March 2025 arXiv papers — page 116

Sicheng Zhou, Zhuozhao Li, Valérie Hayot-Sasson, Haochen Pan

Failures in Task-based Parallel Programming (TBPP) can severely degrade performance and result in incomplete or incorrect outcomes. Existing failure-handling approaches, including reactive, proactive, and resilient methods such as retry and checkpointing mechanisms, often apply uniform retry mechanisms regardless of the root cause of failures, failing to acc

Yifan Zhan, Wangze Xu, Qingtian Zhu, Muyao Niu

We present R3-Avatar, incorporating a temporal codebook, to overcome the inability of human avatars to be both animatable and of high-fidelity rendering quality. Existing video-based reconstruction of 3D human avatars either focuses solely on rendering, lacking animation support, or learns a pose-appearance mapping for animating, which degrades under limited

Hiroshi Okajima, Risa Furukawa, Nobutomo Matsunaga

This paper proposes a system identification algorithm for systems with multi-rate sensors in a discrete-time framework. It is challenging to obtain an accurate mathematical model when the ratios of inputs and outputs are different in the system. A cyclic reformulation-based model for multi-rate systems is formulated, and the multi-rate system can be reduced

Lin-Yue Li, Rong-Hua Wang

Let $n$ be any nonnegative integer and \[ D_n^{(h)}(x)=\sum_{k=0}^{n}\binom{n+k}{2k}^{h}\binom{2k}{k}^{h}{x}^{k} \text{ and } S_{n}^{(h)}(x)=\sum_{k=0}^{n}\binom{n+k}{2k}^{h}C_{k}^{h}{x}^{k} \] be the generalized Delannoy polynomials and Schr\"oder polynomials respectively. Here $C_k$ is the Catalan number and $h$ is a positive integer. In this paper, we pro

Yanyuan Wang, Xiaowei Zhang

We study uncertainty quantification for contextual stochastic optimization, focusing on weighted sample average approximation (wSAA), which uses machine-learned relevance weights based on covariates. Although wSAA is widely used for contextual decisions, its uncertainty quantification remains limited. In addition, computational budgets tie sample size to opt

Siu-Wing Cheng, Haoqiang Huang, Shuo Zhang

Let $\tau$ and $\sigma$ be two polygonal curves in $\mathbb{R}^d$ for any fixed $d$. Suppose that $\tau$ and $\sigma$ have $n$ and $m$ vertices, respectively, and $m\le n$. While conditional lower bounds prevent approximating the Fr\'echet distance between $\tau$ and $\sigma$ within a factor of 3 in strongly subquadratic time, the current best approximation

Patrick Rim, Hyoungseob Park, S. Gangopadhyay, Ziyao Zeng

We present ProtoDepth, a novel prototype-based approach for continual learning of unsupervised depth completion, the multimodal 3D reconstruction task of predicting dense depth maps from RGB images and sparse point clouds. The unsupervised learning paradigm is well-suited for continual learning, as ground truth is not needed. However, when training on new no

Yu Xia, Zhiqiang Xu

This paper investigates the ability of finite samples to identify two-layer irreducible shallow networks with various nonlinear activation functions, including rectified linear units (ReLU) and analytic functions such as the logistic sigmoid and hyperbolic tangent. An ``irreducible" network is one whose function cannot be represented by another network with

Ionuţ Chifan, Adriana Fernández Quero, Denis Osin, Hui Tan

We propose to study a natural version of Connes' Rigidity Conjecture that involves property (T) groups with infinite center. Utilizing techniques at the intersection of von Neumann algebras and geometric group theory, we establish several cases where this conjecture holds. In particular, we provide the first example of a W$^*$-superrigid property (T) group w

Noa Bihlmaier, Nick Ruoff, Philipp Schmale

We present the foundational theory of condensed sets and basic condensed algebra after having introduced key concepts from category theory and homological algebra. In the later sections, we indicate the relevance of condensed mathematics to classical fields such as functional analysis and ergodic theory. We include many pointers to the literature, where most

Yongjia Ma, Donglin Di, Xuan Liu, Xiaokai Chen

Rectified flow models have achieved remarkable performance in image and video generation tasks. However, existing numerical solvers face a trade-off between fast sampling and high accuracy solutions, limiting their effectiveness in downstream applications such as reconstruction and editing. To address this challenge, we propose leveraging the Adams Bashforth

Zhao Joseph Zhang, Kentaro Nagamine, Yuri Oku, Khee-Gan Lee

We investigate the Missing Baryon problem using Fast Radio Bursts (FRBs) to trace cosmic baryons. Our CROCODILE simulations, performed with the GADGET3/4-OSAKA smoothed particle hydrodynamics code, include star formation, supernova (SN) and active galactic nuclei (AGN) feedback. We generate light cones from large-scale structure simulations to compute gas de

Chenxi Li, Xiaochuan Liu, Bao-Feng Feng

In this paper, we study coupled complex modified Korteweg-de Vries (ccmKdV) equation by combining the Hirota's method and the Kadomtsev-Petviashvili (KP) reduction method. First, we show that the bilinear form of the ccmKdV equation under nonzero boundary condition is linked to the discrete BKP hierarchy through Miwa transformation. Based on this finding, we

Onno P Kampman, Michael Xing, Charmaine Lim, Ahmad Ishqi Jabir

This paper explores conversational self-play with LLMs as a scalable approach for analyzing and exploring psychotherapy approaches, evaluating how well AI-generated therapeutic dialogues align with established modalities.

Tianyu Zong, Bingkang Shi, Hongzhu Yi, Jungang Xu

Unsupervised sentence embedding representation has become a hot research topic in natural language processing. As a tensor, sentence embedding has two critical properties: direction and norm. Existing works have been limited to constraining only the orientation of the samples' representations while ignoring the features of their module lengths. To address th

Jeihee Cho, Junyong Lee, Daniel Justice, Shiho Kim

Hybrid quantum-classical computing relies heavily on Variational Quantum Algorithms (VQAs) to tackle challenges in diverse fields like quantum chemistry and machine learning. However, VQAs face a critical limitation: the balance between circuit trainability and expressibility. Trainability, the ease of optimizing circuit parameters for problem-solving, is of

Itamar Vigdorovich

We present the first examples of higher-rank lattices whose reduced $C^{*}$-algebras satisfy strict comparison, stable rank one, selflessness, uniqueness of embeddings of the Jiang--Su algebra, and allow explicit computations of the Cuntz semigroup. This resolves a question raised in recent groundbreaking work of Amrutam, Gao, Kunnawalkam Elayavalli, and Pat

Christian McIver, Karla Avalos, Nikhil Nayak

This paper proposes a model to predict the outcome of the March Madness tournament based on historical NCAA basketball data since 2013. The framework of this project is a simplification of the FiveThrityEight NCAA March Madness prediction model, where the only four predictors of interest are Adjusted Offensive Efficiency (ADJOE), Adjusted Defensive Efficienc

Yeon-Ho Choi, Ueejeong Jeong, Jae-Joon Lee, Hyun-Jeong Kim

Ground-based high-resolution spectroscopy enables precise molecular detections and velocity-resolved atmospheric dynamics, offering a distinct advantage over low-resolution methods for exoplanetary atmospheric studies. IGRINS-2, the successor to IGRINS, features improved throughput and enhanced sensitivity to carbon monoxide by shifting its $\textit{K}$-band

Sam Olesker-Taylor, Lucas Teyssier, Paul Thévenin

We develop a flexible technique to bound the characters of symmetric groups, via the Naruse hook length formula, the Larsen--Shalev character bounds, and appropriate diagram slicings. It allows us to prove a uniform exponential character bound with optimal constant $1/2$. We furthermore prove sharp character bounds for conjugacy classes having a macroscopic

Jianliang He, Xintian Pan, Siyu Chen, Zhuoran Yang

We study how multi-head softmax attention models are trained to perform in-context learning on linear data. Through extensive empirical experiments and rigorous theoretical analysis, we demystify the emergence of elegant attention patterns: a diagonal and homogeneous pattern in the key-query (KQ) weights, and a last-entry-only and zero-sum pattern in the out

Patrick Hytla, Tran T. A. Nghia, Duy Nhat Phan, Andrew Rice

Matrix completion is fundamental for predicting missing data with a wide range of applications in personalized healthcare, e-commerce, recommendation systems, and social network analysis. Traditional matrix completion approaches typically assume centralized data storage, which raises challenges in terms of computational efficiency, scalability, and user priv

Yifei Chen, Lambert Schomaker

Studies in reward shaping for reinforcement learning (RL) have flourished in recent years due to its ability to speed up training. Our previous work proposed an adaptive potential function (APF) and showed that APF can accelerate the Q-learning with a Multi-layer Perceptron algorithm in the low-dimensional domain. This paper proposes to extend APF with an en

Zibin Liu, Banglei Guan, Yang Shang, Yifei Bian

Pose tracking of uncooperative spacecraft is an essential technology for space exploration and on-orbit servicing, which remains an open problem. Event cameras possess numerous advantages, such as high dynamic range, high temporal resolution, and low power consumption. These attributes hold the promise of overcoming challenges encountered by conventional cam

Haoran Ma, Kaihan Zhang, Jiannan Cai

Heat exposure significantly influences pedestrian routing behaviors. Existing methods such as agent-based modeling (ABM) and empirical measurements fail to account for individual physiological variations and environmental perception mechanisms under thermal stress. This results in a lack of human-centred, heat-adaptive routing suggestions. To address these l

Abir Harrasse, Philip Quirke, Clement Neo, Dhruv Nathawani

Mechanistic interpretability research faces a gap between analyzing simple circuits in toy tasks and discovering features in large models. To bridge this gap, we propose text-to-SQL generation as an ideal task to study, as it combines the formal structure of toy tasks with real-world complexity. We introduce TinySQL, a synthetic dataset, progressing from bas

Fangli Yang, Liang Chang, Minghua Pan

The combination of quantum secret sharing (QSS) and continuous-variable quantum key distribution (CV-QKD) has demonstrated clear advantages and has undergone significant development in recent years. However, research on the practical security of CV-QSS remains limited, particularly in the context of free-space channels, which exhibit considerable flexibility

Arka Adhikari, Izumi Okada

We discuss the relationship between the capacity and the geometry for the range of the random walk for $d=3$. In particular, we consider how efficiently the random walk moves or what shape it forms in order to maximize its capacity. In one of our main results, we show a functional law for the capacity of the random walk. In addition, we find that there is a

Brent Sordo, Ellen Rathje, Krishna Kumar

This study utilizes a hybrid Finite Element Method (FEM) and Material Point Method (MPM) to investigate the runout of liquefaction-induced flow slide failures. The key inputs to this analysis are the earthquake ground motion, which induces liquefaction, and the post-liquefaction residual strength. The influence of these factors on runout is evaluated by subj

Fengyun Zhang, Jia Li, Xiaoqing Zhang, Shukai Duan

This paper presents a high-precision positioning system that integrates ultra-wideband (UWB) time difference of arrival (TDoA) measurements, inertial measurement unit (IMU) data, and ultrasonic sensors through factor graph optimization. To overcome the shortcomings of standalone UWB systems in non-line-of-sight (NLOS) scenarios and the inherent drift associa

Soofiyan Atar, Xiao Liang, Calvin Joyce, Florian Richter

The increasing demand for healthcare workers, driven by aging populations and labor shortages, presents a significant challenge for hospitals. Humanoid robots have the potential to alleviate these pressures by leveraging their human-like dexterity and adaptability to assist in medical procedures. This work conducted an exploratory study on the feasibility of

Xing Huang, Peiyue Ma, Mengwu Huo, Chaoxin Huang

We report the systematic synthesis, crystal structure, magnetization, and powder neutron diffraction of single crystalline and polycrystalline CaCo$_2$TeO$_6$ samples. CaCo$_2$TeO$_6$ crystallizes in an orthorhombic structure with $Pnma$ space group, featuring chains of edge-shared CoO$_6$ octahedra arranged in a honeycomb pattern. Two antiferromagnetic tran

Ji-Eun Han, Yoonseok Heo

Incorporating personas into conversational AI models is crucial for achieving authentic and engaging interactions. However, the cultural diversity and adaptability of existing persona datasets is often overlooked, reducing their efficacy in building culturally aware AI systems. To address this issue, we propose a two-step pipeline for generating culture-spec

Jiucheng Chen, Hengyuan Xiao, Tianliang Zhang, Siqin Ding

The energy stability of supercontinuum (SC) significantly impacts its applications. To achieve the most stable SC, we systematically investigated how input pulse energy, numerical aperture (NA), and crystal thickness affect the energy stability of SC generated by femtosecond filamentation in sapphire. Our findings reveal that the SC energy does not always in

Kenneth J. K. Ong, Lye Jia Jun, Hieu Minh "Jord" Nguyen, Seong Hah Cho

As Large Language Models (LLMs) gain autonomous capabilities, their coordination in multi-agent settings becomes increasingly important. However, they often struggle with cooperation, leading to suboptimal outcomes. Inspired by Axelrod's Iterated Prisoner's Dilemma (IPD) tournaments, we explore how personality traits influence LLM cooperation. Using represen

Luca Collini, Andrew Hennessee, Ramesh Karri, Siddharth Garg

Recent Large Language Models (LLMs) such as OpenAI o3-mini and DeepSeek-R1 use enhanced reasoning through Chain-of-Thought (CoT). Their potential in hardware design, which relies on expert-driven iterative optimization, remains unexplored. This paper investigates whether reasoning LLMs can address challenges in High-Level Synthesis (HLS) design space explora

Feng Qiao, Zhexiao Xiong, Eric Xing, Nathan Jacobs

Stereo images are fundamental to numerous applications, including extended reality (XR) devices, autonomous driving, and robotics. Unfortunately, acquiring high-quality stereo images remains challenging due to the precise calibration requirements of dual-camera setups and the complexity of obtaining accurate, dense disparity maps. Existing stereo image gener

Ethan Francolla, Arnav Shah

Atomic swaps have been widely considered to be an ideal solution for cross-chain cryptocurrency transactions due to their trustless and decentralized nature. However, their adoption in practice has been strictly limited compared to centralized exchange order books because of long transaction times (anywhere from 20 to 60 minutes) prohibiting market makers fr

Liankai Zheng, Lijuan Xing, Zhiyu Lin, Wanpeng Zhao

It has been well recognized that there exist high-density deep states in IGZO thin films. Many of the device characteristics of IGZO transistors, such as negative bias illumination stability (NBIS),were understood to be related to these deep states. However, in this work, it is found that deep state density (NtD) of atomic-layer-deposited (ALD) IGZO transist

Shraddha Pradipbhai Shah, Aditya Vilas Deshpande

The integration of Large Language Models (LLMs) into autonomous robotic agents for conducting online transactions poses significant cybersecurity challenges. This study aims to enforce robust cybersecurity constraints to mitigate the risks associated with data breaches, transaction fraud, and system manipulation. The background focuses on the rise of LLM-dri

Keshav Dahiya, Evgeny Mukhin

We use the $q$-characters to compute explicit expressions of the $R$-matrices for first fundamental representations of all types of twisted quantum affine algebras.

U. Camara da Silva

We introduce a renormalization procedure necessary for the complete description of the energy spectra of a one-dimensional stationary Schr\"odinger equation with a potential that exhibits inverse-square singularities. We apply and extend the methods introduced in our recent paper on the hyperbolic P\"oschl-Teller potential (with a single singularity) to its

Rafael Padilla, Özgür Keleş

Recent advancements in virtual reality (VR) technology have enabled the creation of immersive learning environments that provide engineering students with hands-on, interactive experiences. This paper presents a novel framework for virtual laboratory environments (VLEs) focused on embodied learning, specifically designed to teach concepts related to mechanic

Xiao Liang, Junhao Peng, Fugen Wu, Renhai Wang

The hydrogen ions in the superionic ice can move freely, playing the role of electrons in metals. Its electromagnetic behavior is the key to explaining the anomalous magnetic fields of Uranus and Neptune. Based on the ab initio evolutionary algorithm, we searched for the stable H4O crystal structure under pressures of 500-5000 GPa and discovered a new layere

Hanul Jeon

Martin's remarkable proof of $\mathbf{\Pi}^1_2$-determinacy from an iterable rank-into-rank embedding highlighted the connection between large cardinals and determinacy. In this paper, we isolate a large cardinal object called a measurable dilator from Martin's proof of $\mathbf{\Pi}^1_2$-determinacy, which captures the structural essence of Martin's proof o

Suat Barış İplikçioğlu, M. I. Aksun

With the advent of new fabrication technologies, time-varying metasurfaces have emerged as novel platforms for exotic waveform shaping in microwaves and optics, providing an additional degree of freedom to design dynamically controllable and reconfigurable scatterers. Nevertheless, inherent structural properties and material dispersion significantly complica

Spencer Kraisler, Mehran Mesbahi, Behcet Acikmese

A fundamental issue at the core of trajectory optimization on smooth manifolds is handling the implicit manifold constraint within the dynamics. The conventional approach is to enforce the dynamic model as a constraint. However, we show this approach leads to significantly redundant operations, as well as being heavily dependent on the state space representa

Jun Chang, Hantao Lu, Jize Zhao, Hong-Gang Luo

Recently, altermagnetism (AM) has emerged as a new category of magnetism, alongside conventional antiferromagnetism (AFM) and ferromagnetism (FM). In an AM, superconductivity (SC) is faced with a dilemma that the spin-polarized bands, induced by the broken time reversal (T ) symmetry, dominantly supports spin-triplet pairing. In contrast, AM spin fluctuation

Sumin Lee, Namwoo Kang

Modular design maximizes utility by using standardized components in large-scale systems. From a manufacturing perspective, it supports green technology by reducing material waste and improving reusability. Industrially, it offers economic benefits through economies of scale, making it a practical design strategy. Typically, modularization selects a represen

Seunguk Song, Michael Altvater, Wonchan Lee, Hyeon Suk Shin

As silicon-based computing approaches fundamental physical limits in energy efficiency, speed, and density, the search for complementary materials to extend or replace CMOS technology has become increasingly urgent. While two-dimensional (2D) transition metal dichalcogenides have been extensively investigated, van der Waals indium selenides--particularly InS

Joe McIntyre

The early 2020s has seen the rise of two strange and potentially quite impactful social phenomena, namely pseudolaw, where users rely upon pseudolegal arguments that mimic the form and ritual of legal argumentation but fundamentally distort the content of law, and generative AI/LLMs, which generate content that uses probabilistic calculations to create outpu

Iva Laginja, Óscar Carrión-González, Romain Laugier, Elisabeth Matthews

The Habitable Worlds Observatory (HWO) will enable a transformative leap in the direct imaging and characterization of Earth-like exoplanets. For this, NASA is focusing on early investment in technology development prior to mission definition and actively seeking international partnerships earlier than for previous missions. The "R&D for Space-Based HCI in E

Rahul Deshmukh, Avinash Kak

Recent advances in deep-learning based methods for image matching have demonstrated their superiority over traditional algorithms, enabling correspondence estimation in challenging scenes with significant differences in viewing angles, illumination and weather conditions. However, the existing datasets, learning frameworks, and evaluation metrics for the dee

Yang Chen, Hongxin Zhang, Guanyu Xiong, Chenxu Li

With the rapid advancement of brain-computer interface (BCI) technology, the volume of physiological data generated in related research and applications has grown significantly. Data is a critical resource in BCI research and a key factor in the development of BCI technology, making efficient storage and management of this data increasingly vital. In the rea

Tomohiro Soejima, Junkai Dong, Ashvin Vishwanath, Daniel E. Parker

The jellium model is a paradigmatic problem in condensed matter physics, exhibiting a phase transition between metallic and Wigner crystal phases. However, its vanishing Berry curvature makes it ill-suited for studying recent experimental platforms that combine strong interactions with nontrivial quantum geometry. These experiments inspired the anomalous Hal

Samuel Pérez-Ayala, Aaron J. Tyrrell

Let $(M^{n+1},g_+)$ be an asymptotically hyperbolic manifold. We compute the Cheeger constant of conformally compact asymptotically constant mean curvature submanifolds $ \iota : Y^{k+1} \to (M^{n+1},g_+)$ with arbitrary codimension. As an application, we provide two classes of examples of $(n+1)$-dimensional asymptotically hyperbolic manifolds with Cheeger

Giordano Paoletti, Jussara M. Almeida, Luca Vassio, Marcos André Gonçalves

This study delves into the mechanisms that spark user curiosity driving active engagement within public Telegram groups. By analyzing approximately 6 million messages from 29,196 users across 409 groups, we identify and quantify the key factors that stimulate users to actively participate (i.e., send messages) in group discussions. These factors include soci

S. Vallés-Sanclemente, T. H. F. Vroomans, T. R. van Abswoude, F. Brulleman

We experimentally optimize the frequency of flux-tunable couplers in a superconducting quantum processor to minimize the impact of spectator transmons during quantum operations (single-qubit gates, two-qubit gates and readout) on other transmons. We adapt a popular transmon-like tunable-coupling element, achieving high-fidelity, low-leakage controlled-$Z$ ga

Long Hu, Guillaume Olive

In this paper we introduce a method to find the minimal control time for the null controllability of 1D first-order linear hyperbolic systems by one-sided boundary controls when the coefficients are regular enough.

Gabriele Sanguin, Arjun Pakrashi, Marco Viola, Francesco Rinaldi

Handling uncertainty is critical for ensuring reliable decision-making in intelligent systems. Modern neural networks are known to be poorly calibrated, resulting in predicted confidence scores that are difficult to use. This article explores improving confidence estimation and calibration through the application of bilevel optimization, a framework designed

Xin Zhang, Haitao Chu, Lin Liu, Satrajit Roychoudhury

G-computation has become a widely used robust method for estimating unconditional (marginal) treatment effects with covariate adjustment in the analysis of randomized clinical trials. Statistical inference in this context typically relies on the Wald test or Wald interval, which can be easily implemented using a consistent variance estimator. However, existi

Neil Dizon, Jyrki Jauhiainen, Tuomo Valkonen

Online optimisation studies the convergence of optimisation methods as the data embedded in the problem changes. Based on this idea, we propose a primal dual online method for nonlinear time-discrete inverse problems. We analyse the method through regret theory and demonstrate its performance in real-time monitoring of moving bodies in a fluid with Electrica

Matteo Scandella, Michelangelo Bin, Thomas Parisini

Learning models of dynamical systems characterized by specific stability properties is of crucial importance in applications. Existing results mainly focus on linear systems or some limited classes of nonlinear systems and stability notions, and the general problem is still open. This article proposes a kernel-based nonlinear identification procedure to dire

Osmin Lacombe, Lorenzo Paoloni, Francisco G. Pedro

In this paper we study higher-derivative supersymmetric effective field theories focusing on the systematic procedure for the elimination of ghosts from the spectrum. Particular attention is paid to the auxiliary fields, for which the higher-derivative terms induce non-algebraic equations of motion. By employing field redefinitions or the reduction of order

Kehan Shi, Martin Burger

As a generalization of graphs, hypergraphs are widely used to model higher-order relations in data. This paper explores the benefit of the hypergraph structure for the interpolation of point cloud data that contain no explicit structural information. We define the $\varepsilon_n$-ball hypergraph and the $k_n$-nearest neighbor hypergraph on a point cloud and

Gabriel L. Ferreira-Santos, Grzegorz Kowal, Diego A. Falceta-Gonçalves

We investigate the linear tearing instability in weakly collisional plasmas using a non-ideal gyrotropic-MHD framework, uncovering a previously unknown scaling relation for the instability growth rate in high-$\beta$ environments. Even starting from an isotropic equilibrium, our analysis reveals a $\beta$-dependence, with the maximum growth rate scaling as $

Javier Tirado-Garín, Javier Civera

We present AnyCalib, a method for calibrating the intrinsic parameters of a camera from a single in-the-wild image, that is agnostic to the camera model. Current methods are predominantly tailored to specific camera models and/or require extrinsic cues, such as the direction of gravity, to be visible in the image. In contrast, we argue that the perspective a

Jiarui Fei

The notion of denominator vectors can be extended to all generic basis elements of upper cluster algebras in a natural way. Under a weakened version of generic pairing assumption, we provide a representation-theoretic interpretation for this extended notion. We derive several consequences in this generality. We present a counterexample to the conjecture that

Dazhou Guo, Zhanghexuan Ji, Yanzhou Su, Dandan Zheng

Precision medicine in the quantitative management of chronic diseases and oncology would be greatly improved if the Computed Tomography (CT) scan of any patient could be segmented, parsed and analyzed in a precise and detailed way. However, there is no such fully annotated CT dataset with all anatomies delineated for training because of the exceptionally hig

Mohamed Chabab, Samir Iraoui, Hicham Sriba

In this paper we investigate the shadow and energy emission rate of a charged AdS black hole in massive gravity. We develop a new prescription based on maximum frequencies of emission spectrum to probe the thermodynamics of black holes and get an accurate insight into its phase transition criticality. We establish a link between the thermodynamic behavior an

J. W. E. Harrow, A. N. W. Hone

We describe the use of confluent Darboux transformations for Schr\"odinger operators, and how they give rise to explicit Wronskian formulae for certain algebraic solutions of Painlev\'e equations. As a preliminary illustration, we briefly describe how the Yablonskii-Vorob'ev polynomials arise in this way, thus providing well-known expressions for the tau fun

Yuansheng Lian, Ke Zhang, Meng Li

Predicting the future movements of surrounding vehicles is essential for ensuring the safe operation and efficient navigation of autonomous vehicles (AVs) in urban traffic environments. Existing vehicle trajectory prediction methods primarily focus on improving overall performance, yet they struggle to address long-tail scenarios effectively. This limitation

Gurvir Singh, Saptarshi Roy, Arvind

We discover a new class of Gaussian bound entangled states of four-mode continuous-variable systems. These states appear as a transient phase when certain NPT-entangled Gaussian states are evolved under a noisy environment. A thermal bath comprising of harmonic oscillators is allowed to interact with one or modes of the system and a wide variety of initial G

Darin Joseph, Cesare Franchini

In this study, we investigate the formation of electron and hole small polarons in the prototypical ferroelectric material BaTiO3, with a focus on their interaction with ferroelectric distortive fields. To accurately describe the ferroelectric phase in electronically correlated BaTiO3, we employ the HSE06 hybrid density functional, which addresses the limita

Matija Ćuk, Kaustub P. Anand, David A. Minton

The two moons of Mars, Phobos and Deimos, have orbits that are close to martian equator, indicating their formation from a circumplanetary disk. Phobos is currently migrating toward Mars due to tidal dissipation within the planet, and may be disrupted into a ring in few tens of Myr. The past evolution of Phobos is not fully understood, with one possibility b

Anna Roche, Michael R. Koehler, David G. Mandrus, Takashi Taniguchi

Excitons, Coulomb bound electron-hole pairs, dominate the optical response of two-dimensional semiconductors across near-infrared and visible frequencies due to their large binding energy and prominent oscillator strength. Previous measurements of excitons in 2D semiconductors have primarily relied on far-field optical spectroscopy techniques which are diffr

Hengjia Li, Lifan Jiang, Xi Xiao, Tianyang Wang

Video identity customization seeks to produce high-fidelity videos that maintain consistent identity and exhibit significant dynamics based on users' reference images. However, existing approaches face two key challenges: identity degradation over extended video length and reduced dynamics during training, primarily due to their reliance on traditional self-

Tianyuan Wang, Felix Lucka, Daniël M. Pelt, K. Joost Batenburg

In industrial X-ray Computed Tomography (CT), the need for rapid in-line inspection is critical. Sparse-angle tomography plays a significant role in this by reducing the required number of projections, thereby accelerating processing and conserving resources. Most existing methods aim to balance reconstruction quality and scanning time, typically relying on

Naveen Krishnan

This paper examines the evolution, architecture, and practical applications of AI agents from their early, rule-based incarnations to modern sophisticated systems that integrate large language models with dedicated modules for perception, planning, and tool use. Emphasizing both theoretical foundations and real-world deployments, the paper reviews key agent

Jacqueline L. Mitchell, Brian Hyeongseok Kim, Chenyu Zhou, Chao Wang

Large language models (LLMs) are increasingly used for program verification, and yet little is known about \emph{how} they reason about program semantics during this process. In this work, we focus on abstract interpretation based-reasoning for invariant generation and introduce two novel prompting strategies that aim to elicit such reasoning from LLMs. We e

Leonardo Grando, Juan Fernando Galindo Jaramillo, Jose Roberto Emiliano Leite, Edson Luiz Ursini

The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery rec

Daniel J. Rosenkrantz, Madhav V. Marathe, Zirou Qiu, S. S. Ravi

Many researchers have considered multi-agent systems over single-layer networks as models for studying diffusion phenomena. Since real-world networks involve connections between agents with different semantics (e.g., family member, friend, colleague), the study of multi-agent systems over multilayer networks has assumed importance. Our focus is on one class

Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng

Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated. In this work, we propose an algebraic approach to adversarial attacks and study the conditions under which one can generate adversarial ex

Alexander Migdal

We present an exact analytic solution for decaying incompressible magnetohydrodynamic (MHD) turbulence. Our solution reveals a dual formulation in terms of two interacting Euler ensembles--one for hydrodynamic and another for magnetic circulation. This replaces empirical scaling laws with an infinite set of power terms with calculable decay exponents, some o

Pappu Acharya, Martin Trulsson

Alternating shear rotations in dense suspensions have recently shown the ability to reduce both viscosity and dissipation per strain (at a fixed global shear rate). Here, we study alternating shear rotation, with extensive numerical simulations, at various angles and up to their corresponding jamming points. For increasing shear rotation angles, we find that

Steven R. Brill, Britton J. Olson, Guillaume T. Bokman

The localized artificial diffusivity (LAD) method is widely regarded as the preferred multi-material regularization scheme for the compact finite difference method, because it is conservative, easy to implement, and generally robust for a wide range of problems. However, traditional LAD methods face significant challenges when applied to flows with large den

Jeremy A. McCulloch, Ellen Kuhl

When characterizing materials, it can be important to not only predict their mechanical properties, but also to estimate the probability distribution of these properties across a set of samples. Constitutive neural networks allow for the automated discovery of constitutive models that exactly satisfy physical laws given experimental testing data, but are onl

Partho Ghosh, Raisa Bentay Hossain, Mohammad Zunaed, Taufiq Hasan

Automatic video activity recognition is crucial across numerous domains like surveillance, healthcare, and robotics. However, recognizing human activities from video data becomes challenging when training and test data stem from diverse domains. Domain generalization, adapting to unforeseen domains, is thus essential. This paper focuses on office activity re

Roozbeh Siyadatzadeh, Mohsen Ansari, Muhammad Shafique, Alireza Ejlali

Embedded systems power many modern applications and must often meet strict reliability, real-time, thermal, and power requirements. Task replication can improve reliability by duplicating a task's execution to handle transient and permanent faults, but blindly applying replication often leads to excessive overhead and higher temperatures. Existing design-tim

Ayoub Ammar Boudjelal, Rania Yasmine Bir, Huseyin Arslan

In this paper, we introduce the concept of a mother waveform to address key challenges in 5th generation (5G) and 6th generation (6G) networks, including spectral efficiency, backward compatibility, enhanced flexibility, and the integration of joint sensing and communication (JSAC). We propose single-carrier interleaved frequency division multiplexing (SC-IF

Tim Cunningham, Ilaria Caiazzo, Gracjan Sienkiewicz, Peter J. Wheatley

We report the discovery of two new magnetic cataclysmic variables with brown dwarf companions and long orbital periods ($P_{\rm orb}=95\pm1$ and $104\pm2$ min). This discovery increases the sample of candidate magnetic period bouncers with confirmed sub-stellar donors from four to six. We also find their X-ray luminosity from archival XMM-Newton observations

Ananda Roy, Sergei L. Lukyanov, Hubert Saleur

The entanglement spectra for a subsystem in a spin chain fine-tuned to a quantum-critical point contains signatures of the underlying quantum field theory that governs its low-energy properties. For an open chain with given boundary conditions described by a 2D conformal field theory~(CFT), the entanglement spectrum of the left/right half of the system coinc

Andre Nies

The logic blogs 2023 and 2024 have been joined. The present file contains a lot on particular classes of groups and their relationship with logic, as well as entries on ergodic theory and on foundations. There is also a bit on AI proving at the end.

Fernando Tohmé

In this paper, we analyze how global optima of an agent's preferences can be reconstructed from the solutions found for local problems. A sheaf-theoretic analysis provides an abstract characterization of the global solution, and polynomial approximations are obtained when only a few local instances are available.

Leonard Kaufhold, Achim Rosch

We evaluate theoretically the possibility to realize Majorana zero modes in hybrid devices made from topological-insulator (TI) nanowires proximity-coupled to a superconductor. Such systems have been suggested as building blocks of future topological quantum computers, as they have been predicted to realize Majorana zero modes protected by large gaps. A main

Alex Bercik, David A. Craig Penner, David W. Zingg

The construction of stable, conservative, and accurate volume dissipation is extended to discretizations that possess a generalized summation-by-parts (SBP) property within a tensor-product framework. The dissipation operators can be applied to any finite-difference or spectral-element scheme that uses the SBP framework, including high-order entropy-stable s

Boyang Ma, Michael Krüger

Ultrafast science studies the dynamics of electrons in matter with extreme temporal precision, typically in the attosecond and femtosecond time domain. Recent experimental and theoretical progress has put metal-insulator-metal (MIM) tunneling nanojunctions in the spotlight of ultrafast science. Waveform-controlled laser fields can induce ultrafast currents i

Ricardo E. da Silva, Cristiano M. B. Cordeiro

We experimentally demonstrate an acoustically modulated antiresonant nanoweb hollow-core fiber (N-HCF) for the first time. The N-HCF contains two off-center air cores with a diameter difference of 5 microns, separated by a nanoweb of silica. We analytically simulate the influence of the N-HCF core diameter, cladding wall, and nanoweb thicknesses on the confi

Liangyu Wang, Jie Ren, Hang Xu, Junxiao Wang

Fine-tuning large pre-trained LLMs generally demands extensive GPU memory. Traditional first-order optimizers like SGD encounter substantial difficulties due to increased memory requirements from storing activations and gradients during both the forward and backward phases as the model size expands. Alternatively, zeroth-order (ZO) techniques can compute gra

Jacob Chmura, Jonah Dauvet, Sebastian Sabry

Despite advances in language modelling, distributional methods that build semantic representations from co-occurrences fail to discriminate between plausible and implausible events. In this work, we investigate how plausibility prediction can be improved by injecting latent knowledge prompted from large language models using parameter-efficient fine-tuning.