March 2025 arXiv papers — page 215

Jan Bärenfänger, Klaus Zollner, Lukas Cvitkovich, Kenji Watanabe

In this work we report efficient out-of-plane spin injection and detection in an all-van der Waals based heterostructure using only exfoliated 2D materials. We demonstrate spin injection by measuring spin-valve and Hanle signals in non-local transport in a stack of Fe$_3$GeTe$_2$ (FGT), hexagonal boron nitride (hBN) and graphene layers. FGT flakes form the s

J. D. Wagenveld, S. von Hausegger, H-R. Klöckner, D. J. Schwarz

Measurements of the number count dipole with large surveys have shown amplitudes in tension with kinematic predictions based on the observed Doppler dipole of the cosmic microwave background (CMB). These observations seem to be in direct conflict with a homogeneous and isotropic Universe as asserted by the cosmological principle, demanding further investigat

Yuya Ominato, Masahito Mochizuki

We theoretically discover possible dc-current induction and high-harmonic generation from photodriven chiral fermions in B20-type semimetals irradiated with circularly polarized light as nonlinear optical responses with several unconventional properties. First, we find multiple sign changes of the induced bulk dc photocurrent as a function of light parameter

Peace Obioma, Obinna Agbodike, Jenhui Chen, Lei Wang

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-b

Zihao Ren, Lei Wang, Zhengguang Wu, Guodong Shi

In this paper, the distributed strongly convex optimization problem is studied with spatio-temporal compressed communication and equality constraints. For the case where each agent holds an distributed local equality constraint, a distributed saddle-point algorithm is proposed by employing distributed filters to derive errors of the transmitted states for sp

Santosh V. Lohakare

This thesis focuses on late-time cosmic acceleration within modified theories of gravity, using various observational data sets and statistical analysis. The Universe is assumed to be spatially homogeneous and isotropic and is described by the Friedmann Lema\^{i}tre Robertson Walker metric. The late-time acceleration of the Universe has posed a significant c

Simone Di Micco, Beatrice Polacchi, Taira Giordani, Fabio Sciarrino

Quantum memristors represent a promising interface between quantum and neuromorphic computing, combining the nonlinear, memory-dependent behavior of classical memristors with the properties of quantum states. An optical quantum memristor can be realized with a vacuum--one-photon qubit entering a tunable beam splitter whose reflectivity is adapted according t

Yasheerah Yaqoot, Muhammad Ahsan Mustafa, Oleg Sautenkov, Artem Lykov

Emergency search and rescue (SAR) operations often require rapid and precise target identification in complex environments where traditional manual drone control is inefficient. In order to address these scenarios, a rapid SAR system, UAV-VLRR (Vision-Language-Rapid-Response), is developed in this research. This system consists of two aspects: 1) A multimoda

Zhibin Lan, Liqiang Niu, Fandong Meng, Jie Zhou

Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical results indicate that existing LMM-based embedding models trained with the standard InfoNCE loss exhibit a high degree of overlap in similarity distribution between positive and negati

Leonardo Geronzi, Antonio Martinez, Michel Rochette, Kexin Yan

Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth. Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three lo

Thomas Hübner

We study duality gaps in separable optimization problems that contain both convex and nonconvex functions. Classical bounds on the duality gap of such partially nonconvex problems depend solely on the nonconvex functions. As a result, a problem with many convex functions has no tighter bound than one with none at all. To understand the impact of convex funct

Derin Ozer, Sylvain Lamprier, Thomas Cauchy, Nicolas Gutowski

The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which present limitations. To address these, this work proposes the Broad Reaction Set (BRS), a set featuring 20 generic reaction templates written in

Sohan Patnaik, Milan Aggarwal, Sumit Bhatia, Balaji Krishnamurthy

Very large language models (LLMs) such as GPT-4 have shown the ability to handle complex tasks by generating and self-refining step-by-step rationales. Smaller language models (SLMs), typically with < 13B parameters, have been improved by using the data generated from very-large LMs through knowledge distillation. However, various practical constraints such

Fengrui Yao, Volodymyr Multian, Kenji Watanabe, Takashi Taniguchi

Spin valves are essential components in spintronic memory devices, whose conductance is modulated by controlling spin-polarized electron tunneling through the alignment of the magnetization in ferromagnetic elements. Whereas conventional spin valves unavoidably require at least two ferromagnetic elements, here we demonstrate a van der Waals spin valve based

Zhenmin Huang, Ce Hao, Wei Zhan, Jun Ma

Autonomous racing has gained significant attention as a platform for high-speed decision-making and motion control. While existing methods primarily focus on trajectory planning and overtaking strategies, the role of sportsmanship in ensuring fair competition remains largely unexplored. In human racing, rules such as the one-motion rule and the enough-space

V. Martinez-Fernandez, B. Pire, P. Sznajder, J. Wagner

We calculate the kinematic twist-3 and 4 corrections to the leading order amplitude of timelike Compton scattering (TCS) on a (pseudo-)scalar target, in the recently developed framework based on the conformal operator-product expansion. This allows us to compute the complete set of helicity amplitudes of the process, in particular those that vanish at leadin

Pieter Thijs Eendebak

Quantum measurements are not deterministic. For this reason quantum measurements are repeated for a number of shots on identically prepared systems. The uncertainty in each measurement depends on the number of shots and the expected outcome of the measurement. This information can be used to improve the fitting of models to quantum measurements. In this pape

Dengke Zhang, Quan Tang, Fagui Liu, Haiqing Mei

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenMix, a data augmentation technique specifically designed fo

Alexandra Kuznetsova

We study birational automorphisms of algebraic varieties of bounded growth, i.e. such that the norms of the inverse images ${(f^n)}^* \colon \mathrm{NS}(X)\to \mathrm{NS}(X)$ of the powers of the automorphism $f\in\mathrm{Bir}(X)$ are bounded above for $n\geqslant 0$. We prove that some power of an infinite order automorphism of a variety $X$ with such prope

Gino Franco Fazzi, Julie Skoven Hinge, Stefan Heinrich, Paolo Burelli

This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogues. We evaluated state-of-the-art open-weight LLMs to assess their affective expressive range in terms of arousal and valence. Our study employs a novel methodology combining LLM-ba

Tobias Buck, Berkay Günes, Giuseppe Viterbo, William H. Oliver

Galactic chemical abundances provide crucial insights into fundamental galactic parameters, such as the high-mass slope of the initial mass function (IMF) and the normalization of Type Ia supernova (SN Ia) rates. Constraining these parameters is essential for advancing our understanding of stellar feedback, metal enrichment, and galaxy formation processes. H

Emmanuel Alalade, Ashraf Matrawy

Privacy preservation in Internet of Things (IoT) systems requires the use of privacy-enhancing technologies (PETs) built from innovative technologies such as cryptography and artificial intelligence (AI) to create techniques called privacy preservation techniques (PPTs). These PPTs achieve various privacy goals and address different privacy concerns by mitig

Oleg Sautenkov, Aibek Akhmetkazy, Yasheerah Yaqoot, Muhammad Ahsan Mustafa

The UAV-VLPA* (Visual-Language-Planning-and-Action) system represents a cutting-edge advancement in aerial robotics, designed to enhance communication and operational efficiency for unmanned aerial vehicles (UAVs). By integrating advanced planning capabilities, the system addresses the Traveling Salesman Problem (TSP) to optimize flight paths, reducing the t

Yuhao Yang, Zhi Ji, Zhaopeng Li, Yi Li

Generative models have recently gained attention in recommendation systems by directly predicting item identifiers from user interaction sequences. However, existing methods suffer from significant information loss due to the separation of stages such as quantization and sequence modeling, hindering their ability to achieve the modeling precision and accurac

Qipeng Yan, Mingyang Sun, Lihua Zhang

Real-time rendering of high-fidelity and animatable avatars from monocular videos remains a challenging problem in computer vision and graphics. Over the past few years, the Neural Radiance Field (NeRF) has made significant progress in rendering quality but behaves poorly in run-time performance due to the low efficiency of volumetric rendering. Recently, me

Silke Dainese, Simon H. Albrecht

The study of exoplanets has led to many surprises, one of which is the discovery of planets larger than Earth yet smaller than Neptune, super Earths and gas dwarfs. No such planet is a member of the Solar System, yet they appear to be abundant in the local neighbourhood. Their internal structure is not well understood. Super Earths presumably are rocky plane

Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai

Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual's historical data as instructional preference context to customize LLM generation has emerged as a promising directio

Jianyu Wang, Zhengqiao Zhao, Nicolas Dobigeon, Jingdong Chen

Incomplete multiview clustering (IMVC) has gained significant attention for its effectiveness in handling missing sample challenges across various views in real-world multiview clustering applications. Most IMVC approaches tackle this problem by either learning consensus representations from available views or reconstructing missing samples using the underly

Amos Brocco

In this paper, we present an extension to Melda (a library which implements a general purpose delta state JSON CRDT) to support move operations. This enhancement relies on minimal changes to the underlying logic of the data structure, has virtually no runtime overhead and zero storage overhead compared to the original version of the library, ensuring simplic

Qiyi Wang, Yinning Shao, Yunlong Ma, Min Liu

Graph neural architecture search (GraphNAS) has demonstrated advantages in mitigating performance degradation of graph neural networks (GNNs) due to distribution shifts. Recent approaches introduce weight sharing across tailored architectures, generating unique GNN architectures for each graph end-to-end. However, existing GraphNAS methods do not account for

Shalabh K. Anand

We investigate a flexible polymer chain made up of chiral active Brownian particles in two dimensions using computer simulations. In the presence of chiral active Brownian forces, the radius of gyration of the chain reduces significantly. We further identify the formation of spirals using the tangent-tangent correlation to characterize the internal structure

Reiri Miyamoto, Motohiro Sobajima

This paper is concerned with the existence/nonexistence of nontrivial global-in-time solutions to the Cauchy problem \begin{equation} \begin{cases}\tag{P}\partial_tu-\partial_x^2u+Vu=(1+x^2)^{-\frac{m}{2}}u^p,&x\in\mathbb{R},\ t>0,\\ u(x,0)=u_0(x)\ge0,&x\in\mathbb{R}, \end{cases} \end{equation} where $p>1$, $m\ge0$, $u_0\in BC(\mathbb{R})$ and the potential

Hao Li, Yu-Hao Huang, Chang Xu, Viktor Schlegel

Time-series Generation (TSG) is a prominent research area with broad applications in simulations, data augmentation, and counterfactual analysis. While existing methods have shown promise in unconditional single-domain TSG, real-world applications demand for cross-domain approaches capable of controlled generation tailored to domain-specific constraints and

Eleonora Alfinito, Matteo Beccaria

The social organization of microorganisms has long been a fascinating and challenging subject in both biology and sociology. In these organisms, the role of the individual is far less dominant than that of the community, which functions as a superorganism. The coordination is achieved through a communication mechanism known as quorum sensing. When the commun

Iraklis Premptis, Maria Lymperaiou, Giorgos Filandrianos, Orfeas Menis Mastromichalakis

The Unlearning Sensitive Content from Large Language Models task aims to remove targeted datapoints from trained models while minimally affecting their general knowledge. In our work, we leverage parameter-efficient, gradient-based unlearning using low-rank (LoRA) adaptation and layer-focused fine-tuning. To further enhance unlearning effectiveness, we emplo

Dimitra Karkani, Maria Lymperaiou, Giorgos Filandrianos, Nikolaos Spanos

Multilingual hallucination detection stands as an underexplored challenge, which the Mu-SHROOM shared task seeks to address. In this work, we propose an efficient, training-free LLM prompting strategy that enhances detection by translating multilingual text spans into English. Our approach achieves competitive rankings across multiple languages, securing two

Matteo Brosolo, Vinod Puthuvath, Mauro Conti

Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs) have emerged as vital tools for this task. However, issues of robustness and explainability, common in other high risk doma

Ruizhi Zhang, Shengfeng Zhu, Kan Wang, Ding She

Reactor physics is the study of neutron properties, focusing on using models to examine the interactions between neutrons and materials in nuclear reactors. Artificial intelligence (AI) has made significant contributions to reactor physics, e.g., in operational simulations, safety design, real-time monitoring, core management and maintenance. This paper pres

Katharina Klost, Marc van Kreveld, Daniel Perz, Günter Rote

We investigate blob-trees, a new way of connecting a set of points, by a mixture of enclosing them by cycles (as in the convex hull) and connecting them by edges (as in a spanning tree). We show that a minimum-cost blob-tree for $n$ points can be computed in $O(n^3)$ time.

Jiangtao Wang, Jan Ebert, Oleg Filatov, Stefan Kesselheim

Transformer models have revolutionized a wide spectrum of disciplines, especially in language processing. The recent success has proven that model size scalability is crucial for achieving superior performance metrics. However, training large transformer models is challenging even on modern hardware with powerful GPUs and high-speed interconnects. Existing s

Ioseph Buchbinder, Evgeny Ivanov, Nikita Zaigraev

We review the superfield formulation of $\mathcal{N}=2$ higher-spin supergravity theory in harmonic superspace. The analysis of both the hypermultiplet higher-spin supersymmetries and conformal supersymmetries is performed. The analytic superspace gauging of these symmetries gives rise to a set of unconstrained analytical prepotentials describing $\mathcal{N

Antonio Marino, Esteban Restrepo, Claudio Pacchierotti, Paolo Robuffo Giordano

This paper addresses the challenge of allocating heterogeneous resources among multiple agents in a decentralized manner. Our proposed method, Liquid-Graph-Time Clustering-IPPO, builds upon Independent Proximal Policy Optimization (IPPO) by integrating dynamic cluster consensus, a mechanism that allows agents to form and adapt local sub-teams based on resour

Alex Jin, Tarun Dutta, Anh Tu Ngo, Anupam Chattopadhyay

Classification is a fundamental task in machine learning, typically performed using classical models. Quantum machine learning (QML), however, offers distinct advantages, such as enhanced representational power through high-dimensional Hilbert spaces and energy-efficient reversible gate operations. Despite these theoretical benefits, the robustness of QML cl

Mengyi Liu, Jianqiu Xu

As the demand for querying databases in all areas of life continues to grow, researchers have devoted significant attention to the natural language interface for databases (NLIDB). This paper presents a comprehensive survey of recently proposed NLIDBs. We begin with a brief introduction to natural language processing techniques, executable database languages

Irene Mariblanca-Escalona, Luisa M. Lara, Fernando Moreno, Pedro J. Gutiérrez

Comet 7P/Pons-Winnecke was observed from the Calar Alto Observatory (Spain) for four months during the 2021 inbound apparition. Broad-band visible images were taken between 1.71 and 1.25 AU pre-perihelion, while long-slit spectrophotometric data were taken at $\sim$ 1.25 AU pre-perihelion. This dataset has been complemented with three $r$-Sloan images observ

Yu-Ru Chou, Michihiro Takami, Shin-Ping Lai, Emma Whelan

We obtained high spectral resolution spectra ($\Delta v$ $\sim$ 2.5 km s$^{-1}$) for DG Tau A from 4800 \r{A} to 7500 \r{A} using Subaru High Dispersion Spectrograph (HDS) for the first time. The low-velocity components (LVCs, |$v$| < 100 km s$^{-1}$) were observed in the [O I] 5577, 6300, 6364 \r{A}, [S II] 6716, 6731 \r{A} lines. The offset position spectr

Yan Ma, A. Arhrib, S. Moretti, S. Semlali

We analyse light Higgs scalar and pseudoscalar associated hadro-production in the 2-Higgs Doublet Model (2HDM) Type-X (or lepton-specific) within the parameter space allowed by theoretical self-consistency requirements as well as the latest experimental constraints from the Large Hadron Collider (LHC), precision data and $B$ physics. Over the viable regions

Yacine Chitour, Jochen Denzler, Frédéric Jean, Emmanuel Trélat

In this paper, we bring a complete solution to the Ovals problem, as formulated in [3] and [24].

Mads B. Amdisen, Hui Wu, Mikael S. Andersson, Mirjana Dimitrievska

Solid-state batteries are one of the most recent iterations of electrochemical energy storage, and the technology can potentially provide safer and more-energy-dense batteries. The metal closo- and nido-(carba)borates show promise as versatile solid electrolytes and have been shown to have some of the highest ionic conductivities as well as wide electrochemi

Guillaume Chéze, Etienne Fieux

This article deals with ranking methods. We study the situation where a tournament between $n$ players $P_1$, $P_2$, \ldots $P_n$ gives the ranking $P_1 \succ P_2 \succ \cdots \succ P_n$, but, if the results of $P_n$ are no longer taken into account (for example $P_n$ is suspended for doping), then the ranking becomes $P_{n-1} \succ P_{n-2} \succ \cdots \suc

Zichun Ye, Chihao Zhang, Jiahao Zhao

We study the problem of minimizing gap-dependent regret for single-pass streaming stochastic multi-armed bandits (MAB). In this problem, the $n$ arms are present in a stream, and at most $m<n$ arms and their statistics can be stored in the memory. We establish tight non-asymptotic regret bounds regarding all relevant parameters, including the number of arms

Gauthier Thurin

This paper defines quantiles, ranks and statistical depths for image data by leveraging ideas from measure transportation. The first step is to embed a distribution of images in a tangent space, with the framework of linear optimal transport. Therein, Monge-Kantorovich quantiles are shown to provide a meaningful ordering of image data, with outward images ha

Nobutaka Shimizu, Takeharu Shiraga

We present the first nearly-optimal bounds on the consensus time for the well-known synchronous consensus dynamics, specifically 3-Majority and 2-Choices, for an arbitrary number of opinions. In synchronous consensus dynamics, we consider an $n$-vertex complete graph with self-loops, where each vertex holds an opinion from $\{1,\dots,k\}$. At each discrete-t

Jun Wang, M. J. Norden, P. Donker

LOFAR is a low-frequency array distributed across several European countries. Each LOFAR station contains thousands of antennas and associated electronics, making monitoring and thorough testing of those components essential to ensuring station reliability. This paper discusses various anomalies that may arise in LOFAR antennas, tile elements, modems, and su

Wei Luo, Yunkang Cao, Haiming Yao, Xiaotian Zhang

Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest

Usman Ahmed, Mikael P. Johansson, Susi Lehtola, Dage Sundholm

Hydrogen bonding is an important non-covalent interaction that plays a major role in molecular self-organization and supramolecular structures. It can be described accurately with ab initio quantum chemical wave function methods, which become computationally expensive for large molecular assemblies. Density functional theory (DFT) offers a better balance bet

Richard Lindholm, Oscar Marklund, Olof Mogren, John Martinsson

The vast amounts of audio data collected in Sound Event Detection (SED) applications require efficient annotation strategies to enable supervised learning. Manual labeling is expensive and time-consuming, making Active Learning (AL) a promising approach for reducing annotation effort. We introduce Top K Entropy, a novel uncertainty aggregation strategy for A

Chrysa Pratikaki, Panagiotis Filntisis, Athanasios Katsamanis, Anastasios Roussos

Sign Languages are the primary form of communication for Deaf communities across the world. To break the communication barriers between the Deaf and Hard-of-Hearing and the hearing communities, it is imperative to build systems capable of translating the spoken language into sign language and vice versa. Building on insights from previous research, we propos

Sourav Modak, Ahmet Oğuz Saltık, Anthony Stein

Deep learning-based weed control systems often suffer from limited training data diversity and constrained on-board computation, impacting their real-world performance. To overcome these challenges, we propose a framework that leverages Stable Diffusion-based inpainting to augment training data progressively in 10% increments -- up to an additional 200%, thu

Emmanuel Lepinette, Amal Omrani

We propose a constructive framework for the super-hedging problem of a European contingent claim under proportional transaction costs in discrete time. Our main contribution is an explicit recursive scheme that computes both the super-hedging price and the corresponding optimal strategy without relying on martingale arguments. The method is based on convex d

Francesco De Anna, Joshua Kortum

We establish a deep connection between the Prandtl equations linearised around a quadratic shear flow, confluent hypergeometric functions of the first kind, and the Schr\"odinger operator. Our first result concerns an ODE and a spectral condition derived in [10], associated with unstable quasi-eigenmodes of the Prandtl equations. We entirely determine the sp

Dimitry Leites, Alexander S. Tikhomirov

Any supermanifold diffeomorphic to one whose structure sheaf is the sheaf of sections of a~vector bundle over the underlying manifold is called split. Gaw\c{e}dzki (1977) and Batchelor (1979) were the first to prove that any smooth supermanifold is split. In 1981, P.~Green, and Palamodov, found examples of non-split analytic supermanifolds and described obst

Tom Rousseaux, Christophe Crochet, John Aoga, Axel Legay

This article introduces a novel methodology, Network Simulator-centric Compositional Testing (NSCT), to enhance the verification of network protocols with a particular focus on time-varying network properties. NSCT follows a Model-Based Testing (MBT) approach. These approaches usually struggle to test and represent time-varying network properties. NSCT also

Ling Gao, Zhenyu Shu, Shiqing Xin

3D models are widely used in various industries, and mesh data has become an indispensable part of 3D modeling because of its unique advantages. Mesh data can provide an intuitive and practical expression of rich 3D information. However, its disordered, irregular data structure and complex surface information make it challenging to apply with deep learning m

Micol Spitale, Srikar Babu, Serhan Cakmak, Jiaee Cheong

One of the primary goals of Human-Robot Interaction (HRI) research is to develop robots that can interpret human behavior and adapt their responses accordingly. Adaptive learning models, such as continual and reinforcement learning, play a crucial role in improving robots' ability to interact effectively in real-world settings. However, these models face sig

Christophe Crochet, John Aoga, Axel Legay

In this paper, we introduce PANTHER, a modular framework for testing network protocols and formally verifying their specification. The framework incorporates a plugin architecture to enhance flexibility and extensibility for diverse testing scenarios, facilitate reproducible and scalable experiments leveraging Ivy and Shadow, and improve testing efficiency b

Xiaoying Li, Long Xu, Xiaolin Huang, Donglai Xue

Autonomous navigation of car-like robots on uneven terrain poses unique challenges compared to flat terrain, particularly in traversability assessment and terrain-associated kinematic modelling for motion planning. This paper introduces SEB-Naver, a novel SE(2)-based local navigation framework designed to overcome these challenges. First, we propose an effic

Pascale Defraigne, Elisa Pinat, Gérard Petit, Frédéric Meynadier

We present a new approach to report in the Section 4 of BIPM Circular T daily values of the offset between UTC and the predictions of UTC broadcast by the GNSS, this quantity we name bUTC_GNSS. In this approach, the determination of UTC - bUTC_GNSS is based on data collected by several multi-GNSS stations in selected time laboratories worldwide. Test computa

Jiesi Hu, Chenfei Ye, Yanwu Yang, Xutao Guo

In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance from context, making it particularly effective for the intricate demands of neuroimaging. However, current ICL models, limited to 2D inputs and thus exhibiting suboptimal performanc

Kunio Kaneta, Kin-ya Oda, Motohiko Yoshimura

We develop a quantum theory of inflaton and its decay product of various gauge boson pairs to investigate the preheating towards thermalized universe. The inflaton decay into gauge-boson pairs is shown to be inevitably accompanied by tachyon-mass-like correction to inflation potential that ultimately leads to an inflaton escape out of trapped local potential

Zhengzhen Li, Jiahao Shen, Mengyu Ji, Huazi Cao

High-precision manipulation has always been a developmental goal for aerial manipulators. This paper investigates the kinematic coordinate control issue in aerial manipulators. We propose a predictive kinematic coordinate control method, which includes a learning-based modified kinematic model and a model predictive control (MPC) scheme based on weight alloc

Nikita Kazeev, Wei Nong, Ignat Romanov, Ruiming Zhu

Crystal symmetry plays a fundamental role in determining its physical, chemical, and electronic properties such as electrical and thermal conductivity, optical and polarization behavior, and mechanical strength. Almost all known crystalline materials have internal symmetry. However, this is often inadequately addressed by existing generative models, making t

Peter Albers, Gabriele Benedetti, Levin Maier

The subject of this article are magnetic geodesics on odd-dimensional spheres endowed with the round metric and with the magnetic potential given by the standard contact form. We compute the Ma\~n\'e's critical value of the system and show that a value of the energy is supercritical if and only if all pairs of points on the sphere can be connected by a magne

Nico Sutter, Valentin N. Hartmann, Stelian Coros

When manipulating objects in the real world, we need reactive feedback policies that take into account sensor information to inform decisions. This study aims to determine how different encoders can be used in a reinforcement learning (RL) framework to interpret the spatial environment in the local surroundings of a robot arm. Our investigation focuses on co

Sungmo Kang, Rokyeon Kim, Seungwu Han, Young-Woo Son

The newly developed machine learning (ML) empirical pseudopotential (EP) method overcomes the poor transferability of the traditional EP method with the help of ML techniques while preserving its formal simplicity and computational efficiency. We apply the new method to binary and ternary systems such as GeSe and Ge-Sb-Te (GST) compounds, well-known material

Jiahui Sun, Zhichao Hua, Yubin Xia

Comprehensive evaluation of mobile agents can significantly advance their development and real-world applicability. However, existing benchmarks lack practicality and scalability due to the extensive manual effort in defining task reward signals and implementing evaluation codes. We propose AutoEval, an evaluation framework which tests mobile agents without

Ashish Singh, Priti Mohapatra

Retrieving the right level of context for a given query is a perennial challenge in information retrieval - too large a chunk dilutes semantic specificity, while chunks that are too small lack broader context. This paper introduces the Hierarchical Re-ranker Retriever (HRR), a framework designed to achieve both fine-grained and high-level context retrieval f

Zhenpeng Chen, Chong Wang, Weisong Sun, Xuanzhe Liu

Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class s

Seungkwon Kim, GyuTae Park, Sangyeon Kim, Seung-Hun Nam

Story visualization is the transformation of narrative elements into image sequences. While existing research has primarily focused on visual contextual coherence, the deeper narrative essence of stories often remains overlooked. This limitation hinders the practical application of these approaches, as generated images frequently fail to capture the intended

Yunxiao Shi, Wujiang Xu, Zeqi Zhang, Xing Zi

User profile embedded in the prompt template of personalized recommendation agents play a crucial role in shaping their decision-making process. High-quality user profiles are essential for aligning agent behavior with real user interests. Typically, these profiles are constructed by leveraging LLMs for user profile modeling (LLM-UM). However, this process f

Adnan Ali, Jinglong Li, Huanhuan Chen, AlMotasem Bellah Al Ajlouni

Social networks have a vast range of applications with graphs. The available benchmark datasets are citation, co-occurrence, e-commerce networks, etc, with classes ranging from 3 to 15. However, there is no benchmark classification social network dataset for graph machine learning. This paper fills the gap and presents the Binary Classification Social Networ

Sangeeta Sharma, Deepika Gill, Jyoti Krishna, Eddie Harris-Lee

It is now well established that a few femtosecond laser pulse will induce an ultrafast loss of moment in a magnetic material. Here we show that the opposite effect can also occur: an ultrafast increase in moment. Employing both tight-binding and state-of-the-art time dependent density functional theory we find that laser light tuned to the majority spin cond

Z. T. Pei, X. Y. Yue, X. T. Zheng

The G-expectation is a sublinear expectation. It is an important tool for pricing financial products and managing risk thanks to its ability to deal with model uncertainty. The problem is how to efficiently quantify it since the commonly used Monte Carlo method does not work. Fortunately, the expectation of a G-normal random variable can be linked to the vis

Tian Gao, Zhiyuan Zhang, Yu Zhang, Huajun Liu

Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN), offering a potential solution to the deployment challenges faced by Vision Transformers (ViTs) on edge devices. However, due to the structural differences between CNN and Transformer architectures, simply applying b

Lianyu Wang, Meng Wang, Huazhu Fu, Daoqiang Zhang

Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard the intellectual property (IP) of well-trained models. Effective IP protection extends beyond ensuring authorized usage; it also necessitates restricting model deployment to author

Dengke Qi, Xiangyu Wang, Jiayu Ma, Zhenghua Li

Quantum key distribution relying on the principles of quantum mechanics enables two parties to produce a shared random secret key, thereby ensuring the security of data transmission. Continuous variable quantum key distribution (CV-QKD) is widely applied because it can be well combined with standard telecommunication technology. Compared to CV-QKD with a tra

Heng Zhou, Hejia Geng, Xiangyuan Xue, Li Kang

Multi-agent systems (MAS) have emerged as a promising approach for enhancing the reasoning capabilities of large language models in complex problem-solving; however, current MAS frameworks suffer from poor flexibility and scalability with underdeveloped optimization strategies. To address these challenges, we propose ReSo, which integrates task graph generat

Louis Mahon, Benjamin Hoffman, Logan James, Maddie Cusimano

We propose a method for accurately detecting bioacoustic sound events that is robust to overlapping events, a common issue in domains such as ethology, ecology and conservation. While standard methods employ a frame-based, multi-label approach, we introduce an onset-based detection method which we name Voxaboxen. It takes inspiration from object detection me

Wooju Lee, Juhye Park, Dasol Hong, Changki Sung

Accurate localization is essential for autonomous driving, but GNSS-based methods struggle in challenging environments such as urban canyons. Cross-view pose optimization offers an effective solution by directly estimating vehicle pose using satellite-view images. However, existing methods primarily rely on cross-view features at a given pose, neglecting fin

Yifeng Xu, Fan Zhu, Ye Li, Sebastian Ren

Bin picking is a challenging robotic task due to occlusions and physical constraints that limit visual information for object recognition and grasping. Existing approaches often rely on known CAD models or prior object geometries, restricting generalization to novel or unknown objects. Other methods directly regress grasp poses from RGB-D data without object

Qingsong Wang, Yunfei Qu, Chunfeng Cui, Deren Han

Despite the remarkable success of low-rank estimation in data mining, its effectiveness diminishes when applied to data that inherently lacks low-rank structure. To address this limitation, in this paper, we focus on non-negative sparse matrices and aim to investigate the intrinsic low-rank characteristics of the rectified linear unit (ReLU) activation funct

Ryoga Furutani

A link $L$ in $S^3$ is called a symmetric link if it is preserved by a $\pi$ rotation around a closed geodesic in $S^3$. Any symmetric link can be depicted by a diagram with a symmetry axis lying on the plane of the diagram, called a transvergent diagram. Recently, Sugawara proved that any symmetric link can be represented by a divide with cusps, which is a

Mingda Qiao, Eric Zhao

Calibration measures quantify how much a forecaster's predictions violates calibration, which requires that forecasts are unbiased conditioning on the forecasted probabilities. Two important desiderata for a calibration measure are its decision-theoretic implications (i.e., downstream decision-makers that best-respond to the forecasts are always no-regret) a

Maximilian Hilger, Vladimír Kubelka, Daniel Adolfsson, Ralf Becker

Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing tool, especially in vision-obstructed environments, as it is less affected by particles than lidars or cameras. Modern 4D imaging radars provide three-dimensional geometric informatio

Wei Sun, Qianlong Du, Fuwei Cui, Jiajun Zhang

Enhancing the mathematical reasoning capabilities of Large Language Models (LLMs) is of great scientific and practical significance. Researchers typically employ process-supervised reward models (PRMs) to guide the reasoning process, effectively improving the models' reasoning abilities. However, existing methods for constructing process supervision training

Arul Shankar, Jacob Tsimerman

We prove that the smoothed counting function of the set of quartic fields, satisfying any finite set of local conditions, can be written as a linear combination of $X,X^{5/6}\log X,X^{5/6}$, upto an error term of $O(X^{13/16+o(1)})$. For certain sets of local conditions, namely, those cutting out ``$S_4$-families'' of quartic fields, we explicitly determine

Michael R. R. Good, Eric V. Linder

We demonstrate that if the universe started as a vacuum fluctuation rather than from a singular Big Bang state, the universe must have a late-time cosmic acceleration. This is required by a ``cosmological sum rule'' derived using the Schwarzian form of the Friedmann equations. We discuss possible connections to conformal and M\"obius transformations, and als

Jiwan Chung, Saejin Kim, Yongrae Jo, Jaewoo Park

Large language models (LLMs) operate as autoregressive predictors over discrete token vocabularies, a formulation that has enabled their adaptation far beyond natural language to vision, robotics, and multimodal reasoning. However, training against one-hot targets disregards metric relationships between tokens and limits effectiveness on tasks where distance

Xiaoran Li, Jie Qiu, Shuqing Zhang, Liang Hao

In inertial confinement fusion (ICF), the dynamics of plasma mixing in hohlraums critically influence laser-plasma instabilities (LPI) and implosion performance. This study investigates the mixing of hohlraum ablated Au plasmas and filling C$_5$H$_{12}$ plasmas using one-dimensional particle-in-cell (PIC) simulations. We find that ion-ion collisions slow the

Katsuhiko Matsuzaki

On two subsurfaces of a Riemann surface divided by a $p$-Weil-Petersson curve $\gamma$, we consider the spaces of harmonic functions whose $p$-Dirichlet integrals are finite in the complementary domains of $\gamma$. By requiring the coincidence of boundary values on $\gamma$, we establish a correspondence between the harmonic functions in these Banach spaces