October 2024 arXiv papers — page 36

Yirong Sun, Dawei Zhu, Yanjun Chen, Erjia Xiao

Large language models (LLMs) have excelled in various NLP tasks, including machine translation (MT), yet most studies focus on sentence-level translation. This work investigates the inherent capability of instruction-tuned LLMs for document-level translation (docMT). Unlike prior approaches that require specialized techniques, we evaluate LLMs by directly pr

Piotr Przybyła, Euan McGill, Horacio Saggion

Large language models have many beneficial applications, but can they also be used to attack content-filtering algorithms in social media platforms? We investigate the challenge of generating adversarial examples to test the robustness of text classification algorithms detecting low-credibility content, including propaganda, false claims, rumours and hyperpa

Santiago Berrezueta-Guzman, Patrick Bassner, Stefan Wagner, Stephan Krusche

Understanding collaboration patterns in introductory programming courses is essential, as teamwork is a critical skill in computer science. In professional environments, software development relies on effective teamwork, navigating diverse perspectives, and contributing to shared goals. This paper offers a comprehensive analysis of the factors influencing te

Chuchu Chen, Tonghe Dang, Jialin Hong, Fengshan Zhang

In this paper, we propose a new class of splitting methods to solve the stochastic Langevin equation, which can simultaneously preserve the ergodicity and exponential integrability of the original equation. The central idea is to extract a stochastic subsystem that possesses the strict dissipation from the original equation, which is inspired by the inherita

Clea Sunny

The DarkSide-50 (DS-50) experiment aims at the direct detection of weakly interacting massive particles. It is a dual-phase liquid argon time projection chamber (LAr TPC) where Dark Matter (DM), which constitutes five sixths of all matter in the universe, is expected to interact with an argon nucleus resulting in a nuclear recoil. A scintillation signal (S1)

Zenan Li, Yifan Wu, Zhaoyu Li, Xinming Wei

Autoformalization, the task of automatically translating natural language descriptions into a formal language, poses a significant challenge across various domains, especially in mathematics. Recent advancements in large language models (LLMs) have unveiled their promising capabilities to formalize even competition-level math problems. However, we observe a

Simon C. Marshall, Scott Aaronson, Vedran Dunjko

This paper furthers existing evidence that quantum computers are capable of computations beyond classical computers. Specifically, we strengthen the collapse of the polynomial hierarchy to the second level if: (i) Quantum computers with postselection are as powerful as classical computers with postselection ($\mathsf{PostBQP=PostBPP}$), (ii) any one of sever

Nic Fellini

In 1951, Ankeny, Artin, and Chowla published a brief note containing four congruence relations involving the class number of $\mathbb{Q}(\sqrt{d})$ for positive squarefree integers $d\equiv 1 \bmod{4}$. Many of the ideas present in their paper can be seen as the precursors to the now developed theory of cyclotomic fields. Curiously, little attention has been

Ram Karan Choudhary, Sunil Kumar Prajapati

In this article, we present a combinatorial formula for computing the Wedderburn decomposition of the rational group algebra associated with an ordinary metacyclic $p$-group $G$, where $p$ is any prime. We also provide a formula for counting irreducible rational representations of $G$ with distinct degrees and derive a method to explicitly obtain all inequiv

Njagi Mwaniki, Erik Garrison, Nadia Pisanti

In this paper, we introduce flubbles, a new definition of "bubbles" corresponding to variants in a (pan)genome graph $G$. We then show a characterization for flubbles in terms of equivalence classes regarding cycles in an intermediate data structure we built from the spanning tree of the $G$, which leads us to a linear time and space solution for finding all

Beatrice Crippa, Anna Scotti, Andrea Villa

We propose numerical schemes for the approximate solution of problems defined on the edges of a one-dimensional graph. In particular, we consider linear transport and a drift-diffusion equations, and discretize them by extending Finite Volume schemes with upwind flux to domains presenting bifurcation nodes with an arbitrary number of incoming and outgoing ed

Farshad Rostami Ghadi, Kai-Kit Wong, Masoud Kaveh, H. Xu

Fluid antenna system (FAS) is gaining attention as an innovative technology for boosting diversity and multiplexing gains. As a key innovation, it presents the possibility to overcome interference by position reconfigurability on one radio frequency (RF) chain, giving rise to the concept of fluid antenna multiple access (FAMA). While FAMA is originally desig

Felipe Taha Sant'Ana, Hui Liu

We study the effects of the two-spinon excitations on the field-field correlator of the Tonks-Girardeau gas. While these excitations have been previously examined in the ground state of the system, their role at finite temperatures remains unexplored. Here, we extend the analysis to the one-dimensional interacting Bose gas at thermal equilibrium, focusing on

Ana S. Rivero, Giulio Baù, Rafael Vazquez, Claudio Bombardelli

The increasing congestion in the near-Earth space environment has amplified the need for robust and efficient conjunction analysis techniques including the computation of the minimum distance between orbital paths in the presence of perturbations. After showing that classical Minimum Orbit Intersection Distance (MOID) computation schemes are unsuitable to tr

Guanyan Chen, Meiling Wang, Te Cui, Yao Mu

Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable performance in vision and language reasoning capabilities for VIL tasks. Despite the progress, current VIL methods naively employ VLMs to learn high-level p

Aosong Feng, Rex Ying, Leandros Tassiulas

As the demand for processing extended textual data grows, the ability to handle long-range dependencies and maintain computational efficiency is more critical than ever. One of the key issues for long-sequence modeling using attention-based model is the mismatch between the limited-range modeling power of full attention and the long-range token dependency in

Lawrence K. Q. Yan, Qian Niu, Ming Li, Yichao Zhang

With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets employed in medical LLM tasks. These datasets span multiple modalities including text, image, and multimodal benchmark

Paolo Lorenzoni, Sara Perletti, Karoline van Gemst

We extend the generalised hodograph method to regular non- diagonalisable integrable systems of hydrodynamic type, in light of the relation between such systems and F-manifolds with compatible connection. The method allows the construction of solutions starting from the symmetries of the system. In the diagonal case, the completeness of the symmetries follow

M. Bugli, E. F. Lopresti, E. Figueiredo, A. Mignone

Relativistic magnetic reconnection is one of the most fundamental mechanisms considered responsible for the acceleration of relativistic particles in astrophysical jets and magnetospheres of compact objects. Understanding the properties of the dissipation of magnetic fields and the formation of non-ideal electric fields is of paramount importance to quantify

O. P. Jyothilakshmi, Lakshmi J. Naik, Debashree Sen, Atanu Guha

We investigate the behavior of the prominent non-radial fundamental $f$-mode oscillations of dark matter (DM) admixed strange quark stars (DMSQSs), by adopting an equation of state (EoS) developed in Ref.~\cite{Sen:2022pfr}, which considers the possible presence of feebly interacting DM in strange quark stars (SQSs) for the first time. Within the model, feeb

Li Nanbo, Firas Laakom, Yucheng Xu, Wenyi Wang

World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and temporal structures, particularly in scenarios requiring long-

Shangfei Wu, Fei-Ting Huang, Xianghan Xu, Ethan T. Ritz

Symmetry plays an important role in determining the physical properties in condensed matter physics, as the symmetry operations of any physical property must include the symmetry operations of the point group of the crystal. As a consequence, crystallographic polarity and chirality are expected to have an impact on the Cooper pairing in a superconductor. Whi

Wenbo Gu, Ali Esamdin, Chunhai Bai, Xuan Zhang

In this paper, we conducted a detailed analysis of optical turbulence in the Atmospheric Surface Layer (ASL) at Muztagh-ata site during on-site testing. We utilized ultrasonic anemometers positioned on a 30-meter tower to collect and process data at five height levels, obtaining data from October 1, 2021 to the present. We investigated the behavior of optica

Rachid Semmami, Hamid Ezzahraoui, El Hassan Zerouali

This article, is devoted to $n$-EP and $n$-hypo-EP operators. We give some characteristic properties of these two classes and various links with other known classes in the literature, especially the classes of EP, SD, hypo-EP and $n$-normal operators.

Adèle Bréart De Boisanger, Wendy Sims-Schouten, Francois Sicard

Assessing employees' well-being has become central to fostering an environment where employees can thrive and contribute to companies' adaptability and competitiveness in the market. Traditional methods for assessing well-being often face significant challenges, with a major issue being the lack of trust and confidence employees may have in these processes.

Amparo Baíllo, Javier Cárcamo

We introduce the \textit{almost goodness-of-fit} test, a procedure to assess whether a (parametric) model provides a good representation of the probability distribution generating the observed sample. Specifically, given a distribution function $F$ and a parametric family $\mathcal{G}=\{ G(\boldsymbol{\theta}) : \boldsymbol{\theta} \in \Theta\}$, we consider

Lei Yang, Yu-Kun Song, Shu-Yi Wei

The spin correlation of back-to-back dihadron emerges in unpolarized high-energy collision, empowering unpolarized experiments to shed light on the spin-dependent fragmentation functions. This work investigates the transverse spin correlation of back-to-back dihadron in unpolarized $e^+e^-$, $pp$, and $\gamma p$ collisions, which serves as a novel probe of t

Yiqian Yang, Yiqun Duan, Hyejeong Jo, Qiang Zhang

This paper introduces NeuGPT, a groundbreaking multi-modal language generation model designed to harmonize the fragmented landscape of neural recording research. Traditionally, studies in the field have been compartmentalized by signal type, with EEG, MEG, ECoG, SEEG, fMRI, and fNIRS data being analyzed in isolation. Recognizing the untapped potential for cr

Elisa Fusco, Giuseppe Arbia, Francesco Vidoli, Vincenzo Nardelli

In the literature on stochastic frontier models until the early 2000s, the joint consideration of spatial and temporal dimensions was often inadequately addressed, if not completely neglected. However, from an evolutionary economics perspective, the production process of the decision-making units constantly changes over both dimensions: it is not stable over

Diego Román-Cortés, Maxim Mazanov, Rodrigo A. Vicencio, Maxim A. Gorlach

Evanescently coupled waveguide arrays provide a tabletop platform to realize a variety of Hamiltonians, where physical waveguides correspond to the individual sites of a tight-binding lattice. Nontrivial spatial structure of the waveguide modes enriches this picture and uncovers further possibilities. Here, we demonstrate that the effective coupling between

Shuchang Yan, Haoran Sun

In our prior work, we investigated the minimum fuel consumption of a hybrid electric vehicle (HEV) under a state-of-charge (SOC) balance constraint, assuming perfect SOC measurements and accurate reference speed profiles. The constrained optimal fuel consumption (COFC) problem was addressed using a constrained reinforcement learning (CRL) framework. However,

A. Telveenus

The article explores the creation of a cryptosystem using a halidon group ring of a dihedral group. Due to the non-abelian nature of the group, constructing the cryptosystem is more challenging compared to an abelian group. The logic used to develop a decryption programme was also quite complex.

Dario Pasquini, Evgenios M. Kornaropoulos, Giuseppe Ateniese

Large language models (LLMs) are increasingly being harnessed to automate cyberattacks, making sophisticated exploits more accessible and scalable. In response, we propose a new defense strategy tailored to counter LLM-driven cyberattacks. We introduce Mantis, a defensive framework that exploits LLMs' susceptibility to adversarial inputs to undermine malicio

Martine Schut, Patrick Andriolo, Marko Toroš, Sougato Bose

We provide a solution for decoherence in spatial superpositions due to scattering/collision with air molecules. This result reproduces the short- and long-wavelength limits known in the literature. We compare the decoherence rate with several existing interpolations in the literature and evaluate the decoherence rate and experimental parameters when creating

Florian Atzenhofer-Baumgartner, Bernhard C. Geiger, Christoph Trattner, Georg Vogeler

This extended abstract describes the challenges in implementing recommender systems for digital archives in the humanities, focusing on Monasterium.net, a platform for historical legal documents. We discuss three key aspects: (i) the unique characteristics of so-called charters as items for recommendation, (ii) the complex multi-stakeholder environment, and

Thomas Burnett, Thomas Jaki

The standard paradigm for confirmatory clinical trials is to compare experimental treatments with a control, for example the standard of care or a placebo. However, it is not always the case that a suitable control exists. Efficient statistical methodology is well studied in the setting of randomised controlled trials. This is not the case if one wishes to c

Seyed Adel Alizadeh Kolagar, Mehdi Heydari Shahna, Jouni Mattila

Deep reinforcement learning (DRL) is emerging as a promising method for adaptive robotic motion and complex task automation, effectively addressing the limitations of traditional control methods. However, ensuring safety throughout both the learning process and policy deployment remains a key challenge due to the risky exploration inherent in DRL, as well as

Cecile Monthus

For the discrete-time or the continuous-time Markov spin models for image generation when each pixel $n=1,..,N$ can take only two values $S_n=\pm 1$, the finite-time forward propagator depends on the initial and on the final configurations of the $N$ spins only via a single global variable, namely the extensive overlap that counts the number of spins that ha

Hao Miao, Ziqiao Liu, Yan Zhao, Chenjuan Guo

The expanding instrumentation of processes throughout society with sensors yields a proliferation of time series data that may in turn enable important applications, e.g., related to transportation infrastructures or power grids. Machine-learning based methods are increasingly being used to extract value from such data. We provide means of reducing the resul

Gang Dang, Dianhui Wang

Deep learning techniques have shown promise in many domain applications. This paper proposes a novel deep reservoir computing framework, termed deep recurrent stochastic configuration network (DeepRSCN) for modelling nonlinear dynamic systems. DeepRSCNs are incrementally constructed, with all reservoir nodes directly linked to the final output. The random pa

Victoria Isensee, Conrad Caliari, Adrian Oeftiger

This report describes the setup and results of a 2024 experiment on beam-based measurement of the Orbit Response Matrix (ORM) for the GSI Heavy Ion Synchrotron SIS18, characterising the standard doublet optics at extraction energy. The measured ORM is explicitly presented for reference. As an important outcome of this study, an effective linear machine model

Qun Chen, Haochuan Zhang, Huimin Zhu

Approximate Message Passing (AMP), originally developed to address high-dimensional linear inverse problems, has found widespread applications in signal processing and statistical inference. Among its notable variants, Vector Approximate Message Passing (VAMP), Generalized Approximate Survey Propagation (GASP), and Vector Approximate Survey Propagation (VASP

Lukas Ahlheit, Chris Nill, Daniil Svirskiy, Jan de Haan

Magic trapping of ground and Rydberg states, which equalizes the AC Stark shifts of these two levels, enables increased ground-to-Rydberg state coherence times. We measure via photon storage and retrieval how the ground-to-Rydberg state coherence depends on trap wavelength for two different traps and find different optimal wavelengths for a one-dimensional o

Daniel Lokshtanov, Abhishek Sahu, Saket Saurabh, Vaishali Surianarayanan

The \textsc{Capacitated $d$-Hitting Set} problem involves a universe $U$ with a capacity function $\mathsf{cap}: U \rightarrow \mathbb{N}$ and a collection $\mathcal{A}$ of subsets of $U$, each of size at most $d$. The goal is to find a minimum subset $S \subseteq U$ and an assignment $\phi : \mathcal{A} \rightarrow S$ such that for every $A \in \mathcal{A}$

Andoni Elola, Imanol Ania, Xabier Jaureguibeitia, Henry Wang

Objective: The accurate segmentation of capnograms during cardiopulmonary resuscitation (CPR) is essential for effective patient monitoring and advanced airway management. This study aims to develop a robust algorithm using a U-net architecture to segment capnograms into inhalation and non-inhalation phases, and to demonstrate its superiority over state-of-t

Weijian Luo, Colin Zhang, Debing Zhang, Zhengyang Geng

We propose Diff-Instruct* (DI*), a data-efficient post-training approach for one-step text-to-image generative models to improve its human preferences without requiring image data. Our method frames alignment as online reinforcement learning from human feedback (RLHF), which optimizes the one-step model to maximize human reward functions while being regulari

Johan Medrano, Nicholas A. Alexander, Robert A. Seymour, Peter Zeidman

The analysis of neural power spectra plays a crucial role in understanding brain function and dysfunction. While recent efforts have led to the development of methods for decomposing spectral data, challenges remain in performing statistical analysis and group-level comparisons. Here, we introduce Bayesian Spectral Decomposition (BSD), a Bayesian framework f

Emerald Dilworth, Ed Davis, Daniel J. Lawson

Quantifying uncertainty in networks is an important step in modelling relationships and interactions between entities. We consider the challenge of bootstrapping an inhomogeneous random graph when only a single observation of the network is made and the underlying data generating function is unknown. We address this problem by considering embeddings of the o

Pablo de los Riscos, Fernando J. Corbacho

Artificial General Intelligence (AGI) Agents and Robots must be able to cope with everchanging environments and tasks. They must be able to actively construct new internal causal models of their interactions with the environment when new structural changes take place in the environment. Thus, we claim that active causal structure learning with latent variabl

Shengjing Tian, Xiantong Zhao, Yuhao Bian, Yinan Han

In this study, we delve into the robustness of neural network-based LiDAR point cloud tracking models under adversarial attacks, a critical aspect often overlooked in favor of performance enhancement. These models, despite incorporating advanced architectures like Transformer or Bird's Eye View (BEV), tend to neglect robustness in the face of challenges such

Giovanni Ferrami, J. Stuart B. Wyithe

The redshift and size distributions of galaxy scale strong lenses depend on the evolution of early-type galaxies (ETGs). We use this dependence to constrain the velocity dispersion function (VDF) evolution from the Strong Lensing Legacy Survey (SL2S) sample of lenses in the redshift range 0.25 < z < 0.75. Our modeling of the lens population includes lens ide

Zhikang Fan, Weiran Shen

Consider a market where a seller owns an item for sale and a buyer wants to purchase it. Each player has private information, known as their type. It can be costly and difficult for the players to reach an agreement through direct communication. However, with a mediator as a trusted third party, both players can communicate privately with the mediator withou

Philipp Vaeth, Alexander M. Fruehwald, Benjamin Paassen, Magda Gregorova

Recently, several methods have leveraged deep generative modeling to produce example-based explanations of image classifiers. Despite producing visually stunning results, these methods are largely disconnected from classical explainability literature. This conceptual and communication gap leads to misunderstandings and misalignments in goals and expectations

Frank Ball, Abid Ali Lashari, David Sirl, Pieter Trapman

We present a stochastic model for two successive SIR (Susceptible, Infectious, Recovered) epidemics in the same network structured population. Individuals infected during the first epidemic might have (partial) immunity for the second one. The first epidemic is analysed through a bond percolation model, while the second epidemic is approximated by a three-ty

Hang Yuan

We prove that every open-closed homotopy algebra, introduced by Kajiura and Stasheff (arXiv: archive/0410291), naturally gives rise to an open-closed version of Hochschild cochain complex whose cohomology admits a canonical Gerstenhaber algebra structure. We also develop the open-closed brace relations, provide a concise description of OCHAs, and establish a

Tobias Bölle, Christoph Metzl, Kianusch Vahid Yousefnia

Deep convection is one of the most important atmospheric transport mechanisms and associated with various severe weather phenomena. Manifestations of deep convection in the atmosphere are composed of a recurring fundamental building block, called cell, which evolves through a characteristic lifecycle. Despite its importance, no simple, physically consistent

Robin Janssen, Immanuel Sulzer, Tobias Buck

We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlat

Jacob Page

Super-resolution of turbulence is a term used to describe the prediction of high-resolution snapshots of a flow from coarse-grained observations. This is typically accomplished with a deep neural network and training usually requires a dataset of high-resolution images. An approach is presented here in which robust super resolution can be performed without a

Tien-Huy Nguyen, Quang-Khai Tran, Anh-Tuan Quang-Hoang

The cognitive faculty of visual reasoning necessitates the integration of multimodal perceptual processing and commonsense and external knowledge of the world. In recent years, a plethora of large vision-language models (LVLMs) have been proposed, demonstrating outstanding power and exceptional proficiency in commonsense reasoning across diverse domains and

Alexander Becker, Jan D. Wegner, Evans Dawoe, Konrad Schindler

Reconciling agricultural production with climate-change mitigation is a formidable sustainability problem. Retaining trees in agricultural systems is one proposed solution, but the magnitude of the current and future-potential benefit that trees contribute to climate-change mitigation remains uncertain. Here, we help to resolve these issues across a West Afr

Yago Arosa, Alejandro Doval, Raúl de la Fuente

Coupled surface plasmons arise in the surfaces of a dielectric layer between two metallic media when a dim wave propagating in the dielectric generates resonant free charge oscillations at the interfaces. Here, we consider surface plasmon resonance in a Fabry-Perot type cavity with plane metallic mirrors and an inner dielectric medium, optically less dense t

Rajiv Ranjan, Tejasavi Birdh, Nandan Mandal, Dinesh Kumar

This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nit

Meng-Li Guo, Bo Li, Shao-Ming Fei

From the perspective of resource-theoretic approach, this study explores the quantification of imaginary in quantum physics. We propose a well defined measure of imaginarity, the geometric-like measure of imaginarity. Compared with the usual geometric imaginarity measure, this geometric-like measure of imaginarity exhibits smaller decay difference under quan

Dongkyu Kim, Byoungwook Kim, Donggeon Han, Matouš Eibich

Using LLMs (Large Language Models) in conjunction with external documents has made RAG (Retrieval-Augmented Generation) an essential technology. Numerous techniques and modules for RAG are being researched, but their performance can vary across different datasets. Finding RAG modules that perform well on specific datasets is challenging. In this paper, we pr

Ali Tayefeh Younesi, Muhib Omar, Arne Wickenbrock, Dmitry Budker

The negatively-charged nitrogen vacancy (NV$^{-}$) center in diamond is widely used for quantum sensing since the sensitivity of the spin triplet in the electronic ground state to external perturbations such as strain and electromagnetic fields make it an excellent probe for changes in these perturbations. The spin state can be measured through optically det

Eduardo Carneiro Oliveira, Hieke Keuning, Johan Jeuring

Producing code of good quality is an essential skill in software development. Code quality is an aspect of software quality that concerns the directly observable properties of code, such as decomposition, modularization, and code flow. Code quality can often be improved by means of code refactoring -- an internal change made to code that does not alter its o

Jules Grass, Arnaud Guillin, Christophe Poquet

We present a method to obtain sharp local propagation of chaos results for a system of N particles with a diffusion coefficient that it not constant and may depend of the empirical measure. This extends the recent works of Lacker [14] and Wang [24] to the case of non constant diffusions. The proof relies on the BBGKY hierarchy to obtain a system of different

Arne Grobrugge, Nidhi Mishra, Johannes Jakubik, Gerhard Satzger

The integration of artificial intelligence into business processes has significantly enhanced decision-making capabilities across various industries such as finance, healthcare, and retail. However, explaining the decisions made by these AI systems poses a significant challenge due to the opaque nature of recent deep learning models, which typically function

Jaime Ibáñez, Blanka Zicher, Etienne Burdet, Stuart N. Baker

Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. However, technologies used to directly measure CNS activity are limited by their resolution, sensitivity to interferences, and invasiveness. Advances in muscle recordings and deep learning allow us to decode the spiking act

Koffi-Emmanuel Sadzi, Yakir Hadad

When a plasmonic particle is subject to a static magnetic field, ${B}_{\rm dc}=B_{0} \hat{z}$, its gyrotropic response gives rise to nonreciprocal dynamics of the entire ambient surroundings. This dynamics depends on the particle's excitation which in turn depends on the gyrotropic material damping rate $\Gamma$. Thus intuitively speaking, the heavier the gy

Wenfu Cao, Yang Huang, Hongsheng Zhang

We find a novel characteristic for chaotic motion by introducing Shannon entropy for periodic orbits, quasiperiodic orbits, and chaotic orbits.We compare our approach with the previous methods including Poincar\'{e} Section, Lyapunov exponent, Fast Lyapunov Indicator, Recurrence plots(Rps), and Fast Fourier Transform(FFT) for orbits around black hole immerse

Bing Han, Feifei Zhao, Yang Li, Qingqun Kong

Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environments. However, existing spiking neural network-based continual learning algorithms treat each task equally, ignoring the guiding role of different task similarity associations for ne

Ben Hauptvogel, Malte Ostendorff, Georg Rehm, Sebastian Möller

Recent advancements in large language models (LLMs) have led to their increased application across various tasks, with reinforcement learning from human feedback (RLHF) being a crucial part of their training to align responses with user intentions. In the RLHF process, a reward model is trained using responses preferences determined by human labelers or AI s

Qi Liu, Kai Zheng, Rui Huang, Wuchao Li

Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS algorithms are trained and evaluated. However, when these algorithms transition to real world industrial RS, the

Firas Bayram, Bestoun S. Ahmed

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional

Yang Huang, Dao-Jun Liu, Hongsheng Zhang

We obtain the first image of a parity-odd celestial body. Recently, an intriguing parity-odd rotating boson star was proposed. We investigate the lensing effects of these stars in detail. Our analysis demonstrates distinct gravitational distortions around these stars, clearly differentiating them from their parity-even counterparts. Furthermore, we analyze t

Yvan Castin

We review some unresolved theoretical issues in three-dimensional two-component Fermi gases, drawing on recent experiments on cold atoms in immaterial traps close to a magnetic Feshbach resonance. We distinguish successively (i) the open questions arising in the few-body problem with Wigner-Bethe-Peierls contact interactions - essentially the stability of th

John Augustine, Fabien Dufoulon, Gopal Pandurangan

Byzantine agreement is a fundamental problem in fault-tolerant distributed networks that has been studied intensively for the last four decades. Most of these works designed protocols for complete networks. A key goal in Byzantine protocols is to tolerate as many Byzantine nodes as possible. The work of Dwork, Peleg, Pippenger, and Upfal [STOC 1986, SICOMP 1

Xiaomei Zeng, Ting Lv, Xiangyu Zhang, Zhong Zeng

LiNbO3 films doped with vanadium (V) were deposited using RF magnetron sputtering technique. To realize doping with a wider range of V concentration, a 30 mm V metal inlaid target asymmetrically embedded in the 150 mm lithium niobate target was used. The V concentration in the deposited films was a decreasing function of the distance from the V target. The V

Noemi Kurt, Michel Reitmeier, András Tóbiás

We consider the contact process with dormancy, where wake-up times follow a renewal process. Without infection between dormant individuals, we show that the process under certain conditions grows at most logarithmically. On the other hand, if infections between dormant individuals are possible, the process survives with positive probability even on finite gr

José Miguel Moreno, Narseo Vallina-Rodriguez, Juan Tapiador

The JavaScript programming language, which began as a simple scripting language for the Web, has become ubiquitous, spanning desktop, mobile, and server applications. This increase in usage has made JavaScript an attractive target for nefarious actors, resulting in the proliferation of malicious browser extensions that steal user information and supply chain

Nora Bearth

This paper investigates the mental health penalty for women after childbirth in Switzerland. Leveraging insurance data, we employ a staggered difference-in-difference research design. The findings reveal a substantial mental health penalty for women following the birth of their first child. Approximately four years after childbirth, there is a one percentage

Guanyi Wang

Many policy problems involve designing individualized treatment allocation rules to maximize the equilibrium social welfare of interacting agents. Focusing on large-scale simultaneous decision games with strategic complementarities, we develop a method to estimate an optimal treatment allocation rule that is robust to the presence of multiple equilibria. Our

Missie Chercheur, Malkenzie Bovafiz

This study explores the use of AI-driven sentiment analysis as a novel tool for forecasting election outcomes, focusing on Mauritius' 2024 elections. In the absence of reliable polling data, we analyze media sentiment toward two main political parties L'Alliance Lepep and L'Alliance Du Changement by classifying news articles from prominent Mauritian media ou

Louis-Pierre Chaintron, Giovanni Conforti, Julien Reygner

We extend the Gibbs conditioning principle to an abstract setting combining infinitely many linear equality constraints and non-linear inequality constraints, which need not be convex. A conditional large large deviation principle (LDP) is proved in a Wassersteintype topology, and optimality conditions are written in this abstract setting. This setting encom

Francesco Casini, Frank Redig, Hidde van Wiechen

In this paper we consider the multispecies stirring process on the discrete torus. We prove a large deviation principle for the trajectory of the vector of densities of the different species. The technique of proof consists in extending the method of the foundational paper [1] based on the superexponential estimate to the multispecies setting. This requires

Seyed Mohamad Moghadas, Bruno Cornelis, Alexandre Alahi, Adrian Munteanu

Traffic forecasting is pivotal for intelligent transportation systems, where accurate and interpretable predictions can significantly enhance operational efficiency and safety. A key challenge stems from the heterogeneity of traffic conditions across diverse locations, leading to highly varied traffic data distributions. Large language models (LLMs) show exc

Wenyang Liu, Kejun Wu, Tianyi Liu, Yi Wang

Multimedia file fragment classification (MFFC) aims to identify file fragment types, e.g., image/video, audio, and text without system metadata. It is of vital importance in multimedia storage and communication. Existing MFFC methods typically treat fragments as 1D byte sequences and emphasize the relations between separate bytes (interbytes) for classificat

Peter De Maesschalck, Kristian Uldall Kristiansen

In this paper, we study normal forms of analytic saddle-nodes in $\mathbb C^{n+1}$ with any Poincar\'e rank $k\in \mathbb N$. The approach and the results generalize those of Bonckaert and De Maesschalck from 2008 that considered $k=1$. In particular, we introduce a Banach convolutional algebra that is tailored to study differential equations in the Borel pl

Nathaniel Sagman, Ognjen Tošić

We develop a Lie-theoretic perspective on Hitchin's equations for cyclic $G$-Higgs bundles, which we use to study analytic and geometric properties of harmonic maps. Among other things, we prove Dai-Li's conjecture on the monotonicity of the energy density in the case of Coxeter cyclic $G$-Higgs bundles, for all $G$, and Dai-Li's negative curvature conjectur

Xuanyu Liu, Jiao Li, Haoxian Liu, Zongqi Yang

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accur

Chee Kian Yap, Arun Kumar Singh

This study presents a novel approach to modelling economic agents as analogous to spin states in physics, particularly the Ising model. By associating economic activity with spin orientations (up for inactivity, down for activity), the study delves into optimizing market dynamics using concepts from statistical mechanics. Utilizing Monte Carlo simulations, t

Luis Augenstein, Noémie Jaquier, Tamim Asfour, Leonel Rozo

Probabilistic Latent Variable Models (LVMs) excel at modeling complex, high-dimensional data through lower-dimensional representations. Recent advances show that equipping these latent representations with a Riemannian metric unlocks geometry-aware distances and shortest paths that comply with the underlying data structure. This paper focuses on hyperbolic e

Antonio Sojo, Iván Maza, Aníbal Ollero

In this paper we address the optimal planning of autonomous teams for general purpose tasks including a wide spectrum of situations: from project management of human teams to the coordination of an automated assembly lines, focusing in the automated inspection of power grids. There exist many methods for task planning. However, the vast majority of such meth

He Yu, Jing Liu

Designing optimization approaches, whether heuristic or meta-heuristic, usually demands extensive manual intervention and has difficulty generalizing across diverse problem domains. The combination of Large Language Models (LLMs) and Evolutionary Algorithms (EAs) offers a promising new approach to overcome these limitations and make optimization more automat

T. A. Syachina, A. G. Rudnitskiy, P. V. Mzhelskiy, M. A. Shchurov

Millimetron is a space observatory for millimeter and sub-millimeter observations planned for launch around 2030. The 10-meter diameter space unfolded telescope will be cooled down to 10~K and operated in the vicinity of Lagrange point L2. Mission lifetime is 10 years and it includes astronomical observations in two modes: as a space-ground interferometer an

Hana Sebia, Thomas Guyet, Mickaël Pereira, Marco Valdebenito

Segmentation of medical images is a fundamental task with numerous applications. While MRI, CT, and PET modalities have significantly benefited from deep learning segmentation techniques, more recent modalities, like functional ultrasound (fUS), have seen limited progress. fUS is a non invasive imaging method that measures changes in cerebral blood volume (C

Changjian Liu, Yuzhou Tian

The real Jacobian conjecture was posed by Randall in 1983. This conjecture asserts that if $F=\left(f_1,\ldots ,f_n\right):\mathbb{R}^n\rightarrow\mathbb{R}^n$ is a polynomial map such that $\det DF\left(\mathbf{x}\right)\neq0$ for all $\mathbf{x}\in\mathbb{R}^n$, then $F$ is injective. This investigation mainly consists of two parts. Firstly, we use the qua

Jordan Serres

We adapt Stein's method to isoperimetric and geometric inequalities. The main challenge is the treatment of boundary terms. We address this by using an elliptic PDE with an oblique boundary condition. We apply our geometric formulation of Stein's method to obtain stability of the Brock-Weinstock inequality, stability of the isoperimetric inequality under a c

Grégoire Francisco, Francesco Pio Ramunno, Manolis K. Georgoulis, João Fernandes

The solar magnetized corona is responsible for various manifestations with a space weather impact, such as flares, coronal mass ejections (CMEs) and, naturally, the solar wind. Modeling the corona's dynamics and evolution is therefore critical for improving our ability to predict space weather In this work, we demonstrate that generative deep learning method