November 2025 arXiv papers — page 180

Rongbin Ye, Jiaqi Chen

The financial industry faces a significant challenge modeling and risk portfolios: balancing the predictability of advanced machine learning models, neural network models, and explainability required by regulatory entities (such as Office of the Comptroller of the Currency, Consumer Financial Protection Bureau). This paper intends to fill the gap in the appl

Xiongri Shen, Jiaqi Wang, Yi Zhong, Zhenxi Song

Functional and structural connectivity (FC/SC) are key multimodal biomarkers for brain analysis, yet their clinical utility is hindered by costly acquisition, complex preprocessing, and frequent missing modalities. Existing foundation models either process single modalities or lack explicit mechanisms for cross-modal and cross-scale consistency. We propose B

Gimun Bae, Seung Jun Shin

The ROC-SVM, originally proposed by Rakotomamonjy, directly maximizes the area under the ROC curve (AUC) and has become an attractive alternative of the conventional binary classification under the presence of class imbalance. However, its practical use is limited by high computational cost, as training involves evaluating all $O(n^2)$. To overcome this limi

Fang Tian, Yiting Yang, Xiying Yuan

For an integer $r\geqslant 3$, a hypergraph on vertex set $[n]$ is $r$-uniform if each edge is a set of $r$ vertices, and is said to be linear if every two distinct edges share at most one vertex. Given a family $\mathcal{H}$ of linear $r$-uniform hypergraphs,let $Forb_r^L(n,\mathcal{H})$ be the set of linear $r$-uniform hypergraphs on vertex set $[n]$, whic

Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma

Stickers have become a popular form of visual communication, yet understanding their semantic relationships remains challenging due to their highly diverse and symbolic content. In this work, we formally {define the Sticker Semantic Similarity task} and introduce {Triple-S}, the first benchmark for this task, consisting of 905 human-annotated positive and ne

Xin Nie, Zhiyuan Cheng, Yuan Zhang, Chao Ji

We introduce iFlyBot-VLM, a general-purpose Vision-Language Model (VLM) used to improve the domain of Embodied Intelligence. The central objective of iFlyBot-VLM is to bridge the cross-modal semantic gap between high-dimensional environmental perception and low-level robotic motion control. To this end, the model abstracts complex visual and spatial informat

Isaías Bañales, Tomoaki Nishikawa, Yoshihiro Ito, Manuel J. Aguilar-Velázquez

An enhancement in seismic measuring instrumentation has been proven to have implications in the quantity of observed earthquakes, since denser networks usually allow recording more events. However, phenomena such as strong earthquakes or even aseismic transients, as slow slip earthquakes, may alter the occurrence of earthquakes. In the field of seismology, i

Siyuan Li, Yifan Sun, Lei Cheng, Lewen Wang

Generative models for multivariate time series are essential for data augmentation, simulation, and privacy preservation, yet current state-of-the-art diffusion-based approaches are slow and limited to fixed-length windows. We propose FAR-TS, a simple yet effective framework that combines disentangled factorization with an autoregressive Transformer over a d

Dylan Peek, Matthew P. Skerritt, Siddharth Pritam, Stephan Chalup

Topological Data Analysis (TDA) involves techniques of analyzing the underlying structure and connectivity of data. However, traditional methods like persistent homology can be computationally demanding, motivating the development of neural network-based estimators capable of reducing computational overhead and inference time. A key barrier to advancing thes

Esha Chowdhury

Accurate prediction of cardiovascular disease (CVD) risk is crucial for healthcare institutions. This study addresses the growing prevalence of diabetes and its strong link to heart disease by proposing an efficient CVD risk prediction model for diabetic patients using machine learning (ML) and hybrid deep learning (DL) approaches. The BRFSS dataset was prep

Jian Wang, Yixing Yong, Haixia Bi, Lijun He

While both shape and texture are fundamental to visual recognition, research on deep neural networks (DNNs) has predominantly focused on the latter, leaving their geometric understanding poorly probed. Here, we show: first, that optimized shapes can act as potent semantic carriers, generating high-confidence classifications from inputs defined purely by thei

Hiroaki Hashida, Yuichi Kawamoto, Nei Kato

In this study, we investigate the use of intelligent reflecting surfaces (IRSs) in multi-operator communication systems for 6G networks, focusing on sustainable and efficient resource management. This research is motivated by two critical challenges: limited coverage provided by mmWave frequencies and high infrastructure costs associated with current technol

Jingwei Lian, Yao-Bei Liu

Recent CMS analyses report an excess in the diphoton-plus-$b \bar{b}$ channel, indicative of a heavy resonance around 650 GeV decaying into a Standard Model (SM)-like Higgs boson and a lighter scalar near 95 GeV. The case for a 95 GeV state is further supported by diphoton excesses observed by both CMS and ATLAS, as well as a $b\bar{b}$ excess previously obs

Jiayang Niu, Yan Wang, Jie Li, Ke Deng

Reinforcement learning-based Quantum Architecture Search (QAS) offers a promising avenue for automating the design of variational quantum circuits, but existing methods typically decouple discrete structure search from continuous parameter optimization, resulting in inefficient or brittle solutions. We propose HyRLQAS (Hybrid-Action Reinforcement Learning fo

Mao Yuan, Jiarui Niu, Yi Feng, Xu-ning Lv

Fast radio bursts (FRBs) are transient signals exhibiting diverse strengths and emission bandwidths. Traditional single-pulse search techniques are widely employed for FRB detection; yet weak, narrow-band bursts often remain undetectable due to low signal-to-noise ratios (SNR) in integrated profiles. We developed DANCE, a detection tool based on cluster anal

Atsunori Sakurai, Shota Takahashi, Tatsuto Mochizuki, Tomonori Hirano

Vibrational sum frequency generation (SFG) spectroscopy is a powerful technique for investigating molecular structures, orientations, and dynamics at surfaces. However, its spatial resolution is fundamentally restricted to the micrometer scale by the optical diffraction limit. Tip-enhanced SFG (TE-SFG) using a scanning tunneling microscope has been developed

Lingyu Zhang, Mitchell Wang, Boyuan Chen

Scientific discovery begins with ideas, yet evaluating early-stage research concepts is a subtle and subjective human judgment. As large language models (LLMs) are increasingly tasked with generating scientific hypotheses, most systems implicitly assume that scientists' evaluations form a fixed gold standard, assuming that scientists' judgments do not change

Xiongri Shen, Jiaqi Wang, Yi Zhong, Zhenxi Song

Magnetic resonance imaging (MRI), especially functional MRI (fMRI) and diffusion MRI (dMRI), is essential for studying neurodegenerative diseases. However, missing modalities pose a major barrier to their clinical use. Although GAN- and diffusion model-based approaches have shown some promise in modality completion, they remain limited in fMRI-dMRI synthesis

Zihao Yi, Qingxuan Jiang, Ruotian Ma, Xingyu Chen

Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, antagonistic personas remains largely unexamined. We hypothesize that the safety alignment of modern LLMs creates a fundamental conflict with the task of authentically role-playing m

Ruijian Gou, Gerrit Lohmann, Deliang Chen, Shiming Xu

Arctic sea ice is rapidly retreating due to global warming, and emerging evidence suggests that the rate of decline may have been underestimated. A key factor contributing to this underestimation is the coarse resolution of current climate models, which fail to accurately represent eddy floe interactions, climate extremes, and other critical small scale proc

Swetamber Das, Jason R. Green

Infinitesimal volumes stretch and contract as they coevolve with classical phase space trajectories according to linearized dynamics. Unless these tangent-space dynamics are modified, chaotic evolution causes the volume spanned by evolving tangent vectors to collapse. However, this collapse is unphysical and due to their exponential alignment along the most

Daniel Alfonso Santiesteban, Ricardo Abreu Blaya, Daniel Alpay

This paper is devoted to study a fundamental system of equations in Linear Elasticity Theory: the famous Lam\'e-Navier system. The Clifford algebra language allows us to rewrite this system in terms of the Euclidean Dirac operator, which at the same time suggests a very natural generalization involving the so-called structural sets. Our interest lies mainly

Ruihan Ma, Yuqing Cheng, Mengtao Sun

A serial asymmetric transmission (AT) nanodevices based on the grating-film-grating (G-F-G) structure are proposed and studied. By showing the results of three different nanodevices as examples, it is proved that this kind of G-F-G nanostructure can achieve high isolation ratio AT at arbitrary wavelength within a certain range by designing the parameters of

Bruno Fava

As predictive algorithms grow in popularity, using the same dataset to both train and test a new model has become routine across research, policy, and industry. Sample-splitting attains valid inference on model properties by using separate subsamples to estimate the model and to evaluate it. However, this approach has two drawbacks, since each task uses only

Maria Mahbub, Vanessa Lama, Sanjay Das, Brian Starks

High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and

Jinhui Pan, Rui Liu

A question often arises as to why some solar flares are confined in the lower corona while others, termed eruptive flares, are associated with coronal mass ejections (CMEs). Here we intend to rank the importance of pre-flare magnetic parameters of active regions in their potentiality to predict whether an imminent flare will be eruptive or confined. We compi

Christian Ikenmeyer

We prove the bivariate Cayley-Hamilton theorem, a powerful generalization of the classical Cayley-Hamilton theorem. The bivariate Cayley-Hamilton theorem has three direct corollaries that are usually proved independently: The classical Cayley-Hamilton theorem, the Girard-Newton identities, and the fact that the determinant and every coefficient of the charac

Zheng Jiang, Wei Wang, Gaowei Zhang, Yi Wang

Sea Surface Temperature (SST) is crucial for understanding upper-ocean thermal dynamics and ocean-atmosphere interactions, which have profound economic and social impacts. While data-driven models show promise in SST prediction, their black-box nature often limits interpretability and overlooks key physical processes. Recently, physics-informed neural networ

Kristóf Bérczi, Tamás Király, Yutaro Yamaguchi, Yu Yokoi

The rainbow arborescence conjecture posits that if the arcs of a directed graph with $n$ vertices are colored by $n-1$ colors such that each color class forms a spanning arborescence, then there is a spanning arborescence that contains exactly one arc of every color. We prove that the conjecture is true if the underlying undirected graph is a cycle.

Wei Shao, Lingchao Zheng, Pengyu Wang, Peizhen Zheng

Long context inference scenarios have become increasingly important for large language models, yet they introduce significant computational latency. While prior research has optimized long-sequence inference through operators, model architectures, and system frameworks, tokenization remains an overlooked bottleneck. Existing parallel tokenization methods acc

Hexu Zhao, Xiwen Min, Xiaoteng Liu, Moonjun Gong

3D Gaussian Splatting (3DGS) is an increasingly popular novel view synthesis approach due to its fast rendering time, and high-quality output. However, scaling 3DGS to large (or intricate) scenes is challenging due to its large memory requirement, which exceed most GPU's memory capacity. In this paper, we describe CLM, a system that allows 3DGS to render lar

Haoxin Lv, Ijazul Haq, Jin Du, Jiaxin Ma

The advancement of artificial intelligence in oral healthcare relies on the availability of large-scale multimodal datasets that capture the complexity of clinical practice. In this paper, we present a comprehensive multimodal dataset, comprising 8775 dental checkups from 4800 patients collected over eight years (2018-2025), with patients ranging from 10 to

Jinhong Zhao, Bin Guo

We study a class of fourth-order quasilinear degenerate parabolic equations under both time-and space-dependent and time-and space-independent forces, modeling non-Newtonian thin-film flow over a solid surface in the "complete wetting" regime. By analyzing the quantitative properties of solutions to non-autonomous differential inequalities and employing refi

Lei Chen, Erci Xu, Yiming Sun, Shengyu Fan

Fully Homomorphic Encryption (FHE) allows computations to be performed on encrypted data, significantly enhancing user privacy. However, the I/O challenges associated with deploying FHE applications remains understudied. We analyze the impact of storage I/O on the performance of FHE applications and summarize key lessons from the status quo. Key results incl

Huaijing Huang, Daowen Qiu

In this article, we propose a distributed quantum algorithm for solving counting problem using Grover operator and a classical post-processing procedure. We apply the proposed algorithm to estimate inner products and Hamming distances. Simulations are conducted on the Qisikit platform, further demonstrating the effectiveness of our algorithm and its suitabil

Zixiang Ren, Juncong Zhou, Jie Xu, Ling Qiu

Channel knowledge map (CKM) has emerged as a pivotal technology for environment-aware wireless communications and sensing, which provides a priori location-specific channel knowledge to facilitate network optimization. Efficient CKM construction is an important technical problem for its effective implementation. This article provides a comprehensive overview

Shalmali Bandyopadhyay, Maya Chhetri, Briceyda Delgado, Nsoki Mavinga

We consider elliptic systems with superlinear and subcritical boundary conditions and a bifurcation parameter as a multiplicative factor. By combining the rescaling method with degree theory and elliptic regularity theory, we prove the existence of a connected branch of positive weak solutions that bifurcates from infinity as the parameter approaches zero. F

Nikita Guseynov, Mikel Sanz, Ángel Rodríguez-Rozas, Nana Liu

The solution of option-pricing problems may turn out to be computationally demanding due to non-linear and path-dependent payoffs, the high dimensionality arising from multiple underlying assets, and sophisticated models of price dynamics. In this context, quantum computing has been proposed as a means to address these challenges efficiently. Prevailing appr

Valerio Faraoni, Nikki Veilleux

The two-parameter inhomogeneous and time-dependent Pimentel solution of Brans-Dicke theory is analyzed to probe and extend the new thermal view in which General Relativity is the zero-temperature (equilibrium) state of scalar-tensor gravity. As the parameters vary, we uncover phenomenology not found before with other exact solutions, nor contemplated thus fa

Longqing Ye

Deep neural networks, particularly those employing Rectified Linear Units (ReLU), are often perceived as complex, high-dimensional, non-linear systems. This complexity poses a significant challenge to understanding their internal learning mechanisms. In this work, we propose a novel analytical framework that recasts a multi-layer ReLU network into an equival

Jiachen Shen, Jian Shi, Lei Fan, Chenye Wu

In strategic scenarios where decision-makers operate at different hierarchical levels, traditional optimization methods are often inadequate for handling uncertainties from incomplete information or unpredictable external factors. To fill this gap, we introduce a mathematical framework that integrates bi-level game theory with distributionally robust optimiz

Md Romyull Islam, Bobin Deng, Nobel Dhar, Tu N. Nguyen

Cloud-based large language models (LLMs) and their variants have significantly influenced real-world applications. Deploying smaller models (i.e., small language models (SLMs)) on edge devices offers additional advantages, such as reduced latency and independence from network connectivity. However, edge devices' limited computing resources and constrained en

Harshit Nainwani, Hediyeh Baban

Retrieval systems are essential to contemporary AI pipelines, although most confuse two separate processes: finding relevant information and giving enough context for reasoning. We introduce the Search-Is-Not-Retrieve (SINR) framework, a dual-layer architecture that distinguishes between fine-grained search representations and coarse-grained retrieval contex

Davar Khoshnevisan, Cheuk Yin Lee, Fei Pu, Yimin Xiao

Consider the following $p$-dimensional system of It\^o type stochastic PDEs, \begin{align*}\left[\begin{aligned} &\partial_t u(t\,,x) = \partial^2_x u(t\,,x) + b(u(t\,,x)) + \sigma(u(t\,,x)) \xi(t\,,x)\\ &\text{for $(t\,,x)\in(0\,,\infty)\times\mathbb{T}$, subject to $u(0) \equiv u_0$ on $\mathbb{T}$}, \end{aligned}\right.\end{align*} where $\mathbb{T}$ deno

Sabik Aftahee, A. F. M. Farhad, Arpita Mallik, Ratnajit Dhar

Accessing legal help in Bangladesh is hard. People face high fees, complex legal language, a shortage of lawyers, and millions of unresolved court cases. Generative AI models like OpenAI GPT-4.1 Mini, Gemini 2.0 Flash, Meta Llama 3 70B, and DeepSeek R1 could potentially democratize legal assistance by providing quick and affordable legal advice. In this stud

Zhankun Luo, Abolfazl Hashemi

This work investigates the structural properties, cycloid trajectories, and non-asymptotic convergence guarantees of the Expectation-Maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR) with unknown mixing weights and regression parameters. Recent studies have established global convergence for 2MLR with known balanced weights and sup

Siwei Zhao, Penghua Ying, Guoqiang Zhang, Ke Zhou

We reveal intrinsic fracture nonreciprocity, manifesting as directional asymmetry in crack resistance, in two-dimensional heterostructures engineered through lattice-mismatched interfaces. Density-functional theory combined with machine-learning molecular dynamics show that intrinsic lattice mismatch between bonded component crystals imprints asymmetric pres

Cansu Isler

Accurate forecasting of downside risks to economic growth is critically important for policymakers and financial institutions, particularly in the wake of recent economic crises. This paper extends the Growth-at-Risk (GaR) approach by introducing a novel daily sentiment indicator derived from textual analysis of mandatory corporate disclosures (SEC 10-K and

Sarif Khan, Jinsu Kim, Pyungwon Ko

We investigate dark matter (DM) phenomenology and cosmic inflation within a unified framework based on a dark $U(1)_D$ gauge extension of the Standard Model (SM). The associated dark gauge boson, namely the dark photon, serves as a viable DM candidate, which we call dark photon dark matter (DPDM), whilst the dark Higgs field drives inflation. We explore a lo

Zhendong Li, Tong Zhao, Bohan Zhang

Neural network quantum states emerge as a promising tool for solving quantum many-body problems. However, its successes and limitations are still not well-understood in particular for Fermions with complex sign structures. Based on our recent work [J. Chem. Theory Comput. 21, 10252-10262 (2025)], we generalizes the restricted Boltzmann machine to a more gene

Sebastian Petit, Geertrui Van de Voorde

In this paper, we study and characterise the natural embedding of the twisted triality hexagon T(q^3,q) in PG(7,q^3). We begin by describing the possible intersections of subspaces of PG(7,q^3) with T(q^3,q). Then, we provide conditions on a set of lines L which ensures that L forms the line set of a naturally embedded twisted triality hexagon. This work fol

Yongkang Wang, Yair Litman, Minhaeng Cho, Stephen Cox

Graphene supported on a substrate in contact with water underpins a wide range of processes and technologies, yet its wettability remains controversial. Understanding how substrate charges and graphene's properties influence water organization is crucial. Here, we combine heterodyne-detected sum-frequency generation (HD-SFG) spectroscopy with molecular dynam

Mathieu Laurière

The mean field games (MFG) paradigm was introduced to provide tractable approximations of games involving very large populations. The theory typically rests on two key assumptions: homogeneity, meaning that all players share the same dynamics and cost functions, and anonymity, meaning that each player interacts with others only through their empirical distri

Mahek Desai, Apoorva Rumale, Marjan Asadinia, Sherrene Bogle

Phase Change Memory (PCM) has rapidly progressed and surpassed Dynamic Random-Access Memory (DRAM) in terms of scalability and standby energy efficiency. Altering a PCM cell's state during writes demands substantial energy, posing a significant challenge to PCM's role as the primary main memory. Prior research has explored methods to reduce write energy cons

Luchang Niu, Joseph H. Eberly

In a system where two identical two-level atoms interact with their common one-mode cavity field, it is shown that entanglement can become abruptly frozen in time, remaining at a constant value for a period of time until it begins to thaw from this value from the entanglement sharing perspective [Ding et al., Phys. Rev. A 103, 032418 (2021)]. We generalize t

Shixiong Zhao, Hideaki Takeda

Wikidata is currently the largest open knowledge graph on the web, encompassing over 120 million entities. It integrates data from various domain-specific databases and imports a substantial amount of content from Wikipedia, while also allowing users to freely edit its content. This openness has positioned Wikidata as a central resource in knowledge graph re

Rethish Nair Rajendran, Sathish Krishna Anumula, Dileep Kumar Rai, Sachin Agrawal

The microservice bombshells that have been linked with the microservice expansion have altered the application architectures, offered agility and scalability in terms of complexity in security trade-offs. Feeble legacy-based perimeter-based policies are unable to offer safeguard to distributed workloads and temporary interaction among and in between the serv

Ziyang Yuan, Yifei Zhang, Yonggong Teng, Hongxia Wang

Accessing both amplitude and phase of nuclear response functions is central to fully characterizing light-matter interactions in the X-ray-nuclear regime. Recent work has demonstrated phase retrieval in two-dimensional time- and energy-resolved spectra, establishing the feasibility of phase-sensitive nuclear spectroscopy. Here, we introduce Nuclear Ptychosco

P. Vijaya Bharati, J. S. V. Siva Kumar, Sathish K Anumula, P Vamshi Krishna

Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimiz

Anna Kawakami, Jordan Taylor, Sarah Fox, Haiyi Zhu

A growing body of literature has focused on understanding and addressing workplace AI design failures. However, past work has largely overlooked the role of the devaluation of worker expertise in shaping the dynamics of AI development and deployment. In this paper, we examine the case of feminized labor: a class of devalued occupations historically misnomere

Yu Li, Lehui Li, Lin Chen, Qingmin Liao

Large language model agents are becoming increasingly capable at web-centric tasks such as information retrieval, complex reasoning. These emerging capabilities have given rise to surge research interests in developing LLM agent for facilitating scientific quest. One key application in AI research is to automate experiment design through agentic dataset and

Hu Gao, Xiaoning Lei, Ying Zhang, Xichen Xu

Image restoration (IR) aims to recover clean images from degraded observations. Despite remarkable progress, most existing methods focus on a single degradation type, whereas real-world images often suffer from multiple coexisting degradations, such as rain, noise, and haze coexisting in a single image, which limits their practical effectiveness. In this pap

Chandra Vamsi Krishna Alla, Harish Naidu Gaddam, Manohar Kommi

Large Language Models (LLMs) face significant computational and memory constraints when processing long contexts, despite growing demand for applications requiring reasoning over extensive documents, multi-session dialogues, and book length texts. While recent advances have extended context windows to 100K-1M tokens, such approaches incur prohibitive costs f

Ashutosh Agarwal

This paper presents a novel solution, LEVER, designed to address the challenges posed by underperforming infrequent categories in Extreme Classification (XC) tasks. Infrequent categories, often characterized by sparse samples, suffer from high label inconsistency, which undermines classification performance. LEVER mitigates this problem by adopting a robust

A. Ganapathi Rao, Sathish Krishna Anumula, Aditya Kumar Singh, Renukhadevi M

It involves the completely novel ways of integrating ML algorithms with traditional statistical modelling that has changed the way we analyze data, do predictive analytics or make decisions in the fields of the data. In this paper, we study some ML and statistical model connections to understand ways in which some modern ML algorithms help 'enrich' conventio

Shadrack T. Asiedu, Tara Aryal, Zongjie Wang, Hossein Moradi Rekabdarkolaee

The increasing integration of Distributed Energy Resources (DERs) into power systems necessitates the accurate representation of their dynamic behavior at the transmission level. Traditional electromagnetic transient models (EMT), while effective, face scalability challenges due to their reliance on detailed system information. Data-driven approaches, such a

Yun-Tong Yang, Song-Ming Chen, Hong-Gang Luo

The introduction of an asymmetric term into the quantum Rabi model generally lifts energy-level degeneracies. However, when the asymmetry parameter takes specific multiples of the bosonic mode frequency, level degeneracies reappear$-$a phenomenon referred to as the hidden symmetry in the asymmetric quantum Rabi model. Identifying the origin of this hidden sy

Shun Takahashi, Yusuke Wakamoto, Kazuhiro Kuruma, Takuya Maeda

Emerging gallium nitride (GaN) vertical power devices require high-quality GaN crystals with reduced crystal defects, especially threading dislocations (TDs), to harness the high critical electric field and electron mobility of the material. A strong demand for characterizing TDs has driven the development of many imaging techniques, such as multiphoton exci

Hardik B. Sailor, Aw Ai Ti, Chen Fang Yih Nancy, Chiu Ying Lay

We present MERaLiON-SER, a robust speech emotion recognition model designed for English and Southeast Asian languages. The model is trained using a hybrid objective combining weighted categorical cross-entropy and Concordance Correlation Coefficient (CCC) losses for joint discrete and dimensional emotion modelling. This dual approach enables the model to cap

Haoyang Weng, Haisu Wu, Hong Ren, Cunhua Pan

Integrated sensing and communication (ISAC) has emerged as a key enabler for sixth-generation (6G) wireless networks, supporting spectrum sharing and hardware integration. Beyond communication enhancement, ISAC also enables high-accuracy environment reconstruction and imaging, which are crucial for applications such as autonomous driving and digital twins. T

Dongyoung Kim, Young-Il Albert Kim, Haedong Aiden Rho

This paper uses daily Behavioral Risk Factor Surveillance System data to estimate the causal effect of the 2024 U.S. presidential election, a highly competitive race whose outcome resolved lingering uncertainty on election day, on mental-health and life-satisfaction outcomes through a regression discontinuity design. Following the resolution of electoral unc

Piotr Kowalski, Omar León Sánchez, Amador Martin-Pizarro

We provide a differential-algebraic description of forking independence in the stable theory DCF$_{p,m}$ of differentially closed fields of characteristic $p>0$ with $m$-many commuting derivations. As a by-product of this description, we prove that types over algebraically closed subsets of the real sort are stationary. In addition, we prove that the set of

Jaehoon Lee, Sohyun Kim, Wanggeun Park, Geon Lee

Existing benchmarks for visual document retrieval (VDR) largely overlook non-English languages and the structural complexity of official publications. To address this gap, we introduce SDS KoPub VDR, the first large-scale, public benchmark for retrieving and understanding Korean public documents. The benchmark is built upon 361 real-world documents, includin

Paula Rodriguez-Diaz, Kirk Bansak Elisabeth Paulson

Many real-world decisions are made under uncertainty by solving optimization problems using predicted quantities. This predict-then-optimize paradigm has motivated decision-focused learning, which trains models with awareness of how the optimizer uses predictions, improving the performance of downstream decisions. Despite its promise, scaling is challenging:

Haochen Wu, Yuanbin Chen, Yang Ming, Zhaocheng Wang

Benefiting from the rapid development of metamaterials and metasurfaces, the holographic multiple-input and multiple-output surface (HMIMOS) has been regarded as a promising solution for future wireless networks recently. By densely packing numerous radiation elements together, HMIMOS is capable of realizing accurate beamforming with low hardware complexity.

Lunjia Hu, Haipeng Luo, Spandan Senapati, Vatsal Sharan

Multicalibration [HJKRR18] is an algorithmic fairness perspective that demands that the predictions of a predictor are correct conditional on themselves and membership in a collection of potentially overlapping subgroups of a population. The work of [NR23] established a surprising connection between multicalibration for an arbitrary property $\Gamma$ (e.g.,

Rahul Ajit

Suppose $R$ is an excellent ring of dimension $3$ and has rational singularities. Let $\pi:X \longrightarrow \mathrm{Spec} \ R$ be a blow-up and $\phi: W \longrightarrow X$ be any projective, birational morphism such that $X$ and $W$ are both normal, Cohen-Macaulay, and have pseudorational singularities in codimension $2$. Then $R^{i}\phi_{*}\omega_{W}=0 \ \

Maksym Luz, Mykhailo Moklyachuk

The problem of optimal estimation of linear functionals constructed from unobserved values of stochastic sequence with periodically stationary increments based on observations of the sequence with a periodically stationary noise is considered. For sequences with known spectral densities, we obtain formulas for calculating values of the mean square errors and

Bassel Al Omari, Michael Matthews, Alexander Rutherford, Jakob Nicolaus Foerster

Progress in multi-agent reinforcement learning (MARL) requires challenging benchmarks that assess the limits of current methods. However, existing benchmarks often target narrow short-horizon challenges that do not adequately stress the long-term dependencies and generalization capabilities inherent in many multi-agent systems. To address this, we first pres

Dhrubajyoti Ghosh, Samhita Pal

Efficacy testing is a cornerstone of clinical trials, ensuring that medical interventions achieve their intended therapeutic effects. Over the decades, a wide range of statistical methodologies have been developed to address the complexities of clinical trial data, including parametric, nonparametric, Bayesian, and machine learning approaches. Parametric met

Shuvendu Roy, Hossein Hajimirsadeghi, Mengyao Zhai, Golnoosh Samei

Recent advances in large language models have demonstrated the promise of unsupervised reinforcement learning (RL) methods for enhancing reasoning capabilities without external supervision. However, the generalizability of these label-free RL approaches to smaller base models with limited reasoning capabilities remains unexplored. In this work, we systematic

Anthony Gamst, Lawrence Wilson

Prior to recent successes using neural networks, term frequency-inverse document frequency (tf-idf) was clearly regarded as the best choice for identifying documents related to a query. We provide a different score, aver, and observe, on a dataset with ground truth marking for association, that aver does do better at finding assciated pairs than tf-idf. This

Sareh Askari, Youssef Kora, Christoph Simon

Reservoir computing is a promising neuromorphic paradigm, and its quantum implementation using spin networks has shown some advantage when entanglement is present. Here, we consider a distributed scenario in which two distinct input time series are injected into separate qubits of a spin-network reservoir. We investigate how the overall entanglement, as well

Yule Wen, Yixin Ye, Yanzhe Zhang, Diyi Yang

Agents in the real world must make not only logical but also timely judgments. This requires continuous awareness of the dynamic environment: hazards emerge, opportunities arise, and other agents act, while the agent's reasoning is still unfolding. Despite advances in language model reasoning, existing approaches fail to account for this dynamic nature. We i

Monique Kubovsky, Yorick A. Birkhölzer, Luka B. Mitrovic, Hanjong Paik

Metal-insulator transitions and superconductivity in rutile-structured oxides hold promise for advanced electronic applications, yet their thin film synthesis is severely hindered by limited substrate options. Here, we present three single- crystalline substrates, BeAl2O4, Mg2SiO4, and Al2SiO4(F,OH)2, prepared via optimized thermal and chemical treatments to

Tatsushi Shima

We revisit neutrino-matter coupling in the post-shock region of core-collapse supernovae by restoring nuclear recoil in coherent neutrino-nucleus scattering (CEvNS). The resulting local energy transfer (a few keV per ~10 MeV neutrino) accumulates across the ~100 km stalled-shock layer, yielding a total heating of 10^49-10^50 erg, comparable within an order o

Oem Trivedi, Abraham Loeb

Primordial Black Holes (PBHs) represent one of the more interesting ways to address dark matter, at the interface of both cosmology and quantum gravity. It is no surprise then that testing PBHs is a venue of active interest, with several cosmological and astrophysical probes constraining different mass ranges. In this work, we propose novel Solar System scal

Hui-Fei Zhai, Sergey L. Bud'ko, Jacob W. Fritsky, Jason F. Khoury

Mixed valency in intermetallics with lanthanide cations is well established as a pathway to unusual charge transport, complex magnetism, and superconductivity. In this work, we report a comprehensive study of the structural, magnetic, electronic, and thermal properties of the mixed valent compound CeFe$_2$Ga$_8$. Powder X-ray diffraction (PXRD) and X-ray pho

Vasileios Magoulianitis, Catherine A. Alexander, Jiaxin Yang, C. -C. Jay Kuo

Nuclei segmentation is the cornerstone task in histology image reading, shedding light on the underlying molecular patterns and leading to disease or cancer diagnosis. Yet, it is a laborious task that requires expertise from trained physicians. The large nuclei variability across different organ tissues and acquisition processes challenges the automation of

Haris Aziz, Xinhang Lu, Simon Mackenzie, Mashbat Suzuki

We study the problem of fairly allocating indivisible items and a desirable heterogeneous divisible good (i.e., cake) to agents with additive utilities. In our paper, each indivisible item can be a good that yields non-negative utilities to some agents and a chore that yields negative utilities to the other agents. Given a fixed set of divisible and indivisi

Jiahe Wang

We provide a bound for $m$ such that the zero locus of a very general section of an $m$-multiple of some ample line bundle on a weighted projective space with isolated singularities is algebraically hyperbolic.

Jiahe Wang

We show that the moduli space of stable n-pointed rational curves $\overline{M}_{0,n}$ with its boundary $\Delta$ is algebraically hyperbolic.

Saurabh U. Shringarpure, Siheon Park, Sungjoo Cho, Yong Siah Teo

Noise suppression is of paramount importance for reliable quantum information processing and computation. We show that for any single-mode bosonic code (qumode) corrupted by thermal~noise at rate~$\eta$ and mean \mbox{excitation}~$\bar{n}$, a hybrid continuous-discrete-variable~(CV-DV) interferometer using only a single qubit ancilla~(DV) and two controlled~

Fei Yin, Charles Guan, Tyson Aflalo, Jorge Gamez

The motor cortex (MC) is often described as an autonomous dynamical system during movement execution. In an autonomous dynamical system, flexible movement generation depends on reconfiguring the initial conditions, which then unwind along known dynamics. An open question is whether these dynamics govern MC activity during brain-machine interface (BMI) contro

Ahmad Elallaf, Nathan Jacobs, Xinyue Ye, Mei Chen

Roadway traffic accidents represent a global health crisis, responsible for over a million deaths annually and costing many countries up to 3% of their GDP. Traditional traffic safety studies often examine risk factors in isolation, overlooking the spatial complexity and contextual interactions inherent in the built environment. Furthermore, conventional Neu

Caetano Melone, Daniel Nichols, Konstantinos Parasyris, Todd Gamblin

High performance computing (HPC) software ecosystems are inherently heterogeneous, comprising scientific applications that depend on hundreds of external packages, each with distinct build systems, options, and dependency constraints. Tools such as Spack automate dependency resolution and environment management, but their effectiveness relies on manually wri

Geertrui Van de Voorde, José Felipe Voloch

We describe a new construction of a subset of P^4 with no four points on a plane over any finite field of order q in which 3 is not a square. This set has size 2q + 1, is maximal with respect to inclusion, and is the largest known such set.

Di Chen, Jia Li, Michael Zhang

Autonomous vehicles (AVs) are expected to be commercially available in the near future, leading to mixed autonomy traffic consisting of both AVs and human-driven vehicles (HVs). Although numerous studies have shown that AVs can be deployed to benefit the overall traffic system performance by incorporating system-level goals into their decision making, it is

P. L. Oliveira Silva, J. G. A. Ramon, Viviane Peçanha-Antonio, Tatiana Guidi

In this work, we report on the thermomagnetic characterization and crystalline-electric field (CEF) energy scheme of the disordered defect-fluorite Ho2Zr2O7. This structural phase is distinguished by the coexistence of magnetic frustration and extensive disorder, with Ho3+ and Zr4+ sharing randomly the same 4a site with even 50% occupancy, and an average 1/8

Hayden Zammit, Roberto Salazar, Gianluca Valentino, Johann A. Briffa

Quantum Information Processing (QIP) tasks can be efficiently formulated in terms of quantum dynamical maps, whose formalism is able to provide the appropriate mathematical representation of the evolution of open quantum systems. A key QIP task is quantum state transfer (QST) aimed at sharing quantum information between distant nodes of a quantum network, en