March 2025 arXiv papers — page 202

Jun Yang, Wenjie Xue, Sahar Ghavidel, Steven L. Waslander

Estimating the 6D pose of textureless objects from RGB images is an important problem in robotics. Due to appearance ambiguities, rotational symmetries, and severe occlusions, single-view based 6D pose estimators are still unable to handle a wide range of objects, motivating research towards multi-view pose estimation and next-best-view prediction that addre

Jonas Knecht, Anna Zink, Jonathan Kolstad, Maya Petersen

We present a deep learning-based approach to studying dynamic clinical behavioral regimes in diverse non-randomized healthcare settings. Our proposed methodology - deep causal behavioral policy learning (DC-BPL) - uses deep learning algorithms to learn the distribution of high-dimensional clinical action paths, and identifies the causal link between these ac

K. Cichocki, F. J. Dominguez-Gutierrez, L. Kurpaska, K. Muszka

We performed molecular dynamics simulations to investigate the mechanical response of face-centered cubic (FCC) nickel under uniaxial compression and nanoindentation using traditional interatomic potentials, including the Embedded Atom Method (EAM) and Modified Embedded Atom Method (MEAM). By calculating the generalized stacking fault energy (GSFE), we analy

Matias Raja

We introduce a new isomorphic quantity for Banach spaces, the index $\Theta_X$, based on finite convex coverings of the unit ball. This index is closely related to the asymptotic moduli of uniform convexity and uniform smoothness, so that it can be calculated for several classical Banach spaces.

Kuni H Iwasa

Cochlear outer hair cells (OHCs) have two mechanosensitive elements: the hair bundle with mechanotrasducer channels and the piezoelectric lateral wall of the cell body. The present report examines how these elements interact with each other by incorporating OHCs into the simplest local cochlear models. In the frequency range, typically above 1 kHz, where cap

Amanda Burcroff, Kyungyong Lee, Lang Mou

We introduce a new class of combinatorial objects, named tight gradings, which are certain nonnegative integer-valued functions on maximal Dyck paths. Using tight gradings, we derive a manifestly positive formula for any wall-function in a rank-2 generalized cluster scattering diagram. We further prove that any consistent rank-2 scattering diagram is positiv

Hai Jiang, Ke Feng, Runshu Hu, Qiwen Zhan

Despite the successful demonstration of compact free electron lasers (FELs) driven by laser wakefield accelerators (LWFAs), the inherent shot-to-shot fluctuations in LWFAs, including both laser and plasma instabilities, remain a primary obstacle to realizing LWFA-driven FELs with robust operation. Here, we present a conceptual design for LWFA-driven FELs wit

Carlo Dindorf, Fabian Horst, Djordje Slijepčević, Bernhard Dumphart

This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running) and sports biomechanics. It explores the potential of Machine Learning methods to address challenges in biomechanical wor

Christopher Candelora, Muxian Xu, Siyu Cheng, Alessandro De Vita

Altermagnets recently came into the spotlight as a new class of magnetic materials, arising as a consequence of specific crystal symmetries. They are characterized by a spin-polarized electronic band structure similar to ferromagnets, but with net zero magnetization, and touted as a promising platform to host a slew of exotic properties, many of which are ye

Mahdi Arab Loodaricheh, Neh Majmudar, Anita Raja, Ansaf Salleb-Aouissi

Understanding and managing uncertainty is crucial in machine learning, especially in high-stakes domains like healthcare, where class imbalance can impact predictions. This paper introduces RIGA, a novel pipeline that mitigates class imbalance using generative AI. By converting tabular healthcare data into images, RIGA leverages models like cGAN, VQVAE, and

Xing-Yu Zhang, Yu-Qing Wang, Angrui Du, Han Wang

We designed an automatic differentiation-based strategy to generate optical trap arrays that change smoothly in time. Instead of repeatedly regenerating the holograms for each time step, we derive the differential form of the phase dynamics that enables the continuous evolution of the trap coordinates. This differential form is derived from the implicit diff

Asaad Daher, Xin-Li Sheng, David Wagner, Francesco Becattini

We determine the form of dissipative currents at the first order in relativistic spin hydrodynamics with finite chemical potential including gradients of the spin potential. Taking advantage of isotropy in the hydrodynamic local rest frame, using a suitable matching condition for the flow velocity and enforcing the semi-positivity of entropy production, we f

Jie Chen, Chun Chen, Xiaoqun Wang

We study the potential influence of the particle multi-occupations on the stability of many-body localization in the disordered Bose-Hubbard model. Within the higher-energy section of the dynamical phase diagram, we find that there is no apparent finite-size boundary drift between the thermal phase and the many-body localized regime. We substantiate this obs

Elizabeth J. Paul, Abdullah Hyder, Eduardo Rodríguez, Rogério Jorge

The shear Alfv\'en wave (SAW) continuum plays a critical role in the stability of energetic particle-driven Alfv\'{e}n eigenmodes. We develop a theoretical framework to analyze the SAW continuum in three-dimensional quasisymmetric magnetic fields, focusing on its implications for stellarator design. By employing a near-axis model and degenerate perturbation

Xuandong Zhao, Will Cai, Tianneng Shi, David Huang

Existing training-time safety alignment techniques for large language models (LLMs) remain vulnerable to jailbreak attacks. Direct preference optimization (DPO), a widely deployed alignment method, exhibits limitations in both experimental and theoretical contexts as its loss function proves suboptimal for refusal learning. Through gradient-based analysis, w

Krishna R. Akhil, Sreeja S Kartha, Ujjwal Krishnan, Blesson Mathew

Polar Ring Galaxies (PRGs) are a unique class of galaxies characterised by a ring of gas and stars orbiting nearly orthogonal to the main body. This study delves into the evolutionary trajectory of PRGs using the exemplary trio of NGC 3718, NGC 2685, and NGC 4262. We investigate the distinct features of PRGs by analysing their ring and host components to rev

Nianzu Yang, Pandeng Li, Liming Zhao, Yang Li

Existing video tokenizers typically use the traditional Variational Autoencoder (VAE) architecture for video compression and reconstruction. However, to achieve good performance, its training process often relies on complex multi-stage training tricks that go beyond basic reconstruction loss and KL regularization. Among these tricks, the most challenging is

Annie S. Chen, Alec M. Lessing, Yuejiang Liu, Chelsea Finn

Many robot demonstration datasets contain heterogeneous demonstrations of varying quality. This heterogeneity may benefit policy pre-training, but can hinder robot performance when used with a final imitation learning objective. In particular, some strategies in the data may be less reliable than others or may be underrepresented in the data, leading to poor

Ruben Doste, Julia Camps, Zhinuo Jenny Wang, Lucas Arantes Berg

In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In silico trials not only minimise patient risks but also reduce reliance on animal testing. However, the implementation of in silico trials presents several time-consuming challenges.

Mingkang Zhu, Xi Chen, Zhongdao Wang, Bei Yu

As Large language models (LLMs) are increasingly deployed in diverse applications, faithfully integrating evolving factual knowledge into these models remains a critical challenge. Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition. However, this approach is often costly and unreliable, as it relies on

Shen Dong, Shaochen Xu, Pengfei He, Yige Li

Agents powered by large language models (LLMs) have demonstrated strong capabilities in a wide range of complex, real-world applications. However, LLM agents with a compromised memory bank may easily produce harmful outputs when the past records retrieved for demonstration are malicious. In this paper, we propose a novel Memory INJection Attack, MINJA, witho

Hiroyuki Deguchi, Go Kamoda, Yusuke Matsushita, Chihiro Taguchi

Researchers and practitioners in natural language processing and computational linguistics frequently observe and analyze the real language usage in large-scale corpora. For that purpose, they often employ off-the-shelf pattern-matching tools, such as grep, and keyword-in-context concordancers, which is widely used in corpus linguistics for gathering example

Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao

High-quality data resources play a crucial role in learning large language models (LLMs), particularly for low-resource languages like Cantonese. Despite having more than 85 million native speakers, Cantonese is still considered a low-resource language in the field of natural language processing (NLP) due to factors such as the dominance of Mandarin, lack of

Jiaxin Zhao, Nicholas J. Warren, Richard Mandle, Peter Hine

Vitrimers are a relatively new class of polymer materials with unique properties offered by cross-links that can undergo associative exchange dynamics. We here present a new class of vitrimers based on poly(methyl acrylate) with cross-links utilising dioxaboralane metathesis. These vitrimers demonstrate a combination of ultra-stretchability (up to $\sim$ 80

Niklas Witt, Siheon Ryee, Lennart Klebl, Jennifer Cano

Graphene-based multilayer systems serve as versatile platforms for exploring the interplay between electron correlation and topology, thanks to distinctive low-energy bands marked by significant quantum metric and Berry curvature from graphene's Dirac bands. Here, we investigate Mott physics and local spin moments in Dirac bands hybridized with a flat band o

S. P. Cosentino, M. L. Pumo, S. Cherubini

In hydrogen-rich (H-rich) Supernova (SN) events, the collision between the H-rich ejecta and the Circum-Stellar Medium (CSM) can accelerate particles and produce high-energy neutrinos (HE-$\nu$, TeV-PeV) through proton-proton inelastic scattering. Despite understanding the production mechanism of these neutrinos, the lack of direct observations raises questi

Shahram Esmaeilsabzali, Arayi Khalatyan, Zhijun Mo, Sruthi Venkatanarayanan

Programs written in unsafe languages such as C are prone to memory safety errors, which can lead to program compromises and serious real-world security consequences. Recently, Memory-Safe WebAssembly (MSWASM) is introduced as a general-purpose intermediate bytecode with built-in memory safety semantics. Programs written in C can be compiled into MSWASM to ge

Alessandro Pasqui, Sajjad Mahdavi, Benoit Vianay, Alexandra Colin

Three-dimensional biological microscopy has significantly advanced our understanding of complex biological structures. However, limitations due to microscopy techniques, sample properties or phototoxicity often result in poor z-resolution, hindering accurate cellular measurements. Here, we introduce ZAugNet, a fast, accurate, and self-supervised deep learnin

John C. Sunil, Richard A. Blythe, Martin R. Evans, Satya N. Majumdar

We consider a discrete-time continuous-space random walk, with a symmetric jump distribution, under stochastic resetting. Associated with the random walker are cost functions for jumps and resets, and we calculate the distribution of the total cost for the random walker up to the first passage to the target. By using the backward master equation approach we

Matthew Markowitz, Kevin Zelaya, Mohammad-Ali Miri

We show that programmable photonic circuit architectures composed of alternating mixing layers and active layers offer a high degree of flexibility. This alternating configuration enables the systematic tailoring of both the network's depth (number of layers) and width (size of each layer) without compromising computational capabilities. From a mathematical

Zhenhua Wang, Ruoyu Wu

The Join-the-Shortest-Queue-d routing policy is considered for a large system with $n$ servers. Moderate deviation principles (MDP) for the occupancy process and the empirical queue length process are established as $n\to \infty$. Each MDP is formulated in terms of a large deviation principle with an appropriate speed function in a suitable infinite-dimensio

Fuhuan Shen, Xinyi Zhao, Yungui Ma, Jianbin Xu

The recent demonstrations of van der Waals (vdW) nanophotonics have opened new pathways for manipulating the light-matter interaction in an intrinsic manner, leading to fascinating achievements in tunable magneto-optics by self-hybrid polaritons, indirect bandgap lasering, and exceptionally enhanced optical nonlinearity. However, the anisotropic atomic latti

Ungsik Kim

In the field of Explainable Artificial Intelligence (XAI), argumentative XAI approaches have been proposed to represent the internal reasoning process of deep neural networks in a more transparent way by interpreting hidden nodes as arguements. However, as the number of layers increases, existing compression methods simplify all layers at once, which lead to

Xingyi Yang, Constantin Venhoff, Ashkan Khakzar, Christian Schroeder de Witt

Neurons in large language models often exhibit \emph{polysemanticity}, simultaneously encoding multiple unrelated concepts and obscuring interpretability. Instead of relying on post-hoc methods, we present \textbf{MoE-X}, a Mixture-of-Experts (MoE) language model designed to be \emph{intrinsically} interpretable. Our approach is motivated by the observation

Francesco Alessandro Angiulli, Chiara Aimè, Maria Cristina Arena, Davide Biagini

The CH4 Livestock Emission (CH4rLiE) project explores the development of a prototype system for capturing methane emissions in barn environments, offering an alternative approach to mitigating greenhouse gas emissions from livestock farming. Methane (CH4), with a global warming potential significantly higher than CO2 (GWP100 = 27), accounts for ~23% of anthr

Wei Wang, Shefeng Yan, Linlin Mao, Zeping Sui

An ambiguity-free direction-of-arrival (DOA) estimation scheme is proposed for sparse uniform linear arrays under low signal-to-noise ratios (SNRs) and non-stationary broadband signals. First, for achieving better DOA estimation performance at low SNRs while using non-stationary signals compared to the conventional frequency-difference (FD) paradigms, we pro

Necdet Gurkan, Kimathi Njoki, Jordan W. Suchow

As artificial intelligence (AI) continues to advance--particularly in generative models--an open question is whether these systems can replicate foundational models of human social perception. A well-established framework in social cognition suggests that social judgments are organized along two primary dimensions: valence (e.g., trustworthiness, warmth) and

Samuel Mansfield

Let $\mathcal{F}$ be a set of $n$ real analytic functions with linearly independent derivatives restricted to a compact interval $I$. We show that for any finite set $A \subset I$, there is a function $f \in \mathcal{F}$ that satisfies $$|2^{n-1}f(A)-(2^{n-1}-1)f(A)|\gg_{\mathcal{F},I} |A|^{\phi(n)},$$ where $\phi:\mathbb{N} \to \mathbb{R}$ satisfies the rec

Zhao Yang, Zezhong Qian, Xiaofan Li, Weixiang Xu

Accurate and high-fidelity driving scene reconstruction demands the effective utilization of comprehensive scene information as conditional inputs. Existing methods predominantly rely on 3D bounding boxes and BEV road maps for foreground and background control, which fail to capture the full complexity of driving scenes and adequately integrate multimodal in

Daniel S. Alber, Shiheng Zhao, Alexandre O. Jacinto, Eric F. Wieschaus

Tissue deformations during morphogenesis can be active, driven by internal processes, or passive, resulting from stresses applied at their boundaries. Here, we introduce the Drosophila hindgut primordium as a model for studying boundary-driven tissue morphogenesis. We characterize its deformations and show that its complex shape changes can be a passive cons

Zihao Zhao, Chenxiao Fan, Junlong Liu, Zheng Wang

Large language models (LLMs) holds significant promise in achieving general medication recommendation systems owing to their comprehensive interpretation of clinical notes and flexibility to medication encoding. We evaluated both general-purpose and medical-specific LLMs for medication recommendations, showing their unsatisfactory precision and severe overpr

Rui Ye, Shuo Tang, Rui Ge, Yaxin Du

LLM-based multi-agent systems (MAS) have shown significant potential in tackling diverse tasks. However, to design effective MAS, existing approaches heavily rely on manual configurations or multiple calls of advanced LLMs, resulting in inadaptability and high inference costs. In this paper, we simplify the process of building an MAS by reframing it as a gen

Maximilian Buthenhoff, Ercan Sönmez

In this work, we investigate the existence and properties of Gaussian-like densities for weak solutions of multidimensional stochastic differential equations driven by a mixture of completely correlated fractional Brownian motions. We consider both the short-range and long-range dependent regimes, imposing a singular drift in the short-range dependent case a

Alina Basharat, Yijun Bian, Ping Xu, Zhi Tian

This paper develops a comprehensive framework to address three critical trustworthy challenges in federated learning (FL): robustness against Byzantine attacks, fairness, and privacy preservation. To improve the system's defense against Byzantine attacks that send malicious information to bias the system's performance, we develop a Two-sided Norm Based Scree

Omar Darwish

Line-of-sight distortions of the cosmic microwave background (CMB), including gravitational lensing, cosmic birefringence, and patchy screening, encode crucial cosmological information. While quadratic estimators (QE) have been excellent tools for extracting these signals, they become suboptimal for current- and next-generation CMB surveys, failing to maximi

Tatsuya Ishizaki

The concept of vowel tenseness has traditionally been examined through the binary distinction of tense and lax vowels. However, no universally accepted quantitative definition of tenseness has been established in any language. Previous studies, including those by Jakobson, Fant, and Halle (1951) and Chomsky and Halle (1968), have explored the relationship be

Pablo D. Bergamasco, Gabriel G. Carlo, Alejandro M. F. Rivas

Out-of-time-ordered correlators (OTOCs) have emerged as powerful tools for diagnosing quantum chaos and information scrambling. While extensively studied in closed quantum systems, their behavior in dissipative environments remains less understood. In this work, we investigate the spectral decomposition of OTOCs in open quantum systems, using the dissipative

Lucas Winter, Pietro Brighi, Andreas Nunnenkamp

Quantum systems with strong long-range interactions are thought to resist thermalization because of their discrete energy spectra. We show that applying a staggered magnetic field to a strong long-range Heisenberg antiferromagnet restores thermalization for a large class of initial states by breaking permutational symmetry. Using self-consistent mean-field t

Shuoyang Wang, Guanqun Cao, Yuan Huang

The opportunity to utilize complex functional data types for conducting classification tasks is emerging with the growing availability of imaging data. However, the tools capable of effectively managing imaging data are limited, let alone those that can further leverage other one-dimensional functional data. Inspired by the extensive data provided by the Alz

A. Steklain, E. Segreto, A. Machado, M. Adames

We propose a novel concept for the future modules of the DUNE Phase 2 Far Detector Photodetection System, namely the Polymer Wavelength shifter and Enhanced Reflection - PoWER. In this concept, the field cage of the LArTPC is entirely covered with polymeric wavelength shifting foils (PolyEthylene Naphthalate - PEN) to convert the liquid argon scintillation l

Antonio Bonilla, Daniel Seco

We characterize bounded multiplication operators in weighted Dirichlet spaces that are power bounded, Ces\`{a}ro bounded and uniformly Kreiss. Moreover, we show the equivalence in such spaces between mean ergodicity and Ces\`{a}ro boundedness for multiplication operators. We perform the same study for adjoints of multiplication operators. As a particular exa

Maximilian Buthenhoff, Ercan Sönmez

We investigate the well-posedness of stochastic differential equations driven by fractional Brownian motion, focusing on the long-range dependent case $H \in (\frac{1}{2}, 1)$. While existing results on regularization by such noise typically require Hölder continuity of the drift, we establish new strong existence and uniqueness results for certain classes o

Cristian Jimenez-Romero, Alper Yegenoglu, Christian Blum

This work examines the integration of large language models (LLMs) into multi-agent simulations by replacing the hard-coded programs of agents with LLM-driven prompts. The proposed approach is showcased in the context of two examples of complex systems from the field of swarm intelligence: ant colony foraging and bird flocking. Central to this study is a too

Jeremy McMahan, Young Wu, Yudong Chen, Xiaojin Zhu

In this work, we develop a reward design framework for installing a desired behavior as a strict equilibrium across standard solution concepts: dominant strategy equilibrium, Nash equilibrium, correlated equilibrium, and coarse correlated equilibrium. We also extend our framework to capture the Markov-perfect equivalents of each solution concept. Central to

Yang Sun, George H. Rieke, Jianwei Lyu, Meredith A. Stone

The ratio between the stellar mass of a galaxy, $M_{*}$, and that of its central supermassive black hole (SMBH), $M_\bullet$, the ``Magorrian'' relationship, traces their coevolution. JWST observations have suggested significant evolution in $M_\bullet/M_{*}$ relative to local scaling relationships both in low-mass galaxies and in quasars at z $\ge$ 4. We te

Xinze Yang, Alexander F. Kemper, Adrian Gozar, Eduardo H. da Silva Neto

The measurement of quasiparticle scattering patterns on material surfaces using scanning tunneling microscopy (STM) is now an established technique for accessing the momentum-resolved electronic band structure of solids. However, since these quasiparticle interference (QPI) patterns reflect spatial variations related to differences in the band momenta rather

E. Ballico, E. Gasparim, M. P. García del Moral, C. las Heras

We discuss the role of hyperelliptic fibrations in F-theory. For each even integer $n$ we give a noncompact Calabi--Yau threefold $X$ containing a hyperelliptically fibered surface $Y$, such that $X$ and $Y$ are homotopy equivalent and $c_2(X) = n$. We investigate two distinct cases depending on the position of the hyperelliptic fibration. First, we propose

Leonid Ryvkin

We give a survey of Darboux type theorems in multisymplectic geometry. These theorems establish when a closed differential form of a certain type admits a constant-coefficient expression in some local coordinate system. Beyond the classical cases of symplectic and volume forms, 0-deformability (i.e. constancy of linear type) is typically not automatic and ha

Jérémy Dumoulin, Dawit Gebremeskel, Kanchwodia Gashaw, Ingeborg Graabak

The electrification of road transport, as the predominant mode of transportation in Africa, represents a great opportunity to reduce greenhouse gas emissions and dependence on costly fuel imports. However, it introduces major challenges for local energy infrastructures, including the deployment of charging stations and the impact on often fragile electricity

S. Esteban Pozuelo, A. Asensio Ramos, J. Trujillo Bueno, R. Ramelli

Prominences are cool overdensities of plasma supported by magnetic fields that levitate in the solar corona. The physical characterization of these structures is key for understanding the magnetic field in the corona. Our work attempts to shed light on the properties of prominences by using observations at high polarimetric sensitivity in the He I D3 multipl

Bar Karov, Dor Zohar, Yam Marcovitz

We present Attentive Reasoning Queries (ARQs), a novel structured reasoning approach that significantly improves instruction-following in Large Language Models through domain-specialized reasoning blueprints. While LLMs demonstrate remarkable capabilities across diverse tasks, they often fail to maintain adherence to complex, use-case-specific instructions d

Isaac Robinson, John Burden

Large Language Models (LLMs) are increasingly deployed across diverse contexts to support decision-making. While existing evaluations effectively probe latent model capabilities, they often overlook the impact of context framing on perceived rational decision-making. In this study, we introduce a novel evaluation framework that systematically varies evaluati

Subham Agrawal, Nico Ostermann-Myrau, Nils Dengler, Maren Bennewitz

Robots are increasingly being deployed in public spaces such as shopping malls, sidewalks, and hospitals, where safe and socially aware navigation depends on anticipating how pedestrians respond to their presence. However, existing datasets rarely capture the full spectrum of robot-induced reactions, e.g., avoidance, neutrality, attraction, which limits prog

Ankit Shrivastava, Kaushik Dayal, Hae Young Noh

This study investigates computationally the impact of particle size disparity and cohesion on force chain formation in granular media. The granular media considered in this study are bi-disperse systems under uniaxial compression, consisting of spherical, frictionless particles that interact through a modified Hookean model. Force chains in granular media ar

Alisson P. Costa, Andre. L. B. Ribeiro, Flavio R. de M. Neto, Juarez dos S. Junior

In this work, we study the Coma cluster, one of the richest and most well-known systems at low redshifts, to explore the importance of low-flux objects in the identification of cluster substructures. In addition, we conduct a study of the infall flow around Coma, considering the presence or absence of low-flux objects across the projected phase space of the

Gustaw Opiełka, Hannes Rosenbusch, Claire E. Stevenson

Analogical reasoning relies on conceptual abstractions, but it is unclear whether Large Language Models (LLMs) harbor such internal representations. We explore distilled representations from LLM activations and find that function vectors (FVs; Todd et al., 2024) - compact representations for in-context learning (ICL) tasks - are not invariant to simple input

S. Wundrack, M. Bothe, M. Jaime, K. Kuester

Metal intercalation in epitaxial graphene enables the emergence of proximity-induced superconductivity and modified quantum transport properties. However, systematic transport studies of intercalated graphene have been hindered by challenges in device fabrication, including processing-induced deintercalation and instability under standard lithographic techni

Venkat Kumar R, Deepak Saravanan

The convergence of generative artificial intelligence and advanced computer vision technologies introduces a groundbreaking approach to transforming textual descriptions into three-dimensional representations. This research proposes a fully automated pipeline that seamlessly integrates text-to-image generation, various image processing techniques, and deep l

Wei Li, Bing Hu, Rui Shao, Leyang Shen

First-person video assistants are highly anticipated to enhance our daily lives through online video dialogue. However, existing online video assistants often sacrifice assistant efficacy for real-time efficiency by processing low-frame-rate videos with coarse-grained visual features.To overcome the trade-off between efficacy and efficiency, we propose "Fast

Giulia Ramella, Auke Ijspeert, Mohamed Bouri

Hip exoskeletons are known for their versatility in assisting users across varied scenarios. However, current assistive strategies often lack the flexibility to accommodate for individual walking patterns and adapt to diverse locomotion environments. In this work, we present a novel control strategy that adapts the mechanical impedance of the human-exoskelet

Justino Sánchez

We study existence and uniqueness of spherically symmetric solutions of S_k(D^2v)+beta xi\cdot\nabla v+\alpha v+\abs{v}^{q-1}v=0 in R^n, where \alpha,\beta are real parameters, n>2,\, q>k\geq 1 and S_k(D^2v) stands for the k-Hessian operator of v. Our results are based mainly on the analysis of an associated dynamical system and energy methods. We derive som

Liang Hong

In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model. This model-based approach may potentially suffer from several drawbacks: (i) model misspecification, (ii) selection effect, and (iii) lack of finite-sample validity. This article addresses these three issues simultaneousl

Cong Wang

In this paper, we establish the space-time analyticity of global solutions to the incompressible Navier-Stokes equations with small initial data in critical \emph{Besov} spaces $\dot B^{3/p-1}_{p,q}$. Time decay rates of higher order space-time joint derivatives and instantaneous lower bounds of the analyticity radius follow as straightforward consequences.

Chiara Ravazzi, Valentina Breschi, Paolo Frasca, Fabrizio Dabbene

In this paper, we present a novel model to characterize individual tendencies in repeated decision-making scenarios, with the goal of designing model-based control strategies that promote virtuous choices amidst social and external influences. Our approach builds on the classical Friedkin and Johnsen model of social influence, extending it to include random

Tushar Aggarwal, Swayam Singh, Abhijeet Awasthi, Aditya Kanade

Software engineering activities frequently involve edits to existing code. However, contemporary code language models (LMs) lack the ability to handle diverse types of code-edit requirements. In this work, we attempt to overcome this shortcoming through (1) a novel synthetic data generation pipeline and (2) a robust model adaptation algorithm. Starting with

Thomas Pöllabauer, Michael Gasser, Tristan Wirth, Sarah Berkei

6D object pose estimation suffers from reduced accuracy when applied to metallic objects. We set out to improve the state-of-the-art by addressing challenges such as reflections and specular highlights in industrial applications. Our novel BOP-compatible dataset, featuring a diverse set of metallic objects (cans, household, and industrial items) under variou

Yanshu Li

Multimodal in-context learning (ICL) has emerged as a key capability of Large Vision-Language Models (LVLMs), driven by their increasing scale and applicability. Despite its promise, effective ICL in the multimodal setting remains challenging due to the inherent complexity of image-text inputs and the high sensitivity of ICL performance to input configuratio

Jessica Hoffmann, Christiane Ahlheim, Zac Yu, Aria Walfrand

The paper shows that parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models' (LLMs) ability to answer queries on sensitive topics with a Neutral Point of View (NPOV), i.e. to provide significantly more informative, diverse and impartial answers. This is shown by evaluating PE-RL and multiple

Cuiyu He

Many equilibrated flux recovery methods for finite element solutions rely on ad hoc or method-specific techniques, limiting their generalizability and efficiency. In this work, we introduce the Equilibrated Averaging Residual Method (EARM), a unified framework for flux recovery that not only reproduces state-of-the-art locally conservative fluxes but also en

Re'em Harel, Niv Gilboa, Yuval Pinter

The use of language models as remote services requires transmitting private information to external providers, raising significant privacy concerns. This process not only risks exposing sensitive data to untrusted service providers but also leaves it vulnerable to interception by eavesdroppers. Existing privacy-preserving methods for natural language process

Rui Zhao, Weijia Mao, Mike Zheng Shou

Adapting generative models to specific domains presents an effective solution for satisfying specialized requirements. However, adapting to some complex domains remains challenging, especially when these domains require substantial paired data to capture the targeted distributions. Since unpaired data from a single modality, such as vision or language, is mo

Milena Skvortsova

Recently, black hole models in a nonlinear modification of the Maxwell electrodynamics were suggested, possessing simultaneously properties of an extreme charge and regularity (Bronnikov K. A., Phys. Rev. D, 110 (2024) 024021). We study quasinormal modes of a massive scalar field around such black holes and show that they are characterized by a comparatively

Andrei V. Ermolaev, Mathilde Hary, Lev Leybov, Piotr Ryczkowski

We report a generalized nonlinear Schr\"odinger equation simulation model of an extreme learning machine (ELM) based on optical fiber propagation. Using the MNIST handwritten digit dataset as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. For this dataset and with qua

Kornelia Kostrzewska, Paweł Kryszkiewicz

The work aims to propose a new nonlinear characteristics model for a wideband radio amplifier of variable supply voltage. An extended Rapp model proposal is presented. The proposed model has been verified by measurements of three different amplifiers. This model can be used to design frontend-aware 6G systems. -- Praca ma na celu zaproponowanie nowego modelu

C. A. Fonseca-Mora

In this work, we investigate a theory of stochastic integration for operator-valued processes with respect to semimartingales taking values in the dual of a nuclear space. Our construction of this particular stochastic integral relies on previous results from [Electron. J. Probab., Volume 26, paper no. 147, 2021], together with specific tools which share som

Paweł Kryszkiewicz

Multi-access edge computing is a technique that combines the use of communication networks and remote computing resources. It allows to perform complex computational tasks for devices with low computing power while maintaining low latencies. However, it is important to effectively allocate the computing tasks to individual nodes. The work will present how th

Keqi Chen, Zekai Sun, Huijun Lian, Yingming Gao

Large language models (LLMs) are becoming increasingly popular in the field of psychological counseling. However, when human therapists work with LLMs in therapy sessions, it is hard to understand how the model gives the answers. To address this, we have constructed Psy-COT, a graph designed to visualize the thought processes of LLMs during therapy sessions.

Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang

Dongba pictographic is the only pictographic script still in use in the world. Its pictorial ideographic features carry rich cultural and contextual information. However, due to the lack of relevant datasets, research on semantic understanding of Dongba hieroglyphs has progressed slowly. To this end, we constructed \textbf{DongbaMIE} - the first dataset focu

Peter Danchev, Arash Javan, Omid Hasanzadeh, Ahmad Moussavi

We define and consider in-depth the so-called $C\Delta$ rings as those rings $R$ whose elements are a sum of an element in $C(R)$ and of an element in $\Delta(R)$. Our achieved results somewhat strengthen these recently obtained by Ma-Wang-Leroy in Czechoslovak Math. J. (2024) as well as these due to Kurtulmaz-Halicioglu-Harmanci-Chen in Bull. Belg. Math. So

Jingyang Zhao, Zimo Sheng, Mingyu Xiao

The Traveling Salesman Problem (TSP) is a classic and extensively studied problem with numerous real-world applications in artificial intelligence and operations research. It is well-known that TSP admits a constant approximation ratio on metric graphs but becomes NP-hard to approximate within any computable function $f(n)$ on general graphs. This disparity

Seraj Al Mahmud Mostafa, Mike P. Wittie, Utkarsh Goel

The prevailing wisdom is that more network bandwidth does not matter much and that website performance is primarily limited by network latency. However, as mobile websites become more complex and mobile network performance improves, does this adage continue to hold? To understand the effects of small changes in network bandwidth and latency on website perfor

Yuezhe Tian, Kangchen Yao, Xiaoyang Yu

Underwater optical imaging is severely degraded by light absorption, scattering, and color distortion, hindering visibility and accurate image analysis. This paper presents an adaptive enhancement framework integrating illumination compensation, multi-domain filtering, and dynamic color correction. A hybrid illumination compensation strategy combining CLAHE,

Emre Gürsoy, Robert H. Meißner, Gregor B. Vonbun-Feldbauer

Understanding the atomic structure of magnetite-carboxylic acid interfaces is crucial for tailoring nanocomposites involving this interface. We present a Monte Carlo (MC)-based method utilizing iron oxidation state exchange to model magnetite interfaces with tens of thousands of atoms, scales typically inaccessible by electronic structure calculations. Charg

Hongyi Bian, Chunhe Li, Zixiang Lin, Jin Zhu

The study of active matter system has critical importance in revealing the physical essence of biological collective behavior. Dense bacterial suspension - a typical biological active matter, exhibits a wide range of phenomenons, among which bacterial turbulence has received extensive interest in recent years. This seemingly chaotic motion is widely studied

Woo-Jin Jung, Dong-Hee Paek, Seung-Hyun Kong

4-dimensional (4D) radar is increasingly adopted in autonomous driving for perception tasks, owing to its robustness under adverse weather conditions. To better utilize the spatial information inherent in 4D radar data, recent deep learning methods have transitioned from using sparse point cloud to 4D radar tensors. However, the scarcity of publicly availabl

Paweł Hitczenko, Jacek Wesołowski

For a TASEP on $\mathbb Z$ with the step initial condition we identify limits as $t\to\infty$ of the expected total number of jumps until time $t>0$ and the expected number of active particles at a time $t$. We also connect the two quantities proving that non-asymptotically, that is as a function of $t>0$, the latter is the derivative of the former. Our appr

Mateus X. Silva, Edgar Mendoza, Fábio S. L. Ferreira, Alexandre C. R. Gomes

Phosphorus plays an essential role in the chemistry of living organisms, being present in several fundamental biomolecules. The investigation of chemical reactions taking place in different astronomical environments involving phosphorus-containing molecules is essential for understanding how these species are produced and destroyed. Phosphorus monoxide (PO)

Haoze Li, Jun Xie

A major challenge in causal discovery from observational data is the absence of perfect interventions, making it difficult to distinguish causal features from spurious ones. We propose an innovative approach, Feature Matching Intervention (FMI), which uses a matching procedure to mimic perfect interventions. We define causal latent graphs, extending structur

Zhiquan Zhang, Gokul Puthumanaillam, Manav Vora, Melkior Ornik

Autonomous motion planning under unknown nonlinear dynamics presents significant challenges. An agent needs to continuously explore the system dynamics to acquire its properties, such as reachability, in order to guide system navigation adaptively. In this paper, we propose a hybrid planning-control framework designed to compute a feasible trajectory toward

Jianqi Yan, Alex P. Leung, Zhiyuan Pei, David C. Y. Hui

This work introduces a novel deep learning-based approach for gravitational wave anomaly detection, aiming to overcome the limitations of traditional matched filtering techniques in identifying unknown waveform gravitational wave signals. We introduce a modified convolutional neural network architecture inspired by ResNet that leverages residual blocks to ex