April 2020 arXiv papers — page 42

Prashant Singh, Sanjib Sabhapandit, Anupam Kundu

We investigate the run and tumble particle (RTP), also known as persistent Brownian motion, in one dimension. A telegraphic noise $\sigma(t)$ drives the particle which changes between $\pm 1$ values with some rates. Denoting the rate of flip from $1$ to $-1$ as $R_1$ and the converse rate as $R_2$, we consider the position and direction dependent rates of th

Botao Li, Synge Todo, A. C. Maggs, Werner Krauth

We present a multithreaded event-chain Monte Carlo algorithm (ECMC) for hard spheres. Threads synchronize at infrequent breakpoints and otherwise scan for local horizon violations. Using a mapping onto absorbing Markov chains, we rigorously prove the correctness of a sequential-consistency implementation for small test suites. On x86 and ARM processors, a C+

Ruilin Chen, Kaiyan Chang, Kaiyuan Tian

Teamwork is increasingly important in today's society. This paper aims at the problem of team performance evaluation. Through complex network feature extraction, we establishes the passing network and team performance evaluation model. Finally, this paper proposes strategy for Huskies team and extend the model to the general team.

Chu Guo, Kavan Modi, Dario Poletti

We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a database of local input states at different times and the corresponding time-nonlocal output state. In particular, we ana

James R. Wootton

Now that ever more sophisticated devices for quantum computing are being developed, we require ever more sophisticated benchmarks. This includes a need to determine how well these devices support the techniques required for quantum error correction. In this paper we introduce the \texttt{topological\_codes} module of Qiskit-Ignis, which is designed to provid

M. A. Kuntman, E. Kuntman

We introduce a matrix method and we derive a formula for phase retardation effects in plasmonic systems. We analyze the circular dichroic response (CD) of two orthogonal Au nanorods in detail and we show that, although, theoretically, circular dichroism for forward scattering is directly proportional to the dipole-dipole interaction between the particles, CD

Lorenzo Gaudio, Mari Kobayashi, Giuseppe Caire, Giulio Colavolpe

Motivated by future automotive applications, we study the joint target detection and parameter estimation problem using orthogonal time frequency space (OTFS), a digital modulation format robust to time-frequency selective channels. Assuming the transmitter is equipped with a mono-static MIMO radar, we propose an efficient maximum likelihood based approach t

Andre Schenke

We study the tamed magnetohydrodynamics equations, introduced recently in a paper by the author, perturbed by multiplicative Wiener noise of transport type on the whole space $\mathbb{R}^{3}$ and on the torus $\mathbb{T}^{3}$. In a first step, existence of a unique strong solution are established by constructing a weak solution, proving that pathwise uniquen

Dang Quang A, Dang Quang Long

In this paper we consider a fully third order nonlinear boundary value problem which is of great interest of many researchers. First we establish the existence, uniqueness of solution. Next, we propose simple iterative methods on both continuous and discrete levels. We prove that the discrete methods are of second order and third accuracy due to the use of a

J. Alaminos, J. Extremera, M. L. C. Godoy, A. R. Villena

Let $\mathcal{M}$ be a von Neumann algebra, and let $0<p,q\le\infty$. Then the space $\Hom_\mathcal{M}(L^p(\mathcal{M}),L^q(\mathcal{M}))$ of all right $\mathcal{M}$-module homomorphisms from $L^p(\mathcal{M})$ to $L^q(\mathcal{M})$ is a reflexive subspace of the space of all continuous linear maps from $L^p(\mathcal{M})$ to $L^q(\mathcal{M})$. Further, the

Chun-Khiang Chua

We study anomalous magnetic moments and flavor violating processes of $e$, $\mu$ and $\tau$ leptons. We use a data driven approach to investigate the implications of the present data on the parameters of a class of models, which has spin-0 scalar and spin-1/2 fermion fields. We compare two different cases, which has or does not have a built-in cancelation me

Joseph Durnin, M. J. Bhaseen, Benjamin Doyon

Motivated by dynamical experiments on cold atomic gases, we develop a quantum kinetic approach to weakly perturbed integrable models out of equilibrium. Using the exact matrix elements of the underlying integrable model we establish an analytical approach to real-time dynamics. The method addresses a broad range of timescales, from the intermediate regime of

Wadim Zudilin

Some diophantine problems are stated for the Omega constant and, more generally, the values of Lambert $W$-function and their $p$-adic extensions.

Yannick Ponty, Franck Plunian

The dynamo equations are solved numerically with a helical forcing corresponding to the Roberts flow. In the fully turbulent regime the flow behaves as a Roberts flow on long time scales, plus turbulent fluctuations at short time scales. The dynamo onset is controlled by the long time scales of the flow, in agreement with the former Karlsruhe experimental re

Péter Kálmán, Tamás Keszthelyi

In recent electron-positron angular correlation measurements the observed significant enhancements relative to the internal pair creation at large angles was interpreted as indication of the creation of $J^{\pi }=1^{+}$ boson called X17 particle. In this paper it is brought up that such enhancements can be generated by higher order processes. It is found tha

Metin Ata, Francisco-Shu Kitaura, Khee-Gan Lee, Brian C. Lemaux

This work presents the first comprehensive study of structure formation at the peak epoch of cosmic star formation over $1.4\leq z \leq 3.6$ in the COSMOS field, including the most massive high redshift galaxy proto-clusters at that era. We apply the extended COSMIC BIRTH algorithm to account for a multi-tracer and multi-survey Bayesian analysis at Lagrangia

Shashi Narayan, Gonçalo Simoes, Ji Ma, Hannah Craighead

Recent trends in natural language processing using pretraining have shifted focus towards pretraining and fine-tuning approaches for text generation. Often the focus has been on task-agnostic approaches that generalize the language modeling objective. We propose question generation as a pretraining method, which better aligns with the text generation objecti

Geunsu Choi, Yun Sung Choi, Mingu Jung, Miguel Martin

We introduce a weakened notion of norm attainment for bounded linear operators between Banach spaces which we call \emph{quasi norm attaining operators}. An operator $T\colon X \longrightarrow Y$ between the Banach spaces $X$ and $Y$ is quasi norm attaining if there is a sequence $(x_n)$ of norm one elements in $X$ such that $(Tx_n)$ converges to some $u\in

Vid Kocijan, Thomas Lukasiewicz, Ernest Davis, Gary Marcus

The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguous pronoun, resolved differently in the two sentences, that appears to require commonsense knowledge to be reso

Tanvi Jain, Hemant K. Mishra

Associated with every $2n\times 2n$ real positive definite matrix $A,$ there exist $n$ positive numbers called the symplectic eigenvalues of $A,$ and a basis of $\mathbb{R}^{2n}$ called the symplectic eigenbasis of $A$ corresponding to these numbers. In this paper, we discuss the differentiability (analyticity) of the symplectic eigenvalues and corresponding

Benoy Kumar Singh, Raj Pal Singh, Dharm Veer Singh

This paper presents an exact solution of Bardeen black hole in presence of massive gravity, which is characterized by the additional parameter $m$. Here, we focus on $4D$ Bardeen AdS massive black hole solutions as example to discuss the thermodynamical properties such as temperature, entropy and specific heat at constant pressure. We also study the critical

Ziyue Li, Hao Yan, Chen Zhang, Fugee Tsung

Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems. It is typically difficult to be accurately predicted given intrinsic complex spatial and temporal correlations. Most of the existing methods based on various statistical models and regularization terms, fail to preserve innate features in data a

Shrey Dabhi, Kartavya Soni, Utkarsh Patel, Priyanka Sharma

Synthetic Aperture Radar (SAR) images contain a huge amount of information, however, the number of practical use-cases is limited due to the presence of speckle noise in them. In recent years, deep learning based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by th

Namhyuk Ahn, Jaejun Yoo, Kyung-Ah Sohn

In this paper, we tackle a fully unsupervised super-resolution problem, i.e., neither paired images nor ground truth HR images. We assume that low resolution (LR) images are relatively easy to collect compared to high resolution (HR) images. By allowing multiple LR images, we build a set of pseudo pairs by denoising and downsampling LR images and cast the or

Libo Qin, Xiao Xu, Wanxiang Che, Yue Zhang

Recent studies have shown remarkable success in end-to-end task-oriented dialog system. However, most neural models rely on large training data, which are only available for a certain number of task domains, such as navigation and scheduling. This makes it difficult to scalable for a new domain with limited labeled data. However, there has been relatively li

David Hodan, Vojtech Mrazek, Zdenek Vasicek

Despite many successful applications, Cartesian Genetic Programming (CGP) suffers from limited scalability, especially when used for evolutionary circuit design. Considering the multiplier design problem, for example, the 5x5-bit multiplier represents the most complex circuit evolved from a randomly generated initial population. The efficiency of CGP highly

Tom Oomen

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presente

Yael Avni, Rudolf Podgornik, David Andelman

Based on a collective description of electrolytes composed of charge-regulated macro-ions and simple salt ions, we analyze their equilibrium charge state in the bulk and their behavior in the vicinity of an external electrified surface. The mean-field formulation of mobile macro-ions in an electrolyte bathing solution is extended to include interactions betw

Benjamin Hettwer, Tobias Horn, Stefan Gehrer, Tim Güneysu

Side-Channel Attacks (SCAs) are a powerful method to attack implementations of cryptographic algorithms. State-of-the-art techniques such as template attacks and stochastic models usually require a lot of manual preprocessing and feature extraction by the attacker. Deep Learning (DL) methods have been introduced to simplify SCAs and simultaneously lowering t

Taiki Matsushita, Yuki Nagai, Satoshi Fujimoto

We investigate disorder effects on Landau levels in Dirac electron systems with the use of a non-Hermitian quasiparticle Hamiltonian formalism. This formalism reveals that spin-dependent scattering rates induce the spectrum collapse of Landau levels, i.e., the disappearance of the energy gaps between n-th and -n-th levels under a finite external magnetic fie

Gauri Datar, Geeta Vichare, Selvaraj Chelliah

Cosmic rays (CRs) have been studied extensively in the last century to understand the processes in the universe as well as in the solar system. In today's satellite era, although many observations are made from space, CR observations from the ground are still viewed as an important tool. These observations, however, mostly detect the secondary cosmic rays (S

Yu Gu, Xiang Yin, Yonghui Rao, Yuan Wan

This paper presents ByteSing, a Chinese singing voice synthesis (SVS) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders. Different from the conventional SVS models, the proposed ByteSing employs Tacotron-like encoder-decoder structures as the acoustic models, in which the CBHG models and recurrent neural networks (R

Priyadarshini Kapri, Bashab Dey, Tarun Kanti Ghosh

We consider a lateral hetero-junction where the left and right leads are made of monolayer graphene and the middle region is made of a gapped tilted Dirac material (borophene or quinoid graphene) illuminated with off-resonant circularly polarized radiation. The tilt parameter $v_t$ makes the band gap indirect and smaller in magnitude as compared to Dirac mat

Ingrid Beltita, Daniel Beltita

We study simply connected Lie groups $G$ for which the hull-kernel topology of the primitive ideal space $\text{Prim}(G)$ of the group $C^*$-algebra $C^*(G)$ is $T_1$, that is, the finite subsets of $\text{Prim}(G)$ are closed. Thus, we prove that $C^*(G)$ is AF-embeddable. To this end, we show that if $G$ is solvable and its action on the centre of $[G, G]$

Marta Victoria, Kun Zhu, Tom Brown, Gorm B. Andresen

For a given carbon budget over several decades, different transformation rates for the energy system yield starkly different results. Here we consider a budget of 33 GtCO2 for the cumulative carbon dioxide emissions from the European electricity, heating, and transport sectors between 2020 and 2050, which represents Europe's contribution to the Paris Agreeme

Ajitesh Srivastava, Viktor K. Prasanna

Accurate forecasts of COVID-19 is central to resource management and building strategies to deal with the epidemic. We propose a heterogeneous infection rate model with human mobility for epidemic modeling, a preliminary version of which we have successfully used during DARPA Grand Challenge 2014. By linearizing the model and using weighted least squares, ou

Yoshiharu Shinnaka, Hideyo Kawakita, Akito Tajitsu

Comet 21P/Giacobini-Zinner is a peculiar comet from the viewpoints of the chemical and physical properties of its dust grains. We conduct optical high-resolution spectroscopic observations of the comet. The intensity ratios of forbidden oxygen lines (at 557.7, 630.0, and 636.4 nm) and ortho-to-para abundance ratios (OPRs) of water cations (H$_2$O$^+$) and am

Dongsung T. Park, Dongkun Kim, Uhjin Kim, Hwanchul Jung

Through a series of transverse magnetic focusing experiments, we show that hot electrons in a two-dimensional electron gas system undergo an ultrafast relaxation when generated by a quantum dot (QD) instead of a quantum point contact (QPC). We find here that QPC hot electrons were well described by the non-interacting Fermi gas model for excitations up to 1.

K. Zioutas, A. Argiriou, H. Fischer, S. Hofmann

The manifestation of the dark Universe begun with unexpected large scale astronomical observations. Here we are investigating the origin of small scale anomalies, like that of the annually observed temperature anomalies in the stratosphere. Unexpectedly, we observe a planetary relationship of the daily stratospheric temperature distribution. Its spectral sha

Nicole Novielli, Fabio Calefato, Filippo Lanubile

Do you like your code? What kind of code makes developers happiest? What makes them angriest? Is it possible to monitor the mood of a large team of coders to determine when and where a codebase needs additional help?

Dorin Popescu

We show that a valuation ring containing its residue field of characteristic $p>0$ is a filtered direct limit of complete intersection ${\bf F}_p$-algebras.

Wenyuan Wang

Quantum Key Distribution (QKD) can provide information-theoretically secure communications and is a strong candidate for the next generation of cryptography. However, in practice, the performance of QKD is limited by "practical imperfections" in realistic sources, channels, and detectors (such as multi-photon components or imperfect encoding from the sources

Filippo M. Miatto, Nicolás Quesada

Parametrized quantum optical circuits are a class of quantum circuits in which the carriers of quantum information are photons and the gates are optical transformations. Classically optimizing these circuits is challenging due to the infinite dimensionality of the photon number vector space that is associated to each optical mode. Truncating the space dimens

Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta, Dorit Merhof

Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications. However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challen

Hiroyuki Nishidome, Kohei Nagai, Kento Uchida, Yota Ichinose

High-harmonic generation (HHG), which is generation of multiple optical harmonic light, is an unconventional nonlinear optical phenomenon beyond perturbation regime. HHG, which was initially observed in gaseous media, has recently been demonstrated in solid state materials. How to control the extreme nonlinear optical phenomena is a challenging subject. Comp

Zengyuan Guo, Zilin Wang, Zhihui Wang, Wanli Ouyang

Regression-based text detection methods have already achieved promising performances with simple network structure and high efficiency. However, they are behind in accuracy comparing with recent segmentation-based text detectors. In this work, we discover that one important reason to this case is that regression-based methods usually utilize a fixed feature

Ajian Liu, Xuan Li, Jun Wan, Sergio Escalera

Face anti-spoofing is critical to prevent face recognition systems from a security breach. The biometrics community has %possessed achieved impressive progress recently due the excellent performance of deep neural networks and the availability of large datasets. Although ethnic bias has been verified to severely affect the performance of face recognition sys

Dominik Barth, Andreas Wenz

We describe the explicit computation of a family of 4-branch-point rational functions of degree 63 with monodromy group PSL(6,2). This, in particular, negatively answers a question by J. K\"onig whether there exists a such a function with rational coefficients. The computed family also gives rise to non-regular degree-126 realizations of Aut(PSL(6,2)) over Q

Sabeeha Naaz, Jyotsna Singh, R. B. Singh

Highly energetic proton/electron beam fixed target experiments extends an opportunity to probe the sub-GeV dark matter and associated interactions. In this work we have explored the sensitivity of DUNE for sub-GeV leptophobic dark matter i.e. this dark matter barely couples with the leptons. Baryon number gauge theory can predicts the existence of a leptopho

Aiqing Zhu, Pengzhan Jin, Yifa Tang

HNets is a class of neural networks on grounds of physical prior for learning Hamiltonian systems. This paper explains the influences of different integrators as hyper-parameters on the HNets through error analysis. If we define the network target as the map with zero empirical loss on arbitrary training data, then the non-symplectic integrators cannot guara

Sangwook Lee

We investigate how ring isomorphisms between mirror closed states of compact toric Fano manifolds come from homological mirror symmetry. A natural closed-open relation on B-side is also discussed.

Ibrahim Tanriover, Wisnu Hadibrata, Koray Aydin

Machine learning methods have found novel application areas in various disciplines as they offer low-computational cost solutions to complex problems. Recently, metasurface design has joined among these applications, and neural networks enabled significant improvements within a short period of time. However, there are still outstanding challenges that needs

Sabuzima Nayak, Ripon Patgiri

6G communication technology is a revolutionary technology that will revolutionize many technologies and applications. Furthermore, it will be truly AI-driven and will carry on intelligent space. Hence, it will enable Internet of Everything (IoE) which will also impact many technologies and applications. 6G communication technology promises high Quality of Se

Yusuke Ozaki, Kazuma Nagao, Ippei Danshita, Kenichi Kasamatsu

We study quantum dynamics of a dark soliton in a one-dimensional Bose gas in an optical lattice within the truncated Wigner approximation. A previous work has revealed that in the absence of quantum fluctuations, dynamical stability of the dark soliton significantly depends on whether its phase kink is located at a lattice site or a link of two neighboring s

Jiaxi Nie, Sam Spiro, Jacques Verstraete

In this paper, we consider an analog of the well-studied extremal problem for triangle-free subgraphs of graphs for uniform hypergraphs. A loose triangle is a hypergraph $T$ consisting of three edges $e,f$ and $g$ such that $|e \cap f| = |f \cap g| = |g \cap e| = 1$ and $e \cap f \cap g = \emptyset$. We prove that if $H$ is an $n$-vertex $r$-uniform hypergra

Tongxing Li, Giuseppe Viglialoro

This work deals with a parabolic chemotaxis model with nonlinear diffusion and nonlocal reaction source. The problem is formulated on the whole space and, depending on a specific interplay between the coefficients associated to such diffusion and reaction, we establish that all given solutions are uniformly bounded in time.

Man-To Hui, Quan-Zhi Ye

We present a study of C/2019 Y4 (ATLAS) using Sloan $gri$ observations from mid-January to early April 2020. During this timespan, the comet brightened with a growth in the effective cross-section of $\left(2.0 \pm 0.1 \right) \times 10^{2}$ m$^{2}$ s$^{-1}$ from the beginning to $\sim$70 d preperihelion in late March 2020, followed by a brightness fade and

Masayuki Yamaguchi, Kazunori Akiyama, Takashi Tsukagoshi, Takayuki Muto

With an emphasis on improving the fidelity even in super-resolution regimes, new imaging techniques have been intensively developed over the last several years, which may provide substantial improvements to the interferometric observation of protoplanetary disks. In this study, sparse modeling (SpM) is applied for the first time to observational data sets ta

Raffaella Lo Nigro, Emanuela Schilirò, Patrick Fiorenza, Fabrizio Roccaforte

Nanolaminated Al2O3/HfO2 thin films as well as single Al2O3 and HfO2 layers have been grown as gate dielectrics by Plasma Enhanced Atomic Layer Deposition (PEALD) technique on silicon carbide (4H-SiC) substrates. All the three dielectric films have been deposited at temperature as low as 250{\deg}C, with a total thickness of about 30 nm and in particular, th

Qingsen Yan, Bo Wang, Dong Gong, Chuan Luo

A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspe

Rain Kipper, Peeter Tenjes, Taavi Tuvikene, Punyakoti Ganeshaiah Veena

We determine the mass of the Milky Way bar and the torque it causes, using Gaia DR2, by applying the orbital arc method. Based on this, we have found that the gravitational acceleration is not directed towards the centre of our Galaxy but a few degrees away from it. We propose that the tangential acceleration component is caused by the bar of the Galaxy. Cal

Jian-Rong Zhang

Motivated by the Belle Collaboration's new observation of $Y(4626)$, we investigate the possibility of its configuration as a $P$-wave $[cs][\bar{c}\bar{s}]$ tetraquark state from QCD sum rules. Eventually, the extracted mass $4.60^{+0.13}_{-0.19}~\mbox{GeV}$ for the $P$-wave $cs$-scalar-diquark $\bar{c}\bar{s}$-scalar-antidiquark state agrees well with the

Manfred Jaeger, Oliver Schulte

A generative probabilistic model for relational data consists of a family of probability distributions for relational structures over domains of different sizes. In most existing statistical relational learning (SRL) frameworks, these models are not projective in the sense that the marginal of the distribution for size-$n$ structures on induced sub-structure

Soichiro Fujii

The purpose of this note is to provide a gentle introduction to basic universal algebra and (abstract) clones.

Lalitesh Kumar, Prawendra Kumar

In this work perturbed LQR design for fractional-order systems with multiple performance indices is proposed.

Jia Cai, Kexin Lv, Junyi Huo, Xiaolin Huang

Sparse canonical correlation analysis (CCA) is a useful statistical tool to detect latent information with sparse structures. However, sparse CCA works only for two datasets, i.e., there are only two views or two distinct objects. To overcome this limitation, in this paper, we propose a sparse generalized canonical correlation analysis (GCCA), which could de

Woo Seok Lee, Sergej Flach

We train an artificial neural network which distinguishes chaotic and regular dynamics of the two-dimensional Chirikov standard map. We use finite length trajectories and compare the performance with traditional numerical methods which need to evaluate the Lyapunov exponent. The neural network has superior performance for short periods with length down to 10

Michael Trefry, Daniel Lester, Guy Metcalfe, Junhong Wu

We extend previous analyses of the origins of complex transport dynamics in poroelastic media to the case where the input transient signal at a boundary is generated by a multimodal spectrum. By adding harmonic and anharmonic modal frequencies as perturbations to a fundamental mode we examine how such multimodal signals affect the Lagrangian complexity of po

Trung B. Nguyen, Will N. Browne, Mengjie Zhang

A major goal of machine learning is to create techniques that abstract away irrelevant information. The generalisation property of standard Learning Classifier System (LCS) removes such information at the feature level but not at the feature interaction level. Code Fragments (CFs), a form of tree-based programs, introduced feature manipulation to discover im

Hao Yan, Marco Grasso, Kamran Paynabar, Bianca Maria Colosimo

The use of video-imaging data for in-line process monitoring applications has become more and more popular in the industry. In this framework, spatio-temporal statistical process monitoring methods are needed to capture the relevant information content and signal possible out-of-control states. Video-imaging data are characterized by a spatio-temporal variab

Jing Liang, Yi-Ling Qiao, Tianrui Guan, Dinesh Manocha

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estima

Isarun Chamveha, Trongtum Tongdee, Pairash Saiviroonporn, Warasinee Chaisangmongkon

Despite much promising research in the area of artificial intelligence for medical image diagnosis, there has been no large-scale validation study done in Thailand to confirm the accuracy and utility of such algorithms when applied to local datasets. Here we present a wide-reaching development and testing of a deep learning algorithm for automated thoracic d

Platon I. Karpov, Davide Martizzi, Phillip Macias, Enrico Ramirez-Ruiz

Supernova (SN) feedback plays a vital role in the evolution of galaxies. While modern cosmological simulations capture the leading structures within galaxies, they struggle to provide sufficient resolution to study small-scale stellar feedback, such as the detailed evolution of SN remnants. It is thus common practice to assume subgrid models that are rarely

Yujie Zhao, Hao Yan, Sarah Holte, Yajun Mei

We propose an efficient statistical method (denoted as SSR-Tensor) to robustly and quickly detect hot-spots that are sparse and temporal-consistent in a spatial-temporal dataset through the tensor decomposition. Our main idea is first to build an SSR model to decompose the tensor data into a Smooth global trend mean, Sparse local hot-spots, and Residuals. Ne

Akio Kawasaki, Alexander Fieguth, Nadav Priel, Charles P. Blakemore

A high sensitivity force sensor based on dielectric microspheres in vacuum, optically trapped by a single, upward-propagating laser beam, is described. Off-axis parabolic mirrors are used both to focus the 1064~nm trapping beam and to recollimate it to provide information on the horizontal position of the microsphere. The vertical degree of freedom is readou

Jiaao Chen, Yuwei Wu, Diyi Yang

We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses. SMDA utilizes recent transformer-based models to encode each sentence and employs back translation techniques to paraphrase given sentences as augmented data. For labeled sentences, we performed data augme

Corey Lammie, Wei Xiang, Bernabé Linares-Barranco, Mostafa Rahimi Azghadi

Memristive devices have shown great promise to facilitate the acceleration and improve the power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using these Resistive Random-Access Memory (RRAM) devices can be used to efficiently implement various in-memory computing operations, such as Multiply Accumulate (MAC) and unrolled-conv

Qi Hong, Yushun Wang, Yuezheng Gong

We present two novel classes of fully discrete energy-preserving algorithms for the sine-Gordon equation subject to Neumann boundary conditions. The cosine pseudo-spectral method is first used to develop structure-preserving spatial discretizations under two different meshes, which result two finite-dimensional Hamiltonian ODE systems. Then we combine the pr

Sepideh Mahabadi, Ilya Razenshteyn, David P. Woodruff, Samson Zhou

Adaptive sampling is a useful algorithmic tool for data summarization problems in the classical centralized setting, where the entire dataset is available to the single processor performing the computation. Adaptive sampling repeatedly selects rows of an underlying matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$, where $n\gg d$, with probabilities proportional

Kaiyan Chang, Wei Jiang, Jinyu Zhan, Zicheng Gong

Integrating idle embedded devices into cloud computing is a promising approach to support distributed machine learning. In this paper, we approach to address the data hiding problem in such distributed machine learning systems. For the purpose of the data encryption in the distributed machine learning systems, we propose the Tripartite Asymmetric Encryption

Rajesh Ranjan, S. M. Deshpande, Roddam Narasimha

Flow past a high-lift low-pressure turbine (LPT) blade in a cascade could be quite complex as phenomena like separation and transition are often involved. For a highly loadedT106A blade at a high incidence and relatively low Reynolds number(25, 000 < Re < 1, 00, 000), separation-induced transition is observed on the suction side of the blade, making it a cha

Zhaoyu Wang, Engui Fan

In this paper, we study the strong asymptotic for the orthogonal polynomials and universality associated with singularly perturbed Pollaczek-Jacobi type weight $$w_{p_J2}(x,t)=e^{-\frac{t}{x(1-x)}}x^\alpha(1-x)^\beta, $$ where $t \ge 0$, $\alpha >0$, $\beta >0$ and $x \in [0,1].$ Our main results obtained here include two aspects: { I. Strong asymptotics:} W

M. Zubair, Hina Azmat

In this paper, we consider well known Tolman V perfect fluid solution and extend it to its an anisotropic version using gravitational decoupling by minimal geometric deformation approach and analyze the behavior of new version of the solution graphically. The effects of coupling constant on anisotropic factor has been measured and presented graphically. The

Tasmia Tasrin, Md Sultan Al Nahian, Brent Harrison

In this work, we propose a deep neural architecture that uses an attention mechanism which utilizes region based image features, the natural language question asked, and semantic knowledge extracted from the regions of an image to produce open-ended answers for questions asked in a visual question answering (VQA) task. The combination of both region based fe

Hui Yang, Junjie Zeng, Yulei Han, Zhenhua Qiao

We theoretically investigate the localization mechanism of quantum anomalous Hall Effect (QAHE) with large Chern numbers $\mathcal{C}$ in bilayer graphene and magnetic topological insulator thin films, by applying either nonmagnetic or spin-flip (magnetic) disorders. We show that, in the presence of nonmagnetic disorders, the QAHEs in both two systems become

Sanjar M. Abrarov, Rehan Siddiqui, Rajinder K. Jagpal, Brendan M. Quine

Lehmer defined a measure $$ \mu=\sum\limits_{j=1}^J\frac{1}{\log_{10}\left(\left|\beta_j\right|\right)}, $$ where the $\beta_j$ may be either integers or rational numbers in a Machin-like formula for $\pi$. When the $\beta_j$ are integers, Lehmer's measure can be used to determine the computational efficiency of the given Machin-like formula for $\pi$. Howev

Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo

Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and re

Yueming Yin, Zhen Yang, Haifeng Hu, Xiaofu Wu

Domain alignment (DA) has been widely used in unsupervised domain adaptation. Many existing DA methods assume that a low source risk, together with the alignment of distributions of source and target, means a low target risk. In this paper, we show that this does not always hold. We thus propose a novel metric-learning-assisted domain adaptation (MLA-DA) met

Gaurav Pande, Jyun-Yan Siao, Wei-Liang Chen, Chien-Ju Lee

To explore the potential of field-effect transistors (FETs) based on monolayers of the two-dimensional semiconducting channel(SC) for spintronics, the two most important issues are to ensure the formation of variable low resistive tunnel ferromagnetic contacts(FC), and to preserve intrinsic properties of the SC during fabrication. Large Schottky barriers lea

James W. Webber, Eric L. Miller

Here we introduce a new forward model and imaging modality for Bragg Scattering Tomography (BST). The model we propose is based on an X-ray portal scanner with linear detector collimation, currently being developed for use in airport baggage screening. The geometry under consideration leads us to a novel two-dimensional inverse problem, where we aim to recon

Qibo Qin, Lin Gui, Bo Gong, Xiang Ren

For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective (DS) channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low spectral efficiency. In this paper, by exploiting temporal correlation of practical wireless channels, we propose a highly efficient structured distr

Noam Shekel, Ori Katz

Lensless flexible fiber-bundle based endoscopes allow imaging at depths beyond the reach of conventional microscopes with a minimal footprint. These multicore fibers provide a simple solution for widefield fluorescent imaging when the target is adjacent to the fiber facet. However, they suffer from a very limited working distance and out-of-focus background.

Jingwei Song, Shaobo Xia, Jun Wang, Mitesh Patel

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fu

Yiwen Sun, Yulu Wang, Kun Fu, Zheng Wang

Traffic forecasting influences various intelligent transportation system (ITS) services and is of great significance for user experience as well as urban traffic control. It is challenging due to the fact that the road network contains complex and time-varying spatial-temporal dependencies. Recently, deep learning based methods have achieved promising result

Zhiling Dun, Xiaojian Bai, Matthew B. Stone, Haidong Zhou

An indispensable step to understand collective magnetic phenomena in rare-earth compounds is the determination of spatially-anisotropic single-ion properties resulting from spin-orbit coupling and crystal field (CF). The CF Hamiltonian has a discrete energy spectrum -- accessible to spectroscopic probes such as neutron scattering -- controlled by a number of

Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong

The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samples, without forgetting the previously l

Xide Xia, Meng Zhang, Tianfan Xue, Zheng Sun

Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain objectionable artifacts. We propose a n

Vignesh Narayanan, Wei Miao, Jr-Shin Li

Many engineered as well as naturally occurring dynamical systems do not have an accurate mathematical model to describe their dynamic behavior. However, in many applications, it is possible to probe the system with external inputs and measure the process variables, resulting in abundant data repositories. Using the time-series data to infer a mathematical mo

Daniel Max Hoffmann, Chieu-Minh Tran, Jinhe Ye

We classify the stable formulas in the theory of Dense Linear Orders without endpoints, the stable formulas in the theory of Divisible Abelian Groups, and the stable formulas without parameters in the theory of Real Closed Fields. The third result, unexpectedly, requires the Hironaka's theorem on resolution of singularities.

Nazatul Haque Sultan, Maryline Laurent, Vijay Varadharajan

For better data availability and accessibility while ensuring data secrecy, organizations often tend to outsource their encrypted data to the cloud storage servers, thus bringing the challenge of keyword search over encrypted data. In this paper, we propose a novel authorized keyword search scheme using Role-Based Encryption (RBE) technique in a cloud enviro