November 2025 arXiv papers — page 172

Michikuni Eguchi, Takekazu Kitagishi, Yuichi Hiroi, Takefumi Hiraki

The tactile sensation of clothing is critical to wearer comfort. To reveal physical properties that make clothing comfortable, systematic collection of tactile data during sliding motion is required. We propose a robotic arm-based system for collecting tactile data from intact garments. The system performs stroking measurements with a simulated fingertip whi

Takekazu Kitagishi, Chun-Wei Ooi, Yuichi Hiroi, Jun Rekimoto

This paper investigates how visual texture presentation influences tactile perception when interacting with electrostatic cloth displays. We propose a visuo-haptic system that allows users to pinch and rub virtual fabrics while feeling realistic frictional sensations modulated by electrostatic actuation. Through a user study, we examined the cross-modal effe

Qi Wang, Hongzhi Zhang, Jia Fu, Kai Fu

Despite the proliferation of powerful agentic models, the lack of critical post-training details hinders the development of strong counterparts in the open-source community. In this study, we present a comprehensive and fully open-source pipeline for training a high-performance agentic model for interacting with external tools and environments, named Klear-Q

Yutaro Shoji, Masahide Yamaguchi

The seminal work of Coleman, Glaser, and Martin established that, at zero temperature, any non-trivial solution to the equations of motion with the least Euclidean action is $O(D)$-symmetric. This paper extends their foundational analysis to finite temperature. We rigorously prove that for a broad class of scalar potentials, any saddle-point configuration wi

Shota Garuchava

We study the Ising model at fixed magnetization on a triangular ladder with three-spin interactions. By recasting the ground-state determination as a linear programming (LP) problem, we solve it exactly using standard LP techniques. We construct the phase diagram for arbitrary fixed magnetization and identify three types of ground states: periodic, phase-sep

Huimin Hu, Ruihong Jiang, Yanqing Xu, Jiarui Ma

This paper analyzes the age of information (AoI) for a pinching antenna (PA)-assisted wireless powered communication network (WPCN) with probabilistic line-of-sight (LoS) blockage. AoI is a key metric for evaluating the freshness of status updates in IoT networks, and its optimization is crucial for ensuring the performance of time-critical applications. To

Ba-Thinh Nguyen, Thach-Ha Ngoc Pham, Hoang-Long Duc Nguyen, Thi-Duyen Ngo

Remote photoplethysmography (rPPG) is an emerging contactless physiological sensing technique that leverages subtle color variations in facial videos to estimate vital signs such as heart rate and respiratory rate. This non-invasive method has gained traction across diverse domains, including telemedicine, affective computing, driver fatigue detection, and h

Zixuan Li, Xueliang Zhang, Changjiang Zhao, Shuai Gao

The mean squared error (MSE) is a ubiquitous loss function for speech enhancement, but its problem is that the error cannot reflect the auditory perception quality. This is because MSE causes models to over-emphasize low-frequency components which has high energy, leading to the inadequate modeling of perceptually important high-frequency information. To ove

Shiyu Gao, Hao Jiang

The perception of high-definition maps is an integral component of environmental perception in autonomous driving systems. Existing research have often focused on online construction of high-definition maps. For instance, the Maptr[9] series employ a detection-based method to output vectorized map instances parallelly in an end-to-end manner. However, despit

Aravindh Shankar, Pramey Upadhyaya, Supriyo Datta

The many-body ground state of a two-dimensional electron system can be tuned by Coulomb engineering through control of the dielectric environment. However, in conventional dielectrics the static permittivity is restricted to positive values, limiting the accessible interaction regimes. Here we argue that the negative capacitance demonstrated in appropriately

Harikrishna VJ, Vittal Rao, Ramakrishnan K. R

We present an algorithm to solve the Simultaneous Unitary Similarity(S.U.S) problem which is to check if there exists a Similarity transformation determined by a Unitary $U$ s.t $UA_lU^*=B_l$, $l \in \{1,...,p\}$, where $A_l$ and $B_l$ are $nxn$ complex matrices. We observe that the problem is simplest when $U$ is diagonal, where we see that the `paths' in t

Vladimir Kozlov, Oleg Motygin

We consider steady water waves in a two-dimensional channel bounded below by a flat, rigid bottom and above by a free surface. Surface tension is neglected, and the flow is rotational with constant vorticity $a$. We analyze an analytic branch of Stokes waves bifurcating from a subcritical laminar flow, with the wave period serving as the bifurcation paramete

Kang Liu, Enrique Zuazua

Score-based diffusion models have emerged as a powerful class of generative methods, achieving state-of-the-art performance across diverse domains. Despite their empirical success, the mathematical foundations of those models remain only partially understood, particularly regarding the stability and consistency of the underlying stochastic and partial differ

A. Ruiz de Villa, Ll. Badiella

The analysis of randomized trials is often complicated by the occurrence of intercurrent events and missing values. Even though there are different strategies to address missing values it is still common to require missing values imputation. In the present article we explore the estimation of treatment effects in RCTs from a causal inference perspective unde

Jingyi Shi

Facial expression recognition, as a vital computer vision task, is garnering significant attention and undergoing extensive research. Although facial expression recognition algorithms demonstrate impressive performance on high-resolution images, their effectiveness tends to degrade when confronted with low-resolution images. We find it is because: 1) low-res

Wei-Ning Li, Ming Fang, Yuan-Hong Tao, Liu Sun

We investigate the relationships among the wave property, particle property and mixedness of quantum states based on the lp-norm coherence. By conforming that the lp-norm coherence is an appropriate measure of wave property and introducing a measure of particle property based on the differences between the maximal l2-norm coherence and the general l2-norm co

Abhijeet Kumar, Chetan Agarwal, Pronoy B. Neogi, Mayank Goswami

This research explores the fusion of graphology and artificial intelligence to quantify psychological stress levels in students by analyzing their handwritten examination scripts. By leveraging Optical Character Recognition and transformer based sentiment analysis models, we present a data driven approach that transcends traditional grading systems, offering

Soujanya Poria, Navonil Majumder, Chia-Yu Hung, Amir Ali Bagherzadeh

Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena, following the widespread success of their precursors -- LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing development of VLA models -- multimodality, reasoning, data, evaluation,

Lin Li, Chuhan Zhang, Dong Zhang, Chong Sun

Open-vocabulary scene graph generation (OVSGG) extends traditional SGG by recognizing novel objects and relationships beyond predefined categories, leveraging the knowledge from pre-trained large-scale models. Existing OVSGG methods always adopt a two-stage pipeline: 1) \textit{Infusing knowledge} into large-scale models via pre-training on large datasets; 2

Mohammad Rashed Albous, Bedour Alboloushi, Arnaud Lacheret

Comparative evidence on how Gulf Cooperation Council (GCC) states turn artificial intelligence (AI) ambitions into post--New Public Management (post-NPM) outcomes is scarce because most studies examine Western democracies. We analyze constitutional, collective-choice, and operational rules shaping AI uptake in two contrasting GCC members, the United Arab Emi

Hiroaki Hayashi, Bo Pang, Wenting Zhao, Ye Liu

Large language model (LLM) based agents are increasingly used to tackle software engineering tasks that require multi-step reasoning and code modification, demonstrating promising yet limited performance. However, most existing LLM agents typically operate within static execution frameworks, lacking a principled mechanism to learn and self-improve from their

PeiZhen Cheng, XingQian Chen, GuiLin Liao, Qi Zheng

In this manuscript, we recheck the spectroscopic properties of SDSS J134733.36+121724.27 (4C+12.50), confirming the presence of the double-peaked [O~{\sc iii}]$\lambda\lambda4959,5007$\AA\ doublet and a broad H$\alpha$. The former likely results from AGN-driven biconical outflows, while the absence of a broad H$\beta$ supports a classification of the source

Jiaxuan Li, Qing Xu, Xiangjian He, Ziyu Liu

Masked Autoencoders (MAE) achieve self-supervised learning of image representations by randomly removing a portion of visual tokens and reconstructing the original image as a pretext task, thereby significantly enhancing pretraining efficiency and yielding excellent adaptability across downstream tasks. However, MAE and other MAE-style paradigms that adopt r

Ruo-Ran Meng, Pei-Xi Liu, Xiao Liu, Tian-Xiang Zhu

Scalable implementation of quantum networks and photonic processors require integrated photonic memories with high efficiency, yet current integrated systems have been limited to storage efficiencies below 27.8%. Here, we demonstrate highly efficient integrated quantum memories based on rare-earth-iondoped crystals coupled with impedance-matched microcavitie

Mohammad Rashed Albous, Melodena Stephens, Odeh Rashed Al-Jayyousi

The rapid expansion of artificial intelligence (AI) in the Gulf Cooperation Council (GCC) raises a central question: are investments in compute infrastructure matched by an equally robust build-out of skills, incentives, and governance? Grounded in socio-technical systems (STS) theory, this mixed-methods study audits workforce preparedness across Kingdom of

Fatemeh Sohbati, Farzan Haddadi, Hamid Salahinejad

State-space models (SSMs) have recently emerged as efficient alternatives to computationally intensive architectures such as Transformers for sequence modeling. However, their training typically relies on static loss functions, which may be suboptimal at different stages of learning. In this work, we introduce a hybrid model that integrates the Hyena archite

M. Mondelin, S. Codis, J-C. Cuillandre, C. Laigle

The discovery of the large-scale structure has transformed our view of galaxy formation and evolution. Filaments of the cosmic web provide key environments that channel the growth of structures. Guided by predictions from cosmological simulations, we study the morphological distribution of galaxies in the Perseus-Pisces Supercluster, a prominent filamentary

Qiming Li, Zekai Ye, Xiaocheng Feng, Weihong Zhong

Despite the remarkable advancements of Large Vision-Language Models (LVLMs), the mechanistic interpretability remains underexplored. Existing analyses are insufficiently comprehensive and lack examination covering visual and textual tokens, model components, and the full range of layers. This limitation restricts actionable insights to improve the faithfulne

Fraser Sparks

We investigate the tensor-triangular geometry of the categories of isotropic Tate motives, isotropic Artin motives and isotropic Artin--Tate motives. In particular, we study the categories $DTM_{gm}(k/k;\mathbb{F}_2)$, $DAM_{gm}(k/k;\mathbb{F}_2)$ and $DATM_{gm}(k/k;\mathbb{F}_2)$ where $k$ is a flexible field and we fix $\mathbb{F}_2$-coefficients. In this

Ankan Mullick, Sukannya Purkayastha, Saransh Sharma, Pawan Goyal

Voice-controlled dialog systems have become immensely popular due to their ability to perform a wide range of actions in response to diverse user queries. These agents possess a predefined set of skills or intents to fulfill specific user tasks. But every system has its own limitations. There are instances where, even for known intents, if any model exhibits

Chirag Seth, Mehrdad Pirnia, James H Bookbinder

Fruits and vegetables form a vital component of the global economy; however, their distribution poses complex logistical challenges due to high perishability, supply fluctuations, strict quality and safety standards, and environmental sensitivity. In this paper, we propose an adaptive optimization model that accounts for delays, travel time, and associated t

Alina Fastowski, Bardh Prenkaj, Yuxiao Li, Gjergji Kasneci

LLMs are now an integral part of information retrieval. As such, their role as question answering chatbots raises significant concerns due to their shown vulnerability to adversarial man-in-the-middle (MitM) attacks. Here, we propose the first principled attack evaluation on LLM factual memory under prompt injection via Xmera, our novel, theory-grounded MitM

J. Bartel, H. Molique, B. Nerlo-Pomorska, M. Warda

Potential energy surfaces of nine even-even isotopes of Pt, Hg, and Pb around $^{186}$Pt are evaluated within a macroscopic-microscopic model based on the Lublin-Strasbourg-Drop macroscopic energy and the microscopic energy obtained using the Yukawa-folded mean-field potential to establish the Strutinski shell corrections and the pairing correlation energy t

Tuan Nguyen

We provide a characterization of maximal left-compressed families based on their generating sets $\mathcal{G}\subseteq 2^{[n]}$. We show that there is a one-to-one correspondence between maximal left-compressed families $\mathcal{A}\subseteq \binom{[n]}{k}$ and principal generating sets. Moreover, we give a complete description of maximal left-compressed int

Yunyan Lee, Ian R. Petersen, Daoyi Dong

Model predictive control has shown potential to enhance the robustness of quantum control systems. In this work, we propose a tractable Stochastic Model Predictive Control (SMPC) framework for finite-dimensional quantum systems under continuous-time measurement and quantum filtering. Using the almost-sure eigenstate reduction of quantum trajectories, we prov

Guihang Hong, Tao Ouyang, Kongyange Zhao, Zhi Zhou

Motivated by the imperative for real-time responsiveness and data privacy preservation, large language models (LLMs) are increasingly deployed on resource-constrained edge devices to enable localized inference. To improve output quality, retrieval-augmented generation (RAG) is an efficient technique that seamlessly integrates local data into LLMs. However, e

Kevin Wei, Lennart Heim

We introduce a conceptual framework and provide considerations for the institutional design of AI incident reporting systems, i.e., processes for collecting information about safety- and rights-related events caused by general-purpose AI. As general-purpose AI systems are increasingly adopted, they are causing more real-world harms and displaying the potenti

Sajjad Hussain, Conor Brennan

Deterministic electromagnetic (EM) simulators provide accurate radio propagation modeling but often require expert configuration and lack interactive flexibility. We present RadioSim Agent, an agentic framework that integrates large language models (LLMs) with physics-based EM solvers and vision-enabled reasoning to enable interactive and explainable radio m

Marcy Robertson, Chandan Singh

We characterise the profinite Grothendieck-Teichm\"uller group $\widehat{\mathsf{GT}}$ as the group of automorphisms of the profinite completion of a cyclic operad of parenthesised ribbon braids. This operad generates a symmetric monoidal category which is equivalent to the category of framed, oriented tangles, thereby providing an operadic model for profini

Mohsen Khodadi, Nils A. Nilsson, Gaetano Lambiase, Javad T. Firouzjaee

We investigate the potential for current and future gravitational-wave detectors to observe imprints of explicit diffeomorphism violation in primordial signals. Starting from a simple model with known effects, we derive the strain amplitude and power spectrum for primordial gravitational waves, both of which are affected by the symmetry breaking. Through thi

Ali H. Chamseddine

This article surveys the noncommutative-geometric (NCG) approach to fundamental physics, in which geometry is encoded spectrally by a generalized Dirac operator and where dynamics arise from the spectral action. I review historically how the simple idea of marrying a Riemannian manifold to a two point space, progressed to lead to the uniqueness of the Standa

Yaning Zhou, Nianjia Zhang, Ao Zhou, Zhao Zhang

Optical links and knots have attracted growing attention owing to their exotic topologic features and promising applications in next-generation information transfer and storage. However, current protocols for optical topology realization rely on paraxial propagation of spatial modes, which inherently limits their three-dimensional topological structures to l

Archit Agarwal, Meghali Garg, Bibekananda Maji

In 1937, Rademacher refined the circle method of Hardy and Ramanujan to derive an exact convergent series for the partition function $p(n)$. In 1942, Hua derived an exact formula for the distinct part partition function, and in 1971, Hagis generalized this result to the case of $\ell$-regular partitions. More recently, Iskander, Jain, and Talvola established

Hongbin Wu, Zi-Wu Wang, Jin Lan

A lens, a device transforming propagation directions in an organized fashion, is one of the fundamental tools for wave manipulation. Spin wave, the collective excitation of ordered magnetizations, stands out as a promising candidate for future energy-saving information technologies. Here we propose theoretically and verify by micromagnetic simulations, that

Pengwei Zhu

As an important source of small molecule drugs, natural products show remarkable biological activities with their rich types and unique structures. However, due to the limited number of samples and structural complexity, the rapid discovery of lead compounds is limited. Therefore, in this study, natural inhibitors of phosphodiesterase 4 (PDE4) and Phosphodie

Zhengyuan Liu, Stella Xin Yin, Bryan Chen Zhengyu Tan, Roy Ka-Wei Lee

User simulation is important for developing and evaluating human-centered AI, yet current student simulation in educational applications has significant limitations. Existing approaches focus on single learning experiences and do not account for students' gradual knowledge construction and evolving skill sets. Moreover, large language models are optimized to

Tianyu Jiang, Yan Yang, Hongjin Liu, Runyu Han

We address the problem of fast time-varying channel estimation in millimeter-wave (mmWave) MIMO systems with imperfect channel state information (CSI) and facilitate efficient channel reconstruction. Specifically, leveraging the low-rank and sparse characteristics of the mmWave channel matrix, a two-phase rank-aware compressed sensing framework is proposed f

Rui Yang, Matthew Yu Heng Wong, Huitao Li, Xin Li

The rapid growth of medical knowledge and increasing complexity of clinical practice pose challenges. In this context, large language models (LLMs) have demonstrated value; however, inherent limitations remain. Retrieval-augmented generation (RAG) technologies show potential to enhance their clinical applicability. This study reviewed RAG applications in med

Junchun Ding

Under the market background of increasingly personalized product demand and compressed response cycle, the traditional manufacturing model with standardized mass production as the core has been difficult to meet the dual expectations of customers for differentiation and fast delivery. In order to improve the efficiency of resource allocation and market respo

Zhaoyang Wang, Dong Wang

Quantization-aware training (QAT) has achieved remarkable success in low-bit ($\leq$4-bit) quantization for classification networks. However, when applied to more complex visual tasks such as object detection and image segmentation, performance still suffers significant degradation. A key cause of this limitation has been largely overlooked in the literature

Tamoghna Mukhopadhyay, Kamal Majee, Achintya Kumar Dutta

We have developed a reduced-cost non-Dyson third-order algebraic diagrammatic construction theory for the electron-attachment problem based on state-specific frozen natural orbitals. Density fitting and truncated natural auxiliary functions were employed to enhance computational efficiency. The use of state-specific frozen natural orbitals significantly decr

Shruthi Kannappan, Ashwina Kumar, Rupesh Nasre

Maxflow is a fundamental problem in graph theory and combinatorial optimisation, used to determine the maximum flow from a source node to a sink node in a flow network. It finds applications in diverse domains, including computer networks, transportation, and image segmentation. The core idea is to maximise the total flow across the network without violating

Fei Yu, Quan Deng, Shengeng Tang, Yuehua Li

Understanding 3D scenes in open-world settings poses fundamental challenges for vision and robotics, particularly due to the limitations of closed-vocabulary supervision and static annotations. To address this, we propose a unified framework for Open-World 3D Scene Graph Generation with Retrieval-Augmented Reasoning, which enables generalizable and interacti

Hongxia Li, Ying Ji, Yongxin Dong, Yuehua Feng

Low-rank sparse regression models have been widely adopted in face recognition due to their robustness against occlusion and illumination variations. However, existing methods often suffer from insufficient feature representation and limited modeling of structured corruption across samples. To address these issues, this paper proposes a Hybrid second-order g

Thien An Pham, Hannah Bosch, Giovanni Ceccio, Lukas Keller

Moving to larger cell formats in lithium-ion batteries increases overall useable energy but introduces inhomogeneities that influence aging. This study investigates degradation in 21700-type cells with NCM cathodes and graphite/SiOx anodes under cyclic aging, using in operando neutron diffraction, neutron depth profiling, and X-ray computed tomography. Prolo

Linwei Wu

The current surge in supply chain finance has significantly alleviated the "capital challenges" faced by domestic related enterprises, enabling enterprises upstream and subsequent stages of the industrial chain to achieve effective circulation of financing services in the supply chain based on the credit of core enterprises. By gathering essential informatio

Ziqing Ma, Chang Yang, Zhichang Guo, Yao Li

Synthetic Aperture Radar (SAR) images are inherently corrupted by speckle noise, limiting their utility in high-precision applications. While deep learning methods have shown promise in SAR despeckling, most methods employ a single unified network to process the entire image, failing to account for the distinct speckle statistics associated with different sp

Zeyuan Feng, Haimingyue Zhang, Somil Bansal

As robots become increasingly integrated into open-world, human-centered environments, their ability to interpret natural language instructions and adhere to safety constraints is critical for effective and trustworthy interaction. Existing approaches often focus on mapping language to reward functions instead of safety specifications or address only narrow

Yizheng Wang, Yuzhou Lin, Somdatta Goswami, Luyang Zhao

Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these advances, existing DEM approaches require dense collocation near cracks, face stability chal

Younghoon Kim, Sumanta Basu, Samprit Banerjee

Identifying the onset of emotional stress in older patients with mood disorders and chronic pain is crucial in mental health studies. To this end, studying the associations between passively sensed variables that measure human behaviors and self-reported stress levels collected from mobile devices is emerging. Existing algorithms rely on conventional change

Lei Shi, Yongju Kim, Xinzhi Zhong, Wissam Kontar

Ensuring fairness in the coordination of connected and automated vehicles at intersections is essential for equitable access, social acceptance, and long-term system efficiency, yet it remains underexplored in safety-critical, real-time traffic control. This paper proposes a fairness-aware hierarchical control framework that explicitly integrates inequity av

Qiyong Zhong, Jiajie Su, Ming Yang, Yunshan Ma

Sequential recommendations (SR) predict users' future interactions based on their historical behavior. The rise of Large Language Models (LLMs) has brought powerful generative and reasoning capabilities, significantly enhancing SR performance, while Multimodal LLMs (MLLMs) further extend this by introducing data like images and interactive relationships. How

Seyit Okan Kara

We investigate the discovery prospects of a leptophilic gauge boson Z_l at future $e^+e^-$ colliders, focusing on a comparative study of the Circular Electron-Positron Collider (CEPC) and the International Linear Collider (ILC). This state can arise from an additional U(1)'_l gauge symmetry under which quarks are neutral and all leptons have a universal char

Hehai Lin, Hui Liu, Shilei Cao, Jing Li

Numerous multimodal misinformation benchmarks exhibit bias toward specific modalities, allowing detectors to make predictions based solely on one modality. While previous research has quantified bias at the dataset level or manually identified spurious correlations between modalities and labels, these approaches lack meaningful insights at the sample level a

Xinlong Zhao, Tong Jia, Minghua He, Ying Li

Log-based anomaly detection is crucial for ensuring software system stability. However, the scarcity of labeled logs limits rapid deployment to new systems. Cross-system transfer has become an important research direction. State-of-the-art approaches perform well with a few labeled target logs, but limitations remain: small-model methods transfer general kno

Marina V. Yashina, Alexander G. Tatashev

We consider an inhomogeneous symmetric simple exclusion process on a one-dimensional lattice with open boundary conditions. The time scale is continuous. Particles of different types arrive to the utmost left and the utmost right site. If a particle is in a site that is neither the utmost left site nor the utmost right site, then the particle moves onto one

Yiru Zhang

With the rapid advancement of digitization and intelligence, enterprise big data processing platforms have become increasingly important in data management. However, traditional monolithic architectures, due to their high coupling, are unable to cope with increasingly complex demands in the face of business expansion and increased data volume, resulting in l

Xinlong Zhao, Tong Jia, Minghua He, Xixuan Yang

Log-based anomaly detection is critical for ensuring the stability and reliability of web systems. One of the key problems in this task is the lack of sufficient labeled logs, which limits the rapid deployment in new systems. Existing works usually leverage large-scale labeled logs from a mature web system and a small amount of labeled logs from a new system

Ali Elkhazraji, Mohamed-Slim Alouini, Aamir Farooq

High-Altitude Platform Stations (HAPS) are emerging as key enablers of future non-terrestrial networks (NTNs), supporting gigabit-class free-space optical (FSO) backhaul links while hosting laser-based sensing payloads. This tutorial and survey reviews recent advances in HAPS optical communication and integration with atmospheric remote sensing via shared op

Jian Zhu, Xin Zou, Jun Sun, Cheng Luo

In recent years, the advancement of Graph Neural Networks (GNNs) has significantly propelled progress in Multi-View Clustering (MVC). However, existing methods face the problem of coarse-grained graph fusion. Specifically, current approaches typically generate a separate graph structure for each view and then perform weighted fusion of graph structures at th

Haoran Xue, Gias Uddin, Song Wang

Thinking Large Language Models (LLMs) generate explicit intermediate reasoning traces before final answers, potentially improving transparency, interpretability, and solution accuracy for code generation. However, the quality of these reasoning chains remains underexplored. We present a comprehensive empirical study examining the reasoning process and qualit

Tong Chen, Xinyu Ma, Long Bai, Wenyang Wang

Endoscopic images often suffer from diverse and co-occurring degradations such as low lighting, smoke, and bleeding, which obscure critical clinical details. Existing restoration methods are typically task-specific and often require prior knowledge of the degradation type, limiting their robustness in real-world clinical use. We propose EndoIR, an all-in-one

Sayed Sadiqul Islam

For a Noetherian commutative ring $R$, let $H^i_I(R)$ be the $ i$-th local cohomology module of $R$ with respect to $I$. In \cite{Hel-08}, Hellus posed the question of identifying rings $R$ such that $\operatorname{injdim}_R H^i_I(R)=\operatorname{dim}_R(\operatorname{Supp}_R H^i_I(R))$. In this paper, we show that a regular affine domain over a field of cha

Yiqi Liu

Popular empirical strategies for policy evaluation in the panel data literature -- including difference-in-differences (DID), synthetic control (SC) methods, and their variants -- rely on key identifying assumptions that can be expressed through a specific choice of weights $\omega$ relating pre-treatment trends to the counterfactual outcome. While each choi

Lin Qi, Jiaxin Zhang

Fractals represent one of the fundamental manifestations of complexity, and fractal networks serve as tools for characterizing and investigating the fractal structures and properties of large-scale systems. Higher-order networks have emerged as a research hotspot due to their ability to express interactions among multiple nodes. This study proposes an iterat

Hongjun Wang, Jiyuan Chen, Xuan Song, Yinqiang Zheng

Post-training quantization offers an efficient pathway to deploy super-resolution models, yet existing methods treat weight and activation quantization independently, missing their critical interplay. Through controlled experiments on SwinIR, we uncover a striking asymmetry: weight quantization primarily degrades structural similarity, while activation quant

Jiayi Fu, Yuansen Zhang, Yinggui Wang

Large Language Models (LLMs) demonstrate strong capabilities in solving complex tasks when integrated with external tools. The Model Context Protocol (MCP) has become a standard interface for enabling such tool-based interactions. However, these interactions introduce substantial security concerns, particularly when the MCP server is compromised or untrustwo

Suresh Nehra, Aupendu Kar, Jayanta Mukhopadhyay, Prabir Kumar Biswas

A Light Field (LF) camera consists of an additional two-dimensional array of micro-lenses placed between the main lens and sensor, compared to a conventional camera. The sensor pixels under each micro-lens receive light from a sub-aperture of the main lens. This enables the image sensor to capture both spatial information and the angular resolution of a scen

Akira Yasuhara, Takumi Sannomiya, Ryoichi Horisaki

We demonstrate a multi-beam scanning transmission electron microscopy (STEM) imaging that integrates down-sampling with super-resolution image reconstruction via a compressive sensing framework. A custom condenser aperture with six randomly positioned circular holes is employed to produce a multi-beam STEM probe, with the beam shape and distribution tuned th

Yuning Chen, Sha Zhao, Shijian Li, Gang Pan

Emotion recognition from EEG signals is essential for affective computing and has been widely explored using deep learning. While recent deep learning approaches have achieved strong performance on single EEG emotion datasets, their generalization across datasets remains limited due to the heterogeneity in annotation schemes and data formats. Existing models

Xinlong Zhao, Tong Jia, Minghua He, Ying Li

Log-based anomaly detection is an important task in ensuring the stability and reliability of software systems. One of the key problems in this task is the lack of labeled logs. Existing works usually leverage large-scale labeled logs from mature systems to train an anomaly detection model of a target system based on the idea of transfer learning. However, t

Yue Cao, Zongxia Liang, Sheng Wang, Xiang Yu

This paper investigates a time-inconsistent portfolio selection problem in the incomplete mar ket model, integrating expected utility maximization with risk control. The objective functional balances the expected utility and variance on log returns, giving rise to time inconsistency and motivating the search of a time-consistent equilibrium strategy. We char

Chi-Jui Sung, Fan-Hao Lin, Tzu-Hao Huang, Chu-Hsiang Huang

Sixth-generation (6G) networks are envisioned to achieve full-band cognition by jointly utilizing spectrum resources from Frequency Range 1 (FR1) to Frequency Range 3 (FR3, 7-24 GHz). Realizing this vision faces two challenges. First, physicsbased ray tracing (RT), the standard tool for network planning and coverage modeling, becomes computationally prohibit

Dazhao Du, Tao Han, Song Guo

Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while these models implicitly compress historical information into their parameters during training, they are unable to explicitly and dynamicall

Chuanyu Li, Chaoyi Liu, Daotan Wang, Shuyu Zhang

Handheld devices have opened up unprecedented opportunities to collect large-scale, high-quality demonstrations efficiently. However, existing systems often lack robust tactile sensing or reliable pose tracking to handle complex interaction scenarios, especially for bimanual and contact-rich tasks. In this work, we propose ViTaMIn-B, a more capable and effic

Wei Liu, Muhammad Aamir Ali, Yanrong An

This paper presents a systematic study of the calculus of interval-valued functions and its application to interval differential equations. To this end, first, we introduce new interval arithmetic operations. Under new operations, the space of interval numbers becomes a strict linear space, and indeed a Hilbert space, whereas the traditional interval arithme

Rui Huang, Jiang-Tao Li, Wei Cui, Zhijie Qu

As part of the New-ANGELS program, we systematically investigate the X-ray luminosity functions (XLFs) of 4506 X-ray sources projected within a radius of 2.5 deg centering on M31. We construct XLFs for different regions in the disk and halo of M31, accounting for the incompleteness with an effective sensitivity map. Assuming that the halo regions contain (mo

Jiayu Zhou, Qiwei Wu, Jian Li, Zhe Chen

Autonomous execution of long-horizon, contact-rich manipulation tasks traditionally requires extensive real-world data and expert engineering, posing significant cost and scalability challenges. This paper proposes a novel framework integrating hierarchical semantic decomposition, reinforcement learning (RL), visual language models (VLMs), and knowledge dist

Zepeng Bao, Shen Zhou, Qiankun Pi, Jianhao Chen

Hallucination in large language models (LLMs) remains a critical barrier to their safe deployment. For hallucination detection to be practical in real-world scenarios, the use of efficient small models is essential to ensure low latency and minimal resource consumption. However, existing methods rely on fixed verification strategies, where simply tuning smal

Ratun Rahman, Sina Shaham, Dinh C. Nguyen

Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminati

Bingyang Guo, Qiang Zuo, Ruiyun Yu

The effective segmentation of 3D data is crucial for a wide range of industrial applications, especially for detecting subtle defects in the field of integrated circuits (IC). Ceramic package substrates (CPS), as an important electronic material, are essential in IC packaging owing to their superior physical and chemical properties. However, the complex stru

Zhaofan Zhang, Minghao Yang, Sihong Xie, Hui Xiong

The robustness of Unmanned Surface Vehicles (USV) is crucial when facing unknown and complex marine environments, especially when heteroscedastic observational noise poses significant challenges to sensor-based navigation tasks. Recently, Distributional Reinforcement Learning (DistRL) has shown promising results in some challenging autonomous navigation task

Wen-Zhe Xi, Kai-Xing Lu, Jin-Ming Bai, Zhang Yue

We present the results of the 2023 spectroscopic reverberation mapping (RM) campaign for active galactic nuclei (AGN) of NGC 5548, continuing our long-term monitoring program. Using the Lijiang 2.4-meter telescope, we obtained 74 spectra with a median cadence of 1.9 days. Through detailed spectral decomposition, we measured the light curves of the optical co

Max McKinnon

The ability of large language models (LLMs) to recall and retrieve information from long contexts is critical for many real-world applications. Prior work (Liu et al., 2023) reported that LLMs suffer significant drops in retrieval accuracy for facts placed in the middle of large contexts, an effect known as "Lost in the Middle" (LITM). We find the model Gemi

Nan Jiang, Ziyi Wang, Yexiang Xue

Symbolic regression seeks to uncover physical laws from experimental data by searching for closed-form expressions, which is an important task in AI-driven scientific discovery. Yet the exponential growth of the search space of expression renders the task computationally challenging. A promising yet underexplored direction for reducing the search space and a

Sh. Ebrahimi, S. Salimi, F. T. Tabesh, A. S. Khorashad

Although implementing shortcuts to adiabaticity (STA) in open quantum systems remains challenging due to the complex control schemes required for such systems, their powerful ability to rapidly steer the system toward target states and their widespread applicability in quantum technologies have motivated us to explore their potential in quantum energy storag

Adam Rennie, Ben Whale

We prove a limit curve theorem for incomplete metric spaces. Our main application is to Sormani and Vegas' null distance, where our results give strong control on the Lorentzian lengths of limit curves. We also show that regular cosmological time functions and the surface function of a Cauchy surface in a globally hyperbolic manifold define such a null dista

Debottam Daw, Houcine Bouzid, Sung-Gyu Lee, Wujoon Cha

Quantum correlation and hot-carrier transport represent two fundamentally distinct regimes of electronic conduction, rarely accessible within the same device. Here, we report a state-of-the-art monolayer transition metal dichalcogenides transistor architecture on a ferroelectric substrate that enables this crossover by leveraging the strong dielectric screen

Zihao Wang, Tianhao Mao, XiaoFeng Wang, Di Tang

Recommender systems play a central role in digital platforms by providing personalized content. They often use methods such as collaborative filtering and machine learning to accurately predict user preferences. Although these systems offer substantial benefits, they are vulnerable to security and privacy threats, especially data poisoning attacks. By insert

Seyed Alireza Javid, Amirhossein Bagheri, Nuria González-Prelcic

Classifier-guided diffusion models have emerged as a powerful approach for conditional image generation, but they suffer from overconfident predictions during early denoising steps, causing the guidance gradient to vanish. This paper introduces two complementary contributions to address this issue. First, we propose a differentiable calibration objective bas