October 2025 arXiv papers — page 160

Syed Ibad Hasnain

Cardiovascular disease and chronic kidney disease are major complications of diabetes, leading to high morbidity and mortality. Early detection of these conditions is critical, yet traditional diagnostic markers often lack sensitivity in the initial stages. This study integrates conventional statistical methods with machine learning approaches to improve ear

Dorian Martino, Tristan Rivière

We develop the analysis of Lipschitz immersions of $n$-dimensional manifolds into $\mathbb{R}^d$ having their second fundamental forms bounded in the critical Sobolev space $W^{\frac{n}{2}-1,2}$ in dimension $n\geq 4$ even and any codimension. We prove that, while such a weak immersion is not necessary $C^1$, it generates a $C^1$ differential structure on th

Francesco Giovanni Celiberto, Francesca Lonigro

We investigate the inclusive hadroproduction of pseudoscalar heavy quarkonia, $\eta_c$ and $\eta_b$ mesons, in high-energy proton collisions. Our framework bases on the single-parton collinear fragmentation within a variable-flavor number scheme, tailored to describe the moderate to large transverse momentum regime. To this end, we construct a new set of col

Hong Su

Existing studies have introduced method-based reasoning and scope extension as approaches to enhance Large Language Model (LLM) performance beyond direct matrix mappings. Building on these foundations, this paper summarizes and integrates these ideas into a unified Intuition-Method Layered Model with Scope Extension, designed to address indirected (unseen) i

Maël Kupperschmitt, Leo Liberti

In 1979, James B.~Saxe published an extended summary on the complexity of the Distance Geometry Problem in the proceedings of the 17th Allerton Conference. Many of the proofs in his paper are sketches, and even the whole proofs do not have all the details. In this paper we provide a commentary to Saxe's results and hopefully more understandable versions ther

Zachary Selk

Given a metric measure space $M:=(X,d,\mu)$ the Onsager-Machlup (OM) functional is a real valued function that has been seen as a generalized notion of a probability density function. The effect of reweighting the measure on OM functionals has been studied, however analogous reweightings of the metric to the best of our knowledge remain open. In this short n

Jianfeng Hou, Xizhi Liu, Yixiao Zhang, Hongbin Zhao

A classical result of Sidorenko (1989) shows that the Tur\'{a}n density of every $r$-uniform hypergraph with three edges is bounded from above by $1/2$. For even $r$, this bound is tight, as demonstrated by Mantel's theorem on triangles and Frankl's theorem on expanded triangles. In this note, we prove that for odd $r$, the bound $1/2$ is never attained, the

Rohit Mukherjee, Asutosh Dubey

We investigate the effective couplings induced between localized impurities on the surface of a Weyl semimetal (WSM) nanowire within the framework of Ruderman--Kittel--Kasuya--Yosida (RKKY) theory. The itinerant electrons from the chiral Fermi arc surface states mediate impurity-impurity interaction at low energies. As a result, the spin-momentum locking nat

Weina Jin, Elise Li Zheng, Ghassan Hamarneh

The operationalization of ethics in the technical practices of artificial intelligence (AI) is facing significant challenges. To address the problem of ineffective implementation of AI ethics, we present our diagnosis, analysis, and interventional recommendations from a unique perspective of the real-world implementation of AI ethics through explainable AI (

Jingchao Wang, Wenlong Zhang, Dingjiang Huang, Hong Wang

Visual grounding aims to predict the locations of target objects specified by textual descriptions. For this task with linguistic and visual modalities, there is a latest research line that focuses on only selecting the linguistic-relevant visual regions for object localization to reduce the computational overhead. Albeit achieving impressive performance, it

Giulio Ruffini

In the algorithmic (Kolmogorov) view, agents are programs that track and compress sensory streams using generative programs. We propose a framework where the relevant structural prior is simplicity (Solomonoff) understood as \emph{compositional symmetry}: natural streams are well described by (local) actions of finite-parameter Lie pseudogroups on geometrica

Heng Zhang, Yuling Shi, Xiaodong Gu, Haochen You

Multi-agent systems powered by large language models exhibit strong capabilities in collaborative problem-solving. However, these systems suffer from substantial knowledge redundancy. Agents duplicate efforts in retrieval and reasoning processes. This inefficiency stems from a deeper issue: current architectures lack mechanisms to ensure agents share minimal

Shizhen Zhao, Jiahui Liu, Xin Wen, Haoru Tan

Pre-trained vision foundation models have transformed many computer vision tasks. Despite their strong ability to learn discriminative and generalizable features crucial for out-of-distribution (OOD) detection, their impact on this task remains underexplored. Motivated by this gap, we systematically investigate representative vision foundation models for OOD

Adrian Langer

Let $f: X\to Y$ be a proper surjective morphism of varieties defined over an algebraically closed field of positive characteristic. We prove that if $f$ has geometrically connected fibers then the induced homomorphism of $F$-divided fundamental groups is faithfully flat. An important new ingredient in our proof is an analogue of B. Bhatt's and P. Scholze's d

Adrian Langer, Lei Zhang

In this paper we study $F$-divided bundles on irreducible Noetherian normal $F$-finite $\mathbb{F}_p$-schemes and we show that their Tannakian category is governed by the behaviour at the generic point. In particular, if $U\subset X$ is an open subset of a normal variety defined over an algebraically closed field then the corresponding homomorphism of $F$-di

Heng Zhang, Yuling Shi, Xiaodong Gu, Haochen You

Multi-agent systems powered by Large Language Models excel at complex tasks through coordinated collaboration, yet they face high failure rates in multi-turn deep search scenarios. Existing temporal attribution methods struggle to accurately diagnose root causes, particularly when errors propagate across multiple agents. Attempts to automate failure attribut

Jiahao He, Yutao Cui, Cuiping Li, Jikang Jiang

The query optimizer is a fundamental component of database management systems. Recent studies have shown that learned query optimizers outperform traditional cost-based query optimizers. However, they fail to exploit valuable runtime observations generated during query execution to dynamically re-optimize the plan, thereby limiting further improvements in qu

Bekir Can Lütfüoğlu

We investigate the propagation of a massive scalar field and a massless Dirac field in the geometry of a dilaton--de Sitter black hole. Starting from the covariant perturbation equations, we present the corresponding effective potentials and analyze their dependence on the dilaton charge, field mass, and cosmological constant. Using the WKB approximation, we

Haonan Wang, Hanyu Zhou, Haoyue Liu, Luxin Yan

Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from motion blur and insufficient illumination. These conditions lead to weakened texture and amplified noise and deteriorate the appearance saturation and boundary completeness of frame cameras, which are necess

Changxin Yang, Zhongyi Zhu, Heng Lian

Federated learning has attracted significant attention as a privacy-preserving framework for training personalised models on multi-source heterogeneous data. However, most existing approaches are unable to handle scenarios where subgroup structures coexist alongside within-group heterogeneity. In this paper, we propose a federated learning algorithm that add

Zhengrong Yue, Haiyu Zhang, Xiangyu Zeng, Boyu Chen

Tokenizer is a crucial component for both visual understanding and generation. To advance toward the ultimate goal of universal modeling, recent research has focused on developing a unified tokenizer. However, existing tokenizers face a significant performance trade-off between understanding and generation, stemming from the inherent conflict between high-le

Daniel Hennig, Joaquin Garcia-Alfaro

This report presents some technical details on the authentication process of a lightweight key exchange protocol, paying attention on how Man-in-the-Middle (MitM) attacks could undermine its security, e.g., under the scope of lawful interception and its risk to facilitate mass surveillance. We focus only on some technical aspects associated to the attack sce

Farouq Benchallal, Adel Hafiane, Nicolas Ragot, Raphael Canals

Weed species classification represents an important step for the development of automated targeting systems that allow the adoption of precision agriculture practices. To reduce costs and yield losses caused by their presence. The identification of weeds is a challenging problem due to their shared similarities with crop plants and the variability related to

Byeongchan Lee

Self-supervised representation learning has achieved impressive empirical success, yet its theoretical understanding remains limited. In this work, we provide a theoretical perspective by formulating self-supervised representation learning as an approximation to supervised representation learning objectives. Based on this formulation, we derive a loss functi

Chaohua Duan, Hongyu Liu, Qingle Meng, Li Wang

This paper introduces an operator-theoretic paradigm for solving inverse problems in nonlinear balance laws, shifting the focus from identifying specific functional forms to recovering the input-output actions of the associated flux and source operators. It is established that a single local passive boundary observation suffices to uniquely determine realiza

Zirui Wan, Stefan Vlaski

Classical consensus-based strategies for federated and decentralized learning are statistically suboptimal in the presence of heterogeneous local data or task distributions. As a result, in recent years, there has been growing interest in multitask or personalized strategies, which allow individual agents to benefit from one another in pursuing locally optim

Qunqiang Feng, Michael Fuchs, Tsan-Cheng Yu

The Zagreb index, which is defined as the sum of squares of degrees of the nodes of a tree, was studied in previous works by martingale techniques for random non-plane recursive trees and classes of random trees which are close to random plane recursive trees. These techniques are not easily amended to the generalized Zagreb index, which is defined similar b

Hua Sun, Syed A. Jafar

A distributed quantum storage code maps a quantum message to N storage nodes, of arbitrary specified sizes, such that the stored message is robust to an arbitrary specified set of erasure patterns. The sizes of the storage nodes, and erasure patterns may not be homogeneous. The capacity of distributed quantum storage is the maximum feasible size of the quant

Alexander Langmann, Yevhenii Tokarev, Mattia Piccinini, Korbinian Moller

Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, their behavior is often governed by a cost function with manually tuned, static weights, which forces a tactical compromise that is suboptimal across the wide range of scenarios enc

Naoki Maruyama, Masayuki Ohzeki, Kazuyuki Tanaka

Quantum annealing (QA) with a transverse field often fails to sample degenerate ground states fairly, limiting applicability to problems requiring diverse optimal solutions. Although Quantum Monte Carlo (QMC) is widely used to simulate QA, its ability to reproduce such unfair ground-state sampling remains unclear because stochastic and coherent quantum dynam

Pankaj K. Jha, Lakshya Nagpal, Amir Targholizadeh, Utkarsh Mishra

The standard quantum limit (SQL), also known as the shot-noise limit, defines how quantum fluctuations of light constrain measurement precision. In a benchmark experiment using the Mach-Zehnder interferometer (MZI), where a coherent state with the average photon number $\langle n\rangle$ is combined with an ordinary vacuum input, the SQL for the phase uncert

Liang Li, Zhou Yang, Xiaofei Zhu

Sequential recommendation aims to predict the next item based on user interests in historical interaction sequences. Historical interaction sequences often contain irrelevant noisy items, which significantly hinders the performance of recommendation systems. Existing research employs unsupervised methods that indirectly identify item-granularity irrelevant n

Xuyang Zhao, Jiangtao Wang, Xinyu Zhang, Yongchao Wang

This paper proposes an integrated sensing and communication (ISAC) system covert waveform design method for complex clutter environments, with the core objective of maximizing the signal-to-clutter-plus-noise ratio (SCNR). The design achieves efficient clutter suppression while meeting the covertness requirement through joint optimization of the transmit wav

Ali A. Kamli, Sergey A. Moiseev, Jabir W. Hakami

The spectral and statistical properties are explored for surface plasmon (SP) emission in resonance fluorescence from a driven two level emitter in the proximity of 2D single graphene sheet. We derive an exact closed form analytic expression for the emitted SP field valid in the near and far regions. The SP field profile and spectrum function depend on the g

Zhixiong Chen, Hyundong Shin, Arumugam Nallanathan, Jonathon Chambers

Accurate channel prediction and effective beamforming are essential for low Earth orbit (LEO) satellite communications to enhance system capacity and enable high-speed connectivity. Most existing channel prediction and predictive beamforming methods are limited by model generalization capabilities and struggle to adapt to time-varying wireless propagation en

Euhid Aman, Esteban Carlin, Hsing-Kuo Pao, Giovanni Beltrame

Cross-attention transformers and other multimodal vision-language models excel at grounding and generation; however, their extensive, full-precision backbones make it challenging to deploy them on edge devices. Memory-augmented architectures enhance the utilization of past context; however, most works rarely pair them with aggressive edge-oriented quantizati

G. Bargigli, L. Frassineti, A. Lanata', P. Baragli

This review examines the evidence in the literature for physiological co-modulation during human-animal interaction. The aim of this work is to identify studies that assessed co-modulation via simultaneous measurement of physiological signals in both species, performing quantitative comparisons, and evaluate the consistency of the findings.\\ We searched Pub

Weiming Zhao, Xulong Wang, Jun Qi, Yun Yang

Severity assessment of Parkinson's disease (PD) using wearable sensors offers an effective, objective basis for clinical management. However, general-purpose time series models often lack pathological specificity in feature extraction, making it difficult to capture subtle signals highly correlated with PD.Furthermore, the temporal sparsity of PD symptoms ca

Qinglan Xia, Haotian Sun

This article generalizes the study of ramified optimal transport with capacity constraint in transport multi-paths by generalizing the $\mathbf{M}_{\alpha}$ cost to $\mathbf{M}_{\alpha,c}$, which incorporates capacity constraints into the cost function. Equipped with $\mathbf{M}_{\alpha,c}$ cost, we prove the existence of optimal transport path, $\mathbf{M}_

Donglin Zhou, Weike Pan, Zhong Ming

Sequential recommendation (SR) models often capture user preferences based on the historically interacted item IDs, which usually obtain sub-optimal performance when the interaction history is limited. Content-based sequential recommendation has recently emerged as a promising direction that exploits items' textual and visual features to enhance preference l

Zi-Xuan Yang, Lei Li, Tao Huang, Hui Wan

The Sabatier principle establishes a fundamental trade-off in heterogeneous electrocatalysis.In the hydrogen evolution reaction (HER), this trade-off is manifested by the coupling of Volmer step, which requires strong hydrogen adsorption, with the Heyrovsky/Tafel step, which favors facile desorption, thus giving rise to the classical volcano relationship and

YoungJu Choie, Rahul Kumar

In this paper, the second Kronecker ``limit" formula for a real quadratic field is established for the first time. More precisely, we obtain the second Kronecker limit formula of Zagier's zeta function. Using the reduction theory of Zagier, which connects Zagier's zeta function to the zeta function of real quadratic fields, we express the values of the zeta

Siyuan Liu, Junting Lin

Aiming at the problems of missed detection, false detection and low detection efficiency in transmission line foreign object detection under railway environment, we proposed an improved algorithm MRS-YOLO based on YOLO11. Firstly, a multi-scale Adaptive Kernel Depth Feature Fusion (MAKDF) module is proposed and fused with the C3k2 module to form C3k2_MAKDF,

Rafael R. Yumul, Enalyn T. Domingo

The study is anchored to the principles of Nearly-Zero Energy Building (NZEB). It aimed to transform the Tarlac State University Gymnasium into a facility with energy-efficient equipment to contribute to reducing carbon footprints by integrating a solar PV system as its renewable energy source. The researchers found out that the electrical infrastructure of

Baris Ardic, Quentin Le Dilavrec, Andy Zaidman

The integration of generative AI tools like ChatGPT into software engineering workflows opens up new opportunities to boost productivity in tasks such as unit test engineering. However, these AI-assisted workflows can also significantly alter the developer's role, raising concerns about control, output quality, and learning, particularly for novice developer

Jyoti Rani, Arnab Patra

In this article, we conduct a comprehensive study on the continuity, compactness, and spectral properties of Rhaly operators and generalized Ces\`aro operators, acting on weighted null sequence spaces. We determine the point spectrum, continuous spectrum, and residual spectrum for compact Rhaly operators and compact generalized Ces\`aro operators. Additional

Xinbang Dai, Huikang Hu, Yongrui Chen, Jiaqi Li

While large language models (LLMs) excel at many domain-specific tasks, their ability to deeply comprehend and reason about full-length academic papers remains underexplored. Existing benchmarks often fall short of capturing such depth, either due to surface-level question design or unreliable evaluation metrics. To address this gap, we introduce ELAIPBench,

Katsunori Kusakabe, Yoshiyuki Inoue, Daisuke Toyouchi

We present a refined estimation of the stochastic gravitational wave background (SGWB) based on observed dual active galactic nuclei (AGNs) together with AGN X-ray luminosity functions, in light of recent Pulsar Timing Array detections of an nHz SGWB. We identify a characteristic luminosity dependence in dual AGN fractions by compiling recent observational d

Fengping Li, Bheema Lingam Chittari, Chao Lei, Jeil Jung

Magnets with controllable magnetization and high critical temperature are essential for practical spintronics devices, among which the two-dimensional 1T-CrTe2 stands out because of its high experimental critical temperature up to about 300K down to the single layer limit. By using ab initio density functional theory, we investigate the magnetic properties o

Zuha Fatima, Muhammad Anser Sohaib, Muhammad Talha, Sidra Sultana

Glacial Lake Outburst Floods (GLOFs) are rare but destructive hazards in high mountain regions, yet predictive research is hindered by fragmented and unimodal data. Most prior efforts emphasize post-event mapping, whereas forecasting requires harmonized datasets that combine visual indicators with physical precursors. We present GLOFNet, a multimodal dataset

Koki Yamane, Sho Sakaino, Toshiaki Tsuji

Four-channel bilateral control is a method for achieving remote control with force feedback and adjustment operability by synchronizing the positions and forces of two manipulators. This is expected to significantly improve the operability of the remote control in contact-rich tasks. Among these, 4-channel bilateral control on the Cartesian coordinate system

Heng Wu, Houssam el Mrabet Haje, Michiel Dubbelman, Brenden R. Ortiz

Superconductivity represents a macroscopic quantum state notable for its rich manifestations of electronic coherence and collective behavior. Kagome materials AV3Sb5 (A= K, Cs, Rb) possess cascade intertwined quantum phases including superconductivity, symmetry-breaking charge orders, nematic orders and topological states, making them attractive materials fo

Zhiyu Wang, Bingxin Zhou, Jing Wang, Yang Tan

Proteins are essential biological macromolecules that execute life functions. Local structural motifs, such as active sites, are the most critical components for linking structure to function and are key to understanding protein evolution and enabling protein engineering. Existing computational methods struggle to identify and compare these local structures,

Léo Ducas, Lynn Engelberts, Paola de Perthuis

Is module-lattice reduction better than unstructured lattice reduction? This question was highlighted as 'Q8' in the Kyber NIST standardization submission (Avanzi et al., 2021), as potentially affecting the concrete security of Kyber and other module-lattice-based schemes. Foundational works on module-lattice reduction (Lee, Pellet-Mary, Stehl\'e, and Wallet

Yujie Ren, Niklas Gruhlke, Anne Lauscher

As large language models (LLMs) are increasingly applied in sensitive domains such as medicine and law, hallucination detection has become a critical task. Although numerous benchmarks have been proposed to advance research in this area, most of them are artificially constructed--either through deliberate hallucination induction or simulated interactions--ra

Taras Banakh, Oles Mazurenko, Olesia Zavarzina

A metric space is plastic if all its non-expansive bijections are isometries. We prove three main results: (1) every countable dense subspace of a normed space is not plastic, (2) every $k$-crowded separable metric space contains a plastic dense subspace, and (3) every strictly convex separable metric group contains a plastic dense subgroup.

V. V. Nesvizhevsky, J. A. Pioquinto, K. Schreiner, S. Baessler

We discuss small shifts in the interference patterns of gravitational and whispering gallery quantum states that can be observed with neutrons, atoms, antiatoms, muonium, positronium, and other particles. A gravitational shift of interference patterns of neutron gravitational and whispering-gallery states can be easily observed with cold, very cold, or ultra

M Gulliksson, A Oleynik, M Ogren, R Bakhshandeh-Chamazkoti

We develop a new method for solving minimization problems on the Stiefel Manifold using damped dynamical systems. The constraints are satisfied in the limit by an additional damped dynamical system. The method is illustrated by numerical experiments and compared to a state-of-the-art conjugate gradient method.

Binyu Zhao, Wei Zhang, Zhaonian Zou

Multi-modal learning has made significant advances across diverse pattern recognition applications. However, handling missing modalities, especially under imbalanced missing rates, remains a major challenge. This imbalance triggers a vicious cycle: modalities with higher missing rates receive fewer updates, leading to inconsistent learning progress and repre

Elham Shabaninia, Fatemeh Asadi-zeydabadi, Hossein Nezamabadi-pour

This work presents a pattern-aware framework for automatic license plate recognition (ALPR), designed to operate reliably across diverse plate layouts and challenging real-world conditions. The proposed system consists of a modern, high-precision detection network followed by a recognition stage that integrates a transformer-based vision model with an iterat

Guillaume Ambal, George Hodgkins, Mark Madler, Gregory Chockler

Remote Direct Memory Access (RDMA) is a memory technology that allows remote devices to directly write to and read from each other's memory, bypassing components such as the CPU and operating system. This enables low-latency high-throughput networking, as required for many modern data centres, HPC applications and AI/ML workloads. However, baseline RDMA comp

Hanbing Liu, Huaze Tang, Yanru Wu, Yang Li

Continuous Domain Adaptation (CDA) effectively bridges significant domain shifts by progressively adapting from the source domain through intermediate domains to the target domain. However, selecting intermediate domains without explicit metadata remains a substantial challenge that has not been extensively explored in existing studies. To tackle this issue,

Vishal Subramanian, Bikash Kanungo, Vikram Gavini

Density functional theory (DFT) remains the most widely used electronic structure method. Although exact in principle, in practice, it relies on approximations to the exchange-correlation (XC) functional, which is known to be a unique functional of the electron density. Despite 50 years of active research, existing XC approximations remain far from general p

Mingqian Guan, Komei Fujita, Naoya Sueishi, Shota Yasui

This paper proposes a new method for estimating conditional average treatment effects (CATE) in randomized experiments. We adopt inverse probability weighting (IPW) for identification; however, IPW-transformed outcomes are known to be noisy, even when true propensity scores are used. To address this issue, we introduce a noise reduction procedure and estimat

Wo Long, Wenxin Zeng, Xiaoyu Zhang, Ziyao Zhou

This research develops a sentiment-driven quantitative trading system that leverages a large language model, FinGPT, for sentiment analysis, and explores a novel method for signal integration using a reinforcement learning algorithm, Twin Delayed Deep Deterministic Policy Gradient (TD3). We compare the performance of strategies that integrate sentiment and t

Ines Safi

We consider hierarchical quantum Hall edge states with $N$ modes and a spatially local quantum point contact (QPC). In general, the field of an injected anyon does not directly acquire the universal statistical phase $\theta$. Short-range inter-edge interactions split the universal anyon charge and phase into $N$ fractionalized charges associated with nonuni

Yang Liu, Yufei Yin, Chenchen Jing, Muzhi Zhu

In this work, we present COSINE, a unified open-world segmentation model that consolidates open-vocabulary segmentation and in-context segmentation with multi-modal prompts (e.g., text and image). COSINE exploits foundation models to extract representations for an input image and corresponding multi-modal prompts, and a SegDecoder to align these representati

Ricardo Alonso, Milana Čolić

In this paper, we study the polyatomic Boltzmann equation based on continuous internal energy, focusing on physically relevant collision kernels of the hard potentials type with integrable angular part. We establish three main results: smoothing effects of the gain collision operator, propagation of velocity and internal energy first-order derivatives of sol

Xi Zhang, Xiaolin Wu

Recently, several look-up table (LUT) methods were developed to greatly expedite the inference of CNNs in a classical strategy of trading space for speed. However, these LUT methods suffer from a common drawback of limited receptive field of the convolution kernels due to the combinatorial explosion of table size. This research aims to expand the CNN recepti

Baoyu Wang, Lingrui Zou, Tao Wang, Lijun Xu

Van der Waals (vdW) p-n heterojunctions are important building blocks for advanced electronics and optoelectronics, in which high-quality heterojunctions essentially determine device performances or functionalities. Creating tunable depletion regions with substantially suppressed leakage currents presents huge challenges, but is crucial for heterojunction ap

Fuze Sun, Paul Craig, Lingyu Li, Shixiangyue Meng

As the global reach of online higher education continues to grow, universities are increasingly accommodating students from diverse cultural backgrounds (Tereshko et al., 2024). This can present a number of challenges including linguistic barriers (Ullah et al., 2021), cultural differences in learning style (Omidvar & Tan, 2012), cultural sensitivity in cour

Hamed Adami, Anouchah Latifi

We uncover a direct connection between three-dimensional gravity with chiral boundary conditions and a class of forced integrable systems. Starting from the Chern-Simons formulation, we derive consistent boundary conditions on a non-compact spatial slice, leading to boundary dynamics described by the potential modified KdV hierarchy. The dynamics reduce to a

Qunzhong Wang, Jie Liu, Jiajun Liang, Yilei Jiang

Recent advancements in multimodal reward models (RMs) have substantially improved post-training for visual generative models. However, current RMs face inherent limitations: (1) visual inputs consume large context budgets, forcing fewer frames and causing loss of fine-grained details; and (2) all visual information is packed into the initial prompt, exacerba

Su-Hyeon Kim, Joonghyuk Hahn, Sooyoung Cha, Yo-Sub Han

Code runtime optimization-the task of rewriting a given code to a faster one-remains challenging, as it requires reasoning about performance trade-offs involving algorithmic and structural choices. Recent approaches employ code-LLMs with slow-fast code pairs provided as optimization guidance, but such pair-based methods obscure the causal factors of performa

Kanishkha Jaisankar, Xiaoyang Jiang, Feifan Liao, Jeethu Sreenivas Amuthan

Energy-efficient and high-performance motor control remains a critical challenge in robotics, particularly for high-dimensional continuous control tasks with limited onboard resources. While Deep Reinforcement Learning (DRL) has achieved remarkable results, its computational demands and energy consumption limit deployment in resource-constrained environments

Cengiz J. Khan, Oliver J. Irving, Rand A. Al-Waqfi, Giorgio Ferrari

Solid-state nanopore and nanopipette sensors are powerful devices for the detection, quantification and structural analysis of biopolymers such as DNA and proteins, especially in carrier-enhanced resistive-pulse sensing. However, hundreds of different molecules typically need to be sampled from solution and analysed to obtain statistically robust information

Jiaxin Liu, Haoyu Yuan, Xiangli Lei, Wenlong Xu

We investigate the optical linear polarization caused by Thomson scattering of the stellar radiation for gamma-ray binary \lsi61, which likely contains a young pulsar. Based on the pulsar binary scenario, we model the interaction between the pulsar wind and stellar wind from the massive companion star, which creates a shock. To accurately compute the resulti

Md Ibrahim Shikder Mahin, Md Shamsul Arefin, Md Tanvir Hasan

Healthcare research and development face significant obstacles due to data scarcity and stringent privacy regulations, such as HIPAA and the GDPR, restricting access to essential real-world medical data. These limitations impede innovation, delay robust AI model creation, and hinder advancements in patient-centered care. Synthetic data generation offers a tr

Xiaopeng Cheng, Zhichao Zhang

The graph linear canonical transform (GLCT)-based filtering methods often optimize transform parameters and filters separately, which results in high computational costs and limited stability. To address this issue, this paper proposes a trainable joint optimization framework that combines GLCT parameters and Wiener filtering into an end-to-end learning proc

Xu Zhao, Xiaopeng Ye, Chen Xu, Weiran Shen

Improving the long-term user welfare (e.g., sustained user engagement) has become a central objective of recommender systems (RS). In real-world platforms, the creation behaviors of content creators plays a crucial role in shaping long-term welfare beyond short-term recommendation accuracy, making the effective steering of creator behavior essential to foste

Subhodip Panda, Dhruv Tarsadiya, Shashwat Sourav, Prathosh A. P

Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under training randomness: the same example may appear critical in one run and irrelevant in the next. Such instability undermines their use in data curation or cleanup since it is unclea

Zihan Zhang, Xize Cheng, Zhennan Jiang, Dongjie Fu

Universal sound separation faces a fundamental misalignment: models optimized for low-level signal metrics often produce semantically contaminated outputs, failing to suppress perceptually salient interference from acoustically similar sources. We introduce a preference alignment perspective, analogous to aligning LLMs with human intent. To address this, we

Yuhua Zhang, Jukka-Pekka Onnela, Shuo Sun, Ruoyu Wang

Interference--in which a unit's outcome is affected by the treatment of other units--poses significant challenges for the identification and estimation of causal effects. Most existing methods for estimating interference effects assume that the interference networks are known. In many practical settings, this assumption is unrealistic as such networks are ty

Kush Garg, Akshat Dave

Efficient exploration and mapping in unknown indoor environments is a fundamental challenge, with high stakes in time-critical settings. In current systems, robot perception remains confined to line-of-sight; occluded regions remain unknown until physically traversed, leading to inefficient exploration when layouts deviate from prior assumptions. In this wor

Ravindra Singh, Kiran Meena, Kapish Chand Meena

In this paper, we propose \textit{general Chen's first inequality} for Riemannian maps between Riemannian manifolds and manifest its equality and sharpness via non-trivial examples. We also utilize this general inequality by establishing Chen's first inequalities when the target spaces are generalized complex and generalized Sasakian space forms, including r

Kanishkha Jaisankar, Sunidhi Tandel

Motion planning in complex scenarios is a core challenge in autonomous driving. Conventional methods apply predefined rules or learn from driving data to generate trajectories, while recent approaches leverage large language models (LLMs) for decision-making. However, it remains unclear whether LLMs truly capture human driving logic. We propose Align2Act, a

Rong Huang, Jungong Xue

Dealing with zero singular values can be quite challenging, as they have the potential to cause numerous numerical difficulties. This paper presents a method for computing the singular value decomposition (SVD) of a nonnegative bidiagonal product of arbitrary rank, regardless of whether the factors are of full rank or rank-deficient, square or rectangular. A

Heechan Yi, Kayoung Ban, Myeonghun Park, Kyoungchul Kong

Monte Carlo methods play a central role in particle physics, where they are indispensable for simulating scattering processes, modeling detector responses, and performing multi-dimensional integrals. However, traditional Monte Carlo methods often suffer from slow convergence and insufficient precision, particularly for functions with singular features such a

Sizhong Zhou, Qiuxiang Bian, Jiancheng Wu

Let $G$ be a graph. We denote by $e(G)$ and $\rho(G)$ the size and the spectral radius of $G$. A spanning subgraph $F$ of $G$ is called an even factor of $G$ if $d_F(v)\in\{2,4,6,\ldots\}$ for every $v\in V(G)$. Yan and Kano provided a sufficient condition using the number of odd components in $G-S$ for a graph $G$ of even order to contain an even factor, wh

Yutong Hu, Bingxin Zhou, Jing Wang, Weishu Zhao

Social networks often contain dense and overlapping connections that obscure their essential interaction patterns, making analysis and interpretation challenging. Identifying the structural backbone of such networks is crucial for understanding community organization, information flow, and functional relationships. This study introduces a multi-step network

Sizhong Zhou

Let $G$ be a graph. We denote by $c(G)$, $\alpha(G)$ and $q(G)$ the number of components, the independence number and the signless Laplacian spectral radius ($Q$-index for short) of $G$, respectively. The toughness of $G$ is defined by $t(G)=\min\left\{\frac{|S|}{c(G-S)}:S\subseteq V(G), c(G-S)\geq2\right\}$ for $G\neq K_n$ and $t(G)=+\infty$ for $G=K_n$. Ch

Yuteng Ye, Zheng Zhang, Qinchuan Zhang, Di Wang

Controllable 3D style transfer seeks to restyle a 3D asset so that its textures match a reference image while preserving the integrity and multi-view consistency. The prevalent methods either rely on direct reference style token injection or score-distillation from 2D diffusion models, which incurs heavy per-scene optimization and often entangles style with

Ryota Takamido, Chiharu Suzuki, Hiroki Nakamoto

A critical challenge in contemporary sports science lies in filling the gap between group-level insights derived from controlled hypothesis-driven experiments and the real-world need for personalized coaching tailored to individual athletes' unique movement patterns. This study developed a Personalized Motion Guidance Framework (PMGF) to enhance athletic per

Martina G. Vilas, Safoora Yousefi, Besmira Nushi, Eric Horvitz

Reasoning models improve their problem-solving ability through inference-time scaling, allocating more compute via longer token budgets. Identifying which reasoning traces are likely to succeed remains a key opportunity: reliably predicting productive paths can substantially reduce wasted computation and improve overall efficiency. We introduce Latent-Trajec

Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky, Mohamed Amine Ferrag

In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicio

Shanzhi Yin, Bolin Chen, Xinju Wu, Ru-Ling Liao

This paper proposes an efficient 3D avatar coding framework that leverages compact human priors and canonical-to-target transformation to enable high-quality 3D human avatar video compression at ultra-low bit rates. The framework begins by training a canonical Gaussian avatar using articulated splatting in a network-free manner, which serves as the foundatio

Haojie Guo, Emiliano Ventura-Macías, Mariano D. Jiménez-Sánchez, Nicoleta Nicoara

Grain boundaries (GBs) are ubiquitous in large-scale graphene samples, playing a crucial role in their overall performance. Due to their complexity, they are usually investigated as model structures, under the assumption of a fully relaxed interface. Here, we present cantilever-based non-contact atomic force microscopy (ncAFM) as a suitable technique to reso

Prawaal Sharma, Poonam Goyal, Vidisha Sharma, Navneet Goyal

UNESCO has classified 2500 out of 7000 languages spoken worldwide as endangered. Attrition of a language leads to loss of traditional wisdom, folk literature, and the essence of the community that uses it. It is therefore imperative to bring digital inclusion to these languages and avoid its extinction. Low resource languages are at a greater risk of extinct

Jiaye Li, Baoyou Chen, Hui Li, Zilong Dong

Transformers rely on explicit positional encoding to model structure in data. While Rotary Position Embedding (RoPE) excels in 1D domains, its application to image generation reveals significant limitations such as fine-grained spatial relation modeling, color cues, and object counting. This paper identifies key limitations of standard multi-dimensional RoPE

Jeaheang Bang, Changfeng Gui, Hao Liu, Yun Wang

We prove that the steady incompressible Navier-Stokes equations with any given $(-3)$-homogeneous, locally Lipschitz external force on $\mathbb{R}^n\setminus\{0\}$, $4\leq n\leq 16$, have at least one $(-1)$-homogeneous solution which is scale-invariant and regular away from the origin. The global uniqueness of the self-similar solution is obtained as long a