August 2022 arXiv papers — page 128

Ayesha Siddiqua, Muhammad Ashad Kabir, Tanzina Ferdous, Israt Bintea Ali

In this technologically advanced era, with the proliferation of artificial intelligence, many mobile apps are available for plant disease detection, diagnosis, and treatment, each with a variety of features. These apps need to be categorized and reviewed following a proper framework that ensures their quality. This study aims to present an approach to evalua

Yariana Diaz, Cody Gilbert, Ryan Kinser

This paper concerns stability functions for Dynkin quivers, in the generality introduced by Rudakov. We show that relatively few inequalities need to be satisfied for a stability function to be totally stable (i.e. to make every indecomposable stable). Namely, a stability function $\mu$ is totally stable if and only if $\mu(\tau V) < \mu(V)$ for every almost

Long Chen, Jingrong Wei

A transformed primal-dual (TPD) flow is developed for a class of nonlinear smooth saddle point system. The flow for the dual variable contains a Schur complement which is strongly convex. Exponential stability of the saddle point is obtained by showing the strong Lyapunov property. Several TPD iterations are derived by implicit Euler, explicit Euler, implici

Branko Ristic, Alessio Benavoli, Sanjeev Arulampalam

Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human opera

Nang Hung Nguyen, Phi Le Nguyen, Duc Long Nguyen, Trung Thanh Nguyen

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID ty

Muni Zhou, Zhuo Liu, Nuno F. Loureiro

We report analytical and numerical investigations of sub-ion-scale turbulence in low-beta plasmas, focusing on the spectral properties of the fluctuations and electron heating. In the isothermal limit, the numerical results strongly support a description of the turbulence as a critically-balanced Kolmogorov-like cascade of kinetic Alfv\'en wave fluctuations,

Tatsuya Horiguchi

Peterson varieties are subvarieties of flag varieties and their (equivariant) cohomology rings are given by Fukukawa-Harada-Masuda in type A and soon later the author with Harada and Masuda gives an explicit presentation of the (equivariant) cohomology rings of Peterson varieties for arbitrary Lie types. In this note we study the (equivariant) cohomology rin

Min-Gyeom Kim, Minchan Jung, JunGee Hong, Kwang-Ki K. Kim

This paper presents a hybrid trajectory optimization method designed to generate collision-free, smooth trajectories for autonomous mobile robots. By combining sampling-based Model Predictive Path Integral (MPPI) control with gradient-based Interior-Point Differential Dynamic Programming (IPDDP), we leverage their respective strengths in exploration and smoo

Vaibhav Krishna, Nino Antulov-Fantulin

Community Question Answering (CQA) websites have become valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high migration of users in and out of communities, a key challenge is to design effective strategies for recommending experts for new questions. In th

Eduardo Espíndola, Yu Tang

The rigid-body attitude tracking using vector and biased gyro measurements with unknown inertia matrix is studied in this note. First, a gyro-bias observer with global exponential stability is designed. Then, an attitude tracking controller based on this observer is devised, ensuring almost global asymptotic stability and almost semiglobal exponential stabil

Ming Cheng, Yiling Xu, Wang Shen, M. Salman Asif

High-speed, high-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content at fine granularity. The acquisition of H2-Stereo video, however, remains challenging with commodity cameras. Existing spatial super-resolution or temporal frame interpolation methods provide compromised solutions that lack temporal or spatial details, respect

Florence Regol, Soumyasundar Pal, Jianing Sun, Yingxue Zhang

There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned on the observed graph allows to take the graph uncertainty into account. Various

Yuxin Pan, Fangzhen Lin

Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model to reduce interactions with the real environment. The recent model-based RL method considers the way to learn a backward model that specifies the conditional probability of the previous state given the previous action and the current st

Jinhuan Ke, Jiahao Ma, Xiyu Yin, Robin Singh

COVID-19 pandemic has had a disruptive and irreversible impact globally, yet traditional epidemiological modeling approaches such as the susceptible-infected-recovered (SIR) model have exhibited limited effectiveness in forecasting of the up-to-date pandemic situation. In this work, susceptible-vaccinated-exposed-infected-dead-recovered (SVEIDR) model and it

Anh Duy Nguyen, Thuy Dung Nguyen, Huy Hieu Pham, Thanh Hung Nguyen

Identifying pills given their captured images under various conditions and backgrounds has been becoming more and more essential. Several efforts have been devoted to utilizing the deep learning-based approach to tackle the pill recognition problem in the literature. However, due to the high similarity between pills' appearance, misrecognition often occurs,

Qinghui Zhang, Weidong Li, Qian Su, Xuejie Zhang

The intensity of radio waves decays rapidly with increasing propagation distance, and an edge server's antenna needs more power to form a larger signal coverage area. Therefore, the power of the edge server should be controlled to reduce energy consumption. In addition, edge servers with capacitated resources provide services for only a limited number of use

Ali Borji

Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model. A large enough and perceptible perturbation is added to an image such that a model maintains its original decision, whereas a human will mo

Rajmohan Muthaiah, Roshan Sameer Annam, Fatema Tarannum, Ashish Kumar Gupta

In this study, we report the length dependence of thermal conductivity (k) of zinc-blende Zinc-Selenide (ZnSe) and Zinc Telluride (ZnTe) for length scales between 10 nm and 10000 nm using first-principles computations based on density-functional theory. k value of ZnSe is computed to decrease significantly from 11.3 W/mK to 1.75 W/mK as the length scale is d

Fabian Mora Cordero

In this work we present a dynamic analysis tool for analyzing regions of code and how those regions depend between each other via data dependencies encountered during the execution of the program. We also present an abstract method to analyze and study parallelism in a directed graph, by studying a Quotient Graph of the execution graph of a program, and give

Mohamed Mohsen Ahmed

This article presents several numerical techniques for solving Laplace equation. A numerical FORTRAN solver is developed to solve the 2D laplace equation. The numerical approaches implemented in the solver include Jacobi, Gauss-Siedel, Successive Over Relaxation, Alternating Direct Implicit and Multigrid methods. Detailed comparison between different numeric

Laura Pierson, Julian Wellman

A balanced configuration of points on the sphere $S^2$ is a (finite) set of points which are in equilibrium if they act on each other according any force law dependent only on the distance between two points. The configuration is additionally group-balanced if for each point in a configuration $\mathcal{C}$, there is a symmetry of $\mathcal{C}$ fixing only t

San-Dong Guo, Xiao-Shu Guo, Guang-Zhao Wang, Kai Cheng

Valleytronics has been widely investigated for providing new degrees of freedom to future information coding and processing. Here, it is proposed that valley polarization can be achieved by electric field induced magnetic anisotropy (MA) transformation. Through the first-principle calculations, our idea is illustrated by a concrete example of $\mathrm{VSi_2P

Xuting Tang, Jia Xu, Shusen Wang

We study multi-agent reinforcement learning (MARL) with centralized training and decentralized execution. During the training, new agents may join, and existing agents may unexpectedly leave the training. In such situations, a standard deep MARL model must be trained again from scratch, which is very time-consuming. To tackle this problem, we propose a speci

Jiufang Chen, Ye Yuan

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e, learning rate and regularization coefficient, self-adaptation.

Joaquín Sánchez García

Recently solutions to the aggregation equation on compact Riemannian Manifolds have been studied with different techniques. This work demonstrates the small time existence of measure-valued solutions for suitably regular intrinsic potentials. The main tool is the use of the minimizing movement scheme which together with the optimality conditions yield a fini

Zhen-Zhao Tao, Hai-Chen Zhao, Tong-Jie Zhang, Vishal Gajjar

As a major approach to looking for life beyond the Earth, the search for extraterrestrial intelligence (SETI) is committed to detecting technosignatures such as engineered radio signals that are indicative of technologically capable life. In this paper, we report a targeted SETI campaign employing an observation strategy named multi-beam coincidence matching

Jiliang Jing, Sheng Long, Weike Deng, Mengjie Wang

The effective one-body theories, introduced by Buonanno and Damour, are novel approaches to constructing a gravitational waveform template. By taking a gauge in which $\psi_{1}^{B}$ and $\psi_{3}^{B}$ vanish, we find a decoupled equation with separable variables for $\psi^{B}_{4}$ for gravitational perturbation in the effective metric obtained in the post-Mi

Sailun Zhan

We give an explicit stratification of the punctual Hilbert schemes of $n$ points of $\mathbb{A}^{m+1}$ with respect to $m$-dimensional partitions in the Grothendieck group of varieties. As an application, we calculate the classes of the punctual Hilbert schemes of $n$ points of $\mathbb{A}^3$ and the classes of the Hilbert schemes of $n$ points of $\mathbb{A

Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli

In this paper, a novel nonlinear precoding (NLP) technique, namely constellation-oriented perturbation (COP), is proposed to tackle the scalability problem inherent in conventional NLP techniques. The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques. By th

MyeongAh Cho, Tae-young Chun, g Taeoh Kim, Sangyoun Lee

NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different domain characteristics, and the lack of NIR face dataset. In order to reduce domain discrepancy while using the existing face recognition models, we propose a 'Relation Module' whic

Rui Han, Fan Yang

We prove multi-point correlation bounds in $\mathbb{Z}^d$ for arbitrary $d\geq 1$ with symmetrized distances, answering open questions proposed by Sims-Warzel \cite{SW} and Aza-Bru-Siqueira Pedra \cite{ABP}. As applications, we prove multi-point correlation bounds for the Ising model on $\mathbb{Z}^d$, and multi-point dynamical localization in expectation fo

Libei Sun, Xiucai Huang, Yongduan Song

It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed neuroadapt

Mario Motta, Gavin O. Jones, Julia E. Rice, Tanvi P. Gujarati

The computational description of correlated electronic structure, and particularly of excited states of many-electron systems, is an anticipated application for quantum devices. An important ramification is to determine the dominant molecular fragmentation pathways in photo-dissociation experiments of light-sensitive compounds, like sulfonium-based photo-aci

Jinfei Wang, Yi Ma, Na Yi, Rahim Tafazolli

Concerning ultra-reliable low-latency communication (URLLC) for the downlink operating in the frequency-division multiple-access with random channel assignment, a lightweight power allocation approach is proposed to maximize the number of URLLC users subject to transmit-power and individual user-reliability constraints. Provided perfect channel-state-informa

Akanksha Negi, Digvijay Singh Negi

This paper studies identification and estimation of the average treatment effect on the treated (ATT) in difference-in-difference (DID) designs when the variable that classifies individuals into treatment and control groups (treatment status, D) is endogenously misclassified. We show that misclassification in D hampers consistent estimation of ATT because 1)

Sandeep Kulkarni, Duong Nguyen, Lewis Tseng, Nitin Vaidya

In this report, we consider the impact of the consistency model on checkpointing and rollback algorithms for distributed shared memory. In particular, we consider specific implementations of four consistency models for distributed shared memory, namely, linearizability, sequential consistency, causal consistency and eventual consistency, and develop checkpoi

Ovidiu Costin, Gerald V. Dunne, Max Meynig

We analyze the properties of Pade and conformal map approximants for functions with branch points, in the situation where the expansion coefficients are only known with finite precision or are subject to noise. We prove that there is a universal scaling relation between the strength of the noise and the expansion order at which Pade or the conformal map brea

Yuchao Dong

In this paper, we study the optimal stopping problem in the so-called exploratory framework, in which the agent takes actions randomly conditioning on current state and an entropy-regularized term is added to the reward functional. Such a transformation reduces the optimal stopping problem to a standard optimal control problem. We derive the related HJB equa

Jose Miguel Arrieta Ramos, Oscar Perdómo, Fabio A. González

Diabetic retinopathy (DR) is one of the leading causes of blindness in the working-age population of developed countries, caused by a side effect of diabetes that reduces the blood supply to the retina. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. However, these models need a large number of anno

S. M. Richards, J. J. Eldridge, M. M. Briel, H. F. Stevance

Natal supernova kicks, the linear momentum compact remnants receive during their formation, are an essential part of binary population synthesis (BPS) models. Although these kicks are well-supported by evidence, their underlying distributions and incorporation into BPS models is uncertain. In this work, we investigate the nature of natal kicks using a previo

Yiming Li, Linghui Zhu, Xiaojun Jia, Yang Bai

Currently, deep neural networks (DNNs) are widely adopted in different applications. Despite its commercial values, training a well-performing DNN is resource-consuming. Accordingly, the well-trained model is valuable intellectual property for its owner. However, recent studies revealed the threats of model stealing, where the adversaries can obtain a functi

Yanxiong Li, Wenchang Cao, Konstantinos Drossos, Tuomas Virtanen

Automatic estimation of domestic activities from audio can be used to solve many problems, such as reducing the labor cost for nursing the elderly people. This study focuses on solving the problem of domestic activity clustering from audio. The target of domestic activity clustering is to cluster audio clips which belong to the same category of domestic acti

Wei Yan Peh, Yuanyuan Yao, Justin Dauwels

It is well known that electroencephalograms (EEGs) often contain artifacts due to muscle activity, eye blinks, and various other causes. Detecting such artifacts is an essential first step toward a correct interpretation of EEGs. Although much effort has been devoted to semi-automated and automated artifact detection in EEG, the problem of artifact detection

Shung-Ichi Ando

The elastic $\alpha$-$^{12}$C scattering at low energies for $l=0,1,2,3,4,5,6$ is studied in effective field theory. We discuss the construction of the $S$ matrices of elastic $\alpha$-$^{12}$C scattering in terms of the amplitudes of sub-threshold bound and resonant states of $^{16}$O, which are calculated from the effective Lagrangian. The parameters appea

M. Rasel Mahmud, Michael Stewart, Alberto Cordova, John Quarles

This research aims to examine the effects of various vibrotactile feedback techniques on gait (i.e., walking patterns) in virtual reality (VR). Prior studies have demonstrated that gait disturbances in VR users are significant usability barriers. However, adequate research has not been performed to address this problem. In our study, 39 participants (with mo

Vilém Zouhar, Marius Mosbach, Dietrich Klakow

Although masked language models are highly performant and widely adopted by NLP practitioners, they can not be easily used for autoregressive language modelling (next word prediction and sequence probability estimation). We present an LSTM-based autoregressive language model which uses prefix embeddings (from a pretrained masked language model) via fusion (e

Sashi Nepal, Ramesh Dhakal, Iosif Galanakis, Stephen M. Winter

The realization of the stable structure of Heusler compounds and the study of different properties is an important step for their potential application in spintronics and magnetoelectronic devices. In this paper, using the plane-wave pseudopotential method within the framework of density functional theory (DFT), we investigate 25 Quaternary Heusler compounds

Giang Ngo, Rodney Beard, Rohitash Chandra

Ensemble learning has gained success in machine learning with major advantages over other learning methods. Bagging is a prominent ensemble learning method that creates subgroups of data, known as bags, that are trained by individual machine learning methods such as decision trees. Random forest is a prominent example of bagging with additional features in t

Paul M. Gauthier, Mohammad Shirazi

For certain elliptic differential operators $L,$ we study the behaviour of solutions to $Lu=0,$ as we tend to the boundary along radii in strictly starlike domains in $\R^n, n\ge 3.$ Analogous results are obtained in other special domains. Our approach involves introducing harmonic line bundles as instances of Brelot harmonic spaces and approximating continu

Philip Dittmann, Erik Walsberg, Jinhe Ye

We investigate the following question: Given a field $K$, when is the \'etale open topology $\mathcal{E}_K$ induced by a field topology? On the positive side, when $K$ is the fraction field of a local domain $R\neq K$, using a weak form of resolution of singularities due to Gabber, we show that $\mathcal{E}_K$ agrees with the $R$-adic topology when $R$ is qu

Caio da S. Dias, Alceu de S. Britto, Jean P. Barddal, Laurent Heutte

This paper presents a deep learning approach for image retrieval and pattern spotting in digital collections of historical documents. First, a region proposal algorithm detects object candidates in the document page images. Next, deep learning models are used for feature extraction, considering two distinct variants, which provide either real-valued or binar

Yi Yu, Hongsen He, Rodrigo C. de Lamare, Badong Chen

In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we c

Yueling Yang, Liting Wang, Jinshu Huang, Qin Chang

The ground vector $B_{c}^{\ast}$ meson has not yet been experimentally discovered until now. Besides the dominant electromagnetic decays, nonleptonic weak decays provide another choice to search for the mysterious $B_{c}^{\ast}$ mesons. Inspired by the potential prospects of $B_{c}^{\ast}$ mesons in future high-luminosity colliders, nonleptonic $B_{c}^{\ast}

Myeonghyeon Kim, Tenzin Rabga, Yangheon Lee, Junhong Goo

In phase transition dynamics involving symmetry breaking, topological defects can be spontaneously created but it is suppressed in a spatially inhomogeneous system due to the spreading of the ordered phase information. We demonstrate the defect suppression effect in a trapped atomic Bose gas which is quenched into a superfluid phase. The spatial distribution

Juyang Weng, Zejia Zheng, Xiang Wu

Strong AI requires the learning engine to be task non-specific and to automatically construct a dynamic hierarchy of internal features. By hierarchy, we mean, e.g., short road edges and short bush edges amount to intermediate features of landmarks; but intermediate features from tree shadows are distractors that must be disregarded by the high-level landmark

Alexander G. Olenskyj, Brent S. Sams, Zhenghao Fei, Vishal Singh

Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vi

Farhad Aghili

A hierarchical control architecture is presented for energy-efficient control of legged robots subject to variety of linear/nonlinear inequality constraints such as Coulomb friction cones, switching unilateral contacts, actuator saturation limits, and yet minimizing the power losses in the joint actuators. The control formulation can incorporate the nonlinea

Bin-Bin Ruan, Jun-Nan Sun, Yin Chen, Qing-Song Yang

By performing P doping on the Si sites in the topological semimetal Mo$_5$Si$_3$, we discover strong-coupling superconductivity in Mo$_5$Si$_{3-x}$P$_x$ (0.5 $\le$ $x$ $\le$ 2.0). Mo$_5$Si$_3$ crystallizes in the W$_5$Si$_3$-type structure with space group of $I4/mcm$ (No. 140), and is not a superconductor itself. Upon P doping, the lattice parameter $a$ dec

Allen H Boozer

Deuterium-tritium (DT) burning requires a long energy confinement times compared to collision times, so the particle distribution functions must approximate local-Maxwellians. Non-equilibrium thermodynamics is applicable, which gives relations among transport, entropy production, the collision frequency, and the deviation from a Maxwellian. The distribution

Tun S. Tan, J. J. Kas, S. B. Trickey, J. J. Rehr

Effects of finite-temperature quasiparticle self-energy corrections to x-ray absorption spectra are investigated within the finite-temperature quasiparticle local density GW approximation up to temperatures $T$ of order the Fermi temperature. To facilitate the calculations, we parametrize the quasiparticle self-energy using low-order polynomial fits. We show

Jingwei Ji, Renyuan Xu, Ruihao Zhu

Motivated by practical considerations in machine learning for financial decision-making, such as risk aversion and large action space, we consider risk-aware bandits optimization with applications in smart order routing (SOR). Specifically, based on preliminary observations of linear price impacts made from the NASDAQ ITCH dataset, we initiate the study of r

M. Kohno, H. Kamada, K. Miyagawa

An expression of partial wave expansion of three-baryon interactions in chiral effective field theory is presented. The derivation follows the method by Hebeler et al. [Phys. Rev. C{\bf 91}, 044001 (2015)], but the final expression is more general. That is, a systematic treatment of the higher-rank spin-momentum structure of the interaction becomes possible.

Karla Garnica, C. Alenka Negrete, Paola Marziani, Deborah Dultzin

We present an analysis of UV spectra of quasars at intermediate redshifts believed to belong to the extreme Population A (xA), aimed to estimate the chemical abundances of the broad line emitting gas. We follow the approach described in a previous work extending the sample to 42 sources. Our aim is to test the robustness of the analysis carried out previousl

V. Araujo, D. Schaffer, A. B. Costa, S. R. Musse

Animations have become increasingly realistic with the evolution of Computer Graphics (CG). In particular, human models and behaviors were represented through animated virtual humans, sometimes with a high level of realism. In particular, gender is a characteristic that is related to human identification, so that virtual humans assigned to a specific gender

Takashi Kurumaji, Masaki Gen, Shunsuke Kitou, Hajime Sagayama

We investigate the thermodynamic, magnetic, and electrical transport properties of a triangular-lattice antiferromagnet EuZnGe using single crystals grown from Eu-Zn flux in sealed tantalum tubes. Magnetic properties are found to be isotropic in the paramagnetic state while we observe an enhancement of in-plane magnetic susceptibility at the temperature near

Hussein A. Ammar, Raviraj Adve, Shahram Shahbazpanahi, Gary Boudreau

We propose to control handoffs (HOs) in user-centric cell-free massive MIMO networks through a partially observable Markov decision process (POMDP) with the state space representing the discrete versions of the large-scale fading (LSF) and the action space representing the association decisions of the user with the access points. Our proposed formulation acc

Aaron J. Hill, Russ S. Schumacher, Israel Jirak

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4--8. RFs are trained with 9

Axel Huebl, Remi Lehe, Chad E. Mitchell, Ji Qiang

Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency, increasingly sophisticated computational tools are required for their design and optimization. It is key that contemporary software take advantage of the latest advances in computer hardw

Hao Shen

In this survey we review some recent rigorous results on large N problems in quantum field theory, stochastic quantization and singular stochastic PDEs, and their mean field limit problems. In particular we discuss the O(N) linear sigma model on two and three dimensional torus. The stochastic quantization procedure leads to a coupled system of N interacting

Miles Lucas, Michael Bottom, Olivier Guyon, Julien Lozi

We describe the design and initial results from a visible-light Lyot coronagraph for SCExAO/VAMPIRES. The coronagraph is comprised of four hard-edged, partially transmissive focal plane masks with inner working angles of 36 mas, 55 mas, 92 mas, and 129 mas, respectively. The Lyot stop is a reflective, undersized design with a geometric throughput of 65.7%. O

Bennet D. Schwab, Thomas N. Woods, James P. Mason

The Miniature X-ray Solar Spectrometer (MinXSS-1) CubeSat observed solar X-rays between 0.5 and 10 keV. A two-temperature, two-emission measure model is fit to each daily averaged spectrum. These daily average temperatures and emission measures are plotted against the corresponding daily solar 10.7 cm radio flux (F10.7) value and a linear correlation is foun

Ivelisse Rubio, Jaziel Torres

A novel higher-dimensional definition for Costas arrays is introduced. This definition works for arbitrary dimensions and avoids some limitations of previous definitions. Some non-existence results are presented for multidimensional Costas arrays preserving the Costas condition when the array is extended periodically throughout the whole space. In particular

Simon Guiroy, Christopher Pal, Gonçalo Mordido, Sarath Chandar

Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model p

Wangyang Yue, Yuan Zhou, Xiaochuan Zhang, Yuchen Hua

Reinforcement Learning (RL) techniques have drawn great attention in many challenging tasks, but their performance deteriorates dramatically when applied to real-world problems. Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be gene

Preeti Lata Mahapatra, Appu Kumar Singh, Raphael Tromer, Karthik R.

In the last decade, the materials community has been exploring new 2D materials (graphene, metallene, TMDs, TMCs, MXene, among others) that have unique physical and chemical properties. Recently, a new family of 2D materials, the so-called 2D silicates, have been proposed. They are predicted to exhibit exciting properties (such as high catalytic activity, pi

Arun Hegde, Elan Weiss, Wolfgang Windl, Habib N. Najm

Developing reliable interatomic potential models with quantified predictive accuracy is crucial for atomistic simulations. Commonly used potentials, such as those constructed through the embedded atom method (EAM), are derived from semi-empirical considerations and contain unknown parameters that must be fitted based on training data. In the present work, we

Tiago F. F. Santos, Marcelo F. Santos

In this work, we study the efficiency of charging a quantum battery through optical pumping. The battery consists of a qutrit and it is connected to a natural thermal reservoir and an external coherent drive in the limit where its upper energy level can be adiabatically eliminated from the dynamics. In this scenario, the drive plus spontaneous emission optic

Grace Guan, Mark Braverman

Under the Affordable Care Act (ACA), insurers cannot engage in medical underwriting and thus face perverse incentives to engage in risk selection and discourage low-value patients from enrolling in their plans. One ACA program intended to reduce the effects of risk selection is risk adjustment. Under a risk adjustment program, insurers with less healthy enro

Bradley D Hauer, Joshua Combes, John D. Teufel

The ability to prepare a macroscopic mechanical resonator into a quantum superposition state is an outstanding goal of cavity optomechanics. Here, we propose a technique to generate cat states of motion using the intrinsic nonlinearity of a dispersive optomechanical interaction. By applying a bichromatic drive to an optomechanical cavity, our protocol enhanc

Yuiki Takahashi, Masaaki Baba, Katsunari Enomoto, Ayami Hiramoto

Calcium monohydroxide radical (CaOH) is receiving an increasing amount of attention from the astrophysics community as it is expected to be present in the atmospheres of hot rocky super-Earth exoplanets as well as interstellar and circumstellar environments. Here, we report the high-resolution laboratory absorption spectroscopy on low-$J$ transitions in $\ti

Soumyadeep Hore, Ankit Shah, Nathaniel D. Bastian

Cyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as the number of deficiencies in these systems is increasing at a significantly higher rate compared to the expa

Charles Roques-Carmes, Steven E. Kooi, Yi Yang, Nicholas Rivera

When impinging on optical structures or passing in their vicinity, free electrons can spontaneously emit electromagnetic radiation, a phenomenon generally known as cathodoluminescence. Free-electron radiation comes in many guises: Cherenkov, transition, and Smith-Purcell radiation, but also electron scintillation, commonly referred to as incoherent cathodolu

Milad Haddadi, Matthew D. Duez, Francois Foucart, Teresita Ramirez

Detectable electromagnetic counterparts to gravitational waves from compact binary mergers can be produced by outflows from the black hole-accretion disk remnant during the first ten seconds after the merger. Two-dimensional axisymmetric simulations with effective viscosity remain an efficient and informative way to model this late-time post-merger evolution

Isaac Ruiz, Gyorgy Vizkelethy, Anthony E. McDonald, Stephen W. Howell

Graphene's linear bandstructure and two-dimensional density of states provide an implicit advantage for sensing charge. Here, these advantages are leveraged in a deeply depleted graphene-oxide-semiconductor (D2GOS) junction detector architecture to sense carriers created by ionizing radiation. Specifically, the room temperature response of the silicon-based

Simon P. Neville, Michael S. Schuurman

We introduce a perturbative approximation to the combined density functional theory and multireference configuration interaction (DFT/MRCI) method. The method, termed DFT/MRCI(2), results from the application of quasi-degenerate perturbation theory and the Epstein-Nesbet partitioning of the DFT/MRCI Hamiltonian matrix. This results in the replacement of the

Imra Aqeel, Abdul Majid

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In t

Xi-Ning Zhuang, Zhao-Yun Chen, Cheng Xue, Yu-Chun Wu

The continuous time stochastic process is a mainstream mathematical instrument modeling the random world with a wide range of applications involving finance, statistics, physics, and time series analysis, while the simulation and analysis of the continuous time stochastic process is a challenging problem for classical computers. In this work, a general frame

Zoubida Ech-chaffani, Ahmed Aberqi, Touria Karite, Delfim F. M. Torres

We investigate a class of fractional neutral evolution equations on Banach spaces involving Caputo derivatives. Main results establish conditions for the controllability of the fractional-order system and conditions for existence of a solution to an optimal control problem of minimum energy. The results are proved with the help of fixed-point and semigroup t

Samarth Gupta, Daniel N. Hill, Lexing Ying, Inderjit Dhillon

In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model. When applied to the environment of the underlying model, the learned policy results i

Lukasz Tulczyjew, Michal Kawulok, Nicolas Longépé, Bertrand Le Saux

Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth observation satellites equipped with hyperspectral imagers. In this letter, we follow this research pathway and pr

Ryan S. Bennink

In a recent work I developed a formula for efficiently calculating the number of abelian squares of length $t+t$ over an alphabet of size $d$, where $d$ may be very large. Here I show how the expressiveness of a certain class of parameterized quantum circuits can be reduced to the problem of counting abelian squares over a large alphabet, and use the recentl

Hung M. Bui, Daniel A. Goldston, Micah B. Milinovich, Hugh L. Montgomery

We show assuming RH that phenomena concerning pairs of zeros established $via$ pair correlations occur with positive density (with at most a slight adjustment of the constants). Also, while a double zero is commonly considered to be a close pair, we consider the difference between two $distinct$ zeros.

Kenneth L. Baker, David Futer, Jessica S. Purcell, Saul Schleimer

We show that, for hyperbolic fibred knots in the three-sphere, the volume and the genus are unrelated. Furthermore, for such knots, the volume is unrelated to strong quasipositivity and Seifert form.

Samir Canning, Hannah Larson

In this paper, we ask: for which $(g, n)$ is the rational Chow or cohomology ring of $\overline{\mathcal{M}}_{g,n}$ generated by tautological classes? This question has been fully answered in genus $0$ by Keel (the Chow and cohomology rings are tautological for all $n$) and genus $1$ by Belorousski (the rings are tautological if and only if $n \leq 10$). For

Svenja Vollmar, Kai Leckron, Hans Christian Schneider

We analyze theoretically the demagnetization dynamics in a ferromagnetic model system due to the interplay of spin-orbit coupling and electron-electron Coulomb scattering. We compute the $k$-resolved electronic reduced spin-density matrix including precessional dynamics around internal spin-orbit and exchange fields as well as the electron-electron Coulomb s

Ruisheng Su, P. Matthijs van der Sluijs, Yuan Chen, Sandra Cornelissen

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery-vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide ran

H. Jane Bae, Adrian Lozano-Duran

We study the numerical errors of large-eddy simulation (LES) in isotropic and wall-bounded turbulence. A direct-numerical-simulation (DNS)-aided LES formulation, where the subgrid-scale (SGS) term of the LES is computed by using filtered DNS data is introduced. We first verify that this formulation has zero error in the absence of commutation error between t

The Pierre Auger Collaboration, P. Abreu, M. Aglietta, J. M. Albury

Using the data of the Pierre Auger Observatory, we report on a search for signatures that would be suggestive of super-heavy particles decaying in the Galactic halo. From the lack of signal, we present upper limits for different energy thresholds above ${\gtrsim}10^8$\,GeV on the secondary by-product fluxes expected from the decay of the particles. Assuming

Yuntian Teng, Yifeng Wang, Zihao Li, Rui Qiao

In low-permeability porous media, the velocity of a fluid flow exhibits a nonlinear dependence on the imposed pressure gradient. This non-Darcian flow behavior has important implications to geological disposal of nuclear waste, hydrocarbon extraction from shale, and flow and transport in clay-rich aquifers. Temperature has been postulated to affect the thres

E. J. Paul, A. Bhattacharjee, M. Landreman, D. Alex

Collisionless physics primarily determines the transport of fusion-born alpha particles in 3D equilibria. Several transport mechanisms have been implicated in stellarator configurations, including stochastic diffusion due to class transitions, ripple trapping, and banana drift-convective orbits. Given the guiding center dynamics in a set of six quasihelical