October 2020 arXiv papers — page 8

Marcella Palese, Olga Rossi, Fabrizio Zanello

We compare the integration by parts of contact forms - leading to the definition of the interior Euler operator - with the so-called canonical splittings of variational morphisms. In particular, we discuss the possibility of a generalization of the first method to contact forms of lower degree. We define a suitable Residual operator for this case and, workin

Paolo Dolce, Roberto Gualdi

Let $X$ be an arithmetic variety over the ring of integers of a number field $K$, with smooth generic fiber $X_K$. We give a formula that relates the dimension of the first Arakelov-Chow vector space of $X$ with the Mordell-Weil rank of the Albanese variety of $X_K$ and the rank of the N\'eron-Severi group of $X_K$. This is a higher dimensional and arithmeti

Pascal Maillard, Jason Schweinsberg

We consider one-dimensional branching Brownian motion in which particles are absorbed at the origin. We assume that when a particle branches, the offspring distribution is supercritical, but the particles are given a critical drift towards the origin so that the process eventually goes extinct with probability one. We establish precise asymptotics for the pr

Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry

We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-based geometric constraints while being scalable for processing large scale datasets with multiple views. It is based on an autoencoder architecture with graph convolutional neural ne

Rachid Riad, Hadrien Titeux, Laurie Lemoine, Justine Montillot

Conversations between a clinician and a patient, in natural conditions, are valuable sources of information for medical follow-up. The automatic analysis of these dialogues could help extract new language markers and speed-up the clinicians' reports. Yet, it is not clear which speech processing pipeline is the most performing to detect and identify the speak

S Buchwald, G Ciaramella, Julien Salomon

A novel and detailed convergence analysis is presented for a greedy algorithm that was previously introduced for operator reconstruction problems in the field of quantum mechanics. This algorithm is based on an offline/online decomposition of the reconstruction process and on an ansatz for the unknown operator obtained by an a priori chosen set of linearly i

Yuki Atsusaka, Randolph T. Stevenson

The crosswise model is an increasingly popular survey technique to elicit candid answers from respondents on sensitive questions. Recent studies, however, point out that in the presence of inattentive respondents, the conventional estimator of the prevalence of a sensitive attribute is biased toward 0.5. To remedy this problem, we propose a simple design-bas

Roman Kozlov

The note provides the relation between symmetries and first integrals of It\^{o} stochastic differential equations and symmetries of the associated Kolmogorov backward equation. Relation between the symmetries of the Kolmogorov backward equation and the symmetries of the Kolmogorov forward equation is also given.

Ke Ma, Dongxuan He, Hancun Sun, Zhaocheng Wang

Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and non-stationary scenarios. Inspired by the spatial similarity between low-frequency and mmWave channels in non-standalone archite

Shuangyue Liu, Ji Zhou, Haide Wang, Mengqi Guo

In this paper, an optimized detection based on log-maximum a posteriori estimation with the fixed number of surviving states (fixed-state Log-MAP) is proposed to cooperate with equalizers to deal with the spectral distortions caused by limited bandwidth and chromatic dispersion for intensity-modulation and direct-detection (IM/DD) optical systems. The equali

J. F. Wang, G. Qin

Momentum diffusion of the energetic charged particles is an important mechanism of the transport process in astrophysics, physics of the fusion devices, and laboratory plasmas. In addition to the uniform field momentum diffusion, we obtain the modifying term due to the focusing effect of the large-scale magnetic field. After evaluating the modifying term, we

Ye Ding, Zhenjie Li, Wei Wei, Jie Zhang

The calibration process for the hybrid array pixel detector designed for High Energy Photon Source in China, we called HEPS-BPIX, is presented in this paper. Based on the threshold scanning, the relationship between energy and threshold is quantified for the threshold calibration. For the threshold trimming, the precise algorithm basing on LDAC characteristi

Amrita Ghosh, Eytan Grosfeld

We study the phases of hard-core-bosons on a two-dimensional periodic honeycomb lattice in the presence of an on-site potential with alternating sign along the different y-layers of the lattice. Using quantum Monte Carlo simulations supported by analytical calculations, we identify a weak topological insulator, characterized by a zero Chern number but non-ze

Emil Viñas Boström, Angel Rubio, Claudio Verdozzi

We propose a microscopic mechanism for ultrafast skyrmion photo-excitation via a two-orbital electronic model. In the strong correlation limit the $d$-electrons are described by an effective spin Hamiltonian, coupled to itinerant $s$-electrons via $s-d$ exchange. Laser-exciting the system by a direct coupling to the electric charge leads to skyrmion nucleati

Javier G. Orlandi, Jaume Casademunt

Recent experiments have shown that the spontaneous activity of young dissociated neuronal cultures can be described as a process of highly inhomogeneous nucleation and front propagation due to the localization of noise activity, i.e., noise focusing. However, the basic understanding of the mechanisms of noise build-up leading to the nucleation remain an open

Seongcheol Baek, Hiroyasu Ando, Takashi Hikihara

Power packets are proposed as a transmission unit that can deliver power and information simultaneously. They are transferred using the store-and-forward method of power routers. A system that achieves power supply/demand in this manner is called a power packet network (PPN). A PPN is expected to enhance structural robustness and operational reliability in a

Jangwon Ju, Daejun Kim

In this article, we study the representability of integers as sums of pentagonal numbers, where a pentagonal number is an integer of the form $P_5(x)=\frac{3x^2-x}{2}$ for some non-negative integer $x$. In particular, we prove the "pentagonal theorem of $63$", which states that a sum of pentagonal numbers represents every non-negative integer if and only if

Pushkar Kopparla, Ashwin Seshadri, Takeshi Imamura, Yeon Joo Lee

Sulfur dioxide is a radiatively and chemically important trace gas in the atmosphere of Venus and its abundance at the cloud-tops has been observed to vary on interannual to decadal timescales. This variability is thought to come from changes in the strength of convection which transports sulfur dioxide to the cloud-tops, {although} the dynamics behind such

Elena Dmitrieva, Anastasiya Fedosimova, Igor Lebedev, Abzal Temiraliev

An approach for measuring energy of cosmic-ray particles with energies E > 10^12 eV using an ultrathin calorimeter is presented. The method is based on the analysis of the correlation dependence of the cascade size on the rate of development of the cascade process. In order to determine the primary energy, measurements are made based on the number of seconda

V Ya Aleshkin, G Alymov, A A Dubinov, V I Gavrilenko

The dispersion laws of two-dimensional plasmons in narrow-gap HgTe/CdHgTe quantum wells are calculated taking into account the spatial dispersion of the electron susceptibility. At the energy scale of the band gap the dependence of plasmon frequencies on the wave vector is shown to be close to linear that changes significantly the critical concentration of n

Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang

Panoptic segmentation is posed as a new popular test-bed for the state-of-the-art holistic scene understanding methods with the requirement of simultaneously segmenting both foreground things and background stuff. The state-of-the-art panoptic segmentation network exhibits high structural complexity in different network components, i.e. backbone, proposal-ba

Toktarkhan Komesh, Willem Baan, Jarken Esimbek, Jianjun Zhou

Aims. We investigate the role of selective dissociation in the process of star formation by comparing the physical parameters of protostellar-prestellar cores and the distinct regions with the CO isotope distributions in photodissociation regions. We seek to understand whether there is a better connection between the evolutionary age of star forming regions

Stefan Thalhammer, Markus Leitner, Timothy Patten, Markus Vincze

Object pose estimation enables robots to understand and interact with their environments. Training with synthetic data is necessary in order to adapt to novel situations. Unfortunately, pose estimation under domain shift, i.e., training on synthetic data and testing in the real world, is challenging. Deep learning-based approaches currently perform best when

Alvaro Corral

The size that an epidemic can reach, measured in terms of the number of fatalities, is an extremely relevant quantity. It has been recently claimed [Cirillo & Taleb, Nature Physics 2020] that the size distribution of major epidemics in human history is "extremely fat-tailed", i.e., asymptotically a power law, which has important consequences for risk managem

François Baccelli, Sanket S. Kalamkar

Consider a homogeneous Poisson point process of the Euclidean plane and its Voronoi tessellation. The present note discusses the properties of two stationary point processes associated with the latter and depending on a parameter $\theta$. The first one is the set of points that belong to some one-dimensional facet of the Voronoi tessellation and are such th

Gabriel Hondet, Frédéric Blanqui

Dedukti is a type-checker for the $\lambda$$\Pi$-calculus modulo rewriting, an extension of Edinburgh's logicalframework LF where functions and type symbols can be defined by rewrite rules. It thereforecontains an engine for rewriting LF terms and types according to the rewrite rules given by the user.A key component of this engine is the matching algorithm

Seyoon Ko, Hua Zhou, Jin Zhou, Joong-Ho Won

The demand for high-performance computing (HPC) is ever-increasing for everyday statistical computing purposes. The downside is that we need to write specialized code for each HPC environment. CPU-level parallelization needs to be explicitly coded for effective use of multiple nodes in cluster supercomputing environments. Acceleration via graphics processing

Becky Armstrong, Lisa Orloff Clark, Astrid an Huef, Malcolm Jones

We investigate various groupoids associated to an arbitrary inverse semigroup with zero. We show that the groupoid of filters with respect to the natural partial order is isomorphic to the groupoid of germs arising from the standard action of the inverse semigroup on the space of idempotent filters. We also investigate the restriction of this isomorphism to

Frédéric Blanqui

The expressiveness of dependent type theory can be extended by identifying types modulo some additional computation rules. But, for preserving the decidability of type-checking or the logical consistency of the system, one must make sure that those user-defined rewriting rules preserve typing. In this paper, we give a new method to check that property using

S. Yu. Gus'kov, P. A. Kuchugov, R. A. Yakhin, N. V. Zmitrenko

The results of numerical and theoretical studies of the gain of direct-drive inertial confinement fusion (ICF) target, which includes a kinetic description of energy transfer by laser-accelerated fast electrons, are presented. The range of initial temperature of fast electrons and fraction of laser energy contained in these particles were chosen based on the

Rina Tazai, Youichi Yamakawa, Hiroshi Kontani

Spontaneous current orders due to odd-parity order parameters attract increasing attention in various strongly correlated metals. Here, we discover a novel spin-fluctuation-driven charge loop current (cLC) mechanism based on the functional renormalization group (fRG) theory. The present mechanism leads to the ferro-cLC order in a simple frustrated chain Hubb

Ahmed Bensaoud, Nawaf Abudawaood, Jugal Kalita

Due to increasing threats from malicious software (malware) in both number and complexity, researchers have developed approaches to automatic detection and classification of malware, instead of analyzing methods for malware files manually in a time-consuming effort. At the same time, malware authors have developed techniques to evade signature-based detectio

Erik Teichmann

Partial synchronous states appear between full synchrony and asynchrony and exhibit many interesting properties. Most frequently, these states are studied within the framework of phase approximation. The latter is used ubiquitously to analyze coupled oscillatory systems. Typically, the phase dynamics description is obtained in the weak coupling limit, i.e.,

Justin Kottinger, Shaull Almagor, Morteza Lahijanian

Traditional multi-robot motion planning (MMP) focuses on computing trajectories for multiple robots acting in an environment, such that the robots do not collide when the trajectories are taken simultaneously. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. In this work, we propose a notion of ex

Chaoyi Chen, Jiawei Wang, Qing Xu, Jianqiang Wang

The emergence of Connected and Automated Vehicles (CAVs) promises better traffic mobility for future transportation systems. Existing research mostly focused on fully-autonomous scenarios, while the potential of CAV control at a mixed traffic intersection where human-driven vehicles (HDVs) also exist has been less explored. This paper proposes a notion of "1

Yongwei Wang, Mingquan Feng, Rabab Ward, Z. Jane Wang

Deep neural networks are vulnerable to adversarial attacks. White-box adversarial attacks can fool neural networks with small adversarial perturbations, especially for large size images. However, keeping successful adversarial perturbations imperceptible is especially challenging for transfer-based black-box adversarial attacks. Often such adversarial exampl

Lia Gander, Rolf Krause, Michael Multerer, Simone Pezzuto

In electrocardiography, the "classic" inverse problem is the reconstruction of electric potentials at a surface enclosing the heart from remote recordings at the body surface and an accurate description of the anatomy. The latter being affected by noise and obtained with limited resolution due to clinical constraints, a possibly large uncertainty may be perp

Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang

In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We know that GNN is designed to learn single-node representations. When we want to learn a node set representation involving multiple nodes, a com

Jianmin Shi

In this paper we set up an optimal control framework for a hybrid stochastic system with dual or multiple Markov switching diffusion processes, while Markov chains governing these switching diffusions are not identical as assumed by the existing literature. As an application and illustration of this model, we solve a portfolio choice problem for an investor

Motoki Osada, Bai Yang Wang, Kyuho Lee, Danfeng Li

We report the phase diagram of infinite layer Pr$_{1-x}$Sr$_{x}$NiO$_2$ thin films synthesized via topotactic reduction from the perovskite precursor phase using CaH$_2$. Based on the electrical transport properties, we find a doping-dependent superconducting dome extending between $x$ = 0.12 and 0.28, with a maximum superconducting transition temperature $T

Michal Yemini, Elza Erkip, Andrea J. Goldsmith

Virtual cell optimization clusters cells into neighborhoods and performs optimized resource allocation over each neighborhood. In prior works we proposed resource allocation schemes to mitigate the interference caused by transmissions in the same virtual cell. This work aims at mitigating both the interference caused by the transmissions of users in the same

Alvason Zhenhua Li, Karsten Eichholz, Anton Sholukh, Daniel Stone

Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous detection of molecular properties of cells, is an essential tool for characterizing the complex cellular mechanisms in translational research and clinical practice. New image analysis approaches are needed because tissue section stained with a mixture of protein, DNA and RNA biomarkers are

Yuxin Li, Wenhui Yang, Rongzhi Dong, Jianjun Hu

Lattice constants such as unit cell edge lengths and plane angles are important parameters of the periodic structures of crystal materials. Predicting crystal lattice constants has wide applications in crystal structure prediction and materials property prediction. Previous work has used machine learning models such as neural networks and support vector mach

Connor Fredrick, Freja Olsen, Ryan Terrien, Suvrath Mahadevan

We perform heterodyne spectroscopy at 1.56 micron with a tunable laser and thermal radiation from the Sun. The laser tuning is calibrated with a frequency comb, providing a simple spectrometer with absolute frequency tracebility and resolving power of 2,000,000

Kamiokande Collaboration, A. Takenaka, K. Abe, C. Bronner

We have searched for proton decay via $p\to e^+\pi^0$ and $p\to \mu^+\pi^0$ modes with the enlarged fiducial volume data of Super-Kamiokande from April 1996 to May 2018, which corresponds to 450 kton$\cdot$years exposure. We have accumulated about 25% more livetime and enlarged the fiducial volume of the Super-Kamiokande detector from 22.5 kton to 27.2 kton

Haonan Li, Maria Vasardani, Martin Tomko, Timothy Baldwin

Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on BERT, without dependencies on taggers, parsers, curated dictionaries of place names, or other external resources. We show that

Devilal Dahal, Hiroka Warren, Parthapratim Biswas

The structure of the first sharp diffraction peak (FSDP) of amorphous silicon (${\it a}$-Si) near 2 Angstrom$^{-1}$ is addressed with particular emphasis on the position, intensity, and width of the diffraction curve. By studying a number of continuous random network (CRN) models of ${\it a}$-Si, it is shown that the position and the intensity of the FSDP ar

Mrigank Raman, Ojal Kumar, Arpan Chattopadhyay

In this paper, selection of an active sensor subset for tracking a discrete time, finite state Markov chain having an unknown transition probability matrix (TPM) is considered. A total of N sensors are available for making observations of the Markov chain, out of which a subset of sensors are activated each time in order to perform reliable estimation of the

Andrew Zhao, Nicholas C. Rubin, Akimasa Miyake

We propose a tomographic protocol for estimating any $ k $-body reduced density matrix ($ k $-RDM) of an $ n $-mode fermionic state, a ubiquitous step in near-term quantum algorithms for simulating many-body physics, chemistry, and materials. Our approach extends the framework of classical shadows, a randomized approach to learning a collection of quantum-st

Carlo Verschoor

In this paper we are interested in extending Bailey's identity to other classical hypergeometric functions. Bailey's identity states that under a suitable choice of parameters, Appell's $F_4$ decomposes into a product of two ${}_2F_1$'s. We will show how Bailey-type factorizations can be found for Horn's hypergeometric functions $H_1, H_4$ and $H_5$.

Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user e

Yanqiao Zhu, Weizhi Xu, Qiang Liu, Shu Wu

This paper studies active learning (AL) on graphs, whose purpose is to discover the most informative nodes to maximize the performance of graph neural networks (GNNs). Previously, most graph AL methods focus on learning node representations from a carefully selected labeled dataset with large amount of unlabeled data neglected. Motivated by the success of co

Moon-Jin Kang, Alexis Vasseur

We consider the Riemann problem composed of two shocks for the 1D Euler system. We show that the Riemann solution with two shocks is stable and unique in the class of weak inviscid limits of solutions to the Navier-Stokes equations with initial data with bounded energy. This work extends to the case of two shocks a previous result of the authors in the case

Dan Barbasch, Jia-Jun Ma, Binyong Sun, Chen-Bo Zhu

In analogy with the Barbasch-Vogan duality for real reductive linear groups, we introduce a duality notion useful for the representation theory of the real metaplectic groups. This is a map on the set of nilpotent orbits in a complex symplectic Lie algebra, whose range consists of the so-called metaplectic special nilpotent orbits. We relate this duality not

Tian Li, Xiang Chen, Shanghang Zhang, Zhen Dong

Contrastive learning (CL) has been successful as a powerful representation learning method. In this work we propose CLIM: Contrastive Learning with mutual Information Maximization, to explore the potential of CL on cross-domain sentiment classification. To the best of our knowledge, CLIM is the first to adopt contrastive learning for natural language process

Kazuki Nakamura, Ryosuke Kojima, Eiichiro Uchino, Koichi Murashita

Clinical decision making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. However, the remaining prominent issue is the development of objective treatment processes in clinical situations. This

Srijit Bhattacharjee, Shailesh Kumar, Arpan Bhattacharyya

We study the displacement memory effect and its connection with the extended-BMS symmetries near the horizon of black holes. We show there is a permanent shift in the geodesic deviation vector relating two nearby timelike geodesics placed close to the horizon of black holes, upon the passage of gravitational waves. We also relate this memory effect with the

Jian He, Panyue Zhou

Herschend-Liu-Nakaoka introduced the notion of $n$-exangulated categories. It is not only a higher dimensional analogue of extriangulated categories defined by Nakaoka-Palu, but also gives a simultaneous generalization of $(n+2)$-angulated in the sense of Geiss-Keller-Oppermann and $n$-exact categories in the sense of Jasso. In this article, we show that an

Tejas Zodage, Rahul Chakwate, Vinit Sarode, Rangaprasad Arun Srivatsan

Point-cloud registration (PCR) is an important task in various applications such as robotic manipulation, augmented and virtual reality, SLAM, etc. PCR is an optimization problem involving minimization over two different types of interdependent variables: transformation parameters and point-to-point correspondences. Recent developments in deep-learning have

Ye Zhang

This paper examines discrimination by early-stage investors based on startup founders' gender and race using two complementary field experiments with real U.S. venture capitalists. Results show the following. (i) Discrimination varies depending on the context. Investors implicitly discriminate against female and Asian founders when evaluating attractive star

Xiucai Ding, Hong Chang Ji

Consider the random matrix model $A^{1/2} UBU^* A^{1/2},$ where $A$ and $B$ are two $N \times N$ deterministic matrices and $U$ is either an $N \times N$ Haar unitary or orthogonal random matrix. It is well-known that on the macroscopic scale, the limiting empirical spectral distribution (ESD) of the above model is given by the free multiplicative convolutio

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu

The discovery of ferroelectricity in HfO$_2$-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density non-volatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at fini

Xinlu Xu, Fei Li, Frank S. Tsung, Kyle Miller

We show using particle-in-cell (PIC) simulations and theoretical analysis that a high-quality electron beam whose density is modulated at angstrom scales can be generated directly using density downramp injection in a periodically modulated density in nonlinear plasma wave wakefields. The density modulation turns on and off the injection of electrons at the

Hidetoshi Taya, Toshiaki Fujimori, Tatsuhiro Misumi, Muneto Nitta

We study the vacuum pair production by a time-dependent strong electric field based on the exact WKB analysis. We identify the generic structure of a Stokes graph for systems with the vacuum pair production and show that the number of produced pairs is given by a product of connection matrices for Stokes segments connecting pairs of turning points. We derive

Toshihiro Kasuga, David Jewitt

This is an overview of recent research on meteors and the parent bodies from which they are produced. While many meteor showers result from material ejected by comets, two out of the three strongest annual showers (the Geminids and the Quadrantids) are associated with objects whose physical properties are apparently those of asteroids. In the last decades dy

Aradhya Neeraj Mathur, Devansh Batra, Yaman Kumar, Rajiv Ratn Shah

In this work, we explore a new problem of frame interpolation for speech videos. Such content today forms the major form of online communication. We try to solve this problem by using several deep learning video generation algorithms to generate the missing frames. We also provide examples where computer vision models despite showing high performance on conv

Sourav Pal

We find characterization for the distinguished varieties in the symmetrized polydisc $\mathbb G_n \; (n\geq 2)$ and thus generalize the work [\textit{J. Funct. Anal.}, 266 (2014), 5779 -- 5800] on $\mathbb G_2$ by the author and Shalit. We show that a distinguished variety $\Lambda$ in $\mathbb G_n$ is a part of an algebraic curve, which is a set-theoretic c

Xing Huang, Datao Gong, Suen Hou, Guangming Huang

We present the design and test results of a Drivers and Limiting AmplifierS ASIC operating at 10 Gbps (DLAS10) and three Miniature Optical Transmitter/Receiver/Transceiver modules (MTx+, MRx+, and MTRx+) based on DLAS10. DLAS10 can drive two Transmitter Optical Sub-Assemblies (TOSAs) of Vertical Cavity Surface Emitting Lasers (VCSELs), receive the signals fr

Juan Li, Ruoxu Wang, Ningyu Zhang, Wen Zhang

Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unseen relations at test time, we explore the problem of zero-shot relation classification. Previous work regards the problem as reading comprehe

Jong Heum Jung, Hyung Jin Kim, Y. Shin

We study the spatial distributions of the spin and mass currents generated by a moving Gaussian magnetic obstacle in a symmetric, two-component Bose-Einstein condensate in two dimensions. We analytically describe the current distributions for a slow obstacle and show that the spin and the mass currents exhibit characteristic spatial structures resembling tho

Li Zhang, Datao Gong, Suen Hou, Guanming Huang

We present the design principle and test results of a data transmitting ASIC, GBS20, for particle physics experiments. The goal of GBS20 will be an ASIC that employs two serializers each from the 10.24 Gbps lpGBT SerDes, sharing the PLL also from lpGBT. A PAM4 encoder plus a VCSEL driver will be implemented in the same die to use the same clock system, elimi

Cuiyun Gao, Jichuan Zeng, Federica Sarro, David Lo

Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and ba

Teng Zhao, Shao-ping Wu, Guo-qing Yang, Guang-ming Huang

The ultrastrongly coupling (USC) system has very important research significance in quantum simulation and quantum computing. In this paper, the ultranarrow spectrum of a circuit QED system with two qubits ultrastrongly coupled to a single-mode cavity is studied. In the regime of USC, the JC model breaks down and the counter-rotating terms in the quantum Rab

Haiyang Yu, Ningyu Zhang, Shumin Deng, Hongbin Ye

Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood. Unlike prev

Anton V. Eremeev, Anton A. Malakhov, Maxim A. Sakhno, Maria Y. Sosnovskaya

The paper considers the problem of scheduling software modules on a multi-core processor, taking into account the limited bandwidth of the data bus and the precedence constraints. Two problem formulations with different levels of problem-specific detail are suggested and both shown to be NP-hard. A mixed integer linear programming (MILP) model is proposed fo

E. A. Carrillo, J. Flores, G. Monsivais

Doorway states, which are related to the strength function phenomenon and giant resonances, arise when two systems interact, one with a high density eigenvalue spectrum and the other with a comparatively low density. These concepts, first studied in nuclear physics in the 40's, are here analyzed from a theoretical point of view in special and simple graphene

Zhihong Chen, Yan Song, Tsung-Hui Chang, Xiang Wan

Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment. Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists. Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is a

Jinyu Zhao, Yi Hao, Cyrus Rashtchian

Deep embedding methods have influenced many areas of unsupervised learning. However, the best methods for learning hierarchical structure use non-Euclidean representations, whereas Euclidean geometry underlies the theory behind many hierarchical clustering algorithms. To bridge the gap between these two areas, we consider learning a non-linear embedding of d

Le Xuan Dung

Bounds on the Castelnuovo-Mumford regularity of the associated graded modules of k-Buchsbaum modules M are given in terms of k and some other invariants of M.

Panagiotis P. Filntisis, Athanasia Zlatintsi, Niki Efthymiou, Emmanouil Kalisperakis

Digital phenotyping is a nascent multidisciplinary field that has the potential to revolutionize psychiatry and its clinical practice. In this paper, we present a rigorous statistical analysis of short-time features extracted from wearable data, during long-term continuous monitoring of patients with psychotic disorders and healthy control counterparts. Our

Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu

Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However, much of this work only relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages, which is loose and implicit for aligning the contextua

Fabrício Ceschin, Marcus Botacin, Albert Bifet, Bernhard Pfahringer

Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas. One of these challenges is the concept drift, which increases

Chen Liu, Deep Ray, Christopher Thiele, Lu Lin

In this paper, we present an efficient numerical algorithm for solving the time-dependent Cahn--Hilliard--Navier--Stokes equations that model the flow of two phases with different densities. The pressure-correction step in the projection method consists of a Poisson problem with a modified right-hand side. Spatial discretization is based on discontinuous Gal

Shahin Heidarian, Parnian Afshar, Arash Mohammadi, Moezedin Javad Rafiee

The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the spread of the disease and flattening the epidemic curve. Ches

Ricardo Faleiro, Nikola Paunkovic, Marko Vojinovic

This work overviews the single-particle two-way communication protocol recently introduced by del Santo and Daki\'c (dSD), and analyses it using the process matrix formalism. We give a detailed account of the importance and the operational meaning of the interaction of an agent with the vacuum -- in particular its role in the process matrix description. Our

Shahin Heidarian, Parnian Afshar, Nastaran Enshaei, Farnoosh Naderkhani

The newly discovered Corona virus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. Computed tomography (CT) scans have shown distinctive features and higher sensitivity compared to other diagnostic tests, in particular the current gold standard, i.e., the

Shufeng Kong, Junwen Bai, Jae Hee Lee, Di Chen

A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large numbe

Keval Doshi, Yasin Yilmaz

Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors. With the recent advances in technology, especially in computer vision, it is now possible to detect and categorize different types of road damages, which can facilitate efficient maintenance and resource management

Zhi Qiao, Austin Bae, Lucas M. Glass, Cao Xiao

To test the possibility of differentiating chest x-ray images of COVID-19 against other pneumonia and healthy patients using deep neural networks. We construct the X-ray imaging data from two publicly available sources, which include 5508 chest x-ray images across 2874 patients with four classes: normal, bacterial pneumonia, non-COVID-19 viral pneumonia, and

Monisankha Pal, Arindam Jati, Raghuveer Peri, Chin-Cheng Hsu

Deep neural network based speaker recognition systems can easily be deceived by an adversary using minuscule imperceptible perturbations to the input speech samples. These adversarial attacks pose serious security threats to the speaker recognition systems that use speech biometric. To address this concern, in this work, we propose a new defense mechanism ba

Mohamed Trabelsi, Jin Cao, Jeff Heflin

Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking. For example, automatically extracted tables with missing headers can be filled by the predicted schema labels which significantly minimizes human effort. Furthermore, the predicted labels can redu

H. D. Yin, X. X. Li, G. C. Wang, X. Q. Shao

Compared with the idea of universal quantum computation, a direct synthesis of a multiqubit logic gate can greatly improve the efficiency of quantum information processing tasks. Here we propose an efficient scheme to implement a three-qubit controlled-not (Toffoli) gate of neutral atoms based on unconventional Rydberg pumping. By adjusting the strengths of

Deokhwan Han, Namyoon Lee

Cell densification is a key driver to increase area spectral efficiencies in multi-antenna cellular systems. As increasing the densities of base stations (BSs) and users that share the same spectrum, however, both inter-user-interference (IUI) and inter-cell interference (ICI) problems give rise to a significant loss in spectral efficiencies in such systems.

Wonhyeok Jang, Hao Huang, Katherine R. Davis, Thomas J. Overbye

Power system restoration is a highly complex task that must be performed in a timely manner following a blackout. It is crucial to have the capability of developing a reliable restoration plan that can be adjusted quickly to different system conditions. This paper introduces a framework of an automated process that creates a restoration plan for a given powe

Xiangyu Wang, Ting Yang, Yu Wang

Blockchain is an incrementally updated ledger maintained by distributed nodes rather than centralized organizations. The current blockchain technology faces scalability issues, which include two aspects: low transaction throughput and high storage capacity costs. This paper studies the blockchain structure based on state sharding technology, and mainly solve

Hao Huang, Varuneswara Panyam, Mohammad Rasoul Narimani, Astrid Layton

Power systems are susceptible to natural threats including hurricanes and floods. Modern power grids are also increasingly threatened by cyber attacks. Existing approaches that help improve power system security and resilience may not be sufficient; this is evidenced by the continued challenge to supply energy to all customers during severe events. This pape

Hideyuki Umeda, Takashi Yoshida, Chris Nagele, Koh Takahashi

Since the discovery of GW190521, several proposals have been put forward to explain the formation of a black hole in the mass gap caused by (pulsational) pair-instability, $M = 65-130 M_\odot$. We calculate the mass ejection of Pop III stars by the pulsational pair-instability (PPI) process using a stellar evolution and hydrodynamical code. If a relatively s

Wei Li, Yuanjun Xiong, Shuo Yang, Siqi Deng

We present single-shot multi-object tracker (SMOT), a new tracking framework that converts any single-shot detector (SSD) model into an online multiple object tracker, which emphasizes simultaneously detecting and tracking of the object paths. Contrary to the existing tracking by detection approaches which suffer from errors made by the object detectors, SMO

Minz Won, Sergio Oramas, Oriol Nieto, Fabien Gouyon

Tag-based music retrieval is crucial to browse large-scale music libraries efficiently. Hence, automatic music tagging has been actively explored, mostly as a classification task, which has an inherent limitation: a fixed vocabulary. On the other hand, metric learning enables flexible vocabularies by using pretrained word embeddings as side information. Also

Shen-Shi Du, Lin Lan, Jun-Jie Wei, Zi-Ming Zhou

The spectral lags of gamma-ray bursts (GRBs) have been viewed as the most promising probes of the possible violations of Lorentz invariance (LIV). However, these constraints usually depend on the assumption of the unknown intrinsic time lag in different energy bands and the use of a single highest-energy photon. A new approach to test the LIV effects has bee