November 2024 arXiv papers — page 185

Gabriel German

Reheating in inflationary cosmology is essential for understanding the early universe, influencing particle production, thermalization, and the primordial power spectrum. Crucial quantities defined during the reheating epoc, such as the equation of state parameter $\omega_{re}$, reheating temperature $T_{re}$, and the number of $e$-folds $N_{re}$, affect inf

Thomas R. Richardson, Matthias R. Schindler, Roxanne P. Springer

We explore potential explanations for why using large-$N_c$ ($N_c$ is the number of colors) scaling to determine the relative size of few-nucleon low-energy operators agrees with experiment even when dynamical $\Delta$'s are not explicitly included. Given that the large-$N_c$ analysis is predicated on the nucleons and $\Delta$'s being degenerate, this is a c

Nalin Tiwary, Siddarth Aananth

Sharpness-Aware Minimization (SAM) is an optimization technique designed to improve generalization by favoring flatter loss minima. To achieve this, SAM optimizes a modified objective that penalizes sharpness, using computationally efficient approximations. Interestingly, we find that more precise approximations of the proposed SAM objective degrade generali

Junjiao Tian, Chengyue Huang, Zsolt Kira

Modern optimizers such as AdamW, equipped with momentum and adaptive learning rate, are designed to escape local minima and explore the vast parameter space. This exploration is beneficial for finding good loss basins when training from scratch. It is not necessarily ideal when resuming from a powerful foundation model because it can lead to large deviations

Piotr Frąckiewicz, Anna Gorczyca-Goraj, Krzysztof Grzanka, Katarzyna Nowakowska

This paper investigates Nash equilibria in pure strategies for quantum approach to the Prisoner's Dilemma. The quantization process involves extending the classical game by introducing two additional unitary strategies. We consider five classes of such quantum games, which remain invariant under isomorphic transformations of the classical game. For each clas

Dennis Fucci, Marco Gaido, Beatrice Savoldi, Matteo Negri

Spurred by the demand for interpretable models, research on eXplainable AI for language technologies has experienced significant growth, with feature attribution methods emerging as a cornerstone of this progress. While prior work in NLP explored such methods for classification tasks and textual applications, explainability intersecting generation and speech

Futoshi Minato

Nuclear microscopic structural models that treat two-body effective interactions self-consistently becomes available, one of which is second-random-phase-approximation (SRPA). SRPA can be used to study evolutions from 1 particle-1 hole (1p1h) to 2 particle-2 hole (2p2h) states from different point of view from reaction models. We studied nuclear excitations

Asad Mahmood, Raúl Puente, Herman Batelaan, Rafeeq Syed

This study investigates the propagation of an ultrafast laser pulse through a liquid medium. A femtosecond laser oscillator with a pulse duration of less than 8 fs is used. By conducting experiments with coumarin and fluorescein dyes in water, methanol, and chloroform, we analyze two-photon absorption (TPA) fluorescence, a method pioneered by Schr\"oder [Opt

Jingtao Tang, Zining Mao, Hang Ma

We study Multi-Robot Coverage Path Planning (MCPP) on a 4-neighbor 2D grid G, which aims to compute paths for multiple robots to cover all cells of G. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid H and then employ the Spanning Tree Coverage (STC) paradigm to generate paths on G, making them inapplicable

Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop

Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the

Yuefeng Peng, Junda Wang, Hong Yu, Amir Houmansadr

Despite significant advancements, large language models (LLMs) still struggle with providing accurate answers when lacking domain-specific or up-to-date knowledge. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge bases, but it also introduces new attack surfaces. In this paper, we investigate data extraction

Gabriel Nova, Sander van Cranenburgh, Stephane Hess

Discrete Choice Modelling serves as a robust framework for modelling human choice behaviour across various disciplines. Building a choice model is a semi structured research process that involves a combination of a priori assumptions, behavioural theories, and statistical methods. This complex set of decisions, coupled with diverse workflows, can lead to sub

Sejoon Oh, Yiqiao Jin, Megha Sharma, Donghyun Kim

Multimodal large language models (MLLMs) have revolutionized vision-language understanding but remain vulnerable to multimodal jailbreak attacks, where adversarial inputs are meticulously crafted to elicit harmful or inappropriate responses. We propose UniGuard, a novel multimodal safety guardrail that jointly considers the unimodal and cross-modal harmful s

Yifan Shen, David Yarkony

We present a symmetry adapted residual neural network (SAResNet) diabatization method to construct quasi-diabatic Hamiltonians that accurately represent ab initio adiabatic energies, energy gradients, and nonadiabatic couplings for moderate sized systems. Our symmetry adapted neural network inherits from the pioneering symmetry adapted polynomial and fundame

Fuxiang Xu, Ran Wang, Jianan Li, Roberto Neri

We present NOrthern Extended Millimeter Array observations of CO(8-7), (9-8), and (10-9) lines, as well as the underlying continuum for two far-infrared luminous quasars: SDSS J2054-0005 at $\rm z=6.0389$ and SDSS J0129-0035 at $\rm z=5.7788$. Both quasars were previously detected in CO (2-1) and (6-5) transitions, making them candidates for studying the CO

Taran Driver, Zhaoheng Guo, Erik Isele, Gilbert Grell

In molecular systems, the ultrafast motion of electrons initiates the process of chemical change. Tracking this electronic motion across molecules requires coupling attosecond time resolution to atomic-scale spatial sensitivity. In this work, we employ a pair of attosecond x-ray pulses from an x-ray free-electron laser to follow electron motion resulting fro

Vladimir Baryshevsky

Author-made translation of the book published in Russian in 1982. Chapter 1. Channeling of High-Energy Particles in Crystals. Chapter 2. A Channeled Fast Particle as a 2D (1D) Relativistic Atom. Chapter 3. The Foundations of the Theory of $\gamma$-quanta Emission in Crystals under Channeling Conditions. Chapter 4. The Influence of $\gamma$-quanta Refraction

John Lewis

In this paper we first make and discuss a conjecture concerning Newtonian potentials in Euclidean n space which have all their mass on the unit sphere about the origin, and are normalized to be one at the origin. The conjecture essentially divides these potentials into subclasses whose criteria for membership is that a given member have its maximum on the cl

Shaun McDonald, David Campbell

Many models require integrals of high-dimensional functions: for instance, to obtain marginal likelihoods. Such integrals may be intractable, or too expensive to compute numerically. Instead, we can use the Laplace approximation (LA). The LA is exact if the function is proportional to a normal density; its effectiveness therefore depends on the function's tr

Sima Noorani, Orlando Romero, Nicolo Dal Fabbro, Hamed Hassani

Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing CP efficiency during training. We formalize this concept as the problem of conformal risk minimization (CRM). In this di

Mohamed Moumen, Lahcen Taoufiq

Let $B$ be a Banach algebra. The interest of this article lies in the study of the commutativity of B if certain specific algebraic identities hold over a non-empty open subset of B. The limitations imposed in the hypothesis of our results are justified by some examples.

Fekadu L. Bayisa, Christopher L. Seals, Hannah J. Leeper, Todd D. Steury

Interactions among individuals from the same-species of wild animals are an important component of population dynamics. An interaction can be either static (based on overlap of space use) or dynamic (based on movement). The goal of this work is to determine the level of static interactions between individuals from the same-species of wild animals using 95\%

Martin Harrigan, Thomas Lloyd, Daire Ó Broin

Tokens have proliferated across blockchains in terms of number, market capitalisation, and utility. Some tokens are tokenised versions of existing tokens, known variously as wrapped tokens, fractional tokens or shares. The repeated application of this process creates tokens with arbitrarily many layers of composition. We perform an empirical analysis of toke

Alexandre Anahory Simoes, Anthony Bloch, Leonardo Colombo, Efstratios Stratoglou

In this paper, we give sufficient conditions for and deduce a control law under which a mechanical control system converges exponentially fast to a virtual linear nonholonomic constraint that is control invariant via the same feedback control. Virtual constraints are relations imposed on a control system that become invariant via feedback control, as opposed

Luís Cunha, Thiago Lopes, Uéverton Souza, Leonard Bohnenkämper

Genome rearrangement has been an active area of research in computational comparative genomics for the last three decades. While initially mostly an interesting algorithmic endeavor, now the practical application by applying rearrangement distance methods and more advanced phylogenetic tasks is becoming common practice, given the availability of many complet

Xinrui He, Shuo Liu, Jackey Keung, Jingrui He

As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. However, the presence of statistical heterogeneity in FRS, commonly observed due to personalized decision-making patterns, can pose challenges. To ad

Srivatsan Sridhar, Ertem Nusret Tas, Joachim Neu, Dionysis Zindros

A specter is haunting consensus protocols--the specter of adversary majority. Dolev and Strong in 1983 showed an early possibility for up to 99% adversaries. Yet, other works show impossibility results for adversaries above 50% under synchrony, seemingly the same setting as Dolev and Strong's. What gives? It is high time that we pinpoint a key culprit for th

Thomas Poulet, Sandro Truttmann, Victor Boussange, Manolis Veveakis

Recent studies showed that seemingly random Slow Slip Events (SSEs) can display chaotic patterns within the largest source of seismic hazards in New Zealand, the Hikurangi subduction zone. Some irregular SSE occurrences are therefore not arbitrary but behave with short-term predictability. However, the forecasting challenge persists as observations remain to

Yun He, Xuxing Chen, Jiayi Xu, Renqin Cai

In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between the joint learning tasks. However, multi-task learning often suffers from negative transfer: one or several task

E. G. K. M. Gamlath, Andrew Pham, Bing Wei

Given a finite, simple graph $G$, the independent bondage number of $G$ is the minimum size of an edge set such that its deletion results in a graph with strictly larger independent domination number than that of $G$. While the bondage number of graphs under girth constraints has been studied, very few results have yet been established for the independent bo

Walid El Maouaki, Alberto Marchisio, Taoufik Said, Muhammad Shafique

In this study, we investigated the robustness of Quanvolutional Neural Networks (QuNNs) in comparison to their classical counterparts, Convolutional Neural Networks (CNNs), against two adversarial attacks: Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), for the image classification task on both Modified National Institute of Standards

Shaun McDonald, Alexandre Leblanc, Saman Muthukumarana, David Campbell

Within the field of hierarchical modelling, little attention is paid to micro-macro models: those in which group-level outcomes are dependent on covariates measured at the level of individuals within groups. Although such models are perhaps underrepresented in the literature, they have applications in economics, epidemiology, and the social sciences. Despite

Hyun-June Jang, Hyou-Arm Joung, Xiaoao Shi, Rui Ding

To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into

Mohammad Hossein Moslemi, Mostafa Milani

Record matching models typically output a real-valued matching score that is later consumed through thresholding, ranking, or human review. While fairness in record matching has mostly been assessed using binary decisions at a fixed threshold, such evaluations can miss systematic disparities in the entire score distribution and can yield conclusions that cha

Bojan Kuzma, Sushil Singla

The main goal of the article is to prove that if $\mathcal A_1$ and $\mathcal A_2$ are Birkhoff-James isomorphic $C^*$-algebras over the fields $\mathbb F_1$ and $\mathbb F_2$, respectively and if $\mathcal A_1$ finite-dimensional, abelian of dimension greater than one, then $\mathbb F_1=\mathbb F_2$ and $\mathcal A_1$ and $\mathcal A_2$ are (isometrically)

Jungkyoung Na

In this paper, we establish the existence of global self-similar solutions to the 3D Muskat equation when the two fluids have the same viscosity but different densities. These self-similar solutions are globally defined in both space and time, with exact cones as their initial data. Furthermore we estimate the difference between our self-similar solutions an

Nezhla Aghaei, M. K. Pawelkiewicz

We study the Heisenberg double and the Drinfeld double of the Borel half of $Uq (gl(1|1))$ and of the $Uq (gl(1|1))$ when q is a root of unity. We also study the Borel half of Uq (osp(1|2)) for both cases when qis a root of unity and when it is not. We prove the isomorphism between the Heisenberg doubles and the handle algebras, which is missing in the liter

María Camarasa-Gómez, Daniel Hernangómez-Pérez, Jan Wilhelm, Alexej Bagrets

Single-molecule junctions - nanoscale systems where a molecule is connected to metallic electrodes - offer a unique platform for studying charge, spin and energy transport in non-equilibrium many-body quantum systems, with few parallels in other areas of condensed matter physics. Over the past decades, these systems have revealed a wide range of remarkable q

Bo Yuan, Jiazi Hu

Course evaluation plays a critical role in ensuring instructional quality and guiding curriculum development in higher education. However, traditional evaluation methods, such as student surveys, classroom observations, and expert reviews, are often constrained by subjectivity, high labor costs, and limited scalability. With recent advancements in large lang

Piotr Wyrwiński, Krzysztof Krawiec

In evolutionary computation, it is commonly assumed that a search algorithm acquires knowledge about a problem instance by sampling solutions from the search space and evaluating them with a fitness function. This is necessarily inefficient because fitness reveals very little about solutions -- yet they contain more information that can be potentially exploi

Nicolás Astorga, Tennison Liu, Yuanzhang Xiao, Mihaela van der Schaar

Mathematical optimization is fundamental to decision-making across diverse domains, from operations research to healthcare. Yet, translating real-world problems into optimization models remains a difficult task, often demanding specialized expertise. This paper approaches the problem of $\textit{autoformulation}$: the automated creation of solver-ready optim

Yukun Li, Sijia Wang, Lifu Huang, Li-Ping Liu

Reliable confidence estimation is essential for enhancing the trustworthiness of large language models (LLMs), especially in high-stakes scenarios. Despite its importance, accurately estimating confidence in LLM responses remains a significant challenge. In this work, we propose using an auxiliary learning model to assess response correctness based on the se

André Henriques, Nivedita, David Penneys

We study $\mathrm{W}^*$-categories, and explain the ways in which complete $\mathrm{W}^*$-categories behave like categorified Hilbert spaces. Every $\mathrm{W}^*$-category $C$ admits a canonical categorified inner product $\langle\,\,,\,\rangle_{\mathrm{Hilb}}\,:\,\overline C\times C\,\to\, \mathrm{Hilb}$. Moreover, if $C$ and $D$ are complete $\mathrm{W}^*$

Vasudevarao Allu, Raju Biswas, Rajib Mandal

Let $\mathcal{B}(\mathcal{H})$ denote the Banach algebra of all bounded linear operators acting on complex Hilbert spaces $\mathcal{H}$. In this paper, we first establish several sharply refined versions of Bohr's inequality analogues with operator valued functions in the class $\mathcal{B}(\mathbb{D}, \mathcal{B}(\mathcal{H}))$ of bounded analytic functions

Willian Righi Assis, Danilo da Silva Borges, Erick de Moraes Franklin

We inquire into the morphodynamics of barchans under seasonal flows. For that, we carried out grain-scale numerical computations of a subaqueous barchan exposed to two-directional flows, and we varied the angle and frequency of oscillations. We show that when the frequency is lower than the inverse of the characteristic time for barchan formation, the dune a

Vasudevarao Allu, Raju Biswas, Rajib Mandal

In this paper, we derive the sharp Bohr type inequality for the Ces\'aro operator, Bernardi integral operator, and discrete Fourier transform acting on the class of bounded analytic functions defined on shifted disks \beas \Omega_{\gamma}=\left\{z\in\mathbb{C}:\left|z+\frac{\gamma}{1-\gamma}\right|<\frac{1}{1-\gamma}\right\}\quad\text{for}\quad\gamma\in[0,1)

Ivan Zanardi, Alberto Padovan, Daniel J. Bodony, Marco Panesi

State-specific thermochemical collisional models are crucial to accurately describe the physics of systems involving nonequilibrium plasmas, but they are also computationally expensive and impractical for large-scale, multi-dimensional simulations. Historically, computational cost has been mitigated by using empirical and physics-based arguments to reduce th

Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari

People with learning disabilities have a higher mortality rate and premature deaths compared to the general public, as reported in published research in the UK and other countries. This study analyses hospitalisations of 9,618 patients identified with learning disabilities and long-term conditions for the population of Wales using electronic health record (E

Yang Li, Raj K. Koju, Yuri Mishin

Molecular dynamics and parallel-replica dynamics simulations are applied to investigate the atomic structures and diffusion processes at {\text{Al}\{111\}}\parallel{\text{Si}}\{111\} interphase boundaries constructed by simulated vapor deposition of Al(Si) alloy on Si(111) substrates. Different orientation relationships and interface structures are obtained

Jaume de Haro, Llibert Aresté Saló, Supriya Pan

In this article we calculate the reheating temperature in the cosmological scenarios where heavy scalar particles are gravitationally produced, due to a conformally coupled interaction between a massive scalar quantum field and the Ricci scalar, during the oscillations of the inflaton field. We explore two distinct cases, namely the one in which these partic

Chen Shapira, Dan Rosenbaum

Models that adapt their predictions based on some given contexts, also known as in-context learning, have become ubiquitous in recent years. We propose to study the behavior of such models when data is contaminated by noise. Towards this goal we use the Neural Processes (NP) framework, as a simple and rigorous way to learn a distribution over functions, wher

Alisher Ibragimov, Sofya Senotrusova, Arsenii Litvinov, Egor Ushakov

In this study, we introduce a novel method, called MamT$^4$, which is used for simultaneous analysis of four mammography images. A decision is made based on one image of a breast, with attention also devoted to three additional images: another view of the same breast and two images of the other breast. This approach enables the algorithm to closely replicate

Shuang Gao, Roland P. Malhamé

This paper studies a class of linear quadratic mean field games where the coefficients of quadratic cost functions depend on both the mean and the variance of the population's state distribution through its quantile function. Such a formulation allows for modelling agents that are sensitive to not only the population average but also the population variance.

Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf, Martin Grohe

Generative deep learning has become pivotal in molecular design for drug discovery, materials science, and chemical engineering. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on specific objectives. However, string-based models face challenges in ensuring chemical

Thomas F Burns

Southeast Asia is a geopolitically and socio-economically significant region with unique challenges and opportunities. Intensifying progress in generative AI against a backdrop of existing health security threats makes applications of AI to mitigate such threats attractive but also risky if done without due caution. This paper provides a brief sketch of some

Sonam Mehta, Kuldeep Singh Charak

In this paper we generalize a result of Ye, Pang and Yang[12] on the normality of a family of holomorphic curves in $P^N(\mathbb{C})$. Further we obtain a normality criterion for family of meromorphic functions that partially share wandering holomorphic functions with their derivatives. We also devise a tractable representation of complex valued holomorphic

Youngsun Wi, Jayjun Lee, Miquel Oller, Nima Fazeli

Reconstructing unknown external source functions is an important perception capability for a large range of robotics domains including manipulation, aerial, and underwater robotics. In this work, we propose a Physics-Informed Neural Network (PINN [1]) based approach for solving the inverse source problems in robotics, jointly identifying unknown source funct

Subhasish Guha, Ipsita Bar, Bijay Kumar Agarwalla, B. Prasanna Venkatesh

We study the collective dissipative dynamics of dipoles modeled as harmonic oscillators coupled to 1-D electromagnetic reservoirs. The bosonic nature of the dipole oscillators as well as the reservoir modes allows an exact numerical simulation of the dynamics for arbitrary coupling strengths. At weak coupling, apart from essentially recovering the dynamics e

Majid Daliri, Zhao Song, Chiwun Yang

Recently, 1-bit Large Language Models (LLMs) have emerged, showcasing an impressive combination of efficiency and performance that rivals traditional LLMs. Research by Wang et al. (2023); Ma et al. (2024) indicates that the performance of these 1-bit LLMs progressively improves as the number of parameters increases, hinting at the potential existence of a Sc

Antarjami Sahoo, Swayang Priya Mahanta, Subhankar Bedanta

The magnetic Gilbert damping and spin-orbital to charge interconversion phenomenon play vital role in controlling the modern spintronics device performances. Though the ferromagnets (FMs) and heavy metals (HMs) are considered to be the key components of the future spin-orbit torque magnetic random access memory (SOT-MRAM) devices, recently the integration of

Quoc-Huy Trinh, Minh-Van Nguyen, Trong-Hieu Nguyen Mau, Khoa Tran

Singing is one of the most cherished forms of human entertainment. However, creating a beautiful song requires an accompaniment that complements the vocals and aligns well with the song instruments and genre. With advancements in deep learning, previous research has focused on generating suitable accompaniments but often lacks precise alignment with the desi

Thomas Helfer, Thomas D. P. Edwards, Jessica Dafflon, Kaze W. K. Wong

Generating high-resolution simulations is key for advancing our understanding of one of the universe's most violent events: Black Hole mergers. However, generating Black Hole simulations is limited by prohibitive computational costs and scalability issues, reducing the simulation's fidelity and resolution achievable within reasonable time frames and resource

Martin Hsu, Victor Lie

Given a curve $\vec{\gamma}=(t^{\alpha_1}, t^{\alpha_2}, t^{\alpha_3})$ with $\vec{\alpha}=(\alpha_1,\alpha_2,\alpha_3)\in \mathbb{R}_{+}^3$, we define the Carleson-Radon transform along $\vec{\gamma}$ by the formula $$ C_{[\vec{\alpha}]}f(x,y):=\sup_{a\in \mathbb{R}}\left|p.v.\,\int_{\mathbb{R}} f (x-t^{\alpha_1},y-t^{\alpha_2})\,e^{i\,a\,t^{\alpha_3}}\,\fr

Sebastián Muñoz-Thon

We study a version of Calder\'on's problem for harmonic maps between Riemannian manifolds. By using the higher linearization method, we first show that the Dirichlet-to-Neumann map determines the metric on the domain up to a natural gauge in three cases: on surfaces, on analytic manifolds, and in conformally transversally anisotropic manifolds on a fixed con

Tishya Patel, Yusen Ye, Tharindu Fernando

Graphene, a two-dimensional material with tunable electronic properties, holds significant importance in condensed matter physics and material science. In this study, we analyze the curvature of graphene's ground-state energy dispersion band by examining its Gaussian and mean curvature under varying on-site potential values, M, using graphene's Hamiltonian u

Galina Filipuk, Michele Graffeo, Giorgio Gubbiotti, Alexander Stokes

In this paper we propose a geometric approach to study Painlev\'e equations appearing as constrained systems of three first-order ordinary differential equations. We illustrate this approach on a system of three first-order differential equations arising in the theory of semi-classical orthogonal polynomials. We show that it can be restricted to a system of

Xiaole Tang, Xiang Gu, Xiaoyi He, Xin Hu

All-in-one image restoration has emerged as a practical and promising low-level vision task for real-world applications. In this context, the key issue lies in how to deal with different types of degraded images simultaneously. In this work, we present a Degradation-Aware Residual-Conditioned Optimal Transport (DA-RCOT) approach that models (all-in-one) imag

Martin Werner Licht

We study Poincar\'e--Friedrichs--Weber constants for Sobolev differential forms on bounded convex domains and on domains star-shaped with respect to a ball. Generalizing work by Guerini and Savo, our main result shows that the Poincar\'e--Friedrichs--Weber constants in the Sobolev de~Rham complexes on bounded convex domains are nonincreasing in the degree of

Denny M. Oliveira, Eftyhia Zesta, Dibyendu Nandy

In this short communication, we qualitatively analyze possible effects of the 10 October 2024 geomagnetic storm on accelerating the reentry of a Starlink satellite from very low-Earth orbit (VLEO). The storm took place near the maximum of solar cycle (SC) 25, which has shown to be more intense than SC24. Based on preliminary geomagnetic indices, the 10 Octob

Langalibalele Lunga, Suhas Sreehari

Machine learning models are prone to adversarial attacks, where inputs can be manipulated in order to cause misclassifications. While previous research has focused on techniques like Generative Adversarial Networks (GANs), there's limited exploration of GANs and Synthetic Minority Oversampling Technique (SMOTE) in text and image classification models to perf

Saidul Islam, Jamal Bentahar, Robin Cohen, Gaith Rjoub

Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respiratory failure. Accurate and real-time analysis of biomedical signals during CPR is essential for monitoring and decision-making, from the pre-hospital stage to the intensive care un

Qiqi Jiang, Guang-Xing Li, Chandra B. Singh

The Rayleigh-Taylor instability (RTI) arises at the interface between two fluids of different densities, notably when a heavier fluid lies above a lighter one in an effective gravitational field. In astrophysical systems with high velocities, relativistic corrections are necessary. We investigate the linear theory of relativistic Rayleigh-Taylor instability

Laura Wenderoth

This study explores the potential of using training dynamics as an automated alternative to human annotation for evaluating the quality of training data. The framework used is Data Maps, which classifies data points into categories such as easy-to-learn, hard-to-learn, and ambiguous (Swayamdipta et al., 2020). Swayamdipta et al. (2020) highlight that difficu

Vaneeta Ahlawat, Rohit Sharma, Urush

In recent years, the diagnosis of gastrointestinal (GI) diseases has advanced greatly with the advent of high-tech video capsule endoscopy (VCE) technology, which allows for non-invasive observation of the digestive system. The MisaHub Capsule Vision Challenge encourages the development of vendor-independent artificial intelligence models that can autonomous

Hrant Topchyan, Win Nuding, Andreas Klümper, Ara Sedrakyan

The Harris criterion imposes a constraint on the critical behavior of a system upon introduction of new disorder, based on its dimension $d$ and localization length exponent $\nu$. It states that the new disorder can be relevant only if $d \nu < 2$. We analyze the applicability of the Harris criterion to the GKNS network disorder formulated in the paper [I.

Mohamed Boucetta, Hasna Essoufi

Let $(\mathfrak{g}, \bullet)$ be a real left symmetric algebra, and $(\mathfrak{g}^-, [\;,\;])$ the corresponding Lie algebra. We denote by $L$ the left multiplication operator associated with the product $\bullet$. The symmetric bilinear form $\mathrm{B}(X, Y) = \mathrm{tr}(L_{X \bullet Y})$, referred to as the Koszul form of $(\mathfrak{g}, \bullet)$, is i

Wibke Düsterhöft-Wriggers, Thomas Rung

A novel monolithic approach for simulating vessels in waves with granular cargo is presented using a Finite Volume framework. This model integrates a three-phase Volume of Fluid method to represent air, water, and cargo, coupled with a granular material model. The approach incorporates vessel dynamics by assuming rigid-body motion for the vessel's empty hull

Saidul Islam, Gaith Rjoub, Hanae Elmekki, Jamal Bentahar

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from traditional CPR methods to innovative ML-driven approaches, highlighting the impact of predictive modeling, AI-enhanced devices, and real-time data analysis in improving resuscitat

Julien Jiro Langouët

The LHCb Upgrade I introduced a triggerless data acquisition system, crucial for readout across sub-detectors. Upgrade II aims for fivefold throughput enhancement and requires precise clock distribution. The PCIe400 development significantly boosts performance with 400Gbps bandwidth and advanced FPGA capabilities. Key design considerations included thermal d

Zhenbin Wang, Lei Zhang, Lituan Wang, Minjuan Zhu

Medical video generation models are expected to have a profound impact on the healthcare industry, including but not limited to medical education and training, surgical planning, and simulation. Current video diffusion models typically build on image diffusion architecture by incorporating temporal operations (such as 3D convolution and temporal attention).

Ulrich Bunke, Matthias Ludewig

We interpret the coarse symbol and index class of a Callias type Dirac operator $D+\Psi$ on a manifold $M$ as a pairing between the coarse symbol and index classes associated to $D$ and K-theory classes of the coarse corona of $M$ or $M$ itself determined by $\Psi$. Local positivity of $D$ and local invertibility of $\Psi$ are incorporated in terms of suppor

Gjergji Kasneci, Enkelejda Kasneci

Feature engineering is crucial for optimizing machine learning model performance, particularly in tabular data classification tasks. Leveraging advancements in natural language processing, this study presents a systematic approach to enrich tabular datasets with features derived from large language model embeddings. Through a comprehensive ablation study on

Alan Sun, Chiyu Ma, Kenneth Ge, Soroush Vosoughi

We present knowledge continuity, a novel definition inspired by Lipschitz continuity which aims to certify the robustness of neural networks across input domains (such as continuous and discrete domains in vision and language, respectively). Most existing approaches that seek to certify robustness, especially Lipschitz continuity, lie within the continuous d

Shaokun Zhang, Jieyu Zhang, Dujian Ding, Mirian Hipolito Garcia

Recent advancements have enabled Large Language Models (LLMs) to function as agents that can perform actions using external tools. This requires registering, i.e., integrating tool information into the LLM context prior to taking actions. Current methods indiscriminately incorporate all candidate tools into the agent's context and retain them across multiple

Md Abrar Jahin, Md. Akmol Masud, M. F. Mridha, Nilanjan Dey

In high-energy physics, particle jet tagging plays a pivotal role in distinguishing quark from gluon jets using data from collider experiments. While graph-based deep learning methods have advanced this task beyond traditional feature-engineered approaches, the complex data structure and limited labeled samples present ongoing challenges. More broadly, our p

Md Abrar Jahin, Md. Akmol Masud, Md Wahiduzzaman Suva, M. F. Mridha

The rapid data surge from the high-luminosity Large Hadron Collider introduces critical computational challenges requiring novel approaches for efficient data processing in particle physics. Quantum machine learning, with its capability to leverage the extensive Hilbert space of quantum hardware, offers a promising solution. However, current quantum graph ne

A. Neronov, F. Vazza, A. Brandenburg, C. Caprini

We estimate the magnetic field in the jets of the recently discovered 7 Mpc long Porphyrion system. We use non-detection of the system in gamma-rays to derive a lower bound on the co-moving magnetic field strength at the level of ~10 nG. This value is consistent with recent estimates of magnetic fields in the filaments of the Large Scale Structure. We discus

Neel P. Bhatt, Yunhao Yang, Rohan Siva, Daniel Milan

Multimodal foundation models offer a promising framework for robotic perception and planning by processing sensory inputs to generate actionable plans. However, addressing uncertainty in both perception (sensory interpretation) and decision-making (plan generation) remains a critical challenge for ensuring task reliability. We present a comprehensive framewo

Luca Di Domenico, Nadir Murru

Primality testing is an especially useful topic for public-key cryptography. In this paper, a novel primality test algorithm based on the Pell's cubic will be introduced, and its necessary primality conditions will be proved using three integer sequences connected to operations applied in the projectivization of the Pell's cubic. The number of operations inv

Yihan Xiao, Rui Xiong, Haofeng Zhang

We develop a family of new combinatorial models for key polynomials. It is similar to the hybrid pipe dream model for Schubert polynomials defined recently by Knutson and Udell.

Biman Barua, M. Shamim Kaiser

This research investigates the implementation of a real-time, microservices-oriented dynamic pricing system for the travel sector. The system is designed to address factors such as demand, competitor pricing, and other external circumstances in real-time. Both controlled simulation and real-life application showed a respectable gain of 22% in revenue generat

Byungki Ryu, Seunghyun Oh, Wabi Demeke, Jaywan Chung

We derive a thermoelectric inequality in thermoelectric conversion between the material figure of merit (ZT) and the module effective ZT using the Constant Seebeck-coefficient Approximation combining with the Wiedemann-Franz law. In a P-N leg-pair module, the effective ZT lies between the individual ZT values of the P- and N-legs. In a single-leg module, how

Steve Hanneke, Vinod Raman, Amirreza Shaeiri, Unique Subedi

We consider the problem of multiclass transductive online learning when the number of labels can be unbounded. Previous works by Ben-David et al. [1997] and Hanneke et al. [2023b] only consider the case of binary and finite label spaces, respectively. The latter work determined that their techniques fail to extend to the case of unbounded label spaces, and t

Alan Edelman, Sungwoo Jeong, Ron Nissim

In previous work, a description of the result of applying the Householder tridiagonalization algorithm to a G$\beta$E random matrix is provided by Edelman and Dumitriu. The resulting tridiagonal ensemble makes sense for all $\beta>0$, and has spectrum given by the $\beta$-ensemble for all $\beta>0$. Moreover, the tridiagonal model has useful stochastic opera

Xinkang Chen, Sumeet Kumar Gupta

Monolayer TaS$_{2}$ is being explored as a future liner/barrier to circumvent the scalability issues of the state-of-the-art interconnects. However, its large vertical resistivity poses some concerns and mandates a comprehensive circuit analysis to understand the benefits and trade-offs of this technology. In this work, we present a detailed temperature-depe

Florian Besau, Elisabeth M. Werner

We explore analogs of classical centro-affine invariant isoperimetric inequalities, such as the Blaschke--Santal\'o inequality and the $L_p$-affine isoperimetric inequalities, for convex bodies in spherical space. Specifically, we establish an isoperimetric inequality for the floating area and prove a stability result based on the spherical volume difference

Silvia Butti, Alberto Larrauri, Stanislav Živný

A celebrated result of Hastad established that, for any constant $\varepsilon>0$, it is NP-hard to find an assignment satisfying a $(1/|G|+\varepsilon)$-fraction of the constraints of a given 3-LIN instance over an Abelian group $G$ even if one is promised that an assignment satisfying a $(1-\varepsilon)$-fraction of the constraints exists. Engebretsen, Holm

Tengyuan Liang, Kulunu Dharmakeerthi, Takuya Koriyama

Adding noise is easy; what about denoising? Diffusion is easy; what about reverting a diffusion? Diffusion-based generative models aim to denoise a Langevin diffusion chain, moving from a log-concave equilibrium measure $\nu$, say an isotropic Gaussian, back to a complex, possibly non-log-concave initial measure $\mu$. The score function performs denoising,

Asmer Hamid Ali, Mozhgan Navardi, Tinoosh Mohsenin

Tiny Machine Learning (TinyML) has become a growing field in on-device processing for Internet of Things (IoT) applications, capitalizing on AI algorithms that are optimized for their low complexity and energy efficiency. These algorithms are designed to minimize power and memory footprints, making them ideal for the constraints of IoT devices. Within this d

Oscar Ramírez

One advantage of paraconsistent logic is that it can deal with inconsistencies without making the system trivial. However, unlike classical propositional calculus, its deductive system is limited, and the meaning of paraconsistent negation is still not clear. This article presents a logical system that brings together the strengths of both approaches. The Pr