November 2024 arXiv papers — page 21

Michele Dolce, Giulia Mescolini

Building on an approach introduced by Golovkin in the '60s, we show that nonuniqueness in some forced PDEs is a direct consequence of the existence of a self-similar linearly unstable eigenvalue: the key point is a clever choice of the forcing term removing complicated nonlinear interactions. We use this method to give a short and self-contained proof of non

Abhijith S, Arjun Rajesh, Mansi Manoj, Sandra Davis Kollannur

Myocardial infarction (MI), commonly known as a heart attack, is a critical health condition caused by restricted blood flow to the heart. Early-stage detection through continuous ECG monitoring is essential to minimize irreversible damage. This review explores advancements in MI classification methodologies for wearable devices, emphasizing their potential

Daniel Dulog, Martin B. Plenio

Color centers in diamond provide a possible hardware for quantum computation, where the most basic quantum information processing unit are nitrogen-vacancy (NV) centers, each in contact with adjacent carbon nuclear spins. With specifically tailored dynamical decoupling sequences, it is possible to execute selective, high-fidelity two-body gates between the e

Léo Morin, Gabriel Rivière

Given a smooth integral two-form and a smooth potential on the flat torus of dimension 2, we study the high energy properties of the corresponding magnetic Schr\"odinger operator. Under a geometric condition on the magnetic field, we show that every sequence of high energy eigenfunctions satisfies the quantum unique ergodicity property even if the Liouville

Patrick Naivasha, George Musumba, Patrick Gikunda, John Wandeto

Interaction strategies for reward in competitive environments are significantly influenced by the nature and extent of available information. In financial markets, particularly foreign exchange (forex), traders operate independently with limited information, often yielding highly unpredictable outcomes. This study introduces a game-theoretic framework modeli

Marco Pasini, Javier Nistal, Stefan Lattner, George Fazekas

Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive manner is also feasible. However, such Continuous Autoregressive Models (CAMs) can suffer from a decline in generation quality over extended sequences due to error accumulation duri

Zheting Jin, Sohrab Ismail-Beigi

We quantitatively identify the multiple distinct microscopic mechanisms contributing to effective interlayer couplings (EICs) by performing first-principle calculations for two prototype superconducting cuprate families, pristine and doped Bi$_2$Sr$_2$CaCuO$_2$O$_{8+x}$ and Pr$_{x}$Y$_{1-x}$Ba$_2$Cu$_3$O$_7$. The major mechanisms are mediated by interlayer o

Lavanya V Salian, Samala Rathan, Rakesh Kumar

This study aims to construct a stable, high-order compact finite difference method for solving Sobolev-type equations with Dirichlet boundary conditions in one-space dimension. Approximation of higher-order mixed derivatives in some specific Sobolev-type equations requires a bigger stencil information. One can approximate such derivatives on compact stencils

Frederic Kirstein, Terry Ruas, Bela Gipp

The quality of meeting summaries generated by natural language generation (NLG) systems is hard to measure automatically. Established metrics such as ROUGE and BERTScore have a relatively low correlation with human judgments and fail to capture nuanced errors. Recent studies suggest using large language models (LLMs), which have the benefit of better context

Jonathan Lichtenfeld, Kevin Daun, Oskar von Stryk

We propose a real-time dynamic LiDAR odometry pipeline for mobile robots in Urban Search and Rescue (USAR) scenarios. Existing approaches to dynamic object detection often rely on pretrained learned networks or computationally expensive volumetric maps. To enhance efficiency on computationally limited robots, we reuse data between the odometry and detection

Yasin I. Tepeli, Mathijs de Wolf, Joana P. Gonçalves

Selection bias poses a critical challenge for fairness in machine learning, as models trained on data that is less representative of the population might exhibit undesirable behavior for underrepresented profiles. Semi-supervised learning strategies like self-training can mitigate selection bias by incorporating unlabeled data into model training to gain fur

Songyuan Tang, Tekin Bicer, Tao Sun, Kamel Fezzaa

Full-field ultra-high-speed (UHS) x-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of x-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-tempora

Andrew Lizarraga, Eric Hanchen Jiang, Jacob Nowack, Yun Qi Li

Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method for measuring redshift, but it requires about $1000$ times more telescope time than broad-band imaging. That extra cost limits sky coverage and sample size and puts large spectroscop

Bo Tan, Qing-Long Zhou

We fill a gap in the study of the Hausdorff dimension of the set of exact approximation order considered by Fregoli [Proc. Amer. Math. Soc. 152 (2024), no. 8, 3177--3182].

Ka Hei Carrie Lau, Sema Sen, Philipp Stark, Efe Bozkir

Virtual reality (VR) offers promising opportunities for procedural learning, particularly in preserving intangible cultural heritage. Advances in generative artificial intelligence (Gen-AI) further enrich these experiences by enabling adaptive learning pathways. However, evaluating such adaptive systems using traditional temporal metrics remains challenging

Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung, Deqing Sun

Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noisy text prompts and their lack of spatial information. In this paper, we present HoliSDiP, a framework that leverages semantic segmentation to provide both precise textual and spat

Hsiu-Chuan Hsu, Tsung-Wei Chen

The generation of nonlinear spin photocurrents by circularly polarized light in two-dimensional systems is theoretically investigated by calculating the shift spin conductivities. In time-reversal symmetric systems, shift spin photocurrent can be generated under the irradiation of circularly polarized light , while the shift charge photoccurrent is forbidden

Zhuoran Li, Wei Fan

We study the statistical properties of Lanczos coefficients over an ensemble of random initial operators generating the Krylov space. We propose two statistical quantities that are important in characterizing the complexity: the average correlation matrix $\langle x_{i} x_{j}\rangle$ of Lanczos coefficients and the resulting distribution of the variance of L

Jeffrey G. Andrews, Todd E. Humphreys, Tingfang Ji

The contours of 6G -- its key technical components and driving requirements -- are finally coming into focus. Through twenty questions and answers, this article defines the important aspects of 6G across four categories. First, we identify the key themes and forces driving the development of 6G, and what will make 6G unique. We argue that 6G requirements and

Ipsita Mandal

In our quest to unravel the topological properties of nodal points in three-dimensional semimetals, one hallmark property which warrants our attention is the \textit{chiral anomaly}. In the Brillouin zone (BZ), the sign of the Berry-curvature field's monopole charge is referred to as the chirality ($\chi$) of the node, leading to the notion of chiral quasipa

Joshua Daniel Loyal, Xiangyu Wu, Jonathan R. Stewart

Reciprocity, or the stochastic tendency for actors to form mutual relationships, is an essential characteristic of directed network data. Existing latent space approaches to modeling directed networks are severely limited by the assumption that reciprocity is homogeneous across the network. In this work, we introduce a new latent space model for directed net

Xinyu Wang, Yiyang Peng, Wei Ma

Ubiquitous mobile devices have catalyzed the development of vehicle crowd sensing (VCS). In particular, vehicle sensing systems show great potential in the flexible acquisition of spatio-temporal urban data through built-in sensors under diverse sensing scenarios. However, vehicle systems often exhibit biased coverage due to the heterogeneous nature of trip

Bruno Arsioli, Yu-Ling Chang, Luca Ighina

We present the First Cosmic Gamma-ray Horizon (1CGH) catalogue, featuring $\gamma$-ray detections above 10 GeV based on 16 years of observations with the Fermi Large Area Telescope (LAT) satellite. After carefully selecting a sample of blazars and blazar candidates from catalogues in the literature, we performed a binned likelihood analysis and identified 27

Gian-Luca R. Anselmetti, Matthias Degroote, Nikolaj Moll, Raffaele Santagati

Classical shadow tomography has become a powerful tool in learning about quantum states prepared on a quantum computer. Recent works have used classical shadows to variationally enforce N-representability conditions on the 2-particle reduced density matrix. In this paper, we build upon previous research by choice of an improved estimator within classical sha

Onno P. Kampman, Ye Sheng Phang, Stanley Han, Michael Xing

We introduce a general-purpose, human-in-the-loop dual dialogue system to support mental health care professionals. The system, co-designed with care providers, is conceptualized to assist them in interacting with care seekers rather than functioning as a fully automated dialogue system solution. The AI assistant within the system reduces the cognitive load

Ronghui Xu, Hanyin Cheng, Chenjuan Guo, Hongfan Gao

Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on the topological structures from single modality data, i.e., road networks, overlooking the geometric and cont

Zhen Jia, Qing Xiang, Jimeng Xiao, Huajun Zhang

Let $m\geq 2$, $n$ be positive integers, and $R_i=\{k_{i,1} >k_{i,2} >\cdots> k_{i,t_i}\}$ be subsets of $[n]$ for $i=1,2,\ldots,m$. The families $\mathcal{F}_1\subseteq \binom{[n]}{R_1},\mathcal{F}_2\subseteq \binom{[n]}{R_2},\ldots,\mathcal{F}_m\subseteq \binom{[n]}{R_m}$ are said to be non-empty cross-intersecting if for each $i\in [m]$, $\mathcal{F}_i\ne

Rui Li, Marcus Klasson, Arno Solin, Martin Trapp

The rising interest in Bayesian deep learning (BDL) has led to a plethora of methods for estimating the posterior distribution. However, efficient computation of inferences, such as predictions, has been largely overlooked with Monte Carlo integration remaining the standard. In this work we examine streamlining prediction in BDL through a single forward pass

Ao Shen, Zhiyao Li, Mingyu Gao

Serving numerous users and requests concurrently requires good fairness in Large Language Models (LLMs) serving system. This ensures that, at the same cost, the system can meet the Service Level Objectives (SLOs) of more users , such as time to first token (TTFT) and time between tokens (TBT), rather than allowing a few users to experience performance far ex

Leni K. Le Goff, Simón C. Smith

Methods for generative design of robot physical configurations can automatically find optimal and innovative solutions for challenging tasks in complex environments. The vast search-space includes the physical design-space and the controller parameter-space, making it a challenging problem in machine learning and optimisation in general. Evolutionary algorit

Radu Ioan Bot, Konstantin Sonntag

In this paper, we introduce, in a Hilbert space setting, a second order dynamical system with asymptotically vanishing damping and vanishing Tikhonov regularization that approaches a multiobjective optimization problem with convex and differentiable components of the objective function. Trajectory solutions are shown to exist in finite dimensions. We prove f

Matthias Carosi, Björn Garbrecht

Using the 2PI effective action formalism, we study false vacuum decay beyond the quadratic approximation of the path integral. We derive a coupled system of equations for the bounce and the propagator, and we compute a semi-analytic expression for the self-energy of a real scalar field with cubic and quartic interactions from the 2PI effective action truncat

B. Vollmer, J. Freundlich, P. Gratier, Th. Lizee

The specific star-formation rate of star-forming `main sequence' galaxies significantly decreased since z~1.5, due to the decreasing molecular gas fraction and star formation efficiency. However, the radio-infrared (IR) correlation has not changed significantly since z~1.5. The theory of turbulent clumpy starforming gas disks together with the scaling relati

Archer Clayton, Helen Dai, Tianyu Ni, Erick Ross

Let $T_m(N,2k)$ denote the $m$-th Hecke operator on the space $S_{2k}(\Gamma_0(N))$ of cuspidal modular forms of weight $2k$ and level $N$. In this paper, we study the non-repetition of the second coefficient of the characteristic polynomial of $T_m(N,2k)$. We obtain results in the horizontal aspect (where $m$ varies), the vertical aspect (where $k$ varies),

Fernando Alcalde Cuesta, Álvaro Carballido Costas, Matilde Martínez, Alberto Verjovsky

We study the dynamical properties of the laminated horocycle flow on the unit tangent bundles of 2-dimensional smooth solenoidal manifolds of finite type. These laminations are the analog of complete hyperbolic surfaces of finite area.

Dongheng Qian, Huan Wang, Jing Wang

The quantum Mpemba effect (QME) describes the counterintuitive phenomenon in which a system farther from equilibrium reaches steady state faster than one closer to equilibrium. However, ambiguity in defining a suitable distance measure between quantum states has led to varied interpretations across different contexts. Here we propose the intrinsic quantum Mp

Fangyi Wang, Karthik Bharath, Oksana Chkrebtii, Sebastian Kurtek

The reliable recovery and uncertainty quantification of a fixed effect function $\mu$ in a functional mixed model, for modelling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variations along the $x$ and $y$ axes are confounded with additive measurement error, and cannot in general be disenta

Leonhard Rist, Pluvio Stephan, Noah Maul, Linda Vorberg

Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical structures that extend over multiple 2D slices in tomographic volumes is inherently difficult but valuable for decision-making and reporting. Hence, various organ-specific unfolding tech

Laurids Jeppe

A search for scalar or pseudoscalar states decaying to a top quark-antiquark pair ($\mathrm{t \bar{t}}$), using $138\,\mathrm{fb}^{-1}$ of pp collision data taken at $\sqrt{s} = 13\,\mathrm{TeV}$ using the CMS detector, is presented. Events with one or two leptons are analyzed using the invariant $\mathrm{t \bar{t}}$ mass ($m_{\mathrm{t \bar{t}}}$) as well a

Eran Bamani Beeri, Eden Nissinman, Avishai Sintov

Dynamic hand gestures play a crucial role in conveying nonverbal information for Human-Robot Interaction (HRI), eliminating the need for complex interfaces. Current models for dynamic gesture recognition suffer from limitations in effective recognition range, restricting their application to close proximity scenarios. In this letter, we present a novel appro

David Serrano-Lozano, Luis Herranz, Shaolin Su, Javier Vazquez-Corral

Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions. However, these models require all the possible degradation types to be defined during the training stage while showing limited generalization to unseen degradations, which limits their practical application in complex cases. In this

A. Stolarek, W. Jaworek

Artificial Neural Networks (ANNs) require significant amounts of data and computational resources to achieve high effectiveness in performing the tasks for which they are trained. To reduce resource demands, various techniques, such as Neuron Pruning, are applied. Due to the complex structure of ANNs, interpreting the behavior of hidden layers and the featur

Pengfei Lyu, Xiaosheng Yu, Pak-Hei Yeung, Chengdong Wu

The rapid development of deep learning has significantly improved salient object detection (SOD) combining both RGB and thermal (RGB-T) images. However, existing Transformer-based RGB-T SOD models with quadratic complexity are memory-intensive, limiting their application in high-resolution bimodal feature fusion. To overcome this limitation, we propose a pur

Quoc-Hung Nguyen, Dongyi Wei, Zhifei Zhang

This paper investigates nonlinear Landau damping in the 3D Vlasov-Poisson (VP) system. We study the asymptotic stability of the Poisson equilibrium $\mu(v)=\frac{1}{\pi^2(1+|v|^2)^2}$ under small perturbations. Building on the foundational work of Ionescu, Pausader, Wang, and Widmayer \cite{AIonescu2022}, we provide a streamlined proof of nonlinear Landau da

Björn Schenke, Chun Shen, Wenbin Zhao

We review recent theoretical progress in describing collective effects in photon+nucleus collisions. The approaches considered range from the color glass condensate where correlations are encoded in the initial state, to hydrodynamic frameworks, where a strong final state response to the initial geometry of the collision is the key ingredient to generate mom

Tina A. Dardeno, Lawrence A. Bull, Nikolaos Dervilis, Keith Worden

Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. It has been proposed that heterogeneous transfer may be accomplished via intermediate structures that bridge the gap in information between the structures of interest.

Ke-Hong Zhai, Lei-Hua Liu

The Lanczos algorithm offers a framework for constructing wave functions in closed and open quantum systems from their Hamiltonians. Since the early universe is inherently an open system, we employ this algorithm to investigate Krylov complexity across various cosmological phases: inflation, radiation domination (RD), and matter domination (MD). Our results

Xuwei Sun, Jacek Dobaczewski, Markus Kortelainen, David Muir

An iterative adiabatic time-dependent Hartree-Fock-Bogoliubov (ATDHFB) method is developed within the framework of Skyrme density functional theory. The ATDHFB equation is solved iteratively to avoid explicitly calculating the stability matrix. The contribution of the time-odd mean fields to the ATDHF(B) moment of inertia is incorporated self-consistently, a

Patrick Kappl, Tin Ribic, Anna Kauch, Karsten Held

We generalize the three two-particle Bethe-Salpeter equations to ten three-particle ladders. These equations are exact and yield the exact three-particle vertex, if we knew the three-particle vertex irreducible in one of the ten channels. However, as we do not have this three-particle irreducible vertex at hand, we approximate this building block for the lad

Davide Garassino, Vivana Masia, Nicola Brocca, Alice Delorme Benites

This paper aims to provide a comparison between texts produced by French and Italian politicians on polarizing issues, such as immigration and the European Union, and their chatbot counterparts created with ChatGPT 3.5. In this study, we focus on implicit communication, in particular on presuppositions and their functions in discourse, which have been consid

Bolun Hu, Haiyang Du, Xiangyu Jiang, Keh-Fei Liu

Both the Higgs mechanism and strong interactions contribute to the masses of visible matter, yet how the six Higgs-generated quark masses and uniform strong interaction strength determine the hundreds of hadron masses remains unclear. Additionally, the role of massless, flavor-neutral gluons on hadron mass formation is central to the unresolved Millennium Pr

Javier Ron, Zheyuan He, Martin Monperrus

Client diversity is a cornerstone of blockchain resilience, yet most networks suffer from a dangerously skewed distribution of client implementations. This monoculture exposes the network to very risky scenarios, such as massive financial losses in the event of a majority client failure. In this paper, we present a novel framework that combines verifiable ex

Wei Wang, Zhi-Peng Xing, Zhen-Xing Zhao

Motivated by recent LHCb measurements of CP violation in $\Lambda_b$ three-body decays, we conduct an analysis of CP asymmetry in three-body decays of bottom baryons utilizing U-spin symmetry. We first develop a convenient representation that facilitates the incorporation of U-spin symmetry into our analysis. By integrating weak and strong phases into the de

Leander Thiele

Baryonic feedback uncertainty is a limiting systematic for next-generation weak gravitational lensing analyses. At the same time, high-resolution weak lensing maps are best analyzed at the field-level. Thus, robustly accounting for the baryonic effects in the projected matter density field is required. Ideally, constraints on feedback strength from astrophys

Yueyun Zhu, Steven Golovkine, Norma Bargary, Andrew J. Simpkin

Existing approaches for derivative estimation are restricted to univariate functional data. We propose two methods to estimate the principal components and scores for the derivatives of multivariate functional data. As a result, the derivatives can be reconstructed by a multivariate Karhunen-Lo\`eve expansion. The first approach is an extended version of mul

Thomas Hertog, Jef Pauwels, Victoria Venken

We identify a minisuperspace of complex deformations of ABJM theory for which the partition function specifies the amplitude of an eternally inflating universe. The boundary theory predicts that the bosonic bulk is effectively in the Hartle-Hawking no-boundary state, with a subleading 'tunneling' contribution. This holographic model of inflation also reveals

Wyatt Kirkby, Hayder Salman, Thomas Gasenzer, Lauriane Chomaz

We simulate interaction quenches crossing from a superfluid to a supersolid state in a dipolar quantum gas of ${}^{164}\mathrm{Dy}$ atoms, trapped in an elongated tube with periodic boundary conditions, via the extended Gross-Pitaevskii equation. A freeze-out time is observed through a delay in supersolid formation after crossing the critical point. We compu

A. A. Zaitsev, P. I. Zarubin

The formation of the excited states $^{12}$C(0$^+_2$) and $^{12}$C(3$^-$) is investigated in the dissociation of $^{12}$C $\to$ 3$\alpha$ and $^{16}$O $\to$ 4$\alpha$ at the energy of 3.65 GeV per nucleon in the nuclear emulsion. The identification becomes possible by reconstructing the invariant mass from measurements of emission angles in the approximation

L. H. Nguyen, S. Lins, G. Du, A. Sunyaev

The rapid integration of Artificial Intelligence (AI)-based systems offers benefits for various domains of the economy and society but simultaneously raises concerns due to emerging scandals. These scandals have led to the increasing importance of AI accountability to ensure that actors provide justification and victims receive compensation. However, AI acco

Waheed Rehman

The field of steganography has long been focused on developing methods to securely embed information within various digital media while ensuring imperceptibility and robustness. However, the growing sophistication of detection tools and the demand for increased data hiding capacity have revealed limitations in traditional techniques. In this paper, we propos

Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo

The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques. However, even deep learning models can underperform if trained with bad processed data. While preprocessing is essential to the analysis of EEG data

Ying Xiong, Linjing Liu, Yufei Cui, Shangyu Wu

Gene expression profiling provides profound insights into molecular mechanisms, but its time-consuming and costly nature often presents significant challenges. In contrast, whole-slide hematoxylin and eosin (H&E) stained histological images are readily accessible and allow for detailed examinations of tissue structure and composition at the microscopic level

Eduardo M. Mendonça

This paper builds upon J. Nilsson's classification of rank one $\mathcal{U}(\mathfrak{h})$-free modules by extending the analysis to modules without rank restrictions, focusing on the category $\mathfrak{A}$ of $\mathcal{U}(\mathfrak{h})$-finite $\mathfrak{g}$-modules. A deeper investigation of the weighting functor $\mathcal{W}$ and its left derived functor

Seokjoon Cho, David Conlon, Joonkyung Lee, Jozef Skokan

A system of linear equations $L$ is said to be norming if a natural functional $t_L(\cdot)$ giving a weighted count for the set of solutions to the system can be used to define a norm on the space of real-valued functions on $\mathbb{F}_q^n$ for every $n>0$. For example, Gowers uniformity norms arise in this way. In this paper, we initiate the systematic stu

Christoph Linse, Erhardt Barth, Thomas Martinetz

We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all nxn convolution kernels with n>1 are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In the channel-wise convolution part, the 1

Dannuo Li, Quan Xiong, Xuanyi Zhou, Raye Chen-Hua Yeow

Developing kinesthetic haptic devices with advanced haptic rendering capabilities is challenging due to the limitations on driving mechanisms. In this study, we introduce a novel soft electrohydraulic actuator and develop a kinesthetic haptic device utilizing it as the driving unit. We established a mathematical model and conducted testing experiments to dem

Dorian Bouchet, Olivier Stephan, Benjamin Dollet, Philippe Marmottant

Bubbles are ubiquitous in many research applications ranging from ultrasound imaging and drug delivery to the understanding of volcanic eruptions and water circulation in vascular plants. From an acoustic perspective, bubbles are resonant scatterers with remarkable properties, including a large scattering cross-section and strongly sub-wavelength dimensions.

Mattia Giovanni Spina, Edoardo Scalzo, Floriano De Rango, Francesca Guerriero

The rise of programmable data plane (PDP) and in-network computing (INC) paradigms paves the way for the development of network devices (switches, network interface cards, etc.) capable of performing advanced processing tasks. This allows running various types of algorithms, including machine learning, within the network itself to support user and network se

Yifan Sun, Hirofumi Tsuruta, Masaya Kumagai, Ken Kurosaki

Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citizens, grounded in an accurate understandi

Dominique Labbé, Cyril Labbé, Jacques Savoy

Generative AI proposes several large language models (LLMs) to automatically generate a message in response to users' requests. Such scientific breakthroughs promote new writing assistants but with some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT by comparing its generated messages with those of the recent Fr

Gaia Nicosia, Andrea Pacifici, Ulrich Pferschy, Anna Russo Russo

We investigate a scheduling problem arising from a material handling and processing problem in a production line of an Austrian company building prefabricated house walls. The addressed problem is a permutation flow shop with blocking constraints in which the machine of at least one stage can process a number operations of two other stages in the system. Thi

Habiba Farzand, Karola Marky, Mohamed Khamis

Shoulder surfing is a byproduct of smartphone use that enables bystanders to access personal information (such as text and photos) by making screen observations without consent. To mitigate this, several protection mechanisms have been proposed to protect user privacy. However, the mechanisms that users prefer remain unexplored. This paper explores correlati

Ho Hsiao, Ed Bennett, Niccolò Forzano, Deog Ki Hong

We report progress on our lattice calculations for the mass spectra of low-lying composite states in the Sp(4) gauge theory coupled to two and three flavors of Dirac fermions transforming in the fundamental and the two-index antisymmetric representations, respectively. This theory provides an ultraviolet completion to the composite Higgs model with Goldstone

Haoran Di, Zhu Yi, Haihao Shi, Yungui Gong

Fast radio bursts (FRBs) can be explained by collapsing axion stars, imposing constraints on the axion parameter space and providing valuable guidance for experimental axion searches. In the traditional post-inflationary model, axion stars could constitute up to $75\%$ of the dark matter component, suggesting that some axion stars may exist within the Solar

Denys Rozumnyi, Nadine Bertsch, Othman Sbai, Filippo Arcadu

Tracking the full body motions of users in XR (AR/VR) devices is a fundamental challenge to bring a sense of authentic social presence. Due to the absence of dedicated leg sensors, currently available body tracking methods adopt a synthesis approach to generate plausible motions given a 3-point signal from the head and controller tracking. In order to enable

Ryan Lucas, Rahul Mazumder

We present SNOWS, a one-shot post-training pruning framework aimed at reducing the cost of vision network inference without retraining. Current leading one-shot pruning methods minimize layer-wise least squares reconstruction error which does not take into account deeper network representations. We propose to optimize a more global reconstruction objective.

Yiming Wu, Zhenghao Chen, Huan Wang, Dong Xu

The high computational cost and slow inference time are major obstacles to deploying Video Diffusion Models (VDMs). To overcome this, we introduce a new Video Diffusion Model Compression approach using individual content and motion dynamics preserved pruning and consistency loss. First, we empirically observe that deeper VDM layers are crucial for maintainin

Paavo Salminen, Pierre Vallois

In this paper we give excursion theoretical proofs of Lehoczky's formula (in an extended form allowing a lower bound for the underlying diffusion) for the joint distribution of the first drawdown time and the maximum before this time, and of Malyutin's formula for the joint distribution of the first hitting time and the maximum drawdown before this time. It

Damian A. Petersen, Herbert Weigel

We study an extended Proca model with one scalar field and one massive vector field in one space and one time dimensions. We construct the soliton solution and subsequently compute the vacuum polarization energy (VPE) which is the leading quantum correction to the classical energy of the soliton. For this calculation we adopt the spectral methods approach wh

Shima Mohammadi, João Ascenso

Assessing image quality is crucial in image processing tasks such as compression, super-resolution, and denoising. While subjective assessments involving human evaluators provide the most accurate quality scores, they are impractical for large-scale or continuous evaluations due to their high cost and time requirements. Pairwise comparison subjective assessm

Mario Benites, Angel Rosado, Efstratios Manousakis

We present an accurate local density-functional for electronic-structure calculations within the density functional theory (DFT). The functional is derived by analyzing the structure of the standard perturbative expansion of the correlation energy of the interacting uniform electron gas. Then, the expansion is partially re-summed and reorganized as a self-co

Rodrigo Tenorio, Joan-René Mérou, Alicia M. Sintes

Blind continuous gravitational-wave (CWs) searches are a significant computational challenge due to their long duration and weak amplitude of the involved signals. To cope with such problem, the community has developed a variety of data-analysis strategies which are usually tailored to specific CW searches; this prevents their applicability across the nowada

Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen

Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seamless integration of geometric precision and semantic understanding. We present G3Flow, a novel framework that constructs real-time semantic flow, a dynamic, object-centric 3D seman

Abhishek Gupta, Amruta Parulekar, Sameep Chattopadhyay, Preethi Jyothi

Spontaneous or conversational multilingual speech presents many challenges for state-of-the-art automatic speech recognition (ASR) systems. In this work, we present a new technique AMPS that augments a multilingual multimodal ASR system with paraphrase-based supervision for improved conversational ASR in multiple languages, including Hindi, Marathi, Malayala

Ramin Javadi, Hossein Shokouhi

Given a bipartite graph $G=(U\cup V,E)$, a left-perfect many-to-one matching is a subset $M \subseteq E$ such that each vertex in $U$ is incident with exactly one edge in $M$. If $U$ is partitioned into some groups, the matching is called fair if for every $v\in V$, the difference between the number of vertices matched with $v$ in any two groups does not exc

Jacques Savoy

Recently several large language models (LLMs) have demonstrated their capability to generate a message in response to a user request. Such scientific breakthroughs promote new perspectives but also some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT 3.5 by comparing its generated messages with those of the recen

David Auger, Pierre Coucheney, Kossi Roland Etse

We develop a unified framework for rotor-routing that extends the classical model to a broad class of multigraphs equipped with Generalized Rotor Mechanisms (GRM). This perspective places rotor-routing on the same footing as abelian sandpiles by interpreting both as conservative instances of Vector Addition Systems (VAS). Within this framework, routing becom

Qing Jiang, Gen Luo, Yuqin Yang, Yuda Xiong

Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they arguably lack accurate perception abilities, e.g. the stage-of-the-art model Qwen2-VL only achieves a 43.9 recall rate on the COCO dataset, limiting many tasks requiring the combin

Erik Koelink, Pablo Román, Wadim Zudilin

We establish new explicit connections between classical (scalar) and matrix Gegenbauer polynomials, which result in new symmetries of the latter and further give access to several properties that have been out of reach before: generating functions, distribution of zeros for individual entries of the matrices and new type of differential-difference structure.

Matthieu Cadiot, Jonathan Jaquette, Jean-Philippe Lessard, Akitoshi Takayasu

In this paper, we achieve three primary objectives related to the rigorous computational analysis of nonlinear PDEs posed on complex geometries such as disks and cylinders. First, we introduce a validated Matrix Multiplication Transform (MMT) algorithm, analogous to the discrete Fourier transform, which offers a reliable framework for evaluating nonlineariti

Enhui Shi, Hui Xu, Ziqi Yu

Let $\mathbb{A}$ be an annulus in the plane $\mathbb R^2$ and $g:\mathbb{A}\rightarrow \mathbb{A}$ be a boundary components preserving homeomorphism which is distal and has no periodic points. In \cite{SXY}, the authors show that there is a continuous decomposition $\mathcal P$ of $\mathbb{A}$ into $g$-invariant circles such that all the restrictions of $g$

Stefan Adams, Spyros Garouniatis

Consider a large system of $N$ Brownian motions in $\R ^d$ fixed on a time interval $[0,\beta]$ with symmetrized initial and terminal conditions, under the influence of a trap potential. Such systems describe systems of bosons at positive temperatures confined in a spatial domain. We describe the large $N$ behavior of the averaged path (that is, their empiri

Emil R. Ingelsten, Madox C. McGrae-Menge, E. Paulo Alves, Istvan Pusztai

Progress in understanding multi-scale collisionless plasma phenomena requires employing tools which balance computational efficiency and physics fidelity. Collisionless fluid models are able to resolve spatio-temporal scales that are unfeasible with fully kinetic models. However, constructing such models requires truncating the infinite hierarchy of moment e

Robin Piron

Modeling plasmas in terms of atoms or ions is theoretically appealing for several reasons. When it is relevant, the notion of atom or ion in a plasma provides us with an interpretation scheme of the plasma's microscopic structure. From the standpoint of quantitative estimation of plasma properties, atomic models of plasma allow extending many theoretical too

Davide Francesco Redaelli

We prove local (in time) existence and uniqueness for a class of infinite-dimensional Nash systems, namely systems of infinitely many Hamilton-Jacobi-Bellman equations set in an infinite-dimensional Euclidean space. Such systems have been recently showed (see arXiv:2401.06534) to arise in the theory of stochastic differential games with interactions governed

Maria Isabelle Fite, B. A. McKinney

We investigate D-dimensional atomic Bose-Einstein condensates in a hypercylindrical trap with a vortex core along the z-axis and quantized circulation $\hbar m$. We analytically approximate the hypercylindrical Gross-Pitaevskii equation using dimensional perturbation theory with perturbation parameter $\delta=1/(D+2|m|-d)$, \textcolor{black}{where $d$ contro

Youssef Moawad, Andrew Brown, René Steijl, Wim Vanderbauwhede

As the field of quantum computing grows, novel algorithms which take advantage of quantum phenomena need to be developed. As we are currently in the NISQ (noisy intermediate scale quantum) era, quantum algorithm researchers cannot reliably test their algorithms on real quantum hardware, which is still too limited. Instead, quantum computing simulators on cla

Han-Qing Shi, Hai-Qing Zhang

We develop a general theory for computing the Renyi entropy with general multiple disjoint intervals from the swapping operations. Our theory is proposed based on the fact that we have observed the resemblance between the replica trick in quantum field theory and the swapping operation. Consequently, the Renyi entropy can be obtained by evaluating the expect

Andronikos Paliathanasis

We review the Gasperini-Veneziano scale factor duality symmetry for the dilaton field in scalar-tensor theory and its extension in teleparallelism. Within the framework of symmetric teleparallel scalar-tensor theory, we consider a spatially flat Friedmann--Lema\^{\i}tre--Robertson--Walker metric cosmology. For the three possible connections, we write the cor

Stefano Noventa, Roberto Faleh, Augustin Kelava

It is a well-known issue that in Item Response Theory models there is no closed-form for the maximum likelihood estimators of the item parameters. Parameter estimation is therefore typically achieved by means of numerical methods like gradient search. The present work has a two-fold aim: On the one hand, we revise the fundamental notions associated to the it