March 2025 arXiv papers — page 113

Lisa Piccinin, Valentina Breschi, Chiara Ravazzi, Fabrizio Dabbene

Sustainable technologies and services can play a pivotal role in the transition to "greener" habits. Their widespread adoption is thus crucial, and understanding how to foster this phenomenon in a systematic way could have a major impact on our future. With this in mind, in this work we propose an extension of the Friedkin-Johnsen opinion dynamics model towa

Bibi Erum Ayesha, T. Satyanarayana Murthy, Palamakula Ramesh Babu, Ramu Kuchipudi

This research paper presents an innovative ship detection system tailored for applications like maritime surveillance and ecological monitoring. The study employs YOLOv8 and repurposed U-Net, two advanced deep learning models, to significantly enhance ship detection accuracy. Evaluation metrics include Mean Average Precision (mAP), processing speed, and over

Eva Julia Schmitt, Benjamin Noack

Resources such as bandwidth and energy are limited in many wireless communications use cases, especially when large numbers of sensors and fusion centers need to exchange information frequently. One opportunity to overcome resource constraints is the use of event-based transmissions and estimation to transmit only information that contributes significantly t

Hans A. Weidenmüller

In the last 175 years, the physical understanding of nature has seen a revolutionary change. Until about 1850, Newton's theory and the mechanical world view derived from it provided the dominant view of the physical world, later supplemented by Maxwell's theory of the electromagnetic field. That approach was entirely deterministic and free of probabilistic c

Faith Young, Dmitry Alexandrovsky, Daniela Wurhofer, Eva-Maria Krah

This study protocol outlines the design and methodology of a research study investigating collaborative game elements to promote physical activity within digital health interventions. The study aims to examine how social relatedness influences motivation and adherence to step-count goals. Participants will use Shared Achievements, a minimalistic multiplayer

Jean C. Peyen, Ruheyan Nuermaimaiti, Joshua Cunningham

Maximal strength increases with body weight, this is why scoring methods have been developed in order to fairly scale powerlifting performances based on athletes' body weight. The International Powerlifting Federation (IPF) Good Lift (GL) system, introduced in 2020, is a scaling method based on a Von Bertalanffy function. It is specifically tailored to elite

Gal Versano, Itzik Klein

Autonomous mobile robots are widely used for navigation, transportation, and inspection tasks indoors and outdoors. In practical situations of limited satellite signals or poor lighting conditions, navigation depends only on inertial sensors. In such cases, the navigation solution rapidly drifts due to inertial measurement errors. In this work, we propose WM

S. Vijayasree, F. Niederhofer, M. -R. L. Cioni, L. Cullinane

Context: Studying the internal kinematics of galaxies provides insights into their past evolution, current dynamics, and future trajectory. The Large Magellanic Cloud (LMC), as the largest and one of the nearest satellite galaxies of the Milky Way, presents unique opportunities to investigate these phenomena in great detail. In this study, we investigate the

Junjie Chen, Haitao Li, Zhumin Chu, Yiqun Liu

In this paper, we provide an overview of the NTCIR-18 Automatic Evaluation of LLMs (AEOLLM) task. As large language models (LLMs) grow popular in both academia and industry, how to effectively evaluate the capacity of LLMs becomes an increasingly critical but still challenging issue. Existing methods can be divided into two types: manual evaluation, which is

Adam J. Stone, John Paul Gosling

This paper introduces the Heteroscedastic AddiVortes model, a Bayesian non-parametric regression framework that simultaneously models the conditional mean and variance of a response variable using adaptive Voronoi tessellations. By employing a sum-of-tessellations approach for the mean and a product-of-tessellations approach for the variance, the model provi

Haofeng Chen, Jiri Kubik, Bedrich Himmel, Matej Hoffmann

Tactile skins based on electrical impedance tomography (EIT) enable large-area contact localization with few electrodes, but suffer from nonuniform sensitivity that limits force estimation accuracy. This work introduces a dual-channel tactile skin that integrates an EIT layer with a pneumatic pressure layer and a calibration framework that leverages their co

Giuseppe Cosma Brusca, Davide Donati, Chiara Trifone

We discuss a model for phase transitions in which a double-well potential is singularly perturbed by possibly several terms involving different, arbitrarily high orders of derivation. We study by $\Gamma$-convergence the asymptotic behaviour as $\varepsilon\to 0$ of the functionals \begin{equation*} F_\varepsilon(u):=\int_\Omega \Bigl[\frac{1}{\varepsilon}W(

Tinghui Li, Pamuditha Somarathne, Zhanna Sarsenbayeva, Anusha Withana

Continuous prediction of finger joint movement using historical joint positions/rotations is vital in a multitude of applications, especially related to virtual reality, computer graphics, robotics, and rehabilitation. However, finger motions are highly articulated with multiple degrees of freedom, making them significantly harder to model and predict. To ad

Adrian Bekasiewicz, Tom Dhaene, Ivo Couckuyt, Jacek Litka

Design of ultra-wideband antennas for in-door localization applications is a challenging task that involves development of geometry that ensures appropriate balance between the size and performance. In this work, a topologically-flexible monopole has been generated using a stratified framework which embeds a gradient-based trust-region (TR) optimization algo

Jakob Sponholz, Andreas Weilinghoff, Juliane Schopf

In qualitative research, data transcription is often labor-intensive and time-consuming. To expedite this process, a workflow utilizing artificial intelligence (AI) was developed. This workflow not only enhances transcription speed but also addresses the issue of AI-generated transcripts often lacking compatibility with standard content analysis software. Wi

Loic Fache, Francois Copie, Pierre Suret, Stéphane Randoux

We present optical fiber experiments investigating the perturbed, non-integrable evolution of soliton gases (SGs) under weak linear damping and gain. By measuring the amplitude and phase of the optical field in a recirculating loop, we determine the spectral distribution of SGs at various propagation distances. We demonstrate that a SG, initially prepared as

Adrian Bekasiewicz, Mariusz Dzwonkowski, Tom Dhaene, Ivo Couckuyt

Design of antennas for modern applications is a challenging task that combines cognition-driven development of topology intertwined with tuning of its parameters using rigorous numerical optimization. However, the process can be streamlined by neglecting the engineering insight in favor of automatic de-termination of structure geometry. In this work, a speci

Tony Danjun Wang, Lennart Bastian, Tobias Czempiel, Christian Heiliger

Surgical domain models improve workflow optimization through automated predictions of each staff member's surgical role. However, mounting evidence indicates that team familiarity and individuality impact surgical outcomes. We present a novel staff-centric modeling approach that characterizes individual team members through their distinctive movement pattern

Zhijie Liu, Xingyue Ma, Lan Chen, Xiaohong Yan

Electrostatic dielectric capacitors are foundational to advance the electronics and electric power devices due to their ultrafast charging/discharging capability and high-power density. However, the low energy density limits the potential for next generation devices in terms of miniaturization and integration. We propose a strategy that relies on inducing a

Tao Wang, Changxu Cheng, Lingfeng Wang, Senda Chen

The remarkable performance of large multimodal models (LMMs) has attracted significant interest from the image segmentation community. To align with the next-token-prediction paradigm, current LMM-driven segmentation methods either use object boundary points to represent masks or introduce special segmentation tokens, whose hidden states are decoded by a seg

ChangHee Yang, Hyeonseop Song, Seokhun Choi, Seungwoo Lee

Despite considerable efforts to enhance the generalization of 3D pose estimators without costly 3D annotations, existing data augmentation methods struggle in real world scenarios with diverse human appearances and complex poses. We propose PoseSyn, a novel data synthesis framework that transforms abundant in the wild 2D pose dataset into diverse 3D pose ima

T. C. L. Trueman, M. Pignatari, B. Cseh, J. D. Keegans

Thermonuclear explosions of carbon-oxygen white dwarfs as Type Ia supernovae (SNe Ia) play a significant role in the galactic chemical evolution (GCE) of the Milky Way. However, a long-standing and as yet unresolved problem of modern astrophysics concerns the identity of their progenitor. We aim to use GCE predictions to help constrain potential SN Ia progen

Michal Danilowicz, Tomasz Kryjak

Multi-object tracking (MOT) is one of the most important problems in computer vision and a key component of any vision-based perception system used in advanced autonomous mobile robotics. Therefore, its implementation on low-power and real-time embedded platforms is highly desirable. Modern MOT algorithms should be able to track objects of a given class (e.g

Jen-Tsung Hsiang, Bei-Lok Hu

We continue our investigation of multi-partite open quantum systems comprising layers of structure using the atom-field-medium interactions as a familiarly important example. Same as in Paper I~\cite{HH24} we consider a system of $N$ harmonic oscillators, modeling the internal degrees of freedom (idf) of $N$ neutral atoms interacting with a scalar quantum fi

Omri Suissa, Muhiim Ali, Ariana Azarbal, Hui Shen

CLIP has demonstrated exceptional image-text matching capabilities due to its training on contrastive learning tasks. Past research has suggested that whereas CLIP effectively matches text to images when the matching can be achieved just by matching the text with the objects in the image, CLIP struggles when the matching depends on representing the relations

Sanchi Monga, Saswata Bhattacharya

The mechanical exfoliation of two-dimensional materials has sparked significant interest in the study of low-dimensional structures. In this work, we investigate the bulk and low-dimensional derivatives of BiSeI, a quasi-one-dimensional anisotropic crystal known for its remarkable stability and novel electronic properties. Using the density functional theory

Adrian Bekasiewicz, Slawomir Koziel, Tom Dhaene, Marcin Narloch

Numerical methods are important tools for design of modern antennas. Trust-region (TR) methods coupled with data-efficient surrogates based on finite differentiation (FD) represent a popular class of antenna design algorithms. However, TR performance is subject to FD setup, which is normally determined a priori based on rules-of-thumb. In this work, the effe

Silvia Cazacu, Georgia Panagiotidou, Therese Steenberghen, Andrew Vande Moere

Participatory data physicalisation (PDP) is recognised for its potential to support data-driven decisions among stakeholders who collaboratively construct physical elements into commonly insightful visualisations. Like all participatory processes, PDP is however influenced by underlying power dynamics that might lead to issues regarding extractive participat

Philip D. Michel, Paolo A. Mazzali, Daniel A. Perley, K-Ryan Hinds

SN 2023ixf is one of the brightest Core Collapse Supernovae of the 21st century and offers a rare opportunity to investigate the late stage of a Supernova through nebular phase spectroscopy. We present four nebular phase spectra from day +291 to +413 after explosion. This is supplemented with high cadence early phase spectroscopic observations and photometry

Zijia Zhao, Yuqi Huo, Tongtian Yue, Longteng Guo

Most current video MLLMs rely on uniform frame sampling and image-level encoders, resulting in inefficient data processing and limited motion awareness. To address these challenges, we introduce EMA, an Efficient Motion-Aware video MLLM that utilizes compressed video structures as inputs. We propose a motion-aware GOP (Group of Pictures) encoder that fuses s

Eleonora Raimondo, Esteban Garzón, Yixin Shao, Andrea Grimaldi

Probabilistic computing with pbits is emerging as a computational paradigm for machine learning and for facing combinatorial optimization problems (COPs) with the so-called probabilistic Ising machines (PIMs). From a hardware point of view, the key elements that characterize a PIM are the random number generation, the nonlinearity, the network of coupled pbi

Shuang Wang, Fei Deng, Peifan Jiang, Zezheng Ni

Due to limitations such as geographic, physical, or economic factors, collected seismic data often have missing traces. Traditional seismic data reconstruction methods face the challenge of selecting numerous empirical parameters and struggle to handle large-scale continuous missing traces. With the advancement of deep learning, various diffusion models have

Nadine Wetta, Jean-Christophe Pain

Atomic properties of warm dense matter is an active field of research. Understanding transport properties of these states is essential for providing coefficients needed by magneto-radiative hydrodynamics codes for many studies, including hydrodynamic instabilities, energy balances or heating in fusion plasmas, difficult to investigate by experimental means.

L. Gironi, S. Dell'Oro, C. Gotti, N. Manenti

Scintillators are widely used in radiation detection, with continuous advancements enhancing their performance and developing new materials. This study presents a custom experimental setup for the characterization of crystal scintillators under different temperature and pressure conditions. The setup is flexible and capable of providing prompt feedback, whic

Xingguo Lv, Xingbo Dong, Liwen Wang, Jiewen Yang

Despite domain generalization (DG) has significantly addressed the performance degradation of pre-trained models caused by domain shifts, it often falls short in real-world deployment. Test-time adaptation (TTA), which adjusts a learned model using unlabeled test data, presents a promising solution. However, most existing TTA methods struggle to deliver stro

Hao Yang, Lidia Al-Zogbi, Ahmet Yildiz, Nabil Simaan

Laparoscopic surgery constrains instrument motion around a fixed pivot point at the incision into a patient to minimize tissue trauma. Surgical robots achieve this through either hardware to software-based remote center of motion (RCM) constraints. However, accurate RCM alignment is difficult due to manual trocar placement, patient motion, and tissue deforma

Silas Weinert, Jonas Bundschuh, Yvonne Späck-Leigsnering, Herbert De Gersem

Foil windings have, due to their layered structure, different properties than conventional wire windings, which make them advantageous for high frequency applications. Both electromagnetic and thermal analyses are relevant for foil windings. These two physical areas are coupled through Joule losses and temperature dependent material properties. For an effici

Jarne Van Mulders, Gilles Callebaut

This paper presents a preliminary study exploring the feasibility of designing batteryless electronic shelf labels (ESLs) powered by radio frequency wireless power transfer using commercial off-the-shelf components. The proposed ESL design is validated through a dedicated testbed and involves a detailed analysis of design choices, including energy consumptio

David E. Hernandez, Jose Ramon Chang, Torbjörn E. M. Nordling

Efficient deployment of deep neural networks on resource-constrained devices demands advanced compression techniques that preserve accuracy and interoperability. This paper proposes a machine learning framework that augments Knowledge Distillation (KD) with Integrated Gradients (IG), an attribution method, to optimise the compression of convolutional neural

Carlos Lozano, Jorge Ponsin

The characteristic structure of the two-dimensional adjoint Euler equations is examined. The behavior is similar to that of the original Euler equations, but with the information travelling in the opposite direction. The compatibility conditions obeyed by the adjoint variables along characteristic lines are derived. It is also shown that adjoint variables ca

Noémie C. Combe

We establish that the Grothendieck-Teichmuller conjecture, which predicts an isomorphism between the Grothendieck-Teichmuller group GT and the absolute Galois group of rational numbers Gal, holds in the setting of profinite spaces. To access arithmetic information within this framework, we introduce a generalization of the notion of path integrals, defined s

László Kérchy, Carl Pearcy

We show that if a nonscalar operator on a separable Hilbert space has a nontrivial invariant subspace, then it has also a nontrivial hyperinvariant subspace. Thus the hyperinvariant subspace problem is equivalent to the invariant subspace problem. As a consequence we obtain that every bilateral weighted shift has a proper hyprinvariant subspace. Our proof is

Jiaxu Liu, Li Li, Hubert P. H. Shum, Toby P. Breckon

Diffusion models currently demonstrate impressive performance over various generative tasks. Recent work on image diffusion highlights the strong capabilities of Mamba (state space models) due to its efficient handling of long-range dependencies and sequential data modeling. Unfortunately, joint consideration of state space models with 3D point cloud generat

Shiva Sinaei, Daisuke Iwai, Kousuke Sato

Saliency modulation has significant potential for various applications. In our pursuit of implementing saliency modulation for optical see-through near-eye displays, we decided to introduce a blur effect to reduce the sharpness of specific areas while preserving the sharpness of others. In this study, we used a digital micromirror device (DMD) to separate th

Emily Laue Christensen, Maria Paasivaara, Iflaah Salman

The Covid-19 pandemic established hybrid work as the new norm in software development companies. In large-scale agile, meetings of different types are pivotal for collaboration, and decisions need to be taken on how they are organized and carried out in hybrid work. This study investigates how recurring meetings are organized and carried out in hybrid work i

Leo Zanotti

It is shown that any continuous piecewise affine (CPA) function $\mathbb{R}^2\to\mathbb{R}$ with $p$ pieces can be represented by a ReLU neural network with two hidden layers and $O(p)$ neurons. Unlike prior work, which focused on convex pieces, this analysis considers CPA functions with connected but potentially non-convex pieces.

Fengrui Yao, Menghan Liao, Marco Gibertini, Cheol-Yeon Cheon

Antiferromagnetic conductors with suitably broken spatial symmetries host spin-polarized bands, which lead to transport phenomena commonly observed in metallic ferromagnets. In bulk materials, it is the given crystalline structure that determines whether symmetries are broken and spin-polarized bands are present. Here we demonstrate experimentally that doubl

Thibaut Bourdais, Nadia Oudjane, Francesco Russo

This paper focuses on stochastic optimal control problems with constraints in law, which are rewritten as optimization (minimization) of probability measures problem on the canonical space. We introduce a penalized version of this type of problems by splitting the optimization variable and adding an entropic penalization term. We prove that this penalized ve

Samuel M. Corson, Saharon Shelah

It is shown, from $\sigma$-centered Martin's Axiom, that there exists a proper dense subgroup of the symmetric group on a countably infinite set whose natural action on sufficiently flexible relational structures is transitive. This allows us to give consistent positive answers to some questions of Peter M. Neumann from the 1980s.

Avinandan Das, Pierre Fraigniaud, Ami Paz, Adi Rosen

We introduce the {\em certification} of solutions to graph problems when access to the input is restricted. This topic has received a lot of attention in the distributed computing setting, and we introduce it here in the context of \emph{streaming} algorithms, where the input is too large to be stored in memory. Given a graph property $\mbox{P}$, a \emph{str

Julien Roques

Using Hahn series, one can attach to any linear Mahler equation a basis of solutions at 0 reminiscent of the solutions of linear differential equations at a regular singularity. We show that such a basis of solutions can be produced by using a variant of Frobenius method.

Noé Cecillon, Vincent Labatut, Richard Dufour

Abusive behavior is common on online social networks, and forces the hosts of such platforms to find new solutions to address this problem. Various methods have been proposed to automate this task in the past decade. Most of them rely on the exchanged content, but ignore the structure and dynamics of the conversation, which could provide some relevant inform

Geoff Beck

The dual wave-particle nature of quantum objects is a notoriously unintuitive feature of quantum theories. However, it is often deemed essential, due to quantum objects exhibiting diffraction and interference. We extend the work of Land\'{e} and L\'{e}vy-Leblond to demonstrate that de Broglie wavelengths are not relativistically covariant as simultaneous spa

Muhan Hou, Koen Hindriks, A. E. Eiben, Kim Baraka

Transfer Learning (TL) is a powerful tool that enables robots to transfer learned policies across different environments, tasks, or embodiments. To further facilitate this process, efforts have been made to combine it with Learning from Demonstrations (LfD) for more flexible and efficient policy transfer. However, these approaches are almost exclusively limi

Michael Pichat, William Pogrund, Paloma Pichat, Armanouche Gasparian

This study investigates the ability of perceptron-type neurons in language models to perform intra-neuronal attention; that is, to identify different homogeneous categorical segments within the synthetic thought category they encode, based on a segmentation of specific activation zones for the tokens to which they are particularly responsive. The objective o

Sam Albin, Garhan Attebury, Kenneth Bloom, Brian Paul Bockelman

As a part of the IRIS-HEP "Analysis Grand Challenge" activities, the Coffea-casa AF team executed a "200 Gbps Challenge". One of the goals of this challenge was to provide a setup for execution of a test notebook-style analysis on the facility that could process a 200 TB CMS NanoAOD dataset in 20 minutes. We describe the solutions we deployed at the facility

Ben Bals

Researchers, policy makers, and engineers need to make sense of data on spreading processes as diverse as viral infections, water contamination, and misinformation in social networks. Classical questions include predicting infection behavior in a given network or deducing the structure of a network from infection data. We study two central problems in this a

Roba Al Majzoub, Hashmat Malik, Muzammal Naseer, Zaigham Zaheer

Recently, histopathology vision-language foundation models (VLMs) have gained popularity due to their enhanced performance and generalizability across different downstream tasks. However, most existing histopathology benchmarks are either unimodal or limited in terms of diversity of clinical tasks, organs, and acquisition instruments, as well as their partia

Palakorn Achananuparp, Ee-Peng Lim, Yao Lu

Automatically annotating job data with standardized occupations from taxonomies, known as occupation classification, is crucial for labor market analysis. However, this task is often hindered by data scarcity and the challenges of manual annotations. While large language models (LLMs) hold promise due to their extensive world knowledge and in-context learnin

Wenqiang Wang, Yijia Zhang, Zikai Zhang, Guanting Huo

As large language models (LLMs) demonstrate powerful capabilities, deploying them on edge devices has become increasingly crucial, offering advantages in privacy and real-time interaction. QLoRA has emerged as the standard approach for on-device LLMs, leveraging quantized models to reduce memory and computational costs while utilizing LoRA for task-specific

Karolına Sehnalová, Didier Henrion, Milan Korda, Martin Kružík

The behaviour of the moment-sums-of-squares (moment-SOS) hierarchy for polynomial optimal control problems on compact sets has been explored to a large extent. Our contribution focuses on the case of non-compact control sets. We describe a new approach to optimal control problems with unbounded controls, using compactification by partial homogenization, lead

Kala Agbo Bidi

The implementation of the Sterile Insect Technique (SIT) to manage a target population has been the focus of numerous recent scientific studies. The present work focuses on a feedback law that depends linearly on the state variables of the SIT control system. We provide both mathematical proof and numerical illustrations demonstrating the global asymptotic s

Rukmani Mohanta

Neutrinos, being elusive subatomic particles having only weak interactions, provide an ideal platform to look for physics beyond the Standard Model. In the present era of neutrino physics, various experiments are focusing towards the precision measurements of the oscillation parameters. Hence, various new physics scenarios which can affect the neutrino oscil

Harikrishnan KP, Varun Harbola, Jaehong Choi, Kevin J. Crust

Modern electromechanical actuators and sensors rely on the piezoelectric effect that linearly couples strain and electric polarization. However, this effect is restricted to materials that lack inversion symmetry. In contrast, the flexoelectric effect couples strain gradients to electric polarization, and is a universal property in insulating materials of ar

Hala Houmsi, Benoit Sklénard, Marc Guillaumont, François Triozon

Accurately determining the coupling strength between polaron states is essential to describe charge-hopping transport in materials. In this work, we revisit methodologies to extract coupling strengths using ab initio approaches. Our findings underscore the critical role of incorporating anharmonic effects in the model Hamiltonian when analyzing total energy

Pak Shen Choong, Aqilah Rasat, Afiqa Nik Aimi, Nurisya Mohd Shah

Concepts on quantum physics are generally difficult for the general public to understand and grasp due to its counter-intuitive nature and requirement for higher level of mathematical literacy. With categorical quantum mechanics (CQM), quantum theory is re-formalized into a more intuitive diagrammatic approach, which we will refer to as the first level of tr

Yunshuang Yuan, Yan Xia, Daniel Cremers, Monika Sester

Cooperative perception can increase the view field and decrease the occlusion of an ego vehicle, hence improving the perception performance and safety of autonomous driving. Despite the success of previous works on cooperative object detection, they mostly operate on dense Bird's Eye View (BEV) feature maps, which are computationally demanding and can hardly

Thu Tran, Kenny Tsu Wei Choo, Shaohui Foong, Hitesh Bhardwaj

Monitoring swimmer performance is crucial for improving training and enhancing athletic techniques. Traditional methods for tracking swimmers, such as above-water and underwater cameras, face limitations due to the need for multiple cameras and obstructions from water splashes. This paper presents a novel approach for tracking swimmers using a moving UAV. Th

Marta Menci, Thierry Paul, Stefano Rossi, Tommaso Tenna

In this paper, we propose a numerical investigation of topological interactions in flocking dynamics. Starting from a microscopic description of the phenomena, mesoscopic and macroscopic models have been previously derived under specific assumptions. We explore the role of topological interactions by describing the convergence speed to consensus in both micr

Emiliano Ambrosi, Giuseppe Ancona

We prove a characteristic two version of the famous criterion of Artin and Mumford for irrationality of conic bundles. On the one hand, combined with the pathological behaviour of conic bundles in characteristic two, this allows us to construct easier and more explicit examples of irrational conic bundles. On the other hand, degeneration techniques \`a la Vo

Yang Ji, Ying Sun, Hengshu Zhu

In the era of the knowledge economy, understanding how job skills influence salary is crucial for promoting recruitment with competitive salary systems and aligned salary expectations. Despite efforts on salary prediction based on job positions and talent demographics, there still lacks methods to effectively discern the set-structured skills' intricate comp

Leonard E. C. Romano, Manuel Behrendt, Andreas Burkert

Aims. We introduce the SISSI (Supernovae In a Stratified, Shearing Interstellar medium) simulation suite, which aims to enable a more comprehensive understanding of supernova remnants (SNRs) evolving in a complex interstellar medium (ISM) structured under the influence of galactic rotation, gravity and turbulence. Methods. We utilize zoom-in simulations of 3

Yan Kim, Wojciech Jamroga, Wojciech Penczek, Laure Petrucci

Model checking of temporal logics in a well established technique to verify and validate properties of multi-agent systems (MAS). However, practical model checking requires input models of manageable size. In this paper, we extend the model reduction method by variable-based abstraction, proposed recently by Jamroga and Kim, to the verification of real-time

Colin Cros, Laurent Ferro-Famil

This paper presents a non-parametric method for 3-D imaging of natural volumes using Synthetic Aperture Radar tomography. This array processing-based technique aims at characterizing a spatially distributed density of incoherent sources, whose shape is imprecisely known. The proposed technique estimates the moments of the reflectivity density using a low-com

Xueying Jiang, Wenhao Li, Xiaoqin Zhang, Ling Shao

3D task planning has attracted increasing attention in human-robot interaction and embodied AI thanks to the recent advances in multimodal learning. However, most existing studies are facing two common challenges: 1) heavy reliance on explicit instructions with little reasoning on implicit user intention; 2) negligence of inter-step route planning on robot m

Colin Prieur, Nassim Ait Ali Braham, Paul Tresson, Grégoire Vincent

Airborne hyperspectral imaging is a promising method for identifying tropical species, but spectral variability between acquisitions hinders consistent results. This paper proposes using Self-Supervised Learning (SSL) to encode spectral features that are robust to abiotic variability and relevant for species identification. By employing the state-of-the-art

Junming Liu, Siyuan Meng, Yanting Gao, Song Mao

Multimodal reasoning in Large Language Models (LLMs) struggles with incomplete knowledge and hallucination artifacts, challenges that textual Knowledge Graphs (KGs) only partially mitigate due to their modality isolation. While Multimodal Knowledge Graphs (MMKGs) promise enhanced cross-modal understanding, their practical construction is impeded by semantic

Tianlin Zhou, Fei Gao, Qinghua Zhang, Yuansha Chen

Interfacing complex oxides in atomically engineered layered structures can give rise to a wealth of exceptional electronic and magnetic properties that surpass those of the individual building blocks. Herein, we demonstrate a ferromagnetic spin order with a high Curie temperature of 608 K in superlattices consisting of otherwise paramagnetic perovskite LaNiO

B. Kamala Latha, V. S. S. Sastry, S. R. Shenoy

The Lebwohl-Lasher model of uniaxial liquid crystals with (\textit{n} = 3, \textit{d} = 2) was reported earlier to undergo a crossover transition to a novel nematic phase at a temperature $T=T_{n}$. This phase has unbound topological defects in a nematic background, that pair at a lower $T_{\text{BKT}} < T_{n}$. The transition has zero latent heat, and a spe

Kazuki Omi, Jion Oshima, Toru Tamaki

This paper proposes a method for spatio-temporal action detection (STAD) that directly generates action tubes from the original video without relying on post-processing steps such as IoU-based linking and clip splitting. Our approach applies query-based detection (DETR) to each frame and matches DETR queries to link the same person across frames. We introduc

Guanhua Ding, Yuxuan Xia, Runwei Guan, Qinchen Wu

Accurate 3D multi-object tracking (MOT) is crucial for autonomous driving, as it enables robust perception, navigation, and planning in complex environments. While deep learning-based solutions have demonstrated impressive 3D MOT performance, model-based approaches remain appealing for their simplicity, interpretability, and data efficiency. Conventional mod

Avi Levy, Meir Hemmo

We define criteria for a hidden variables theory to be Lorentz invariant and prove that it implies no signaling. As a result, we show that a Lorentz invariant and contextual theory (e.g., quantum field theory) must be genuinely stochastic, i.e., it cannot have a deterministic (strong or weak) hidden variables model. This proof is an improved version of a the

Eliot Beyler, Francis Bach

Score-based generative models achieve state-of-the-art sampling performance by denoising a distribution perturbed by Gaussian noise. In this paper, we focus on a single deterministic denoising step, and compare the optimal denoiser for the quadratic loss, we name ''full-denoising'', to the alternative ''half-denoising'' introduced by Hyv{\"a}rinen (2025). We

Andrea De Domenico, Ali Farjami, Krishna Manoorkar, Alessandra Palmigiano

We continue to develop a research line initiated in \cite{wollic22}, studying I/O logic from an algebraic approach based on subordination algebras. We introduce the classes of slanted (co-)Heyting algebras as equivalent presentations of distributive lattices with subordination relations. Interpreting subordination relations as the algebraic counterparts of i

Zeeshan Patel, Ethan He, Parth Mannan, Xiaowei Ren

Video Foundation Models (VFMs) have recently been used to simulate the real world to train physical AI systems and develop creative visual experiences. However, there are significant challenges in training large-scale, high quality VFMs that can generate high-quality videos. We present a scalable, open-source VFM training pipeline with NVIDIA NeMo, providing

Chaolong Yang, Kai Yao, Yuyao Yan, Chenru Jiang

Audio-driven single-image talking portrait generation plays a crucial role in virtual reality, digital human creation, and filmmaking. Existing approaches are generally categorized into keypoint-based and image-based methods. Keypoint-based methods effectively preserve character identity but struggle to capture fine facial details due to the fixed points lim

Abolfazl Zakeri, Mohammad Moltafet, Marian Codreanu

We address the real-time remote tracking problem in a status update system comprising two sensors, two independent information sources, and a remote monitor. The status updating follows a pull-based communication, where the monitor commands/pulls the sensors for status updates, i.e., the actual state of the sources. We consider that the observations are \tex

Doosung Park

The purpose of this second part of the series is to show a technical result on Chow groups of toric varieties. This is a crucial ingredient for the first part.

Yuta Michimura, Koji Nagano, Kentaro Komori, Kiwamu Izumi

DECIGO (DECi-hertz Interferometer Gravitational Wave Observatory) is a space-based gravitational wave antenna concept targeting the 0.1-10 Hz band. It consists of three spacecraft arranged in an equilateral triangle with 1,000 km sides, forming Fabry-P\'erot cavities between them. A precursor mission, B-DECIGO, is also planned, featuring a smaller 100 km tri

Ovidiu Cristinel Stoica

Both empirical and theoretical objective science can only access relations, revealing nothing about the intrinsic nature of the entities "in relation". We typically refer to these entities as "matter", assuming their nature is irrelevant and that relational structures alone explain all phenomena, including consciousness. This would imply that consciousness a

Yunbo Li, Jiaping Gui, Yue Wu

This paper proposes FedSVA, an explainable differential privacy (DP) mechanism for federated learning (FL) that dynamically calibrates noise injection based on the privacy contribution of attributes via Shapley Values. Unlike heuristic DP methods, FedSVA quantifies each attribute's influence on model training and adjusts noise accordingly, providing rigorous

Tomohiro Taniguchi

Physical reservoir computing by using artificial spin ice (ASI) has been proposed on the basis of both numerical and experimental analyses. ASI is a many-body system consisting of ferromagnets with various interactions. Recently, fabricating magnetic tunnel junctions (MTJs) as ferromagnets in an ASI was achieved in the experiment, which enables an electrical

Animesh Choudhury, Jagabandhu Panda

Numerical Weather Prediction (NWP) has advanced significantly in recent decades but still faces challenges in accuracy, computational efficiency, and scalability. Data-driven weather models have shown great promise, sometimes surpassing operational NWP systems. However, training these models on massive datasets incurs high computational costs. A regional dat

Jiahe Zhao, Ruibing Hou, Zejie Tian, Hong Chang

We propose a new task to benchmark human-in-scene understanding for embodied agents: Human-In-Scene Question Answering (HIS-QA). Given a human motion within a 3D scene, HIS-QA requires the agent to comprehend human states and behaviors, reason about its surrounding environment, and answer human-related questions within the scene. To support this new task, we

Rouven Maier, Cheng-I Ho, Hitoshi Sumiya, Shinobu Onoda

Nuclear magnetic resonance (NMR) spectroscopy is widely used in fields ranging from chemistry, material science to neuroscience. Nanoscale NMR spectroscopy using Nitrogen-vacancy (NV) centers in diamond has emerged as a promising platform due to an unprecedented sensitivity down to the single spin level. At the nanoscale, high nuclear spin polarization throu

Zheyuan Liu, Junyan Wang, Zicheng Duan, Cristian Rodriguez-Opazo

Text-video prediction (TVP) is a downstream video generation task that requires a model to produce subsequent video frames given a series of initial video frames and text describing the required motion. In practice TVP methods focus on a particular category of videos depicting manipulations of objects carried out by human beings or robot arms. Previous metho

Elif Dicle Demir, Buse Bilgin, Mehmet Cengiz Onbasli

As quantum computing advances, modern cryptographic standards face an existential threat, necessitating a transition to post-quantum cryptography (PQC). The National Institute of Standards and Technology (NIST) has selected CRYSTALS-Kyber and CRYSTALS-Dilithium as standardized PQC algorithms for secure key exchange and digital signatures, respectively. This

Guojie Zheng, Xin Yu

This paper studies the state observation problems for the semilinear heat equation in R^n. We derive observation estimates for the equation using the logarithmic convexity property of the frequency function (see [12]). As an application, we show that if two solutions coincide on a nonempty open subset \omega\subset\Omega at some time T>0, then they must be i

Tamás Szalai, Szanna Zsíros, Jacob Jencson, Ori D. Fox

Core-collapse supernovae (CCSNe) have long been considered to contribute significantly to the cosmic dust budget. New dust cools quickly and is therefore detectable at mid-infrared (mid-IR) wavelengths. However, before the era of the James Webb Space Telescope (JWST), direct observational evidence for dust condensation was found in only a handful of nearby C

Umut Güvengir, Dilek Küçük, Serkan Buhan, Cuma Ali Mantaş

Automatic classification of electric power quality events with respect to their root causes is critical for electrical grid management. In this paper, we present comparative evaluation results of an extensive set of machine learning models for the classification of power quality events, based on their root causes. After extensive experiments using different