October 2024 arXiv papers — page 109

Yingtao Ren, Yu-Cheng Chang, Thomas Do, Zehong Cao

Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with high-dimensional data, especially with noise, FNNs encounter challenges such as vanishing gradients, excessive fuzzy rules, and limited access to prior knowledge. To address these c

Alessio Dallabona, Patrik Schermann, Mogens Blanke, Dimitrios Papageorgiou

The application of nonlinear control schemes to electro-hydraulic actuators often requires several alterations in the design of the controllers during their implementation. This is to overcome challenges that frequently arise in such control algorithms owing to model nonlinearities. Moreover, advanced control solutions for this type of systems often introduc

Bishal Sonar, Ravi Srivastava

The spectrum of Laplacian and signless Laplacian matrix for a graph product is obtained, where both underlying graphs are regular. As an application of this, we have been able to generate the Kirchhoff Index and Wiener Index and determine the number of spanning trees. Additionally, we derived the conditions necessary for obtaining a Laplacian and signless La

Chaoran Chen, Daodao Zhou, Yanfang Ye, Toby Jia-jun Li

The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However, many users remain unaware of the risks. This paper explores methods to increase user awareness of privacy risks associated with LLMs in end-us

Stéphane Devismes, Yoann Dieudonné, Arnaud Labourel

A mobile agent, starting from a node $s$ of a simple undirected connected graph $G=(V,E)$, has to explore all nodes and edges of $G$ using the minimum number of edge traversals. To do so, the agent uses a deterministic algorithm that allows it to gain information on $G$ as it traverses its edges. During its exploration, the agent must always respect the cons

Daniel Roncel, Federico Costa, Javier Hernando

With the significant progress of speech technologies, spoken goal-oriented dialogue systems are becoming increasingly popular. One of the main modules of a dialogue system is typically the dialogue policy, which is responsible for determining system actions. This component usually relies only on audio transcriptions, being strongly dependent on their quality

Zhuoran Liu, Danpei Zhao, Bo Yuan

Current methods for disaster scene interpretation in remote sensing images (RSIs) mostly focus on isolated tasks such as segmentation, detection, or visual question-answering (VQA). However, current interpretation methods often fail at tasks that require the combination of multiple perception methods and specialized tools. To fill this gap, this paper introd

Florian Wulff, Bernd Schaeufele, Julian Pfeifer, Ilja Radusch

Automated vehicles rely on an accurate and robust perception of the environment. Similarly to automated cars, highly automated trains require an environmental perception. Although there is a lot of research based on either camera or LiDAR sensors in the automotive domain, very few contributions for this task exist yet for automated trains. Additionally, no p

Theodore Modis

The number of deaths from car accidents and from the unlawful use of guns can be described by logistic growth curves. The annual rates of both have traced completed logistic trajectories following which they have been self-regulated for many decades at what seems to be a homeostatic equilibrium level through legislative actions. Exception constitutes the num

S. Balamoorthy, T. Kavaskar

Let $\varepsilon(G)$ be the eccentricity matrix of a graph $G$ and $Spec(\varepsilon(G))$ be the eccentricity spectrum of $G$. Let $H[G_1,G_2,\ldots, G_k]$ be the $H$-join of graphs $G_1,G_2,\ldots, G_k$ and let $H[G]$ be lexicographic product of $H$ and $G$. This paper finds the eccentricity matrix of a $H$-join of graphs. Using this result, we find (i) $Sp

Kyuseong Choi, Jacob Feitelberg, Caleb Chin, Anish Agarwal

Consider a setting with multiple units (e.g., individuals, cohorts, geographic locations) and outcomes (e.g., treatments, times, items), where the goal is to learn a multivariate distribution for each unit-outcome entry, such as the distribution of a user's weekly spend and engagement under a specific mobile app version. A common challenge is the prevalence

Giuliano Difranco, Lindsay Bassman Oftelie

A key hurdle to the success of quantum computers is the ability to initialize qubits into a pure state, which can be achieved by cooling qubits down to very low temperatures. Computational cooling of qubits, whereby a subset of the qubits is cooled at the expense of heating the other qubits via the application of special sets of logic gates, offers a route t

Andres A. Contreras Hip, Ewain Gwynne

We present a proof of the folklore result that any length metric on $\mathbb R^d$ can be approximated by conformally flat Riemannian distance functions in the uniform distance. This result is used to study Liouville quantum gravity in another paper by the same authors.

Li Yu, Lianzheng Shi, Jianhua Zhang, Jialin Wang

6G is envisaged to provide multimodal sensing, pervasive intelligence, global coverage, global coverage, etc., which poses extreme intricacy and new challenges to the network design and optimization. As the core part of 6G, wireless channel is the carrier and enabler for the flourishing technologies and novel services, which intrinsically determines the ulti

Patrick H. Cahill, Georg A. Gottwald

The Hegselmann--Krause model is a prototypical model for opinion dynamics. It models the stochastic time evolution of an agent's or voter's opinion in response to the opinion of other like-minded agents. The Hegselmann--Krause model only considers the opinions of voters; we extend it here by incorporating the dynamics of political parties which influence and

Camille Jorge, Denis Bartolo

By confining colloidal active fluids in microchannel networks, we demonstrate that their degenerate flows corresponds to the configurations of the six-vertex model. We use this quantitative correspondence to control and explain the active flows that emerge in square grid networks. In particular, we show that the Lagrangian trajectories of active particles re

Reworr, Dmitrii Volkov

Attacks powered by Large Language Model (LLM) agents represent a growing threat to modern cybersecurity. To address this concern, we present LLM Honeypot, a system designed to monitor autonomous AI hacking agents. By augmenting a standard SSH honeypot with prompt injection and time-based analysis techniques, our framework aims to distinguish LLM agents among

Krzysztof Ptaszynski, Massimiliano Esposito

We investigate the multipartite mutual information between $N$ discrete-state stochastic units interacting in a network that is invariant under unit permutations. We show that when the system relaxes to fixed point attractors, multipartite correlations in the stationary state either do not scale extensively with $N$, or the extensive scaling is not robust to

Peter Tibensky, Michal Kompan

Recommenders take place on a wide scale of e-commerce systems, reducing the problem of information overload. The most common approach is to choose a recommender used by the system to make predictions. However, users vary from each other; thus, a one-fits-all approach seems to be sub-optimal. In this paper, we propose a meta-hybrid recommender that uses machi

Kangkang Lu, Yanhua Yu, Zhiyong Huang, Yunshan Ma

Graph neural networks (GNNs) have demonstrated excellent performance in semi-supervised node classification tasks. Despite this, two primary challenges persist: heterogeneity and heterophily. Each of these two challenges can significantly hinder the performance of GNNs. Heterogeneity refers to a graph with multiple types of nodes or edges, while heterophily

Xinyuan Huang, Yueqiao Qu, Yu Liao, Qian Zheng

The coexistence of high-temperature intrinsic ferromagnetic ordering, large magnetic anisotropy, along with novel mechanical properties such as ferroelasticity and flexibility, in experimental feasible two-dimensional (2D) crystals is greatly appealing for nanoscale spintronics. However, the progress in identifying such materials is limited. Here, by first-p

Zezhun Shi

Camera calibration is fundamental to 3D vision, and the choice of calibration pattern greatly affects the accuracy. To address aberration issue, star-shaped pattern has been proposed as alternatives to traditional checkerboard. However, such pattern suffers from aliasing artifacts. In this paper, we present a novel solution by employing a series of checkerbo

Donghao Zhou, Jiancheng Huang, Jinbin Bai, Jiaze Wang

Text-to-image diffusion models can generate high-quality images but lack fine-grained control of visual concepts, limiting their creativity. Thus, we introduce component-controllable personalization, a new task that enables users to customize and reconfigure individual components within concepts. This task faces two challenges: semantic pollution, where unde

Jiahuan Zhu, Hua Feng, Tong Liu

The brightest ever gamma-ray burst (GRB) 221009A displays a significant emission line component around 10 MeV. As the GRB central engine is neutron-rich, we propose that the emission line could be originally due to the 2.223 MeV gamma-rays following neutron capture with protons. The measured line profile can be adequately fitted with a neutron capture model

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Utilizing 4.5${~\rm{fb}}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at center-of-mass energies between 4.600 and 4.699 GeV, the first observation of the singly Cabibbo-suppressed decay $\Lambda_c^{+}\to p\pi^0$ is presented, with a statistical significance of $5.4\sigma$. The ratio of the branching fractions

Y. J. Jin, X. Chen, H. F. Zhu, Z. J. Jiang

With data observed by the Hard X-ray Modulation Telescope (\textit{Insight}-HXMT) and the Neutron star Interior Composition Explorer (\textit {NICER}), we study low-frequency quasi-periodic oscillations (LFQPOs) of the black hole candidate MAXI J1803$-$298 during the 2021 outburst. Based on hardness intensity diagram and difference of the QPOs properties, Ty

Peter Gracar, Marilyn Korfhage, Peter Mörters

We study the Poisson Boolean model where the grains are random convex bodies with a rotation-invariant distribution. We say that a grain distribution is dense if the union of the grains covers the entire space and robust if the union of the grains has an unbounded connected component irrespective of the intensity of the underlying Poisson process. If the gra

D. K. Patel, K. M. Fijalkowski, M. Kruskopf, N. Liu

The quantum anomalous Hall effect holds promise as a disruptive innovation in condensed matter physics and metrology, as it gives access to Hall resistance quantization in terms of the von-Klitzing constant RK = h/e2 at zero external magnetic field. In this work, we study the accuracy of Hall resistance quantization in a device based on the magnetic topologi

Feiyu Peng, Hongfei Shu

We study the zero-dimensional prototype of the path integrals in quantum mechanics and quantum field theory, with the action $S(\phi)=\frac{\sigma }{2}\phi^{2}+\frac{\lambda}{4}\phi^{4}$. Using the Lefschetz thimble decomposition and the saddle point expansion, we derive multiple asymptotic formal series of the correlation function associated with the pertur

Rosemary He, Ichiro Takeuchi

Alzheimer's Disease is challenging to diagnose due to our limited understanding of its mechanism and large heterogeneity among patients. Neurodegeneration is studied widely as a biomarker for clinical diagnosis, which can be measured from time series MRI progression. On the other hand, generative AI has shown promise in anomaly detection in medical imaging a

David Dudal, Ken Vandermeersch

This paper investigates a recent construction using bumpified Haar wavelets to demonstrate explicit violations of the Bell-Clauser-Horne-Shimony-Holt inequality within the vacuum state in quantum field theory. The construction was tested for massless spinor fields in $(1+1)$-dimensional Minkowski spacetime and is claimed to achieve violations arbitrarily clo

Mihaly Novak

A new model, for the accurate simulation of multiple Coulomb scattering (MSC) of e-/e+, has been implemented in Geant4 recently and made available with version Geant4-10.4. The model is based on Goudsmit-Saunderson (GS) angular distributions computed by utilising the screen Rutherford (SR) DCS and follows very closely the formulation developed by Kawrakow [1

Haoran Hao, Jiaming Han, Changsheng Li, Yu-Feng Li

The development of large language models (LLMs) has significantly enhanced the capabilities of multimodal LLMs (MLLMs) as general assistants. However, lack of user-specific knowledge still restricts their application in human's daily life. In this paper, we introduce the Retrieval Augmented Personalization (RAP) framework for MLLMs' personalization. Starting

Anton A. Kulbakov, Ellen Häußler, Kaushick K. Parui, Aswathi Mannathanath Chakkingal

CuSn(OH)$_6$ is a quantum spin system from the family of magnetic double perovskite hydroxides, having a frustrated magnetic sublattice. It is also known as the natural mineral mushistonite, whose crystal structure has remained elusive for decades. Here we employ x-ray and neutron powder diffraction to solve the crystal structure of CuSn(OH)$_6$ and propose

Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang

The rapid growth of blockchain technology has driven the widespread adoption of smart contracts. However, their inherent vulnerabilities have led to significant financial losses. Traditional auditing methods, while essential, struggle to keep pace with the increasing complexity and scale of smart contracts. Large Language Models (LLMs) offer promising capabi

Xiaoying Dai, Yunying Fan, Zhiqiang Sheng

In this paper, we propose a subspace method based on neural networks for eigenvalue problems with high accuracy and low cost. We first construct a neural network-based orthogonal basis by some deep learning method and dimensionality reduction technique, and then calculate the Galerkin projection of the eigenvalue problem onto the subspace spanned by the orth

José Giraldo, Martí Llopart-Font, Alex Peiró-Lilja, Carme Armentano-Oller

High-quality audio data is a critical prerequisite for training robust text-to-speech models, which often limits the use of opportunistic or crowdsourced datasets. This paper presents an approach to overcome this limitation by implementing a denoising pipeline on the Catalan subset of Commonvoice, a crowd-sourced corpus known for its inherent noise and varia

Vincenzo Amato, Alba Lia Masiello, Carlo Nitsch, Cristina Trombetti

We study the behaviour, as $p \to +\infty$, of the second eigenvalues of the $p$-Laplacian with Robin boundary conditions and the limit of the associated eigenfunctions. We prove that, up to some regularity of the set, the limit of the second eigenvalues is actually the second eigenvalue of the so-called $\infty$-Laplacian.

Xuezhi Xiang, Xi Wang, Lei Zhang, Denis Ombati

Scene flow estimation aims to generate the 3D motion field of points between two consecutive frames of point clouds, which has wide applications in various fields. Existing point-based methods ignore the irregularity of point clouds and have difficulty capturing long-range dependencies due to the inefficiency of point-level computation. Voxel-based methods s

Urei Miura, Keisuke Totsuka

We investigate the emergence of spacetime supersymmetry (SUSY) in an interacting Kitaev chain model with explicit microscopic $\mathcal{N}=1$ quantum mechanical SUSY. As the interaction strength is varied, the model transitions from a weak-coupling gapless phase with spontaneously broken SUSY to a strong-coupling phase with restored SUSY. In this paper, we n

Marina Ruiz-García, Miguel Querejeta, Santiago García-Burillo, Eric Emsellem

Bars are remarkable stellar structures that can transport gas toward centers and drive the secular evolution of galaxies. In this context, it is important to locate dynamical resonances associated with bars. For this study, we used ${Spitzer}$ near-infrared images as a proxy for the stellar gravitational potential and the ALMA CO(J=2-1) gas distribution from

Odysseas S. Chlapanis, Dimitrios Galanis, Ion Androutsopoulos

We present Legal Argument Reasoning (LAR), a novel task designed to evaluate the legal reasoning capabilities of Large Language Models (LLMs). The task requires selecting the correct next statement (from multiple choice options) in a chain of legal arguments from court proceedings, given the facts of the case. We constructed a dataset (LAR-ECHR) for this tas

Vijay Prakash Dwivedi, Viktor Schlegel, Andy T. Liu, Thanh-Tung Nguyen

Large Language Models (LLMs) have demonstrated remarkable performance across various domains, including healthcare. However, their ability to effectively represent structured non-textual data, such as the alphanumeric medical codes used in records like ICD-10 or SNOMED-CT, is limited and has been particularly exposed in recent research. This paper examines t

Archana Soam, Ka Ho Yuen, Ian Stephens, Chi Yan Law

Magnetic fields (B-fields) are ubiquitous in the interstellar medium (ISM), and they play an essential role in the formation of molecular clouds and subsequent star formation. However, B-fields in interstellar environments remain challenging to measure, and their properties typically need to be inferred from dust polarization observations over multiple physi

Shuichang Lai, Letian Huang, Jie Guo, Kai Cheng

Reconstructing objects from posed images is a crucial and complex task in computer graphics and computer vision. While NeRF-based neural reconstruction methods have exhibited impressive reconstruction ability, they tend to be time-comsuming. Recent strategies have adopted 3D Gaussian Splatting (3D-GS) for inverse rendering, which have led to quick and effect

Maksym Fritsak, Hubert S. Gabryś, Preethi Mohan, Matthias Guckenberger

The Standardized Uptake Value (SUV) is a critical metric in positron emission tomography (PET) imaging, used to assess metabolic activity. However, calculating SUV from DICOM files presents challenges due to vendor-specific DICOM attributes and variations in the encoding of radiotracer accumulation times. This technical note introduces a robust, vendor-speci

Romain Petrides

We prove the existence of optimal metrics for a wide class of combinations of Laplace eigenvalues on closed orientable surfaces of any genus. The optimal metrics are explicitely related to Laplace minimal eigenmaps, defined as branched minimal immersions into ellipsoids parametrized by the eigenvalues of the critical metrics whose coordinates are eigenfuncti

M. Kulig, T. Maslowski, K. A. Kouzakov, V. K. Dugaev

Altermagnetism became very popular because of unique features, namely coupling between magnetic properties and momentum of itinerant electrons. The particular model of the altermagnetic system of our interest has already been studied in recent publications in a different context: Phys. Rev. B \textbf{108}, L140408 (2023). Here, we study the scattering proces

Resmawan Resmawan, Agus Suryanto, Isnani Darti, Hasan S Panigoro

In this article, we develop a predator-prey model with Allee effect and prey group defense. The model has three equilibrium points i.e. the trivial point, the predator extinction point, and the coexistence point. All equilibrium points are locally asymptotically stable under certain conditions. The Allee effect in this model influences the stability of the e

Yuxuan Liu, Wenyuan Li, Laizhong Cui, Hailiang Yang

Large language models (LLMs) often face a bottleneck in inference speed due to their reliance on auto-regressive decoding. Recently, parallel decoding has shown significant promise in enhancing inference efficiency. However, we have identified two key issues with existing parallel decoding frameworks: (1) decoding heads fail to balance prediction accuracy an

Jiacong Zhou, Xianyun Wang, Min Zhang, Jun Yu

Aligning large language models with human preferences is essential for improving interaction quality and safety by ensuring outputs better reflect human values. A promising strategy involves Reinforcement Learning from Human Feedback (RLHF), starting with collecting and ranking responses generated by a supervised fine-tuning model to refine alignment. Existi

Yu Yuan, Lili Zhao, Kai Zhang, Guangting Zheng

Large Language Models (LLMs) have shown remarkable capabilities in various natural language processing tasks. However, LLMs may rely on dataset biases as shortcuts for prediction, which can significantly impair their robustness and generalization capabilities. This paper presents Shortcut Suite, a comprehensive test suite designed to evaluate the impact of s

Jan Melechovsky, Ambuj Mehrish, Berrak Sisman, Dorien Herremans

Recent advancements in Text-to-Speech (TTS) systems have enabled the generation of natural and expressive speech from textual input. Accented TTS aims to enhance user experience by making the synthesized speech more relatable to minority group listeners, and useful across various applications and context. Speech synthesis can further be made more flexible by

Florian E. Dorner, Vivian Y. Nastl, Moritz Hardt

High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem. Scalable evaluation methods that avoid costly annotation have therefore become an important research ambition. Many hope to use strong existing models in lieu of costly labels to provide cheap model evaluations. Unfortunately, this method of using mo

M. Atoui, M. Hoballah, M. Lassaut, J. Van de Wiele

The present paper proposes a robust evaluation of any radial density at small distances using negative-order radial moments evaluated in momentum space. This evaluation provides a valuable insight into the behavior of a given radial density in the vicinity of $r=0$, and puts strong emphasis on the importance of measuring form factors at large squared four-mo

Ingeol Baek, Hwan Chang, Byeongjeong Kim, Jimin Lee

Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge. However, traditional retrieve-and-generate processes may not be optimized for real-world scenarios, where queries might require multiple retrieval steps or none at all. In this paper, we propose a Probing-RAG, which utilizes the hidden s

Hongfan Gao, Wangmeng Shen, Xiangfei Qiu, Ronghui Xu

Probabilistic time series imputation has been widely applied in real-world scenarios due to its ability for uncertainty estimation and denoising diffusion probabilistic models~(DDPMs) have achieved great success in probabilistic time series imputation tasks with its power to model complex distributions. However, current DDPM-based probabilistic time series i

Zao Zhang, Huaming Chen, Pei Ning, Nan Yang

In knowledge distillation, a primary focus has been on transforming and balancing multiple distillation components. In this work, we emphasize the importance of thoroughly examining each distillation component, as we observe that not all elements are equally crucial. From this perspective,we decouple the Kullback-Leibler (KL) divergence into three unique ele

Benoît Valiron

This thesis (Habilitation \`a diriger des recherches) presents some of my research contributions since my Ph.D defense in 2008. I have had the chance to participate in the development of quantum programming languages since their early developments: the presentation aims to present my point of view on the evolution of the subject, my contributions, and the cu

Lucas Giroto de Oliveira, Yueheng Li, Benedikt Geiger, Laurent Schmalen

Integrated sensing and communication (ISAC) is a novel capability expected for sixth generation (6G) cellular networks. To that end, several challenges must be addressed to enable both mono- and bistatic sensing in existing deployments. A common impairment in both architectures is oscillator phase noise (PN), which not only degrades communication performance

Joaquín Domínguez-de-Tena, Aníbal Rodríguez-Bernal

In this work, we study the asymptotic behaviour of solutions to the heat equation in exterior domains, i.e., domains which are the complement of a smooth compact set in $\mathbb{R}^N$. Different homogeneous boundary conditions are considered, including Dirichlet, Robin, and Neumann ones. In this second part of our work, we consider the case of bounded initia

Isack Lee, Haebin Seong

Although large language models (LLMs) demonstrate impressive proficiency in various tasks, they present potential safety risks, such as `jailbreaks', where malicious inputs can coerce LLMs into generating harmful content bypassing safety alignments. In this paper, we delve into the ethical biases in LLMs and examine how those biases could be exploited for ja

Haoyang Li, Fangcheng Fu, Hao Ge, Sheng Lin

As the scale of models and training data continues to grow, there is an expanding reliance on more GPUs to train large-scale models, which inevitably increases the likelihood of encountering dynamic stragglers that some devices lag behind in performance occasionally. However, hybrid parallel training, one of the de facto paradigms to train large models, is t

Zeren Shui, Petros Karypis, Daniel S. Karls, Mingjian Wen

Citation intention Classification (CIC) tools classify citations by their intention (e.g., background, motivation) and assist readers in evaluating the contribution of scientific literature. Prior research has shown that pretrained language models (PLMs) such as SciBERT can achieve state-of-the-art performance on CIC benchmarks. PLMs are trained via self-sup

Rushi Shah, Mingyuan Yan, Michael Curtis Mozer, Dianbo Liu

The Straight-Through Estimator (STE) is the dominant method for training neural networks with discrete variables, enabling gradient-based optimisation by routing gradients through a differentiable surrogate. However, existing STE variants conflate two fundamentally distinct concerns: forward-pass stochasticity, which controls exploration and latent space uti

Markus Doepfert, Soner Candas, Hermann Kraus, Peter Tzscheutschler

The shift towards decentralized and renewable energy sources has introduced significant challenges to traditional power systems, necessitating innovative market designs. Local energy markets present a viable solution for integrating distributed energy resources such as photovoltaic systems, electric vehicles, and heat pumps within various grid topologies. Th

Marie Doumic, Sophie Hecht, Marc Hoffmann, Diane Peurichard

Originally motivated by the morphogenesis of bacterial microcolonies, the aim of this article is to explore models through different scales for a spatial population of interacting, growing and dividing particles. We start from a microscopic stochastic model, write the corresponding stochastic differential equation satisfied by the empirical measure, and rigo

Zhehui Zhang

Recent SELD research has predominantly focused on long-time segment scenarios (typically 5 to 10 seconds, occasionally 2 seconds), improving benchmark performance but lacking the temporal granularity needed for real-world applications. To bridge this gap, this paper investigates SELD with distance estimation (3D SELD) systems under short-time segments, speci

Ziye Mo, Chunyi Li, Wenting Zhang, Chang Liu

Uniaxial strain is a powerful tuning parameter that can control symmetry and anisotropic electronic properties in iron-based superconductors. However, accurately characterizing anisotropic strain can be challenging and complex. Here, we utilize a cryogenic optical system equipped with a high-spatial-resolution microscope to characterize surface strains in ir

Leonardo Pasquarelli, Charles Koutcheme, Arto Hellas

Providing sufficient support for students requires substantial resources, especially considering the growing enrollment numbers. Students need help in a variety of tasks, ranging from information-seeking to requiring support with course assignments. To explore the utility of recent large language models (LLMs) as a support mechanism, we developed an LLM-powe

Jie Peng, Zhang Cao, Huaizhi Qu, Zhengyu Zhang

Although Large Language Models (LLMs) have demonstrated remarkable capabilities, their massive parameter counts and associated extensive computing make LLMs' deployment the main part of carbon emission from nowadays AI applications. Compared to modern GPUs like H$100$, it would be significantly carbon-sustainable if we could leverage old-fashioned GPUs such

Sabrina Renaud, Léa Amar, Pascale Chevret, Caroline Romestaing

The semicircular canals of the inner ear are involved in balance and velocity control. Being crucial to ensure efficient mobility, their morphology exhibits an evolutionary conservatism attributed to stabilizing selection. Release of selection in slow-moving animals has been argued to lead to morphological divergence and increased inter-individual variation.

Martin O'Byrne, Badre Kerzabi, Marco Abbarchi, Alejo Lifschitz

This work describes the elaboration of rutile titanium dioxide films with high refractive indices and low scattering by sol-gel process and controlled crystallization. The evolutions of the optical properties and crystalline structure of sol-gel processed titania coatings on fused silica were investigated for different thermal budgets of the annealing post-t

Raphaël Gass, Zhongliang Li, Rachid Outbib, Samir Jemei

Technologies based on the use of hydrogen are promising for future energy requirements in a more sustainable world. Consequently, modelling fuel cells is crucial, for instance, to optimize their control to achieve excellent performance, to test new materials and configurations on a limited budget, or to consider their degradation for improved lifespan. To de

Giovanni Braglia, Davide Tebaldi, André Eugenio Lazzaretti, Luigi Biagiotti

In robotics, Learning from Demonstration (LfD) aims to transfer skills to robots by using multiple demonstrations of the same task. These demonstrations are recorded and processed to extract a consistent skill representation. This process typically requires temporal alignment through techniques such as Dynamic Time Warping (DTW). In this paper, we consider a

Kyungmin Min, Minbeom Kim, Kang-il Lee, Dongryeol Lee

Large Vision-Language Models (LVLMs) demonstrate impressive capabilities in generating detailed and coherent responses from visual inputs. However, they are prone to generate hallucinations due to an over-reliance on language priors. To address this issue, we investigate the language priors in LVLMs and make two key observations: (1) Even when predicting the

Novel Certad, Cristina Olaverri-Monreal, Friedrich Wiesinger, Tomasz E. Burghardt

Road markings were reported as critical road safety features, equally needed for both human drivers and for machine vision technologies utilised by advanced driver assistance systems (ADAS) and in driving automation. Visibility of road markings is achieved because of their colour contrasting with the roadway surface. During recent testing of an open-source c

Ruixian Liu, Qi Tang, Chang Liu, Chunyi Li

In iron-based superconductors (FeSCs), the interplay between electronic nematicity and superconductivity is essential for understanding the exotic superconducting ground state. In the nematic regime, uniaxial-strain ($\varepsilon$) tuning of the superconducting transition temperature $T_c$ [$\Delta T_c(\varepsilon)=\alpha\varepsilon+\beta\varepsilon^2$] offe

Caroline Sabty

Natural Language Processing (NLP) is a vital computational method for addressing language processing, analysis, and generation. NLP tasks form the core of many daily applications, from automatic text correction to speech recognition. While significant research has focused on NLP tasks for the English language, less attention has been given to Modern Standard

A. V. Guglielmi

The elementary theory of relaxation of the source cooling down after the main shock of an earthquake is presented axiomatically. The names of the objects under study are given and the relationships between them are determined. A new basic concept of earthquake source deactivation is introduced and a procedure for calculating the deactivation coefficient from

Mohsen Raoufi, Pawel Romanczuk, Heiko Hamann

We present the Light Augmented Reality System LARS as an open-source and cost-effective tool. LARS leverages light-projected visual scenes for indirect robot-robot and human-robot interaction through the real environment. It operates in real-time and is compatible with a range of robotic platforms, from miniature to middle-sized robots. LARS can support rese

E. Ahmadi-Azar, K. Atazadeh, A. Eghbali

In solving the Brans-Dicke (BD) equations in the BD theory of gravity, their linear independence is important. This is due to fact that in solving these equations in cosmology, if the number of unknown quantities is equal to the number of independent equations, then the unknowns can be uniquely determined. In the BD theory, the tensor field $g_{\mu \nu}$ and

Federico Bernini, Bartosz Bieganowski, Daniel Strzelecki

We show an abstract critical point theorem about existence of infinitely many critical orbits to strongly indefinite functionals with sign-changing nonlinear part defined on a dislocation space with a discrete group action. We apply the abstract result to a Schr\"odinger equation $$ -\Delta u + V(x) u = f(u) - \lambda g(u) $$ with $0$ in the spectral gap of

ChaoRong Li, XuDong Ling, YiLan Xue, Wenjie Luo

Short-term precipitation forecasting remains challenging due to the difficulty in capturing long-term spatiotemporal dependencies. Current deep learning methods fall short in establishing effective dependencies between conditions and forecast results, while also lacking interpretability. To address this issue, we propose a Precipitation Nowcasting Using Diff

Xinmeng Hou

This study introduces a prescriptive annotation benchmark grounded in humanities research to ensure consistent, unbiased labeling of offensive language, particularly for casual and non-mainstream language uses. We contribute two newly annotated datasets that achieve higher inter-annotator agreement between human and language model (LLM) annotations compared

Huiguang Zhang, Baoguo Liu

Compressed Spectrum Sensing (CSS) is widely employed in spectral analysis due to its sampling efficiency. However, conventional CSS assumes a standard sparse spectrum, which is affected by Spectral Leakage (SL). Despite the widespread use of CSS, the impact of SL on its performance has not been systematically and thoroughly investigated. This study addresses

Chuhao Zhou, Chenxi Jiang, Yi Xie, Haozhi Cao

Dataset Distillation (DD) seeks to create a condensed dataset that, when used to train a model, enables the model to achieve performance similar to that of a model trained on the entire original dataset. It relieves the model training from processing massive data and thus reduces the computation resources, storage, and time costs. This paper illustrates our

Ruud JG van Sloun

Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. Nevertheless US image quality and diagnostic efficacy are still highly operator- and patient-dependent. In difficult-to-image patients, image quality is often insufficient for relia

Natalia Accomazzo, Daniel Carando, Rocio Nores, Victoria Paternostro

We study the short-time Fourier transform phase retrieval problem in locally compact abelian groups. Using probabilistic methods, we show that for a large class of groups $G$ and compact subsets $K\subseteq G$ there exists a window function and a uniformly discrete set in $G\times \widehat{G}$ allowing phase retrieval in $L^2(K)$. We also study the obstructi

Hayat. Laassiri, Ahmed. Daassou, Rachid. Benbrik

In this paper, we investigate the phase transitions and critical behavior of a nonlinear magnetically charged rotating AdS black hole, with a particular emphasis on the influence of a quintessence field. Our comprehensive thermodynamic analysis explores the impact of thermal fluctuations on the black hole's properties. We observe that for larger black holes,

Kotone Tajiri, Riki Murakami, Shunsuke Kobayashi, Ryuichi Tarumi

Knitted fabrics are two-dimensional-like structures formed by stitching one-dimensional yarn into three-dimensional curves. Plain stitch or stockinette stitch, one of the most fundamental knitting stitches, consists of periodic lattices of bent yarns, where three-dimensional (3D) curling behavior naturally emerges at the edges. The elasticity and geometry of

Ruiyue Li, Fei He, Licai Deng, Xiaodian Chen

The cloud cover and meteorological parameters serve as fundamental criteria for the qualification of an astronomical observatory working in optical and infrared wavelengths. In this paper, we present a systematic assessment of key meteorological parameters at the Lenghu site. The datasets adopted in this study includes the meteorological parameters collected

Thao Do, Dinh Phu Tran, An Vo, Daeyoung Kim

Extracting fine-grained OCR text from aged documents in diacritic languages remains challenging due to unexpected artifacts, time-induced degradation, and lack of datasets. While standalone spell correction approaches have been proposed, they show limited performance for historical documents due to numerous possible OCR error combinations and differences bet

Ulrich Haisch, Luc Schnell

We calculate the one- and two-loop matching corrections in the Standard Model effective field theory (SMEFT) that impact electroweak precision measurements and flavour physics observables, focusing on the contributions of third-generation four-quark operators. Our results provide a crucial ingredient for a model-independent analysis of constraints on beyond

Chongyang Wan, Shunbo Lei, Yuan Luo

The increasing severity of climate change necessitates an urgent transition to renewable energy sources, making the large-scale adoption of wind energy crucial for mitigating environmental impact. However, the inherent uncertainty of wind power poses challenges for grid stability, underscoring the need for accurate wind energy prediction models to enable eff

Wen-Bin He, Giedrius Žlabys, Hoshu Hiyane, Sarika Sasidharan Nair

Lieb excitations are fundamental to the understanding of the low energy behaviour of many-body quantum gases. Here we study the spectral function of a Tonks-Girardeau gas in a finite sized Kronig-Penney potential and show that the Lieb-I and Lieb-II excitations can become gapped as a function of the barrier height. Moreover, we reveal the existence of a topo

Louis Mahon, Mirella Lapata

The proliferation of creative video content has driven demand for textual descriptions or summaries that allow users to recall key plot points or get an overview without watching. The volume of movie content and speed of turnover motivates automatic summarisation, which is nevertheless challenging, requiring identifying character intentions and very long-ran

Jaime Jiménez

This paper presents the IETF Insights project, an automated system that streamlines the generation of comprehensive reports on the activities of the Internet Engineering Task Force (IETF) Working Groups. The system collects, consolidates, and analyzes data from various IETF sources, including meeting minutes, participant lists, drafts and agendas. The core c

Roman Soletskyi, Marylou Gabrié, Bruno Loureiro

While deep learning has expanded the possibilities for highly expressive variational families, the practical benefits of these tools for variational inference (VI) are often limited by the minimization of the traditional Kullback-Leibler objective, which can yield suboptimal solutions. A major challenge in this context is \emph{mode collapse}: the phenomenon

David Hoffmann, Kailash Budhathoki, Matthaeus Kleindessner

The evolving capabilities of large language models are accompanied by growing sizes and deployment costs, necessitating effective inference optimisation techniques. We propose a novel pruning method utilising centrality measures from graph theory, reducing both the computational requirements and the memory footprint of these models. Specifically, we devise a