February 2024 arXiv papers — page 90

Mingchen Li, Chen Ling, Rui Zhang, Liang Zhao

Zero-shot link prediction (ZSLP) on knowledge graphs aims at automatically identifying relations between given entities. Existing methods primarily employ auxiliary information to predict tail entity given head entity and its relation, yet face challenges due to the occasional unavailability of such detailed information and the inherent simplicity of predict

Timo Birr, Christoph Pohl, Abdelrahman Younes, Tamim Asfour

Recent advances in task planning leverage Large Language Models (LLMs) to improve generalizability by combining such models with classical planning algorithms to address their inherent limitations in reasoning capabilities. However, these approaches face the challenge of dynamically capturing the initial state of the task planning problem. To alleviate this

Lakmal Silva, Michael Unterkalmsteiner, Krzysztof Wnuk

Background: Bugs and bug management consumes a significant amount of time and effort from software development organizations. A reduction in bugs can significantly improve the capacity for new feature development. Aims: We categorize and visualize dimensions of bug reports to identify accruing technical debt. This evidence can serve practitioners and decisio

H. Fabelo, S. Ortega, A. Szolna, D. Bulters

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of

Janka Biznárová, J. C. Rivera Hernández, Daniel Forchheimer, Jonas Bylander

Two-level system (TLS) loss is typically limiting the coherence of superconducting quantum circuits. The loss induced by TLS defects is nonlinear, resulting in quality factors with a strong dependence on the circulating microwave power. We observe frequency mixing due to this nonlinearity by applying a two-tone drive to a coplanar waveguide resonator and mea

Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko

Error Feedback (EF) is a highly popular and immensely effective mechanism for fixing convergence issues which arise in distributed training methods (such as distributed GD or SGD) when these are enhanced with greedy communication compression techniques such as TopK. While EF was proposed almost a decade ago (Seide et al., 2014), and despite concentrated effo

Nazanin Bayati Chaleshtari, Yoann Marquer, Fabrizio Pastore, Lionel C. Briand

Although the security testing of Web systems can be automated by generating crafted inputs, solutions to automate the test oracle, i.e., vulnerability detection, remain difficult to apply in practice. Specifically, though previous work has demonstrated the potential of metamorphic testing, security failures can be determined by metamorphic relations that tur

Hariram Veeramani, Surendrabikram Thapa, Usman Naseem

In the evolving landscape of Environmental, Social, and Corporate Governance (ESG) impact assessment, the ML-ESG-2 shared task proposes identifying ESG impact types. To address this challenge, we present a comprehensive system leveraging ensemble learning techniques, capitalizing on early and late fusion approaches. Our approach employs four distinct models:

Albert Chua, Yang Yang

Let $\mathcal{M}$ be a compact, smooth, $n$-dimensional Riemannian manifold without boundary. In this paper, we generalize nonwindowed geometric scattering transforms, which we formulate as $\mathbf{L}^q(\mathcal{M})$ norms of a cascade of geometric wavelet transforms and modulus operators. We then provide weighted measures for these operators, prove that th

Ehsan Doostmohammadi, Oskar Holmström, Marco Kuhlmann

Work on instruction-tuned Large Language Models (LLMs) has used automatic methods based on text overlap and LLM judgments as cost-effective alternatives to human evaluation. In this paper, we perform a meta-evaluation of such methods and assess their reliability across a broad range of tasks. In evaluating how well automatic methods align with human evaluati

Yongqi Li, Zhen Zhang, Wenjie Wang, Liqiang Nie

Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generative language models, distinct from traditional sparse or dense retrieval methods. In this work, we identify a viable direction to further enhance generative retrieval via di

Hao Liu, Suresh P. Sethi, Tak Kwong Wong, Sheung Chi Phillip Yam

We extend the work on optimal investment and consumption of a population considered in [2] to a general stochastic setting over a finite time horizon. We incorporate the Cobb-Douglas production function in the capital dynamics while the consumption utility function and the drift rate in the population dynamics can be general, in contrast with [2, 30, 31]. Th

Greg Wilson, Jorge Aranda, Michael Hoye, Brittany Johnson

We have been trying to get software engineering researchers and practitioners to talk to one another for over a decade. This paper describes what we have done, assesses our impact, and recommends an approach that we hope will have greater success.

Dhairya Dalal, Marco Valentino, André Freitas, Paul Buitelaar

While Large Language Models (LLMs) have found success in real-world applications, their underlying explanatory process is still poorly understood. This paper proposes IBE-Eval, a framework inspired by philosophical accounts on Inference to the Best Explanation (IBE) to advance the interpretation and evaluation of LLMs' explanations. IBE-Eval estimates the pl

James Eills, Morgan W. Mitchell, Irene Marco Rius, Michael C. D. Tayler

Hyperpolarized nuclear spins in molecules exhibit high magnetization that is unachievable by classical polarization techniques, making them widely used as sensors in physics, chemistry, and medicine. The state of a hyperpolarized material, however, is typically only studied indirectly and with partial destruction of magnetization, due to the nature of conven

Linh Le Pham Van, Hung The Tran, Sunil Gupta

Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In many real-world scenarios, this approach is not practical due to the high costs of data collection and safety concerns. As a result, a common strategy is to transfer a policy traine

Santiago Barbieri, Gerard Farré

We prove that the solutions of H\"older-differentiable Hamiltonian systems, associated to initial conditions in a small ball of radius $\rho>0$ around a Lagrangian, $(\gamma,\tau)-$Diophantine, quasi-periodic torus, are stable over a time $t^{\text{stab}}\simeq 1/(|\rho|^{1+\frac{\ell-1}{\tau+1}}|\ln \rho|^{\ell-1})$, where $\ell>2d+1, \ell \in \mathbb R$, i

Maria Arias, Ping Zhou, Alexandros Chiotellis, Carlos De Breuck

Supernova remnants (SNRs) are profoundly affected by their ambient medium. We present carbon monoxide (CO) observations around two mixed morphology SNRs, VRO 42.05.01 and G 350.0-2.0, that look remarkably similar in continuum radio emission, showing what we refer to as a shell and wing shape. It has been proposed that the shell and wing shape is the result o

Christos Fragkathoulas, Vasiliki Papanikou, Danae Pla Karidi, Evaggelia Pitoura

Algorithmic fairness and explainability are foundational elements for achieving responsible AI. In this paper, we focus on their interplay, a research area that is recently receiving increasing attention. To this end, we first present two comprehensive taxonomies, each representing one of the two complementary fields of study: fairness and explanations. Then

Sambatra Andrianomena, Sultan Hassan, Francisco Villaescusa-Navarro

We demonstrate the use of deep network to learn the distribution of data from state-of-the-art hydrodynamic simulations of the CAMELS project. To this end, we train a generative adversarial network to generate images composed of three different channels that represent gas density (Mgas), neutral hydrogen density (HI), and magnetic field amplitudes (B). We co

Benjamin Sullivan, Jingjing Jiang, Georgios Mavros, Wen-Hua Chen

Autonomous Emergency Braking (AEB) potentially brings significant improvements in automotive safety due to its ability to autonomously prevent collisions in situations where the driver may not be able to do so. Driven by the poor performance of the state of the art in recent testing, this work provides an online solution to identify critical parameters such

Jingyi Gu, Wenlu Du, Guiling Wang

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propo

Athanasios Beslikas

In this note we study the problem of determining the holomorphic self maps of the unit disc that induce a bounded composition operator on Dirichlet-type spaces. We find a class of symbols $\varphi$ that induce a bounded composition operator on the Dirichlet-type spaces, by applying results of the multidimensional theory of composition operators for the weigh

Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus

Building upon score-based learning, new interest in stochastic localization techniques has recently emerged. In these models, one seeks to noise a sample from the data distribution through a stochastic process, called observation process, and progressively learns a denoiser associated to this dynamics. Apart from specific applications, the use of stochastic

Georgios Andreadis, Tanja Alderliesten, Peter A. N. Bosman

For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued GOMEA (RV-GOMEA) that uses a linkage model to capture dependencie

Xinyu Wang, Kang Chen, Lei Liu, Tao Han

Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect the causal relationships between these physical variables, failing to fully capture the spatial and temporal patterns required for intensity

Siamak Ghodsi, Seyed Amjad Seyedi, Eirini Ntoutsi

Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability. To address these is

John Ellis

"If one could ever prove the existence of gravitational waves, the processes responsible for their generation would probably be much more curious and interesting than even the waves themselves." (Gustav Mie, 1868 - 1957) The discovery of gravitational waves has opened new windows on astrophysics, cosmology and physics beyond the Standard Model (BSM). Measure

Chengpeng Wang, Wuqi Zhang, Zian Su, Xiangzhe Xu

Dataflow analysis is a fundamental code analysis technique that identifies dependencies between program values. Traditional approaches typically necessitate successful compilation and expert customization, hindering their applicability and usability for analyzing uncompilable programs with evolving analysis needs in real-world scenarios. This paper presents

Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang

Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes leveraging tools to augment LLMs, it frequently neglects emerging safety considerations tied to their application. To fill this gap, we present *ToolSword*, a comprehensive framework de

Noreen Anwar, Guillaume-Alexandre Bilodeau, Wassim Bouachir

Consecutive frames in a video contain redundancy, but they may also contain relevant complementary information for the detection task. The objective of our work is to leverage this complementary information to improve detection. Therefore, we propose a spatio-temporal fusion framework (STF). We first introduce multi-frame and single-frame attention modules t

Aven Le Zhou, Lei Xi, Kang Zhang

In Flusserian view of media history, humans often misperceive the world projected by media to be the world itself, leading to a loss of freedom. This paper examines Flusserian Freedom in the context of Mixed Reality (MR) and explores how humans can recognize the obscuration of the world within the media (i.e., MR) and understand their relationship. The autho

Massimo Bartoletti, Fabio Fioravanti, Giulia Matricardi, Roberto Pettinau

Formal verification of smart contracts has become a hot topic in academic and industrial research, given the growing value of assets managed by decentralized applications and the consequent incentive for adversaries to tamper with them. Most of the current research on the verification of contracts revolves around Solidity, the main high-level language suppor

Jérémie Vidal, Yves Colin de Verdière

Pancake-like vortices are often generated by turbulence in geophysical flows. Here, we study the inertia-gravity oscillations that can exist within such geophysical vortices, due to the combined action of rotation and gravity. We consider a fluid enclosed within a triaxial ellipsoid, which is stratified in density with a constant Brunt-V\"ais\"al\"a frequenc

Paola Busia, Matteo Antonio Scrugli, Victor Jean-Baptiste Jung, Luca Benini

Wearable systems for the continuous and real-time monitoring of cardiovascular diseases are becoming widespread and valuable assets in diagnosis and therapy. A promising approach for real-time analysis of the electrocardiographic (ECG) signal and the detection of heart conditions, such as arrhythmia, is represented by the transformer machine learning model.

Peter Pavlík, Martin Výboh, Anna Bou Ezzeddine, Viera Rozinajová

This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge. We propose LUPIN, a Lagrangian Double U-Net for Physics-Informed Nowcasting, that draws from existing extrapolation-based nowcasting methods. It consists of a U-Net that dynamically produces mesoscal

K. Mouloudakis, V. Koutrouli, I. K. Kominis, M. W. Mitchell

Present protocols for obtaining the ultimate magnetic sensitivity of optically pumped magnetometers (OPMs) utilizing alkali-metal ensembles rely on uncorrelated atoms in stretched states. A new approach for calculating the spin projection noise (SPN)-limited signal to noise ratio (SNR) and the magnetic sensitivity of OPMs is proposed. Our model is based sole

Erik Høg

In 1953 I heard of an experiment in 1925 by Bengt Str\"omgren where he observed transit times with the meridian circle at the Copenhagen University Observatory measuring the current in a photocell behind slits when a star was crossing. In 1954 just 22 years old I was given the task as a student to make first test observations with a new meridian circle of th

Sreraman Muralidharan

Distributed quantum computing (DQC) provides a way to scale quantum computers using multiple quantum processing units (QPU) connected through quantum communication links. In this paper, we have built a distributed quantum computing simulator and used the simulator to investigate quantum algorithms such as the quantum Fourier transform, quantum phase estimati

Lukas Ramlow, Benjamin Lindner

Stochastic transitions between discrete microscopic states play an important role in many physical and biological systems. Often, these transitions lead to fluctuations on a macroscopic scale. A classic example from neuroscience is the stochastic opening and closing of ion channels and the resulting fluctuations in membrane current. When the microscopic tran

Pengcheng Jiang, Jiacheng Lin, Zifeng Wang, Jimeng Sun

The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). However, we discovered that traditional relation extraction (RE) metrics like precision and recall fall short in evaluating GRE methods. This shortfall arises because these metrics re

T. J. J. M. van Overveld, W. G. Ellenbroek, J. M. Meijer, H. J. H. Clercx

The self-organization of clusters of particles is a fundamental phenomenon across various physical systems, including hydrodynamic and colloidal systems. One example is that of dense spherical particles submerged in a fluid and subjected to horizontal oscillations. The interaction of the particles with the oscillating flow leads to the formation of one-parti

Hanqing Zhao, Yuehan Li

Entity and relationship extraction is a crucial component in natural language processing tasks such as knowledge graph construction, question answering system design, and semantic analysis. Most of the information of the Yishui school of traditional Chinese Medicine (TCM) is stored in the form of unstructured classical Chinese text. The key information extra

Atsushi Taguchi, Yamato Fukui, Keiji Sasaki

We explore chiroptical phenomena in 3D chiral nano-gap antennas using topology optimization. The characteristic helical geometries of the topology-designed antennas exhibit giant chiral dissymmetry (g=-1.70) considering the gap intensity, circular-to-linear polarization conversion, and circularly polarized light emission from a linear dipole coupled with the

Laurenţiu Bubuianu, Douglas Singleton, Sergiu I. Vacaru, Elşen Veli Veliev

This article consists of an introduction to the theory of nonassociative geometric classical and quantum information flows defined by star products with R-flux deformations in string gravity. Corresponding nonassociative generalizations of the concepts of classical Shannon entropy, quantum von Neumann entropy, R\'{e}nyi entropy are formulated. The fundamenta

Wensi Wu, Mitchell Daneker, Kevin T. Turner, Matthew A. Jolley

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently,

Qinxun Li, Martin Kilbinger, Wentao Luo, Kai Wang

This letter presents, for the first time, direct constraints on the black-hole-to-halo-mass relation using weak gravitational lensing measurements. We construct type I and type II Active Galactic Nuclei (AGNs) samples from the Sloan Digital Sky Survey (SDSS), with a mean redshift of 0.4 0.1 for type I (type II) AGNs. This sample is cross-correlated with weak

Dingkang Liang, Xin Zhou, Wei Xu, Xingkui Zhu

Transformers have become one of the foundational architectures in point cloud analysis tasks due to their excellent global modeling ability. However, the attention mechanism has quadratic complexity, making the design of a linear complexity method with global modeling appealing. In this paper, we propose PointMamba, transferring the success of Mamba, a recen

Yinpeng Liu, Jiawei Liu, Xiang Shi, Qikai Cheng

Demonstration ordering, which is an important strategy for in-context learning (ICL), can significantly affects the performance of large language models (LLMs). However, most of the current approaches of ordering require high computational costs to introduce the priori knowledge. In this paper, inspired by the human learning process, we propose a simple but

Stephen D. Cohen

Let $\F$ be the finite field of odd prime power order $q$, We find explicit expressions for the number of triples $\{\al-1,\al,\al+1 \}$ of consecutive non-zero squares in $\F$ and similarly for the number of triples of consecutive non-square elements. A key ingredient is the evaluation of Jacobsthal sums over general finite fields by Katre and Rajwade. This

Christian Le Merdy

Let $X$ be a Banach space, let $(\Omega,\mu)$ be a $\sigma$-finite measure space and let $A,B\colon\Omega\to B(X)$ be strongly measurable $\gamma$-bounded functions. We show that for all $x\in X$ and all $x^*\in X^*$, there exist a Hilbert space $K$ and two measurable functions $a_1\in L^\infty(\Omega;K)$ and $a_2\in L^\infty(\Omega;K)$ such that $\langle B(

John Dougrez-Lewis, Mahmud Elahi Akhter, Federico Ruggeri, Sebastian Löbbers

Although LLMs have shown great performance on Mathematics and Coding related reasoning tasks, the reasoning capabilities of LLMs regarding other forms of reasoning are still an open problem. Here, we examine the issue of reasoning from the perspective of claim verification. We propose a framework designed to break down any claim paired with evidence into ato

Huili Hou, David Dlaka, Jon Pugh, Ruth Oulton

Quantum and classical telecommunications require efficient sources of light. Semiconductorsources, owing to the high refractive index of the medium, often exploit photonic cavities to enhance the external emission of photons into a well-defined optical mode. Optical Tamm States (OTS), in which light is confined between a distributed Bragg reflector and a thi

Zhihong Ye, Haojie Zhang, Yaopeng Zhang, Haocen Zhao

This article explores the significant advancements in Short-Range Correlation (SRC) research enabled by the latest Chinese nuclear physics facilities- CSR at HIRFL, HIAF, SHINE, and the upcoming EicC. These facilities introduce cutting-edge technologies and methodologies, addressing existing challenges and broadening the scope for SRC studies. By providing d

Jesper Lykke Jacobsen, Kay Joerg Wiese

The two-dimensional $Q$-state Potts model with real couplings has a first-order transition for $Q>4$. We study a loop-model realization in which $Q$ is a continuous parameter. This model allows for the collision of a critical and a tricritical fixed point at $Q=4$, which then emerge as complex conformally invariant theories at $Q>4$, or even complex $Q$, for

J. van Marrewijk, T. W. Morris, T. Mroczkowski, C. Cicone

Millimeter-wave single-dish telescopes offer two key advantages compared to interferometers: they can efficiently map larger portions of the sky, and they can recover larger spatial scales. Nonetheless, fluctuations in the atmosphere limit the accurate retrieval of signals from astronomical sources. To efficiently reduce atmospheric noise and filtering effec

Otavio A. D. Molitor, Łukasz Rudnicki

In recent years many works have explored possible advantages of indefinite causal order, with the main focus on its controlled implementation known as quantum switch. In this paper, we tackle advantages in quantum thermodynamics, studying whether quantum switch is capable of activating a passive state: either alone or with extra resources (active control sta

Viswa Narayanan Sankaranarayanan, Akshit Saradagi, Sumeet Satpute, George Nikolakopoulos

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the as

Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo, Qianye Yang

For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupervised losses are unavailable or ineffective. This correspondence-informing supervision entails cost in annota

Nouar AlDahoul, Talal Rahwan, Yasir Zaki

A quarter of US adults regularly get their news from YouTube. Yet, despite the massive political content available on the platform, to date no classifier has been proposed to identify the political leaning of YouTube videos. To fill this gap, we propose a novel classifier based on Bert -- a language model from Google -- to classify YouTube videos merely base

Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč, Éric Moulines

There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwise risk into components, associated with different sources of predictive uncertainty, namely aleatoric uncertainty (inherent data variability) and epistemic uncertainty (model-rela

Tomáš Balyo, Martin Suda, Lukáš Chrpa, Dominik Šafránek

Existing planning action domain model acquisition approaches consider different types of state traces from which they learn. The differences in state traces refer to the level of observability of state changes (from full to none) and whether the observations have some noise (the state changes might be inaccurately logged). However, to the best of our knowled

Slavomír Švancár, Lukáš Chrpa, Filip Dvořák, Tomáš Balyo

The global food delivery market provides many opportunities for AI-based services that can improve the efficiency of feeding the world. This paper presents the Cloud Kitchen platform as a decision-making tool for restaurants with food delivery and a simulator to evaluate the impact of the decisions. The platform contains a Technology-Specific Bridge (TSB) th

Henning Schwarz, Micha Überrück, Jens-Peter M. Zemke, Thomas Rung

We present approaches to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstruction of the spatial loads using a convolutional autoencoder (CAE) and the transient evolution of these loads in a subsequent part. Different CAE strategies are assessed and combined

Alireza Javanmardi, David Stutz, Eyke Hüllermeier

Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attracted attention as an appealing formalism for uncertainty representation, in particular due to their ability to represent both the aleatoric and epistemic uncertainty in a predicti

Tomáš Balyo, G. Michael Youngblood, Filip Dvořák, Lukáš Chrpa

In this paper, we propose a method and workflow for automating regression testing of certain video game aspects using automated planning and incremental action model learning techniques. The basic idea is to use detailed game logs and incremental action model learning techniques to maintain a formal model in the planning domain description language (PDDL) of

Sándor Frey, Judit Fogasy, Krisztina Perger, Kateryna Kulish

The prominent radio quasar PKS 2215+020 (J2217+0220) was once labelled as a new laboratory for core--jet physics at redshift z=3.572 because of its exceptionally extended jet structure traceable with very long baseline interferometric (VLBI) observations up to a ~600 pc projected distance from the compact core and a hint of an arcsec-scale radio and an X-ray

J. Butterworth, S. Viti, P. P. Van der Werf, J. G. Mangum

Determining the evolution of the CNO isotopes in the interstellar medium (ISM) of starburst galaxies can yield important constraints on the ages of superstar clusters (SSCs), or on other aspects and contributing factors of their evolution. Due to the time-dependent nature of the abundances of isotopes within the ISM as they are supplied from processes such a

Holger Lyre

Do LLMs understand the meaning of the texts they generate? Do they possess a semantic grounding? And how could we understand whether and what they understand? I start the paper with the observation that we have recently witnessed a generative turn in AI, since generative models, including LLMs, are key for self-supervised learning. To assess the question of

Khanh Ngoc Pham, Marcin Modrzejewski, Jiří Klimeš

The relative energies of different phases or polymorphs of molecular solids can be small, less than a kiloJoule/mol. Reliable description of such energy differences requires high quality treatment of electron correlations, typically beyond that achievable by routinely applicable density functional theory approximations (DFT). At the same time, high-level wav

Tommaso Cortopassi

We consider the problem of proving uniqueness of the solution of the continuity equation with a vector field $u \in [L^1 (0,T; W^{1,p}(\mathbb{T}^d)) \cap L^\infty ((0,T) \times \mathbb{T}^d)]^d$ with $\operatorname{div}(u) ^- \in L^1 (0,T; L^\infty (\mathbb{T}^d))$ and an initial datum $\rho_0 \in L^q (\mathbb{T}^d)$, where $\mathbb{T}^d$ is the $d$-dimensi

Daniel Alpay, Ilwoo Cho

We define the corresponding Hardy space, Schur multipliers and their realizations, and interpolation. Possible applications of the present work include matrices of quaternions, matrices of split quaternions, and other algebras of hypercomplex numbers.

Raktim Kumar Mondol, Ewan K. A. Millar, Arcot Sowmya, Erik Meijering

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holisti

Piotr B. Mucha, Maja Szlenk, Ewelina Zatorska

We analyze the pressureless Navier-Stokes system with nonlocal attraction-repulsion forces. Such systems appear in the context of models of collective behavior. We prove the existence of weak solutions on the whole space $\mathbb{R}^3$ in the case of density-dependent degenerate viscosity. For the nonlocal term it is assumed that the interaction kernel has t

Bettina Kozák, Sándor Frey, Krisztina Éva Gabányi

Narrow-line Seyfert 1 (NLS1) galaxies are a peculiar subclass of active galactic nuclei (AGN). Among them, TXS 1206+549 belongs to a small group of radio-loud and gamma-ray-emitting NLS1 galaxies. We focus on the radio properties of this galaxy by analysing archival, high-resolution, very long baseline interferometry (VLBI) imaging observations taken at 8 GH

Ralf-Dieter Scholz

Hypervelocity stars (HVSs) unbound to the Galaxy can be formed with extreme stellar interactions. Observational evidence comes from measurements of radial velocities (RVs) of objects crossing the Galactic halo and of tangential velocities based on high proper motions (HPMs) and distances of relatively nearby stars. I searched for new HVS candidates and revie

Serhii Kondratov

An approach to modeling the oligomer composition distribution function in the irreversible step growth homopolymerization process based on a mixture of oligomers of arbitrary composition is developed. The approach is based on consideration of probabilities of processes in the system, proceeding on the principle of P. Flory and obtaining on this basis an infi

Atsuki Yamaguchi, Aline Villavicencio, Nikolaos Aletras

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorates when generating text in languages other than English. This

Ruixuan Liu, Kangle Deng, Ziwei Wang, Changliu Liu

Structural stability is a necessary condition for successful construction of an assembly. However, designing a stable assembly requires a non-trivial effort since a slight variation in the design could significantly affect the structural stability. To address the challenge, this paper studies the stability of assembly structures, in particular, block stackin

Diptarka Hait, Dean Lahana, O. Jonathan Fajen, Amiel S. P. Paz

Simulations of photochemical reaction dynamics have been a challenge to the theoretical chemistry community for some time. In an effort to determine the predictive character of current approaches, we predict the results of an upcoming ultrafast diffraction experiment on the photodynamics of cyclobutanone after excitation to the lowest lying Rydberg state (S$

Kate Attard, Alessia Gualandris, Justin I. Read, Walter Dehnen

The coalescence of the most massive black hole (MBH) binaries releases gravitational waves (GWs) within the detectable frequency range of Pulsar Timing Arrays (PTAs) $(10^{-9} - 10^{-6})$ Hz. The incoherent superposition of GWs from MBH mergers, the stochastic Gravitational Wave Background (GWB), can provide unique information on MBH parameters and the large

Hendrik Kleikamp

In this contribution we apply an adaptive model hierarchy, consisting of a full-order model, a reduced basis reduced order model, and a machine learning surrogate, to parametrized linear-quadratic optimal control problems. The involved reduced order models are constructed adaptively and are called in such a way that the model hierarchy returns an approximate

Santiago Barbieri

The steepness property is a local geometric transversality condition on the gradient of a $C^2$-function which is fundamental in order to ensure the stability of sufficiently-regular nearly-integrable Hamiltonian systems over long timespans. Steep functions were originally introduced by Nekhoroshev, who also proved their genericity. Namely, given a pair of p

Martin Teuscher, Aurélien Barrau, Killian Martineau

We examine the main properties of gravitational waves (GWs) emitted by transient hyperbolic encounters of black holes. We begin by building the set of basic variables most relevant to setting our problem. After exposing the ranges of masses and eccentricities accessible at a given GW frequency, we analyze the dependence of the gravitational strain on those p

Yiwen Sun, Furong Ye, Xianyin Zhang, Shiyu Huang

Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance. Modern CDCL solvers, such as MiniSat and Kissat, commonly incorporate several heuristics and select one to use according to simple rules,

Andrew D. Bragg, Stephen M. de Bruyn Kops

In an important study, Maffioli et al. (J. Fluid Mech., Vol. 794 , 2016) used a scaling analysis to predict that in the weakly stratified flow regime $Fr_h\gg1$ ($Fr_h$ is the horizontal Froude number), the mixing coefficient $\Gamma$ (defined as the ratio of the dissipation rates of potential to kinetic energy) scales as $\Gamma\sim O(Fr_h^{-2})$. Direct nu

Sumit Kumar Rano, Rudra P. Sarkar

A theorem of Strichartz states that if a uniformly bounded bi-infinite sequence of functions on the Euclidean spaces, satisfies the condition that the Laplacian acting on a function in this sequence yields the next one, then each function in this sequence is an eigenfunction of the Laplacian. We consider a generalization of this result for homogeneous trees,

Hans-Thomas Elze, Kenichi Konishi

The concept of {\it quantum ratio} emerged in the recent efforts to understand how Newton's equations appear for the center of mass (CM) of an isolated macroscopic body at finite body-temperatures, as the first approximation to quantum-mechanical equations. It is defined as $Q\equiv R_q/L_0$, where the quantum fluctuation range $R_q$ is the spatial extension

Sarah Al-Shareeda, Sema F. Oktug, Yusuf Yaslan, Gokhan Yurdakul

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase

J. Gamboa, F. Méndez

The motion of gravitational axion-like particles (ALP) around a Kerr black hole is analyzed, paying attention to resonance and distribution of spectral radiation. We first discuss the computation of $\sqrt{g}{\tilde R}_{\mu \nu \rho \rho \sigma}R^{\mu \nu \rho \sigma}$ and its implications with Pontryagin's theorem and a detailed analysis of Teukolsky's mast

Hongbin Na, Zimu Wang, Mieradilijiang Maimaiti, Tong Chen

Large language models (LLMs) have demonstrated promising potential in various downstream tasks, including machine translation. However, prior work on LLM-based machine translation has mainly focused on better utilizing training data, demonstrations, or pre-defined and universal knowledge to improve performance, with a lack of consideration of decision-making

David Romero, Thamar Solorio

We present Q-ViD, a simple approach for video question answering (video QA), that unlike prior methods, which are based on complex architectures, computationally expensive pipelines or use closed models like GPTs, Q-ViD relies on a single instruction-aware open vision-language model (InstructBLIP) to tackle videoQA using frame descriptions. Specifically, we

J. Mena-Fernández, M. Rodríguez-Monroy, S. Avila, A. Porredon

In this paper we present and validate the galaxy sample used for the analysis of the baryon acoustic oscillation (BAO) signal in the Dark Energy Survey (DES) Y6 data. The definition is based on a color and redshift-dependent magnitude cut optimized to select galaxies at redshifts higher than 0.6, while ensuring a high-quality photo-$z$ determination. The opt

DES Collaboration, T. M. C. Abbott, M. Adamow, M. Aguena

We present the angular diameter distance measurement obtained with the Baryonic Acoustic Oscillation feature from galaxy clustering in the completed Dark Energy Survey, consisting of six years (Y6) of observations. We use the Y6 BAO galaxy sample, optimized for BAO science in the redshift range 0.6<$z$<1.2, with an effective redshift at $z_{\rm eff}$=0.85 an

Jiajun Tan, Fei Sun, Ruichen Qiu, Du Su

As concerns over data privacy intensify, unlearning in Graph Neural Networks (GNNs) has emerged as a prominent research frontier in academia. This concept is pivotal in enforcing the \textit{right to be forgotten}, which entails the selective removal of specific data from trained GNNs upon user request. Our research focuses on edge unlearning, a process of p

Xiaofa Chen

We introduce the notion of exact dg category, which provides a differential graded enhancement of Nakaoka--Palu's notion of extriangulated category. We give a definition in complete analogy with Quillen's but where the category of kernel-cokernel pairs is replaced with a more sophisticated homotopy category. We introduce the notion of stable dg category, and

Florian Le Bronnec, Alexandre Verine, Benjamin Negrevergne, Yann Chevaleyre

We introduce a novel evaluation framework for Large Language Models (LLMs) such as \textsc{Llama-2} and \textsc{Mistral}, focusing on importing Precision and Recall metrics from image generation to text generation. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora. By conducting

Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin

Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. Currently, PoT primarily uses Python. However, relying solely on a single language may result in suboptimal solutions and overlook the potential benefits of other programming languages. I

Collin Cherubim, Robin Wordsworth, Renyu Hu, Evgenya Shkolnik

We simulate atmospheric fractionation in escaping planetary atmospheres using IsoFATE, a new open-source numerical model. We expand the parameter space studied previously to planets with tenuous atmospheres that exhibit the greatest helium and deuterium enhancement. We simulate the effects of EUV-driven photoevaporation and core-powered mass loss on deuteriu

Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum

Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents MANGO, a methodology for distilling high-accuracy, high-recall assertions of cultural knowledge. We judiciously and iteratively prompt LLMs for this purpose from two entry points,