January 2022 arXiv papers — page 98

James Freitag, Rémi Jaoui, David Marker, Joel Nagloo

We study the structure of the solution sets in universal differential fields of certain differential equations of order two, the Poizat equations, which are particular cases of Li\'enard equations. We give a necessary and sufficient condition for strong minimality for equations in this class and a complete classification of the algebraic relations for soluti

Yuting Fana, Enxu Zhua, Chaoying Zhaoa

With the increasing demand for potential applications in almost all fields in modern optics, the generation of vortex beams has attracted significant interest. Based on a flat plate made of electro-optical crystals, we propose an electrically controlling method to generate vortex beams assisted by the Pockels effect. Compared with traditional methods, our me

Brando Rettino, Thomas Hayward-Schneider, Alessandro Biancalani, Alberto Bottino

Energetic particles produced by neutral beams are observed to excite energetic-particle-driven geodesic acoustic modes (EGAMs) in tokamaks. We study the effects of anisotropy of distribution function of the energetic particles on the excitation of such instabilities with ORB5, a gyrokinetic particle-in-cell code. Numerical results are shown for linear electr

Jun Xu, Panagiota Papakonstantinou

Bayesian analyses on both isoscalar and isovector nuclear interaction parameters are carried out based on the Korea-IBS-Daegu-SKKU (KIDS) model under the constraints of nuclear structure data of $^{208}$Pb and $^{120}$Sn. Under the constraint of the neutron-skin thickness, it is found that incorporating the curvature parameter $K_{sym}$ of nuclear symmetry e

Jesus Bujalance Martin, Fabien Moutarde

In the search for more sample-efficient reinforcement-learning (RL) algorithms, a promising direction is to leverage as much external off-policy data as possible. For instance, expert demonstrations. In the past, multiple ideas have been proposed to make good use of the demonstrations added to the replay buffer, such as pretraining on demonstrations only or

Georg Oberdieck

We interprete results of Markman on monodromy operators as a universality statement for descendent integrals over moduli spaces of stable sheaves on $K3$ surfaces. This yields effective methods to reduce these descendent integrals to integrals over the punctual Hilbert scheme of the $K3$ surface. As an application we establish the higher rank Segre-Verlinde

Konstantin G. Zloshchastiev

The logarithmic superfluid theory of physical vacuum predicts that gravity is an induced phenomenon, which has a multiple-scale structure. At astronomical scales, as the distance from a gravitating center increases, gravitational potential and corresponding spacetime metric are dominated by a Newtonian (Schwarzschild) term, followed by a logarithmic term, fi

Florian Cafiero, Jeremy Ward

International public health policies increasingly favor mandatory immunization. If its short-term effects on vaccine coverage are well documented, there has been little consideration to its effects on public attitudes towards vaccines. In this paper, we examine Google searches related to vaccines in five countries (Australia, France, Germany, Italy, Serbia)

Yufei Tao

In PODS'21, Hu presented an algorithm in the massively parallel computation (MPC) model that processes any acyclic join with an asymptotically optimal load. In this paper, we present an alternative analysis of her algorithm. The novelty of our analysis is in the revelation of a new mathematical structure -- which we name "canonical edge cover" -- for acyclic

Bernard Bonnard, Olivier Cots, Joseph Gergaud, Boris Wembe

In this article, based on two case studies, we discuss the role of abnormal geodesics in planar Zermelo navigation problems. Such curves are limit curves of the accessibility set, in the domain where the current is strong. The problem is set in the frame of geometric time optimal control, where the control is the heading angle of the ship and in this context

Jenis Thongam, Lenin S. Shagolsem

The effect of chain topology on the statics and dynamics of chain at the interface of two immiscible fluids is studied by means of molecular dynamics simulations. For three topologically different chains, namely, linear, ring, and trefoil-knot of same molecular weight the effect of varying both polymer--fluid and fluid--fluid interaction nature on the width

Yuting Yang, Pei Huang, FeiFei Ma, Juan Cao

Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addressed. Along this line, we propose a formal framework to evaluate word-level robustness. First, to study safe regions for a model, we introduce robustness radius which is the boundar

Amin Mosallanezhad, Fatemeh Zahra Zeraatgari, Liquan Mei, De-Fu Bu

We explore the effects of anisotropic thermal conduction, anisotropic pressure, and magnetic field strength on the hot accretion flows around black holes by solving the axisymmetric, steady-state magnetohydrodynamic equations. The anisotropic pressure is known as a mechanism for transporting angular momentum in weakly collisional plasmas in hot accretion flo

Bo Wang, Dongdong Liu, Hailiang Chen

Accretion-induced collapse (AIC) of massive white-dwarfs (WDs) has been proposed as an important way for the formation of neutron star (NS) systems. An oxygen-neon (ONe) WD that accretes H-rich material from a red-giant (RG) star may experience the AIC process, eventually producing millisecond pulsars (MSPs), known as the RG donor channel. Previous studies i

Irving Rondon

We present a general expression for the optical theorem in terms of Localized Waves. This representation is well-known and commonly used to generate Frozen waves, Xwaves, and other propagation invariant beams. We analyze several examples using different input beam sources on a circular detector to measure the extinction cross-section.

Clement Demeslay, Philippe Rostaing, Roland Gautier

This paper presents a semi-analytical approximation of Symbol Error Rate (SER) for the well known LoRa Internet of Things (IoT) modulation scheme in the following two scenarios: 1) in multi-path frequency selective fading channel with Additive White Gaussian Noise (AW GN) and 2) in the presence of a second interfering LoRa user in flat-fading AW GN channel.

Rahma Dandan, Sylvie Despres, Karima Sedki

Our food preferences guide our food choices and in turn affect our personal health and our social life. In this paper, we adopt an approach using a domain ontology expressed in OWL2 to support the acquisition and representation of preferences in formalism CP-Net. Specifically, we present the construction of the domain ontology and questionnaire design to acq

Raphaël Danchin, Patrick Tolksdorf

We are concerned with the barotropic compressible Navier-Stokes system in a bounded domain of $\mathbb{R}^d$ (with $d\geq2$). In a critical regularity setting, we establish local well-posedness for large data with no vacuum and global well-posedness for small perturbations of a stable constant equilibrium state.Our results rely on new maximal regularity esti

M. Meuwly

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in integrating and combining this with machine learning techniques provides a unique opportunity to bring such dynamics simulation

Tetsuo Hatakeyama, Hirohisa Hirai, Mitsuru Simetani, Dai Okamoto

In this work, the origin of the low free electron mobility in SiC MOSFETs is investigated using the scattering theory of two-dimensional electron gases. We first establish that neither phonon scattering nor Coulomb scattering can be the cause of the low observed mobility in SiC MOSFETs; we establish this fact by comparing the theoretically calculated mobilit

Neeldhara Misra, Saraswati Nanoti

Eternal Vertex Cover problem is a dynamic variant of the vertex cover problem. We have a two player game in which guards are placed on some vertices of a graph. In every move, one player (the attacker) attacks an edge. In response to the attack, the second player (defender) moves the guards along the edges of the graph in such a manner that at least one guar

Swann Marx, Edouard Pauwels

We consider flows of ordinary differential equations (ODEs) driven by path differentiable vector fields. Path differentiable functions constitute a proper subclass of Lipschitz functions which admit conservative gradients, a notion of generalized derivative compatible with basic calculus rules. Our main result states that such flows inherit the path differen

Zhifei Yu, Bo Fang, Shuying Chen, Pan Liu

The SU (1,1)-type atom-light hybrid interferometer (SALHI) is a kind of interferometer that is sensitive to both the optical phase and atomic phase. However, the loss has been an unavoidable problem in practical applications and greatly limits the use of interferometers. Visibility is an important parameter to evaluate the sensing performance of interferomet

Tianlang He, Jiajie Tan, Weipeng Zhuo, Maximilian Printz

Proximity detection is to determine whether an IoT receiver is within a certain distance from a signal transmitter. Due to its low cost and high popularity, Bluetooth low energy (BLE) has been used to detect proximity based on the received signal strength indicator (RSSI). To address the fact that RSSI can be markedly influenced by device carriage states, pr

Md Nasim

An experimental overview of the energy dependence of strangeness production is presented. The strange hadrons are considered a good probe to study the QCD matter created in relativistic nucleus-nucleus collisions. The heavy-ion experiments at SPS, RHIC, and LHC have recorded a wealth of data in proton-proton, proton-nucleus and nucleus-nucleus collisions at

Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection informa

J. Jaume Bestard, J. Trujillo Bueno, M. Bianda, J. Štěpán

We present novel spectropolarimetric observations of the hydrogen H$\alpha$ line taken with the Z\"urich Imaging Polarimeter (ZIMPOL) at the Gregory Coud\'e Telescope of the Istituto Ricerche Solari Locarno (IRSOL). The linear polarization is clearly dominated by the scattering of anisotropic radiation and the Hanle effect, while the circular polarization by

Shuai Chang

Embracing the deep learning techniques for representation learning in clustering research has attracted broad attention in recent years, yielding a newly developed clustering paradigm, viz. the deep clustering (DC). Typically, the DC models capitalize on autoencoders to learn the intrinsic features which facilitate the clustering process in consequence. Nowa

Giorgio Iavicoli, Claudio Zito

Graph-SLAM is a well-established algorithm for constructing a topological map of the environment while simultaneously attempting the localisation of the robot. It relies on scan matching algorithms to align noisy observations along robot's movements to compute an estimate of the current robot's location. We propose a fundamentally different approach to scan

Vladimir Shenmaier

The maximum traveling salesman problem (Max~TSP) consists of finding a Hamiltonian cycle with the maximum total weight of the edges in a given complete weighted graph. We prove that, in the case when the edge weights are induced by a metric space of bounded doubling dimension, asymptotically optimal solutions of the problem can be found by the simple greedy

Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si

Recent works explore learning graph representations in a self-supervised manner. In graph contrastive learning, benchmark methods apply various graph augmentation approaches. However, most of the augmentation methods are non-learnable, which causes the issue of generating unbeneficial augmented graphs. Such augmentation may degenerate the representation abil

Hongjie Dong, Seick Kim, Sungjin Lee

We construct the fundamental solution of second order parabolic equations in non-divergence form under the assumption that the coefficients are of Dini mean oscillation in the spatial variables. We also prove that the fundamental solution satisfies a sub-Gaussian estimate. In the case when the coefficients are Dini continuous in the spatial variables and mea

Debo Cheng, Jiuyong Li, Lin Liu, Jiji Zhang

Unobserved confounding is the main obstacle to causal effect estimation from observational data. Instrumental variables (IVs) are widely used for causal effect estimation when there exist latent confounders. With the standard IV method, when a given IV is valid, unbiased estimation can be obtained, but the validity requirement on a standard IV is strict and

Yoonjeon Kim, Joel Jang, Sumin Shin

Creation of images using generative adversarial networks has been widely adapted into multi-modal regime with the advent of multi-modal representation models pre-trained on large corpus. Various modalities sharing a common representation space could be utilized to guide the generative models to create images from text or even from audio source. Departing fro

Zhiliang Xu, Zhibin Hong, Changxing Ding, Zhen Zhu

Advanced face swapping methods have achieved appealing results. However, most of these methods have many parameters and computations, which makes it challenging to apply them in real-time applications or deploy them on edge devices like mobile phones. In this work, we propose a lightweight Identity-aware Dynamic Network (IDN) for subject-agnostic face swappi

Fengchang Bu, Lei Xue, Mengyang Zhai, Chao Xu

The concept of joint persistence has been widely used to study the mechanics and failure processes of rock masses benefitting from the simplicity of statistical linear weighing of the discontinuity. Nevertheless, this term neglects the scale effects of rock bridges, meaning that the same joint persistence may refer to different numbers and spacings of rock b

Robi Bhattacharjee, Gaurav Mahajan

We consider a lifelong learning scenario in which a learner faces a neverending and arbitrary stream of facts and has to decide which ones to retain in its limited memory. We introduce a mathematical model based on the online learning framework, in which the learner measures itself against a collection of experts that are also memory-constrained and that ref

K. K. Jena, S. Senapati, B. B. Sahu, Jajati K. Nayak

We use a phenomenological nucleus-nucleus optical potential constructed in the light of a potential developed by Ginocchio to study the elastic angular distributions of different nuclear systems near Coulomb barrier. The differential cross section ratios of elastic to Rutherford are studied for systems $^{14}$N+$^{56}$Fe and $^{14}$N+$^{90}$Zr for several co

Wenliang Dai, Samuel Cahyawijaya, Tiezheng Yu, Elham J. Barezi

With the rise of deep learning and intelligent vehicle, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, there is a

Haopeng Hou

The unsupervised domain adaptive person re-identification (re-ID) task has been a challenge because, unlike the general domain adaptive tasks, there is no overlap between the classes of source and target domain data in the person re-ID, which leads to a significant domain gap. State-of-the-art unsupervised re-ID methods train the neural networks using a memo

Hui-Hui Duan, Yong-Lu Liu, Ming-Qiu Huang

Semileptonic decay processes of $\Xi_c \to \Xi\ell\nu_\ell$ are studied by light-cone QCD sum rules in this paper. The six form factors of $\Xi_c \to \Xi$ semileptonic transition matrix elements are calculated by this method with the light-cone distribution amplitudes of $\Xi$ baryon up to twist six. With the six form factors, the absolute branching ratios o

Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera

This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and a

M. Raghunathan, N. B. George, V. R Unni, J. Kurths

This experimental study investigates the dynamical transition from stable operation to thermoacoustic instability in a turbulent bluff-body stabilized dump combustor. We conduct experiments to characterize the dynamical transition utilizing acoustic pressure and local heat release rate fluctuations. We observe the transition to thermoacoustic instability for

Shek Yeung, Ming-Chung Chu

Recent observations suggest that there are violations of the isotropy of the universe at large scales, an important part of the cosmological principle. In this paper, we use the Cosmic Microwave Background (CMB) data to search for spatial variations of the cosmological parameters in the $\Lambda\mathrm{CDM}$ model. We fit the Planck temperature angular power

Pravin Khanal, Bowei Zhou, Magda Andrade, Yanliu Dang

Future generations of magnetic random access memory demand magnetic tunnel junctions that can provide simultaneously high magnetoresistance, strong retention, low switching energy and small cell size below 10nm. Here we study perpendicular magnetic tunnel junctions with composite free layers where multiple ferromagnet/nonmagnet interfaces can contribute to t

Lei Liu, Weimin Zheng, Jian Fu, Zhijun Xu

We present OmniUV, a multi-purpose simulation toolkit for space and ground VLBI observations. It supports various kinds of VLBI stations, including Earth (ground) fixed, Earth orbit, Lunar fixed, Lunar orbit, Moon-Earth and Earth-Sun Lagrange 1 and 2 points, etc. The main functionalities of this toolkit are: (1) Trajectory calculation; (2) Baseline uv calcul

Lin Nie, Yang Liu, Zejun Jiang, Xiongfei Geng

It has been long debated whether the high-energy gamma-ray radiation from the Crab nebula stems from leptonic or hadronic processes. In this work, we investigate the multi-band non-thermal radiation from the Crab pulsar wind nebula with the leptonic and leptonic-hadronic hybrid models, respectively. Then we use the Markov Chain Monte Carlo(MCMC) sampling tec

Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang

Glaucoma is one of the ophthalmic diseases that may cause blindness, for which early detection and treatment are very important. Fundus images and optical coherence tomography (OCT) images are both widely-used modalities in diagnosing glaucoma. However, existing glaucoma grading approaches mainly utilize a single modality, ignoring the complementary informat

Bin Xia, Yucheng Hang, Yapeng Tian, Wenming Yang

Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However, NLA gives noisy information large weights and consumes quadratic computation resources with respect to the input size, limiting its performance and application. In this paper, we propose a n

Jun Qi, Javier Tejedor

This work aims to design a low complexity spoken command recognition (SCR) system by considering different trade-offs between the number of model parameters and classification accuracy. More specifically, we exploit a deep hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC pipeline. Our command recognition system, namely CNN+(TT-DN

James W. Webber, Eric Todd Quinto

We present a microlocal analysis of two novel Radon transforms of interest in Compton Scattering Tomography (CST), which map compactly supported $L^2$ functions to their integrals over seven-dimensional sets of apple and lemon surfaces. Specifically, we show that the apple and lemon transforms are elliptic Fourier Integral Operators (FIO), which satisfy the

Z. Yousaf, Kazuharu Bamba, M. Z. Bhatti, U. Farwa

In this paper, the quasi static-approximation on the hydrodynamics of compact objects is proposed in $f(R, T)$ gravity, where $R$ is the scalar curvature and $T$ is the trace of stress-energy tensor, by exploring the axial and reflection symmetric space time stuffed with anisotropic and dissipative matter contents. The set of invariant-velocities is defined

Philipp Hohlfeld, Tobias Ostermeier, Dominik Brandl

Classification problems are common in Computer Vision. Despite this, there is no dedicated work for the classification of beer bottles. As part of the challenge of the master course Deep Learning, a dataset of 5207 beer bottle images and brand labels was created. An image contains exactly one beer bottle. In this paper we present a deep learning model which

Mrinal K. Ghosh, Subrata Golui, Chandan Pal, Somnath Pradhan

We study zero-sum stochastic games for controlled discrete time Markov chains with risk-sensitive average cost criterion with countable state space and Borel action spaces. The payoff function is nonnegative and possibly unbounded. Under a certain Lyapunov stability assumption on the dynamics, we establish the existence of a value and saddle point equilibriu

Sunwoo Lee, Anit Kumar Sahu, Chaoyang He, Salman Avestimehr

Local Stochastic Gradient Descent (SGD) with periodic model averaging (FedAvg) is a foundational algorithm in Federated Learning. The algorithm independently runs SGD on multiple workers and periodically averages the model across all the workers. When local SGD runs with many workers, however, the periodic averaging causes a significant model discrepancy acr

Donal F. Connon

We present a new representation of the Stieltjes constants in the form of a limit of a Fourier series.

Yang Shen, Yu-Chao Hua, Han-Ling Li, S. L. Sobolev

To develop efficient thermal management strategies for wide bandgap (WBG) semiconductor devices, it is essential to have a clear understanding of the heat transport process within the device and accurately predict the junction temperature. In this paper, we used the phonon Monte Carlo (MC) method with the phonon dispersion of various typical WBG semiconducto

Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra, Robert M. Gray

In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a promising option; however, thus far they have been largely limited by the lack of energy-efficient nonlinear optical functions. Here, we experimentally demonstrate an all-optical

Lizhi Yang, Ruhang Ma, Avideh Zakhor

Object detection using aerial drone imagery has received a great deal of attention in recent years. While visible light images are adequate for detecting objects in most scenarios, thermal cameras can extend the capabilities of object detection to night-time or occluded objects. As such, RGB and Infrared (IR) fusion methods for object detection are useful an

Delvydo Melvernaldo

In this paper, we show the additive separation of the Hamilton-Jacobi equation, present the 4-velocity of the test particles, and attempt to find the equatorial circular photon orbit (ECPO) in the Kerr-Sen-Taub-NUT (KSTN) solution of the low energy limit of heterotic string theory. In the process of trying to find the ECPO, we instead encounter a ring singul

A. S. Agrawal, S. K. Tripathy, Sarmistha Pal, B. Mishra

In this work, we have studied some bouncing cosmologies in the frame work of $f(R,T)$ gravity. The bouncing scenario has been formulated to avoid the big bang singularity. The physical and geometrical parameters are investigated. The effect of the extended gravity theory on the dynamical parameters of the model is investigated. It is found that, the $f(R,T)$

Wei Zhu

Planet multiplicities are useful in constraining the formation and evolution of planetary systems but usually difficult to constrain observationally. Here, we develop a general method that can properly take into account the survey incompleteness and recover the intrinsic planet multiplicity distribution. We then apply it to the radial velocity (RV) planet sa

Laichuan Shen, Jing Xia, Zehan Chen, Xiaoguang Li

Magnetic bimerons are topologically nontrivial spin textures in in-plane easy-axis magnets, which can be used as particle-like information carriers. Here, we report a theoretical study on the nonreciprocal dynamics of asymmetrical ferrimagnetic (FiM) bimerons induced by spin currents. The FiM bimerons have the ability to move at a speed of kilometers per sec

Jinglin Zhao, Eric B. Ford, Chris G. Tinney

The radial velocity (RV) detection of exoplanets is challenged by stellar spectroscopic variability that can mimic the presence of planets and by instrumental instability that can further obscure the detection. Both stellar and instrumental changes can distort the spectral line profiles and be misinterpreted as apparent RV shifts. We present an improved Four

Raghavendra Srikanth Hundi

In this work, we have studied lepton flavor violating (LFV) decays of $Z$ gauge boson and Higgs boson ($H$) in the scotogenic model. We have computed branching ratios for the decays $Z\to\ell_\alpha\ell_\beta$ and $H\to\ell_\alpha\ell_\beta$ in this model. Here, $\ell_\alpha$ and $\ell_\beta$ are different charged lepton fields. After fitting to the neutrino

S. V. Mousavi, S. Miret-Artes

Some unexplored decoherence aspects within the Caldeira-Leggett master equation are analyzed and discussed. The decoherence process is controlled by the two environment parameters, the relaxation rate or friction and the temperature, leading to a gradual transition from the quantum to classical regime. Arrival time distributions, nonminimum-uncertainty-produ

Himashi Peiris, Zhaolin Chen, Gary Egan, Mehrtash Harandi

This paper proposes an adversarial learning based training approach for brain tumor segmentation task. In this concept, the 3D segmentation network learns from dual reciprocal adversarial learning approaches. To enhance the generalization across the segmentation predictions and to make the segmentation network robust, we adhere to the Virtual Adversarial Tra

Christian N. K. Anderson, Mark K. Transtrum

Bifurcation phenomena are common in multi-dimensional multi-parameter dynamical systems. Normal form theory suggests that the bifurcations themselves are driven by relatively few parameters; however, these are often nonlinear combinations of the bare parameters in which the equations are expressed. Discovering reparameterizations to transform such complex or

Huw Price

Many scientists have expressed concerns about potential catastrophic risks associated with new technologies. But expressing concern is one thing, identifying serious candidates another. Such risks are likely to be novel, rare, and difficult to study; data will be scarce, making speculation necessary. Scientists who raise such concerns may face disapproval no

K. K. Abdurasulov, J. Q. Adashev

The present article is a part of the study of solvable Leibniz algebras with a given nilradical. In this paper solvable Leibniz algebras, whose nilradicals is naturally graded quasi-filiform algebra and the complemented space to the nilradical has one dimension, are described up to isomorphism.

Valentin Duruisseaux, Melvin Leok

A variational framework for accelerated optimization was recently introduced on normed vector spaces and Riemannian manifolds in Wibisono et al. (2016) and Duruisseaux and Leok (2021). It was observed that a careful combination of timeadaptivity and symplecticity in the numerical integration can result in a significant gain in computational efficiency. It is

Kevin J. Doherty, David M. Rosen, John J. Leonard

In this work we present the first initialization methods equipped with explicit performance guarantees adapted to the pose-graph simultaneous localization and mapping (SLAM) and rotation averaging (RA) problems. SLAM and rotation averaging are typically formalized as large-scale nonconvex point estimation problems, with many bad local minima that can entrap

Yongkang Wang, Dihua Zhai, Yufeng Zhan, Yuanqing Xia

Federated learning (FL) is a distributed machine learning paradigm where enormous scattered clients (e.g. mobile devices or IoT devices) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. Unfortunately, FL is susceptible to a variety of attacks, including backdoor

Junjie Zhao, Paulo C. C. Freire, Michael Kramer, Lijing Shao

Benefitting from the unequaled precision of the pulsar timing technique, binary pulsars are important testbeds of gravity theories, providing some of the tightest bounds on alternative theories of gravity. One class of well-motivated alternative gravity theories, the scalar-tensor gravity, predict large deviations from general relativity for neutron stars th

Marc Bruyere, Christoff Visser, Daphne Tuncer

The task of determining which network architectures provide the best ratio in terms of operation and management efforts \textit{vs.} performance guarantees is not trivial. In this paper, we investigate the complexity of operating different types of architectures from the perspective of the space of network parameters that need to be monitored and configured.

Daniel Cirkovic, Tiandong Wang, Sidney Resnick

Reciprocity in social networks helps understand information exchange between two individuals, and indicates interaction patterns between pairs of users. A recent study indicates the reciprocity coefficient of a classical directed preferential attachment (PA) model does not match empirical evidence. In this paper, we extend the classical 3-scenario directed P

Zigeng Li, Xiaomiao Li, Xiaolan Zhong

We present an all-fiber emitter-cavity quantum electrodynamics (QED) system which consists of two two-level emitters and a nanofiber cavity. Our scheme makes it possible to observe the higher-order exceptional points based on the coupling between the emitters and the nanofiber cavity. The effective gain of this cavity can be obtained by weakly driven to the

Jinyu Lu, Guoqiang Liu, Bing Sun, Chao Li

In CRYPTO 2019, Gohr made a pioneering attempt and successfully applied deep learning to the differential cryptanalysis against NSA block cipher SPECK32/64, achieving higher accuracy than the pure differential distinguishers. By its very nature, mining effective features in data plays a crucial role in data-driven deep learning. In this paper, in addition to

Gande Naga Raju, Harshita Madduri

In this article, we propose a higher order approximation to Caputo fractional (C-F) derivative using graded mesh and standard central difference approximation for space derivatives, in order to obtain the approximate solution of time fractional partial differential equations (TFPDE). The proposed approximation for C-F derivative tackles the singularity at or

Sumita Datta, Vanja Dunjko, Maxim Olshanii

In this paper, the quantum fluctuations of the relative velocity of constituent solitons in a Gross-Pitaevskii breather are studied. The breather is confined in a weak harmonic trap. These fluctuations are monitored,indirectly, using a two-body correlation function measured at a quarter of the harmonic period after the breather creation. The results of an ab

Hongjie Dong, Seick Kim, Sungjin Lee

We improve a result in Kim and Lee (Ann. Appl. Math. 37(2):111--130, 2021): showing that if the coefficients of an elliptic operator in non-divergence form are of Dini mean oscillation, then its Green's function has the same asymptotic behavior near the pole $x_0$ as that of the corresponding Green's function for the elliptic equation with constant coefficie

Li-Zheng Yin, Jin Zhao, Ming-Zhe Chong, Feng-Yuan Han

Retroreflectors that can accurately redirect the incident wave in free space back along its original channel provide unprecedented opportunities for light manipulation in wireless communication. However, to the best of our knowledge, the existing methods of designing retroreflectors suffer from either the bulky size, narrow angular bandwidth, or time-consumi

Yu. G. Peisakhovich, A. A. Shtygashev

Within the framework of the density matrix method, general formulas obtained that are convenient for describing fast pulsed photoemission that occurs in a time less than or on the order of the times of relaxation processes inside the photocathode. Expressions for the elements of the density matrix are found by solving the kinetic equation that takes into acc

Petter Holme

This is an essay about understanding complexity science, via some peculiarities of the field, as a meeting place for a special kind of scientist. It comes out of my hobby of reading popular-science complex systems books, and builds on notes that have been collecting dust for almost a decade.

Song Wang, Liyan Tang, Mingquan Lin, George Shih

Radiology report generation aims to produce computer-aided diagnoses to alleviate the workload of radiologists and has drawn increasing attention recently. However, previous deep learning methods tend to neglect the mutual influences between medical findings, which can be the bottleneck that limits the quality of generated reports. In this work, we propose t

Jing Yue, Jian Li, Wen Zhang

In this article, we present an efficient deep learning method called coupled deep neural networks (CDNNs) for coupled physical problems. Our method compiles the interface conditions of the coupled PDEs into the networks properly and can be served as an efficient alternative to the complex coupled problems. To impose energy conservation constraints, the CDNNs

Ting Lv, Tian-Cheng Yi, Liangsheng Li, Gaoyong Sun

We investigate the quantum criticality and universality in Aubry-Andr\'{e}-Harper (AAH) model with $p$-wave superconducting pairing $\Delta$ in terms of the generalized fidelity susceptibility (GFS). We show that the higher-order GFS is more efficient in spotlighting the critical points than lower-order ones, and thus the enhanced sensitivity is propitious f

Yichuan Li, Petros G. Voulgaris, Nikolaos M. Freris

We propose a communication efficient quasi-Newton method for large-scale multi-agent convex composite optimization. We assume the setting of a network of agents that cooperatively solve a global minimization problem with strongly convex local cost functions augmented with a non-smooth convex regularizer. By introducing consensus variables, we obtain a block-

Daye Nam, Baishakhi Ray, Seohyun Kim, Xianshan Qu

Today's programmers, especially data science practitioners, make heavy use of data-processing libraries (APIs) such as PyTorch, Tensorflow, NumPy, Pandas, and the like. Program synthesizers can provide significant coding assistance to this community of users; however program synthesis also can be slow due to enormous search spaces. In this work, we examine w

Lior Shamir

In the past several decades, multiple cosmological theories that are based on the contention that the Universe has a major axis have been proposed. Such theories can be based on the geometry of the Universe, or multiverse theories such as black hole cosmology. The contention of a cosmological-scale axis is supported by certain evidence such as the dipole axi

Jin Zhang, Chao Zhang, Jin Yang, Barbara Capogrosso-Sansone

By means of quantum Monte Carlo simulations we study phase diagrams of dipolar bosons in a square optical lattice. The dipoles in the system are parallel to each other and their orientation can be fixed in any direction of the three-dimensional space. Starting from experimentally tunable parameters like scattering length and dipolar interaction strength, we

Hwancheol Jeong, Carleton DeTar, Steven Gottlieb

We investigate the state-of-the-art Lanczos eigensolvers available in the Grid and QUDA libraries. They include Implicitly Restarted Lanczos, Thick-Restart Lanczos, and Block Lanczos. We measure and analyze their performance for the Highly Improved Staggered Quark (HISQ) Dirac operator. We also discuss optimization of Chebyshev acceleration.

Benquan Lu, Xiaotong Lu, Tao Wang, Hong Chang

Large scale ab initio calculations of the electric contribution (i.e. the electric field gradient) to the electric quadrupole hyperfine interaction constants $B$ for the $5p~^2P_{3/2}$ state in $^{109}$Cd$^+$ ion and the $5s5p~^3P_{1,2}$ states in neutral $^{109}$Cd atom were performed. To probe the sensitivity of $B$ to different electron correlation effect

Takuma Ikeda, Yasunobu Uchiyama, Toshiki Sato, Ryota Higurashi

The origin of the jet-like structures observed in Cassiopeia A is still unclear, although it seems to be related to its explosion mechanism. X-ray observations of the characteristic structures could provide us useful information on the explosive nucleosynthesis via the observation of elements, which is a unique approach to understand its origin. We here repo

Anna Ijjas

Advances in our understanding of the origin, evolution and structure of the universe have long been driven by cosmological perturbation theory, model building and effective field theory. In this review, we introduce numerical relativity as a powerful new complementary tool for fundamental cosmology. To illustrate its power, we discuss applications of numeric

Giacomo Micheli, Severin Schraven, Simran Tinani, Violetta Weger

The local to global principle for densities is a very convenient tool proposed by Poonen and Stoll to compute the density of a given subset of the integers. In this paper we provide an effective criterion to find all higher moments of the density (e.g. the mean, the variance) of a subset of a finite dimensional free module over the ring of algebraic integers

Xueying Shao, He Gao

In this paper, we simulate the gamma-ray bursts (GRBs) prompt emission light curve, spectrum and $E_p$ evolution patterns within the framework of the Internal-Collision-induced MAgnetic Reconnection and Turbulence (ICMART) model. We show that this model can produce a Band shape spectrum, whose parameters ($E_p$, $\alpha$, $\beta$) could distribute in the typ

Peter Rassolov, Hadi Mohammadigoushki

We investigate the effects of micellar entanglement number on the kinetics of shear banding flow formation in a Taylor-Couette flow. Three sets of wormlike micellar solutions, each set with a similar fluid elasticity and zero-shear-rate viscosity, but with varying entanglement densities, are studied under start-up of steady shear. Our experiments indicate th

Péter Garamvölgyi, Yuxi Liu, Dong Zhou, Fan Long

Traditional public blockchain systems typically had very limited transaction throughput because of the bottleneck of the consensus protocol itself. With recent advances in consensus technology, the performance limit has been greatly lifted, typically to thousands of transactions per second. With this, transaction execution has become a new performance bottle

Shang-Jie Yu, Yue Jiang, John A. Roberts, Markus A. Huber

van der Waals nanomaterials supporting phonon polariton quasiparticles possess unprecedented light confinement capabilities, making them ideal systems for molecular sensing, thermal emission, and subwavelength imaging applications, but they require defect-free crystallinity and nanostructured form factors to fully showcase these capabilities. We introduce bo