March 2026 arXiv papers — page 95

Haifang Cao, Yu Wang, Timing Li, Xinjie Yao

Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a single Riemannian manifold. While emerging mixed-curvature methods attempt to capture such diversity, they often rely on implicit, task-driven routing that lacks fundamental geometric grounding. To address this challenge, we propose a

Zicheng Lyu, Zengfeng Huang

Many Wasserstein analyses of diffusion samplers control reverse-time propagation by global stability summaries of the learned drift. These summaries can hide radial geometry: equal-height expansive regions of different width can yield different propagation costs. We give a profile-adapted propagation interface for scalar-isotropic reverse-SDE windows with ce

Tapa Manna, Supriyo Dutta, Baby Bhattacharya

Given a finite group $G$, the \emph{Prime Order Element (POE) Graph} $\Gamma(G)$ consists of the group elements as the vertices, and two vertices $x$ and $y$ are adjacent if and only if $o(xy)$ is prime. This paper presents a thorough structural and spectral analysis of the POE graphs associated with the finite Abelian groups of different types. The order of

Salim Al Mandhari, Hieu Pham Dinh, Mo El-Haj, Paul Rayson

This paper presents a novel prompt engineering framework for trait specific Automatic Essay Scoring (AES) in Arabic, leveraging large language models (LLMs) under zero-shot and few-shot configurations. Addressing the scarcity of scalable, linguistically informed AES tools for Arabic, we introduce a three-tier prompting strategy (standard, hybrid, and rubric-

Zhijian Gong, Tianren Yao, Wenjia Dong, Xueyuan Xu

Human visual reconstruction aims to reconstruct fine-grained visual stimuli based on subject-provided descriptions and corresponding neural signals. As a widely adopted modality, Electroencephalography (EEG) captures rich visual cognition information, encompassing complex spatial relationships and chromatic details within scenes. However, current approaches

Vidya S, Sunny Kumar Sharma, Prasanna Poojary, Vadiraja Bhatta G R

The undirected zero divisor graph of a commutative ring with unity \( R \), denoted by \( \Gamma(R) = (V(\Gamma(R)), E(\Gamma(R))) \). The vertex set \( V(\Gamma(R)) \) consists of all the non-zero zero-divisors of \( R \). The edge set \( E(\Gamma(R)) \) is defined by the set \( \{ e = a_1 a_2 \mid a_1 \cdot a_2 = 0 \text{ and } a_1, a_2 \in V(\Gamma(R)) \}

Kaleem Ullah Qasim, Jiashu Zhang, Muhammad Kafeel Shaheen, Razan Alharith

The key-value (KV) cache is widely treated as essential state in transformer inference, and a large body of work engineers policies to compress, evict, or approximate its entries. We prove that this state is entirely redundant: keys and values at every layer are deterministic projections of the residual stream, and recomputing them from a single residual vec

Chunhua Jin

In this paper, we concentrate on investigating the self-similar singular solutions of Keller-Segel model with signal consumption ($-uv^{\alpha}$) and singular sensitivity. We perform a detailed exploration into the existence and decay rate of self-similar solutions, particularly, the permissibility of arbitrary mass for these solutions across all possible ca

Masaya Maeda, Tetsu Mizumachi

In this short note, we prove the non-existence of slow and fast small nontrivial compact solutions for the Euler-Poisson system in $1$D. The proof is based on the virial estimate which provides local in space average decay of bounded small solutions.

Cristina G. Wilson, Marion Nachon, Shipeng Liu, John G. Ruck

The ability to efficiently and effectively explore planetary surfaces is currently limited by the capability of wheeled rovers to traverse challenging terrains, and by pre-programmed data acquisition plans with limited in-situ flexibility. In this paper, we present two novel approaches to address these limitations: (i) high-mobility legged robots that use di

Yichen Zeng, Hebaixu Wang, Meng Liu, Yu Zhou

Audio-visual navigation enables embodied agents to navigate toward sound-emitting targets by leveraging both auditory and visual cues. However, most existing approaches rely on precomputed room impulse responses (RIRs) for binaural audio rendering, restricting agents to discrete grid positions and leading to spatially discontinuous observations. To establish

Yuyang Zheng, Mingda Zhang, Jianglong Qin, Qi Mo

Recently Mamba-based methods have shown promise in abdominal organ segmentation. However, existing approaches neglect cross-channel anatomical semantic collaboration and lack explicit boundary-aware feature fusion mechanisms. To address these limitations, we propose CS-MUNet with two purpose-built modules. The Boundary-Aware State Mamba module employs a Baye

Xuebo Qiu, Mingqi Lv, Yimei Zhang, Tiantian Zhu

Advanced Persistent Threats (APTs) remain difficult to detect due to their stealthy nature and long-term persistence. To tackle this challenge, provenance-based threat hunting has gained traction as a proactive defense mechanism. This technique models audit logs as a whole-system provenance graph and searches for subgraphs that match APT patterns recorded in

Xinyu Liu, Hai Zhang

We study model-order selection and component-mean estimation for multidimensional Gaussian mixture models with a known common covariance matrix. Using empirical characteristic-function measurements, we construct Fourier covariance matrices whose population counterparts have rank equal to the number of mixture components. We establish a minimax lower bound sh

Bhuvaneswari A, Kamalika Bhattacharjee

An equidistribution is a theoretical quality criteria that measures the uniformity of a linear pseudo-random number generator (PRNG). In this work, we first show that all existing linear cellular automaton (CA) based pseudo-random number generators (PRNGs) are weak in the equidistribution characteristic. Then we propose a list of light-weight combined CA-bas

Henrik Krauss, Johann Licher, Naoya Takeishi, Annika Raatz

This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, that provide mechanistically interpretable 2D oscillator latents through an attention broadcast decoder (ABCD). Open-loop, single-shooting optimal control is performed in latent space to trac

Haichao Zhu, Qian Zhang

Gravity estimation is fundamental to visual-inertial perception, augmented reality, and robotics, yet gravity priors from IMUs are often unreliable under linear acceleration, vibration, and transient motion. Existing methods often estimate gravity directly from images or assume reasonably accurate inertial input, leaving the practical problem of correcting a

Federico Formica, Stefano Gregis, Andrea Rota, Aurora Francesca Zanenga

Recent Deep Neural Networks (DNN) applications ask for techniques that can explain their behavior. Existing solutions, such as Feature Guided Analysis (FGA), extract rules on their internal behaviors, e.g., by providing explanations related to neurons activation. Results from the literature show that these rules have considerable precision (i.e., they correc

Linshan Sun, Hao Zhang, Cameron Leary, Alex Amador

Programmable shaping of femtosecond ultraviolet (UV) pulses is still much less flexible than at visible and near-infrared wavelengths, mainly because direct UV modulators remain limited in bandwidth, throughput and damage threshold. Here we show that dispersive four-wave mixing (DFWM) in a gas-filled hollow-cappillary fibre (HCF) can transfer programmed spec

Boyuan Li, Carolyn C. Chang, Jake J. Kim, Jia Wang

Objectives: This study aims to characterize the dose-performance relationship for opportunistic CT and disentangle the contributions of segmentation failure and dose-dependent HU bias to performance degradation. Methods: Simulated low-dose CT images at 1-75% of full dose were generated from 50 paired full- and low-dose chest CT scans. An independent dataset

Guyu Jin

We know that there exist semi-groups for contact type Hamilton-Jacobi equations, which refers to \cite{KLJ2}. Guy Barles and Agn\`es Tourin give a proof of the commutation properties for normal Hamilton-Jacobi equations at \cite{GA}. In this article, we provide a proof of the commutation property of semi-groups for contact type Hamilton-Jacobi equations.

Renhong Huang, Ning Tang, Jiarong Xu, Yuxuan Cao

Social platforms serve as central hubs for information exchange, where user behaviors and platform interventions jointly shape opinions. However, intervention policies like recommendation and content filtering, can unintentionally amplify echo chambers and polarization, posing significant societal risks. Proactively evaluating the impact of such policies is

Siddharth Chandak, Anuj Yadav, Ayfer Ozgur, Nicholas Bambos

Stochastic approximation (SA) is a fundamental iterative framework with broad applications in reinforcement learning and optimization. Classical analyses typically rely on martingale difference or Markov noise with bounded second moments, but many practical settings, including finance and communications, frequently encounter heavy-tailed and long-range depen

Ningxin Liu, Zhichao Peng

In implicit time marching of the radiative transfer equation (RTE), the resulting linear systems are commonly solved using source iteration with diffusion synthetic acceleration (SI-DSA). Despite its widespread success, the performance of the DSA preconditioner may deteriorate when the RTE cannot be well approximated by its diffusion limit. Moreover, classic

Jekwin Dabhi, Prakash Dabhi

Let $G$ be a locally compact abelian group, and let $\omega:G \to [1,\infty)$ be a measurable weight, i.e., $\omega$ is measurable, and $\omega(s+t)\leq \omega(s)\omega(t)$ for all $s, t \in G$. Let $\mathcal{A}$ be a semisimple commutative Banach algebra with a predual $\mathcal A_\ast$ such that the Gel'fand space $\Phi_{\mathcal A}\subset \mathcal{A}_\ast

Xinyang Yu, Yin Huang, Karin Yamamura, Chenyi Wang

Converting mid-infrared (MIR) radiation to visible or near-infrared wavelengths is essential for imaging and sensing, yet achieving sensitive, low-power, and scalable detection remains challenging. Lanthanide nanocrystals provide an alternative through ratiometric luminescence but are typically constrained by Boltzmann statistics, which tie population distri

Weixuan Zeng, Pengcheng Wei, Huaiqing Wang, Boheng Zhang

Despite the rapid advancement of Virtual Try-On (VTON) and Try-Off (VTOFF) technologies, existing VTON methods face challenges with fine-grained detail preservation, generalization to complex scenes, complicated pipeline, and efficient inference. To tackle these problems, we propose OmniDiT, an omni Virtual Try-On framework based on the Diffusion Transformer

Zecheng Zhang, Han Zheng, Yue Xu

Evaluating production LLM responses and routing requests across providers in LLM gateways requires fine-grained quality signals and operationally grounded decisions. To address this gap, we present SEAR, a schema-based evaluation and routing system for multi-model, multi-provider LLM gateways. SEAR defines an extensible relational schema covering both LLM ev

Zhengdao Li, Penggao Yan, Li-Ta Hsu

This paper develops a logistic-aided Huber (LAH) M-estimator for robust GNSS positioning under long-tailed, multipath-affected measurement errors. The key idea is to leverage a logistic measurement error assumption and establish a one-to-one approximation between the logistic-based loglikelihood (i.e., quasi-log-cosh) and the Huber kernel by matching their s

Beibei Xu, Yutong Ye, Chuyun Shen, Yingbo Zhou

Although agentic workflows have demonstrated strong potential for solving complex tasks, existing automated generation methods remain inefficient and underperform, as they rely on predefined operator libraries and homogeneous LLM-only workflows in which all task-level computation is performed through probabilistic inference. To address these limitations, we

Chuang Zhong, Masaki Kashima, Yaping Mao, Yan Zhao

The induced Ramsey number $r_{\mathrm{ind}}(G,H)$ is defined as the minimum order of a graph $F$ on such that any 2-coloring of its edges with red and blue leads to either a red induced copy of $G$ or a blue induced copy of $H$. Motivated by the Kohayakawa-Pr\"omel-R\"odl conjecture, we prove that a quadratic upper bound $\mathrm{r}_{\text {ind}}\left(G, F_n

Caiyi Sun, Yujing Sun, Xiangyu Li, Yuhang Zheng

Deepface generation has traditionally followed a task-driven paradigm, where distinct tasks (e.g., face transfer and hair transfer) are addressed by task-specific models. Nevertheless, this single-task setting severely limits model generalization and scalability. A unified model capable of solving multiple deepface generation tasks in a single pass represent

Zhengpei Hu, Kai Li, Dapeng Fu, Chang Zeng

The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference latency and information utilization. Existing compression methods often suffer from high training costs or semantic fragmentation due to aggressive token pruning. In this paper, we propose BEAVER, a nov

Anjali Singh, Karan Taneja, Zhitong Guan, Soo Young Rieh

Generative AI (GenAI) search tools are increasingly used for information seeking, yet their design tends to encourage cognitive offloading, which may lead to passive engagement, selective attention, and informational homogenization. Effective use requires metacognitive engagement to craft good prompts, verify AI outputs, and critically engage with informatio

Hirohane Takagi, Atsushi Nitanda

This work introduces a new approximate proximal sampler that operates solely with zeroth-order information of the potential function. Prior theoretical analyses have revealed that proximal sampling corresponds to alternating forward and backward iterations of the heat flow. The backward step was originally implemented by rejection sampling, whereas we direct

Vrushabh Zinage, Narek Harutyunyan, Eric Verheyden, Fred Y. Hadaegh

Legged locomotion in unstructured environments demands not only high-performance control policies but also formal guarantees to ensure robustness under perturbations. Control methods often require carefully designed reference trajectories, which are challenging to construct in high-dimensional, contact-rich systems such as quadruped robots. In contrast, Rein

May Lynn Reese, Markela Zeneli, Mindy Ng, Jacob Haimes

General-purpose Large Language Models (LLMs) are becoming widely adopted by people for mental health support. Yet emerging evidence suggests there are significant risks associated with high-frequency use, particularly for individuals suffering from psychosis, as LLMs may reinforce delusions and hallucinations. Existing evaluations of LLMs in mental health co

Jiahao Pi, Xiangjia Liu, Junle Cao, Pengfei Wang

Quantum systems promise to revolutionize information processing science and technology [1-3]. The preservation of quantum coherence, the defining property of qubits, fundamentally constrains the performance of quantum information processing with quantum memories [4]. While trapped atomic ions theoretically support million-year coherence based on spontaneous

Evgenii Barts, Paolo Barone, Maxim Mostovoy

Spin-orbit coupling (SOC) gives rise to complex magnetic states such as spin liquids, skyrmion crystals, and topological spin-wave excitations. We consider exchange interactions in multi-orbital Mott insulators where SOC is strong on ligand ions. SOC on the ligands enables electron hopping accompanied by spin flips and fluctuations in the orbital state of th

Ali Siahkoohi, Davide Sabeddu

Learned priors based on deep generative models offer data-driven regularization for seismic inversion, but training them requires a dataset of representative subsurface models -- a resource that is inherently scarce in geoscience applications. Since the training objective of most generative models can be cast as maximum likelihood on a finite dataset, any su

Yiheng Wang, Changhong Fu, Liangliang Yao, Haobo Zuo

Robust feature encoding constitutes the foundation of UAV tracking by enabling the nuanced perception of target appearance and motion, thereby playing a pivotal role in ensuring reliable tracking. However, existing feature encoding methods often overlook critical illumination and viewpoint cues, which are essential for robust perception under challenging nig

Jing Xu, Weiqiang Wang, Cunjian Chen, Jun Liu

Group dance generation from music requires synchronizing multiple dancers while maintaining spatial coordination, making it highly relevant to applications such as film production, gaming, and animation. Recent group dance generation models have achieved promising generation quality, but they remain difficult to deploy in interactive scenarios due to bidirec

Mingfeng Qin, Jian-Ning Fu, Weikai Zong, Tianqi Cang

Asteroseismology of member pulsators provides a robust physical constraint on cluster parameters by linking internal stellar structures to the global properties of the host cluster. However, the parameters of NGC 1647 remains poorly constrained due to limited investigation, a situation that cluster asteroseismology can significantly refine. In this study, we

Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev, Ceren Budak

We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify which posts were shown to desktop social media users, randomly assigning 9,386 users to a control group or one of five alternative ranking algorithms which simultaneously altered

Jun Wang, Xiaoyan Huang

Relative pose estimation is fundamental for SLAM, visual localization, and 3D reconstruction. Existing Relative Pose Regression (RPR) methods face a key trade-off: feature-matching pipelines achieve high accuracy but block gradient flow via non-differentiable RANSAC, while ViT-based regressors are end-to-end trainable but prohibitively expensive for real-tim

Piyush Kaushik Bhattacharyya, Devansh Tomar, Shubham Mishra, Divyanshu Rai

Conventional machine learning pipelines often struggle to recognize categories absent from the original trainingset. This gap typically reduces accuracy, as fixed datasets rarely capture the full diversity of a domain. To address this, we propose a continual learning framework for text-guided food classification. Unlike approaches that require retraining fro

Chunlei Zhang, Jiahao Xia, Yun Xiao, Bo Jiang

Multimodal image registration is a fundamental task and a prerequisite for downstream cross-modal analysis. Despite recent progress in shared feature extraction and multi-scale architectures, two key limitations remain. First, some methods use disentanglement to learn shared features but mainly regularize the shared part, allowing modality-private cues to le

Jiancheng Wang, Jirong Mao

The Next-Generation Atmospheric Cherenkov Telescope Array (NG-ACTA) is proposed as a prospective infrastructure for very high energy (VHE) gamma-ray astronomy, consisting of a mixed-aperture array of 88 telescopes with a maximum array diameter of 10 km. The array adopts a three-tier configuration of 30 m large-aperture Large Size Telescopes (LSTs), 12 m medi

Yaqi Xie, Xinru Hao, Jiaxi Liu, Will Ma

Deep Reinforcement Learning (DRL) provides a general-purpose methodology for training inventory policies that can leverage big data and compute. However, off-the-shelf implementations of DRL have seen mixed success, often plagued by high sensitivity to the hyperparameters used during training. In this paper, we show that by imposing policy regularizations, g

Sena Watanabe, Yukitoshi Motome, Haruki Watanabe

The proton-disordered molecular phase of water ice (ice-VII) and its ultrahigh-pressure non-molecular phase (ice-X) share identical macroscopic crystal symmetry (space group $Pn\bar{3}m$). This raises a fundamental thermodynamic question: are they distinct phases separated by a singularity, or are they adiabatically connected via a continuous crossover? To r

Weifeng Xie, Libo Wang, Xiong Xu, Yunliang Yue

Stable and remarkable valley polarization effect is the key to utilizing valley degree of freedom in valleytronic devices. According to first-principles calculations and symmetry analysis, we reveal that valley polarization effect in monolayer V2Se2O altermagnet is correlated with the net magnetic moment between magnetic V atoms under uniaxial strain, thereb

Qiping Lai, Yi Shen, Chen Shen

In high-penetration renewable power systems with complex and highly variable operating scenarios, grid-connected inverters (GCIs) may transition between different control modes to adapt to diverse grid conditions. Among these, the switching between grid-following (GFL) and grid-forming (GFM) control modes is particularly critical. Nevertheless, safe and robu

Mohammadjavad Ebrahimi, Daniel Burbano, Farzad Yousefian

Federated learning (FL) has emerged as a communication-efficient algorithmic framework for distributed learning across multiple agents. While standard FL formulations capture unconstrained or globally constrained problems, many practical settings involve heterogeneous resource or model constraints, leading to optimization problems with agent-specific feasibl

Chuanrui Zhang, Yingshuang Zou, ZhengXian Wu, Yonggen Ling

Perceiving and reconstructing objects from images are critical for real-to-sim transfer tasks, which are widely used in the robotics community. Existing methods rely on multiple submodules such as detection, segmentation, shape reconstruction, and pose estimation to complete the pipeline. However, such modular pipelines suffer from inefficiency and cumulativ

Insung Lee, Taeyoung Jeong, Haejun Yoo, Du-Seong Chang

While Large Audio-Language Models (LALMs) have advanced audio captioning, robust evaluation remains difficult. Reference-based metrics are expensive and often fail to assess acoustic fidelity, while Contrastive Language-Audio Pretraining (CLAP)-based approaches frequently overlook syntactic errors and fine-grained details. We propose CAF-Score, a reference-f

Mengting Fan, Ning-An Lai, Hiroyuki Takamura

In our recent precious work, we established the finite time blow up result and upper bound of lifespan estimate to the singular Cauchy problem of semilinear Euler-Poisson-Darboux equation in R^n with subcritical power type nonlinearity. By introducing an improved test function, we obtain an enhanced lower bound for the functional including the spacetime inte

Jinglin Liang, Zijian Zhou, Rui Huang, Shuangping Huang

Novel View Synthesis (NVS) aims to generate unseen views of a 3D object given a limited number of known views. Existing methods often struggle to synthesize plausible views for unobserved regions, particularly under single-view input, and still face challenges in maintaining geometry- and appearance-consistency. To address these issues, we propose OrbitNVS,

Shin-Liang Chen, Nikolai Miklin

Self-testing is a phenomenon where the use of specific quantum states or measurements can be inferred solely from the correlations they generate. We introduce a universal method for conducting robustness analysis in the self-testing of various quantum resources. Unlike previous numerical approaches, which rely on selecting specific isometries, our method opt

Xuhan Tong, Yuchen Zeng, Jiawei Zhang

In-Context Learning (ICL) enables pretrained LLMs to adapt to downstream tasks by conditioning on a small set of input-output demonstrations, without any parameter updates. Although there have been many theoretical efforts to explain how ICL works, most either rely on strong architectural or data assumptions, or fail to capture the impact of key practical fa

Haoran Su, Hanxiao Deng, Yandong Sun

Emergency vehicle (EV) response time is a critical determinant of survival outcomes, yet deployed signal preemption strategies remain reactive and uncontrollable. We propose a return-conditioned framework for emergency corridor optimization based on the Decision Transformer (DT). By casting corridor optimization as offline, return-conditioned sequence modeli

Ratna Kandala, Niva Manchanda, Akshata Kishore Moharir

Online dating has become the dominant way romantic relationships begin, yet current platforms strip the nonverbal cues: gaze, facial expression, body posture, response timing, that humans rely on to signal comfort, disinterest, and consent, creating a communication gap with disproportionate safety consequences for women. We argue that this gap represents bot

Quan Kong, Yuhao Shen, Yicheng Ji, Huan Li

Although current Video-LLMs achieve impressive performance in video understanding tasks, their autoregressive decoding efficiency remains constrained by the massive number of video tokens. Visual token pruning can partially ease this bottleneck, yet existing approaches still suffer from information loss and yield only modest acceleration in decoding. In this

Aram Ansary Ogholbake, Hannah Choi, Spencer Brandenburg, Alyssa Antuna

We propose AttentionMixer, a unified deep learning framework for multimodal detection of brain edema that combines structural head CT (HCT) with routine clinical metadata. While HCT provides rich spatial information, clinical variables such as age, laboratory values, and scan timing capture complementary context that might be ignored or naively concatenated.

Cosimo Spera

We prove that capability safety admits an exact representation as propositional Datalog evaluation (Datalogprop: the monadic, ground, function-free fragment of first-order logic), enabling the transfer of algorithmic and structural results unavailable in the native formulation. This addresses two structural limitations of the capability hypergraph framework

Shuaibang Peng, Juelin Zhu, Xia Li, Kun Yang

We present LoD-Loc v3, a novel method for generalized aerial visual localization in dense urban environments. While prior work LoD-Loc v2 achieves localization through semantic building silhouette alignment with low-detail city models, it suffers from two key limitations: poor cross-scene generalization and frequent failure in dense building scenes. Our meth

Ming Hu, Yongsheng Huo, Mingyu Dou, Jianfu Yin

Fine-grained anomaly detection is crucial in industrial and medical applications, but labeled anomalies are often scarce, making zero-shot detection challenging. While vision-language models like CLIP offer promising solutions, they struggle with foreground-background feature entanglement and coarse textual semantics. We propose FB-CLIP, a framework that enh

Qin Zhang, Peiyu Jing, Hong-Xing Yu, Fangqiang Ding

Video generation models are increasingly used as world simulators for storytelling, simulation, and embodied AI. As these models advance, a key question arises: do generated videos obey the physical laws of the real world? Existing evaluations largely rely on automated metrics or coarse human judgments such as preferences or rubric-based checks. While useful

Peisong Niu, Haifan Zhang, Yang Zhao, Tian Zhou

Tropical cyclones (TCs) pose severe threats to life, infrastructure, and economies in tropical and subtropical regions, underscoring the critical need for accurate and timely forecasts of both track and intensity. Recent advances in AI-based weather forecasting have shown promise in improving TC track forecasts. However, these systems are typically trained o

Zhenyu Yang, Gensheng Pei, Tao Chen, Xia Yuan

Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependencies at a prohibitive computational cost. This paper introduces ChangeRWKV, a new architecture that reconciles this conflict. By building upon the Receptance Weighted Key Value (RWKV

Ontima Pankoon, Nimit Nimana, Yeol Je Cho

In this paper, we consider the nonsmooth convex optimization problems over the fixed point constraint sets of firmly nonexpansive operators. To find an optimal solution of the problem, we present an iterative method based on the hybrid steepest descent method and the idea of a delayed subgradient scheme in which allows the use of staled subgradients from the

Ali Saeedi, S Chockalingam, Mrityunjay Kothari

Cavitation refers to the sudden, unstable expansion of a defect or cavity within a material in response to applied loads, when the loads reach a critical threshold. It is widely recognized as a common failure nucleation mechanism in soft and biological materials. For an isolated cavity in the bulk of an incompressible neo-Hookean solid loaded by remote hydro

Haoyu Xi, Mingao Tan, Xinming Zhang, Siwei Cheng

Visual navigation for cross-embodiment robots is challenging due to variations in robot and camera configurations, which can lead to the failure of navigation tasks. Previous approaches typically rely on collecting massive datasets across different robots, which is highly data-intensive, or fine-tuning models, which is time-consuming. Furthermore, both metho

ZhiMing Li

Tracking non-stationary covariance matrices is fundamental to vision yet hindered by existing estimators that either neglect manifold constraints or rely on first-order updates, incurring inevitable phase lag during rapid evolution. We propose K-GMRF, an online, training-free framework for covariance tracking that reformulates the problem as forced rigid-bod

Pierre Chanial, Simon Biquard, Wassim Kabalan, Wuhyun Sohn

The Framework for Unified and Robust data Analysis with JAX (Furax) is an open-source Python framework for modeling data acquisition systems and solving inverse problems in astrophysics and cosmology. Built on JAX, Furax provides composable building blocks in the form of general-purpose and domain-specific linear operators, along with preconditioners and sol

Guangyin Jin, Xiaohan Ni, Yanjie Song, Kun Wei

With society entering the Internet era, the volume and speed of data and information have been increasing. Predicting the popularity of information cascades can help with high-value information delivery and public opinion monitoring on the internet platforms. The current state-of-the-art models for predicting information popularity utilize deep learning meth

Zhifei Yang, Guangyao Zhai, Keyang Lu, YuYang Yin

Scene generation has extensive industrial applications, demanding both high realism and precise control over geometry and appearance. Language-driven retrieval methods compose plausible scenes from a large object database, but overlook object-level control and often fail to enforce scene-level style coherence. Graph-based formulations offer higher controllab

Ruihu Li, Guanmin Guo, Yang Liu, Hao Song

We introduce a stabilizer formalism for EAQECCs with noise ebits, using special subgroups of product groups of two Pauli groups. This formalism includes the two coding schemes,given by Lai and Brun (C.Y. Lai and T. A. Brun, PHYSICAL REVIEW A 86, 032319 (2012)), for EAQECCs with imperfect ebits as special cases. Then two equivalent formalisms of the formalism

Jinming Xing, Muhammad Shahzad

The integration of Large Language Models (LLMs) and Graph Neural Networks (GNNs) promises to unify semantic understanding with structural reasoning, yet existing methods typically rely on static, unidirectional pipelines. These approaches suffer from fundamental limitations: (1) Bidirectional Error Propagation, where semantic hallucinations in LLMs or struct

Ernest Fokoué, Gregory Babbitt, Yuval Levental

Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of \textbf

Danish Gufran, Akhil Singampalli, Sudeep Pasricha

Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-preserving approach by training a centralized global model (GM) using distributed data from mobile devices without sharing raw data. However, real-world deployments require a continua

Shuo Wang, Ming-Zhong Ai, Jing-Wei Fan, Junchen Ye

Nanodiamonds containing nitrogen-vacancy (NV) centers are promising quantum sensors for biological applications thanks to their sub-micron spatial resolution, biocompatibility, and versatile multi-modal responses. However, the optically detected magnetic resonance (ODMR) measurement requires laser irradiation, creating a trade-off between high-throughput and

Jinyu Wu, Jiawen Zhang, Toni Shiroka, Shams Sohel Islam

The dense Kondo lattice Ce$_5$CoGe$_2$ exhibits superconductivity once the magnetic ordering is suppressed by pressure. Here the ambient pressure magnetic state is investigated via magnetization, heat capacity, powder neutron diffraction, and muon spin relaxation ($\mu$SR) measurements. Neutron diffraction results reveal a noncollinear ferromagnetic structur

Yongzhi Huang

Traditional liquid identification instruments are often unavailable to the general public. This paper shows the feasibility of identifying unknown liquids with commercial lightweight devices, such as a smartphone. The key insight is that different liquid molecules have different viscosity coefficients and therefore must overcome different energy barriers dur

Qiusheng Huang, Xiaohui Zhong, Anboyu Guo, Ziyi Peng

Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1{\deg} grid up to 365 days for sea-surface temperat

Lijie Zhou, Luran Wang

The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from high false alarm rates, while conventional machine learning me

Harshitha A, Madhumitha K, Sabitha D'Souza

Graph energy has been widely investigated in spectral graph theory. However, the manner in which this energy is distributed among individual vertices has received attention only more recently, following the introduction of energy of a vertex by O. Arizmendi et al. in 2018. Since then, several studies have explored this concept. In the present work, we focus

Lijie Zhou, Luran Wang

The rapid aging of global populations has created an urgent need for intelligent healthcare monitoring systems to ensure the safety of elderly individuals living independently. Existing cloud-centric platforms face critical limitations, including high latency unsuitable for emergency response, privacy risks from continuous transmission of sensitive data, and

Hassan Noghrei, Murad Abdullah

This paper presents a refined analysis of the block error rate (BLER) of polar codes over symmetric binary-input discrete memoryless channels under successive cancellation (SC) and successive cancellation list (SCL) decoding. A novel expression for the BLER under SC decoding is derived directly in terms of the decoder's LLRs. Building on this formulation, we

Taejun Kim, Vimal Mollyn, Riku Arakawa, Chris Harrison

We present a new and accurate approach for gaze estimation on consumer computing devices. We take advantage of continued strides in the quality of user-facing cameras found in e.g., smartphones, laptops, and desktops - 4K or greater in high-end devices - such that it is now possible to capture the 2D reflection of a device's screen in the user's eyes. This a

Luis Cid

Let k be an algebraically closed field of characteristic zero and let B be a finitely generated k-domain. We study semisimple derivations on B, with special emphasis on those whose eigenvalues are integers. For such derivations, after passing to the field of fractions and choosing a rational slice s with D(s) = s, we describe the kernel of D explicitly in te

Cunyi Nan

In this paper, building on previous work, we extend the thermodynamic formalism for random open dynamical systems generated by piecewise monotone interval maps with countably many branches. Under summable and contracting assumptions on the potential, we establish the Ruelle-Perron-Frobenius type theorem for the associated random open operator and prove expon

Xingyu Feng, Chang Sun, Yuzhu Wang, Zhangbing Zhou

Battery life remains a critical challenge for mobile devices, yet existing power management mechanisms rely on static rules or coarse-grained heuristics that ignore user activities and personal preferences. We present PowerLens, a system that tames the reasoning power of Large Language Models (LLMs) for safe and personalized mobile power management on Androi

Yiming Li, Yuhan Cheng, Mingchen Ma, Yihang Zou

Large language models (LLMs) and agentic systems have shown promise for automated software development, but applying them to hardware-in-the-loop (HIL) embedded and Internet-of-Things (IoT) systems remains challenging due to the tight coupling between software logic and physical hardware behavior. Code that compiles successfully may still fail when deployed

Jianqiang Wang, Shuaiqun Pan, Alvaro Serra-Gomez, Xiaohan Wei

The intelligent behavior of robots does not emerge solely from control systems, but from the tight coupling between body and brain, a principle known as embodied intelligence. Designing soft robots that leverage this interaction remains a significant challenge, particularly when morphology and control require simultaneous optimization. A significant obstacle

Najme Ebrahimi, Haoling Li, Gun Suer, Kin Chung Fong

The increasing demand for high-speed wireless connectivity and scalable quantum information processing has driven parallel advancements in millimeter-wave (MMW) communication transmitters and cryogenic qubit controllers. Despite serving different applications, both systems rely on the precise generation of radio frequency (RF) waveforms with stringent requir

Tianmeng Hu, Biao Luo

Multi-objective reinforcement learning (MORL) provides an effective solution for decision-making problems involving conflicting objectives. However, achieving high-quality approximations to the Pareto policy set remains challenging, especially in complex tasks with continuous or high-dimensional state-action space. In this paper, we propose the Pareto Ascent

Yuan Ma, Mike Ballou, Kyle Richard, Hessam Mahdavifar

Future integrated sensing and communication (ISAC) systems require simultaneous multibeam operation with low-latency hardware and robust isolation under synchronization error and fading. Conventional code-division multiplexing using Walsh-Hadamard codes is extremely time-sensitive. This paper demonstrates that conventional temporal-only coded multibeam array

Arnab Ganguly, Hye-Won Kang

Many biological processes exhibit oscillatory behavior. Among these, glycolytic oscillations have been extensively studied due to their well-characterized biochemical reaction networks. However, the complexity of these networks necessitates low-dimensional ordinary differential equation (ODE) models to identify core mechanisms and perform stability analysis.

Kaixin Cai, Pengzhen Ren, Jianhua Han, Yi Zhu

Open-world semantic segmentation presently relies significantly on extensive image-text pair datasets, which often suffer from a lack of fine-grained pixel annotations on sufficient categories. The acquisition of such data is rendered economically prohibitive due to the substantial investments of both human labor and time. In light of the formidable image ge

Soorya Ram Shimgekar, Vipin Gunda, Jiwon Kim, Violeta J. Rodriguez

Conversational AI systems are increasingly used for personal reflection and emotional disclosure, raising concerns about their effects on vulnerable users. Recent anecdotal reports suggest that prolonged interactions with AI may reinforce delusional thinking -- a phenomenon sometimes described as AI Psychosis. However, empirical evidence on this phenomenon r