October 2020 arXiv papers — page 28

Meiqi Hu, Chen Wu, Liangpei Zhang, Bo Du

With the hyperspectral imaging technology, hyperspectral data provides abundant spectral information and plays a more important role in geological survey, vegetation analysis and military reconnaissance. Different from normal change detection, hyperspectral anomaly change detection (HACD) helps to find those small but important anomaly changes between multi-

Zhongyu Jin, Qingchun Tian, Yuan Zhou

In this paper we study multiple Dedekind symbols and the associated multiple reciprocity functions. There is a bijection between the two sets of them after a normalization. By this bijection we define products of multiple reciprocity functions, and study the relationship to the shuffle property. We construct and calculate shuffled multiple Dedekind symbols a

Raymon S. Watson, John J. McFerran

We model the efficiency of loading atoms of various species into a one dimensional optical lattice from a cold ensemble taking into account the initial cloud temperature and size, the lattice laser properties affecting the trapping potential, and atomic parameters. Stochastic sampling and dynamical evolution are used to simulate the transfer, leading to esti

A. S. Samsonov, I. Yu. Kostyukov, E. N. Nerush

Development of the self-sustained quantum-electrodynamical (QED) cascade in a single strong laser pulse is studied analytically and numerically. The hydrodynamical approach is used to construct the analytical model of the cascade evolution, which includes the key features of the cascade observed in 3D QED particle-in-cell (QED-PIC) simulations such as the ma

Bin Xu, Junzhe Shi, Sixu Li, Huayi Li

Propulsion system electrification revolution has been undergoing in the automotive industry. The electrified propulsion system improves energy efficiency and reduces the dependence on fossil fuel. However, the batteries of electric vehicles experience degradation process during vehicle operation. Research considering both battery degradation and energy consu

Bo-Eun Choi, Hee-Won Lee

Young planetary nebulae (PNe) retain a large amount of neutral material that was shed in the previous asymptotic giant branch stage. The thick H I region in young PNe can be effectively probed by illuminating far UV radiation that may be inelastically scattered to appear in the optical region. Raman-scattered features are unique spectroscopic tracers of neut

Philip Chen, Edward J Oughton, Pete Tyler, Mo Jia

Basic broadband connectivity is regarded as generally having a positive macroeconomic effect. However, over the past decade there has been an emerging school of thought suggesting the impacts of upgrading to higher speed broadband have been overstated, potentially leading to the inefficient allocation of taxpayer-funded subsidies. In this analysis we model t

Marius Müller

We consider a parabolic obstacle problem for Euler's elastic energy of graphs with fixed ends. We show global existence, well-posedness and subconvergence provided that the obstacle and the initial datum are suitably 'small'. For symmetric cone obstacles we can improve the subconvergence to convergence. Qualitative aspects such as energy dissipation, coincid

Xiaoli Liu, Yang Xu, Jiali Li, Xuanwei Ong

Precise control over dimension of nanocrystals is critical to tune the properties for various applications. However, the traditional control through experimental optimization is slow, tedious and time consuming. Herein a robust deep neural network-based regression algorithm has been developed for precise prediction of length, width, and aspect ratios of semi

Soumyadeep Datta, Dheeraj Naidu Amudala, Ekant Sharma, Rohit Budhiraja

Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited-capacity fronthaul links. We derive clo

Akio Hayakawa, Takuya Narihira

While large neural networks demonstrate higher performance in various tasks, training large networks is difficult due to limitations on GPU memory size. We propose a novel out-of-core algorithm that enables faster training of extremely large-scale neural networks with sizes larger than allotted GPU memory. Under a given memory budget constraint, our scheduli

Soumyadeep Datta, Ekant Sharma, Dheeraj Naidu Amudala, Rohit Budhiraja

Cell-free (CF) massive multiple-input-multiple-output (mMIMO) deployments are usually investigated with half-duplex nodes and high-capacity fronthaul links. To leverage the possible gains in throughput and energy efficiency (EE) of full-duplex (FD) communications, we consider a FD CF mMIMO system with practical limited-capacity fronthaul links. We derive clo

Mingjun Zhao, Shengli Yan, Bang Liu, Xinwang Zhong

Query-based document summarization aims to extract or generate a summary of a document which directly answers or is relevant to the search query. It is an important technique that can be beneficial to a variety of applications such as search engines, document-level machine reading comprehension, and chatbots. Currently, datasets designed for query-based summ

K. L. Chiu, D. G. Qian, J. W. Qiu, W. Y. Liu

Weyl semimetals for their exotic topological properties have drawn considerable attention in many research fields. When in combination with s-wave superconductors, the supercurrent can be carried by their topological surface channels, forming junctions mimic the behavior of Majorana bound states. Here, we present a transmon-like superconducting quantum inter

Soumya S. Bhat, Satadeep Bhattacharjee, Jung-Hae Choi, Seung-Cheol Lee

The surface segregation of indium atoms in InGaAs is investigated using first-principles calculations based on density functional theory. Through the calculation of segregation energies for (100), (110), and (111) surfaces of GaAs we analyze the decisive role of surface orientation on indium segregation. Further, our calculations reveal that the variation of

Hongwei Li, Rameshwara G. N. Prasad, Anjany Sekuboyina, Chen Niu

Existing medical image super-resolution methods rely on pairs of low- and high- resolution images to learn a mapping in a fully supervised manner. However, such image pairs are often not available in clinical practice. In this paper, we address super-resolution problem in a real-world scenario using unpaired data and synthesize linearly \textbf{eight times}

Björn Bebensee, Byoung-Tak Zhang

Inspired by recent trends in vision and language learning, we explore applications of attention mechanisms for visio-lingual fusion within an application to story-based video understanding. Like other video-based QA tasks, video story understanding requires agents to grasp complex temporal dependencies. However, as it focuses on the narrative aspect of video

Katherine Aristizabal, Andreas Katzensteiner, Andrea Bachmaier, Frank Muecklich

Carbon nanotube (CNT)-reinforced nickel matrix composites with different concentrations were processed by high pressure torsion (HPT). We thoroughly characterized the CNT agglomerates' spatial arrangement at different stages of deformation in order to extract information valuable for the optimization of the processing parameters and to elucidate the mechanis

Wen Wu, Chao Zhang, Philip C. Woodland

In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB). To capture correlations between each word and its acoustic realisation, the TSB combines speech and text modalities at each input window frame and then does po

T. Giang Nguyen, Nicolas B. Cowan, Agnibha Banerjee, John E. Moores

Transit searches have uncovered Earth-size planets orbiting so close to their host star that their surface should be molten, so-called lava planets. We present idealized simulations of the atmosphere of lava planet K2-141b and calculate the return flow of material via circulation in the magma ocean. We then compare how pure Na, SiO, or SiO$_2$ atmospheres wo

W. Zhang, H. Liu, P. Li, L. Han

The goal of convective storm nowcasting is local prediction of severe and imminent convective storms. Here, we consider the convective storm nowcasting problem from the perspective of machine learning. First, we use a pixel-wise sampling method to construct spatiotemporal features for nowcasting, and flexibly adjust the proportions of positive and negative s

Zhifu Gao, Shiliang Zhang, Ming Lei, Ian McLoughlin

Recently, online end-to-end ASR has gained increasing attention. However, the performance of online systems still lags far behind that of offline systems, with a large gap in quality of recognition. For specific scenarios, we can trade-off between performance and latency, and can train multiple systems with different delays to match the performance and laten

Kazuki Ikeda, Shoto Aoki

The quest of this work is to present discussions of some fundamental questions of economics in the era of quantum technology, which require a treatment different from economics studied thus far in the literature. A study of quantum economic behavior will become the center of attention of economists in the coming decades. We analyze a quantum economy in which

L. Edwards, H. Lane, A. M. Arevalo-Lopez, M. Songvilay

$\alpha$-CoV$_{2}$O$_{6}$ consists of $j_{\mathrm{eff}}={1 \over 2}$ Ising spins located on an anisotropic triangular motif with magnetization plateaus in an applied field. We combine neutron diffraction with low temperature magnetization to investigate the magnetic periodicity in the vicinity of these plateaus. We find these steps to be characterized by met

Somnath Dutta, Chin-Fei Lee, Tie Liu, Naomi Hirano

Planck Galactic Cold Clumps (PGCCs) are contemplated to be the ideal targets to probe the early phases of star formation. We have conducted a survey of 72 young dense cores inside PGCCs in the Orion complex with the Atacama Large Millimeter/submillimeter Array (ALMA) at 1.3\,mm (band 6) using three different configurations (resolutions $\sim$ 0$\farcs$35, 1$

Y. Xu, C. Liu, H. Tian, H. J. Newberg

The recent discovery of a spiral feature in $Z-V_Z$ phase plane in the solar neighborhood implies that the Galactic disk has been remarkably affected by a dwarf galaxy passing through it some hundreds of millions of years ago. Using 429,500 LAMOST K giants stars, we show that the spiral feature exits not only in the solar vicinity; it also extends to about 1

Aisha Urooj Khan, Amir Mazaheri, Niels da Vitoria Lobo, Mubarak Shah

We present MMFT-BERT(MultiModal Fusion Transformer with BERT encodings), to solve Visual Question Answering (VQA) ensuring individual and combined processing of multiple input modalities. Our approach benefits from processing multimodal data (video and text) adopting the BERT encodings individually and using a novel transformer-based fusion method to fuse th

Felipe Bañados Schwerter, Alison M. Clark, Khurram A. Jafery, Ronald Garcia

Abstracting Gradual Typing (AGT) is a systematic approach to designing gradually-typed languages. Languages developed using AGT automatically satisfy the formal semantic criteria for gradual languages identified by Siek et al. [2015]. Nonetheless, vanilla AGT semantics can still have important shortcomings. First, a gradual language's runtime checks should p

Jingjun Wen, Daowei Dou, Jinfu Zhu, Zhi Zeng

This paper describes a FADC Readout system developed for the tap water $\alpha/\beta$ dose monitoring system which is based on EJ444 phoswich scintillation detector and wavelength shifting fiber readout. The Readout system contains dual sampling channels that can supply sampling rate up to 1 GSPS, 14-Bit vertical resolution and adequate effective number of b

H. -L. Guo, B. -Q. Chen, H. -B. Yuan, Y. Huang

We present a three-dimensional (3D) extinction map of the southern sky. The map covers the SkyMapper Southern Survey (SMSS) area of $\sim$ 14,000 ${\rm deg^{2}}$ and has spatial resolutions between 6.9 and 27 arcmin. Based on the multi-band photometry of SMSS, the Two Micron All Sky Survey, the Wide-Field Infrared Survey Explorer Survey and the Gaia mission,

Jianjia Zhang

The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients and breaking the person-to-person transmission. Chest X-ray (CXR) based computer-aided diagnosis of COVID-19 using deep

Yasser Fuentes-Edfuf, José A. Sánchez-Gil, Marina Garcia Pardo, Rosalía Serna

Exposure of metal surfaces to multiple ultrashort laser pulses under certain conditions leads to the formation of well-defined periodic surface structures. We show how the period of such structures in steel can be tuned over a wide range by controlling the complex interaction mechanisms triggered in the material. Amongst the different irradiation parameters

Yingchao Lu, Fan Guo, Patrick Kilian, Hui Li

An oblique-rotating pulsar generates a relativistic striped wind in a pulsar wind nebula (PWN). The termination shock of the PWN compresses the Poynting-flux-dominated flow and drives magnetic reconnection. By carrying out particle-in-cell (PIC) simulations of the termination shock of the PWN, we study the shock structure as well as the energy conversion pro

Yuyan Chen, Bin Dong, Jinchao Xu

This paper studies numerical solutions for parameterized partial differential equations (P-PDEs) with deep learning (DL). P-PDEs arise in many important application areas and the computational cost using traditional numerical schemes can be exorbitant, especially when the parameters fall into a particular range and the underlying PDE is required to be solved

Jeongho Kim, Jaeuk Shin, Insoon Yang

In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal control problems with Lipschitz continuous controls. Our method is based on a new class of Hamilton-Jacobi-Bellman (HJB) equations derived from applying the dynamic programming principle to continuous-time Q-functions. A novel semi-discrete version of the HJB equation i

Mostfa Shams Kojanaghi, Kazem Haghnejad Azar

Let $B$ be a Banach $A-bimodule$. We introduce the weak topological centers of left module action and we show it by $\tilde{{Z}}^\ell_{B^{**}}(A^{**})$. For a compact group, we show that $L^1(G)=\tilde{Z}_{M(G)^{**}}^\ell(L^1(G)^{**})$ and on the other hand we have $\tilde{Z}_1^\ell{(c_0^{**})}\neq c_0^{**}$. Thus the weak topological centers are different w

Rohan E. Louis, Christian Beck, Debi P. Choudhary

We use a combination of full-disk data from the Solar Dynamics Observatory and high-resolution data from the Dunn Solar Telescope (DST) to study the formation, structure, and evolution of an atypical light bridge (LB) in a regular sunspot. The LB results from the emergence of magnetic flux with one footpoint rooted in a pore outside the parent sunspot that a

F. Lach, F. K. Roepke, I. R. Seitenzahl, B. Coté

We analyze the nucleosynthesis yields of various Type Ia supernova explosion simulations including pure detonations in sub- Chandrasekhar mass white dwarfs, double detonations and pure helium detonations of sub-Chandrasekhar mass white dwarfs with an accreted helium envelope, a violent merger model of two white dwarfs and deflagrations as well as delayed det

Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi

This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gradient ascent scheme for optimizing the rate reduction of learned features naturally leads to a multi-layer deep network, one iteration per layer. The layered architectures, linear

C. A. Bertulani, R. Lobato

We study the effects of final state interactions in the non-mesonic weak decay $\Lambda N \rightarrow nN$ (n is a neutron and N is either a neutron or a proton) of the hypernucleus $_\Lambda^4$He. Using a three-body model the effects of distortion of the interaction of the emitted nucleon pair with the residual nucleus is considered. We also study the influe

Bin Du, Kun Qian, Christian Claudel, Dengfeng Sun

This paper presents a novel Jacobi-style iteration algorithm for solving the problem of distributed submodular maximization, in which each agent determines its own strategy from a finite set so that the global submodular objective function is jointly maximized. Building on the multi-linear extension of the global submodular function, we expect to achieve the

Yoo-Lim Cheon, Seok-Ho Moon, Moses Chung, Dong-O Jeon

For modern high-intensity linear accelerators, the well-known envelope instability and recently reported fourth-order particle resonance impose a fundamental operational limit (i.e., zero-current phase advance should be less than 90 deg). Motivated by the stability of spinning flying objects, we propose a novel approach of using spinning beams to surpass thi

Jesse Goodman, Felipe Hernandez, Daniel Spector

In this paper we prove two approximation results for divergence free measures. The first is a form of an assertion of J. Bourgain and H. Brezis concerning the approximation of solenoidal charges in the strict topology: Given $F \in M_b(\mathbb{R}^d;\mathbb{R}^d)$ such that $\operatorname*{div} F=0$ in the sense of distributions, there exist oriented $C^1$ lo

Tongtong Li, Xing Wang, Ivan Yotov

In this paper, we focus on investigating the influence on hydrodynamic factors of different coupled computational models describing the interaction between an incompressible fluid and two symmetric elastic or poroelastic structures. The fluid region is governed by time dependent Navier-Stokes equations; while for the structure region, we employ two different

Navodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture features fro

Nicholas Sedlmayr, Alex Levchenko

In this communication we consider generalities of the proximity effect in a contact between a conventional $s$-wave superconductor (S) nano-island and a thin film of a topological insulator (TI). A local hybridization coupling mechanism is considered and a corresponding model is corroborated that captures not only the induced unconventional superconductivity

Dmitry Kleinbock, Shahriar Mirzadeh

Let $X = G/\Gamma$, where $G$ is a Lie group and $\Gamma$ is a lattice in $G$, let $U$ be an open subset of $X$, and let $\{g_t\}$ be a one-parameter subgroup of $G$. Consider the set of points in $X$ whose $g_t$-orbit misses $U$; it has measure zero if the flow is ergodic. It has been conjectured that this set has Hausdorff dimension strictly smaller than t

Xuli Shen, Qing Xu, Xiangyang Xue

For basic machine learning problems, expected error is used to evaluate model performance. Since the distribution of data is usually unknown, we can make simple hypothesis that the data are sampled independently and identically distributed (i.i.d.) and the mean value of loss function is used as the empirical risk by Law of Large Numbers (LLN). This is known

Shi-Yan Weng, Berlin Chen

The attention-based encoder-decoder modeling paradigm has achieved promising results on a variety of speech processing tasks like automatic speech recognition (ASR), text-to-speech (TTS) and among others. This paradigm takes advantage of the generalization ability of neural networks to learn a direct mapping from an input sequence to an output sequence, with

Xinjie Lan, Kenneth E. Barner

In this paper, we propose a probabilistic representation of MultiLayer Perceptrons (MLPs) to improve the information-theoretic interpretability. Above all, we demonstrate that the activations being i.i.d. is not valid for all the hidden layers of MLPs, thus the existing mutual information estimators based on non-parametric inference methods, e.g., empirical

Huikai Xu, Weiyang Liu, Zhiyuan Li, Jiaxiu Han

High fidelity two-qubit gates are fundamental for scaling up the superconducting number. We use two qubits coupled via a frequency-tunable coupler which can adjust the coupling strength, and demonstrate the CZ gate using two different schemes, adiabatic and diabatic methods. The Clifford based Randomized Benchmarking (RB) method is used to assess and optimiz

Jiaxiu Han, Zhiyuan Li, Jingning Zhang, Huikai Xu

The identification of spacial noise correlation is of critical importance in developing error-corrected quantum devices, but it has barely been studied so far. In this work, we utilize an effective new method called qubit motion, to efficiently determine the noise correlations between any pair of qubits in a 7-qubit superconducting quantum system. The noise

Tipawan Silwattananusarn, Wanida Kanarkard, Kulthida Tuamsuk

In this paper ensemble learning based feature selection and classifier ensemble model is proposed to improve classification accuracy. The hypothesis is that good feature sets contain features that are highly correlated with the class from ensemble feature selection to SVM ensembles which can be achieved on the performance of classification accuracy. The prop

W. B. Dong, H. S. Tang, Y. J. Liu

A study is presented on the convergence of the computation of coupled advection-diffusion-reaction equations. In the computation, the equations with different coefficients and even types are assigned in two subdomains, and Schwarz iteration is made between the equations when marching from a time level to the next one. The analysis starts with the linear syst

Wen-Ting Tseng, Tien-Hong Lo, Yung-Chang Hsu, Berlin Chen

Frequently asked question (FAQ) retrieval, with the purpose of providing information on frequent questions or concerns, has far-reaching applications in many areas, where a collection of question-answer (Q-A) pairs compiled a priori can be employed to retrieve an appropriate answer in response to a user\u2019s query that is likely to reoccur frequently. To t

Pin-Wen Guan, Venkatasubramanian Viswanathan

Thermodynamics is fundamental for understanding and synthesizing multi-component materials, while efficient and accurate prediction of it still remain urgent and challenging. As a demonstration of the "Divide and conquer" strategy decomposing a phase diagram into different learnable features, quantitative prediction of melting temperature of binary alloys is

Zenan Xu, Zijing Ou, Qinliang Su, Jianxing Yu

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this iss

Neer Bhardwaj, Lou van den Dries

This expository paper gives an account of the Pila-Wilkie counting theorem and some of its extensions and generalizations. We use semialgebraic cell decomposition to simplify part of the original proof. We also include complete treatments of a result due to Pila and Bombieri and of the o-minimal Yomdin-Gromov theorem that are used in this proof. For the latt

Xing-Jie Han, Philipp Werner, Carsten Honerkamp

The effective interaction of downfolded low-energy models for electrons in solids can be obtained by integrating out the high energy bands away from the target band near the Fermi level. Here, we apply the constrained random-phase approximation (cRPA) and constrained functional renormalization group (cfRG), which can go beyond cRPA by including all one-loop

Naseem Ud Din, Volodymyr Turkowski, Talat S Rahman

Our examination of the interplay of ultrafast charge dynamics and electron-phonon interaction in bilayer MoS2 provides a microscopic basis for understanding the features (two peaks) in the emission spectrum. We demonstrate that while the initial accumulation of excited charge occurs at and near the Q point of the two-dimensional Brillioun zone, emission take

Akash Kumar, Hanqi Zhang, Adish Singla, Yuxin Chen

Algorithmic machine teaching has been studied under the linear setting where exact teaching is possible. However, little is known for teaching nonlinear learners. Here, we establish the sample complexity of teaching, aka teaching dimension, for kernelized perceptrons for different families of feature maps. As a warm-up, we show that the teaching complexity i

Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan

Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success. However, training these models can be costly both from an economic and environmental standp

Mohammed A. Chamma, Fereshteh Rajabi, Christopher M. Wyenberg, Abhilash Mathews

We study the spectro-temporal characteristics of two repeating fast radio bursts (FRBs), namely, FRB 20180916B and FRB 20180814A, and combine the results with those from our earlier analysis on FRB 20121102A. The relationship between the frequency drift rate, or slope, of individual sub-bursts and their temporal duration is investigated. We consider a broad

Xueheng Kuang, Zhen Zhan, Shengjun Yuan

Twisted bilayer graphene with tiny rotation angles have drawn significant attention due to the observation of the unconventional superconducting and correlated insulating behaviors. In this paper, we employ a full tight-binding model to investigate collective excitations in twisted bilayer graphene near magic angle. The polarization function is obtained from

Yucheng Liu

We prove some positivity results on the coefficients in the complexified Hilbert polynomial of a semi-stable object. After applying these results on the classical slope stability conditions, we get sequences of quadratic inequalities for semi-stable objects on product varieties. The leading quadratic inequality can be viewed as a weak version of Bogomolov's

Yajue Yang, Pinxin Long, Jia Pan, Xinbin Song

In this paper, we present a novel optimization-based framework for autonomous excavator trajectory generation under various objectives, including minimum joint displacement and minimum time. Traditional methods on excavation trajectory generation usually separate the excavation motion into a sequence of fixed phases, resulting in limited trajectory searching

Marten van Dijk, Nhuong V. Nguyen, Toan N. Nguyen, Lam M. Nguyen

Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where multiple threads in parallel access a common repository containing training data, perform SGD iterations and update shared state that represents a jointly learned (global) model. We consider big data analysis where training data is distributed among local data sets in a heterogeneous wa

Wanxin Li, Collin Meese, Hao Guo, Mark Nejad

The on-demand mobility market, including ridesharing, is becoming increasingly important with e-hailing fares growing at a rate of approximately 130% per annum since 2013. By increasing utilization of existing vehicles and empty seats, ridesharing can provide many benefits including reduced traffic congestion and environmental impact from vehicle usage and p

Yu Sun, Qian Bao, Wu Liu, Wenpeng Gao

Recovering 3D human mesh from monocular images is a popular topic in computer vision and has a wide range of applications. This paper aims to estimate 3D mesh of multiple body parts (e.g., body, hands) with large-scale differences from a single RGB image. Existing methods are mostly based on iterative optimization, which is very time-consuming. We propose to

Songbing Liang, Stylianos D. Assimonis

In this article, three new inter-symbol interference (ISI)-free pulses with enhanced performance compared to the state-of-the-art are proposed and studied in terms of frequency and time domain characteristics. They are based on inverse hyperbolic functions and on the concept of inner and outer functions, which was first introduced by the authors. New pulses

Prahlad Vaidyanathan

Given an action of a compact group on a complex vector bundle, there is an induced action of the group on the associated Cuntz-Pimsner algebra. We determine conditions under which this action has finite Rokhlin dimension.

Mosarof Sarkar, Shubh N. Singh

Let $X$ be a set and $\mathcal{T}_X$ be the full transformation semigroup on $X$. For a partition $\mathcal{P}$ of $X$, we consider semigroups $T(X, \mathcal{P}) = \{f\in \mathcal{T}_X| (\forall X_i\in \mathcal{P}) (\exists X_j \in \mathcal{P})\;X_i f \subseteq X_j\}$, $\Sigma(X, \mathcal{P}) = \{f\in T(X, \mathcal{P})|(\forall X_i \in \mathcal{P})\; Xf \cap

Robert Sison, Toby Murray

Proving only over source code that programs do not leak sensitive data leaves a gap between reasoning and reality that can only be filled by accounting for the behaviour of the compiler. Furthermore, software does not always have the luxury of limiting itself to single-threaded computation with resources statically dedicated to each user to ensure the confid

Jun Suk Kim, Minh Dao Tran, Sung-Tae Kim, Daehan Yoo

Although van der Waals layered transition metal dichalcogenides from transient absorption spectroscopy have successfully demonstrated an ideal carrier multiplication (CM) performance with an onset of nearly 2Eg,interpretation of the CM effect from the optical approach remains unresolved owing to the complexity of many-body electron-hole pairs. We demonstrate

Hans U. Boden, Homayun Karimi

We use an extension of Gordon-Litherland pairing to thickened surfaces to give a topological characterization of alternating links in thickened surfaces. If $\Sigma$ is a closed oriented surface and $F$ is a compact unoriented surface in $\Sigma \times I$, then the Gordon-Litherland pairing defines a symmetric bilinear pairing on the first homology of $F$. A

Runxin Xu, Zhuo Zhi, Jun Cao, Mingxuan Wang

In this paper, we describe our submissions to the WMT20 shared task on parallel corpus filtering and alignment for low-resource conditions. The task requires the participants to align potential parallel sentence pairs out of the given document pairs, and score them so that low-quality pairs can be filtered. Our system, Volctrans, is made of two modules, i.e.

Christopher D. Carone

We consider the possibility of an axion-like particle (ALP) that is a ghost, with wrong-sign kinetic and mass terms. Such an ALP can arise as the partner to an ordinary particle in theories with higher-derivative quadratic terms. We compute the photon regeneration probability in light-shining-through-wall experiments and show that the presence of such a ghos

Qirui Yang, Runyu Jin, Nabil Gandhi, Xiongzi Ge

Edge computing has been developed to utilize multiple tiers of resources for privacy, cost and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven and latency-sensitive. Because of this, edge systems have developed to be both heterogeneous and distributed. The unique characteristics of edge workloads and edge systems have

Matthias Kormaksson, Luke J. Kelly, Xuan Zhu, Sibylle Haemmerle

Knockoffs provide a general framework for controlling the false discovery rate when performing variable selection. Much of the Knockoffs literature focuses on theoretical challenges and we recognize a need for bringing some of the current ideas into practice. In this paper we propose a sequential algorithm for generating knockoffs when underlying data consis

Xin Gao, Jeno Szep, Pratik Satam, Salim Hariri

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The

Carlos Martinez-Ranero, Javier Utreras, Xavier Vidaux

We investigate the problem of deciding whether a system of linear equations, together with divisibility conditions on the variables, has a solution over holomorphy subrings of global fields. We obtain decidability results when we allow poles at a cofinite set of places, and undecidability results when at a finite set of places.

Seng Pei Liew, Tsubasa Takahashi

Transfer learning is a useful machine learning framework that allows one to build task-specific models (student models) without significantly incurring training costs using a single powerful model (teacher model) pre-trained with a large amount of data. The teacher model may contain private data, or interact with private inputs. We investigate if one can lea

Xingjian Du, Zhesong Yu, Bilei Zhu, Xiaoou Chen

We present in this paper ByteCover, which is a new feature learning method for cover song identification (CSI). ByteCover is built based on the classical ResNet model, and two major improvements are designed to further enhance the capability of the model for CSI. In the first improvement, we introduce the integration of instance normalization (IN) and batch

Reuben Tate, Majid Farhadi, Creston Herold, Greg Mohler

We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem. Near-term (noisy) quantum devices are only able to (accurately) execute QAOA at low circuit depths while QAOA requires a relatively high circuit-depth in order to "see" the whole graph. We introduce a classical pre-processing step that initializes QAOA with

J. S. Liu, S. C. Huan, Z. H. Liu, W. L. Liu

The new-found two-dimensional antiferromagnetic GdTe$_3$ is attractive owing to its highest carrier mobility among all known layered magnetic materials, as well as its potential application for novel magnetic twistronic and spintronic devices. Here, we have used high-resolution angle-resolved photoemission spectroscopy to investigate its Fermi surface topolo

Akshatha Kamath, Dwaraknath Gnaneshwar, Matias Valdenegro-Toro

Estimating epistemic uncertainty of models used in low-latency applications and Out-Of-Distribution samples detection is a challenge due to the computationally demanding nature of uncertainty estimation techniques. Estimating model uncertainty using approximation techniques like Monte Carlo Dropout (MCD), DropConnect (MCDC) requires a large number of forward

Axel Ringh, Johan Karlsson, Anders Lindquist

In this work we consider robust stabilization of uncertain dynamical systems and show that this can be achieved by solving a non-classically constrained analytic interpolation problem. In particular, this non-classical constraint confines the range of the interpolant, when evaluated on the imaginary axis, to a frequency-dependent set. By considering a suffic

Zipeng Wang

In this first part of our project, we prove a classical Hardy-Littlewood-Sobolev result for a new family of fractional integral operators whose kernel has singularity appeared on the light cone in R^n+1.

Alireza Fereidouni, Julius Susanto, Pierluigi Mancarella, Nicky Hong

In small/medium-sized isolated power networks with low rotational inertia and high penetration of renewables, generation/load contingency events may cause large frequency excursions, potentially leading to cascading failures and even blackouts. Therefore, it is crucial for system operators to be able to monitor the state of the network in real-time and predi

Subash Adhikari, Ji-Hee Kim, Bumsub Song, Manh-Ha Doan

Many-body effect and strong Coulomb interaction in monolayer transition metal dichalcogenides lead to shrink the intrinsic bandgap, originating from the renormalization of electrical/optical bandgap, exciton binding energy, and spin-orbit splitting. This renormalization phenomenon has been commonly observed at low temperature and requires high photon excitat

Yu Shen, Sijie Zhu, Taojiannan Yang, Chen Chen

Fast and effective responses are required when a natural disaster (e.g., earthquake, hurricane, etc.) strikes. Building damage assessment from satellite imagery is critical before an effective response is conducted. High-resolution satellite images provide rich information with pre- and post-disaster scenes for analysis. However, most existing works simply u

Yitong Meng, Jie Liu, Xiao Yan, James Cheng

When a new user just signs up on a website, we usually have no information about him/her, i.e. no interaction with items, no user profile and no social links with other users. Under such circumstances, we still expect our recommender systems could attract the users at the first time so that the users decide to stay on the website and become active users. Thi

Runrun Xu, Fengyuan Xuan, Su Ying Quek

Mixed-dimensional magnetic heterostructures are intriguing, newly available platforms to explore quantum physics and its applications. Using state-of-the-art many-body perturbation theory, we predict the energy level alignment for a self-assembled monolayer of cobalt phthalocyanine (CoPc) molecules on magnetic VSe 2 monolayers. The predicted projected densit

Ze Li

In this paper, we study the skew mean curvature flow. The results are threefold. First, we prove the global regularity of solutions with initial data which are small perturbations of planes in Sobolev spaces. Second, we prove the modified scattering and the existence of wave operators for small data, which completely determines the set of asymptotic states.

Ruodu Wang

We introduce the notion of p*-values (p*-variables), which generalizes p-values (p-variables) in several senses. The new notion has four natural interpretations: operational, probabilistic, Bayesian, and frequentist. A main example of a p*-value is a mid p-value, which arises in the presence of discrete test statistics. A unified stochastic representation fo

Zihao Wang, Zhifei Xu, Jiayi He, Chulsoon Hwang

In this work we propose a neuromorphic hardware based signal equalizer by based on the deep learning implementation. The proposed neural equalizer is plasticity trainable equalizer which is different from traditional model designed based DFE. A trainable Long Short-Term memory neural network based DFE architecture is proposed for signal recovering and digita

L. Yu, S. Y. Huang, Z. G. Yuan, K. Jiang

We present a statistical analysis for the characteristics and radial evolution of linear magnetic holes (LMHs) in the solar wind from 0.166 to 0.82 AU using Parker Solar Probe observations of the first two orbits. It is found that the LMHs mainly have a duration less than 25 s and the depth is in the range from 0.25 to 0.7. The durations slightly increase an

Junjie Yan, Tamara Bonaci, Howard Jay Chizeck

In this paper, a new behavioral biometric password based on haptic interaction is described. This "haptic password" system is built on a device that has a force-sensitive screen. The user authentication process leverages maximal overlap discrete wavelet transform (MODWT) based feature extraction along with a customized classifier as well as an adaptive templ

Junjie Yan, Kevin Huang, Kyle Lindgren, Tamara Bonaci

In this paper, we present a novel approach for continuous operator authentication in teleoperated robotic processes based on Hidden Markov Models (HMM). While HMMs were originally developed and widely used in speech recognition, they have shown great performance in human motion and activity modeling. We make an analogy between human language and teleoperated

Haibo Yuan, Dingshan Deng, Yang Sun

The Chinese Space Station Telescope (CSST) spectroscopic survey aims to deliver high-quality low-resolution ($R > 200$) slitless spectra for hundreds of millions of targets down to a limiting magnitude of about 21 mag, distributed within a large survey area (17500 deg$^2$) and covering a wide wavelength range (255-1000 nm by 3 bands GU, GV, and GI). As slitl