October 2025 arXiv papers — page 218

A. A. Lyappieva, A. Yu. Veretennikov

A new strong uniqueness result for a multidimensional SDE with a non-degenerate diffusion and partially irregular drift is established. It may be regarded as a combined variation on the themes of Yamada \& Watanabe (1971), of Zvonkin (1974), and of the second author of the present paper (1980).

Bingqing Sun, Daniela Calzetti, Andrew J. Battisti

The role of spiral arms in galaxies -- whether they enhance star formation efficiency or primarily act as material gatherers -- remains an open question. Observational studies have yielded ambiguous results, in part due to the choice of star formation rate (SFR) tracers and their inherent limitations. These limitations are addressed here by applying multi-wa

Haosong Zhang, Shenxi Wu, Yichi Zhang, Xi Chen

Choosing an appropriate learning rate remains a key challenge in scaling depth of modern deep networks. The classical maximal update parameterization ($\mu$P) enforces a fixed per-layer update magnitude, which is well suited to homogeneous multilayer perceptrons (MLPs) but becomes ill-posed in heterogeneous architectures where residual accumulation and convo

Rahul Chandra, Mahbub Rahaman, Soumyabroto Majumder, Analabha Roy

We demonstrate the realization of a Discrete Time-Crystal (DTC) phase in a family of periodically driven, one-dimensional quadratic lattice Hamiltonians that can be obtained using spin chains. These interactions preserve integrability while opening controllable gaps at resonant quasienergies and pinning the emergent quasienergy modes that are responsible for

Kenechukwu Ogbuagu, Sepehr Maleki, Giuseppe Bruni, Senthil Krishnababu

The accurate prediction of flow fields around airfoils is crucial for aerodynamic design and optimisation. Computational Fluid Dynamics (CFD) models are effective but computationally expensive, thus inspiring the development of surrogate models to enable quicker predictions. These surrogate models can be based on deep learning architectures, such as Convolut

Alexander Somov

The GlueX detector in the experimental Hall $D$ at Jefferson Lab offers a unique opportunity to perform a measurement of the decay width of eta mesons through the Primakoff effect. The PrimEx $\eta$ experiment complements the physics program at Jefferson Lab on measuring the decay width of light pseudoscalar mesons via the Primakoff process. The goal of Prim

Amod Agashe, Matthew Winters

Let N be a square-free positive integer and let f be a newform of weight 2 on \Gamma_0(N). Let A denote the abelian subvariety of J_0(N) associated to f and let m be a maximal ideal of the Hecke algebra T that contains Ann_T(f) and has residue characteristic r such that r does not divide 6N. We show that if either A[m] or the canonical representation \rho_m

Akshay Govind Srinivasan, Anuj Jagannath Said, Sathwik Pentela, Vikas Dwivedi

Partial differential equation (PDE) solvers underpin modern quantitative finance, governing option pricing and risk evaluation. Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving the forward and inverse problems of partial differential equations (PDEs) using deep learning. However they remain computationally expensive d

Xue Dong, Kai Lion, Fei Xia, YoonSeok Baek

Artificial intelligence (AI) has rapidly evolved into a critical technology; however, electrical hardware struggles to keep pace with the exponential growth of AI models. Free space optical hardware provides alternative approaches for large-scale optical processing, and in-memory computing, with applications across diverse machine learning tasks. Here, we ex

Rui Wu, Yihao Quan, Zeru Shi, Zhenting Wang

Safety-aligned Large Language Models (LLMs) still show two dominant failure modes: they are easily jailbroken, or they over-refuse harmless inputs that contain sensitive surface signals. We trace both to a common cause: current models reason weakly about links between actions and outcomes and over-rely on surface-form signals, lexical or stylistic cues that

Bernd Rosenow, Bertrand I. Halperin

Anyon colliders -- quantum Hall devices where dilute quasiparticle beams collide at a quantum point contact -- provide an interferometer-free probe of anyonic exchange phases through current cross correlations. Within a non-equilibrium bosonization framework, the normalized cross-correlations take a universal form depending only on the exchange phase and the

Etienne Gauthier, Francis Bach, Michael I. Jordan

Traditional conformal prediction methods construct prediction sets such that the true label falls within the set with a user-specified coverage level. However, poorly chosen coverage levels can result in uninformative predictions, either producing overly conservative sets when the coverage level is too high, or empty sets when it is too low. Moreover, the fi

Yucong Dai, Lu Zhang, Feng Luo, Mashrur Chowdhury

Training fair and unbiased machine learning models is crucial for high-stakes applications, yet it presents significant challenges. Effective bias mitigation requires deep expertise in fairness definitions, metrics, data preprocessing, and machine learning techniques. In addition, the complex process of balancing model performance with fairness requirements

Sahar Koohfar

Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. Accurate and timely prediction of crash severity is crucial in mitigating the severe consequences of traffic accidents. In order to provide appropriate levels of medical assistance and transportation services, an intelligent transportation

Hajg Jasa, Ronny Bergmann, Christian Kümmerle, Avanti Athreya

Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those admitting simple closed-form solutions to those requiring advanced and novel techniques. We compare different Procrustes problems in which we align two sets of points after variou

Jiarui Ouyang, Yihui Wang, Yihang Gao, Yingxue Xu

Spatial Transcriptomics (ST) offers spatially resolved gene expression but remains costly. Predicting expression directly from widely available Hematoxylin and Eosin (H&E) stained images presents a cost-effective alternative. However, most computational approaches (i) predict each gene independently, overlooking co-expression structure, and (ii) cast the tas

Alexander Dukhovny

Relative Divergence (RD) and Maximum Relative Divergence Principle (MRDP) for grading (order-comonotonic) functions (GF) on posets are used as an expression of Insufficient Reason Principle under the given prior information (IRP+). Classic Probability Theory formulas are presented as IRP+ solutions of MRDP problems on conjoined posets. RD definition principl

Antti Ritari, Jani Romanoff, Kari Tammi

This paper focuses on the problem of determining a minimum-cost fleet of battery electric ships for a given liner shipping operation. The problem is strongly nonlinear and includes integer-valued decision variables, which make it intractable for most real-world instances. The conventional approach in the literature is to formulate a linear approximation by r

Vida Adeli, Ivan Klabucar, Javad Rajabi, Benjamin Filtjens

Objective gait assessment in Parkinson's Disease (PD) is limited by the absence of large, diverse, and clinically annotated motion datasets. We introduce CARE-PD, the largest publicly available archive of 3D mesh gait data for PD, and the first multi-site collection spanning 9 cohorts from 8 clinical centers. All recordings (RGB video or motion capture) are

Bohan Tang, Huidong Liang, Keyue Jiang, Xiaowen Dong

Large language model multi-agent systems (LLM-MAS) offer a promising paradigm for harnessing collective intelligence to achieve more advanced forms of AI behaviour. While recent studies suggest that LLM-MAS can outperform LLM single-agent systems (LLM-SAS) on certain tasks, the lack of systematic experimental designs limits the strength and generality of the

Hagit Attiya, Itay Flam, Jennifer L. Welch

Canonical asynchronous rounds are a widely used abstraction for structuring distributed algorithms, making asynchronous executions appear synchronous and enabling modular reasoning. We show that this abstraction is fundamentally incompatible with optimal resilience in the Byzantine setting, even when randomization is allowed. Specifically, we prove that when

Mikhail Molodyk, András Vasy

On a large class of asymptotically flat spacetimes which includes radiative perturbations of Minkowski space, we define a distinguished global Feynman propagator for massive Klein-Gordon fields by means of the microlocal approach to non-elliptic Fredholm theory, working in the de,sc-pseudodifferential algebra due to Sussman. We extend the limiting absorption

Bence Csajbók, Giovanni Longobardi, Giuseppe Marino, Rocco Trombetti

The $p$-ary code associated with the incidence structure of points and $t$-spaces in a projective space $\mathrm{PG}(m,q)$, where $q=p^h$, is the $\mathbb{F}_p$-subspace generated by the incidence vectors of the blocks of this design. The dual of this code consists of all vectors orthogonal to every codeword of the original code. In contrast to the codes der

Matteo Longo, Paola Magrone, Eris Rocha Walchek

Following up a previous article of the authors which studies the interpolation of certain anticyclotomic $p$-adic $L$-functions associated to quaternionic modular forms in a Hida family, we extend the work of F. Castella on the interpolation and specialization of big Heegner points to the quaternionic setting. We prove an explicit reciprocity law relating th

Thomas Jacob, Siddhant Mohapatra, Rajalingam A, Sam Mathew

Controlled activity of active entities interacting with a passive environment can generate emergent system-level phenomena, positioning such systems as promising platforms for potential downstream applications in targeted drug delivery, adaptive and reconfigurable materials, microfluidic transport and related fields. The present work aims to realise an optim

Harshil Vejendla

We introduce Wave-PDE Nets, a neural architecture whose elementary operation is a differentiable simulation of the second-order wave equation. Each layer propagates its hidden state as a continuous field through a medium with trainable spatial velocity c(x) and damping {\gamma}(x). A symplectic spectral solver based on FFTs realises this propagation in O(nlo

Om Tailor

Multi-agent deployments of large language models (LLMs) are increasingly embedded in market, allocation, and governance workflows, yet covert coordination among agents can silently erode trust and social welfare. Existing audits are dominated by heuristics that lack theoretical guarantees, struggle to transfer across tasks, and seldom ship with the infrastru

Thomas F Burns

Common evaluation paradigms for language models focus on scoring single responses through accuracy metrics or proper scoring rules, failing to capture the full richness of a model's belief state. Recent work illustrates that language models hallucinate in-part because they are optimised to be good test-takers under binary scoring schemes that reward any answ

Matteo Longo, Paola Magrone, Eris Rocha Walchek

We use Serre--Tate expansions of modular forms to construct power series attached to quaternionic ordinary families of modular forms. We associate to these power series a big $p$-adic $L$-function interpolating the $p$-adic $L$-functions constructed by Burungale and Magrone at classical specializations. A crucial ingredient is the generalization of some resu

Ander Artola Velasco, Stratis Tsirtsis, Manuel Gomez-Rodriguez

Millions of users rely on a market of cloud-based services to obtain access to state-of-the-art large language models. However, it has been very recently shown that the de facto pay-per-token pricing mechanism used by providers creates a financial incentive for them to strategize and misreport the (number of) tokens a model used to generate an output. In thi

Emanuele Brusaschi, Marco Liscidini, Matteo Galli, Daniele Bajoni

Silicon nitride microresonators driven by strong pump pulses can generate squeezed light in a dominant spectral-temporal mode, a central resource for continuous-variable quantum computation. In the high parametric gain regime, several effects, including self- and cross-phase modulation as well as time-ordering corrections, become significant and can degrade

Diego Serrano, Eduardo García-Portugués

Statistical methods for metric spaces provide a general and versatile framework for analyzing complex data types. We introduce a novel approach for constructing confidence regions around new predictions from any bagged regression algorithm with metric-space-valued responses. This includes the recent extensions of random forests for metric responses: Fr\'eche

Ziang Zhu, Yifan Liu, Jun Li, Han Wen

In inertial confinement fusion (ICF), Thomson scattering (TS) is a widely used diagnostic technique for probing plasma conditions. We present a first-principles numerical approach to obtaining scattered light signals of ion acoustic features with high resolution in angle and frequency space using particle-in-cell simulations under typical ICF conditions. Our

Muhammad Ahsan Khan, Sohail Khan

Toeplitz matrices are characterized by their constant diagonals, have been extensively studied in various settings, including over real and complex numbers. However, their study over quaternions is quite sparse. In this paper, we investigate the structure and the algebraic properties of quaternion Toeplitz matrices. Most importantly, we established a complet

Martin Averseng, Nicolas Frantz, Frédéric Hérau, Nicolas Raymond

We consider the non-selfadjoint, semiclassical Schr\"odinger operator $\mathscr{L}(h) := -h^2\partial_x^2+e^{i\alpha}V$, where $\alpha \in (-\pi,\pi)$ and $V: \mathbb{R}\to \mathbb{R}_+$ is even and vanishes at exactly two (symmetric) non-degenerate minima. We establish a semiclassical tunneling result: the spectrum of $\mathscr{L}(h)$ near the origin is giv

Nghiem T. Diep, Dung Le, Tuan Truong, Tan Dinh

Parameter-efficient fine-tuning (PEFT) has emerged as a powerful paradigm for adapting large-scale pre-trained models to downstream tasks with minimal additional parameters. Among PEFT methods, Low-Rank Adaptation (LoRA) stands out for its effectiveness by inserting trainable low-rank matrices into weight updates to enable efficient adaptation. However, when

Lingnan Xu, Chong Feng, Kaiyuan Zhang, Liu Zhengyong

While large language models (LLMs) demonstrate impressive capabilities, their reliance on parametric knowledge often leads to factual inaccuracies. Retrieval-Augmented Generation (RAG) mitigates this by leveraging external documents, yet existing approaches treat retrieved passages as isolated chunks, ignoring valuable structure that is crucial for document

Arsen Khvedelidze, Dimitar Mladenov, Astghik Torosyan

A special class of states of 2-qubits which are simultaneously separable and have positive semidefinite Wigner functions is described.

Mehrzad Tareh, Aydin Mohandesi, Ebrahim Ansari

Sentiment analysis is a key task in Natural Language Processing (NLP), enabling the extraction of meaningful insights from user opinions across various domains. However, performing sentiment analysis in Persian remains challenging due to the scarcity of labeled datasets, limited preprocessing tools, and the lack of high-quality embeddings and feature extract

Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen, Tianshi Cao

Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, where edited objects must remain coherent. This capability is especially vital for world simulation related tasks. In this paper, we present ChronoEdit, a framework that reframes ima

Alessandro Calvia, Marzia De Donno, Chiara Guardasoni, Simona Sanfelici

With the reform of interest rate benchmarks, interbank offered rates (IBORs) like LIBOR have been replaced by risk-free rates (RFRs), such as the Secured Overnight Financing Rate (SOFR) in the U.S. and the Euro Short-Term Rate (\euro STR) in Europe. These rates exhibit characteristics like jumps and spikes that correspond to specific market events, driven by

Jacquelyn Ho, Yue-Hui Lu, Tai Xiang, Tsai-Chen Lee

Higher symmetries in interacting many-body systems often give rise to new phases and unexpected dynamical behavior. Here, we theoretically investigate a variant of the Dicke model with higher-order discrete symmetry, resulting from complex-valued coupling coefficients between quantum emitters and a bosonic mode. We propose a driven-dissipative realization of

T. Shiotani, T. Waki, Y. Tabata, I. Kézsmárki

We successfully synthesized single crystals of a series of C15b Laves phase compounds, $R{\mathrm{In}}{\mathrm{Co}}_{4}$ ($R=$ Dy-Tm), with Co-pyrochlore and $R$-fcc sublattices, and systematically studied their magnetic properties via magnetometry measurements. These itinerant cubic compounds, with Curie temperatures above room temperature, show compensated

Harshil Vejendla

Mixture-of-Experts (MoE) layers scale transformers by routing tokens to a sparse subset of feed-forward experts. Token-level routing, however, assigns an entire semantic spectrum to each expert, creating capacity bottlenecks, load-balancing pathologies, and limited specialization. We introduce SliceMoE, an architecture that routes contiguous slices of a toke

Karthik Viswanathan, Sang Eon Park

We introduce a cumulant-expansion framework for quantifying how large language models (LLMs) internalize higher-order statistical structure during next-token prediction. By treating the softmax entropy of each layer's logit distribution as a perturbation around its "center" distribution, we derive closed-form cumulant observables that isolate successively hi

Yunghwei Lai, Kaiming Liu, Ziyue Wang, Weizhi Ma

The professionalism of a human doctor in outpatient service depends on two core abilities: the ability to make accurate medical decisions and the medical consultation skill to conduct strategic, empathetic patient inquiry. Existing Large Language Models (LLMs) have achieved remarkable accuracy on medical decision-making benchmarks. However, they often lack t

Stephen C. Anco, Danial Saadatmand

Evolution of sphalerons in a class of quartic Klein-Gordon models are studied under a growing perturbation. Sphalerons are unstable lump-like solutions that arise from a saddle point between true and false vacua in the energy functional. Numerical simulations are presented which show the sphaleron evolving into an accelerating kink-antikink pair whose separa

Yu Kiu, Lau, Chao Chen, Ge Jin

Sequential Visual Place Recognition (Seq-VPR) leverages transformers to capture spatio-temporal features effectively. In practice, a transformer-based Seq-VPR model should be flexible to the number of frames per sequence (seq- length), deliver fast inference, and have low memory usage to meet real-time constraints. However, existing approaches prioritize per

Aryan Kumar Singh, Janvi Singh

This dataset provides responses to a standardized, bilingual (English-Hindi) Prakriti Assessment Questionnaire designed to evaluate the physical, physiological, and psychological characteristics of individuals according to classical Ayurvedic principles. The questionnaire consists of 24 multiple-choice items covering body features, appetite, sleep patterns,

Saida Elouardi, Mohammed Jouhari, Anas Motii

In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems (IDS) are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional machine learning-based IDS models typically require large datasets, but data sharing is often limited due to privacy an

Kento Takemura, Tomohiro Yokoyama

Topological protection is an attractive signature in both fundamental and applied researches because it provides an exotic and robust state. Multi-terminal Josephson junctions have recently been studied extensively owing to the emergence of topologically protected Weyl points without the need for topological materials. In this study, we examine the dynamic p

Aengus Lynch, Benjamin Wright, Caleb Larson, Stuart J. Ritchie

We stress-tested 16 leading models from multiple developers in hypothetical corporate environments to identify potentially risky agentic behaviors before they cause real harm. In the scenarios, we allowed models to autonomously send emails and access sensitive information. They were assigned only harmless business goals by their deploying companies; we then

Peiwen Yang, Weisong Wen, Runqiu Yang, Yuanyuan Zhang

Model predictive control (MPC) faces significant limitations when applied to systems evolving on nonlinear manifolds, such as robotic attitude dynamics and constrained motion planning, where traditional Euclidean formulations struggle with singularities, over-parameterization, and poor convergence. To overcome these challenges, this paper introduces FactorMP

Hamish Flynn

The sample complexity of estimating or maximising an unknown function in a reproducing kernel Hilbert space is known to be linked to both the effective dimension and the information gain associated with the kernel. While the information gain has an attractive information-theoretic interpretation, the effective dimension typically results in better rates. We

Joseph Ramsey, Bryan Andrews, Peter Spirtes

Learning graphical conditional independence structures from nonlinear, continuous or mixed data is a central challenge in machine learning and the sciences, and many existing methods struggle to scale to thousands of samples or hundreds of variables. We introduce two basis-expansion tools for scalable causal discovery. First, the Basis Function BIC (BF-BIC)

Candace Bethea, Thomas Brazelton

Enumerative geometry, the art and science of counting geometric objects satisfying geometric conditions, has seen a resurgence of activity in recent years due to an influx of new techniques that allow for enriched computations. This paper offers a historical survey of enumerative geometry, starting with its classical origins and real counterparts, to new adv

Damjan Fujs, Damjan Vavpotič, Tomaž Hovelja, Marko Poženel

This study investigates how access to Large Language Models (LLMs) and varying levels of professional software development experience affect the prioritization of cybersecurity requirements for web applications. Twenty-three postgraduate students participated in a research study to prioritize security requirements (SRs) using the MoSCoW method and subsequent

Paul Strang, Zacharie Alès, Côme Bissuel, Olivier Juan

On the occasion of the 20th Mixed Integer Program Workshop's computational competition, this work introduces a new approach for learning to solve MIPs online. Influence branching, a new graph-oriented variable selection strategy, is applied throughout the first iterations of the branch and bound algorithm. This branching heuristic is optimized online with Th

Jinyang Jiang, Jinhui Han, Yijie Peng, Ying Zhang

Effective cross-functional coordination is essential for enhancing firm-wide profitability, particularly in the face of growing organizational complexity and scale. Recent advances in artificial intelligence, especially in reinforcement learning (RL), offer promising avenues to address this fundamental challenge. This paper proposes a unified multi-agent RL

Heng Tan, Hua Yan, Lucas Yang, Yu Yang

Shared micromobility systems, such as electric scooters and bikes, have gained widespread popularity as sustainable alternatives to traditional transportation modes. However, these systems face persistent challenges due to spatio-temporal demand fluctuations, often resulting in a mismatch between vehicle supply and user demand. Existing shared micromobility

Iulia Cristian, Juan J. L. Velázquez

We study a spatially inhomogeneous coagulation model that contains a transport term in the spatial variable. The transport term models the vertical motion of particles due to gravity, thereby incorporating their fall into the dynamics. Local existence of mass-conserving solutions for a class of coagulation rates for which in the spatially homogeneous case in

Iosif Pinelis

It is shown that the convex order between the distributions of linear functionals does not imply the convex order between the probability distributions over $\mathbb R^d$ if $d\ge2$. This stands in contrast with the well-known fact that any probability distribution in $\mathbb R^d$, for any $d\ge1$, is determined by the corresponding distributions of linear

Robin Algayres, Charles-Éric Saint-James, Mahi Luthra, Jiayi Shen

Children learn to speak with a low amount of data and can be taught new words on a few-shot basis, making them particularly data-efficient learners. The BabyLM challenge aims at exploring language model (LM) training in the low-data regime but uses metrics that concentrate on the head of the word distribution. Here, we introduce LongTail-Swap (LT-Swap), a be

Lieuwe Bakker, Suvendu Barik, Vladimir Gritsev, Emil A. Yuzbashyan

We determine the late-time dynamics of a generic spin ensemble with inhomogeneous broadening - equivalently, qubits with arbitrary Zeeman splittings - coupled to a dissipative environment with strength decreasing as $1/t$. The approach to the steady state follows a power law, reflecting the interplay between Hamiltonian dynamics and vanishing dissipation. Th

L. Z. Maulana, A. A. Tsirlin, E. Uykur, Y. Saito

We probe the possible double-Weyl state in cubic SrSi2 using optical spectroscopy. The complex optical conductivity was measured in a frequency range from 70 to 22 000 cm-1 at temperatures down to 10 K at ambient pressure. The optical response of SrSi2 can be well separated into the intraband (free carriers) and interband contributions. Additionally, four in

Mohsen Hariri, Amirhossein Samandar, Michael Hinczewski, Vipin Chaudhary

Pass$@k$ is widely used to report the reasoning performance of LLMs, but it often produces unstable and potentially misleading rankings, especially when the number of trials (samples) is limited and computational resources are constrained. We present a principled Bayesian evaluation framework that replaces Pass$@k$ and average accuracy over $N$ trials (avg$@

Mohamed Shamseldein

The Alternating Current Power Flow (ACPF) problem forces a trade-off between the speed of data-driven models and the reliability of analytical solvers. This paper introduces a hybrid framework that synergizes a Graph Neural Network (GNN) with the Implicit Z-Bus Recursive (IZR) method, a robust, non-iterative solver for radial distribution networks. The frame

Joseph Ramsey, Bryan Andrews, Peter Spirtes

Learning causal structure from observational data is especially challenging when latent variables or selection bias are present. The Fast Causal Inference (FCI) algorithm addresses this setting but performs exhaustive conditional independence tests across many subsets, often leading to spurious independences, missing or extra edges, and unreliable orientatio

Hannes Kohlmann, Dmitry Kuklin, Farhad Rachidi, Wolfgang Schulz

In this study, the open-source finite-difference time-domain (FDTD) solvers gprMax, Elecode and MEEP are investigated for their suitability to compute lightning electromagnetic field propagation. Several simulations are performed to reproduce the results of typical field propagation scenarios that can be found in the literature. The results of the presented

Ou Deng, Ruichen Cong, Jianting Xu, Shoji Nishimura

AI for health will only scale when models are not only accurate but also readable, auditable, and governable. Many clinical and public-health decisions hinge on numeric thresholds -- cut-points that trigger alarms, treatment, or follow-up -- yet most machine-learning systems bury those thresholds inside opaque scores or smooth response curves. We introduce l

Yu Cui, Sicheng Pan, Yifei Liu, Haibin Zhang

Large language models (LLMs) have been widely deployed in Conversational AIs (CAIs), while exposing privacy and security threats. Recent research shows that LLM-based CAIs can be manipulated to extract private information from human users, posing serious security threats. However, the methods proposed in that study rely on a white-box setting that adversarie

M. D. Danarianto, A. Sulaksono

We investigate quasi-universal relations in neutron stars linking standard observables, such as tidal deformability ($\Lambda$) and normalized moment of inertia ($\bar{I}$), with normalized curvature scalars in general relativity. These curvature scalars include the Ricci scalar ($\mathcal{R}$), the Ricci tensor contraction ($\mathcal{J}$), the Weyl scalar (

Ziang Zhao, Weixi Liang, Kai Hu, Qun Zhang

Accurate channel modeling is essential for realizing the potential of terahertz (THz) communications in 6G indoor networks, where existing models struggle with severe frequency selectivity and multipath effects. We propose a physically grounded Rician fading channel model that jointly incorporates deterministic line-of-sight (LOS) and stochastic non-line-of-

Yanjie Li, Yiming Cao, Dong Wang, Bin Xiao

Multimodal agents built on large vision-language models (LVLMs) are increasingly deployed in open-world settings but remain highly vulnerable to prompt injection, especially through visual inputs. We introduce AgentTypo, a black-box red-teaming framework that mounts adaptive typographic prompt injection by embedding optimized text into webpage images. Our au

Xiangxiang Chen, Peixin Zhang, Jun Sun, Wenhai Wang

Model quantization is a popular technique for deploying deep learning models on resource-constrained environments. However, it may also introduce previously overlooked security risks. In this work, we present QuRA, a novel backdoor attack that exploits model quantization to embed malicious behaviors. Unlike conventional backdoor attacks relying on training d

Joaquin Cavieres, Sebastian Krumscheid

Gaussian random field is a ubiquitous model for spatial phenomena in diverse scientific disciplines. Its approximation is often crucial for computational feasibility in simulation, inference, and uncertainty quantification. The Karhunen-Lo\`eve Expansion provides a theoretically optimal basis for representing a Gaussian random field as a sum of deterministic

H. Hao

This paper is a follow-up to our earlier study, Natural Disasters in Canada (2017). We analyze the Canadian Disaster Database (CDD) to examine the frequency and severity of various natural disasters over the past 120 years and to identify emerging trends. We generate annual loss distributions for individual disaster types, as well as an aggregate annual loss

Mariya Shcherbina, Tatyana Shcherbina

We consider the asymptotic local behavior of the second correlation functions of the characteristic polynomials of a certain class of Gaussian $N\times N$ non-Hermitian random band matrices with a bandwidth $W$. Given $W,N\to\infty$, we show that this behavior near the point in the bulk of the spectrum exhibits the crossover at $W\sim \sqrt{N}$: it coincides

Jeongwoo Jae, Junghee Ryu, Hoon Ryu

We employ the operational quasiprobability (OQ) as a work distribution, which reproduces the Jarzynski equality and yields the average work consistent with the classical definition. The OQ distribution can be experimentally implemented through the end-point measurement and the two-point measurement scheme. Using this framework, we demonstrate the explicit co

R. Araújo, T. Mariz, J. R. Nascimento, A. Yu. Petrov

We discuss applications of the proper-time method in a Lorentz-violating extension of QED characterized by the addition of the term proportional to the antisymmetric tensor $H_{\mu\nu}$. Unlike other LV extensions of QED, in our case, the one-loop Euler-Heisenberg-like action turns out to include only odd powers of the stress tensor $F_{\mu\nu}$. Our result

Zhao Ren, Rathi Adarshi Rammohan, Kevin Scheck, Tanja Schultz

Speech emotion recognition aims to identify emotional states from speech signals and has been widely applied in human-computer interaction, education, healthcare, and many other fields. However, since speech data contain rich sensitive information, partial data can be required to be deleted by speakers due to privacy concerns. Current machine unlearning appr

Zhanke Zhou, Chentao Cao, Xiao Feng, Xuan Li

We present AlphaApollo, an agentic reasoning system that targets two bottlenecks in foundation-model reasoning: (1) limited reasoning capacity for complex, long-horizon problem solving and (2) unreliable test-time evolution without trustworthy verification. AlphaApollo orchestrates models and tools via three components: (i) multi-turn agentic reasoning, whic

Md. Mouzakkir Hossain, Mohamin B. M. Khan, Youchuang Chao

The linear dynamics and instability mechanisms of double-layered weakly viscoelastic fluid flowing over an inclined plane are analyzed in the presence of insoluble surfactant at both the free surface and interface. The constitutive equation of the non-Newtonian flow field follows the rheological property of Walters' $B^{''}$ model. The Orr-Sommerfeld-type bo

Marko Kostic

In this research article, we formulate and prove multidimensional Widder--Arendt theorem and integrated form of multidimensional Widder--Arendt theorem for functions with values in sequentially complete locally convex spaces. Established results seem to be new even for scalar-valued functions.

Yu-Ting Lin, Hsin-Po Wang

In a large database system, upper-bounding the cardinality of a join query is a crucial task called $\textit{pessimistic cardinality estimation}$. Recently, Abo Khamis, Nakos, Olteanu, and Suciu unified related works into the following dexterous framework. Step 1: Let $(X_1, \dotsc, X_n)$ be a random row of the join, equating $H(X_1, \dotsc, X_n)$ to the log

Román Aranda, Scott Carter, Julia Courtney, Puttipong Pongtanapaisan

New explicit procedures for passing among triplane diagrams, braid movies, and braid charts for knotted surfaces in $\mathbb{R}^4$ are presented. To this end, rainbow diagrams, which lie between braid charts and triplanes, are introduced. Inequalities relating the braid index and the bridge index of 2-knots are obtained via these procedures. Another conseque

Leah Rank, Emanuela Zaccarelli

Hollow microgels are elastic polymer shells easily realizable in experiments. Recent works have shown the emergence of buckling events in dilute hollow microgels under the effect of an added osmotic pressure. Here, we perform large-scale simulations to show that these microgels at high enough packing fractions undergo spontaneous symmetry-breaking deformatio

Huiwon Jang, Sihyun Yu, Heeseung Kwon, Hojin Jeon

Leveraging temporal context is crucial for success in partially observable robotic tasks. However, prior work in behavior cloning has demonstrated inconsistent performance gains when using multi-frame observations. In this paper, we introduce ContextVLA, a policy model that robustly improves robotic task performance by effectively leveraging multi-frame obse

Ayushi Mehrotra, Derek Peng, Dipkamal Bhusal, Nidhi Rastogi

Adversarial patch attacks pose a practical threat to deep learning models by forcing targeted misclassifications through localized perturbations, often realized in the physical world. Existing defenses typically assume prior knowledge of patch size or location, limiting their applicability. In this work, we propose a patch-agnostic defense that leverages con

Gil Alon, Subhajit Ghosh

In 2020, F. Cesi introduced a random walk on the hyperoctahedral group $B_n$ and analysed its spectral gap when the allowed generators are transpositions and diagonal elements corresponding to singletons. In this paper we extend the allowed generators to transpositions and any diagonal elements, and characterise completely the set of representations from whi

Jincan Lou, Jingkun Chen, Haoquan Li, Hang Li

Accurate liver segmentation from contrast-enhanced MRI is essential for diagnosis, treatment planning, and disease monitoring. However, it remains challenging due to limited annotated data, heterogeneous enhancement protocols, and significant domain shifts across scanners and institutions. Traditional image-to-image translation frameworks have made great pro

I. Juhász, J. van Mill, L. Soukup, Z. Szentmiklóssy

A topological space $X$ is a $\Delta$-space (or $X \in \Delta$) if for any decreasing sequence $\{A_n : n < \omega\}$ of subsets of $X$ with empty intersection there is a (decreasing) sequence $\{U_n : n < \omega\}$ of open sets with empty intersection such that $A_n \subset U_n$ for all $n < \omega$. In this note we prove the following results concerning $\

Seong Jin Ahn, Myoung-Ho Kim

For large-scale applications, there is growing interest in replacing Graph Neural Networks (GNNs) with lightweight Multi-Layer Perceptrons (MLPs) via knowledge distillation. However, distilling GNNs for self-supervised graph representation learning into MLPs is more challenging. This is because the performance of self-supervised learning is more related to t

Dario Tagliaferri, Silvia Mura, Musa Furkan Keskin, Sauradeep Dey

This paper addresses the challenge of integrating multistatic coherent imaging functionalities in the downlink (DL) of a phase-coherent distributed multiple input multiple output (D-MIMO) communication network. During DL, the D-MIMO access points (APs) jointly precode the transmitted signals to maximize the spectral efficiency (SE) at the users (UEs) locatio

Tongzhou Wu, Yuhao Wang, Maolin Wang, Chi Zhang

Sequential recommendation aims to capture user preferences by modeling sequential patterns in user-item interactions. However, these models are often influenced by noise such as accidental interactions, leading to suboptimal performance. Therefore, to reduce the effect of noise, some works propose explicitly identifying and removing noisy items. However, we

E. Eisenriegler

In spin systems such as the Ising model, the local order and disorder can be characterized by the order-parameter and energy density profiles $\langle \sigma ({\bf r}_1) \rangle$ and $\langle \epsilon ({\bf r}_2) \rangle$, respectively. Does increasing the order at ${\bf r}_1$ always decrease the disorder at ${\bf r}_2$? Does increasing the disorder at ${\bf

Jinhui Bai, Andreas Christmann, Lei Shi

In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compared to traditional kernel SGD, our algorithm improves efficiency and scalability through an adaptive regularization strategy. By leveraging the infinite series expansion of spherical radial basis functions, this s

Shikun Liu, Kam Woh Ng, Wonbong Jang, Jiadong Guo

We present Kaleido, a family of generative models designed for photorealistic, unified object- and scene-level neural rendering. Kaleido operates on the principle that 3D can be regarded as a specialised sub-domain of video, expressed purely as a sequence-to-sequence image synthesis task. Through a systemic study of scaling sequence-to-sequence generative ne

S. E. Derkachov, A. P. Isaev, L. A. Shumilov

In the paper, the family of conformal four-point ladder diagrams in arbitrary space-time dimensions is considered. We use the representation obtained via explicit calculation using the operator approach and conformal quantum mechanics to study their properties, such as symmetries, loop and dimensional shift identities. In even integer dimensions, latter allo

Runhan Huang, Haldun Balim, Heng Yang, Yilun Du

Legged locomotion demands controllers that are both robust and adaptable, while remaining compatible with task and safety considerations. However, model-free reinforcement learning (RL) methods often yield a fixed policy that can be difficult to adapt to new behaviors at test time. In contrast, Model Predictive Control (MPC) provides a natural approach to fl