May 2025 arXiv papers — page 98

Marko Znidaric

One of the premier utilities of present day noisy quantum computers is simulation of many-body quantum systems. We study how long in time is such a discrete-time simulation representative of a continuous time Hamiltonian evolution, namely, a finite time-step introduces so-called Trotterization errors. We demonstrate that the truncated operator propagator (Ru

Ziyue Liu, Meredith L. Carr, Norberto C. Nadal-Caraballo, Luke A. Aucoin

Coastal compound floods (CCFs) are triggered by the interaction of multiple mechanisms, such as storm surges, storm rainfall, tides, and river flow. These events can bring significant damage to communities, and there is an increasing demand for accurate and efficient probabilistic analyses of CCFs to support risk assessments and decision-making. In this stud

Michele Zhu, Francesco Linsalata, Silvia Mura, Lorenzo Cazzella

The Line-of-Sight (LoS) identification is crucial to ensure reliable high-frequency communication links, especially those vulnerable to blockages. Network Digital Twins and Artificial Intelligence are key technologies enabling blockage detection (LoS identification) for high-frequency wireless systems, e.g., 6>GHz. In this work, we enhance Network Digital Tw

Chang Liu

To overcome the constraints of the underwater environment and improve the accuracy and robustness of underwater target detection models, this paper develops a specialized dataset for underwater target detection and proposes an efficient algorithm for underwater multi-target detection. A self-supervised learning based on the SimSiam structure is employed for

Kaiyuan Chen, Shuangyu Xie, Zehan Ma, Pannag R Sanketi

Vision-Language Models (VLMs) acquire real-world knowledge and general reasoning ability through Internet-scale image-text corpora. They can augment robotic systems with scene understanding and task planning, and assist visuomotor policies that are trained on robot trajectory data. We explore the reverse paradigm - using rich, real, multi-modal robot traject

Kanghui He, Shengling Shi, Ton van den Boom, Bart De Schutter

Ensuring safety in the sense of constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-based setting by modifying unsafe control inputs, they typically rely on predictive models derived from physics or data. This reliance limits their applicabilit

Soham Sane

Proximal Policy Optimization (PPO) is a widely used reinforcement learning algorithm that heavily relies on accurate advantage estimates for stable and efficient training. However, raw advantage signals can exhibit significant variance, noise, and scale-related issues, impeding optimal learning performance. To address this challenge, we introduce Advantage M

Qi Lei, Hongyu Liu, Zhi-Qiang Miao, Guang-Hui Zheng

In this paper, we investigate the hybridization theory of plasmon resonance in metallic nanostructures, which has been validated by the authors in [31] through a series of experiments. In an electrostatic field, we establish a mathematical framework for the Neumann-Poincar\'{e}(NP) type operators for metallic nanoparticles with general geometries related to

Brandon Duderstadt, Zach Nussbaum, Laurens van der Maaten

The rapid adoption of generative AI has driven an explosion in the size of datasets consumed and produced by AI models. Traditional methods for unstructured data visualization, such as t-SNE and UMAP, have not kept up with the pace of dataset scaling. This presents a significant challenge for AI explainability, which relies on methods such as t-SNE and UMAP

Biao Hu, Minyue Wang

This paper investigates the generative mechanism of the p-order cloud model, which is a mathematical framework for representing uncertainty with applications in image processing, evaluation, and decision-making systems. By employing a reparameterization technique, we reformulate the cloud model as a stochastic recurrence equation (SRE) with a nonlinear trans

Zihui Cheng, Qiguang Chen, Xiao Xu, Jiaqi Wang

Large Vision-Language Models (LVLMs) have achieved significant success in multimodal tasks, with multimodal chain-of-thought (MCoT) further enhancing performance and interpretability. Recent MCoT methods fall into two categories: (i) Textual-MCoT (T-MCoT), which takes multimodal input and produces textual output; and (ii) Interleaved-MCoT (I-MCoT), which gen

Christopher Rauhögger

We study strong approximation of $d$-dimensional stochastic differential equations (SDEs) with a discontinuous drift coefficient driven by a $d$-dimensional Brownian motion $W$. More precisely, we essentially assume that the drift coefficient $\mu$ is piecewise Lipschitz continuous with an exceptional set $\Theta\subset \mathbb{R}^d$ that is an orientable $C

Prasoon Bajpai, Tanmoy Chakraborty

Test-time scaling has emerged as a widely adopted inference-time strategy for boosting reasoning performance. However, its effectiveness has been studied almost exclusively in English, leaving its behavior in other languages largely unexplored. We present the first systematic study of test-time scaling in multilingual settings, evaluating DeepSeek-R1-Distill

Mahesh Godavarti

We introduce a new algebraic structure for multi-dimensional compositional embeddings, built on directional non-commutative monoidal operators. The core contribution of this work is this novel framework, which exhibits appealing theoretical properties (associativity along each dimension and an interchange law ensuring global consistency) while remaining comp

Debarshi Brahma, Anuska Roy, Soma Biswas

Recently, Vision-Language foundation models like CLIP and ALIGN, which are pre-trained on large-scale data have shown remarkable zero-shot generalization to diverse datasets with different classes and even domains. In this work, we take a step further and analyze whether these models can be adapted to target datasets having very different distributions and c

Abdul Samad Shaik, Shashaank Mattur Aswatha, Rahul Jashvantbhai Pandya

Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous changes and prevent disease progression. In this study, we performed a focused analysis by segmenting the cellular boundaries and drawing bounding boxes to isolate the cancer cells. A novel Deep Learning (DL) archit

Conghao Xiong, Zhengrui Guo, Zhe Xu, Yifei Zhang

Few-shot Whole Slide Image (WSI) classification is severely hampered by overfitting. We argue that this is not merely a data-scarcity issue but a fundamentally geometric problem. Grounded in the manifold hypothesis, our analysis shows that features from pathology foundation models exhibit a low-dimensional manifold geometry that is easily perturbed by downst

Tom Silver, Rajat Kumar Jenamani, Ziang Liu, Ben Dodson

Generalist robots must personalize in-the-wild to meet the diverse needs and preferences of long-term users. How can we enable flexible personalization without sacrificing safety or competency? This paper proposes Coloring Between the Lines (CBTL), a method for personalization that exploits the null space of constraint satisfaction problems (CSPs) used in ro

Christian Röver, Tim Friede

Meta-analytic-predictive (MAP) priors have been proposed as a generic approach to deriving informative prior distributions, where external empirical data are processed to learn about certain parameter distributions. The use of MAP priors is also closely related to shrinkage estimation (also sometimes referred to as dynamic borrowing). A potentially odd situa

Federico Ranaldi, Andrea Zugarini, Leonardo Ranaldi, Fabio Massimo Zanzotto

We introduce the concept of protoknowledge to formalize and measure how sequences of tokens encoding Knowledge Graphs are internalized during pretraining and utilized at inference time by Large Language Models (LLMs). Indeed, LLMs have demonstrated the ability to memorize vast amounts of token sequences during pretraining, and a central open question is how

Jasper Riebesehl, David C. Nak, Darko Zibar

Self-heterodyne techniques are widely used for laser phase noise characterization due to their simple experimental setup and the removed need for a reference laser. However, when investigating low-noise lasers, optical delay paths shorter than the laser coherence length become necessary. This introduces interference patterns that distort the measured phase n

Kévin Perrot, Sylvain Sené, Léah Tapin

In the context of discrete dynamical systems and their applications, fixed points often have a clear interpretation. This is indeed a central topic of gene regulatory mechanisms modeled by Boolean automata networks (BANs), where a collection of Boolean entities (the automata) update their state depending on the states of others. Fixed points represent phenot

Efe Yelesti, Onur Erten, M. O. Oktel

Flat-band systems have attracted significant attention as platforms for studying strongly correlated electron physics, where the dominance of electron-electron interactions over kinetic energy gives rise to a variety of emergent phenomena. Quasicrystals are compelling systems for studying these phenomena as they host degenerate strictly localized states at z

Hossein Zakerinia, Christoph H. Lampert

We present new fast-rate PAC-Bayesian generalization bounds for multi-task and meta-learning in the unbalanced setting, i.e. when the tasks have training sets of different sizes, as is typically the case in real-world scenarios. Previously, only standard-rate bounds were known for this situation, while fast-rate bounds were limited to the setting where all t

Elijah Mullens, Britney Schmidt, Lisa Kaltenegger, Nikole K. Lewis

Most stars end their main-sequence (MS) lives by evolving through the red-giant and asymptotic-giant branches before ending as a quiescent, stable white dwarf. Therefore, it is imperative to model the post-MS as it relates to long-term stability of environments potentially suitable for life. Recent work has shown that gas giants can exist in the habitable zo

Grace Bridge, Wen Wu

This study investigates laminar boundary layer separation over a fully porous Gaussian bump using pore-resolved direct numerical simulations. The bump is formed by randomly packed spheres. Compared to a solid bump, the porous surface significantly alters the flow by enabling cross-bump mass flux. Near-wall fluid is drawn into the bump by favorable pressure g

Kiarash Hassas Irani, Yongwei Huang, Sergiy A. Vorobyov

This paper addresses the robust adaptive beamforming (RAB) problem via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty sets for the random interference-plus-noise covariance (INC) matrix and desired signal steering vector. Our study explores two distinct uncertainty sets for the INC matrix and three

Sanghyuk Lee, Sewook Oh

Let $H\subset \R^{d+1}$ be a compact, convex, analytic hypersurface of finite type with a smooth measure $\sigma $ on $H$. Let $\kappa$ denote the Gaussian curvature on $H$. We consider the oscillatory integral $(\kappa^{1/2} \sigma)^\wedge$ with the damping factor $\kappa^{1/2}$ and prove the optimal decay estimate \[ |(\kappa^{1/2} \sigma )^\wedge(\xi)|\le

Ce Zhang, Zifu Wan, Simon Stepputtis, Katia Sycara

Semantic segmentation relying solely on RGB data often struggles in challenging conditions such as low illumination and obscured views, limiting its reliability in critical applications like autonomous driving. To address this, integrating additional thermal radiation data with RGB images demonstrates enhanced performance and robustness. However, how to effe

Can Rong, Xin Zhang, Yanxin Xi, Hongjie Sui

Commuting Origin-destination~(OD) flows, capturing daily population mobility of citizens, are vital for sustainable development across cities around the world. However, it is challenging to obtain the data due to the high cost of travel surveys and privacy concerns. Surprisingly, we find that satellite imagery, publicly available across the globe, contains r

Isidora Jeknic, Alex Duchnowski, Alexander Koller

Dialogue agents that support human users in solving complex tasks have received much attention recently. Many such tasks are NP-hard optimization problems that require careful collaborative exploration of the solution space. We introduce a novel dialogue game in which the agents collaboratively solve a two-player Traveling Salesman problem, along with an age

Jiaying Wu, Fanxiao Li, Zihang Fu, Min-Yen Kan

The impact of multimodal misinformation arises not only from factual inaccuracies but also from the misleading narratives that creators deliberately embed. Interpreting such creator intent is therefore essential for multimodal misinformation detection (MMD) and effective information governance. To this end, we introduce DeceptionDecoded, a large-scale benchm

Shubhrangshu Debsarkar, Bijoy Kundu

Dynamic FDG PET imaging study of n = 52 rats including 26 control Wistar-Kyoto (WKY) rats and 26 experimental spontaneously hypertensive rats (SHR) were performed using a Siemens microPET and Albira trimodal scanner longitudinally at 1, 2, 3, 5, 9, 12 and 18 months of age. A 15-parameter dual output model correcting for spill over contamination and partial v

Sheng Wang, Jun-Xia Chen, Defu Hou, Hai-Cang Ren

The analytic strong coupling expansion of the gluodynamics under a rotation with an angular velocity $\omega$ is reported. While the expansion is systematic, free from additional assumptions, the deconfinement temperature determined by the onset of the Polyakov loop expectation value decreases with the angular velocity up to $\omega^2$, opposite to the tende

Xia Zhou, Zhenliang Ma, Mark Wallace, Daniel D. Harabor

Departure time management is an efficient way in addressing the peak-hour crowding in public transport by reducing the temporal imbalance between service supply and travel demand. From the demand management perspective, the problem is to determine an equilibrium distribution of departure times for which no user can reduce their generalized cost by changing t

Chuangchang Lei, Xiang Wu, Yaohua Li, Xu Xu

Europium halide perovskites have emerged as promising candidates for environmental-friendly blue-emitting materials. However, their development is hindered by relative low photoluminescence quantum yields (PLQY, e.g. ~2-5% for intrinsic CsEuCl3) and poor stability against air. Here, we introduce a one-step-procedure for synthesizing Eu$^{2+}$-doped CsSrCl$_3

Oliver Schnetz

We present the renormalization functions of dimensionally regularized $\phi^3$ theory in six dimensions up to loop order six in the minimal subtraction scheme.

Runzhang Xu, Yifan Gao, Junwei Liu

The crystal-symmetry-paired spin-momentum locking (CSML) arisen from the intrinsic crystal symmetry connecting different magnetic sublattices in altermagnets enables many exotic spintronics properties such as unconventional piezomagnetism and noncollinear spin current. However, the shortage of monolayer altermagnets restricts further exploration of dimension

Ye Zheng, Sumita Mishra, Yidan Hu

Numerical data with bounded domains is a common data type in personal devices, such as wearable sensors. While the collection of such data is essential for third-party platforms, it raises significant privacy concerns. Local differential privacy (LDP) has been shown as a framework providing provable individual privacy, even when the third-party platform is u

Lisa Seckler, Amit Ben Antony Bennan, Niklas Wahl

As depth increases, linear energy transfer (LET) rises toward the distal edge of the Bragg peak, boosting the radiobiological effectiveness (RBE). To manage the biological variation and limit normal-tissue damage, LET-modifying objective functions on, e.g., dose-weighted LET or dirty dose and/or usage of variable RBE models were introduced. Because shaping L

Frederic Alberti, Matthias Birkner, Wai-Tong Louis Fan, John Wakeley

We study coalescent processes conditional on the population pedigree under the exchangeable diploid bi-parental population model of \citet{BirknerEtAl2018}. While classical coalescent models average over all reproductive histories, thereby marginalizing the pedigree, our work analyzes the genealogical structure embedded within a fixed pedigree generated by t

Can Rong, Jingtao Ding, Meng Li, Yong Li

Commuting Origin-Destination (OD) flows capture movements of people from residences to workplaces, representing the predominant form of intra-city mobility and serving as a critical reference for understanding urban dynamics and supporting sustainable policies. However, acquiring such data requires costly, time-consuming censuses. In this study, we introduce

Qihuang Zhong, Liang Ding, Xiantao Cai, Juhua Liu

Supervised fine-tuning (SFT) is a common approach to improve the domain-specific question-answering (QA) performance of large language models (LLMs). However, recent literature reveals that due to the conflicts between LLMs' internal knowledge and the context knowledge of training data, vanilla SFT using the full QA training set is usually suboptimal. In thi

Anagha Savit, Harikrishna Sahu, Shivank Shukla, Wei Xiong

Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, w

Alexandre Bougakov, Melaine Saillenfest, Marc Fouchard

Context. Integrating the motion of stars in a smoothed potential is necessary in many stellar and galactic studies. Previous works have often used numerical integrators that alternate between linear drifts and velocity kicks (such as the Leapfrog scheme). This approach contrasts with the sophisticated methods developed in planetary dynamics, for which integr

Michele Zhu, Silvia Mura, Francesco Linsalata, Lorenzo Cazzella

The identification of Line-of-Sight (LoS) conditions is critical for ensuring reliable high-frequency communication links, which are particularly vulnerable to blockages and rapid channel variations. Network Digital Twins (NDTs) and Ray-Tracing (RT) techniques can significantly automate the large-scale collection and labeling of channel data, tailored to spe

Yan Hao, Hannah Lax, Marc-Thorsten Hütt, Daniel J. Graham

Brain network communication models typically assume that signals propagate independently, despite the high network density and small diameter of mammalian connectomes, where interactions among simultaneously propagating messages are likely. We investigate these interactions using the copy-spread-annihilate (CSA) model, a synchronous Markovian message-passing

Guotao Xu, Bowen Zhao, Yang Xiao, Yantao Zhong

Face recognition is an effective technology for identifying a target person by facial images. However, sensitive facial images raises privacy concerns. Although privacy-preserving face recognition is one of potential solutions, this solution neither fully addresses the privacy concerns nor is efficient enough. To this end, we propose an efficient privacy-pre

Zhanyue Qin, Yue Ding, Deyuan Liu, Qingbin Liu

Nowadays, Large Language Models (LLMs) have attracted widespread attention due to their powerful performance. However, due to the unavoidable exposure to socially biased data during training, LLMs tend to exhibit social biases, particularly gender bias. To better explore and quantifying the degree of gender bias in LLMs, we propose a pair of datasets named G

Projesh Kumar Roy

This letter highlights the entropy exchange phenomenon in a coupled binary inter-correlating system following Haldane's non-linear statistical correlation. A unique coupling between a classical and a quantum-like system at the marginal distribution is observed. It is shown that the quantum nature of a system can arise without any self-correlation. Extending

Sven Schmidt, Aaron Thielmann, Thomas Niederprüm, Herwig Ott

We propose a novel non-destructive method for the detection of single Rydberg excitations in a mesoscopic ensemble. The protocol achieves high fidelities on a microsecond timescale and is robust against changes in the probe laser frequency. The technique relies on optical pumping in Autler Townes configuration, whose efficiency is controlled by the presence/

Song Dai, Yibo Yan, Jiamin Su, Dongfang Zihao

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in diverse reasoning tasks, yet their application to complex physics reasoning remains underexplored. Physics reasoning presents unique challenges, requiring grounding in physical conditions and the interpretation of multimodal information. Current physics benchmarks are limit

Yiyun Zhou, Chang Yao, Jingyuan Chen

The scaling law of Large Language Models (LLMs) reveals a power-law relationship, showing diminishing return on performance as model scale increases. While training LLMs from scratch is resource-intensive, fine-tuning a pre-trained model for specific tasks has become a practical alternative. Full fine-tuning (FFT) achieves strong performance; however, it is

D. Navarro-Gironés, M. Crocce, E. Gaztañaga, A. Wittje

We present the measurements and constraints of intrinsic alignments (IA) in the Physics of the Accelerating Universe Survey (PAUS) deep wide fields, which include the W1 and W3 fields from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) and the G09 field from the Kilo-Degree Survey (KiDS). Our analyses cover 51deg$^{2}$, in the photometric redshift

Jonathan Katzy, Yongcheng Huang, Gopal-Raj Panchu, Maksym Ziemlewski

Large Language Models are essential coding assistants, yet their training is predominantly English-centric. In this study, we evaluate the performance of code language models in non-English contexts, identifying challenges in their adoption and integration into multilingual workflows. We conduct an open-coding study to analyze errors in code comments generat

Gaétan Leclerc, Sampo Paukkonen, Tuomas Sahlsten

We establish power Fourier decay for equilibrium states of parabolic $C^{1+\alpha}$ iterated function systems with overlaps satisfying a multiscale nonlinearity condition. This class includes the Lyons conductance measures $\nu_t$, $0<t<1$, associated to Galton-Watson trees with equal weights yielding advance towards a conjecture of Lyons on the absolute con

Yukun Zhao, Lingyong Yan, Zhenyang Li, Shuaiqiang Wang

Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally due to catastrophic forgetting. Existing approaches rely on experience replay, optimization constraints, or task differentiation, which encounter strict limitations in real-world scenarios. To address these issues, we

Valeria Cesaroni, Eleonora Pasqua, Piercosma Bisconti, Martina Galletti

AI-based technologies have significant potential to enhance inclusive education and clinical-rehabilitative contexts for children with Special Educational Needs and Disabilities. AI can enhance learning experiences, empower students, and support both teachers and rehabilitators. However, their usage presents challenges that require a systemic-ecological visi

Guilherme de Oliveira, Matheus M. dos Santos, Paulo L. J. Drews-Jr

This paper introduces Synthetic Enclosed Echoes (SEE), a novel dataset designed to enhance robot perception and 3D reconstruction capabilities in underwater environments. SEE comprises high-fidelity synthetic sonar data, complemented by a smaller subset of real-world sonar data. To facilitate flexible data acquisition, a simulated environment has been develo

Mauro Florez, Anna Gottard, Carrie McAdams, Michele Guindani

Mixed data refers to a type of data in which variables can be of multiple types, such as continuous, discrete, or categorical. This data is routinely collected in various fields, including healthcare and social sciences. A common goal in the analysis of such data is to identify dependence relationships between variables, for an understanding of their associa

Youri H. Lemm, Xander M. de Wit, Rudie P. J. Kunnen

Turbulence is an out-of-equilibrium flow state that is characterised by nonzero net fluxes of kinetic energy between different scales of the flow. These fluxes play a crucial role in the formation of characteristic flow structures in many turbulent flows encountered in nature. However, measuring these energy fluxes in practical settings can be challenging as

Michal Kuchař, Jaromír Fišer, Cyril Oswald, Tomáš Vyhlídal

The paper presents a decision support system for the long-term preservation of aeronautical heritage exhibited/stored in sheltered sites. The aeronautical heritage is characterized by diverse materials of which this heritage is constituted. Heritage aircraft are made of ancient aluminum alloys, (ply)wood, and particularly fabrics. The decision support system

Soumaya Latour, Mathias Lebihain, Harsha S. Bhat, Cédric Twardzik

We present a direct measurement of the slip-rate function from a natural coseismic rupture, recorded on March 28, 2025, during the $M_w$ 7.7 Mandalay earthquake (Myanmar). This measurement was made on video footage of the surface rupture captured by a security camera located only meters away from the fault trace. Using direct image analysis, we measured the

Youfan He, Ralf Peter Brinkmann, Efe Kemaneci, Andrew R. Gibson

Reactive species produced by atmospheric pressure plasma jets have high application potential in the fields of biomedicine and surface processing. An extensive validation between the simulation results in this work and measurement data from various research groups is carried out in order to reliably understand the complicated chemical kinetics defining the r

Junlin Li, Guodong DU, Jing Li, Sim Kuan Goh

Fine-tuning Large Language Models (LLMs) with multimodal encoders on modality-specific data expands the modalities that LLMs can handle, leading to the formation of Multimodal LLMs (MLLMs). However, this paradigm heavily relies on resource-intensive and inflexible fine-tuning from scratch with new multimodal data. In this paper, we propose MMER (Multi-modali

Robert Harland, Tao Wang, David Codling, Catherine Polling

Electronic health records (EHRs) provide comprehensive patient data which could be better used to enhance informed decision-making, resource allocation, and coordinated care, thereby optimising healthcare delivery. However, in mental healthcare, critical information, such as on risk factors, precipitants, and treatment responses, is often embedded in unstruc

Felipe Hernández

In this article we describe all possible infinite linear configurations that can be found in a shift of any set of positive upper Banach density. This simultaneously generalizes Szemer\'edi's theorem on arithmetic progressions and the recent density finite sums theorem of Kra, Moreira, Richter, and Robertson.

Jonas D. Ziegler, Sotirios Papadopoulos, Antti J. Moilanen, Marcelo M. Valenzuela

Van der Waals materials have become a promising building block for future electronics and photonics. The two-dimensional magnet CrSBr came into the spotlight of solid state research due to its intriguing combination of antiferromagnetic order, strong light-matter coupling and unusual quasi-1D electronic bandstructure. This study reports the electrical excita

Weixiang Zhao, Xingyu Sui, Yulin Hu, Jiahe Guo

Personalized alignment is essential for enabling large language models (LLMs) to engage effectively in user-centric dialogue. While recent prompt-based and offline optimization methods offer preliminary solutions, they fall short in cold-start scenarios and long-term personalization due to their inherently static and shallow designs. In this work, we introdu

J. Kleimann, H. Fichtner, M. Stein, R. -J. Dettmar

A detailed understanding of cosmic-ray transport in galactic halos is essential for explaining various observations, such as the radio continuum measurements of synchrotron radiation from energetic electrons. Of central importance is the spatial diffusion tensor of cosmic rays, which can be computed in an ab~initio manner if the turbulence in the background

Nanxiang Zhou, Jing Dong, Baoxiang Wang

In this work, we introduce the concept of non-negative weighted regret, an extension of non-negative regret \cite{anagnostides2022last} in games. Investigating games with non-negative weighted regret helps us to understand games with conflicting interests, including harmonic games and important classes of zero-sum games.We show that optimistic variants of cl

Damien Depannemaecker, Adrien d'Hollande, Jiaming Wu, Marcelo J. Rozenberg

Common wisdom indicates that to implement a Dynamical Memory with spiking neurons two ingredients are necessary: recurrence and a neuron population. Here we shall show that the second requirement is not needed. We shall demonstrate that under very general assumptions a single recursive spiking neuron can realize a robust model of a dynamical memory. We demon

Prince Romeo Mensah

We consider a solute-solvent-structure mutually coupled system of equations given by an Oldroyd-type model for a two-dimensional dilute corotational polymer fluid with solute diffusion and damping that is interacting with a one-dimensional viscoelastic shell. Firstly, we give the rate at which its solution decays exponentially in time to the equilibrium solu

G. Galletta, S. Colombo, L. Prisinzano, G. Micela

Flares are short-lived but energetic manifestations of stellar activity. Studying them is crucial, as they emit intense high-energy radiation that can impact the circumstellar environment, especially the atmospheres of orbiting planets. This is particularly relevant for M dwarfs, which frequently flare and often host planets within their habitable zones. Fla

Die Chen, Zhiwen Li, Cen Chen, Yuexiang Xie

Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the generation of not-safe-for-work (NSFW) content, posing significant risks to their safe deployment. While several concept erasur

D. Arisa, R. M. Dos Santos, Isaac M. Carvalho, Vivian V. França

Quantum systems under electric fields provide a powerful framework for uncovering and controlling novel quantum phases, especially in low-dimensional systems with strong correlations. In this work, we investigate quantum phase transitions induced by an electric potential difference in a one-dimensional half-filled Hubbard chain. By analyzing (i) tunneling an

Baoxiang Wang, Tao Yang

The Advanced LIGO and Virgo collaborations recently detected a gravitational wave event, GW230529\_181500, during the fourth observing run, which is most plausibly attributed to the merger of a neutron star and a black hole. This observation provides an opportunity to test a class of gravitational theories that deviate from general relativity. In such theori

Ziqiang Xu, Qi Dai, Tian Xie, Yifan Yang

Video understanding is inherently intention-driven-humans naturally focus on relevant frames based on their goals. Recent advancements in multimodal large language models (MLLMs) have enabled flexible query-driven reasoning; however, video-based frameworks like Video Chain-of-Thought lack direct training signals to effectively identify relevant frames. Curre

Hiba Ayoub, Soukaina Zayat, Darine Al-Mniny

A cycle C(k1,k2,...,kn) is the oriented cycle formed of n blocks of lengths k1,k2,...,kn-1 and kn respectively. In 2018 Cohen et al. conjectured that for every positive integers k1,k2,...,kn there exists a constant g(k1,k2,...,kn) such that every strongly connected digraph containing no subdivisions of C(k1,k2,...,kn) has a chromatic number at most g(k1,k2,.

Yutao Zhu, Jiajie Jin, Hongjin Qian, Zheng Liu

Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizations into a unified framework remains challenging. To tackle this problem, this work proposes RoleRAG, a unified RAG framework that achieves efficient multi-task processing through

Artem Zabolotnyi, Roman Makarov, Mile Mitrovic, Polina Proskura

Uncertainty estimation remains a key challenge when adapting pre-trained language models to downstream classification tasks, with overconfidence often observed for difficult inputs. While predictive entropy provides a strong baseline for uncertainty estimation, it considers mainly aleatoric uncertainty and has limited capacity to capture effects, such as cla

Suhas Kamasetty Ramesh, Ayan Sengupta, Tanmoy Chakraborty

Knowledge distillation (KD) is a key technique for compressing large language models into smaller ones while preserving performance. Despite the recent traction of KD research, its effectiveness for smaller language models (LMs) and the mechanisms driving knowledge transfer remain underexplored. In this work, we present the first large-scale empirical and st

Brett Binst, Lien Michiels, Annelien Smets

Serendipity has been associated with numerous benefits in the context of recommender systems, e.g., increased user satisfaction and consumption of long-tail items. Despite this, serendipity in the context of recommender systems has thus far remained conceptually ambiguous. This conceptual ambiguity has led to inconsistent operationalizations between studies,

Ge Meng, Zhongnan Cai, Ruizhe Chen, Jingyan Tu

Generating hyperspectral images (HSIs) from RGB images through spectral reconstruction can significantly reduce the cost of HSI acquisition. In this paper, we propose a Fractal-Based Recursive Spectral Reconstruction Network (FRN), which differs from existing paradigms that attempt to directly integrate the full-spectrum information from the R, G, and B chan

Fahrettin Emin Tiras, Hayriye Serra Altinoluk

Radio Frequency (RF) fingerprinting offers a promising approach for drone identification and security, although it suffers from significant performance degradation when operating on different transmission channels. This paper presents CrossRF, a domain-invariant deep learning approach that addresses the problem of cross-channel RF fingerprinting for Unmanned

Jianyuan Guo, Peike Li, Trevor Cohn

Sign Language Translation (SLT) aims to map sign language videos to spoken language text. A common approach relies on gloss annotations as an intermediate representation, decomposing SLT into two sub-tasks: video-to-gloss recognition and gloss-to-text translation. While effective, this paradigm depends on expert-annotated gloss labels, which are costly and r

Xintong Zhang, Zhi Gao, Bofei Zhang, Pengxiang Li

Vision language models (VLMs) have achieved impressive performance across a variety of computer vision tasks. However, the multimodal reasoning capability has not been fully explored in existing models. In this paper, we propose a Chain-of-Focus (CoF) method that allows VLMs to perform adaptive focusing and zooming in on key image regions based on obtained v

Zeqing Wang, Shiyuan Zhang, Chengpei Tang, Keze Wang

Reasoning about temporal causality, particularly irreversible transformations of objects governed by real-world knowledge (e.g., fruit decay and human aging), is a fundamental aspect of human visual understanding. Unlike temporal perception based on simple event sequences, this form of reasoning requires a deeper comprehension of how object states change ove

Vsevolod Chernyshev, Johannes Rauch, Dieter Rautenbach, Liliia Redina

The previously fastest algorithm for deciding the existence of an independent cut had a runtime of $\mathcal{O}^*(1.4423^n)$, where $n$ is the order of the input graph. We improve this to $\mathcal{O}^*(1.4143^n)$. In fact, we prove a runtime of $\mathcal{O}^*\left( 2^{(\frac{1}{2}-\alpha_\Delta)n} \right)$ on graphs of order $n$ and maximum degree at most $

Beni Egressy, Jan Stühmer

While large language models (LLMs) demonstrate impressive capabilities across numerous applications, their robustness remains a critical concern. This paper is motivated by a specific vulnerability: the order sensitivity of LLMs. This vulnerability manifests itself as the order bias observed when LLMs decide between possible options (for example, a preferenc

Corbet Elkins, Alexander Tsymbaliuk

In this note, we establish the convexity and monotonicity for affine standard Lyndon words in all types, generalizing the $A$-type results of arXiv:2305.16299. We also derive partial results on the structure of imaginary standard Lyndon words and present a conjecture for their general form. Additionally, we provide computer code in Appendix which, in particu

Tencent Hunyuan Team, Ao Liu, Botong Zhou, Can Xu

As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mamba's long-sequence processing efficiency with Transformer's superior contextual understanding. Hunyuan-TurboS features an adaptive long-short chain-of-thought (CoT) mechanism, dyna

Zhaolin Wang, Chongjun Ouyang, Yuanwei Liu, Arumugam Nallanathan

A wireless sensing architecture via pinching antenna systems is proposed. Compared to conventional wireless systems, PASS offers flexible antenna deployment and improved probing performance for wireless sensing by leveraging dielectric waveguides and pinching antennas (PAs). To enhance signal reception, leaky coaxial (LCX) cables are used to uniformly collec

Pritam Anand

This paper explores Uncertainty Quantification (UQ) in SVM predictions, particularly for regression and forecasting tasks. Unlike the Neural Network, the SVM solutions are typically more stable, sparse, optimal and interpretable. However, there are only few literature which addresses the UQ in SVM prediction. At first, we provide a comprehensive summary of e

Momose Oyama, Ryo Kishino, Hiroaki Yamagiwa, Hidetoshi Shimodaira

We address the computational cost of constructing a model map, which embeds diverse language models into a common space for comparison via KL divergence. The map relies on log-likelihoods over a large text set, making the cost proportional to the number of texts. To reduce this cost, we propose a resampling method that selects important texts with weights pr

Zhiwen Li, Die Chen, Mingyuan Fan, Cen Chen

The remarkable ability of diffusion models to generate high-fidelity images has led to their widespread adoption. However, concerns have also arisen regarding their potential to produce Not Safe for Work (NSFW) content and exhibit social biases, hindering their practical use in real-world applications. In response to this challenge, prior work has focused on

Aleksandra Tomaszewska, Dariusz Czerski, Bartosz Żuk, Maciej Ogrodniczuk

NeoN, a tool for detecting and analyzing Polish neologisms. Unlike traditional dictionary-based methods requiring extensive manual review, NeoN combines reference corpora, Polish-specific linguistic filters, an LLM-driven precision-boosting filter, and daily RSS monitoring in a multi-layered pipeline. The system uses context-aware lemmatization, frequency an

Raza Imam, Rufael Marew, Mohammad Yaqub

Medical Vision-Language Models (MVLMs) have achieved par excellence generalization in medical image analysis, yet their performance under noisy, corrupted conditions remains largely untested. Clinical imaging is inherently susceptible to acquisition artifacts and noise; however, existing evaluations predominantly assess generally clean datasets, overlooking

Yan-Shuo Liang, Jia-Rui Chen, Wu-Jun Li

Continual learning (CL), which requires the model to learn multiple tasks sequentially, is crucial for large language models (LLMs). Recently, low-rank adaptation~(LoRA), one of the most representative parameter-efficient fine-tuning (PEFT) methods, has gained increasing attention in CL of LLMs. However, most existing CL methods based on LoRA typically expan

Marcell T. Kurbucz, Nikolaos Tzivanakis, Nilufer Sari Aslam, Adam M. Sykulski

Capturing nonlinear relationships without sacrificing interpretability remains a persistent challenge in regression modeling. We introduce SplitWise, a novel framework that enhances stepwise regression. It adaptively transforms numeric predictors into threshold-based binary features using shallow decision trees, but only when such transformations improve mod