October 2025 arXiv papers — page 90

Tuan Pham, Thanh-Tung Le, Xiaohui Xie, Stephan Mandt

We introduce a novel framework for metric depth estimation that enhances pretrained diffusion-based monocular depth estimation (DB-MDE) models with stereo vision guidance. While existing DB-MDE methods excel at predicting relative depth, estimating absolute metric depth remains challenging due to scale ambiguities in single-image scenarios. To address this,

Yuki Takazawa, Tomonari Sei

The inference of evolutionary histories is a central problem in evolutionary biology. The analysis of a sample of phylogenetic trees can be conducted in Billera-Holmes-Vogtmann tree space, which is a CAT(0) metric space of phylogenetic trees. The globally non-positively curved (CAT(0)) property of this space enables the extension of various statistical techn

Jialin Wu, Shreya Saha, Yiqing Bo, Meenakshi Khosla

The extent to which different neural or artificial neural networks (models) rely on equivalent representations to support similar tasks remains a central question in neuroscience and machine learning. Prior work has typically compared systems using a single representational similarity metric, yet each captures only one facet of representational structure. To

Andrew J. Medford, Todd N. Whittaker, Bjarne Kreitz, David W. Flaherty

Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the "many-to-one" challenge of linking intrinsic kinetics to observables. Advances in machine-learned force fields, microkinetics, and r

Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao

Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarcity and ambiguities in mixed-text contexts. We construct English and Chinese Braille Mixed Datasets (EBMD/CBMD) with mathematical formulas to support diverse Braille domain researc

Vishal Vinod

Identity preserving editing of faces is a generative task that enables modifying the illumination, adding/removing eyeglasses, face aging, editing hairstyles, modifying expression etc., while preserving the identity of the face. Recent progress in 2D generative models have enabled photorealistic editing of faces using simple techniques leveraging the composi

Hideyuki Miyahara

Natural gradient is an advanced optimization method based on information geometry, where the Fisher metric plays a crucial role. Its quantum counterpart, known as quantum natural gradient (QNG), employs the symmetric logarithmic derivative (SLD) metric, one of the quantum Fisher metrics. While quantization in physics is typically well-defined via the canonic

Kanghuai Liu, Lin Chen, Jihong Yu, Junyi Huang

We revisit the problem of missing tag identification in RFID networks by making three contributions. Firstly, we quantitatively compare and gauge the existing propositions spanning over a decade on missing tag identification. We show that the expected execution time of the best solution in the literature is $\Theta \left(N+\frac{(1-\alpha)^2(1-\delta)^2}{ \e

Benjamin Kubwimana, Qijing Huang

Edge intelligence paradigm is increasingly demanded by the emerging autonomous systems, such as robotics. Beyond ensuring privacy-preserving operation and resilience in connectivity-limited environments, edge deployment offers significant energy and cost advantages over cloud-based solutions. However, deploying large language models (LLMs) for reasoning task

Nathan Grieve

We address the question of effectivity for calculation of local Weil functions from the viewpoint of presentations of Cartier divisors. This builds on the approach of Bombieri and Gubler as well as the perspective of our earlier works. Among other features, our approach here gives rise to theoretical effective algorithms for calculating local Weil functions

Daniel G. Schwartz

This paper provides a new and more direct proof of the assertion that a Turing computable function of the natural numbers is primitive recursive if and only if the time complexity of the corresponding Turing machine is bounded by a primitive recursive function of the function's arguments. In addition, it provides detailed proofs of two consequences of this f

Jonathan Treviño-Marroquín

Pseudotopological spaces are the Cartesian closed hull of the category of \v{C}ech closure spaces. In this paper, we give a direct proof that the model category of the pseudotopological spaces constructed by Rieser is Quillen equivalent to the category of simplicial sets. In addition to noting that every pseudotopological space is weak homotopy equivalent to

Zijian Li, Changze Zhou, Minghao Fu, Sanjay Manjunath

This paper is concerned with online time series forecasting, where unknown distribution shifts occur over time, i.e., latent variables influence the mapping from historical to future observations. To develop an automated way of online time series forecasting, we propose a Theoretical framework for Online Time-series forecasting (TOT in short) with theoretica

Yongren Shi, Edo Airoldi, Nicholas A. Christakis

Human social networks are inherently multiplex, comprising overlapping layers of relationships. Different layers may have distinct structural properties and interpersonal dynamics, but also may interact to form complex interdependent pathways for social contagion. This poses a fundamental problem in understanding behavioral diffusion and in devising effectiv

Yanhong Li, Zixuan Lan, Jiawei Zhou

Large language models (LLMs) and their multimodal variants can now process visual inputs, including images of text. This raises an intriguing question: can we compress textual inputs by feeding them as images to reduce token usage while preserving performance? In this paper, we show that visual text representations are a practical and surprisingly effective

Karelia Salinas, Victor Barella, André Luiz Cunha, Gabriel Martins de Oliveira

Analyzing origin-destination flows is an important problem that has been extensively investigated in several scientific fields, particularly by the visualization community. The problem becomes especially challenging when involving massive data, demanding mechanisms such as data aggregation and interactive filtering to make the exploratory process doable. How

Nikolaos Belibasakis, Anastasios Giannaros, Ioanna Giannoukou, Spyros Sioutas

The increasing number of data a booking platform such as Booking.com and AirBnB offers make it challenging for interested parties to browse through the available accommodations and analyze reviews in an efficient way. Efforts have been made from the booking platform providers to utilize recommender systems in an effort to enable the user to filter the result

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

Using an $e^+e^-$ sample of $20.3\,\rm fb^{-1}$ collected at the center-of-mass energy $\sqrt{s}=$ 3.773 GeV with the BESIII detector, we report measurements of several four-body hadronic decays of the $D$ mesons. The absolute branching fractions are determined to be ${\mathcal B}(D^0\to K^0_S K^+K^-\pi^0 )=( 18.4^{+2.6}_{-2.5}\pm 2.4)\times 10^{-5}$, ${\mat

Joshua O. Aggrey, Leonard Bleiziffer, Frank Hagelberg

Edge-functionalized Transition Metal dichalcogenide nanoribbons of the zigzag type (zTMDCNRs) are explored in terms of their spin transmission properties. Specifically, systems of the type 5-zWXYNR + nA (X, Y = S, Se; n = 0, 1, 2; A = H, B, C, N, O), involving five rows of a zWXY unit, are investigated as transmission elements between semi-infinite electrode

Yonggang Jiang, Danupon Nanongkai, Pachara Sawettamalya

We study the problem of computing a minimum $s$--$t$ cut in an unweighted, undirected graph via \emph{cut queries}. In this model, the input graph is accessed through an oracle that, given a subset of vertices $S \subseteq V$, returns the size of the cut $(S, V \setminus S)$. This line of work was initiated by Rubinstein, Schramm, and Weinberg (ITCS 2018), w

Mohammadreza Doostmohammadian, Sergio Pequito

Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Conventional DRA approaches require reliable communication, yet real-world networks frequently suffer from link failures, packet drops, and communication delays due to environmental c

Ziquan Wei, Tingting Dan, Guorong Wu

A reliable foundation model of functional neuroimages is critical to promote clinical applications where the performance of current AI models is significantly impeded by a limited sample size. To that end, tremendous efforts have been made to pretraining large models on extensive unlabeled fMRI data using scalable self-supervised learning. Since self-supervi

Junlin Xiong, Jiawei Jiang, Yanwei Cui, Han Gao

Antiferromagnets have garnered significant attention due to their negligible stray field and ultrafast magnetic dynamics, which are promising for high-density and ultrafast spintronic applications. Their dual functionality as both spin sources and information carriers could enable all-electrical self-induced switching of antiferromagnetic order, offering gre

Xiao Xue, Deyu Zhou, Ming Zhang, Fei-Yue Wang

This is the first part of the comprehensive review, focusing on the historical development of Agent-Based Modeling (ABM) and its classic cases. It begins by discussing the development history and design principles of Agent-Based Modeling (ABM), helping readers understand the significant challenges that traditional physical simulation methods face in the soci

Yang Chen, Toufique Ahmed, Reyhaneh Jabbarvand, Martin Hirzel

Test suites in real-world projects are often large and achieve high code coverage, yet they remain insufficient for detecting all bugs. The abundance of unresolved issues in open-source project trackers highlights this gap. While regression tests are typically designed to ensure past functionality is preserved in the new version, they can also serve a comple

Xueyi Chen, Keda Tao, Kele Shao, Huan Wang

Unlike offline processing, streaming video vision-language models face two fundamental constraints: causality and accumulation. Causality prevents access to future frames that offline methods exploit, while accumulation causes tokens to grow unbounded, creating efficiency bottlenecks. However, existing approaches only regulate post-LLM kv-cache, leaving cost

Yucheng Song, Chenxi Li, Haokang Ding, Zhining Liao

In medical image segmentation tasks, Domain Generalization (DG) under the Federated Learning (FL) framework is crucial for addressing challenges related to privacy protection and data heterogeneity. However, traditional federated learning methods fail to account for the imbalance in information aggregation across clients in cross-domain scenarios, leading to

Hongyi He, Xiao Liu, Zhenghao Lin, Mingni Tang

High-quality pre-training data is crutial for large language models, where quality captures factual reliability and semantic value, and diversity ensures broad coverage and distributional heterogeneity. Existing approaches typically rely on single or multiple-dimensional score-based selection. However, directly selecting top-scored data often degrades perfor

Xiang Zhang, Suping Wu, Sheng Yang

Existing 3D human mesh recovery methods often fail to fully exploit the latent information (e.g., human motion, shape alignment), leading to issues with limb misalignment and insufficient local details in the reconstructed human mesh (especially in complex scenes). Furthermore, the performance improvement gained by modelling mesh vertices and pose node inter

Hua Su, Lei Zhang, Jin Zhao

We introduce the Stable Physics-Informed Kernel Evolution (SPIKE) method for numerical computation of inviscid hyperbolic conservation laws. SPIKE resolves a fundamental paradox: how strong-form residual minimization can capture weak solutions containing discontinuities. SPIKE employs reproducing kernel representations with regularized parameter evolution, w

Erik Dahlen

A $b$-coloring is a proper coloring such that for each color class, there exists at least one vertex that is adjacent to at least one vertex in every other color class. The $b$-chromatic number of a graph $G$ is the maximum number $k$ such that $G$ admits a $b$-coloring with $k$ colors. This paper focuses on the $b$-chromatic number of the power graph of the

Ziwei Huang, Ying Shu, Hao Fang, Quanyu Long

Subject-driven image generation models face a fundamental trade-off between identity preservation (fidelity) and prompt adherence (editability). While online reinforcement learning (RL), specifically GPRO, offers a promising solution, we find that a naive application of GRPO leads to competitive degradation, as the simple linear aggregation of rewards with s

Da Zhang, Chenggang Rong, Bingyu Li, Feiyu Wang

Large vision-language models (VLMs) have achieved remarkable success in natural scene understanding, yet their application to underwater environments remains largely unexplored. Underwater imagery presents unique challenges including severe light attenuation, color distortion, and suspended particle scattering, while requiring specialized knowledge of marine

Andreas Stavrou

Let $\Sigma_{g,*}$ be a once-punctured oriented surface of genus $g$. We study the action of the mapping class group $\Gamma_{g,*}$ on the $n^{th}$ rational cohomology of the configuration space $\text{Conf}_n(\Sigma_{g,*})$ of injections $\{1,\ldots, n\}\hookrightarrow \Sigma_{g,*}$, and compare the kernel $J_{g,*}^{cfg}(n)$ of this action with the $n^{th}$

Minh Hoang Trinh, Hyo-Sung Ahn

This paper proposes Warshall algorithm-a greedy algorithm for determining connectedness and clustering in matrix-weighted graphs. Built upon a sufficient condition for connectedness, the algorithm reveals a simple but important difference between matrix-weighted and scalar-weighted graphs. In a scalar-weighted graph, once a path exists between two vertices,

Dechen Zhang, Junwei Su, Difan Zou

The use of low-bit quantization has emerged as an indispensable technique for enabling the efficient training of large-scale models. Despite its widespread empirical success, a rigorous theoretical understanding of its impact on learning performance remains notably absent, even in the simplest linear regression setting. We present the first systematic theore

Xiaohan Qin, Xiaoxing Wang, Ning Liao, Junchi Yan

Multi-Task Learning (MTL) enables a single model to learn multiple tasks simultaneously, leveraging knowledge transfer among tasks for enhanced generalization, and has been widely applied across various domains. However, task imbalance remains a major challenge in MTL. Although balancing the convergence speeds of different tasks is an effective approach to a

Tao Tao, Guanghui Zhu, Lang Guo, Hongyi Chen

Prompt Optimization has emerged as a crucial approach due to its capabilities in steering Large Language Models to solve various tasks. However, current works mainly rely on the random rewriting ability of LLMs, and the optimization process generally focus on specific influencing factors, which makes it easy to fall into local optimum. Besides, the performan

Xiang Zhang, Suping Wu, Weibin Qiu, Zhaocheng Jin

3D human meshes show a natural hierarchical structure (like torso-limbs-fingers). But existing video-based 3D human mesh recovery methods usually learn mesh features in Euclidean space. It's hard to catch this hierarchical structure accurately. So wrong human meshes are reconstructed. To solve this problem, we propose a hyperbolic space learning method lever

Sion Weatherhead, Flora Salim, Aaron Belbasis

Humans do not just find mistakes after the fact -- we often catch them mid-stream because 'reflection' is tied to the goal and its constraints. Today's large language models produce reasoning tokens and 'reflective' text, but is it functionally equivalent with human reflective reasoning? Prior work on closed-ended tasks -- with clear, external 'correctness'

Tianyu Huang, Runnan Chen, Dongting Hu, Fengming Huang

Understanding 3D scenes is pivotal for autonomous driving, robotics, and augmented reality. Recent semantic Gaussian Splatting approaches leverage large-scale 2D vision models to project 2D semantic features onto 3D scenes. However, they suffer from two major limitations: (1) insufficient contextual cues for individual masks during preprocessing and (2) inco

Zhao Liu, Yichen Zhu, Yiqing Yang, Xiao Lv

Generative recommendation (GR) is an emerging paradigm that represents each item via a tokenizer as an n-digit semantic ID (SID) and predicts the next item by autoregressively generating its SID conditioned on the user's history. However, two structural properties of SIDs make ARMs ill-suited. First, intra-item consistency: the n digits jointly specify o

Atanu Manna, Suvra Kanti Chakraborty

This paper extends Hridaya Kolam patterns to cases where the number of dots ($m$) and arms ($n$) are not coprime, i.e., $\gcd(m, n) \ne 1$. Such configurations give rise to multiple disjoint closed loops. We propose a modular-arithmetic-based algorithm to systematically generate such patterns, and illustrative patterns for various non-coprime $(m, n)$ pairs

Luis H. Chia

Credit scoring models face a critical challenge: severe class imbalance, with default rates typically below 10%, which hampers model learning and predictive performance. While synthetic data augmentation techniques such as SMOTE and ADASYN have been proposed to address this issue, the optimal augmentation ratio remains unclear, with practitioners often defau

Kanta Kitajima, Shu-ichiro Inutsuka, Izumi Seno

This paper proposes a novel numerical method based on Godunov Smoothed Particle Hydrodynamics for special relativistic fluid dynamics. Our method utilizes a Riemann solver to describe shock, enhancing accuracy in strong shock waves. The formulation maintains conservation laws and achieves higher accuracy through convolution integrals that define physical qua

Xiaohan Qin, Xiaoxing Wang, Ning Liao, Cancheng Zhang

Data quality plays a critical role in enhancing supervised fine-tuning (SFT) for large language models (LLMs), and token-level data selection has emerged as a promising direction for its fine-grained nature. Despite their strong empirical performance, existing token-level selection methods share two key limitations: (1) requiring training or accessing an add

Edgar S. Arroyo-Rivera, Long D. Nguyen, Surendar Vijayakumar, Akbar Safari

Light experiences drag effects when it propagates through a moving medium. The study of light drag has provided foundational insights into light-matter interactions. While longitudinal drag has been extensively characterized, transverse drag, where the medium moves perpendicular to the light's propagation, is subtler and requires advanced techniques for dete

Akiyoshi Tsujita, Seiji Fujimoto, Andreas Faisst, Meédéric Boquien

Characterizing dust attenuation is crucial for revealing the intrinsic physical properties of galaxies. We present an analysis of dust attenuation in 18 spectroscopically confirmed star-forming main-sequence galaxies at $z = 4.4-5.7$ observed with JWST/NIRSpec IFU and NIRCam, selected from the ALPINE and CRISTAL ALMA large programs. We fit the emission line

Jiazhen Xu, Andrew T. A. Wood, Tao Zou

Non-Euclidean object-valued time series are playing a growing role in modern data analysis, and periodicity is a fundamental characteristic of many time series. However, quantifying periodicity in general non-Euclidean random objects remains largely unexplored. In this work, we introduce a novel nonparametric framework for quantifying periodicity in random o

Xihe Li, Runshan Wang

Motivated by problems in Ramsey theory, we study edge-colorings of 3-uniform hypergraphs that contain no rainbow paths of length 3. We consider the following three natural 3-uniform paths of length 3: the tight path $\mathcal{T}=\{v_1v_2v_3, v_2v_3v_4, v_3v_4v_5\}$, the messy path $\mathcal{M}=\{v_1v_2v_3, v_2v_3v_4, v_4v_5v_6\}$ and the loose path $\mathcal

Ajinkya Khoche, Gergő László Nagy, Maciej Wozniak, Thomas Gustafsson

Zero-shot 3D object classification is crucial for real-world applications like autonomous driving, however it is often hindered by a significant domain gap between the synthetic data used for training and the sparse, noisy LiDAR scans encountered in the real-world. Current methods trained solely on synthetic data fail to generalize to outdoor scenes, while t

Xihe Li, Xiangxiang Liu

Given a graph $H$ and a positive integer $k$, the {\it $k$-colored Ramsey number} $R_k(H)$ is the minimum integer $n$ such that in every $k$-edge-coloring of the complete graph $K_{n}$, there is a monochromatic copy of $H$. Given two graphs $H$ and $G$, the {\it constrained Ramsey number} (also called {\it rainbow Ramsey number}) $f(H,G)$ is defined as the m

Jaideep Mahajan, Kaihong Zhang, Feng Liang, Jingbo Liu

We study parallel sampling from high-dimensional strongly log-concave distributions. Langevin-based samplers converge rapidly in continuous time, but their discretizations are typically sequential and often require polynomially many steps in the dimension $d$, the target accuracy $\varepsilon^{-1}$, or both. Picard-based parallel sampling methods reduce this

Bahareh Ghanbari, Pavel Krupskiy, Laleh Tafakori, Yan Wang

Parametric factor copula models typically work well in modeling multivariate dependencies due to their flexibility and ability to capture complex dependency structures. However, accurately estimating the linking copulas within these models remains challenging, especially when working with high-dimensional data. This paper proposes a novel approach for estima

Haobin Li, Yijie Lin, Peng Hu, Mouxing Yang

Multi-modal entity alignment (MMEA) aims to identify equivalent entities across heterogeneous multi-modal knowledge graphs (MMKGs), where each entity is described by attributes from various modalities. Existing methods typically assume that both intra-entity and inter-graph correspondences are faultless, which is often violated in real-world MMKGs due to the

Yunjiang Jiang, Ayush Agarwal, Yang Liu, Bi Xue

Scaling large recommendation systems requires advancing three major frontiers: processing longer user histories, expanding candidate sets, and increasing model capacity. While promising, transformers' computational cost scales quadratically with the user sequence length and linearly with the number of candidates. This trade-off makes it prohibitively expensi

Bryan Wilder, Angela Zhou

There has been increasing research interest in AI/ML for social impact, and correspondingly more publication venues have refined review criteria for practice-driven AI/ML research. However, these review guidelines tend to most concretely recognize projects that simultaneously achieve deployment and novel ML methodological innovation. We argue that this intro

Yang Hu, William Kuszmaul, Jingxun Liang, Huacheng Yu

In the static retrieval problem, a data structure must answer retrieval queries mapping a set of $n$ keys in a universe $[U]$ to $v$-bit values. Information-theoretically, retrieval data structures can use as little as $nv$ bits of space. For small value sizes $v$, it is possible to achieve $O(1)$ query time while using space $nv + o(n)$ bits -- whether or n

Mario Ghossoub, Qinghua Ren, Ruodu Wang

We study Pareto-optimal risk sharing in economies with heterogeneous attitudes toward risk, where agents' preferences are modeled by distortion risk measures. Building on comonotonic and counter-monotonic improvement results, we show that agents with similar attitudes optimally share risks comonotonically (risk-averse) or counter-monotonically (risk-seeking)

Zhitong He, Zijing Wang, Lingxi Li

Urban Air Mobility (UAM) offers a transformative approach to addressing urban congestion, improving accessibility, and advancing environmental sustainability. Rapid progress has emerged in three tightly linked domains since 2020: (1) Communication, where dynamic spectrum allocation and low-altitude channel characterization support reliable air-ground data ex

Haoran Wei, Yaofeng Sun, Yukun Li

We present DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically, DeepEncoder serves as the core engine, designed to maintain low activations under high-resolution input while achieving high c

Wenjing Zhang, Jizhe Cui, Xiaoyang Wang, Shubo Zhang

Alloying by addition of oxygen interstitials during additive manufacturing provides new routes to strengthen and toughen metals and alloys. The underlying mechanisms by which such interstitial atoms lead to enhanced properties remain, however, unclear, not least due a lack of quantitative atomic-scale models linking microstructure to properties. Here using q

Yuzheng Hu, Ryan McKenna, Da Yu, Shanshan Wu

Generating high-quality synthetic text under differential privacy (DP) is critical for training and evaluating language models without compromising user privacy. Prior work on synthesizing DP datasets often fail to preserve key statistical attributes, suffer utility loss from the noise required by DP, and lack fine-grained control over generation. To address

Rosalia O'Brien, Richard G. Arendt, Rogier A. Windhorst, Tejovrash Acharya

We present an improved zodiacal light model, optimized for optical wavelengths, using archival Hubble Space Telescope (HST) imaging from the SKYSURF program. The Kelsall et al. 1998 model used infrared imaging from the Diffuse Infrared Background Experiment (DIRBE) on board the Cosmic Background Explorer to create a 3D structure of the interplanetary dust cl

Shilaj Baral, Youngkyu Lee, Sangam Khanal, Joongoo Jeon

Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical drift during long-term rollouts. Although hybrid strategies combining surrogate models and physics-based solvers have been proposed, they are limited to manual implementations for low-dimensional benchmarks. In t

Yuxuan Xiong, Ziwen Zhou, Jixing Ren, Jingze Liu

The growing demands of artificial intelligence and immersive media require communication beyond bit-level accuracy to meaning awareness. Conventional optical systems that focused on syntactic precision suffer significant inefficiencies. Here, we introduce a multi-dimensional semantic communication framework that bridges this gap by directly mapping high-leve

Xinhao Cai, Liulei Li, Gensheng Pei, Tao Chen

This paper presents a generation-based debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of samples, while naive generative augmentation often preserves the biases it aims to solve. Moreover, our analysis reveals that simply generating more data for rare classes is suboptimal due to two core i

Zhendong Mi, Qitao Tan, Grace Li Zhang, Zhaozhuo Xu

Fine-tuning large language models (LLMs) using zeroth-order (ZO) optimization has emerged as a promising alternative to traditional gradient-based methods due to its reduced memory footprint requirement. However, existing ZO methods suffer from high variance in gradient estimation, leading to slow convergence and suboptimal performance on large-scale models.

Yin Wu, Haining Li, Yang Huang, Xiang-Xiang Xue

Escape velocity has long been used to constrain the mass of the Dark Matter (DM) halo in the Milky Way (MW). Here we present a study of the escape velocity curve using a sample of high-velocity K giants with full 6D phase-space information and relatively good quality, selected from LAMOST DR8 and cross-matched with Gaia DR3. To expand the high-velocity stars

Jianming Li

A p-n junction is the basic building block for various semiconductor devices. A Schottky diode has characteristics that are essentially similar to those of the p-n junction diode. As is known, the leakage currents of p-n and Schottky junctions affect the overall performance of the devices and reduce the reliability of the devices. In order to achieve optimum

Xueyao Zhang, Bo Yang, Zhiwen Yu, Xuelin Cao

This paper investigates underwater cooperative target detection using autonomous underwater vehicles (AUVs), with a focus on the critical trade-off between cooperation efficiency and communication covertness. To tackle this challenge, we first formulate a joint trajectory and power control optimization problem, and then present an innovative hierarchical act

Jiangong Chen, Mingyu Zhu, Bin Li

Mixed Reality (MR)-aided operation overlays digital objects on the physical world to provide a more immersive and intuitive operation process. A primary challenge is the precise and fast auto-verification of whether the user follows MR guidance by comparing frames before and after each operation. The pre-operation frame includes virtual guiding objects, whil

Sani Biswas, Joaquin Fontbona

This paper introduces a randomized tamed Euler scheme tailored for L\'evy-driven stochastic differential equations (SDEs) with superlinear random coefficients and Carath\'eodory-type drift. Under assumptions that allow for time-irregular drifts while ensuring appropriate time-regularity of the diffusion and jump coefficients, the proposed scheme is shown to

Joseph Bejjani, Chase Van Amburg, Chengrui Wang, Chloe Huangyuan Su

We explore how physical scale and population size shape the emergence of complex behaviors in open-ended ecological environments. In our setting, agents are unsupervised and have no explicit rewards or learning objectives but instead evolve over time according to reproduction, mutation, and selection. As they act, agents also shape their environment and the

Ho Yin Ng, Emily S. C. Ching

In this paper, we propose a space-dependent eddy thermal diffusivity model for turbulent vertical natural convection in a fluid between two infinite vertical walls at different temperatures. Using this model, we derive analytical results for the mean temperature profile. Our results reveal that mean temperature profiles for different Rayleigh and Prandtl num

Sayan Bagchi, Riju Basak, Joydwip Singh, Manasa N. Vempati

In this article, we establish the unweighted and weighted $L^p$-boundedness of pseudo-multipliers associated with a class of Schrödinger operators, this generalizes the result of our first author and Thangavelu [Bagchi \& Thangavelu, J. Funct. Anal. 2015] for Hermite pseudo-multipliers. The weight classes we consider are tailored to this framework and strict

Dennis Tang, Jon Donnelly, Alina Jade Barnett, Lesia Semenova

The presence of interictal epileptiform discharges (IEDs) in electroencephalogram (EEG) recordings is a critical biomarker of epilepsy. Even trained neurologists find detecting IEDs difficult, leading many practitioners to turn to machine learning for help. While existing machine learning algorithms can achieve strong accuracy on this task, most models are u

Luxuan Li, Xiao Wang, Chunfeng Cui

Deep hashing converts high-dimensional feature vectors into compact binary codes, enabling efficient large-scale retrieval. A fundamental challenge in deep hashing stems from the discrete nature of quantization in generating the codes. W-type regularizations, such as $||z|-1|$, have been proven effective as they encourage variables toward binary values. Howe

Moon-ki Choi, Daniel Palmer, Harley T. Johnson

We introduce a force following active learning algorithm that integrates density functional theory DFT with the Gaussian Approximation Potential GAP framework to develop a robust interatomic potential IP for a dislocation in a topological insulator Bi1xSbx. Starting from an initial potential IP0 trained on unit cell data from strained Bi Sb binaries our acti

Carmen Berta C De Saituma Cagiza

This study investigates how Development Finance Institutions (DFIs) contribute to de-risking development in Sub-Saharan Africa by shaping Foreign Direct Investment (FDI) flows and supporting sustainable economic transformation. Focusing on Angola as a representative case, the research draws on qualitative interviews with international development advisors, f

Hua Sun, Huixiang Chen, Yinhuo Zhang

In this paper, we investigate the representations of the Drinfeld doubles $D(H_{\mathcal{D}})$ of pointed rank one Hopf algebras $H_{\mathcal{D}}$ over an algebraically closed field $\Bbbk$ of characteristic zero. We provide a complete classification of all finite-dimensional indecomposable $D(H_{\mathcal{D}})$-modules up to isomorphism and explicitly descri

Haixiang Lan, Luofeng Liao, Adam N. Elmachtoub, Christian Kroer

Data-driven stochastic optimization is ubiquitous in machine learning and operational decision-making problems. Sample average approximation (SAA) and model-based approaches such as estimate-then-optimize (ETO) or integrated estimation-optimization (IEO) are all popular, with model-based approaches being able to circumvent some of the issues with SAA in comp

Shruti Palaskar, Leon Gatys, Mona Abdelrahman, Mar Jacobo

Safety evaluation of multimodal foundation models often treats vision and language inputs separately, missing risks from joint interpretation where benign content becomes harmful in combination. Existing approaches also fail to distinguish clearly unsafe content from borderline cases, leading to problematic over-blocking or under-refusal of genuinely harmful

Maryam Dialameh, Hossein Rajabzadeh, Jung Suk Sim, Hyock Ju Kwon

Papillary thyroid microcarcinoma (PTMC) is increasingly managed with radio-frequency ablation (RFA), yet accurate lesion segmentation in ultrasound videos remains difficult due to low contrast, probe-induced motion, and heat-related artifacts. The recent Segment Anything Model 2 (SAM-2) generalizes well to static images, but its frame-independent design yiel

Dan Hendrycks, Dawn Song, Christian Szegedy, Honglak Lee

The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in

Taehyun Yoon, Augustinos D. Saravanos, Evangelos A. Theodorou

Many real-world multi-agent systems exhibit nonlinear dynamics and complex inter-agent interactions. As these systems increase in scale, the main challenges arise from achieving scalability and handling nonconvexity. To address these challenges, this paper presents a distributed sampling-based optimization framework for multi-agent model predictive control (

Qianxia Wang, Alexander Stanforth, William Andrew LePain, Edgar Gelover

Background: Voxel-based analysis (VBA) is an analytic approach to evaluate correlations between local dose and the development of different toxicities. DVHs are used for toxicity prediction as well. Compared with DVH, no contours are required for VBA technique and results tell specific voxels that may be related to the toxicity instead of the whole contoured

Satoru Hayami, Rikuto Oiwa, Akane Inda

In condensed matter physics, a broad spectrum of physical characteristics, such as chirality, axiality, and polarity, arises as a direct consequence of the underlying symmetry of the system. We here theoretically investigate the effective coupling between chirality and axiality at their domain boundaries, mediated by polarity. Based on symmetry consideration

Qiuhao Chen, Yuling Jiao, Yinan Li, Xiliang Lu

Understanding the theoretical capabilities and limitations of quantum machine learning (QML) models to solve machine learning tasks is crucial to advancing both quantum software and hardware developments. Similarly to the classical setting, the performance of QML models can be significantly affected by the limited access to the underlying data set. Previous

Shu-Xu Yi, Chen-Wei Wang, Shao-Lin Xiong, Shuang-Nan Zhang

To understand the physical mechanisms underlying the prompt emission of gamma-ray bursts (GRB), single FRED (Fast-Rise-Exponential-Decay) profile GRBs serve as an ideal sample, as they origin from single epoch central engine activity. These GRBs have been found to exhibit a peculiar morphology-including the elegant cross-energy-similarity across energy bands

Hanyu Meng, Vidhyasaharan Sethu, Eliathamby Ambikairajah, Qiquan Zhang

In audio signal processing, learnable front-ends have shown strong performance across diverse tasks by optimizing task-specific representation. However, their parameters remain fixed once trained, lacking flexibility during inference and limiting robustness under dynamic complex acoustic environments. In this paper, we introduce a novel adaptive paradigm for

S. Takagi, S. Harada, Y. Aritomo, K. Hirose

Actinide nuclei exhibit mass-asymmetric fission at low energy due to shell structure. The fission-fragment mass distributions produced at high energy tend to have a symmetric shape due to smearing of shell effects. On the other hand, the distribution can be changed by neutron emission occurring before fission, as this decreases the excitation energy of the f

Jiahao Shi, Tianyi Zhang

Despite recent advances, Large Language Models (LLMs) still generate vulnerable code. Retrieval-Augmented Generation (RAG) has the potential to enhance LLMs for secure code generation by incorporating external security knowledge. However, the conventional RAG design struggles with the noise of raw security-related documents, and existing retrieval methods ov

Chenyue Liu, Ali Mostafavi

Current wildfire risk assessments rely on coarse hazard maps and opaque machine learning models that optimize regional accuracy while sacrificing interpretability at the decision scale. WildfireGenome addresses these gaps through three components: (1) fusion of seven federal wildfire indicators into a sign-aligned, PCA-based composite risk label at H3 Level-

Serena Dipierro, David Pfefferlé, Enrico Valdinoci

This is a textbook on Fourier Series, suitable for both undergraduate and graduate courses. The textbook is endowed with exercises, and full solutions are provided at the end of the book.

Yelyzaveta Satynska, Daniel T. Robb

Dynamic phase transitions of periodically forced mean-field ferromagnets are often described by a single order parameter and a scalar conjugate field. Building from previous work, we show that, at the critical period $P_c$ of the mean-field Ginzburg-Landau (MFGL) dynamics with energy $F(m)=am^2+bm^4-hm$, the correct conjugate field is the entire even-Fourier

Tetsuya Shiromizu, Keisuke Izumi, Hirotaka Yoshino, Yoshimune Tomikawa

We discuss the local and quasilocal properties of the loosely trapped surface (LTS) and the attractive gravity probe surface (AGPS), which have been proposed to characterize the strength of gravity in both strong and weak gravity regions using the mean curvature. In terms of local mass defined in a region surrounded by the two AGPSs and of Geroch quasilocal

Sriharsh Bhyravajjula, Ujwal Narayan, Manish Shrivastava

Character arcs are important theoretical devices employed in literary studies to understand character journeys, identify tropes across literary genres, and establish similarities between narratives. This work addresses the novel task of computationally generating event-centric, relation-based character arcs from narratives. Providing a quantitative represent

Minseok Jung, Abhas Ricky, Muhammad Rameez Chatni

AI inference scaling is often tuned through 1D heuristics (a fixed reasoning pass) or 2D bivariate trade-offs (e.g., accuracy vs. compute), which fail to consider cost and latency constraints. We introduce a 3D optimization framework that jointly calibrates accuracy, cost, and latency within a unified decision space, enabling constraints-aware inference scal

Chu Wing So, Chun Shing Jason Pun, Shengjie Liu

Spectroscopic study of the night sky has been a common way to assess the impacts of artificial light at night at remote astronomical observatories. However, the spectroscopic properties of the urban night sky remain poorly documented. We addressed this gap by collecting more than 12,000 zenith sky spectra with compact spectrometers at urban and suburban site