October 2025 arXiv papers — page 164

Hyogo Hiruma, Hiroshi Ito, Tetsuya Ogata

Training robots to operate effectively in environments with uncertain states, such as ambiguous object properties or unpredictable interactions, remains a longstanding challenge in robotics. Imitation learning methods typically rely on successful examples and often neglect failure scenarios where uncertainty is most pronounced. To address this limitation, we

Ashley Chen

Autism spectrum disorder (ASD) has been associated with structural alterations across cortical and subcortical regions. Quantitative neuroimaging enables large-scale analysis of these neuroanatomical patterns. This project used structural MRI (T1-weighted) data from the publicly available ABIDE I dataset (n = 1,112) to classify ASD and control participants u

Zhechong Huang, Zhao Zhang, Ruyi Ji, Tingxuan Xia

Language models have shown remarkable proficiency in code generation; nevertheless, ensuring type correctness remains a challenge. Although traditional methods, such as constrained decoding, alleviate this problem by externally rejecting untypable code, the model itself does not effectively learn type reasoning internally, which ultimately limits its overall

Yiyi Huo, Mohammadhossein Golchin, Kaiyu Zhou, Ashwin Abraham

Our study investigates the segregation of bitumen residues within the transport pipeline before disposal in the presence of coal particles in carriers and microbubbles. Coal particles decreased the bitumen recovery by 17% without the injection of microbubbles. In addition, the improvement in bitumen recovery efficiency by 6 mL of H2O2 is negligible due to a

Pranav Gupta, Ravi Banavar, Anastasia Bizyaeva

Local bifurcation analysis plays a central role in understanding qualitative transitions in networked nonlinear dynamical systems, including dynamic neural network and opinion dynamics models. In this article we establish explicit bounds of validity for the classification of bifurcation diagrams in two classes of continuous-time networked dynamical systems,

Jingyi Wu, Chao Ning, Yang Shi

Wasserstein distributionally robust control (DRC) recently emerges as a principled paradigm for handling uncertainty in stochastic dynamical systems. However, it constructs data-driven ambiguity sets via uniform distribution shifts before sequentially incorporating them into downstream control synthesis. This segregation between ambiguity set construction an

Ilyas Kalimullin, Eduard Lerner

Consider a connected pseudograph $H$ such that each edge is associated with weight $x_e$, $x_e \in \mathbb{F}_3$; $\mathcal{T}(H)$ is the set of spanning trees of graph $H$. Assume that $s(H;{\mathbf x})=\sum_{T\in\mathcal{T}(H)} \prod_{e\in E(T)} x_e$. Let $G$ be a maximal planar graph (arbitrary planar triangulation) such that each face $F$ is assigned the

Bin Li, Dongdong Yang, Lei Liu, Dusit Niyato

Reconfigurable intelligent surface (RIS) has emerged as a pivotal technology for enhancing wireless networks. Compared to terrestrial RIS deployed on building facades, aerial RIS (ARIS) mounted on quadrotor unmanned aerial vehicle (UAV) offers superior flexibility and extended coverage. However, the inevitable tilt and altitude variations of a quadrotor UAV

Yurii Kulinich, Bohdan Novosyadlyj, Maksym Tsizh, Nazar Fortuna

We investigate how primordial magnetic fields (PMFs) affect the formation kinetics of the first molecules, H$_2$, HD, and HeH$^+$, as well as the populations of rovibrational levels and the global signals in the rovibrational transitions of H$_2$ and HD. We show that PMFs can significantly speed up the formation and destruction of the first molecules, leadin

Yida Xiong, Jiameng Chen, Kun Li, Hongzhi Zhang

Molecular graph generation (MGG) is essentially a multi-class generative task, aimed at predicting categories of atoms and bonds under strict chemical and structural constraints. However, many prevailing diffusion paradigms learn to regress numerical embeddings and rely on a hard discretization rule during sampling to recover discrete labels. This introduces

Rishabh Shukla, Wasim Akram, Manil T. Mohan

We study the following nonlinear heat equation with damping and pumping effects (a reaction-diffusion equation) posed on a bounded simply connected convex domain $\Omega \subset \mathbb{R}^d$, $d \geq 1$ with Lipschitz boundary $\partial\Omega$: $$ \frac{\partial u(t)}{\partial t} - \nu \Delta u(t) + \alpha |u(t)|^{p-2}u(t) - \sum_{\ell=1}^M \beta_{\ell} |u(

Massinissa Merouani, Afif Boudaoud, Riyadh Baghdadi

The advancement of machine learning for compiler optimization, particularly within the polyhedral model, is constrained by the scarcity of large-scale, public performance datasets. This data bottleneck forces researchers to undertake costly data generation campaigns, slowing down innovation and hindering reproducible research learned code optimization. To ad

Bo Yuan, Yulin Chen, Yin Zhang

Parameter-efficient fine-tuning (PEFT) large language models (LLMs) have shown impressive performance in various downstream tasks. However, in many real-world scenarios, the collected training data inevitably contains noisy labels. To learn from noisy labels, most solutions select samples with small losses for model training. However, the selected samples, i

Yujian Zhang, Keyu Chen, Zhifeng Shen, Ruizhi Qiao

Although Long Reasoning Models (LRMs) have achieved superior performance on various reasoning scenarios, they often suffer from increased computational costs and inference latency caused by overthinking. To address these limitations, we propose Adaptive Dual Reasoner, which supports two reasoning modes: fast thinking and slow thinking. ADR dynamically altern

Zuhong Liu, Junhao Ge, Minhao Xiong, Jiahao Gu

The true promise of humanoid robotics lies beyond single-agent autonomy: two or more humanoids must engage in physically grounded, socially meaningful whole-body interactions that echo the richness of human social interaction. However, single-humanoid methods suffer from the isolation issue, ignoring inter-agent dynamics and causing misaligned contacts, inte

Manjiang Yu, Hongji Li, Priyanka Singh, Xue Li

Reliable behavior control is central to deploying large language models (LLMs) on the web. Activation steering offers a tuning-free route to align attributes (e.g., truthfulness) that ensure trustworthy generation. Prevailing approaches rely on coarse heuristics and lack a principled account of where to steer and how strongly to intervene. To this end, we pr

Aradhita Chattopadhyaya, Jan Manschot

We study Appell functions associated to an arbitrary positive definite lattice $\Lambda$ and a choice of $M\leq {\rm dim}(\Lambda)$ linearly independent vectors $d_r\in \Lambda$, $r=1,\dots,M$. These functions are instances of multi-variable quasi-elliptic functions, and specific examples have appeared at various places in mathematics and theoretical physics

Dingyi Yao, Xinyao Han, Ruibo Ming, Zhihang Song

Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets have therefore emerged as a promising alternative, offering advantages such as cost-effectiveness, bias free labeling, and controllable scenarios. However, the domain gap between syn

Ayush Rai, Shaoshuai Mou, Brian D. O. Anderson

The design of the performance index, also referred to as cost or reward shaping, is central to both optimal control and reinforcement learning, as it directly determines the behaviors, trade-offs, and objectives that the resulting control laws seek to achieve. A commonly used approach for this inference task in recent years is differentiable trajectory optim

Jinghao Zhang, Naishan Zheng, Ruilin Li, Dongzhou Cheng

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising framework for improving reasoning abilities in Large Language Models (LLMs). However, policy optimized with binary verification prone to overlook potential valuable exploration in reasoning trajectory. In view of heavy annotation cost of golden Process Reward Models (PR

Haocan Sun, Weizi Liu, Di Wu, Guoming Yu

Trust is one of the most important factors shaping whether and how people adopt and rely on artificial intelligence (AI). Yet most existing studies measure trust in terms of functionality, focusing on whether a system is reliable, accurate, or easy to use, while giving less attention to the social and emotional dimensions that are increasingly relevant for t

Siyuan Lu, Zechuan Wang, Hongxuan Zhang, Qintong Wu

Large Language Model (LLM) agents show great promise for complex, multi-turn tool-use tasks, but their development is often hampered by the extreme scarcity of high-quality training data. Supervised fine-tuning (SFT) on synthetic data leads to overfitting, whereas standard reinforcement learning (RL) struggles with a critical cold-start problem and training

Kristo Väljako

In this note it is proven that an idempotent ring cannot be Morita equivalent to its idempotent proper ideal.

Yizhi Wang, Li Chen, Qiang Huang, Tian Guan

Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise, these models still lack accuracy and generalizability. General foundation models offer a broader reach but remain limited in capturing subspecialty-specific features and task adap

Hong-Kun Zhang, Xin Li, Sikun Yang, Zhihong Xia

A novel neural network inspired by Cauchy's integral formula, is proposed for function approximation tasks that include time series forecasting, missing data imputation, etc. Hence, the novel neural network is named CauchyNet. By embedding real-valued data into the complex plane, CauchyNet efficiently captures complex temporal dependencies, surpassing tradit

Feng Xiao, Hongbin Xu, Hai Ci, Wenxiong Kang

Localizing 3D objects using natural language is essential for robotic scene understanding. The descriptions often involve multiple spatial relationships to distinguish similar objects, making 3D-language alignment difficult. Current methods only model relationships for pairwise objects, ignoring the global perceptual significance of n-ary combinations in mul

Qingni Wang, Yue Fan, Xin Eric Wang

As large language models (LLMs) are increasingly deployed in risk-sensitive applications such as real-world open-ended question answering (QA), ensuring the trustworthiness of their outputs has become critical. Existing selective conformal prediction (SCP) methods provide statistical guarantees by constructing prediction sets with a constrained miscoverage r

Benjamin Dupont, Revekka Kyriakoglou, Vassilis Metaftsis, Efstratios Prassidis

Coverings of the Riemann sphere by itself, ramified over two points, are given by so-called Shabat polynomials. The correspondence between Grothendieck's dessins d'enfants and Belyi maps then implies a bijection between Shabat polynomials and tree dessins (bicolored plane trees). Dessins can be assigned a combinatorial invariant known as their passport, whic

Haohua Dong, Ana Manzano Rodríguez, Camille Guinaudeau, Shin'ichi Satoh

Face gender classification models often reflect and amplify demographic biases present in their training data, leading to uneven performance across gender and racial subgroups. We introduce pseudo-balancing, a simple and effective strategy for mitigating such biases in semi-supervised learning. Our method enforces demographic balance during pseudo-label sele

Sina Beyraghi, Javad Shabanpour, Giovanni Geraci, Paul Almasan

This paper presents a fully automated, data-driven framework for the large-scale deployment of reconfigurable intelligent surfaces (RISs) in cellular networks. Leveraging physically consistent ray tracing and empirical data from a commercial deployment in the UK, the proposed method jointly optimizes RIS placement, orientation, configuration, and base statio

David Wang, Mohammad Abdulaziz

We present an approach to unsolvability certification of temporal planning. Our approach is based on encoding the planning problem into a network of timed automata, and then using an efficient model checker on the network followed by a certificate checker to certify the output of the model checker. Our approach prioritises trustworthiness of the certificatio

Linfei Li, Fengyi Zhang, Zhong Wang, Lin Zhang

Implicit Neural Representations (INRs) have gained success in various signal processing tasks due to their advantages of continuity and infinite resolution. However, the factors influencing their effectiveness and limitations remain underexplored. To better understand these factors, we leverage insights from Neural Tangent Kernel (NTK) theory to analyze how

Shuo Dai, Zeqing Wang, Liang-Liang Wan, Weidong Li

Quantum synchronization (QS) in open many-body systems offers a promising route for controlling collective quantum dynamics, yet existing manipulation schemes often rely on dissipation engineering, which distorts limit cycles, lacks scalability, and is strongly system-dependent. Here, we propose a universal and scalable method for continuously tuning QS from

Dekai Zhang, Simone Conia, Antonio Rago

Ontologies have become essential in today's digital age as a way of organising the vast amount of readily available unstructured text. In providing formal structure to this information, ontologies have immense value and application across various domains, e.g., e-commerce, where countless product listings necessitate proper product organisation. However, the

Artem A. Tsygankov, Bulat N. Galimzyanov, Anatolii V. Mokshin

In liquid pnictogens, quasi-stable structures can be formed near melting temperature. The nature of their stability does not have the unified point of view. In the present work, the task of determining the degree of atomic bonding in these structures is solved using the Crystal Orbital Hamilton Population (COHP) method. The original results of ab-initio simu

Levin Hornischer

Analog computation is an alternative to digital computation, that has recently re-gained prominence, since it includes neural networks and neuromorphic computing. Further important examples are cellular automata and differential analyzers. While analog computers offer many advantages, they lack a notion of universality akin to universal digital computers. Si

Xiaohui Yao, Jiahui Ji, Xuanchi Zhou

The metal-insulator transition (MIT) in correlated oxide systems opens up a new paradigm to trigger the abruption in multiple physical functionalities, enabling the possibility in unlocking exotic quantum states beyond conventional phase diagram. Nevertheless, the critical challenge for practical device implementation lies in achieving the precise control ov

Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang

Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamental types, which is characterized by its particular-to-general thinking process and the non-uniqueness of its answers. The inductive mode is crucial for knowledge generalization and aligns better with human cognition,

Shaokai Wu, Yanbiao Ji, Qiuchang Li, Zhiyi Zhang

Embodied agents face a fundamental limitation: once deployed in real-world environments, they cannot easily acquire new knowledge to improve task performance. In this paper, we propose Dejavu, a general post-deployment learning framework that augments a frozen Vision-Language-Action (VLA) policy with retrieved execution memories through an Experience Feedbac

Linghan Huang, Peizhou Zhao, Huaming Chen

The rapid development of large language models (LLMs) has revolutionized software testing, particularly fuzz testing, by automating the generation of diverse and effective test inputs. This advancement holds great promise for improving software reliability. Meanwhile, the introduction of MOJO, a high-performance AI programming language blending Python's usab

Marco Linton

We refine Feighn--Handel's results on subgroups of mapping tori of free groups to the special case of free-by-cyclic groups. We use these refinements to show that any finitely generated free-by-cyclic group embeds in a {finitely generated free}-by-cyclic group. When the free-by-cyclic group is hyperbolic, it embeds in a hyperbolic {finitely generated free}-b

Yulin Wang, Mengting Hu, Hongli Li, Chen Luo

In pose estimation for seen objects, a prevalent pipeline involves using neural networks to predict dense 3D coordinates of the object surface on 2D images, which are then used to establish dense 2D-3D correspondences. However, current methods primarily focus on more efficient encoding techniques to improve the precision of predicted 3D coordinates on the ob

Linda Rocco

The proliferation of Large Language Models in higher education presents a fundamental challenge to traditional pedagogical frameworks. Drawing on Jacques Ranci\`ere's theory of intellectual emancipation, this paper examines how generative AI risks becoming a "mechanical yes-man" that reinforces passivity rather than fostering intellectual autonomy. Generativ

Xian He, Wei Zeng, Ye Wang

Highly-informed Expressive Performance Rendering (EPR) systems transform music scores with rich musical annotations into human-like expressive performance MIDI files. While these systems have achieved promising results, the availability of detailed music scores is limited compared to MIDI files and are less flexible to work with using a digital audio worksta

Cristiano Patrício, Luís F. Teixeira, João C. Neves

Concept-based models aim to explain model decisions with human-understandable concepts. However, most existing approaches treat concepts as numerical attributes, without providing complementary visual explanations that could localize the predicted concepts. This limits their utility in real-world applications and particularly in high-stakes scenarios, such a

Paul Haimes

This paper introduces Chord Colourizer, a near real-time system that detects the musical key of an audio signal and visually represents it through a novel graphical user interface (GUI). The system assigns colours to musical notes based on Isaac Newton's original colour wheel, preserving historical links between pitch and hue, and also integrates an Arduino-

Mihail N. Kolountzakis, Nir Lev, Máté Matolcsi

We study two optimization problems for positive definite functions on Euclidean space with restrictions on their support and sign: the Turan problem and the Delsarte problem. These problems have been studied also for their connections to geometric problems of tiling and packing. In the finite group setting the weak and strong linear duality for these problem

Alexander McFarlane

We derive a slippage-aware toxicity condition for on-chain liquidations executed via a constant-product automated market maker (CP-AMM). For a fixed (constant) liquidation incentive $i$, the familiar toxicity frontier $\nu < 1/(1+i)$ tightens to $\nu < 1/((1+i)\lambda)$ for a liquidity penalty factor $\lambda$ that we derive for both the CP-AMM and a general

Mohammad adarbeh, Mohammad Saleh

In this paper, we introduce the notion of uniformly S-pseudo-projective (u-S-pseudo-projective) modules as a generalization of u-S-projective modules. Let R be a ring and S a multiplicative subset of R. An R-module P is said to be u-S-pseudo-projective if for any submodule K of P, there is s\in S such that for any u-S-epimorphism f:P\to \frac{P}{K}, sf can b

Michele Romani, Devis Zanoni, Elisabetta Farella, Luca Turchet

$\textit{BrainForm}$ is a gamified Brain-Computer Interface (BCI) training system designed for scalable data collection using consumer hardware and a minimal setup. We investigated (1) how users develop BCI control skills across repeated sessions and (2) perceptual and performance effects of two visual stimulation textures. Game Experience Questionnaire (GEQ

Chengqian Gao, Haonan Li, Taylor W. Killian, Jianshu She

Concise reasoning in large language models seeks to generate only essential intermediate steps needed to arrive at a final answer, thereby alleviating issues of overthinking. Most proposed approaches hinge on carefully hand-crafted heuristics, struggling to balance concision with performance, often failing to adapt across domains and model scales. In this wo

Shuanping Du, Zhaofang Bai

Large-scale quantum networks have been employed to overcome practical constraints on transmission and storage for single entangled systems. The deterministic preparation of entangled states is one of the key factors for realization of quantum networks. There is no efficient method to verify whether single multipartite entanglement can be prepared by multisou

Suhrid Gupta, Muhammed Tawfiqul Islam, Rajkumar Buyya

Edge computing allows for the decentralization of computing resources. This decentralization is achieved through implementing microservice architectures, which require low latencies to meet stringent service level agreements (SLA) such as performance, reliability, and availability metrics. While cloud computing offers the large data storage and computation r

Feiyang Xu, Poonacha K. Medappa, Murat M. Tunc, Martijn Vroegindeweij

GenAI solutions like GitHub Copilot have been shown to increase the productivity of software developers. Yet prior work remains unclear on the quality of code produced and the challenges of maintaining it in software projects. If quality declines as volume grows, technical debt accumulates as experienced developers face increased workloads reviewing and rewo

Yanhui Chen, Chaomi Duan, Baokun Sun

With the progressive release of data from numerous sky surveys, humanity has entered the era of astronomical big data. Multi-wavelength, multi-method research is playing an increasingly crucial role. Binaries account for a substantial fraction of all stellar systems and research into binaries is of fundamental importance. LAMOST J064137.77+045743.8 has not y

Cesar Borja, Carlos Plou, Ruben Martinez-Cantin, Ana C. Murillo

Semantic segmentation is essential for automating remote sensing analysis in fields like ecology. However, fine-grained analysis of complex aerial or underwater imagery remains an open challenge, even for state-of-the-art models. Progress is frequently hindered by the high cost of obtaining the dense, expert-annotated labels required for model supervision. W

Javier Gutiérrez García, Ulrich Höhle

This paper provides a comprehensive overview of some of the foundational properties of categories enriched over quantaloids, along with several new results. We demonstrate that the category whose objects are quantaloid-enriched categories and whose morphisms are left adjoint distributors admits an (epi, extremal mono)--factorization system. Furthermore, we p

Liang Pang, Jia Gu, Sunhao Dai, Zihao Wei

Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspectives is essential. This survey presents a systematic investigation organized around four interr

Zhenjie Mao, Yuhuan Yang, Chaofan Ma, Dongsheng Jiang

Referring Image Segmentation (RIS) aims to segment the target object in an image given a natural language expression. While recent methods leverage pre-trained vision backbones and more training corpus to achieve impressive results, they predominantly focus on simple expressions--short, clear noun phrases like "red car" or "left girl". This simplification of

Jaap Jumelet, Abdellah Fourtassi, Akari Haga, Bastian Bunzeck

We present BabyBabelLM, a multilingual collection of datasets modeling the language a person observes from birth until they acquire a native language. We curate developmentally plausible pretraining data aiming to cover the equivalent of 100M English words of content in each of 45 languages. We compile evaluation suites and train baseline models in each lang

Ziyi Liu, Qingyue Long, Zhiwen Xue, Huandong Wang

User mobility trajectory and mobile traffic data are essential for a wide spectrum of applications including urban planning, network optimization, and emergency management. However, large-scale and fine-grained mobility data remains difficult to obtain due to privacy concerns and collection costs, making it essential to simulate realistic mobility and traffi

Tsung-Min Pai, Jui-I Wang, Li-Chun Lu, Shao-Hua Sun

Multi-LLM systems enhance the creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. To address these limitations, we propose BILLY (BlendIng persona vectors for Large Language model creativitY), a training-free framework that captures the

Benjia Zhou, Bin Fu, Pei Cheng, Yanru Wang

Recent advances in large-scale text-to-image diffusion models (e.g., FLUX.1) have greatly improved visual fidelity in consistent character generation and editing. However, existing methods rarely unify these tasks within a single framework. Generation-based approaches struggle with fine-grained identity consistency across instances, while editing-based metho

Mohamed Hamad, Muhammad Khan, Tamer Khattab, Mohamed Mabrok

A key challenge in ischemic stroke diagnosis using medical imaging is the accurate localization of the occluded vessel. Current machine learning methods in focus primarily on lesion segmentation, with limited work on vessel localization. In this study, we introduce Stroke Locus Net, an end-to-end deep learning pipeline for detection, segmentation, and occlud

LinFeng Li, Jian Zhao, Yuan Xie, Xin Tan

The dominant paradigm for training Large Vision-Language Models (LVLMs) in navigation relies on imitating expert trajectories. This approach reduces the complex navigation task to a sequence-to-sequence replication of a single correct path, fundamentally limiting the agent's ability to explore and generalize. In this work, we argue for and introduce a new pa

L. Berguiga, T. Girerd, X. Letartre, T. Benyattou

It is commonly accepted that phase singularities in refractive index sensors can provide highly sensitive detection. To address this issue, we studied the phase sensitivity and the limit of detection of Tamm photonic crystals used as temperature sensors, taken here as a model system by exploring critical coupling and its associated phase singularity. To fine

Yecong Wan, Mingwen Shao, Renlong Wu, Wangmeng Zuo

In this work, we present Color3D, a highly adaptable framework for colorizing both static and dynamic 3D scenes from monochromatic inputs, delivering visually diverse and chromatically vibrant reconstructions with flexible user-guided control. In contrast to existing methods that focus solely on static scenarios and enforce multi-view consistency by averagin

Tianyi Tao, Bohan Yang

Markov's equation x^2 + y^2 + z^2 = 3xyz is a widely studied topic in number theory, and the structure of its solutions has profound connections with mathematical fields such as combinatorics, hyperbolic geometry, approximation theory, and cluster algebras. In this paper, we prove that Markov's equation is not partition regular, which also confirms a necessa

Zhezheng Hao, Hong Wang, Haoyang Liu, Jian Luo

Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, its training is often plagued by \emph{entropy collapse}, a rapid decline in policy entropy that limits exploration and undermines training effectiveness. While recent works attempt to miti

Xin Chen, Gillian Dobbie, Xinyu Wang, Feng Liu

Conditional diffusion models have the generative controllability by incorporating external conditions. However, their performance significantly degrades with noisy conditions, such as corrupted labels in the image generation or unreliable observations or states in the control policy generation. This paper introduces a robust learning framework to address ext

Mengyao Zhao, Kaixuan Li, Lyuye Zhang, Wenjing Dang

Recent advances in Large Language Models (LLMs) have brought remarkable progress in code understanding and reasoning, creating new opportunities and raising new concerns for software security. Among many downstream tasks, generating Proof-of-Concept (PoC) exploits plays a central role in vulnerability reproduction, comprehension, and mitigation. While previo

Boris A. Khanikati, Konstantin Y. Bliokh

Wave vortices constitute a large family of wave entities, closely related to phase singularities and orbital angular momentum (OAM). So far, two main classes of localized wave vortices have been explored: (i) transversely-localized monochromatic vortex beams that carry well-defined longitudinal OAM and propagate/diffract along the longitudinal $z$-axis in sp

Kaveh Eftekharinasab

We introduce a class of functionals on the space of rapidly decreasing sequences $s$, called $\mathcal{F}_s$-functionals, defined as decomposable sums of quadratic and convex terms with quadratic growth. We prove that such functionals satisfy the Palais-Smale condition and admit a unique global minimum. Furthermore, we show that the Palais-Smale condition is

Cheng He, Xijie Liang, Zengrong Zheng, Patrick P. C. Lee

Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers. They often encode time series data in a black-box manner and rely on trial-and-error optimization solely based on forecasting performance, leading to limited interpretability and theoretical u

Bruno Vallette

This is a short survey on the recent developments made in the integration theory with effective formulas of algebraic structures stronger or higher than Lie algebras.

Matthew Faust, Frank Sottile

The Spectral Edges Conjecture is a well-known and widely believed conjecture in the theory of discrete periodic operators. It states that the extrema of the dispersion relation are isolated, non-degenerate, and occur in a single band. We present two infinite families of periodic graphs which satisfy the Spectral Edges Conjecture. For each, every extremum of

Tingxu Han, Wei Song, Ziqi Ding, Ziming Li

Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear, but gives little insight into the mechanisms that generate them, leaving existing mitigations largely fragile. In this paper, we conduct a systematic investigation LLM unfairness

Hongxing Peng, Haopei Xie, Weijia Lia, Huanai Liuc

Litchi is a high-value fruit, yet traditional manual selection methods are increasingly inadequate for modern production demands. Integrating UAV-based aerial imagery with deep learning offers a promising solution to enhance efficiency and reduce costs. This paper introduces YOLOv11-Litchi, a lightweight and robust detection model specifically designed for U

Fei-Fan Song, Jirong Mao

GRB 250702DBE was time-consequently triggered by GBM onboard the Fermi satellite. It is uncertain which celestial catalog is suitable for this special ultra-long event to belong to. In this paper, we comprehensively investigate the lightcurves obtained by Fermi-GBM detectors. In the energy band of 8-1000 keV, no Quasi-Period Oscillation (QPO) signals are fou

Yichen Yan, Ming Zhong, Qi Zhu, Xiaoling Gu

Multimodal large language models (MLLMs) rely heavily on instruction tuning to align vision and language capabilities, yet the computational cost of training on large-scale datasets remains a major bottleneck. Existing data selection methods aim to mitigate this by selecting important and diverse subsets, but they often suffer from two critical drawbacks: hi

Zilong Wang, Xiaoyu Shen

Information extraction from copy-heavy documents, characterized by massive volumes of structurally similar content, represents a critical yet understudied challenge in enterprise document processing. We present a systematic framework that strategically combines OCR engines with Large Language Models (LLMs) to optimize the accuracy-efficiency trade-off inhere

Pietro De Checchi, Federico Gallina, Barbara Fresch, Giulio G. Giusteri

Stochastic evolution underpins several approaches to the dynamics of open quantum systems, such as random modulation of Hamiltonian parameters, the stochastic Schrodinger equation (SSE), and the stochastic Liouville equation (SLE). These approaches replace the explicit system-environment coupling with an effective system-only dynamics, where dissipative beha

Lancheng Zou, Shuo Yin, Zehua Pei, Tsung-Yi Ho

Channel permutation is a powerful technique for enhancing the accuracy of N:M sparse models by reordering the channels of weight matrices to prioritize the retention of important weights. However, traditional channel permutation methods rely on handcrafted quality metrics, which often fail to accurately capture the true impact of pruning on model performance

Zhongsheng Wang, Ming Lin, Zhedong Lin, Yaser Shakib

Ensuring character identity consistency across varying prompts remains a fundamental limitation in diffusion-based text-to-image generation. We propose CharCom, a modular and parameter-efficient framework that achieves character-consistent story illustration through composable LoRA adapters, enabling efficient per-character customization without retraining t

Yves Dumont, Marc Hétier, Valaire Ivric Yatat-Djeumen

Anthropisation and excessive hunting in tropical forests threaten biodiversity, ecosystem maintenance and human food security. In this article, we focus on the issue of coexistence between humans and wildlife in an anthropised environment. Assuming that the human population moves between its residential area and the surrounding forest to hunt, we study a res

Kabir Khan, Manju Sarkar, Anita Kar, Suresh Ghosh

Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings due to heavy computation and communication and statistical/system heterogeneity. We propose FedGen-Edge, a framework that decouples a frozen, pre-trained global backbone from light

Anjelin Mariya Johnson, S. N. Fathima

Recently, Kaur and Rana introduced the partition function denoted by $\rho(n)$, where the largest part $\lambda$ appears exactly once, and the remaining parts constitute a partition of $\lambda$. In this paper, we establish new generating functions for certain variants of $\rho(n)$. Further, we obtain a linear recurrence relation for our new generating funct

Ioannis Dassios

We study a material modeled as a network of nodes connected by edges. Using a discrete approach, we build a nonlinear algebraic system that connects applied forces to internal forces and node positions. The model can describe elasticity, plasticity, and possibly cracking. The goal is to solve this system and understand how the material responds. Students are

Jian Fang, Yican Sun, Yingfei Xiong

One important approach to software verification is interactive theorem proving. However, writing formal proofs often requires substantial human effort, making proof automation highly important. Traditionally, proof automation has relied on symbolic provers. Recently, large language models (LLMs) have demonstrated strong capabilities in theorem proving, compl

Mihai Ivan

The main purpose of this paper is to study the fractional-order system with Caputo derivative associated to single Stokes pulse. The dynamic behavior for this fractional model (called the fractional Stokes system) is investigated, including: the asymptotic stability around zero equilibrium state, the stabilization problem using appropriate linear controls an

Sunita Khod, Vinay Kamma, Ravi Kumar Verma, Mayank Goswami

This study proposes an Artificial Intelligence (AI) driven methodology for predicting a combination of brazed ceramic-metal composite materials. Multiple machine learning (ML) algorithms are compared with the deep learning (DL) model. The developed models are tested using k-fold validation. Nine different input-output feature configurations are evaluated to

Sehar Zehra, Hassan Jamil Syed, Ummay Faseeha

Kubernetes multi-cluster deployments demand scalable and privacy-preserving anomaly detection. Existing eBPF-based monitors provide low-overhead system and network visibility but are limited to single clusters, while centralized approaches incur bandwidth, privacy, and heterogeneity challenges. We propose FedMon, a federated eBPF framework that unifies kerne

Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen, Chelsea Finn

Generalist robot policies can now perform a wide range of manipulation skills, but evaluating and improving their ability with unfamiliar objects and instructions remains a significant challenge. Rigorous evaluation requires a large number of real-world rollouts, while systematic improvement demands additional corrective data with expert labels. Both of thes

Nassim Athmouni

We establish a local rigidity theorem for quasi--Lie brackets on quaternionic Banach right modules. Under quantitative control of antisymmetry and Jacobi defects, we construct an explicit bilinear correction that preserves right $\mathbb{H}$--linearity and restores the exact Lie property. The approach combines a radial homotopy operator, a controlled Neumann

Joydeep Chandra, Satyam Kumar Navneet, Yong Zhang

The proliferation of complex, multimodal datasets has exposed a critical gap between the capabilities of specialized vector databases and traditional graph databases. While vector databases excel at semantic similarity search, they lack the capacity for deep relational querying. Conversely, graph databases master complex traversals but are not natively optim

Halil Hüseyin Çalışkan, Talha Koruk

Computer vision and image processing applications suffer from dark and low-light images, particularly during real-time image transmission. Currently, low light and dark images are converted to bright and colored forms using autoencoders; however, these methods often achieve low SSIM and PSNR scores and require high computational power due to their large numb

Abu Saleh Musa Miah, Najmul Hassan, Md Maruf Al Hossain, Yuichi Okuyama

Accurate evaluation of Parkinsons disease (PD) severity is essential for effective clinical management and intervention development. Despite the proposal of several gesture based PD recognition systems, including those using the finger tapping task to assess Parkinsonian symptoms, their performance remains unsatisfactory. In this study, we present a multi cl

Hua Chen, Hong-Ge Chen, Jin-Ning Li, Xin Liao

We study the following perturbation from symmetry problem for the semilinear subelliptic equation \[ \left\{ \begin{array}{cc} -\triangle_{X} u=f(x,u)+g(x,u) & \mbox{in}~\Omega, \\[2mm] u\in H_{X,0}^{1}(\Omega),\hfill \end{array} \right. \] where $\triangle_{X}=-\sum_{i=1}^{m}X_{i}^{*}X_{i}$ is the self-adjoint sub-elliptic operator associated with H\"{o}rma

Liutong Han, Zhiyuan Tan, Hongbin Zhang, Pengcheng Wang

The use of intrinsic functions to leverage hardware-specific capabilities is a crucial approach for optimizing library performance. Many mainstream libraries implement a large number of vectorized algorithms on Arm or x86 SIMD (Single-Instruction, Multiple-Data) intrinsic functions. Translating existing vectorized intrinsic code into the intrinsics of an eme

Emanuele Costa, Axel Pérez-Obiol, Javier Menéndez, Arnau Rios

Quantum computing is emerging as a promising tool in nuclear physics. However, the cost of encoding fermionic operators hampers the application of algorithms in current noisy quantum devices. In this work, we analyze an encoding scheme based on pairing nucleon modes. This approach significantly reduces the complexity of the encoding, while maintaining a high