November 2025 arXiv papers — page 144

Li-Chao Peng, Dian Wu, Xue-Mei Gu, Jian Qin

Photon loss is the biggest enemy in quantum communications. Direct transmission typically results in a small fraction of photons surviving over long distances, hindering advanced applications such as loophole-free Bell tests and device-independent quantum key distribution. Quantum teleportation offers a "disembodied" way of transferring particles through a v

Silin Zhou, Yao Chen, Shuo Shang, Lisi Chen

Trajectory representation learning (TRL) maps trajectories to vector embeddings and facilitates tasks such as trajectory classification and similarity search. State-of-the-art (SOTA) TRL methods transform raw GPS trajectories to grid or road trajectories to capture high-level travel semantics, i.e., regions and roads. However, they lose fine-grained spatial-

Zijian Wang, Leyun Gao, Zhuo Chen, Cheng-en Liu

Sub-GeV light dark matter often requires new light mediators, such as a dark $Z$ boson in the $L_\mu - L_\tau$ gauge theory. We study the search potential for such a $Z^\prime$ boson via the process $\mu e^- \to \mu e^- X$, with $X$ decaying invisibly, in a muon on-target experiment using a high-intensity 1-10 GeV muon beam from facilities such as HIAF-HIRIB

Jay Tharwani, Shobhit Aggarwal, Arnab A Purkayastha

This paper evaluates HPC-style CPU performance and cost in virtualized cloud infrastructures using a subset of OpenMP workloads in the SPEC ACCEL suite. Four major cloud providers by market share AWS, Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI) are compared across Intel, AMD, and ARM general purpose instance types under both on-

Xiaohan Zhang, Tian Gao, Mingyue Cheng, Bokai Pan

Time series forecasting plays a crucial role in decision-making across many real-world applications. Despite substantial progress, most existing methods still treat forecasting as a static, single-pass regression problem. In contrast, human experts form predictions through iterative reasoning that integrates temporal features, domain knowledge, case-based re

Tuhin Subhra De

Variational Autoencoders and Generative Adversarial Networks remained the state-of-the-art (SOTA) generative models until 2022. Now they are superseded by diffusion-based models. Efforts to improve traditional models have stagnated as a result. In old-school fashion, we explore image generation with conditional Variational Autoencoders (CVAE) to incorporate

Haowei Zhang, Yuanpei Zhao, Ji-Zhe Zhou, Mao Li

Improving the diversity of generated results while maintaining high visual quality remains a significant challenge in image generation tasks. Fractal Generative Models (FGMs) are efficient in generating high-quality images, but their inherent self-similarity limits the diversity of output images. To address this issue, we propose a novel approach based on th

Kazuki Iwahana, Yusuke Yamasaki, Akira Ito, Takayuki Miura

Backdoor attacks pose a critical threat to machine learning models, causing them to behave normally on clean data but misclassify poisoned data into a poisoned class. Existing defenses often attempt to identify and remove backdoor neurons based on Trigger-Activated Changes (TAC) which is the activation differences between clean and poisoned data. These metho

Zhehao Xu, Lok Ming Lui

Free-boundary diffeomorphism optimization, an important and widely occurring task in geometric modeling, computer graphics, and biological imaging, requires simultaneously determining a planar target domain and a locally bijective map with well-controlled distortion. We formulate this task through the least-squares quasiconformal (LSQC) operator and establis

Wenlai Mu, Nisar Muhammad, Huaihong Guo, Zsolt Gulacsi

Supramolecular device (SMD) with topological end states and a noncovalent junction is rarely investigated but deemed promising for thermoelectric (TE) applications. We designed a new kind of SMD based on the Su-Schrieffer-Heeger (SSH) chains, and calculated TE properties of it using the non-equilibrium Green's function (NEGF) method. By scaling TE performanc

Mobin Habibpour, Fatemeh Afghah

While Vision-Language Models (VLMs) are set to transform robotic navigation, existing methods often underutilize their reasoning capabilities. To unlock the full potential of VLMs in robotics, we shift their role from passive observers to active strategists in the navigation process. Our framework outsources high-level planning to a VLM, which leverages its

Chenhao Wang, Shanshan Feng, Lisi Chen, Fan Li

Next point-of-interest (POI) recommendation improves personalized location-based services by predicting users' next destinations based on their historical check-ins. However, most existing methods rely on static datasets and fixed models, limiting their ability to adapt to changes in user behavior over time. To address this limitation, we explore a novel tas

Pedro Chumpitaz-Flores, My Duong, Ying Mao, Kaixun Hua

Hyperparameter optimization (HPO) for neural networks on tabular data is critical to a wide range of applications, yet it remains challenging due to large, non-convex search spaces and the cost of exhaustive tuning. We introduce the Quantum-Inspired Bilevel Optimizer for Neural Networks (QIBONN), a bilevel framework that encodes feature selection, architectu

Yingtong Dou, Zhimeng Jiang, Tianyi Zhang, Mingzhi Hu

We present TransactionGPT (TGPT), a foundation model for consumer transaction data within one of the world's largest payment networks. TGPT is designed to understand and generate transaction trajectories while simultaneously supporting a variety of downstream prediction and classification tasks. We introduce a novel 3D-Transformer architecture specifically t

Yuxi Liu, Dengchao Jin, Shuai Huo, Jiawen Gu

Neural video compression (NVC) has made significant progress in recent years, while neural B-frame video compression (NBVC) remains underexplored compared to P-frame compression. NBVC can adopt bi-directional reference frames for better compression performance. However, NBVC's hierarchical coding may complicate continuous temporal prediction, especially at s

Juntu Zhao, Jialing Zhang, Chongxuan Li, Dequan Wang

Recent closed-source multimodal systems have made great advances, but their hidden language for understanding the world remains opaque because of their black-box architectures. In this paper, we use the systems' preference bias to study their hidden language: During the process of compressing the input images (typically containing multiple concepts) into tex

Yipeng Zou, Qin Liu, Jie Wu, Yu Peng

Ensemble attacks integrate the outputs of surrogate models with diverse architectures, which can be combined with various gradient-based attacks to improve adversarial transferability. However, previous work shows unsatisfactory attack performance when transferring across heterogeneous model architectures. The main reason is that the gradient update directio

Liuzixuan Lin, Andrew A. Chien

The exploding power consumption of AI and cloud datacenters (DCs) intensifies the long-standing concerns about their carbon footprint, especially because DCs' need for constant power clashes with volatile renewable generation needed for grid decarbonization. DC flexibility (a.k.a. load adaptation) is a key to reducing DC carbon emissions by improving grid re

Ningnan Wang, Weihuang Chen, Liming Chen, Haoxuan Ji

Embodied visual navigation remains a challenging task, as agents must explore unknown environments with limited knowledge. Existing zero-shot studies have shown that incorporating memory mechanisms to support goal-directed behavior can improve long-horizon planning performance. However, they overlook visual frontier boundaries, which fundamentally dictate fu

Di Liao, Ruijia Liang, Ziyi Ye

With the deepening of digital transformation, business process optimisation has become the key to improve the competitiveness of enterprises. This study constructs a business process optimisation model integrating artificial intelligence and big data to achieve intelligent management of the whole life cycle of processes. The model adopts a three-layer archit

Ha-Thanh Nguyen, Ken Satoh, Francesca Toni, Randy Goebel

Reasoning is an essential component of human intelligence in that it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language processing,

Minjin Kim, Suyeon Son, Luis C. Ho

We examine the continuum variability of active galactic nuclei (AGNs) by analyzing the multi-epoch spectroscopic data from the Sloan Digital Sky Survey. To achieve this, we utilized approximately 2 million spectroscopy pairwise combinations observed across different epochs for $\sim90,000$ AGNs. We estimate the ensemble variability structure function (SF) fo

Danqing Wang, Yufeng Wu, Naomi Pieczulewski, Prachi Garg

The development of large-scale quantum processors benefits from superconducting qubits that can operate at elevated temperatures and be fabricated with scalable, foundry-compatible processes. Atomic layer deposition (ALD) is increasingly being adopted as an industrial standard for thin-film growth, particularly in applications requiring precise control over

Hanbo Cheng, Peng Wang, Kaixiang Lei, Qi Li

The inference latency of diffusion models remains a critical barrier to their real-time application. While trajectory-based and distribution-based step distillation methods offer solutions, they present a fundamental trade-off. Trajectory-based methods preserve global structure but act as a "lossy compressor", sacrificing high-frequency details. Conversely,

Q. Qian, D. Seipt, M. Vranic, T. Grismayer

Modern ultra-intense laser facilities can generate electromagnetic fields strong enough to accelerate particles to near-light speeds over micron-scale distances and also approach the QED critical field, resulting in highly nonlinear and relativistic quantum phenomena. For such conditions, ab-initio modeling techniques are required that capture the electromag

Phattara Khumprom, Wanatchapong Kongkaew, Antoun Yaacoub, Nattakit Thanawitsatien

In today competitive business environment, efficient logistics are essential, especially in industries where timely delivery matters. This research aims to improve warehouse picking cycle time through simulation-based analysis, using a leading plumbing equipment distributor in Thailand as a case study. The study identifies inefficiencies such as disorganized

Graham L. Bishop

Current bioacoustic AI systems achieve impressive cross-species performance by processing animal communication through transformer architectures, foundation model paradigms, and other computational approaches. However, these approaches overlook a fundamental question: what happens when one form of recursive cognition--AI systems with their attention mechanis

Zhuhui Li, Chunbo Luo, Liming Huang, Luyu Qi

Multi-agent multi-objective systems (MAMOS) have emerged as powerful frameworks for modelling complex decision-making problems across various real-world domains, such as robotic exploration, autonomous traffic management, and sensor network optimisation. MAMOS offers enhanced scalability and robustness through decentralised control and more accurately reflec

Marius Lemm, Simone Rademacher, Jingxuan Zhang

We consider the quantum dynamics of interacting bosons in the mean-field regime when they are subjected to a disordered potential, which is either random or quasi-periodic. Starting from a spatially localized Bose-Einstein condensate, we prove that fluctuations around the condensate propagate with a small velocity due to the disorder. This provides an exampl

Jingyu Liu, Xin Dong, Zhifan Ye, Rishabh Mehta

Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to e

Yunchang Ma, Tenglong Liu, Yixing Lan, Xin Yin

In offline reinforcement learning, value overestimation caused by out-of-distribution (OOD) actions significantly limits policy performance. Recently, diffusion models have been leveraged for their strong distribution-matching capabilities, enforcing conservatism through behavior policy constraints. However, existing methods often apply indiscriminate regula

Shuting Jin, Yi Jiang, Yimin Liu, Tengfei Ma

Background: Identifying new indications for approved drugs is a complex and time-consuming process that requires extensive knowledge of pharmacology, clinical data, and advanced computational methods. Recently, deep learning (DL) methods have shown their capability for the accurate prediction of drug repositioning. However, implementing DL-based modeling req

Joshua Paik

This paper introduces Dedieu-Shub measures and surveys their appearance in the literature.

Arsenii Onuchin, Konstantin Sorokin, Maxim Beketov, Liubov Tupikina

Community detection in complex networks is a fundamental problem, open to new approaches in various scientific settings. We introduce a novel community detection method, based on Ricci flow on graphs. Our technique iteratively updates edge weights (their metric lengths) according to their (combinatorial) Foster version of Ricci curvature computed from effect

Kai Hu, Han Wang, Renhe Liu, Zhilin Li

Region of Interest (ROI)-based image compression has rapidly developed due to its ability to maintain high fidelity in important regions while reducing data redundancy. However, existing compression methods primarily apply masks to suppress background information before quantization. This explicit bit allocation strategy, which uses hard gating, significantl

Kapil Garg, Xinru Tang, Jimin Heo, Dwayne R. Morgan

Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues--such as blur, misframing, and rotation--affect the accura

Yaxin Zhao, Yu Zhang

Large language models (LLMs) have achieved impressive performance across a wide range of natural language processing tasks, yet they often produce hallucinated content that undermines factual reliability. To address this challenge, we introduce HalluClean, a lightweight and task-agnostic framework for detecting and correcting hallucinations in LLM-generated

Zifu Zhang, Shengxi Li, Xiancheng Sun, Mai Xu

Human-machine collaborative compression has been receiving increasing research efforts for reducing image/video data, serving as the basis for both human perception and machine intelligence. Existing collaborative methods are dominantly built upon the de facto human-vision compression pipeline, witnessing deficiency on complexity and bit-rates when aggregati

Tianyu Guo, Shanwei Zhao, Shiai Zhu, Chenguang Ma

Deploying Vision-Language Models (VLMs) on edge devices (e.g., smartphones and robots) is crucial for enabling low-latency and privacy-preserving intelligent applications. Given the resource constraints of these devices, quantization offers a promising solution by improving memory efficiency and reducing bandwidth requirements, thereby facilitating the deplo

Yichen Lu, Yingshan Guo, Yiyi Zhang, Tong Zhu

We introduce a frustration parameter $\alpha$ into the Vicsek-Kuramoto systems of self-propelled particles. While the system exhibits conventional synchronized states, such as global phase synchronization and swarming, for low frustration ($\alpha < \pi/2$), beyond the critical point $\alpha = \pi/2$, a Hopf-Turing bifurcation drives a transition to a restin

Jinyu Zhang, Lijun Han, Feng Jian, Lingxi Zhang

In mobile robot shared control, effectively understanding human motion intention is critical for seamless human-robot collaboration. This paper presents a novel shared control framework featuring planning-level intention prediction. A path replanning algorithm is designed to adjust the robot's desired trajectory according to inferred human intentions. To rep

Zhongchen Xu, Yi Yan, Zhihao Liu, Jie Pang

Materials featuring hypervalent bismuth motifs have generated immense interest due to their extraordinary electronic structure and exotic quantum transport. In this study, we synthesized high-quality single crystals of La3ScBi5 characterized by one-dimensional hypervalent bismuth chains and performed a systematic investigation of the magnetoresistive behavio

Jiuqi Yan, Chendong Xu, Dongyu Liu

Human activity recognition (HAR) with millimeter-wave (mmWave) radar offers a privacy-preserving and robust alternative to camera- and wearable-based approaches. In this work, we propose the Occupancy-Gated Parallel-CNN Bi-LSTM (OG-PCL) network to process sparse 3D radar point clouds produced by mmWave sensing. Designed for lightweight deployment, the parame

Zimao Lu, Hui Xu, Bing Liu, Ke Wang

Text-only training provides an attractive approach to address data scarcity challenges in zero-shot image captioning (ZIC), avoiding the expense of collecting paired image-text annotations. However, although these approaches perform well within training domains, they suffer from poor cross-domain generalization, often producing hallucinated content when enco

Shuji Ono

While convolutional neural network (CNN)-based object detection is widely used, it exhibits a shape dependency that degrades performance for postures not included in the training data. Building upon our previous simulation study published in this journal, this study implements and evaluates the spectral-based approach on physical hardware to address this lim

Patrick Mayeda

We prove a structural result concerning the exit path category associated to a manifold $M$ equipped with a smooth action of a finite group $G$. Specifically, the functor $\Pi: \mathsf{Exit}(M) \rightarrow \mathsf{Exit}(M/G)$ is a right fibration and $\mathsf{Enter}(M/G)$ is classified by a natural functor $\mathsf{Enter}(M/G) \rightarrow O_G$, where $O_G$ i

Hanyu Wu, Bo Yang

We study function theory and K\"ahler geometry on total spaces of vector bundles on an elliptic curve. For rank two vector bundles of degree zero, we show that any two total spaces are biholomorphic if and only if the corresponding vector bundles are isomorphic. We also construct complete Gauduchon Hermitian metrics with flat Chern-Ricci curvature on these t

Zixun Xiong, Gaoyi Wu, Qingyang Yu, Mingyu Derek Ma

Given the high cost of large language model (LLM) training from scratch, safeguarding LLM intellectual property (IP) has become increasingly crucial. As the standard paradigm for IP ownership verification, LLM fingerprinting thus plays a vital role in addressing this challenge. Existing LLM fingerprinting methods verify ownership by extracting or injecting m

Yating Liu, Yan Lu

Unsupervised remote sensing change detection aims to monitor and analyze changes from multi-temporal remote sensing images in the same geometric region at different times, without the need for labeled training data. Previous unsupervised methods attempt to achieve style transfer across multi-temporal remote sensing images through reconstruction by a generato

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Document layout understanding remains data-intensive despite advances in semi-supervised learning. We present a framework that enhances semi-supervised detection by fusing visual predictions with structural priors from text-pretrained LLMs via principled probabilistic weighting. Given unlabeled documents, an OCR-LLM pipeline infers hierarchical regions which

Yingjie Sun, Guyue Li, Hongfu Chou, Aiqun Hu

Ultra-low latency, the hallmark of fifth-generation mobile communications (5G), imposes exacting timing demands on identification as well. Current cryptographic solutions introduce additional computational overhead, which results in heightened identification delays. Radio frequency fingerprint (RFF) identifies devices at the physical layer, blocking imperson

Riling Wei, Kelu Yao, Chuanguang Yang, Jin Wang

Cross-modal Knowledge Distillation has demonstrated promising performance on paired modalities with strong semantic connections, referred to as Symmetric Cross-modal Knowledge Distillation (SCKD). However, implementing SCKD becomes exceedingly constrained in real-world scenarios due to the limited availability of paired modalities. To this end, we investigat

Yuyi Yao, Gongliu Yang, Runzhuo Xu, Yongqiang Tu

The high-temperature glassblowing-fabricated micro hemispherical resonator (MHR) exhibits high symmetry and high Q-value for precision inertial navigation. However, MHR design entails a comprehensive evaluation of multiple possible configurations and demands extremely time-consuming simulation of key parameters combination. To address this problem, this pape

Howard Ho, Ebenezer Essel, Pierre Sullivan

Three-dimensional unsteady numerical simulations are performed to investigate the effects of blowing ratio $C_B$ ($0.85 < \overline{U}_j/U_\infty < 1.7$), stroke ratio $L^+$ ($10.6 < \overline{U}_j /(fd) < 21.3$), and boundary-layer height ratio $D^+$ ($2.1<\delta/d<8.0$) on circular synthetic jet actuator (SJA) performance in crossflow. Nine cases are exami

Mehdi Fatan Serj, C. Alejandro Parraga, Xavier Otazu

Object recognition plays a fundamental role in how biological organisms perceive and interact with their environment. While the human visual system performs this task with remarkable efficiency, reproducing similar capabilities in artificial systems remains challenging. This study investigates VisNet, a biologically inspired neural network model, and several

Sanyukta Adap, Ujjwal Baid, Spyridon Bakas

Glioblastoma (GBM) is the most common aggressive, fast-growing brain tumor, with a grim prognosis. Despite clinical diagnostic advancements, there have not been any substantial improvements to patient prognosis. Histopathological assessment of excised tumors is the first line of clinical diagnostic routine. We hypothesize that automated, robust, and accurate

Ashish Kattamuri, Arpita Vats, Harshwardhan Fartale, Rahul Raja

Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics across three generations of recursive text generation using three complementary evaluation frameworks: rule-based pattern matching, embedding-based semantic similarity, and downstream

Zhiqiang Miao, Huan Yang

We present a novel resonance mechanism that can occur in {compact-star} binaries: a spin-orbit resonance. This resonance locks the binary into a unique state where {the spin of one component} evolves alongside the orbit. The resonance requires this component to possess a finite ellipticity $ε$, and we find that the locking probability is proportional to $\sq

Susobhan Mandal

Thermal field theory is an essential tool for comprehending various physical phenomena, including astrophysical objects such as neutron stars and white dwarfs, as well as the early stages of the universe. Nonetheless, the traditional thermal field theory formulated in Minkowski spacetime is not capable of considering the effects originating from the curved s

Johannes H. Uhl, Maxwell C. Cook, Cibele Amaral, Stefan Leyk

The increasing occurrence of natural hazards such as wildfires and drought, along with urban expansion and land consumption, causes increasing levels of fire risk to populations and human settlements. Moreover, increasing geopolitical instability in many regions of the world requires evaluation of scenarios related to potential hazards caused by military ope

Weihao Tan, Xiangyang Li, Yunhao Fang, Heyuan Yao

We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environments. Lumine adopts a human-like interaction paradigm that unifies perception, reasoning, and action in an end-to-end manner, powered by a vision-language model. It processes raw p

Tianqi Cang, Pascal Petit, Jean-François Donati, Hui Tian

Magnetic fields in the upper atmospheres of solar-like stars are believed to provide an enormous amount of energy to power the hot coronae and drive large-scale eruptions that could impact the habitability of planetary systems around these stars. However, these magnetic fields have never been routinely measured on stars beyond the solar system. Through decad

Sensen Lin, Yang Gao, Yongheng Lu, Yongkuan Zhang

The high-temperature performance of liquid $Li_{17}Pb_{83}$, a key fusion reactor material, is governed by its atomic-scale dynamics. Using ab initio molecular dynamics, we discover that lithium diffusion is not free but confined within cages formed by lead atoms, a phenomenon we term the chemical cage effect. Structurally, RDF and CSRO analyses confirm a st

Yang-Cheng Ye, Fulu Zheng, Ajay Jha, Hong-Guang Duan

The geometric phase is a fundamental quantum mechanical phenomenon uniquely associated with conical intersections (CI) between potential energy surfaces and serves as a definitive signature of their presence. In this study, we propose a novel spectroscopic approach to directly detect the geometric phase using two-dimensional electronic spectroscopy (2DES) en

Jiahang He, Rishi Ramachandran, Neel Ramachandran, Aryan Katakam

As large language models (LLMs) are adopted in an increasingly wide range of applications, user-model interactions have grown in both frequency and scale. Consequently, research has focused on evaluating the robustness of LLMs, an essential quality for real-world tasks. In this paper, we employ simple multi-turn follow-up prompts to evaluate models' answer c

Christopher Cheong, Gary Davis, Seongjin Choi

Spatiotemporal forecasting on transportation networks is a complex task that requires understanding how traffic nodes interact within a dynamic, evolving system dictated by traffic flow dynamics and social behavioral patterns. The importance of transportation networks and ITS for modern mobility and commerce necessitates forecasting models that are not only

Tommy Sha, Zhan Cheng, Haotian Zhai, Xuwei Ding

Stroke is an acute cerebrovascular disease, and timely diagnosis significantly improves patient survival. However, existing automated diagnosis methods suffer from fairness issues across demographic groups, potentially exacerbating healthcare disparities. In this work we propose FAST-CAD, a theoretically grounded framework that combines domain-adversarial tr

Gang Zhao, Yiming Zhu, Jiangpei Dou, Yili Chen

To support the development of the data processing pipeline and the scientific performance assessment for the Cool Planet Imaging Coronagraph (CPI-C) on the Chinese Space Station Survey Telescope (CSST), we have developed the end-to-end instrument simulation program, CPISM. This paper details the core modules of CPISM that simulate the CPI-C instrument, focus

Sitao Ling, Wenxuan Ma, Musheng Wei

In signal processing and identification, generalized singular value decomposition (GSVD), related to a sequence of matrices in product/quotient form are essential numerical linear algebra tools. On behalf of the growing demand for efficient processing of coupled rotation-translation signals in modern engineering, we introduce the restricted SVD of a dual qua

Oliver Wang, Pengrui Quan, Kang Yang, Mani Srivastava

Practitioners deploying time series forecasting models face a dilemma: exhaustively validating dozens of models is computationally prohibitive, yet choosing the wrong model risks poor performance. We show that spectral predictability~$\Omega$ -- a simple signal processing metric -- systematically stratifies model family performance, enabling fast model selec

Alexander Ingold, Richard G. Baird, Dasmeet Kaur, Nidhi Dwivedi

We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel

Cheng Wang, Shuisheng Zhou, Fengjiao Peng, Jin Sheng

In the field of image clustering, the widely used contrastive learning networks improve clustering performance by maximizing the similarity between positive pairs and the dissimilarity of negative pairs of the inputs. Extant contrastive learning networks, whose two encoders often implicitly interact with each other by parameter sharing or momentum updating,

Mustafa Avci

We study a time-inhomogeneous nonlinear SDE with drift and diffusion governed by state-dependent variable exponents. This framework generalizes models like the geometric Brownian motion (GBM) and the constant elasticity of variance (CEV), offering flexibility to capture complex dynamics while posing analytical challenges. Using a fixed-point approach, we pro

A. J. Brinson, B. J. Rickey, J. M. Allmond, A. Dockery

This manuscript reports on the commissioning of the Resonance Ionization Spectroscopy Experiment (RISE) at the BECOLA facility at FRIB. The new instrument implements the collinear resonance ionization spectroscopy technique for sensitive measurements of isotope shifts and hyperfine structure of short-lived isotopes produced at FRIB. The existing BECOLA beaml

Chayapatr Archiwaranguprok, Constanze Albrecht, Pattie Maes, Karrie Karahalios

As AI systems become increasingly integrated into daily life, their potential to exacerbate or trigger severe psychological harms remains poorly understood and inadequately tested. This paper presents a proactive methodology for systematically exploring psychological risks in simulated human-AI interactions based on documented real-world cases involving AI-i

Felix Schremmer, Eva Viehmann

Affine Deligne-Lusztig varieties in the fully Hodge-Newton decomposable (or minute) case are the only larger class of ADLVs which could be described completely in the past. Instances of them play important roles in arithmetic geometry, from Harris-Taylor's proof of the local Langlands correspondence to applications in the Kudla program. We study generalizati

Andrea Cavallo, Ayushman Raghuvanshi, Sundeep Prabhakar Chepuri, Elvin Isufi

Machine learning and data processing techniques relying on covariance information are widespread as they identify meaningful patterns in unsupervised and unlabeled settings. As a prominent example, Principal Component Analysis (PCA) projects data points onto the eigenvectors of their covariance matrix, capturing the directions of maximum variance. This mappi

Junichiro Niimi

Large language models (LLMs) generate fluent text across a wide range of tasks, but the fabrication of non-existent academic citations remains a critical and well-documented failure mode. Building on prior work that frames hallucination and verbatim memorization as outcomes of the same probabilistic process, this study uses citation count as a proxy for trai

Fang Li, Duan Xingyu, Guo Zhenhua

The evolution of two partially miscible, nonhomogeneous, incompressible viscous fluids of non-Newtonian type, can be governed by the Navier-Stokes-Cahn-Hilliard system. In the present work, we prove the global existence of weak solutions for the case of initial density containing zero and the concentration depending viscosity with free energy potential equal

Phuc Tran, Van Vu

Let $A$ be an $m \times n$ matrix with rank $r$ and singular value decomposition $A = \sum _{i=1}^r σ_i u_i v_i^\top, $ where the $σ_i$ are its singular values, ordered decreasingly, and $u_i, v_i$ are the corresponding left and right singular vectors. For an integer $1 \le p \le r$, $A_p := \sum_{i=1}^p σ_i u_i v_i^\top$ is the best rank-$p$ approximation o

Shihao Ru, Liheng An, Haidong Liang, Zhengzhi Jiang

Negatively charged boron vacancy ($\mathrm{V_B^-}$) in hexagonal boron nitride (hBN) is the most extensively studied room-temperature quantum spin system in two-dimensional (2D) materials. Nevertheless, the current effective readout of $\mathrm{V_B^-}$ spin states is carried out by systematically optical methods. This limits their exploitation in compact and

Shouang Wei, Min Zhang, Xin Lin, Bo Jiang

Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning methods only learn surface teaching patterns without dynamic adaptation capabilities. Recent reinforcement learning approaches address this limitation but face two critical challenges. First, they evaluate teaching

Hu Cui, Wenqiang Hua, Renjing Huang, Shurui Jia

Recently, the Mamba architecture based on State Space Models (SSMs) has gained attention in 3D human pose estimation due to its linear complexity and strong global modeling capability. However, existing SSM-based methods typically apply manually designed scan operations to flatten detected 2D pose sequences into purely temporal sequences, either locally or g

Jonathan Kay, François Monard

If $d$ is a boundary defining function for the Euclidean unit disk and $I$ denotes the geodesic X-ray transform, for $\gamma\in (-1,1)$, we study the singularly-weighted X-ray transforms $I_m d^\gamma$ acting on symmetric $m$-tensors. For any $m$, we provide a sharp range decomposition and characterization in terms of a distinguished Hilbert basis of the dat

Ziqian Tang, Zizhao Han, Zikuan Kan, Chen Yang

Massive quantum systems have emerged as compelling tabletop interface-systems for testing the quantum nature of gravity. However, conventional schemes that focus on directly using gravity to induce entanglement suffer from overwhelming environmental decoherence: maintaining entanglement between two oscillators requires an impractically high mechanical qualit

Yuze Liu, Yunhan Wang, Tiehua Zhang, Zhishu Shen

The surge in intelligent applications driven by large language models (LLMs) has made it increasingly difficult for bandwidth-limited cloud servers to process extensive LLM workloads in real time without compromising user data privacy. To solve these problems, recent research has focused on constructing cloud-edge consortia that integrate server-based LLM wi

Taewan Kim, Dayou Luo, Behçet Açıkmeşe

This paper presents a convex optimization-based framework for synthesizing time-varying controlled invariant funnels and associated feedback control around a given nominal trajectory for nonlinear systems subject to bounded disturbances. Nonlinearities are modeled using incremental quadratic constraints, including Lipschitz, L-smooth, and sector-bounded nonl

Xingchao Jian, Purui Zhang, Lan Tian, Feng Ji

Detecting the origin of information or infection spread in networks is a fundamental challenge with applications in misinformation tracking, epidemiology, and beyond. We study the multi-source detection problem: given snapshot observations of node infection status on a graph, estimate the set of source nodes that initiated the propagation. Existing methods e

Fuyi Yang, Chenchen Ye, Mingyu Derek Ma, Yijia Xiao

Hypothesis generation in biomedical research has traditionally centered on uncovering hidden relationships within vast scientific literature, often using methods like Literature-Based Discovery (LBD). Despite progress, current approaches typically depend on single data types or predefined extraction patterns, which restricts the discovery of novel and comple

Cong Tai, Hansheng Wu, Haixu Long, Zhengbin Long

In this work, we present a PICO-based robot remote operating framework that enables low-cost, real-time acquisition of hand motion and pose data, outperforming mainstream visual tracking and motion capture solutions in terms of cost-effectiveness. The framework is natively compatible with the RealMirror ecosystem, offering ready-to-use functionality for stab

Woosuk Chung, Seokwoo Hong, Wonhyeok Lee, Sangyoon Bae

Manual sleep staging from polysomnography (PSG) is labor-intensive and prone to inter-scorer variability. While recent deep learning models have advanced automated staging, most rely solely on raw PSG signals and neglect contextual cues used by human experts. We propose a two-stage architecture that combines a Transformer-based per-epoch encoder with a 1D CN

Hyesu Jang, Wooseong Yang, Ayoung Kim, Dongje Lee

X-band radar serves as the primary sensor on maritime vessels, however, its application in autonomous navigation has been limited due to low sensor resolution and insufficient information content. To enable X-band radar-only autonomous navigation in maritime environments, this paper proposes a place recognition algorithm specifically tailored for X-band rada

Justin Hom, Schuyler G. Wolff, Catherine A. Clark, David R. Ciardi

The upcoming Roman Coronagraph will be the first high-contrast instrument in space capable of high-order wavefront sensing and control technologies, a critical technology demonstration for the proposed Habitable Worlds Observatory (HWO) that aims to directly image and characterize habitable exoEarths. The nominal Roman Coronagraph observing plan involves alt

Navid Mohammadi Foumani, Soheila Ghane, Nam Nguyen, Mahsa Salehi

Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and configurations, and (2) the low signal-to-noise ratio (SNR) of EEG, where brain signals are often buried under artifacts and non-brain sources. To address these challenges, we present EEG

Cleverson Andrade Goulart, Gleb Oshanin, Mauricio Porto Pato

Non-Hermitian PT-symmetric models have been extensively studied in recent years. Following the seminal work that reduced classical random matrix ensembles to a tridiagonal form, several efforts have aimed to generalize this framework to non-Hermitian extensions of the so-called \b{eta}-ensembles. In particular, while the transition of eigenvalues from the re

Krishu K Thapa, Supriya Savalkar, Bhupinderjeet Singh, Trong Nghia Hoang

Various complex water management decisions are made in snow-dominant watersheds with the knowledge of Snow-Water Equivalent (SWE) -- a key measure widely used to estimate the water content of a snowpack. However, forecasting SWE is challenging because SWE is influenced by various factors including topography and an array of environmental conditions, and has

Maud Lemercier, Paola Arrubarrena, Salvatore Di Giorgio, Julia Brettschneider

Detecting chemical modifications on RNA molecules remains a key challenge in epitranscriptomics. Traditional reverse transcription-based sequencing methods introduce enzyme- and sequence-dependent biases and fragment RNA molecules, confounding the accurate mapping of modifications across the transcriptome. Nanopore direct RNA sequencing offers a powerful alt

Casper L. Christensen, Logan Riggs

Recent work in mechanistic interpretability has shown that decomposing models in parameter space may yield clean handles for analysis and intervention. Previous methods have demonstrated successful applications on a wide range of toy models, but the gap to "real models" has not yet been bridged. In this work, we extend Stochastic Parameter Decomposition (SPD

Pragya Singh, Islem Rekik

Graph super-resolution, the task of inferring high-resolution (HR) graphs from low-resolution (LR) counterparts, is an underexplored yet crucial research direction that circumvents the need for costly data acquisition. This makes it especially desirable for resource-constrained fields such as the medical domain. While recent GNN-based approaches show promise

Ahmad Peyvan, Khemraj Shukla, George Em Karniadakis

We have developed a hypersonic high-order, high-performance code (H$^3$PC) utilizing the ``Trixi.jl" framework in order to simulate both non-reactive and chemically reactive compressible Euler and Navier-Stokes equations for complex three-dimensional geometries. H$^3$PC is parallel on CPU platforms and can perform exascale parallel computations of hypersonic