December 2024 arXiv papers — page 20

Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Emil Janulewicz

High-quality Service Function Chaining (SFC) provisioning is provided by the timely execution of Virtual Network Functions (VNFs) in a defined sequence. Advanced Deep Reinforcement Learning (DRL) solutions are utilized in many studies to contribute to fast and reliable autonomous SFC provisioning. However, under a large-scale network environment, centralized

Yang Han, Ziping Wan, Lu Chen, Kai Yu

Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with th

Xingcheng Fu, Jian Wang, Yisen Gao, Qingyun Sun

Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to study graph connectivity and information propagation efficiency with a geometric perspective, and has been raised in recent years to explore the efficient message-passing structure of

Yuang Wang, Pengfei Jin, Li Zhang, Quanzheng Li

Diffusion bridge models have demonstrated promising performance in conditional image generation tasks, such as image restoration and translation, by initializing the generative process from corrupted images instead of pure Gaussian noise. However, existing diffusion bridge models often rely on Stochastic Differential Equation (SDE) samplers, which result in

Shilong Wang, Jianchun Liu, Hongli Xu, Chunming Qiao

In a federated learning (FL) system, many devices, such as smartphones, are often undependable (e.g., frequently disconnected from WiFi) during training. Existing FL frameworks always assume a dependable environment and exclude undependable devices from training, leading to poor model performance and resource wastage. In this paper, we propose FLUDE to effec

Shengbo Tan, Rundong Xue, Shipeng Luo, Zeyu Zhang

Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain vessel integrity and posing significant challenges for vessel segmentation. To address this issue, we propose an innovative model: SegKAN. First, we improve the conventional embedding module by adopting a novel convolutio

Jiaming Yan, Jianchun Liu, Hongli Xu, Liusheng Huang

Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under compression will lead to unexpected model/gradient deviation for the FL training, significantly degrading the training performance, especially under the challenges of data heterogenei

Yasmin Rahmati

The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detection and monitoring, achieving over 90%

Ling-Fang Li, Shun-Cai Zhao, Lu-Xin Xu

Revealing the role of quantum entanglement in charge-transport in the Photosystem II reaction center (PSII RC) is of great significance. In this work, we theoretically demonstrate that the robust quantum entanglement provides regulatory benefits to the charge-transport via a quantum heat engine (QHE) model with two absorbed photon channels. The calculation r

Lisa Walter, Jun Hua, Sebastian Lahrtz, Lingquan Ma

We present the first lattice QCD calculation of the quark transverse spin-momentum correlation, i.e., the T-odd Boer-Mulders function, of the pion, using large-momentum effective theory (LaMET). The calculation is done at three lattice spacings $a=(0.098, 0.085, 0.064)$ fm and pion masses $\sim350$ MeV, with pion momenta up to $1.8$ GeV. The matrix elements

Xinyi Hu

Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to train a shared model collaboratively while preserving privacy. However, the scaling of real-world FL systems is often limited by two communication bottlenecks:(a) while the increasing computing power of edge devices enables the deployment of large-scale Deep N

Ryan Blair, Patricia Cahn, Alexandra Kjuchukova, Hannah Schwartz

An $n$-dimensional rep-tile is a compact, connected submanifold of $\mathbb{R}^n$ with non-empty interior which can be decomposed into pairwise isometric rescaled copies of itself whose interiors are disjoint. We show that every smooth compact $n$-dimensional submanifold of $\mathbb{R}^n$ with connected boundary is topologically isotopic to a polycube that t

Yusuke Morimoto

This paper introduces a new algorithmic execution model that integrates interbank limit and market orders with internal liquidity generated through market making. Based on the Cartea et al.\cite{cartea2015algorithmic} framework, we incorporate market impact in interbank orders while excluding it for internal market-making transactions. Our model aims to opti

Christopher Brix, Stanley Bak, Taylor T. Johnson, Haoze Wu

This report summarizes the 5th International Verification of Neural Networks Competition (VNN-COMP 2024), held as a part of the 7th International Symposium on AI Verification (SAIV), that was collocated with the 36th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of sta

Siyi Wang, Yanxiang Feng, Xiaoling Li, Guanghui Zhang

The distributed assembly flowshop scheduling problem (DAFSP) can be applied to immense manufacturing environments. In DAFSP, jobs are first processed in distributed flowshops, and then assembled into final products by an assembly machine, which usually has limited buffers in practical application. This limited capacity can lead to deadlocks, halting job comp

Ze Zhong, Yao Chen, Y. W. Ni, P. F. Chen

Over the past three decades, a lot of coronal fast-mode waves were detected by space missions, but their counterparts in the chromosphere, called the Moreton waves, were rarely captured. How this happens remains a mystery. Here, to shed light on this problem, we investigate the photospheric vector magnetograms of the Moreton wave events associated with M- an

Peixin Weng, Bingtao Li, Yiming Lyu, Song Shu

We investigate the properties of nucleons at finite temperature and density using a two-flavor quark meson model with Gaussian fluctuations that extend beyond the mean-field approximation. Our findings suggest that Gaussian fluctuations lead to a non-monotonic behavior of the nucleon mass as a function of temperature and density, which may play an important

Bingtao Li, Yiming Lyu, Song Shu, PeiXin Weng

We introduce the vector meson $\omega$ into the Quark Meson model, and study the impact of vector interactions on the properties of static hadrons using the mean-field approximation. The short-range repulsive force associated with vector interactions leads to an expansion of the root mean square radius of nucleons. While the mass of hadrons increases, the ga

Dongfang Zhao

Outsourced databases powered by fully homomorphic encryption (FHE) offer the promise of secure data processing on untrusted cloud servers. A crucial aspect of database functionality, and one that has remained challenging to integrate efficiently within FHE schemes, is the ability to perform comparisons on encrypted data. Such comparisons are fundamental for

Li Dong, Yubo Peng, Feibo Jiang, Kezhi Wang

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and

Haoyu Zheng, Wenqiao Zhang, Zheqi Lv, Yu Zhong

Diffusion-based text-to-image (T2I) models have demonstrated remarkable results in global video editing tasks. However, their focus is primarily on global video modifications, and achieving desired attribute-specific changes remains a challenging task, specifically in multi-attribute editing (MAE) in video. Contemporary video editing approaches either requir

Jifa Jiang, Xi Sheng, Yi Wang

The current series of papers is concerned with stochastic stability of monotone dynamical systems by identifying the basic dynamical units that can survive in the presence of noise interference. In the first of the series, for the cooperative and irreducible systems, we will establish the stochastic stability of a dynamical order, that is, the zero-noise lim

Di Jin, Xing Liu, Yu Liu, Jia Qing Yap

The rapid development of large language models (LLMs) and large vision models (LVMs) have propelled the evolution of multi-modal AI systems, which have demonstrated the remarkable potential for industrial applications by emulating human-like cognition. However, they also pose significant ethical challenges, including amplifying harmful content and reinforcin

Xiaoge Zhang, Tao Wang, Chao Yan, Fedaa Najdawi

Ensuring trustworthiness is fundamental to the development of artificial intelligence (AI) that is considered societally responsible, particularly in cancer diagnostics, where a misdiagnosis can have dire consequences. Current digital pathology AI models lack systematic solutions to address trustworthiness concerns arising from model limitations and data dis

Ekaterina Novozhilova, Jiacheng Huang, Le He, Ziling Li

This work investigates the causal mechanism behind the effect of chatbot personification and persuasion strategies on users' perceptions and donation likelihood. In a 2 (personified vs. non-personified chatbot) x 2 (emotional vs. logical persuasion strategy) between-subjects experiment (N=76), participants engaged with a chatbot that represented a non-profit

Jiseong Kim

In this paper, by assuming a zero-free region for Dirichlet L-functions, we show that almost all even integers $n$ in a short interval $[x,x+x^{2/3+\varepsilon}]$ with a missing digit are Goldbach numbers.

Jingjing Zhao, Quan Zhou, Xidong Mu, Kaiquan Cai

A novel movable-element enabled simultaneously transmitting and reflecting surface (ME-STARS) communication system is proposed, where ME-STARS elements positions can be adjusted to enhance the degress-of-freedom for transmission and reflection. For each ME-STARS operating protocols, namely energy-splitting (ES), mode switching (MS), and time switching (TS),

Yuxuan Song, Yong Zeng, Yuhang Yang, Zixiang Ren

Low-altitude unmanned aerial vehicles (UAVs) are expected to play an important role in future wireless networks, either as aerial base stations (BSs) or aerial users connected to the cellular network. In addition, integrated sensing and communication (ISAC) has been identified as one of the six usage scenarios for the forthcoming sixth-generation (6G) mobile

Nianli Peng, Yilin Wang

Mean Field Control Games (MFCGs) provide a powerful theoretical framework for analyzing systems of infinitely many interacting agents, blending elements from Mean Field Games (MFGs) and Mean Field Control (MFC). However, solving the coupled Hamilton-Jacobi-Bellman and Fokker-Planck equations that characterize MFCG equilibria remains a significant computation

Ivan Cheltsov, Maksym Fedorchuk, Kento Fujita, Anne-Sophie Kaloghiros

We find all K-polystable limits of divisors in $(\mathbb{P}^1)^4$ of degree $(1,1,1,1)$ and explicitly describe the associated irreducible component of the K-moduli space.

Emilio Gallicchio

We present the Alchemical Transfer with Coordinate Swapping (ATS) method to enable the calculation of the relative binding free energies between large congeneric ligands and single-point mutant peptides to protein receptors with the Alchemical Transfer Method (ATM) framework. Similarly to ATM, the new method implements the alchemical transformation as a coor

Guofang Shen, Zihao Bo, Wei Chen, Xun Chen

We present a novel constraint on light dark matter utilizing $1.54$ tonne$\cdot$year of data acquired from the PandaX-4T dual-phase xenon time projection chamber. This constraint is derived through detecting electronic recoil signals resulting from the interaction with solar-enhanced dark matter flux. Low-mass dark matter particles, lighter than a few MeV/$c

Go Takeuchi, Mikio Eto

Superconducting diode effect (SDE) is theoretically examined in double quantum dot coupled to three superconducting leads, $L$, $R1$ and $R2$. Lead $L$ is commonly connected to two quantum dots (QD1, QD2) while lead $R1$ ($R2$) is connected to QD1 (QD2) only. The phase differences $\varphi_{1}$ between leads $L$ and $R1$ and $\varphi_{2}$ between leads $L$ a

Pablo Perrella

We study the stability of pullback foliations under morphisms and rational maps via Grothendieck's Drapeaux scheme. In the local setting, a foliated version of Schlessinger's Theorem on rigidity of conical singularities was achieved. We apply these techniques to provide a criterion for the stability of algebraic leaves of a foliation.

Hui Pan, Yanxuan Yu, Jilun Ye, Xu Zhang

This study proposes a novel lightweight neural network model leveraging features extracted from electrocardiogram (ECG) and respiratory signals for early OSA screening. ECG signals are used to generate feature spectrograms to predict sleep stages, while respiratory signals are employed to detect sleep-related breathing abnormalities. By integrating these pre

Shrestha Mohanty, Sarah Xuan, Jacob Jobraeel, Anurag Kumar

We focus on enhancing comprehension in small-group recorded conversations, which serve as a medium to bring people together and provide a space for sharing personal stories and experiences on crucial social matters. One way to parse and convey information from these conversations is by sharing highlighted excerpts in subsequent conversations. This can help p

T. C. Son, N. T. Dung, P. T. P Thuy, T. M. Cuong

In this paper, we consider a class of the Caputo fractional stochastic differential equations of fractional order $\alpha \in (\frac{1}{2},1]$. Our aim is to analyze of the continuous dependence of solutions on the fractional order $\alpha.$ We first provide explicit estimates for the rate of weak convergence the solutions. We then describe the exact asympto

Zelin Meng, Zhichen Wang

Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are unavailable in many real-world scenarios, such as autonomous driving. In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various nois

Yu-Zheng Lin, Qinxuan Shi, Zhanglong Yang, Banafsheh Saber Latibari

Digital twin (DT) technology enables real-time simulation, prediction, and optimization of physical systems, but practical deployment faces challenges from high data requirements, proprietary data constraints, and limited adaptability to evolving conditions. This work introduces DDD-GenDT, a dynamic data-driven generative digital twin framework grounded in t

Seongoh Park, Seongjin Lee, Nguyen Thi Hai Yen, Nguyen Phuoc Long

One of the common challenges faced by researchers in recent data analysis is missing values. In the context of penalized linear regression, which has been extensively explored over several decades, missing values introduce bias and yield a non-positive definite covariance matrix of the covariates, rendering the least square loss function non-convex. In this

Neelam Shukla, Jeremy R Armstrong

We study a system of ultra cold dipolar Bose gas atoms confined in a two-dimensional (2D) harmonic trap with a dipolar impurity implanted at the center of the trap. Due to recent experimental progress in dipolar condensates, we focused on calculating properties of dipolar impurity systems that might guide experimentalists if they choose to study impurities i

Yifan Lu, K. Supriya, Shanna Shaked, Elizabeth H. Simmons

Inequities in student access to trigonometry and calculus are often associated with racial and socioeconomic privilege, and often influence introductory physics course performance. To mitigate these disparities in student preparedness, we developed a two-pronged intervention consisting of (1) incentivized supplemental math assignments and (2) AI-generated le

Anmolika Singh, Yuhang Diao

Effective item categorization is vital for businesses, enabling the transformation of unstructured datasets into organized categories that streamline inventory management. Despite its importance, item categorization remains highly subjective and lacks a uniform standard across industries and businesses. The United Nations Standard Products and Services Code

Davoud Mirzaei

These lecture notes focus on some numerical linear algebra algorithms in scientific computing. We assume that students are familiar with elementary linear algebra concepts such as vector spaces, systems of equations, matrices, norms, eigenvalues, and eigenvectors. In the numerical part, we do not pursue Gaussian elimination and other LU factorization algorit

Fernando Martinez-Lopez, Lesther Santana, Mohamed Rahouti

Network intrusion detection systems face significant challenges in identifying emerging attack patterns, especially when limited data samples are available. To address this, we propose a novel Multi-Space Prototypical Learning (MSPL) framework tailored for few-shot attack detection. The framework operates across multiple metric spaces-Euclidean, Cosine, Cheb

Davoud Mirzaei

Welcome to a beautiful subject in scientific computing: numerical solution of ordinary differential equations (ODEs) with initial conditions.

Maedeh Izadi, A. T. J. R. Cobbenhagen, Ruben Sommer, A. R. P. Andriën

In this paper, we present a novel cascade control structure with formal guarantees of uniform almost global asymptotic stability for the state tracking error dynamics of a quadcopter. The proposed approach features a model predictive control strategy for the outer loop, explicitly accounting for the non-zero total thrust constraint. The outer-loop controller

P. S. Kolesnikov, B. K. Sartayev

The classical Dong Lemma for distributions over a Lie algebra lies in the foundation of vertex algebras theory. In this paper, we find necessary and sufficient condition for a variety of nonassociative algebras with binary operations to satisfy the analogue of the Dong Lemma. In particular, it turns out that Novikov and Novikov--Poisson algebras satisfy the

Davoud Mirzaei

This lecture addresses some general ideas behind numerical computations ranging from representation of numbers in computers to stability and accuracy of standard algorithms for some simple mathematical problems.

Jonas Köppl, Nicolas Lanchier, Max Mercer

Interacting particle systems of interest in evolutionary game theory introduced in the probability literature consist of variants of the voter model in which each site is occupied by one player. The goal of this paper is to initiate the study of evolutionary games based more realistically on the multitype contact process in which each site is either empty or

Shengze Duan, Minh-Quy Pham, Donggeun Ryou

For $0 < \alpha \leq 1$, let $E$ be a compact subset of the $d$-dimensional moment curve in $\mathbb{R}^d$ such that $N(E,\varepsilon) \lesssim \varepsilon^{-\alpha}$ for $0 <\varepsilon <1$ where $N(E,\varepsilon)$ is the smallest number of $\varepsilon$-balls needed to cover $E$. We proved that if $f \in L^p(\mathbb{R}^d)$ with \begin{align*} 1 \leq p\leq

Lamya Benaddi, Charaf Ouaddi, Adnane Souha, Abdeslam Jakimi

A chatbot is an intelligent software application that automates conversations and engages users in natural language through messaging platforms. Leveraging artificial intelligence (AI), chatbots serve various functions, including customer service, information gathering, and casual conversation. Existing virtual assistant chatbots, such as ChatGPT and Gemini,

Abigail Flowers, Jackson H. O'Donnell, Tesla E. Jeltema, Vernon Wetzell

We model the total mass and galactic substructure in the strong lensing galaxy cluster MACS J0138.0-2155 using a combination of Chandra X-ray data, Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy, and Hubble Space Telescope imaging. MACS J0138.0-2155 lenses a source galaxy at $z=1.95$ which hosts two strongly lensed supernovae, Requiem and Encore. We f

A. J. Foraci, R. P. Woodard

We compute the 1-loop contribution to the graviton self-energy from a loop of massless fermions on a general cosmological background. The result is used to quantum-correct the linearized Einstein equation on de Sitter background and work out 1-loop corrections to gravitational radiation and to the response to a point mass. The renormalization group is employ

Chao Fan, Qipei Mei, Xiaonan Wang, Xinming Li

In the construction sector, workers often endure prolonged periods of high-intensity physical work and prolonged use of tools, resulting in injuries and illnesses primarily linked to postural ergonomic risks, a longstanding predominant health concern. To mitigate these risks, researchers have applied various technological methods to identify the ergonomic ri

Vicente Corral Arreola, Arturo Rodriguez, Vinod Kumar

Turbulence cascade has been modeled using various methods; the one we have used applies to a more exact representation of turbulence where people use the multifractal representation. The nature of the energy dissipation is usually governed by partial differential equations that have been described, such as Navier-Stokes Equations, although usually in climate

CMS Collaboration

A novel machine-learning-based flavor-tagging algorithm combining same-side and opposite-side tagging is used to obtain the equivalent of 27$\,$000 tagged B$^0_\mathrm{s}$ $\to$ J/$\psi\, \phi$(1020) decays from pp collisions at $\sqrt{s}$ $=$ 13 TeV, collected by the CMS experiment and corresponding to an integrated luminosity of 96.5 fb$^{-1}$. A time- and

Thor E. Andreassen, Donald R. Hume, Landon D. Hamilton, Stormy L. Hegg

Calibration to experimental data is vital when developing subject-specific models towards developing digital twins. Yet, to date, subject-specific models are largely based on cadaveric testing, as in vivo data to calibrate against has been difficult to obtain until recently. To support our overall goal of building subject-specific models of the living knee,

Trung Hau Hoang

Many applications like subseismic fault modeling, fractured reservoir modeling and interpretation/validation of fault connectivity involve the solution to an elliptic boundary value problem in a background medium perturbed by the presence of cracks that take the form of one or many pieces of surface (with boundary). When the background medium can be consider

Eric Hirsch, Christian Friedrich

Accurate tool wear prediction is essential for maintaining productivity and minimizing costs in machining. However, the complex nature of the tool wear process poses significant challenges to achieving reliable predictions. This study explores data-driven methods, in particular deep learning, for tool wear prediction. Traditional data-driven approaches often

Ludwik Dąbrowski, Yang Liu, Sugato Mukhopadhyay

We relate the recently defined spectral torsion with the algebraic torsion of noncommutative differential calculi on the example of the almost-commutative geometry of the product of a closed oriented Riemannian spin manifold $M$ with the two-point space $\mathcal Z_2$.

J. Carvalho, A. Le, P. Kicki, D. Koert

The performance of optimization-based robot motion planning algorithms is highly dependent on the initial solutions, commonly obtained by running a sampling-based planner to obtain a collision-free path. However, these methods can be slow in high-dimensional and complex scenes and produce non-smooth solutions. Given previously solved path-planning problems,

Olukorede Fakorede, Modeste Atsague, Jin Tian

Adversarial Training (AT) has been demonstrated to improve the robustness of deep neural networks (DNNs) against adversarial attacks. AT is a min-max optimization procedure where in adversarial examples are generated to train a more robust DNN. The inner maximization step of AT increases the losses of inputs with respect to their actual classes. The outer mi

Beyza E. Ortlek, Ozgur B. Akan

Molecular communication (MC) is a bio-inspired communication paradigm that utilizes molecules to transfer information and offers a robust framework for understanding biological signaling systems. This paper introduces a novel end-to-end MC framework for short-chain fatty acid (SCFA)-driven vagus nerve signaling within the gut-brain axis (GBA) to enhance our

Lukas Picek, Vojtěch Čermák, Marek Hanzl

This paper presents our submission to the COOOL competition, a novel benchmark for detecting and classifying out-of-label hazards in autonomous driving. Our approach integrates diverse methods across three core tasks: (i) driver reaction detection, (ii) hazard object identification, and (iii) hazard captioning. We propose kernel-based change point detection

Nicholas Wawrykow

How hard is it to program $n$ robots to move about a long narrow aisle while making a series of $r-2$ intermediate stops, provided only $w$ of the robots can fit across the width of the aisle? In this paper, we answer this question by calculating the $r^{\text{th}}$-sequential topological complexity of $\text{conf}(n,w)$, the ordered configuration space of $

James O'Keeffe

An active approach to fault tolerance, the combined processes of fault detection, diagnosis, and recovery, is essential for long term autonomy in robots -- particularly multi-robot systems and swarms. Previous efforts have primarily focussed on spontaneously occurring electro-mechanical failures in the sensors and actuators of a minority sub-population of ro

Alice Allen, Kimberly DuPrie

This presentation covered the benefits of registering astronomy research software with the Astrophysics Source Code Library (ASCL, ascl.net), a free online registry for software used in astronomy research. Indexed by ADS and Clarivate's Web of Science, the ASCL currently contains over 3600 codes, and its entries have been cited over 17,000 times. Registering

Crystianne L. De Andrade, Alexander A. Kiselev

Chemotaxis and reactions are fundamental processes in biology, often intricately intertwined. Chemotaxis, in particular, can be crucial in maintaining and accelerating a reaction. In this work, we extend the investigation initiated by kiselev et al. [17] by examining the impact of chemotactic attraction on reproduction and other processes in the context of a

Bhavana Jonnalagadda, Stephen Becker

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulat

Samuel J. Eschker, Chuanhai Liu

Strong artificial intelligence (AI) is envisioned to possess general cognitive abilities and scientific creativity comparable to human intelligence, encompassing both knowledge acquisition and problem-solving. While remarkable progress has been made in weak AI, the realization of strong AI remains a topic of intense debate and critical examination. In this p

Alfredo Rial, Ania M. Piotrowska, Harry Halpin

Anonymous communication relies on encrypted packet formats that resist traffic analysis and ensure unlinkability. Sphinx, the current standard for mixnets, provides strong anonymity but relies on classical public-key cryptography, making it vulnerable to quantum attacks. In this paper, we present Outfox, a simplified variant of Sphinx tailored for mixnets wi

Eugenia Ellis, Emanuel Rodríguez Cirone

Algebraic $kk$-theory, introduced by Corti\~nas and Thom, is a bivariant $K$-theory defined on the category $\mathrm{Alg}$ of algebras over a commutative unital ring $\ell$. It consists of a triangulated category $kk$ endowed with a functor from $\mathrm{Alg}$ to $kk$ that is the universal excisive, homotopy invariant and matrix-stable homology theory. Moreo

Christian H. Hannah, Nicholas C. Stone, Anil C. Seth, Sjoert van Velzen

Tidal disruption events (TDEs) are a class of transients that occur when a star is destroyed by the tides of a massive black hole (MBH). Their rates encode valuable MBH demographic information, but this can only be extracted if accurate TDE rate predictions are available for comparisons with observed rates. In this work, we present a new, observer-friendly P

Yenifer Angarita, Germán Chaparro, Stuart L. Lumsden, Catherine Walsh

Massive stars play a pivotal role in shaping their galactic surroundings due to their high luminosity and intense ionizing radiation. However, the precise mechanisms governing the formation of massive stars remain elusive. Complex organic molecules (COMs) offer an avenue for studying star formation across the low- to high-mass spectrum because COMs are found

Biaoshuai Tao, Chengkai Zhang, Houyu Zhou

Justified Representation (JR)/Extended Justified Representation (EJR) is a desirable axiom in multiwinner approval voting. In contrast to that (E)JR only requires at least \emph{one} voter to be represented in every cohesive group, we study its optimization version that maximizes the \emph{number} of represented voters in each group. Given an instance, we sa

Kamil Ł. Szydłowski, Jarosław A. Chudziak

This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolution of Transformer models, from their inception to their adaptation for time series analysis in financial contexts, is reviewed and discussed.

Clara Filosa, Marin Jovanovic, Lara Agostini, Anna Nosella

The landscape of digital servitization in the manufacturing sector is evolving, marked by a strategic shift from traditional product-centric to platform business models (BMs). Manufacturing firms often employ a blend of approaches to develop business-to-business (B2B) platforms, leading to significant reconfigurations in their BMs. However, they frequently e

Jorge P. Zubelli, Jennifer Loria, Vinicius V. L. Albani

The COVID-19 pandemic highlighted the need to improve the modeling, estimation, and prediction of how infectious diseases spread. SEIR-like models have been particularly successful in providing accurate short-term predictions. This study fills a notable literature gap by exploring the following question: Is it possible to incorporate a nonparametric suscepti

Michael J. Collins

Gosper developed an algorithm for performing arithmetic operations on continued fractions (CFs), getting a CF as the result. Straightforward implementation of the algorithm leads to infinite loops on some inputs. Here we present a modified version of the arithmetic algorithm and prove that it avoids all difficulties with infinite loops. We then combine CF ar

Adamu Gaston Philipo, Doreen Sebastian Sarwatt, Jianguo Ding, Mahmoud Daneshmand

Cyberbullying significantly contributes to mental health issues in communities by negatively impacting the psychology of victims. It is a prevalent problem on social media platforms, necessitating effective, real-time detection and monitoring systems to identify harmful messages. However, current cyberbullying detection systems face challenges related to per

Shengyu Chen, Peyman Givi, Can Zheng, Xiaowei Jia

The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-efficient manufacturing. Within the realm of simulating turbulent flows, large eddy simulation (LES) has emerged as a prevalent alternative to direct numerical simulation (DNS), offe

Jiaqing Yuan, Pradeep K. Murukannaiah, Munindar P. Singh

An ethical dilemma describes a choice between two "right" options involving conflicting moral values. We present a comprehensive evaluation of how LLMs navigate ethical dilemmas. Specifically, we investigate LLMs on their (1) sensitivity in comprehending ethical dilemmas, (2) consistency in moral value choice, (3) consideration of consequences, and (4) abili

Ze Yang, Yihong Jin, Xinhe Xu

Large Language Models (LLMs) have revolutionized natural language processing by understanding and generating human-like text. However, the increasing demand for more sophisticated LLMs presents significant computational challenges due to their scale and complexity. This paper introduces Hardware Accelerated Decoding (HADES), a novel approach to enhance the p

Tobias Strauch

With increasing aging problems of advanced technologies, in-field testing becomes an inevitable challenge, on top of the already demanding requirements, such as the ISO26262 for automotive safety. SOCs used in space, automotive or military applications in particular are worst affected as the in-field failures in these applications could even be life threaten

G. D. Alexeev, M. G. Alexeev, C. Alice, A. Amoroso

A new and detailed measurement of the cross section for hard exclusive neutral-pion muoproduction on the proton was performed in a wide kinematic region, with the photon virtuality $Q^2$ ranging from 1 to 8 (GeV/$c$)$^{\rm\, 2}$ and the Bjorken variable $x_{\rm Bj}$ ranging from 0.02 to 0.45. The data were collected at COMPASS at CERN using 160 GeV/$c$ longi

Jacek Dziubański

Let $L=-\Delta + V(x)$ be a Schr\"odinger operator on $\mathbb R^d$, where $V(x)\geq 0$, $V\in L^2_{\rm loc} (\mathbb R^d)$. We give a short proof of dimension free $L^p(\mathbb R^d)$ estimates, $1<p\leq 2$, for the vector of the Riesz transforms $$\big(\frac{\partial}{\partial x_1}L^{-1/2}, \frac{\partial}{\partial x_2}L^{-1/2},\dots,\frac{\partial}{\partia

Ivan Gatica, Alfonso Bustamente

In this paper we present a brief study of the $\sigma$-set-$\sigma$-antiset duality that occurs in $\sigma$-set theory and we also present the development of the integer space $3^{A}=\left\langle 2^{A}, 2^{A^{-}} \right\rangle$ for the cardinals $|A|=2,3$ together with its algebraic properties. In this article, we also develop a presentation of some of the p

Artem Chernikov, Nadja Hempel

We develop some model theory of multi-linear forms, generalizing Granger in the bi-linear case. In particular, after proving a quantifier elimination result, we show that for an NIP field K, the theory of infinite dimensional non-degenerate alternating n-linear spaces over K is strictly n-dependent; and it is NSOP1 if K is. This relies on a new Composition L

Mateusz Michalkiewicz, Sheena Bai, Mahsa Baktashmotlagh, Varun Jampani

In this paper, we analyze the viewpoint stability of foundational models - specifically, their sensitivity to changes in viewpoint- and define instability as significant feature variations resulting from minor changes in viewing angle, leading to generalization gaps in 3D reasoning tasks. We investigate nine foundational models, focusing on their responses t

Austin Kaczmarek, Andrea Capa Salinas, Stephen D. Wilson, Katja C. Nowack

We report measurements of the local temperature-dependent penetration depth, $\lambda(T)$, in the Kagome superconductors AV$_3$Sb$_5$ (A = Cs, K, Rb) using scanning superconducting quantum interference device (SQUID) microscopy. Our results suggest that the superconducting order in all three compounds is fully gapped, in contrast to reports of nodal supercon

Enze Xie, Jiaho Lyu, Daiqing Wu, Huawen Shen

The recent emergence of the Segment Anything Model (SAM) enables various domain-specific segmentation tasks to be tackled cost-effectively by using bounding boxes as prompts. However, in scene text segmentation, SAM can not achieve desirable performance. The word-level bounding box as prompts is too coarse for characters, while the character-level bounding b

Egor Shulgin, Peter Richtárik

Stochastic Gradient Descent (SGD) with gradient clipping is a powerful technique for enabling differentially private optimization. Although prior works extensively investigated clipping with a constant threshold, private training remains highly sensitive to threshold selection, which can be expensive or even infeasible to tune. This sensitivity motivates the

Wenxiu Xu, Saba Ghorbani Bazegar, Dong Sheng, Manuel Toledo-Hernandez

Cocoa is a multi-billion-dollar industry but research on improving yields through pollination remains limited. New embedded hardware and AI-based data analysis is advancing information on cocoa flower visitors, their identity and implications for yields. We present the first cocoa flower visitor dataset containing 5,792 images of Ceratopogonidae, Formicidae,

Amit Acharya

A scheme for treating the Second Law of thermodynamics as a constraint and accounting for the approximate nature of constitutive assumptions in continuum thermomechanics is discussed. An unconstrained, concave, variational principle is designed for solving the resulting mathematical problem. Cases when the Second Law becomes an over-constraint on the mechani

Ningning Xu, Jidong J. Yang

Image deraining holds great potential for enhancing the vision of autonomous vehicles in rainy conditions, contributing to safer driving. Previous works have primarily focused on employing a single network architecture to generate derained images. However, they often fail to fully exploit the rich prior knowledge embedded in the scenes. Particularly, most me

Richard Lang, Nicolás Sanhueza-Matamala

We show that every graph $G$ on $n$ vertices with $\delta(G) \geq (1/2+\varepsilon)n$ is spanned by a complete blow-up of a cycle with clusters of nearly uniform size $\Omega(\log n)$. The proof is based on a recently introduced approach for finding vertex-spanning substructures via blow-up covers.

Ahmed Abdeljawad, Thomas Dittrich

Operator learning is a recent development in the simulation of Partial Differential Equations (PDEs) by means of neural networks. The idea behind this approach is to learn the behavior of an operator, such that the resulting neural network is an (approximate) mapping in infinite-dimensional spaces that is capable of (approximately) simulating the solution op

Bruno El-Bennich

The gauge covariance of the quark gap equation is compared for three quark-gluon vertices: the bare vertex, a Ball-Chiu like vertex constrained by the corresponding Slavnov-Taylor identity, and a full vertex including the transverse components derived from transverse Slavnov-Taylor identities. The covariance properties are verified with the chiral quark cond

M. C. Araújo, J. G. Lima, J. Furtado, T. Mariz

In this paper, we study a Bhabha-like scattering in a massive Rarita-Schwinger model at finite temperature. The analysis is conducted at the tree level and addresses temperature effects through the thermofield dynamics formalism. We consider the usual fermion-antifermion into fermion-antifermion scattering and compute the cross-section in order to investigat