November 2025 arXiv papers — page 9

Muhammad Muneeb, David B. Ascher, Ahsan Baidar Bakht

Context-based question answering (CBQA) models provide more accurate and relevant answers by considering the contextual information. They effectively extract specific information given a context, making them functional in various applications involving user support, information retrieval, and educational platforms. In this manuscript, we benchmarked the perf

Zhibo Liu, Hiroaki Nakajima, Hiroaki Abuki, Akira Watanabe

The gravitational form factors (GFFs) of the baryon octet, including hyperons, are investigated in a bottom-up holographic QCD model that explicitly incorporates the SU(3) flavor symmetry breaking through the strange quark mass. We fit the model parameters to reproduce the empirical masses of the baryon octet and examine the dependence of GFFs on the probe m

Issa Oe, Keiichiro Yamamura, Hiroki Ishikura, Ryo Hamahira

Deep learning models are used in safety-critical tasks such as automated driving and face recognition. However, small perturbations in the model input can significantly change the predictions. Adversarial attacks are used to identify small perturbations that can lead to misclassifications. More powerful black-box adversarial attacks are required to develop m

Shivani Mruthyunjaya, Anandi Dutta, Kazi Sifatul Islam

Power consumption has become a critical aspect of modern life due to the consistent reliance on technological advancements. Reducing power consumption or following power usage predictions can lead to lower monthly costs and improved electrical reliability. The proposal of a holistic framework to establish a foundation for IoT systems with a focus on contextu

Shishu Pal Singh, Sudeep Kundu

In this paper, we propose a \( C^0 \)-conforming finite element method for the Chafee-Infante equation with a finite-parameter feedback control. We establish error analysis for both the state variable and the control variable for the spatially discretized solution. Furthermore, we employ the backward Euler method for time discretization and discuss the stabi

Katherine Goldman, Jingyin Huang

We show that a certain class of affine hyperplane arrangements are $K(\pi,1)$ by endowing their Falk complexes with an injective metric. This gives new examples of infinite $K(\pi,1)$ arrangements in dimension $n>2$.

Isaac Anderson, Wesley Stevick, Katrina Koehler

The Bible is packed with references from start to finish. This study aims to analyze a specific branch of these references: citations. While there are several types of references, both explicit and implicit, this study focuses on the types of references that can be detected with a simple algorithmic string comparison, or an n-gram string comparison. Words we

Shishu Pal Singh, Sudeep Kundu

This article focuses on a nonlinear Neumann boundary feedback control formulation for the viscous Burgers' equation and develops a class of finite difference schemes to achieve global stabilization. The proposed procedure, known as the $\theta$-scheme with $\theta \in [0,1]$, unifies explicit and implicit time discretizations and is suitable for handling the

Onrina Chandra, Min-ge Xie

Ranking populations such as institutions based on certain characteristics is often of interest, and these ranks are typically estimated using samples drawn from the populations. Due to sample randomness, it is important to quantify the uncertainty associated with the estimated ranks. This becomes crucial when latent characteristics are poorly separated and w

Marcílio Ferreira dos Santos, Cleiton de Lima Ricardo, Andreza dos Santos Rodrigues de Melo

We investigate whether the structural connectivity of urban road networks helps explain dengue incidence in Recife, Brazil (2015--2024). For each neighborhood, we compute the average \emph{communicability curvature}, a graph-theoretic measure capturing the ability of a locality to influence others through multiple network paths. We integrate this metric into

Alexis de Colnet, Kuldeep S. Meel, Umang Mathur

In this work, we study the problems of counting and sampling Mazurkiewicz traces that a regular language touches. Fix an alphabet $\Sigma$ and an independence relation $\mathbb{I} \subseteq \Sigma \times \Sigma$. The input consists of a regular language $L \subseteq \Sigma^*$, given by a finite automaton with $m$ states, and a natural number $n$ (in unary).

Zhitong Guan, Yi Wang

Large Language Models (LLMs) are rapidly reshaping information retrieval by enabling interactive, generative, and inference-driven search. While traditional keyword-based search remains central to web and academic information access, it often struggles to support multi-step reasoning and exploratory learning tasks. LLM-powered search interfaces, such as Chat

Kenny Watts, Jonathan Pipping-Gamón

Following a penalty in rugby union, teams typically choose between attempting a shot at goal or kicking to touch to pursue a try. We develop an Expected Points (EP) framework that quantifies the value of each option as a function of both field location and game context. Using phase-level data from the 2018/19 Premiership Rugby season (35,199 phases across 13

Jungyang Park, Suho Kang, Jaewoo Park, Jaehong Kim

Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutoring Systems. However, traditional KT methods face fundamental limitations in explainability, as they rely solely on the response correctness, neglecting the rich information embedded in students' problem-solving pr

Qinyi Cao, Jianan Fan, Weidong Cai

Unsupervised anomaly detection aims to identify anomalies without pixel-level annotations. Synthetic anomaly-based methods exhibit a unique capacity to introduce controllable irregularities with known masks, enabling explicit supervision during training. However, existing methods often produce synthetic anomalies that are visually distinct from real patholog

Xiao Cui, Yulei Qin, Wengang Zhou, Hongsheng Li

Dataset distillation seeks to synthesize a compact distilled dataset, enabling models trained on it to achieve performance comparable to models trained on the full dataset. Recent methods for large-scale datasets focus on matching global distributional statistics (e.g., mean and variance), but overlook critical instance-level characteristics and intraclass v

Haobin Ke, Sen Zhang, Qingqing Ye, Xun Ran

Signed graphs with positive and negative edges can model complex relationships in social networks. Leveraging on balance theory that deduces edge signs from multi-hop node pairs, signed graph learning can generate node embeddings that preserve both structural and sign information. However, training on sensitive signed graphs raises significant privacy concer

Zhijian Wei, Runze Ma, Zichen Wang, Zhongmin Li

Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization remains constrained by data qual

Zhengzhuo Xu, SiNan Du, Yiyan Qi, SiwenLu

Multimodal Large Language Models (MLLMs) have emerged as powerful tools for chart comprehension. However, they heavily rely on extracted content via OCR, which leads to numerical hallucinations when chart textual annotations are sparse. While existing methods focus on scaling instructions, they fail to address the fundamental challenge, i.e., reasoning with

Shigeru Ida, Ya-Ping Li, Jun-Peng Pan, Yi-Xian Chen

Type II orbital migration is a key process to regulate the mass and semimajor axis distribution of exoplanetary giant planets. The conventional formula of type II migration generally predicts too rapid inward migration to reconcile with the observed pile-up of gas giant beyond 1 au. Analyzing the recent high-resolution hydrodynamical simulations by Li et al.

Shenghong He

Federated reinforcement learning (FRL) enables distributed learning of optimal policies while preserving local data privacy through gradient sharing.However, FRL faces the risk of data privacy leaks, where attackers exploit shared gradients to reconstruct local training data.Compared to traditional supervised federated learning, successful reconstruction in

Jinyuan Liu, Yong Liang Guan, Tuo Wu, Kai-Kit Wong

As 6G networks demand massive connectivity and stronger interference control, rate-splitting multiple access (RSMA) is attractive because it superposes a common stream and user-private streams and remains effective under imperfect CSIT and heterogeneous traffic. In practical multiuser deployments, two considerations arise: the common stream decoding constrai

Peiheng Jiang, Jie Li, Yu-Han Cao, Xiaodong Cao

We uncover a dual mechanism by which oxygen defects suppress superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_{7+\delta}$ using density functional theory, dynamical mean-field theory, and functional renormalization group analysis. Apical vacancies and interbilayer interstitials emerge as the dominant low-energy defect species and are further stabili

Rui Qian, Haozhi Cao, Tianchen Deng, Tianxin Hu

Embodied 3D Semantic Scene Completion (SSC) infers dense geometry and semantics from continuous egocentric observations. Most existing Gaussian-based methods rely on random initialization of many primitives within predefined spatial bounds, resulting in redundancy and poor scalability to unbounded scenes. Recent depth-guided approach alleviates this issue bu

Zeyun Hu, Yang Liu

We investigate the static portfolio selection problem of S-shaped and non-concave utility maximization under first-order and second-order stochastic dominance (SD) constraints. In many S-shaped utility optimization problems, one should require a liquidation boundary to guarantee the existence of a finite concave envelope function. A first-order SD (FSD) cons

González Trigueros Jesús Eduardo, Alonso Sánchez Alejandro, Muñoz Rivera Emilio, Peñarán Prieto Mariana Jaqueline

This study analyzes the impact of heterogeneity ("Variety") in Big Data by comparing classification strategies across structured (Epsilon) and unstructured (Rest-Mex, IMDB) domains. A dual methodology was implemented: evolutionary and Bayesian hyperparameter optimization (Genetic Algorithms, Optuna) in Python for numerical data, and distributed processing in

Michael Wehar

We reinvestigate known lower bounds for the Intersection Non-Emptiness Problem for Deterministic Finite Automata (DFA's). We first strengthen conditional time complexity lower bounds from T. Kasai and S. Iwata (1985) which showed that Intersection Non-Emptiness is not solvable more efficiently unless there exist more efficient algorithms for non-deterministi

Aashish Gupta, Antonio S. Hales, L. Ilsedore Cleeves, Felipe Alves

Recent discoveries of streamer-like structures around protostellar sources challenge the traditional picture of isolated, axisymmetric star formation. Here, we present new ALMA observations of [BHB2007]1, a flat-spectrum source connected to at least three such elongated structures. Two of these features are symmetrically located to the north and south of the

Lixing Guo, Tobias Höllerer

Traditional augmented reality (AR) systems predominantly rely on fixed class detectors or fiducial markers, limiting their ability to interpret complex, open-vocabulary natural language queries. We present a modular AR agent system that integrates multimodal large language models (MLLMs) with grounded vision models to enable relational reasoning in space and

Yafei Lyu, Hao Zhou, Lu Zhang, Xu Yang

Time series forecasting is central to data analysis and web technologies. The recent success of Large Language Models (LLMs) offers significant potential for this field, especially from the cross-modality aspect. Most methods adopt an LLM-as-Predictor paradigm, using LLM as the forecasting backbone and designing modality alignment mechanisms to enable LLM to

Jovin Ryan Joseph, Do Hoon Kiem, Sinchul Yeom, Mina Yoon

Delafossites (ABC2, where A and B are metals and C is a chalcogen) are a versatile family of quantum materials and layered oxides/chalcogenides whose properties are highly sensitive to atomic composition and stacking geometry. Their broad chemical tunability makes them an ideal platform for large-scale combinatorial exploration and high-throughput computatio

Zhikai Li, Tanda Li, Yixiao Zhou, Shaolan Bi

Standard 1D stellar evolution model has poor descriptions of the near-surface layers of stars, and this can be improved by using the atmosphere model computed from 3D hydrodynamical simulations. In this work, we validated the model inferences of the 1D-3D coupling models using 18 well-studied stars from the Kepler LEGACY Sample. We compared our estimates of

Guoqing Ma, Jia Zhu, Hanghui Guo, Weijie Shi

Large language models (LLMs) demonstrate significant potential for educational applications. However, their unscrutinized deployment poses risks to educational standards, underscoring the need for rigorous evaluation. We introduce EduEval, a comprehensive hierarchical benchmark for evaluating LLMs in Chinese K-12 education. This benchmark makes three key con

Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj, Jacob Levy

We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exhibit inherent model-form uncertainty, motivating the need for data-driven correction. Moreover, non-autonomous dynamics are governed by time-dependent control inputs, which pose ch

Danial Yazdani, Mai Peng, Delaram Yazdani, Shima F. Yazdi

Benchmarking is central to optimization research, yet existing test suites for continuous optimization remain limited: classical collections are fixed and rigid, while previous generators cover only narrow families of landscapes with restricted variability and control over details. This paper introduces PORTAL (Platform for Optimization Research, Testing, An

Yuzheng Gao, Yuxing Long, Lei Kang, Yuchong Guo

Existing appliance assets suffer from poor rendering, incomplete mechanisms, and misalignment with manuals, leading to simulation-reality gaps that hinder appliance manipulation development. In this work, we introduce the RealAppliance dataset, comprising 100 high-fidelity appliances with complete physical, electronic mechanisms, and program logic aligned wi

Shukun Wang

In this paper, we first study Rota-Baxter Hopf algebras of weight $-1$ and construct a matched pair of Hopf algebras on every Rota-Baxter Hopf algebra of weight $-1$. Then we propose the notion of projection homomorphism pairs on a matched pair of Hopf algebras, and show that every projection homomorphism pair $(C,\wtd{C})$ induces a Rota-Baxter Hopf algebra

Zamora-Aviles Manuel, Ballesteros-Paredes Javier, Palau Aina, Vázquez-Semadeni Enrique

It has recently been suggested that the typical separation between cores in molecular clouds dominated by turbulence is determined by the sonic scale, the size scale at which the turbulent velocity dispersion equals the sound speed. In this work, we test this hypothesis using a suite of turbulent simulations with Mach numbers $\mathcal{M}=4$ and 8, and three

Zihao Wu

Diffusion models deliver high-fidelity synthesis but remain slow due to iterative sampling. We empirically observe there exists feature invariance in deterministic sampling, and present InvarDiff, a training-free acceleration method that exploits the relative temporal invariance across timestep-scale and layer-scale. From a few deterministic runs, we compute

Connor Casey, Albert Williams, Catherine McCaffrey, Eugene Rotherham

Quantum networking seeks to enable global entanglement distribution through terrestrial and free space channels; however, the exponential loss in these channels necessitates quantum repeaters with efficient, long lived quantum memories (QMs). Space based architectures, particularly satellite assisted links, offer a path to truly global connectivity, yet they

Justin Grieshop

We study a Fejer-type smoothing kernel on the finite cyclic group Z/NZ. For each smoothing radius we give explicit l1 and l2 norms, compute the discrete Fourier transform, and record bounds that are uniform in N. As an application we prove a smoothed discrepancy estimate with explicit constants that can be used in quantitative problems on finite cyclic group

Domonkos F. Vamossy

This paper moves beyond aggregate measures of retail intensity to explore investment horizon as a distinguishing feature of earnings-related return patterns. Using self-reported holding periods from StockTwits (2010-2021), we observe that separating retail activity into "long-horizon" and "short-horizon" cohorts reveals divergent price anomalies. Long-horizo

Zhenliang Ni, Xiaowen Ma, Zhenkai Wu, Shuai Xiao

Multivariate time series forecasts are widely used, such as industrial, transportation and financial forecasts. However, the dominant frequencies in time series may shift with the evolving spectral distribution of the data. Traditional Mixture of Experts (MoE) models, which employ a fixed number of experts, struggle to adapt to these changes, resulting in fr

Meng Li, Yu Zhang, Leshan Li

The Industry 4.0 refers to a industrial ecology which will merge the information system, physical system and service system into an integrate platform. Since now the industrial designers either conceive the physical part of products, or design the User Interfaces of computer systems, the new industrial ecology will give them a chance to redefine their roles

Oluyinka Lindblad, Ezra Guerrero

We consider the Anderson model on the finite grid $G = \mathbb Z/L_1\mathbb Z\times\cdots\times\mathbb Z/L_d\mathbb Z$, defined by the random Hamiltonian $H_t=\Delta+tV$, where $\Delta$ is the discrete Laplacian and $V=\mathrm{diag}(\{\omega_{x}\}_{x\in G})$ is a random onsite potential with $\omega_x\sim\mu$ i.i.d. We ask the natural question of when $H_t$

Andrew Cooper, Justin Strait, Mary Frances Dorn, Robert B. Gramacy

Angular data are commonly encountered in settings with a directional or orientational component. Regressing an angular response on real-valued features requires intrinsically capturing the circular or spherical manifold the data lie on, or using an appropriate extrinsic transformation. A popular example of the latter is the technique of distributional wrappi

Jiachen Li, Shihao Li, Jiamin Xu, Soovadeep Bakshi

Data-Enabled Predictive Control (DeePC) has emerged as a powerful framework for controlling unknown systems directly from input-output data. For nonlinear systems, recent work has proposed selecting relevant subsets of data columns based on geometric proximity to the current operating point. However, such proximity-based selection ignores the control objecti

Yi Liu, Yi Wan, Xinyi Liu, Qiong Wu

In remote sensing applications, such as disaster detection and response, real-time efficiency and model lightweighting are of critical importance. Consequently, existing remote sensing image super-resolution methods often face a trade-off between model performance and computational efficiency. In this paper, we propose a lightweight super-resolution framewor

Prakrithi Shivaprakash, Diptadhi Mukherjee, Lekhansh Shukla, Animesh Mukherjee

Background: Large Language Models show promise in psychiatry but are English-centric. Their ability to understand mood states in other languages is unclear, as different languages have their own idioms of distress. Aim: To quantify the ability of language models to faithfully represent phrases (idioms of distress) of four distinct mood states (depression, eu

Venkata Ramana Makkapati, Tulasi Ram Vechalapu, Vinodhini Comandur, Seth Hutchinson

This paper introduces a novel reachability problem for the scenario involving two agents, where one agent follows another agent using a feedback strategy. The geometry of the reachable set for an agent, termed \emph{dependent reachable set}, is characterized using the constant bearing pursuit strategy as a case study. Key theoretical results are presented th

Mohammad M Maheri, Xavier Cadet, Peter Chin, Hamed Haddadi

Approximate machine unlearning aims to efficiently remove the influence of specific data points from a trained model, offering a practical alternative to full retraining. However, it introduces privacy risks: an adversary with access to pre- and post-unlearning models can exploit their differences for membership inference or data reconstruction. We show thes

Woo Hyeon Lim, Ji Young Lee, Jong Hyuk Lee, Saehoon Kim

Purpose: To benchmark open-source or commercial medical image-specific VLMs against real-world radiologist-written reports. Methods: This retrospective study included adult patients who presented to the emergency department between January 2022 and April 2025 and underwent same-day CXR and CT for febrile or respiratory symptoms. Reports from five VLMs (AIRea

Satoshi Kura, Hiroshi Unno

We propose new supermartingale-based certificates for verifying almost sure satisfaction of $\omega$-regular properties: (1) generalised Streett supermartingales (GSSMs) and their lexicographic extension (LexGSSMs), (2) distribution-valued Streett supermartingales (DVSSMs), and (3) progress-measure supermartingales (PMSMs) and their lexicographic extension (

Jun Wang, Peirong Liu

Understanding the relationship between pathological and healthy brain structures is fundamental to neuroimaging, connecting disease diagnosis and detection with modeling, prediction, and treatment planning. However, paired pathological-healthy data are extremely difficult to obtain, as they rely on pre- and post-treatment imaging, constrained by clinical out

Hong Wang

Consensus optimization enables autonomous agents to solve joint tasks through peer-to-peer exchanges alone. Classical decentralized gradient descent is appealing for its minimal state but fails to achieve exact consensus with fixed stepsizes unless additional trackers or dual variables are introduced. We revisit penalty methods and introduce a decentralized

Anab Maulana Barik, Shou Ziyi, Yang Kaiwen, Yang Qi

Technological advancement allows information to be shared in just a single click, which has enabled the rapid spread of false information. This makes automated fact-checking system necessary to ensure the safety and integrity of our online media ecosystem. Previous methods have demonstrated the effectiveness of decomposing the claim into simpler sub-tasks an

Eleftheria Kelekidou

This paper is an exposition of how BRIDGE and adaptive LASSO can be used in a two-stage least squares problem, to estimate the second-stage coefficients when the number of parameters p in both stages is growing with the sample size n. Facing a larger class of problems compared to the usual analysis in the literature, i.e., replacing the assumption of normal

Zhengda Ma, Abhirup Banerjee

We present the first geometric deep learning framework based on point cloud representation for 3D four-chamber cardiac reconstruction from cine MRI data. This work addresses a long-standing limitation in conventional cine MRI, which typically provides only 2D slice images of the heart, thereby restricting a comprehensive understanding of cardiac morphology a

Dang Vo Phuc

Let $q=p^f$ be a prime power, $H \leq \mathrm{GL}_d(q)$ a subgroup containing a genuine Singer cycle $s$ of order $q^d-1$, and $W$ an $\mathbb{F}_q H$-module whose scalar extension restricts to an untwisted polynomial tensor representation $\bigotimes L(\lambda^{(t)})$ of the algebraic group $\mathrm{GL}_d$. If the total polynomial degree satisfies $K < q-1$

Maria Teresa Parreira, Ruidong Zhang, Sukruth Gowdru Lingaraju, Alexandra Bremers

How do humans recognize and rectify social missteps? We achieve social competence by looking around at our peers, decoding subtle cues from bystanders - a raised eyebrow, a laugh - to evaluate the environment and our actions. Robots, however, struggle to perceive and make use of these nuanced reactions. By employing a novel neck-mounted device that records f

Yuzhen Hu, Saurabh Prasad

Sparse annotations fundamentally constrain multimodal remote sensing: even recent state-of-the-art supervised methods such as MSFMamba are limited by the availability of labeled data, restricting their practical deployment despite architectural advances. ImageNet-pretrained models provide rich visual representations, but adapting them to heterogeneous modali

Y. Sungtaek Ju

Kolmogorov-Arnold Networks (KANs) offer a promising alternative to Multi-Layer Perceptron (MLP) by placing learnable univariate functions on network edges, enhancing interpretability. However, standard KANs lack probabilistic outputs, limiting their utility in applications requiring uncertainty quantification. While recent Gaussian Process (GP) extensions to

Diogo Ferreira, Pedro Ribeiro, André Coelho, Rui Campos

Autonomous Flying Networks (FNs) are emerging as a key enabler of on-demand connectivity in dynamic and infrastructure-limited environments. However, current approaches mainly focus on UAV placement, routing, and resource management, neglecting the autonomous perception of users and their service demands - a critical capability for zero-touch network operati

Davi Lopes Medeiros, José Edson Sampaio, Eder Leandro Sanchez Quiceno

We investigate the (ambient) bi-Lipschitz V-equivalence of two-variable mixed polynomials satisfying the Newton inner non-degeneracy condition. Concerning triviality, we show that ambient bi-Lipschitz V-triviality for families $\{f + \varepsilon \theta\}_{\varepsilon \in \mathbb{R}}$ is guaranteed when $f$ is semi-radially weighted homogeneous and the weight

Hend Baza, Fei Chen, Taras Turiv, Sergij V. Shiyanovskii

Flagellated microswimmers B. Subtilis dispersed in a nematic phase of a lyotropic chromonic liquid crystal form a living liquid crystal (LLC). The combination of the passive and active components allows us to analyze how the active component transitions from the shear-imposed alignment into topological turbulence. The lateral extension of the experimental ce

Kunwar Maheep Singh, Jianchun Chen, Vladislav Golyanik, Stephan J. Garbin

We present Relightable Holoported Characters (RHC), a novel person-specific method for free-view rendering and relighting of full-body and highly dynamic humans solely observed from sparse-view RGB videos at inference. In contrast to classical one-light-at-a-time (OLAT)-based human relighting, our transformer-based RelightNet predicts relit appearance within

Matheus Bongestab, David Pinto-Ramos, Ricardo Martinez-Garcia

Mutualisms are key for structuring ecological communities, but they are sensitive to environmental change and fluctuations in population size. Consequently, how mutualisms achieve stability remains an open question in ecological theory. Motivated by previous results in competitive and predator-prey interactions, we hypothesize that self-organized pattern for

Micaela E. Primer, Anna A. Berseneva, Ayesha Ulde, Wenhao Sun

Subtle synthetic variables can have an outsizes influence on the crystal structure and magnetic properties of a material, particularly those of quantum materials. In this work, we investigate the impact of synthesis under a magnetic field (magnetosynthesis) on the crystal structure and magnetic properties of several Cu$^{2+}$ ($S=1/2$) based materials with a

Dinanath Padhya, Jenish Pant, Krishna Acharya, Sajen Maharjan

Despite the prevalence of severe hearing loss affecting over 430 million people globally, access to sign language interpretation remains critically scarce, particularly in low-resource settings like Nepal. Assistive technologies divide into two flawed categories: prohibitively expensive commercial gloves (often exceeding \$3,000) or fragile research prototyp

Ke Fang, Kohta Murase

We summarize recent results of the observations of high (1 TeV-100 PeV) and ultrahigh ($\geq 100$ PeV) energy neutrinos, including the detection of a diffuse cosmic high-energy neutrino background, the identification of the first neutrino source candidates, and the observation of high-energy neutrinos from the Galactic plane. These findings open a new window

Zhiqiang Zhao, Siyao Shuang, Kepeng Ouyang, Maolin Yu

The origin of the superior high-temperature strength of γ-TiAl with high Nb addition remains highly controversial, largely due to the unclear role of Nb atoms. Using large-scale hybrid Monte Carlo and molecular dynamics simulations with a self-developed neural network potential,we show that Nb atoms predominantly occupy Ti sites and form short-range order wi

Jiaxun Zhang, Qian Xu, Zhenning Li, Yuan Wu

Vehicle-to-Everything (V2X) cooperation is reshaping traffic safety from an ego-centric sensing problem into a networked intelligence problem involving distributed sensing, cooperative perception, and coordinated decision-making. This survey reviews recent progress in V2X-enabled cooperative safety intelligence through a unified Sensor-Perception-Decision (S

Yan-Xiao Wang, Yin Zhong

Heavy-fermion superconductors often display upper critical fields that exceed the conventional Pauli paramagnetic limit, indicating that strong correlations and hybridized quasiparticle bands play an essential role in the paramagnetic pair-breaking process. Within the two-dimensional Kondo-Heisenberg model, we perform a self-consistent mean-field analysis of

Yefeng Wu, Yuchen Song, Yecheng Zhao, Ling Wu

Large language models (LLMs) are increasingly deployed as conversational tutors in STEM education, yet most systems still rely on a single LLM with a static retrieval-augmented generation (RAG) pipeline over course materials. This design struggles in complex domains such as digital signal processing (DSP), where tutors must maintain coherent long-term studen

Kaihong Lu, Huanshui Zhang, Long Wang

In this paper, the problem of distributively seeking the equilibria of aggregative games with bilevel structures is studied. Different from the traditional aggregative games, here the aggregation is determined by the minimizer of a virtual leader&#39;s objective function in the inner level, which depends on the actions of the players in the outer level. More

Henry Onyeka, Emmanuel Samson, Liang Hong, Tariqul Islam

The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service (DoS) attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional Generative Adversarial Network framework enhanced with Sinkhorn

Shrinivass Arunachalam Balasubramanian

Reactivity is central to Angular applications, yet subtle misuse of Observables, Signals, and change-detection often leads to performance regressions that are difficult to diagnose. Although Angular 17 introduced a unified, signal-first model, most enterprise codebases still rely heavily on legacy RxJS patterns that create unpredictable update flows, memory

Scotty Black, Christian Darken

In the domain of combat simulations in support of wargaming, the development of intelligent agents has predominantly been characterized by rule-based, scripted methodologies with deep reinforcement learning (RL) approaches only recently being introduced. While scripted agents offer predictability and consistency in controlled environments, they fall short in

Ronald Orozco López

We use a new $q$-exponential operator based on the $q^{\pm1}$-derivative $\D_{q^{\pm1}}$ of order 1 to derive summation formulas for bilateral basic hypergeometric series ${}_{0}\psi_{1}$, ${}_{1}\psi_{1}$, ${}_{1}\psi_{2}$, and ${}_{2}\psi_{2}$. In addition, we provide summation formulas for bilateral series whose terms are basic hypergeometric functions.

Rohan Goyal, Prahladh Harsha, Mrinal Kumar, Ashutosh Shankar

A recent work of Goyal, Harsha, Kumar and Shankar gave nearly linear time algorithms for the list decoding of Folded Reed-Solomon codes (FRS) and univariate multiplicity codes up to list decoding capacity in their natural setting of parameters. A curious aspect of this work was that unlike most list decoding algorithms for codes that also naturally extend to

Wenlong Tang

This study proposes a multi-agent language framework that enables continual strategy evolution without fine-tuning the language model's parameters. The core idea is to liberate the latent vectors of abstract concepts from traditional static semantic representations, allowing them to be continuously updated through environmental interaction and reinforcement

José Rojas, Melvin Arias

We study the dynamics of multiparticle Carroll-Schr\"odinger (CS) quantum systems in $1{+}1$ dimensions, where $x$ acts as the evolution variable and $t$ as the configuration coordinate. We derive the $N$-body theory on equal-$x$ slices as the Carrollian limit of a relativistic multi-time Klein-Gordon model, introducing temporal interactions via minimal coup

Stephen L. Skinner, Manuel Guedel

We report results of Chandra X-ray observations of CI Tau, a young magnetically active classical T Tauri star for which previous studies have reported periodic variability attributed to a massive planet in a short-period orbit. CI Tau was clearly detected by Chandra in four separate observations acquired in late 2023. The X-ray emission was steady in the fir

Rajesh Pandiyan, Sanyasi Bobbara, Somayeh Mirzaee, Su-Peng Yu

Micro-Electro-Mechanical, so called 'MEMs,' vapor cells are a key component in atom-based quantum sensors, such as clocks, gyroscopes, electric field sensors and magnetometers. MEMs vapor cell fabrication for Rydberg atom radio frequency sensors is particularly demanding. The Rydberg states used for the sensor can shift in a constant electric field which can

David Cruz-Uribe, Scott Rodney

We prove a weak maximum principle for subsolutions of a degenerate, linear, second order elliptic operator with lower order terms, building on the existence results recently proved by the authors and \c{C}etin, Dal and Zeren.

Paulo Roberto de Melo Barros Junior, Monica Alexandra Vilar Ribeiro De Meireles, Jose Luis Lima de Jesus Silva

Our work investigates the economic efficiency of the prevailing "ladder-step" investment strategy in oil and gas exploration, which advocates for the incremental acquisition of geological information throughout the project lifecycle. By employing a multi-agent Deep Reinforcement Learning (DRL) framework, we model an alternative strategy that prioritizes the

Ruslan Kravchuk, Oleksandr Kurochkin, Vassili G. Nazarenko, Volodymyr Sashuk

Surface alignment of a recently discovered ferroelectric nematic liquid crystal (NF) is usually achieved by buffed polymer films, which produce a unidirectional polar alignment of the spontaneous electric polarization. We demonstrate that photosensitive polymer substrates could provide a wider variety of alignment modes. Namely, a polyvinyl cinnamate polymer

Yingdong Yang

This study presents a comprehensive Bayesian analysis of hotel booking cancellations using PyMC, comparing three model specifications of increasing complexity. We investigate how lead time, special requests, and parking requirements affect cancellation probability, and explore interaction effects with hotel type. Using MCMC sampling (NUTS algorithm) on 5,000

R. T. Cavalcanti, J. M. Hoff da Silva

Recent developments in the construction of generalized Dirac duals have revealed, within the structure of the Clifford algebra $\mathbb{C}\otimes\mathcal{C}\ell_{1,3},$ the existence of distinct algebraic formulations of spinors duals with potential applications in quantum field theoretic models. In this work, after reviewing the matrix formulation, we emplo

Ufuk Beyaztas, Han Lin Shang, Gizel Bakicierler Sezer

The function-on-function regression model is fundamental for analyzing relationships between functional covariates and responses. However, most existing function-on-function regression methodologies assume independence between observations, which is often unrealistic for spatially structured functional data. We propose a novel penalized spatial function-on-f

Hongjiang Qian

In this paper, we proved moderate deviation principles for a fully coupled two-time-scale stochastic systems, where the slow process is given by stochastic differential equations with small noise, while the fast process is a rapidly changing purely jump process on finite state space. The system is fully coupled in that the drift and diffusion coefficients of

Miguel A. Cardona, Diego A. Mejía, Ismael E. Rivera-Madrid

We introduce the notion of directed scheme of ideals to characterize peculiar ideals on the reals, which comes from a formalization of the framework of Yorioka ideals for strong measure zero sets. We prove general theorems for directed schemes and propose a directed scheme $\vec{\mathcal{M}} = \{\mathcal{M}_I \colon I\in\mathbb{I}\}$ for the ideal $\mathcal{

Sai Koneru, Matthias Huck, Jan Niehues

There has been significant progress in open-source text-only translation large language models (LLMs) with better language coverage and quality. However, these models can be only used in cascaded pipelines for speech translation (ST), performing automatic speech recognition first followed by translation. This introduces additional latency, which is particula

Davide Rucci, Sebastian Parfeniuc, Matteo Mordacchini, Emanuele Carlini

In this paper, we investigate the parallelization of $k$-core decomposition, a method used in graph analysis to identify cohesive substructures and assess node centrality. Although efficient sequential algorithms exist for this task, the scale of modern networks requires faster, multicore-ready approaches. To this end, we adapt a distributed $k$-core algorit

Jan de Leeuw

We rewrite the metric/nonmetric and weighted/unweighted versions of the smacof program for square symmetric data as one monolithic C program. R is used for taking care of the data and parameter setup, the I/O, and of issuing a single call to .C() to start the computations. This makes this new smacofSS() program five to fifty times as fast (for our examples)

Daniil Sukhorukov, Andrei Zakharov, Nikita Glazkov, Katsiaryna Yanchanka

Weather forecasting is not only a predictive task but an interpretive scientific process requiring explanation, contextualization, and hypothesis generation. This paper introduces AI-Meteorologist, an explainable LLM-agent framework that converts raw numerical forecasts into scientifically grounded narrative reports with transparent reasoning steps. Unlike c

Monique Louise Monteiro, George G. Cabral, Adriano L. I. OLiveira

This work introduces CodeFlowLM, an incremental learning framework for Just-In-Time Software Defect Prediction (JIT-SDP) that leverages pre-trained language models (PLMs). Unlike traditional online learners, CodeFlowLM employs continual fine-tuning to address concept drift, class imbalance, and verification latency without retraining from scratch. We evaluat

Vivan Doshi, Mengyuan Li

The proliferation of Large Language Models (LLMs) raises a critical question about what it means to be human when we share an increasingly symbiotic relationship with persuasive and creative machines. This paper examines patterns of human-AI coevolution in creative writing, investigating how human craft and agency are adapting alongside machine capabilities.

Edited by Tessai Hayama, Takayuki Ito, Takahiro Uchiya, Motoki Miura

This volume presents the proceedings of the 20th International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2025), held in Nagaoka, Japan, on December 3-5, 2025. The conference, organized in cooperation with the IEICE Proceedings Series, provides a multidisciplinary forum for researchers in artificial intelligence, knowledge eng

Michael Hrušák, Luis Sáenz

Answering questions raised in \cite{Leonetti, Uzcategui} we characterize ideals $\mathcal I\subseteq \mathcal P(\omega)$ such that $c_{0,\mathcal I}$ is complemented in $\ell_\infty$ as exactly those ideals for which the space $K_{\mathcal I}= \mathsf{Stone}(\mathcal P(\omega)/\mathcal I)$ is approximable, i.e., the unit ball of the space $M(K_{\mathcal I})$