October 2025 arXiv papers — page 8

Min Hou, Le Wu, Yuxin Liao, Yonghui Yang

Recommender systems serve as foundational infrastructure in modern information ecosystems, helping users navigate digital content and discover items aligned with their preferences. At their core, recommender systems address a fundamental problem: matching users with items. Over the past decades, the field has experienced successive paradigm shifts, from coll

Xian-Jun Long, Kang Zeng, Gao-Xi Li, Minh N. Dao

In this paper, we consider a broad class of nonconvex and nonsmooth optimization problems, where one objective component is a nonsmooth weakly convex function composed with a linear operator. By integrating variable smoothing techniques with first-order methods, we propose a variable smoothing alternating proximal gradient algorithm that features flexible pa

Yuanhao Tang, Xuechao Zou, Zhengpei Hu, Junliang Xing

Remote sensing image scene classification remains a challenging task, primarily due to the complex spatial structures and multi-scale characteristics of ground objects. Although CNN-based methods excel at extracting local inductive biases, and Mamba-based approaches demonstrate impressive capabilities in efficiently capturing global sequential context, relyi

Hugo Guadalupe Reyna-Castañeda, María de los Ángeles Sandoval-Romero

This work develops, from a functional analytic perspective, the construction of random variables in Lebesgue spaces L^p. It extends classical notions of measurability, integrability, and expectation to L^p valued functions, using Pettis's theorem and the Riesz representation theorem to define the Bochner integral as a natural generalization of classical expe

Tong Zhao, Jiacheng Li, Yuanchang Zhou, Guangming Tan

Finding lower and better-generalizing minima is crucial for deep learning. However, most existing optimizers stop searching the parameter space once they reach a local minimum. Given the complex geometric properties of the loss landscape, it is difficult to guarantee that such a point is the lowest or provides the best generalization. To address this, we pro

Erica Coppolillo, Giuseppe Manco

We study how targeted content injection can strategically disrupt social networks. Using the Friedkin-Johnsen (FJ) model, we utilize a measure of social dissensus and show that (i) simple FJ variants cannot significantly perturb the network, (ii) extending the model enables valid graph structures where disruption at equilibrium exceeds the initial state, and

Xueliang Cheng, Kanzhong Yao, Andrew West, Ognjen Marjanovic

Positioning of underwater robots in confined and cluttered spaces remains a key challenge for field operations. Existing systems are mostly designed for large, open-water environments and struggle in industrial settings due to poor coverage, reliance on external infrastructure, and the need for feature-rich surroundings. Multipath effects from continuous sou

Olena Podladchikova

We present a comprehensive physical model explaining the origin of Periodic Density Structures (PDS) observed in white-light coronagraphs with characteristic periods of approximately 45, 80, and 120 minutes. Through systematic investigation of potential resonant cavities in the solar atmosphere, we demonstrate that traditional large-scale cavities yield fund

Jiacheng Hong, Kunzhen Wu, Mingrui Yu, Yichao Gu

Three-dimensional scene generation holds significant potential in gaming, film, and virtual reality. However, most existing methods adopt a single-step generation process, making it difficult to balance scene complexity with minimal user input. Inspired by the human cognitive process in scene modeling, which progresses from global to local, focuses on key el

Gaoyuan Zhang, Ruisong Si, Boyuan Li, Zijian Li

We pay our attention towards secure and robust communication in the presence of a Reconfigurable Intelligent Surface (RIS)-enhanced mobile eavesdropping attacker in Multiple-Input Multiple-Output (MIMO)wireless networks.Specifically,we first provide a unifying framework that generalizes specific intelligent wiretap model wherein the passive eavesdropper conf

Emilio Mastriani, Alessandro Costa, Federico Incardona, Kevin Munari

Objective: ServiMon is designed to offer a scalable and intelligent pipeline for data collection and auditing to monitor distributed astronomical systems such as the ASTRI Mini-Array. The system enhances quality control, predictive maintenance, and real-time anomaly detection for telescope operations. Methods: ServiMon integrates cloud-native technologies-in

Sein Kwon, Seulgi Baek, Hyunseo Yang, Youngwan Jo

Database Management Systems (DBMSs) are fundamental for managing large-scale and heterogeneous data, and their performance is critically influenced by configuration parameters. Effective tuning of these parameters is essential for adapting to diverse workloads and maximizing throughput while minimizing latency. Recent research has focused on automated config

Takahiro Iwami, Naohisa Inoue, Akira Omoto

This paper proposes a novel beamforming framework in the reproducing kernel domain, derived from a unified interpretation of directional response as spatial differentiation of the sound field. By representing directional response using polynomial differential operators, the proposed method enables the formulation of arbitrary beam patterns including non-axis

Chunxiao Ye, Xiao Yan, Eric Lo

The increasing prevalence of hybrid vector and relational data necessitates efficient, general support for queries that combine high-dimensional vector search with complex relational filtering. However, existing filtered search solutions are fundamentally limited by specialized indices, which restrict arbitrary filtering and hinder integration with general-p

Xingtao Ling Yingying Zhu

Cross-view object geo-localization has recently gained attention due to potential applications. Existing methods aim to capture spatial dependencies of query objects between different views through attention mechanisms to obtain spatial relationship feature maps, which are then used to predict object locations. Although promising, these approaches fail to ef

Takumi Matsuo, Tomoya Higo, Daisuke Nishio-Hamane, Takuya Matsuda

Intensive studies have been made on antiferromagnets as candidate materials for next generation memory bits due to their ultrafast dynamics reaching picosecond time scales. Recent demonstrations of electrical bidirectional switching of antiferromagnetic states have attracted significant attention. However, under the presence of significant Joule heating that

Minh Phu Vuong, Chul-Ho Lee, Do Young Eun

Patching nodes is an effective network defense strategy for malware control at early stages, and its performance is primarily dependent on how accurately the infection propagation is characterized. In this paper, we aim to design a novel patching policy based on the susceptible-infected epidemic network model by incorporating the influence of patching delay-

Minh Phu Vuong, Young-Ju Lee, Iván Ojeda-Ruiz, Chul-Ho Lee

Due to the growing concern about unsavory behaviors of machine learning models toward certain demographic groups, the notion of 'fairness' has recently drawn much attention from the community, thereby motivating the study of fairness in graph clustering. Fair graph clustering aims to partition the set of nodes in a graph into $k$ disjoint clusters such that

Denis Sidorov, Alexander Sinitsyn, Omar Toledo Leguizamón, Liguo Wang

In order to avoid the electron oscillation of the cathode and enhance the work efficiency of a vacuum diode, an approach for analyzing the solutions and complex bifurcation has been proposed and used to determine the optimal trajectory of electron motion of the vacuum diode. This work is focusing on the stationary self-consistent problem of magnetic insulati

Tong Shen, Jingai Yu, Dong Zhou, Dong Li

Diffusion models have shown strong capabilities in generating high-quality images from text prompts. However, these models often require large-scale training data and significant computational resources to train, or suffer from heavy structure with high latency. To this end, we propose Efficient Multimodal Diffusion Transformer (E-MMDiT), an efficient and li

Kosei Watanabe

It is shown by Mizuno and Sato that the Bartholdi zeta function of a covering graph is decomposed as a product of Bartholdi zeta functions of a base graph that are associated with representations. In this paper, we extend their result to the case of a hypergraph covering.

Zhicong Sun, Jacqueline Lo, Jinxing Hu

3D Gaussian splatting (3DGS) and its subsequent variants have led to remarkable progress in simultaneous localization and mapping (SLAM). While most recent 3DGS-based SLAM works focus on small-scale indoor scenes, developing 3DGS-based SLAM methods for large-scale forest scenes holds great potential for many real-world applications, especially for wildfire e

Wenxuan Zhang, Yixiao Guo, Benzhuo Lu

This paper proposes the Exact Terminal Condition Neural Network (ETCNN), a deep learning framework for accurately pricing American options by solving the Black-Scholes-Merton (BSM) equations. The ETCNN incorporates carefully designed functions that ensure the numerical solution not only exactly satisfies the terminal condition of the BSM equations but also m

Hong Jiao, Hanna Choi, Haowei Hua

This study explored the utilities of rationales generated by GPT-4.1 and GPT-5 in automated scoring using Prompt 6 essays from the 2012 Kaggle ASAP data. Essay-based scoring was compared with rationale-based scoring. The study found in general essay-based scoring performed better than rationale-based scoring with higher Quadratic Weighted Kappa (QWK). Howeve

Srijit Seal, Dinh Long Huynh, Moudather Chelbi, Sara Khosravi

Artificial intelligence (AI) agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act, and learn through complicated research workflows. Building on large language models (LLMs) coupled with perception, computation, action, and memory tools, these agentic AI systems could integrate diverse biomedical data, e

Antonio Guillen-Perez, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour

The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the absence of benchmarks that realistically capture the interplay of time-varying environmental factors (grid carbon intensity, electricity prices, weather), detailed data center physic

Alex Cohen, Felipe Hernández

The Coulomb gas models an interacting system of $N$ negatively charged particles. We give a new proof that, at sufficiently low temperature, smooth linear statistics $\sum_j \varphi(x_j)$ are bounded by $C N^{1-2/d}$.

Haonan Wang, Jingyu Lu, Hongrui Li, Xiaomeng Li

Recent advances in neural decoding have enabled the reconstruction of visual experiences from brain activity, positioning fMRI-to-image reconstruction as a promising bridge between neuroscience and computer vision. However, current methods predominantly rely on subject-specific models or require subject-specific fine-tuning, limiting their scalability and re

Avisek Naug, Antonio Guillen, Vineet Kumar, Scott Greenwood

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) contro

Kunming Yang, Ling Chen

With the widespread adoption of deep neural networks (DNNs), protecting intellectual property and detecting unauthorized tampering of models have become pressing challenges. Recently, Perceptual hashing has emerged as an effective approach for identifying pirated models. However, existing methods either rely on neural networks for feature extraction, demandi

Jinwen Tang, Yi Shang

Conventional online surveys provide limited personalization, often resulting in low engagement and superficial responses. Although AI survey chatbots improve convenience, most are still reactive: they rely on fixed dialogue trees or static prompt templates and therefore cannot adapt within a session to fit individual users, which leads to generic follow-ups

R. Li, E. Litvinova, M. N. Harakeh, D. Verney

Gamma ($\gamma$) decay shapes the synthesis of heavy elements in neutron-rich nuclear environments of neutron star mergers, supplying the Universe with heavy elements. The low-energy pygmy dipole resonance (PDR) influences nuclear reaction rates of the rapid nucleosynthesis through enhanced $\gamma$ transitions. However, since it is difficult to reproduce as

Neng Wang, Shuyong Chen, Guo Ping Wang

We investigate temporal scattering in lossless Drude media and reveal an overlooked role of the zero-frequency flat band associated with static polarization charge. This flat band forms an exceptional line spanning all wavenumbers and can be directly excited during temporal scattering at photonic time interfaces, generating non-propagating static fields alon

Yihong Guo, Junjie Luo, Guodong Gao, Ritu Agarwal

Offline contextual bandits allow one to learn policies from historical/offline data without requiring online interaction. However, offline policy optimization that maximizes overall expected rewards can unintentionally amplify the reward disparities across groups. As a result, some groups might benefit more than others from the learned policy, raising concer

Shumpei Masuda

We theoretically investigate reflection and transmission measurements of two-photon and four-photon Kerr parametric oscillators (KPOs), introducing interference effects between inter-level transitions. Due to the level degeneracy of a KPO, a probe field can be resonant with multiple inter-level transitions. We extend the previous theory of reflection measure

Luis Antonio L. F. da Costa, Rodrigo C. de Lamare, Rafael Kunst, Edison Pignaton de Freitas

The sixth generation (6G) wireless networks are envisioned to deliver ultra-low latency, massive connectivity, and high data rates, enabling advanced applications such as autonomous {unmaned aerial vehicles (UAV)} swarms and aerial edge computing. However, realizing this vision in Flying Ad Hoc Networks (FANETs) requires intelligent and adaptive clustering m

Yongxin Chen, Tryphon Georgiou, Michele Pavon

We show that the continuous-time gradient descent in Rn can be viewed as an optimal controlled evolution for a suitable action functional; a similar result holds for stochastic gradient descent. We then provide an analogous characterization for the Wasserstein gradient flow of the (relative) entropy, with an action that mirrors the classical case where the E

Haoyuan Li, Yuanbo Tong, Yuchen Li, Zirui Wang

Personality recognition from text is typically cast as hard-label classification, which obscures the graded, prototype-like nature of human personality judgments. We present ProtoMBTI, a cognitively aligned framework for MBTI inference that operationalizes prototype theory within an LLM-based pipeline. First, we construct a balanced, quality-controlled corpu

Qiyan Deng, Jianhui Li, Chengliang Chai, Jinqi Liu

Nowadays, the explosion of unstructured data presents immense analytical value. Leveraging the remarkable capability of large language models (LLMs) in extracting attributes of structured tables from unstructured data, researchers are developing LLM-powered data systems for users to analyze unstructured documents as working with a database. These unstructure

Ningji Wei, Jiaming Liang

We present GFORS, a GPU-accelerated framework for large binary integer programs. It couples a first-order (PDHG-style) routine that guides the search in the continuous relaxation with a randomized, feasibility-aware sampling module that generates batched binary candidates. Both components are designed to run end-to-end on GPUs with minimal CPU-GPU synchroniz

Yogesh M. Joshi, Alexander Ya. Malkin

Many soft jammed materials, such as pastes, gels, concentrated emulsions, and suspensions, possess a threshold stress, known as yield stress, that must be exceeded to cause permanent deformation or flow. In rheology, the term plastic flow is commonly used to describe continuous flow (unbounded increase in strain with time) that a material undergoes above a y

Hirohito Aizawa

In this study, we theoretically investigated the magnetic properties of the quasi-two-dimensional organic conductor $\lambda$-(BETS)$_2$GaCl$_4$ using a multi-band Hubbard model and the two-particle self-consistent method. We employed a four-band model in which each BETS molecule is considered as a site and a two-band model that considers each BETS dimer as

Dohyeok Lee, Jung Min Lee, Munkyung Kim, Seokhun Ju

Behavior cloning methods for robot learning suffer from poor generalization due to limited data support beyond expert demonstrations. Recent approaches leveraging video prediction models have shown promising results by learning rich spatiotemporal representations from large-scale datasets. However, these models learn action-agnostic dynamics that cannot dist

Man Zhang, Yuechen Li, Tao Yue, Kai-Yuan Cai

In recent years, quantum, quantum-inspired, and hybrid algorithms are increasingly showing promise for solving software engineering optimization problems. However, best-intended practices for conducting empirical studies have not yet well established. In this paper, based on the primary studies identified from the latest systematic literature review on quant

Klajdi Hoxha

E-commerce platforms are rolling out ambitious targeted advertising initiatives that rely on merchants sharing customer data with each other via the platform. Yet current platform designs fail to address participating merchants' concerns about customer poaching. This paper proposes a model of designing targeted advertising platforms that incentivizes merchan

Heehwan Kim, Sungjune Park, Daeseon Choi

Large Language Models (LLMs) are generally equipped with guardrails to block the generation of harmful responses. However, existing defenses always assume that an external attacker crafts the harmful query, and the possibility of a model's own output becoming a new attack vector has not been sufficiently explored. In this study, we propose the Self-HarmLLM s

Yunfeng Jiang, Zhiming Huang, Jianping Pan

The analytical characterization of coverage probability in finite three-dimensional wireless networks has long remained an open problem, hindered by the loss of spatial independence in finite-node settings and the coupling between link distances and interference in bounded geometries. This paper closes this gap by presenting the first exact analytical framew

Gal Shtendel, Ayush Bhandari

In this paper, we address the problem of reconstructing multiband signals from modulo-folded, pointwise samples within the Unlimited Sensing Framework (USF). Focusing on a low-complexity, single-channel acquisition setup, we establish recovery guarantees demonstrating that sub-Nyquist sampling is achievable under the USF paradigm. In doing so, we also tighte

Sajad Salami

We study the family of algebraic curves of genus $\geq 1$ defined by the affine equations $y^s=ax^r+b$ over a number field $k$, where $r \geq 2$ and $s\geq 2$ are fixed integers. Assuming the strong version of Lang's conjecture on varieties of general type, we prove that the Mordell-Weil rank of the Jacobian varieties of these curves is uniformly bounded. Th

Shuo Zhu, Siyu Lin

Massive message transmissions, unpredictable aperiodic messages, and high-speed moving vehicles contribute to the complex wireless environment, resulting in inefficient resource collisions in Vehicle to Everything (V2X). In order to achieve better medium access control (MAC) layer performance, 3GPP introduced several new features in NR-V2X. One of the most i

Jeff Brown, Andrew Kirjner, Annika Vivekananthan, Ed Boyden

Connectomics - the mapping of neural connections in an organism's brain - currently requires extraordinary human effort to proofread the data collected from imaging and machine-learning assisted segmentation. With the growing excitement around using AI agents to automate important scientific tasks, we explore whether current AI systems can perform multiple t

Zhipeng Liao, Kunming Shao, Jiangnan Yu, Liang Zhao

With powerful and integrative large language models (LLMs), medical AI agents have demonstrated unique advantages in providing personalized medical consultations, continuous health monitoring, and precise treatment plans. Retrieval-Augmented Generation (RAG) integrates personal medical documents into LLMs by an external retrievable database to address the co

Rajarshi Haldar, Julia Hockenmaier

As Natural Language Generation (NLG) continues to be widely adopted, properly assessing it has become quite difficult. Lately, using large language models (LLMs) for evaluating these generations has gained traction, as they tend to align more closely with human preferences than conventional n-gram or embedding-based metrics. In our experiments, we show that

Navid Mojahed, Mahdis Rabbani, Shima Nazari

This paper develops a predictive compensation framework for finite-horizon, discrete-time linear quadratic dynamic games subject to Gauss-Markov execution deviations from feedback Nash strategies. One player's control is corrupted by temporally correlated stochastic perturbations modeled as a first-order autoregressive (AR(1)) process, while the opposing pla

Arka Dutta, Sujan Dutta, Rijul Magu, Soumyajit Datta

Hallucinations pose a critical challenge to the real-world deployment of large language models (LLMs) in high-stakes domains. In this paper, we present a framework for stress testing factual fidelity in LLMs in the presence of adversarial nudge. Our framework consists of three steps. In the first step, we instruct the LLM to produce sets of truths and lies c

Lei Chang, Zi-Chen Kan, Jing-Jing Ma, Saikat Chakraborty Thakur

This paper reports an innovative concept of ``plasma fibre" using bright-core helicon plasma, inspired by its spatial and spectral similarities to the well-known optical fibre. Theoretical analyses are presented for both ideal case of step-like density profile and the realistic case of Gaussian density profile in radius. The total reflection of electromagnet

Michelle Mastrianni, Rucha Deshpande, Frank W. Samuelson, Yee Lam Elim Thompson

The deployment of multiple AI-triage devices in radiology departments has grown rapidly, yet the cumulative impact on patient wait-times across different disease conditions remains poorly understood. This research develops a comprehensive mathematical and simulation framework to quantify wait-time trade-offs when multiple AI-triage devices operate simultaneo

Jack Lonergan, Andrew Benson, Xiaolong Du

We present a comparative analysis of warm dark matter (WDM) subhalo populations generated by the semi-analytic model {\sc Galacticus} and the COZMIC suite of dark matter-only $N$-body simulations. Using a range of thermal relic WDM particle masses (3--10 keV), we examine key summary statistics -- including the subhalo mass function, spatial distribution, max

Jonathan Morse, Azadeh Naderi, Swen Gaudl, Mark Cartwright

Text-to-audio models are a type of generative model that produces audio output in response to a given textual prompt. Although level generators and the properties of the functional content that they create (e.g., playability) dominate most discourse in procedurally generated content (PCG), games that emotionally resonate with players tend to weave together a

Reda Snaiki, Jiachen Lu, Shaopeng Li, Negin Nazarian

Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. However, conventional approaches usually only yield low-frequency predictions (essentially averaging values from proximate pixels), missing critical high-frequency details such as s

Yuxi Han, Son Tu

We study the periodic homogenization of convex Hamilton-Jacobi equations on perforated domains with Dirichlet boundary conditions. By analyzing the optimal control representation of the solutions and the properties of the metric function associated with the running cost, we establish the optimal convergence rate $\mathcal{O}(\varepsilon)$ for homogenization.

Kotaro Hisa, Yasuhito Miyamoto

We establish nonuniqueness of solutions for Cauchy problems of semilinear heat equations with a wide class of nonlinearities. Specifically, we consider \[ \begin{cases} \partial_tu-\Delta u=f(u), & x\in\mathbb{R}^N,\ t>0,\\ u(x,0)=u_0(x), & x\in\mathbb{R}^N, \end{cases} \] where $N>2$. We assume that the growth rate of $f$ is less than the Joseph-Lundgren ex

Crystal Su, Kuai Yu, Mingyuan Shao, Daniel Bauer

Bulk tissue RNA sequencing of heterogeneous samples provides averaged gene expression profiles, obscuring cell type-specific dynamics. To address this, we present a probabilistic hierarchical Bayesian model that deconvolves bulk RNA-seq data into constituent cell-type expression profiles and proportions, leveraging a high-resolution single-cell reference. We

Kaito Nishimiya, Rambabu Rajpoot, Eiji J Takahashi

The duration of isolated attosecond pulses created via high-order harmonic generation is determined by the number of optical cycles in the driving laser. Achieving shorter attosecond soft X-ray pulses requires minimizing the number of cycles while maintaining a high pulse energy. Here, we demonstrate a carrier-envelope-phase-stable, 100-mJ-class sub-cycle mi

Shubham Jadhav, Kaustav Roy, Luis Amaro, Thejas Basavarajappa

Neuromorphic computing demands synaptic elements that can store and update weights with high precision while being read non-destructively. Conventional ferroelectric synapses store weights in remnant polarization states and might require destructive electrical readout, limiting endurance and reliability. We demonstrate a ferroelectric MEMS (FeMEMS) based syn

Hamed Mahdavi, Pouria Mahdavinia, Alireza Farhadi, Pegah Mohammadipour

State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 of 6 problems. Given this progress, we assess how well these models can grade proofs: detecting errors, judging their severity, and assigning fair scores beyond binary correctness.

Jinlu Li

In this paper, we calculate the covering constants for single-valued mappings in Euclidean space by using Mordukhovich derivatives (or coderivatives). At first, we prove the guideline for calculating the Frechet derivatives of single-valued mappings by their partial derivatives. Then, by using the connections between Frechet derivatives and Mordukhovich deri

Ziyu Huang, Masatoshi Hirabayashi, Thomas M. Orlando

Nanophase metallic iron ( $\mathrm{npFe}^0$ ) is a key indicator of space weathering on the lunar surface, primarily attributed to solar wind irradiation and micrometeoroid impacts. Recent discoveries of hematite ( $\mathrm{Fe}_2 \mathrm{O}_3$ ), a highly oxidized form of iron, in the lunar polar regions challenge the prevailing understanding of the Moon's r

Yiwei Chen, Kehuan Yan, Yu Pan, Daoyi Dong

Quantum theory provides non-classical principles, such as superposition and entanglement, that inspires promising paradigms in machine learning. However, most existing quantum-inspired fusion models rely solely on unitary or unitary-like transformations to generate quantum entanglement. While theoretically expressive, such approaches often suffer from traini

Sina Javadzadeh, Rahil Soroushmojdehi, S. Alireza Seyyed Mousavi, Mehrnaz Asadi

Aggregating intracranial recordings across subjects is challenging since electrode count, placement, and covered regions vary widely. Spatial normalization methods like MNI coordinates offer a shared anatomical reference, but often fail to capture true functional similarity, particularly when localization is imprecise; even at matched anatomical coordinates,

Takeshi Suzuki, Kozo Okazaki

Recent advancements in ultrafast laser systems and high harmonic generation (HHG) techniques have enabled time-resolved photoemission spectroscopy on femtosecond timescales, opening up unprecedented opportunities to explore quantum materials in both time and momentum space. In this review, we present recent representative studies utilizing HHG-laser-based ti

Aditya Vora, Lily Goli, Andrea Tagliasacchi, Hao Zhang

We introduce HiT, a novel hierarchical neural field representation for 3D shapes that learns general hierarchies in a coarse-to-fine manner across different shape categories in an unsupervised setting. Our key contribution is a hierarchical transformer (HiT), where each level learns parent-child relationships of the tree hierarchy using a compressed codebook

Adel Khorramrouz, Sharon Levy

Safety guardrails in large language models(LLMs) are developed to prevent malicious users from generating toxic content at a large scale. However, these measures can inadvertently introduce or reflect new biases, as LLMs may refuse to generate harmful content targeting some demographic groups and not others. We explore this selective refusal bias in LLM guar

Tian-Yang Sun, Tian-Nuo Li, He Wang, Jing-Fei Zhang

The cosmic microwave background power spectra are a primary window into the early universe. However, achieving interpretable compression and fast inference diagnostics under weak model assumptions remains challenging. We propose a parameter-conditioned variational autoencoder (CVAE) that aligns a data-driven latent representation with cosmological parameters

Spyros Alexakis, Matti Lassas

We address the question of whether a Riemannian manifold-with-boundary (M,g) in dimension two is uniquely determined from knowledge of the distances between points on its boundary. An affirmative answer is called boundary rigidity for (M,g); it is closely related to lens rigidity. The latter question originates in the problem of reconstructing the speed of s

Samuel Bolduc-St-Aubin, Antony R. Humphries

The Ghil-Zaliapin-Thompson (GZT) model, a scalar delay differential equation with periodic forcing and time-delayed feedback, captures key features of the El Nino-Southern Oscillation (ENSO) phenomenon. Numerical studies of the GZT model have revealed stable period-one orbits under strong forcing and locked, quasiperiodic, or even chaotic regimes under weake

Annika Gonzalez-Zugasti, Ryan Lynch, Dylan Snustad

We study the stable configurations of the labeled chip-firing game on an infinitely subdivided $k$-star graph starting with $km$ chips on the center vertex. We prove a sorting property of this game and analyze special stable configurations corresponding to standard Young tableaux.

Bilgi Yilmaz, Alper Hekimoglu

This paper derives the exact transition density and cumulative distribution function of a linear combination of two independent Cox-Ingersoll-Ross (CIR) processes. By combining the Poisson Gamma mixture representation of the noncentral chi-square law with the Kummer type convolution of Gamma densities, we obtain a closed-form analytical expression involving

Arnabh Borah, Md Tanvirul Alam, Nidhi Rastogi

Security applications are increasingly relying on large language models (LLMs) for cyber threat detection; however, their opaque reasoning often limits trust, particularly in decisions that require domain-specific cybersecurity knowledge. Because security threats evolve rapidly, LLMs must not only recall historical incidents but also adapt to emerging vulner

Jeffrey Kuan, Krutika Tawri, Konstantina Trivisa

We study the long time behavior of isentropic compressible Euler equations with linear damping driven by a white-in-time noise, on a one-dimensional torus. We prove the existence of a statistically stationary solution in the class of weak martingale entropy solutions for any adiabatic constant $\gamma>1$, which satisfies an associated entropy inequality. To

Meles Weldegebriel, Zihan Li, Greg Hellbourg, Ning Zhang

Protecting passive radio astronomy observatories from unintended radio-frequency interference (RFI) is increasingly challenging as wireless activity expands near protected bands. While radio quiet zones, database-driven coordination, and post-processing mitigation can reduce interference risk, they often lack the ability to attribute detected RFI to a specif

Peng-Fei Zhang, Ying Cheng, Xiaofan Sun, Shijie Wang

Autonomous agents are increasingly expected to operate in complex, dynamic, and uncertain environments, performing tasks such as manipulation, navigation, and decision-making. Achieving these capabilities requires agents to understand the underlying mechanisms and dynamics of the world, moving beyond reactive control or simple replication of observed states.

Hanae Elmekki, Amanda Spilkin, Ehsan Zakeri, Antonela Mariel Zanuttini

Cardiac ultrasound (US) is among the most widely used diagnostic tools in cardiology for assessing heart health, but its effectiveness is limited by operator dependence, time constraints, and human error. The shortage of trained professionals, especially in remote areas, further restricts access. These issues underscore the need for automated solutions that

Jiasen Zheng, Huajun Zhang, Xu Yan, Ran Hao

This paper addresses the limitations of large-scale language models in safety alignment and robustness by proposing a fine-tuning method that combines contrastive distillation with noise-robust training. The method freezes the backbone model and transfers the knowledge boundaries of the teacher model to the student model through distillation, thereby improvi

Lei Cao, Shen-Fu Tsai

We introduce two related notions of pattern enforcement in $(0,1)$-matrices: $Q$-forcing and strongly $Q$-forcing, which formalize distinct ways a fixed pattern $Q$ must appear within a larger matrix. A matrix is $Q$-forcing if every submatrix can realize $Q$ after turning any number of $1$-entries into $0$-entries, and strongly $Q$-forcing if every $1$-entr

Byoung-Hee Kwon, Minji Lee, Seong-Whan Lee

This study introduces a pioneering approach in brain-computer interface (BCI) technology, featuring our novel concept of high-level visual imagery for non-invasive electroencephalography (EEG)-based communication. High-level visual imagery, as proposed in our work, involves the user engaging in the mental visualization of complex upper limb movements. This i

Carlos Bustamante, Christian Kaiser, Erik Lindahl, Robert Sosa

How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than 6 decades ago, this code remains undefined. While we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how sequences dictate

Otávio José R. Silveira, Nycolas B. da Silva, Saulo L. L. da Silva, Angélica S. da Mata

Quantum communication is a growing area of research, with quantum internet being one of the most promising applications. Studying the statistical properties of this network is essential to understanding its connectivity and the efficiency of the entanglement distribution. However, the models proposed in the literature often assume homogeneous distributions i

Justin Yang Chae, Md Tanvirul Alam, Nidhi Rastogi

Recent advances in large language model (LLM) reasoning, led by reinforcement learning with verifiable rewards (RLVR), have inspired self-play post-training, where models improve by generating and solving their own problems. While self-play has shown strong in-domain and out-of-domain gains, the mechanisms behind these improvements remain poorly understood.

Dengming Xu, Mengmeng LI

Constant dimension codes (CDCs) have become an important object in coding theory due to their application in random network coding. The multilevel construction is one of the most effective ways to construct constant dimension codes. The paper is devoted to constructing CDCs by the multilevel construction. Precisely, we first choose an appropriate skeleton co

Dong Tong

The security and efficiency of modern computing systems are fundamentally undermined by the absence of a native architectural mechanism to propagate high-level program semantics, such as object identity, bounds, and lifetime, across the hardware/software interface. This paper presents a comprehensive survey of the architectural paradigm designed to bridge th

Mohammad Hossein Shokouhi, Vincent W. S. Wong

Cell-free massive multiple-input multiple-output (MIMO) is a key technology for next-generation wireless systems. The integration of cell-free massive MIMO within the open radio access network (O-RAN) architecture addresses the growing need for decentralized, scalable, and high-capacity networks that can support different use cases. Precoding is a crucial st

Xiaoyi Tian, Qingxiang Xu, Chunhong Fu

A quasi-projection pair consists of two operators $P$ and $Q$ acting on a Hilbert $C^*$-module $H$, where $P$ is a projection and $Q$ is an idempotent satisfying $Q^*=(2P-I)Q(2P-I)$, in which $Q^*$ denotes the adjoint operator of $Q$, and $I$ is the identity operator on $H$. Such a pair is said to be harmonious if both $P(I-Q)$ and $(I-P)Q$ admit polar decom

Marouane Benbetka, Merwan Bekkar, Riyadh Baghdadi, Martin Kong

Qubit Mapping is a critical task in Quantum Compilation, as modern Quantum Processing Units (QPUs) are constrained to nearest-neighbor interactions defined by a qubit coupling graph. This compiler pass repairs the connectivity of two-qubit gates whose operands are not adjacent by inserting SWAP gates that move the state of qubits between directly connected q

Radoyeh Shojaei, Predrag Djurdjevic, Mostafa El-Khamy, James Goel

We present MLPerf Automotive, the first standardized public benchmark for evaluating Machine Learning systems that are deployed for AI acceleration in automotive systems. Developed through a collaborative partnership between MLCommons and the Autonomous Vehicle Computing Consortium, this benchmark addresses the need for standardized performance evaluation me

Guo-Dong Zhang, Shuang-Yong Zhou, Meng-Fan Zhu

It was recently discovered that waves scattering off a $Q$-ball can extract energy from it. We present an analytical treatment of this process by adopting a multi-step function approximation for the background field, which yields perturbative solutions expressed in terms of Bessel functions. For thin-wall $Q$-balls, the amplification factors reduce to simple

Shairoz Sohail, Taher Ali

Given two algorithms for the same problem, can we determine whether they are meaningfully different? In full generality, the question is uncomputable, and empirically it is muddied by competing notions of similarity. Yet, in many applications (such as clone detection or program synthesis) a pragmatic and consistent similarity metric is necessary. We review e

Alex Irpan, Alexander Matt Turner, Mark Kurzeja, David K. Elson

An LLM's factuality and refusal training can be compromised by simple changes to a prompt. Models often adopt user beliefs (sycophancy) or satisfy inappropriate requests which are wrapped within special text (jailbreaking). We explore \emph{consistency training}, a self-supervised paradigm that teaches a model to be invariant to certain irrelevant cues in th

J. Dick, Q. T. Le Gia, K. Mustapha

This work uses the Bayesian inference technique to infer the Young modulus from the stochastic linear elasticity equation. The Young modulus is modeled by a finite Karhunen Lo\'{e}ve expansion, while the solution to the linear elasticity equation is approximated by the finite element method. The high-dimensional integral involving the posterior density and t

Momo Ellwarth, Joe Llama, Lily L. Zhao, John M. Brewer

Stellar activity remains one of the primary challenges in the detection and characterization of low-mass exoplanets, as it can induce radial velocity (RV) variations that mask or mimic planetary signals. Identifying reliable activity proxies is essential in order to distinguish stellar variability from genuine planetary signatures. In this study, we examine