenomena at macroscopic length scales. However, realizing quantum states that feature quantum correlations among macroscopic mechanical objects remains an experimental challenge. Here, we propose a quantum system in which two micro-electromechanical oscillators interact through a chain of Ryd

Peter Albers, Lael Edwards-Costa, Serge Tabachnikov

We present some foundational results about the outer length billiard system, including its generating function and the invariant area form. We describe the limiting behavior of the orbits far away from the billiard table: the orbits of the map lie on the origin-centered circles, and the second iteration of the map is approximated by the flow of a Hamiltonian

Viacheslav Meshchaninov, Egor Shibaev, Artem Makoian, Ivan Klimov

The performance of pre-trained masked diffusion models is often constrained by their sampling procedure, which makes decisions irreversible and struggles in low-step generation regimes. We introduce a novel sampling algorithm that works with pre-trained models and, after a lightweight fine-tuning of a single layer, significantly improves sample quality and e

Yi Zhang, Yue Xie, Tao Sun, Fumiya Iida

Robotic performance emerges from the coupling of body and controller, yet it remains unclear when morphology-control co-design is necessary. We present a unified framework that embeds morphology and control parameters within a single neural network, enabling end-to-end joint optimization. Through case studies in static-obstacle-constrained reaching, we evalu

P. Limaye, L. G. Spitler, N. Manaswini, J. Benáček

We present Effelsberg 100-m telescope observations of the hyperactive repeating fast radio burst source FRB 20240110A, discovered by CHIME/FRB in January 2024. Using the Ultra BroadBand (UBB) receiver, spanning 1.3-6.0 GHz, we detected over 700 unique bursts across four observing epochs. A comprehensive analysis of their temporal and spectral properties reve

Yukai Song, Pengfei Zhou, César Escobar-Viera, Candice Biernesser

Suicide rates have risen worldwide in recent years, underscoring the urgent need for proactive prevention strategies. Social media provides valuable signals, as many at-risk individuals - who often avoid formal help due to stigma - choose instead to share their distress online. Yet detecting implicit suicidal ideation, conveyed indirectly through metaphor, s

Han Wu, Hamdi Joudeh

We study the information bottleneck (IB) source coding problem, also known as remote lossy source coding under logarithmic loss. Based on a rate-limited description of noisy observations, the receiver produces a soft estimate for the remote source, i.e., a probability distribution, evaluated under the logarithmic loss. We focus on the excess distortion proba

Mattia Ferrari, Lorenzo Bruzzone

The introduction of new generation hyperspectral satellite sensors, combined with advancements in deep learning methodologies, has significantly enhanced the ability to discriminate detailed land-cover classes at medium-large scales. However, a significant challenge in deep learning methods is the risk of overfitting when training networks with small labeled

Margarida Companys Franzke, Alexander Tichai, Kai Hebeler, Achim Schwenk

Understanding the emergence of complex structures of nuclei from chiral effective field theory (EFT) is a central challenge. The large number of low-energy couplings (LECs) in the EFT expansion and the significant cost of $\textit{ab initio}$ many-body calculations render large-scale sensitivity studies of many-body observables computationally prohibitive, n

Peter Borg, Dayle Scicluna

Given a set $\mathcal{F}$ of graphs, we call a copy of a graph in $\mathcal{F}$ an $\mathcal{F}$-graph. The $\mathcal{F}$-isolation number of a graph $G$, denoted by $\iota(G, \mathcal{F})$, is the size of a smallest set $D$ of vertices of $G$ such that the closed neighbourhood of $D$ intersects the vertex sets of the $\mathcal{F}$-graphs contained by $G$ (e

Jinho Cha, Eunchan Cha

Micro-randomized trials (MRTs) are increasingly used to evaluate mobile health interventions with binary proximal outcomes. Standard inverse probability weighting (IPW) estimators are unbiased but unstable in small samples or under extreme randomization. Estimated mean excursion effect (EMEE) improves efficiency but lacks double robustness. We propose a doub

Alexander Belyaev, Pierre-Alain Fayolle, Michael Cohen

We introduce a simple and efficient method to enhance and clarify images. More specifically, we deal with low light image enhancement and clarification of hazy imagery (hazy/foggy images, images containing sand dust, and underwater images). Our method involves constructing an image filter to simulate low-light or hazy conditions and deriving approximate reve

Wenlong Shi, Dingwei Wang, Liming Liu, Zhaoyu Wang

Electrification and decarbonization are transforming power system demand and recovery dynamics, yet their implications for post-outage load surges remain poorly understood. Here we analyze a metropolitan-scale heterogeneous dataset for Indianapolis comprising 30,046 feeder-level outages between 2020 and 2024, linked to smart meters and submetering, to quanti

Wenlong Shi, Hongyi Li, Zhaoyu Wang

Underground power distribution systems (PDSs) are increasingly deployed in urban areas. The integration of smart devices including smart switchgears, pad-mounted distribution transformers and inverter-based resources (IBRs) enhance system resilience, however simultaneously introducing unique challenges. The challenges include inrush currents caused by trappe

Jianan Mu, Mingyu Shi, Yining Wang, Tianmeng Yang

LLM-based RTL generation is an interesting research direction, as it holds the potential to liberate the least automated stage in the current chip design. However, due to the substantial semantic gap between high-level specifications and RTL, coupled with limited training data, existing models struggle with generation accuracy. Drawing on human experience, d

Zechang Sun, Yuan-Sen Ting, Yaobo Liang, Nan Duan

Astronomical research has long relied on human expertise to interpret complex data and formulate scientific hypotheses. In this study, we introduce Mephisto -- a multi-agent collaboration framework powered by large language models (LLMs) that emulates human-like reasoning for analyzing multi-band galaxy observations. Mephisto interfaces with the CIGALE codeb

Alexus Annika Kumwembe, Claire Lamman, Daniel Eisenstein, Jessica Nicole Aguilar

We demonstrate that measurements of the gravitational tidal field made with spectroscopic redshifts can be improved with information from imaging surveys. The average orientation of small groups of galaxies, or "multiplets" is correlated with large-scale structure and is used to measure the direction of tidal forces. Previously, multiplet intrinsic alignment

Nikos Theodoridis, Tim Brophy, Reenu Mohandas, Ganesh Sistu

Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on a variety of tasks that require both visual and textual understanding. Their strong generalisation abilities make them a promising component for automated driving systems, which must handle unexpected corner cases. However, to be trusted in such safety-criti

Qingxiu Liu, Jiazhen Cai, Siyuan Sheng, Yuhui Chen

Fast and scalable metadata management across multiple metadata servers is crucial for distributed file systems to handle numerous files and directories. Client-side caching of frequently accessed metadata can mitigate server loads, but it incurs significant overhead and complexity in maintaining cache consistency when the number of clients increases. We prop

Joe Watson, Ivan O'Connor, Chia-Wen Chen, Luning Sun

Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories. Although rich, unstructured text is often captured alongside these scales, it rarely contributes to measuring the target trait because it lacks direct mapping to the latent scale. We introduce the Information-De

Yi-Xin Wang, Yan Zhang, Lei Du, Lingzhen Guo

Atomic metasurfaces (AMs) provide a powerful nanophotonic platform for integrating topological effects into quantum many-body systems. In this Letter, we investigate the quantum optical and topological properties of a two-dimensional Kagome AM, going beyond the tight-binding approximation and incorporating all-to-all interactions. We reveal selective higher-

Étienne Objois, Adrian Vladu

We introduce a generic framework for solving linear programs (LPs) with many constraints $(n \gg d)$ via adaptive sparsification. Our approach provides a principled generalization of the techniques of [Assadi '23] from matching problems to general LPs and robustifies [Clarkson's '95] celebrated algorithm for the exact setting. The framework reduces LP solvin

Mohamed Essenhajy, Said Fahlaoui

Recent advances have extended the Dunkl transform to the setting of Clifford algebras. In particular, the two-sided quaternionic Dunkl transform has been introduced as a Dunkl analogue of the two-dimensional quaternionic Fourier transform. In this paper, we develop the two-sided Clifford Dunkl transform, defined using two square roots of -1 in Cl_{p,q}. We e

Renee Jerome

In the past, the Voter Model has been explicitly used to model the impact of propaganda on a dynamic, interconnected population, and certain factors have been identified that influence the behavior of voters when under outside influence. The Blotto Game has also been explicitly used to study information wars between two opposing parties, whether in regards t

Thomas Ruf

We introduce the property of countable separation for a locally convex Hausdorff space $X$ and relate it to the existence of a metrizable coarser topology. Building on this, we demonstrate how the separability of $X$ is equivalent to the existence of a locally convex topology on the dual $X'$ that is metrizable and coarser than the weak topology $\sigma(X',

Serena Dipierro, Sven Jarohs, Enrico Valdinoci

In this paper we introduce a very general setting dealing with the superposition of operators of any positive order and provide a systematic study of them. We also provide examples and counterexamples, as well as characterizing properties of the measures and the functional spaces under consideration. Moreover, we present some applications regarding the exist

Anne Müller, Mohd Kashif, Nico Döttling

Fully Homomorphic Encryption (FHE) enables the evaluation of programs directly on encrypted data. However, because only basic operations can be performed on ciphertexts, programs must be expressed as boolean or arithmetic circuits. This low-level representation makes implementing applications for FHE significantly more cumbersome than writing code in a high-

Jordan Cotler, Clément Hongler, Barbora Hudcová

Self-replication is central to all life, and yet how it dynamically emerges in physical, non-equilibrium systems remains poorly understood. Von Neumann's pioneering work in the 1940s and subsequent developments suggest a natural hypothesis: that any physical system capable of Turing-universal computation can support self-replicating objects. In this work, we

Bjarne Stroustrup

We present programming techniques to illustrate the facilities and principles of C++ generic programming using concepts. Concepts are C++'s way to express constraints on generic code. As an initial example, we provide a simple type system that eliminates narrowing conversions and provides range checking without unnecessary notational or run-time overheads. C

Pál Zsámboki, Benjamin Levi, David Ansel Josef Smith, Mitansh Kagalwala

Length Generalization is the essential capacity of autonomous agents to perform tasks in longer contexts than those encountered during training. To systematically study this feat, we test how well models can approximate the next token distributions in algorithmic tasks. This is to take into account the realistic possibility of multiple next tokens being lega

Beatrice Cipriani, Hender Lopez

Nanoparticles (NPs) demonstrate considerable potential in medical applications, including targeted drug delivery and diagnostic probes. However, their efficacy depends on their ability to navigate through the complex biological environments inside living organisms. In such environments, NPs interact with a dense mixture of biomolecules, which can reduce thei

Himanshu Chaudhary, Vipin Kumar Sharma, Salvatore Capozziello, G. Mustafa

Observational data play a pivotal role in identifying cosmological models that are both theoretically consistent and empirically viable. In this work, we investigate the level of preference for dynamical dark energy over a cosmological constant using current late-time observational datasets, including Cosmic Chronometers , Baryon Acoustic Oscillations from D

Benjamin F. Maier, Ulf Aslak, Luca Fiaschi, Nina Rismal

Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic similarity rating (SSR), a method that elicits textual responses

Aliya Turegeldinova, Bakytzhan Amralinova, Mate Miklos Fodor, Akerkin Eraliyeva

Generative AI does more than cut costs. It pulls products toward a shared template, making offerings look and feel more alike while making true originality disproportionately expensive. We capture this centripetal force in a standard two-stage differentiated-competition framework and show how a single capability shift simultaneously compresses perceived diff

Maxim van den Berg, Matthias Christandl, Vladimir Lysikov, Harold Nieuwboer

Tensors are fundamental in mathematics, computer science, and physics. Their study through algebraic geometry and representation theory has proved very fruitful in the context of algebraic complexity theory and quantum information. In particular, moment polytopes have been understood to play a key role. In quantum information, moment polytopes (also known as

Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov

Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead t

Weixin Li, Rundong Yuan, Fenglin Zhong, Bo Peng

Excitation and control of antiferromagnetic magnon modes lie at the heart of coherent antiferromagnetic spintronics. Here, we propose a topological surface magnon-polariton as a new approach in the prototypical magnonic material hematite. We show that in an insulating canted antiferromagnet, where strong-coupled magnon-photon modes can be achieved using elec

Mikaëla Ngamboé, Jean-Simon Marrocco, Jean-Yves Ouattara, José M. Fernandez

With the growing reliance on the vulnerable Automatic Dependent Surveillance-Broadcast (ADS-B) protocol in air traffic management (ATM), ensuring security is critical. This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems (IDS) for ADS-B. Focusing on ground-based ATM systems, we evalu

Mengdi Chu, Zefeng Qiu, Meng Ling, Shuning Jiang

We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images. We then examined how these scores align with 12 image-based metrics spanning information-theoretic, clutter, color, and

Chao Liu, Kaicheng Zhang, Xin Zhou, Mingjian Wu

Perovskite-organic tandem solar cells (P-O TSCs) hold great promise for next-generation thin-film photovoltaics, with steadily improving power conversion efficiency (PCE). However, the development of optimal interconnecting layers (ICLs) remains one major challenge for further efficiency gains, and progress in understanding the improved long-term stability o

Muxi Diao, Yutao Mou, Keqing He, Hanbo Song

The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits the semantic diversity of the synthesized adversarial prompts. We propose AutoRed, a free-form adversarial prompt generation framework that removes the need for seed instructions.

Kalyan Ramana Gattoz, Prasad S. Onkar

In the early stages of engineering design, it is essential to know how a product behaves, especially how it moves. As designers must keep adjusting the motion until it meets the intended requirements, this process is often repetitive and time-consuming. Although the physics behind these motions is usually based on simple equations, manually working through t

Lorenzo Nikiforos, Luciano Prono, Charalampos Antoniadis, Fabio Pareschi

The increasing scale of Deep Neural Networks (DNNs) introduces the need for compression techniques such as pruning, quantization, and low-rank decomposition. While these methods are very effective at reducing memory, computation, and energy consumption, they may introduce severe accuracy degradation, which is often mitigated by using iterative, gradual compr

Alexander Madurowicz, Jean-Baptiste Ruffio, Bruce Macintosh, Marshall Perrin

We present high-contrast direct spectroscopy of the low-mass, cool exoplanet 51 Eridani b (2-4 M$_\textrm{Jup}$, $\sim$750 K) using JWST / NIRSpec in a fixed-slit configuration (F290LP / G395H, $3-5\,\mu$m, R$\sim$2,700). A cross correlation analysis between the continuum-subtracted data and atmospheric forward models indicates a detection of molecular signa

Yaning Li, Yutong Chen, Yihan Hou, Chenyi Chen

Lacquerware, a representative craft of Chinese intangible cultural heritage, is renowned for its layered aesthetics and durability but faces declining engagement. While prior human-computer interaction research has explored embedding interactive circuits to transform lacquerware into responsive artifacts, most studies have focused on fabrication techniques r

Marius Dragoi, Ioana Pintilie, Florin Gogianu, Florin Brad

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm to improve Large Language Models on reasoning tasks such as coding, math or logic. To assess the reasoning boundary (the fraction of problems a model can solve) researchers often report Pass@k at large sampling budgets. Recent results reveal a crossover phenomenon: while

Jonathan Schulte, Eric Schwegler, Ute A. Hellmich, Nina Morgner

Biological processes rely on finely tuned homo- and heteromeric interactions between (biomacro)molecules. The strength of an interaction, typically given by the dissociation constant (KD), plays a crucial role in basic research and must be monitored throughout the development of drugs and agrochemicals. An ideal method for KD determination is applicable to v

Chanania Steinbock, Daniel A. Beller

We study the over-damped dynamics of individual one-dimensional elastic filaments subjected to a chiral active force which propels each point of the filament at a fixed angle relative to the tangent vector of the filament at that point. Such a model is a reasonable starting point for describing the behavior of polymers such as microtubules in gliding assay e

Laura Shou

The Walsh-quantized baker's maps are models for quantum chaos on the torus. We show that for all baker's map scaling factors $D\ge2$ except for $D=4$, typically (in the sense of Haar measure on the eigenspaces, which are degenerate) the empirical distribution of the scaled matrix element fluctuations $\sqrt{N}\{\langle \varphi^{(j)}|\operatorname{Op}_{k,\ell

Patryk Lipka-Bartosik, Jessica Bavaresco, Nicolas Brunner, Pavel Sekatski

A key result in entanglement theory is that the addition of a catalyst dramatically enlarges the set of possible state transformations via local operations and classical communication (LOCC). However, it remains unclear what is the interplay between classical communication and quantum catalysis. Here our aim is to disentangle the effect of the catalyst from

E. Cristo, J. P. Faria, N. C. Santos, W. Dethier

We present and describe a new version of the spot oscillation and planet code, SOAPv4. Our aim is to demonstrate its capabilities in modeling stellar activity in the context of RV measurements and its effects on transmission spectra. To do this, we employed solar observations alongside synthetic spectra and compared the resulting simulations. We used SOAPv4

Yushi Huang, Xingtong Ge, Ruihao Gong, Chengtao Lv

Video diffusion models (DMs) have enabled high-quality video synthesis. However, their computation costs scale quadratically with sequence length because self-attention has quadratic complexity. While linear attention lowers the cost, fully replacing quadratic attention requires expensive pretraining due to the limited expressiveness of linear attention and

Zhuo-Yang Song, Zeyu Cai, Shutao Zhang, Jiashen Wei

Symbolic regression (SR), the automated discovery of mathematical expressions from data, is a cornerstone of scientific inquiry. However, it is often hindered by the combinatorial explosion of the search space and a tendency to overfit. Popular methods, rooted in genetic programming, explore this space syntactically, often yielding overly complex, uninterpre

Yu Huang, Zelin Peng, Changsong Wen, Xiaokang Yang

Affordance segmentation aims to decompose 3D objects into parts that serve distinct functional roles, enabling models to reason about object interactions rather than mere recognition. Existing methods, mostly following the paradigm of 3D semantic segmentation or prompt-based frameworks, struggle when geometric cues are weak or ambiguous, as sparse point clou

Jean-Luc Garden

We extend the canonical Gibbs distribution, originally formulated for systems at equilibrium, to systems driven out of equilibrium. The stochastic dynamics of a small system are described by a probability distribution over discrete energy levels. Within this framework, we derive a microscopic expression for work and introduce a microscopic definition of entr

Andrea Pugnana, Giovanni De Toni, Cesare Barbera, Roberto Pellungrini

Developing decision-support systems that complement human performance in classification tasks remains an open challenge. A popular approach, Learning to Defer (LtD), allows a Machine Learning (ML) model to pass difficult cases to a human expert. However, LtD treats humans and ML models as mutually exclusive decision-makers, restricting the expert contributio

Kosuke Matsui, Jun-Yi Wu, Hayata Yamasaki, Min-Hsiu Hsieh

Scaling up the number of qubits available on quantum processors remains technically demanding even in the long term; it is therefore crucial to clarify the number of qubits required to implement a given quantum operation. For the most general class of quantum operations, known as quantum instruments, the qubit requirements are not well understood, especially

Soichiro Yamazaki, Seiseki Akibue

We developed a general framework for synthesizing target gates by using a finite set of basic gates, which is a crucial step in quantum compilation. When approximating a gate in SU($n$), a naive brute-force search requires a computational complexity of $O(1/\varepsilon^{(n^2 - 1)})$ to achieve an approximation with error $\varepsilon$. In contrast, by using

Yanran Chen, Lynn Greschner, Roman Klinger, Michael Klenk

Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness is inherently subjective. We present the first computational study of how emotional framing interacts with fallacies and convincingness, using large language models (LLMs) to system

Abdellah El Mrini, Sadegh Farhadkhan, Rachid Guerraoui

Collaborative machine learning is challenged by training-time adversarial behaviors. Existing approaches to tolerate such behaviors either rely on a central server or induce high communication costs. We propose Robust Pull-based Epidemic Learning (RPEL), a novel, scalable collaborative approach to ensure robust learning despite adversaries. RPEL does not rel

Herbert Fleischner, Enrico Iurlano, Günther R. Raidl

Using the graphs of prisms and Tutte Fragments, we construct an infinite family of hamiltonian and non-hamiltonian graphs in which Tutte's counterexample to Tait's conjecture appears in a certain sense as a minimal element. We observe that generalizations of the minimum-cardinality counterexamples of Holton and McKay to Tait's conjecture are as well containe

Michael T. Gorczyca, Jenna D. Li, Charissa M. Newkirk, Arjun S. Srivatsa

Gene expression levels, hormone secretion, and internal body temperature each oscillate over an approximately 24-hour cycle, or display circadian rhythms. Many circadian biology studies have investigated how these rhythms vary across cohorts, uncovering associations between atypical rhythms and diseases such as cancer, metabolic syndrome, and sleep disorders

Liwei Kang, Yue Deng, Yao Xiao, Zhanfeng Mo

Large language models have recently demonstrated significant gains in reasoning ability, often attributed to their capacity to generate longer chains of thought and engage in reflective reasoning. However, the contribution of reflections to performance improvement remains unclear. In this paper, we systematically analyze the rollouts of eight reasoning model

C. Sardón, X. Zhao

In this work, we conduct a systematic study of Hamiltonian and quasi-Hamiltonian systems within the framework of nondecomposable generalized Poisson geometry. Our focus lies on the interplay between the algebraic structure of nondecomposable generalized Poisson brackets and the dynamical behavior of systems exhibiting specific symmetry properties. In particu

A. A. Farafontova, M. S. Kirsanova, S. V. Salii

In this work, we present the program MOLLId (MOLecular Line Identification) for automated molecular lines approximation with gaussian profile. Molecular identification was performed using multi-level comparison of the lines' center frequencies and rest frequencies from the spectroscopic database. The program was tested using identification of the molecular l

Cilin Yan, Jingyun Wang, Guoliang Kang

Referring Video Segmentation (RVOS) aims to segment objects in videos given linguistic expressions. The key to solving RVOS is to extract long-range temporal context information from the interactions of expressions and videos to depict the dynamic attributes of each object. Previous works either adopt attention across all the frames or stack dense local atte

Matteo Amestoy, Mark van de Wiel, Jeroen Lakerveld, Wessel van Wieringen

Exposure to diverse non-genetic factors, known as the exposome, is a critical determinant of health outcomes. However, analyzing the exposome presents significant methodological challenges, including: high collinearity among exposures, the longitudinal nature of repeated measurements, and potential complex interactions with individual characteristics. In thi

Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, Merim Dzaferagic

As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven by multi-vendor deployments, hardware upgrades, and evolving service requirements. To address this growing need for flexible learning in non-stationary environments, this vision p

Fiorenza Morini, Marco Antonio Pellegrini

An integer Heffter array $H(m,n;s;k)$ is an $m\times n$ partially filled array whose entries are the elements of a subset $\Omega\subset \mathbb{Z}$ such that $\{\Omega,-\Omega\}$ is a partition of the set $\{1,2,\ldots,2nk\}$ and such that the following conditions are satisfied: each row contains $s$ filled cells, each column contains $k$ filled cells, the

Patrick Lotz, Luca Bosetti, André Bardow, Sergio Lucia

Optimization problems in chemical process design involve a significant number of discrete and continuous decisions. When taking into account uncertainties, the search space is very difficult to explore, even for experienced engineers. Moreover, it should be taken into account that while some decisions are fixed at the design stage, other parameters can be ad

Bart Kuipers, Freek Byrman, Daniel Uyterlinde, Alejandro García-Castellanos

Amortized optimization accelerates the solution of related optimization problems by learning mappings that exploit shared structure across problem instances. We explore the use of Scale Equivariant Graph Metanetworks (ScaleGMNs) for this purpose. By operating directly in weight space, ScaleGMNs enable single-shot fine-tuning of existing models, reducing the

Igor G. Vladimirov, Ian R. Petersen, Guodong Shi

This paper is concerned with open quantum memory systems for approximately retaining quantum information, such as initial dynamic variables or quantum states to be stored over a bounded time interval. In the Heisenberg picture of quantum dynamics, the deviation of the system variables from their initial values lends itself to closed-form computation in terms

Maite Arcos, Philippe Faist, Takahiro Sagawa, Jonathan Oppenheim

In thermodynamics, an agent's ability to extract work is fundamentally constrained by their environment. Traditional frameworks struggle to capture how strategic decision-making under uncertainty, particularly an agent's tolerance for risk, determines the trade-off between extractable work and probability of success in finite-scale experiments. Here, we deve

Simon Meierhans, Maximilian Probst Gutenberg

Whether a graph $G=(V,E)$ is connected is arguably its most fundamental property. Naturally, connectivity was the first characteristic studied for dynamic graphs, i.e. graphs that undergo edge insertions and deletions. While connectivity algorithms with polylogarithmic amortized update time have been known since the 90s, achieving worst-case guarantees has p

D. J. Priour

Percolation in systems made up of randomly placed impermeable grains is often examined in the context of system spanning clusters of connected solids forming above a relatively low critical grain density $\rho_{c1}$ or networks of interstitial void volumes ceasing to exist above a signficantly higher threshold $\rho_{c2}$. In this work, we interpret these pe

Tsuyoshi Okita

Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-inf

Fabio De Sousa Ribeiro, Ainkaran Santhirasekaram, Ben Glocker

We address the open question of counterfactual identification for high-dimensional multivariate outcomes from observational data. Pearl (2000) argues that counterfactuals must be identifiable (i.e., recoverable from the observed data distribution) to justify causal claims. A recent line of work on counterfactual inference shows promising results but lacks id

Nathaniel P. Johnson, Andrew R. Zentner

We show that host cold dark matter (CDM) haloes cluster in a manner that depends upon the anisotropy and planarity of their subhaloes, indicating an environmental dependence to subhalo anisotropy and planarity. The spatial distributions of both satellite galaxies about central galaxies and subhaloes about host haloes have been subjects of interest for two de

Haomu Yuan, Daniel Stilck França, Ilia Luchnikov, Egor Tiunov

Quadratic Unconstrained Binary Optimization (QUBO) problems are prevalent in various applications and are known to be NP-hard. The seminal work of Goemans and Williamson introduced a semidefinite programming (SDP) relaxation for such problems, solvable in polynomial time that upper bounds the optimal value. Their approach also enables randomized rounding tec

Xiaobing Feng, Liet Vo

This paper focuses on deriving optimal-order full moment error estimates in strong norms for both velocity and pressure approximations in the Euler-Maruyama time discretization of the stochastic Navier-Stokes equations with multiplicative noise. Additionally, it introduces a novel approach and framework for the numerical analysis of nonlinear stochastic part

Xiaodan Li, Mengjie Wu, Yao Zhu, Yunna Lv

Large models (LMs) are powerful content generators, yet their open-ended nature can also introduce potential risks, such as generating harmful or biased content. Existing guardrails mostly perform post-hoc detection that may expose unsafe content before it is caught, and the latency constraints further push them toward lightweight models, limiting detection

Yu. D. Panov, V. A. Ulitko, D. N. Yasinskaya, A. S. Moskvin

The results of numerical simulation using a modified Monte Carlo method with a heat bath algorithm for the pseudospin model of cuprates are presented. The temperature phase diagrams are constructed for various degrees of doping and for various parameters of the model, and the effect of local correlations on the critical temperatures of the model cuprate is i

Hamid R. Ossareh, William Shayne, Samuel Chevalier

Constraint management is a central challenge in modern control systems. A solution is the Reference Governor (RG), which is an add-on strategy to pre-stabilized feedback control systems to enforce state and input constraints by shaping the reference command. While robust formulations of RG exist for linear systems, their extension to nonlinear systems is oft

Monica Conti, Stefania Gatti, Andrea Giorgini, Giulio Schimperna

We investigate the Cahn-Hilliard equation with nonlinear diffusion and non-degenerate mobility modeling phase separation phenomena in complex systems (e.g., crystals and polymers). Previous results in the literature on this model relied on the strong convexity assumption of the gradient part of the energy, which excludes relevant cases. In this work, we remo

Oscar Hill

We explore some of the properties of consecutive, equally-summed arithmetic progressions of odd numbers, particularly their offsets and sums, before using them to prove that no $3\times3$ magic squares of distinct square integers exist.

N. K. Raut, N. Stilin, J. Vennekate, S. Wang

Superconducting radio frequency accelerating cavities made with different material layers, such as copper, Nb or Nb3Sn, are susceptible to thermoelectric effects due to differences in Seebeck coefficients between the metals. A temperature gradient across the surfaces can drive thermoelectric currents, which may impact the cavity performance. A layered Cu/Nb/

Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala, Hitomi Yanaka

As large language models (LLMs) are increasingly deployed worldwide, ensuring their fair and comprehensive cultural understanding is important. However, LLMs exhibit cultural bias and limited awareness of underrepresented cultures, while the mechanisms underlying their cultural understanding remain underexplored. To fill this gap, we conduct a neuron-level a

Cristina Sardón

This paper presents a geometric and analytic derivation of Dirac-Dunkl operators as symmetry reductions of the flat Dirac operator on Euclidean space. Starting from the standard Dirac operator, we restrict to a fundamental Weyl chamber of a finite Coxeter group equipped with the Heckman-Opdam measure, and determine the necessary drift and reflection correcti

Jayeeta Chattopadhyay, Simon Guldager Andersen, Kristian Thijssen, Amin Doostmohammadi

We investigate how coupling to fluid flow influences defect-mediated transitions in two-dimensional passive nematic fluids using fluctuating nematohydrodynamic simulations. The system is driven by tuning the fluctuation strength, with increasing (decreasing) fluctuations defining the forward (backward) protocol. In the absence of flow coupling, the transitio

Tianwei Li, Yu Zhao, Yunze Li, Sheng Li

For Internet platforms operating real-time bidding (RTB) advertising service, a comprehensive understanding of user lifetime value (LTV) plays a pivotal role in optimizing advertisement allocation efficiency and maximizing the return on investment (ROI) for advertisement sponsors, thereby facilitating growth of commercialization revenue for the platform. How

Lorenzo Peerani, Saburo Howard, Ravit Helled

The composition and internal structure of gas giant exoplanets encode key information about their formation and evolution. We investigate how different assumed interior structures affect the inferred bulk metallicity and its correlation with planetary mass. For a sample of 44 giant exoplanets (0.12-5.98 MJ), we computed evolutionary models with CEPAM and ret

Michael Niemeyer, Fabian Manhardt, Marie-Julie Rakotosaona, Michael Oechsle

Recent advances in neural scene representations have led to unprecedented quality in 3D reconstruction and view synthesis. Despite achieving high-quality results for common benchmarks with curated data, outputs often degrade for data that contain per image variations such as strong exposure changes, present, e.g., in most scenes with indoor and outdoor areas

Fevziye Irem Eyiokur, Dogucan Yaman, Hazım Kemal Ekenel, Alexander Waibel

Embodied Reference Understanding requires identifying a target object in a visual scene based on both language instructions and pointing cues. While prior works have shown progress in open-vocabulary object detection, they often fail in ambiguous scenarios where multiple candidate objects exist in the scene. To address these challenges, we propose a novel ER

Wen Wang, Han-Ze Li, Jian-Xin Zhong

Understanding the relationship between many-body localization and spectra in non-Hermitian many-body systems is crucial. In a one-dimensional clean, long-range interaction-induced non-Hermitian many-body localization system, we have discovered the coexistence of static and dynamic spectral real-complex phase transitions, along with many-body ergodic-localize