November 2025 arXiv papers — page 21

Heng Ma, Pascal Maillard

We consider a sparse Erd\H{o}s--R\'{e}nyi graph $\mathcal{G}(n,\lambda/n)$ where each edge is independently assigned a random signed weight. For two uniformly chosen vertices, we study the joint distribution of the total weights and hopcounts (number of edges) of the near-minimum weight paths connecting them. Under certain conditions on the weight distributi

Maria B. Stone, Roberto De Propris, Clare Wethers, Jari Kotilainen

We investigated the star formation history and stellar populations of a sample of 205 Type I quasar host galaxies (0.1$<$z$<$0.35) and compared with normal (non-active) galaxies of the same mass and redshift within the volume of the Galaxy and Mass Assembly (GAMA) redshift survey. We find that quasar host galaxies tend to be star-forming galaxies ($\sim$ 80%

Ala Eddine Nouali, Mohamed Sana, Jean-Paul Jamont

The cell-free architecture represents a significant advancement in network design, where each User Equipment (UE) is served by a group of distributed Access Points (APs), aimed at delivering uniformly high data rates to UEs across all locations. To ensure network scalability, user-centric clustering (UCC) has emerged as a practical approach, wherein only a s

Eyal Briman, Ehud Shapiro, Nimrod Talmon

The challenge of finding compromises between agent proposals is fundamental to AI sub-fields such as argumentation, mediation, and negotiation. Building on this tradition, Elkind et al. (2021) introduced a process for coalition formation that seeks majority-supported proposals preferable to the status quo, using a metric space where each agent has an ideal p

Valeria Díaz Moreno, Ryan P Khalili, Daniel Schug, Patrick J. Walsh

Semiconductor quantum dots (QDs) are a leading platform for scalable quantum processors. However, scaling to large arrays requires reliable, automated tuning strategies for devices' bootstrapping, calibration, and operation, with many tuning aspects depending on accurately identifying QD device states from charge-stability diagrams (CSDs). In this work, we p

Massimo Blasone, Loredana Gastaldo, Francesco Romeo

In a recent work [B. J. P. Jones and J. A. Formaggio, Phys. Rev. Lett. 135, 111801 (2025)], the possibility of superradiant neutrino emission from atomic condensates has been theoretically proposed. Subsequent analysis by Y. K. Lu, H. Lin, and W. Ketterle [arXiv:2510.21705] questioned this scenario, emphasizing the limiting role of the fermionic nature of th

Kazuki Okajima, Tetsuri Nishikawa, Hiroshi Abe, Koichi Murata

Defect charge-state dynamics are central to both spin-photon interfaces and photoelectrical spin readout. Despite the significance of silicon vacancies (V1/V2) in silicon carbide (4H-SiC) for both applications, their ionization behavior has remained unclear because their lack of optical blinking prevents conventional charge-state analysis. Here, we employ ph

Pritam Deka, Barry Devereux

Business Process Model and Notation (BPMN) is a widely adopted standard for representing complex business workflows. While BPMN diagrams are often exchanged as visual images, existing methods primarily rely on XML representations for computational analysis. In this work, we present a pipeline that leverages Vision-Language Models (VLMs) to extract structured

Xinyi Che, Wenbo Wang, Yuanbo Hou, Mingjie Xie

Multimodal Emotion Recognition in Conversation (MERC) aims to enhance emotion understanding by integrating complementary cues from text, audio, and visual modalities. Existing MERC approaches predominantly focus on cross-modal shared features, often overlooking modality-specific features that capture subtle yet critical emotional cues such as micro-expressio

Owen Garnier, Igor Haladjian

The family of J-groups was introduced by Achar and Aubert with the goal of providing Coxeter-like combinatorial tools for studying rank 2 complex reflection groups. However, J-groups lack an explicit presentation with abstract reflections as generators. This gap was filled by Gobet, and later by the second author, for the subfamily of so-called J-reflection

Duling Xu, Tong Li, Zegang Sun, Zheng Chen

The deployment of databases across geographically distributed regions has become increasingly critical for ensuring data reliability and scalability. Recent studies indicate that distributed databases exhibit significantly higher latency than single-node databases, primarily due to consensus protocols maintaining data consistency across multiple nodes. We ar

Maheswar Bora, Tashvik Dhamija, Shukesh Reddy, Baptiste Chopin

Deepfake generation has witnessed remarkable progress, contributing to highly realistic generated images, videos, and audio. While technically intriguing, such progress has raised serious concerns related to the misuse of manipulated media. To mitigate such misuse, robust and reliable deepfake detection is urgently needed. Towards this, we propose a novel ne

Sébastien Piérard, Adrien Deliège, Marc Van Droogenbroeck

Ranking methods or models based on their performance is of prime importance but is tricky because performance is fundamentally multidimensional. In the case of classification, precision and recall are scores with probabilistic interpretations that are both important to consider and complementary. The rankings induced by these two scores are often in partial

Xinyu Zhang, Yixin Wu, Boyang Zhang, Chenhao Lin

Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Models (LVLMs) now enables accurate geolocation even for ordinary users. However, existing approaches are not optimized for this task. To explore the full potential and associated priv

G. Grünwald, L. Hensel, M. Deisenhofer, S. Lautenbach

Active Flux (AF) is a modified Finite Volume method that evolves additional Degrees of Freedom (DoF) located on the cell interfaces to compute high-order approximations to the numerical fluxes through the respective interface. We present an AF-based scheme for the simulation of collisionless plasmas described by the Vlasov equation coupled with Maxwell's equ

Huanran Li, Manh Nguyen, Daniel Pimentel-Alarcón

Contrastive learning has emerged as a powerful method in deep learning, excelling at learning effective representations through contrasting samples from different distributions. However, dimensional collapse, where embeddings converge into a lower-dimensional space, poses a significant challenge, especially in semi-supervised and self-supervised setups. In t

Žan Kokalj, Tommaso Favalli, Andrea Trombettoni

Studying the performance of a quantum interferometer coupled to an external environment is a problem of conceptual and practical importance. If we consider a quantum interferometer featuring Heisenberg-limited sensitivity, then a typical result is that introducing coupling with the environment degrades the sensitivity to the shot-noise limit. Here we argue t

Manh Nguyen, Daniel Pimentel-Alarcón

Nonnegative matrix factorization (NMF) is a widely used tool for learning parts-based, low-dimensional representations of nonnegative data, with applications in vision, text, and bioinformatics. In clustering applications, orthogonal NMF (ONMF) variants further impose (approximate) orthogonality on the representation matrix so that its rows behave like soft

Adam Fredriksson, Erik Sjöqvist

Berry monopoles always cancel when summing over a complete set of energy eigenstates. We demonstrate that analogous sum rules exist for geometric phases and their underlying 2-forms in non-adiabatic evolution. Our result has implications for qudit computation as it limits the types of gates that can be implemented by purely geometric means.

Runzhi Deng, Yundi Hu, Xinshuang Zhang, Zhao Wang

Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-language models offer strong generalization, modeling multiple distinct category manifolds concurrently without actual anomalous data causes feature space collapse and cross-class int

Manh Nguyen

Given augmented views of each input graph, contrastive learning methods (e.g., InfoNCE) optimize pairwise alignment of graph embeddings across views while providing no mechanism to control the global structure of the view specific graph-of-graphs built from these embeddings. We introduce SpecMatch-CL, a novel loss function that aligns the view specific graph

Ali Ghasemi, Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri

Recent work has extended the invariance principle for out-of-distribution (OOD) generalization from Euclidean to graph data, where challenges arise due to complex structures and diverse distribution shifts in node attributes and topology. To handle these, Chen et al. proposed CIGA (Chen et al., 2022b), which uses causal modeling and an information-theoretic

Wenbo Song, Xinxin Fan, Quanliang Jing, Shaoye Luo

The deep learning (DL) has been penetrating daily life in many domains, how to keep the DL model inference secure and sample privacy in an encrypted environment has become an urgent and increasingly important issue for various security-critical applications. To date, several approaches have been proposed based on the Residue Number System variant of the Cheo

Hongda Liu, Yunfan Liu, Changlu Wang, Yunlong Wang

Recent advances in skeleton-based action recognition increasingly leverage semantic priors from Large Language Models (LLMs) to enrich skeletal representations. However, the LLM is typically queried in isolation from the recognition model and receives no performance feedback. As a result, it often fails to deliver the targeted discriminative cues critical to

Camille Frévent

We have developed a new signature-based spatial scan statistic for functional data (SigFSS). This scan statistic can be applied to both univariate and multivariate functional data. In a simulation study, SigFSS almost always performed better than the literature approaches and yielded more precise clusters in geographic terms. Lastly, we used SigFSS to search

Tingzhang Shi, Chentong Qi, H. T. Quan

We investigate the non-equilibrium four-vector work in an expanding relativistic piston. We derive the exact work distribution in this pedagogical model and find that the joint distribution of four-vector work $(W^0, W^1)$ concentrates on the origin and some curves in the $(W^0, W^1)$ space, rather than being smoothly distributed. In the non-relativistic lim

Dexin Chen

The rotation of multi-dimensional arrays, or tensors, is a fundamental operation in computer science with applications ranging from data processing to scientific computing. While various methods exist, achieving this rotation in-place (i.e., with O(1) auxiliary space) presents a significant algorithmic challenge. The elegant three-reversal algorithm provides

Alexandre Delporte, Susanne Ditlevsen, Adeline Samson

Advances in tracking technologies for animal movement require new statistical tools to better exploit the increasing amount of data. Animal positions are usually calculated using the GPS or Argos satellite system and include potentially non-Gaussian and heavy-tailed measurement error patterns. Errors are usually handled through a Kalman filter algorithm, whi

Weining Ren, Hongjun Wang, Xiao Tan, Kai Han

We present Fin3R, a simple, effective, and general fine-tuning method for feed-forward 3D reconstruction models. The family of feed-forward reconstruction model regresses pointmap of all input images to a reference frame coordinate system, along with other auxiliary outputs, in a single forward pass. However, we find that current models struggle with fine ge

Alain Bensoussan, Ziyu Huang, Sheung Chi Phillip Yam

Variational methods have been used to study stochastic control for long, see Bensoussan (1982) and Bensoussan-Lions (1978) for the early works. More precisely, variational approaches apply to the study of Bellman equation as a parabolic quasi-linear equation, when the nonlinearity affects only the gradient of the solution, and the second order derivative ter

Tanupat Trakulthongchai

The Lonely Runner Conjecture of Wills and Cusick states that if $k+1$ runners start running at distinct constant speeds around a unit-length circular track, then for each runner there is a time when he/she is at least $1/(k+1)$ away from all other runners. Rosenfeld recently obtained a computer-assisted proof of the conjecture for $8$ runners. By refining hi

Xiaolong Su, Weiwei Liu, Ruiqian Cheng, Haoru Zhang

Convolution, a cornerstone of signal processing and optical neural networks, has traditionally been implemented by mapping mathematical operations onto complex hardware. Here, we overcome this challenge by revealing that wave dynamics in translation-symmetric lattices intrinsically performs convolution, with the dispersion relation uniquely defining the comp

Minghao Yin, Yukang Cao, Kai Han

We present WUKONG, a novel training-free framework for high-fidelity textured 3D morphing that takes a pair of source and target prompts (image or text) as input. Unlike conventional methods -- which rely on manual correspondence matching and deformation trajectory estimation (limiting generalization and requiring costly preprocessing) -- WUKONG leverages th

Shu Xu, Liqun Cao

Numerical investigations of partial differential equations with hysteresis have largely focused on simulations, leaving numerical error analysis unexplored and relying mainly on derivative-free nonlinear solvers. This work establishes rigorous finite element error estimates for the backward Euler fully discrete scheme applied to semilinear and quasilinear pa

Meneka Banik, Ranjini Bandyopadhyay

The evaporation of particle-laden droplets on engineered surfaces underpins a wide range of technologies, from printed electronics to biosensing. While the influence of substrate topography on passive particle deposition is well established, the combined effects of active matter dynamics, catalytic gas generation, and surface structuring remain unexplored. H

Ayoub Lailoune, Valerio Loi, Stefano Serra-Capizzano

The present work contains a comprehensive treatment of Weyl eigenvalue and singular value distributions, \val{Schatten $p$-norm estimates, spectral localization and positive-definiteness criteria} for single-axis quaternion block multilevel Toeplitz matrix sequences generated by $s\times t$ quaternion matrix-valued, $d$-variate, Lebesgue integrable generatin

Amogh Anshu N, Harish BP

Network-on-Chip (NoC) design requires exploring a high-dimensional configuration space to satisfy stringent throughput requirements and latency constraints. Traditional design space exploration techniques are often slow and struggle to handle complex, non-linear parameter interactions. This work presents a machine learning-driven framework that automates NoC

Hanshuai Cui, Zhiqing Tang, Zhi Yao, Weijia Jia

Text-to-image generation employing diffusion models has attained significant popularity due to its capability to produce high-quality images that adhere to textual prompts. However, the integration of diffusion models faces critical challenges into resource-constrained mobile and edge environments because it requires multiple denoising steps from the origina

Sebe Vanbrabant, Gustavo Rovelo Ruiz, Davy Vanacken

While the increased integration of AI technologies into interactive systems enables them to solve an increasing number of tasks, the black-box problem of AI models continues to spread throughout the interactive system as a whole. Explainable AI (XAI) techniques can make AI models more accessible by employing post-hoc methods or transitioning to inherently in

Ken Sakayori, Andrea Colledan, Ugo Dal Lago

In this paper, a monad-based denotational model is introduced and shown adequate for the Proto-Quipper family of calculi, themselves being idealized versions of the Quipper programming language. The use of a monadic approach allows us to separate the value to which a term reduces from the circuit that the term itself produces as a side effect. In turn, this

Leo Kao

Constant-size cryptographic evidence records are increasingly used to build audit trails for regulated AI workloads in clinical, pharmaceutical, and financial settings, where each execution is summarized by a compact, verifiable record of code identity, model version, data digests, and platform measurements. Existing instantiations, however, typically rely o

Shuaishuai Han, Emad Alsusa, Arafat Al-Dweik

In this paper, a symbol error rate (SER) analysis is provided to evaluate the impact of localization inaccuracy on the communication performance under Zero-Forcing (ZF) and Minimum Mean-Square Error (MMSE) equalizers. Specifically, we adopt a pilot-aided simultaneous communication and localization (PASCAL) system, in which multiple drones actively transmit s

Ane Sanz, Eire Salegi, Asier Atutxa, David Franco

The advent of quantum computing threatens classical cryptographic mechanisms, demanding new strategies for securing communication networks. Since real-world networks cannot be fully Quantum Key Distribution (QKD)-enabled due to infrastructure constraints, practical security solutions must support hybrid operation. This paper presents an adaptive security fra

Shayne Longpre, Christopher Akiki, Campbell Lund, Atharva Kulkarni

Since 2019, the Hugging Face Model Hub has been the primary global platform for sharing open weight AI models. By releasing a dataset of the complete history of weekly model downloads (June 2020-August 2025) alongside model metadata, we provide the most rigorous examination to-date of concentration dynamics and evolving characteristics in the open model econ

Bokang Zhang, Chaojun Lu, Jianhui Li, Junfeng Wu

Reinforcement learning (RL) has achieved remarkable success across diverse domains, enabling autonomous systems to learn and adapt to dynamic environments by optimizing a reward function. However, this reliance on reward signals creates a significant security vulnerability. In this paper, we study a stealthy backdoor attack that manipulates an agent's policy

Camille Frévent

We developed two new alternatives to signature-based, spatial autoregressive models. In a simulation study, we found that the new models performed at least as well as existing approaches but presented shorter computation times. We then used the new models to analyze the premature mortality rate and the mortality rate for people aged 65 and over.

Guillermo Díez-Pastor, Julio I. de Vicente

Catalysis refers to the possibility of performing an otherwise impossible local state transformation by sharing an additional state, i.e. a catalyst, which is returned at the end of the protocol. There is a stronger version, known as supercatalysis, in which the borrowed catalyst is returned in an enhanced form, i.e. more entangled. However, this phenomenon

Hu Keyi

Current AI paradigms, as "architects of experience," face fundamental challenges in explainability and value alignment. This paper introduces "Weight-Calculatism," a novel cognitive architecture grounded in first principles, and demonstrates its potential as a viable pathway toward Artificial General Intelligence (AGI). The architecture deconstructs cognitio

Xueqing Deng

In real life, psychological and physiological states rarely change along a single dimension. Through self-tracking and discussions with clinicians, I have come to recognise with increasing clarity that sleep patterns, autonomic arousal, bodily sensations, and cognitive load are in constant interaction. Existing models often fail to capture this complexity. M

Polydoros Giannouris, Sophia Ananiadou

Large Language Models (LLMs) are increasingly utilised in software engineering, yet their ability to generate structured artefacts such as UML diagrams remains underexplored. In this work we present NOMAD, a cognitively inspired, modular multi-agent framework that decomposes UML generation into a series of role-specialised subtasks. Each agent handles a dist

Yizhi He, Sayed Amir Hoseini, Mahbub Hassan

Intelligent Reflecting Surfaces (IRS) promise low-power coverage extension, yet practical deployments must curb hardware complexity and control overhead. This paper quantifies the performance impact of two cost-saving measures, column-wise element grouping and 1-bit (binary) phase quantization, relative to the ideal fully-controlled, continuous-phase baselin

Necati Çelik

We propose a quantum-enhanced picostrain sensor that achieves Heisenberg-limited strain sensing using superconducting qubits. A strain-sensitive qubit s Hamiltonian is coupled to the momentum quadrature of a microwave resonator, transducing mechanical strain $\epsilon$ into amplified spatial displacements of the resonator s phase space. Using homodyne detect

Roland Stolz, Michael Eichelbeck, Matthias Althoff

In reinforcement learning (RL), it is often advantageous to consider additional constraints on the action space to ensure safety or action relevance. Existing work on such action-constrained RL faces challenges regarding effective policy updates, computational efficiency, and predictable runtime. Recent work proposes to use truncated normal distributions for

Bo Zhao, Chao-Hui Wang, Shao-Wen Wei

In this paper, we study the energy extraction from Kerr-AdS black holes following the magnetic reconnection process. The parameter space regions that satisfy the energy extraction condition, as well as the efficiency and power of the extracted energy, are analyzed. The study shows that the presence of a negative cosmological constant extends the range of dom

Arthur G. Suvorov, George Pappas

Spacetimes in general relativity can be uniquely decomposed into a set of multipole moments. Given the usefulness of moments in the categorization of radiation patterns, tidal deformations, and other phenomena associated with compact objects, a number of studies have explored their construction in beyond-Einstein theories of gravity. It is shown here that un

Longkun Zou, Jiale Wang, Rongqin Liang, Hai Wu

Accurate perception of UAVs in complex low-altitude environments is critical for airspace security and related intelligent systems. Developing reliable solutions requires large-scale, accurately annotated, and multimodal data. However, real-world UAV data collection faces inherent constraints due to airspace regulations, privacy concerns, and environmental v

Valeria Rossi, Sergio Luigi Cacciatori, Alessandro Pesci

Theories of emergent gravity have established a deep connection between entropy and the geometry of spacetime by looking at the latter through a thermodynamic lens. In this framework, the macroscopic properties of gravity arise in a statistical way from an effective small scale discrete structure of spacetime and its information content. In this review we be

Srivarshinee Sridhar, Raghav Kaushik Ravi, Kripabandhu Ghosh

Large Language Models (LLMs) are increasingly used in clinical settings, where sensitivity to linguistic uncertainty can influence diagnostic interpretation and decision-making. Yet little is known about where such epistemic cues are internally represented within these models. Distinct from uncertainty quantification, which measures output confidence, this w

I. P. Zabolotnyi, V. V. Izmodenov

The regions in which stellar winds interact with the interstellar medium, also known as astrospheres, can be observed in detail through the thermal emission of the interstellar dust particles, resided in plasma. Interstellar dust is also directly observed in the vicinity of the Sun with dust detectors onboard spacecraft, and it is known to be affected by the

Wenxue He, Shijin Li, Jinhao Cheng, Yingpeng Zhang

We investigate quantum transport in graphene/InSe heterostructures and find major asymmetries in the longitudinal resistance ($R_{xx}$) and vanishing $R_{xx}$ peaks at high magnetic fields, particularly at the charge-neutrality point. Our Landauer-Buttiker analysis and numerical simulations show that a monotonically varying density gradient combined with a f

Euclid Collaboration, A. Nersesian, Abdurro'uf, M. Baes

We analyzed the spatially resolved and global star formation histories (SFHs) for a sample of 25 TNG50-SKIRT Atlas galaxies to assess the feasibility of reconstructing accurate SFHs from Euclid-like data. This study provides a proof of concept for extracting the spatially resolved SFHs of local galaxies with Euclid, highlighting the strengths and limitations

Qingguang Xie, Jens Harting

The coalescence of liquid lenses is relevant in various applications, including inkjet printing and fog harvesting. However, the dynamics of liquid-lens coalescence have been relatively underexplored, particularly in the case of liquid lenses with larger contact angles. We numerically investigate the coalescence of low-viscosity liquid lenses by means of the

Irina Abaloszewa, Viktor V. Chabanenko, Aleksander Abaloszew

We report the first direct visualization of flux avalanche propagation dynamics in bulk superconducting NbTi, tracking individual events and measuring their velocities using high-speed magneto-optical imaging. Unlike thin films with electromagnetic avalanches at km/s speeds, we observe velocities of 15--25 m/s, which are orders of magnitude slower. Analysis

Run Shao, Ziyu Li, Zhaoyang Zhang, Linrui Xu

Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where

Ganeshan Niroshan, Uthayasanker Thayasivam

Self-supervised representation learning, particularly through contrastive methods like TS2Vec, has advanced the analysis of time series data. However, these models often falter in forecasting tasks because their objective functions prioritize instance discrimination over capturing the deterministic patterns, such as seasonality and trend, that are critical f

Devin M. Mulvey, Kenneth D. Jordan, Alston J. Misquitta

It is generally assumed that the carbon atoms of graphitic samples and their finite analogs have sizable quadrupole moments, with the out-of-plane component ($Q^{\rm C}_{20}$ in traceless spherical coordinates) being the dominate contribution. However, there is no consensus on what the quantity is for such carbon-based systems and values reported in the lite

Julian Haddad, C. Hugo Jiménez, Rafael Villa

Given two symmetric convex bodies $L \subseteq K \subseteq \R^n$ with $L$ strictly convex, we prove that there exist at least $n$ hyperplanes $H$ tangent to $L$, such that the center of mass of $H \cap K$ belongs to $\partial L$. The theorem makes use of Lusternik-Schnirelmann category theory.

Hans van Ditmarsch

In the 2013 Advent calender of the Berlin Mathematics Research Center MATH+, Gerhard Woeginger presents a novel hat problem with an uncommon initial announcement. Although the information given is insufficient for the hat bearers to learn their colour, they are informed that the colours have been chosen so that they can learn their colour. We formalize this

Yuanzhe Yang

Impure simplicial complexes are a powerful tool to model multi-agent epistemic situations where agents may die, but it is difficult to define a satisfactory semantics for the ordinary propositional modal language on such models, since many conceptually dubious expressions involving dead agents can be expressed in this language. In this paper, we introduce a

Hans van Ditmarsch, Tim French, Rustam Galimullin, Louwe B. Kuijer

Simulation and refinement are variations of the bisimulation relation, where in the former we keep only atoms and forth, and in the latter only atoms and back. Quantifying over simulations and refinements captures the effects of information change in a multi-agent system. In the case of quantification over refinements, we are looking at all the ways the agen

Daniil Kozhemiachenko, Igor Sedlár

Modal probabilistic logics provide a framework for reasoning about probability in modal contexts, involving notions such as knowledge, belief, time, and action. In this paper, we study a particular family of these logics, extending the modal {\L}ukasiewicz many-valued logic. These logics are shown to be capable of expressing nuanced probabilistic concepts, i

Nicodemo De Vito

We study cautious reasoning in finite sequential games played by agents with perfect recall. Our contribution lies in formulating a definition of prudent rationalizability (Heifetz et al. 2021, BEJTE) as an iterative reduction procedure of beliefs. To this end, we represent the players' beliefs by systems of conditional non-standard probability measures. The

Sourav Chakraborty, Sujata Ghosh, Smiha Samanta

We discuss winning possibilities of players in various variants of cops and robber game played on large random graphs, a testbed for various kinds of network queries, search problems in particular. We explore the use of logic frameworks to investigate such results; in particular, we show that whenever a winning condition for either player can be expressed as

Edoardo Baccini, Zoé Christoff, Nina Gierasimczuk, Rineke Verbrugge

The principle of minimal change in belief revision theory requires that, when accepting new information, one keeps one's belief state as close to the initial belief state as possible. This is precisely what the method known as minimal revision does. However, unlike less conservative belief revision methods, minimal revision falls short in learning power: It

Chenwei Shi

In this paper, we generalize epistemic logic so that it can help reason about ways of combining common knowledge and distributed knowledge such as "common distributed knowledge", "distributed common knowledge", "distributed common distributed knowledge" and so on. Moreover, we study the logic of its dynamic update by arbitrary reading events. We axiomatize t

Laryssa Horn, Paul Nüsken, Jörg Rothe, Tessa Seeger

The Skating System, which originated from the scrutineering system in dance sport tournaments, can be formulated as a voting system: We introduce and formalize the Skating System Single (SkS, for short), a new voting system embedded into the framework of computational social choice. Although SkS has similarities with Bucklin voting, it differs from it becaus

Teppei Kitahara, Jun Miyamoto, Kota Sasaki

Recently, a violation of the CKM unitarity condition has been reported in the latest charm-meson data and the latest lattice results, once the universal electroweak correction is taken into account. In this article, we analytically derive for the first time the complete one-loop electroweak (EW) and QED corrections to the $D_{s}^+ \to \ell^+ \nu_\ell$ decays

Gregor Behnke, Malvin Gattinger, Avijeet Ghosh, Haitian Wang

Model checking with the standard Kripke models used in (Dynamic) Epistemic Logic leads to scalability issues. Hence alternative representations have been developed, in particular symbolic structures based on Binary Decision Diagrams (BDDs) and succinct models based on mental programs. While symbolic structures have been shown to perform well in practice, the

Emiliano Lorini, Dmitry Rozplokhas

We introduce a new logic of graded distributed belief that allows us to express the fact that a group of agents distributively believe that a certain fact holds with at least strength k. We interpret our logic by means of computationally grounded semantics relying on the concept of belief base. The strength of the group's distributed belief is directly compu

Kaya Alpturer, Ron van der Meyden, Sushmita Ruj, Godfrey Wong

Work on the development of optimal fault-tolerant Agreement protocols using the logic of knowledge has concentrated on the "full information" approach to information exchange, which is costly with respect to message size. Alpturer, Halpern, and van der Meyden (PODC 2023) introduced the notion of optimality with respect to a limited information exchange, and

Alexandru Baltag, Sonja Smets

In recent years, epistemic logics have been extended with operators K_ax for knowledge of (the value of) a variable x (by an agent a). We study dynamic versions of these logics, enriched with modalities for semi-public data-exchange events (e.g., public announcements, data-sharing within a subgroup, or changing the value of a variable). To obtain a complete

Anna Pazola, Mohammad Shamsudduha, Richard G. Taylor, Allan Tucker

Geospatial observational datasets are often limited to point measurements, making temporal prediction and spatial interpolation essential for constructing continuous fields. This study evaluates two deep learning strategies for addressing this challenge: (1) a grid-to-grid approach, where gridded predictors are used to model rasterised targets (aggregation b

Tommaso Flaminio, Lluis Godo, Gluliano Rosella

Methods for probability updating, of which Bayesian conditionalization is the most well-known and widely used, are modeling tools that aim to represent the process of modifying an initial epistemic state, typically represented by a prior probability function P, which is adjusted in light of new information. Notably, updating methods and conditional sentences

Uri Andrews, Luca San Mauro

Argumentation frameworks, consisting of arguments and an attack relation representing conflicts, are fundamental for formally studying reasoning under conflicting information. We use methods from mathematical logic, specifically computability and set theory, to analyze the grounded extension, a widely-used model of maximally skeptical reasoning, defined as t

Boning Yu

This paper examines the conditions under which Bayesian conditioning aligns with Maximum Entropy. Specifically, I address cases in which newly learned information does not correspond to an event in the probability space defined on the sample space of outcomes. To facilitate Bayesian conditioning in such cases, one must therefore extend the probability space

Bin Liu, Yanjing Wang

Distributed knowledge is a key concept in the standard epistemic logic of knowledge-that. In this paper, we propose a corresponding notion of distributed knowledge-how and study its logic. Our framework generalizes two existing traditions in the logic of know-how: the individual-based multi-step framework and the coalition-based single-step framework. In par

Nguyen Xuan Hong

Let $\varphi$ be a plurisubharmonic function defined in a neighborhood of the origin in $\mathbb C^n$. For each real number $t>-n$, we associate to $\varphi$ the weighted log canonical threshold \[ c_t(\varphi):=\sup\Bigl\{c\geq 0:\|z\|^{2t}e^{-2c\varphi}\in L^1_{\mathrm{loc}} \text{ near }0\Bigr\}. \] In this paper, we prove a sharp slope inequality showing

Eric Pacuit, Leo Yang

Aumann's famous Agreeing to Disagree Theorem states that if a group of agents share a common prior, update their beliefs by Bayesian conditioning based on private information, and have common knowledge of their posterior beliefs regarding some event, these posteriors must be identical. There is an elegant generalization of this theorem by Monderer and Samet,

Daniil Khaitovich, Aybüke Özgün

Intentions are crucial for our practical reasoning. The rational intention obeys some simple logical principles, such as agglomeration and consistency, among others, motivating the search for a proper logic of intention. However, such a logic should be weak enough not to force the closure under entailment; otherwise, we cannot distinguish between intended co

Jörg Rothe, Ildikó Schlotter

Hedonic games -- at the interface of cooperative game theory and computational social choice -- are coalition formation games in which the players have preferences over the coalitions they can join. Kerkmann et al. [13] introduced altruistic hedonic games where the players' utilities depend not only on their own but also on their friends' valuations of coali

Kym Pram, Burkhard C. Schipper

We study the design of mechanisms under asymmetric awareness and information. While the mechanism designer cannot necessarily commit to a particular social choice function in the face of unawareness, she can at least commit to properties of social choice functions such as efficiency given ex post awareness. Assuming quasi-linear utilities and private values,

Ali Azarbahram, Shenyu Liu, Gian Paolo Incremona

This paper presents a unified and scalable framework for predictive and safe autonomous navigation in dynamic transportation environments by integrating model predictive control (MPC) with distributed Koopman operator learning. High-dimensional sensory data are employed to model and forecast the motion of surrounding dynamic obstacles. A consensus-based dist

Hugo Hazard, Zafeirios Fountas, Martin A. Benfeghoul, Adnan Oomerjee

Continual learning, one's ability to adapt to a sequence of tasks without forgetting previously acquired knowledge, remains a major challenge in machine learning and a key gap between artificial and human intelligence. While regularisation and replay perform well in vision, they lag behind multi-task learning for large language models (LLMs), especially at s

Bekir Can Lütfüoğlu, Abubakir Shermatov, Javlon Rayimbaev, Muhammad Matyoqubov

We investigate gravitational perturbations, quasinormal modes, grey-body factors, and absorption cross-sections of the recently proposed regular and asymptotically flat black hole supported by a Dehnen-type dark-matter halo. This geometry provides a remarkably simple analytic model of supermassive black holes embedded in galactic environments, having a lapse

Y. Gao, A. Galicia, J. D. Da Costa Jesus, Y. Liu

One of the main enablers in quantum computing is having qubit control that is precise and fast. However, qubits typically have multilevel structures making them prone to unwanted transitions from fast gates. This leakage out of the computational subspace is especially detrimental to algorithms as it has been observed to cause long-lived errors, such as in qu

Lingfeng Zhou, Yi Xu, Zhenyu Wang, Dequan Wang

Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce FlockVote, a novel framework that uses Large Language Models (L

Seongwon Cho, Daechul Ahn, Donghyun Shin, Hyeonbeom Choi

Open-vocabulary mobile manipulation (OVMM) requires robots to follow language instructions, navigate, and manipulate while updating their world representation under dynamic environmental changes. However, most prior approaches update their world representation only at discrete update points such as navigation targets, waypoints, or the end of an action step,

Sergio Giardino

In this article one introduces a formalism of classical mechanics where complex Lagrangian functions are admitted. The results include complex versions of the Lagrangian function, of the Euler-Lagrange equation, of the Hamilton principle, a geometric formulation, and the relation to a previous complex Hamiltonian formalism. The framework is particularly suit

Merey Orazaly, Fariza Temirkhanova, Jurn-Gyu Park

Transformer-based models have gained considerable attention in the field of physiological signal analysis. They leverage long-range dependencies and complex patterns in temporal signals, allowing them to achieve performance superior to traditional RNN and CNN models. However, they require high computational intensity and memory demands. In this work, we pres

Liwen Lian, Jinfeng Liu, Mengyuan Niu, Xiumei Wang

A connected nontrivial graph $G$ is {\it matching covered} if every edge of $G$ is contained in some perfect matching of $G$. A matching covered graph $G$ is {\it minimal} if $G-e$ is not matching covered for each edge $e$ of $G$. A graph is said to be {\it factor-critical} if $G-v$ has a perfect matching for every $v\in V(G)$. A factor-critical graph $G$ is