May 2024 arXiv papers — page 114

Andrii Khrabustovskyi, Vladimir Lotoreichik

We address the homogenization of the two-dimensional Dirac operator with position-dependent mass. The mass is piecewise constant and supported on small pairwise disjoint inclusions evenly distributed along an $\varepsilon$-periodic square lattice. Under rather general assumptions on geometry of these inclusions we prove that the corresponding family of Dirac

Milan Bhan, Jean-Noel Vittaut, Nina Achache, Victor Legrand

Toxicity mitigation consists in rephrasing text in order to remove offensive or harmful meaning. Neural natural language processing (NLP) models have been widely used to target and mitigate textual toxicity. However, existing methods fail to detoxify text while preserving the initial non-toxic meaning at the same time. In this work, we propose to apply count

Mao Sheng, Hao Sun, Jianping Wang

In this paper, we prove a nonabelian Hodge correspondence for principal bundles on a smooth variety $X$ in positive characteristic, which generalizes the Ogus-Vologodsky correspondence for vector bundles. Then we extend the correspondence to logahoric torsors over a log pair $(X,D)$, where $D$ a reduced normal crossing divisor in $X$. As an intermediate step

Hugo Herbelin

We study the logical structure of Teichm{\"u}ller-Tukey lemma, a maximality principle equivalent to the axiom of choice and show that it corresponds to the generalisation to arbitrary cardinals of update induction, a well-foundedness principle from constructive mathematics classically equivalent to the axiom of dependent choice.From there, we state general f

Surajit Das, Surojit Dalui, Rickmoy Samanta

We investigate chaos in the dynamics of massless particles near the horizon of static spherically symmetric black holes in two well-motivated models of $f(R)$ gravity. In both these models, we probe chaos in the particle trajectories (under suitable harmonic confinement) in the vicinity of the black hole horizons, for a set of initial conditions. The particl

Elena Berardini, Xavier Caruso

We introduce the sum-rank metric analogue of Reed--Muller codes, which we called linearized Reed--Muller codes, using multivariate Ore polynomials. We study the parameters of these codes, compute their dimension and give a lower bound for their minimum distance. Our codes exhibit quite good parameters, respecting a similar bound to Reed--Muller codes in the

Tino Werner

A crucial part of data analysis is the validation of the resulting estimators, in particular, if several competing estimators need to be compared. Whether an estimator can be objectively validated is not a trivial property. If there exists a loss function such that the theoretical risk is minimized by the quantity of interest, this quantity is called elicita

Siliang Ma, Yong Xu

Bounding box regression is one of the important steps of object detection. However, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. Most of the existing loss functions for rotated object detection calculate the difference between two bounding boxes only focus on the deviation of area o

Azzeddine Tiba, Thibault Dairay, Florian de Vuyst, Iraj Mortazavi

Stable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple coupling strategies have been developed to accelerate these simulations, but often use predictors in the form of simple finite-difference extrapolations. In this work, we propose a non-in

Ruiqi Li, Yu Zhang, Yongqi Wang, Zhiqing Hong

Note-level Automatic Singing Voice Transcription (AST) converts singing recordings into note sequences, facilitating the automatic annotation of singing datasets for Singing Voice Synthesis (SVS) applications. Current AST methods, however, struggle with accuracy and robustness when used for practical annotation. This paper presents ROSVOT, the first robust A

Yuwei Wan, Yixuan Liu, Aswathy Ajith, Clara Grazian

We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). SciQAG consists of a QA generator and a QA evaluator, which work together to extract diverse and research-level questions and answers from scientific papers. Utilizi

Goulven Monnier, Benjamin Camus, Yann-Hervé Hellouvry, Pierre Dubois

In this paper, we introduce HEEPS/MARE, the end-to-end simulator developed for the SAR oceanographic products of ESA Earth Explorer 10 mission, Harmony, expected to launch in Decembre 2029. Harmony is primarily dedicated to the observation of small-scale motion and deformation fields of the Earth surface (oceans, glaciers and ice sheets, solid Earth), thanks

Raphaël Danchin

We are concerned with the construction of global-in-time strong solutions for the incompressible Vlasov-Navier-Stokes system in the whole three-dimensional space. One of our goals is to establish that small initial velocities with critical Sobolev regularity and sufficiently well localized initial kinetic distribution functions give rise to global and unique

Michael Rogenmoser, Alessandro Ottaviano, Thomas Benz, Robert Balas

In the last decade, we have witnessed exponential growth in the complexity of control systems for safety-critical applications (automotive, robots, industrial automation) and their transition to heterogeneous mixed-criticality systems (MCSs). The growth of the RISC-V ecosystem is creating a major opportunity to develop open-source, vendor-neutral reference p

Xinning Yia, Tianguang Lua, Jing Li, Shaocong Wu

Most planning of the traditional hydrogen energy supply chain (HSC) focuses on the storage and transportation links between production and consumption ends. It ignores the energy flows and interactions between each link, making it unsuitable for energy system planning analysis. Therefore, we propose the concept of a hydrogen energy chain (HEC) based on the H

Alex Kim, Keonwoo Kim, Sangwon Yoon

As natural language generation (NLG) models have become prevalent, systematically assessing the quality of machine-generated texts has become increasingly important. Recent studies introduce LLM-based evaluators that operate as reference-free metrics, demonstrating their capability to adeptly handle novel tasks. However, these models generally rely on a sing

Milda Pocevičiūtė, Gabriel Eilertsen, Stina Garvin, Claes Lundström

Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not clear how sensitive MIL is to clinically realistic domain shifts, i.e., differences in data distribution that could negatively affect performance, and if already existing metrics fo

Fengjie Wang, Chengming Liu, Lei Shi, Pang Haibo

Previous industrial anomaly detection methods often struggle to handle the extensive diversity in training sets, particularly when they contain stylistically diverse and feature-rich samples, which we categorize as feature-rich anomaly detection datasets (FRADs). This challenge is evident in applications such as multi-view and multi-class scenarios. To addre

Amirhossein Aminimehr, Amin Aminimehr, Hamid Moradi Kamali, Sauleh Eetemadi

Studies conducted on financial market prediction lack a comprehensive feature set that can carry a broad range of contributing factors; therefore, leading to imprecise results. Furthermore, while cooperating with the most recent innovations in explainable AI, studies have not provided an illustrative summary of market-driving factors using this powerful tool

Yuchen Zhou, Linkai Liu, Chao Gou

Most existing attention prediction research focuses on salient instances like humans and objects. However, the more complex interaction-oriented attention, arising from the comprehension of interactions between instances by human observers, remains largely unexplored. This is equally crucial for advancing human-machine interaction and human-centered artifici

Christian Bargetz, Jerzy Kąkol, Damian Sobota

We study the existence of continuous (linear) operators from the Banach spaces $\mbox{Lip}_0(M)$ of Lipschitz functions on infinite metric spaces $M$ vanishing at a distinguished point and from their predual spaces $\mathcal{F}(M)$ onto certain Banach spaces, including $C(K)$-spaces and the spaces $c_0$ and $\ell_1$. For pairs of spaces $\mbox{Lip}_0(M)$ and

Samuel Forbes

Empirical evidence shows stock returns are often heavy-tailed rather than normally distributed. The $\kappa$-generalised distribution, originated in the context of statistical physics by Kaniadakis, is characterised by the $\kappa$-exponential function that is asymptotically exponential for small values and asymptotically power law for large values. This pro

Wei Jiang, Hans D. Schotten

Cell-free massive multi-input multi-output (MIMO) has recently gained a lot of attention due to its high potential in sixth-generation (6G) wireless systems. The goal of this paper is to first present a unified modeling for massive MIMO, encompassing both cellular and cell-free architectures with a variable number of antennas per access point. We derive sign

Risheng Liu, Zhu Liu, Wei Yao, Shangzhi Zeng

This work focuses on addressing two major challenges in the context of large-scale nonconvex Bi-Level Optimization (BLO) problems, which are increasingly applied in machine learning due to their ability to model nested structures. These challenges involve ensuring computational efficiency and providing theoretical guarantees. While recent advances in scalabl

Michal Praszalowicz

Twenty years ago, in 2003, two experimental groups, LEPS and DIANA, announced the discovery of a light, narrow, exotic baryon with mass within the range of 1540 MeV, which was later dubbed as $\Theta^+$. In this talk we recall the history of this discovery and its theoretical foundations. We also discuss possible future experiments that could determine the e

Eric Lucet, Farès Kfoury

This paper presents the definition of a teleoperated robotic system for non-destructive corrosion inspection of Steel Cylinder Concrete Pipes (SCCP) from the inside. A general description of in-pipe environment and a state of the art of in-pipe navigation solutions are exposed, with a zoom on the characteristics of the SCCP case of interest (pipe dimensions,

Xiaopei Zhu, Yuqiu Liu, Zhanhao Hu, Jianmin Li

Infrared physical adversarial examples are of great significance for studying the security of infrared AI systems that are widely used in our lives such as autonomous driving. Previous infrared physical attacks mainly focused on 2D infrared pedestrian detection which may not fully manifest its destructiveness to AI systems. In this work, we propose a physica

Jie Liang, Radu Timofte, Qiaosi Yi, Shuaizheng Liu

In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, including real-world images with/without reference ground truth in various scenarios from real applications. The participants were required to restore the real-captured images from comp

Valerio Marsocci, Nicolas Audebert

Large-scale ''foundation models'' have gained traction as a way to leverage the vast amounts of unlabeled remote sensing data collected every day. However, due to the multiplicity of Earth Observation satellites, these models should learn ''sensor agnostic'' representations, that generalize across sensor characteristics with minimal fine-tuning. This is comp

Yabreb Mohamed Egueh, Karim Kellay, Mohamed Zarrabi

Tolokonnikov's Corona Theorem is used to obtain two results on cyclicity in Besov-Dirichlet spaces.

Maria Cherifa, Clément Calauzènes, Vianney Perchet

Inspired by sequential budgeted allocation problems, we study the online matching problem with budget refills. In this context, we consider an online bipartite graph $G=(U,V,E)$, where the nodes in $V$ are discovered sequentially and nodes in $U$ are known beforehand. Each $u\in U$ is endowed with a budget $b_{u,t}\in \mathbb{N}$ that dynamically evolves ove

Alexandre Huchet

Galaxy cluster masses help to constrain cosmological parameters through the halo mass function. To get rid of major biases in the mass measurement, we directly probe the cluster gravitational potentials by observing their gravitational lensing on the Cosmic Microwave Background (CMB). We measured the average mass of a 468-cluster sample using SPT-SZ and Plan

İnci Akkaya Oralhan, Hikmet Çakmak, Yüksel Karataş, Raúl Michel

We derive astrophysical parameters of the open cluster NGC 1513 by means of colour indices built with new $CCD\,UBV(RI)_{KC}$ photometry. Based on early-type members, the mean foreground reddening and total to selective extinction ratio are E(B-V)=0.79$\pm$0.09 mag and $R_{V}$=2.85$\pm$0.05. Through the differential grid method, we derive the metal abundance

Jozef Bobok, Jernej Činč, Piotr Oprocha, Serge Troubetzkoy

We consider continuous maps of the interval which preserve the Lebesgue measure. Except for the identity map or $1 - \id$ all such maps have topological entropy at least $\log2/2$ and generically they have infinite topological entropy. In this article we show that the generic map has zero measure-theoretic entropy. This implies that there are dramatic differ

Tooba Faisal, Emmanuel Marilly

In the context of industrial environment, devices, such as robots and drones, are vulnerable to malicious activities such device tampering (e.g., hardware and software changes). The problem becomes even worse in a multi-stakeholder environment where multiple players contribute to an ecosystem. In such scenarios, particularly, when devices are deployed in rem

Sai Dinesh Kancharana, Madhusudan Kumar Sinha, Arun Pachai Kannu

Motivated by hyper-reliable low-latency communication in 6G, we consider error control coding for short block lengths in multi-antenna fading channels. In general, the channel fading coefficients are unknown at both the transmitter and receiver, which is referred to as non-coherent channels. Conventionally, pilot symbols are transmitted to facilitate channel

Christian Forsch, Alexander Karataev, Laura Cottatellucci

We consider the uplink of a grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) system. We propose an algorithm for distributed joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP) called JACD-EP. We develop the algorithm by factorizing the a posteriori probability (APP) of activ

Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schütze

Transliterating related languages that use different scripts into a common script is effective for improving crosslingual transfer in downstream tasks. However, this methodology often makes pretraining a model from scratch unavoidable, as transliteration brings about new subwords not covered in existing multilingual pretrained language models (mPLMs). This i

Zhan Sui, Ke Lan

The ignition at the National Ignition Facility (NIF) set off a global wave of research on the inertial fusion energy (IFE). However, IFE requires a necessary target gain G of 30-100, while it is hard to achieve the fusions at such high gain with the energy, configuration, and technical route of the NIF. We will present a conceptual design for the next genera

Robert Hogan, Sean R. Mathieson, Aurel Luca, Soraia Ventura

Background: Neonatal seizures are a neurological emergency that require urgent treatment. They are hard to diagnose clinically and can go undetected if EEG monitoring is unavailable. EEG interpretation requires specialised expertise which is not widely available. Algorithms to detect EEG seizures can address this limitation but have yet to reach widespread c

Yijiang Peng, Zike Wang, Bo Gao, Yiyue Tang

Photomultiplier tubes (PMTs) with large-area cathodes are increasingly being used in cosmic-ray experiments to enhance detection efficiency. The optical modules (OMs) of the High-Energy Underwater Neutrino Telescope (HUNT) have employed a brand new N6205 20-inch microchannel plate photomultiplier tube (MCP-PMT) developed by the North Night Vision Science & T

Arwin Gansekoele, Alexios Balatsoukas-Stimming, Tom Brusse, Mark Hoogendoorn

As telecommunication systems evolve to meet increasing demands, integrating deep neural networks (DNNs) has shown promise in enhancing performance. However, the trade-off between accuracy and flexibility remains challenging when replacing traditional receivers with DNNs. This paper introduces a novel probabilistic framework that allows a single DNN demapper

Jiarui Zhang, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng

Stance detection classifies stance relations (namely, Favor, Against, or Neither) between comments and targets. Pretrained language models (PLMs) are widely used to mine the stance relation to improve the performance of stance detection through pretrained knowledge. However, PLMs also embed ``bad'' pretrained knowledge concerning stance into the extracted st

Sergio Hernandez F., Christophe Peucheret, Francesco Da Ros, Darko Zibar

The use of directly modulated lasers (DMLs) is attractive in low-power, cost-constrained short-reach optical links. However, their limited modulation bandwidth can induce waveform distortion, undermining their data throughput. Traditional distortion mitigation techniques have relied mainly on the separate training of transmitter-side pre-distortion and recei

Tomoya Wakayama, Sudipto Banerjee

Rapid developments in streaming data technologies have enabled real-time monitoring of human activity that can deliver high-resolution data on health variables over trajectories or paths carved out by subjects as they conduct their daily physical activities. Wearable devices, such as wrist-worn sensors that monitor gross motor activity, have become prevalent

Wei Jiang, Hans D. Schotten

Terahertz (THz) frequencies have recently garnered considerable attention due to their potential to offer abundant spectral resources for communication, as well as distinct advantages in sensing, positioning, and imaging. Nevertheless, practical implementation encounters challenges stemming from the limited distances of signal transmission, primarily due to

Sergei Nayakshin, Fernando Cruz Sáenz de Miera, Ágnes Kóspál

Recent imaging observations with ALMA and other telescopes found widespread signatures of planet presence in protoplanetary discs at tens of au separations from their host stars. Here we point out that the presence of very massive planets at 0.1 au sized orbits can be deduced for protostars accreting gas at very high rates, when their discs display powerful

Enrique Mármol Campos, Aurora González Vidal, José Luis Hernández Ramos, Antonio Skarmeta

Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning (ML) models while data sources' privacy is still preserved. However, most of existing FL approaches are based on supervised techniques, which could require resource-intensive activities and human intervention to obtain labelled datasets. Furthermore, in the sc

Arwin Gansekoele, Tycho Bot, Rob van der Mei, Sandjai Bhulai

Encryption on the internet with the shift to HTTPS has been an important step to improve the privacy of internet users. However, there is an increasing body of work about extracting information from encrypted internet traffic without having to decrypt it. Such attacks bypass security guarantees assumed to be given by HTTPS and thus need to be understood. Pri

Ziyu Wang, Lejun Min, Gus Xia

Recent deep music generation studies have put much emphasis on long-term generation with structures. However, we are yet to see high-quality, well-structured whole-song generation. In this paper, we make the first attempt to model a full music piece under the realization of compositional hierarchy. With a focus on symbolic representations of pop songs, we de

A. Vazan, C. W. Ormel, M. G. Brouwers

The characterization of Super-Earth-to-Neptune sized exoplanets relies heavily on our understanding of their formation and evolution. In this study, we link a model of planet formation by pebble accretion to the planets' long-term observational properties by calculating the interior evolution, starting from the dissipation of the protoplanetary disk. We inve

Yi-Qiao Xu, Bing-Zhao Li

Following the idea of the fractional space-time Fourier transform, a linear canonical space-time transform for 16-dimensional space-time $C\ell_{3,1}$-valued signals is investigated in this paper. First, the definition of the proposed linear canonical space-time transform is given, and some related properties of this transform are obtained. Second, the convo

Minwei Shi, Guzhi Bao, Jinxian Guo, Weiping Zhang

Exceptional points (EPs), which arose early from non-Hermitian physics, significantly amplify the system's response to minor perturbations, and they act as a useful concept to enhance measurement in metrology. In particular, such a metrological enhancement grows dramatically with the EP's order. However, the Langevin noises intrinsically existing in the non-

Naresh Kumar, Yogendra K. Gautam, Soni Mishra, Anuj Kumar

First-principles based calculations were executed to investigate the sensing properties of ammonia gas molecules on two-dimensional pristine black phosphorene towards its application as a gas sensor and related applications. We discuss in detail, the interaction of ammonia gas molecules on the phosphorene single sheet through the structural change analysis,

Ahmad Deeb, Denys Dutykh

Over the past decade, Finite Element Method (FEM) has served as a foundational numerical framework for approximating the terms of Time Series Expansion (TSE) as solutions to transient Partial Differential Equation (PDE). However, the application of high-order Finite Element (FE) to certain classes of PDEs, such as diffusion equations and the Navier-Stokes (N

Vinay I. Hegde, Miroslava Peterson, Sarah I. Allec, Xiaonan Lu

Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design. In this perspective, we present a few guiding principles for applying informatics-based methods towards the design of novel nuclear waste forms. We advocate for adopting a s

Pujiang He, Shan Zhou, Changqing Li, Wenhuan Huang

Large language models (LLMs) hold tremendous potential for addressing numerous real-world challenges, yet they typically demand significant computational resources and memory. Deploying LLMs onto a resource-limited hardware device with restricted memory capacity presents considerable challenges. Distributed computing emerges as a prevalent strategy to mitiga

Adi Ditkowski, Anne Le Blanc, Chi-Wang Shu

Finite Difference methods (FD) are one of the oldest and simplest methods for solving partial differential equations (PDE). Block Finite Difference methods (BFD) are FD methods in which the domain is divided into blocks, or cells, containing two or more grid points, with a different scheme used for each grid point, unlike the standard FD method. It was shown

Sudipan Saha

Unsupervised transfer learning-based change detection methods exploit the feature extraction capability of pre-trained networks to distinguish changed pixels from the unchanged ones. However, their performance may vary significantly depending on several geographical and model-related aspects. In many applications, it is of utmost importance to provide trustw

Julian Frattini, Jannik Fischbach, Davide Fucci, Michael Unterkalmsteiner

Requirements engineering aims to fulfill a purpose, i.e., inform subsequent software development activities about stakeholders' needs and constraints that must be met by the system under development. The quality of requirements artifacts and processes is determined by how fit for this purpose they are, i.e., how they impact activities affected by them. Howev

P. Barai, G. Leroy, P. Bisht, J. M. Rothman

Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we pr

Nicolas Faroß

We introduce a quantum automorphism group for hypergraphs, which turns out to generalize the quantum automorphism group of Bichon for classical graphs. Further, we show that our quantum automorphism group acts on hypergraph C*-algebras as recently defined. In particular, this action generalizes the one on graph C*-algebras by Schmidt-Weber in 2018.

Rabii Younès, Cook Michael

A game's theme is an important part of its design -- it conveys narrative information, rhetorical messages, helps the player intuit strategies, aids in tutorialisation and more. Thematic elements of games are notoriously difficult for AI systems to understand and manipulate, however, and often rely on large amounts of hand-written interpretations and knowled

Kunda Yan, Sen Cui, Abudukelimu Wuerkaixi, Jingfeng Zhang

In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively using data while maintaining user privacy. One key challenge in FL is managing statistical heterogeneity, such as non-i.i.d. data, arising from numerous clients and diverse data sources. This requires strategic cooperation, often with clients having similar character

Chih-Wei Chang, Zhen Tian, Richard L. J. Qiu, H. Scott McGinnis

This study aims to develop a digital twin (DT) framework to enhance adaptive proton stereotactic body radiation therapy (SBRT) for prostate cancer. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes. Thi

Shuhei Mita, Shoh Tagawa, Kei Hirose, Nagi Ikuta

Fe and FeH form a binary eutectic system above ~40 GPa. Here we performed melting experiments in a laser-heated diamond-anvil cell (DAC) and obtained the Fe-FeH eutectic melting curve between 52 and 175 GPa. Its extrapolation shows the eutectic temperature to be 4700 K at the inner core boundary (ICB), which is lower than that in Fe-FeSi but is higher than t

Lucia Anna Tarasovičová, Justin Mohs, Anton Andronic, Hannah Elfner

We present a comparison of directed and elliptic flow data by the FOPI collaboration in Au--Au, Xe--CsI, and Ni--Ni collisions at beam kinetic energies from 0.25 to 1.5 GeV per nucleon to simulations using the SMASH hadronic transport model. The Equation of State is parameterized as a function of nuclear density and momentum dependent potentials are newly in

Josephine Nanyondo, Joseph Y. T. Mugisha, Henry Kasumba

In this paper, a multi-class Aw-Rascle \textrm{(AR)} model with time fractional order derivative is presented. The conservative form of the proposed model is considered for the natural extension and generalization of equations involved. The fractional order derivative involved in the model equations is computed by applying the Caputo fractional derivative de

Ruizhe Chen, Tianxiang Hu, Yang Feng, Zuozhu Liu

Concerns regarding Large Language Models (LLMs) to memorize and disclose private information, particularly Personally Identifiable Information (PII), become prominent within the community. Many efforts have been made to mitigate the privacy risks. However, the mechanism through which LLMs memorize PII remains poorly understood. To bridge this gap, we introdu

Guerlain Lambert, Céline Helbert, Claire Lauvernet

Numerical modeling is essential for comprehending intricate physical phenomena in different domains. To handle complexity, sensitivity analysis, particularly screening, is crucial for identifying influential input parameters. Kernel-based methods, such as the Hilbert Schmidt Independence Criterion (HSIC), are valuable for analyzing dependencies between input

Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak

Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms. Although MTL has been successfully applied to ether regression or classification tasks alone, incorporating mixed types of tasks into a unified MTL framework remains challenging, primarily due to variations in the magnitudes of losses

Enrique Hernandez, Elmer Suarez, Igor Lesanovsky, Beatriz Olmos

Dissipative light-matter systems can display emergent collective behavior. Here, we report a $\mathbb{Z}_2$-symmetry-breaking phase transition in a system of multi-level $^{87}$Rb atoms strongly coupled to a weakly driven two-mode optical cavity. In the symmetry-broken phase, non-ergodic dynamics manifests in the emergence of multiple stationary states with

Miguel Coloma Puga, Barbara Balmaverde, Alessandro Capetti, Cristina Ramos Almeida

The study of ionized gas kinematics in high-z active galaxies plays a key part in our understanding of galactic evolution, in an age where nuclear activity was widespread and star formation close to its peak. We present a study of TXS 0952-217, a radio galaxy at z=2.95, using VLT/MUSE integral field optical spectroscopy as part of a project aimed studying of

Xiaosu Zhu, Hualian Sheng, Sijia Cai, Bing Deng

We introduce RoScenes, the largest multi-view roadside perception dataset, which aims to shed light on the development of vision-centric Bird's Eye View (BEV) approaches for more challenging traffic scenes. The highlights of RoScenes include significantly large perception area, full scene coverage and crowded traffic. More specifically, our dataset achieves

Yuhao Sun, Lingyun Yu, Hongtao Xie, Jiaming Li

With the rapid development of face recognition (FR) systems, the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However, the generated adversarial examples, i.e., the pr

Yoshihiro Mori, Toshihiko Sasaki, Rikizo Ikuta, Kentaro Teramoto

The optical Bell State Analyzer (BSA) plays a key role in the optical generation of entanglement in quantum networks. The optical BSA is effective in controlling the timing of arriving photons to achieve interference. It is unclear whether timing synchronization is possible even in multi-hop and complex large-scale networks, and if so, how efficient it is. W

Federico Talamucci

The aim of this study is to present an alternative way to deduce the equations of motion of general (i.e., also nonlinear) nonholonomic constrained systems starting from the d'Alembert principle and proceeding by an algebraic procedure. The two classical approaches in nonholonomic mechanics -- Cetaev method and vakonomic method -- are treated on equal terms,

Yuchen Guo, Hanqun Cao, Lok Ming Lui

3D face registration is an important process in which a 3D face model is aligned and mapped to a template face. However, the task of 3D face registration becomes particularly challenging when dealing with partial face data, where only limited facial information is available. To address this challenge, this paper presents a novel deep learning-based approach

Juwon Seo, Sung-Hoon Lee, Tae-Young Lee, Seungjun Moon

Recent advances in generative models trained on large-scale datasets have made it possible to synthesize high-quality samples across various domains. Moreover, the emergence of strong inversion networks enables not only a reconstruction of real-world images but also the modification of attributes through various editing methods. However, in certain domains r

Hans-Peter Beise

We leverage the framework of hyperplane arrangements to analyze potential regions of (stable) fixed points. We provide an upper bound on the number of fixed points for multi-layer neural networks equipped with piecewise linear (PWL) activation functions with arbitrary many linear pieces. The theoretical optimality of the exponential growth in the number of l

Cosme G. Ayani, Michele Pisarra, Iván M. Ibarburu, Clara Rebanal

The prominent role of electron-electron interactions in two-dimensional (2D) materials versus three-dimensional (3D) ones is at the origin of the great variety of fermionic correlated states reported in the literature. In this respect, artificial van der Waals heterostructures comprising single layers of highly correlated insulators allow one to explore the

Naphan Benchasattabuse, Michal Hajdušek, Rodney Van Meter

Quantum networking, heralded as the next frontier in communication networks, envisions a realm where quantum computers and devices collaborate to unlock capabilities beyond what is possible with the Internet. A critical component for realizing a long-distance quantum network, and ultimately, the Quantum Internet, is the quantum repeater. As with the race to

Piotr Gorczyca, Dörthe Arndt, Martin Diller, Jochen Hampe

We propose the Riskman ontology and shapes for representing and analysing information about risk management for medical devices. Risk management is concerned with taking necessary precautions to ensure that a medical device does not cause harms for users or the environment. To date, risk management documentation is submitted to notified bodies (for certifica

Xinyang Li, Zhangyu Lai, Linning Xu, Jianfei Guo

We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only $1$ minute.The key component is a dual-mode multi-view latent diffusion model. Given the noisy multi-view latents, the 2D mode can efficiently denoise them with a single latent denoising network, while the 3D mode can generate a tri-plane neura

Yongsong Huang, Tomo Miyazaki, Xiaofeng Liu, Shinichiro Omachi

Infrared image super-resolution demands long-range dependency modeling and multi-scale feature extraction to address challenges such as homogeneous backgrounds, weak edges, and sparse textures. While Mamba-based state-space models (SSMs) excel in global dependency modeling with linear complexity, their block-wise processing disrupts spatial consistency, limi

Mingxiang Li

Given a conformal metric with finite total Q-curvature, we show that the assumptions on scalar curvature sensitively govern the Q-curvature integral. Additionally, we introduce a conformal mass for such manifolds. Using such mass, we provides a necessary and sufficient condition for the metric to be normal without assuming metric completeness. As application

Jinjie Li, Junichiro Sugihara, Moju Zhao

Utilizing a servo to tilt each rotor transforms quadrotors from underactuated to overactuated systems, allowing for independent control of both attitude and position, which provides advantages for aerial manipulation. However, this enhancement also introduces model nonlinearity, sluggish servo response, and limited operational range into the system, posing c

Christian Biello

We report on the implementation of a new NNLO+PS event generator for the Higgs production via bottom annihilation using the MiNNLOPS method in the POWHEG framework. The calculation has been carried out in the five flavour scheme (5FS), where the bottom mass is neglected. We compare our results against fixed-order predictions at NNLO as well as resummed predi

Nguyen Van Tuyen, Kwan Deok Bae, Do Sang Kim

This paper is devoted to study of optimality conditions at infinity in nonsmooth minimax programming problems and applications. By means of the limiting subdifferential and normal cone at infinity, we dirive necessary and sufficient optimality conditions of Karush--Kuhn--Tucker type for nonsmooth minimax programming problems with constraint. The obtained res

Abel de Burgos, Zsolt Keszthelyi, Sergio Simón-Díaz, Miguel A. Urbaneja

The properties of blue supergiants are key for constraining the end of the main sequence (MS) of massive stars. Whether the observed drop in the relative number of fast-rotating stars below $\sim$21$\,$kK is due to enhanced mass-loss rates at the location of the bistability jump, or the result of the end of the MS is still debated. Here, we combine newly der

Bo Reipurth, C. Briceno, T. R. Geballe, C. Baranec

We have discovered that the Halpha emission line star Haro 5-2, located in the 3-6 Myr old Ori OB1b association, is a young quadruple system. The system has a 2+2 configuration with an outer separation of 2.6 arcseconds and with resolved subarcsecond inner binary components. The brightest component, Aa, dominates the A-binary, it is a weakline T Tauri star w

Eleonora Grassucci, Jinho Choi, Jihong Park, Riccardo F. Gramaccioni

In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing. Among them, semantic communication obtained promising results especially when combined with state-of-the-art deep generative models, such as large language or diffusio

Kevin J Wilson, Nina Wilson

Cross country running races are different to track and road races in that the courses are not typically accurately measured and the condition of the course can have a strong effect on the finish times of the participants. In this paper we investigate these effects by modelling the finish times of all participants in 28 cross country running races over 5 seas

A. Asensio Ramos

The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post-facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmo

Haonan An, Guang Hua, Zhiping Lin, Yuguang Fang

Box-free model watermarking is an emerging technique to safeguard the intellectual property of deep learning models, particularly those for low-level image processing tasks. Existing works have verified and improved its effectiveness in several aspects. However, in this paper, we reveal that box-free model watermarking is prone to removal attacks, even under

Kento Samuel Soon, Naphan Benchasattabuse, Michal Hajdušek, Kentaro Teramoto

The heterogeneity of quantum link architectures is an essential theme in designing quantum networks for technological interoperability and possibly performance optimization. However, the performance of heterogeneously connected quantum links has not yet been addressed. Here, we investigate the integration of two inherently different technologies, with one li

Kento Samuel Soon, Michal Hajdušek, Shota Nagayama, Naphan Benchasattabuse

Quantum repeater networks play a crucial role in distributing entanglement. Various link architectures have been proposed to facilitate the creation of Bell pairs between distant nodes, with entangled photon sources emerging as a primary technology for building quantum networks. Our work advances the Memory-Source-Memory (MSM) link architecture, addressing t

Marii Koyama, Claire Yun, Amin Taherkhani, Naphan Benchasattabuse

To scale quantum computers to useful levels, we must build networks of quantum computational nodes that can share entanglement for use in distributed forms of quantum algorithms. In one proposed architecture, node-to-node entanglement is created when nodes emit photons entangled with stationary memories, with the photons routed through a switched interconnec

Poramet Pathumsoot, Theerapat Tansuwannont, Naphan Benchasattabuse, Ryosuke Satoh

Quantum networks are expected to enhance distributed quantum computing and quantum communication over long distances while providing security dependent upon physical effects rather than mathematical assumptions. Through simulation, we show that a quantum network utilizing only entanglement purification or only quantum error correction as error management str

Nicolas Christianson, Bo Sun, Steven Low, Adam Wierman

We study the design of risk-sensitive online algorithms, in which risk measures are used in the competitive analysis of randomized online algorithms. We introduce the CVaR$_\delta$-competitive ratio ($\delta$-CR) using the conditional value-at-risk of an algorithm's cost, which measures the expectation of the $(1-\delta)$-fraction of worst outcomes against t