November 2025 arXiv papers — page 110

Martin Ridout

Shortly before he died in 2014, Donald Preece gave two talks about what he called tredoku tilings, inspired by the puzzle of the same name. In these talks he presented a conjecture about the existence of these tilings that has been proved recently by Simon Blackburn. This paper provides an overview of Donald's work in this area, including his work on a natur

JaeSeong Kim, Suan Lee

Multilingual LLMs demonstrate strong performance across diverse languages, yet there has been limited systematic analysis of how language information is structured within their internal representation space and how it emerges across layers. We conduct a comprehensive probing study on six multilingual LLMs, covering all 268 transformer layers, using linear an

Yingzhuo Sun, Yulan Gao, Ming Xiao, Zhu Han

Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility i

Oliver Joergensen, Dominik Drexler, Jendrik Seipp

A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compress

Subin Varghese, Joshua Gao, Asad Ur Rahman, Vedhus Hoskere

Deploying embodied agents that can answer questions about their surroundings in realistic real-world settings remains difficult, partly due to the scarcity of benchmarks for episodic memory Embodied Question Answering (EQA). Inspired by the challenges of infrastructure inspections, we propose Inspection EQA as a compelling problem class for advancing episodi

Nil Mont-Geli, Ariel Tarifeño-Saldivia, Max Pallàs, Ángel Perea

The miniBELEN detector is a moderated neutron counter based on the use of $^3$He tubes and high-density polyethylene as moderator. The detector has been designed to have a neutron detection efficiency that is nearly independent from the initial neutron energy for ($\alpha$,n) reactions with alpha-particle energies up to 15 MeV. In order to achieve that, an i

Saar Stern, Ido Sobol, Or Litany

The goal of Novel View Synthesis (NVS) is to generate realistic images of a given content from unseen viewpoints. But how can we trust that a generated image truly reflects the intended transformation? Evaluating its reliability remains a major challenge. While recent generative models, particularly diffusion-based approaches, have significantly improved NVS

Zhihua Zheng, Xiaolong Yao, Cailian Yu, Menghao Gao

The interplay between lattice distortions and charge carriers governs the properties of many functional oxides. In alkali-doped LiMgPO4, a significant enhancement in dosimetric response is observed, but its microscopic origin is not understood. Using non-adiabatic molecular dynamics, we reveal a fundamental mechanism of carrier decoupling driven by a hierarc

Matteo Maltoni

Even if some experimental evidence suggests the existence of physics beyond the SM, no clues of new resonances can be found in the data. In the case their masses are much larger than the energies of current experiments, the SMEFT formalism can be used to introduce new operators that parametrise small deviations from the SM predictions at the LHC, induced by

Maciej Korpalski, Grzegorz Plebanek

The classical Banach spaces $L_\infty[0,1]$ and $\ell_\infty$ are isomorphic. We present here some lower and upper bounds for their Banach-Mazur distance.

Tushar Anand, Advik Sinha, Abhijit Das

In this work, we propose an accurate and real-time optical flow and disparity estimation model by fusing pairwise input images in the proposed non-causal selective state space for dense perception tasks. We propose a non-causal Mamba block-based model that is fast and efficient and aptly manages the constraints present in a real-time applications. Our propos

G. Mustafa, O. Donmez, A. Errehymy, F. Javed

We investigate the physical phenomena occurring around a spherically symmetric, non-rotating charged black hole (BH) to explore the effects of scalar-tensor Gauss-Bonnet gravity on circular motion, accretion disk properties, and Bondi-Hoyle-Lyttleton (BHL) accretion flow. By analytically and numerically examining the influence of the Gauss-Bonnet coupling co

Yuxuan Hu, Jian Chen, Yuhao Wang, Zixuan Li

Stickers are widely used in online communication to convey emotions and implicit intentions. The Sticker Response Selection (SRS) task aims to select the most contextually appropriate sticker based on the dialogue. However, existing methods typically rely on semantic matching and model emotional and intentional cues separately, which can lead to mismatches w

K. Folias, Ch. C. Moustakidis

The structure and basic properties of dense nuclear matter still remain one of the open problems of Physics. In particular, the composition of the matter that composes neutron stars is under theoretical and experimental investigation. Among the theories that have been proposed, apart from the classical one where the composition is dominated by hadrons, the e

Samuel Nathanson, Alexander Lee, Catherine Chen Kieffer, Jared Junkin

Assurance for artificial intelligence (AI) systems remains fragmented across software supply-chain security, adversarial machine learning, and governance documentation. Existing transparency mechanisms - including Model Cards, Datasheets, and Software Bills of Materials (SBOMs) - advance provenance reporting but rarely provide verifiable, machine-readable ev

Sepideh Masoudi, Mark Edward Michael Daly, Jannis Kiesel

As data mesh architectures grow, organizations increasingly build consumer-specific data-sharing pipelines from modular, cloud-based transformation services. While reusable transformation services can improve cost and energy efficiency, applying traditional cloud design patterns can reduce reusability of services in different pipelines. We present a Kubernet

Fabio Cumbo, Rui-Hao Li, Bryan Raubenolt, Jayadev Joshi

A significant challenge in quantum computing (QC) is developing learning models that truly align with quantum principles, as many current approaches are complex adaptations of classical frameworks. In this work, we introduce Quantum Hyperdimensional Computing (QHDC), a fundamentally new paradigm. We demonstrate that the core operations of its classical count

Hongbin Huang, Junwei Li, Tianxin Xie, Zhuang Li

High-fidelity digital humans are increasingly used in interactive applications, yet achieving both visual realism and real-time responsiveness remains a major challenge. We present a high-fidelity, real-time conversational digital human system that seamlessly combines a visually realistic 3D avatar, persona-driven expressive speech synthesis, and knowledge-g

Yuchen Wu, Liang Ding, Li Shen, Dacheng Tao

Aligning Large Language Models (LLMs) to be faithful to new knowledge in complex, multi-hop reasoning tasks is a critical, yet unsolved, challenge. We find that SFT-based methods, e.g., Reason-KE, while state-of-the-art, suffer from a "faithfulness gap": they optimize for format mimicry rather than sound reasoning. This gap enables the LLM's powerful paramet

Songnian Xu, Dein Wong, Wenhao Zhen

Let $G$ be a finite group and $m \geq 2$ a positive integer. We say that $G$ admits an \emph{oriented $m$-semiregular representation} (abbreviated as OmSR) if there exists a $m$-Cayley digraph $\Gamma$ over $G$ such that $\Gamma$ is oriented and $\mathrm{Aut}(\Gamma) \cong G$. In \cite{xu1}, we classified finite groups generated by at most two elements that

Alon Cohen, Liad Erez, Steve Hanneke, Tomer Koren

The fundamental theorem of statistical learning states that binary PAC learning is governed by a single parameter -- the Vapnik-Chervonenkis (VC) dimension -- which determines both learnability and sample complexity. Extending this to multiclass classification has long been challenging, since Natarajan's work in the late 80s proposing the Natarajan dimension

Zeqin Yu, Haotao Xie, Jian Zhang, Jiangqun Ni

Existing Text Image Forgery Localization (T-IFL) methods often suffer from poor generalization due to the limited scale of real-world datasets and the distribution gap caused by synthetic data that fails to capture the complexity of real-world tampering. To tackle this issue, we propose Fourier Series-based Tampering Synthesis (FSTS), a structured and interp

Aris Martinian, Benjamin Steinberg

Fiedorowicz suggested that it was likely that every finite simply connected CW complex is homotopy equivalent to the classifying space of a finite semigroup. We prove that every finite wedge of simply connected Moore spaces of finitely generated abelian groups is homotopy equivalent to the classifying space of a finite semigroup. Consequently, homology group

Dana Fisman, Noa Izsak

Cellular automata (CA) are well-studied models of decentralized parallel computation, known for their ability to exhibit complex global behavior from simple local rules. While their dynamics have been widely explored through simulations, a formal treatment of CA as genuine language generators remains underdeveloped. We formalize CA-expressible languages as s

Jiajun Chen, Bosai Lyu, Liguo Wang, Shuo Lou

Two-dimensional moir\'e superlattices have been extensively studied, and a variety of correlated phenomena have been observed. However, their lower-dimensional counterpart, one-dimensional (1D) moir\'e superlattices, remain largely unexplored. Electrons in 1D are generally described by Luttinger liquid theory, with universal scaling relations depending only

Offer Kopelevitch

We look at the eigenvalues of the complex Ginibre Ensemble of random matrices consisting of $N$ eigenvalues. We study the event that for $ {c \in [0,1]}$, $\lfloor cN \rfloor$ of the eigenvalues are located outside of a disk of radius $ R \in (\sqrt{1-c},1)$. Except for the case $c=1$ the eigenvalue process conditioned on this event is not determinantal. Nev

Cheng Liao

Deep learning-based Visual SLAM (vSLAM) systems exhibit exceptional geometric reasoning capabilities, yet their prohibitive computational overhead severely restricts deployment on resource-constrained autonomous platforms. This paper presents a hierarchical quantization optimization framework, DPVO-QAT++ (DPVO-QAT++: Heterogeneous QAT and CUDA Kernel Fusion

Claude Carlet, Marko Ðurasevic, Domagoj Jakobovic, Luca Mariot

Boolean functions with strong cryptographic properties, such as high nonlinearity and algebraic degree, are important for the security of stream and block ciphers. These functions can be designed using algebraic constructions or metaheuristics. This paper examines the use of Evolutionary Algorithms (EAs) to evolve homogeneous bent Boolean functions, that is,

Nicolò Drago, Lorenzo Pettinari, Christiaan J. F. van de Ven

We provide new sufficient conditions for subcriticality of classical and quantum spin lattice systems, formulated in terms of the uniqueness of Kubo-Martin-Schwinger (KMS) states. This is achieved by exploiting a non-commutative analog of the Kirkwood-Salzburg equations together with a novel decomposition of local observables. In contrast to standard approac

Arvind Kumar Mishra, Sohom Chakrabarty

In this work, Yoshikawa's manipulability index is used to investigate reinforcement learning (RL) as a framework for morphology optimization in planar robotic manipulators. A 2R manipulator tracking a circular end-effector path is first examined because this case has a known analytical optimum: equal link lengths and the second joint orthogonal to the first.

Georgy L. Alfimov, Pavel A. Korchagin, Dmitry E. Pelinovsky

We study the discrete nonlinear Schrodinger equation with competing powers (p,q) satisfying 2 <= p < q. The physically relevant cases are given by (p,q) = (2,3), (p,q) = (3,4), and (p,q) = (3,5). In the anticontinuum limit, all intrinsic localized modes are compact and can be classified by their codes, which record one of two nonzero (smaller and larger) sta

Rathin Chandra Shit, Sharmila Subudhi

The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes are unable to provide sub-10 ms (milliseconds) anomaly detectio

Junwei Li, Wenqing Wang, Huiliu Mao, Jiazhe Ni

As generative AI enters enterprise workflows, ensuring compliance with legal, ethical, and reputational standards becomes a pressing challenge. In beauty tech, where biometric and personal data are central, traditional reviews are often manual, fragmented, and reactive. To examine these challenges, we conducted a formative study with six experts (four IT man

Haotian Jin, Yang Li, Haihui Fan, Lin Shen

Backdoor attacks pose a serious threat to the security of large language models (LLMs), causing them to exhibit anomalous behavior under specific trigger conditions. The design of backdoor triggers has evolved from fixed triggers to dynamic or implicit triggers. This increased flexibility in trigger design makes it challenging for defenders to identify their

Sascha Lange, Roland Hafner, Martin Riedmiller

This article revisits the 20-year-old neural fitted Q-iteration (NFQ) algorithm on its classical CartPole benchmark. NFQ was a pioneering approach towards modern Deep Reinforcement Learning (Deep RL) in applying multi-layer neural networks to reinforcement learning for real-world control problems. We explore the algorithm's conceptual simplicity and its tran

Ahmed Sameh, Sahar Selim

Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these limitations, this paper proposes an Adaptive Dual-Layer WAF em

Suyog Garg, Feng-Li Lin, Kipp Cannon

Upgrades to current gravitational wave detectors for the next observation run and upcoming third-generation observatories, like the Einstein telescope, are expected to have enormous improvements in detection sensitivities and compact object merger event rates. Estimation of source parameters for a wider parameter space that these detectable signals will lie

Caleb Cheung, Bryan Shader

Patterns of m by n matrices of term-rank m for which every real matrix with the pattern has no multiple singular value are characterized. This generalizes Fiedler's characterization of the paths being the only graphs for which every real symmetric matrix with the given graph has no repeated eigenvalue.

Samuel Nathanson, Rebecca Williams, Cynthia Matuszek

Large language models (LLMs) increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adversarially. This study examines whether larger models can systematically jailbreak smaller ones - eliciting harmful or restricted behavior despite alignment safeguards. Using stand

Sofiia Dolgikh, Bodan Potanin

We propose plug-in (PI) and double machine learning (DML) estimators of average treatment effect (ATE), average treatment effect on the treated (ATET) and local average treatment effect (LATE) in the multivariate sample selection model with ordinal selection equations. Our DML estimators are doubly-robust and based on the efficient influence functions. Finit

Ye Du, Nanxi Yu, Shujun Wang

Vision-language foundation models (VLMs) have shown great potential in feature transfer and generalization across a wide spectrum of medical-related downstream tasks. However, fine-tuning these models is resource-intensive due to their large number of parameters. Prompt tuning has emerged as a viable solution to mitigate memory usage and reduce training time

Rathin Chandra Shit, Sharmila Subudhi

The consensus strategies used in collaborative multi-agent systems (MAS) face notable challenges related to adaptability, scalability, and convergence certainties. These approaches, including structured workflows, debate models, and iterative voting, often lead to communication bottlenecks, stringent decision-making processes, and delayed responses in solvin

Benjamin Driscoll, Kshitij Dubey, Anjiang Wei, Neeraj Kayal

With the rapid progress of deep learning and large language models (LLMs), companies spend enormous sums executing GPU kernels. These kernels have become prime targets for aggressive optimization. Recent efforts increasingly leverage LLMs to generate GPU kernels, but make no formal guarantees about the generated kernels. We present the first equivalence chec

Filippo Santoro, Alberto Petri, Francesca Tria

Unprecedented events intertwine with the repetition of the past in natural phenomena and human activities. Key statistical patterns, such as Heaps&#39; and Taylor&#39;s laws and Zipf&#39;s law, have been identified as characterizing the dynamical processes that govern the emergence of novelties and the abundance of repeated elements. Observing these statisti

Emanuele Tasso

In this paper we establish a Besicovitch-Federer type projection theorem for general measures. Specifically, let $\mu$ be a finite Borel measure on $\mathbb{R}^n$ and let $0 < m < n$ be an integer. We show that, under the sole assumption that the slice $\mu \cap W$ is atomic for a typical $(n-m)$-plane $W \subset \mathbb{R}^n$, pure unrectifiability can be c

Manuel Rissel, Marius Tucsnak

Given a finite-dimensional time continuous control system and $\varepsilon>0$, we address the question of the existence of controls that maintain the corresponding state trajectories in the $\varepsilon$-neighborhood of any prescribed path in the state space. We investigate this property, called approximate tracking controllability, for linear and quadratic

Xunzhi Xiang, Xingye Tian, Guiyu Zhang, Yabo Chen

Variational autoencoders (VAEs) typically encode images into a compact latent space, reducing computational cost but introducing an optimization dilemma: a higher-dimensional latent space improves reconstruction fidelity but often hampers generative performance. Recent methods attempt to address this dilemma by regularizing high-dimensional latent spaces usi

Gal Barkai, Leonid Mirkin, Daniel Zelazo

We propose a distributed two-degrees-of-freedom (2DOF) architecture for driving autonomous, possibly heterogeneous, agents to agreement. The scheme mirrors classical servo structures, separating local feedback from network filtering. This separation enables independent network-filter design for prescribed noise attenuation and allows controller heterogeneity

Yushe Cao, Dianxi Shi, Xing Fu, Xuechao Zou

While significant progress has been achieved in multimodal facial generation using semantic masks and textual descriptions, conventional feature fusion approaches often fail to enable effective cross-modal interactions, thereby leading to suboptimal generation outcomes. To address this challenge, we introduce MDiTFace--a customized diffusion transformer fram

Maoqi Liu, Quan Fang, Yang Yang, Can Zhao

Notice to Air Missions (NOTAMs) serve as a critical channel for disseminating key flight safety information, yet their complex linguistic structures and implicit reasoning pose significant challenges for automated parsing. Existing research mainly focuses on surface-level tasks such as classification and named entity recognition, lacking deep semantic unders

Shiyun Lin

The housing market, also known as one-sided matching market, is a classic exchange economy model where each agent on the demand side initially owns an indivisible good (a house) and has a personal preference over all goods. The goal is to find a core-stable allocation that exhausts all mutually beneficial exchanges among subgroups of agents. While this model

Ke Hu, Liyao Xiang, Peng Tang, Weidong Qiu

Current federated-learning models deteriorate under heterogeneous (non-I.I.D.) client data, as their feature representations diverge and pixel- or patch-level objectives fail to capture the global topology which is essential for high-dimensional visual tasks. We propose FedTopo, a framework that integrates Topological-Guided Block Screening (TGBS) and Topolo

Baber Jan, Aiman H. El-Maleh, Abdul Jabbar Siddiqui, Abdul Bais

Camouflaged object detection identifies objects that blend seamlessly with their surroundings through similar colors, textures, and patterns. This task challenges both traditional segmentation methods and modern foundation models, which fail dramatically on camouflaged objects. We identify six fundamental challenges in COD: Intrinsic Similarity, Edge Disrupt

Brian O'Donovan, Andrew Finley, James Sweeney

Valuing residential property is inherently complex, requiring consideration of numerous environmental, economic, and property-specific factors. These complexities present significant challenges for automated valuation models (AVMs), which are increasingly used to provide objective assessments for property taxation and mortgage financing. The challenge of obt

Weiping Yang, Shilin Zhou, Hui Xu, Jiawei Xue

With the rise of compute-in-memory (CIM) accelerators, floating-point multiply-and-accumulate (FP-MAC) operations have gained extensive attention for their higher accuracy over integer MACs in neural networks. However, the hardware overhead caused by exponent comparison and mantissa alignment, along with the delay introduced by bit-serial input methods, rema

Antonin Barbe, Benjamin De Bruyne, Romain Allez

We investigate the overlap matrix between the eigenvectors of a Wigner matrix $H_{N+K}$ of size $(N+K)\times(N+K)$ and those of its principal minor $H_N$ of size $N\times N$, for both the real symmetric ($\beta=1$) and complex Hermitian ($\beta=2$) ensembles, in the regime where $N \to \infty$ while $K$ remains fixed. Our analysis yields two main results. (i

Satish Ramakrishna

The observed vacuum energy lies far below quantum-field-theoretic estimates. Weinberg&#39;s theorem shows that no field can dynamically relax the cosmological constant to zero in a local theory with a translationally invariant vacuum. Approaching this question from a different point of view, the Cohen-Kaplan-Nelson (CKN) bound ties an effective theory&#39;s

Difan Deng, Shengfei Geng, Pin Liu

We prove that under a mild condition, a multiset of tagged permissible arcs over a skew-tiling is uniquely determined by its intersection vector. As an application, it is proved that -- up to isomorphism -- different $\tau$-rigid modules over a skew-gentle algebra $A$ arising from a skew-triple $(Q,Sp,I)$ have different dimension vectors if and only if $(Q,I

Jordan Leyva, Nahim J. Moran Vera, Yihan Xu, Adrien Durasno

Obstacle avoidance path planning for uncrewed aerial vehicles (UAVs), or drones, is rarely addressed in most flight path planning schemes, despite obstacles being a realistic condition. Obstacle avoidance can also be energy-intensive, making it a critical factor in efficient point-to-point drone flights. To address these gaps, we propose EcoFlight, an energy

Stefano Markidis, Luca Pennati, Marco Pasquale, Gilbert Netzer

We introduce QPU micro-kernels: shallow quantum circuits that perform a stencil node update and return a Monte Carlo estimate from repeated measurements. We show how to use them to solve Partial Differential Equations (PDEs) explicitly discretized on a computational stencil. From this point of view, the QPU serves as a sampling accelerator. Each micro-kernel

Arya Parameshwara

This paper presents SynapticCore-X, a modular and resource-efficient neural processing architecture optimized for deployment on low-cost FPGA platforms. The design integrates a lightweight RV32IMC RISC-V control core with a configurable neural compute tile that supports fused matrix, activation, and data-movement operations. Unlike existing FPGA accelerators

Rico Zöllner

This paper presents and proves an equation for the time horizon of symmetric trajectories with zero boundary conditions and bounded derivatives of arbitrary order. This equation holds regardless of the number of phases comprising the associated motion. This avoids case distinctions in calculations. Application examples of motions with minimum time, minimum v

Artem Moroz, Vít Zeman, Martin Mikšík, Elizaveta Isianova

We introduce a unified, end-to-end framework that seamlessly integrates object detection and pose estimation with a versatile onboarding process. Our pipeline begins with an onboarding stage that generates object representations from either traditional 3D CAD models or, in their absence, by rapidly reconstructing a high-fidelity neural representation (NeRF)

Pietro Zanotta, Ljubomir Budinski, Caglar Aytekin, Valtteri Lahtinen

This paper introduces Quantum Orthogonal Separable Physics-Informed Neural Networks (QO-SPINNs), a novel architecture for solving Partial Differential Equations, integrating quantum computing principles to address the computational bottlenecks of classical methods. We leverage a quantum algorithm for accelerating matrix multiplication within each layer, achi

Yuanfeng Yin, Bokai Zhang, H. P. Zhang, Shuo Guo

Dense bacterial suspensions at fluid interfaces provide a natural platform to explore active turbulence in a dimensional mismatch: active units are restricted to a two-dimensional surface, while the induced flows extend into the surrounding three-dimensional liquid. Using hydrophobic Serratia marcescens at the air-water interface, we realize interfacial bact

Jing-Yu Yi, Ze-Rui Liang, Liuming Liu, De-Liang Yao

We perform a lattice QCD calculation of the $S$-wave interactions between the ground-state spin-$1/2$ doubly charmed baryons and Goldstone bosons. The lattice QCD simulations are carried out on four $2+1$ flavor Wilson-Clover ensembles generated by the CLQCD collaboration, with a lattice spacing $a=0.07746$ fm and two different pion masses, $M_\pi \sim 210$

Marcos Jardim, Leonardo Roa-Leguizamón, Renato Vidal Martins

We present stability conditions for the category of coherent systems on an integral curve. We define a three-parameter family of pre-stability conditions in its derived category using tilting, and we then investigate when these conditions qualify as true stability conditions. Additionally, we examine the semistability of specific objects under these conditio

Yunxin Li, Xinyu Chen, Shenyuan Jiang, Haoyuan Shi

We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal understanding, reasoning, and generating. Based on the dense LLM, we build Uni-MoE-2.0-Omni from scratch through three core contributions: dynamic-capacity Mixture-of-Experts (MoE)

Takahiro Matsushita

The closed neighborhood complex $\mathcal{N}[G]$ of a simple graph $G$ is the simplicial complex whose simplices are finite sets of vertices contained in a closed neighborhood of a vertex in $G$. We reveal that the closed neighborhood complex has close connections with other concepts, including the independence complex of the canonical double covering and th

Luca Accorsi, Demetrio Laganà, Federico Michelotto, Roberto Musmanno

We propose a parallel shared-memory schema to cooperatively optimize the solution of a Capacitated Vehicle Routing Problem instance with minimal synchronization effort and without the need for an explicit decomposition. To this end, we design FILO2$^x$ as a single-trajectory parallel adaptation of the FILO2 algorithm originally proposed for extremely large-s

Ziqiong Liu, Yushun Tang, Junyang Ji, Zhihai He

Test-time adaptation (TTA) refers to adjusting the model during the testing phase to cope with changes in sample distribution and enhance the model's adaptability to new environments. In real-world scenarios, models often encounter samples from unseen (out-of-distribution, OOD) categories. Misclassifying these as known (in-distribution, ID) classes not only

Drishya Karki, Merey Ramazanova, Anthony Cioppa, Silvio Giancola

Group Activity Recognition (GAR) is well studied on the video modality for surveillance and indoor team sports (e.g., volleyball, basketball). Yet, other modalities such as agent positions and trajectories over time, i.e. tracking, remain comparatively under-explored despite being compact, agent-centric signals that explicitly encode spatial interactions. Un

Huijie Guo, Jingyao Wang, Peizheng Guo, Xingchen Shen

In this paper, we explore the transferability of SSL by addressing two central questions: (i) what is the representation transferability of SSL, and (ii) how can we effectively model this transferability? Transferability is defined as the ability of a representation learned from one task to support the objective of another. Inspired by the meta-learning para

Yizhe Liu, Bo Sun

The presence of interfaces in semiconductor devices substantially hinders thermal transport, contributing disproportionately to the overall thermal resistance. However, approaches to enhance interfacial thermal transport remain scarce without changing the interface structure, as the intrinsic electron and phonon properties of constituent materials set an upp

Hongyi Chen, Jianhai Shu, Jingtao Ding, Yong Li

Langevin dynamics sampling suffers from extremely low generation speed, fundamentally limited by numerous fine-grained iterations to converge to the target distribution. We introduce PID-controlled Langevin Dynamics (PIDLD), a novel sampling acceleration algorithm that reinterprets the sampling process using control-theoretic principles. By treating energy g

Ria Shekhawat, Sushrut Patwardhan, Raghavendra Ramachandra, Praveen Kumar Chandaliya

Face Recognition Systems (FRS) are critical for security but remain vulnerable to morphing attacks, where synthetic images blend biometric features from multiple individuals. We propose a novel Single-Image Morphing Attack Detection (S-MAD) approach using a teacher-student framework, where a CNN-based teacher model refines a ViT-based student model. To impro

Odysseas Boufalis, Jorge Carrasco-Pollo, Joshua Rosenthal, Eduardo Terres-Caballero

Neural network parameterizations exhibit inherent symmetries that yield multiple equivalent minima within the loss landscape. Scale Graph Metanetworks (ScaleGMNs) explicitly leverage these symmetries by proposing an architecture equivariant to both permutation and parameter scaling transformations. Previous work by Ainsworth et al. (2023) addressed permutati

Marina Khismatullina, Bernhard van der Sluis

This paper proposes a novel framework to test for slope heterogeneity between time-varying coefficients in panel data models. Our test not only allows us to detect whether the coefficient functions are the same across all units or not, but also determines which of them are different and where these differences are located. We establish the asymptotic validit

Bijia Liu, Ronghao Dang

Large language models (LLMs) have shown strong reasoning capabilities and are increasingly explored for financial trading. Existing LLM-based trading agents, however, largely focus on single-step prediction and lack integrated mechanisms for risk management, which reduces their effectiveness in volatile markets. We introduce FinRS, a risk-sensitive trading f

Hongda Qiu

This work pose an example of a smooth closed surface in $\mathbb{R}^3$ which has genus $0$, normal curvatures at most $1$ in absolute value and encloses a volume smaller than the volume of a unit ball. It gives a negative answer to a question asked by Dmitri Burago and Anton Petrunin.

Mengyao Gao, Chongming Gao, Haoyan Liu, Qingpeng Cai

Recent advancements in large language model-based recommendation systems often represent items as text or semantic IDs and generate recommendations in an auto-regressive manner. However, due to the left-to-right greedy decoding strategy and the unidirectional logical flow, such methods often fail to produce globally optimal recommendations. In contrast, huma

Oron Anschel, Alon Shoshan, Adam Botach, Shunit Haviv Hakimi

Large Language Models (LLMs) often suffer from mode collapse, repeatedly generating the same few completions even when many valid answers exist, limiting their diversity across a wide range of tasks. We introduce Group-Aware Policy Optimization (GAPO), a simple extension of the recent and popular Group Relative Policy Optimization (GRPO) that computes reward

Shenxing Zhang

Let $S_g$ be a closed surface of genus $g$ and $\mathcal{H}_g$ be the moduli space of Abelian differentials on $S_g$. A stratum of $\mathcal{H}_g$, endowed with the Masur-Veech measure, becomes a probability space. Then the number of closed saddle connections with lengths in $[\frac{a}{\sqrt{g}},\frac{b}{\sqrt{g}}]$ on a random translation surface in the str

Rongkun Zheng, Lu Qi, Xi Chen, Yi Wang

While visual autoregressive modeling (VAR) strategies have shed light on image generation with the autoregressive models, their potential for segmentation, a task that requires precise low-level spatial perception, remains unexplored. Inspired by the multi-scale modeling of classic Mask2Former-based models, we propose Seg-VAR, a novel framework that rethinks

Gustavo de Paula Ramos

Suppose that either (i) $N = 2$, $\alpha \in \mathbb{R}$ and $p > 2$ or (ii) $N = 3$, $\alpha < 0$ and $2 < p < 3$. We prove that there exists an explicitly computable $\mu_0 = \mu_0 (N, \alpha, p) > 0$ such that if $0 < \mu < \mu_0$, then the following normalized semilinear elliptic problem with a point interaction admits ground states: \[ \begin{cases} - \

Pratik Jawahar, Caterina Doglioni, Maurizio Pierini

Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline performance as classifiers and could improve trigger performance, thereby improving downstream physics analyses. However, inference on such large models does not satisfy the $40\text{

Darya Meniailava, Michael Petrov, Josef Soucek, Milos Nesladek

This is a preliminary version. Improvements and additional analysis will be included in a revised manuscript. We investigate the charge-state stability of individual nitrogen-vacancy (NV) centers in weakly doped HPHT IIa diamond containing sub-ppm concentrations of boron and nitrogen. Using Ti/Al coplanar electrodes on an oxygen-terminated surface, we study

Guoqing Xu, Yiheng Li, Yang Yang

Precise modeling of lane topology is essential for autonomous driving, as it directly impacts navigation and control decisions. Existing methods typically represent each lane with a single query and infer topological connectivity based on the similarity between lane queries. However, this kind of design struggles to accurately model complex lane structures,

Ji-Xin Yu, Shan Cheng, Jia-Jie Han, Hsiang-nan Li

We present the first high-twist study of the proton form factors $F_{1,2}(Q^2)$ in $ep$ elastic scattering based on the perturbative QCD $k_T$ factorization, $Q^2$ being momentum transfer squared. It is motivated by unexpectedly large higher-power contributions from subleading-twist light-cone distribution amplitudes (LCDAs), which are attributed to the enha

Yue Ling, Peiqi Zhang, Zhenyi Zhang, Peijie Zhou

Single-cell RNA sequencing (scRNA-seq), especially temporally resolved datasets, enables genome-wide profiling of gene expression dynamics at single-cell resolution across discrete time points. However, current technologies provide only sparse, static snapshots of cell states and are inherently influenced by technical noise, complicating the inference and re

Zuqi Huang, Mengxin Tian, Huan Liu, Wentao Li

Accurate cell counting in immunohistochemistry (IHC) images is critical for quantifying protein expression and aiding cancer diagnosis. However, the task remains challenging due to the chromogen overlap, variable biomarker staining, and diverse cellular morphologies. Regression-based counting methods offer advantages over detection-based ones in handling ove

El-Mehdi Mehiri

The M-polynomial, introduced by Deutsch and Klav\v{z}ar in 2015, provides a unifying algebraic framework for the computation of numerous degree-based topological indices such as the Zagreb, Randic, harmonic, and forgotten indices. Despite its broad applications in chemical graph theory and network analysis, closed expressions of the M-polynomial remain unkno

Pu-Hai Yang, Heyan Huang, Heng-Da Xu, Fanshu Sun

Task-oriented dialogue systems have garnered significant attention due to their conversational ability to accomplish goals, such as booking airline tickets for users. Traditionally, task-oriented dialogue systems are conceptualized as intelligent agents that interact with users using natural language and have access to customized back-end APIs. However, in r

Qinran Wei, Suyu Ding, Yang Zhao, Yuanpeng Ma

Numerical simulations of surface acoustic wave (SAW)-induced acoustic streaming are highly sensitive to the choice of second-order boundary conditions. This study systematically compares the no-slip (NS) and Stokes slip (SD) boundary conditions through different numerical approaches. Two- and three-dimensional simulations based on the Reynolds stress method

Jiaqi Fu

The derived geometry approach to Donaldson--Thomas theory (over $\mathbb{C}$) is built on Pantev--To\"en--Vezzosi--Vaqui\'e's existence theorem of $(-1)$-shifted symplectic forms \cite{pantev2013shifted} and Brav--Bussi--Joyce's shifted Darboux theorem \cite{brav2019darboux}. In this paper, we prove a Darboux theorem in characteristic $p>2$ for the $(-1)$-sh

Yao Li, Jiatong Sun

Many stochastic differential equations in various applications like coupled neuronal oscillators are driven by time-periodic forces. In this paper, we extend several data-driven computational tools from autonomous Fokker-Planck equation to the time-periodic setting. This allows us to efficiently compute the time-periodic invariant probability measure using e

Ying Gao, Hongfei Fu, Xiaoying Wang

This paper presents a linear, decoupled, mass- and energy-conserving numerical scheme for the multi-dimensional coupled nonlinear Schr\"odinger (CNLS) system. The scheme combines the fourth-order compact difference approximation in space with the relaxation technique in a time-staggered mesh framework, solving the primal unknowns and introduced auxiliary rel

Hajime Kobayashi, Shinji Mukohyama, Naritaka Oshita, Kazufumi Takahashi

The response of a black hole (BH) to tidal forces encodes key information about the underlying gravitational theory and affects the waveform of gravitational waves emitted during binary inspiral processes. In this paper, we analyze the dynamical tidal response of static and spherically symmetric BHs in a low-frequency regime within general relativity (GR), b

Yongwen Ren, Chao Wang, Peng Du, Chuan Qin

Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To further enhance accuracy and mitigate hallucination, many methods integrate PLMs with knowledge graphs (KGs), but face key challenges: failing to fully exploit PLM reasoning over gr

Yukuo Ma, Cong Liu, Junke Wang, Junqi Liu

We present TempoMaster, a novel framework that formulates long video generation as next-frame-rate prediction. Specifically, we first generate a low-frame-rate clip that serves as a coarse blueprint of the entire video sequence, and then progressively increase the frame rate to refine visual details and motion continuity. During generation, TempoMaster emplo