April 2024 arXiv papers — page 174

L. Ya. Glozman, A. V. Nefediev, R. Wagenbrunn

Multiple lattice evidences support the existence of a confining but chirally symmetric regime of QCD above the chiral symmetry restoration crossover at Tch ~ 155 MeV. This regime is characterised by an approximate chiral spin symmetry of the partition function, which is a symmetry of the colour charge and the confining electric part of the QCD Lagrangian. It

Barbara Franci, Filippo Fabiani, Martin Schmidt, Mathias Staudigl

We design a computational approach to find equilibria in a class of Nash games possessing a hierarchical structure. By using tools from mixed-integer optimization and the characterization of variational equilibria in terms of the Karush-Kuhn-Tucker conditions, we propose a mixed-integer game formulation for solving this challenging class of problems. Besides

A. García Muñoz, A. Asensio Ramos, A. Faure

The hydrogen and water molecules respond very differently to the collisional-radiative processes taking place in planetary atmospheres. Naturally, the question arises whether H2O-rich atmospheres are more (or less) resilient to long-term mass loss than H2-dominated ones if they radiate away the incident stellar energy more (or less) efficiently. If confirmed

D. Patgiri, R. Rathi, V. Yadav, D. Chakrabarty

In general, nighttime thermospheric 557.7 nm emission over mid-latitudes is predominantly masked by significantly larger mesospheric component, and hence, F-region plasma structures are rarely observed in this emission. This paper reports the first rare simultaneous detection of F-region plasma depleted structure in O($^1$D) 630.0 nm and O($^1$S) 557.7 nm ai

H. Hirvonen, K. J. Eskola, H. Niemi

We demonstrate how deep convolutional neural networks can be trained to predict 2+1 D hydrodynamic simulation results for flow coefficients, mean-transverse-momentum and charged particle multiplicity from the initial energy density profile. We show that this method provides results that are accurate enough, so that one can use neural networks to reliably est

Yifan Yang

In this paper, we study the forward self-similar solutions to the three-dimensional Magnetohydrodynamic equations (MHD equations) in the whole space. By employing the Leray-Schauder theorem and blow-up argument, we construct a global-time forward self-similar solutions, which is smooth in $\R^{3}\times(0,\infty)$. Furthermore, by investigating the regularity

Timothy G. Myers, Marc Calvo-Schwarzwalder, Francesc Font, Abel Valverde

A mathematical model is developed to describe column adsorption when the contaminant constitutes a significant amount of the fluid. This requires modelling the variation of pressure and velocity, in addition to the usual advection-diffusion-adsorption and kinetic equations describing concentration and adsorption rates. The model builds on previous work based

Lennart Vater, Sven Tarlowski, Michael Schuldes, Lutz Eckstein

The selection of relevant test scenarios for the scenario-based testing and safety validation of automated driving systems (ADSs) remains challenging. An important aspect of the relevance of a scenario is the challenge it poses for an ADS. Existing methods for calculating the challenge of a scenario aim to express the challenge in terms of a metric value. Me

Xi-Han Zhou, Xiyin Ye, Lihui Bai, Tao Yu

Recent experiments observe the spin-wave-Meissner-current modes in ferromagnetic insulator-superconductor heterostructures, in which the coherently excited spin waves seemingly do not decay as usual beneath the superconductor strip [Borst et al., Science 382, 430 (2023)]. We interpret this phenomenon by demonstrating that the stray magnetic field emitted by

Jintu Zhang, Odin Zhang, Luigi Bonati, TingJun Hou

Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used. We pr

Atreyee Kundu

This paper deals with input/output-to-state stability (IOSS) of switched nonlinear systems whose switching signals obey pre-specified restrictions on admissible switches between the subsystems and admissible dwell times on the subsystems. We present sufficient conditions on the subsystems, admissible switches between them and admissible dwell times on them,

Nouhaila Innan, Alberto Marchisio, Mohamed Bennai, Muhammad Shafique

This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD e

Charlotte Castel, Zhi Zhao, Magne Thoresen

Variable selection in relation to regression modeling has constituted a methodological problem for more than 60 years. Especially in the context of high-dimensional regression, developing stable and reliable methods, algorithms, and computational tools for variable selection has become an important research topic. Omics data is one source of such high-dimens

Rong Du, Yiting Wang, Dazhi Zhang

We demonstrate the existence of a uniform and nonhomogeneous vector bundle $E$ of rank $(n-d)(m+1)-1$ over Grassmannian $\mathbb{G}(d,n)$, where $m>d$ and $1\le d \le n-d-1$ with a $\mathbb{P}$-homogeneity degree $h(E)=d$. Particularly, we establish an upper bound of $3(n-d)-2$ for the uniform-homogeneous shreshold of $\mathbb{G}(d,n)$. Additionally, we cons

Yejin Jeon, Yunsu Kim, Gary Geunbae Lee

Contemporary neural speech synthesis models have indeed demonstrated remarkable proficiency in synthetic speech generation as they have attained a level of quality comparable to that of human-produced speech. Nevertheless, it is important to note that these achievements have predominantly been verified within the context of high-resource languages such as En

Jerker Denrell

The Hot Stove Effect is a negativity bias resulting from the adaptive character of learning. The mechanism is that learning algorithms that pursue alternatives with positive estimated values, but avoid alternatives with negative estimated values, will correct errors of overestimation but fail to correct errors of underestimation. Here, we generalize the theo

Watthanan Jatuviriyapornchai, Stefan Grosskinsky

We introduce a simple zero-range process with constant rates and one fast rate for a particular occupation number, which diverges with the system size. Surprisingly, this minor modification induces a condensation transition in the thermodynamic limit, where the structure of the condensed phase depends on the scaling of the fast rate. We study this transition

Zhiyuan Wen, Jiannong Cao, Yu Yang, Ruosong Yang

Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. It is essential for providing personalized services in various applications of Human-Computer Interaction (HCI), such as AI-based mental therapy and companion robots for the elderly. Most recent studies analyze the dialog conten

Jakub Hoscilowicz, Pawel Pawlowski, Marcin Skorupa, Marcin Sowański

Spoken Language Understanding (SLU) models are a core component of voice assistants (VA), such as Alexa, Bixby, and Google Assistant. In this paper, we introduce a pipeline designed to extend SLU systems to new languages, utilizing Large Language Models (LLMs) that we fine-tune for machine translation of slot-annotated SLU training data. Our approach improve

Abhijit Anand, Venktesh V, Vinay Setty, Avishek Anand

An important problem in text-ranking systems is handling the hard queries that form the tail end of the query distribution. The difficulty may arise due to the presence of uncommon, underspecified, or incomplete queries. In this work, we improve the ranking performance of hard or difficult queries without compromising the performance of other queries. Firstl

Halimah Harfah, Yusuf Wicaksono, Gagus K. Sunnardianto, Muhammad Aziz Majidi

We investigate the impact of monoatomic vacancies in 2D materials on the performance of magnetic tunnel junction (MTJ) devices using first-principles calculations within Density Functional Theory (DFT). Specifically, we analyze the influence on hexagonal boron nitride (hBN) with various layer configurations, uncovering distinct transmission probability patte

Jiahao Lu, Xingyi Yang, Xinchao Wang

Foundation segmentation models, while powerful, pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click, potentially leading to copyright infringement or malicious misuse. To mitigate this risk, we introduce a new task "Anything Unsegmentable" to grant any image "the right to be unsegmented"

Sylvain Barde, Rowan Cherodian, Guy Tchuente

We propose a novel estimation procedure for models with endogenous variables in the presence of spatial correlation based on Eigenvector Spatial Filtering. The procedure, called Moran's $I$ 2-Stage Lasso (Mi-2SL), uses a two-stage Lasso estimator where the Standardised Moran's I is used to set the Lasso tuning parameter. Unlike existing spatial econometric m

Francesco Catalano, Laura Nasello, Daniel Guterding

Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to

Eunseong Choi, Hyeri Lee, Jongwuk Lee

In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses concatenated multiple contexts in the decoding phase, i.e., Fusion-in-Decoder, demonstrates promising performance but generates incorrect outputs from seemingly plausible contexts

Bart M. van Marrewijk, Charbel Dandjinou, Dan Jeric Arcega Rustia, Nicolas Franco Gonzalez

Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which every pixel must be annotated. A potential strategy to mitigate annotation effort is active learning. Active learning facilitates the identification and selection of the most inform

David Nieves, María José Ramírez-Quintana, Carlos Monserrat, César Ferri

A common way of learning to perform a task is to observe how it is carried out by experts. However, it is well known that for most tasks there is no unique way to perform them. This is especially noticeable the more complex the task is because factors such as the skill or the know-how of the expert may well affect the way she solves the task. In addition, le

G. F. Thomas, G. Battaglia, F. Gran, E. Fernandez-Alvar

The emergence of large spectroscopic surveys requires homogenising on the same scale the quantities they measure in order to increase their scientific legacy. We developed the SpectroTranslator, a data-driven deep neural network algorithm that can convert spectroscopic parameters from the base of one survey to another. The algorithm also includes a method to

Fei Kong

Let $Q$ be a non-degenerated even lattice, let $V_Q$ be the lattice vertex algebra associated to $Q$, and let $V_Q^\eta$ be a quantum lattice vertex algebra. In this paper, we prove the equivalence between the category $V_Q$-modules and the category of $V_Q^\eta$-modules. As a consequence, we show that every $V_Q^\eta$-module is completely reducible, and the

Abhijeet Pendyala, Asma Atamna, Tobias Glasmachers

We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput waste sorting facility. Our work addresses the challenge of effectively balancing the competing objectives of operational safety, volume optimization, and minimizing resource usag

Fei Kong

Let $\mathfrak g$ be a symmetrizable Kac-Moody Lie algebra, and let $V_{\hat{\mathfrak g},\hbar}^\ell$, $L_{\hat{\mathfrak g},\hbar}^\ell$ be the quantum affine vertex algebras constructed in [11]. For any complex numbers $\ell$ and $\ell'$, we present an $\hbar$-adic quantum vertex algebra homomorphism $\Delta$ from $V_{\hat{\mathfrak g},\hbar}^{\ell+\ell'}

E. C. Cortés García, P. Niedermayer, R. Singh, R. Taylor

The beam response to an external periodic excitation delivers relevant information about the optics, tune distribution and stability of a circulating beam in a storage ring. In this contribution the horizontal beam response to the excitation (transfer function) under conditions typical for slow extraction is presented for a coasting beam. The resulting spect

Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Tzu-iunn Ong

Algorithmic reasoning refers to the ability to understand the complex patterns behind the problem and decompose them into a sequence of reasoning steps towards the solution. Such nature of algorithmic reasoning makes it a challenge for large language models (LLMs), even though they have demonstrated promising performance in other reasoning tasks. Within this

Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang

Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will dec

Simiao Li, Yun Zhang, Wei Li, Hanting Chen

Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher model to a compact student model. Previous methods for image super-resolution (SR) mostly compare the feature maps directly or after standardizing the dimensions with basic algebraic

Kleanthis Malialis, Jin Li, Christos G. Panayiotou, Marios M. Polycarpou

Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical i

Ying Fang, Hezhu Shao

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of

Shijia Zhou, Huangyan Shan, Barbara Plank, Robert Litschko

This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), on Track C: Cross-lingual. The task aims to detect semantic relatedness of two sentences in a given target language without access to direct supervision (i.e. zero-shot cross-lingual transfer). To this end, we focus on different source language selection

Cristian C. Beltran-Hernandez, Nicolas Erbetti, Masashi Hamaya

Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environments, especially when handling dangerous tools such as kitchen knives. This study focuses on enabling a robot to autonomously and safely learn food-cutting tasks. More specificall

Wouter Zomerdijk, Peter Palensky, Tarek AlSkaif, Pedro P. Vergara

The energy sector's digital transformation brings mutually dependent communication and energy infrastructure, tightening the relationship between the physical and the digital world. Digital twins (DT) are the key concept for this. This paper initially discusses the evolution of the DT concept across various engineering applications before narrowing its focus

Luca Crupi, Elia Cereda, Daniele Palossi

Autonomous nano-drones (~10 cm in diameter), thanks to their ultra-low power TinyML-based brains, are capable of coping with real-world environments. However, due to their simplified sensors and compute units, they are still far from the sense-and-act capabilities shown in their bigger counterparts. This system paper presents a novel deep learning-based pipe

Yuki Takei, Daichi Tsuna

Some hydrogen-poor (Type Ibc) supernovae (SNe) are known to have massive circumstellar matter (CSM) that are well detached from the star. Using the open-source code CHIPS, we construct a grid of models of SN Ibc interacting with detached CSM, inspired by a recently proposed scenario of such CSM generated by a mass eruption and feedback process from the lefto

Sreela Kodali, Cihualpilli Camino Cruz, Thomas C. Bulea, Kevin S. Rao Diana Bharucha-Goebel

A host of medical conditions, including amputations, diabetes, stroke, and genetic disease, result in loss of touch sensation. Because most types of sensory loss have no pharmacological treatment or rehabilitative therapy, we propose a haptic sensory prosthesis that provides substitutive feedback. The wrist and forearm are compelling locations for feedback d

Aki Dote, Koji Hukushima

The effects of constraint relaxation on dynamic critical phenomena in the Minimum Vertex Cover (MVC) problem on Erd\H{o}s-R\'enyi random graphs are investigated using Markov chain Monte Carlo simulations. Following our previous work that revealed the reduction of the critical temperature by constraint relaxation based on the penalty function method, this stu

Corné Koks, Martin P. van Exter

We measure the fluorescence spectrum of broadband emitters in an open optical microcavity with radius of curvature R = 17.7(3) um and finesse F ~= 1000. This geometry enables a combined measurement of emission spectra versus cavity length, which has several benefits over measurements at fixed wavelength or fixed cavity length alone. We demonstrate the role o

Zhonglin Liu, Shujie Chen, Jianfeng Dong, Xun Wang

Achieving high-performance in multi-object tracking algorithms heavily relies on modeling spatio-temporal relationships during the data association stage. Mainstream approaches encompass rule-based and deep learning-based methods for spatio-temporal relationship modeling. While the former relies on physical motion laws, offering wider applicability but yield

Michael Schuldes, Christoph Glasmacher, Lutz Eckstein

Scenario-based testing is a promising method to develop, verify and validate automated driving systems (ADS) since pure on-road testing seems inefficient for complex traffic environments. A major challenge for this approach is the provision and management of a sufficient number of scenarios to test a system. The provision, generation, and management of scena

R. Folk

The late eighteenth and early nineteenth centuries have long been considered as a formative period for modern Irish political traditions such as nationalism, republicanism and unionism. For Europe it was the time of a turnover in science moving from observation to experiment and from speculation to fact. Richard Kirwan was a well known natural philosopher in

Roberto Dvornicich, Davide Lombardo, Francesco Veneziano, Umberto Zannier

This paper is a continuation of an earlier one, and completes a classification of the configurations of points in a plane lattice that determine angles that are rational multiples of ${\pi}$. We give a complete and explicit description of lattices according to which of these configurations can be found among their points.

Ximena Salgado Uribe, Martí Bosch, Jérôme Chenal

Advances in Artificial Intelligence are challenged by the biases rooted in the datasets used to train the models. In image geolocation estimation, models are mostly trained using data from specific geographic regions, notably the Western world, and as a result, they may struggle to comprehend the complexities of underrepresented regions. To assess this issue

François Vurpillot, Constantinos Hatzoglou, Benjamin Klaes, Loic Rousseau

Atom probe tomography data is composed of a list of coordinates of the reconstructed atoms in the probed volume. The elemental identity of each atom is derived from time-of-flight mass spectrometry, with no local energetic or chemical information readily available within the mass spectrum. Here, we used a new data processing technique referred to as field ev

Hendrik Wilka, Jens Lang

High-dimensional interpolation problems appear in various applications of uncertainty quantification, stochastic optimization and machine learning. Such problems are computationally expensive and request the use of adaptive grid generation strategies like anisotropic sparse grids to mitigate the curse of dimensionality. However, it is well known that the sta

Hanxuan Wang, Na Lu, Zixuan Wang, Jiacheng Liu

Deep learning based transient stability assessment (TSA) has achieved great success, yet the lack of interpretability hinders its industrial application. Although a great number of studies have tried to explore the interpretability of network solutions, many problems still remain unsolved: (1) the difference between the widely accepted power system knowledge

Tiangang Cui, Xin Tong, Olivier Zahm

Poincar\'e inequality is a fundamental property that rises naturally in different branches of mathematics. The associated Poincar\'e constant plays a central role in many applications since it governs the convergence of various practical algorithms. For instance, the convergence rate of the Langevin dynamics is exactly given by the Poincar\'e constant. This

Jelena Sjakste, Maxime Markov, Raja Sen, Lorenzo Paulatto

In this work, we discuss the possibility of reaching the Ziman conditions for collective heat transport in cubic bulk semiconductors, such as Si, Ge, AlAs and AlP. In natural and enriched silicon and germanium, the collective heat transport limit is impossible to reach due to strong isotopic scattering. However, we show that in hyperenriched silicon and germ

Francesco P. Ramunno, S. Hackstein, V. Kinakh, M. Drozdova

Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient data. This study proposes using generative deep learning models, specifically a Denoising Diffusion Probabilistic Model (DDPM), to create synthetic images of solar phenomena, inclu

Shmuel Onn

The degree sequence optimization problem is to find a subgraph of a given graph which maximizes the sum of given functions evaluated at the subgraph degrees. Here we study this problem by replacing degree sequences, via suitable nonlinear transformations, by suitable degree enumerators, and we introduce suitable degree enumerator polytopes. We characterize t

Gi-Chan Bae, Seung-Yeal Ha, Gyuyoung Hwang, Tommaso Ruggeri

We study the compression between the phenomenological and kinetic models for a mixture of gases from the viewpoint of collective dynamics. In the case in which constituents are Eulerian gases, balance equations for mass, momentum, and energy are the same in the main differential part, but production terms due to the interchanges between constituents are diff

Victor K. Kornev, Alena N. Nikolaeva, Nikolay V. Kolotinskiy

Obstacles to the two-line Josephson traveling wave parametric amplifier (JTWPA) designs, aimed at increasing the allowed pump wave energy and, hence, the gain growth through the combination of linear and nonlinear lines as pump and signal lines is discussed. This analysis takes into account both the mutual coupling and the discreteness of the artificial wave

Eduard Frankford, Clemens Sauerwein, Patrick Bassner, Stephan Krusche

With the rapid advancement of artificial intelligence (AI) in various domains, the education sector is set for transformation. The potential of AI-driven tools in enhancing the learning experience, especially in programming, is immense. However, the scientific evaluation of Large Language Models (LLMs) used in Automated Programming Assessment Systems (APASs)

Kai Du, Ruoyang Liu

In this paper, we establish the existence, uniqueness and stability results for the obstacle problem associated with a degenerate nonlinear diffusion equation perturbed by conservative gradient noise. Our approach revolves round introducing a new entropy formulation for stochastic variational inequalities. As a consequence, we obtain a novel well-posedness r

Badreddine Yacine Yacheur, Toufik Ahmed, Mohamed Mosbah

Cooperative intelligent transport systems rely on a set of Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) applications depend on a significant amount of shared data and require high reliability, low end-to-end (E2E) latency,

Wei Gong, Dongdong Liang

In this paper, we investigate an optimal control problem governed by parabolic equations with measure-valued controls over time. We establish the well-posedness of the optimal control problem and derive the first-order optimality condition using Clarke's subgradients, revealing a sparsity structure in time for the optimal control. Consequently, these optimal

Yi Shen, Hanyan Huang

Offline reinforcement learning learns from a static dataset without interacting with environments, which ensures security and thus owns a good application prospect. However, directly applying naive reinforcement learning algorithm usually fails in an offline environment due to inaccurate Q value approximation caused by out-of-distribution (OOD) state-actions

Huayi Zhou, Fei Jiang, Jin Yuan, Yong Rui

Existing research on unconstrained in-the-wild head pose estimation suffers from the flaws of its datasets, which consist of either numerous samples by non-realistic synthesis or constrained collection, or small-scale natural images yet with plausible manual annotations. This makes fully-supervised solutions compromised due to the reliance on generous labels

Ouassine Younes, Zahir Jihad, Conruyt Noël, Kayal Mohsen

Coral reefs are vital ecosystems that are under increasing threat due to local human impacts and climate change. Efficient and accurate monitoring of coral reefs is crucial for their conservation and management. In this paper, we present an automatic coral detection system utilizing the You Only Look Once (YOLO) deep learning model, which is specifically tai

Philipp Hager, Romain Deffayet, Jean-Michel Renders, Onno Zoeter

Unbiased learning-to-rank (ULTR) is a well-established framework for learning from user clicks, which are often biased by the ranker collecting the data. While theoretically justified and extensively tested in simulation, ULTR techniques lack empirical validation, especially on modern search engines. The Baidu-ULTR dataset released for the WSDM Cup 2023, col

Gilles Blanchard, Guillermo Durand, Ariane Marandon-Carlhian, Romain Périer

In Marandon (2023), the author introduces a procedure to detect true edges from a partially observed graph using a conformal prediction fashion: first computing scores from a trained function, deriving conformal p-values from them and finally applying a multiple testing procedure. In this paper, we prove that the resulting procedure indeed controls the FDR,

Raphaël Danchin

We consider the evolution of two-dimensional incompressible flows with variable density, only bounded and bounded away from zero. Assuming that the initial velocity belongs to a suitable critical subspace of L^2 , we prove a global-in-time existence and stability result for the initial (boundary) value problem. Our proof relies on new time decay estimates fo

Lisa Blum Moyse, Hugues Berry

The standard consolidation theory states that short-term memories located in the hippocampus enable the consolidation of long-term memories in the neocortex. In other words, the neocortex slowly learns long-term memories with a transient support of the hippocampus that quickly learns unstable memories. However, it is not clear yet what could be the neurobiol

Nishat Raihan, Dhiman Goswami, Sadiya Sayara Chowdhury Puspo, Christian Newman

Recent advances in AI, machine learning, and NLP have led to the development of a new generation of Large Language Models (LLMs) that are trained on massive amounts of data and often have trillions of parameters. Commercial applications (e.g., ChatGPT) have made this technology available to the general public, thus making it possible to use LLMs to produce h

Marie-Jose Chaaya, Sophie Chauvet, Florence Hubert, Fanny Mann

The pancreatic innervation undergoes dynamic remodeling during the development of pancreatic ductal adenocarcinoma (PDAC). Denervation experiments have shown that different types of axons can exert either pro- or anti-tumor effects, but conflicting results exist in the literature, leaving the overall influence of the nervous system on PDAC incompletely under

Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan

We present theoretical convergence guarantees for ODE-based generative models, specifically flow matching. We use a pre-trained autoencoder network to map high-dimensional original inputs to a low-dimensional latent space, where a transformer network is trained to predict the velocity field of the transformation from a standard normal distribution to the tar

Daniel Ceverino, Yurina Nakazato, Naoki Yoshida, Ralf Klessen

Current models of the formation of first galaxies predict low masses and faint objects at extremely high redshifts, z=9-15. However, the first observations of this epoch indicate a higher-than-expected number of bright (sometimes massive) galaxies. Numerical simulations can help to elucidate the mild evolution of the bright end of the UV luminosity function

Satyanad Kichenassamy

We show that relativistic rotation transformations represent transfer maps between the laboratory system and a local observer on an observer manifold, rather than an event manifold, in the spirit of C-equivalence. Rotation is, therefore, not a parameterised motion on a background space or spacetime, but is determined by a particular sequence of tetrads relat

Toshitaka Aoki, Emerson G. Escolar, Shunsuke Tada

In persistent homology analysis, interval modules play a central role in describing the birth and death of topological features across a filtration. In this work, we extend this setting, and propose the use of bipath persistent homology, which can be used to study the persistence of topological features across a pair of filtrations connected at their ends, t

José Miguel Balado-Alves, Anna Siffert

We provide a construction method for biharmonic submanifolds in cohomogeneity one manifolds. In particular, we give new examples of biharmonic submanifolds and study the normal index of these submanifolds. We use this strategy to construct metrics on the sphere admitting biharmonic non-minimal hypersurfaces with three distinct principal curvatures. Finally,

Osvaldo Luamba Quinjica, David Ifeoluwa Adelani

In recent years, the development of pre-trained language models (PLMs) has gained momentum, showcasing their capacity to transcend linguistic barriers and facilitate knowledge transfer across diverse languages. However, this progress has predominantly bypassed the inclusion of very-low resource languages, creating a notable void in the multilingual landscape

Xavier Bekaert, Andrea Campoleoni, Simon Pekar

We provide holographic realisations in Minkowski spacetime of a free conformal Carrollian scalar field living at null infinity. To this end, we first show that the electric and magnetic limits of a relativistic conformal scalar are equivalent and we study the representation of the Carroll, Poincar\'e and BMS algebras that is realised on the resulting solutio

Qianqiao Xu, Zhiliang Tian, Hongyan Wu, Zhen Huang

With the enhanced performance of large models on natural language processing tasks, potential moral and ethical issues of large models arise. There exist malicious attackers who induce large models to jailbreak and generate information containing illegal, privacy-invasive information through techniques such as prompt engineering. As a result, large models co

Adrien de Jarmy

The development of digital humanities has opened new perspectives in the history of Islam: whether dealing with thin or sometimes vast source corpora (such as S\=ira, al-Tbar\=i, al-Dahab\=i, etc.), these tools allow us to approach texts much more effectively from a statistical perspective, in order to support more general hypotheses and move beyond case stu

Guanghui Chen, Zheng Wang, Hongxin Lin, Yongming Huang

In this paper, we consider robust joint access point (AP) clustering and beamforming design with imperfect channel state information (CSI) in cell-free systems. Specifically, we jointly optimize AP clustering and beamforming with imperfect CSI to simultaneously maximize the worst-case sum rate and minimize the number of AP clustering under power constraint a

Jordan Vice, Naveed Akhtar, Richard Hartley, Ajmal Mian

Text-to-image (T2I) generative models have gained increased popularity in the public domain. While boasting impressive user-guided generative abilities, their black-box nature exposes users to intentionally- and intrinsically-biased outputs. Bias manipulation (and mitigation) techniques typically rely on careful tuning of learning parameters and training dat

Maja Stahl, Nadine Michel, Sebastian Kilsbach, Julian Schmidtke

Learning argumentative writing is challenging. Besides writing fundamentals such as syntax and grammar, learners must select and arrange argument components meaningfully to create high-quality essays. To support argumentative writing computationally, one step is to mine the argumentative structure. When combined with automatic essay scoring, interactions of

Ying Che, Tianyue Zhang, Xiaowei Liu, Dejiao Hu

Artificial nanostructures with ultrafine and deep-subwavelength feature sizes have emerged as a paradigm-shifting platform to advanced light field management, becoming a key building block for high-performance integrated optoelectronics and flat optics. However, direct optical inspection of such integrated chips with densely packed complex and small features

Xu Wang, Yifan Li, Qiudan Zhang, Wenhui Wu

Learning to build 3D scene graphs is essential for real-world perception in a structured and rich fashion. However, previous 3D scene graph generation methods utilize a fully supervised learning manner and require a large amount of entity-level annotation data of objects and relations, which is extremely resource-consuming and tedious to obtain. To tackle th

Bidyut Hazarika, Naba Jyoti Gogoi, Prabwal Phukon

In this work, we propose a common vector field to study the thermodynamic topology of the Davies type and Hawking-Page phase transitions. Existing literature has shown that studying these two types of phase transitions typically requires defining two separate vector fields . In our approach, we adopt Duan's $\phi$-mapping topological current theory to define

Diego Vallarino

By integrating survival analysis, machine learning algorithms, and economic interpretation, this research examines the temporal dynamics associated with attaining a 5 percent rise in purchasing power parity-adjusted GDP per capita over a period of 120 months (2013-2022). A comparative investigation reveals that DeepSurv is proficient at capturing non-linear

Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, t

Zhiyu Huang, Zixu Zhang, Ameya Vaidya, Yuxiao Chen

Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants, thereby limiting their utility in the evaluation and validation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generatio

Tomoya Yoshida, Shuhei Kurita, Taichi Nishimura, Shinsuke Mori

Visual affordance learning is a key component for robots to understand how to interact with objects. Conventional approaches in this field rely on pre-defined objects and actions, falling short of capturing diverse interactions in realworld scenarios. The key idea of our approach is employing textual instruction, targeting various affordances for a wide rang

Ignacio Magaña Hernandez, Anarya Ray

Gravitational wave standard sirens typically require electromagnetic (EM) data to obtain redshift information to constrain cosmology. Difficult to find EM counterparts for bright sirens and galaxy survey systematics for dark sirens make cosmological constraints with spectral sirens, a gravitational wave data-only approach, extremely appealing. In this work,

Abdul Qadir Ibrahim, Sebastian Götschel, Daniel Ruprecht

Iterative parallel-in-time algorithms like Parareal can extend scaling beyond the saturation of purely spatial parallelization when solving initial value problems. However, they require the user to build coarse models to handle the inevitably serial transport of information in time.This is a time consuming and difficult process since there is still only limi

Ana M. Montero, Álvaro Rodríguez-Rivas, Santos B. Yuste, Andrés Santos

In the statistical mechanics approach to liquid-state theory, understanding the role of the intermolecular potential in determining thermodynamic and structural properties is crucial. The Fisher--Widom (FW) line, which separates regions in the temperature vs density plane where the decay of the total correlation function is monotonic or oscillatory, provides

Xusen Guo, Qiming Zhang, Junyue Jiang, Mingxing Peng

Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted result

Tong Lin, Jerome P. Reiter

Several official statistics agencies release synthetic data as public use microdata files. In practice, synthetic data do not admit accurate results for every analysis. Thus, it is beneficial for agencies to provide users with feedback on the quality of their analyses of the synthetic data. One approach is to couple synthetic data with a verification server

D. Umerenkov, S. Kudin, M. Peksheva, D. Pavlov

We introduce the CPAISD: Core-Penumbra Acute Ischemic Stroke Dataset, aimed at enhancing the early detection and segmentation of ischemic stroke using Non-Contrast Computed Tomography (NCCT) scans. Addressing the challenges in diagnosing acute ischemic stroke during its early stages due to often non-revealing native CT findings, the dataset provides a collec

Chowdhury Shahriar Muzammel, Maria Spichkova, James Harland

Autonomous vehicles and other intelligent transport systems have been evolving rapidly and are being increasingly deployed worldwide. Previous work has shown that perceptions of autonomous vehicles and attitudes towards them depend on various attributes, including the respondent's age, education level and background. These findings with respect to age and ed

Zhongyu Xia, ZhiWei Lin, Xinhao Wang, Yongtao Wang

Three-dimensional perception from multi-view cameras is a crucial component in autonomous driving systems, which involves multiple tasks like 3D object detection and bird's-eye-view (BEV) semantic segmentation. To improve perception precision, large image encoders, high-resolution images, and long-term temporal inputs have been adopted in recent 3D perceptio

Dimitrios Chatziparaschis, Hanzhe Teng, Yipeng Wang, Pamodya Peiris

By-tree information gathering is an essential task in precision agriculture achieved by ground mobile sensors, but it can be time- and labor-intensive. In this paper we present an algorithmic framework to perform real-time and on-the-go detection of trees and key geometric characteristics (namely, width and height) with wheeled mobile robots in the field. Ou