November 2024 arXiv papers — page 63

Mohammad Arafat Ullah, Arthy Anjum, Rashedul Amin Tuhin, Shamim Akhter

A distributed denial-of-service (DDoS) attack is an attempt to produce humongous traffic within a network by overwhelming a targeted server or its neighboring infrastructure with a flood of service requests ceaselessly coming from multiple remotely controlled malware-infected computers or network-connected devices. Thus, exploring DDoS attacks by recognizing

Fei Er Yan, Hugo Frezat, Julien Le Sommer, Julian Mak

For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations in these parameterizations affect the modeled ocean circulation and impact on predictive skill. An increasingly popular approach is to leverage machine learning approaches for para

Hong-Bin Chen, Jean-Christophe Mourrat

We consider mean-field vector spin glasses with possibly non-convex interactions. Up to a small perturbation of the parameters defining the model, the asymptotic behavior of the Gibbs measure is described in terms of a critical point of an explicit functional. In this paper, we study some properties of these critical points. Under modest assumptions ensuring

Sramana Das, Sauvik Roy, Subhasish Dutta Gupta, Nirmalya Ghosh

The optical Spin-Hall effect originates from the interaction between the spin angular momentum (SAM) and extrinsic orbital angular momentum (OAM) of light, leading to mutual interrelations between the polarization and trajectory of light in case of non-paraxial fields. Here, we extensively study the SHE and the resultant Spin-Hall shifts (SHS) in optical twe

Lovish Madaan, David Esiobu, Pontus Stenetorp, Barbara Plank

In the recent past, a popular way of evaluating natural language understanding (NLU), was to consider a model's ability to perform natural language inference (NLI) tasks. In this paper, we investigate if NLI tasks, that are rarely used for LLM evaluation, can still be informative for evaluating LLMs. Focusing on five different NLI benchmarks across six model

Germain Poullot

The monotone path polytope of a polytope $P$ encapsulates the combinatorial behavior of the shadow vertex rule (a pivot rule used in linear programming) on $P$. Computing monotone path polytopes is the entry door to the larger subject of fiber polytopes, for which explicitly computing examples remains a challenge. We first give a detailed presentation on how

Keisuke Watanabe, Tadaaki Nagao, Masanobu Iwanaga

Dielectric metasurfaces operating at quasi-bound states in the continuum (qBICs) can achieve exceptionally high radiative quality ($\textit{Q}$) factors by introducing small asymmetries into their unit cells. However, fabrication imperfections often impose major limitations on the experimentally observed $\textit{Q}$ factors. In this study, we experimentally

Anup Singh, Kris Demuynck, Vipul Arora

Spoken term detection (STD) is often hindered by reliance on frame-level features and the computationally intensive DTW-based template matching, limiting its practicality. To address these challenges, we propose a novel approach that encodes speech into discrete, speaker-agnostic semantic tokens. This facilitates fast retrieval using text-based search algori

Irene Ferri, Emanuele Cozzo, Aleix Nicolas-Olive, Albert Diaz-Guilera

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a dec

Shao-Hung Chung, Wei Chen, H. H. Jen

An atomic array coupled to a photonic crystal waveguide forms a strongly coupled quantum interface, exhibiting various intriguing collective features of quantum dynamics. Here we consider a homogeneous atomic array and theoretically investigate its steady-state distribution when the incident fields drive the atoms from both sides at asymmetric angles. This e

A. Hadavand

Let $E$ be an elliptic curve over $\mathbb{Q}$ and $G=\langle\sigma_1, \dots, \sigma_n\rangle$ be a finitely generated subgroup of $\operatorname{Gal}(\overline{\mathbb{Q}}/ \mathbb{Q})$. Larsen's conjecture claims that the rank of the Mordell-Weil group $E(\overline{\mathbb{Q}}^G)$ is infinite where ${\overline{\mathbb Q}}^G$ is the $G$-fixed sub-field of $

Francisco Javier Del Arco Santos, Jakob S. Kottmann

Efficient encoding of electronic operators into qubits is essential for quantum chemistry simulations. The majority of methods map single electron states to qubits, effectively handling electron interactions. Alternatively, pairs of electrons can be represented as quasi-particles and encoded into qubits, significantly simplifying calculations. This work pres

Shubham Agarwal, Raz Birman, Ofer Hadar

This paper introduces WARLearn, a novel framework designed for adaptive representation learning in challenging and adversarial weather conditions. Leveraging the in-variance principal used in Barlow Twins, we demonstrate the capability to port the existing models initially trained on clear weather data to effectively handle adverse weather conditions. With m

Tianqi Zhao, Megha Khosla

Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superiority of these methods is usually supported by either evaluating their performance on small subset of benchmark datasets or by reasoning about their expressive power in terms of cer

Bin Gao, Renfeng Peng, Ya-xiang Yuan

Low-rank tensors appear to be prosperous in many applications. However, the sets of bounded-rank tensors are non-smooth and non-convex algebraic varieties, rendering the low-rank optimization problems to be challenging. To this end, we delve into the geometry of bounded-rank tensor sets, including Tucker and tensor train formats. We propose a desingularizati

Thomas Woehrle, Arun N. Sivakumar, Naveen Uppalapati, Girish Chowdhary

Autonomous under-canopy navigation faces additional challenges compared to over-canopy settings - for example the tight spacing between the crop rows, degraded GPS accuracy and excessive clutter. Keypoint-based visual navigation has been shown to perform well in these conditions, however the differences between agricultural environments in terms of lighting,

Rongxun He, Wei Ke

We consider the uniqueness of the following positive solutions of anisotropic elliptic equation: \begin{equation} \nonumber \left\{ \begin{aligned} -\Delta^F_p u&=u^q \quad \text{in} \quad \Omega, u&=0 \quad \text{on} \quad \partial \Omega, \end{aligned} \right. \end{equation} where $p>\frac{3}{2}$ is a constant. We utilize the linearized method to derive th

Tobias Binder, Marco Drewes, Yannis Georis, Michael Klasen

We present a first assessment of how the previously unexplored effect of positronium formation can impact on the value of the effective number of neutrino species in the Standard Model, $N_{\rm eff}^{\rm SM}$. Adopting a Yukawa form for the electrostatic potential, we discuss two possible scenarios that differ primarily in their assumptions about entropy evo

Martin Moesmann, Torben Bach Pedersen

Prescriptive Analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehensive survey of existing applications within PSA in terms of their use cases, methodologies, and possible future

Xing Huang, Huaiqian Li, Liying Mu

Being concerned with ergodicity of McKean--Vlasov SDEs, we establish a general result on exponential ergodicity in the $L^1$-Wasserstein distance. The result is successfully applied to non-degenerate and multiplicative Brownian motion cases, degenerate second order systems, and even the additive $\alpha$-stable noise, where the coefficients before the noise

Abel C. H. Chen

With the maturation of quantum computing technology, research has gradually shifted towards exploring its applications. Alongside the rise of artificial intelligence, various machine learning methods have been developed into quantum circuits and algorithms. Among them, Quantum Neural Networks (QNNs) can map inputs to quantum circuits through Feature Maps (FM

Alexey Golovnev

In recent years, it has been rather fashionable to talk about geometric trinity of gravity. The main idea is that one can formally present the gravity equations in different terms, those of either torsion or nonmetricity instead of curvature. It starts from a very erroneous claim that the Levi-Civita connection, and therefore the (pseudo-)Riemannian geometry

Weicong Chen, Yu Han, Chao-Kai Wen, Xiao Li

The deployment of multiple reconfigurable intelligent surfaces (RISs) enhances the propagation environment by improving channel quality, but it also complicates channel estimation. Following the conventional wireless communication system design, which involves full channel state information (CSI) acquisition followed by RIS configuration, can reduce transmis

Minjia Shi, Shitao Li, Tor Helleseth, Ferruh Ozbudak

For an integer $s\ge 1$, let $\mathcal{C}_s(q_0)$ be the generalized Zetterberg code of length $q_0^s+1$ over the finite field $\F_{q_0}$ of odd characteristic. Recently, Shi, Helleseth, and \"{O}zbudak (IEEE Trans. Inf. Theory 69(11): 7025-7048, 2023) determined the covering radius of $\mathcal{C}_s(q_0)$ for $q_0^s \not \equiv 7 \pmod{8}$, and left the rem

Mingke Lu, Han Gao, Haijie Dai, Qianli Lei

We propose a Path-Tracking Hybrid A* planner coupled with a hierarchical Model Predictive Control (MPC) framework for path smoothing in agricultural vehicles. The goal is to minimize deviation from reference paths during cross-furrow operations, thereby optimizing operational efficiency, preventing crop and soil damage, while also enforcing curvature constra

Paul-Antoine Le Tolguenec, Yann Besse, Florent Teichteil-Koenigsbuch, Dennis G. Wilson

The ability to explore efficiently and effectively is a central challenge of reinforcement learning. In this work, we consider exploration through the lens of information theory. Specifically, we cast exploration as a problem of maximizing the Shannon entropy of the state occupation measure. This is done by maximizing a sequence of divergences between distri

Mehdi Elasmi, Felix Krumbiegel, Roland Maier

Numerical homogenization methods aim at providing appropriate coarse-scale approximations of solutions to (elliptic) partial differential equations that involve highly oscillatory coefficients. The localized orthogonal decomposition (LOD) method is an effective way of dealing with such coefficients, especially if they are non-periodic and non-smooth. It modi

Saroj Si, Ankik Kumar Giri

The exchange-driven growth (EDG) model describes the evolution of clusters through the exchange of single monomers between pairs of interacting clusters. The dynamics of this process are primarily influenced by the interaction kernel $K_{j,k}$. In this paper, the global existence of classical solutions to the EDG equations is established for non-negative, sy

Xiaoming Chen

This paper introduces CKTSO (abbreviation of "circuit solver"), a novel sparse linear solver specially designed for the simulation program with integrated circuit emphasis (SPICE). CKTSO is a parallel solver and can be run on a multi-core, shared-memory computer. The algorithms of CKTSO are designed by considering the features of matrices involved in SPICE s

Yuming Qin, Xiaolei Dong, Xiuqing Wang

This book aims to present some recent results on Prandtl equations and MHD boundary layer equations. This book is essentially divided into two parts. Chapter 1 as the first part systematically surveys the results till 2020 on Prandtl equations and MHD boundary layer equations. Chapter 2 to 6 are the main part of the book, which presents the local and the glo

Marta Casanellas, Luis Sierra, Piotr Zwiernik

Tensors are ubiquitous in statistics and data analysis. The central object that links data science to tensor theory and algebra is that of a model with latent variables. We provide an overview of tensor theory, with a particular emphasis on its applications in algebraic statistics. This high-level treatment is supported by numerous examples to illustrate key

Jiaxuan Shen, Haitao Zhang, Yunjie Wang, Yilong Wang

This study pioneers the application of Recursive Feature Machines (RFM) in QSPR modeling, introducing a tailored feature importance analysis approach to enhance interpretability. By leveraging deep feature learning through AGOP, RFM achieves state-of-the-art (SOTA) results in predicting molecular properties, as demonstrated through solubility prediction acro

Thomas Cecconello, Simone Riggi, Ugo Becciani, Fabio Vitello

The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional visual models pretrained on optical photography images may not perform optimally on radio interferometry images, which have distinct visual characteristics. Self-Supervised Learnin

Felix Agner, Anders Rantzer

This paper concerns control of a class of systems where multiple dynamically stable agents share a nonlinear and bounded control-interconnection. The agents are subject to a disturbance which is too large to reject with the available control action, making it impossible to stabilize all agents in their desired states. In this nonlinear setting, we consider t

Dmitry Svintsov, Zhanna Devizorova

Photon drag represents a mechanism of photocurrent generation wherein the electromagnetic (EM) field momentum is transferred directly to the charge carriers. It is believed to be small by the virtue of low photon momentum compared to the typical momenta of the charge carriers. Here, we show that photon drag becomes particularly strong at the junctions betwee

Asad Ali, Samira Elghaayda, Saif Al-Kuwari, M. I. Hussain

We investigate the performance of a novel model based on a one-dimensional (1D) spin-$1/2$ Heisenberg $XY-\Gamma(\gamma)$ quantum chain, also known as 1D Kitaev chain, as a working medium for a quantum battery (QB) in both closed and open system scenarios. We analyze the closed QB scenario by analytically evaluating ergotropy across different spin-spin coupl

Arno Simons

This paper explores the potential of contextualized word embeddings (CWEs) as a new tool in the history, philosophy, and sociology of science (HPSS) for studying contextual and evolving meanings of scientific concepts. Using the term "Planck" as a test case, I evaluate five BERT-based models with varying degrees of domain-specific pretraining, including my c

Shu Zhou, Xin Wang, Zhengda Zhou, Haohan Yi

In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapid patent updates, and the problem of information redundancy caused by insufficient consideration of the high professionalism, accuracy, and

Alok Kumar Ranjan, Dayakrishna Nailwal, MV Sunil Krishna, Akash Kumar

During intense geomagnetic storms, the rapid and significant production of NO followed by its associated infrared radiative emission in lower thermosphere contributes crucially to the energetics of the upper atmosphere. This makes NO infrared radiative cooling a very important phenomenon which needs to be considered for accurate density forecasting in thermo

Rastko Gajanin, Anastasiya Danilenka, Andrea Morichetta, Stefan Nastic

In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized Machine Learning. Solving this issue is very important in IoT scenarios, where data coming from various sources, collected by heterogeneous devices, serve the learning of a distributed ML model through Federated Lear

Yung-Kun Liu, Ching-En Lin, Jiwoo Nam, Pisin Chen

This paper presents an enhanced optical configuration for a single-pass quantitative Schlieren imaging system that achieves an optical resolution of approximately 4.6 micrometers. The modified setup decouples sensitivity from resolution, enabling independent optimization of these critical parameters. Using this high-resolution system, we conduct quantitative

James B. Glattfelder, Thomas Houweling

We present a series of equations that track the total realized and unrealized profits and losses at any time, incorporating the spread. The resulting formalism is ideally suited to evaluate the performance of trading model algorithms.

Jan Friso Groote, Jan Martens

We show that deciding simulation equivalence and simulation preorder have quadratic lower bounds assuming that the Strong Exponential Time Hypothesis holds. This is in line with the best know quadratic upper bounds of simulation equivalence. This means that deciding simulation is inherently quadratic. A typical consequence of this result is that computing si

Arpita Ghosh, Surojit Ghosh

The set of sums of two squares plays a significant role in elementary number theory. In this article, we establish the existence of several rich monochromatic configurations in the natural numbers by exploiting algebraic structures induced by the set of sums of two squares. The proofs rely largely on the algebraic properties arising from the induced structur

Hongwei Yu, Xiaojun Zhang, Zhihai Wang, Jin Wang

We study the dynamics of two giant atoms interacting with a coupled resonator waveguide (CRW) beyond the Markovian approximation. The distinct atomic configurations determine the number of bound states in the continuum (BIC), leading to different dynamical behaviors. Our results show that when the system supports two BICs, Rabi oscillations dominate the dyna

Ege Kesim, Selahattin Serdar Helli

Parameter efficient finetuning (PEFT) methods are widely used in LLMs and generative models in computer vision. Especially one can use multiple of these during inference to change the behavior of the base model. In this paper we investigated whether multiple LoRA adapters trained on computer vision tasks can be merged together and used during inference witho

Mayank Nautiyal, Andrey Shternshis, Andreas Hellander, Prashant Singh

We present a generative modeling approach based on the variational inference framework for likelihood-free simulation-based inference. The method leverages latent variables within variational autoencoders to efficiently estimate complex posterior distributions arising from stochastic simulations. We explore two variations of this approach distinguished by th

Karol Radziszewski, Piotr Ociepka

We present a large-scale news recommendation system implemented at Ringier Axel Springer Polska, focusing on enhancing prediction models with reinforcement learning techniques. The system, named Aureus, integrates a variety of algorithms, including multi-armed bandit methods and deep learning models based on large language models (LLMs). We detail the archit

Aymeric Vinot, Nicolas Perez

In most team-based esports, voice communications are prominent in the team efficiency and synergy. In fact it has been observed that not only the skill aspect of the team but also the team effective voice communication comes into play when trying to have good performance in official matches. With the recent emergence of LLM (Large Language Models) tools rega

Divya Tahelyani, Arpan Bhattacharyya, Anand S. Sengupta

In this paper, we evaluate the potential of multiband gravitational wave observations from a deci-Hz space-based detector and third-generation ground-based gravitational wave detectors to constrain the properties of dark matter spikes around intermediate-mass ratio inspirals. The presence of dark matter influences the orbital evolution of the secondary compa

Hailang Huang, Yong Wang, Zixuan Huang, Huaqiu Li

Large Multimodal Models (LMMs) demonstrate impressive capabilities. However, current benchmarks predominantly focus on image comprehension in specific domains, and these benchmarks are labor-intensive to construct. Moreover, their answers tend to be brief, making it difficult to assess the ability of LMMs to generate detailed descriptions of images. To addre

Liangfei Wu, Yuxuan Xu, Guo Chen, Junfeng Ding

A high-efficient one-step synthesis of cubic gauche polymeric nitrogen was developed just by thermal treatment of KN3 powders. The Raman and infrared spectra confirm the formation of polymeric nitrogen networks. Thermogravimetric differential scanning calorimeter measurements show that the content of cubic gauche polymeric nitrogen is as high as 1.5 wt% with

Zixiang Zhou

In this paper we prove the vanishing of the bounded cohomology of $\text{Diff}^r_+(S^n)$ with real coefficients when $n\geq 4$ and $1\leq r\leq \infty$. This answers the question raised in \cite{FNS24} for $\geq 4$ dimensional spheres.

Tatsuru Kikuchi

In this study, we perform some analysis for the probability distributions in the space of frequency and time variables. However, in the domain of high frequencies, it behaves in such a way as the highly non-linear dynamics. The wavelet analysis is a powerful tool to perform such analysis in order to search for the characteristics of frequency variations over

Marc Hellmuth, Anna Lindeberg

We explore the connections between clusters and least common ancestors (LCAs) in directed acyclic graphs (DAGs), focusing on the interplay between so-called $I$-lca-relevant DAGs and DAGs with the $I$-lca-property. Here, $I$ denotes a set of integers. In $I$-lca-relevant DAGs, each vertex is the unique LCA for some subset $A$ of leaves of size $|A|\in I$, wh

H. N. Smitha, Alexander I. Shapiro, Veronika Witzke, Nadiia M. Kostogryz

Accurate calculations of starspot spectra are essential for multiple applications in astronomy. The current standard is to represent starspot spectra by spectra of stars that are cooler than the quiet star regions. This implies approximating a starspot as a non-magnetic 1D structure in radiative-convective equilibrium, parametrizing convective energy transpo

Hexuan Deng, Wenxiang Jiao, Xuebo Liu, Jing Li

Large language models (LLMs) deliver impressive results but face challenges from increasing model sizes and computational costs. Structured pruning reduces model size and speeds up inference but often causes uneven degradation across domains, leading to biased performance. To address this, we propose DRPruning, a method that dynamically adjusts the data dist

Shinbok Lee, Gaeun Seo, Daniel Lee, Byeongil Ko

This study investigates language models' generative capabilities in tool-use dialogs. We categorize the models' outputs in tool-use dialogs into four distinct types: Tool Call, Answer Completion, Slot Question, and Relevance Detection, which serve as aspects for evaluation. We introduce FunctionChat-Bench, comprising 700 evaluation items and automated assess

Xianda Guo, Chenming Zhang, Youmin Zhang, Ruilin Wang

Stereo matching serves as a cornerstone in 3D vision, aiming to establish pixel-wise correspondences between stereo image pairs for depth recovery. Despite remarkable progress driven by deep neural architectures, current models often exhibit severe performance degradation when deployed in unseen domains, primarily due to the limited diversity of training dat

Fei Song, Zhe Wang, Jun Li, Long Shi

In ultra-dense unmanned aerial vehicle (UAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale UAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense UAV communication network, w

Gia Dvali, Otari Sakhelashvili, Anja Stuhlfauth

The color-triplet partner of the Higgs doublet, called a $T$-particle, is a universal feature of Grand Unification. It has been shown some time ago that this particle can be accessible for direct production in collider experiments. In this paper we point out that the $T$-particle represents a simultaneous low-energy probe of baryon number violation as well a

Federico Buccioni, Fabrizio Caola, Federica Devoto, Giulio Gambuti

We investigate $2 \to 3$ QCD scattering amplitudes in multi-Regge kinematics, i.e. where the final partons are strongly ordered in rapidity. In this regime amplitudes exhibit intriguing factorisation properties which can be understood in terms of effective degrees of freedom called \emph{reggeons}. Working within the Balitsky/JIMWLK framework, we predict the

Haiyun Yao, Zongbo Han, Huazhu Fu, Xi Peng

Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers (e.g., data from the web or other datasets) in training. However, we experimentally reveal that these methods still struggle to generalize their detection capabilities to unkno

Kayn A. Forbes, Vittorio Aita, Anatoly V. Zayats

Recent developments in the understanding of optical angular momentum have resulted in many demonstrations of unusual optical phenomena, such as optical beams with orbital angular momentum and transverse spinning light. Here we detail novel contributions to spin and orbital angular momentum generated by the gradient of wavefront curvature that becomes relevan

Li Hu, Rong-Gen Cai, Shao-Jiang Wang

In recent years, the long-term effects of non-linear perturbations were found to be important for the evolution of the hierarchical triple system, which, for the central third body of a larger mass, can significantly suppress the occurrences of orbital flip that changes the sign of angular momentum of inner binary. However, as the third-body mass increases s

Qingxiang Liu, Sheng Sun, Yuxuan Liang, Xiaolong Xu

Multiple federated learning (FL) methods are proposed for traffic flow forecasting (TFF) to avoid heavy-transmission and privacy-leaking concerns resulting from the disclosure of raw data in centralized methods. However, these FL methods adopt offline learning which may yield subpar performance, when concept drift occurs, i.e., distributions of historical an

Prasanta Chowdhury, Jyotirmay Sau, Mohamad Numan, Jhuma Sannigrahi

We conducted a comprehensive analysis of the magnetic and electronic transport properties of the layered chalcogenide Cr$_{1+\delta}$Te$_2$ in its single crystalline form. This material exhibits a ferromagnetic transition at a critical temperature of $T_C = 191$ K, characterized by significant thermal hysteresis in the magnetization data below this temperatu

Darwin Alexander O. Co, Denny Lane B. Sombillo

Identifying the nature of near-threshold enhancements is hindered by the limited resolution of experimental data leading to multiple conflicting interpretations. A prominent example of ambiguous line shape is the set of pentaquark signals observed by LHCb in 2019. Some of these signals can be interpreted as hadronic molecule, compact state, virtual state, or

Giorgio Lo Giudice, Lorenzo Leone, Fedele Lizzi

The primary focus of this work is to investigate how the most emblematic classical probability density, namely a Gaussian, can be mapped to a valid quantum states. To explore this issue, we consider a Gaussian whose squared variance depends on a parameter $\lambda$. Specifically, depending on the value of $\lambda$, we study what happens in the classical-qua

Daehoon Gwak, Junwoo Park, Minho Park, Chaehun Park

Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and geopolitics. However, existing datasets available for this task are often limited in quality, hindering the progress of related research. In this paper, we introduce WORLDREP (WORLD

Ying-nan Mao, Kechen Wang, Yiheng Xiong

We propose a search strategy at the HL-LHC for a new neutral particle $X$ that couples to $W$-bosons, using the process $p p \rightarrow W^{\pm} X (\rightarrow W^{+} W^{-})$ with a tri-$W$-boson final state. Focusing on events with two same-sign leptonic $W$-boson decays into muons and a hadronically decaying $W$-boson, our method leverages the enhanced sign

Da-Chun Qiang, Jie Zheng, Zhi-Qiang You, Sheng Yang

Fast Radio Bursts (FRBs) are highly energetic millisecond-duration astrophysical phenomena typically categorized as repeaters or non-repeaters. However, observational limitations may result in misclassifications, potentially leading to a higher proportion of repeaters than currently identified. In this study, we leverage unsupervised machine learning techniq

Abdennour Boulesnane, Boutheina Mokhtari, Oumnia Rana Segueni, Slimane Segueni

Medical imaging has significantly revolutionized medical diagnostics and treatment planning, progressing from early X-ray usage to sophisticated methods like MRIs, CT scans, and ultrasounds. This paper investigates the use of deep learning for medical image captioning, with a particular focus on uterine ultrasound images. These images are vital in obstetrics

Ivan Limonchenko, Rade Živaljević

We prove that a Murai sphere is flag if and only if it is a nerve complex of a flag nestohedron and classify all the polytopes arising in this way. Our classification implies that flag Murai spheres satisfy the Nevo-Petersen conjecture on $\gamma$-vectors of flag homology spheres. We continue by showing that a Bier sphere is minimally non-Golod if and only i

Yunhui Liu, Xinyi Gao, Tieke He, Jianhua Zhao

Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate relationships and diverse relational semantics inherent in heterogeneous graph structures. However, the neighborhood-fetching latency incurred by structure dependency in HGNNs makes i

Xinjie Sun, Qi Liu, Kai Zhang, Shuanghong Shen

Cognitive diagnosis (CD) aims to reveal students' proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students' knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students' explicit response

Federico Ottomano, John Y. Goulermas, Vladimir Gusev, Rahul Savani

Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face constraints imposed by the quantity and quality of available data. Moreover, ML is often employed in tandem with simulated

Frédéric Berdoz, Roger Wattenhofer

While autonomous agents often surpass humans in their ability to handle vast and complex data, their potential misalignment (i.e., lack of transparency regarding their true objective) has thus far hindered their use in critical applications such as social decision processes. More importantly, existing alignment methods provide no formal guarantees on the saf

Yingxuan Yang, Qiuying Peng, Jun Wang, Ying Wen

In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get feedback from the environment so as to accomplish the given tasks in an autonomous manner. Besides the environment-interaction property, the LLM agents can call various external tools

Belle, Belle II Collaborations, :, I. Adachi

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KE

Enrique Fernandez-Cara, Roberto Morales, Diego A. Souza

In this paper, we study several theoretical and numerical questions concerning the null controllability problems for linear parabolic equations and systems for several dimensions. The control is distributed and acts on a small subset of the domain. The main goal is to compute numerically a control that drives a numerical approximation of the state from presc

Jun Qian, Ross Murch, Khaled B. Letaief

Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) are being explored for sixth-generation (6G) wireless networks. A promising configuration for their deployment is within cell-free massive multiple-input multiple-output (MIMO) systems. However, despite the advantages that STAR-RISs could bring, challenges such as electr

Jinting Zhu, Julian Jang-Jaccard, Ian Welch, Harith AI-Sahaf

When malware employs an unseen zero-day exploit, traditional security measures such as vulnerability scanners and antivirus software can fail to detect them. This is because these tools rely on known patches and signatures, which do not exist for new zero-day attacks. Furthermore, existing machine learning methods, which are trained on specific and occasiona

William Borrelli, Umberto Morellini

Graphene is a monolayer graphitic film in which electrons behave like two-dimensional Dirac fermions without mass. Its study has attracted a wide interest in the domain of condensed matter physics. In particular, it represents an ideal system to test the comprehension of 2D massless relativistic particles in a laboratory, the Fermi velocity being 300 times s

Hossein Larki

We generalize the Li-Yang notion of self-similar $k$-graph $(G,\Lambda)$ and its $C^*$-algebra $\mathcal{O}_{G,\Lambda}$ to any finitely aligned $k$-graph $\Lambda$. We then introduce an inverse semigroup model for $\mathcal{O}_{G,\Lambda}$ and analyze its tight groupoid and $C^*$-algebra via inverse semigroup methods.

Shayma Wahdan, On behalf of the ATLAS collaboration

These proceedings present a search for flavour-changing neutral-current (FCNC) interaction involving the top quark, Higgs boson and either the up or the charm quark, using 140 fb$^{-1}$ of 13 TeV proton--proton collision data from the ATLAS detector at the Large Hadron Collider. Two channels are considered: the production of top quark-antiquark pair with one

Chenghao Chen, Xiaolin Zhang, Kailun Qin, Tengfei Wang

RISC-V's limited security features hinder its use in confidential computing and heterogeneous platforms. This paper introduces RISecure-PUF, a security extension utilizing existing Physical Unclonable Functions for key generation and secure protocol purposes. A one-way hash function is integrated to ensure provable security against modeling attacks, while a

A. J. Pan-Collantes, C. Muriel, A. Ruiz

The $\mathcal{C}^{\infty}$-structure-based method of integration of distributions of vector fields is used to classify all the traveling wave solutions of the modified Zakharov--Kuznetsov equation. This work unifies and generalizes the particular results obtained in the recent literature by using specific ansatz-based methods.

Nidhish Shah, Zulkuf Genc, Dogu Araci

We present two comprehensive benchmarks to evaluate the performance of language models in coding assistance tasks, covering code writing, debugging, code review, and conceptual understanding. Our main contribution includes two curated datasets: StackEval, a large-scale benchmark derived from Stack Overflow questions, and StackUnseen, a dynamic benchmark feat

Yuan Shi, Pengjie Zhang, Furen Deng, Shuren Zhou

Cosmic shear surveys serve as a powerful tool for mapping the underlying matter density field, including non-visible dark matter. A key challenge in cosmic shear surveys is the accurate reconstruction of lensing convergence ($\kappa$) maps from shear catalogs impacted by survey boundaries and masks, which seminal Kaiser-Squires (KS) method are not designed t

Iain Davies

This PhD thesis is submitted to arXiv as a pedagogical resource on gravitational effective field theories and the laws of black hole mechanics. It begins with an introduction to the mathematics and intuition behind effective field theory (EFT) corrections to General Relativity (GR) pitched at the level of a first year postgraduate course. Chapter 3 contains

Claudio Bonanno, Claudio Bonati, Mario Papace, Davide Vadacchino

In this talk we will report on a study of the $\theta$-dependence of the string tension and of the mass gap of four-dimensional SU($N$) Yang--Mills theories. The spectrum at $N=3$ and $N=6$ was obtained on the lattice at various imaginary values of the $\theta$-parameter, using Parallel Tempering on Boundary Conditions to avoid topological freezing at fine l

Wanyu Zhang, Ruili Gou, Huikang Liu, Zhiguo Wang

The determination of molecular orientations is crucial for the three-dimensional reconstruction of Cryo-EM images. Traditionally addressed using the common-line method, this challenge is reformulated as a self-consistency error minimization problem constrained to rotation groups. In this paper, we consider the least-squared deviation (LUD) formulation and em

Utsav Dewan

One of the most celebrated problems in Euclidean Harmonic analysis is the Carleson's problem: determining the optimal regularity of the initial condition $f$ of the Schr\"odinger equation given by \begin{equation*} \begin{cases} i\frac{\partial u}{\partial t} =\Delta u\:,\: (x,t) \in \mathbb{R}^n \times \mathbb{R} \newline u(0,\cdot)=f\:, \text{ on } \mathbb

Mahammad Humayoo

Q-Learning is a fundamental off-policy reinforcement learning (RL) algorithm that has the objective of approximating action-value functions in order to learn optimal policies. Nonetheless, it has difficulties in reconciling bias with variance, particularly in the context of long-term rewards. This paper introduces Q($\Delta$)-Learning, an extension of TD($\D

Julio Arrechea, Carlos Barceló, Gerardo García-Moreno, José Polo-Gómez

One of the theoretical motivations behind the belief that black holes as described by general relativity exist in nature is that it is hard to find matter configurations that mimic their properties, especially their compactness. One of the classic results that goes in this direction is the socalled Buchdahl limit: a bound for the maximum compactness that sph

Mathilde Faanes, Ragnhild Holden Helland, Ole Solheim, Sébastien Muller

Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during resection surgery. The lack of large annotated datasets limits the current models performances. In this paper, we investigated the use of tumor annotations in magnetic resonance imaging (MRI) scans, which are more accessible

Xin Wang

In this paper, we propose $\lambda_{g}$ conjecture for Hodge integrals with target varieties. Then we establish relations between Virasoro conjecture and $\lambda_{g}$ conjecture, in particular, we prove $\lambda_{g}$ conjecture in all genus for smooth projective varieties with semisimple quantum cohomology or smooth algebraic curves. Meanwhile, we also prov

Hongfei Wang, Long Feng, Ping Zhao, Zhaojun Wang

In this article, we address the challenge of identifying skilled mutual funds among a large pool of candidates, utilizing the linear factor pricing model. Assuming observable factors with a weak correlation structure for the idiosyncratic error, we propose a spatial-sign based multiple testing procedure (SS-BH). When latent factors are present, we first extr

Mohamad Alameddine

We consider isomonodromic deformations of connections with a simple pole on the torus, motivated by the elliptic version of the sixth Painlev\'e equation. We establish an extended symmetry, complementing known results. The Calogero-Moser system in its elliptic version is shown to fit nicely in the geometric framework, the extended symplectic two-form is intr