December 2025 arXiv papers — page 115

David Strnadel

This paper presents an exploratory, simulator-based proof of concept investigating whether differentiable energy terms derived from parameterized quantum circuits can serve as auxiliary regularization signals in Generative Adversarial Networks (GANs). We augment the Auxiliary Classifier GAN (ACGAN) generator objective with a Variational Quantum Eigensolver (

Sidhant Sundrani, Francesco Tudisco, Pasquale Minervini

Approaches for compressing large-language models using low-rank decomposition have made strides, particularly with the introduction of activation and loss-aware SVD, which improves the trade-off between decomposition rank and downstream task performance. Despite these advancements, a persistent challenge remains--selecting the optimal ranks for each layer to

Malinda Iluppangama, Dilmi Abeywardana, Chris Tsokos

Parkinson's Disease (PD) is a devastating neurodegenerative disorder that affects millions of people around the globe. Many researchers are continuously working to understand PD and develop treatments to improve the condition of PD patients, which affects their day-to-day lives. Since the last decades, the treatment, Deep Brain Stimulation (DBS) has given pr

Zhenyu Chen, Bin Cheng, Minbo Gao, Xiaodie Lin

Noise in quantum hardware is the primary obstacle to realizing the transformative potential of quantum computing. Quantum error mitigation (QEM) offers a promising pathway to enhance computational accuracy on near-term devices, yet existing methods face a difficult trade-off between performance, resource overhead, and theoretical guarantees. In this work, we

Hong Liu, Jingjing Tian, Yiping Li, Yuyang Zhong

This study constructs a dual-model framework integrating Ordinary Differential Equations (ODE) and Petri Nets (PN) to analyze the 2025 Chikungunya outbreak in Foshan City, China. We employ SEICR compartmental modeling to compare two distinct approaches under identical epidemiological scenarios and evaluate intervention effectiveness through three-phase fitti

Suraj Kumar Sah, Love Kumar Sah

Modern computing systems face security threats, including memory corruption attacks, speculative execution vulnerabilities, and control-flow hijacking. Although existing solutions address these threats individually, they frequently introduce performance overhead and leave security gaps. This paper presents a Variable Record Table (VRT) with a unified hardwar

Srimanta Middey, Nandana Bhattacharya, Rukma Nevgi, Suresh Chandra Joshi

Perovskite oxides ($AB$O$_3$) have long been central to the advancement of modern condensed matter physics, owing to their rich and tunable electronic and magnetic properties. The quest to understand their various entangled phases has spurred the development of both cutting-edge experimental tools and innovative theoretical frameworks. In recent times, the e

Isaac Adjetey, Yiyuan She

Modeling response surfaces with abrupt jumps and discontinuities remains a major challenge across scientific and engineering domains. Although Gaussian process models excel at capturing smooth nonlinear relationships, their stationarity assumptions limit their ability to adapt to sudden input-output variations. Existing nonstationary extensions, particularly

Pei-Kun Yang

Classical molecular docking is fundamentally constrained by the combinatorial growth of ligand translational and rotational degrees of freedom, rendering exhaustive pose enumeration infeasible on classical hardware. This work introduces a quantum-native formulation that encodes ligand occupancy on discretized three-dimensional grids and coherently generates

Takayuki Yokoyama, Yasuhiro Tada

We theoretically study Landau quantization of itinerant Majorana quasiparticles induced by lattice strain in a Kitaev spin liquid with Majorana Fermi surfaces. We consider the isotropic spin-1/2 Kitaev model on the honeycomb lattice with a perturbation such as a staggered Zeeman field and an electromagnetic field, which generates small Majorana Fermi surface

Weijie Yang, Xun Zhang, Simin Jiang, Yubao Zhou

The estimation of high-dimensional physical parameters constrained by partial differential equations (PDEs) from limited and indirect measurements is a highly ill-posed problem. Traditional methods face significant accuracy and efficiency bottlenecks, particularly when observations are sparse, irregularly sampled, and constrained by real-world sensor placeme

Anton Kobelev, Nikolay Andrianov, Yuri Barsukov, Alexander Smirnov

Consideration is given to inductively coupled BCl3-plasma (ICP) treatment of the GaN surface, which is a promising technique to get the low resistance ohmic contacts in GaN-based transistors. In some cases, BCl3 plasma treatment results in ohmic contact degradation because BClx radicals tend to form a polymer thin film BxCly on the surface. In the present wo

Erdhi Widyarto Nugroho, R. Rizal Isnanto, Luhur Bayuaji

The Federated Byzantine Agreement (FBA) achieves rapid consensus by relying on overlapping quorum slices. But this architecture leads to a high dependence on the availability of validators when about one fourth of validators go down, the classical FBA can lose liveness or fail to reach agreement. We thus come up with an Adaptive FBA architecture that can rec

Zachary Chase, Steve Hanneke, Shay Moran, Jonathan Shafer

We resolve a 30-year-old open problem concerning the power of unlabeled data in online learning by tightly quantifying the gap between transductive and standard online learning. In the standard setting, the optimal mistake bound is characterized by the Littlestone dimension $d$ of the concept class $H$ (Littlestone 1987). We prove that in the transductive se

Ling-Qi Huang, Shih-Chung Chen, Chia-Hao Lin, Li-Tzu Wang

We present a plane--convex open microcavity that supports room-temperature polariton spectroscopy and offers a simple geometric handle on the coupling rate. The effective length ($L_{\mathrm{eff}}$) is absolutely calibrated from the free-spectral range, and piezo tuning is performed at near-normal incidence ($k_{\parallel}!\approx!0$) to avoid angle-induced

Weikang Bai, Yongkun Du, Yuchen Su, Yazhen Xie

Mathematical Expression Recognition (MER) has made significant progress in recognizing simple expressions, but the robust recognition of complex mathematical expressions with many tokens and multiple lines remains a formidable challenge. In this paper, we first introduce CMER-Bench, a carefully constructed benchmark that categorizes expressions into three di

Shujing Pan, Julian Scheuer

We study a new notion of convexity for subsets of the unit sphere, which closely resembles the horo-convexity for subsets of the hyperbolic space. We call this notion, accordingly, horo-convexity. For horo-convex hypersurfaces of the unit sphere, we prove the smooth convergence of the classical Guan/Li flow of inverse type and use this result to prove the fu

Omar Abdul-Aziz, Danilo Comini, Johannes Lang, Nils Bartel

Revealing the interactions binding electronic and lattice components of cooperative quantum order is central to sculpting new states of matter. This challenge is epitomized by the charge density wave material 1T-TiSe$_2$, where photoexcitation disrupts its presumed hybrid exciton-phonon order. This exposes a paradox: the electronic component collapses within

Tian Shi, Wenkun Wen, Peiran Wu, Minghua Xia

Low-altitude wireless networks are increasingly vital for the low-altitude economy, enabling wireless coverage in high-mobility and hard-to-reach environments. However, providing reliable connectivity to sparsely distributed aerial users in dynamic three-dimensional (3D) spaces remains a significant challenge. This paper investigates downlink coverage enhanc

Víctor Hernández-Santamaría, Subrata Majumdar, Luz de Teresa

This paper investigates the global controllability properties of the Cahn--Hilliard equation posed on the $d$-dimensional flat torus $\mathbb{T}^d$. We first establish small-time global approximate controllability of the system by means of controls acting on finitely many Fourier modes, relying on techniques inspired by geometric control theory. We then prov

Kedarnath Buda, B. V. Rathish Kumar, Anil Rathi

This paper investigates the Nash equilibrium of a bi-objective optimal control problem governed by the Stokes equations. A multi-objective Nash strategy is formulated, and fundamental theoretical results are established, including the existence, uniqueness, and analytical characterization of the equilibrium. A finite element framework is developed to approxi

Xinqi Jin, Hanxun Yu, Bohan Yu, Kebin Liu

Online video understanding is essential for applications like public surveillance and AI glasses. However, applying Multimodal Large Language Models (MLLMs) to this domain is challenging due to the large number of video frames, resulting in high GPU memory usage and computational latency. To address these challenges, we propose token pruning as a means to re

Ahmed Ryan, Junaid Mansur Ifti, Md Erfan, Akond Ashfaque Ur Rahman

The widespread adoption of open-source ecosystems enables developers to integrate third-party packages, but also exposes them to malicious packages crafted to execute harmful behavior via public repositories such as PyPI. Existing datasets (e.g., pypi-malregistry, DataDog, OpenSSF, MalwareBench) label packages as malicious or benign at the package level, but

Erwin Frey, Henrik Weyer

Intracellular protein patterns govern essential cellular functions by dynamically redistributing proteins between membrane-bound and cytosolic states, conserving their total numbers. This review presents a theoretical framework for understanding such patterns based on mass-conserving reaction--diffusion systems. The emergence, selection, and evolution of pat

Shining Yang, Jianbo Lu, Mou Xu, Yu Liu

This paper primarily explores the dynamics of charged particle in the magnetized SV spacetime, and constrains the parameters of the SV spacetime along with its surrounding magnetic fields. The constraints are given by using $\chi^2$ analysis combined with high-frequency quasi-periodic oscillation (HFQPO) data observed from three microquasars: GRS 1915+105, X

Daniel Sebbag

We study simple extensions of pointed finite tensor categories, that is, tensor categories $\mathcal{C}$ admitting an abelian decomposition $\mathcal{C} \cong \mathcal{D} \oplus \mathcal{M}$ where $\mathcal{D}$ is a pointed tensor subcategory and $\mathcal{M}$ has a unique simple projective object. Such categories provide a natural generalization of near-gro

Friedemann Krannich

We present a dynamical version for the multi-marginal optimal transport problem with infimal convolution cost, using the theory of Wasserstein barycentres. We show, how our formulation relates to the dynamical version of the multi-marginal optimal transport problem developed by Pass and Shenfeld (arXiv:2509.22494v2).

Junfeng Liu, Qinghua Zhao, Liwei Ni, Jingren Wang

Logic optimization constitutes a critical phase within the Electronic Design Automation (EDA) flow, essential for achieving desired circuit power, performance, and area (PPA) targets. These logic circuits are typically represented as Directed Acyclic Graphs (DAGs), where the structural depth, quantified by node level, critically correlates with timing perfor

Muhammad Hassnain, Anirudh Basu, Ethan Ng, Caleb Stanford

Supply chain attacks threaten open-source software ecosystems. This paper proposes a formal framework for quantifying trust in third-party software dependencies that is both formally checkable - formalized in satisfiability modulo theories (SMT) - while at the same time incorporating human factors, like the number of downloads, authors, and other metadata th

Jifei Liu, Zhi Chen, Yuanguang Zhong

Problem definition: Although large language models (LLMs) are increasingly integrated into business decision making, their potential to replicate and even amplify human cognitive biases cautions a significant, yet not well-understood, risk. This is particularly critical in high-stakes operational contexts like supply chain management. To address this, we inv

Dewen Suo, Lei Xue, Weihao Huang, Runze Tan

As the dominant mobile operating system, Android continues to attract a substantial influx of new applications each year. However, this growth is accompanied by increased attention from malicious actors, resulting in a significant rise in security threats to the Android ecosystem. Among these threats, the adoption of Anti-Runtime Analysis (ARA) techniques by

Jie Wang

Distributionally robust optimization (DRO) has emerged as a powerful paradigm for reliable decision-making under uncertainty. This paper focuses on DRO with ambiguity sets defined via the Sinkhorn discrepancy: an entropy-regularized Wasserstein distance, referred to as Sinkhorn DRO. Existing work primarily addresses Sinkhorn DRO from a dual perspective, leve

Shaif Chowdhury, Mushfika Rahman, Greg Hamerly

We propose a supervised contrastive learning framework for video representation learning that leverages temporally global context. We introduce a video to image aggregation strategy that spatially arranges multiple frames from each video into a single input image. This design enables the use of pre trained convolutional neural network backbones such as ResNe

Yesid Fonseca, Manuel S. Ríos, Nicanor Quijano, Luis F. Giraldo

Patch foraging involves the deliberate and planned process of determining the optimal time to depart from a resource-rich region and investigate potentially more beneficial alternatives. The Marginal Value Theorem (MVT) is frequently used to characterize this process, offering an optimality model for such foraging behaviors. Although this model has been wide

Madhukrishna Chakraborty, Subenoy Chakraborty

Traversable wormholes are one of the most exciting predictions of General Relativity that offer short-cuts through space-time. However, their feasibility requires the violation of the null energy condition and makes their detection a bit difficult. This paper aims to show the new avenues delving deep into the observational prospects of TWHs via quasinormal m

Andrew R. Booker, Min Lee

We prove a conjecture of Ross concerning the value distribution of $\dim S_2^{\rm new}(\Gamma_0(N))$ for $N\in\mathbb{N}$, as well as analogous results for general weight $k\in2\mathbb{N}$ and the full and twist-minimal spaces $S_k(\Gamma_0(N))$, $S_k^{\rm min}(\Gamma_0(N))$.

Bin Mu, Yuxuan Chen, Shijin Yuan, Bo Qin

Accurate subseasonal-to-seasonal (S2S) prediction of extreme events is critical for resource planning and disaster mitigation under accelerating climate change. However, such predictions remain challenging due to complex multi-sphere interactions and intrinsic atmospheric uncertainty. Here we present TianXing-S2S, a multi-sphere coupled probabilistic model f

Yiming Zeng, Jinghan Cao, Zexin Li, Wanhao Yu

Instruction-based text editing is increasingly critical for real-world applications such as code editors (e.g., Cursor), but Large Language Models (LLMs) continue to struggle with this task. Unlike free-form generation, editing requires faithfully implementing user instructions while preserving unchanged content, as even minor unintended modifications can br

Shaif Chowdhury, Soham Biren Katlariwala, Devleena Kashyap

In social media networks a small number of highly influential users can drive large scale changes in discourse across multiple communities. Small shifts in the behavior of these users are often sufficient to propagate widely throughout the network. A similar phenomenon occurs during neural network fine tuning. Conventional fine tuning of convolutional neural

Taekyun Kim, Dae san Kim

This paper introduces a degenerate version of the Euler-Seidel matrix method by incorporating a parameter lambda into the classical recurrence relation. The standard Euler-Seidel method relates the generating functions of an initial sequence and its final sequence via Seidel's formula, Our generalized method establishes transformation formulas using lambda-g

Osvaldo L. Santos-Pereira, Everton M. C. Abreu, Marcelo B. Ribeiro

This work analyzes the Darmois junction conditions matching an interior Alcubierre warp drive spacetime to an exterior Minkowski geometry. The joining hypersurface requires that the shift vector of the warp drive spacetime must satisfy the solution of a particular inviscid Burgers equation, namely, the gauge where the shift vector is not a function of the $y

Jin Woo Jang, Seok-Bae Yun

We study steady solutions to the relativistic Boltzmann equation with hard-sphere interactions in a slab geometry. Under a spatial symmetry assumption in the transverse variables $x_2$ and $x_3$, the problem reduces to a one-dimensional spatial slab $x_1 \in [0,1]$ while retaining full three-dimensional momentum dependence. For non-negative inflow boundary c

Huan Huang, Michele Esposito, Chen Zhao

Accurate coronary artery segmentation from coronary computed tomography angiography is essential for quantitative coronary analysis and clinical decision support. Nevertheless, reliable segmentation remains challenging because of small vessel calibers, complex branching, blurred boundaries, and myocardial interference. We propose a coronary artery segmentati

Martin J. Gander, Yao-Lin Jiang, Hui Zhang

The oscillatory waves require sufficient degrees of freedom to resolve. That restriction usually applies also to coarse problems for Schwarz methods. The resulting coarse problem is then too large. To address the issue, a new form of Schwarz methods with coarse correction is proposed for the Helmholtz problem. There are two components in the proposed form: r

Agniva Maiti, Manya Pandey, Murari Mandal

The vast majority of the world's languages, particularly creoles like Nagamese, remain severely under-resourced in Natural Language Processing (NLP), creating a significant barrier to their representation in digital technology. This paper introduces NagaNLP, a comprehensive open-source toolkit for Nagamese, bootstrapped through a novel methodology that relie

Arastoo Zibaeirad, Marco Vieira

Large Language Models (LLMs) are increasingly being studied for Software Vulnerability Detection (SVD) and Repair (SVR). Individual LLMs have demonstrated code understanding abilities, but they frequently struggle when identifying complex vulnerabilities and generating fixes. This study presents DVDR-LLM, an ensemble framework that combines outputs from dive

Paul Buckingham

Let $r$ be a non-zero rational number. In a paper in the Transactions of the AMS in 2023, O'Desky and Richman gave a construction of a $p$-adic incomplete gamma-function $\Gamma_p(\cdot,r)$ for each prime $p$ for which $|r - 1|_p < 1$. Aside from the special case where $r = 1$, only finitely many primes satisfy that condition for a given $r$, so it is desira

Qi Sun, Can Wang, Jiaxiang Shang, Wensen Feng

We present Animus3D, a text-driven 3D animation framework that generates motion field given a static 3D asset and text prompt. Previous methods mostly leverage the vanilla Score Distillation Sampling (SDS) objective to distill motion from pretrained text-to-video diffusion, leading to animations with minimal movement or noticeable jitter. To address this, ou

Haryanto M. Siahaan

We construct the Kerr/CFT correspondence for extremal Kerr--Bertotti--Robinson (Kerr--BR) black holes, which are exact stationary solutions of the Einstein--Maxwell equations describing a rotating black hole immersed in a uniform Bertotti--Robinson electromagnetic universe. After reviewing the geometry, horizon structure, and thermodynamics of the Kerr--BR f

Duc A. Tran, Dung Truong, Duy Le

Server deployment is a fundamental task in mobile edge computing: where to place the edge servers and what user cells to assign to them. To make this decision is context-specific, but common goals are 1) computing efficiency: maximize the amount of workload processed by the edge, and 2) communication efficiency: minimize the communication cost between the ce

Gaofeng Dong, Kang Yang, Mani Srivastava

Existing Wi-Fi sensing systems rely on injecting high-rate probing packets to extract channel state information (CSI), leading to communication degradation and poor deployability. Although Integrated Sensing and Communication (ISAC) is a promising direction, existing solutions still rely on auxiliary packet injection because they exploit only CSI from data f

Giovanni Ambrosi, Carla Aramo, Mattia Barbanera, Chiara Bartolini

The Cherenkov Telescope Array Observatory (CTAO) is a ground-based observatory that will improve upon the sensitivities of the current generation of very-high-energy gamma-ray instruments. The Schwarzschild-Couder Telescope (SCT) is a dual-mirror candidate design for a CTAO Medium-Sized Telescope (MST). The prototype Schwarzschild-Couder Telescope (pSCT) was

Indrakshi Dey, Ilias Cherkaoui, Mohamed Khalafalla Hassan

This note develops the first-ever noise-centric anomaly prediction method for a fused discrete-time signal. A Wavelet Packet Transform (WPT) provides a time--frequency expansion in which structure and residual can be separated via orthogonal projection. Higher-Order Statistics (HOS), particularly the third-order cumulant (and its bispectral interpretation),

Petrus J. Gerrits, Efe Erünal, M. Erdem Kabadayi, Ana Basiri

Accurate reconstruction of historical population distributions from the 1970s to the 1990s remains a significant limitation in global gridded population products due to coarse built-up data and limited census records. This study is, to our knowledge, the first to integrate declassified Hexagon KH-9 reconnaissance imagery into gridded population mapping. We e

Wang Jiaqi, Lan Yi, Chen Xiang

This study investigates AI integration in architectural education through a teaching experiment in Zhejiang University's 2024-25 grade three undergraduate design studio. Adopting a dual-module framework (20-hour AI training + embedded ethics discussions), the course introduced deep learning models, LLMs, AIGC, LoRA, and ComfyUI while maintaining the original

Agustín M. de los Riscos, Julio E. Sandubete, Diego Carmona-Fernández, León Beleña

This study applies Empirical Mode Decomposition (EMD) to the MSCI World index and converts the resulting intrinsic mode functions (IMFs) into graph representations to enable modeling with graph neural networks (GNNs). Using CEEMDAN, we extract nine IMFs spanning high-frequency fluctuations to long-term trends. Each IMF is transformed into a graph using four

Amirmasoud Geevechi, Robert L Jerrard

We construct novel solutions in $d\ge 3$ space dimensions of a family of nonlinear evolutions equations that includes the critical hyperbolic Abelian Higgs model (AHM). For the AHM, these solutions exhibit an ensemble of $N\ge 1$ slowly-moving, nearly parallel vortex filaments, whose leading-order dynamics are described by a wave map from ${\mathbb R}^{d-2}$

Linbin Yang, Taoran Zhou

In this paper, we consider the non-resistive axially symmetric Hall-MHD system. We show that the lifespan of their strong solutions can be arbitrarily large if their initial magnetic gradient are small enough. Precise lifespan lower bounds for both viscid and inviscid cases are given.

Wenqi Fang, Ye Li

Detecting critical transitions in complex, noisy time-series data is a fundamental challenge across science and engineering. Such transitions may be anticipated by the emergence of a low-dimensional order parameter, whose signature is often masked by high-amplitude stochastic variability. Standard contrastive learning approaches based on deep neural networks

Vandana Gupta, Shagun, Jasleen Kaur

This research paper examines the geometric structure of screen generic lightlike (SGL) submanifolds in an indefinite Sasakian statistical manifold equipped with a quarter-symmetric (QS) metric connection. The study focuses on analyzing the integrability conditions and the parallelism properties of various distributions associated with these submanifolds. It

Logan Carlson, Devesh Bhattarai, Mamata Subedi, Haifeng Wang

We have implemented principal component analysis for microscopic wide-field chemical imaging via coherent Raman spectroscopy. Microscopic imaging of nitrogen gas has been challenging due to extremely weak signals stemming from low order Raman interaction. Wide-field coherent Raman micro-spectroscopy has demonstrated the ability to chemically distinguish nitr

Abby Liu, Armando Gil, Moon-ki Choi, Berna Akgenc Hanedar

(Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ alloys are non-degenerate topological insulators (TIs) whose Dirac point (DP) can be tuned within the bulk bandgap by varying the composition, effectively reducing bulk conduction while allowing surface carrier conduction. Magnetotransport measurements of a series of (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ thin films indicate electron-domin

Jiabin Wang, Jinquan Luo

In this paper, we study the enumerative and asymptotic properties related to Hermitian $\ell$-complementary codes on the unitary space over $\F_{q^2}$. We provide some closed form expressions for the counting formulas of Hermitian $\ell$-complementary codes. There is a similarity in the asymptotic weight distribution between Hermitian self-orthogonal codes a

Alan Chen, Shuixin Xiao, Hailan Ma, Daoyi Dong

Quantum state tomography is a core task in quantum system identification. Real experimental conditions often deviate from nominal designs, introducing errors in both the measurement devices and the Hamiltonian governing the system's dynamics. In this paper, we investigate the robustness of quantum state tomography against such perturbations in both static an

Yue Hu

We investigate how neutral-ion collisional damping modifies the spectral properties and energy partition of compressible MHD turbulence using a suite of 3D two-fluid simulations. By systematically varying the neutral-ion coupling strength and decomposing the turbulent velocity field into Alfv\'en, slow, and fast (polarization) modes, we quantify how each mod

Md Zakir Hossain, Nitish K. Panigrahy, Walter O. Krawec, Don Towsley

Satellite-based quantum key distribution (QKD), leveraging low photon loss in free-space quantum communication, is widely regarded as one of the most promising directions to achieve global-scale QKD. With a constellation of satellites and a set of ground stations in a satellite-based QKD system, how to schedule satellites to achieve efficient QKD is an impor

Victoria Tiki, Eliu Huerta

Machine-learning methods offer a computationally efficient complement to conventional gravitational-wave searches, but their performance depends strongly on training data, preprocessing, inference design, and robustness to nonstationary detector noise. We present a framework for developing and evaluating lightweight neural detectors using real LIGO data. The

Xingyun Feng

Quantum convolutional neural networks (QCNNs) offer a promising architecture for near-term quantum machine learning by combining hierarchical feature extraction with modest parameter growth. However, any QCNN operating on classical data must rely on an encoding scheme to embed inputs into quantum states, and this choice can dominate both performance and reso

Zeyu Zhang, Hongkai Chen, Nicola Paoletti, Shan Lin

Resiliency is the ability of a system to quickly recover from a violation (recoverability) and avoid future violations for as long as possible (durability). In the spatial setting, recoverability and durability (now known as persistency) are measured in units of distance. Like its temporal counterpart, spatial resiliency is of fundamental importance for Cybe

Alicia, Lee, Mai Lee Chang, Sreehana Mandava

The increasing number of older adults who experience cognitive decline places a burden on informal caregivers, whose support with tasks of daily living determines whether older adults can remain in their homes. To explore how agents might help lower-SES older adults to age-in-place, we interviewed ten pairs of older adults experiencing cognitive decline and

Taylor A. Hutchison, Rebecca L. Larson, Pablo Arrabal Haro, Erini Lambrides

To date, many spectroscopic confirmations of z>7 galaxies have been obtained using JWST/NIRSpec prism observations, with most of their physical properties inferred from these observations and corresponding imaging. What is needed are higher-resolution spectra at deeper depths to study these sources in detail. We present The High-(Redshift+Ionization) Line Se

Jinfan Zhou, Lixin Luo, Sungmin Eum, Heesung Kwon

We explore spatiotemporal data augmentation using video foundation models to diversify both camera viewpoints and scene dynamics. Unlike existing approaches based on simple geometric transforms or appearance perturbations, our method leverages off-the-shelf video diffusion models to generate realistic 3D spatial and temporal variations from a given image dat

Agustín García-García, Pablo Hidalgo, Julio E. Sandubete

Explainable Artificial Intelligence (XAI) is increasingly required in computational economics, where machine-learning forecasters can outperform classical econometric models but remain difficult to audit and use for policy. This survey reviews and organizes the growing literature on XAI for economic time series, where autocorrelation, non-stationarity, seaso

Martin M. Roth

The Mega-MUSE Nearby Galaxy Serendipity Survey is a White Paper in response to the ESO Expanding Horizons Call for Ideas. It is based on the notion that novel observing facilities for astronomy almost always make discoveries that were not anticipated at the time of planning and conceptual design. Unlike other surveys it is not targeting the solution of a sin

K. S. Mallesh

We give a compact, frame-independent characterization of the set of classical second-order moments for a single spin-1 particle. Defining the moment matrix M = 2Q + (1/3) I, we show that a moment pair (s, Q) arises from a positive mixture of spin-coherent states if and only if M is positive semidefinite, M minus ss^T is positive semidefinite, and the trace o

Mingrui Ye, Chanjin Zheng, Zengyi Yu, Chenyu Xiang

Multimodal Large Language Models (MLLMs) show remarkable progress across many visual-language tasks; however, their capacity to evaluate artistic expression remains limited. Aesthetic concepts are inherently abstract and open-ended, and multimodal artwork annotations are scarce. We introduce KidsArtBench, a new benchmark of over 1k children's artworks (ages

Matthew K Tam, Liam Timms, Lele Zhang

Consider the problem of finding a zero of a finite sum of maximally monotone operators, where some operators are Lipschitz continuous and the rest are potentially set-valued. We propose a forward-backward-type algorithm for this problem suitable for decentralised implementation. In each iteration, agents evaluate a Lipschitz continuous operator and the resol

Dang Phuong Nam, Nguyen Kieu, Pham Thanh Hieu

Generative Artificial Intelligence (AI) has created unprecedented opportunities for creative expression, education, and research. Text-to-image systems such as DALL.E, Stable Diffusion, and Midjourney can now convert ideas into visuals within seconds, but they also present a dual-use dilemma, raising critical ethical concerns: amplifying societal biases, pro

Xuhai Xu, Haoyu Hu, Haoran Zhang, Will Ke Wang

Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two la

André Cirne, Patrícia R. Sousa, João S. Resende, Luís Antunes

The widespread adoption of embedded systems has led to their deployment in critical real-world applications, making them attractive targets for malicious actors. These devices face unique challenges in mitigating vulnerabilities due to intrinsic constraints, such as low energy consumption requirements and limited computational resources. This paper presents

Xiaofeng Wu, Xin Wang, Sixun Jia, Bo Xiong

The experimental detection of non-equilibrium quantum criticality remains a challenge, as traditional signatures like dynamical quantum phase transitions rely on hard-to-measure global properties. Here, we demonstrate that local connected correlation functions provide a superior, practical means to directly probe the dynamics of magnetic order in a quenched

Saadh Jawwadh, Guhanathan Poravi

The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional software vulnerability detection techniques, such as static and dynamic analysis, have been shown to be ineffective at

Jincheng Yu, Jiachen Li, Chaoqun Zhang, Tianyu Zhu

The accurate description of electron correlation and excitation energies remains a fundamental challenge in quantum chemistry. The particle-particle random phase approximation (ppRPA) has emerged as a promising method for capturing a broad range of excited-state properties. However, the implementation of ppRPA has been largely limited to in-house software, r

Najmul Hasan, Prashanth BusiReddyGari, Haitao Zhao, Yihao Ren

Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language Models (LLMs) applications, these systems face evolving phishing email threats that exploit their fundamental architectures. Current LLMs require substantial hardening before deployment in email security systems, pa

Mikhail V. Bondarko, Kirill S. Ladny, Konstantin I. Pimenov

For a wildly ramified extension $K/k$ of complete discrete valuation fields we study collections of elements of $k[G]$ (where $G=Gal(K/k)$) that fit well for constructing bases of various associated Galois modules and orders. In the case $G=(Z/pZ)^2$ (where $p$ is the characteristic of residue fields) we are able to compute the action of the elements $(σ_1-1

Ignatios Antoniadis, Auttakit Chatrabhuti, Hiroshi Isono

We study the possibility that the Right-handed neutrino is a five-dimensional state propagating along a micron size extra dimension, as required in the dark dimension proposal. We work out the signatures of R-neutrino production in KATRIN experiment and compare them with those of a sterile neutrino which manifests by a kink in the electron energy spectrum of

Sidhanta Sahu, Anupama Chauhan, Poulami Ghosh, Sayan Routh

Spin-orbit entangled excitons (SOEE) in two-dimensional (2D) antiferromagnets provide direct access to explore unconventional many body interactions in correlated electron systems. In this work, we carry out a detailed investigation using non-degenerate isotropic and anisotropic pump-probe reflection spectroscopy to probe the ultrafast dynamics of SOEE and t

Pablo Hidalgo, Julio E. Sandubete, Agustín García-García

This study investigates the contribution of Intrinsic Mode Functions (IMFs) derived from economic time series to the predictive performance of neural network models, specifically Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks. To enhance interpretability, DeepSHAP is applied, which estimates the marginal contribution of each IMF whil

Tasweer Ahmad, Arindam Sikdar, Sandip Pradhan, Ardhendu Behera

Few-shot image classification remains difficult under limited supervision and visual domain shift. Recent cache-based adaptation approaches (e.g., Tip-Adapter) address this challenge to some extent by learning lightweight residual adapters over frozen features, yet they still inherit CLIP's tendency to encode global, general-purpose representations that are

Ioannis Anagnostides, Zachary W. Sollie, Arman Kilic, Tuomas Sandholm

Heart transplantation is a viable path for patients suffering from advanced heart failure, but this lifesaving option is severely limited due to donor shortage. Although the current allocation policy was recently revised in 2018, a major concern is that it does not adequately take into account pretransplant and post-transplant mortality. In this paper, we ta

Ying Cui, Dongzhe Zheng, Ke Yu, Xiyin Zheng

Clear cell renal cell carcinoma (ccRCC) exhibits extensive intratumoral heterogeneity on multiple biological scales, contributing to variable clinical outcomes and limiting the effectiveness of conventional TNM staging, which highlights the urgent need for multiscale integrative analytic frameworks. The lipid-deficient de-clear cell differentiated (DCCD) ccR

Joshua Cooper, Hays Whitlatch

A square, upper-triangular matrix $U$ is a Cholesky root of a matrix $M$ provided $U^*U=M$, where $\cdot^*$ represents the conjugate transpose when working over the complex field and $U^*=U^T$ over the reals and finite fields. In this paper, we investigate the number of such factorizations over the finite field with two elements, $\mathbb{F}_2$, and prove th

Alexei Rybkin

We present a unified operator-theoretic framework for constructing deterministic KdV soliton gases and step-type KdV solutions. Starting from Dyson's determinantal formula, we obtain a broad class of reflectionless solutions and describe their basic spectral and analytic properties, including their interpretation as deterministic soliton gases. We then intro

Raj Kishor Joshi, Antonios Tsokaros, Sanjit Debnath, Indranil Chattopadhyay

Theoretical studies of transonic accretion onto black holes reveal a wide range of possible solutions, broadly classified into smooth flows and flows featuring shocks. Accretion solutions that involve the formation of shocks are particularly intriguing, as they are expected to naturally produce observable variability features. However, despite their theoreti

Vaarunay Kaushal, Rajib Mall

Early identification of at-risk students is critical for effective intervention in online learning environments. This study extends temporal prediction analysis to Week 20 (50% of course duration), comparing Decision Tree and Long Short- Term Memory (LSTM) models across six temporal snapshots. Our analysis reveals that different performance metrics matter at

Shengkai Xu, Hsiang Lun Kao, Tianxiang Xu, Honghui Zhang

Polyp detectors trained on clean datasets often underperform in real-world endoscopy, where illumination changes, motion blur, and occlusions degrade image quality. Existing approaches struggle with the domain gap between controlled laboratory conditions and clinical practice, where adverse imaging conditions are prevalent. In this work, we propose AdaptiveD

Ziyi Yang, Sakshi Gupta, Jehan Shalabi, Daien He

High-sensitivity long-wave infrared (LWIR) detection is crucial for observing weak thermal radiation. Recently, the Poisson bolometer has been proposed as a fundamentally new platform for uncooled infrared detection. In contrast to traditional analog detectors, where signal and noise are determined by continuous currents or voltages, the Poisson bolometer&#3

Shuhui Qu, Cheolwoo Park

While graph neural networks have shown remarkable success in molecular property prediction, current approaches like the Equivariant Subgraph Aggregation Networks (ESAN) treat molecules as bags of independent substructures, overlooking crucial relationships between these components. We present Graph of Molecule Substructures (GoMS), a novel architecture that

James Luther, Donald Brown

Culture is the bedrock of human interaction; it dictates how we perceive and respond to everyday interactions. As the field of human-computer interaction grows via the rise of generative Large Language Models (LLMs), the cultural alignment of these models become an important field of study. This work, using the VSM13 International Survey and Hofstede's cultu

Hoang Anh Just, Yifei Fan, Handong Zhao, Jiuxiang Gu

Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. However, RLVR-trained VLMs still exhibit two persistent failure modes: inaccurate visual extraction (missing or hallucinating details) and logically inconsistent chains-of-thought, la