March 2025 arXiv papers — page 94

Jonathan M. Fraser, Firdavs Rakhmonov

We consider the problem of bounding the number of exceptional projections (projections which are smaller than typical) of a subset of a vector space over a finite field onto subspaces. We establish bounds that depend on $L^p$ estimates for the Fourier transform, improving various known bounds for sets with sufficiently good Fourier analytic properties. The s

Fábio Natali, Dmitry E. Pelinovsky, Shuoyang Wang

The traveling wave with the peaked profile arises in the limit of the family of traveling waves with the smooth profiles. We study the linear and nonlinear stability of the peaked traveling wave by using a local model for shallow water waves, which is related to the Hunter--Saxton equation. The evolution problem is well-defined in the function space $H^1_{\r

Kana Kurata, Hitoshi Niigaki, Xiaojun Wu, Ryuichi Tanida

Optical sensor applications have become popular through digital transformation. Linking observed data to real-world locations and combining different image sensors is essential to make the applications practical and efficient. However, data preparation to try different sensor combinations requires high sensing and image processing expertise. To make data pre

Carlos A. R. Herdeiro, Eugen Radu

We add an $S^1$ extra dimension (size $L$) to the well known $D=4$ static, spherically symmetric $Q$-balls and boson stars. We show that the resulting uniform horizonless boson strings possess a static zero-mode for a critical value of $L$. This is at the threshold of a Gregory-Laflamme instability of these objects, occurring for larger values of $L$.The non

Cui-Cui Ding, Qin Zhou, B. A. Malomed

We demonstrate that the model of a spatially non-uniform two-component Bose-Einstein condensate (BEC) featuring the helicoidal spin-orbit coupling (SOC), gives rise to dark-bright soliton complexes characterized by spatiotemporal periodic oscillations in each component. These solitons are formed by the superposition of dark and bright ones, and exhibit a bea

Sam Taziaux, Ancla Müller, Björn Adebahr, Aritra Basu

The study of radio emission in starburst dwarf galaxies provides a unique opportunity to investigate the mechanisms responsible for the amplification and transport of magnetic fields. Local dwarfs are often considered proxies for early-Universe galaxies, so this study may provide insights into the role of non-thermal components in the formation and evolution

So Tanaka, Teruaki Suyama

Gravitational lensing of gravitational waves provides a powerful probe of the mass density distribution in the universe. Wave optics effects, such as diffraction, make the lensing effect sensitive to the structure around the Fresnel scale, which depends on the gravitational wave frequency and is typically sub-Galactic for realistic observations. Contrary to

Andrea Oliveri, Davide Balzarotti

Memory forensics is a powerful technique commonly adopted to investigate compromised machines and to detect stealthy computer attacks that do not store data on non-volatile storage. To employ this technique effectively, the analyst has to first acquire a faithful copy of the system's volatile memory after the incident. However, almost all memory acquisition

V. Rodriguez-Franco, M. M. Spiering, F. Ritort, M. Mañosas

DNA helicases are molecular motors that use the energy from ATP hydrolysis to move along DNA, promoting the unwinding or rewinding of the double helix. Here, we use magnetic and optical tweezers to track the motion of three helicases, gp41, RecQ, and RecG, while they unwind or rewind a DNA hairpin. Their activity is characterized by measuring the helicase ra

Carlos Molero, Pablo H. Zapata-Cano, Antonio Alex-Amor

This paper introduces a formal definition of the transfer ABCD parameters in time-varying electromagnetic systems. The formal definition comes after the rearrangement of the fields $D$ and $B$ at the inputs and outputs of the temporal system based on the time-varying boundary conditions. Then, we derive the ABCD parameters of a temporal transmission line, i.

Yixin Li, Xueyu Geng

This paper presents an improved non-ordinary state-based peridynamics (NOSB PD) framework for modelling the elastoplastic behaviour and damage of geomaterials, such as soil, rock, and concrete, under quasi static conditions. Conventional NOSB PD for elastoplastic materials faces two primary challenges: the surface effect due to the low accuracy of the approx

Gonçalo Hora de Carvalho

This study investigated visual impairment complaints in a sample of 948 Acquired Brain Injury (ABI) patients using the DiaNAH dataset, emphasizing advanced machine learning techniques for managing missing data. Patients completed a CVS questionnaire capturing eight types of visual symptoms, including blurred vision and altered contrast perception. Due to inc

Indranil Ghosh, Mina Norouzirad, Filipe J. Marques

Although the specification of bivariate probability models using a collection of assumed conditional distributions is not a novel concept, it has received considerable attention in the last decade. In this study, a bivariate distribution-the bivariate Poisson-Gamma conditional distribution-is introduced, combining both univariate continuous and discrete dist

Ran Si, Chaofan Shi, Nan Xue, Xiangjin Kong

The $^{229}\text{Th}$ isotope is a promising candidate for nuclear clocks, with its transition frequency influenced by electron-induced nuclear frequency shifts. This effect is comparatively small and requires high-precision theoretical calculations. In this work, we employed a non-perturbative multi-configuration Dirac-Hartree-Fock (MCDHF) method, in contra

Imanol G. Estepa, Jesús M. Rodríguez-de-Vera, Ignacio Sarasúa, Bhalaji Nagarajan

While representation learning and generative modeling seek to understand visual data, unifying both domains remains unexplored. Recent Unified Self-Supervised Learning (SSL) methods have started to bridge the gap between both paradigms. However, they rely solely on semantic token reconstruction, which requires an external tokenizer during training -- introdu

A. Karska, M. Figueira, A. Mirocha, M. Kaźmierczak-Barthel

Massive star formation is associated with energetic processes that may influence the physics and chemistry of parental molecular clouds and impact galaxy evolution. The high-mass protostar DR21 Main in Cygnus X possesses one of the largest and most luminous outflows ever detected in the Galaxy, but the origin of its structure and driving mechanisms is still

Francesco Di Feola, Ludovica Pompilio, Cecilia Assolito, Valerio Guarrasi

Computed Tomography (CT) plays a pivotal role in medical diagnosis; however, variability across reconstruction kernels hinders data-driven approaches, such as deep learning models, from achieving reliable and generalized performance. To this end, CT data harmonization has emerged as a promising solution to minimize such non-biological variances by standardiz

Jaihyun Park, Ryan Cordell

This study investigates the usage of "slave" and "servant" in the 19th century US newspapers using computational methods. While both terms were used to refer to enslaved African Americans, they were used in distinct ways. In the Chronicling America corpus, we included possible OCR errors by using FastText embedding and excluded text reprints to consider text

Suhyeon Lee, Kwanyoung Kim, Jong Chul Ye

Unpaired image-to-image translation has seen significant progress since the introduction of CycleGAN. However, methods based on diffusion models or Schr\"odinger bridges have yet to be widely adopted in real-world applications due to their iterative sampling nature. To address this challenge, we propose a novel framework, Implicit Bridge Consistency Distilla

Arina Razmyslovich, Kseniia Murasheva, Sofia Sedlova, Julien Capitaine

We introduce Efficient LLM Token Extraction (ELTEX), a framework addressing the critical challenge of LLM domain specialization by systematically extracting and integrating domain indicators throughout synthetic data generation. Unlike approaches relying on implicit knowledge transfer, ELTEX explicitly leverages domain signals to maintain specialized knowled

Kecheng Zhang, Ya-Feng Liu, Zhongbin Wang, Weijie Yuan

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial resp

Emily Barnard, Jean-Christophe Novelli, Vincent Pilaud

A congruence of the weak order is simple if its quotientope is a simple polytope. We provide an alternative elementary proof of the characterization of the simple congruences in terms of forbidden up and down arcs. For this, we provide a combinatorial description of the vertices of the corresponding quotientopes in terms of separating trees. This also yields

Junji Xia, Xingjian Lv, Kun Fang, Siming Liu

In gamma-ray astronomy and cosmic-ray physics, the continuous approximation of inverse Compton scattering (ICS) is widely adopted to model the evolution of electron energy. However, when the initial electron energy approaches $\sim100$ TeV, the discrete nature of ICS becomes prominent, and the energy of evolved electrons should be considered as a broad distr

Elisabeth Menendez, Michael Gienger, Santiago Martínez, Carlos Balaguer

Large Language Models (LLMs) have substantially improved the conversational capabilities of social robots. Nevertheless, for an intuitive and fluent human-robot interaction, robots should be able to ground the conversation by relating ambiguous or underspecified spoken utterances to the current physical situation and to the intents expressed nonverbally by t

M. Werwiński, J. Kaczkowski, P. Leśniak, W. L. Malinowski

High-pressure induced structural transition from zircon-type phase into scheelite-type phase in YVO$_4$ is studied using ab initio calculations. Several structures with compressed volumes are evaluated, where for every considered volume the c/a ratio and atomic positions are optimised. The transition pressure and transition volume change are calculated. The

Aolin Chen, Haojun Wu, Qi Xin, Steven P. Reiss

Automated program repair (APR) is designed to automate the process of bug-fixing. In recent years, thanks to the rapid development of large language models (LLMs), automated repair has achieved remarkable progress. Advanced APR techniques powered by conversational LLMs, most notably ChatGPT, have exhibited impressive repair abilities and gained increasing po

Cheng Wang, Lingxin Kong, Massimiliano Tamborski, Stefano V. Albrecht

Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (AVs). However, contemporary methodologies, such as deterministic and imitation learning-based driver models, struggle to capture the variability of human-like driving behavior. Given these challenges, we propose HAD-Gen, a general framework for realist

Saadaoui Islem, Ilahi Hayet, Robin Bryant Christopher, Rejeb Hichem

The study was conducted during 2013 in Bouchebka, located in the central west of Tunisia.Such territory has a typical landscape of the transfrontier region. The series of the forest in Bouchebka is a part of the great mass of Aleppo pine. It is distinguished by the importance of the forest area which covers 92 % of the surface area (19,700 ha). The study att

J. Kleykamp, S. Akhter, Z. Ahmad Dar, N. S. Alex

The first simultaneous measurements of the $\nu_{\mu}$ quasielastic-like cross section on C, CH, H$_2$0, Fe, and Pb targets as a function of kinematic imbalance variables in the plane transverse to the incoming neutrino direction are presented. These variables combine the muon and proton information to provide a new way to disentangle the effects of the nucl

Vladimir Spokoiny

The recent paper \cite{GSZ2023} on estimation and inference for top-ranking problem in Bradley-Terry-Lice (BTL) model presented a surprising result: component-wise estimation and inference can be done under much weaker conditions on the number of comparison then it is required for the full dimensional estimation. The present paper revisits this finding from

Haoyi Li, Angela Yifei Yuan, Soyeon Caren Han, Christopher Leckie

The increasing capability of large language models (LLMs) to generate synthetic content has heightened concerns about their misuse, driving the development of Machine-Generated Text (MGT) detection models. However, these detectors face significant challenges due to the lack of high-quality synthetic datasets for training. To address this issue, we propose SP

Roger Balsach, Alessandro Broggio, Simone Devoto, Andrea Ferroglia

We present new, state-of-the-art predictions for the associated production of the SM Higgs boson with top quarks, computed in accordance with the recommendations of the LHC Higgs Working Group. The NNLO QCD predictions, derived through suitable approximations of the two-loop virtual contribution, are supplemented with soft-gluon resummation up to NNLL accura

Meng-Quan Yang, Wei-Xi Kong, Peng Ru, Ben-Wei Zhang

Particle azimuthal anisotropies inside jets, defined within the momentum plane perpendicular to the jet axis, carry the information of the QCD cascade process for jet formation. In this work, we propose to measure the medium-induced modifications of the elliptic anisotropy inside jets in relativistic heavy-ion collisions to provide novel insight into the jet

Saïd Maanan, Azzouz Dermoune, Ahmed El Ghini

This paper investigates the asymptotic properties of quantile regression estimators in linear models, with a particular focus on polynomial regressors and robustness to heavy-tailed noise. Under independent and identically distributed (i.i.d.) errors with continuous density around the quantile of interest, we establish a general Central Limit Theorem (CLT) f

Valentin Blomer, Ashay Burungale, Philippe Michel, Jun-Hwi Min

Let $f$ be a non-CM elliptic newform without a quadratic inner twist, $p$ an odd prime and $\chi$ a Dirichlet character of $p$-power order and sufficiently large $p$-power conductor. We show that the compositum $\mathbb{Q}_{f}(\chi)$ of the Hecke fields associated to $f$ and $\chi$ is generated by the square of the absolute value of the corresponding central

Florian Heinrichs

This work extends local linear regression to Banach space-valued time series for estimating smoothly varying means and their derivatives in non-stationary data. The asymptotic properties of both the standard and bias-reduced Jackknife estimators are analyzed under mild moment conditions, establishing their convergence rates. Simulation studies assess the fin

Wenhan Xi, Shun-Li Yu, Jian-Xin Li

We utilize the fluctuation-exchange approximation on a bilayer two-orbital model, incorporating $d_{x^2-y^2}$ and $d_{z^2}$ orbitals, to explore potential pairing symmetries in the bilayer nickelate La$_3$Ni$_2$O$_7$. Our study particularly examines the impact of interlayer Coulomb interactions. In the absence of these interactions, the superconducting gap e

Enhan Li

We prove the full Fock--Goncharov conjecture for $\mathcal{A}_{SL_2,\Sigma_{g,p}}$, the $\mathcal{A}$-cluster variety of the moduli of decorated twisted $SL_2$-local systems on triangulable surfaces $\Sigma_{g,p}$ with at least 2 punctures. Equivalently, we show that the tagged skein algebra $Sk^{ta}(\Sigma)$, or the middle cluster algebra $\mathrm{mid}(\mat

Jinsheng Yuan, Yun Tang, Weisi Guo

Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-the-Air Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while co

Satyajeet Sahoo, Jhareswar Maiti

An important aspect of text mining involves information retrieval in form of discovery of semantic themes (topics) from documents using topic modelling. While generative topic models like Latent Dirichlet Allocation (LDA) or Latent Semantic Analysis (LSA) elegantly model topics as probability distributions and are useful in identifying latent topics from lar

Sungjae Lee, Yeonjoo Hong, Kwang In Kim

Despite significant advancements in robotic manipulation, achieving consistent and stable grasping remains a fundamental challenge, often limiting the successful execution of complex tasks. Our analysis reveals that even state-of-the-art policy models frequently exhibit unstable grasping behaviors, leading to failure cases that create bottlenecks in real-wor

Gabriel Emperauger, Mu Qiao, Guillaume Bornet, Cheng Chen

We report on the experimental characterization of various types of spin-exchange interactions between two individual atoms, where pseudo-spin degrees of freedom are encoded in different Rydberg states. For the case of the direct dipole-dipole interaction between states of opposite parity, such as between $nS$ and $nP$, we investigate the effects of positiona

Patrick Donovan

Simply-connected four-dimensional gradient Ricci solitons that are invariant under a compact cohomogeneity one group action have been studied extensively. However, the special case where the group is $SU(2)$ (the smallest possible example) has received comparatively little attention. The purpose of this article is to give a comprehensive study of simply-conn

Julien Courtiel, Matthieu Dien, Paul Dorbec

An increasing 1,2-tree is a labeled graph formed by starting with a vertex and then repeatedly attaching a leaf to a vertex or a triangle to an edge, the labeling of the vertices corresponding to the order in which the vertices are added. Equivalently, increasing 1,2-trees are connected chordal graphs of treewidth at most 2 labeled with a reversed perfect el

Peir-Ru Wang, Yen-Cheng Chang

In this tutorial, we provide the natural derivation of symmetrical, gauge-invariant canonical energy-momentum tensor for the abelian gauge field, i.e., the electromagnetic field.

Vael Hajahmad, Murhaf Alsayed Ali

In this study, we investigate the lepton flavor violation (LFV) of Z gauge boson decaying into two different flavor charged leptons $Z\rightarrow l_i l_j$ ($Z\rightarrow \tau \mu$, $Z\rightarrow \tau e$ and $Z\rightarrow \mu e$). This work is performed in the framework of the constrained minimal supersymmetric standard model (CMSSM) which is extended by the

Jianbo Zhao, Taiyu Ban, Zhihao Liu, Hangning Zhou

Accurate and efficient modeling of agent interactions is essential for trajectory generation, the core of autonomous driving systems. Existing methods, scene-centric, agent-centric, and query-centric frameworks, each present distinct advantages and drawbacks, creating an impossible triangle among accuracy, computational time, and memory efficiency. To break

Young-Jun Choi, Kang-Hyurk Lee, Aeryeong Seo

In this paper, we characterize the K\"ahler-hyperbolicity length of a bounded symmetric domain, defined by its rank and genus, as a unique constant determined by a constant gradient length of a special Bergman potential. Additionally, we establish a characterization of the lower bound of $L^\infty$ norm of the gradient length of any Bergman potential.

Soma Shiraki, Eli Barkai, Takuma Akimoto

Laser cooling of atomic motion enables advances in quantum information and precision metrology. However, the spatial spreading of subrecoil-laser-cooled atoms---crucial for understanding cooling mechanisms and atomic confinement---remains largely unexplored. Here, we analyze anomalous diffusion in subrecoil-laser-cooled atoms, where a velocity-dependent fluo

Xia-Ji Liu, Hui Hu

By using the exact Bethe wavefunctions of the one-dimensional Hubbard model with $N$ spin-up fermions and one spin-down impurity, we derive an analytic expression of the impurity form factor, in the form of a determinant of a $(N+1)$ by $(N+1)$ matrix. This analytic expression enables us to exactly calculate spectral properties of one-dimensional Fermi polar

Riccardo Reho, Arnold H. Kole, Nils Wittemeier, Andrés R. Botello-Méndez

We investigate the superconducting properties of monolayer FeSe, both freestanding (ML FeSe) and on SrTiO$_3$ (STO), by simultaneously solving the Kohn-Sham Density Functional Theory and Bogoliubov--de Gennes equations. Our results demonstrate that the substrate profoundly alters both the normal-state and superconducting properties of FeSe. We identify proxi

Jin Wang, Chenghui Lv, Xian Li, Shichao Dong

Recently, the rapid development of AIGC has significantly boosted the diversities of fake media spread in the Internet, posing unprecedented threats to social security, politics, law, and etc. To detect the ever-increasingly diverse malicious fake media in the new era of AIGC, recent studies have proposed to exploit Large Vision Language Models (LVLMs) to de

Chaouki Boufenar, Mehdi Ayoub Rabiai, Boualem Nadjib Zahaf, Khelil Rafik Ouaras

Handwritten Arabic script recognition is a challenging task due to the script's dynamic letter forms and contextual variations. This paper proposes a hybrid approach combining convolutional neural networks (CNNs) and Transformer-based architectures to address these complexities. We evaluated custom and fine-tuned models, including EfficientNet-B7 and Vision

Saad Lahlali, Sandra Kara, Hejer Ammar, Florian Chabot

Object discovery, which refers to the task of localizing objects without human annotations, has gained significant attention in 2D image analysis. However, despite this growing interest, it remains under-explored in 3D data, where approaches rely exclusively on 3D motion, despite its several challenges. In this paper, we present a novel framework that levera

Luıs Soeiro, Thomas Robert, Stefano Zacchiroli

Developers gain productivity by reusing readily available Free and Open Source Software (FOSS) components. Such practices also bring some difficulties, such as managing licensing, components and related security. One approach to handle those difficulties is to use Software Bill of Materials (SBOMs). While there have been studies on the readiness of practitio

El-Mehdi El Arar, Silviu-Ioan Filip, Theo Mary, Elisa Riccietti

This work proposes a mathematically founded mixed precision accumulation strategy for the inference of neural networks. Our strategy is based on a new componentwise forward error analysis that explains the propagation of errors in the forward pass of neural networks. Specifically, our analysis shows that the error in each component of the output of a linear

Xinxin Zhang, S. Hassan HosseinNia

This study presents a shaped reset feedback control strategy to enhance the performance of precision motion systems. The approach utilizes a phase-lead compensator as a shaping filter to tune the phase of reset instants, thereby shaping the nonlinearity in the first-order reset control. {The design achieves either an increased phase margin while maintaining

Thomas Rahab Lacroix, Pierre Lemaire

The observation of celestial objects is a fundamental activity in astronomy. Ground-based and space telescopes are used to gather electromagnetic radiation from space, allowing astronomers to study a wide range of celestial objects and phenomena, such as stars, planets, galaxies, and black holes. The European Southern Observatory (ESO) charges each night 83

Shengqiong Wu, Hao Fei, Jingkang Yang, Xiangtai Li

The latest emerged 4D Panoptic Scene Graph (4D-PSG) provides an advanced-ever representation for comprehensively modeling the dynamic 4D visual real world. Unfortunately, current pioneering 4D-PSG research can primarily suffer from data scarcity issues severely, as well as the resulting out-of-vocabulary problems; also, the pipeline nature of the benchmark g

Thomas Weiss

The system of interacting Brownian motions, where a particle is reflected asymmetrically from its left neighbor, belongs to the KPZ universality class, with multi-point asymptotics having been derived in previous works. In this paper we show upper tail large deviation principles for all three fundamental initial conditions, including explicit calculation of

Yunwei Lan, Zhigao Cui, Chang Liu, Jialun Peng

Unpaired training has been verified as one of the most effective paradigms for real scene dehazing by learning from unpaired real-world hazy and clear images. Although numerous studies have been proposed, current methods demonstrate limited generalization for various real scenes due to limited feature representation and insufficient use of real-world prior.

Fethi Harkat, Guillaume Gey, Valérie Perrier, Kévin Polisano

Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacenc

Yunlong Mao, Mingyang Niu, Ziqin Dang, Chengxi Li

Efficient and secure federated learning (FL) is a critical challenge for resource-limited devices, especially mobile devices. Existing secure FL solutions commonly incur significant overhead, leading to a contradiction between efficiency and security. As a result, these two concerns are typically addressed separately. This paper proposes Opportunistic Federa

Jianye Xu, Bassam Alrifaee

We examine the complexity of the standard High-Order Control Barrier Function (HOCBF) approach and propose a truncated Taylor-based approach that reduces design parameters. First, we derive the explicit inequality condition for the HOCBF approach and show that the corresponding equality condition sets a lower bound on the barrier function value that regulate

Caifeng Zou, Zachary E. Ross, Robert W. Clayton, Fan-Chi Lin

Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastody

Alice Chevaux, Ali Fahkar, Kévin Polisano, Irène Gannaz

Inferring a binary connectivity graph from resting-state fMRI data for a single subject requires making several methodological choices and assumptions that can significantly affect the results. In this study, we investigate the robustness of existing edge detection methods when relaxing a common assumption: the sparsity of the graph. We propose a new pipelin

Jérémie Chalopin, Victor Chepoi, Feodor Dragan, Guillaume Ducoffe

For every weight assignment $\pi$ to the vertices in a graph $G$, the radius function $r_\pi$ maps every vertex of $G$ to its largest weighted distance to the other vertices. The center problem asks to find a center, i.e., a vertex of $G$ that minimizes $r_\pi$. We here study some local properties of radius functions in graphs, and their algorithmic implicat

Zineb Lahrichi, Gaëtan Hadjeres, Gael Richard, Geoffroy Peeters

Neural audio codecs, neural networks which compress a waveform into discrete tokens, play a crucial role in the recent development of audio generative models. State-of-the-art codecs rely on the end-to-end training of an autoencoder and a quantization bottleneck. However, this approach restricts the choice of the quantization methods as it requires to define

D. Fernández-Martínez, E. J. Vega, A. M. Gañán-Calvo, J. M. Montanero

We propose using a dielectric beveled nozzle for electrospray and electrohydrodynamic jet printing. This nozzle stabilizes the liquid ejection of low-conductivity liquids, considerably reducing the minimum flow rate below which the flow becomes unstable. This translates into a significant reduction of the minimum jet diameter. Due to its dielectric character

Etienne Ménager, Tanguy Navez, Paul Chaillou, Olivier Goury

The Finite Element Method (FEM) is a powerful modeling tool for predicting soft robots' behavior, but its computation time can limit practical applications. In this paper, a learning-based approach based on condensation of the FEM model is detailed. The proposed method handles several kinds of actuators and contacts with the environment. We demonstrate that

Aamir Mehmood, Yue Hu, Saddam Hussain Khan

Recent advancements in detecting tumors using deep learning on breast ultrasound images (BUSI) have demonstrated significant success. Deep CNNs and vision-transformers (ViTs) have demonstrated individually promising initial performance. However, challenges related to model complexity and contrast, texture, and tumor morphology variations introduce uncertaint

Pablo Dopico

The supervaluationist approach to fixed-point semantics is, arguably, the most celebrated and studied competitor to the Strong Kleene approach within Kripkean truth. In this paper, we show how to obtain supervaluationist fixed-point theories of truth for intuitionistic logic. In particular, we show how to do supervaluations over Kripke structures for intuiti

Davide Bergamasco, Federico Clazzer, Andrea Munari, Paolo Casari

Orthogonal time frequency space (OTFS) modulation has been proposed recently as a new waveform in the context of doubly-selective multi-path channels. This article proposes a novel pilot design that improves OTFS spectral efficiency (SE) while reducing its peak-to-average power ratio (PAPR). Instead of adopting an embedded data-orthogonal pilot for channel e

Yanchen Luo, Zhiyuan Liu, Yi Zhao, Sihang Li

3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordinates. A key challenge is integrating these modalities of different shapes while maintaining SE(3) equivariance for 3D coordinates. To achieve this, existing approaches typically m

Shengqiong Wu, Hao Fei, Tat-Seng Chua

Scene graph (SG) representations can neatly and efficiently describe scene semantics, which has driven sustained intensive research in SG generation. In the real world, multiple modalities often coexist, with different types, such as images, text, video, and 3D data, expressing distinct characteristics. Unfortunately, current SG research is largely confined

Annalena Blänsdorf, Tristan Wirth, Arne Rak, Thomas Pöllabauer

Segmenting transparent structures in images is challenging since they are difficult to distinguish from the background. Common examples are drinking glasses, which are a ubiquitous part of our lives and appear in many different shapes and sizes. In this work we propose TransCaGNet, a modified version of the zero-shot model CaGNet. We exchange the segmentatio

Amr Keleg

Large language models (LLMs) have the potential of being useful tools that can automate tasks and assist humans. However, these models are more fluent in English and more aligned with Western cultures, norms, and values. Arabic-specific LLMs are being developed to better capture the nuances of the Arabic language, as well as the views of the Arabs. Yet, Arab

Hao Zhang, Wei Chen, Xingyu Zhao, Jianpeng Qi

Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in hand

Michael Neri, Federica Battisti

During the compression, transmission, and rendering of point clouds, various artifacts are introduced, affecting the quality perceived by the end user. However, evaluating the impact of these distortions on the overall quality is a challenging task. This study introduces PST-PCQA, a no-reference point cloud quality metric based on a low-complexity, learning-

Thomas C. Lang, Andreas M. Läuchli

We perform large scale quantum Monte Carlo simulations of the Hubbard model at half filling with a single Dirac cone close to the critical point, which separates a Dirac semi-metal from an antiferromagnetically ordered phase where SU(2) spin rotational symmetry is spontaneously broken. We discuss the implementation of a single Dirac cone in the SLAC formulat

Yulan Ju, Xiaru Meng, Harunobu Taguchi, Tamil Selvan Gunasekaran

Nowadays, touch remains essential for emotional conveyance and interpersonal communication as more interactions are mediated remotely. While many studies have discussed the effectiveness of using haptics to communicate emotions, incorporating affect into haptic design still faces challenges due to individual user tactile acuity and preferences. We assessed t

Marta Hasny, Maxime Di Folco, Keno Bressem, Julia Schnabel

Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align paired modalities. However, these approaches often overlook semantic relationships between distinct instances, leading to false negatives when semantically similar samples are treated as negatives. This limitatio

Sihan Wang, Suiyang Jiang, Yibo Gao, Boming Wang

Traditional AI-based healthcare systems often rely on single-modal data, limiting diagnostic accuracy due to incomplete information. However, recent advancements in foundation models show promising potential for enhancing diagnosis combining multi-modal information. While these models excel in static tasks, they struggle with dynamic diagnosis, failing to ma

Francesco Maria Molfese, Luca Moroni, Luca Gioffré, Alessandro Scirè

One of the most widely used tasks for evaluating Large Language Models (LLMs) is Multiple-Choice Question Answering (MCQA). While open-ended question answering tasks are more challenging to evaluate, MCQA tasks are, in principle, easier to assess, as the model's answer is thought to be simple to extract and is compared directly to a set of predefined choices

Chris S. Hanson, Vivek Menon, Shravan Hanasoge, Katepalli R. Sreenivasan

Solar inertial modes have the potential to surpass the diagnostic capabilities of acoustic waves in probing the deep interior of the Sun. The fulfillment of this potential requires an accurate identification and characterization of these modes. Among the set of detected inertial modes, the equatorially anti-symmetric "high-frequency retrograde'' (HFR) modes

Augustin Skopal, Natalia Shagatova

In 2019, the classical nova V1047 Cen experienced an unusual outburst, the nature of which has not yet been clearly determined. In this paper, we show that the 2019 V1047~Cen outburst is of Z And-type -- a type that is characteristic and has so far been observed only in symbiotic binaries. We support our claim by modeling the near-ultraviolet to near-infrare

Yun-Yun Bai, Yan Gao, Weikang Wu, Yong Liu

The study of the Weyl fermions and Kagome bands has recently attracted significant attention in condensed matter physics. However, realizing of perfect Weyl semimetals and double Kagome bands remains challenging. Here, we report a new class of distorted armchair-type fully sp2-hybridized carbon networks, termed DACN-n. The DACN-n family is characterized by o

Saddam Hussain Khan, Rashid Iqbal

Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped convolutions, depthwise separable convolutions, an

Octavio Arizmendi, Takahiro Hasebe, Yu Kitagawa

We develop analytic tools for studying the free multiplicative convolution of any measure on the real line and any measure on the nonnegative real line. More precisely, we construct the subordination functions and the $S$-transform of an arbitrary probability measure. The important multiplicativity of $S$-transform is proved with the help of subordination fu

Stefan Arnold

Differential Privacy (DP) for text has recently taken the form of text paraphrasing using language models and temperature sampling to better balance privacy and utility. However, the geometric distortion of DP regarding the structure and complexity in the representation space remains unexplored. By estimating the intrinsic dimension of paraphrased text acros

Kévin Polisano, Sylvain Meignen, Nils Laurent, Hubert Leterme

In this paper, we investigate how the spectrogram of multicomponent signals can be decomposed into a mode part and an interference part. We explore two approaches: (i) a variational method inspired by texture-geometry decomposition in image processing, and (ii) a supervised learning approach using a U-Net architecture, trained on a dataset encompassing diver

Stefano Longhi

This work explores the emergence of Mpemba-like effects within the quantum theory of lasers. By examining the temporal dynamics of photon number statistics in a single-mode laser above threshold, we reveal the curious and counterintuitive possibility that a laser system, starting with photon statistics far from equilibrium, may reach its stationary nearly-Po

Peilin Du

Hybridizing different physical systems or degrees of freedom offers significant advantages for realizing practical, universal, scalable, and fault-tolerant quantum computation (FTQC). Here, we propose optical FTQC schemes with low squeezing thresholds by leveraging the strengths of both discrete-variable (DV) and continuous-variable (CV) systems while utiliz

Kai-Kai Zhang, Wen-Xuan Zhang, Duojie Jia

Masses, magnetic moments and color-spin structures of nonstrange and strange tetraquarks with two heavy quarks are systematically studied in QCD string picture with chromomagnetic interaction. Our mass computations combined with weak and radiative decays indicate that there are two doubly-heavy tetraquarks, the bottom-charmed tetraquark $T_{cb}(7173,01^+)^0$

Haotian Mao, Khashayar Khorasani, Yingqing Guo

This study proposes a novel paradigm for enhancing fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct a pioneering investigation into the challenges and opportunities for FDI brought about by APU electrification. Our analysis revea

Dewei Wang, Wei Zhu, Liyang Ling, Ettore Tiotto

In the era of LLMs, dense operations such as GEMM and MHA are critical components. These operations are well-suited for parallel execution using a tilebased approach. While traditional GPU programming often relies on low level interfaces like CUDA or SYCL, Triton has emerged as a DSL that offers a more user-friendly and portable alternative by programming at

Xinyan Chen, Jiaxin Ge, Hongming Dai, Qiang Zhou

Empathy is fundamental to human interactions, yet it remains unclear whether embodied agents can provide human-like empathetic support. Existing works have studied agents' tasks solving and social interactions abilities, but whether agents can understand empathetic needs and conduct empathetic behaviors remains overlooked. To address this, we introduce Empat

Long-Xing Huang, Shi-Xian Sun, Yu-Peng Zhang, Zhen-Hua Zhao

In a recent study [1], the Bardeen-boson star (BBS) model involving a scalar field minimally coupled to Einstein gravity and a Bardeen's nonlinear electromagnetic field was investigated. It was found that when the magnetic charge $q$ of the electromagnetic field exceeds a certain critical value $q_c$, a frozen Bardeen-boson star (FBBS) can be obtained with t

Zanting Ye, Xiaolong Niu, Xuanbin Wu, Wenxiang Yi

Deep learning-based medical image segmentation has shown remarkable success; however, it typically requires extensive pixel-level annotations, which are both expensive and time-intensive. Semi-supervised medical image segmentation (SSMIS) offers a viable alternative, driven by advancements in CNNs and ViTs. However, these networks often rely on single fixed

Esra Ilke Albar, Valeriia P. Kosheleva, Heiko Appel, Angel Rubio

We study the high harmonic generation with vortex beams beyond the dipole approximation. To do so we employ the full minimal coupling approach to account for multipolar coupling without truncation and describe the full spatio-temporal properties of the electromagnetic field. This allows us to investigate the beyond-dipole deviations in electron trajectories