October 2025 arXiv papers — page 72

I. Ishant, Z. Guguchia, V. Fritsch, O. Stockert

A field-induced quantum spin liquid (QSL) state is an extraordinary phenomenon, hitherto unobserved in metallic frustrated compounds. Recent bulk measurements have revealed intriguing field-induced magnetic states in metallic frustrated CePdAl. However, the nature of these field-induced states, potentially including a QSL state, remains unclear due to the la

Pengfei Su, Yan Gong, Qi Xiong, Dingao Hu

We present a comprehensive forecast for cosmological constraints using the joint observation of the cosmic shear signal from the Chinese Space Station Survey Telescope (CSST) and the clustering signal from the next-generation gravitational wave (GW) detector networks, e.g. Einstein Telescope (ET) and Cosmic Explorer (CE). By leveraging the angular clustering

Massimiliano de Sa, Pio Ong, Aaron D. Ames

Control barrier functions (CBFs) have a well-established theory in Euclidean spaces, yet still lack general formulations and constructive synthesis tools for systems evolving on manifolds common in robotics and aerospace applications. In this paper, we develop a general theory of geometric CBFs on bundles and, for control-affine systems, recover the standard

Masao Hirokawa, Fumio Hiroshima, DongYun Lee

The anisotropic quantum Rabi model provides a continuous interpolation between the quantum Rabi model and the Jaynes-Cummings model obtained via the rotating-wave approximation. In this paper, we investigate the limit as the coupling constant tends to infinity from an operator-theoretic viewpoint. After an explicit quadratic renormalization and a suitable un

Max Hopkins, Russell Impagliazzo, Christopher Ye

Replicability, introduced by (Impagliazzo et al. STOC '22), is the notion that algorithms should remain stable under a resampling of their inputs (given access to shared randomness). While a strong and interesting notion of stability, the cost of replicability can be prohibitive: there is no replicable algorithm, for instance, for tasks as simple as threshol

Jane H. Lee, Baturay Saglam, Spyridon Pougkakiotis, Amin Karbasi

Constrained optimization provides a common framework for dealing with conflicting objectives in reinforcement learning (RL). In most of these settings, the objectives (and constraints) are expressed though the expected accumulated reward. However, this formulation neglects risky or even possibly catastrophic events at the tails of the reward distribution, an

Maggie Bai, Ava Kim Cohen, Eleanor Koss, Charlie Lichtenbaum

This paper explores the spatial reasoning capability of large language models (LLMs) over textual input through a suite of five tasks aimed at probing their spatial understanding and computational abilities. The models were tested on both fundamental spatial reasoning and multi-step problem-solving within structured grid-based environments using tasks such a

Keita Allen, Lucas Piessevaux

We prove a topological reconstruction result for the category of cellular $A$-equivariant motivic spectra over the complex numbers where $A$ is a finite abelian group: after completion at an arbitrary prime, this is equivalent to the completion of a category of synthetic $A$-equivariant spectra. The latter is a deformation of equivariant spectra which catego

Minh Sao Khue Luu, Margaret V. Benedichuk, Ekaterina I. Roppert, Roman M. Kenzhin

The development of foundation models for brain MRI depends critically on the scale, diversity, and consistency of available data, yet systematic assessments of these factors remain scarce. In this study, we analyze 54 publicly accessible brain MRI datasets encompassing over 538,031 to provide a structured, multi-level overview tailored to foundation model de

John Nijim, Zoran Ninkov, Dmitry Vorobiev, Kevin Kearney

Hadamard Transform Spectral Imaging (HTSI) is a multiplexing technique used to recover spectra via encoding with multi-slit masks, and is particularly useful in low photon flux applications where signal-independent noise is the dominant noise source. This work focuses on the procedure that is used to recover spectra encoded with multi-slit masks generated fr

Mayank Pandey, Maksym Radziwiłł

Let $f$ be a real-valued $1$-bounded multiplicative function. Suppose that the mean-value of $f^{2}$ exists, and $$\int_{0}^{1} \Big | \sum_{n \leq N} f(n)e^{2\pi i n \alpha} \Big | d \alpha\leq N^{o(1)}$$ as $N \rightarrow \infty$, then there exists a quadratic character $\chi$ such that for every $\delta > 0$ the (logarithmic) proportion of primes $p \leq

Rahul Raja, Arpita Vats

Question Answering (QA) systems have traditionally relied on structured text data, but the rapid growth of multimedia content (images, audio, video, and structured metadata) has introduced new challenges and opportunities for retrieval-augmented QA. In this survey, we review recent advancements in QA systems that integrate multimedia retrieval pipelines, foc

Zhuang Ma, Xianke Li, Hongyi Shi, Ruonan Guo

Parametric modulation, valued for its versatility, is widely employed in superconducting circuits for quantum simulations and high-fidelity two-qubit gates. Conventionally, the qubit coupling strength is determined by the amplitude of the parametric flux pulse, which affects the qubit parameters dramatically. In this paper, we propose and implement a phase-m

Mingxuan Ge, Dae Woong Ham

Quasi-experimental causal inference methods have become central in empirical operations management for guiding managerial decisions. Among these, empiricists utilize the Difference-in-Differences (DiD) estimator, which relies on the parallel trends assumption. To improve its plausibility, researchers often match treated and control units before applying DiD,

Yangze Zhou, Ruiyang Yao, Dalin Qin, Yixiong Jia

Decision-making for multi-energy system (MES) dispatch depends on accurate load forecasting. Traditionally, load forecasting and decision-making for MES are implemented separately. Forecasting models are typically trained to minimize forecasting errors, overlooking their impact on downstream decision-making. To address this, decision-focused learning (DFL) h

Marcelo Maciel Amaral, Raymond Aschheim

Large language models (LLMs) remain broadly open and highly steerable: they imitate at scale, accept arbitrary system prompts, and readily adopt multiple personae. By analogy to human development, we hypothesize that progress toward artificial general intelligence (AGI) involves a lock-in phase: a transition from open imitation to identity consolidation, in

Xinyi Hu, Yuran Wang, Ruixu Zhang, Yue Li

Temporal Intention Localization (TIL) is crucial for video surveillance, focusing on identifying varying levels of suspicious intentions to improve security monitoring. However, existing discrete classification methods fail to capture the continuous nature of suspicious intentions, limiting early intervention and explainability. In this paper, we propose the

Morris Yu-Chao Huang, Zhen Tan, Mohan Zhang, Pingzhi Li

Large Language Models generate complex reasoning chains that reveal their decision-making, yet verifying the faithfulness and harmlessness of these intermediate steps remains a critical unsolved problem. Existing auditing methods are centralized, opaque, and hard to scale, creating significant risks for deploying proprietary models in high-stakes domains. We

Dian Yu, Yulai Zhao, Kishan Panaganti, Linfeng Song

We propose Reinforcement Learning with Explicit Human Values (RLEV), a method that aligns Large Language Model (LLM) optimization directly with quantifiable human value signals. While Reinforcement Learning with Verifiable Rewards (RLVR) effectively trains models in objective domains using binary correctness rewards, it overlooks that not all tasks are equal

Zhecheng Hu, Fei Dai, Wei Zhu, Mu-Tian Wang

Recent observations suggest that the incidence of near-resonant planets declines as planetary systems age, making young planetary systems key signposts of early dynamical evolution. Here we investigate the dynamical states of three of the youngest multi-transiting planetary systems: AU Mic (3-planet, $\sim$20-Myr-old), V1298 Tau (4-planet, $\sim$23-Myr-old),

Tobia Marcucci

A Graph of Convex Sets (GCS) is a graph in which vertices are associated with convex programs and edges couple pairs of programs through additional convex costs and constraints. Any optimization problem over an ordinary weighted graph (e.g., the shortest-path, the traveling-salesman, and the minimum-spanning-tree problems) can be naturally generalized to a G

M. Z. Bhatti, Kazuharu Bamba, I. Siddique, Bander Almutairi

We explore the thermodynamics of a novel solution for the Reissner-Nordstr\"{o}m-Anti-de Sitter (AdS) black hole, uniquely incorporating the Gauss-Bonnet term. Unlike previous studies that primarily focused on standard General Relativity or other modifications, this inclusion allows for a modified entropy formulation, facilitating the computation of key ther

Sk Jamaluddin, Warit Nisaiyok, Yu Zhang, Hari Bhandari

Magnetic topology and its associated emergent phenomena are central to realizing intriguing quantum states and spintronics functionalities. Designing spin textures to achieve strong and distinct electrical responses remains a significant challenge. Layered transition metal dichalcogenides offer a versatile platform for tailoring structural and magnetic prope

Hans Guente Dosch, Guy F. de Teramond, Stanley J. Brodsky

This brief review presents the key components of light-front holographic quantum chromodynamics (HLFQCD). Particular attention is given to the introduction of the QCD color confinement scale within the context of a graded superconformal algebra. A concise overview of applications is provided, including spectroscopy, form factors, parton distributions of hadr

Tadashi Wadayama

We propose a gradient-based framework for optimizing parametric nonlinear Gaussian channels via mutual information maximization. Leveraging the score-to-Fisher bridge (SFB) methodology, we derive a computationally tractable formula for the information gradient that is the gradient of mutual information with respect to the parameters of the nonlinear front-en

Yun Wang, Junjie Hu, Qiaole Dong, Yongjian Zhang

Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersion of users. Despite its importance, this task remains challenging due to the difficulty in modeling long-term temporal consistency in a computationally efficient manner. Previous

Muhammad Suhail Saleem, Lai Yuan, Maxim Likhachev

Robots often face manipulation tasks in environments where vision is inadequate due to clutter, occlusions, or poor lighting--for example, reaching a shutoff valve at the back of a sink cabinet or locating a light switch above a crowded shelf. In such settings, robots, much like humans, must rely on contact feedback to distinguish free from occupied space an

Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang

Understanding and reasoning over tables is a critical capability for many real-world applications. Large language models (LLMs) have shown promise on this task, but current approaches remain limited. Fine-tuning based methods strengthen language reasoning; yet they are prone to arithmetic errors and hallucination. In contrast, tool-based methods enable preci

Zhumagali Shomanov, Frank Garvan

We prove an infinite family of Hecke-like congruences for the overpartition function modulo powers of 2. Starting from a recent identity of Garvan and Morrow and iterating Atkin's $U_2$ operator, we determine lower bounds on the 2-adic valuations of the coefficients that arise at each step. Our approach yields new modular equations relating the Hauptmoduln $

Yong Um, Young-Ha Shin, Joon-Ha Kim, Soonpyo Kwon

We present a reinforcement learning framework for quadrupedal wall-climbing locomotion that explicitly addresses uncertainty in magnetic foot adhesion. A physics-based adhesion model of a quadrupedal magnetic climbing robot is incorporated into simulation to capture partial contact, air-gap sensitivity, and probabilistic attachment failures. To stabilize lea

Wenzhen Luo, Wei Guan, Yifan Yao, Yimin Pan

We introduce Falcon, a cross-domain Chinese text-to-SQL benchmark grounded in an enterprise-compatible dialect (MaxCompute/Hive). It contains 600 Chinese questions over 28 databases; 77% require multi-table reasoning and over half touch more than four tables. Each example is annotated along SQL-computation features and Chinese semantics. For evaluation, we r

Cossas K. -W. Wu, Chih-Teng Ling, Tomotsugu Goto, Amos Y. -A. Chen

High-redshift protoclusters are crucial for understanding the formation of galaxy clusters and the evolution of galaxies in dense environments. The James Webb Space Telescope (JWST), with its unprecedented near-infrared sensitivity, enables the first exploration of protoclusters beyond $z>$10. Among JWST surveys, COSMOS-Web Data Release 0.5 offers the larges

Min Si, Pavan Balaji, Yongzhou Chen, Ching-Hsiang Chu

The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of thousands of GPUs. Traditional communication methods face significant throughput and latency limitations at this scale, hindering both the development and deployment of state-of-the-a

Ryotaro Isobe, Shinya Kumashiro

We provide a characterization of one-dimensional almost Gorenstein rings in terms of the trace ideal. As an application, we investigate the almost Gorenstein property of certain $\mathbb{Z}_2$-graded rings.

Tongkai Lu, Shuai Ma, Chongyang Tao

Traveling Salesman Problem (TSP) is a classic NP-hard problem that has garnered significant attention from both academia and industry. While neural-based methods have shown promise for solving TSPs, they still face challenges in scaling to larger instances, particularly in memory constraints associated with global heatmaps, edge weights, or access matrices,

Tian Lan, Bin Zhu, Qianghuai Jia, Junyang Ren

Current search agents fundamentally lack the ability to simultaneously perform \textit{deep} reasoning over multi-hop retrieval and \textit{wide}-scale information collection-a critical deficiency for real-world applications like comprehensive market analysis and business development. To bridge this gap, we introduce DeepWideSearch, the first benchmark expli

Roman Bacik

We demonstrate that any function $f$ from a finite set $Y$ to itself can be represented linearly. Specifically, we prove the existence of an injective map $j$ from $Y$ into a modular ring $\mathbb{Z}/m\mathbb{Z}$ and a constant $a \in \mathbb{Z}/m\mathbb{Z}$ such that $j(f(y)) = a \cdot j(y)$ in $\mathbb{Z}/m\mathbb{Z}$ holds for all $y \in Y$. This result i

Xu-Hua Tian, Long-Gang Pang

In this work, we propose a novel approach for probing the neutron skin thickness of gold (Au) by analyzing the event-by-event distribution of $\pi^{-}$ and $\pi^{+}$ yield differences. This is achieved through SMASH simulations of ultra-peripheral Au+Au collisions at $\sqrt{s_{\rm NN}}=3$ GeV. Our results demonstrate that the mean value of $\Delta n_{\pi} =

Insu Jeon, Wonkwang Lee, Myeongjang Pyeon, Gunhee Kim

We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framework to the optimization of GAN, thereby named IB-GAN. The architecture of IB-GAN is partially similar to that of InfoGAN but has a critical difference; an intermediate layer of the

Di Wu, Shuang-Qing Wu

In this paper, we investigate thermodynamical aspects of a novel static non-extremal black hole solution in the five-dimensional anti-de Sitter (AdS) space carrying four independent electric charge parameters and with a negative cosmological constant, within the framework of $D=5$, $\mathcal{N} = 2$ gauged supergravity coupled to three vector multiplets spec

Anthony Zhang, Mahmut Gokmen, Dennis Hein, Rongjun Ge

The Poisson Flow Consistency Model (PFCM) is a consistency-style model based on the robust Poisson Flow Generative Model++ (PFGM++) which has achieved success in unconditional image generation and CT image denoising. Yet the PFCM can only be trained in distillation which limits the potential of the PFCM in many data modalities. The objective of this research

Xudong Yan, Songhe Feng

Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performance degradation caused by the distribution shift of label space at test time, which stems from the inclusion of unseen compositions recombined from attributes and objects. To overcom

Ahmed Alanazi, Duy Ho, Yugyung Lee

Robotic arms require precise, task-aware trajectory planning, yet sequence models that ignore motion structure often yield invalid or inefficient executions. We present a Path-based Transformer that encodes robot motion with a 3-grid (where/what/when) representation and constraint-masked decoding, enforcing lattice-adjacent moves and workspace bounds while r

Jia-Xin Zhong, Jing Lin, Kai Chen, Jing Lu

Real-valued band structures are foundational to analyzing periodic systems within the Hermitian description and have been experimentally well-established over recent decades. In contrast, non-Hermitian systems exhibit complex band structures where both energy and momentum have imaginary parts, underpinning phenomena like the non-Hermitian skin effect and ano

Eduardo R. Corral-Soto, Yang Liu, Yuan Ren, Bai Dongfeng

In Autonomous Driving, cyclists belong to the safety-critical class of Vulnerable Road Users (VRU), and accurate estimation of their pose is critical for cyclist crossing intention classification, behavior prediction, and collision avoidance. Unlike rigid objects, articulated bicycles are composed of movable rigid parts linked by joints and constrained by a

Xiaoming Wu, Teng Liu, Xin Wang, Ming Yang

Differential privacy is widely employed in decentralized learning to safeguard sensitive data by introducing noise into model updates. However, existing approaches that use fixed-variance noise often degrade model performance and reduce training efficiency. To address these limitations, we propose a novel approach called decentralized learning with adaptive

Anthony Morales

We show that the chiral algebra bootstrap, which computes form factors of twistorial theories, can help determine two-loop amplitudes in massless QCD. We give an $n$-gluon result for a previously unknown partial amplitude of the two-loop all-plus-helicity QCD amplitude by utilizing supersymmetry Ward identities and known chiral algebra bootstrap results. We

Penghao Wang, Yiyang He, Xin Lv, Yukai Zhou

Understanding objects at the level of their constituent parts is fundamental to advancing computer vision, graphics, and robotics. While datasets like PartNet have driven progress in 3D part understanding, their reliance on untextured geometries and expert-dependent annotation limits scalability and usability. We introduce PartNeXt, a next-generation dataset

Anthony Dubreuil, Antoine Gourru, Christine Largeron, Amine Trabelsi

Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many Natural Language Processing tasks, such as hateful speech detection or sentiment analysis. Surprisingly, the evaluation of this kind of bias in stance detection methods has been largely overlooked by the community. Stance Det

Tristan Pollner, Amin Saberi, Anders Wikum

We study two-stage bipartite matching, in which the edges of a bipartite graph on vertices $(B_1 \cup B_2, I)$ are revealed in two batches. In stage one, a matching must be selected from among revealed edges $E \subseteq B_1 \times I$. In stage two, edges $E^\theta \subseteq B_2 \times I$ are sampled from a known distribution, and a second matching must be s

Haoyuan Li, Zhengyuan Shen, Sullam Jeoung, Yueyan Chen

Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation into semantically meaningful components, which cannot be effectively handled by conventional sentence-level segmentation methods. To address this, we propose BoundRL, a novel approa

Yaochen Zhu, Harald Steck, Dawen Liang, Yinhan He

Large language models (LLMs) are reshaping the recommender system paradigm by enabling users to express preferences and receive recommendations through conversations. Yet, aligning LLMs to the recommendation task remains challenging: pretrained LLMs often generate out-of-catalog items, violate required output formats, and their ranking quality degrades sharp

Zhenyu Zhao, Xiaoxia Xu, Tiankui Zhang, Junjie Li

This paper proposes a novel multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) framework, where the computing tasks of terminal devices (TDs) can be decomposed into serial or parallel sub-tasks and offloaded to collaborative UAVs. We first model the dependencies among all sub-tasks as a directed acyclic graph (DAG) and des

Tingting Dan, Xinwei Huang, Jiaqi Ding, Yinggang Zheng

Emerging neuroimaging evidence shows that pathological tau proteins build up along specific brain networks, suggesting that large-scale network architecture plays a key role in the progression of Alzheimer's disease (AD). However, how structural connectivity (SC) and functional connectivity (FC) interact to influence tau propagation remains unclear. Leveragi

Qingyang Liu, Sanvesh Srivastava, Dipankar Bandyopadhyay

We propose a regression model with matrix-variate skew-t response (REGMVST) for analyzing irregular longitudinal data with skewness, symmetry, or heavy tails. REGMVST models matrix-variate responses and predictors, with rows indexing longitudinal measurements per subject. It uses the matrix-variate skew-t (MVST) distribution to handle skewness and heavy tail

Yongning Qi, Tao Zhou, Zuowei Xiang, Liu Liu

The cell-free massive multi-input multi-output (CF-mMIMO) is a promising technology for the six generation (6G) communication systems. Channel prediction will play an important role in obtaining the accurate CSI to improve the performance of CF-mMIMO systems. This paper studies a deep learning (DL) based joint space-time-frequency domain channel prediction f

José E. Cruz Serrallés, Oluwadara Ogunkoya, Do{g}a Murat Kürkçüo{g}lu, Nicholas Bornman

We propose a novel floating-point encoding scheme that builds on prior work involving fixed-point encodings. We encode floating-point numbers using Two's Complement fixed-point mantissas and Two's Complement integral exponents. We used our proposed approach to develop quantum algorithms for fundamental arithmetic operations, such as bit-shifting, reciprocati

Alejandro Hnilo

Years ago, Itamar Pitowski asked two relevant questions: Why microphysical (quantum) phenomena and classical phenomena differ in the way they do? and, what kind of explanation could qualify as a reasonable one? I argue that both questions can be answered by the comparison of quantum phenomena with some features of vectors in real space. In particular, I show

Sheng Wang, Juan Huang

This paper concerns the existence and related properties of solutions to the Schr\"{o}dinger-Bopp-Podolsky system, which reduces to a nonlinear and nonlocal partial differential equation describing a Schr\"{o}dinger field coupled with its electromagnetic field in Bopp-Podolsky theory under purely electrostatic conditions. Firstly, by applying the mountain-pa

Xiaoyang Wang, Yiqi Gu

We propose a novel class of neural network-like parametrized functions, i.e., general transformation neural networks (GTNNs), for high-dimensional approximation. Conventional deep neural networks sometimes perform less accurately on learning problems trained with gradient descent, especially when the target function is oscillatory. To improve accuracy, we ge

Leonid Polterovich

This book offers an introduction to Hofer's metric on the group of Hamiltonian diffeomorphisms. It presents results on the diameter, geodesics, and the growth of one-parameter subgroups, along with applications to dynamics and ergodic theory.

Somayajulu L. N. Dhulipala, Deep Ray, Nicholas Forman

Simulating coupled PDE systems is computationally intensive, and prior efforts have largely focused on training surrogates on the joint (coupled) data, which requires a large amount of data. In the paper, we study compositional diffusion approaches where diffusion models are only trained on the decoupled PDE data and are composed at inference time to recover

Jiangong Chen, Xia Lei, Kaitao Meng, Kawon Han

In this paper, we explore sensing security in near-field (NF) integrated sensing and communication (ISAC) scenarios by exploiting known scatterers in the sensing scene. We propose a location deception (LD) scheme where scatterers are deliberately illuminated with probing power that is higher than that directed toward targets of interest, with the goal of dec

A. V. Kotikov, A. V. Lipatov

We present the main results of our recent papers, where we derived an analytical solution of the QCD evolution equations for parton distribution functions. The valence and non-singlet quark components satisfy the Gross-Llewellyn-Smith and Gottfried sum rules, respectively, while momentum conservation is maintained for the singlet quark and gluon densities. S

Ryosuke Ooe

We define the abelian fundamental group with modulus of a regular flat scheme over a discrete valuation ring, taking into account wild ramification along a divisor. Our definition provides a mixed-characteristic analogue of the abelian fundamental group with modulus introduced by Kerz--Saito for smooth schemes over a perfect field. In this setting, we prove

Hasnain A. Ziad, Ashiq A. Sakib

The design of approximate adders has been widely researched to advance energy-efficient hardware for computation-intensive multimedia applications, such as image, audio, or video processing. The design of approximate adders has been widely researched to advance energy-efficient hardware for computation intensive multimedia applications, such as image/audio/v

Yao Xiao, Anne Gelb

Conventional priors used for signal recovery are often limited by the assumption that the type of a signal's variability, such as piecewise constant or linear behavior, is known and fixed. This assumption is problematic for complex signals that exhibit different behaviors across the domain. The recently developed {\em residual transform operator} effectively

Zhiyuan Fan, Bolun Xu

The dual challenge of decarbonizing the economy and meeting rising global energy demand underscores the need for scalable and cost-effective carbon dioxide removal technologies. Direct air capture (DAC) is among the most promising approaches, but its high energy intensity, particularly the thermal energy required for sorbent regeneration, remains a critical

Jiachen Liang, Ruibing Hou, Minyang Hu, Hong Chang

Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning models in open-world applications. While post-hoc methods are favored for their efficiency and ease of deployment, existing approaches often underexploit the rich information embedded in the model's logits space. In this paper, we propose LogitGap, a novel post-hoc

Jan Minac, Nguyen Duy Tan, Nguyen Thi Tra

Zassenhaus filtrations of profinite groups are an important tool to study profinite groups.In this paper, we describe Zassenhaus filtrations of profinite groups as intersections of kernels of certain representations. In this way we introduce a link between studying profinite groups with methods of Zassenhaus filtrations and representation theory.

Hyunjun Jung, Hae-Gon Jeon

A concept of light-fields computed from multiple view images on regular grids has proven its benefit for scene representations, and supported realistic renderings of novel views and photographic effects such as refocusing and shallow depth of field. In spite of its effectiveness of light flow computations, obtaining light fields requires either computational

Mohammed Barhoush

Pseudorandom generators (PRGs) are a foundational primitive in classical cryptography, underpinning a wide range of constructions. In the quantum setting, pseudorandom quantum states (PRSs) were proposed as a potentially weaker assumption that might serve as a substitute for PRGs in cryptographic applications. Two primary size regimes of PRSs have been studi

Lei Xu, Shuhang Mai, Manzhang Xu, Xue Yang

The missing first Shapiro step in microwave-irradiated Josephson junctions has been widely interpreted as a hallmark of Majorana bound states. However, conventional mechanisms like junction underdamping or Joule heating can produce similar signatures. Here, we demonstrate that the intrinsic non-linear current-voltage characteristic of low-to-moderate transpa

Yulong Chen, Qi Zhang, Jiawen Zhang, Yadong Liu

Large Language Models (LLMs) remain vulnerable to jailbreak attacks, where adversarially crafted prompts induce policy-violating responses despite safety alignment. Existing defenses typically improve safety through external filtering, auxiliary guardrails, or decoding-time control. However, these interventions often reduce practical deployability because th

Xin Zhan, K. Grace Johnson, Aniello Esposito, Barbara Chapman

To address the growing needs for scalable High Performance Computing (HPC) and Quantum Computing (QC) integration, we present our HPC-QC full stack framework and its hybrid workload development capability with modular hardware/device-agnostic software integration approach. The latest development in extensible interfaces for quantum programming, dispatching,

Munawar Ali, Qi Feng

In this paper, we introduce the branched signature model, motivated by the branched rough path framework of [Gubinelli, Journal of Differential Equations, 248(4), 2010], which generalizes the classical geometric rough path. We establish a universal approximation theorem for the branched signature model and demonstrate that iterative compositions of lower-lev

Nicholas M. Christopher, Deniz E. Stiegemann, Abhijeet Alase, Thomas M. Stace

Superconducting protected qubits aim to achieve sufficiently low error rates so as to allow realization of error-corrected, utility-scale quantum computers. A recent proposal encodes a protected qubit in the quasicharge degree of freedom of the conventional transmon device. Operating such a protected `quasicharge qubit' requires implementing new strategies.

Prithvi Raj Singh, Raju Gottumukkala, Anthony S. Maida, Alan B. Barhorst

While computer vision has advanced considerably for general object detection and tracking, the specific problem of fast-moving tiny objects remains underexplored. This paper addresses the significant challenge of detecting and tracking rapidly moving small objects using an RGB-D camera. Our novel system combines deep learning-based detection with physics-bas

Cayden Maguire, Christofer Hardcastle, Trevor Hastings, Raymundo Arróyave

In alloy design, the search for candidate materials is often framed as an optimization problem, with the goal of identifying Pareto-optimal solutions across multiple objectives. However, Pareto-optimal solutions do not necessarily satisfy all minimum performance thresholds required for practical deployment. An alternative approach is to treat alloy design as

Yao Li, Jingyi Xie, Ya-Fang Lin, He Zhang

Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Although AI is increasingly present in learning environments, most systems remain child-centered and overlook the collaborative, distributed nature of family education. This paper investigates how AI can mediate family collaboration

Carlos J. Fernandez-Candel, Jesus Garcia-Molina, Francisco Javier Bermudez Ruiz, Jose Ramon Hoyos Barcelo

Model-driven software engineering (MDE) techniques are not only useful in forward engineering scenarios, but can also be successfully applied to evolve existing systems. RAD (Rapid Application Development) platforms emerged in the nineties, but the success of modern software technologies motivated that a large number of enterprises tackled the migration of t

A. H. Gevorgyan, A. A. Malinchenko, N. A. Vanyushkin, S. S. Golik

Magneto-optical effects in structured anisotropic media have attracted significant attention due to their potential in tunable photonic devices. In particular, dichroic cholesteric liquid crystals (i.e., cholesterics in which the real parts of the local dielectric permeability tensor are equal each other, and the imaginary parts differ) provide a unique syst

Arian Prabowo, Flora D. Salim

Timeseries foundation models (TSFMs) have multiplied, yet lightweight supervised baselines and even classical models often match them. We argue this gap stems from the naive importation of NLP or CV pipelines. In language and vision, large web-scale corpora densely capture human concepts i.e. there are countless images and text of apples. In contrast, timese

Huan-Yu Liu, Tai-Ping Sun, Zhao-Yun Chen, Cheng Xue

Open quantum systems host a wide range of intriguing phenomena, yet their simulation on well-controlled quantum devices is challenging, owing to the exponential growth of the Hilbert space and the inherently non-unitary nature of the dynamics. Here we propose and experimentally demonstrate a variational quantum algorithm capable of scalable simulation of non

Robert M. Corless, C. Yalçın Kaya

Given the set of discrete solution points or nodes, called the skeleton, generated by an ODE solver, we study the problem of fitting a curve passing through the nodes in the skeleton minimizing a norm of the residual vector of the ODE. We reformulate this interpolation problem as a multi-stage optimal control problem and, for the minimization of two differen

Takahiro Chiba, Ryunosuke Suzuki, Takashi Otaki, Hiroaki Matsueda

We theoretically study nonperturbative strong-coupling phenomena in cavity magnonics systems in which the uniform magnetization dynamics (magnons) in a ferromagnet is coupled to the microwave magnetic field (photons) of a single LC resonator. Starting from an effective circuit model that accounts for the magnetization dynamics described by the Landau-Lifshit

Raahul Potluri, Rohin Tangirala, Jiangteng Liu, Alejandro Barrios

Tantalum (Ta) has emerged as a promising low-loss material, enabling record coherence times in superconducting qubits. This enhanced performance is largely attributed to its stable native oxide, which may host fewer two-level system (TLS) defects, which are the key contributors to decoherence in superconducting circuits. Nevertheless, aluminum oxide remains

Haowei Lou, Chengkai Huang, Hye-young Paik, Yongquan Hu

Speech is essential for human communication, yet millions of people face impairments such as dysarthria, stuttering, and aphasia conditions that often lead to social isolation and reduced participation. Despite recent progress in automatic speech recognition (ASR) and text-to-speech (TTS) technologies, accessible web and mobile infrastructures for users with

Shanshan Dong, Lu Wang, Xiangxiang Chen, Guanqing Wang

The discretization of velocity space plays a crucial role in the accuracy and efficiency of multiscale Boltzmann solvers. Conventional velocity space discretization methods suffer from uneven node distribution and mismatch issues, limiting the performance of numerical simulations. To address this, a Gaussian quadrature scheme with a parameterized weight func

Borui Du, Yumeng Zhang, Christos Masouros, Bruno Clerckx

Orthogonal time frequency space (OTFS) offers significant advantages in managing mobility for both wireless sensing and communication systems, making it a promising candidate for dual-functional radar-communication (DFRC). However, the optimal signal design that fully exploits OTFS's potential in DFRC has not been sufficiently explored. This paper addresses

Huawei Bai, Yifan Huang, Wenqi Shi, Ansheng You

The training efficiency and scalability of language models on massive clusters currently remain a critical bottleneck. Mainstream approaches like ND parallelism are often cumbersome and complex, while flexible alternatives such as the Zero Redundancy Optimizer (ZeRO) are frequently hampered by communication overhead. In this paper, we propose Asynchronous Hi

Ming Sheng, Shuliang Wang, Yong Zhang, Yi Luo

Numerous multi- or high-dimensional indexes with distinct advantages have been proposed on various platforms to meet application requirements. To achieve higher-performance queries, most indexes employ enhancement methods, including structure-oriented and layout-oriented enhancement methods. Existing structure-oriented methods tailored to specific indexes wo

Joshua Yuvaraj

It is often claimed that machine learning-based generative AI products will drastically streamline and reduce the cost of legal practice. This enthusiasm assumes lawyers can effectively manage AI's risks. Cases in Australia and elsewhere in which lawyers have been reprimanded for submitting inaccurate AI-generated content to courts suggest this paradigm must

Premala Chandra, Piers Coleman, Clare C. Yu

Philip Warren Anderson was a pioneering theoretical physicist whose work fundamentally shaped our understanding of complex systems. Anderson received the Nobel Prize in Physics in 1977 for his groundbreaking research on localization and magnetism, yet he did so much more. His work on magnetism included antiferromagnetism, superexchange, the Kondo problem and

Gabriel Y. Arteaga, Marius Aasan, Rwiddhi Chakraborty, Martine Hjelkrem-Tan

Prototypical self-supervised learning methods consistently suffer from partial prototype collapse, where multiple prototypes converge to nearly identical representations. This undermines their central purpose -- providing diverse and informative targets to guide encoders toward rich representations -- and has led practitioners to over-parameterize prototype

Ayatullah Faruk Mollah

Studies on various facets of pattern classification is often imperative while working with multi-dimensional samples pertaining to diverse application scenarios. In this notion, weighted dimension-based distance measure has been one of the vital considerations in pattern analysis as it reflects the degree of similarity between samples. Though it is often pre

Amartya Roy, Souvik Chakraborty

Causal discovery remains a central challenge in machine learning, yet existing methods face a fundamental gap: algorithms like GES and GraN-DAG achieve strong empirical performance but lack finite-sample guarantees, while theoretically principled approaches fail to scale. We close this gap by introducing a game-theoretic reinforcement learning framework for

Michael E. Glinsky

By returning to the topological basics of fusion target design, Generative Artificial Intelligence (genAI) is used to specify how to initially configure and drive the optimally entangled topological state, and stabilize that topological state from disruption. This can be applied to all methods; including tokamaks, laser-driven schemes, and pulsed-power drive

Yuhan Chu

The point-line incidence problem has been widely studied in Euclidean spaces and vector spaces over finite fields, whereas the analogous problem has rarely been considered over finite $p$-adic rings. In this paper, we investigate incidences in the $p$-adic setting and prove new incidence bounds for points and lines in $(\mathbb{Z}/p^k\mathbb{Z})^2$. Our firs

Rishabh Dey, Michael Brocidiacono, Kushal Koirala, Alexander Tropsha

The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to explicit solvent models, limiting its use in precise thermodynamic calculations. Recent advancements in machine learning (ML) present an opportunity to overcome these limitations b