December 2025 arXiv papers — page 47

H M Quamran Hasan, Housam Khalifa Bashier, Jiayi Dai, Mi-Young Kim

Despite the wide adoption of Large Language Models (LLM)s, clinical decision support systems face a critical challenge: achieving high predictive accuracy while generating explanations aligned with the predictions. Current approaches suffer from exposure bias leading to misaligned explanations. We propose Reason2Decide, a two-stage training framework that ad

Hongyang Shang, Shuai Dong, Ye Ke, Arindam Basu

This work proposes a 3D Stack In-Sensor-Computing (3DS-ISC) architecture for efficient event-based vision processing. A real-time normalization method using an exponential decay function is introduced to construct the time-surface, reducing hardware usage while preserving temporal information. The circuit design utilizes the leakage characterization of Dynam

Qing Zhang, Siyun Wang, Jianting Dong, Jia Zhang

The emergence of altermagnets has driven groundbreaking advances in spintronics. Notably, d-wave altermagnets support non-relativistic spin transport, efficient charge-to-spin conversion, and T-odd spin currents. In addition, their integration as electrodes in antiferromagnetic tunnel junctions (AFMTJs) enables a tunneling magnetoresistance (TMR) effect, all

Ling Zhuang, Ximing Xie, Fang Fang, Ali Attaran

Reconfigurable intelligent surfaces (RISs) have been extensively applied in integrated sensing and communication (ISAC) systems due to the capability of enhancing physical layer security (PLS). However, conventional static RIS architectures lack the flexibility required for adaptive beam control in multi-user and multifunctional scenarios. To address this is

Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee

Recent advances in learned image codecs have extended from human perception toward machine perception However, progressive image compression with fine granular scalability (FGS)-which enables decoding a single bitstream at multiple quality levels-remains unexplored for machine-oriented codecs. In this work, we propose PICM-Net, a progressive learned image co

Lixin Liu, Han Yan, Leyi Loh, Kamal Kumar Paul

Excellent gate electrostatics in field effect transistors (FETs) based on two-dimensional transition metal dichalcogenide (2D TMD) channels can dramatically decrease static power dissipation. Energy efficient FETs operate in enhancement mode with small and positive threshold voltage (Vth) for n-type devices. However, most state-of-the-art FETs based on monol

Conor Kresin, Boris Baeumer, Sophie Phillips

Many self-exciting systems change because endogenous amplification, as opposed to exogenous forcing, varies. We study a Hawkes process with fixed background rate and kernel, but piecewise time-varying productivity. For exponential kernels we derive closed-form mean-field relaxation after a change and a deterministic surrogate for post-change Fisher informati

Houtianfu Wang, Haofan Dong, Hanlin Cai, Ozgur B. Akan

Ka-band low-Earth-orbit (LEO) downlinks can suffer second-scale reliability collapses during flare-driven ionospheric disturbances, where fixed fade margins and reactive adaptive coding and modulation (ACM) are either overly conservative or too slow. This paper presents a GNSS-free, link-internal predictive controller that senses the same downlink via a geom

R. Vahedifard, E. Salahinejad

This study is focused on the mechanism of in vitro biomineralization on the surface of CaO.MgO.2SiO2 (diopside) nanostructured coatings by scanning electron microscopy, energy-dispersive X-ray spectroscopy and inductively coupled plasma spectroscopy assessments. A homogeneous diopside coating of almost 2 um in thickness was deposited on a medical-grade stain

Houtianfu Wang, Ozgur Akan

Low-Earth-orbit (LEO) inter-satellite links must cope with strongly doubly selective channels and aged channel state information (CSI). In this paper, the term ``sensing'' refers to the receiver-side identifiability of a small set of dominant delay--Doppler path parameters, quantified via CRLB-type proxies, rather than a full-fledged target-sensing pipeline.

N. Zirak, A. Maadani, E. Salahinejad, N. Abbasnezhad

Porous ceramic microspheres are a desirable substance for bone tissue reconstruction and delivery applications. This study focuses on Mg-Ca silicate microspheres encapsulated in biodegradable poly (lactic-co-glycolic acid) (PLGA) to serve as a biocompatible carrier for the controlled release of vancomycin hydrochloride. In this regard, diopside (MgCaSi2O6),

Yaojian Chen, Si-Qiu Gong, Lin Gan, Yanfei Liu

Matrix Product State (MPS) is a versatile tensor network representation widely applied in quantum physics, quantum chemistry, and machine learning, etc. MPS sampling serves as a critical fundamental operation in these fields. As the problems become more complex, the scale of MPS is rapidly increasing. Traditional data parallelism is limited by memory and hea

Sangryu Park, Gihyuk Ko, Homook Cho

Large Language Models (LLMs) show significant promise in automating software vulnerability analysis, a critical task given the impact of security failure of modern software systems. However, current approaches in using LLMs to automate vulnerability analysis mostly rely on using online API-based LLM services, requiring the user to disclose the source code in

Hamed Firooz, Rui Liu, Yuchen Lu, Zhenyu Hou

Content moderation at scale remains one of the most pressing challenges in today's digital ecosystem, where billions of user- and AI-generated artifacts must be continuously evaluated for policy violations. Although recent advances in large language models (LLMs) have demonstrated strong potential for policy-grounded moderation, the practical challenges of t

Aasim Jan, Sophia Nicolella, Deirdre Shoemaker, Richard O'Shaughnessy

The gravitational-wave event GW231123_135430 is the heaviest binary black hole system observed by the LIGO--Virgo--KAGRA Collaboration to date, with the initial analysis indicating the individual black hole masses lie within or above the theorized pair-instability mass gap of roughly $60$--$130\,M_\odot$. The inference further suggests that both black holes

Ziyang Fan, Li Tao, Yi Wang, Jingwei Qu

Student engagement is a critical factor influencing academic success and learning outcomes. Accurately predicting student engagement is essential for optimizing teaching strategies and providing personalized interventions. However, most approaches focus on single-dimensional feature analysis and assessing engagement based on individual student factors. In th

H. Li, J. Sun, Z. Zhang

We consider operator learning for efficiently solving parametric non-self-adjoint eigenvalue problems. To overcome the spectral instability and mode switching associated with non-self-adjoint operators, we choose to learn the eigenspace rather than individual eigenfunctions. In particular, we propose a Deep Eigenspace Network (DEN) architecture integrating F

Dianjun Lin, Bing Li, Lingzhou Xue

We introduce two nonlinear sufficient dimension reduction methods for regressions with tensor-valued predictors. Our goal is two-fold: the first is to preserve the tensor structure when performing dimension reduction, particularly the meaning of the tensor modes, for improved interpretation; the second is to substantially reduce the number of parameters in d

Hao Li, Fabian Deuser, Wenping Yin, Steffen Knoblauch

As Earth's climate changes, it is impacting disasters and extreme weather events across the planet. Record-breaking heat waves, drenching rainfalls, extreme wildfires, and widespread flooding during hurricanes are all becoming more frequent and more intense. Rapid and efficient response to disaster events is essential for climate resilience and sustainabilit

Quanyu Tang, Shengtong Zhang

Fix an integer $k\ge 3$. Call a set $A\subseteq [N]$ LCM-$k$-free if it does not contain distinct $a_1,\dots,a_k$ such that $\mathrm{lcm}(a_i,a_j)$ is the same for all $1\le i<j\le k$. Define $$ f_k(N):=\max\left\{\sum_{a\in A}\frac1a: A\subseteq [N] \text{ is LCM-$k$-free}\right\}. $$ Addressing a problem of Erd\H{o}s, we prove an explicit unconditional low

Peter N. Loxley

Controllable Markov chains describe the dynamics of sequential decision making tasks and are the central component in optimal control and reinforcement learning. In this work, we give the general form of an optimal policy for learning controllable dynamics in an unknown environment by exploring over a limited time horizon. This policy is simple to implement

Hung-Chieh Fang, Kuo-Han Hung, Chu-Rong Chen, Po-Jung Chou

Learning from videos offers a promising path toward generalist robots by providing rich visual and temporal priors beyond what real robot datasets contain. While existing video generative models produce impressive visual predictions, they are difficult to translate into low-level actions. Conversely, latent-action models better align videos with actions, but

Hedibert Lopes, Nick Polson, Vadim Sokolov

\noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while fully Bayesian hyper-parameter learning can be difficult due to the cost of posterior sampling. We develop a generative

Sourav Duari, Nilanjan Chaudhuri, Pradip Roy, Sourav Sarkar

The dynamical chromoelectric color conductivity of a chiral plasma has been extracted from one loop gluon self energy by using linear response theory at finite temperature and density. It is shown that due to the P and CP violation the conductivity tensor has an anomalous contribution in addition to the longitudinal and transverse components. We identify thi

Bo Fu, Chang-An Li, Björn Trauzettel

We propose a novel type of topological superconductivity based on Bogoliubov Fermi surfaces (BFSs) in an altermagnetic topological insulator proximitized by an s-wave superconductor. The 3D altermagnetic topological insulator is characterized by zero-energy surface states in bulk nodal-ring phases and anisotropically shifted surface Dirac cones in topologica

Wei Xu

Any non-abelian finite $p$-group has a non-inner automorphism of order $p$.

Yifan Gao, Alvin Valera, Winston K. G. Seah

Quantum entanglement routing in dynamic Low Earth Orbit (LEO) satellite networks is important for achieving scalable and high-fidelity quantum communication. However, the dynamic characteristics of satellite network topology, limited quantum resources, and strict coherence time constraints pose significant challenges to reliable entanglement routing. An enta

Yujia Gu, Lin Liu, Wei Ma

Adjusting for (baseline) covariates with working regression models becomes standard practice in the analysis of randomized clinical trials (RCT). When the dimension $p$ of the covariates is large relative to the sample size $n$, specifically $p = o (n)$, adjusting for covariates even in a linear working model by ordinary least squares can yield overly large

Md. Nazmus Sakib, Golam Mahmud, Md. Maruf Bangabashi, Umme Ara Mahinur Istia

Bengali, spoken by over 300 million people, is a morphologically rich and lowresource language, posing challenges for automatic speech recognition (ASR). This research presents an end-to-end framework for Bengali ASR, building on a Conformer-CTC backbone with a multi-level embedding fusion mechanism that incorporates phoneme, syllable, and wordpiece represen

Pavao Mardesic, Dmitry Novikov, Laura Ortiz-Bobadilla, Jessie Pontigo-Herrera

We study foliations in $\mathbb{C}^2$ given by polynomial deformations of the form $dH+\epsilon \eta=0$, with $\gamma(t)\subset H^{-1}(t)$ a family of cycles. The \emph{Poincar\'e first return map} is of the form $P(t)=t+\sum_j \epsilon^j M_j^\gamma(t).$ The functions $M_j^\gamma$ are called \emph{Melnikov functions} and are given by \emph{iterated integrals

Yingchao Yu, Pengfei Sun, Yaochu Jin, Kuangrong Hao

Most computational accounts of cognitive maps assume that stability is achieved primarily through sensory anchoring, with self-motion contributing to incremental positional updates only. However, biological spatial representations often remain coherent even when sensory cues degrade or conflict, suggesting that self-motion may play a deeper organizational ro

Nguyen Lam Phu Quy, Pham Phu Hoa, Tran Chi Nguyen, Dao Sy Duy Minh

Real-world image captions often lack contextual depth, omitting crucial details such as event background, temporal cues, outcomes, and named entities that are not visually discernible. This gap limits the effectiveness of image understanding in domains like journalism, education, and digital archives, where richer, more informative descriptions are essential

Jingkai Cui, Qian Qin

We study the convergence properties of a class of data augmentation algorithms targeting posterior distributions of Bayesian lasso models with log-concave likelihoods. Leveraging isoperimetric inequalities, we derive a generic convergence bound for this class of algorithms and apply it to Bayesian probit, logistic, and heteroskedastic Gaussian linear lasso m

Guangpu Wu, Shibei Xue, Guofeng Zhang, Rebing Wu

An augmented system model provides an effective way to model non-Markovian quantum systems, which is useful in filtering and control for this class of systems. However, since a large number of ancillary quantum oscillators representing internal modes of a non-Markovian environment directly interact with the principal system in these models, the dimension of

Shubhanshu Shekhar

We consider the problem of designing optimal level-$\alpha$ power-one tests for composite nulls. Given a parameter $\alpha \in (0,1)$ and a stream of $\mathcal{X}$-valued observations $\{X_n: n \geq 1\} \overset{i.i.d.}{\sim} P$, the goal is to design a level-$\alpha$ power-one test $\tau_\alpha$ for the null $H_0: P \in \mathcal{P}_0 \subset \mathcal{P}(\ma

Alexander Brudnyi, Mahishanka Withanachchi

In connection with the still unsolved multidimensional corona problem for algebras of bounded holomorphic functions on convex domains, we study the solvability of the B\'ezout equation for the algebra of bounded holomorphic functions on the polydisk with values in a complex Banach algebra. Assuming local solvability of the B\'ezout equation on a special open

Wen-Ji Hua, Yi-Ran Xiao, Yu Bao, Hua-Lei Yin

Multipartite entanglement enables secure group key distribution among multiple users while providing immunity against hacking attacks targeting source devices, thereby realizing source-independent quantum conference key agreement (SI-QCKA). However, previous experimental demonstrations of SI-QCKA have encountered substantial technical challenges, primarily d

Himanshu Verma, Priyanka Sarmah, Joseph Silk, Kingman Cheung

The resolution of photon rings of Sgr~A$^*$ and M87 is the next milestone of upcoming EHT-like interferometries. We extend the formalism developed in our previous work~\cite{Verma:2023hes} to constrain primordial black hole (PBH) dark matter using microlensing-induced distortions of black hole shadows. Building upon the theoretical framework for microlensing

Renjun Duan, Weiqiang Wang, Yong Wang

In this paper, we investigate the existence of 2-D Taylor-Couette flow for a rarefied gas between two coaxial rotating cylinders, characterized by differing angular velocities at the outer boundary $\{r=1\}$ and the inner boundary $\{r=r_{1}>0\}$, with a small relative strength denoted by $\alpha$. We formulate the problem using the steady Boltzmann equation

Jayden Lang, Wan Tang

In this paper, we consider the (1+2)-dimensional oscillatory integral with degenerate cubic homogeneous polynomial phase. We prove that the $L^{2}$ decay rate of 3/8 given in (Archiv der Mathematik, 122: 437-447, 2024) is sharp.

HaoNan Tang

Vision Transformers (ViT) have demonstrated significant promise in dense prediction tasks such as pose estimation. However, their performance is frequently constrained by the overly simplistic front-end designs employed in models like ViTPose. This naive patchification mechanism struggles to effectively handle multi-scale variations and results in irreversib

Xian Wu, Ming Zhang, Zhiyu Fang, Fei Li

The automation of user interface development has the potential to accelerate software delivery by mitigating intensive manual implementation. Despite the advancements in Large Multimodal Models for design-to-code translation, existing methodologies predominantly yield unstructured, flat codebases that lack compatibility with component-oriented libraries such

Andreas Zinonos, Michał Stypułkowski, Antoni Bigata, Stavros Petridis

We present FlashLips, a two-stage, mask-free lip-sync system that decouples lips control from rendering and achieves real-time performance, with our U-Net variant running at over 100 FPS on a single GPU, while matching the visual quality of larger state-of-the-art models. Stage 1 is a compact, one-step latent-space editor that reconstructs an image using a r

Chang Sun, Dongliang Xie, Wanpeng Xie, Bo Qin

Visual speech recognition (VSR) aims to transcribe spoken content from silent lip-motion videos and is particularly challenging in Mandarin due to severe viseme ambiguity and pervasive homophones. We propose VALLR-Pin, a two-stage Mandarin VSR framework that extends the VALLR architecture by explicitly incorporating Pinyin as an intermediate representation.

Jiefeng Xu, Xueli Bai, Dong-Hui Li

We study the general $(\boldsymbol{\sigma},\mathbf{p})$-eigenvalue problem of nonnegative tensors introduced by A. Gautier, F. Tudisco, and M. Hein [SIAM J. Matrix Anal. Appl., 40 (2019), pp. 1206--1231], which unifies several well-studied tensor eigenvalue and singular value problems. First, we propose an alternative min-max Collatz--Wielandt formula for th

Arjun Sharma, Donald L. Koch

We present a semi-analytical framework to compute the suspension stress in dilute particle-laden non-Newtonian fluids, separating Newtonian and non-Newtonian contributions. The ensemble-averaged stress includes both the particle-induced non-Newtonian stress (PINNS) and an interaction stresslet arising from surface tractions due to the non-Newtonian stress an

Lin Li, Jiahui Li, Jiaming Lei, Jun Xiao

Compositional zero-shot learning (CZSL) aims to recognize unseen state-object compositions by generalizing from a training set of their primitives (state and object). Current methods often overlook the rich hierarchical structures, such as the semantic hierarchy of primitives (e.g., apple fruit) and the conceptual hierarchy between primitives and composition

Yuan Gao, Zhenguo Dong, Xuelong Wang, Zhiqiang Wang

Accurate and interpretable forecasting of multivariate time series is crucial for understanding the complex dynamics of cryptocurrency markets in digital asset systems. Advanced deep learning methodologies, particularly Transformer-based and MLP-based architectures, have achieved competitive predictive performance in cryptocurrency forecasting tasks. However

Ming Gu, David Hirshleifer, Siew Hong Teoh, Shijia Wu

We study dynamic visual representations as a proxy for investor sentiment about the stock market. Our sentiment index, GIFsentiment, is constructed from millions of posts in the Graphics Interchange Format (GIF) on a leading investment social media platform. GIFsentiment correlates with seasonal mood variations and the severity of COVID lockdowns. It is posi

Ziwei Qin, Xuhui Song, Deqing Huang, Na Qin

Graph neural networks are increasingly applied to multimodal medical diagnosis for their inherent relational modeling capabilities. However, their efficacy is often compromised by the prevailing reliance on a single, static graph built from indiscriminate features, hindering the ability to model patient-specific pathological relationships. To this end, the p

Anthony Dontoh, Stephanie Ivey, Armstrong Aboah

Despite increasing interest in computer vision-based distracted driving detection, most existing models rely exclusively on driver-facing views and overlook crucial environmental context that influences driving behavior. This study investigates whether incorporating road-facing views alongside driver-facing footage improves distraction detection accuracy in

Naishan Zheng, Jie Huang, Qingpei Guo, Feng Zhao

Understanding long videos with multimodal large language models (MLLMs) remains challenging due to the heavy redundancy across frames and the need for temporally coherent representations. Existing static strategies, such as sparse sampling, frame compression, and clustering, are optimized for offline settings and often produce fragmented or over-compressed o

Shi-Chao Chen, Chuan-Chuan Wu

Let $n\ge1$, $r\ge0$ and $s\ge0$ be integers satisfying $4+r+3 s\le3^{n+1}$. Given linear polynomials $f_{i}(x)=m_{i} x+n_{i}$ for $1 \le i \le r+s$, where the coefficients $m_{i} , n_{i}$ are positive integers satisfying certain conditions, we prove that there exist infinitely many fundamental discriminants $D>0$ such that the 3-rank of the class group of e

Kian Godhwani, David Benrimoh

Introduction: Recent work suggests large language models (LLMs) can accelerate screening, but prior evaluations focus on earlier LLMs, standardized Cochrane reviews, single-model setups, and accuracy as the primary metric, leaving generalizability, configuration effects, and calibration largely unexamined. Methods: We developed OLIVER (Optimized LLM-based In

Anna R. Flowers, Christopher T. Franck, Robert B. Gramacy, Justin A. Krometis

Collecting operationally realistic data to inform machine learning models can be costly. Before collecting new data, it is helpful to understand where a model is deficient. For example, object detectors trained on images of rare objects may not be good at identification in poorly represented conditions. We offer a way of informing subsequent data acquisition

Marios Papamichalis, Regina Ruane

Spatial networks are typically assortative: well-connected nodes link to other well-connected nodes, and the usual reading is sorting, popular nodes seeking each other out. In space there is a rival explanation: nearby nodes draw on the same pool of potential neighbors, so their degrees move together even when popularity and location are unrelated. This pape

Xiaobo Zeng, Pan Liu, Liangcai Chen, Ruonan Deng

We propose and demonstrate an elastic digital-analog radio-over-fiber (RoF) modulation and demodulation architecture, seamlessly bridging A-RoF and D-RoF solutions, achieving quasilinear SNR scaling with respect to 1/{\eta}, and evidenced by R^2=0.9908.

Hexu Zhao, Xiaoteng Liu, Xiwen Min, Jianhao Huang

Point-based Differentiable Rendering (PBDR) enables high-fidelity 3D scene reconstruction, but scaling PBDR to high-resolution and large scenes requires efficient distributed training systems. Existing systems are tightly coupled to a specific PBDR method. And they suffer from severe communication overhead due to poor data locality. In this paper, we present

Anubhav Gupta, Abhinav Gupta

The Heyland circle diagram is a classical graphical tool for representing the steady-state behavior of induction machines using no-load and blocked-rotor test data. While widely used in alternating-current machinery texts, the diagram is typically presented as a hand-constructed aid and lacks a standardized computational formulation. This paper presents Heyl

Zepeng Xin, Kaiyu Li, Luodi Chen, Wanchen Li

Effectively grounding complex language to pixels in remote sensing (RS) images is a critical challenge for applications like disaster response and environmental monitoring. Current models can parse simple, single-target commands but fail when presented with complex geospatial scenarios, e.g., segmenting objects at various granularities, executing multi-targe

Qiushuo Hou, Sangwoo Park, Matteo Zecchin, Yunlong Cai

Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretation, and network optimization. However, their deployment in practice must balance inference cost, latency, and reliability. In this work, we study an edge-cloud-expert cascaded LLM

Xiaobo Zeng, Liangcai Chen, Pan Liu, Ruonan Deng

We propose and demonstrate a power-fading-aware noise-shaping technique for C-band IMDD system with low resolution DAC, which shapes and concentrates quantization noise within the fading-induced notch areas, yielding 94% improvement in data-rate over traditional counterpart.

Tuo Wu, Xiazhi Lai, Shihang Lu, Zihao Chen

The exponential proliferation of mobile devices and data-intensive applications in future wireless networks imposes substantial computational burdens on resource-constrained devices, thereby fostering the emergence of over-the-air computation (AirComp) as a transformative paradigm for edge intelligence.} To enhance the efficiency and scalability of AirComp s

Zhihan Huang, Ziang Niu

Goodness-of-fit (GoF) tests are fundamental for assessing model adequacy. Score-based tests are appealing because they require fitting the model only once under the null. However, extending them to powerful nonparametric alternatives is difficult due to the lack of suitable score functions. Through a class of exponentially tilted models, we show that the res

Sukumar Kishanthan, Asela Hevapathige

Class imbalance is a common challenge in machine learning and data mining, often leading to suboptimal performance in classifiers. While deep learning excels in feature extraction, its performance still deteriorates under imbalanced data. In this work, we propose a novel activation function, named OGAB, designed to alleviate class imbalance in deep learning

Xinyang Sun, Nipon Sarmah, Miao Guo

The extraction of structured knowledge from scientific literature remains a major bottleneck in nutraceutical research, particularly when identifying microbial strains involved in compound biosynthesis. This study presents a domain-adapted system powered by large language models (LLMs) and guided by advanced prompt engineering techniques to automate the iden

Chaofeng Yuan, Sainan Xu, Xingbing Kong, Jianhua Guo

In this study, we propose a novel model called the Markov-switching dynamic matrix factor (Ms-DMF) model, which serves the dual purpose of structural interpretation and prediction for high-dimensional matrix time series. When estimating the parameters of the Ms-DMF model, an EM (expectation maximization) algorithm was used to get a quasi-maximum likelihood e

Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, Longzhi Yang

Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings. First, we demonstrate the effectiveness of graph-based class

Khaled Kahouli, Romuald Elie, Klaus-Robert Müller, Quentin Berthet

Sampling from unnormalized probability densities is a pervasive challenge across the computational and physical sciences. Diffusion models provide a powerful generative framework for this task, but their success relies on accurately estimating the score of the perturbed target distribution. Current approaches face a dichotomy between two standard estimation

Jiacheng You, Jingcheng Yang, Yuhang Xie, Zhongxuan Wu

Time-series forecasting in real-world applications such as finance and energy often faces challenges due to limited training data and complex, noisy temporal dynamics. Existing deep forecasting models typically supervise predictions using full-length temporal windows, which include substantial high-frequency noise and obscure long-term trends. Moreover, auxi

Zhenhao Li, Shaohan Yi, Zheng Liu, Leonartinus Gao

Diffusion models (DMs) have recently achieved impressive photorealism in image and video generation. However, their application to image animation remains limited, even when trained on large-scale datasets. Two primary challenges contribute to this: the high dimensionality of video signals leads to a scarcity of training data, causing DMs to favor memorizati

Rebecca Szabó, Valentin D. Ivanov, M. Švanda

As of late 2025 there are about 70 exoplanets that meet the formal criterion of having equilibrium temperatures allowing the presence of liquid water and about 50 of them orbit M-stars, known for their strong chromospheric activity. Most of these stars are close to the Sun and the planet-to-star mass and luminosity ratios are advantageous, allowing for a mor

Kexin Nie, Xin Tang, Mengyao Guo, Ze Gao

This workshop explores innovative human-AI collaboration methodologies in HCI visual storytelling education through our established "gap-and-fill" approach. Drawing on Eastern aesthetic philosophies of intentional emptiness, including Chinese negative-space traditions, Japanese "ma" concepts, and contemporary design minimalism, we demonstrate how educators c

Kang Lu, Weiqiang Wang, Alex Weekes

Associated to all quasi-split Satake diagrams of type ADE and even spherical coweights $\mu$, we introduce the shifted iYangians ${}^\imath Y_\mu$ and establish their PBW bases. We construct the iGKLO representations of ${}^\imath Y_\mu$, which factor through quotients called truncated shifted iYangians ${}^\imath Y_\mu^\lambda$. In type AI with $\mu$ domina

Yanjing Yang, He Zhang, Bohan Liu, Jinwei Xu

Broken Access Control (BAC) violations, which consistently rank among the top five security risks in the OWASP API Security Top 10, refer to unauthorized access attempts arising from BAC vulnerabilities, whose successful exploitation can impose significant risks on exposed application programming interfaces (APIs). In recent years, learning-based methods hav

Suman Chakraborty, Bongseok Kim, Li Qiao

The study of interfacial fluxes under evaporative or condensation processes are ubiquitous in thermal systems, propulsion devices, and many other engineering applications. Most continuum scale models fail to capture the true nature of thermodynamic property variation across the interface, particularly under high-temperature and high-pressure conditions. An i

Ming Li, Chenrui Fan, Yize Cheng, Soheil Feizi

Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statistics. We adopt Schoenfeld's Episode Theory as an inductive, intermediate-scale lens and introduce ThinkARM (Anatomy of Reasoning in Models), a scalable framework that explicitly abs

James Kotary, Natalie Isenberg, Draguna Vrabie

A central challenge in the design of energy-efficient wind farms is the presence of wake effects between turbines. When a wind turbine harvests energy from free wind, it produces a turbulent region with reduced energy for downstream turbines. Strategies for increasing the efficiency of wind farms by mitigating wake effects have been the subject of much compu

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on $(2.712\pm0.014)\times10^9$ $\psi(3686)$ events collected with the BESIII detector, the decays $\chi_{cJ} \to p \bar{p} K^{0}_{S} K^{0}_{S}$ ($J=0,1,2$) are observed for the first time with statistical significances exceeding $5\sigma$.The measured branching fractions are $\mathcal{B}(\chi_{c0}\to p \bar p K^{0}_{S} K^{0}_{S})=(6.94\pm0.30\pm0.38)\t

Zeraoulia Rafik, Alvaro H Salas, David L Ocampo

In this note we present a new special function that behaves like the error function and we provide an approximated accurate closed form for its CDF in terms of both Chebyshev polynomials of the first kind and the error function. Also, we provide its series representation using Pad\'e approximant. We show convincing numerical evidence of an accuracy of $10^{-

Zhe Sun, Xueyuan Yang, Yujie Lu, Zhenliang Zhang

The integration of embodied agents into human environments demands embodied social intelligence: reasoning over both social norms and physical constraints. However, existing evaluations fail to address this integration, as they are limited to either disembodied social reasoning (e.g., in text) or socially-agnostic physical tasks. Both approaches fail to asse

John Cartmell, Mihaela Cardei, Ionut Cardei

We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array representation using hash-based encoding, producing a fixed-length feature space that reduces memory usage and obfuscates original feature values. The encoding does not rely on keyed hashing; however, a key can optio

Peng Gao, Ke Li, Di Wang, Yongshan Zhu

Cross-resolution land cover mapping aims to produce high-resolution semantic predictions from coarse or low-resolution supervision, yet the severe resolution mismatch makes effective learning highly challenging. Existing weakly supervised approaches often struggle to align fine-grained spatial structures with coarse labels, leading to noisy supervision and d

Tamim Ahasan Rijon, Yeasin Arafath

As a significant agricultural country, Bangladesh utilizes its fertile land for guava cultivation and dedicated labor to boost its economic development. In a nation like Bangladesh, enhancing guava production and agricultural practices plays a crucial role in its economy. Anthracnose and fruit fly infection can lower the quality and productivity of guava, a

Wenwu Gao, Le Hu, Xingping Sun, Xuan Zhou

We propose and study a general quasi-interpolation framework for stochastic function approximation, which stems and draws motivation from convolution-type solutions for certain practical weighted variational problems. We obtain our quasi-interpolants using Monte Carlo discretization of the pertinent integrals and establish a family of $L^p$-McDiarmid-type co

Nikolaos Iliopoulos

Metaheuristic algorithms for cardinality-constrained portfolio optimization require repair operators to map infeasible candidates onto the feasible region. Standard Euclidean projection treats assets as independent and can ignore the covariance structure that governs portfolio risk, potentially producing less diversified portfolios. This paper introduces Cov

Ali Zeytoon-Nejad

This paper presents a novel quantitative approach for comparative economic studies, addressing limitations in current classification methods. Conventional approaches in comparative economics often rely on ad hoc and categorical classifications, leading to subjective judgments and disregarding the continuous nature of the spectrum of economic systems. These c

Ali Zeytoon-Nejad

This article expands Milton Friedman's spending matrix to analyse 'spending efficiency' and 'preference compatibility' across different economic systems against five key outcome criteria. By generalising Friedman's typology, it compares efficiency and freedom as systems shift from laissez-faire capitalism to communism, illustrating a gradual deterioration in

Ziyuan Guo, Jie Guo, Zhenghao Chen, Bin Song

Multimodal recommender systems (MRSs) are critical for various online platforms, offering users more accurate personalized recommendations by incorporating multimodal information of items. Structure-based MRSs have achieved state-of-the-art performance by constructing semantic item graphs, which explicitly model relationships between items based on modality

Le Feng, Li Xiao

In recent years, the integration of pre-trained foundational models with multiple instance learning (MIL) has improved diagnostic accuracy in computational pathology. However, existing MIL methods focus on optimizing feature extractors and aggregation strategies while overlooking the complex semantic relationships among instances within whole slide image (WS

Mengyue Zhang, Changhui Li

Various optical ultrasound microsensors have been developed with size ranging from tens to hundreds of micrometers. However, it becomes challenging to further minimize these sensors' size. In this work, we proposed a method that use a tapered fiber-based micro-waveguide (TFMW) attaching to the optical microsensor to bypass this challenge. The TFMW not only s

Zhe Yin, Xiaodong Gu, Beijun Shen

Code language models excel on code intelligence tasks, yet their internal interpretability is underexplored. Existing neuron interpretability techniques from NLP are suboptimal for source code due to programming languages formal, hierarchical, and executable nature. We empirically investigate code LLMs at the neuron level, localizing language-specific neuron

Yun-Mei Li, Yongwei Huang, Kai Chang

We introduce a class of superconductors termed "quantized quadrupole superconductors" that support Majorana corner modes according to the bulk-corner correspondence, distinct from previous works on the second-order topological superconductors. An intrinsic physical quantity for superconductors, i.e., the quadrupole moment serves as the topological invariant,

Fernando M. de Paula Neto, Lucas dos Reis Silva, Paulo S. G. de Mattos Neto, Felipe F. Fanchini

The performance of quantum neural network models depends strongly on architectural decisions, including circuit depth, placement of parametrized operations, and data-encoding strategies. Selecting an effective architecture is challenging and closely related to the classical difficulty of choosing suitable neural-network topologies, which is computationally h

A. Yu. Volkov, G. A. Koroteev, Yu. S. Volkov

We introduce Quantum Index Algebra (QIA) as a finite, index-based algebraic framework for representing and manipulating quantum operators on Hilbert spaces of dimension $2^m$. In QIA, operators are expressed as structured combinations of basis elements indexed by Boolean codes, allowing products, commutators, and conjugations to be computed through finite ru

C. Ramírez-Dolores, J. C. Zamora-Luria, J. A. Altamirano-Acosta, L. Sarao-Cruz

The use of Earth-Air-Water Heat Exchangers (EAWHE) for sustainable air conditioning has not been widely studied. Due to their experimental nature, methods of characterizing internal thermal air distribution impose high dependence on instrumentation by sensors and entail data acquisition and computational costs. This document presents an alternative method th

Vincent E. Coll

Over a field of characteristic $0$ we give a concrete, computation--ready description of Jordan algebra structures and their low--order deformation theory. The Jordan identity is quartic in the elements and cubic in the multiplication, and in characteristic $0$ it is equivalent to its standard four--variable polarization. We encode this polarization as a cub

Borui Du, Kawon Han, Christos Masouros

As integrated sensing and communication (ISAC) systems are deployed in next-generation wireless networks, a new security vulnerability emerges, particularly in terms of sensing privacy. Unauthorized sensing eavesdroppers (Eve) can potentially exploit the ISAC signal for their own independent passive sensing. However, solutions for sensing-secure ISAC remain

Jun Yuan, Shan Liu, Shangwei Lin, Aixia Liu

The $S$-Steiner tree packing problem provides mathematical foundations for optimizing multi-path information transmission, particularly in designing fault-tolerant parallelized routing architectures for massive-scale network infrastructures. In this article, we propose the definitions of completely independent $S$-Steiner trees (CISSTs for short) and general

Pengchao Han, Xi Huang, Yi Fang, Guojun Han

Collaborative learning has emerged as a key paradigm in large-scale intelligent systems, enabling distributed agents to cooperatively train their models while addressing their privacy concerns. Central to this paradigm is knowledge distillation (KD), a technique that facilitates efficient knowledge transfer among agents. However, the underlying mechanisms by