August 2022 arXiv papers — page 45

Takumi Funato, Ai Yamakage, Mamoru Matsuo

We theoretically study the generation of spin current due to a surface acoustic wave (SAW) in a superconductor. We model an s-wave superconductor as the mean-field Hamiltonian and calculate spin current generated via spin-vorticity coupling based on kinetic theory. The results suggest that the spin current can be driven in a single superconductor layer, and

Chitralekha Gupta, Yize Wei, Zequn Gong, Purnima Kamath

Standard evaluation metrics such as the Inception score and Fr\'echet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean audio. However, the sensitivity of these metrics to variations in the statistical parameters that define an audio texture is not well studied. In this work, we provide a systema

Foad Ghasemi, Abdollah Salimi

Two-dimensional (2D) materials have been used extensively in various fields due to their unique physical and chemical properties. Among their diverse applications, field-effect transistor biosensors (bio-FETs) promise a brilliant prospect in the fabrication of biodevices for diagnostics especially point of care (PoC) based biomedical testing. The introductio

Jungho Lee, Minhyeok Lee, Dogyoon Lee, Sangyoun Lee

Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance. Generating adjacency matrices with semantically meaningful edges is particularly important for this task, but extracting such edges is challenging problem. To solve this, we propose a hierarchically decomposed

Masaaki Hiramatsu, S_Asagiri, Stella. G. Amano, Naohiro Takanashi

Many forefront observatories are located in remote areas and are difficult to visit, and the global pandemic made visits even harder. Several virtual tours have been executed on YouTube or Facebook Live, however, it is difficult to feel a sense of immersion and these are far from the actual experience of visiting a site. To solve this problem, we pursued an

Chunlei Cai, Yi Wang, Xiaobo Li, Tianxiao Ye

Providing quality-constant streams can simultaneously guarantee user experience and prevent wasting bit-rate. In this paper, we propose a novel deep learning based two-pass encoder parameter prediction framework to decide rate factor (RF), with which encoder can output streams with constant quality. For each one-shot segment in a video, the proposed method f

Marco Pesavento, Marco Volino, Adrian Hilton

We propose a novel framework to reconstruct super-resolution human shape from a single low-resolution input image. The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-det

Chethan Reddy G. P., Pullagurla Abhijith Reddy, Vidyashree R. Kanabur, Deepu Vijayasenan

In this paper, a semi-automatic annotation of bacteria genera and species from DIBaS dataset is implemented using clustering and thresholding algorithms. A Deep learning model is trained to achieve the semantic segmentation and classification of the bacteria species. Classification accuracy of 95% is achieved. Deep learning models find tremendous application

Winston Hurst, Yasamin Mostofi

This paper considers trajectory planning for a mobile robot which persistently relays data between pairs of far-away communication nodes. Data accumulates stochastically at each source, and the robot must move to appropriate positions to enable data offload to the corresponding destination. The robot needs to minimize the average time that data waits at a so

Zhou Yang, Jing Zhang, Chao Zhou

A robust control problem is considered in this paper, where the controlled stochastic differential equations (SDEs) include ambiguity parameters and their coefficients satisfy non-Lipschitz continuous and non-linear growth conditions, the objective function is expressed as a backward stochastic differential equation (BSDE) with the generator depending on the

Xiaohang Tang, Yi Zhou, Danushka Bollegala

Dynamic contextualised word embeddings (DCWEs) represent the temporal semantic variations of words. We propose a method for learning DCWEs by time-adapting a pretrained Masked Language Model (MLM) using time-sensitive templates. Given two snapshots $C_1$ and $C_2$ of a corpus taken respectively at two distinct timestamps $T_1$ and $T_2$, we first propose an

Fernando Castañeda, Jason J. Choi, Wonsuhk Jung, Bike Zhang

Learning-based control has recently shown great efficacy in performing complex tasks for various applications. However, to deploy it in real systems, it is of vital importance to guarantee the system will stay safe. Control Barrier Functions (CBFs) offer mathematical tools for designing safety-preserving controllers for systems with known dynamics. In this a

Zhendong Peng, Cunhua Pan, Gui Zhou, Hong Ren

In this paper, we propose a novel two-stage based uplink channel estimation strategy with reduced pilot overhead and error propagation for a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. Specifically, in Stage I, with the carefully designed RIS phase shift matrix and introduced matching matrices, all users jo

Qi Guo, Yong Qi, Saiyu Qi, Di Wu

Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, Non-IID data of clients presents a tough challenge for FL. Existing personalized FL approaches rely heavily on the default treatment of one complete model as a basic unit and ignore the significance of different layers o

Ming-Yang Ho, Min-Sheng Wu, Che-Ming Wu

While hematoxylin and eosin (H&E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KI

Chun-Hung Liu

The defective chromatic number of a graph class is the infimum $k$ such that there exists an integer $d$ such that every graph in this class can be partitioned into at most $k$ induced subgraphs with maximum degree at most $d$. Finding the defective chromatic number is a fundamental graph partitioning problem and received attention recently partially due to

Tetsuya Ito, Alexander Stoimenow

As an extension of positive and almost positive diagrams and links, we study two classes of links we call successively almost positive and weakly successively almost positive links. We prove various properties of polynomial invariants and signatures of such links, extending previous results or answering open questions about positive or almost positive links.

Thomas Bellitto, Christopher Duffy, Gary MacGillivray

Homomorphically full graphs are those for which every homomorphic image is isomorphic to a subgraph. We extend the definition of homomorphically full to oriented graphs in two different ways. For the first of these, we show that homomorphically full oriented graphs arise as quasi-transitive orientations of homomorphically full graphs. This in turn yields an

Indu K. Dihingia, Bhargav Vaidya, Christian Fendt

Variable features in black hole X-ray Binaries (BH-XRBs) are observed in different energy ranges and time scales. The physical origin of different spectral states in BH-XRBs and their relations with the underlying accretion disc are still elusive. To investigate the intermediate state of BH-XRBs during outburst, we simulate a truncated accretion disc around

Fitsum Debebe Tilahun, Ameha Tsegaye Abebe, Chung G. Kang

In this paper, with the aim of addressing the stringent computing and quality-of-service (QoS) requirements of recently introduced advanced multimedia services, we consider a cell-free massive MIMO-enabled mobile edge network. In particular, benefited from the reliable cell-free links to offload intensive computation to the edge server, resource-constrained

Michele Nguyen, Almut E. D. Veraart, Benoit Taisne, Tan Chiou Ting

Extreme events such as natural and economic disasters leave lasting impacts on society and motivate the analysis of extremes from data. While classical statistical tools based on Gaussian distributions focus on average behaviour and can lead to persistent biases when estimating extremes, extreme value theory (EVT) provides the mathematical foundations to acc

Tan N. Nguyen, Dinh-Hieu Tran, Trinh Van Chien, Van-Duc Phan

This work investigates the security and reliability analysis for a novel satellite-terrestrial (SatTer) network. Specifically, a satellite attempts to transmit confidential information to a ground user (GU) via the support of multiple relay nodes in the presence of an eavesdropper that tries to overhear the information. A friendly jammer is deployed to impro

Zining Chen, Weiqiu Wang, Zhicheng Zhao, Aidong Men

Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue. However, most existing algorithms for OOD generalization are complicated and specifically designed for certai

Andrew Skemer, R. Deno Stelter, Stephanie Sallum, Nicholas MacDonald

We present the design of SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) a new 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. SCALES enables low-resolution (R~50) spectroscopy, as well as medium-resolution (R~4,000) spectroscopy with the goal of discovering and characterizing cold exop

Shunhao Oh, Dana Randall, Andréa W. Richa

The foraging problem asks how a collective of particles with limited computational, communication and movement capabilities can autonomously compress around a food source and disperse when the food is depleted or shifted, which may occur at arbitrary times. We would like the particles to iteratively self-organize, using only local interactions, to correctly

Li Ding, Ryan Boldi, Thomas Helmuth, Lee Spector

Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variant

Kisoo Kwon, Kuhwan Jung, Junghyun Park, Hwidong Na

In this paper, we investigate the problem of string-based molecular generation via variational autoencoders (VAEs) that have served a popular generative approach for various tasks in artificial intelligence. We propose a simple, yet effective idea to improve the performance of VAE for the task. Our main idea is to maintain multiple decoders while sharing a s

Dawei Chen

Gendron proved that the strata of holomorphic differentials with prescribed orders of zeros do not contain complete algebraic curves by applying the maximum modulus principle to saddle connections. Here we provide an alternative proof for this result by using positivity of divisor classes on moduli spaces of curves.

Weihao Yan, Yeqiang Qian, Chunxiang Wang, Ming Yang

Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning methods require large amounts of labeled data for training. Manual annotation is expensive, while simulators can provide accurate annotations. However, the performance of the semantic segmentation model trained with the data of the simulat

Ellis R. Crabtree, Juan M. Bello-Rivas, Andrew L. Ferguson, Ioannis G. Kevrekidis

Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in many fields, from protein folding to materials discovery. These problems are often multiscale in nature: they can be described in terms of low-dimensional effective free energy surf

Nilanjan Roy, Jagannath Sutradhar, Sumilan Banerjee

We provide real-space and Fock-space (FS) characterizations of ergodic, nonergodic extended (NEE) and many-body localized (MBL) phases in an interacting quasiperiodic system, namely generalized Aubry-Andr\'e-Harper model, which possesses a mobility edge in the non-interacting limit. We show that a mobility edge in the single-particle (SP) excitations survive

Ajit Desai, Mohammad Khalil, Chris L. Pettit, Dominique Poirel

Realistic physical phenomena exhibit random fluctuations across many scales in the input and output processes. Models of these phenomena require stochastic PDEs. For three-dimensional coupled (vector-valued) stochastic PDEs (SPDEs), for instance, arising in linear elasticity, the existing two-level domain decomposition solvers with the vertex-based coarse gr

Valliappan Muthukaruppan, Ashwin Shirsat, Rongxing Hu, Victor Paduani

Forming a microgrid on a distribution system with large scale outage after a severe weather event is emerging as a viable solution to improve resiliency at the distribution level. This option becomes more attractive when the distribution system has high levels of distributed PV. The management of such feeder-level microgrid has however many challenges, such

Zixiao Zhao, Qinghe Du, George K. Karagiannidis

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and

Shu Tang, Yang Wu, Hongxing Qin, Xianzhong Xie

Most existing deep-learning-based single image dynamic scene blind deblurring (SIDSBD) methods usually design deep networks to directly remove the spatially-variant motion blurs from one inputted motion blurred image, without blur kernels estimation. In this paper, inspired by the Projective Motion Path Blur (PMPB) model and deformable convolution, we propos

PengHui Chen, Chang Geng, XiangHua Zeng, Zhao-Qing Feng

Within the framework of the dinuclear system model, the influence of mass asymmetry and the isospin effect on the production of exotic actinides have been investigated systematically. The isotopic yields populate in multinucleon transfer reactions of Ca(48), Kr(86), Xe(136), and U(238) bombarding on Cm(248) are analyzed and compared to the available experime

Yutao Luo, Menghua Lu, Gongshen Liu, Shilin Wang

Neural table-to-text generation approaches are data-hungry, limiting their adaptation for low-resource real-world applications. Previous works mostly resort to Pre-trained Language Models (PLMs) to generate fluent summaries of a table. However, they often contain hallucinated contents due to the uncontrolled nature of PLMs. Moreover, the topological differen

Xinbin Liang, Yaru Liu, Yang Yu, Kaixuan Liu

Objective: Convolutional Neural Networks (CNNs) have shown great potential in the field of Brain-Computer Interfaces (BCIs). The raw Electroencephalogram (EEG) signal is usually represented as 2-Dimensional (2-D) matrix composed of channels and time points, which ignores the spatial topological information. Our goal is to make the CNN with the raw EEG signal

Boyi Jin

This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The objective function of the optimization model is the weighted sum of the expectation and value at risk(VaR) of portfolio cumulative return. The proposed algorithm is based on actor-critic architecture, in which the main task of criti

H. T. Tuan, L. V. Thinh

This paper addresses the qualitative theory of mixed-order positive linear coupled systems with bounded or unbounded delays. First, we introduce a general result on the existence and uniqueness of solutions to mixed-order linear coupled systems with time-varying delays. Next, we obtain the necessary and sufficient criteria which characterize the positivity o

Banafsheh Mohajeri, Jinghui Cheng

While smart mobile health apps that adapt to users' progressive individual needs are proliferating, many of them struggle to fulfill their promises due to an inferior user experience. Understanding the concerns of real-world users related to those apps, and their smart components in particular, could help advance the app design to attract and retain users. I

Haodong Li

This paper studies the offloading service improvement of multi-access edge computing (MEC) based on backscatter communication (BackCom) assisted non-orthogonal multiple access (BAC-NOMA). A hybrid BAC-NOMA protocol is proposed, where the uplink users backscatter their tasks by leveraging the downlink signal, and then the remaining data is transmitted through

Zhi-Yuan Fan, Liu Qiu, Simon Gröblacher, Jie Li

Microwave-optics entanglement is a vital component for building hybrid quantum networks. Here, a new mechanism for preparing stationary entanglement between microwave and optical cavity fields in a cavity optomagnomechanical system is proposed. It consists of a magnon mode in a ferrimagnetic crystal that couples directly to a microwave cavity mode via the ma

Rong Wei, Saisai Yang, Jianliang Zhai

In this paper, we consider a class of Mckean-Vlasov stochastic differential equation with oblique reflection over an non-smooth time dependent domain. We establish the existence and uniqueness results of this class, address the propagation of chaos and prove a Fredlin-Wentzell type large deviations principle (LDP). One of the main difficulties is raised by t

Jinkai Lv, Yuyong Hu, Quanshui Fu, Zhiwang Zhang

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge,

Mackenzie Simper

Contingency tables are useful objects in statistics for representing 2-way data. With fixed row and column sums, and a total of $n$ entries, contingency tables correspond to parabolic double cosets of $S_n$. The uniform distribution on $S_n$ induces the Fisher-Yates distribution, classical for its use in the chi-squared test for independence. A Markov chain

Hechao Liu

The Sombor index is one of the geometry-based descriptors, which was defined as $$SO(G)=\sum_{uv\in E(G)}\sqrt{d^{2}(u)+d^{2}(v)},$$ where $d(u)$ (resp. $d(v)$) denotes the degree of vertex $u$ (resp. $v$) in $G$. In this note, we determine the maximum and minimum graphs with respect to the Sombor index among the set of graphs with vertex connectivity (resp.

Persi Diaconis, Arun Ram, Mackenzie Simper

Let $G$ be a finite group. Let $H, K$ be subgroups of $G$ and $H \backslash G / K$ the double coset space. Let $Q$ be a probability on $G$ which is constant on conjugacy classes ($Q(s^{-1} t s) = Q(t)$). The random walk driven by $Q$ on $G$ projects to a Markov chain on $H \backslash G /K$. This allows analysis of the lumped chain using the representation th

Xin Wei, Huaiwei Cong, Zheng Zhang, Junran Peng

Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis. Computed tomography (CT) is a common clinical examination to screen for this disease at early stages. However, the faint radiological appearances and unspecific symptoms lead to a high risk of missed diagnosis, especially for the

Shuai Hou, Yunhe Sheng

In this paper, first we give the notion of a compatible $3$-Lie algebra and construct a bidifferential graded Lie algebra whose Maurer-Cartan elements are compatible $3$-Lie algebras. We also obtain the bidifferential graded Lie algebra that governs deformations of a compatible $3$-Lie algebra. Then we introduce a cohomology theory of a compatible $3$-Lie al

Guodong Wang, Bijun Zuo

We give a sufficient condition for the nonlinear stability of steady flows of a two-dimensional ideal fluid in a bounded multiply-connected domain, which generalizes a stability criterion proved by Arnold in the 1960s. The most important ingredient of the proof is to establish a variational characterization for the steady flow under consideration, which is a

Daiki Yamasaki, Shin'ichi Nagata, Kiyoshi Ichimoto

The Tandem Etalon Magnetograph (TEM) is one of the instruments of the Solar Magnetic Activity Research Telescope of Hida Observatory. The TEM is a partial disk (320" x240") filter magnetograph which scans the wavelength around a Fe I line at 6303 angstrom and achieves polarimetric sensitivity of < 5x10^-4 for each wavelength. To obtain the polarimeter respon

Lingfeng Li, Huaiwei Cong, Gangming Zhao, Junran Peng

In recent years, several works have adopted the convolutional neural network (CNN) to diagnose the avascular necrosis of the femoral head (AVNFH) based on X-ray images or magnetic resonance imaging (MRI). However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis. MRI, on the other hand, has a long ima

Penghua Zhai, Enwei Zhu, Baolian Qi, Xin Wei

Computed tomography (CT) samples with pathological annotations are difficult to obtain. As a result, the computer-aided diagnosis (CAD) algorithms are trained on small datasets (e.g., LIDC-IDRI with 1,018 samples), limiting their accuracies and reliability. In the past five years, several works have tailored for unsupervised representations of CT lesions via

Chin-Fei Lee, Claudio Codella, Cecilia Ceccarelli, Ana Lopez-Sepulcre

Formamide (NH2CHO) is considered an important prebiotic molecule because of its potential to form peptide bonds. It was recently detected in the atmosphere of the HH 212 protostellar disk on the Solar-System scale where planets will form. Here we have mapped it and its potential parent molecules HNCO and H2CO, along with other molecules CH3OH and CH3CHO, in

Sichun Luo, Yuanzhang Xiao, Yang Liu, Congduan Li

Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations. Though recent success in the federated recommender system, several vital challenges remain to be addressed: (i) The majority of federated recommendation models only consider the model performance and the privacy-preserving ability, while igno

Xuehua Li, Dongmei Yu, Yinong Yang, Deren Han

In this paper, an inverse-free dynamical system with fixed-time convergence is presented to solve the system of absolute value equations (AVEs). Under a mild condition, it is proved that the solution of the proposed dynamical system converges to the solution of the AVEs. Moreover, in contrast to the existing inverse-free dynamical system \cite{chen2021}, a c

Minwoo Kim, Sangil Han, Jeongyoun Ahn, Sungkyu Jung

We propose a method for variable selection and basis learning for high-dimensional classification with ordinal responses. The proposed method extends sparse multiclass linear discriminant analysis, with the aim of identifying not only the variables relevant to discrimination but also the variables that are order-concordant with the responses. For this purpos

Dhruba R. Adhikari

Let $X$ be a real locally uniformly convex Banach space and $X^*$ be the dual space of $X$. Let $\varphi:\mathbf R_+\to \mathbf R_+$ be a strictly increasing and continuous function such that $\varphi(0) = 0$, $\varphi(r) \to \infty$ as $r\to\infty$, and let $J_\varphi$ be the duality mapping corresponding to $\varphi$. We will prove that for every $R>0$ and

Simin Li, Huangxinxin Xu, Jiakai Wang, Aishan Liu

Billions of people are sharing their daily life images on social media every day. However, their biometric information (e.g., fingerprint) could be easily stolen from these images. The threat of fingerprint leakage from social media raises a strong desire for anonymizing shared images while maintaining image qualities, since fingerprints act as a lifelong in

Gaurav R. Ghosal, Matthew Zurek, Daniel S. Brown, Anca D. Dragan

When inferring reward functions from human behavior (be it demonstrations, comparisons, physical corrections, or e-stops), it has proven useful to model the human as making noisy-rational choices, with a "rationality coefficient" capturing how much noise or entropy we expect to see in the human behavior. Prior work typically sets the rationality level to a c

Kosuke Ohnishi, Motomi Aoki, Ryo Ohshima, Ei Shigematsu

Topological quantum materials (TQMs) possess abundant and attractive spin physics, and a Weyl semimetal is the representative material because of the generation of spin polarization that is available for spin devices due to fictitious Weyl monopoles at the edge of the Weyl node. Meanwhile, a Weyl semimetal allows the other but unexplored spin polarization du

Ya-Fei Guo, Gao-Chan Yong

A concept of \emph{isospin blocking} in the process of isospin diffusion in heavy-ion collisions is raised. Generally, in the process of isospin diffusion, isospin asymmetry would diffuse from the place with large asymmetry to the place with small asymmetry. However, our study shows that the isospin diffusion could be blocked in case the local value of the s

Jean Lee, Taejun Lim, Heejun Lee, Bogeun Jo

Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides

Boshen Zhang, Yuxi Li, Yuanpeng Tu, Jinlong Peng

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fac

Jing Zhu, Danai Koutra, Mark Heimann

Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel alignment framework that Coarsens the input graphs, Aligns the coarsened graphs, Projects the alignment solution to finer levels and Refines the alignment solution. We show that C

Yongjia Wang, Zepeng Gao, Hongliang Lü, Qingfeng Li

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are used in these approaches, however, observables can be obtained event-by-event both in experiments and transport model simul

M. Marques Alves

We propose and study the weak convergence of a projective splitting algorithm for solving multi-term composite monotone inclusion problems involving the finite sum of $n$ maximal monotone operators, each of which having an inner four-block structure: sum of maximal monotone, Lipschitz continuous, cocoercive and smooth differentiable operators. We show how to

Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil, Moninder Singh

This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption from many sensor measurements, we often have a situation where some observed samples may significantly deviate from their prediction. It may be due to a sub-optimal black-box model

Taewook Ko, Chong-Kwon Kim

A signed directed graph is a graph with sign and direction information on the edges. Even though signed directed graphs are more informative than unsigned or undirected graphs, they are more complicated to analyze and have received less research attention. This paper investigates a spectral graph convolution model to fully utilize the information embedded in

Rigoberto Martinez

This is a review with some comments on the A. Einstein`s 1911 paper, which he published as one of his many attempts of the general theory of relativity. The main point of the idea is to propose a new approach about light and its motion as well as study the assumption made by A. Einstein concerning the possibility of the variation of the speed of light in pre

Nathan S. Sitaraman, Zeming Sun, Ben Francis, Ajinkya C. Hire

Niobium-zirconium (Nb-Zr) alloy is an old superconductor that is a promising new candidate for superconducting radio-frequency (SRF) cavity applications. Using density-functional and Eliashberg theories, we show that addition of Zr to a Nb surface in small concentrations increases the critical temperature $T_c$ and improves other superconducting properties.

Danshi Wang, Chunyu Zhang, Wenbin Chen, Hui Yang

Failure management plays a significant role in optical networks. It ensures secure operation, mitigates potential risks, and executes proactive protection. Machine learning (ML) is considered to be an extremely powerful technique for performing comprehensive data analysis and complex network management and is widely utilized for failure management in optical

Yining Chen, Ke Wang, Guanghua Song, Xiaohong Jiang

In most existing studies on large-scale multi-agent coordination, the control methods aim to learn discrete policies for agents with finite choices. They rarely consider selecting actions directly from continuous action spaces to provide more accurate control, which makes them unsuitable for more complex tasks. To solve the control issue due to large-scale m

Soojeong Baek, Sang Hyun Park, Donghak Oh, Kanghee Lee

Non-Hermitian degeneracies, also known as exceptional points (EPs), have been the focus of much attention due to their singular eigenvalue surface structure. Nevertheless, as pertaining to a non-Hermitian metasurface platform, the reduction of an eigenspace dimensionality at the EP has been investigated mostly in a passive repetitive manner. Here, we propose

Tsuyoshi Idé, Rudy Raymond

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others. As such, we propose a decentralized machine learning framework that is carefully designed to respect the values of democracy, diversity, and privacy. Specifi

Yao-Lun Yang, Joel D. Green, Klaus M. Pontoppidan, Jennifer B. Bergner

The origin of complex organic molecules (COMs) in young Class 0 protostars has been one of the major questions in astrochemistry and star formation. While COMs are thought to form on icy dust grains via gas-grain chemistry, observational constraints on their formation pathways have been limited to gas-phase detection. Sensitive mid-infrared spectroscopy with

Taciana O. Souza, Cesar A. Ipanaque Zapata

In this paper we present new results about the topology of the Milnor fibrations of analytic function-germs with a special attention to the topology of the fibers. In particular, we provide a short review on the existence of the Milnor fibrations in the real and complex cases. This allows us to compare our results with the previous ones.

Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe

We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solution

J. D. Miller, J. F. McNulty, B. J. Ruck, M. Al Khalfioui

The rare-earth nitrides form a series of structurally simple $intrinsic$ ferromagnetic semiconductors, a rare class of both fundamental interest and application potential. Within the series there is a wide range of magnetic properties relating to the spin/orbit contributions to the ferromagnetic ground states. We report an x-ray magnetic circular dichroism i

Sidharth Malhotra, Girik Malik

Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received. While RL has outperformed existing state-of-the-art methods used for playing simple video games and popular

Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural da

Julian Gerstenberg

Motivated by statistical practice, category theory terminology is used to introduce Borel data structures and study exchangeability in an abstract framework. A generalization of de Finetti's theorem is shown and natural transformations are used to present functional representation theorems (FRTs). Proofs of the latter are based on a classical result by D.N.H

Jaime Cuadros, Joe Lope

We calculate the homology groups of certain 2-connected 7-manifolds admitting quasi-regular Sasaki-Einstein metrics. These manifolds are links that arise as Thom-Sebastiani sums of chain type singularities and cycle type singularities. Among these links, we found 52 new examples of Sasaki-Einstein rational homology 7-spheres and 124 new examples of Sasaki-Ei

Zonglin Chi, Shuai Ren, Jingbo Qiao, Jinglong Qu

Processing maps are key to guiding the thermo-mechanical processing (TMP) of superalloys. However, traditional processing maps are incapable of delimiting failure, which is an essential factor to be concerned about during the TMP of superalloys. Employing isothermal hot compression experiments and finite element analysis (FEA), the present study examined the

Bright Antwi Boasiako, John Staudenmayer

This paper studies the asymptotic behavior of penalized spline estimates of derivatives. In particular, we show that simply differentiating the penalized spline estimator of the mean regression function itself to estimate the corresponding derivative achieves the optimal L2 rate of convergence.

Chenmu Zhang, Ruoyu Wang, Himani Mishra, Yuanyue Liu

Two-dimensional (2D) semiconductors have demonstrated great potential for next-generation electronics and optoelectronics. However, the current 2D semiconductors suffer from intrinsically low carrier mobility at room temperature, which significantly limits its applications. Here we discover a variety of new 2D semiconductors with mobility one order of magnit

Alzayat Saleh, Marcus Sheaves, Dean Jerry, Mostafa Rahimi Azghadi

Tracking fish movements and sizes of fish is crucial to understanding their ecology and behaviour. Knowing where fish migrate, how they interact with their environment, and how their size affects their behaviour can help ecologists develop more effective conservation and management strategies to protect fish populations and their habitats. Deep learning is a

Jialong Jiang, Sisi Chen, Tiffany Tsou, Christopher S. McGinnis

Therapeutic modulation of immune states is central to the treatment of human disease. However, how drugs and drug combinations impact the diverse cell types in the human immune system remains poorly understood at the transcriptome scale. Here, we apply single-cell mRNA-seq to profile the response of human immune cells to 502 immunomodulatory drugs alone and

Fan-Yun Sun, Isaac Kauvar, Ruohan Zhang, Jiachen Li

Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we introduce a method for accurately modeling multi-agent systems. We present Interaction Modeling with Multiplex Atte

B. Hamil, B. C. Lütfüoğlu

Graphene-based materials are thought to revolutionize entire industries. Therefore, many research are being carried on graphene theoretically and experimentally. On the other hand, recent studies show that the use of Dunkl derivative, instead of ordinary derivative, allows the concept of parity to be interpreted together with other physical quantities. In th

Prabhjot Kaur, Qifan Wang, Weisong Shi

Fall detection for the elderly is a well-researched problem with several proposed solutions, including wearable and non-wearable techniques. While the existing techniques have excellent detection rates, their adoption by the target population is lacking due to the need for wearing devices and user privacy concerns. Our paper provides a novel, non-wearable, n

Nan Li, Lianbo Ma, Guo Yu, Bing Xue

Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often needs high computational cost and human expertise. To mitigate the above issue, evolutionary computation (EC) as a powerful heuristic search approach has shown significant merits in

Rayson Laroca, Marcelo Santos, Valter Estevam, Eduardo Luz

Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on each dataset. In this scenario, they have

Kazumi Murata, Tsutomu T. Takeuchi

We propose a regularization-based deblurring method that works efficiently for galaxy images. The spatial resolution of a ground-based telescope is generally limited by seeing conditions and much worse than space-based telescopes. This circumstance has generated considerable research interest in restoration of spatial resolution. Since image deblurring is a

Qiang Zhu, Byungkyun Kang, Kevin Parrish

The ability to understand the atomistic mechanisms that occur in the solid phase transition is of crucial importance in materials research. To investigate the displacive phase transition at the atomic scale, we have implemented a numerical algorithm to automate the detection of the symmetry relations between any two candidate crystal structures. Using this a

Megan Morrison, Lai-Sang Young

Key features of biological activity can often be captured by transitions between a finite number of semi-stable states that correspond to behaviors or decisions. We present here a broad class of dynamical systems that are ideal for modeling such activity. The models we propose are chaotic heteroclinic networks with nontrivial intersections of stable and unst

Ran Wei

The packet throughput in 5th Generation (5G) New Radio (NR) Vehicle-to-Everything (V2X) is highly dependent on the Medium Access Control (MAC) based scheduling algorithm with no base station participation. In particular, the Semi-Persistent Scheduling (SPS) algorithm has been standardized by the 3rd Generation Partnership Project (3GPP) for V2X resource sche

Chun-Han Yao, Jimei Yang, Duygu Ceylan, Yi Zhou

Estimating 3D human pose and shape from 2D images is a crucial yet challenging task. While prior methods with model-based representations can perform reasonably well on whole-body images, they often fail when parts of the body are occluded or outside the frame. Moreover, these results usually do not faithfully capture the human silhouettes due to their limit

Michael DiPasquale, Thai Thanh Nguyen, Alexandra Seceleanu

The starting point of this paper is a duality for sequences of natural numbers which, under mild hypotheses, interchanges subadditive and superadditive sequences and inverts their asymptotic growth constants. We are motivated to explore this sequence duality since it arises naturally in at least two important algebraic-geometric contexts. The first context i