November 2024 arXiv papers — page 23

Shao-Feng Ge, Chui-Fan Kong, Pedro Pasquini

We propose the possibility of using the near detector at reactor neutrino experiments to probe the renormalization group (RG) running effect on the leptonic Dirac CP phase $\delta_D$. Although the reactor neutrino oscillation cannot directly measure $\delta_D$, it can probe the deviation $\Delta \delta \equiv \delta_D(Q^2_d) - \delta_D(Q^2_p)$ caused by the

Da Chang, Deliang Wang, Xiao Yang

Weight initialization significantly impacts the convergence and performance of neural networks. While traditional methods like Xavier and Kaiming initialization are widely used, they often fall short for spiking neural networks (SNNs), which have distinct requirements compared to artificial neural networks (ANNs). To address this, we introduce \textbf{IKUN},

George Yiasemis, Jan-Jakob Sonke, Jonas Teuwen

Dynamic MRI enables a range of clinical applications, including cardiac function assessment, organ motion tracking, and radiotherapy guidance. However, fully sampling the dynamic k-space data is often infeasible due to time constraints and physiological motion such as respiratory and cardiac motion. This necessitates undersampling, which degrades the quality

Albert Feijoo, Isaac Vidaña

We analyze the possible existence of a strangeness $S=-3$, isospin $I=1$ pentaquark state $P_{sss}$ generated dynamically from the $\bar{K}\Xi$ interaction. We employ a unitarized scheme in coupled channels based on the chiral Lagrangian expanded up to next-to-leading order (NLO), and show that the inclusion of the NLO terms is crucial to provide the necessa

Daniel Scalena, Elisabetta Fersini, Malvina Nissim

Adapting models to a language that was only partially present in the pre-training data requires fine-tuning, which is expensive in terms of both data and computational resources. As an alternative to fine-tuning, we explore the potential of activation steering-based techniques to enhance model performance on Italian tasks. Through our experiments we show tha

Arturo de Giorgi, Marta Fuentes Zamoro, Luca Merlo

We present a model where a GeV axion-like-particle (ALP) is predicted in a large portion of the parameter space due to the presence of explicit Peccei-Quinn symmetry-breaking terms in an exotic leptonic sector. The latter provides a solution to the muon $g-2$ anomaly, within the framework of the Linear Seesaw neutrino mechanism. The spectrum is extended by a

Thomas P. Sheerin, Chris A. Hooley

We study the non-Fermi-liquid state formed by an isotropic, degenerate Fermi gas in two spatial dimensions interacting with a U(1) gauge field. Our calculation uses the functional renormalization group (fRG) with a soft frequency cutoff for the fermions. The fRG scheme we employ takes account of the gauge symmetry, which imposes relations (modified Ward-Taka

Priti Prasanna Mondal, Basit Auyoob Mir, Fouzul Atik

Consider a group $\mathbb{G}$ and construct its power graph, whose vertex set consists of the elements of $\mathbb{G}$. Two distinct vertices (elements) are adjacent in the graph if and only if one element can be expressed as an integral power of the other. In this article, we improved the bounds of the spectral radius of the power graphs of the cyclic group

M. D. Caballero-Garcia, E. Gogus, J. Navarro-Gonzalez, K. E. Atapin

We present a timing study of the gamma and X-ray observations and analysis of a sample of bright gamma-ray bursts (GRBs; i.e. GRB 180720B, GRB 181222B, GRB 211211A and GRB 220910A), including the very bright and long GRB 211211A (a.k.a. kilonova candidate). They have been detected and observed by the Atmosphere-Space Interactions Monitor (ASIM) installed on

KBTG Labs, Atthakorn Petchsod, Pornchanan Balee, Danupat Khamnuansin

Large Language Models (LLMs) excel in general tasks but struggle with domain-specific challenges, such as specialized terminology and localized regulations. Existing financial LLMs, like FinGPT and BloombergGPT, lack support for the Thai financial domain. We developed a Thai Financial LLM using the Investment Consultant (IC) exam dataset from the Stock Excha

Zhihua Duan, Jialin Wang

With the rapid development of large model technology, the application of agent technology in various fields is becoming increasingly widespread, profoundly changing people's work and lifestyles. In complex and dynamic systems, multi-agents achieve complex tasks that are difficult for a single agent to complete through division of labor and collaboration amon

Weiwei Zhang, Wei Suo, Jiahao Song, Wenbo Cao

In recent years, Physics-Informed Neural Networks (PINNs) have become a representative method for solving partial differential equations (PDEs) with neural networks. PINNs provide a novel approach to solving PDEs through optimization algorithms, offering a unified framework for solving both forward and inverse problems. However, some limitations in terms of

Jonas Kasper, Awal Awal, Ronja Hetzel, Magdalena Kołodziej

Objective: Proton therapy is a precision-focused cancer treatment where accurate proton beam range monitoring is critical to ensure effective dose delivery. This can be achieved by prompt gamma detection with a Compton camera like the SiFi-CC. This study aims to show the feasibility of optimising the geometry of SiFi-CC Compton camera for verification of dos

Nicola Abatangelo, Serena Dipierro, Enrico Valdinoci

This book is intended as a self-contained introduction to selected topics in the fractional world, focusing particularly on aspects that arise in the study of equations driven by the fractional Laplacian. The scope of this work is not intended to be exhaustive or all-encompassing. We have chosen topics that we believe will appeal to readers embarking on thei

Stefano Capitani, Stephan Durr

Karsten-Wilczek and Borici-Creutz fermions show a near-degeneracy of the $2$ species involved, similar to the $2^{d/2}$ species of staggered fermions. Hence in $d=2$ dimensions all three formulations happen to be minimally doubled (two species). This near-degeneracy shows up both in the eigenvalue spectrum of the respective Dirac operator and in spectroscopi

Danijel Krizmanic

For a stationary sequence of random variables we derive a self-normalized functional limit theorem under joint regular variation with index $\alpha \in (0,2)$ and weak dependence conditions. The convergence takes place in the space of real-valued cadlag functions on $[0,1]$ with the Skorokhod $M_{1}$ topology.

Zhouxing Shi, Haoyu Li, Cho-Jui Hsieh, Huan Zhang

We study the problem of learning verifiably Lyapunov-stable neural controllers that provably satisfy the Lyapunov asymptotic stability condition within a region-of-attraction (ROA). Unlike previous works that adopted counterexample-guided training without considering the computation of verification in training, we introduce Certified Training with Branch-and

Jonas Thoen Faber, Reetika Joshi, Luc Rouppe van der Voort, Sven Wedemeyer

Context. Since the mechanism of energy release from solar flares is still not fully understood, the study of fine-scale features developing during flares becomes important for progressing towards a consistent picture of the essential physical mechanisms. Aims. We aim to probe the fine structures in flare ribbons at the chromospheric level using high-resoluti

Kerr Maxwell, Mark R Dennis

We consider the jacobian of a random transverse polarisation field, from the transverse plane to the Poincar\'e sphere, as a Skyrme density partially covering the sphere. Connected domains of the plane where the jacobian has the same sign -- patches -- map to facets subtending some general solid angle on the Poincar\'e sphere. As a generic continuous mapping

Nadine M. Trummer, Amit Reza, Michael A. Steindorfer, Christiane Helling

The growing number of man-made debris in Earth's orbit poses a threat to active satellite missions due to the risk of collision. Characterizing unknown debris is, therefore, of high interest. Light Curves (LCs) are temporal variations of object brightness and have been shown to contain information such as shape, attitude, and rotational state. Since 2015, th

Xiang Cheng, Zhi Mao, Ying Wang, Wen Wu

In this paper, we propose a novel dependency-aware task scheduling strategy for dynamic unmanned aerial vehicle-assisted connected autonomous vehicles (CAVs). Specifically, different computation tasks of CAVs consisting of multiple dependency subtasks are judiciously assigned to nearby CAVs or the base station for promptly completing tasks. Therefore, we for

Duc-Hai Pham, Tung Do, Phong Nguyen, Binh-Son Hua

We propose SharpDepth, a novel approach to monocular metric depth estimation that combines the metric accuracy of discriminative depth estimation methods (e.g., Metric3D, UniDepth) with the fine-grained boundary sharpness typically achieved by generative methods (e.g., Marigold, Lotus). Traditional discriminative models trained on real-world data with sparse

Aikaterini Argyrou, Rafaela Maria Giappa, Emmanouil Gagaoudakis, Vassilios Binas

Lead halide perovskites have attracted considerable attention as potential gas sensing elements due to their unique ability to detect and respond to an external stimulus with measurable electrical or optical signals. The distinctive characteristics of these materials lie in their ability to operate upon gas exposure at room temperature. However, the presence

Yuanji Li, Ruotong Yin, Mingzhe Li, Jiashuo Gong

The hybrid ferromagnet-superconductor heterostructures have attracted extensive attention as they potentially host topological superconductivity. Relevant experimental signatures have recently been reported in CrBr3/NbSe2 ferromagnet-superconductor heterostructure, but controversies remain. Here, we reinvestigate CrBr3/NbSe2 by an ultralow temperature scanni

Ruslan Idelfonso Magana Vsevolodovna

Integrating new features into existing software projects can be a complex and time-consuming process. Feature-Factory leverages Generative AI with WatsonX.ai to automate the analysis, planning, and implementation of feature requests. By combining advanced project parsing, dependency resolution, and AI-generated code, the program ensures seamless integration

Zak Buzzard, Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik

Computational analysis of whole slide images (WSIs) has seen significant research progress in recent years, with applications ranging across important diagnostic and prognostic tasks such as survival or cancer subtype prediction. Many state-of-the-art models process the entire slide - which may be as large as $150,000 \times 150,000$ pixels - as a bag of man

Karthik Mohan, Hanxiao Wang, Xiatian Zhu

This paper presents an experimental study of Kolmogorov-Arnold Networks (KANs) applied to computer vision tasks, particularly image classification. KANs introduce learnable activation functions on edges, offering flexible non-linear transformations compared to traditional pre-fixed activation functions with specific neural work like Multi-Layer Perceptrons (

Annegret Glitzky, Matthias Liero

We establish a novel uniqueness result for an instationary drift-diffusion model for perovskite solar cells. This model for vacancy-assisted charge transport uses Fermi--Dirac statistics for electrons and holes and Blakemore statistics for the mobile ionic vacancies in the perovskite. Existence of weak solutions and their boundedness was proven in a previous

Pablo M. Delgado, Jürgen Herre

Efficient audio quality assessment is vital for streamlining audio codec development. Objective assessment tools have been developed over time to algorithmically predict quality ratings from subjective assessments, the gold standard for quality judgment. Many of these tools use perceptual auditory models to extract audio features that are mapped to a basic a

Theodorus Maria Nieuwenhuizen

Weak lensing exhibits that rotation curves of isolated galaxies remain flat up to Mpc scale (Mistele et al, 2024). Recently we proposed that dark matter is a combination of electrostatic and vacuum energy in standard physics. In this theory, isolated galaxies may be embedded in ``charged cocoons'', spheres with $\pm 1/r^2$ charge density. The related circula

Aladin Djuhera, Vlad C. Andrei, Mohsen Pourghasemian, Haris Gacanin

Multi-task large language models (MTLLMs) are important for many applications at the wireless edge, where users demand specialized models to handle multiple tasks efficiently. However, training MTLLMs is complex and exhaustive, particularly when tasks are subject to change. Recently, the concept of model fusion via task vectors has emerged as an efficient ap

Jaap Eising, Florian Dörfler

We consider optimization algorithms that are open systems, that is, with external inputs and outputs. Such algorithms arise for instance, when analyzing the effect of noise or disturbance on an algorithm, or when an algorithm is part of control loop without timescale separation. Bridging between monotone operator theory and energy-based modeling, we consider

Johann Ostmeyer

Recently, it has been shown that the hybrid Monte Carlo (HMC) algorithm is guaranteed to converge exponentially to a given target probability distribution $p(x)\propto e^{-V(x)}$ on non-compact spaces if augmented by an appropriate radial update. In this work we present a simple way to derive efficient radial updates meeting the necessary requirements for an

Shih-Heng Wang, Zih-Ching Chen, Jiatong Shi, Ming-To Chuang

The utilization of speech Self-Supervised Learning (SSL) models achieves impressive performance on Automatic Speech Recognition (ASR). However, in low-resource language ASR, they encounter the domain mismatch problem between pre-trained and low-resource languages. Typical solutions like fine-tuning the SSL model suffer from high computation costs while using

Samuele Pasini, Jinhan Kim, Tommaso Aiello, Rocio Cabrera Lozoya

Large Language Models (LLMs) are increasingly used in software development to generate functions, such as attack detectors, that implement security requirements. A key challenge is ensuring the LLMs have enough knowledge to address specific security requirements, such as information about existing attacks. For this, we propose an approach integrating Retriev

Judhajeet Basu, Ravi Kumar, G. C. Anupama, Sudhanshu Barway

We present a detailed analysis of an extragalactic slow classical nova in M31 exhibiting multiple peaks in its light curve. Spectroscopic and photometric observations were used to investigate the underlying physical processes. Shock-induced heating events resulting in the expansion and contraction of the photosphere are likely responsible for the observed mu

Daniel J. G. Pearce, Berta Martínez-Prat, Jordi Ignés-Mullol, Francesc Sagués

Active nematics are fluids in which the components have nematic symmetry and are driven out of equilibrium due to the microscopic generation of an active stress. When the active stress is high, it drives flows in the nematic and can lead to the proliferation of topological defects, a state we refer to as defect chaos. Using numerical simulations of active ne

Esmaeal Ghavanloo, Patrizio Neff

In this paper, we consider the isotropic relaxed micromorphic model in polar coordinates and use this representation to solve explicitly an elastostatic axisymmetric extension problem involving a linear system of ordinary differential equations. To obtain an analytical solution, modified Bessel functions are utilized and closed-form solutions for the displac

Shimin Chen, Xiaohan Lan, Yitian Yuan, Zequn Jie

Rapid development of large language models (LLMs) has significantly advanced multimodal large language models (LMMs), particularly in vision-language tasks. However, existing video-language models often overlook precise temporal localization and struggle with videos of varying lengths. We introduce TimeMarker, a versatile Video-LLM designed for high-quality

Houda Barkouki, Khalide Jbilou

In this paper, we investigate the use of multilinear algebra for reducing the order of multidimensional linear time-invariant (MLTI) systems. Our main tools are tensor rational Krylov subspace methods, which enable us to approximate the systems solution within a low-dimensional subspace. We introduce the tensor rational block Arnoldi and tensor rational bloc

Knut Zoch

This talk presents the ATLAS Collaboration's first measurement of the inclusive cross-section for top-quark pair production in association with charm quarks. Using the full Run 2 proton-proton collision data sample at $\sqrt{s}$ = 13 TeV, collected with the ATLAS experiment at the LHC between 2015 and 2018, the measurement selects $t\bar{t}$ events with one

Valentina Anita Carriero, Antonia Azzini, Ilaria Baroni, Mario Scrocca

Procedural Knowledge is the know-how expressed in the form of sequences of steps needed to perform some tasks. Procedures are usually described by means of natural language texts, such as recipes or maintenance manuals, possibly spread across different documents and systems, and their interpretation and subsequent execution is often left to the reader. Repre

Luca Schiavone

We present an alternative proof of the Coisotropic Embedding Theorem in which the geometric choice of a connection is recast as the algebraic choice of an embedding into the cotangent bundle. The symplectic thickening is then identified as the submanifold determined by the Hamiltonian momenta conjugate to the kernel directions of the pre-symplectic form.

Zizhao Li, Zhengkang Xiang, Joseph West, Kourosh Khoshelham

Traditional object detection methods operate under the closed-set assumption, where models can only detect a fixed number of objects predefined in the training set. Recent works on open vocabulary object detection (OVD) enable the detection of objects defined by an in-principle unbounded vocabulary, which reduces the cost of training models for specific task

Stefano Cavuoti, Lars Doorenbos, Demetra De Cicco, Gianluca Sasanelli

The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibilities offered by transfer learning techniques in addressing these challenges across various domains of astronomical research. We present a set of recent applications of transfer lea

M. Straub, F. Petocchi, C. Witteveen, F. B. Kugler

We study the electronic structure of bulk 1T-TaSe$_2$ in the charge density wave phase at low temperature. Our spatially and angle resolved photoemission (ARPES) data show insulating areas coexisting with metallic regions characterized by a chiral Fermi surface and moderately correlated quasiparticle bands. Additionally, high-resolution laser ARPES reveals v

F. de Gasperin, H. W. Edler, A. Boselli, P. Serra

The Virgo cluster is the closest richest nearby galaxy cluster. It is in the formation process, with a number of sub-clusters undergoing merging and interactions. Although a great laboratory to study galaxy evolution and cluster formation, its large apparent size and the severe dynamic range limitations due to the presence of the bright radio source Virgo A

Di Zhang, Junxian Li, Jingdi Lei, Xunzhi Wang

Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues like hallucinated image understandings or unrefined reasoning paths. To address these challenges, we introduce Critic-V, a novel framework inspired by the Actor-Critic paradigm to

G. Y. Thiancourt, S. M. Ngom, N. Bardou, T. Devolder

Efficient coupling between magnons and phonons requires material platforms that contain magnetic multilayers with versatile high-frequency properties grown on piezoelectric substrates with large electromechanical coupling coefficients. One of these systems is the CoFeB/Ru/CoFeB Synthetic antiferromagnet grown on Lithium Niobate substrate. We investigate its

Jie-Jing Shao, Hao-Ran Hao, Xiao-Wen Yang, Yu-Feng Li

Recent learning-to-imitation methods have shown promising results in planning via imitating within the observation-action space. However, their ability in open environments remains constrained, particularly in long-horizon tasks. In contrast, traditional symbolic planning excels in long-horizon tasks through logical reasoning over human-defined symbolic spac

R. Poitevineau, F. Combes, S. Garcia-Burillo, D. Cornu

The detailed feeding and feedback mechanisms of Active Galactic Nuclei (AGN) are not yet well known. For low-luminosity and obscured AGN, as well as late-type galaxies, determining the central black hole (BH) masses is challenging. Our goal with the GATOS sample is to study circum-nuclear regions and better estimate BH masses with more precision than scaling

Milin Zhang, Mohammad Abdi, Venkat R. Dasari, Francesco Restuccia

Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to chan

Frederic Bippus, Benedikt Schneider, Björn Sbierski

The numerical study of high-dimensional frustrated quantum magnets remains a challenging problem. Here we present an extension of the pseudo-Majorana functional renormalization group to spin-1/2 XXZ type Hamiltonians with field or magnetization along spin-Z direction at finite temperature. We consider a $U(1)$ symmetry-adapted fermionic spin representation a

Zhiyang Guo, Jinxu Xiang, Kai Ma, Wengang Zhou

3D characters are essential to modern creative industries, but making them animatable often demands extensive manual work in tasks like rigging and skinning. Existing automatic rigging tools face several limitations, including the necessity for manual annotations, rigid skeleton topologies, and limited generalization across diverse shapes and poses. An alter

Soubhik De, Vedhanayagi R., S. V. M. Satyanarayana, Alok Sharan

We propose a controlled quantum teleportation protocol for securely transferring an unknown $n$-qubit state from a sender to a receiver, under the supervision of $m$ controller participants. The protocol uses $n$ copies of an $m$-qubit Greenberger-Horne-Zeilinger state as the quantum resource. Message qubits can be distributed among participants to enhance s

Jia-Shian Wang, Manoj Kumar Singh, Hau-Bin Li, Henry Tsz-King Wong

The p-type point-contact germanium (pPCGe) detectors have been widely adopted in searches for low energy physics events such as neutrinos and dark matter. This is due to their enhanced capabilities of background rejection, sensitivity at energies as low as the sub-keV range and particularly fine energy resolution. Nonetheless, the pPCGe is subject to irregul

Dimitris Michailidis, Willem Röpke, Diederik M. Roijers, Sennay Ghebreab

Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more complex than single-objective RL, particularly as the number of objectives increases. Additionally, when objectives involve the preferences of agents or groups, incorporating fairn

Lars Moreels, Ian Lateur, Diego De Gusem, Jeroen Mulkers

We present mumax+, an extensible GPU-accelerated micromagnetic simulator with a Python user interface, to address the challenges posed by current magnetism research into systems with complex magnetic ordering and interfaces. It is a general solver for the space- and time-dependent evolution of the magnetization and related vector quantities, using finite dif

Takero Yoshihisa, Takaaki Yokoyama, Takafumi Kaneko

We performed numerical simulations to study mechanisms of solar prominence formation triggered by a single heating event. In the widely accepted ``chromospheric-evaporation condensation" model, localized heating at footpoints of a coronal loop drives plasma evaporation and eventually triggers condensation. The occurrence of condensation is strongly influence

Galina Filipuk, Juan F. Mañas-Mañas, Juan J. Moreno-Balcázar, Cristina Rodríguez-Perales

We tackle the regularisation of a differential system related to generalised Krawtchouk polynomials. We show a straightforward connection between certain auxiliary quantities involving the recurrence coefficients of these polynomials and Painlev\'e V equation via iterative regularisation. Furthermore, we explore how iterative regularisation yields polynomial

Xinyao Zheng, Husheng Han, Shangyi Shi, Qiyan Fang

Large language models (LLMs) possess extensive knowledge and question-answering capabilities, having been widely deployed in privacy-sensitive domains like finance and medical consultation. During LLM inferences, cache-sharing methods are commonly employed to enhance efficiency by reusing cached states or responses for the same or similar inference requests.

Yixuan Zhang, Hui Yang, Chuanchen Luo, Junran Peng

Generating realistic 3D human-object interactions (HOIs) from text descriptions is a active research topic with potential applications in virtual and augmented reality, robotics, and animation. However, creating high-quality 3D HOIs remains challenging due to the lack of large-scale interaction data and the difficulty of ensuring physical plausibility, espec

R. N. Rogalyov

We discuss the distribution of fireballs produced in heavy-ion collisions in the net-baryon number and argue that neither the Free-Quark Model (FQM) nor the Hadron Resonance Gas (HRG) model can provide a comprehensive explanation of the distribution observed at the LHC. The concept of net-baryon number freezeout temperature is suggested and the role of sea q

Abeer Banerjee, Sanjay Singh

The field of computational imaging has witnessed a promising paradigm shift with the emergence of untrained neural networks, offering novel solutions to inverse computational imaging problems. While existing techniques have demonstrated impressive results, they often operate either in the high-data regime, leveraging Generative Adversarial Networks (GANs) as

Remi Yvant Temgoua

In this paper, we study the effect of symmetric radial decreasing rearrangement on fractional Orlicz-Sobolev seminorm in domains. Roughly speaking, we prove that symmetric radial decreasing rearrangement can increase the fractional Orlicz-Sobolev seminorm in domains. Our result extends that of Li-Wang [Commun. Contemp. Math. 21.07 (2019): 1850059.] to the se

Stefan Le Coz, Boris Shakarov

We consider the nonlinear Schr\''odinger equation on a strip with Neumann boundary conditions and a delta condition on the $x$-axis. First, we show the existence of ground states as minimizers of the action or of the energy under suitable constraints. Second, we prove that the energy minimizers converge to the ground state on the line with a delta condition

Clarence Kineider, Georgios Kydonakis, Eugen Rogozinnikov, Valdo Tatitscheff

This book offers a comprehensive introduction to spectral networks from a unified viewpoint that bridges geometry with the physics of supersymmetric gauge theories. It provides the foundational background needed to approach the frontiers of this rapidly evolving field, treating geometric and physical aspects in parallel. After surveying fundamental topics in

María Emilia Alonso García, Henri Lombardi, Stefan Neuwirth

We compare two henselisations of a residually discrete valuation domain. Our constructive proof that a certain natural morphism is an isomorphism is also a proof in classical mathematics. Although this isomorphism is implicitly accepted as obvious in the literature, it seems that no proof was previously available.

Nathan Szwarcberg, Christophe Vergez, Tom Colinot, Michaël Jousserand

Localized nonlinear losses are taken into account in a simple Raman clarinet model.The complete system is expressed as an iterated map, enabling to study the stability of the different playing regimes. A parametric study is carried out with respect to three major parameters: blowing pressure, embouchure and nonlinear losses coefficient.The model exhibits the

Jean-baptiste Casteras, Juraj Földes, Itamar Oliveira, Gennady Uraltsev

In this paper, we study the local well-posedness of the cubic Schr\"odinger equation $$(i\partial_t + \mathcal{L}) u = \pm |u|^2 u \qquad \textrm{on} \quad \ I\times \mathbb{R}^d ,$$ with initial data being a Wiener randomization at unit scale of a given function $f$ and $\mathcal{L}$ being an operator of degree $\sigma\geq 2$. In particular, we prove that a

Riad Mokadem, Abdelkader Hameurlain, Franck Morvan

Evaluation of join queries is very challenging since they have to deal with an increasing data size. We study the relational join query processing realized by hash tables and we focus on the case of equi join queries. We propose to use a new form of signatures, the algebraic signatures, for fast comparison between values of two attributes in relations partic

Sergey Khoperskov, Paola Di Matteo, Matthias Steinmetz, Bridget Ratcliffe

The innermost parts of the Milky Way (MW) are very difficult to observe due to the high extinction along the line of sight, especially close to the disc mid-plane. However, this region contains the most massive complex stellar component of the MW, the bulge, primarily composed of disc stars whose structure is (re-)shaped by the evolution of the bar. In this

A. L. Semenov, S. F. Soprunov

We investigate the definability (reducts) lattice of the order of integers and describe a sublattice generated by relations 'between', 'cycle', 'separation', 'neighbor', '1-codirection', 'order' and equality'. Some open questions are proposed.

Bo Fang, Wenhao Wu, Qiangqiang Wu, Yuxin Song

Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment. Automatic generation of ADs remains challenging due to: i) the domain gap between movie-AD data and existing data used to train vision-language models, and ii) the issue of contextual re

Yanjiang Guo, Yucheng Hu, Jianke Zhang, Yen-Jen Wang

Diffusion models have demonstrated remarkable capabilities in image generation tasks, including image editing and video creation, representing a good understanding of the physical world. On the other line, diffusion models have also shown promise in robotic control tasks by denoising actions, known as diffusion policy. Although the diffusion generative model

Aron Zingler, Stephane Fliscounakis, Patrick Panciatici, Alexander Mitsos

Motivated by the increasing need to hedge against load and generation uncertainty in the operation of power grids, we propose flexibility maximization during operation. We consider flexibility explicitly as the amount of uncertainty that can be handled while still ensuring nominal grid operation in the worst-case. We apply the proposed flexibility optimizati

Emma Reyner-Fuentes, Esther Rituerto-Gonzalez, Carmen Pelaez-Moreno

Gender-based violence is a pervasive public health issue that severely impacts women's mental health, often leading to conditions such as in anxiety, depression, post-traumatic stress disorder, and substance abuse. Identifying the combination of these various mental health conditions could then point to someone who is a victim of gender-based violence. And w

Miha Malenšek, Blaž Škrlj, Blaž Mramor, Jure Demšar

Synthetic datasets are important for evaluating and testing machine learning models. When evaluating real-life recommender systems, high-dimensional categorical (and sparse) datasets are often considered. Unfortunately, there are not many solutions that would allow generation of artificial datasets with such characteristics. For that purpose, we developed a

Amina Doumane, Samuel Humeau, Damien Pous

We provide a finite equational presentation of graphs of treewidth at most three, solving an instanceof an open problem by Courcelle and Engelfriet. We use a syntax generalising series-parallel expressions, denoting graphs with a small interface. Weintroduce appropriate notions of connectivity for such graphs (components, cutvertices, separationpairs). We us

Soumyadip Chatterjee, Subhranshu Sekhar Sahu, Kanchan Singh Rana, Swagata Bhunia

Inhomogeneity-governed carrier localization has been investigated in three sets of InGaN/GaN multi-quantum-disk light-emitting diode (LED) structures grown by plasma-assisted molecular beam epitaxy (PAMBE) under different process conditions. A temperature-dependent study of the luminescence peak positions reveals that samples prepared under certain process c

Yangrui Dong, Weisheng Gong, Qingyong Li, Kaijie Su

This paper proposes an enhancement to the ORB-SLAM3 algorithm, tailored for applications on rugged road surfaces. Our improved algorithm adeptly combines feature point matching with optical flow methods, capitalizing on the high robustness of optical flow in complex terrains and the high precision of feature points on smooth surfaces. By refining the inter-f

A. Durán, A. Esfahani, G. Muslu

The Klein-Gordon-Boussinesq (KGB) system is proposed in the literature as a model problem to study the validity of approximations in the long wave limit provided by simpler equations such as KdV, nonlinear Schr\"{o}dinger or Whitham equations. In this paper, the KGB system is analyzed as a mathematical model in three specific points. The first one concerns w

Yudong Zhang, Ruobing Xie, Xingwu Sun, Yiqing Huang

Large vision-language models (LVLMs) have demonstrated exceptional performance on complex multimodal tasks. However, they continue to suffer from significant hallucination issues, including object, attribute, and relational hallucinations. To accurately detect these hallucinations, we investigated the variations in cross-modal attention patterns between hall

Zekun Shi, Zheyuan Hu, Min Lin, Kenji Kawaguchi

Optimizing neural networks with loss that contain high-dimensional and high-order differential operators is expensive to evaluate with back-propagation due to $\mathcal{O}(d^{k})$ scaling of the derivative tensor size and the $\mathcal{O}(2^{k-1}L)$ scaling in the computation graph, where $d$ is the dimension of the domain, $L$ is the number of ops in the fo

Simon Vandevelde, Laurent Mertens, Sverre Lauwers, Joost Vennekens

Artificial Neural Networks excel at identifying individual components in an image. However, out-of-the-box, they do not manage to correctly integrate and interpret these components as a whole. One way to alleviate this weakness is to expand the network with explicit knowledge and a separate reasoning component. In this paper, we evaluate an approach to this

Alexandre Benoist, Jean Kieffer

We generalize the notion of Elkies primes for elliptic curves to the setting of abelian varieties with real multiplication (RM), and prove the following. Let $A$ be an abelian variety with RM over a number field whose attached Galois representation has large image. Then the number of Elkies primes (in a suitable range) for reductions of $A$ modulo primes con

Dianze Li, Jianing Li, Xu Liu, Zhaokun Zhou

Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two independent Artificial Neural Network (ANN) branches, limiting cross-modality information interaction across the two visual streams and encountering challenges in extracting temporal cues

Benjamin J. Choi, Hiroshi Ohno, Takayuki Sumimoto, Akio Tomiya

We present our preliminary results on the machine learning estimation of $\text{Tr} \, M^{-n}$ from other observables with the gradient boosting decision tree regression, where $M$ is the Dirac operator. Ordinarily, $\text{Tr} \, M^{-n}$ is obtained by linear CG solver for stochastic sources which needs considerable computational cost. Hence, we explore the

Mengya Xu, Wenjin Mo, Guankun Wang, Huxin Gao

Purpose: Endoscopic surgical environments present challenges for dissection zone segmentation due to unclear boundaries between tissue types, leading to segmentation errors where models misidentify or overlook edges. This study aims to provide precise dissection zone suggestions during endoscopic submucosal dissection (ESD) procedures, enhancing ESD safety.

Avin Seneviratne, Peter L. Walters, Fei Wang

In this work, we developed an efficient quantum algorithm for the simulation of non-Markovian quantum dynamics, based on the Feynman path integral formulation. The algorithm scales polynomially with the number of native gates and the number of qubits, and has no classical overhead. It demonstrates the quantum advantage by overcoming the exponential cost on c

Yuqian Zhao, Zhaohua Ma, Zhangzhen He, Haijun Liao

Quantum annealing, which involves quantum tunnelling among possible solutions, has state-of-the-art applications not only in quickly finding the lowest-energy configuration of a complex system, but also in quantum computing. Here we report a single-crystal study of the frustrated magnet $\alpha$-CoV$_2$O$_6$, consisting of a triangular arrangement of ferroma

Sampath Kumar Mulagaleti, Alberto Bemporad

Dynamical models identified from data are frequently employed in control system design. However, decoupling system identification from controller synthesis can result in situations where no suitable controller exists after a model has been identified. In this work, we introduce a novel control-oriented regularization in the identification procedure to ensure

Dong Han, Yong Li, Joachim Denzler

With the advancement of face reconstruction (FR) systems, privacy-preserving face recognition (PPFR) has gained popularity for its secure face recognition, enhanced facial privacy protection, and robustness to various attacks. Besides, specific models and algorithms are proposed for face embedding protection by mapping embeddings to a secure space. However,

Mohamad Abubaker, Zubayda Alsadder, Hamed Abdelhaq, Maik Boltes

The automatic detection of pedestrian heads in crowded environments is essential for crowd analysis and management tasks, particularly in high-risk settings such as railway platforms and event entrances. These environments, characterized by dense crowds and dynamic movements, are underrepresented in public datasets, posing challenges for existing deep learni

Sadataka Furui, Serge Dos Santos

Localization and classification of scattered nonlinear ultrasonic signatures in 2 dimensional complex damaged media using Time Reversal based Nonlinear Elastic Wave Spectroscopy (TR-NEWS) approach is extended to 3 dimensional complex damaged media. In (2+1)D, i.e. space 2 dimensional time 1 dimensional spacetime, we used quaternion bases for analyses, while

Beomjong Kwak

In this paper, we prove the global well-posedness of the energy-critical nonlinear Schr\"odinger equations on the torus $\mathbb{T}^{d}$ for general dimensions. This result is new for dimensions $d\ge5$, extending previous results for $d=3,4$ [10,22]. Compared to the cases $d=3,4$, the regularity theory for higher $d$, developed in the underlying local well-

Jie Wang, Yichen Wang, Zhilin Zhang, Jianhao Zeng

With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex contexts. To address this, we propose the Sentiment Cross-Lingual Recognition and Logic Framework (SentiXRL), wh

Alonso Rodriguez-Navarro

The contribution to pushing the boundaries of knowledge is a critical metric for evaluating the research performance of countries and institutions, which in many cases is not revealed by common bibliometric indicators. The Rk-index was specifically designed to assess such contributions, and the Rn-index is a variant that corrects the weakness of the Rk-index

Po-Shen Hsin, Jaume Gomis

The Standard Model of particle physics stands as one of the most profound and successful frameworks for describing the fundamental workings of nature. The global form of the Standard Model gauge group, however, remains an open question: it can be $\left(SU(3)_C\times SU(2)_W\times U(1)_Y\right)/\Gamma$ with $\Gamma=1,\mathbb{Z}_2,\mathbb{Z}_3$ or $\mathbb{Z}

Wataru Shimoda, Naoto Inoue, Daichi Haraguchi, Hayato Mitani

While recent text-to-image models can generate photorealistic images from text prompts that reflect detailed instructions, they still face significant challenges in accurately rendering words in the image. In this paper, we propose to retouch erroneous text renderings in the post-processing pipeline. Our approach, called Type-R, identifies typographical erro