December 2025 arXiv papers — page 107

Patrick Bennett, Quentin Dubroff, Alan Frieze, Wesley Pegden

We discuss the expected minimum cost of rainbow spanning trees and Hamilton cycles in randomly edge colored random graphs.

N. V. Porokhov, M. A. Dryazgov, A. R. Shevchenko, A. M. Mumlyakov

This work investigates the effect of a scandium nitride buffer layer on the superconducting properties of niobium nitride thin films. The use of a ScN buffer layer significantly improves the characteristics of 29 nm thick NbN films: the critical temperature Tc increases from 9 K to 12.5 K, while the resistivity at 20 K decreases from 330 mkOhm*cm to 210 mkOh

Tomohito Kawabata, Xinyu Zhang, Ling Xiao

For robots navigating in human-populated environments, safety and social compliance are equally critical, yet prior work has mostly emphasized safety. Socially compliant navigation that accounts for human comfort, social norms, and contextual appropriateness remains underexplored. Vision language models (VLMs) show promise for this task; however, large-scale

Simonetta Abenda, Türkü Özlüm Çelik, Claudia Fevola, Yelena Mandelshtam

The Kadomtsev-Petviashvili (KP) equation is the cornerstone of integrable systems, whose solutions reflect deep connections in algebraic geometry. Banana curves are reducible rational curves obtained as a degeneration of hyperelliptic curves. In this work, we relate the family of KP multi-solitons arising from banana curves together with non-special divisors

A. I. Perminov

This paper presents a parallel random-search method for reducing additive complexity in fast matrix multiplication algorithms with ternary coefficients $\{-1,0,1\}$. The approach replaces expensive exact evaluation with fast heuristic scoring, including the new Greedy-Intersections strategy. The method runs many independent common subexpression elimination p

Aisling Power, Cara-Lena Nies, Stefan Schulz

Controlling the crystal phase and lattice mismatch of semiconductors offers a powerful route to engineer electronic and optical properties of heterostructures. As a consequence, semiconductors in the wurtzite phase are increasingly sought after, superseding the thermodynamically favored cubic zinc blende phase. Empirical atomistic modeling, required for larg

Shibani Sankpal

This study investigates emotion drift: the change in emotional state across a single text, within mental health-related messages. While sentiment analysis typically classifies an entire message as positive, negative, or neutral, the nuanced shift of emotions over the course of a message is often overlooked. This study detects sentence-level emotions and meas

Luis Fernández, Ka Shen, Leandro O. Nascimento, Van Sérgio Alves

We present a unified quantum field theory for Dirac magnons coupled to emergent gauge fields. At zero temperature, any space- and time-dependent gauge perturbation drives magnons out of equilibrium, generating spin currents and magnon accumulation without conventional thermal or chemical potential gradients. For a honeycomb ferromagnet, we derive closed-form

Elizaveta Prozorova, Anton Konev, Vladimir Faerman

The article analyzes the use of thermal imaging technologies for biometric identification based on facial thermograms. It presents a comparative analysis of infrared spectral ranges (NIR, SWIR, MWIR, and LWIR). The paper also defines key requirements for thermal cameras used in biometric systems, including sensor resolution, thermal sensitivity, and a frame

Shuyuan Xiao, Yiran Zhang, Weisong Sun, Xiaohong Chen

Use cases are widely employed to specify functional requirements, yet existing benchmarks are scarce and face the risk of being misaligned with actual system behavior, similarly limiting the rigorous evaluation of large language models (LLMs) in generating use cases from source code. We address this gap by introducing code-aligned use case benchmarks, constr

Sümer Tunçay, Alain Andres, Ignacio Carlucho

Autonomous Underwater Vehicles (AUVs) require reliable six-degree-of-freedom (6-DOF) position control to operate effectively in complex and dynamic marine environments. Traditional controllers are effective under nominal conditions but exhibit degraded performance when faced with unmodeled dynamics or environmental disturbances. Reinforcement learning (RL) p

Antoine Monier, Brielle Byerley, Julien Renaudeau, H. Daniel Ou-Yang

Diffusiophoresis (DP) refers to the migration of particles driven by a solute concentration gradient in a liquid. Observations in the case of molecular neutral solutes are rather scarce, due to the low drift velocities in dilute solutions, and the difficulty in distinguishing DP from other phenomena in concentrated solutions. We investigated experimentally D

Ming-Xiao Li, Yuqi Li, Rui-Bin Xu, Mo-Ran Zhu

Quantum networks are essential for advancing scalable quantum information processing. Quantum nonlocality sharing provides a crucial strategy for the resource-efficient recycling of quantum correlations, offering a promising pathway toward scaling quantum networks. Despite its potential, the limited availability of resources introduces a fundamental trade-of

Alessandro Sozza, Andrea Maffioli

We investigate three-dimensional turbulence in a stably stratified fluid driven by a vertically sheared Kolmogorov flow using direct numerical simulations of the Boussinesq equations. As stratification increases, mean profiles evolve toward piecewise-linear shapes while layered density structures emerge, with sharp interfaces separating well-mixed bulk layer

Geremy Loachamín, Eleni D. Koronaki, Dimitrios G. Giovanis, Martin Kathrein

This study presents a Bayesian framework for (inverse) uncertainty quantification and parameter estimation in a two-step Chemical Vapor Deposition coating process using production data. We develop an XGBoost surrogate model that maps reactor setup parameters to coating thickness measurements, enabling efficient Bayesian analysis while reducing sampling costs

Dennis Koopmans, Lingyu Wang, Berta Margalef-Bentabol, Antonio La Marca

Dusty star-forming galaxies (DSFGs) dominate the far-infrared and sub-millimetre number counts, but single-dish surveys suffer from poor angular resolution, complicating mult-wavelength counterpart identification. Prior-driven deblending techniques require extensive fine-tuning and struggle to process large fields. This work aims to develop a fast, reliable

Ali Al Sahili, Ali Chehab, Razane Tajeddine

Large Language Models (LLMs) are prone to memorizing training data, which poses serious privacy risks. Two of the most prominent concerns are training data extraction and Membership Inference Attacks (MIAs). Prior research has shown that these threats are interconnected: adversaries can extract training data from an LLM by querying the model to generate a la

Navin Kartik, Elliot Lipnowski, Harry Pei

Does electoral replacement ensure that officeholders eventually act in voters' interests? We study a reputational model of accountability. Voters observe incumbents' performance and decide whether to replace them. Politicians may be "good" types who always exert effort or opportunists who may shirk. We find that good long-run outcomes are always attainable,

Christian Nicolaisen Hansen, Line Jelver, Christos Tserkezis

We introduce theoretical methods for describing the optical response of two-dimensional (2D) materials patterned at the nanoscale into both arrays of ribbons along a planar surface and spherical particles. Fourier-Floquet decompositions of the electromagnetic fields are used in order to obtain the reflectance, transmittance and absorbance of the nanoribbon a

David Barreiro-Villaverde, Antonio Cantiani, Miguel A. Mendez

Accurate prediction of self-pressurization in cryogenic tanks requires resolving the coupled effects of heat ingress, natural convection, and phase change. This work introduces a segregated numerical framework in which the liquid and vapor phases are treated with incompressible and compressible solvers, respectively, and the liquid-vapor interface is modeled

Yucheng Wang

The eigenstate thermalization hypothesis (ETH) provides a powerful framework for understanding thermalization in isolated quantum many-body systems, yet a complete and conceptually transparent derivation has remained elusive. In this work, we derive the structure of ETH from a minimal dynamical principle, which we term the eigenstate typicality principle (ET

Magnus Aspenberg, Erik Martens, Kristofer Wollein Waldetoft

We consider the Lotka-Volterra system and provide necessary conditions for an equilibrium to be stable. Our results naturally complement earlier fundamental results by N. Adachi, Y. Takeuchi, and H. Tokumaru, who, in a series of papers, give sufficient (and for some cases necessary) conditions for the existence of a stable equilibrium point.

Yukun Lu, Bingjie Li, Zhigang Yao

Identifying preclinical biomarkers of neurodegenerative diseases remains a major challenge in aging research. In this study, we demonstrate that frequent "Don't know/can't remember" (DK) responses, often treated as missing data in touchscreen questionnaires, serve as a novel digital behavioral biomarker of early cognitive vulnerability and neurodegenerative

TK Lee

Large language models (LLMs) are widely deployed as general-purpose tools, yet extended interaction can reveal behavioral patterns not captured by standard quantitative benchmarks. We present a qualitative case-study methodology for auditing policy-linked behavioral selectivity in long-horizon interaction. In a single 86-turn dialogue session, the same model

Shulei Cao, Amit Kumar Mandal, Michal Zajaček, Bharat Ratra

Active galactic nuclei (AGN) have been studied as alternate probes in cosmology due to their large and stable luminosities and broad redshift range. Previously it was shown that higher-redshift AGN that were reverberation-mapped (RM) using broad Mg\,\textsc{ii} and C\,\textsc{iv} lines are standardizable and yield weak cosmological constraints that are consi

Vaishnavi Jagabathula, Pushpak Jagtap

Signal Temporal Logic (STL) provides a powerful framework to describe complex tasks involving temporal and logical behavior in dynamical systems. This work addresses controller synthesis for continuous-time systems subject to STL specifications and input constraints. We propose a neural network-based framework for synthesizing time-varying control barrier fu

Yiran Lin

We introduce a new Hermitian metric on the cohomology ring of compact K\"ahlerian manifolds with a pair $(v,w)$ satisfying certain Hodge-Riemann relations. An Hermitian metric on the exterior algebra of the cotangent bundle is also defined and we establish the corresponding theory of harmonic forms, relating the global metric and local metric. This generaliz

Sheikh Shakil Akhtar, Pranabendu Misra, Geevarghese Philip

We study "space efficient" FPT algorithms for graph problems with limited memory. Let n be the size of the input graph and k be the parameter. We present algorithms that run in time f(k)*poly(n) and use g(k)*polylog(n) working space, where f and g are functions of k alone, for k-Path, MaxLeaf SubTree and Multicut in Trees. These algorithms are motivated by b

Gabriel Fuhrmann, Chunlin Liu

We study minimal idempotents $J^{\mathrm{min}}(X)$ in the Ellis semigroup $E(X)$ associated with a Floyd-Auslander system $(X,T)$. We show that $(X,T)$ is non-tame if and only if $|J^{\mathrm{min}}(X)| > 2^{\aleph_0}$, which happens exactly when the factor map onto the maximal equicontinuous factor possesses uncountably many non-invertible fibres. This yield

Henrik C. M. Frederiksen, Junya Shiraishi, Cedomir Stefanovic, Hei Victor Cheng

The use of lightweight machine learning (ML) models in internet of things (IoT) networks enables resource constrained IoT devices to perform on-device inference for several critical applications. However, the inference accuracy deteriorates due to the non-stationarity in the IoT environment and limited initial training data. To counteract this, the deployed

Gianluca Aglieri Rinella, Luca Aglietta, Matias Antonelli, Francesco Barile

Monolithic Active Pixel Sensors (MAPS) in advanced CMOS imaging technologies are key to next-generation tracking systems for high-energy physics, where radiation hardness and precise vertex reconstruction are essential. As part of the ALICE ITS3 R&D program in synergy with the CERN R&D, we evaluated the performance of the Analog Pixel Test Structures (APTS)

Volker Branding, Nicolas Ginoux, Georges Habib

The magnetic Dirac operator describes the relativistic motion of a charged particle in a magnetic field. Although this operator got a lot of attention in physics many of its fundamental mathematical properties remain unexplored and this article is a first step towards filling this gap. To this end we provide a number of eigenvalue estimates for the magnetic

Si Qi Goh, Yongsen Zheng, Ziyao Liu, Sami Hormi

Machine unlearning (MU) seeks to eliminate the influence of specific training examples from deployed models. As large language models (LLMs) become widely used, managing risks arising from insufficient forgetting or utility loss is increasingly crucial. Current MU techniques lack effective mechanisms for evaluating and controlling these risks, hindering the

Karim Bounja, Lahcen Laayouni, Abdeljalil Sakat

This work introduces Knowledge-Distilled Physics-Informed Neural Networks (KD-PINN), a framework that transfers the predictive accuracy of a high-capacity teacher model to a compact student through a continuous adaptation of the Kullback-Leibler divergence. In order to confirm its generality for various dynamics and dimensionalities, the framework is evaluat

Anette Messinger, Christophe Goeller, Wolfgang Lechner

We present a set of efficiently implementable logical multi-qubit gates in concatenated quantum error correction codes using parity qubits. In particular, we show how fault-tolerant high-weight rotation gates of arbitrary angle can be implemented on single physical qubits of a classical stabilizer code, or on localized regions of full quantum error correctio

Eduard Vilalta

The Global Glimm Problem lies at the heart of several open questions regarding regularity properties of C*-algebras. The problem has been open for over two decades, and has recently garnered significant attention due to its strong ties to non-simple versions of well-known dimension reduction phenomena for simple C*-algebras, such as the weakly purely infinit

Tushin Mallick, Maya Zeldin, Murat Cenk, Cristina Nita-Rotaru

As quantum computing advances, classical cryptographic systems - signature schemes, key exchange protocols, public-key encryption, and certain hash constructions - that secure most blockchain platforms come under threat, raising serious concerns about their long-term security and integrity. Transitioning to post-quantum primitives is rarely straightforward:

Joona Kytöniemi, Jousia Piha, Akseli Reunamo, Fedor Vitiugin

We introduce FIN-bench-v2, a unified benchmark suite for evaluating large language models in Finnish. FIN-bench-v2 consolidates Finnish versions of widely used benchmarks together with an updated and expanded version of the original FIN-bench into a single, consistently formatted collection, covering multiple-choice and generative tasks across reading compre

Bibhatsu Kuiri, Rittwick Mondal, Dipankar Biswas, Soumyajyoti Kabi

We develop a unified scaling framework for the end-position distributions of tethered polymers confined in finite cylindrical geometries. Two observables are analysed: the longitudinal distribution P(x), along the confinement axis, and the transverse distribution P(y), perpendicular to the confinement axis. Using exact Fourier-sine and image-method represent

Tsanimir Angelov, Rasim Bekir, Galin Gyulchev, Petya Nedkova

We study the influence of the plasma environment on the shadows of stationary axisymmetric wormholes. We consider a sample of several wormhole solutions and plasma distributions for which the Hamilton-Jacobi equation for the light rays is separable. This allows us to derive analytical expressions for the shadow boundary and examine the behavior of the photon

Leif Lönnblad, Torbjörn Sjöstrand

We present recent developments in PYTHIA for the modelling of hadronic cascades in a medium. Several improvements have been made in the Angantyr model for collisions with nuclei, especially in the limit of low collision energies, allowing it to be used throughout the hadronic cascades. Also the simplified nuclear model in the PythiaCascade} module has been u

Malte Hellmeier

Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital watermarking provides additional protection by embedding an

Thomas Deck, Carlos Mudarra

Given a superreflexive Banach space $X$, and a set $E \subset X$, we characterise the $1$-jets $(f,G)$ on $E$ that admit $C^{1,\omega}$ convex extensions $(F,DF)$ to all of $X$; where $\omega$ is any admissible modulus of continuity depending on the regularity of $X$. Moreover, we obtain precise estimates for the growth of the $C^{1,\omega}$ seminorm of the

Sakshi Shukla, Praveen C. Srivastava, Kazunari Kaneko

In order to comprehend the process underlying mirror energy differences in mirror pairs, we have performed shell-model calculations for $T_z= \pm 2$ $sd$-shell nuclei in the mass range $A$= 20 to 36 and neutron number varying from $N$= 8 to 20. Isospin-symmetry breaking (ISB) is responsible for the mirror energy difference of excited states. We have investig

Alexander Roberts, Huixin Guo, Joseph Askey, Vani Lanka

Three-dimensional (3D) magnetic nanostructures offer unprecedented opportunities for engineering emergent spin textures, but controlling their configuration remains a central challenge. Here we show that conformally coated Ni Nanotubes arranged in a woodpile geometry with lattice spacings ranging from 800 to 1200 nm, realised by two-photon lithography and at

Shivam Parashar, Dibyendu Chakrabarty, Prashant Kumar, Abhishek Kumar

The interaction between interplanetary Coronal Mass Ejection (ICME) structures can alter the geo-effectiveness of the ICME events in myriad ways. Many aspects of these interaction processes are not well-understood till date. Using the energy spectra measured in two mutually orthogonal top hat analyzers (THA 1 and 2), which are part of the Solar Wind Ion Spec

Hassan Razavi, Ángel F. García-Fernández, Simo Särkkä

This paper proposes a parallel-in-time method for computing continuous-time maximum-a-posteriori (MAP) trajectory estimates of the states of partially observed stochastic differential equations (SDEs), with the goal of improving computational speed on parallel architectures. The MAP estimation problem is reformulated as a continuous-time optimal control prob

Carlo Nicolini

We establish an exact analytic relation between random spanning forests and the heat-kernel partition function. This identity enables estimation of partition functions, energies, and the Von Neumann entropy by Wilson sampling of forests, avoiding costly Laplacian eigendecompositions. We validate inverse-Laplace reconstructions stabilized by a Stieltjes spect

Mikhail Zakharov

Face recognition systems rely on learning highly discriminative and compact identity clusters to enable accurate retrieval. However, as with other surveillance-oriented technologies, such systems raise serious privacy concerns due to their potential for unauthorized identity tracking. While several works have explored machine unlearning as a means of privacy

Mayank Gulati, Benedikt Groß, Gerhard Wunder

We present ALIGN-FL, a novel approach to distributed learning that addresses the challenge of learning from highly disjoint data distributions through selective sharing of generative components. Instead of exchanging full model parameters, our framework enables privacy-preserving learning by transferring only generative capabilities across clients, while the

Kling Team, Jialu Chen, Yikang Ding, Zhixue Fang

Avatar video generation models have achieved remarkable progress in recent years. However, prior work exhibits limited efficiency in generating long-duration high-resolution videos, suffering from temporal drifting, quality degradation, and weak prompt following as video length increases. To address these challenges, we propose KlingAvatar 2.0, a spatio-temp

Yu Mao, Mohamed Saidi

In \cite{Ho3}, Hoshi proved that open homomorphisms between solvably closed Galois groups of number fields which are compatible with the cyclotomic characters arise from field embeddings. In this paper, we will prove an $m$-step solvable version of Hoshi's result. More precisely, if $K$ and $L$ are number fields, we will prove that given an open homomorphism

S. Mandal, P. Kumar, Z. Pi, H. Y. Kim

High harmonic generation (HHG) in solids has emerged as a powerful spectroscopic method for resolving ultrafast electron dynamics and band structure properties across a wide range of materials. However, quantitative HHG studies require instrumentation capable of delivering stable driving fields, precise crystal alignment, and broadband detection spanning the

Bufan Li, Xinghao Qiao, Weichi Wu, Holger Dette

Second-order characteristics including covariance and spectral density functions are fundamentally important for both statistical applications and theoretical analysis in functional time series. In the high-dimensional setting where the number of functional variables is large relative to the length of functional time series, non-asymptotic theory for covaria

Gabriel Fuhrmann

Given a continuous self-map $f$ on some compact metrisable space $X$, it is natural to ask for the visiting frequencies of points $x\in X$ to sufficiently ``nice'' sets $C\subseteq X$ under iteration of $f$. For example, if $f$ is an irrational rotation on the circle, it is well-known that the Birkhoff average $\lim_{n\to\infty}1/n\cdot \sum_{i=0}^{n-1}\math

Elsiddig Awadelkarim, David Bolin, Alexandre B. Simas

Given a compact metric graph $\Gamma$ and the Laplacian $\Delta_{\Gamma}$ coupled with standard (Kirchhoff) vertex conditions, solutions to fractional elliptic partial differential equations of the form $(\kappa^2 - \Delta_{\Gamma})^{\alpha/2}u=f$ on $\Gamma$ exhibit a distinctive regularity structure: even-order derivatives are continuous across vertices, w

Thomas Baier, Michele Bolognesi, Johan Martens, Christian Pauly

We prove that the Hitchin connection for $\operatorname{SL}_2$ at level four can be understood in terms of the Mumford-Welters connections on bundles of abelian theta functions for Prym torsors of all unramified double covers, and use this to show that its monodromy is finite. This builds on earlier works, for individual curves, of the last named author with

Manuel A. Jimenez, Sarang Kahvazadeh, Ignacio Labrador, Josep Mangues-Bafalluy

6G networks envision a pervasive service infrastructure spanning from centralized cloud to distributed edge and highly dynamic extreme-edge domains. This vision introduces significant challenges in orchestrating services over heterogeneous, volatile, and often mobile resources beyond traditional operator control. To address these challenges, this demo presen

V. S. Ryumshin

The results of numerical simulation using a classical Monte Carlo method with a kinematic accounting of the bosons concentration for a pseudospin model of orthonickelates are presented. Type of the phase transitions of the model orthonickelates is investigated.

Kenneth H. Karlsen, Yan Rybalko

We investigate the Cauchy problem for a two-component generalization of the Novikov equation with cubic nonlinearity -- an integrable system whose solutions may develop strong nonlinear phenomena such as gradient blow-up and interactions between peakon-like structures. Our study has two main objectives: first, to analyze the generic regularity of global cons

Zhihang Liu, Xiaoyi Bao, Pandeng Li, Junjie Zhou

While existing generation and unified models excel at general image generation, they struggle with tasks requiring deep reasoning, planning, and precise data-to-visual mapping abilities beyond general scenarios. To push beyond the existing limitations, we introduce a new and challenging task: creative table visualization, requiring the model to generate an i

Ioanna Theophilou, Georgia M. Kapitsaki

Data privacy legislation, such as GDPR and CCPA/CPRA, has rendered data privacy law compliance a requirement of all software systems. Developers need to implement various kinds of functionalities to cover law needs, including user rights and law principles. As data compliance is tightly coupled with legal knowledge, it is not always easy to perform such inte

Haijun Yang

DarkSHINE is an electron fixed target experiment under proposal that aims to probe light dark matter in the MeV-GeV mass range via the invisible decay of dark photons, leveraging the High repetition rate 8 GeV electron beam from the Shanghai High repetition-rate XFEL and Extreme Light Facility. This proceeding presents the core detector design of the experim

Qinglin Jia, Zhaocheng Du, Chuhan Wu, Huifeng Guo

In most real-world online advertising systems, advertisers typically have diverse customer acquisition goals. A common solution is to use multi-task learning (MTL) to train a unified model on post-click data to estimate the conversion rate (CVR) for these diverse targets. In practice, CVR prediction often encounters missing conversion data as many advertiser

Mehrdad Faraji, Alexander Schleusener, Sirous Khabbaz Abkenar, Andrea Griesi

Low-dimensional metal halide perovskites provide exciting opportunities to fabricate new semiconductor materials. Semiconductor technology relies on electronic heterojunctions, and cost-efficient and flexible approaches to realize functional heterostructures are of fundamental importance. Lateral heterostructures define the energy landscape in the plane of t

Anna Aksenova, Boris Zverkov, Nicola Dainese, Alexander Nikitin

Large language models are powerful but often limited by high computational cost, privacy concerns, and English-centric training. Recent progress demonstrates that small, efficient models with around one billion parameters can deliver strong results and enable on-device use. This paper introduces MiniLingua, a multilingual open-source LLM of one billion param

Zhenghao Zhu, Chuxue Cao, Sirui Han, Yuanfeng Song

In medical data analysis, extracting deep insights from complex, multi-modal datasets is essential for improving patient care, increasing diagnostic accuracy, and optimizing healthcare operations. However, there is currently a lack of high-quality datasets specifically designed to evaluate the ability of large multi-modal models (LMMs) to discover medical in

Amit Kumar Mandal, Francisco Pozo Nuñez, Vikram Kumar Jaiswal, Mohammad Hassan Naddaf

We investigate the origin of inter-band continuum time delays in active galactic nuclei (AGNs) to study the structure and properties of their accretion disks. We aim to measure the inter-band continuum time delays through photometric monitoring of Seyfert galaxy Fairall 9 to construct the lag-spectrum. Additionally, we explain the observed features in the Fa

P. Amster, M. Rodríguez Cartabia

A non-autonomous discrete delayed system for a one-species chemostat based on an Ellermeyer model for the continuous case is studied. Conditions for the persistence or the extinction of the solutions are obtained respectively in terms of the lower and upper Bohl exponents for a scalar linear equation associated to the problem. Furthermore, the condition for

Divya Acharya, Prasanna Poojary, Vadiraja Bhatta G. R

In this article, we study the algebraic structure of double cyclic codes of length $(m, n)$ over $\mathbb{F}_4$ and we give a necessary and sufficient condition for a double cyclic code over $\mathbb{F}_4$ to be reversible. Also, we determine the algebraic structure of double cyclic codes of length $(m, n)$ over $\mathbb{F}_4+v\mathbb{F}_4$ with $v^2=v$, sat

Sooryansh Asthana, Yeshma Ibrahim, Norman Tze Wei Koo, Sai Vinjanampathy

We unify Ramsey, twist-untwist, and random quantum sensors using operator algebra and account for the Fisher scaling of various sensor designs. We illustrate how the operator orbits associated with state preparation inform the scaling of the sensitivity with the number of subsystems. Using our unified model, we design a novel set of sensors in which a projec

Hao Fu, Wei Liu, Shuai Zhou

This paper investigates the application of reinforcement learning (RL) to multi-robot social formation navigation, a critical capability for enabling seamless human-robot coexistence. While RL offers a promising paradigm, the inherent unpredictability and often uncooperative dynamics of pedestrian behavior pose substantial challenges, particularly concerning

Farshad Rostami Ghadi, F. Javier Lopez-Martinez, Kai-Kit Wong, Christos Masouros

This paper develops a unified information-theoretic framework for artificial-intelligence (AI)-aided integrated sensing and communication (ISAC), where a learning component with limited representational capacity is embedded within the transceiver loop. The study introduces the concept of an AI capacity budget to quantify how the finite ability of a learning

Chunlei Liu

The counting function for the numbers satisfying the Collatz conjecture is studied. A related exponential congruence equation is investigated, yielding a method to construct its solutions from free variables, and enabling us to find at least $x^{0.3227}$ Collatz numbers in the interval $[1,x]$. The historical record is 0.84.

Sergio Junquera

In this paper we study a non-local diffusion system of two equations with both coupled and uncoupled singular absorption terms of the type $u^{-p}$. We prove that there exist necessary and sufficient conditions for the existence of both stationary and quenching solutions. We also characterize in terms of the exponents of the absorption terms when the quenchi

Shu Yu, Chaochao Lu

Diffusion models (DMs) have achieved remarkable success in image and video generation. However, they still struggle with (1) physical alignment and (2) out-of-distribution (OOD) instruction following. We argue that these issues stem from the models' failure to learn causal directions and to disentangle causal factors for novel recombination. We introduce the

Fanny Augeri, Ofer Zeitouni

We compute the second order asymptotics of the maximum of the absolute value of the log-characteristic polynomial of random Jacobi matrices whose coefficients satisfy some exponential integrability condition. In particular, by the triadiagonal representation of Dumitriu and Eldelman of Gaussian $\beta$ Ensembles, this result partially confirms the Fydorov-Si

Hamza Harraf, Mohamed Amazioug, Rachid Ahl Laamara

Entropy production is a fundamental concept in nonequilibrium thermodynamics, providing a direct measure of the irreversibility inherent in any physical process. In this work, we investigate in steady-state the enhancement of irreversibility employing coherent feedback loop. We evaluate the steady-state entropy production rate and quantum correlations by app

Lucy A. Z. Arditi, Anna Lisa Varri

We present a new three-parameter family of self-consistent equilibrium models for quasi-relaxed stellar systems that are subject to the combined action of external tides and rigid internal rotation. These models provide an idealised description of globular clusters that rotate asynchronously with respect to their orbital motion around a host galaxy. Model co

Youssra Rebboud, Pasquale Lisena, Raphael Troncy

In fact-checking applications, a common reason to reject a claim is to detect the presence of erroneous cause-effect relationships between the events at play. However, current automated fact-checking methods lack dedicated causal-based reasoning, potentially missing a valuable opportunity for semantically rich explainability. To address this gap, we propose

Bo Liu, Qiao Qin, Qinghui He

The rapid advancement of generative models has increased the demand for generated image detectors capable of generalizing across diverse and evolving generation techniques. However, existing methods, including those leveraging pre-trained vision-language models, often produce highly entangled representations, mixing task-relevant forensic cues (causal featur

Adheep Arya G R, Vaibhav Pratap Singh, Mayank Kumar, Niyathi Shenoy

This paper describes an automatic bird call recording system called SAMAY, which is developed to study bird species by creating a database of large amounts of bird acoustic data. By analysing the recorded bird call data, the system can also be used for automatic classification of bird species, monitoring bird populations and analysing the impact of environme

Anna Sigalou, Yunhe Tong, Charlie Pilgrim, Richard P. Mann

Moving groups are routinely faced with a choice of different routes as part of their daily lives, such as choosing between exits from a building. Differences in moving speeds and environmental constraints often lead to individuals being able to observe the choices of others ahead. This social information can inform their decision-making, but exactly how this

Endri Taka, Andre Roesti, Joseph Melber, Pranathi Vasireddy

The high computational and memory demands of modern deep learning (DL) workloads have led to the development of specialized hardware devices from cloud to edge, such as AMD's Ryzen AI XDNA NPUs. Optimizing general matrix multiplication (GEMM) algorithms for these architectures is critical for improving DL workload performance. To this end, this paper present

Jiaqi Wang, Weijia Wu, Yi Zhan, Rui Zhao

With AI-generated videos increasingly indistinguishable from reality, current benchmarks primarily focus on broad semantic alignment and basic physical consistency, offering limited discriminative power for evaluating them. To address this, we introduce VideoASMR-Bench, a benchmark based on Autonomous Sensory Meridian Response (ASMR) videos that emphasizes f

Yifei He, Haoting Zhen, Mithilesh K. Parit, Mingchen Huang

Fluctuations typically destroy long-range order in two-dimensional (2D) systems, posing a fundamental challenge to the existence of exotic states like supersolids, which paradoxically combine solid-like structure with frictionless superfluid flow. While long-predicted, the definitive observation of a 2D supersolid has remained an outstanding experimental goa

Zibin Liu, Cheng Zhang, Xi Zhao, Yunfei Feng

Large Language Model (LLM) agents are increasingly deployed to automate complex workflows in mobile and desktop environments. However, current model-centric agent architectures struggle to self-evolve post-deployment: improving personalization, capability, and efficiency typically requires continuous model retraining/fine-tuning, which incurs prohibitive com

Jinrui Liu, Jeff Wu, Xuanguang Pan, Gavin Cheung

LLMs achieve remarkable multi-step reasoning capabilities, yet effectively transferring these skills via post-training distillation remains challenging. Existing data selection methods, ranging from manual curation to heuristics based on length, entropy, or overall loss, fail to capture the causal importance of individual reasoning steps, limiting distillati

D. L. Neuhäuser, R. Neuhäuser, V. Hambaryan, J. Chapman

Connections between novae with shells and historical observations are crucial for astrophysical understanding of long-term evolution of shells and cataclysmic variables. Three of five previously considered links are revisited here: extended features in M22 in BC48, Te-11 in 483, and AT Cnc in 1645. We aim to develop a procedure to check whether these links a

Yan Li, Lin Liu, Xiaopeng Zhang, Wei Xue

Instruction-based image editing with diffusion models has achieved impressive results, yet existing methods struggle with fine-grained instructions specifying precise attributes such as colors, positions, and quantities. While recent approaches employ Group Relative Policy Optimization (GRPO) for alignment, they optimize only at individual sampling steps, pr

M. H. M. Rashid

This paper establishes comprehensive stability results for quasi-variational inequalities (QVIs) under monotone perturbations of the governing operator. We prove strong convergence of both minimal and maximal solutions when sequences of operators converge pointwise while preserving fundamental properties including homogeneity, strong monotonicity, Lipschitz

Wenwei Zhang, Jintao Wang, Tianyu Ye, Changgeng Liao

Classical deep neural network models struggle to represent data uncertainty and capture dependencies between features simultaneously, especially under fuzzy or noisy conditions. Although a quantum-assisted hierarchical fuzzy neural network (QA-HFNN) was proposed to learn fuzzy membership for each feature, it cannot model dependencies between features due to

Jieyu Chen, Zengqiang Lin

Let $\mathcal{A}$ be an abelian category with a torsion pair $(\mathcal{T},\mathcal{F})$. Happel-Reiten-Smalo tilting provides a method to construct a new abelian category $\mathcal{B}$ with a torsion pair associated to $(\mathcal{T},\mathcal{F})$, which is exactly the heart of a certain $t$-structure on the bounded derived category $D^b(\mathcal{A})$. In th

Ching-Yeh Chen, Shih-Wei Lin, Ching-Ping Lee, J. C. Chen

Three-level Lambda systems provide a versatile platform for quantum optical phenomena such as Electromagnetically Induced Transparency (EIT), slow light, and quantum memory. Such Lambda systems have been realized in several quantum hardware platforms including atomic systems, superconducting artificial atoms, and meta-structures. Previous experiments involvi

Fangju Yang, Hang Yang, Ibrahim Alsarraj, Yuhao Wang

Cable-driven continuum robots (CDCRs) require accurate, real-time dynamic models for high-speed dynamics prediction or model-based control, making such capability an urgent need. In this paper, we propose the Lightweight Actuation-Space Energy Modeling (LASEM) framework for CDCRs, which formulates actuation potential energy directly in actuation space to ena

Grigory Franguridi, Jinyong Hahn, Pierre Hoonhout, Arie Kapteyn

In panel data subject to nonignorable attrition, auxiliary (refreshment) sampling may restore full identification under weak assumptions on the attrition process. Despite their generality, these identification strategies have seen limited empirical use, largely because the implied estimation procedure requires solving a functional minimization problem for th

Edgar A. Patiño-Nariño, Nicolas Moreno, Marco Ellero

We present a Lagrangian Heterogeneous Multiscale Method (LHMM) for simulating the non-Newtonian rheology of polymer melts in complex two-dimensional flows. The method couples Dissipative Particle Dynamics (DPD) at the microscale with a GENERIC-compliant Smoothed Particle Hydrodynamics (SPH) at the macroscale, in a concurrent framework, overcoming the limitat

Yukun Zhang, Xiaoxue Gong, Xu Zhang, Lei Guo

In this paper, a low-complexity multi-dimensional architecture for IQ imbalance compensation is proposed, which reduces the effects of in-phase (I) and quadrature (Q) imbalance. The architecture use a transceiver IQ skew estimation structure to compensate for IQ skew, and then use a low-complexity MIMO equalizer to compensate for IQ amplitude/phase imbalance

Alibek Adilzhan, Damir Yeliussizov

We obtain Hamel--Goulden-type ribbon decomposition determinantal formulas for flagged supersymmetric Schur functions. As an application, we derive corresponding new determinantal formulas dual refined canonical stable Grothendieck polynomials. These results generalize and produce a number of new determinantal formulas for these symmetric functions including

Devibala Esakkimuthu, Basherrudin Mahmud Ahmed Abduljaffer

Creation of high fidelity photonic quantum states in the continuous variable regime is indispensable for the implementation of quantum technologies universally. However, this is a challenging task as it requires higher nonlinearity or larger Fock states. In this article, we surmount this necessity by using a linear optical setup with a cascaded arrangement o