November 2025 arXiv papers — page 70

Nannan Chen, Miao Liu, Yuzhen Qi, Caihong Yang

We study the Tur\'{a}n numbers of $3$-graphs avoiding $3$-graphs $F$ and $M_{s+1}^3$, a matching of size $s+1$. We disprove a conjecture of Gerbner, Tompkins, and Zhou [European Journal of Combinatorics, 2025, 127:104155] on $\ex(n,\{F,M^3_{s+1}\})$ for $3$-graph $F$ with $\chi(F)=2$ by constructing infinitely many counterexamples. For this family, we determ

Tanmoy Biswas, Chandan Datta, Luis Pedro Garcia-Pintos

The removal of heat generated during computation poses a major challenge for both classical and quantum information processing. In particular, heat removal is directly linked to a fundamental requirement of quantum computation: the ability to reset a system to a pure state before computation. Efficient cooling is therefore crucial both for advancing our unde

Qianyi Shao, Yuanfan Zhang, Renxiang Xiao, Liang Hu

Reliable visual perception under adverse weather conditions, such as rain, haze, snow, or a mixture of them, is desirable yet challenging for autonomous driving and outdoor robots. In this paper, we propose a unified Memory-Enhanced Visual-Language Recovery (MVLR) model that restores images from different degradation levels under various weather conditions.

Qingbin Zeng, Bingbing Fan, Zhiyu Chen, Sijian Ren

The emergence of AI Scientists has demonstrated remarkable potential in automating scientific research. However, current approaches largely conceptualize scientific discovery as a solitary optimization or search process, overlooking that knowledge production is inherently a social and historical endeavor. Human scientific insight stems from two distinct yet

Xingli Li, Yan Li, Yangqian Yan

The Mpemba effect, where a hotter system cools faster than a colder one under otherwise identical conditions, has been extensively studied in classical systems. In this work, we present the quantum analogue of the Mpemba effect using a dissipative qubit, which is referred to as the canonical quantum Mpemba effect. We demonstrate that, under the identical con

Bahrul Jalaali, Kie Okabayashi

This study proposes a rotationally invariant data-driven subgrid-scale (SGS) model for large-eddy simulation (LES) of wall-bounded turbulent flows. Building upon the multiscale convolutional neural network subgrid-scale model, which outputs SGS stress tensors ($\tau_{ij}$) as the baseline, the deep neural network (DNN) architecture is modified to satisfy the

Changyu Zhou

We carry out a Kovalevskaya analysis of the Riccati hierarchy. We determine all indicial loci and Kovalevskaya exponents and identify a rigid recursive structure governing how free parameters enter Laurent solutions. We further identify a nontrivial quasi--homogeneous vector field commuting with the hierarchy and use it to obtain an explicit parametrization

Navneet Singh, Shiva Raj Pokhrel

Trajectory forecasting for autonomous driving must deliver accurate, calibrated multi-modal futures under tight compute and latency constraints. We propose a compact hybrid quantum architecture that aligns quantum inductive bias with road-scene structure by operating in an ego-centric, lane-aligned frame and predicting residual corrections to a kinematic bas

Junhong Min, Jimin Kim, Minwook Kim, Cheol-Hui Min

Depth in the real world is rarely singular. Transmissive materials create layered ambiguities that confound conventional perception systems. Existing models remain passive; conventional approaches typically estimate static depth maps anchored to the nearest surface, and even recent multi-head extensions suffer from a representational bottleneck due to fixed

Jonathan Skaza, Parsa Madinei, Ziqi Wen, Miguel Eckstein

Visual complexity prediction is a fundamental problem in computer vision with applications in image compression, retrieval, and classification. Understanding what makes humans perceive an image as complex is also a long-standing question in cognitive science. Recent approaches have leveraged multimodal models that combine visual and linguistic representation

Liling Li, Guoyang Xu, Xiongri Shen, Zhifei Xu

Multimodal Sentiment Analysis (MSA) is critical for human-computer interaction but faces challenges when the modalities are incomplete or missing. Existing methods often assume pre-defined missing modalities or fixed missing rates, limiting their real-world applicability. To address this challenge, we propose Senti-iFusion, an integrity-centered hierarchical

Weiyi Wang, Lanqing Yang, Linqian Gan, Guangtao Xue

Advancements in information technology have increased demand for natural human-computer interaction in areas such as gaming, smart homes, and vehicles. However, conventional approaches like physical buttons or cameras are often limited by contact requirements, privacy concerns, and high costs.Motivated by the observation that these EM signals are not only st

Chang-Yong Song, David Hyde

Differentiable particle-based simulation can produce physically plausible motion, but target-driven volumetric shape morphing remains underconstrained: physics-only mass matching captures coarse global structure yet struggles with fine geometric detail, while naive image-space coupling destabilizes elastic dynamics. We present PhysMorph-GS, a render-guided m

Fupei Guo, Kerry Pan, Songyang Zhang, Yue Wang

Radiomap serves as a vital tool for wireless network management and deployment by providing powerful spatial knowledge of signal propagation and coverage. However, increasingly complex radio propagation behavior and surrounding environments pose strong challenges for radiomap estimation (RME). In this work, we propose a knowledge-guided RME framework that in

An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh, Zhuang Li

Online conversations have become more prevalent on public discussion platforms (e.g. Reddit). With growing controversial topics, it is desirable to summarize not only diverse arguments, but also their rationale and justification. Early studies on text summarization focus on capturing general salient information in source documents, overlooking the argumentat

Rui Li, Yi Li, Kai-Ya Zhang, Qing Ai

Recently, the coherent modified Redfield theory (CMRT) has been widely used to simulate the excitation-energy-transfer (EET) processes in photosynthetic systems. However, the numerical simulation of the CMRT is computationally expensive when dealing with large-scale systems, e.g. photosystem I (PSI) and II (PSII). On the other hand, the chemical reaction and

Xiao Yang, Shuai Ma, Yong Liang, Guangming Shi

Semantic communication can improve transmission efficiency by focusing on task-relevant information. However, under packet-based communication protocols, any error typically results in the loss of an entire packet, making semantic communication particularly vulnerable to packet loss. Since high-dimensional semantic features must be partitioned into one-dimen

Sai Nath Chowdary Medikonduru, Hongpeng Jin, Yanzhao Wu

Plant diseases pose a significant threat to global agriculture, causing over $220 billion in annual economic losses and jeopardizing food security. The timely and accurate detection of these diseases from plant leaf images is critical to mitigating their adverse effects. Deep neural network Ensembles (Deep Ensembles) have emerged as a powerful approach to en

A. Arziev, K. Kudaybergenov. P. Orinbaev

The study presents a vector-valued extension of the classical Mercer theorem within the framework of reproducing kernel Hilbert spaces defined over Kaplansky-Hilbert modules associated with the algebra of essentially bounded measurable functions. The analysis focuses on a partial integral operator with a positive definite kernel depending on a measurable par

Xiaobin Deng, Qiuli Yu, Changyu Diao, Min Li

3DGS employs a large number of Gaussian primitives to fit scenes, resulting in substantial storage and computational overhead. Existing pruning methods rely on manually designed criteria or introduce additional learnable parameters, yielding suboptimal results. To address this, we propose an natural selection inspired pruning framework that models survival p

Yunyun Wang, Zheng Duan, Xinyue Liao, Ke-Jia Chen

Open-Set Domain Generalization (OSDG) tackles the realistic scenario where deployed models encounter both domain shifts and novel object categories. Despite impressive progress with vision-language models like CLIP, existing methods still fall into the dilemma between structural risk of known-classes and open-space risk from unknown-classes, and easily suffe

Satyajeet Moharana, Chang Won Lee, Shinyoung Kim, Eun Jung Chung

We present the results of molecular line observations toward the W40 and Serpens South regions of the Aquila molecular cloud complex, conducted as part of the TRAO-FUNS project to investigate the role of filamentary structures in the formation of dense cores and stars in molecular clouds. We performed a Gaussian decomposition of the C$^{18}$O spectra to dise

Hiroki Tateishi, Daisuke Yoshida, Tomoki Tsuno, Takuto Nihashi

Multiplexing is essential for improving entanglement distribution rates in quantum communication. Frequency multiplexing provides a promising and scalable path toward large-capacity quantum networks. Further progress requires increasing the number of frequency modes and developing broadband photon-pair sources and quantum memories that are spectrally compati

Sanjit Dandapanthula, Aaditya Ramdas

Deep equilibrium models (DEQs) have recently emerged as a powerful paradigm for training infinitely deep weight-tied neural networks that achieve state of the art performance across many modern machine learning tasks. Despite their practical success, theoretically understanding the gradient descent dynamics for training DEQs remains an area of active researc

Hartosh Singh Bal

We show that the infinite product defined by \[ P(z) = -\prod_{n=1}^{\infty} (\Phi_n(z))^{-1/n}, \] where \( \Phi_n(z) \) is the \( n \)-th cyclotomic polynomial, is constant inside the unit disk. The proof translates a result of Ramanujan on Ramanujan sums, equivalent to the prime number theorem, to the setting of infinite products. We also show that simila

MST Rumi Akter, Anamitra Pal, Rajasekhar Anguluri

Low-frequency oscillations remain a major challenge in bulk power systems with high renewable penetration, long lines, and large loads. Existing damping strategies based on power modulation of high voltage DC (HVDC) or energy storage, are often limited by fixed control architectures, leaving some modes poorly damped. This paper introduces a state-derivative

Shukai Chen, Xu Yang, Xiaowen Zhou

For a class of time-inhomogeneous SDEs with jumps, we establish criteria for the existence and uniqueness of the nonnegative solutions, and examine the extinction, the explosion together with the contractivity of the solutions, which generalize and improve upon earlier results in the literature. As an application, we study the aforementioned properties for a

Shuyang Yu, Jianan Liang, Hui Hu

Optimizing patent claims is a critical yet challenging task, demanding careful balance between maximizing novelty and preserving legal scope. Manual claim drafting is labor-intensive, costly, and inherently inconsistent, while conventional Large Language Models (LLMs) often lack the structured, iterative reasoning essential for precise claim refinement. To a

Kien Nguyen, Feng Liu, Clinton Fookes, Sridha Sridharan

The rapid emergence of airborne platforms and imaging sensors is enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment, and covert observation capabilities. This paper provides a comprehensive overview of 150+ papers over the last 10 years of human-centric aerial surveillance tasks from a computer visi

Wenlin Hou, Jianxiang Liu, Kai Liao

Gravitational lensing observables, including anomalies in image positions, flux ratios, and time delays, serve as usual probes of dark matter (DM) substructure. When dark matter substructure possesses sufficient perturbations, it may lead to the formation of extra images in otherwise canonical doubly or quadruply imaged systems. With the advent of increasing

Jacques Delabrouille, Oliver Jeong, Michel Piat, Alexander Steier

Stray radiation of various origin is a major source of degradation of centimeter to sub-millimeter astronomical observations. This is particularly problematic for the detection of signals such as faint cosmic microwave background polarization B modes, or for mapping large-scale extragalactic or Galactic diffuse emission. In this paper, we propose a double-ri

Joseph McDonald

In this note, we study the operation of Sasaki hook within the setting of quantum cylindric algebras by introducing cylindric quasi-implication algebras. It is first demonstrated that every quantum cylindric algebra can be converted into a cylindric quasi-implication algebra and conversely that every cylindric quasi-implication algebra gives rise to a quantu

Oliver Jeong, Jacques Delabrouille, Michel Piat

Next-generation centimeter to sub-millimeter telescopes require exquisite control over instrumental far-sidelobe response to accurately measure faint signals like the Cosmic Microwave Background B modes. Because existing electromagnetic modeling methods are computationally expensive, we developed a novel, diffraction-based beam modeling method for rapid and

Yiheng Bian, Zechen Li, Lanqing Yang, Hao Pan

Reconstructing 3D Radiance Field (RF) scenes through opaque obstacles is a long-standing goal, yet it is fundamentally constrained by a laborious data acquisition process requiring thousands of static measurements, which treats human motion as noise to be filtered. This work introduces a new paradigm with a core objective: to perform fast, data-efficient, an

Jigyasa Gupta, Soumya Goyal, Anil Kumar, Ishan Jindal

Synthesizing realistic cooked food images from raw inputs on edge devices is a challenging generative task, requiring models to capture complex changes in texture, color and structure during cooking. Existing image-to-image generation methods often produce unrealistic results or are too resource-intensive for edge deployment. We introduce the first oven-base

Hsuan Yuan, Shao-Yu Weng, I-Hsuan Lo, Wei-Chen Chiu

Previous methods have demonstrated remarkable performance in single image super-resolution (SISR) tasks with known and fixed degradation (e.g., bicubic downsampling). However, when the actual degradation deviates from these assumptions, these methods may experience significant declines in performance. In this paper, we propose a Dual Branch Degradation Extra

Shin-ichiro Kai, Ryota Horiguchi, Jian Xing, Kentaro Wada

This study validates the continuum traffic flow model of capacity drop at sag and tunnel bottlenecks, as proposed by Jin (2018) and Wada et al. (2020), through empirical analysis. Specifically, after addressing the limitations in the existing studies, we calibrate the model using data from multiple congestion events at several expressway bottlenecks. We then

Erik P. Nyberg, Steven Mascaro, Ingrid Zukerman, Michael Wybrow

Bayesian Networks (BNs) are an important tool for assisting probabilistic reasoning, but despite being considered transparent models, people have trouble understanding them. Further, current User Interfaces (UIs) still do not clarify the reasoning of BNs. To address this problem, we have designed verbal and visual extensions to the standard BN UI, which can

Shibshankar Dey, Sanjay Mehrotra, Anirudh Subramanyam

We study linear chance-constrained problems where the coefficients follow a Gaussian mixture distribution. We provide mixed-binary quadratic programs that give inner and outer approximations of the chance constraint based on piecewise linear approximations of the standard normal cumulative density function. We show that $O\left(\sqrt{\ln(1/\tau)/\tau} \right

Saúl A. Blanco, Mikhail P. Golubyatnikov, Elena V. Konstantinova, Natalia V. Maslova

The cubic pancake graphs are Cayley graphs over the symmetric group $\mathrm{Sym}_n$ generated by three prefix reversals. There is the following open problem: characterize all the sets of three prefix reversals that generate $\mathrm{Sym}_n$. As the largest prefix reversal of length $n$ is always included in a triple, we give a complete solution of the probl

I. Sebastian Buhai

We model the cadence of AI product releases, i.e. quiet spells, reversible patches, and rarer pivots, as optimal exercise of strategic real options under reputational learning. A privately observed technical state follows a diffusion. The firm controls two upgrade options with asymmetric costs and reversibility (a cheap patch and a costly pivot) and a public

Di Luo, Shuhui Yang, Mingxin Yang, Jiawei Lu

Physically-based rendering (PBR) materials are fundamental to photorealistic graphics, yet their creation remains labor-intensive and requires specialized expertise. While generative models have advanced material synthesis, existing methods lack a unified representation bridging natural image appearance and PBR properties, leading to fragmented task-specific

Yinjie Zhao, Heng Zhao, Bihan Wen, Joey Tianyi Zhou

As the development of AI-generated contents (AIGC), multi-modal Large Language Models (LLM) struggle to identify generated visual inputs from real ones. Such shortcoming causes vulnerability against visual deceptions, where the models are deceived by generated contents, and the reliability of reasoning processes is jeopardized. Therefore, facing rapidly emer

Masakazu Yamamoto

In this paper, the initial value problem for the Debye--Hueckel drift-diffusion equation is studied. This equation was introduced as a model describing plasma behavior and is also known as a simulation model of MOSFET, and so its solution describes charge density. It is well-known that, if the initial density is localized, then the density is adjusted to be

Dailan He, Guanlin Feng, Xingtong Ge, Yazhe Niu

Group Relative Policy Optimization (GRPO) has shown promise in aligning image and video generative models with human preferences. However, applying it to modern flow matching models is challenging because of its deterministic sampling paradigm. Current methods address this issue by converting Ordinary Differential Equations (ODEs) to Stochastic Differential

Qiyuan Liu, Qirui Zhang, Jinhong Du, Siming Zhao

The Perturbation Discrimination Score (PDS) is increasingly used to evaluate whether predicted perturbation effects remain distinguishable, including in Systema and the Virtual Cell Challenge. However, its behavior in high-dimensional gene-expression settings has not been examined in detail. We show that PDS is highly sensitive to the choice of similarity or

Travis Gagie

We show how to merge two run-length compressed Burrows-Wheeler Transforms (RLBWTs) into a run-length compressed extended Burrows-Wheeler Transform (eBWT) in $O (r)$ space and $O ((r + L) \log (m + n))$ time, where $m$ and $n$ are the lengths of the uncompressed strings, $r$ is the number of runs in the final eBWT and $L$ is the sum of its irreducible LCP val

Yicheng Deng, Hideaki Hayashi, Hajime Nagahara

Automatic facial expression spotting, which aims to identify facial expression instances in untrimmed videos, is crucial for facial expression analysis. Existing methods primarily focus on fully-supervised learning and rely on costly, time-consuming temporal boundary annotations. In this paper, we investigate point-supervised facial expression spotting (P-FE

Xin Shen, Rui Zhu, Lei Shen, Xinyu Wang

Understanding fine-grained human hand motion is fundamental to visual perception, embodied intelligence, and multimodal communication. In this work, we propose Fine-grained Finger-level Hand Motion Captioning (FingerCap), which aims to generate textual descriptions that capture detailed finger-level semantics of hand actions. To support this task, we curate

Qijun You, Lingshuo Meng, Fangrui Quan, Wei Cao

We present a novel axial ptychographic coherent diffractive imaging (AP-CDI) technique designed to overcome the critical throughput bottleneck of conventional methods. By replacing the 2D raster scan with a simple 1D axial scan, our approach reduces the number of required diffraction patterns by approximately an order of magnitude while maintaining high-fide

Atharv Singh Patlan, Peiyao Sheng, S. Ashwin Hebbar, Prateek Mittal

Language agents are rapidly expanding from single-user assistants to multi-user collaborators in shared workspaces and groups. However, today's language models lack a mechanism for isolating user interactions and concurrent tasks, creating a new attack vector inherent to this new setting: cross-user poisoning (CUP). In a CUP attack, an adversary injects ordi

Junseo Kim, Guido Dumont, Xinyu Gao, Gang Chen

Dense 3D semantic occupancy perception is critical for mobile robots operating in pedestrian-rich environments, yet it remains underexplored compared to its application in autonomous driving. To address this gap, we present MobileOcc, a semantic occupancy dataset for mobile robots operating in crowded human environments. Our dataset is built using an annotat

Ding Yu Shao, Han-Qing Yu, Cheng Zhang, Jian Zhou

Azimuthal anisotropies in heavy-ion collisions are conventionally interpreted as signatures of hydrodynamic flow. We demonstrate that in peripheral collisions, a significant $\cos 2\phi$ asymmetry in the decay leptons of coherently photoproduced $J/\psi$ mesons arises purely from the initial-state geometry of the nuclear electromagnetic field. This modulatio

Baoqing Li, Yuanyuan Liu, Congcong Liu, Qingyong Zhu

Dynamic magnetic resonance imaging (dMRI) captures temporally-resolved anatomy but is often challenged by limited sampling and motion-induced artifacts. Conventional motion-compensated reconstructions typically rely on pre-estimated optical flow, which is inaccurate under undersampling and degrades reconstruction quality. In this work, we propose a novel imp

Chenqi Zhao, Wenfei Wu, Linhai Song, Yuchen Xu

Mixture-of-Experts (MoE) has emerged as a promising approach to scale up deep learning models due to its significant reduction in computational resources. However, the dynamic nature of MoE leads to load imbalance among experts, severely impacting training efficiency. While previous research has attempted to address the load balancing challenge, existing sol

Mariko Kato, Izumi Hachisu, Hideyuki Saio

The rising phase toward the optical maximum of a classical nova is one of the last frontiers of nova study. Constructing free-free emission model light curves based on our fully self-consistent nova explosion models, we present several theoretical light curves of classical novae and compare them with the four novae having the observed rising phase toward the

Wouter Verstraelen, Stanisław Świerczewski, Andrzej Opala, Andrew Haky

Characterising optical quantum states is essential for the development of quantum technologies. While traditional approaches to perform full quantum state tomography are often experimentally demanding, neuromorphic architectures may provide an effective alternative. In this work, we demonstrate how a quantum nonlinear driven-dissipative mode is sufficient to

Yuan-bo Xie, Yun-dong Wu, Wei Hong, Tong-jie Zhang

In the context of fitting cosmological models, parameter degeneracy remains a central issue. This paper critically examines traditional methods for constraining parameters and focuses on the G factor as a tool for evaluating the quality of observational data. To ensure analytical independence, two datasets--Cosmic Chronometers (CC) and Baryon Acoustic Oscill

Tianyu Zhan, Kairui Fu, Zheqi Lv, Shengyu Zhang

Generative recommendation systems typically leverage Semantic Identifiers (SIDs), which represent each item as a sequence of tokens that encode semantic information. However, representing item ID with multiple SIDs significantly increases input sequence length, which is a major determinant of computational complexity and memory consumption. While existing ef

Shu Luo

The field of neutrino physics has made significant progress in measuring the strength and frequency of neutrino and antineutrino oscillations in the past two decades. It is clear that the amplitudes involved in the neutrino oscillation probabilities are all rephaping invariants of the quartet forms of the elements of the PMNS mixing matrix. We show in this p

Scott Willenbrock

In honor of Dave Roper's 90th birthday, I present a pedagogical introduction to our modern understanding of unstable particles in Quantum Field Theory, based on the analytic structure of the propagator, with occasional remarks on the Roper resonance. I discuss the mass and decay rate of unstable particles, Breit-Wigner resonance formulae and width, poles and

Shuai Wang, Harrisen Scells, Bevan Koopman, Guido Zuccon

We present AutoBool, a reinforcement learning (RL) framework that trains large language models (LLMs) to generate effective Boolean queries for medical systematic reviews. Boolean queries are the primary mechanism for literature retrieval in this domain and must achieve high recall while maintaining reasonable precision - a challenging balance that existing

Yongtao Ma, Wenjie Dou

In mixed quantum-classical simulations of molecule-metal surface interactions, the discretization of the metallic electronic continuum typically results in a closed-system representation that fails to capture the open-system nature of the true physical process. This approximation can introduce significant artifacts, including deviations in the dynamical evol

Xianwei Lv, Debin Tang, Zhecheng Shi, Wang Wang

Meeting real-time constraints for high-performance Approximate Nearest Neighbor (ANN) search remains a critical challenge in remote sensing edge devices, which are essentially fusion systems like micro-satellites and UAVs, largely due to stringent limitations in primary (RAM) and secondary (disk) storage. To address this challenge, we propose Edge-ANN, an in

Hong Gao, Jingyu Wu, Xiangkai Xu, Kangni Xie

Spatio-Temporal Video Grounding (STVG) aims to localize target objects in videos based on natural language descriptions. Despite recent advances in Multimodal Large Language Models, a significant gap remains between current models and real-world demands involving diverse objects and complex queries. We attribute this to limited benchmark scope, causing model

Lin Yu, Xiaofei Han, Yifei Kang, Chiung-Yi Tseng

Recent advances in large language models (LLMs) have enabled fluent dialogue systems, but most remain reactive and struggle in emotionally rich, goal-oriented settings such as marketing conversations. To address this limitation, we propose AffectMind, a multimodal affective dialogue agent that performs proactive reasoning and dynamic knowledge grounding to s

Arkady Bolotin

This work proposes a geometric-statistical reinterpretation of the dark sector, grounded in a discrete spacetime framework composed of non-material spatial units termed hodons. Unlike particle-based dark matter models, hodons are kinematically inert and possess ultra-light effective mass derived from vacuum energy density and holographic volume bounds. We in

Zongrui Ji, Zhiming Cui, Na Li, Qianhan Zheng

Background:Accurate tooth segmentation from cone beam computed tomography (CBCT) images is crucial for digital dentistry but remains challenging in cases of interdental adhesions, which cause severe anatomical shape distortion. Methods: To address this, we propose a deep learning framework that integrates semantic and shape awareness for shape-preserving seg

Sierra A. T. Moxon, Harold Solbrig, Nomi L. Harris, Patrick Kalita

Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, non-standardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. LinkML (Linked Data Modeling Language) is an open framewo

Ram Kumar, Aditya H. Kelkar, Neeraj Shukla, Paras Poswal

Medium-energy ion irradiation is a promising technique for inducing magnetism in materials with partially filled d or f electron bands. This approach enables precise control over the density and spatial distribution of irradiation-induced defects, which play a crucial role in modifying the electronic and magnetic properties of the system. The primary objecti

Angelina Yan, Matt L. Sampson, Peter Melchior

12-lead ECGs with high sampling frequency are the clinical gold standard for arrhythmia detection, but their short-term, spot-check nature often misses intermittent events. Wearable ECGs enable long-term monitoring but suffer from irregular, lower sampling frequencies due to battery constraints, making morphology analysis challenging. We present an end-to-en

Chang Zhai, Ping Chen, Zhuo Jin, David Pitt

Epidemic risk assessment poses inherent challenges, with traditional approaches often failing to balance health outcomes and economic constraints. This paper presents a data-driven decision support tool that models epidemiological dynamics and optimises vaccination strategies to control disease spread whilst minimising economic losses. The proposed economic-

Chenyang Shao, Dehao Huang, Yu Li, Keyu Zhao

With the rapid development of Large Language Models (LLMs), AI agents have demonstrated increasing proficiency in scientific tasks, ranging from hypothesis generation and experimental design to manuscript writing. Such agent systems are commonly referred to as "AI Scientists." However, existing AI Scientists predominantly formulate scientific discovery as a

Ashish, Aparajita Jaiswal, Sudip Vhaduri, Niveditha Nerella

High-performance computing (HPC) and parallel computing increasingly rely on global collaboration among diverse teams, yet traditional computing curricula inadequately prepare students for cross-cultural teamwork essential in modern computational research environments. This paper presents Empa, an AI-powered virtual mentor that integrates intercultural colla

Vivek Chawla, Stephen Puplampu, Haochen Zhu, Philip D. Rack

Accelerating the discovery of mechanical properties in combinatorial materials requires autonomous experimentation that accounts for both instrument behavior and experimental cost. Here, an automated nanoindentation (AE-NI) framework is developed and validated for adaptive mechanical mapping of combinatorial thin-film libraries. The method integrates heteros

Rui Xue, Dan He, Fengmei Jin, Chen Zhang

Detecting trajectory anomalies is a vital task in modern Intelligent Transportation Systems (ITS), enabling the identification of unsafe, inefficient, or irregular travel behaviours. While deep learning has emerged as the dominant approach, several key challenges remain unresolved. First, sub-trajectory anomaly detection, capable of pinpointing the precise s

Jingyi Xu, Meisong Zheng, Ying Chen, Minglang Qiao

Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. However, they suffer from error accumulation, spatial artifacts, and a trade-off between perceptual quality and fidelity, primarily caused by inaccurate alignment and insufficient compensation between video frames. In this paper, within the DM-based VSR

Ziyuan Gao

Medical vision-language segmentation models suffer from catastrophic forgetting when adapting to new anatomical structures, requiring complete retraining that limits their clinical deployment. Although continual learning approaches have been studied for various applications, targeted research on continual learning approaches specifically designed for medical

Vinaya P C

In 1941, G. Gr\"unwald proved the convergence of a sequence of operators constructed using classical Lagrange interpolation at Chebyshev nodes. In this work, we establish a perturbed version of Gr\"unwald's result, thereby extending the class of admissible nodal points. Specifically, we provide sufficient conditions for convergence when the interpolation nod

Xun Zhang, Weijie Yang, Jiangjiang Zhang, Simin Jiang

We propose the Diffusion-Inversion-Net (DIN) framework for inverse modeling of groundwater flow and solute transport processes. DIN utilizes an offline-trained Denoising Diffusion Probabilistic Model (DDPM) as a powerful prior leaner, which flexibly incorporates sparse, multi-source observational data, including hydraulic head, solute concentration, and hard

Andrés Aradillas Fernández, José Blanchet, José Luis Montiel Olea, Chen Qiu

We consider a class of hypothesis testing problems where the null hypothesis postulates $M$ distributions for the observed data, and there is only one possible distribution under the alternative. We show that one can use a stochastic mirror descent routine for convex optimization to provably obtain - after finitely many iterations - both an approximate least

Pankaj Bhagwat, Linglong Kong, Bei Jiang

Conformal prediction has emerged as a popular technique for facilitating valid predictive inference across a spectrum of machine learning models, under minimal assumption of exchangeability. Recently, Hoff (2023) showed that full conformal Bayes provides the most efficient prediction sets (smallest by expected volume) among all prediction sets that are valid

Ali Anaissi, Deshao Liu, Yuanzhe Jia, Weidong Huang

Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated d

Shri H. Viswanathan, Ankit Joshi, Isabella DeClair, Bryce Twidwell

Sweat evaporation is the body's primary cooling mechanism, yet the physical factors governing it are not fully understood. We identify a dueling buoyancy effect in the context of the human body, in which perspiration vapor reduces the near skin air density, counteracting the downward flow driven by cooling of warm air upon contact with the skin. In hot,

Liming Xiang, Jing Feng, Ziqi Yin, Zijian Li

Approximate nearest neighbor (ANN) search in high-dimensional spaces is a foundational component of many modern retrieval and recommendation systems. Currently, almost all algorithms follow an $\epsilon$-Recall-Bounded principle when comparing performance: they require the ANN search results to achieve a recall of more than $1-\epsilon$ and then compare quer

Gehao Wang

The open intersection theory has been initiated by R. Pandharipande, J. P. Solomon and R. J. Tessler. In the scope of matrix model theory, A. Buryak and R. J. Tessler have constructed a matrix model $\mathcal{Z}^o$ for the open partition function based on a Kontsevich type combinatorial formula for the open intersection numbers found by R. J. Tessler. In thi

Yuichi Yoshida, Zihan Zhang

In this work, we study the maximum matching problem from the perspective of sensitivity. The sensitivity of an algorithm $A$ on a graph $G$ is defined as the maximum Wasserstein distance between the output distributions of $A$ on $G$ and on $G - e$, where $G - e$ is the graph obtained by deleting an edge $e$ from $G$. The maximum is taken over all edges $e$,

Chi Zhang, Jiepeng Wang, Youming Wang, Yuanzhi Liang

We present UniModel, a unified generative model that jointly supports visual understanding and visual generation within a single pixel-to-pixel diffusion framework. Our goal is to achieve unification along three axes: the model, the tasks, and the representations. At the representation level, we eliminate modality discrepancies by mapping both text and image

Ye Han, Lijun Zhang, Dejian Meng, Zhuang Zhang

In multi-vehicle cooperative driving tasks involving high-frequency continuous control, traditional state-based reward functions suffer from the issue of vanishing reward differences. This phenomenon results in a low signal-to-noise ratio (SNR) for policy gradients, significantly hindering algorithm convergence and performance improvement. To address this ch

Mohammad Javad Habibi Vosta Kolaei

In this paper, we study the evolution of smooth, closed planar curves under a fourth order biharmonic flow with an external forcing term. Such flows arise naturally in the theory of biharmonic maps and geometric variational problems involving bending energy. We first establish the global existence of smooth solutions to the associated initial value problem,

Andrés Pedroza

We introduce a generalization of Weinstein's morphism, defined on \pi_{2k-1}(Ham(M,\omega)) for 1 < k \leq n, where (M,\omega) is a 2n-dimensional symplectic manifold. Using this morphism, we show that for n > 1 and 1 < k \leq n, the homotopy groups \pi_{2k-1}(Ham(CP^n,\omega_{FS})) and \pi_{2k-1}(Ham(\tilde CP^n,\tilde\omega_\rho)) are nontrivial. Here, (\t

Xueming Li, Bing Guo

This paper investigates noise-robust phase retrieval by enhancing the prDeep architecture with difference of convex functions (DC) and DnCNN-based denoising regularization. This research introduces two novel algorithms, prDeep-DC and prDeep-L2, which demonstrably achieve excellent quantitative and visual performance, as confirmed by extensive numerical exper

Trieu Nguyen, Hao-Wei Pang, Shasha Feng

Macrocyclic peptides are an emerging modality that combines biologics-like affinity with small-molecule-like developability, but their vast combinatorial space and multi-parameter objectives make lead optimization slow and challenging. Prior generative approaches such as PepINVENT require chemists to pre-specify mutable positions for optimization, choices th

Yendo Hu, Yiliang Wu, Weican Chen

In multi UAV scenarios,the traditional Artificial Potential Field (APF) method often leads to redundant flight paths and frequent abrupt heading changes due to unreasonable obstacle avoidance path planning,and is highly prone to inter UAV collisions during the obstacle avoidance process.To address these issues,this study proposes a novel hybrid algorithm tha

Hugo Messias, Laura Gomez, Harold Francke, Bill Dent

(abridged) After more than 10yr of ALMA operations, the community interest in conducting deep, extra-galactic, millimetre surveys resulted in varying strategic compromises between areal size and map depth to survey the sky. The current bias leans towards a galaxy population found in the field or towards rich star-bursty proto-cluster groups, both tendentious

Shushi Wang, Zicheng Zhang, Chunyi Li, Wei Wang

Quality assessment of AI-generated content is crucial for evaluating model capability and guiding model optimization. However, most existing quality assessment datasets and models provide only a single quality score, which is too coarse to offer targeted guidance for improving generative models. In current applications of AI-generated images, realism and pla

Leyun Wu, Chilin Zhang

In this series of papers, we investigate coupled systems arising in the study of two-component Bose-Einstein condensates, and we establish classification results for solutions of De Giorgi conjecture type. In the present (second) paper of the series, we focus on the nonlocal problem of the form \begin{equation*} \left\{\begin{aligned} (-\Delta)^{s}u+u(u^{2}+

Hewang Zhao, Zhendong Li

A major challenge in using spin-flip time-dependent density functional theory (SF-TD-DFT) for spin-flip-down excitations is the presence of spin contamination. While several improved methods have been developed in the past, a simple and accurate method remains elusive. Here, based on our previous development on spin-adapted open-shell TD-DFT for spin-conserv

Bilguun Batsaikhan, Hiroyuki Fukuda

Early detection of rising talents is of paramount importance in the field of advertising. In this paper, we define a concept of talent breakout and propose a method to detect Japanese talents before their rise to stardom. The main focus of the study is to determine the effectiveness of combining Twitter and TV data on predicting time-dependent changes in soc

Hao-Chien Hsueh, Chi-En Yen, Wen-Hsiao Peng, Ching-Chun Huang

Diffusion probabilistic models have achieved remarkable success in generative tasks across diverse data types. While recent studies have explored alternative degradation processes beyond Gaussian noise, this paper bridges two key diffusion paradigms: hot diffusion, which relies entirely on noise, and cold diffusion, which uses only blurring without noise. We