February 2024 arXiv papers — page 159

Guilhem Balvet, Jean-Pierre Minier, Yelva Roustan, Martin Ferrand

Lagrangian stochastic methods are widely used to model turbulent flows. Scarce consideration has, however, been devoted to the treatment of the near-wall region and to the formulation of a proper wall-boundary condition. With respect to this issue, the main purpose of this paper is to present an in-depth analysis of such flows when relying on particle/mesh f

R. M. K. L. Ratnayake, D. M. U. P. Sumanasekara, H. M. K. D. Wickramathilaka, G. M. R. I. Godaliyadda

In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. The attention mechanism within transformers facilitates input-dependent weighting and enhances contextual awareness during training. Drawing inspiration from this, we propose a novel attention-based H

Oleksandr Kuznetsov, Alex Rusnak, Anton Yezhov, Dzianis Kanonik

Blockchain technology has emerged as a revolutionary tool in ensuring data integrity and security in digital transactions. However, the current approaches to data verification in blockchain systems, particularly in Ethereum, face challenges in terms of efficiency and computational overhead. The traditional use of Merkle Trees and cryptographic hash functions

Wei Huang, Yangdong Liu, Haotong Qin, Ying Li

Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a powerful compression technology, binarization can extremely reduce model weights to a mere 1 bit, lowering the expensive computation and memory requirements. However, existing quant

G. Madhuri, Atul Negi

Kernel-based nonlinear dictionary learning methods operate in a feature space obtained by an implicit feature map, and they are not independent of computationally expensive operations like Singular Value Decomposition (SVD). This paper presents an SVD-free lightweight approach to learning a nonlinear dictionary using a randomized functional link called a Ran

Khanh Cao Nguyen, Mike Zhang, Syrielle Montariol, Antoine Bosselut

Skill Extraction involves identifying skills and qualifications mentioned in documents such as job postings and resumes. The task is commonly tackled by training supervised models using a sequence labeling approach with BIO tags. However, the reliance on manually annotated data limits the generalizability of such approaches. Moreover, the common BIO setting

Riki Dutta, Gautam K Saharia, Sagardeep Talukdar, Sudipta Nandy

We investigate the propagation of an ultrashort optical pulse using Fokas-Lenells equation (FLE) under varying dispersion, nonlinear effects and perturbation. Such a system can be said to be under soliton management (SM) scheme. At first, under a gauge transformation, followed by shifting of variables, we transform FLE under SM into a simplified form, which

Guohang Yan, Jiahao Pi, Jianfei Guo, Zhaotong Luo

With deep learning and computer vision technology development, autonomous driving provides new solutions to improve traffic safety and efficiency. The importance of building high-quality datasets is self-evident, especially with the rise of end-to-end autonomous driving algorithms in recent years. Data plays a core role in the algorithm closed-loop system. H

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

Using data samples collected with the BESIII detector at the BEPCII collider at center-of-mass energies ranging from 3.80 to 4.95 GeV, corresponding to an integrated luminosity of 20 fb$^{-1}$, a measurement of Born cross sections for the $e^+e^-\to D^{0}\bar{D}^{0}$ and $D^{+}D^{-}$ processes is presented with unprecedented precision. Many clear peaks in th

Alexander Kolesov, Petr Mokrov, Igor Udovichenko, Milena Gazdieva

Given a collection of probability measures, a practitioner sometimes needs to find an "average" distribution which adequately aggregates reference distributions. A theoretically appealing notion of such an average is the Wasserstein barycenter, which is the primal focus of our work. By building upon the dual formulation of Optimal Transport (OT), we propose

Luis Sanz

In this kind of model, the main characteristic that determines population viability in the long term is the stochastic growth rate (SGR) denoted $\lambda_S$. When $\lambda_S$ is larger than one, the population grows exponentially with probability one and when it is smaller than one, the population goes extinct with probability one. However, even in very simp

Giovanni Marini, Matteo Calandra, Pierluigi Cudazzo

The photoluminescence spectrum of a single-layer boron nitride remains elusive, marked by enigmatic satellites that hint at a significant but unidentified exciton-phonon coupling.Here, by employing a first principles approach based on the many-body cumulant expansion of the charge response, we calculate the optical absorption and photoluminescence of a singl

Fred Brackx

Two specific families of distributions in harmonic and Clifford analysis are further studied through a spherical co-ordinates approach. In particular actions involving spherical co-ordinates, such as the radial derivative and the multiplication and division by the radial distance, are computed, giving rise to two families of so--called signumdistributions, i

Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl

We propose Embodied AI as the next fundamental step in the pursuit of Artificial General Intelligence, juxtaposing it against current AI advancements, particularly Large Language Models. We traverse the evolution of the embodiment concept across diverse fields - philosophy, psychology, neuroscience, and robotics - to highlight how EAI distinguishes itself fr

Andreas Dounas, Tudor-Stefan Cotet, Alexander Yermanos

Protein language models (PLMs) learn contextual representations from protein sequences and are profoundly impacting various scientific disciplines spanning protein design, drug discovery, and structural predictions. One particular research area where PLMs have gained considerable attention is adaptive immune receptors, whose tremendous sequence diversity dic

Si Shen, Peijun Shen, Danhao Zhu

This paper presents RevOrder, a novel technique aimed at improving arithmetic operations in large language models (LLMs) by reversing the output digits in addition, subtraction, and n-digit by 1-digit (nD by 1D) multiplication tasks. Our method significantly reduces the Count of Sequential Intermediate Digits (CSID) to $\mathcal{O}(1)$, a new metric we intro

Quentin Chauleur

We study the approximation by a semi-discrete finite-volume scheme of the Gross-Pitaevskii equation with time-dependent potential in two dimensions, performing a two-point flux approximation scheme in space. We rigorously analyze the error bounds relying on discrete uniform Sobolev inequalities. We finally perform some numerical simulations to investigate co

Yuta Kawachi, Mitsuru Ambai, Yuichi Yoshida, Gaku Takano

Speed responses of motors, especially Permanent Magnet Synchronous Motors (PMSMs), are increasing in importance for recent applications, such as electric vehicles or quadrotors. These applications require quick acceleration performance. However, commercial controllers are based mainly on Proportional-Integral (PI) controllers, which are suitable for eliminat

Abdoulaye Sakho, Emmanuel Malherbe, Erwan Scornet

Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In this paper, we derive several non-asymptotic upper bound on SMOTE density. From these results, we prove that SMOTE (with default parameter) tends to copy the original minority samples

O. Duranthon, L. Zdeborová

While graph convolutional networks show great practical promises, the theoretical understanding of their generalization properties as a function of the number of samples is still in its infancy compared to the more broadly studied case of supervised fully connected neural networks. In this article, we predict the performances of a single-layer graph convolut

Florian Bacho, Dminique Chu

There is an interest in finding energy efficient alternatives to current state of the art neural network training algorithms. Spiking neural network are a promising approach, because they can be simulated energy efficiently on neuromorphic hardware platforms. However, these platforms come with limitations on the design of the training algorithm. Most importa

Liwei Yin, Yongjiang Shu, Heng Zhang, Yuefei Dai

Reasonable vibration reduction design is an important way to achieve low phase noise index of airborne frequency source output signal. Aiming at the problem of phase noise deterioration of an airborne frequency source under random condition, this paper proposes to improve the vibration reduction mode crystal oscillator and reduce the distance between the bar

Hannah Collier, Olivier Limousin, Hualin Xiao, Arnaud Claret

The Spectrometer/Telescope for Imaging X-rays (STIX) is a hard X-ray imaging spectrometer on board the ESA and NASA heliospheric mission Solar Orbiter. STIX has been operational for three years and has observed X-ray emission from ~35,000 solar flares. Throughout its lifetime, Solar Orbiter has been frequently struck by a high flux of energetic particles usu

Xiaoxin Su, Yipeng Zhou, Laizhong Cui, Song Guo

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinat

Ziwen Zhao, Yuhua Li, Yixiong Zou, Jiliang Tang

Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete edge masking and binary link reconstruction strategies are insufficient to learn topologically informative representations, from the perspective of message propagation on graph neura

Renato Tinós, Liang Zhao, Francisco Chicano, Darrell Whitley

The NK hybrid genetic algorithm for clustering is proposed in this paper. In order to evaluate the solutions, the hybrid algorithm uses the NK clustering validation criterion 2 (NKCV2). NKCV2 uses information about the disposition of $N$ small groups of objects. Each group is composed of $K+1$ objects of the dataset. Experimental results show that density-ba

Oussama Zoubia, Nagaraj Bahubali Asundi, Adamantios Koumpis, Christoph Lange

In the digital age, data has emerged as one of the most valuable assets across various sectors, including academia, industry, and healthcare. Effective data preservation involves the management of data to ensure its long-term accessibility and usability. Given the importance and sensitivity of data, the need for effective management is a crucial necessity. O

Elena Aparicio-Esteve, Jesús Ureña, Álvaro Hernández, Daniel Pizarro

The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver.The receiver is based on a quadrant photodiode angular div

Mickael Binois, Victor Picheny

Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification. However, they suffer from the curse of dimensionality whenever the number of variables increases. This challenge is generally addressed by assuming additi

Yu-Tung Liu, Kuan-Chen Wang, Kai-Chun Liu, Sheng-Yu Peng

Surface electromyography (sEMG) recordings can be influenced by electrocardiogram (ECG) signals when the muscle being monitored is close to the heart. Several existing methods use signal-processing-based approaches, such as high-pass filter and template subtraction, while some derive mapping functions to restore clean sEMG signals from noisy sEMG (sEMG with

Jiafei Lyu, Xiaoteng Ma, Le Wan, Runze Liu

Offline reinforcement learning (RL) has attracted much attention due to its ability in learning from static offline datasets and eliminating the need of interacting with the environment. Nevertheless, the success of offline RL relies heavily on the offline transitions annotated with reward labels. In practice, we often need to hand-craft the reward function,

Marc Schmitt

This paper explores the integration of Explainable Automated Machine Learning (AutoML) in the realm of financial engineering, specifically focusing on its application in credit decision-making. The rapid evolution of Artificial Intelligence (AI) in finance has necessitated a balance between sophisticated algorithmic decision-making and the need for transpare

Thanh Trong Vu, Tuan-Dung Bui, Thanh-Dat Do, Thu-Trang Nguyen

Software patches are pivotal in refining and evolving codebases, addressing bugs, vulnerabilities, and optimizations. Patch descriptions provide detailed accounts of changes, aiding comprehension and collaboration among developers. However, manual description creation poses challenges in terms of time consumption and variations in quality and detail. In this

Zhengyan Zhang, Yixin Song, Guanghui Yu, Xu Han

Sparse computation offers a compelling solution for the inference of Large Language Models (LLMs) in low-resource scenarios by dynamically skipping the computation of inactive neurons. While traditional approaches focus on ReLU-based LLMs, leveraging zeros in activation values, we broaden the scope of sparse LLMs beyond zero activation values. We introduce a

Vineeth Teeda, K Sujatha, Rakesh Mutukuru

Robotic assistants reduce the manual efforts being put in by humans in their day-to-day tasks. In this paper, we develop a voice-controlled personal assistant robot. The robot takes the human voice commands by its own built-in microphone. This robot not only takes the commands and executes them but also acknowledges them through speech output. This robot can

U. Özdem

We perform a systematic study of the electromagnetic properties of exotic states to shed light on their nature, which is still controversial and not fully understood. The magnetic dipole and higher multipole moments of a hadronic state are as fundamental a dynamical quantity as its mass, and they contain valuable information about the deep structure underlyi

Tao Zhang, Luwei Yang, Zhibo Xiao, Wen Jiang

Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy. However, this pursuit of accuracy has inadvertently given rise to echo chamber/filter bubble effects. Especially in industry, it could impair user's experiences and prevent user from accessing a wider range of ite

Emanuel Kohlscheen, Richhild Moessner, Elod Takats

We provide ex-post empirical analysis of the effects of climate policies on carbon dioxide emissions at the aggregate national level. Our results are based on a comprehensive database of 121 countries. As climate policies we examine carbon taxes and emissions trading systems (ETS), as well as the overall stringency of climate policies. We use dynamic panel r

Qingying Deng, Fengming Dong, Xian'an Jin, Qi Yan

Recently, Chmutov proved that the partial-dual polynomial considered as a function on chord diagrams satisfies the four-term relations. In this paper, we show that this function on framed chord diagrams also satisfies the four-term relations, i.e., is a framed weight system.

Guido Cavallaro, Carlo Marchioro

We study existence and uniqueness of the solution to the gravitational Vlasov-Poisson system evolving in $\mathbb{R}^3$. It is assumed that initially the particles are distributed according to a spatial density with a power-law decay in space, allowing for unbounded mass, and an exponential decay in velocities given by a Maxwell-Boltzmann law. We extend a cl

Junchao Gong, Lei Bai, Peng Ye, Wanghan Xu

Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management. Despite progresses have been made based on deep learning, two key challenges of precipitation nowcasting are not well-solved: (i) the modeling of complex precipitation system evolutions with different scales, and

G. Brochard, C. Liu, X. Wei, W. Heidbrink

Gyrokinetic and kinetic-MHD simulations are performed for the fishbone instability in the DIII-D discharge #178631, chosen for validation of first-principles simulations to predict the energetic particle (EP) transport in an ITER prefusion baseline scenario. Fishbone modes are found to generate zonal flows, which dominate the fishbone saturation. The underly

Purnendu Prabhat, Himanshu Gupta, Ajeet Kumar Vishwakarma

The majority of computer vision applications that handle images featuring humans use face detection as a core component. Face detection still has issues, despite much research on the topic. Face detection's accuracy and speed might yet be increased. This review paper shows the progress made in this area as well as the substantial issues that still need to be

Jinjing Zhu, Zhedong Hu, Tae-Kyun Kim, Lin Wang

Recent endeavors have been made to leverage self-supervised depth estimation as guidance in unsupervised domain adaptation (UDA) for semantic segmentation. Prior arts, however, overlook the discrepancy between semantic and depth features, as well as the reliability of feature fusion, thus leading to suboptimal segmentation performance. To address this issue,

Akanksha Kapahtia, Tirthankar Roy Choudhury

The reionization of the second electron of helium (HeII) leaves important imprints on the thermal and ionization state of the intergalactic medium (IGM). Observational evidence suggests that HeII reionization ended at $z \simeq 3$ due to ionizing photons emitted predominantly by quasars. We present efficient semi-numerical simulations of helium reionization

Sanu Bera, Sugata Mandal, Soumendu Nandy

In this article, the two-parameter quantum Heisenberg enveloping algebra, which serves as a model for certain quantum generalized Heisenberg algebras, have been studied at roots of unity. In this context, the quantum Heisenberg enveloping algebra becomes a polynomial identity algebra, and the dimension of simple modules is bounded by its PI degree. The PI de

Davide Maran, Alberto Maria Metelli, Matteo Papini, Marcello Restell

Obtaining no-regret guarantees for reinforcement learning (RL) in the case of problems with continuous state and/or action spaces is still one of the major open challenges in the field. Recently, a variety of solutions have been proposed, but besides very specific settings, the general problem remains unsolved. In this paper, we introduce a novel structural

Ding Tang, Lijuan Jiang, Jiecheng Zhou, Minxi Jin

Large-scale models rely heavily on 3D parallelism for distributed training, which utilizes tensor parallelism (TP) as the intra-operator parallelism to partition model states across GPUs. However, TP introduces significant communication overheads and complexity in modifying single-GPU code. In this paper, we propose a TP-free distributed framework ZeroPP, wh

Mariam Al-Maskari, Samir Karaa

In this paper, we consider the numerical approximation of a time-fractional stochastic Cahn--Hilliard equation driven by an additive fractionally integrated Gaussian noise. The model involves a Caputo fractional derivative in time of order $\alpha\in(0,1)$ and a fractional time-integral noise of order $\gamma\in[0,1]$. The numerical scheme approximates the m

Yutack Park, Jaesun Kim, Seungwoo Hwang, Seungwu Han

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due to their data efficiency and high accuracy. However, parallelizing GNN-IPs poses challenges because multiple message-passing layers complicate data communication within the spatial

Rafael de la Rosa, María de los Santos Bruzón

In this paper we consider a generalized Kuramoto-Sivashinsky equation. The equivalence group of the class under consideration has been constructed. This group allows us to perform a comprehensive study and a clear and concise formulation of the results. We have constructed the optimal system of subalgebras of the projections of the equivalence algebra on the

Tamir Bendory, Dan Edidin, Oscar Mickelin

The classical beltway problem entails recovering a set of points from their unordered pairwise distances on the circle. This problem can be viewed as a special case of the crystallographic phase retrieval problem of recovering a sparse signal from its periodic autocorrelation. Based on this interpretation, and motivated by cryo-electron microscopy, we sugges

Humberto González, Lluís Martínez-León, Fernando Soldevila, María Araiza-Esquivel

A single-pixel digital holography system with phase-encoded illumination using a digital micromirror device (DMD) as a spatial light modulator (SLM) is presented. The enhanced switching rate of DMDs, far exceeding the stringent frame-rate of liquid crystal SLMs, allows recording and reconstruction of complex amplitude distributions in just a few seconds. A s

Haihong Zhao, Chenyi Zi, Yang Liu, Chen Zhang

Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupervised learning, are hard to reach satisfactory detection accuracy due to the lack of labels. Weakly Supervised Anomaly Detection (WSAD) has

Kethmi Hirushini Hettige, Jiahao Ji, Shili Xiang, Cheng Long

Air quality prediction and modelling plays a pivotal role in public health and environment management, for individuals and authorities to make informed decisions. Although traditional data-driven models have shown promise in this domain, their long-term prediction accuracy can be limited, especially in scenarios with sparse or incomplete data and they often

Fudan Zheng, Jindong Cao, Weijiang Yu, Zhiguang Chen

Most advances in medical image recognition supporting clinical auxiliary diagnosis meet challenges due to the low-resource situation in the medical field, where annotations are highly expensive and professional. This low-resource problem can be alleviated by leveraging the transferable representations of large-scale pre-trained vision-language models via rel

Fred Philippy, Siwen Guo, Shohreh Haddadan, Cedric Lothritz

Soft Prompt Tuning (SPT) is a parameter-efficient method for adapting pre-trained language models (PLMs) to specific tasks by inserting learnable embeddings, or soft prompts, at the input layer of the PLM, without modifying its parameters. This paper investigates the potential of SPT for cross-lingual transfer. Unlike previous studies on SPT for cross-lingua

Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang

Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of large language models (LLMs), known for their vast knowledge repositories and advanced logical inference capabilities, has emerged as a promising way for efficient and effective MRL.

Géraud Faye, Benjamin Icard, Morgane Casanova, Julien Chanson

This paper investigates the language of propaganda and its stylistic features. It presents the PPN dataset, standing for Propagandist Pseudo-News, a multisource, multilingual, multimodal dataset composed of news articles extracted from websites identified as propaganda sources by expert agencies. A limited sample from this set was randomly mixed with papers

Florian Valade, Mohamed Hebiri, Paul Gay

The increasing complexity of advanced machine learning models requires innovative approaches to manage computational resources effectively. One such method is the Early Exit strategy, which allows for adaptive computation by providing a mechanism to shorten the processing path for simpler data instances. In this paper, we propose EERO, a new methodology to t

George Sarantoglou, Adonis Bogris, Charis Mesaritakis

In this work, we present numerical results concerning an integrated photonic non-linear activation function that relies on a power independent, non-linear phase to amplitude conversion in a passive optical resonator. The underlying mechanism is universal to all optical filters, whereas here, simulations were based on micro-ring resonators (MRRs). Investigati

Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet

Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from the feedback of experienced reviewers; however, it often brings a large workload and stress to reviewers. To alleviate this burden, the field of automated code reviews aims to aut

Yufang Cui, Anders Lindquist

Simultaneous stabilization problem arises in various systems and control applications. This paper introduces a new approach to addressing this problem in the multivariable scenario, building upon our previous findings in the scalar case. The method utilizes a Riccati-type matrix equation known as the Covariance Extension Equation, which yields all solutions

Shahriar Golchin, Nikhil Garuda, Christopher Impey, Matthew Wenger

Massive open online courses (MOOCs) unlock the doors to free education for anyone around the globe with access to a computer and the internet. Despite this democratization of learning, the massive enrollment in these courses means it is almost impossible for one instructor to assess every student's writing assignment. As a result, peer grading, often guided

Bushra Ali, Sweta Singh, Anuj Chandra, Shakeel Ahmad

Physical quantities such as, entropy, dimensions and multifractal characteristics of multiplicity distributions of charged particles produced in $^{197}$Au-AgBr collisions are examined and the findings are compared with the predictions of Monte Carlo model URQMD (Ultra-Relativistic Quantum Molecular Dynamics) and HIJING (Heavy Ion Jet INteraction Generator)

Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang

Decision trees are renowned for their ability to achieve high predictive performance while remaining interpretable, especially on tabular data. Traditionally, they are constructed through recursive algorithms, where they partition the data at every node in a tree. However, identifying a good partition is challenging, as decision trees optimized for local seg

Huy Nguyen, Christoph Treude, Patanamon Thongtanunam

With the exponential growth of AI tools that generate source code, understanding software has become crucial. When developers comprehend a program, they may refer to additional contexts to look for information, e.g. program documentation or historical code versions. Therefore, we argue that encoding this additional contextual information could also benefit c

Zeyan Zhuang, Xin Zhang, Dongfang Xu, Shenghui Song

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been

Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu

In Reinforcement Learning (RL), it is commonly assumed that an immediate reward signal is generated for each action taken by the agent, helping the agent maximize cumulative rewards to obtain the optimal policy. However, in many real-world scenarios, designing immediate reward signals is difficult; instead, agents receive a single reward that is contingent u

Xiaoxin Su, Yipeng Zhou, Laizhong Cui, John C. S. Lui

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy; yet the communication between the PS and sca

Oleksandr Kuznetsov, Dmytro Zakharov, Emanuele Frontoni, Andrea Maranesi

Biometric security is the cornerstone of modern identity verification and authentication systems, where the integrity and reliability of biometric samples is of paramount importance. This paper introduces AttackNet, a bespoke Convolutional Neural Network architecture, meticulously designed to combat spoofing threats in biometric systems. Rooted in deep learn

Takahiro Morimoto, Naoto Nagaosa

We study direct current (DC) generation induced by microwave irradiation to ferroelectric materials. The DC generation originates from microwave absorption called dielectric loss due to the delay of dielectric response. Such current generation can be formulated as the low-frequency limit of the phonon shift current which arises from an increase of electric p

Aijaz H. Lone, Meng Tang, Daniel N. Rahimi, Xuecui Zou

Spintronic devices, such as the domain walls and skyrmions, have shown significant potential for applications in energy-efficient data storage and beyond CMOS computing architectures. In recent years, spiking neural networks have shown more bio-plausibility. Based on the magnetic multilayer spintronic devices, we demonstrate the magnetic field-gated Leaky in

Xiangxiang Chu, Limeng Qiao, Xinyu Zhang, Shuang Xu

We introduce MobileVLM V2, a family of significantly improved vision language models upon MobileVLM, which proves that a delicate orchestration of novel architectural design, an improved training scheme tailored for mobile VLMs, and rich high-quality dataset curation can substantially benefit VLMs' performance. Specifically, MobileVLM V2 1.7B achieves better

Yi-Chen Yang, Soohyun Cho, Tong-Rui Li, Xiang-Qi Liu

The charge density wave (CDW), as a hallmark of vanadium-based kagome superconductor AV3Sb5 (A = K, Rb, Cs), has attracted intensive attention. However, the fundamental controversy regarding the underlying mechanism of CDW therein persists. Recently, the vanadium-based bi-layered kagome metal ScV6Sn6, reported to exhibit a long-range charge order below 94 K,

Yyanis Johnson-Llambias, Philippe H. Trinh

In the study of low-speed or low-Froude flows of a potential gravity-driven fluid past a wave-generating object, the traditional asymptotic expansion in powers of the Froude number predicts a waveless free-surface at every order. This is due to the fact that the waves are, in fact, exponentially small and beyond-all-orders of the naive expansion. The theory

Gianluca Nogara, Francesco Pierri, Stefano Cresci, Luca Luceri

The kick-off of vaccination campaigns in Europe, starting in late December 2020, has been followed by the online spread of controversies and conspiracies surrounding vaccine validity and efficacy. We study Twitter discussions in three major European languages (Italian, German, and French) during the vaccination campaign. Moving beyond content analysis to exp

Heng Zhou, Zhetao Guo, Shuhong Liu, Lechen Zhang

Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints. However, existing monocular SLAM systems are designed for bounded scenes, restricting the applicability of SLAM systems. To address this limitation, we propose MoD-SLAM, the first monocular NeRF-based dense mapping method that allows 3D

David Black, Jaidev Gill, Andrew Xie, Benoit Liquet

Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods that are unable to capture the non-linear

Yali Yuan, Jian Ge, Guang Cheng

The network flow watermarking technique associates the two communicating parties by actively modifying certain characteristics of the stream generated by the sender so that it covertly carries some special marking information. Some curious users communicating with the hidden server as a Tor client may attempt de-anonymization attacks to uncover the real iden

H. Ebata, Y. Nishigami, H. Fujiwara, S. Kidoaki

One of the essential functions of living organisms is spontaneous migration through the deformation of their body, such as crawling, swimming, and walking. Depending on the size of the object, the efficient migratory mode should be altered because the contribution from the inertial and frictional forces acting on the object switches. Although the self-propel

Mingyue Guo, Binghui Chen, Zhaoyi Yan, Yaowei Wang

Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all domains, which is known as domain bias. In this study, we propose a simple-yet-effective Modulating Domain-specific Knowledge Network (MDKNet) to handle the domain bias issue in mu

Tianyang Han, Qing Lian, Rui Pan, Renjie Pi

Large language models (LLMs) have recently experienced remarkable progress, where the advent of multi-modal large language models (MLLMs) has endowed LLMs with visual capabilities, leading to impressive performances in various multi-modal tasks. However, those powerful MLLMs such as GPT-4V still fail spectacularly when presented with certain image and text i

Kota Takeda, Takashi Sakajo

Data assimilation is a method of uncertainty quantification to estimate the hidden true state by updating the prediction owing to model dynamics with observation data. As a prediction model, we consider a class of nonlinear dynamical systems on Hilbert spaces including the two-dimensional Navier-Stokes equations and the Lorenz '63 and '96 equations. For nonl

Saizhuo Wang, Hang Yuan, Lionel M. Ni, Jian Guo

Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence.However, tailoring these agents for specialized domains like quantitative investment remains a formidable task. The core challenge involves efficiently building and integrating a domain-specific knowledge base for the agent's lear

Fudan Zheng, Mengfei Li, Ying Wang, Weijiang Yu

Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view str

Aik Rui Tan, Johannes C. B. Dietschreit, Rafael Gomez-Bombarelli

Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (MLIP). However, the complexity of molecular systems, characterized by intricate potential energy surfaces (PESs) with numerous local minima and energy barriers, presents a significan

Jen Hong Tan

Can a lightweight Vision Transformer (ViT) match or exceed the performance of Convolutional Neural Networks (CNNs) like ResNet on small datasets with small image resolutions? This report demonstrates that a pure ViT can indeed achieve superior performance through pre-training, using a masked auto-encoder technique with minimal image scaling. Our experiments

Man Ho Chan, Chak Man Lee

Black hole dynamics suggests that dark matter would re-distribute near a supermassive black hole to form a density spike. However, no direct evidence of dark matter density spike around a supermassive black hole has been identified. In this letter, we present the first robust evidence showing a dark matter density spike around a supermassive black hole. We r

Xin Chen, Mingliang Hou, Tao Tang, Achhardeep Kaur

With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system. Mobility profiling can extract potential patterns in urban traffic from mobility data and is critical for a variety of traffic-related applications. However, due to the high level o

Jianyuan Guo, Hanting Chen, Chengcheng Wang, Kai Han

Recent advancements in large language models have sparked interest in their extraordinary and near-superhuman capabilities, leading researchers to explore methods for evaluating and optimizing these abilities, which is called superalignment. In this context, our paper delves into the realm of vision foundation models, focusing on the concept of weak-to-stron

Girish Balakrishnan, Sankardeep Chakraborty, N S Narayanaswamy, Kunihiko Sadakane

We improve the worst-case information theoretic lower bound of Munro and Wu (ISAAC 2018) for $n-$vertex unlabeled chordal graphs when vertex leafage is bounded and leafage is unbounded. The class of unlabeled $k-$vertex leafage chordal graphs that consists of all chordal graphs with vertex leafage at most $k$ and unbounded leafage, denoted $\mathcal{G}_k$, i

Chao Chen, Hui Li, Xiaowei Jin

The discovery of partial differential equations (PDEs) from datasets has attracted increased attention. However, the discovery of governing equations from sparse data with high noise is still very challenging due to the difficulty of derivatives computation and the disturbance of noise. Moreover, the selection principles for the candidate library to meet phy

Daechul Ahn, Yura Choi, Youngjae Yu, Dongyeop Kang

Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). The previous approaches for VLMMs involved Supervised Fine-Tuning (SFT) with instruction-tuned datasets, integrating LLM with visual encoders, and adding additional learnable modules. Video and text multimodal alignment remains challenging,

Kun Wang, Hao Wu, Guibin Zhang, Junfeng Fang

In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames. Traditional numerical simulation systems depend largely on the initial settings and correctness of the constructed partial differential equations (PDEs). Despite recent efforts yie

Kiran Kumar A. S., Shambhu Nath Maurya, Koushik Saha

In this paper, we derive a unified method for establishing the distributional convergence of linear eigenvalue statistics (LES) for generalized patterned random matrices. We prove that for an $N \times N$ generalized patterned random matrix with independent subexponential entries and even degree monomial test functions of degree $p_n=o(\log N/\log \log N)$,

Chao Chen, Kai Liu, Ze Chen, Yi Gu

Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs. Previous efforts in detecting hallucinations have been employed at logit-level uncertainty estimation or language-level self-consistency evaluation, where the semantic information is inevitably lost during the token-decoding procedure. Thus, we propose

Xiaobin Ding, Runxia Zhao, Cunqiang Wu, Denghong Zhang

We have calculated the transition energies and probabilities of one-electron one photon and one-electron two photon transitions of middle-Z and high-Z He-like ions using the fully relativistic multiconfiguration Dirac-Hartree-Fock method with active space method. The relativistic, electron correlation, Breit and QED effects are systemically taken into accoun

Tenzin Rabga, Yangheon Lee, Yong-il Shin

We experimentally investigate the evolution of spatial phase correlations in a rapidly quenched inhomogeneous Bose gas of rubidium using off-resonant matter-wave interferometry. We measure the phase coherence length $\ell$ of the sample and directly probe its increase during the early stage of condensate growth before vortices are formed. Once the vortices a

Oubo Ma, Yuwen Pu, Linkang Du, Yang Dai

Recent advancements in multi-agent reinforcement learning (MARL) have opened up vast application prospects, such as swarm control of drones, collaborative manipulation by robotic arms, and multi-target encirclement. However, potential security threats during the MARL deployment need more attention and thorough investigation. Recent research reveals that atta