November 2025 arXiv papers — page 158

Kai A. Horstmann, Maxim Clouser, Kia Khezeli

Infrared imagery enables temperature-based scene understanding using passive sensors, particularly under conditions of low visibility where traditional RGB imaging fails. Yet, developing downstream vision models for infrared applications is hindered by the scarcity of high-quality annotated data, due to the specialized expertise required for infrared annotat

Antoine Amarilli, Mikaël Monet, Rémi De Pretto

The class of local languages is a well-known subclass of the regular languages that admits many equivalent characterizations. In this short note we establish the PSPACE-completeness of the problem of determining, given as input a nondeterministic finite automaton (NFA) A, whether the language recognized by A is local or not. This contrasts with the case of d

D. Bazeia, R. Menezes

This perspective deals with real scalar fields in two-dimensional spacetime. We focus on models described by one and two real scalar fields, paying closer attention to kinks and lumps, which are localized structures of current interest in high energy physics and in other areas of nonlinear science. We briefly review some of the main results presented in the

Wei Li, Qiye Shou, Fuqiang Wang

The chiral magnetic effect (CME) refers to a predicted phenomena in quantum chromodynamics that manifests as a charge separation along an external magnetic field, driven by an imbalance of quark chirality. Searches for the CME has been carried out by azimuthal particle correlations in relativistic heavy ion collisions where such a chirality imbalance is anti

Sašo Grozdanov, Mile Vrbica

In this work, we systematically treat the ambiguities that generically arise in the gradient expansion of any hydrodynamic theory. While these ambiguities do not affect the physical content of the equations, they induce two types of transformations in the space of transport coefficients. The first type is known as the 'frame' transformations, and amounts to

Claudemir Alcantara, Makson Santos

We examine the interior regularity of solutions to a degenerate normalized $p$-Laplace equation, where the degeneracy is governed by a modulus of continuity whose inverse satisfies a Dini continuity condition. We prove that under very general assumptions on the degeneracy law, solutions belong to the $C^1$ class. We argue by approximating the solutions by a

Marthinus Michael Dreeckmeier

In this article, we will use elementary number theory techniques to investigate a sequence of integers defined by a sifting process called the lucky numbers. Ulam introduced lucky numbers as a sieve-based analogue of prime numbers. We derive an exact formula for the $n$th lucky number, providing a new tool for quantitative analysis. We formulate and prove a

Anna Van Boven, Kyri Baker

Wholesale power markets often use linear approximations of power system constraints. Because it does not consider inequality constraints, using AC power flow for feasibility post-processing can violate bounds on reactive power, voltage magnitudes, or thermal limits. There remains a need for a streamlined analytical approach that can guarantee AC feasibility

Claudio Bonanno, Jorge Luis Dasilva Golán, Margarita García Pérez, Massimo D'Elia

We set the scale of SU($N$) Yang--Mills theories for $N=3,5,8$ and in the large-$N$ limit via gradient flow, as a first step towards the computation of the large-$N$ $\Lambda$-parameter using step scaling. We adopt twisted boundary conditions to achieve large-$N$ volume reduction and the Parallel Tempering on Boundary Conditions algorithm to tame topological

Shay Solomon, Amitai Uzrad

In the dynamic set cover (SC) problem, the input is a dynamic universe of at most $n$ elements and a fixed collection of $m$ sets, where each element belongs to at most $f$ sets and each set has cost in $[1/C, 1]$. The objective is to efficiently maintain an approximate minimum SC under element updates; efficiency is primarily measured by the update time, bu

Ruoyu Li, Rob Deardon, Na Li, John Conly

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium that leads to severe infections in hospitalized patients. Previous epidemiological research has focused on MRSA transmission, but few studies have examined the influence of both hospital-acquired MRSA (HA-MRSA) and community-acquired MRSA (CA-MRSA) on MRSA spread in hospitals. In this study, w

Muhammad Usman, Sana Waheed, Aamir Mubashar

Voids are one of the many material defects present at the microscopic length scale. They are primarily responsible for the formation of cracks and hence contribute to ductile fracture. Circular voids tend to deform into elliptical voids just before their coalescence to form cracks. The principle aim of this study is to investigate the effect of void shape on

Benjamin Proudfoot, Will Grundy, Darin Ragozzine, Estela Fernández-Valenzuela

We present a detailed dynamical analysis of the Quaoar-Weywot system based on nearly 20 years of high-precision astrometric data, including new HST observations and stellar occultations. Our study reveals that Weywot's orbit deviates significantly from a purely Keplerian model, requiring the inclusion of Quaoar's non-spherical gravitational field and center-

Mriganka Basu Roy Chowdhury, Shirshendu Ganguly, Vilas Winstein

Independent sets in graphs are sets of vertices containing no neighbors, and they represent a canonical spin system with hardcore constraints. Of particular interest is the setting of the boolean hypercube, where counting independent sets was the original motivator for Sapozhenko's famous graph container method. A modern perspective on such problems is to co

Marcel Menner, Eugene Lavretsky

This paper presents a novel modeling approach for unsteady aircraft airflow, leveraging the Lorenz attractor framework. The proposed model is based on the force distribution exerted by a lift-generating wing on the surrounding fluid. It distinguishes between turbulent and nominal components of the force distribution, with the nominal force distribution model

Chiara Marletto, Jonathan Oppenheim, Vlatko Vedral, Elizabeth Wilson

In Nature, 646, 813 (2025), Aziz and Howl claim that classical (unquantised) gravity produces entanglement. We show that their model does not produce entanglement. Even if the model produced entanglement, it would be mediated by the quantised matter interaction, and not gravity. Hence entanglement mediated by gravity remains an unambiguous witness of gravity

L. J. Broussard, H. Acharya, R. Alarcon, S. Baeßler

Our understanding of the weak mixing of quarks, described by the Cabibbo Kobayashi Maskawa (CKM) matrix, currently presents an anomaly. Thanks to major strides in both theory and experiment, improved precision in determinations of the first row of matrix elements has revealed disagreement with the expectation of unitarity. The Nab experiment at the Spallatio

Sujoy Bhore, Baris Can Esmer, Daniel Marx, Karol Wegrzycki

We study the Steiner Tree problem on the intersection graph of most natural families of geometric objects, e.g., disks, squares, polygons, etc. Given a set of $n$ objects in the plane and a subset $T$ of $t$ terminal objects, the task is to find a subset $S$ of $k$ objects such that the intersection graph of $S\cup T$ is connected. Given how typical paramete

Roberto Morales, Javier-Ramírez-Ganga

In this article, we study an inverse problem consisting in the identification of a space-time dependent source term in the Ginzburg-Landau equation from final-time observations. We adopt a weak-solution framework and analyze Tikhonov's functional, deriving an explicit gradient formula via an adjoint system and proving its Lipschitz continuity. We then establ

Mohamma Reza Salehi, Ali BashirGonbadi, Hamid Soltanian-Zadeh

The human brain is a complex system defined by multi-way, higher-order interactions invisible to traditional pairwise network models. Although a diverse array of analytical methods has been developed to address this shortcoming, the field remains fragmented, lacking a unifying conceptual framework that integrates and organizes the rapidly expanding methodolo

Zeman Li, Ali Behrouz, Yuan Deng, Peilin Zhong

Recurrent neural networks (RNNs) with deep test-time memorization modules, such as Titans and TTT, represent a promising, linearly-scaling paradigm distinct from Transformers. While these expressive models do not yet match the peak performance of state-of-the-art Transformers, their potential has been largely untapped due to prohibitively slow training and l

Lorenzo Baldi, Rainer Sinn, Máté L. Telek, Julian Weigert

The classical version of P\'olya's theorem provides a simple method for certifying that a homogeneous polynomial of degree d is strictly copositive, that is, it takes only positive values on the nonnegative real orthant. However, this method might fail to detect copositivity of polynomials that are missing certain degree d monomials. In this paper, we presen

Andrew Chin, Akihiko Nishimura

Combining a continuous "slab" density with discrete "spike" mass at zero, spike-and-slab priors provide important tools for inducing sparsity and carrying out variable selection in Bayesian models. However, the presence of discrete mass makes posterior inference challenging. "Sticky" extensions to piecewise-deterministic Markov process samplers have shown pr

Reza Marzban, Ashkan Zandi, Ali Adibi

Designing large-scale metasurfaces with nonlocal optical effects remains challenging due to the immense dimensionality and fabrication constraints of conventional optimization methods. We introduce GiBS (Generative Input-side Basis-driven Structures), an inverse-design framework that represents the entire device using a compact set of coefficients from smoot

Zhen Wang, Yufan Zhou, Zhongyan Luo, Lyumanshan Ye

Simulating human profiles by instilling personas into large language models (LLMs) is rapidly transforming research in agentic behavioral simulation, LLM personalization, and human-AI alignment. However, most existing synthetic personas remain shallow and simplistic, capturing minimal attributes and failing to reflect the rich complexity and diversity of rea

Long-Hin Fung, Che Cheng, Jie-Hong Roland Jiang, Friedrich Slivovsky

Dependency Quantified Boolean Formulas (DQBF) generalize QBF by explicitly specifying which universal variables each existential variable depends on, instead of relying on a linear quantifier order. The satisfiability problem of DQBF is NEXP-complete, and many hard problems can be succinctly encoded as DQBF. Recent work has revealed a strong analogy between

Joshua Mukherjee, Giorgos Christopoulos, Zhouyang Shen, Sriram Subramanian

Acoustic Holography is an emerging field where mid-air ultrasound is controlled and manipulated for novel and exciting applications. These range from mid-air haptics, volumetric displays, contactless fabrication, and even chemical and biomedical applications such as drug delivery. To develop these applications, a software framework to predict acoustic behavi

Marcel Menner, Eugene Lavretsky

This paper presents a systematic approach for designing robust linear proportional-integral (PI) servo-controllers that effectively manage control input and output constraints in flight control systems. The control design leverages the Nagumo Theorem and the Comparison Lemma to prove constraint satisfaction, while employing min-norm optimal controllers in a

Spenser Talkington, Debarghya Mallick, An-Hsi Chen, Benjamin F. Mead

We study diffusive magnetotransport in highly p-doped large area twisted bilayer graphene in 1{\deg}, 7{\deg}, 9{\deg} and 20{\deg} samples. We report weak localization in twisted bilayer graphene for the first time. All samples exhibit weak localization, from which we extract the phase coherence length and intervalley scattering lengths, and from that deter

Peng Xie, Sabin Diaconescu, Florin Stoican, Amr Alanwar

This paper introduces Roundabout Constrained Convex Generators (RCGs), a set representation framework for modeling multiply connected regions in control and verification applications. The RCG representation extends the constrained convex generators framework by incorporating an inner exclusion zone, creating sets with topological holes that naturally arise i

Artyom Sorokin, Nazar Buzun, Alexander Anokhin, Oleg Inozemcev

Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most existing RAG methods focus on single-step retrieval, which is often insufficient for answering complex questions that require multi-step search. Recently, multi-step retrieval appro

Guoxin Chen, Zile Qiao, Xuanzhong Chen, Donglei Yu

Recent advances in deep-research agents have shown promise for autonomous knowledge construction through dynamic reasoning over external sources. However, existing approaches rely on a mono-contextual paradigm that accumulates all information in a single, expanding context window, leading to context suffocation and noise contamination that limit their effect

Lucas Davron, Pierre Lissy

This paper provides a complete characterization of the Dirichlet boundary outputs that can be exactly tracked in the one-dimensional heat equation with Neumann boundary control. The problem consists in describing the set of boundary traces generated by square-integrable controls over a finite or infinite time horizon. We show that these outputs form a precis

R. Kumar, A. Lall, S. Chaudhari, M. Kale

This paper proposes a smart way to manage municipal solid waste by using the Internet of Things (IoT) and computer vision (CV) to monitor illegal waste dumping at garbage vulnerable points (GVPs) in urban areas. The system can quickly detect and monitor dumped waste using a street-level camera and object detection algorithm. Data was collected from the Sanga

Andrea Autieri, Stefano Camarda, Leandro Cieri, Giancarlo Ferrera

We consider the transverse momentum ($q_T$) distribution of neutral charged bosons at hadron colliders. We perform the resummation of the logarithmically-enhanced effects due to simultaneous QCD and QED initial-state radiation, up to mixed next-to-next-to-leading logarithmic (NNLL) accuracy. We study the impact of such mixed QCD$\otimes$QED resummed contribu

Papa Yaw Owusu-Obeng, Mai Shi, Max Vanatta, Michael T. Craig

The feasibility and cost-effectiveness of continued growth in solar photovoltaics are closely tied to siting decisions. But trade-offs between costs and technical potential between land categories, especially brownfields and rooftop sites, have not been quantified, despite increasing resistance to and policy interest in reducing use of greenfield sites (e.g.

Raymond Zhang, Hédi Hadiji, Richard Combes

We consider the maximization of $x^\top \theta$ over $(x,\theta) \in \mathcal{X} \times \Theta$, with $\mathcal{X} \subset \mathbb{R}^d$ convex and $\Theta \subset \mathbb{R}^d$ an ellipsoid. This problem is fundamental in linear bandits, as the learner must solve it at every time step using optimistic algorithms. We first show that for some sets $\mathcal{X

Botao Ye, Boqi Chen, Haofei Xu, Daniel Barath

Fast and flexible 3D scene reconstruction from unstructured image collections remains a significant challenge. We present YoNoSplat, a feedforward model that reconstructs high-quality 3D Gaussian Splatting representations from an arbitrary number of images. Our model is highly versatile, operating effectively with both posed and unposed, calibrated and uncal

Daniel Kaplan, Pavel A. Volkov, Andrea Cavalleri, Premala Chandra

Faraday waves, typically observed in driven fluids, result from the confluence of nonlinearity and parametric amplification. Here we show that optical pulses can generate analogous phenomena that persist much longer than the pump time-scales in ordered quantum solids. We present a theory of ultrafast light-matter interactions within a symmetry-broken state;

Andreas Schmitt

Cold and dense matter is expected to be in a color-superconducting state. Here we review two calculations, relevant for fundamental properties and applications of color superconductivity, respectively: the weak-coupling QCD calculation of the fermionic energy gap together with the magnetic screening masses of the gauge bosons, and the calculation of bulk vis

Shaowen Wang, Yiqi Dong, Ruinian Chang, Tansheng Zhu

Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this paper, we highlight a critical yet previously underexplored class of hallucinations driven by spurious correlations -- superficial but statistically prominent associations between features (e.g., surnames) and a

Barbara Betti, Viktoriia Borovik, Bella Finkel, Bernd Sturmfels

The CHY scattering equations on the moduli space $M_{0,n}$ play a prominent role at the interface of particle physics and algebraic statistics. We study the scattering correspondence when the Mandelstam invariants are restricted to a fixed graph on $n$ vertices.

Yuxuan Zhou, Yang Bai, Kuofeng Gao, Tao Dai

The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leading to poor sample diversity and scalability. To address these ga

Bryce Clarke, Gabriel Scherer, Noam Zeilberger

We consider the problem of constructing the free bifibration generated by a functor of categories $p : D \to C$. This problem was previously considered by Lamarche, and is closely related to the problem, considered by Dawson, Par\'e, and Pronk, of ``freely adjoining adjoints'' to a category. We develop a proof-theoretic approach to the problem, beginning wit

Aaron Jacobson, Tingting Dan, Martin Styner, Guorong Wu

Functional connectivity has been widely investigated to understand brain disease in clinical studies and imaging-based neuroscience, and analyzing changes in functional connectivity has proven to be valuable for understanding and computationally evaluating the effects on brain function caused by diseases or experimental stimuli. By using Mahalanobis data whi

Samuel Sokota, Eugene Vinitsky, Hengyuan Hu, J. Zico Kolter

Few classical games have been regarded as such significant benchmarks of artificial intelligence as to have justified training costs in the millions of dollars. Among these, Stratego -- a board wargame exemplifying the challenge of strategic decision making under massive amounts of hidden information -- stands apart as a case where such efforts failed to pro

Tuan-Dung Le, Shohreh Haddadan, Thanh Q. Thieu

Automatic ICD coding, the task of assigning disease and procedure codes to electronic medical records, is crucial for clinical documentation and billing. While existing methods primarily enhance model understanding of code hierarchies and synonyms, they often overlook the pervasive use of medical acronyms in clinical notes, a key factor in ICD code inference

Han Xiao, Xiaoyan Hu, Wenjie Wang, Kai-Kit Wong

In response to the security blind zone challenges faced by traditional reconfigurable intelligent surface (RIS)-aided covert communication (CC) systems, the joint distance-angle beamforming capability of frequency diverse RIS (FD-RIS) shows significant potential for addressing these limitations. Therefore, this paper initially incorporates the FD-RIS into th

Frederik J. Zuiderveen Borgesius

Information about millions of people is collected for behavioural targeting, a type of marketing that involves tracking people's online behaviour for targeted advertising. It is hotly debated whether data protection law applies to behavioural targeting. Many behavioural targeting companies say that, as long as they do not tie names to data they hold about in

Frederik Zuiderveen Borgesius

An attorney submitted a 'right to be forgotten' delisting request to Google, regarding a blog post about a criminal conviction of the attorney in another country. The Rotterdam District Court ruled that Google may no longer link to the blog post when people search for the attorney's name. The court granted the attorney's request because the blog post concern

Asia Belfiore, Jonathan Passerat-Palmbach, Dmitrii Usynin

The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explores the use of language models (LMs) for the generation of synthetic genetic mutation profiles, leveraging differential privacy (DP) for the protection of sensitive genetic data. We

Sourav Saha, K M Nafi Asib, Mohammed Moshiul Hoque

This paper addresses the problem of Bangla hate speech identification, a socially impactful yet linguistically challenging task. As part of the "Bangla Multi-task Hate Speech Identification" shared task at the BLP Workshop, IJCNLP-AACL 2025, our team "Retriv" participated in all three subtasks: (1A) hate type classification, (1B) target group identification,

Qiaoyi Li, Dai-Wei Qu, Bin-Bin Chen, Tao Shi

Numerical simulations of strongly correlated fermions at finite temperature are essential for studying high-temperature superconductivity and other quantum many-body phenomena. The recently developed tangent-space tensor renormalization group (tanTRG) provides an efficient and accurate framework by representing thermal density operators as matrix product ope

Benjamin Brindle

In 2015, Bachmann \cite{Ba3} conjectured that the~$\Q$-vector space~$\Zq$ of (formal)~$q$-analogues of Multiple Zeta Values (\qmzv s) is spanned by a very particular set compared to known spanning sets. In this work, we prove that this conjecture is true for a subspace of~$\Zq$ spanned by words satisfying some condition on their number of zeros and depth. Ac

Galina Weinstein

This essay offers an epistemological reinterpretation of the foundational divide between matrix mechanics and wave mechanics. Though formally equivalent, the two theories embody distinct modes of knowing: procedural construction and recognitional verification. These epistemic architectures anticipate, in philosophical form, the logical asymmetry expressed by

Huizai Yao, Sicheng Zhao, Pengteng Li, Yi Cui

Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often results in biased pseudo-labels, thereby hindering both transf

Luca Marzari, Manuele Bicego, Ferdinando Cicalese, Alessandro Farinelli

Although recent provable methods have been developed to compute preimage bounds for neural networks, their scalability is fundamentally limited by the #P-hardness of the problem. In this work, we adopt a novel probabilistic perspective, aiming to deliver solutions with high-confidence guarantees and bounded error. To this end, we investigate the potential of

Ying Cheng, Yu-Ho Lin, Min-Hung Chen, Fu-En Yang

Video anomaly understanding (VAU) aims to provide detailed interpretation and semantic comprehension of anomalous events within videos, addressing limitations of traditional methods that focus solely on detecting and localizing anomalies. However, existing approaches often neglect the deeper causal relationships and interactions between objects, which are cr

Kagan Celik, Mehmet Ozan Unal, Metin Ertas, Isa Yildirim

Low-dose computed tomography (CT) represents a significant improvement in patient safety through lower radiation doses, but increased noise, blur, and contrast loss can diminish diagnostic quality. Therefore, consistency and robustness in image quality assessment become essential for clinical applications. In this study, we propose an LLM-based quality asses

Jorge Gabín, M. Eduardo Ares, Javier Parapar

News articles often reference numerous organizations, but traditional Named Entity Recognition (NER) treats all mentions equally, obscuring which entities genuinely drive the narrative. This limits downstream tasks that rely on understanding event salience, influence, or narrative focus. We introduce Protagonist Entity Recognition (PER), a task that identifi

Tianrui Song, Wen-Shuo Chao, Hao Liu

Implicit feedback, employed in training recommender systems, unavoidably confronts noise due to factors such as misclicks and position bias. Previous studies have attempted to identify noisy samples through their diverged data patterns, such as higher loss values, and mitigate their influence through sample dropping or reweighting. However, we observed that

J. Collins, M. Hohlmann

We present the first micropattern gaseous detector that employs a small 3D-printed Thick-GEM as its sole gain element. The detector can achieve sufficient gas gain for regular operation without the need for pre-amplification by additional gain elements. We describe the design, quality control, assembly, and test of this detector. The 10 cm $\times$ 10 cm act

Qi Junyi

The Tower of Hanoi continues to provide a surprisingly rich meeting point for recursive reasoning, combinatorial geometry, and computational verification. Motivated by the editorial standards of the Bulletin of the Australian Mathematical Society, we revisit the classical three-peg problem through Sierpinski-style self-similarity, bring Stockmeyer's uniquene

Mohammad Afzal, S. Akshay, Blaise Genest, Ashutosh Gupta

In the last decade, a large body of work has emerged on robustness of neural networks, i.e., checking if the decision remains unchanged when the input is slightly perturbed. However, most of these approaches ignore the confidence of a neural network on its output. In this work, we aim to develop a generalized framework for formally reasoning about the confid

Simon Gerstenecker, Andreas Geiger, Katrin Renz

Most recent work in autonomous driving has prioritized benchmark performance and methodological innovation over in-depth analysis of model failures, biases, and shortcut learning. This has led to incremental improvements without a deep understanding of the current failures. While it is straightforward to look at situations where the model fails, it is hard t

Aesol Jeon, Ki-Ahm Lee

This study investigates an SEIS PDE model with a free boundary, which captures the dynamics of epidemic transmission, including diseases like COVID-19. This parabolic PDE system is analyzed in a rotationally symmetric domain, and the existence and uniqueness of the local solution are established through the straightening lemma. Furthermore, the existence and

Xinyi Wang, Angeliki Katsenou, Junxiao Shen, David Bull

The prevalence of user-generated content (UGC) on platforms such as YouTube and TikTok has rendered no-reference (NR) perceptual video quality assessment (VQA) vital for optimizing video delivery. Nonetheless, the characteristics of non-professional acquisition and the subsequent transcoding of UGC video on sharing platforms present significant challenges fo

Madison Smith, Michael Gaiewski, Sam Dulin, Laurel Williams

Supply chains' increasing globalization and complexity have recently produced unpredictable disruptions, ripple effects, and cascading resulting failures. Proposed practices for managing these concerns include the advanced field of forward stress testing, where threats and predicted impacts to the supply chain are evaluated to harden the system against the m

Maurice Flechtner

Assessing the quality of group deliberation is essential for improving our understanding of deliberative processes. The Deliberative Reason Index (DRI) offers a sophisticated metric for evaluating group reasoning, but its implementation has been constrained by the complex and time-consuming process of statement generation. This thesis introduces an innovativ

Daniele De luca

This paper introduces a geometric framework for analyzing power relations in games, independent of their strategic form. We define a canonical preference space where each player's relational stance is a normalized vector. This model eliminates the arbitrariness of selecting utility functions, a limitation of recent approaches. We show how classical concepts-

Roman Malashin, Svetlana Pashkevich, Daniil Ilyukhin, Arseniy Volkov

We present Glioma C6, a new open dataset for instance segmentation of glioma C6 cells, designed as both a benchmark and a training resource for deep learning models. The dataset comprises 75 high-resolution phase-contrast microscopy images with over 12,000 annotated cells, providing a realistic testbed for biomedical image analysis. It includes soma annotati

Babak Ghanbari, Robert Šámal

The Cycle double cover (CDC) conjecture states that for every bridgeless graph $G$, there exists a family $\mathcal{F}$ of cycles such that each edge of the graph is contained in exactly two members of $\mathcal{F}$. Given an embedding of a graph~$G$, an edge $e$ is called a \emph{singular edge} if it is visited twice by the boundary of one face. The CDC con

Oskar Leibnitz, Peter Mlkvik, Nicola A. Spaldin, Claude Ederer

We present a density-functional theory study of the effects of oxygen vacancies on the structural and electronic properties of vanadium dioxide (VO$_2$). Our motivation is the reported suppression of the metal-insulator transition by oxygen vacancies and the lack of a clear consensus on its origin. We use the DFT$+V$ method with a static intersite vanadium-v

Anupam Gupta, Marco Molinaro, Matteo Russo

In the random-order online set cover problem, the instance with $m$ sets and $n$ elements is chosen in a worst-case fashion, but then the elements arrive in a uniformly random order. Can this random-order model allow us to circumvent the bound of $O(\log m \log n)$-competitiveness for the adversarial arrival order model? This long-standing question was recen

Yibu Wang, Zhaoxin Zhang, Ning Li, Xinlong Zhao

Received signal strength indicator (RSSI) is the primary representation of Wi-Fi fingerprints and serves as a crucial tool for indoor localization. However, existing RSSI-based positioning methods often suffer from reduced accuracy due to environmental complexity and challenges in processing multi-source information. To address these issues, we propose a nov

R. P. Chowdhury, T. Rahman

The accurate understanding of ischemic stroke lesions is critical for efficient therapy and prognosis of stroke patients. Magnetic resonance imaging (MRI) is sensitive to acute ischemic stroke and is a common diagnostic method for stroke. However, manual lesion segmentation performed by experts is tedious, time-consuming, and prone to observer inconsistency.

Yuvraj Singh, Chandan K. Choudhury, Rakesh S. Singh

Nanoparticles (NPs) are fundamental building blocks for engineering functional soft materials, where precise control over the solvent-mediated inter-particle effective interaction (Ueff) is essential for tailoring bulk structure and properties. These solvent-mediated interactions are strongly influenced by NP's surface chemistry, solvent properties, and ther

Yilong Chen, Xiang Bai, Zhibin Wang, Chengyu Bai

Video Large Language Models (Video-LLMs) have demonstrated significant potential in the areas of video captioning, search, and summarization. However, current Video-LLMs still face challenges with long real-world videos. Recent methods have introduced a retrieval mechanism that retrieves query-relevant KV caches for question answering, enhancing the efficien

Michelle Huang, Violeta J. Rodriguez, Koustuv Saha, Tal August

Limited English proficiency (LEP) patients in the U.S. face systemic barriers to healthcare beyond language and interpreter access, encompassing procedural and institutional constraints. AI advances may support communication and care through on-demand translation and visit preparation, but also risk exacerbating existing inequalities. We conducted storyboard

Salem AlMarri, Muhammad Irzam Liaqat, Muhammad Zaigham Zaheer, Shah Nawaz

In recent years, multimodal anomaly detection methods have demonstrated remarkable performance improvements over video-only models. However, real-world multimodal data is often corrupted due to unforeseen environmental distortions. In this paper, we present the first-of-its-kind work that comprehensively investigates the adverse effects of corrupted modaliti

David Black, Septimiu Salcudean

Diagnostic medical ultrasound is widely used, safe, and relatively low cost but requires a high degree of expertise to acquire and interpret the images. Personnel with this expertise are often not available outside of larger cities, leading to difficult, costly travel and long wait times for rural populations. To address this issue, tele-ultrasound technique

Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li

Multiple clustering aims to discover diverse latent structures from different perspectives, yet existing methods generate exhaustive clusterings without discerning user interest, necessitating laborious manual screening. Current multi-modal solutions suffer from static semantic rigidity: predefined candidate words fail to adapt to dataset-specific concepts,

Jefferson Delgado-Quesada, Edgar A. Rojas-González

A quantum walk in an ordered medium exhibits ballistic propagation. A related process is the driven quantum walk, in which the number of walkers varies along the propagation. In this work, we show that a driven quantum walk of biphoton states in an array of nonlinear waveguides does not propagate ballistically, but instead presents two transport regimes: sup

Allal Guessab, Federico Nudo

In this paper we introduce three complementary three-dimensional weighted quadratic enrichment strategies to improve the accuracy of local histopolation on tetrahedral meshes. The first combines face and interior weighted moments (face-volume strategy), the second uses only volumetric quadratic moments (purely volumetric strategy), and the third enriches the

Youngjoo Yun, Rishabh Dudeja

In differential privacy, random noise is introduced to privatize summary statistics of a sensitive dataset before releasing them. The noise level determines the privacy loss, which quantifies how easily an adversary can detect a target individual's presence in the dataset using the published statistic. Most privacy analyses provide upper bounds on the privac

Ziyue Liu, Mohammad Ahmadi Gharehtoragh, Brenna Kari Losch, David R. Johnson

Coastal communities face significant risk from storm-induced coastal flooding, which causes substantial societal and economic losses worldwide. Machine learning techniques have increasingly been integrated into coastal hazard modeling, particularly for storm surge prediction, due to advances in computational capacity. However, incorporating multiple projecte

Jonathan Lehmkuhl, Ábel Ilyés-Kun, Nico Bremes, Cemhan Kaan Özaltan

Although a variety of transformers have been proposed for symbolic music generation in recent years, there is still little comprehensive study on how specific design choices affect the quality of the generated music. In this work, we systematically compare different datasets, model architectures, model sizes, and training strategies for the task of symbolic

Chen Han, Yijia Ma, Jin Tan, Wenzhen Zheng

Multi-agent debate (MAD) frameworks have emerged as promising approaches for misinformation detection by simulating adversarial reasoning. While prior work has focused on detection accuracy, it overlooks the importance of helping users understand the reasoning behind factual judgments and develop future resilience. The debate transcripts generated during MAD

Marco Roccetti

The integration of advanced analytical tools, including Machine Learning (ML) and massive data processing, has revolutionized health research, promising unprecedented accuracy in diagnosis and risk prediction. However, the rigor of these complex methods is fundamentally dependent on the quality and integrity of the underlying datasets and the validity of the

Keisuke Inomata, Kazunori Kohri, Takahiro Terada

Gravitational waves induced by primordial density perturbations provide a powerful probe of the Universe's thermal history, which may include an early matter-dominated (eMD) era predicted by well-motivated particle-physics models. The induced GWs can be significantly enhanced when the Universe undergoes a sudden transition from an eMD era to an era with pres

Gamal Refai-Ahmed, Mallik Tatipamula, Victor Zhirnov, Ahmed Refaey Hussein

The exponential growth of AI agents and connected devices fundamentally transforms the structure and capacity demands of global digital infrastructure. This paper introduces a unified forecasting model that projects AI agent populations to increase by more than 100 times between 2026 and 2036+, reaching trillions of instances globally. In parallel, bandwidth

Liliane Siqueira de Oliveira, Davi Vieira Teixeira da Silva, Lucileno Rodrigues da Trindade, Diego dos Santos Baião

Food polysaccharides have emerged as suitable carriers of active substances and as additives to food and nutraceutical formulations, showing potential to stabilize bioactive compounds during the storage of microencapsulate preparations, even in the gastrointestinal tract following the intake of bioactive compounds, thereby improving their bioaccessibility an

Saba Latif, Huma Latif, Touseef Ur Rehman, Muhammad Rameez Ur Rahman

Object-centric process mining addresses the limitations of traditional approaches, which often involve the lossy flattening of event data and obscure vital relationships among interacting objects. This paper presents a novel formal framework for Object-centric Event Data (OCED) that ensures the correctness of the meta-model and preserves native object-centri

Qile Jiang, George Karniadakis

Scientific Machine Learning (SciML) integrates data-driven inference with physical modeling to solve complex problems in science and engineering. However, the design of SciML architectures, loss formulations, and training strategies remains an expert-driven research process, requiring extensive experimentation and problem-specific insights. Here we introduce

Kasper Bågmark, Filip Rydin

In this work, we systematically benchmark two recently developed deep density methods for nonlinear filtering. We model the filtering density of a discretely observed stochastic differential equation through the associated Fokker--Planck equation, coupled with Bayesian updates at discrete observation times. The two filters: the deep splitting filter and the

Hohei Chan, Xinzhi Zhang, Antao Xiang, Weinan Zhang

Ad hoc teamwork (AHT) requires agents to collaborate with previously unseen teammates, which is crucial for many real-world applications. The core challenge of AHT is to develop an ego agent that can predict and adapt to unknown teammates on the fly. Conventional RL-based approaches optimize a single expected return, which often causes policies to collapse i

Francesco Dell'Accio, Allal Guessab, Mohammed Kbiri Alaoui, Federico Nudo

In this paper, we study the reconstruction of a bivariate function from weighted integrals along the edges of a triangular mesh, a problem of central importance in tomography, computer vision, and numerical approximation. Our approach relies on local histopolation methods defined through unisolvent triples, where the edge weights are induced by suitable prob

Brayan M. Shali, Rodolphe Sepulchre

Reciprocity is a fundamental symmetry property observed across many physical domains, including acoustics, elasticity, electromagnetics, and thermodynamics. In systems and control theory, it provides key insights into the internal structure of linear time-invariant (LTI) systems and is closely linked to properties such as passivity, relaxation, and time-reve

Hanya Elhashemy, Youssef Lotfy, Yongjian Tang

The increasing adoption of Jupyter notebooks in data science and machine learning workflows has created a gap between exploratory code development and production-ready software systems. While notebooks excel at iterative development and visualization, they often lack proper software engineering principles, making their transition to production environments c

Charles Livingston

In 1928, Alexander defined a sequence of knot polynomials, D_i(K). The first, D_1(K), is the classical Alexander polynomial. These are easily defined in terms of the homology of the infinite cyclic cover of the knot. In theory they can be computed by putting an associated Alexander matrix in Smith normal form. However, standard algorithms for computing the S