March 2025 arXiv papers — page 101

Siyu Jiang, Fa Peng Huang

In many well-motivated new physics models, the pseudo-Nambu-Goldstone boson (pNGB) from U(1) symmetry breaking emerges as a promising dark matter candidate. Its coupling, suppressed by the symmetry breaking scale, prevents thermal equilibrium in the early Universe for high scale symmetry breaking. Thus, pNGB dark matter is predominantly produced via non-ther

Kang-Sin Choi

We present an amplitude-generating formula in renormalizable quantum field theory. It reflects the self-similarity of loop amplitudes, in which an amplitude can also be a subamplitude of another. Amplitudes are generated by a small number of "irreducible" ones, which may replace tree-level couplings to form more complex amplitudes.

Yufei Zhu, Yiming Zhong, Zemin Yang, Peishan Cong

Dexterous robotic hands often struggle to generalize effectively in complex environments due to the limitations of models trained on low-diversity data. However, the real world presents an inherently unbounded range of scenarios, making it impractical to account for every possible variation. A natural solution is to enable robots learning from experience in

Renzi Wang, Mathijs Schuurmans, Panagiotis Patrinos

This paper considers risk-sensitive model predictive control for stochastic systems with a decision-dependent distribution. This class of systems is commonly found in human-robot interaction scenarios. We derive computationally tractable convex upper bounds to both the objective function, and to frequently used penalty terms for collision avoidance, allowing

Idris Temitope Bello, Hassan Raza, Madithedu Muneeswara, Neha Tewari

The reliability and safety of Lithium-ion batteries (LiBs) are of great concern in the energy storage industry. Nevertheless, the real-time monitoring of their degradation remains challenging due to limited quantitative metrics available during cycling. This study addresses this limitation by employing a novel approach that combines external optical fiber se

Max Fischer, Arianna Poli, Lorenzo Crippa, Dumitru Călugăru

The Kondo screening of a localized magnetic moment crucially depends on the spectral properties of the electronic bath to which it is coupled. Unlike textbook examples, realistic systems as well as dynamical mean-field theory of correlated lattice models force us to consider sharp features in the hybridization function near the Fermi energy. Divergencies of

Wanxin Yu, Zhemin Zhu, Cong Wang, Yihang Bao

To meet the clinical demand for accurate 3D lumbar spine assessment in a weight-bearing position, this study presents a novel, fully automatic framework for high-precision 3D reconstruction from biplanar X-ray images, overcoming the limitations of existing methods. The core of this method involves a novel multi-task deep learning network that simultaneously

Yu Cheng, Fajie Yuan

Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video data into a compact latent space. However, as LVDM training scales, the computational overhead of Video VAEs becomes a critical bottleneck, particularly for encoding high-resolutio

Wei Song, Yuran Wang, Zijia Song, Yadong Li

The differing representation spaces required for visual understanding and generation pose a challenge in unifying them within the autoregressive paradigm of large language models. A vision tokenizer trained for reconstruction excels at capturing low-level visual appearance, making it well-suited for visual generation but lacking high-level semantic represent

Jameel-Un Nabi, Asim Ullah, Zeeshan Khan

Reliable and precise knowledge of the $\beta$-decay properties of neutron-rich nuclei is important for a better understanding of the $r$-process. We report the computation of $\beta$-decay properties of neutron-rich Cerium isotopes calculated within the proton-neutron quasiparticle random phase approximation (pn-QRPA) approach. A total of 34 isotopes of Ce i

Tiago Vasconcelos Afonso, Florian Heinrichs

Electroencephalography-based eye tracking (EEG-ET) leverages eye movement artifacts in EEG signals as an alternative to camera-based tracking. While EEG-ET offers advantages such as robustness in low-light conditions and better integration with brain-computer interfaces, its development lags behind traditional methods, particularly in consumer-grade settings

Adrián Javaloy, Antonio Vergari, Isabel Valera

In machine learning (ML), we often need to choose one among hundreds of trained ML models at hand, based on various objectives such as accuracy, robustness, fairness or scalability. However, it is often unclear how to compare, aggregate and, ultimately, trade-off these objectives, making it a time-consuming task that requires expert knowledge, as objectives

Dimitra Kyriakopoulou

We show that Boundary Control method, a method for hyperbolic inverse problems, is also capable of dealing directly with certain classes of elliptic and parabolic Inverse Boundary Value Problems; thus pointing towards Boundary Control method potentially constituting a means of unification of Inverse Boundary Value Problems. As an application we show that the

Amanda Parker, J. M. Schwarz

In vitro collagen networks and in silico fiber network models are typically used to represent extracellular matrix in tissues. Such networks exhibit the phenomenon of strain-stiffening, or an increase in elastic modulus with increasing strain, both under isotropic expansion and shear. However, the deformations induced in an extracellular matrix environment i

Richard Griffon, Samuel Le Fourn, Fabien Pazuki

We give optimal estimates on the variation of the differential and modular heights within an isogeny class of abelian varieties defined over the function field of a curve (in any characteristic). We also prove a parallelogram inequality for abelian varieties in this context, and deduce corollaries of these results.

Ahmed Hussain, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil

Ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for 6G networks, offering exceptional high data rates in millimeter-wave (mmWave) and Terahertz (THz) frequency bands. The deployment of large antenna arrays at high frequencies transitions wireless communication into the radiative near-field, where precise beam alignment beco

Junliang Luo, Hong Kang, Shuhao Zheng, Xue Liu

Airdrops issued by platforms are to distribute tokens, drive user adoption, and promote decentralized services. The distributions attract airdrop hunters (attackers), who exploit the system by employing Sybil attacks, i.e., using multiple identities to manipulate token allocations to meet eligibility criteria. While debates around airdrop hunting question th

David A. Hague, David G. Felton

This letter presents a method for synthesizing equiripple MIMO transmit beampatterns using Chebyshev approximation. The MIMO beampattern is represented as a non-negative real-valued trigonometric polynomial where the $\ell^{\text{th}}$ order polynomial coefficient is the sum of the $\ell^{\text{th}}$ order diagonal of the waveform correlation matrix. The opt

Santiago Acerenza, Julian Martinez-Iriarte, Alejandro Sánchez-Becerra, Pietro Emilio Spini

We obtain partial identification of direct and spillover effects in settings with strategic interaction and discrete treatments, outcome and independent instruments. We consider a framework with two decision-makers who play pure-strategy Nash equilibria in treatment take-up, whose outcomes are determined by their joint take-up decisions. We obtain a latent-t

Peng Zong, Jian-Ning Fu, Jie Su, Bing-Kai Zhang

In this study, we conduct a comparative analysis of the properties of Blazhko and non-Blazhko RRab stars. We identified 1054 non-Blazhko and 785 Blazhko RRab stars in the photometric data observed by K2 mission, which, combined with those 37 stars observed in the original Kepler field, constituted our study sample. Using the Fourier Decomposition method, we

Anthony D. Stephens, David R. Walwyn

In 2024, the UK Government made two striking announcements on its plans to decarbonise the energy system; it pledged GBP22 billion to establish carbon capture and storage hubs on Teesside and Merseyside and released the Clean Power 2030 Action Plan. This paper questions the validity of both plans, arguing that they do not take adequate account of the consequ

M. Raquel Serial, Luca Schmidt, Muhammad Adrian, Grit Brauckmann

Enzyme immobilization plays a crucial role in enhancing the stability and recyclability of enzymes for industrial applications. However, traditional methods for quantifying enzyme loading within porous carriers are limited by time-consuming workflows, cumulative errors, and the inability to probe enzymes adsorbed inside the pores. In this study, we introduce

Hisamitsu Awaki, Matthew G. Baring, Richard Bose, Dana Braun

We report measurements of the linear polarisation degree (PD) and angle (PA) for hard X-ray emission from the Crab pulsar and wind nebula. Measurements were made with the XL-Calibur ($\sim$15-80 keV) balloon-borne Compton-scattering polarimeter in July 2024. The polarisation parameters are determined using a Bayesian analysis of Stokes parameters obtained fr

Bing-Shu Hu, Xiao-Ming Lu

The Mach-Zehnder interferometer is a fundamental tool for measuring phase shifts between two light paths, serving as a crucial prototype for achieving high-precision measurements in various scientific and technological applications. In this study, we analyze different models for estimating relative phase shift in a general two-arm Mach-Zehnder interferometer

Adeel Akram, Xiangyang Ju, Michael Papenbrock, Jenny Taylor

We present track reconstruction algorithms based on deep learning, tailored to overcome specific central challenges in the field of hadron physics. Two approaches are used: (i) deep learning (DL) model known as fully-connected neural networks (FCNs), and (ii) a geometric deep learning (GDL) model known as graph neural networks (GNNs). The models have been im

Yuheng Li, Mingzhe Hu, Richard L. J. Qiu, Maria Thor

Deep learning-based segmentation of genito-pelvic structures in MRI and CT is crucial for applications such as radiation therapy, surgical planning, and disease diagnosis. However, existing segmentation models often struggle with generalizability across imaging modalities, and anatomical variations. In this work, we propose RoMedFormer, a rotary-embedding tr

Hassan S. Al Khatib, Sudip Mittal, Shahram Rahimi, Nina Marhamati

The transition towards patient-centric healthcare necessitates a comprehensive understanding of patient journeys, which encompass all healthcare experiences and interactions across the care spectrum. Existing healthcare data systems are often fragmented and lack a holistic representation of patient trajectories, creating challenges for coordinated care and p

Huizhang Yang, Zhong Liu, Jian Yang

Synthetic aperture radar (SAR) imaging traditionally requires precise knowledge of system parameters to implement focusing algorithms that transform raw data into high-resolution images. These algorithms require knowledge of SAR system parameters, such as wavelength, center slant range, fast time sampling rate, pulse repetition interval (PRI), waveform param

Nikolaos Dimakis, Alex Giacomini, Andronikos Paliathanasis, Grigorios Panotopoulos

We investigate static spherically symmetric spacetimes within the framework of symmetric teleparallel $f(Q)$ gravity in order to describe relativistic stars. We adopt a specific ansatz for the background geometry corresponding to a singularity-free space-time. We obtain an expression for the connection, which allows the derivation of solutions for any $f(Q)$

Kamil Książek, Hubert Jastrzębski, Bartosz Trojan, Krzysztof Pniaczek

The ability of deep learning models to learn continuously is essential for adapting to new data categories and evolving data distributions. In recent years, approaches leveraging frozen feature extractors after an initial learning phase have been extensively studied. Many of these methods estimate per-class covariance matrices and prototypes based on backbon

Mohammad Hossein Rahimi Abkenar, Ahmad Mohamadnejad, Reza Sepahvand

This paper studies gravitational waves in a dark matter model composed of three types of particles with distinct spins, along with a scalar field $\phi$ that mediates interactions between Standard Model particles and dark matter. It discusses the electroweak phase transition following the Big Bang, during which all particles are initially massless due to the

Lucas Gnecco-Heredia, Matteo Sammut, Muni Sreenivas Pydi, Rafael Pinot

Randomization as a mean to improve the adversarial robustness of machine learning models has recently attracted significant attention. Unfortunately, much of the theoretical analysis so far has focused on binary classification, providing only limited insights into the more complex multiclass setting. In this paper, we take a step toward closing this gap by d

Ambarish Moharil, Indika Kumara, Damian Andrew Tamburri, Majid Mohammadi

This paper conceptualizes the Deep Weight Spaces (DWS) of neural architectures as hierarchical, fractal-like, coarse geometric structures observable at discrete integer scales through recursive dilation. We introduce a coarse group action termed the fractal transformation, $T_{r_k} $, acting under the symmetry group $G = (\mathbb{Z}, +) $, to analyze neural

Usman Syed, Bin Hu

Computing tight Lipschitz bounds for deep neural networks is crucial for analyzing their robustness and stability, but existing approaches either produce relatively conservative estimates or rely on semidefinite programming (SDP) formulations (namely the LipSDP condition) that face scalability issues. Building upon ECLipsE-Fast, the state-of-the-art Lipschit

Jorge Ruiz-Cases

We establish a complete Widder Theory for the fractional fast diffusion equation. Our work focuses on nonnegative solutions satisfying a certain integral size condition at infinity. We prove that these solutions possess a Radon measure as initial trace, and prove the existence and uniqueness of solutions originating from such initial data. The uniqueness res

Liang Wan, Qingsong Mei, Haowen Liu, Huafeng Zhang

Despite its widespread use in materials science, conventional molecular dynamics (MD) simulations are severely constrained by timescale limitations. To address this shortcoming, we propose an empirical formulation of accelerated MD method, adapted from a collective-variable-based extended system dynamics framework. While this framework is originally develope

Sakib Matin, Emily Shinkle, Yulia Pimonova, Galen T. Craven

The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Datasets generated with high-fidelity QC methods are typically restricted to small molecules and may be missing energy gradients, which make it difficult to train accurate MLIPs. We prese

Marco Meyer-Conde, Nobuyuki Kanda, Hirotaka Takahashi, Ken-ichi Oohara

High-Energy Physics (HEP) and Gravitational Wave (GW) communities serve different scientific purposes. However, their methodologies might potentially offer mutual enrichment through common software developments. A suite of libraries is currently being prototyped and made available at https://git.ligo.org/kagra/libraries-addons/root, extending at no cost the

Santiago Oribe, Marcela Peláez, Urko Reinosa

We discuss the existence of Landau-pole-free renormalization group trajectories in the Minkowskian version of the Curci-Ferrari model as a function of a running parameter $q^2$ associated to the four-vector $q$ at which renormalization conditions are imposed, and which can take both space-like ($\smash{q^2<0}$) and time-like ($\smash{q^2>0}$) values. We disc

I. L. Buchbinder, S. A. Fedoruk, V. A. Krykhtin

We give a brief overview of the BRST approach to the gauge invariant Lagrangian formulation for free massive higher-spin bosonic fields focusing on two specific aspects. First, the theory is considered in four dimensional flat space in terms of spin-tensor fields with two component undotted and dotted indices. This leads to a significant simplification of th

Lukas Reicht, Lukas Legenstein, Sandro Wieser, Egbert Zojer

Heat transport can be modelled with a variety of approaches in real space (using molecular dynamics) or in reciprocal space (using the Boltzmann transport equation). Employing two conceptually different approaches of each type, we study heat transport in crystalline polyethylene and polythiophene. We find that consistent results can be obtained when using hi

Justus Westerhoff, Golzar Atefi, Mario Koddenbrock, Alexei Figueroa

The capacity of foundation models allows for their application to new, unseen tasks. The adaptation to such tasks is called transfer learning. An efficient transfer learning method that circumvents parameter optimization is imprinting. The conceptual differences between studies on imprinting form the basis of our systematic investigation. In this work, we pr

Marco Schlichting

We show that for finite dimensional regular Noetherian rings that contain a field or are smooth over a Dedekind domain, the comparison map from the Hermitian K-theory of genuine symmetric forms to that of symmetric forms is an equivalence in degrees greater or equal -1 and a monomorphism in degree -2. In particular, the spaces of Hermitian K-theory of genuin

Enrico Tosi, Panwei Hu, Aleksandar Ichkov, Marina Petrova

Millimeter-wave (mm-wave) communications requirebeamforming and consequent precise beam alignmentbetween the gNodeB (gNB) and the user equipment (UE) toovercome high propagation losses. This beam alignment needs tobe constantly updated for different UE locations based on beamsweepingradio frequency measurements, leading to significantbeam management overhead

Nicolas Le Roux, Marc G. Bellemare, Jonathan Lebensold, Arnaud Bergeron

We propose a new algorithm for fine-tuning large language models using reinforcement learning. Tapered Off-Policy REINFORCE (TOPR) uses an asymmetric, tapered variant of importance sampling to speed up learning while maintaining stable learning dynamics, even without the use of KL regularization. TOPR can be applied in a fully offline fashion, allows the han

Eduard Lerner

Consider a finite field $\mathbb F_q$, $q=p^d$, where $p$ is an odd number. Let $M=(E,r)$ be a regular matroid; denote by ${\mathcal B}$ the family of its bases, $\bar s(M;\alpha)=\sum_{B\in {\mathcal B}}\prod_{e\not\in B} \alpha_e$, where ${\alpha_e\in \mathbb F_q}$, $\alpha_e\neq 0$. Let a subset $A\equiv A(\alpha)$ in $E$ have the maximal cardinality and

Jiacen Xu, Chenang Li, Yu Zheng, Zhou Li

Graph-based Network Intrusion Detection Systems (GNIDS) have gained significant momentum in detecting sophisticated cyber-attacks, such as Advanced Persistent Threats (APTs), within and across organizational boundaries. Though achieving satisfying detection accuracy and demonstrating adaptability to ever-changing attacks and normal patterns, existing GNIDS p

Pascal Stiefenhofer

The rise of Artificial General Intelligence (AGI) marks an existential rupture in economic and political order, dissolving the historic boundaries between labor and capital. Unlike past technological advancements, AGI is both a worker and an owner, producing economic value while concentrating power in those who control its infrastructure. Left unchecked, thi

Joseph W. Baron

The remarkable universality of the eigenvalue correlation functions is perhaps one of the most salient findings in random matrix theory. Particularly for short-range separations of the eigenvalues, the correlation functions have been shown to be robust to many changes in the random matrix ensemble, and are often well-predicted by results corresponding to Gau

Adam Štorek, Mukur Gupta, Noopur Bhatt, Aditya Gupta

AI coding assistants are widely used for tasks like code generation. These tools now require large and complex contexts, automatically sourced from various origins$\unicode{x2014}$across files, projects, and contributors$\unicode{x2014}$forming part of the prompt fed to underlying LLMs. This automatic context-gathering introduces new vulnerabilities, allowin

Marko Shuntov, Pascal A. Oesch, Sune Toft, Romain A. Meyer

We develop a theoretical framework to provide observational constraints on the early Universe galaxy-halo connection by combining measurements of the UV luminosity function (UVLF) and galaxy clustering via the 2-point correlation function (2PCF). We implemented this framework in the FRESCO and CONGRESS JWST NIRCam/grism surveys by measuring the 2PCF of spect

Maryam Shaygan Tabar, Johannes Kortz, Paul Staat, Harald Elders-Boll

Many computing systems need to be protected against physical attacks using active tamper detection based on sensors. One technical solution is to employ an ATR (Anti-Tamper Radio) approach, analyzing the radio wave propagation effects within a protected device to detect unauthorized physical alterations. However, ATR systems face key challenges in terms of s

Dan Goreac, Juan Li, Xinru Zhang

In this paper we explore several novel notions of exact controllability for mean-field linear controlled stochastic differential equations (SDEs). A key feature of our study is that the noise coefficient is not required to be of full rank. We begin by demonstrating that classical exact controllability with $\mathbb{L}^2$-controls necessarily requires both ra

Fedor Zolotarev, Borek Reich, Tuomas Eerola, Tomi Kauppi

In this work, we propose a novel method to synthetically generate realistic 3D representations of wooden logs. Efficient sawmilling heavily relies on accurate measurement of logs and the distribution of knots inside them. Computed Tomography (CT) can be used to obtain accurate information about the knots but is often not feasible in a sawmill environment. A

Changkeun Oh

In this short note, we prove that the restriction conjecture for the (hyperbolic) paraboloid in $\mathbb{R}^d$ implies the $l^p$-decoupling theorem for the (hyperbolic) paraboloid in $\mathbb{R}^{2d-1}$. In particular, this gives a simple proof of the $l^p$ decoupling theorem for the (hyperbolic) paraboloid in $\mathbb{R}^3$.

Jiang Qin, Senmao Li, Alexandra Gomez-Villa, Shiqi Yang

Recent advances in Text-to-Image (T2I) diffusion models have transformed image generation, enabling significant progress in stylized generation using only a few style reference images. However, current diffusion-based methods struggle with fine-grained style customization due to challenges in controlling multiple style attributes, such as color and texture.

Glenn Grubert, Florian Barthel, Anna Hilsmann, Peter Eisert

3D Gaussian Splatting (3DGS) has become one of the most influential works in the past year. Due to its efficient and high-quality novel view synthesis capabilities, it has been widely adopted in many research fields and applications. Nevertheless, 3DGS still faces challenges to properly manage the number of Gaussian primitives that are used during scene reco

Matthew J. Allen, Harry J. F. Owen, Stuart W. D. Grieve, Emily R. Lines

Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based forest inventories are labor-intensive and limited in spatial coverage. Advances in remote sensing using drone-acquired RGB imagery combined with deep learning models have promised

Runyi Li, Bin Chen, Jian Zhang, Radu Timofte

Real-world image super-resolution is a critical image processing task, where two key evaluation criteria are the fidelity to the original image and the visual realness of the generated results. Although existing methods based on diffusion models excel in visual realness by leveraging strong priors, they often struggle to achieve an effective balance between

Ziyu Zhong, Mufan Liu, Le Yang, Yifan Wang

In this paper, we present Kairos, a model predictive control (MPC)-based adaptive bitrate (ABR) scheme that integrates streaming-aware throughput predictions to enhance video streaming quality. Kairos features an attention-based throughput predictor with buffer-aware uncertainty control, improving prediction accuracy and adaptability to network conditions. S

Vaibhav Aggarwal, Ojasv Kamal, Abhinav Japesh, Zhijing Jin

Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development, by enabling automation. In software engineering, LLM-powered coding agents have garnered significant attention due to their potential to automate complex development tasks, assist in debugging, and enhance producti

Patrizio Perugini, Jens Lundell, Katharina Friedl, Danica Kragic

We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization problem relaxed from an exact mixed integer non-linear trajectory optimization formulation. The critical insight is recasting the external pushers (environment) as a discrete probabilit

Sounak Sinha, Barry Bradlyn

Recent work on Abelian topological phases with rotational symmetry has raised the question of whether rotational symmetry can protect gapless propagating edge modes. Here we address this issue by considering the coupling of topological phases to the extrinsic geometry of the background. First, we analyze an effective hydrodynamic theory for an Abelian topolo

Yi Wang

This study introduced a Multimodal Mindfulness-Training System. Our installation, 'EmotionCarrier', correlates traditional calligraphy interactions with real-time physiological data from an Apple Watch. We aim to enhance mindfulness training effectiveness, aiding in achieving physiological calmness through calligraphy practice. Our experiments with varied pa

Hongjun Cui, Zhixiao Ren, Xinwei Ma, Minqing Zhu

Dockless bike-sharing (DBS) users often encounter difficulties in finding available bikes at their preferred times and locations. This study examines the determinants of the users' mode shifts in the context of bike absence, using survey data from Nanjing, China. An integrated choice and latent variable based on multinomial logit was employed to investigate

Alaa Ibrahim

Deciding the positivity of a sequence defined by a linear recurrence and initial conditions is, in general, a hard problem. When the coefficients of the recurrences are constants, decidability has only been proven up to order 5. The difficulty arises when the characteristic polynomial of the recurrence has several roots of maximal modulus, called dominant ro

Francesca Meimeti, Loukas Triantafyllopoulos, Aikaterini Sakagianni, Vasileios Kaldis

The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource allocation. This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital by leveraging the comprehensive MIMIC-IV dataset. After preprocessing the

Soohwan Lee, Seoyeong Hwang, Dajung Kim, Kyungho Lee

Group decision-making processes frequently suffer when social influence and power dynamics suppress minority viewpoints, leading to compliance and groupthink. Conversational agents can counteract these harmful dynamics by encouraging critical thinking. This study investigates how LLM-powered devil's advocate systems affect psychological safety, opinion expre

Pascal Stiefenhofer, Cafer Deniz, Liangxun Xie, Jing Qian

This paper investigates effective strategies for dealing with workplace bullying perpetrated by a narcissistic boss. Adopting a game-theoretic framework, we propose a three-stage sequential game with a simultaneous form game, incorporating a war of attrition in the final stage. Our findings demonstrate that victims of bullying should consistently choose to s

Sudheendran Vasudevan, Steffen M. Giesen, Simon T. Ranecky, Lutz Marder

Excited electronic states of fenchone, thiofenchone, and selenofenchone are characterized and assigned with different gas-phase spectroscopic methods and \textit{ab initio} quantum chemical calculations. With an increasing atomic number of the chalcogen, we observe increasing bathochromic (red) shifts, which vary in strength for Rydberg states, valence-excit

Arindam Saha, Baramee Charoensombutamon, Thibault Michel, V. Vijendran

As free-space optical systems grow in scale and complexity, troubleshooting becomes increasingly time-consuming and, in the case of remote installations, perhaps impractical. An example of a task that is often laborious is the alignment of a high-finesse optical resonator, which is highly sensitive to the mode of the input beam. In this work, we demonstrate

Ziyad Sheebaelhamd, Michael Tschannen, Michael Muehlebach, Claire Vernade

Current transformer-based imitation learning approaches introduce discrete action representations and train an autoregressive transformer decoder on the resulting latent code. However, the initial quantization breaks the continuous structure of the action space thereby limiting the capabilities of the generative model. We propose a quantization-free method i

Weihang Su, Baoqing Yue, Qingyao Ai, Yiran Hu

This paper introduces JuDGE (Judgment Document Generation Evaluation), a novel benchmark for evaluating the performance of judgment document generation in the Chinese legal system. We define the task as generating a complete legal judgment document from the given factual description of the case. To facilitate this benchmark, we construct a comprehensive data

Guang Dai, Pinhao Wang, Cheng Yao, Fangtian Ying

One's own voice is one of the most frequently heard voices. Studies found that hearing and talking to oneself have positive psychological effects. However, the design and implementation of self-voice for emotional regulation in HCI have yet to be explored. In this paper, we introduce InnerSelf, an innovative voice system based on speech synthesis technologie

Sebastian Foks

Absolute continuity of polynomially bounded $n$-tuples of commuting contractions is studied. A necessary and sufficient condition is found in Constantin Apostol's "weakened $C_{0,\cdot}$ assumption", asserting the convergence to 0 of the powers of each operator in a specific topology. Kosiek with Octavio considered tuples of Hilbert space contractions satisf

Aman Singh, Bhavya Giri Goswami, Ketan Nehete, Shishir N. Y. Kolathaya

This paper introduces a chain-driven, sandwich-legged mid-size quadruped robot designed as an accessible research platform. The design prioritizes enhanced locomotion, improved actuation reliability and safety, and simplified, cost-effective manufacturing. Locomotion performance is improved through a sandwiched leg architecture and dual-motor configuration,

Chunyu Yang, Shengben Bi, Yihui Xu, Xin Zhang

With the increasing demand for efficient and flexible robotic exploration solutions, Reinforcement Learning (RL) is becoming a promising approach in the field of autonomous robotic exploration. However, current RL-based exploration algorithms often face limited environmental reasoning capabilities, slow convergence rates, and substantial challenges in Sim-To

Jakub Malinowski, Marcin Kostrzewa, Michał Balcerek, Weronika Tomczuk

Change point detection has become an important part of the analysis of the single-particle tracking data, as it allows one to identify moments, in which the motion patterns of observed particles undergo significant changes. The segmentation of diffusive trajectories based on those moments may provide insight into various phenomena in soft condensed matter an

Shuyue Fu, Shengping Gong, Di Wu, Peng Shi

This paper proposes an analytical framework for the orbital Target-Attacker-Defender game with a non-maneuvering target along elliptic orbits. Focusing on the linear quadratic game, we derive an analytical solution to the matrix Riccati equation, which yields analytical Nash-equilibrium strategies for the game. Based on the analytical strategies, we derive t

Yu Feng, Puzhen Zhang, Guohui Xiao, Linfang Ding

A Barrier-Free GeoQA Portal: Enhancing Geospatial Data Accessibility with a Multi-Agent LLM Framework Geoportals are vital for accessing and analyzing geospatial data, promoting open spatial data sharing and online geo-information management. Designed with GIS-like interaction and layered visualization, they often challenge non-expert users with complex func

Yuxuan Wang, Meng Long, Qiang Wu, Wei Liu

Adaptive traffic signal control (ATSC) can effectively reduce vehicle travel times by dynamically adjusting signal timings but poses a critical challenge in real-world scenarios due to the complexity of real-time decision-making in dynamic and uncertain traffic conditions. The burgeoning field of intelligent transportation systems, bolstered by artificial in

Raúl Rodríguez-Barrera, Francisco Torres-Ayala

We show that the Beurling algebra with a weight-dependent convolution and the group algebra $L^1(G)$ are isomorphic. In particular, using this isomorphism, we extend some results of the algebra $\mathscr{L}^1(G,\omega)$ presented in recent articles. As a main result, we explicitly construct the equivalence between unitary representations of the group and non

Ruben Queiros, Megumi Kaneko, Helder Fontes, Rui Campos

Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensur

Tingyang Xiao, Xiaolin Zhou, Liu Liu, Wei Sui

This paper presents GeoFlow-SLAM, a robust and effective Tightly-Coupled RGBD-inertial SLAM for legged robotics undergoing aggressive and high-frequency motions.By integrating geometric consistency, legged odometry constraints, and dual-stream optical flow (GeoFlow), our method addresses three critical challenges:feature matching and pose initialization fail

Mattia Jacopo Villani, Emanuele Natale, Frederik Mallmann-Trenn

Leveraging the training-by-pruning paradigm introduced by Zhou et al. and Isik et al. introduced a federated learning protocol that achieves a 34-fold reduction in communication cost. We achieve a compression improvements of orders of orders of magnitude over the state-of-the-art. The central idea of our framework is to encode the network weights $\vec w$ by

Wen Zhou, Zhong-Xi Shen, Dong-Ping Xuan, Zhi-Xi Wang

In this paper, we propose a novel class of parameterized entanglement measures which are named as $G_\omega$-concurrence ($G_\omega$C) ($0<\omega\leq1$), and demonstrate comprehensively that they satisfy all the necessary axiomatic conditions required for an entanglement measure. Furthermore, we derive an analytical formula relating $G_\omega$C to concurrenc

Fedor Zolotarev, Tuomas Eerola, Tomi Kauppi

In sawmills, it is essential to accurately measure the raw material, i.e. wooden logs, to optimise the sawing process. Earlier studies have shown that accurate predictions of the inner structure of the logs can be obtained using just surface point clouds produced by a laser scanner. This provides a cost-efficient and fast alternative to the X-ray CT-based me

Wenxia Qu, Wenston J. T. Zang

In this paper, we present the bilateral truncated identity of the quintuple product identity, which is a generalization of the truncated quintuple product identities given by Chan, Ho and Mao [J. Number Theory 169 (2016) 420--438]. Additionally, we provide the bilateral truncated forms of two $q$-series identities, which are well-known consequences of the qu

Jack Kuipers, Giusi Moffa

Previously [Journal of Causal Inference, 10, 90-105 (2022)], we computed the variance of two estimators of causal effects for a v-structure of binary variables. Here we show that a linear combination of these estimators has lower variance than either. Furthermore, we show that this holds also when the treatment variable is block randomised with a predefined

Micael Toledo, Alejandra Ramos, Primoz Potocnik, Stephen Wilson

In a simple graph, a shunt is a symmetry which sends an edge to an incident edge (without fixing their shared vertex). The orbit of this edge under the shunt forms a consistent cycle. The important theorem of Biggs and Conway says that in a dart-transitive graph of valence q, there are exactly q-1 orbits of consistent cycles. These ideas have become a useful

Viet The Nguyen, Duy Anh Pham, An Thai Le, Jans Peter

The effectiveness of Spatio-temporal Graph Neural Networks (STGNNs) in time-series applications is often limited by their dependence on fixed, hand-crafted input graph structures. Motivated by insights from the Topological Data Analysis (TDA) paradigm, of which real-world data exhibits multi-scale patterns, we construct several graphs using Persistent Homolo

Muhammet Alkan, Gruschen Veldtman, Fani Deligianni

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables including oxygen consumption ($VO_2$), carbon dioxide production ($VCO_2$), and pulmonary ventilation ($VE$) during exercise. Previous research has established that parameters such as peak $VO_2$ and $VE/VCO_2$ ratio ser

Hitesh Agarwal, Antoine Reserbat-Plantey, David Barcons Ruiz, Karuppasammy Soundarapandian

Van der Waals heterostructures are at the forefront in materials heterostructure engineering, offering the ultimate control in layer selectivity and capability to combine virtually any material. Hexagonal boron nitride (hBN), the most commonly used dielectric material, has proven indispensable in this field, allowing the encapsulation of active 2D materials

Chenting Wang, Kunchang Li, Tianxiang Jiang, Xiangyu Zeng

Popular video training methods mainly operate on a fixed number of tokens sampled from a predetermined spatiotemporal grid, resulting in sub-optimal accuracy-computation trade-offs due to inherent video redundancy. They also lack adaptability to varying computational budgets for downstream tasks, hindering applications of the most competitive model in real-w

J. Javaraiah

The tilts of bipolar magnetic regions are believed to be caused by the action of Coriolis force on rising magnetic flux tubes. Here we analysed the combined Greenwich and Debrecen observatories sunspot-group data during the period 1874-2017 and the tilt angles of sunspot groups measured at Mt. Wilson Observatory during the period 1917-1986 and Debrecen Obser

Meisam Jamshidi Seikavandi, Jostein Fimland, Maria Barrett, Paolo Burelli

Accurate emotion recognition is pivotal for nuanced and engaging human-computer interactions, yet remains difficult to achieve, especially in dynamic, conversation-like settings. In this study, we showcase how integrating eye-tracking data, temporal dynamics, and personality traits can substantially enhance the detection of both perceived and felt emotions.

Roman Pol, Mirosława Reńska

We shall prove that if X, Y are compact metrizable spaces of positive dimension and h: X x Y --> X is a continuous map with zero-dimensional fibers then X contains a non-trivial continuum without one-dimensional subsets; in particular X is not a countable union of zero-dimensional sets, which provides a negative answer to a question of J. Dud\'ak and B. Vejn

Ruiyi Yang, Hao Xue, Imran Razzak, Hakim Hacid

Graph Retrieval-Augmented Generation (GraphRAG) has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge. By leveraging Knowledge Graphs (KGs), GraphRAG improves information retrieval for complex reasoning tasks, providing more precise and comprehensive retrieval and generating more acc

Bo Wu

In empirical research, this article uses daily climate data provided by the National Oceanic and Atmospheric Administration (NOAA) of the United States to construct a temperature box with a range of 5\( ^\circ\text{C} \), focusing on analyzing the impact of extreme high temperatures (\( >30^\circ\text{C} \)) and extreme low temperatures (\( \le -10^\circ\tex

Yuyang Xue, Edward Moroshko, Feng Chen, Jingyu Sun

Text-to-Image diffusion models can produce undesirable content that necessitates concept erasure. However, existing methods struggle with under-erasure, leaving residual traces of targeted concepts, or over-erasure, mistakenly eliminating unrelated but visually similar concepts. To address these limitations, we introduce CRCE, a novel concept erasure framewo