March 2024 arXiv papers — page 19

Shoei Takahashi, Hikaru Manabe, Ryohei Miyadera

In this study, we study the relation between Grundy numbers of a Maximum Nim and Josephus problem. Let f(x) = floor(x/k), where floor( ) is the floor function and k is a positive integer. We prove that there is a simple relation with a Maximum Nim with the rule function f and the Josephus problem in which every k-th numbers are to be removed.

Wooseok Kang, Ho Seong Hwang, Hyunmi Song, Changbom Park

We study the global kinematics of the Perseus galaxy cluster (Abell 426) at redshift z = 0.017 using a large sample of galaxies from our new MMT/Hectospec spectroscopic observation for this cluster. The sample includes 1447 galaxies with measured redshifts within 60' from the cluster center (1148 from this MMT/Hectospec program and 299 from the literature).

Chuyang Shang, Tian Ma, Wanzhu Ren, Yuancheng Li

Existing pseudo label generation methods for point weakly supervised object detection are inadequate in low data volume and dense object detection tasks. We consider the generation of weakly supervised pseudo labels as the model's sparse output, and propose Sparse Generation as a solution to make pseudo labels sparse. The method employs three processing stag

Yu Li, Shenyu Zhang, Rui Wu, Xiutian Huang

Recent advancements in generative Large Language Models(LLMs) have been remarkable, however, the quality of the text generated by these models often reveals persistent issues. Evaluating the quality of text generated by these models, especially in open-ended text, has consistently presented a significant challenge. Addressing this, recent work has explored t

J. W. E. Mallinson, K. Lind, A. M. Amarsi, K. Youakim

Rapidly growing datasets from stellar spectroscopic surveys are providing unprecedented opportunities to analyse the chemical evolution history of our Galaxy. However, spectral analysis requires accurate modelling of synthetic stellar spectra for late-type stars, for which the assumption of local thermodynamic equilibrium (LTE) has been shown to be insuffici

Joshua Ebere Chukwuere

This research explores the quickly changing field of generative artificial intelligence (GAI) chatbots in higher education, an industry that is undergoing major technological changes. AI chatbots, such as ChatGPT, HuggingChat, and Google Bard, are becoming more and more common in a variety of sectors, including education. Their acceptance is still in its ear

Zahra Abbasiantaeb, Simon Lupart, Mohammad Aliannejadi

Conversational information seeking (CIS) systems aim to model the user's information need within the conversational context and retrieve the relevant information. One major approach to modeling the conversational context aims to rewrite the user utterance in the conversation to represent the information need independently. Recent work has shown the benefit o

Morgan Facchin, Saba N. Khan, Kishan Dholakia, Graham D. Bruce

Speckle patterns are a powerful tool for high-precision metrology, as they allow remarkable performance in relatively simple setups. Nonetheless, researchers in this field follow rather distinct paths due to underappreciated general principles underlying speckle phenomena. Here, we advise on a universal metric of intrinsic speckle sensitivity, and on the adv

Hugo Jaquard, Pierre-Olivier Amblard, Simon Barthelmé, Nicolas Tremblay

Random diffusions are a popular tool in Monte-Carlo estimations, with well established algorithms such as Walk-on-Spheres (WoS) going back several decades. In this work, we introduce diffusion estimators for the problems of angular synchronization and smoothing on graphs, in the presence of a rotation associated to each edge. Unlike classical WoS algorithms

Tanmay Tripathi, Abhinav Awasthi, Shaurya Pratap Singh, Atul Chaturvedi

Cryptography plays a pivotal role in safeguarding sensitive information and facilitating secure communication. Classical cryptography relies on mathematical computations, whereas quantum cryptography operates on the principles of quantum mechanics, offering a new frontier in secure communication. Quantum cryptographic systems introduce novel dimensions to se

Davide Ferrari, Ilya Peshkov, Evgeniy Romenski, Michael Dumbser

In this paper, we present a unified nonequilibrium model of continuum mechanics for compressible multiphase flows. The model, which is formulated within the framework of Symmetric Hyperbolic Thermodynamically Compatible (SHTC) equations, can describe the arbitrary number of phases that can be heat-conducting inviscid and viscous fluids, as well as elastoplas

Yuzuru Kato

We have recently proposed a fully quantum-mechanical definition of the asymptotic phase for quantum nonlinear oscillators, which is also applicable in the strong quantum regime [Kato and Nakao 2022 Chaos 32 063133]. In this study, we propose a definition of the quantum asymptotic amplitude for quantum oscillatory systems, which naturally extends the asymptot

Soichi Shirai, Takahiro Horiba, Hirotoshi Hirai

Quantum chemical calculations have attracted much attention as a practical application of quantum computing. Quantum computers can prepare superpositions of electronic states with various numbers of electrons on qubits. This special feature could be used to construct an efficient method for analyzing the structural variations of molecules and chemical reacti

Manuel Friedrich, Wojciech Górny, Ulisse Stefanelli

We characterize the unique minimizer of the three-dimensional double-bubble problem with respect to the $\ell_1$-norm for volume ratios between $1/2$ and $2$.

Yiyang Sun, Zhiyuan Xu, Xiaonian Wang, Jing Yao

Self-supervised multi-frame methods have currently achieved promising results in depth estimation. However, these methods often suffer from mismatch problems due to the moving objects, which break the static assumption. Additionally, unfairness can occur when calculating photometric errors in high-freq or low-texture regions of the images. To address these i

Jayathi Hewapathirana, Deshan Sumanathilaka

This work explores the utilization of Romanized Sinhala social media data to identify individuals at risk of depression. A machine learning-based framework is presented for the automatic screening of depression symptoms by analyzing language patterns, sentiment, and behavioural cues within a comprehensive dataset of social media posts. The research has been

Thomas Reichenbach, Johannes Clar, Andreas Pott, Alexander Verl

This paper presents a method for dynamic adjustment of cable preloads based on the actuation redundancy of \acp{CDPR}, which allows increasing or decreasing the platform stiffness depending on task requirements. This is achieved by computing preload parameters with an extended nullspace formulation of the kinematics. The method facilitates the operator's abi

Byungjun Kim, Christoph Mecklenbräuker, Peter Gerstoft

In this study, the modulation of symbols on OFDM subcarriers is classified for transmissions following Wi-Fi~6 and 5G downlink specifications. First, our approach estimates the OFDM symbol duration and cyclic prefix length based on the cyclic autocorrelation function. We propose a feature extraction algorithm characterizing the modulation of OFDM signals, wh

Kim L. Kreienkamp, Sabine H. L. Klapp

Heterogeneity is ubiquitous in biological and synthetic active matter systems that are inherently out of equilibrium. Typically, such active mixtures involve not only conservative interactions between the constituents, but also non-reciprocal couplings, whose full consequences for the collective behavior still remain elusive. Here, we study a minimal active

Nils Asmussen, Michael Roitzsch

Disaggregation is an ongoing trend to increase flexibility in datacenters. With interconnect technologies like CXL, pools of CPUs, accelerators, and memory can be connected via a datacenter fabric. Applications can then pick from those pools the resources necessary for their specific workload. However, this vision becomes less clear when we consider data mov

Afvensu Enoch Ibisu

This study designed a personality-based gamification model for E-learning systems. It also implemented the model and evaluated the performance of the gamification model implemented. These were with a view to developing a model for gamifying personalization of e-learning systems. Personalization requirements for motivational tendencies based on the Myers-Brig

Lili Yang

By identifying each standard flag with a trivalent Feynman diagram, the corresponding propagators can be read directly from the flag itself. Within the flag representation, the kinematic Jacobi identity (equivalently, the residue theorem on moduli spaces) admits a natural interpretation as the equivalence between a complete flag and its gapped counterpart. U

George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang

Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such treatment effects without actual intervention is a standard practice to diminish the risk. However, existing methods for treatment effect prediction tend to rely on training sets o

B. Li, D. Vretenar, T. Nikšić, D. D. Zhang

Nuclear reactions present an interesting case for studies of the time-evolution of entanglement between complex quantum systems. In this work, the time-dependent nuclear density functional theory is employed to explore entanglement in multinucleon transfer reactions. As an illustrative example, for the reaction $^{40}$Ca $+$ $^{208}$Pb at $E_{\rm lab} = 249$

Zhengran Zeng, Yidong Wang, Rui Xie, Wei Ye

In the evolving landscape of large language models (LLMs) tailored for software engineering, the need for benchmarks that accurately reflect real-world development scenarios is paramount. Current benchmarks are either too simplistic or fail to capture the multi-tasking nature of software development. To address this, we introduce CoderUJB, a new benchmark de

Till Aust, Eirini Balta, Argiro Vatakis, Heiko Hamann

In high-pressure environments where human individuals must simultaneously monitor multiple entities, communicate effectively, and maintain intense focus, the perception of time becomes a critical factor influencing performance and well-being. One indicator of well-being can be the person's subjective time perception. In our project $ChronoPilot$, we aim to d

Dany Rios, Felix Scholz, Thomas Takacs

In isogeometric analysis, isogeometric function spaces are employed for accurately representing the solution to a partial differential equation (PDE) on a parameterized domain. They are generated from a tensor-product spline space by composing the basis functions with the inverse of the parameterization. Depending on the geometry of the domain and on the dat

Chenming Tang, Zhixiang Wang, Yunfang Wu

In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task. How to select informative examples remains an open issue. Previous works on in-context example selection for machine translation (MT) focus on superficial word-level features whi

Linlian Xiao, Jiaqian Yuan, Jian Zhou, Yunshun Wu

In this paper, we consider the existence of solutions of the following Kirchhoff-type problem \[ \left\{ \begin{array} [c]{ll} -\left(a+b\int_{\mathbb{R}^3}|\nabla u|^2dx\right)\Delta u+ V(x)u=f(x,u),~{\rm{in}}~ \mathbb{R}^{3},\\ u\in H^1(\mathbb{R}^3), \end{array} \right. \] where $a,b$ are postive constants, and the potential $V(x)$ is continuous and indef

Chenming Tang, Fanyi Qu, Yunfang Wu

In the era of large language models (LLMs), in-context learning (ICL) stands out as an effective prompting strategy that explores LLMs' potency across various tasks. However, applying LLMs to grammatical error correction (GEC) is still a challenging task. In this paper, we propose a novel ungrammatical-syntax-based in-context example selection strategy for G

Ryu Tomonaga

We classify two-dimensional complete local rings $(R,\mathfrak{m},k)$ of finite Cohen-Macaulay type where $k$ is an arbitrary field of characteristic zero, generalizing works of Auslander and Esnault for algebraically closed case. Our main result shows that they are precisely of the form $R=l[[x_1,x_2]]^G$ where $l/k$ is a finite Galois extension and $G$ is

Philippe Bolle, Marco Mazzucchelli, Andrea Venturelli

A level orbit of a mechanical Hamiltonian system is a solution of Newton equation that is contained in a level set of the potential energy. In 2003, Mark Levi asked for a characterization of the smooth potential energy functions on the plane with the property that any point on the plane lies on a level orbit; we call such functions Levi potentials. The basic

José A. Almanza-Marrero, Gonzalo Manzano

Quantum coherence has been shown to impact the operational capabilities of quantum systems performing thermodynamic tasks in a significant way, and yet the possibility and conditions for genuine coherence-enhanced thermodynamic operation remain unclear. Introducing a comparison with classical machines using the same set of thermodynamic resources, we show th

Hao Lang, Fei Huang, Yongbin Li

Reinforcement learning from human feedback (RLHF) has emerged as an effective approach to aligning large language models (LLMs) to human preferences. RLHF contains three steps, i.e., human preference collecting, reward learning, and policy optimization, which are usually performed serially. Despite its popularity, however, (fixed) reward models may suffer fr

Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, Robby T. Tan

Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher framework. However, a fundamental issue arises from the class imbalance in the labelled training set, which can result in inaccurate pseudo-labe

Mahsa Seyed Heydari, Wolfgang Belzig, Niklas Rohling

Motivated by experimental work showing enhancement of spin transport between Yttrium Iron Garnet and Platinum by a thin antiferromagnetic insulator between them, we consider spin transport through the interface of a non-magnetic metal and compensated antiferromagnetically ordered insulator and focus on the significance of the interface itself. The spin trans

Kexin Shi, Jing Zhang, Linjiajie Fang, Wenjia Wang

In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning. However, the inadvertent selection of false negatives remains a major concern in hard negative sampling, as these false negatives can provide incorrect information and mislead the model learning. To date, only a smal

Junkai Zhou, Liang Pang, Ya Jing, Jia Gu

Constructing personalized and anthropomorphic agents holds significant importance in the simulation of social networks. However, there are still two key problems in existing works: the agent possesses world knowledge that does not belong to its personas, and it cannot eliminate the interference of diverse persona information on current actions, which reduces

Valeria Chiadò Piat, Virginia De Cicco, Anderson Melchor Hernandez

In this study, we approach the analysis of a degenerate nonlinear functional in one dimension, accommodating a degenerate weight $w$. Our investigation focuses on establishing an explicit relaxation formula for a functional exhibiting $p$-growth for $1< p<+\infty$. We adopt the approach developed in [6], where some assumptions like doubling or Muckenhoupt co

Menglin Li, Kwan Hui Lim

To address the challenges of scarcity in geotagged data for social user geolocation, we propose FewUser, a novel framework for Few-shot social User geolocation. We incorporate a contrastive learning strategy between users and locations to improve geolocation performance with no or limited training data. FewUser features a user representation module that harn

Robin Chemnitz, Maximilian Engel, Péter Koltai

Coherent sets are time-dependent regions in the physical space of nonautonomous flows that exhibit little mixing with their neighborhoods, robustly under small random perturbations of the flow. They thus characterize the global long-term transport behavior of the system. We propose a framework to extract such time-dependent families of coherent sets for nona

Forkan Uddin Ahmed, Annesha Das, Md Zubair

The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However, choosing crops with better production rates and efficiently controlling crop disease are obstacles that farmers have to face.

Lei Lan, Zixuan Lu, Jingyi Long, Chun Yuan

This paper pushes the performance of cloth simulation, making the simulation interactive even for high-resolution garment models while keeping every triangle untangled. The penetration-free guarantee is inspired by the interior point method, which converts the inequality constraints to barrier potentials. We propose a major overhaul of this modality within t

Antonio Guerriero, Roberto Pietrantuono, Stefano Russo

Deep Neural Networks (DNN) are core components for classification and regression tasks of many software systems. Companies incur in high costs for testing DNN with datasets representative of the inputs expected in operation, as these need to be manually labelled. The challenge is to select a representative set of test inputs as small as possible to reduce th

Dahyun Kim, Yungi Kim, Wonho Song, Hyeonwoo Kim

As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important. We propose stepwise DPO (sDPO), an extension of the recently popularized direct preference optimization (DPO) for alignment tuning. This approach involves dividing the available preference datasets and utilizing them in a stepwise

O. Feher, S. E. Ragan, F. D. Priestley, P. C. Clark

Recent advances in identifying giant molecular filaments in galactic surveys allow us to study the interstellar material and its dense, potentially star forming phase on scales comparable to resolved extragalactic clouds. Two large filaments detected in the CHIMPS $^{13}$CO(3-2) survey, one in the Sagittarius-arm and one in an inter-arm region, were mapped w

Wei Wei

We establish the Liouville theorem for positive constant $\sigma_{k}$-curvature equation in $\mathbb{R}_{+}^{n}$ and positive constant boundary $\mathcal{B}_{k}^{g}$ curvature equation, where the boundary curvature $\mathcal{B}_{k}^{g}$ is discovered by Sophie Chen \cite{Chen} from the natural variational functional for $\sigma_{k}(A_{g})$.

Xianhao Yu, Jiaqi Fu, Renjia Deng, Wenjuan Han

While Vision-Language Models (VLMs) hold promise for tasks requiring extensive collaboration, traditional multi-agent simulators have facilitated rich explorations of an interactive artificial society that reflects collective behavior. However, these existing simulators face significant limitations. Firstly, they struggle with handling large numbers of agent

Christopher J. Banks, Aeron Sanchez, Vicki Stewart, Kate Bowen

Diagnostic tests that can detect pre-clinical or sub-clinical infection, are one of the most powerful tools in our armoury of weapons to control infectious diseases. Considerable effort has been paid to improving diagnostic testing for human, plant and animal diseases, including strategies for targeting the use of diagnostic tests towards individuals who are

Qingqing Peng, Dawei Yin, Dongxu Chang, Yuan Li

Efficient decoding is crucial to high-throughput and power-sensitive wireless communication scenarios. A theoretical analysis of the performance-complexity tradeoff toward low-complexity decoding is required for a better understanding of the fundamental limits in the above-mentioned scenarios. This study aims to explore the performance of LDPC codes under be

Beerend G. A. Gerats, Jelmer M. Wolterink, Seb P. Mol, Ivo A. M. J. Broeders

Laparoscopic video tracking primarily focuses on two target types: surgical instruments and anatomy. The former could be used for skill assessment, while the latter is necessary for the projection of virtual overlays. Where instrument and anatomy tracking have often been considered two separate problems, in this paper, we propose a method for joint tracking

Fatama Tuz Johora, Md Shahedul Islam Khan, Esrath Kanon, Mohammad Abu Tareq Rony

Cyber risk refers to the risk of defacing reputation, monetary losses, or disruption of an organization or individuals, and this situation usually occurs by the unconscious use of cyber systems. The cyber risk is unhurriedly increasing day by day and it is right now a global threat. Developing countries like Bangladesh face major cyber risk challenges. The g

Mohammad Hassan Shirdareh Haghighi, Amir Mohammad Ghazanfari, Seyed Ali Reza Talebpour Shirazi Fard

For a graph $G$, a $k$-coloring $c:V(G)\to \{1,2,\ldots, k\}$ is called distinguishing, if the only automorphism $f$ of $G$ with the property $c(v)=c(f(v))$ for every vertex $v\in G$ (color-preserving automorphism), is the identity. In this paper, we show that the number of distinguishing $k$-colorings of $G$ is a monic polynomial in $k$, calling it the dist

Giulia d'Addato, Ruggero Carli, Eurico Pedrosa, Artur Pereira

Accurate dynamic models are crucial for many robotic applications. Traditional approaches to deriving these models are based on the application of Lagrangian or Newtonian mechanics. Although these methods provide a good insight into the physical behaviour of the system, they rely on the exact knowledge of parameters such as inertia, friction and joint flexib

David Rath, Vojtěch Mikerásek, Chunlei Wang, Moritz Eder

Infrared Reflection Absorption Spectroscopy (IRAS) on dielectric single crystals is challenging because the optimal incidence angles for light-adsorbate interaction coincide with regions of low IR reflectivity. Here, we introduce an optimized IRAS setup that maximizes the signal-to-noise ratio for non-metals. This is achieved by maximizing light throughput,

Dieter Coppens, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter

Indoor positioning using UWB technology has gained interest due to its centimeter-level accuracy potential. However, multipath effects and non-line-of-sight conditions cause ranging errors between anchors and tags. Existing approaches for mitigating these ranging errors rely on collecting large labeled datasets, making them impractical for real-world deploym

Phongpichit Channuie

In this work, we extend the investigation of the consequences of thermal fluctuations on the Casimir effect within the context of a traversable wormhole, recently proposed by Garattini \& Faizal, arXiv:2403.15174 [gr-qc], subject to charge contributions. Specifically, we focus on scenarios where the plates exhibit both constant and radial variations. In our

Manuel Tonneau, Pedro Vitor Quinta de Castro, Karim Lasri, Ibrahim Farouq

To address the global issue of online hate, hate speech detection (HSD) systems are typically developed on datasets from the United States, thereby failing to generalize to English dialects from the Majority World. Furthermore, HSD models are often evaluated on non-representative samples, raising concerns about overestimating model performance in real-world

Nobuhiro Ueda, Hideko Habe, Yoko Matsui, Akishige Yuguchi

Understanding expressions that refer to the physical world is crucial for such human-assisting systems in the real world, as robots that must perform actions that are expected by users. In real-world reference resolution, a system must ground the verbal information that appears in user interactions to the visual information observed in egocentric views. To t

Zhi Zeng, Yin-Kai Yu, Zhi-Xuan Li, Zi-Xiang Li

The conventional Kibble-Zurek mechanism and the finite-time scaling provide universal descriptions of the driven critical dynamics from gapped initial states based on the adiabatic-impulse scenario. Here we investigate the driven critical dynamics in two-dimensional Dirac systems, which harbor semimetal and Mott insulator phases separated by the quantum crit

Yifei Li, Ryan Chard, Yadu Babuji, Kyle Chard

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programm

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

Using $e^+ e^-$ collision data collected at the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV, corresponding to an integrated luminosity of $7.33~{\rm fb}^{-1}$, we determine the absolute branching fractions of fifteen hadronic $D_s^{+}$ decays with a double-tag technique. In particular, we make precise measurements of the branching

Davide Soranzio, Denny Puntel, Manuel Tuniz, Paulina E. Majchrzak

Understanding the thermoelastic response of a nanostructure is crucial for the choice of materials and interfaces in electronic devices with improved and tailored transport properties, at the length scales of the present technology. Here we show how the deposition of a $MoS_2$ monolayer can strongly modify the nanoscale thermoelastic dynamics of silicon subs

Namhyuk Ahn, Wonhyuk Ahn, KiYoon Yoo, Daesik Kim

Recent progress in diffusion models has profoundly enhanced the fidelity of image generation, but it has raised concerns about copyright infringements. While prior methods have introduced adversarial perturbations to prevent style imitation, most are accompanied by the degradation of artworks' visual quality. Recognizing the importance of maintaining this, w

Wei Duan, Jie Lu, Junyu Xuan

Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by graph structures, prevailing graph learning methods in MARL are limited. They rely solely on one-step observations, neglecting crucial historical experiences, leading to deficient graphs that foster redundant or detr

Sonu Tabitha Paulson, K. K. Mallick, D. K. Ojha

In this paper, we have conducted an investigation focused on a segment of the $Spitzer$ mid-infrared bubble N59, specifically referred to as R1 within our study. Situated in the inner Galactic plane, this region stands out for its hosting of five 6.7 GHz methanol masers, as well as numerous compact H II regions, massive clumps, filaments, and prominent brigh

Guangpu Wu, Shibei Xue, Shan Ma, Sen Kuang

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. With coherent vector representation, we propose a switching control strategy, which can prevent the state of the qubit from entering invariant sets and singular value sets, effectively driving the system ultimately to a sufficiently small

Julian Hellstern, Christoph Tillkorn, Tim Hieronymus, Myriam Kaiser

We present an overview on the development and characterization of multiscale laser processing optics for versatile material modifications across more than six orders of magnitude. Starting with solutions for micromachining we present high-NA microscope objectives creating sub-wavelength material modifications on macroscopic scales with highest peak intensiti

Ruoyu Li, Qing Li, Yu Zhang, Dan Zhao

Anomaly-based network intrusion detection systems (A-NIDS) use unsupervised models to detect unforeseen attacks. However, existing A-NIDS solutions suffer from low throughput, lack of interpretability, and high maintenance costs. Recent in-network intelligence (INI) exploits programmable switches to offer line-rate deployment of NIDS. Nevertheless, current i

Roberto Salazar, Fereshte Shahbeigi

Advancing quantum technologies necessitates an in-depth exploration of how operations generate quantum resources and respond to noise. Crucial are gates generating quantum coherence and the challenge of mitigating gate dephasing noise. Precisely, we study the dephasing noise that reduces the coherence-generating power of quantum gates, its simulation, and cr

Marco Bongiovanni, Luca Gallo, Roberto Grasso, Alfredo Pulvirenti

Graph representation learning has rapidly emerged as a pivotal field of study. Despite its growing popularity, the majority of research has been confined to embedding single-layer graphs, which fall short in representing complex systems with multifaceted relationships. To bridge this gap, we introduce MPXGAT, an innovative attention-based deep learning model

Zhijun Zhao, Feng Zhang, Wenwen Li, Jingwei Li

Satellite remote sensing serves as a crucial means to acquire cloud physical parameters. However, existing official cloud products derived from the advanced geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary satellite suffer from limitations in computational precision and efficiency. In this study, an image-based transfer learning mod

Joshua Ebere Chukwuere

Higher education scholars are interested in an artificial intelligence (AI) technology called ChatGPT, which was developed by OpenAI. Whether ChatGPT can improve learning is still a topic of debate among experts. This concise overview of the literature examines the application of ChatGPT in higher education to comprehend and produce high-level instruction. B

A. Boyce, E. Martínez-Pañeda, P. R. Shearing

In the race to reduce global CO2 emissions and achieve net-zero, chemomechanics must play a critical role in the technological development of current and next-generation batteries to improve their energy storage capabilities and their lifetime. Many degradation processes arise through mechanics via the development of diffusion-induced stress and volumetric s

Yiping Ji, Hemanth Saratchandran, Cameron Gordon, Zeyu Zhang

Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learning. These techniques significantly lower the number of parameters, striking a balance between compactness and performance. However, a common challenge has been the compromise betw

Julien Malard-Adam, Jan Adamowski, Héctor Tuy, Hugo Melgar-Quiñonez

Given the increasing pressures exerted by climate change on small-scale agriculture, the importance of participatory modelling methodologies that can consider both the human and environmental components of these systems has become more and more evident. The current study presents a socioeconomic system dynamics model of the food and environmental systems of

Yuqing Huang, Xin Li, Zikun Zhou, Yaowei Wang

Existing tracking methods mainly focus on learning better target representation or developing more robust prediction models to improve tracking performance. While tracking performance has significantly improved, the target loss issue occurs frequently due to tracking failures, complete occlusion, or out-of-view situations. However, considerably less attentio

Aditya Jha, Yacine Amarouchene, Thomas Salez

Two cylinders rotating next to each other generate a large hydrodynamic force if the intermediate space is filled with a viscous fluid. Herein, we explore the case where the cylinders are separated by two layers of viscous immiscible fluids, in the limit of small capillary deformation of the fluid interface. As the interface deformation breaks the system's s

Sayantan Nag Chowdhury, Md Sayeed Anwar, Dibakar Ghosh

Ensembles of coupled nonlinear oscillators are a popular paradigm and an ideal benchmark for analyzing complex collective behaviors. The onset of cluster synchronization is found to be at the core of various technological and biological processes. The current literature has investigated cluster synchronization by focusing mostly on the case of attractive cou

Julia Puig, Denis Friboulet, Hang Jung Ling, François Varray

Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ventricular filling. Concurrently, deep learning is demonstrating promising outcomes in post-processing of ech

Ting Yang

Let $(W_{t}(\lambda))_{t\ge 0}$, parametrized by $\lambda\in\mathbb{R}$, be the additive martingale related to a supercritical super-Brownian motion on the real line and let $W_{\infty}(\lambda)$ be its limit. Under a natural condition for the martingale limit to be non-degenerate, we investigate the rate at which the martingale approaches its limit. Indeed,

Sidi Yang, Binxiao Huang, Mingdeng Cao, Yatai Ji

The widespread use of high-definition screens in edge devices, such as end-user cameras, smartphones, and televisions, is spurring a significant demand for image enhancement. Existing enhancement models often optimize for high performance while falling short of reducing hardware inference time and power consumption, especially on edge devices with constraine

Kevin Bleakley

We examine rules for predicting whether a point in $\mathbb{R}$ generated from a 50-50 mixture of two different probability distributions came from one distribution or the other, given limited (or no) information on the two distributions, and, as clues, one point generated randomly from each of the two distributions. We prove that nearest-neighbor prediction

Alex Krolewski, Will J. Percival

The amplitude of the baryon signature in galaxy clustering depends on the cosmological baryon fraction. We consider two ways to isolate this signal in galaxy redshift surveys. First, we extend standard template-based Baryon Acoustic Oscillation (BAO) models to include the amplitude of the baryonic signature by splitting the transfer function into baryon and

Pierre Denelle, Boris Leroy, Maxime Lenormand

Bioregionalization consists in the identification of spatial units with similar species composition and is a classical approach in the fields of biogeography and macroecology. The recent emergence of global databases, improvements in computational power, and the development of clustering algorithms coming from the network theory have led to several major upd

Haonan Lin, Mengmeng Wang, Yan Chen, Wenbin An

While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editing": precisely modifying specific features of a subject while maintaining its inherent identity and context. Existing personalization methods either require time-consuming optimiza

Vicky Kyrimi, Kosmas L. Tsakmakidis

Bubble-based metamaterials have been extensively studied both theoretically and experimentally thanks to their simple geometry and their ability to manipulate acoustic waves. The latter is partly dependent on the structural characteristics of the metamaterial and partly dependent on the incident acoustic wave. Initially, the selection of specific structural

Michael Feischl, Caroline Lasser, Christian Lubich, Jörg Nick

This paper studies the numerical approximation of evolution equations by nonlinear parametrizations $u(t)=\Phi(\param(t))$ with time-dependent parameters $\param(t)$, which are to be determined in the computation. The motivation comes from approximations in quantum dynamics by multiple Gaussians and approximations of various dynamical problems by tensor netw

Takahiro Urata, Wataru Hattori, Hiroshi Ikuta

A newly identified magnetic phase called altermagnet is being actively studied because of its unprecedented spin-dependent phenomena. Among the candidate materials, CrSb has a particularly high ordering temperature and a large spin-splitting energy, but its transport properties have remained unexplored. In this study, we report the magnetotransport propertie

Junghyup Lee, Bumsub Ham

Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies, capturing network characteristics related to the final performance. However, network rankings estimated by previous training-free NAS methods have shown weak correlations with the performance. To address this issue, we propose AZ-NAS, a novel app

Bruce M Boghosian, François Dubois, Pierre Lallemand

In this contribution, we address the numerical solutions of high-order asymptotic equivalent partial differential equations with the results of a lattice Boltzmann scheme for an inhomogeneous advection problem in one spatial dimension. We first derive a family of equivalent partial differential equations at various orders, and we compare the lattice Boltzman

Paula Mouriño, Laura Mercadé, Miguel Sinusía Lozano, Raquel Resta

Gallium phosphide (GaP) has recently received considerable attention as a suitable material for building photonic integrated circuits due to its remarkable optical and piezoelectric properties. Usually, GaP is grown epitaxially on III-V substrates to keep its crystallinity and later transferred to silicon wafers for further processing. Here, an alternative p

Pabitra Tripathy

The present work delves into examining the thermodynamic properties of the four-dimensional Kerr-Newmann-AdS black hole, employing the recently proposed framework of restricted phase space thermodynamics (RPST). This approach introduces a novel set of paired thermodynamic variables: the central charge $C$ of the corresponding dual conformal field theory (CFT

Naotaka Kubo

We study the duality between the ABJ(M) theory at Chern-Simons level $k=4$ and the orientifold ABJ theory at Chern-Simons level $k=1$ by using the $S^{3}$ partition function. The partition function can be computed using the supersymmetric localization in terms of a matrix model, and we derive an ideal Fermi gas system by applying the Fermi gas formalism to t

Alex Krolewski, Will J. Percival, Alex Woodfinden

We introduce a new method for measuring the Hubble parameter from low-redshift large-scale observations that is independent of the comoving sound horizon. The method uses the baryon-to-photon ratio determined by the primordial deuterium abundance, together with Big Bang Nucleosynthesis (BBN) calculations and the present-day CMB temperature to determine the p

Denis Périce, Nicolas Rougerie

We study the dynamics of two-dimensional interacting fermions submitted to a homogeneous transverse magnetic field. We consider a large magnetic field regime, with the gap between Landau levels set to the same order as that of potential energy contributions. Within the mean-field approximation, i.e. starting from Hartree's equation for the first reduced dens

Xinran Li

This work analyzes unobservable directions of Vision-aided Inertial Navigation System (VINS) and Lidar-aided Inertial Navigation System (LINS) nonlinear model. Under the assumption that there exist two features observed by the camera without occlusion, the unobservable directions of VINS are uniformly globally translation and global rotations about the gravi

Nadège Alavoine, Gaëlle Laperriere, Christophe Servan, Sahar Ghannay

Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fifteen years, models achieving both of themjointly and exploiting their mutual enhancement have been proposed. A multilingual module using a joint modelwas envisioned to create a to

Angchi Xu, Wei-Shi Zheng

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and

Siyuan Shen, Yu Gao, Feng Liu, Hanyang Wang

The mainstream paradigm of speech emotion recognition (SER) is identifying the single emotion label of the entire utterance. This line of works neglect the emotion dynamics at fine temporal granularity and mostly fail to leverage linguistic information of speech signal explicitly. In this paper, we propose Emotion Neural Transducer for fine-grained speech em