April 2023 arXiv papers — page 140

Bita Farsi, Ahmad Sheykhi, Mohsen Khodadi

Employing the spherical collapse (SC) formalism, we investigate the linear evolution of the matter over-density for energy-momentum-squared gravity (EMSG), which in practical phenomenological terms, one may imagine as an extension of the {\Lambda}CDM model of cosmology. The underlying model, while still having a cosmological constant, is a non-linear materia

Tiago L. Campante, Tanda Li, J. M. Joel Ong, Enrico Corsaro

Asteroseismology is playing an increasingly important role in the characterization of red-giant host stars and their planetary systems. Here, we conduct detailed asteroseismic modeling of the evolved red-giant branch (RGB) hosts KOI-3886 and $\iota$ Draconis, making use of end-of-mission Kepler (KOI-3886) and multi-sector TESS ($\iota$ Draconis) time-series

Yao Lu, Pengyuan Zhou, Yong Liao, Haiyong Xie

Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance. Existing methods typically use deep learning to capture the intra-dependencies in spatial and temporal dimensions. However, numerous key challenges remain unsolved, for instance, sparse zero-inflated data due to

Ninghao Pu, Zhongxing Wu, Ao Wang, Hanshi Sun

Reasonably and effectively monitoring arrhythmias through ECG signals has significant implications for human health. With the development of deep learning, numerous ECG classification algorithms based on deep learning have emerged. However, most existing algorithms trade off high accuracy for complex models, resulting in high storage usage and power consumpt

Hannes Fassold, Karlheinz Gutjahr, Anna Weber, Roland Perko

Monitoring the movement and actions of humans in video in real-time is an important task. We present a deep learning based algorithm for human action recognition for both RGB and thermal cameras. It is able to detect and track humans and recognize four basic actions (standing, walking, running, lying) in real-time on a notebook with a NVIDIA GPU. For this, i

Pascal Heid

The focus of the present work is the (theoretical) approximation of a solution of the p(x)-Poisson equation. To devise an iterative solver with guaranteed convergence, we will consider a relaxation of the original problem in terms of a truncation of the nonlinearity from below and from above by using a pair of positive cut-off parameters. We will then verify

Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung

Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few years, there is an increasing need for surveys of diffusion models on specific fields. In this work, we are com

Jarah Evslin

The definition of a quantum state corresponding to a wave packet far from a global soliton is considered. We define an asymptotic quantum state corresponding to a localized wave packet of elementary quanta far from a kink. We demand that the state satisfies two properties. First, it must evolve in time via a rigid translation of the wave packet, up to the us

Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity

Jen-Hsu Chang

One constructs the parity-time symmetric solitons in the complex KP Equation using the totally non-negative Grassmannian. We obtain that every element in the totally non-negative orthogonal Grassmannian corresponds to a parity-time symmetric solitons solution.

Yunfei Yang, Ding-Xuan Zhou

We study the approximation capacity of some variation spaces corresponding to shallow ReLU$^k$ neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established. Using these results, we are able t

Daewon Seo, Yongjune Kim

In practical simultaneous information and energy transmission (SIET), the exact energy harvesting function is usually unavailable because an energy harvesting circuit is nonlinear and nonideal. In this work, we consider a SIET problem where the harvesting function is accessible only at experimentally-taken sample points and study how close we can design SIET

Lixia Wu, Jianlin Liu, Junhong Lou, Haoyuan Hu

Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant t

Xin Huang, Han Lin Shang, Tak Kuen Siu

An important issue in functional time series analysis is whether an observed series comes from a purely random process. We extend the BDS test, a widely-used nonlinear independence test, to the functional time series. Like the BDS test in the univariate case, the functional BDS test can act as the model specification test to evaluate the adequacy of various

Christophe Didier, Elliot P. Gilbert, Jitendra Mata, Vanessa Peterson

Despite being the major cause of battery safety issues and detrimental performance, a comprehensive growth mechanism for metallic lithium deposited at electrode surfaces in lithium metal batteries remains elusive. While lithium surface morphology is often derived indirectly, here, detailed information is directly obtained using in situ small and ultra-small

Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao

OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is demonstrated to be one small step for generative AI (GAI), but one giant leap for artificial general intelligence (AGI). Since its official release in November 2022, ChatGPT has quickly attracted numerous users with extensive media coverage. Such unprecedented attention has also motivated num

Xuesen Na

We study the limiting behavior of the solutions $h_t$ of the Hitchin's equation associated with a family of stable SU(1,2) Higgs bundles $(L,F,t\beta,t\gamma)$ on a compact connected Riemann surface $X$ as $t\to\infty$ under the assumption that the quadratic differential $q=\beta\cdot\gamma$ have simple zeros at $D$. The spectral data of the SU(1,2) Higgs bu

Jyothi Hariharan, Rahul Rama Varior, Sunil Karunakaran

As road accident cases are increasing due to the inattention of the driver, automated driver monitoring systems (DMS) have gained an increase in acceptance. In this report, we present a real-time DMS system that runs on a hardware-accelerator-based edge device. The system consists of an InfraRed camera to record the driver footage and an edge device to proce

Ashish Sinha, Jonghyun Choi

Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabelled target domain and labelled source domain. While the single target domain adaptation (STDA) is well studied in the literature for both 2D and 3D vision tasks, multi-target domain adaptation (MTDA) is barely explored for 3D data despite its wide real-world a

Vishal Upendran

The solar atmosphere shows anomalous variation in temperature, starting from the 5500 K photosphere to the million-degree Kelvin corona. The corona itself expands into the interstellar medium as the free streaming solar wind, which modulates and impacts the near-Earth space weather. The precise source regions of different structures in the solar wind, their

Suhyun Kang, Duhun Hwang, Moonjung Eo, Taesup Kim

Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared initialization and the inner-loop process optimizes task-specific weights. Although MAML relies on the standard gradient descent in the inner-loop, recent studies have shown that con

Xing-Yu Zhang, Shuang Liang, Hai-Jun Liao, Wei Li

We present a general computational framework to investigate ground state properties of quantum spin models on infinite two-dimensional lattices using automatic differentiation-based gradient optimization of infinite projected entangled-pair states. The approach exploits the variational uniform matrix product states to contract infinite tensor networks with u

Masataka Koide, Yuta Nagoya, Satoshi Yamaguchi

We study quantum field theories with boundary by utilizing non-invertible symmetries. We consider three kinds of boundary conditions of the four dimensional $\mathbb{Z}_2$ lattice gauge theory at the critical point as examples. The weights of the elements on the boundary is determined so that these boundary conditions are related by the Kramers-Wannier-Wegne

Samir F Matar

Original carbon allotropes C12 and C16 called superglitter and squarodiamond from relationships with literature glitter and squaroglitter respectively are shown through DFT-based geometry to be cohesive with energy dependent properties as hardness from the elastic constants, the phonon band structures, and thermal behavior related to diamond. Like C6 glitter

Constantinos Kitsos, Rami Katz, Emilia Fridman

This work deals with the exponential stabilization of a system of three semilinear parabolic partial differential equations (PDEs), written in a strict feedforward form. The diffusion coefficients are considered distinct and the PDEs are interconnected via both a reaction matrix and a nonlinearity. Only one of the PDEs is assumed to be controlled internally,

Eleni Oikonomaki, Dimitris Belivanis

In an era of knowledge-based economy, commercialized research and globalized competition for talent, the creation of innovation ecosystems and innovation networks is at the forefront of efforts of cities. In this context, public authorities, private organizations, and academics respond to the question of the most promising indicators that can predict innovat

Talal Algumaei, Ruben Solozabal, Reda Alami, Hakim Hacid

This work studies non-cooperative Multi-Agent Reinforcement Learning (MARL) where multiple agents interact in the same environment and whose goal is to maximize the individual returns. Challenges arise when scaling up the number of agents due to the resultant non-stationarity that the many agents introduce. In order to address this issue, Mean Field Games (M

Hana Gil, Nobuo Hinohara, Chang Ho Hyun, Kenichi Yoshida

Background: Nuclear energy-density functional (EDF) approach has been widely used to describe nuclear-matter equations of state (EoS) and properties of finite nuclei. Recent advancements in neutron-star (NS) observations have put constraints on the nuclear EoS. The Korea-IBS-Daegu-SKKU (KIDS) functional has been then developed to satisfy the NS observations

Heesoo Shin, Mario Rüttgers, Sangseung Lee

This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited research has explored the impact of different spatial and temporal scales of input wind data on the learnability of

Ata Nazari, Changchang Wang, Ruichen He, Farzad Taghizadeh-Hesary

Airborne transmission of SARS-CoV-2 via virus-laden aerosols in enclosed spaces poses a significant concern. Elevators, commonly utilized enclosed spaces in modern tall buildings, present a challenge as the impact of varying heating, ventilation, and air conditioning (HVAC) systems on virus transmission within these cabins remains unclear. In this study, we

Zahra Sadeghi, Roohallah Alizadehsani, Mehmet Akif Cifci, Samina Kausar

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model exp

Lei Liu, Changbong Hyeon

Polymer chains composing a polymer solution in strict two dimensions (2D) are characterized with irregular domain boundaries, whose fractal dimension ($\mathcal{D}^{\partial}$) varies with the area fraction of the solution and the solvent quality. {\color{black}Our analysis of numerical simulations of polymer solutions finds} that $\mathcal{D}^{\partial}$ in

Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

Privacy and communication constraints are two major bottlenecks in federated learning (FL) and analytics (FA). We study the optimal accuracy of mean and frequency estimation (canonical models for FL and FA respectively) under joint communication and $(\varepsilon, \delta)$-differential privacy (DP) constraints. We show that in order to achieve the optimal er

U. A. Rozikov, S. K. Shoyimardonov, R. Varro

In this paper we study the discrete-time dynamical systems associated with gonosomal algebras used as algebraic model in the sex-linked genes inheritance. We show that the class of gonosomal algebras is disjoint from the other non-associative algebras usually studied (Lie, alternative, Jordan, associative power). To each gonosomal algebra, with the mapping $

Keehang Kwon

We present a novel definition of an algorithm and its corresponding algorithm language called CoLweb. The merit of CoLweb [1] is that it makes algorithm design so versatile. That is, it forces us to a high-level, proof-carrying, distributed-style approach to algorithm design for both non-distributed computing and distributed one. We argue that this approach

Álvaro Briz-Redón

In this paper, Poisson time series models are considered to describe the number of field goals made by a basketball team or player at both the game (within-season) and the minute (within-game) level. To deal with the existence of temporal autocorrelation in the data, the model is endowed with a doubly self-exciting structure, following the INGARCH(1,1) speci

Minsu Kim, Seungryong Kim, JungIn Park, Seongheon Park

Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored. In this paper, we present a novel data augmentatio

Alessandro Carderi, Alice Giraud, François Le Maître

We extend Dye's reconstruction theorem, which classifies isomorphisms between full groups, to a classification of homomorphisms between full groups. For full groups of ergodic p.m.p. equivalence relations, our result roughly says that such homomorphisms come only from actions of the equivalence relation, or of one of its symmetric powers. This has several ri

Lin-Jun Li, Li-Lu Feng, Jia-Hao Dai, Yu-Yu Zhang

We present exotic quantum phases in a quantum Rabi hexagonal ring, which is derived by an analytical solution. We find that an artificial magnetic field applied in the ring induces an effect magnetic flux in the even and odd subring. It gives rise to two chiral quantum phases besides a ferro-superradiant and an antiferro-superradiant phases. With analogy to

Rui Song, Runsheng Xu, Andreas Festag, Jiaqi Ma

Bird's eye view (BEV) perception is becoming increasingly important in the field of autonomous driving. It uses multi-view camera data to learn a transformer model that directly projects the perception of the road environment onto the BEV perspective. However, training a transformer model often requires a large amount of data, and as camera data for road tra

Zibo Ren, Huan Han, Hao Zeng, Chao Sun

We investigate experimentally and theoretically the interactions between a cavitation bubble and a hemispherical pendant oil droplet immersed in water. In experiments, the cavitation bubble is generated by a focused laser pulse right below the pendant droplet with well-controlled bubble-wall distances and bubble-droplet size ratios. By high-speed imaging, fo

ATLAS Collaboration

This Letter reports on a search for off-shell production of the Higgs boson using 139 $\textrm{fb}^{-1}$ of $pp$ collision data at $\sqrt{s}=$ 13 TeV collected by the ATLAS detector at the Large Hadron Collider. The signature is a pair of $Z$ bosons, with contributions from both the production and subsequent decay of a virtual Higgs boson and the interferenc

Xiaoran Li, Siebe Dijcks, Anbang Sun, Sander Nijdam

We investigate the propagation of positive streamers in CO$_2$ through 3D particle-in-cell simulations, which are qualitatively compared against experimental results at 50 mbar. The experiments show that CO$_2$ streamers are much more stochastic than air streamers at the same applied voltage, indicating that few electrons are available in front of the stream

Turgay Bayraktar, Ali Ulaş Özgür Kişisel

We compute the expected multivolume of the amoeba of a random half dimensional complete intersection in $\mathbb{CP}^{2n}$. We also give a relative generalization of our result to the toric case.

Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao, Gang Li

The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising. State-of-the-art learning based methods focus on training neural networks to infer filtered displacements and directly shift noisy points onto the under

Hershy Kisilevsky, Masato Kuwata

For an elliptic curve $E/\mathbb{Q}$ we show that there are infinitely many cyclic sextic extensions $K/\mathbb{Q}$ such that the Mordell-Weil group $E(K)$ has rank greater than the subgroup of $E(K)$ generated by all the $E(F)$ for the proper subfields $F \subset K$. For certain curves $E/\mathbb{Q}$ we show that the number of such fields $K$ of conductor l

Nitu Lakhmara, Hari Shankar Mahato

A diffused interface model describing the evolution of two conterminous incompressible fluids in a porous medium is discussed. The system consists of the Cahn-Hilliard equation with Flory-Huggins logarithmic potential, coupled via surface tension term with the evolutionary Stokes equation at the pore scale. An evolving diffused interface of finite thickness,

Tochukwu Elijah Ogri, Zachary I. Bell, Rushikesh Kamalapurkar

Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, model-based reinforcement learning architecture that simultaneously learns and implements an optimal controller while maintaining stability d

Swetha Ganesh, Alexandre Reiffers-Masson, Gugan Thoppe

We introduce an observation-matrix-based framework for fully asynchronous online Federated Learning (FL) with adversaries. In this work, we demonstrate its effectiveness in estimating the mean of a random vector. Our main result is that the proposed algorithm almost surely converges to the desired mean $\mu.$ This makes ours the first asynchronous FL method

Kohav Dey, Krishna Bajaj, K S Ramalakshmi, Samuel Thomas

Marine ecosystems are vital for the planet's health, but human activities such as climate change, pollution, and overfishing pose a constant threat to marine species. Accurate classification and monitoring of these species can aid in understanding their distribution, population dynamics, and the impact of human activities on them. However, classifying marine

Bishal Lakha, Kalyan Bhetwal, Nasir U. Eisty

Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering prac

Kazumi Okuyama

We study the high temperature (or small inverse temperature $\beta$) expansion of the free energy of double scaled SYK model. We find that this expansion is a convergent series with a finite radius of convergence. It turns out that the radius of convergence is determined by the first zero of the partition function on the imaginary $\beta$-axis. We also show

Soumi Dey, Ayan Banerjee, Debashree Chowdhury, Awadhesh Narayan

In recent years, non-Hermitian (NH) topological semimetals have garnered significant attention due to their unconventional properties. In this work, we explore the transport properties of a three-dimensional dissipative Weyl semi-metal formed as a result of the stacking of two-dimensional Chern insulators. We find that unlike Hermitian systems where the Hall

P. T. Nhung, D. T. Hoai, P. Darriulat, P. N. Diep

New analyses of earlier ALMA observations of oxygen-rich AGB star EP Aquarii are presented, which contribute major progress to our understanding of the morpho-kinematics of the circumstellar envelope (CSE). The birth of the equatorial density enhancement (EDE) is shown to occur very close to the star where evidence for rotation has been obtained. High Dopple

Haitao Yang, Zaiwei Zhang, Xiangru Huang, Min Bai

Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors. However, little research has been done to investigate how to incorporate additional supervision on the BEV features to improve proposal generation in the detector head, while still balancing the number

Myong Chol Jung, He Zhao, Joanna Dipnall, Lan Du

Uncertainty estimation is an important research area to make deep neural networks (DNNs) more trustworthy. While extensive research on uncertainty estimation has been conducted with unimodal data, uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for

Xu Chen, Zhiyong Feng, Zhiqing Wei, Ping Zhang

The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load-saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar sensing signals to improv

Haowei Shi, Zaijun Chen, Scott E. Fraser, Mengjie Yu

Dual-comb interferometry harnesses the interference of two laser frequency combs to provide unprecedented capability in spectroscopy applications. In the past decade, the state-of-the-art systems have reached a point where the signal-to-noise ratio per unit acquisition time is fundamentally limited by shot noise from vacuum fluctuations. To address the issue

Jaewoong Lee, Sangwon Jang, Jaehyeong Jo, Jaehong Yoon

Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performance to diffusion-based models, their generation performance is still suboptimal as they sample multiple tokens simultaneously without considering the dependence among them. We empiri

Haobo Jiang, Zheng Dang, Zhen Wei, Jin Xie

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. In this paper, we develop a novel variational non-local network-based outlier re

Kai Du, Xianghan Xu, Choongjae Won, Kefeng Wang

Antiferromagnetic states with no stray magnetic fields can enable high-density ultra-fast spintronic technologies. However, the detection and control of antiferromagnetic Neel vectors remain challenging. Linear magnetoelectric antiferromagnets (LMAs) may provide new pathways, but applying simultaneous electric and magnetic fields, necessary to control Neel v

Ziyi Liu, Rakshitha Godahewa, Kasun Bandara, Christoph Bergmeir

Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts. However, in many real-world situations, the data distributions are not stationary and they can change over time while reducing the accuracy of the forecasting models, which in the ML literature is known as

M. Sharif, T. Naseer

This paper investigates some particular anisotropic star models in $f(\mathcal{R},\mathcal{T},\mathcal{Q})$ gravity, where $\mathcal{Q}=\mathcal{R}_{\omega\alpha}\mathcal{T}^{\omega\alpha}$. We adopt a standard model $f(\mathcal{R},\mathcal{T},\mathcal{Q})=\mathcal{R}+\varpi\mathcal{Q}$, where $\varpi$ indicates a coupling constant. We take spherically symme

Syed Eqbal Alam, Dhirendra Shukla, Shrisha Rao

Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the research community. In this context, agents demand resources based on their local computation. Due to the exchang

Xu Chen, Zhiyong Feng, Zhiqing Wei, Feifei Gao

We propose a novel cooperative joint sensing-communication (JSC) unmanned aerial vehicle (UAV) network that can achieve downward-looking detection and transmit detection data simultaneously using the same time and frequency resources by exploiting the beam sharing scheme. The UAV network consists of a UAV that works as a fusion center (FCUAV) and multiple su

Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju

Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent research has revealed that deep neural networks for skin lesion recognition may overly depend on disease-irrelevant image artifacts (i.e., dark corners, dense hairs), leading to poor ge

Wenxuan Tu, Qing Liao, Sihang Zhou, Xin Peng

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perform the mask-then-reconstruct operation in the raw data space as is done in computer vision (CV) and natural language processing (NLP) areas, while neglecting the important non-Euc

Xiao He, Ye Li, Jian Tan, Bin Wu

Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and forecasting. However, existing decomposition methods rely on batch processing with a time complexity of O(W), where W is the number of data points within a time window. Therefore, the

Yi-Fei Dong, Ying-Chen Mao, Ji-Chong Yang

Searching for new physics~(NP) is one of the areas of high-energy physics that requires the most processing of large amounts of data. At the same time, quantum computing has huge potential advantages when dealing with large amounts of data. The principal component analysis~(PCA) algorithm may be one of the bridges connecting these two aspects. On the one han

Kyle Hayden, Sungkyung Kang, Anubhav Mukherjee

In this brief note, we show that there exist smooth 4-manifolds (with nonempty boundary) containing pairs of exotically knotted 2-spheres that remain exotic after one (either external or internal) stabilization. It follows that the ``one is enough'' theorem of Auckly-Kim-Melvin-Ruberman-Schwartz does not hold for closed surfaces whose homology classes are ch

Jonathan D. Freedman, Ian A. Nappier

The Plastic Surgery In-Service Training Exam (PSITE) is an important indicator of resident proficiency and serves as a useful benchmark for evaluating OpenAI's GPT. Unlike many of the simulated tests or practice questions shown in the GPT-4 Technical Paper, the multiple-choice questions evaluated here are authentic PSITE questions. These questions offer real

Tianchen Zhou, Zhanyi Hu, Bingzhe Wu, Cen Chen

Data privacy concerns has made centralized training of data, which is scattered across silos, infeasible, leading to the need for collaborative learning frameworks. To address that, two prominent frameworks emerged, i.e., federated learning (FL) and split learning (SL). While FL has established various benchmark frameworks and research libraries,SL currently

Allan Wing-Bocanegra, Salvador E. Venegas-Andraca

Several models have been proposed to build evolution operators to perform quantum walks in a theoretical way, although when wanting to map the resulting evolution operators into quantum circuits to run them in quantum computers, it is often the case that the mapping process is in fact complicated. Nevertheless, when the adjacency matrix of a graph can be dec

Shanglin Zhou, Yingjie Li, Minhan Lou, Weilu Gao

As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption. However, there is a mismatch, i.e., significant prediction accuracy loss, between the DONN

J. G. Dai, Peter Glynn, Yaosheng Xu

We prove that under a multi-scale heavy traffic condition, the stationary distribution of the scaled queue length vector process in any generalized Jackson network has a product-form limit. Each component in the product form follows an exponential distribution, corresponding to the Brownian approximation of a single station queue. The ``single station'' can

Wencong Wu, Guannan Lv, Yingying Duan, Peng Liang

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often d

Guangfu Cao, Li He

It is well known that the composition operator on Hardy or Bergman space has a closed range if and only if its Navanlinna counting function induces a reverse Carleson measure. Similar conclusion is not available on the Dirichlet space. Specifically, the reverse Carleson measure is not enough to ensure that the range of the corresponding composition operator

Nick R. Schwartz, Ian G. Abel, Adil B. Hassam, Myles Kelly

The centrifugal mirror confinement scheme incorporates supersonic rotation of a plasma into a magnetic mirror device. This concept has been shown experimentally to drastically decrease parallel losses and increase plasma stability as compared to prior axisymmetric mirrors. MCTrans++ is a 0D scoping tool which rapidly models experimental operating points in t

Yang Tian, Yunhui Xu, Pei Sun

Swarming phenomena are ubiquitous in various physical, biological, and social systems, where simple local interactions between individual units lead to complex global patterns. A common feature of diverse swarming phenomena is that the units exhibit either convergent or divergent evolution in their behaviors, i.e., becoming increasingly similar or distinct,

Setare Hajarolasvadi, Paolo Celli, Brian L. Kim, Ahmed E. Elbanna

In this letter, we provide an experimental demonstration of amplitude-dependent dispersion tuning of surface acoustic waves interacting with nonlinear resonators. Leveraging the similarity between the dispersion properties of plate edge waves and surface waves propagating in a semi-infinite medium, we use a setup consisting of a plate with a periodic arrange

Ebru Toprak

We study the scattering poles of $\sqrt{-\Delta} + V$, where $V$ is a compactly supported, bounded and complex valued potential. We show that the resolvent operator $ \chi R_V \chi$ has a meromorphic continuation to the whole Riemannian surface of $\Lambda$ of $ \log z $ as an operator $L^2 \to L^2 $. We then obtain the upper bound on the counting function $

Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, the

Md Asif Bin Syed, Imtiaz Ahmed

For decades, track association has been a challenging problem in marine surveillance, which involves the identification and association of vessel observations over time. However, the Automatic Identification System (AIS) has provided a new opportunity for researchers to tackle this problem by offering a large database of dynamic and geo-spatial information o

Finn Lattimore, Daniel M. Steinberg, Anna Zhu

Pursuing educational qualifications later in life is an increasingly common phenomenon within OECD countries since technological change and automation continues to drive the evolution of skills needed in many professions. We focus on the causal impacts to economic returns of degrees completed later in life, where motivations and capabilities to acquire addit

Junyang Wang, Yuanhong Xu, Juhua Hu, Ming Yan

Fine-tuning a visual pre-trained model can leverage the semantic information from large-scale pre-training data and mitigate the over-fitting problem on downstream vision tasks with limited training examples. While the problem of catastrophic forgetting in pre-trained backbone has been extensively studied for fine-tuning, its potential bias from the correspo

Xiaojie Zhang, Mingjun Li, Andrew Hilton, Amitangshu Pal

In order to plan rapid response during disasters, first responder agencies often adopt `bring your own device' (BYOD) model with inexpensive mobile edge devices (e.g., drones, robots, tablets) for complex video analytics applications, e.g., 3D reconstruction of a disaster scene. Unlike simpler video applications, widely used Multi-view Stereo (MVS) based 3D

Alessandro Pegoraro, Kavita Kumari, Hossein Fereidooni, Ahmad-Reza Sadeghi

ChatGPT has become a global sensation. As ChatGPT and other Large Language Models (LLMs) emerge, concerns of misusing them in various ways increase, such as disseminating fake news, plagiarism, manipulating public opinion, cheating, and fraud. Hence, distinguishing AI-generated from human-generated becomes increasingly essential. Researchers have proposed va

Michael May, Hong Qin

We develop an algebraic formulation for the discrete quantum harmonic oscillator (DQHO) with a finite, equally-spaced energy spectrum and energy eigenfunctions defined on a discrete domain, which is known as the su(2) or Kravchuk oscillator. Unlike previous approaches, ours does not depend on the discretization of the Schr\"odinger equation and recurrence re

Chunyang Ma, Qian Lu, Yen Wah Tong

The radiative/scattering properties of cyanobacterial aggregates are crucial for understanding microalgal cultivation. This study analyzed scattering matrix elements and cross-sections of cyanobacterial aggregates using the discrete dipole approximation (DDA) method. The stochastic random walk approach was adopted to generate a force-biased packing model for

Xinyi Ying, Li Liu, Yingqian Wang, Ruojing Li

Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt to achieve infrared small target detect

Gaochen Dong, Wei Chen

Transformer-based models, exemplified by GPT-3, ChatGPT, and GPT-4, have recently garnered considerable attention in both academia and industry due to their promising performance in general language tasks. Nevertheless, these models typically involve computationally encoding processes, and in some cases, decoding processes as well, both of which are fundamen

Ajinkya Tejankar, Maziar Sanjabi, Qifan Wang, Sinong Wang

Recently, self-supervised learning (SSL) was shown to be vulnerable to patch-based data poisoning backdoor attacks. It was shown that an adversary can poison a small part of the unlabeled data so that when a victim trains an SSL model on it, the final model will have a backdoor that the adversary can exploit. This work aims to defend self-supervised learning

Dimitri Coelho Mollo, Raphaël Millière

Large language models (LLMs) produce seemingly meaningful outputs, yet they are trained on text alone without direct interaction with the world. This leads to a modern variant of the classical symbol grounding problem in AI: can LLMs' internal states and outputs be about extra-linguistic reality, independently of the meaning human interpreters project onto t

Noah Stier, Anurag Ranjan, Alex Colburn, Yajie Yan

Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency. However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coa

Blanka Horvath, Maud Lemercier, Chong Liu, Terry Lyons

Distribution Regression on path-space refers to the task of learning functions mapping the law of a stochastic process to a scalar target. The learning procedure based on the notion of path-signature, i.e. a classical transform from rough path theory, was widely used to approximate weakly continuous functionals, such as the pricing functionals of path--depen

Peter Kuchment

The article surveys the known results and conjectures about the analytic properties of dispersion relations and Fermi surfaces for periodic equations of mathematical physics and their spectral incarnations.

Wenjie Ji, Siyuan Wang, Jiguang Hao, J. M. Floryan

Droplet velocities used in impact studies were investigated using high-speed photography. It was determined that droplets do not reach terminal velocity before a typical impact, raising the question of how to predict impact velocity. This question was investigated experimentally, and the results were used to validate a theoretical model. Experiments used dro

Vadim R. Munirov, Nathaniel J. Fisch

We study the effects of redistributing superthermal electrons on Bremsstrahlung radiation from hot relativistic plasma. We consider thermal and nonthermal distribution of electrons with an energy cutoff in the phase space and explore the impact of the energy cutoff on Bremsstrahlung losses. We discover that the redistribution of the superthermal electrons in

Yoshinari Takayama, Kazumune Hashimoto, Toshiyuki Ohtsuka

This study considers the control problem with signal temporal logic (STL) specifications. Prior works have adopted smoothing techniques to address this problem within a feasible time frame and solve the problem by applying sequential quadratic programming (SQP) methods naively. However, one of the drawbacks of this approach is that solutions can easily becom

Rui Niu, Shuai Wan, Tian-Peng Hua, Wei-Qiang Wang

For the applications of the frequency comb in microresonators, it is essential to obtain a fully frequency-stabilized microcomb laser source. Here, we demonstrate an atom-referenced stabilized soliton microcomb generation system based on the integrated microring resonator. The pump light around $1560.48\,\mathrm{nm}$ locked to an ultra-low-expansion (ULE) ca