October 2024 arXiv papers — page 7

Björn Kischelewski, Gregory Cathcart, David Wahl, Benjamin Guedj

The detection and clearance of explosive ordnance (EO) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its efficiency and effectiveness. Research on artificial intelligence (AI) for EO detection in clearance operations has grown significantly in recent years. However, this research spans a

Xiusheng Huang, Yequan Wang, Jun Zhao, Kang Liu

Knowledge editing technology is crucial for maintaining the accuracy and timeliness of large language models (LLMs) . However, the setting of this task overlooks a significant portion of commonsense knowledge based on free-text in the real world, characterized by broad knowledge scope, long content and non instantiation. The editing objects of previous metho

Xiusheng Huang, Jiaxiang Liu, Yequan Wang, Kang Liu

Knowledge editing technology has received widespread attention for low-cost updates of incorrect or outdated knowledge in large-scale language models. However, recent research has found that edited models often exhibit varying degrees of performance degradation. The reasons behind this phenomenon and potential solutions have not yet been provided. In order t

Raphael Hernandes, Giulio Corsi

This study examines the influence of Google's search algorithm on news diversity by analyzing search results in Brazil, the UK, and the US. It explores how Google's system preferentially favors a limited number of news outlets. Utilizing algorithm auditing techniques, the research measures source concentration with the Herfindahl-Hirschman Index (HHI) and Gi

Jianqun Zhou, Yuanlei Zheng, Wei Chen, Qianqian Zheng

Instruction-following capabilities in LLMs have progressed significantly, enabling more complex user interactions through detailed prompts. However, retrieval systems have not matched these advances, most of them still relies on traditional lexical and semantic matching techniques that fail to fully capture user intent. Recent efforts have introduced instruc

Sebastian Griesbach, Carlo D'Eramo

Exploration is a crucial and distinctive aspect of reinforcement learning (RL) that remains a fundamental open problem. Several methods have been proposed to tackle this challenge. Commonly used methods inject random noise directly into the actions, indirectly via entropy maximization, or add intrinsic rewards that encourage the agent to steer to novel regio

Bo-Xuan Ge, Eugene A. Lim, Ulrich Sperhake, Tamara Evstafyeva

We explore the gravitational-wave emission from head-on collisions of equal-mass solitonic boson-star binaries from simulations spanning a two-dimensional parameter space, consisting of the central scalar-field amplitude of the stars and the solitonic potential parameter. We report the gravitational-wave energies emitted by boson-star binaries which, due to

Chaoyi Lu, Daniele Durante, Nial Friel

Criminal networks arise from the unique attempt to balance a need of establishing frequent ties among affiliates to facilitate the coordination of illegal activities, with the necessity to sparsify the overall connectivity architecture to hide from law enforcement. This efficiency-security tradeoff is also combined with the creation of groups of redundant cr

Junming Duan, Wasilij Barsukow, Christian Klingenberg

The active flux (AF) method is a compact high-order finite volume method that simultaneously evolves cell averages and point values at cell interfaces. Within the method of lines framework, the existing Jacobian splitting-based point value update incorporates the upwind idea but suffers from a stagnation issue for nonlinear problems due to inaccurate estimat

Atsuhisa Ota

We conduct a mode analysis of a general $U(1)$-charged first-order relativistic hydrodynamics within the framework of effective field theory for dissipative fluids in flat Minkowski spacetime. We derive the most general quadratic action for hydrodynamic modes, including stochastic noise, and analyze the corresponding dispersion relations in a consistent grad

Xiang Deng, Youxin Pang, Xiaochen Zhao, Chao Xu

This paper introduces Stereo-Talker, a novel one-shot audio-driven human video synthesis system that generates 3D talking videos with precise lip synchronization, expressive body gestures, temporally consistent photo-realistic quality, and continuous viewpoint control. The process follows a two-stage approach. In the first stage, the system maps audio input

Pedro Morão, Joao Santinha, Yasna Forghani, Nuno Loução

Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual magnetic resonance (MR) images that simulate different IAP without alteri

Cosmin I. Bercea, Philippe C. Cattin, Julia A. Schnabel, Julia Wolleb

This review explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their applicatio

Lorenzo Giannessi, Franck Cadoux, Sebastien Cap, Jaafar Chakrani

T2K is a long baseline neutrino experiment, entering Phase II with a Near Detector upgrade. The T2K near detector (ND280) upgrade consists of the installation of three new detector systems: a plastic scintillator neutrino active target (Super-FGD), two time projection chambers (HA-TPC) and a time of flight detector (TOF). The Super-FGD is composed of 2-milli

C. T. H. Davies, A. S. Kronfeld, G. P. Lepage, C. McNeile

An accurate calculation of the leading-order hadronic vacuum polarisation (LOHVP) contribution to the anomalous magnetic moment of the muon ($a_\mu$) is key to determining whether a discrepancy, suggesting new physics, exists between the Standard Model and experimental results. This calculation can be expressed as an integral over Euclidean time of a current

Tahar Chettaoui, Naser Damer, Fadi Boutros

Foundation models are predominantly trained in an unsupervised or self-supervised manner on highly diverse and large-scale datasets, making them broadly applicable to various downstream tasks. In this work, we investigate for the first time whether such models are suitable for the specific domain of face recognition (FR). We further propose and demonstrate t

Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras

In this work, we generalize the ideas of Kaiming initialization to Graph Neural Networks (GNNs) and propose a new scheme (G-Init) that reduces oversmoothing, leading to very good results in node and graph classification tasks. GNNs are commonly initialized using methods designed for other types of Neural Networks, overlooking the underlying graph topology. W

Feng Zhang, Li Deng, Yanjie Ge, Jiaxing Wen

Muons, which play a crucial role in both fundamental and applied physics, have traditionally been generated through proton accelerators or from cosmic rays. With the advent of ultra-short high-intensity lasers capable of accelerating electrons to GeV levels, it has become possible to generate muons in laser laboratories. In this work, we show the first proof

Ke Li, Fuyu Dong, Di Wang, Shaofeng Li

Remote sensing change detection aims to perceive changes occurring on the Earth's surface from remote sensing data in different periods, and feed these changes back to humans. However, most existing methods only focus on detecting change regions, lacking the capability to interact with users to identify changes that the users expect. In this paper, we introd

Katherine Collins, Siaw-Lynn Ng

This article describes our invention of a new poetic form based on projective geometry. In doing this we also explore the 'what ifs' in mathematics and poetry which spark the creative processes of poet and mathematician. In other words, throughout our collaboration we often asked one another, is this what it's like for you? Do you think in this way, too? How

Yuval Gitlitz, Ofer Neiman, Richard Spence

An $(\alpha,\beta)$-spanner of a weighted graph $G=(V,E)$, is a subgraph $H$ such that for every $u,v\in V$, $d_G(u,v) \le d_H(u,v)\le\alpha\cdot d_G(u,v)+\beta$. The main parameters of interest for spanners are their size (number of edges) and their lightness (the ratio between the total weight of $H$ to the weight of a minimum spanning tree). In this paper

Amir Hossein Kargaran, François Yvon, Hinrich Schütze

The need for large text corpora has increased with the advent of pretrained language models and, in particular, the discovery of scaling laws for these models. Most available corpora have sufficient data only for languages with large dominant communities. However, there is no corpus available that (i) covers a wide range of minority languages; (ii) is genera

Youngjoon Lee, Jinu Gong, Joonhyuk Kang

Federated learning enables edge devices to collaboratively train a global model while maintaining data privacy by keeping data localized. However, the Non-IID nature of data distribution across devices often hinders model convergence and reduces performance. In this paper, we propose a novel plugin for federated optimization methods that approximates Non-IID

Eduard A. Podshivaylov, Pavel A. Frantsuzov

A theoretical model for the recently discovered effect of all-optical photoswitching in lead tribromide perovskite single microcrystals is proposed. The model takes into account the spatially distributed kinetics of the charge carrier recombination and the creation/destruction of trap states. It successfully reproduces the key features of the photoswitching

Jinlong He, Pengfei Li, Gang Liu, Shenjun Zhong

Multimodal Large Language Models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve excellent results in the general domain of multimodal tasks. However, in the medical domain, the substantial training costs and the requirement for extensive medical data pose challe

Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas, Javier Del Ser

Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, these AI-based systems encounter reliability and safety challenges when handling continuously generated data streams in complex and dynamic scenarios. This work explores the concept

Ilaria Colazzo, Arne Van Antwerpen

We extend the cabling method by Lebed, Ram\'irez and Vendramin from involutive to bijective non-degenerate set-theoretic solutions of the Yang--Baxter equation by working in the Yang--Baxter monoid $M(X,r)$ rather than the group $G(X,r)$. This shift in approach overcomes the obstruction that, for non-involutive solutions, the canonical map from $X$ to the Ya

Youngjun Jun, Jiwoo Park, Kyobin Choo, Tae Eun Choi

Disentangled representation learning (DRL) aims to break down observed data into core intrinsic factors for a profound understanding of the data. In real-world scenarios, manually defining and labeling these factors are non-trivial, making unsupervised methods attractive. Recently, there have been limited explorations of utilizing diffusion models (DMs), whi

Seijin Kobayashi, Yassir Akram, Johannes Von Oswald

The effect of regularizers such as weight decay when training deep neural networks is not well understood. We study the influence of weight decay as well as $L2$-regularization when training neural network models in which parameter matrices interact multiplicatively. This combination is of particular interest as this parametrization is common in attention la

A. Chaika, A. O. Oliinyk, I. V. Yatsuta, N. P. Proukakis

Persistent currents--inviscid quantized flow around an atomic circuit--are a crucial building block of atomtronic devices. We investigate how acceleration influences the transfer of persistent currents between two density-connected, ring-shaped atomic Bose-Einstein condensates, joined by a tunable weak link that controls system topology. We find that the acc

P. Muggli, M. Bergamaschi, J. Pucek, D. Easton

We outline plans to use plasma light emitted as atomic lines radiation as a diagnostic for wakefields driven in plasma by a self-modulating, long proton bunch. This diagnostic is built into the design of a new vapor/plasma source that will also allow for imposing a plasma density step of various height at various locations. Such a step of a few percent in re

Yannis Voet, Espen Sande, Annalisa Buffa

Mass scaling is widely used in finite element models of structural dynamics for increasing the critical time step of explicit time integration methods. While the field has been flourishing over the years, it still lacks a strong theoretical basis and mostly relies on numerical experiments as the only means of assessment. This contribution thoroughly reviews

Dake Guo, Jixun Yao, Xinfa Zhu, Kangxiang Xia

This paper presents the NPU-HWC system submitted to the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge 2024 (ICAGC). Our system consists of two modules: a speech generator for Track 1 and a background audio generator for Track 2. In Track 1, we employ Single-Codec to tokenize the speech into discrete tokens and use a language-model-based

Aliaksandr Herasimenka, Xianlingchen Wang, Ralph Schroeder

We present a systematic review of peer-reviewed research into ways to mitigate manipulative information about climate change on social media. Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We find that commonly recommended approaches to addressing manipulation about climate cha

R. J. Hernández-Pinto, L. X. Gutiérrez-Guerrero, M. A. Bedolla, A. Bashir

We present a detailed survey of electric, magnetic and quadrupole form factors of light and heavy spin-1 vector mesons. It complements our analogous analysis of the electromagnetic form factors of pseudoscalar and scalar mesons reported earlier. Our formalism is based upon the Schwinger-Dyson equations treatment of a vector $\times$ vector contact interactio

Federico Nardi, Jinpei Han, Shlomi Haar, A. Aldo Faisal

Graph Neural Networks (GNN) can capture the geometric properties of neural representations in EEG data. Here we utilise those to study how reinforcement-based motor learning affects neural activity patterns during motor planning, leveraging the inherent graph structure of EEG channels to capture the spatial relationships in brain activity. By exploiting task

Anurag Anshu, Jonas Haferkamp, Yeongwoo Hwang, Quynh T. Nguyen

We study the long-standing open question on the power of unique witnesses in quantum protocols, which asks if $\textsf{UniqueQMA}$, a variant of $\textsf{QMA}$ whose accepting witness space is 1-dimensional, contains $\mathsf{QMA}$ under quantum reductions. This work rules out any black-box reduction from $\mathsf{QMA}$ to $\mathsf{UniqueQMA}$ by showing a q

Jesse Farebrother, Pablo Samuel Castro

We introduce the Continuous Arcade Learning Environment (CALE), an extension of the well-known Arcade Learning Environment (ALE) [Bellemare et al., 2013]. The CALE uses the same underlying emulator of the Atari 2600 gaming system (Stella), but adds support for continuous actions. This enables the benchmarking and evaluation of continuous-control agents (such

Håvard Bakke Bjerkevik, Linda Kleist, Torsten Ueckerdt, Birgit Vogtenhuber

For a set $P$ of $n$ points in general position in the plane, the flip graph $F(P)$ has a vertex for each non-crossing spanning tree on $P$ and an edge between any two spanning trees that can be transformed into each other by one edge flip. The diameter ${\rm diam}(F(P))$ of this graph is subject of intensive study. For points in general position, it is betw

Junjie Guo

This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock portfolios. Two datasets comprising randomly selected stocks from the S&P500 and NASDAQ indices, each spanning a decade of

Weida Li, Yaoliang Yu

The concept of probabilistic values, such as Beta Shapley values and weighted Banzhaf values, has gained recent attention in applications like feature attribution and data valuation. However, exact computation of these values is often exponentially expensive, necessitating approximation techniques. Prior research has shown that the choice of probabilistic va

Antonia Saske, Laura Koesten, Torsten Möller, Judith Staudner

How audiences read, interpret, and critique data visualizations is mainly assessed through performance tests featuring tasks like value retrieval. Yet, other factors shown to shape visualization understanding, such as numeracy, graph familiarity, and aesthetic perception, remain underrepresented in existing instruments. To address this, we design and test a

Faisal Mehmood, Enqing Chen, Touqeer Abbas, Samah M. Alzanin

Human Action Recognition (HAR) is an interesting research area in human-computer interaction used to monitor the activities of elderly and disabled individuals affected by physical and mental health. In the recent era, skeleton-based HAR has received much attention because skeleton data has shown that it can handle changes in striking, body size, camera view

Sitian Chen, Amelie Chi Zhou, Yucheng Shi, Yusen Li

Approximate Nearest Neighbor Search (ANNS) is a critical component of modern AI systems, such as recommendation engines and retrieval-augmented large language models (RAG-LLMs). However, scaling ANNS to billion-entry datasets exposes critical inefficiencies: CPU-based solutions are bottlenecked by memory bandwidth limitations, while GPU implementations under

Paul Rosenberger, Andri Darmawan, Olena Fedchenko, Olena Tkach

Spin-polarized two-dimensional electron gases (2DEGs) are of particular interest for functional oxide electronics applications. The redox-created 2DEG residing on the strontium titanate, SrTiO$_3$ (STO), side of a europium monoxide (EuO)/SrTiO$_3$ (001) interface is expected to be significantly spin-polarized due to the proximity to the strong ($7\,\mu_B/f.u

Jens Grubert, Junlong Chen, Per Ola Kristensson

Artificial Intelligence-Generated Content (AIGC) has the potential to transform how people build and interact with virtual environments. Within this paper, we discuss potential benefits but also challenges that AIGC has for the creation of inclusive and accessible virtual environments. Specifically, we touch upon the decreased need for 3D modeling expertise,

Xiaojie Zhang, Donglei Wang, Qing Li, Rongzong Huang

A volumetric lattice Boltzmann (LB) method is developed for the particle-resolved direct numerical simulation of thermal particulate flows with conjugate heat transfer. This method is devised as a single-domain approach by applying the volumetric interpretation of the LB equation and introducing a solid fraction field to represent the particle. The volumetri

Martin Dindoš, Linhan Li, Jill Pipher

In this paper, we fully resolve the question of whether the Regularity problem for the parabolic PDE $-\partial_tu + \mbox{div}(A\nabla u)=0$ on a Lipschitz cylinder $\mathcal O\times\mathbb R$ is solvable for some $p\in (1,\infty)$ under the assumption that the matrix $A$ is elliptic, has bounded and measurable coefficients and its coefficients satisfy a na

Zhuoyang Pan, Angjoo Kanazawa, Hang Gao

Self-occlusion is common when capturing people in the wild, where the performer do not follow predefined motion scripts. This challenges existing monocular human reconstruction systems that assume full body visibility. We introduce Self-Occluded Avatar Recovery (SOAR), a method for complete human reconstruction from partial observations where parts of the bo

Rikuya Miyashita, Shiori Hironaka, Kazuyuki Shudo

Hypergraphs are generalizations of simple graphs that allow for the representation of complex group interactions beyond pairwise relationships. Clustering coefficients quantify local link density in networks and have been widely studied for both simple graphs and hypergraphs. However, existing clustering coefficients for hypergraphs treat each hyperedge as a

Hui Li, Weiren Zhao

In this paper, we study the instability effect of viscous dissipation in a domain without boundaries. We construct a shear flow that is initially spectrally stable but evolves into a spectrally unstable state under the influence of viscous dissipation. To the best of our knowledge, this is the first result of viscosity driven instability that is not caused b

Qi Wang, Deng Pan, Yuliang Jin

The Gardner transition in structural glasses is characterized by full-replica symmetry breaking of the free-energy landscape and the onset of anomalous aging dynamics due to marginal stability. Here we show that this transition also has a structural signature in finite-dimensional glasses consisting of hard spheres and disks. By analyzing the distribution of

F. D. Gonzalez-Martinez, J. J. Carabias-Orti, F. J. Canadas-Quesada, N. Ruiz-Reyes

Snoring, an acoustic biomarker commonly observed in individuals with Obstructive Sleep Apnoea Syndrome (OSAS), holds significant potential for diagnosing and monitoring this recognized clinical disorder. Irrespective of snoring types, most snoring instances exhibit identifiable harmonic patterns manifested through distinctive energy distributions over time.

Grzegorz Banaszak, Dorota Blinkiewicz

In this paper we present families of wild 1-motives, i.e., families of pairwise non-isomorphic Deligne 1-motives, over rings of $S$-integers $\mathcal{O}_{F,S}$, which have the same reductions to torsion 1-motives for all $v\notin S$. Our proof is based on a technical result concerning a local to global principle for multiple base discrete logarithm problem

Jeffy Yu

Autonomous AI is driving new intersections between culture, cognition, and finance, fundamentally reshaping the digital landscape. Zerebro, an AI fine-tuned on schizophrenic responses and scraped conversations of Andy Ayrey's infinite backrooms, autonomously creates and spreads disruptive memes across online platforms. It also mints unique ASCII artwork on b

Marek Wadinger, Rastislav Fáber, Erika Pavlovičová, Radoslav Paulen

This paper presents a comprehensive framework aimed at enhancing education in modeling, optimal control, and nonlinear Model Predictive Control~(MPC) through a practical greenhouse climate control model. The framework includes a detailed mathematical model of lettuce growth and greenhouse, which are influenced by real-time external weather conditions obtaine

Marcos Pena-Rocha, Maria Rocio Gomez-Crisostomo, Vicente Pablo Guerrero-Bote, Felix de Moya-Anegon

This study presents a comparative analysis between two scientific document classification systems. The first system employs the Scopus journal-based assignment method, adapted to a fractional model, while the second system uses an item-by-item system based on reclassified references according to the origin of the citers. The study's results are divided into

Nina Girotto Erhardt, Aloïs Castellano, J. P. Alvarinhas Batista, Raffaello Bianco

The Raman active G mode in graphene exhibits strong coupling to electrons, yet the comprehensive treatment of this interaction in the calculation of its temperature-dependent Raman spectrum remains incomplete. In this study, we calculate the temperature dependence of the G mode frequency and linewidth, and successfully explain the experimental trend, by acco

Mirco Giacobbe, Daniel Kroening, Abhinandan Pal, Michael Tautschnig

We introduce a machine learning approach to model checking temporal logic, with application to formal hardware verification. Model checking answers the question of whether every execution of a given system satisfies a desired temporal logic specification. Unlike testing, model checking provides formal guarantees. Its application is expected standard in silic

Robert de Mello Koch, Bo-Qiang Lu, Pedro Ornelas, Isaac Nape

The topology of a pure state of two entangled photons is leveraged to provide a discretization of quantum information. Since discrete signals are inherently more resilient to the effects of perturbations, this discrete class of entanglement observables may offer an advantage against noise. Establishing this is the primary objective of this paper. We develop

Xinwang Chen, Ning Liu, Yichen Zhu, Feifei Feng

Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs, yet their extensive computational requirements hinder widespread practical applications. To reduce the computation budget of transformer-based DPMs, this work proposes the Efficient Diffusion Transformer (EDT) framework. The framework includes a lightweight

Jean-Christophe Pain

We present an integral expression of the Catalan numbers, based on F\'eaux' integral representation of $\log\left[\Gamma(x)\right]$, $\Gamma$ being the usual Gamma function. The obtained formula may be the starting point of the derivation of new relations involving central binomial coefficients or Catalan numbers.

Facundo Argañaraz, Juan Carlos Escanciano

Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a charact

Xiuyang Xia, Yuhan Peng, Ka Ki Li, Ran Ni

To unlock the potential for assembling complex colloidal "molecules", we investigate a minimal binary system of programmable colloidal atom-electron equivalents (PAE-EE), where electron equivalents (EEs) are multivalent linkers with two distinct types of single-stranded DNA (ssDNA) ends complementary to those ssDNAs on binary programmable atom equivalents (P

Ya-Peng Hu, Yu-Sen An, Gao-Yong Sun, Wen-Long You

Scaling laws for critical phenomena take pivotal status in almost all branches of physics. However, as scaling laws are commonly guaranteed by the renormalization group theory, systems that violate them have rarely been found. In this letter, we demonstrate that gravitational system can break scaling laws. We derive this result through investigating phase tr

Seon-Ho Lee, Jue Wang, Zhikang Zhang, David Fan

As the scale of data and models for video understanding rapidly expand, handling long-form video input in transformer-based models presents a practical challenge. Rather than resorting to input sampling or token dropping, which may result in information loss, token merging shows promising results when used in collaboration with transformers. However, the app

A. S. Khrykin

We present evidence of the existence of the six-quark component of the deuteron ($d_{6q}$) found in the experimental photon energy spectra of the proton deuteron bremsstrahlung measured at the proton incident energy of 200 MeV by the Grenoble group and of 195 MeV by the Michigan state group. A comparison of these spectra with the theoretically predicted ones

Xinyuan Chang, Maixuan Xue, Xinran Liu, Zheng Pan

Ensuring adherence to traffic sign regulations is essential for both human and autonomous vehicle navigation. While current online mapping solutions often prioritize the construction of the geometric and connectivity layers of HD maps, overlooking the construction of the traffic regulation layer within HD maps. Addressing this gap, we introduce MapDR, a nove

John F Kam, Spiro Gicev, Kavan Modi, Angus Southwell

The realization of fault-tolerant quantum computers hinges on effective quantum error correction protocols, whose performance significantly relies on the nature of the underlying noise. In this work, we directly study the structure of non-Markovian correlated errors and their impact on surface code memory performance. Specifically, we compare surface code pe

Joysankar Majumdar, Sakshi Maurya, Raj Prince

In October 2024, The object BL Lac experienced a brightest flaring event in gamma-ray ($>$100 MeV) with a historical $\gamma$-ray flux of $\sim$10$^{-5}$ erg cm$^{-2}$ s$^{-1}$. Soon after the event was followed across the waveband and in X-ray (0.3-10 keV) it was also found to be flaring with the maximum flux achieved during this event as 8.30$\times$10$^{-

José M. Espinar, Diego A. Marín

In this article, we study domains $\Omega \subset \mathbb{S}^2$ that support positive solutions of the overdetermined problem $$ \Delta u + f(u,|\nabla u|)=0 \quad \text{in } \Omega, $$ subject to the boundary conditions $u=0$ on $\partial\Omega$ and $|\nabla u|$ being locally constant along $\partial\Omega$. We refer to such domains as $f$--extremal domains

Weijie Ke, Mina Khoei, Dylan Muir

XyloAudio is a line of ultra-low-power audio inference chips, designed for in- and near-microphone analysis of audio in real-time energy-constrained scenarios. Xylo is designed around a highly efficient integer-logic processor which simulates parameter- and activity-sparse spiking neural networks (SNNs) using a leaky integrate-and-fire (LIF) neuron model. Ne

Lianghua Huang, Wei Wang, Zhi-Fan Wu, Yupeng Shi

Recent research arXiv:2410.15027 has explored the use of diffusion transformers (DiTs) for task-agnostic image generation by simply concatenating attention tokens across images. However, despite substantial computational resources, the fidelity of the generated images remains suboptimal. In this study, we reevaluate and streamline this framework by hypothesi

Hongying Liu, Hao Wang, Haoran Chu, Yibo Wu

An unsolved issue in widely used methods such as Support Vector Data Description (SVDD) and Small Sphere and Large Margin SVM (SSLM) for anomaly detection is their nonconvexity, which hampers the analysis of optimal solutions in a manner similar to SVMs and limits their applicability in large-scale scenarios. In this paper, we introduce a novel convex SSLM f

Jérome Eertmans, Nicola Di Cicco, Claude Oestges, Laurent Jacques

Radio propagation modeling is essential in telecommunication research, as radio channels result from complex interactions with environmental objects. Recently, Machine Learning has been attracting attention as a potential alternative to computationally demanding tools, like Ray Tracing, which can model these interactions in detail. However, existing Machine

Gunnar König, Eric Günther, Ulrike von Luxburg

In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one importance score for each feature. But often, predicting the target variable requires interactions between several features (such as in the XOR function), and features might have

Kangxiang Xia, Dake Guo, Jixun Yao, Liumeng Xue

The ISCSLP 2024 Conversational Voice Clone (CoVoC) Challenge aims to benchmark and advance zero-shot spontaneous style voice cloning, particularly focusing on generating spontaneous behaviors in conversational speech. The challenge comprises two tracks: an unconstrained track without limitation on data and model usage, and a constrained track only allowing t

Lizhe Fang, Yifei Wang, Zhaoyang Liu, Chenheng Zhang

Handling long-context inputs is crucial for large language models (LLMs) in tasks such as extended conversations, document summarization, and many-shot in-context learning. While recent approaches have extended the context windows of LLMs and employed perplexity (PPL) as a standard evaluation metric, PPL has proven unreliable for assessing long-context capab

Dhruba Prakash Biswas, Priti Sharma, Sandip Jana, Jens Schwaiger

In this paper, we shall compare two metrics in terms of orderly dependence, a notion developed in exponential vector space in the article 'Basis and Dimension of Exponential Vector Space' by Jayeeta Saha and Sandip Jana in Transactions of A. Razmadze Mathematical Institute Vol. 175 (2021), issue 1, 101-115. Exponential vector space, in short 'evs', is a part

Sebastián Barbieri, Felipe García-Ramos, Siamak Taati

We establish a connection between percolation on the Cayley graphs of a group and the dynamical diversity of cellular automata on that group. Specifically, we demonstrate that Gilman's dichotomy between equicontinuity and sensitivity with respect to Bernoulli measures holds on a finitely generated group if and only if the group has a trivial percolation thre

Yunqi Zhu, Wen Tang, Huayu Yang, Jinghao Niu

In this work, we leverage LLMs to produce medical qualification exam questions and the corresponding answers through few-shot prompts, investigating in-depth how LLMs meet the requirements in terms of coherence, evidence of statement, factual consistency, and professionalism etc. Utilizing a multicenter bidirectional anonymized database with respect to comor

E. Kongkui Berinyuy, Jia-Xin Peng, P. Djorwe, Abdourahimi

We investigate perfect optical nonreciprocal transmission in a hybrid optomechanical system that incorporates an atomic ensemble. By introducing complex coupling strengths between the atomic ensemble and a mechanical oscillator, nonreciprocity is induced through interference between distinct optical pathways. The nonreciprocal transmission is governed by the

Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette

Autonomous systems rely on accurate 3D object detection from LiDAR data, yet most detectors are limited to a predefined set of known classes, making them vulnerable to unexpected out-of-distribution (OOD) objects. In this work, we present HD-OOD3D, a novel two-stage method for detecting unknown objects. We demonstrate the superiority of two-stage approaches

Stefan-Claudiu Susan

Even though much progress has been made in identifying and mitigating smart contract vulnerabilities, we often hear about coding or design issues leading to great financial losses. This paper presents our progress toward finding defects that are sometimes not detected or completely detected by state-of-the-art analysis tools. Although it is still in its inci

Dafina Trufaş

In this paper we present a formalization of Intuitionistic Propositional Logic in the Lean proof assistant. Our approach focuses on verifying two completeness proofs for the studied logical system, as well as exploring the relation between the two analyzed semantical paradigms - Kripke and algebraic. In addition, we prove a large number of theorems and deriv

Vlad-Alexandru Teodorescu, Dorel Lucanu

Pointers are a powerful, but dangerous feature provided by the C and C++ programming languages, and incorrect use of pointers is a common source of bugs and security vulnerabilities. Making secure software is crucial, as vulnerabilities exploited by malicious actors not only lead to monetary losses, but possibly loss of human lives. Fixing these vulnerabilit

Georgiana Caltais, Mahboobeh Zangiabady, Ervin Zvirbulis

Software Defined Networking (SDN) has become a new paradigm in computer networking, introducing a decoupled architecture that separates the network into the data plane and the control plane. The control plane acts as the centralized brain, managing configuration updates and network management tasks, while the data plane handles traffic based on the configura

Lorenzo Capra, Marco Gribaudo

Petri Nets (PN) are extensively used as a robust formalism to model concurrent and distributed systems; however, they encounter difficulties in accurately modeling adaptive systems. To address this issue, we defined rewritable PT nets (RwPT) using Maude, a declarative language that ensures consistent rewriting logic semantics. Recently, we proposed a modular

Gabriel Istrate

Motivated by phase transitions in combinatorial optimization problems, we define two kinds of geometry-preserving reductions between constraint satisfaction problems and other NP-search problems. We give a couple of examples and counterexamples for these reductions.

Eneia Nicolae Todoran, Gabriel Ciobanu

We develop denotational and operational semantics designed with continuations for process calculi based on CCS extended with mechanisms offering support for multiparty interactions. We investigate the abstractness of this continuation semantics. We show that our continuation-based denotational models are weakly abstract with respect to the corresponding oper

Ádám Kurucz, Péter Bereczky, Dániel Horpácsi

Matching logic is a logical framework for specifying and reasoning about programs using pattern matching semantics. A pattern is made up of a number of structural components and constraints. Structural components are syntactically matched, while constraints need to be satisfied. Having multiple structural patterns poses a practical problem as it requires mul

Georgios V. Pitsiladis, Petros S. Stefaneas

The ecosystem of Privacy Calculus is a formal framework for privacy comprising (a) the Privacy Calculus, a Turing-complete language of message-exchanging processes based on the pi-calculus, (b) a privacy policy language, and (c) a type checker that checks adherence of Privacy Calculus terms to privacy policies. BPMN is a standard for the graphical descriptio

Shunmei Dong, Qinglong Wang, Haiqing Wang, Qianqian Wang

The star tracker is generally affected by the atmospheric background light and the aerodynamic environment when working in near space, which results in missing stars or false stars. Moreover, high-speed maneuvering may cause star trailing, which reduces the accuracy of the star position. To address the challenges for starmap identification, a reverse attitud

Yue Liu, Shihao Zhu, Tianyuan Yang, Jian Ma

Group Recommendation (GR), which aims to recommend items to groups of users, has become a promising and practical direction for recommendation systems. This paper points out two issues of the state-of-the-art GR models. (1) The pre-defined and fixed number of user groups is inadequate for real-time industrial recommendation systems, where the group distribut

P. T. Quyen, Son Cao, N. T. Hong Van

This study examines the precise measurement of the largest leptonic mixing angle $\theta_{23}$ through the analysis of neutrino oscillation data samples. Our findings indicate that, contrary to common understanding, the ${\nu}_{\mu}(\bar{\nu}_{\mu})\rightarrow {\nu}_{e}(\bar{\nu}_{e})$ appearance samples, rather than the ${\nu}_{\mu}(\bar{\nu}_{\mu})\rightar

Adil Rehman, Volodymyr Petriakov, Ivan Yahniuk, Aleksandr Kazakov

Hg$_{1-x}$Cd$_x$Te is a unique material with the band-gap tunable by the temperature, pressure, and cadmium content in a wide range, from 1.6 eV to inverted band-gap of -0.3 eV. This makes Hg$_{1-x}$Cd$_x$Te one of the key materials for infrared and terahertz detectors, whose characteristics largely depend on the material noise properties. In this work, we i

Dongyang Li, Haoyang Qin, Mingyang Wu, Jiahua Tang

Decoding visual stimuli from neural recordings is a critical challenge in the development of brain-computer interfaces (BCIs). Although recent EEG-based decoding approaches have made progress in tasks such as visual classification, retrieval, and reconstruction, they remain constrained by unstable representation learning and a lack of interpretability. This

Tomas Rigaux, Hisashi Kashima

Mastering games is a hard task, as games can be extremely complex, and still fundamentally different in structure from one another. While the AlphaZero algorithm has demonstrated an impressive ability to learn the rules and strategy of a large variety of games, ranging from Go and Chess, to Atari games, its reliance on extensive computational resources and r

Dingli Yuan, Shitong Wu, Haoran Tang, Lu Yang

Multiple-input multiple-output (MIMO) is pivotal for wireless systems, yet its high-dimensional, stochastic channel poses significant challenges for accurate estimation, highlighting the critical need for robust estimation techniques. In this paper, we introduce a novel channel estimation method for the MIMO system. The main idea is to construct a fixed-poin

S Balasubramanian, M Sai Subramaniam, Sai Sriram Talasu, Yedu Krishna P

Deep neural networks (DNNS) excel at learning from static datasets but struggle with continual learning, where data arrives sequentially. Catastrophic forgetting, the phenomenon of forgetting previously learned knowledge, is a primary challenge. This paper introduces EXponentially Averaged Class-wise Feature Significance (EXACFS) to mitigate this issue in th