December 2020 arXiv papers — page 147

Shuyu Zheng, Haoyu Wang, Lei Wu, Gang Huang

WebAssemly is an emerging runtime for Web applications and has been supported in almost all browsers. Recently, WebAssembly is further regarded to be a the next-generation environment for blockchain applications, and has been adopted by Ethereum, namely eWASM, to replace the state-of-the-art EVM. However, whether and how well current eWASM outperforms EVM on

Philipp Terhörst, Daniel Fährmann, Jan Niklas Kolf, Naser Damer

Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threatens the user's privacy, or are valuable for commercial aspects. Current face databases are specifically constructed for the development of face recognition applications. Consequently, these databases contain large amount of fa

Wenchao Gu, Zongjie Li, Cuiyun Gao, Chaozheng Wang

Code retrieval is a common practice for programmers to reuse existing code snippets in open-source repositories. Given a user query (i.e., a natural language description), code retrieval aims at searching for the most relevant ones from a set of code snippets. The main challenge of effective code retrieval lies in mitigating the semantic gap between natural

Inácio Bó, Rustamdjan Hakimov

We introduce a new family of mechanisms for one-sided matching markets, denoted pick-an-object (PAO) mechanisms. When implementing an allocation rule via PAO, agents are asked to pick an object from individualized menus. These choices may be rejected later on, and these agents are presented with new menus. When the procedure ends, agents are assigned the las

Ritwik Gupta, Eric T. Heim

These are the "proceedings" of the 1st AI + HADR workshop which was held in Vancouver, Canada on December 13, 2019 as part of the Neural Information Processing Systems conference. These are non-archival and serve solely as a collation of all the papers accepted to the workshop.

Sourav Bhattacharjee, Souvik Bandyopadhyay, Diptiman Sen, Amit Dutta

We present a complete topological characterization of a bilayer composite of two Chern insulators (specifically, Haldane models) and explicitly establish the bulk-boundary correspondences. We show that an appropriately defined Chern number accurately maps out all the possible phases of the system and remains well-defined even in the presence of degeneracies

Qiang Zhang, Zhenwei Xie, Luping Du, Peng Shi

Magnetic skyrmions are topological quasiparticles in magnetic field. Until recently, as one of their photonic counterparts, N\'eel-type photonic skyrmion is discovered in surface plasmon polaritons. The deep-subwavelength features of the photonic skyrmions suggest their potentials in quantum technologies and data storage. So far, the Bloch-type photonic skyr

Julie Gerlings, Arisa Shollo, Ioanna Constantiou

The diffusion of artificial intelligence (AI) applications in organizations and society has fueled research on explaining AI decisions. The explainable AI (xAI) field is rapidly expanding with numerous ways of extracting information and visualizing the output of AI technologies (e.g. deep neural networks). Yet, we have a limited understanding of how xAI rese

X. L. Zhao, M. Li, T. H. Qiu, L. B. Chen

We focus on the dynamical feature for a $p$-wave superconductor model in different parameter regions in terms of the appearance of gapless edge modes in reel geometry. Firstly, we show the parameter region with gapless edge modes versus a parameter and quasi-momentum. The parameter diagram can be reflected by the expectations of Pauli matrices in global mann

Mustafa Oğuz Afacan, Inácio Bó

We consider the allocation of indivisible objects when agents have preferences over their own allocations, but share the ownership of the resources to be distributed. Examples might include seats in public schools, faculty offices, and time slots in public tennis courts. Given an allocation, groups of agents who would prefer an alternative allocation might c

Alexander Zlotnik, Raimondas Čiegis

We study necessary conditions for stability of a Numerov-type compact higher-order finite-difference scheme for the 1D homogeneous wave equation in the case of non-uniform spatial meshes. We first show that the uniform in time stability cannot be valid in any spatial norm provided that the complex eigenvalues appear in the associated mesh eigenvalue problem.

J. Emmanuel Johnson, Sairam Sundaresan, Tansu Daylan, Lisseth Gavilan

Magnetic activity in stars manifests as dark spots on their surfaces that modulate the brightness observed by telescopes. These light curves contain important information on stellar rotation. However, the accurate estimation of rotation periods is computationally expensive due to scarce ground truth information, noisy data, and large parameter spaces that le

M. Ilyas

The present work is to introduce a new kind of modified gravitational theory, named as $f(\mathcal{R,G,T})$ (also $f(\mathcal{R,T,G})$) gravity, where $\mathcal{R}$ is the Ricci scalar, $\mathcal{G}$ is Gauss-Bonnet invariant and $\mathcal{T}$ is the trace of the energy-momentum tensor. With the help of different models in this gravity, we investigate some p

Jinfeng Wen, Yi Liu, Zhenpeng Chen, Junkai Chen

Serverless computing has become a new trending paradigm in cloud computing, allowing developers to focus on the development of core application logic and rapidly construct the prototype via the composition of independent functions. With the development and prosperity of serverless computing, major cloud vendors have successively rolled out their commodity se

Natalia Nolde, Jennifer L. Wadsworth

The study of multivariate extremes is dominated by multivariate regular variation, although it is well known that this approach does not provide adequate distinction between random vectors whose components are not always simultaneously large. Various alternative dependence measures and representations have been proposed, with the most well-known being hidden

Yi Wei, Ziyi Wang, Yongming Rao, Jiwen Lu

In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle t

Natsumi Makigusa

In this article, we introduce a novel discrepancy called the maximum variance discrepancy for the purpose of measuring the difference between two distributions in Hilbert spaces that cannot be found via the maximum mean discrepancy. We also propose a two-sample goodness of fit test based on this discrepancy. We obtain the asymptotic null distribution of this

Jonathan Jedwab, Shuxing Li

A packing of partial difference sets is a collection of disjoint partial difference sets in a finite group $G$. This configuration has received considerable attention in design theory, finite geometry, coding theory, and graph theory over many years, although often only implicitly. We consider packings of certain Latin square type partial difference sets in

G. A. Grigorian

In this paper we use the Riccati equation method with other ones to establish global solvability, stability and oscillation criteria for a class of two dimensional nonlinear systems of ordinary differential equations, which is a generalization of wide classes of second order nonlinear ordinary differential equations, studied by many authors. The applicabilit

Punyashloka Debashis, Aman K. Maskay, Pramey Upadhyaya, Zhihong Chen

Stochastic oscillators based on emerging nanodevices are attractive because of their ultra-low power requirement and ability to exhibit stochastic resonance, a phenomenon where synchronization to weak input signals is enabled due to ambient noise. In this work, a low barrier nanomagnet based stochastic oscillator is demonstrated, whose output jumps spontaneo

Han Aung, Daisuke Nagai, Erwin T. Lau

Recent advances in simulations and observations of galaxy clusters suggest that there exists a physical outer boundary of massive cluster-size dark matter haloes. In this work, we investigate the locations of the outer boundaries of dark matter and gas around cluster-size dark matter haloes, by analyzing a sample of 65 massive dark matter halos extracted fro

Gaurav Bhardwaj, Utkarsh A. Mishra, N. Sukavanam, R. Balasubramanian

In this paper, a novel optimal technique for joint angles trajectory tracking control with energy optimization for a biped robot with toe foot is proposed. For the task of climbing stairs by a 9-link biped model, a cycloid trajectory for swing phase is proposed in such a way that the cycloid variables depend on the staircase dimensions. Zero Moment Point(ZMP

Tomoyuki Ichiba, Guodong Pang, Murad S. Taqqu

We study the semimartingale properties for the generalized fractional Brownian motion (GFBM) introduced by Pang and Taqqu (2019) and discuss the applications of the GFBM and its mixtures to financial asset pricing. The GFBM is self-similar and has non-stationary increments, whose Hurst index $H \in (0,1)$ is determined by two parameters. We identify the regi

Kevin Lybarger, Mari Ostendorf, Matthew Thompson, Meliha Yetisgen

Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for

Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang

A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to refine the estimated pose in a coarse-to-fine approach hierarchically. An attentive cost volume i

Kang Gao, Bertrand Hochwald

We analyze phase transitions in the conditional entropy of a sequence caused by a change in the conditional variables. Such transitions happen, for example, when training to learn the parameters of a system, since the transition from the training phase to the data phase causes a discontinuous jump in the conditional entropy of the measured system response. F

Kang Gao, Xiangbo Meng, J. Nicholas Laneman, Jonathan Chisum

We show that a quantized large-scale system with unknown parameters and training signals can be analyzed by examining an equivalent system with known parameters by modifying the signal power and noise variance in a prescribed manner. Applications to training in wireless communications and signal processing are shown. In wireless communications, we show that

Kürşat Tekbıyık, Güneş Karabulut Kurt, Ali Rıza Ekti, Halim Yanikomeroglu

Next-generation communication technology will be made possible by cooperation between terrestrial networks with non-terrestrial networks (NTN) comprised of high-altitude platform stations and satellites. Further, as humanity embarks on the long road to establish new habitats on other planets, cooperation between NTN and deep-space networks (DSN) will be nece

Ambra Lattanzi, Giuseppe Dattoli, Giuseppe Baldacchini

The photoluminescence (PL) of thermally evaporated Alq3 thin films has been studied in a few samples annealed and non-annealed and afterwards exposed to the laboratory atmosphere for over six years. It was found that the measured emission intensity decays with a long lifespan and with four different time-spectral behaviors, which imply the existence of four

Yu-Ting Wei, Meng-Yun Guan, Jin-Chang Liu, Ze-Yuan Yu

We propose to measure the coherent elastic neutrino nucleus scattering (CE$ν$NS) using a dual-phase liquid argon time projection chamber (TPC) with 200kg fiducial mass. The detector is expected to be adjacent to the JUNO-TAO experiment and to be about 35m from a reactor core with 4.6GW thermal power at Taishan. The antineutrino flux is approximately 6$\times

Ihar Lobach, Sergei Nagaitsev, Valeri Lebedev, Aleksandr Romanov

Generally, turn-to-turn fluctuations of synchrotron radiation power in a storage ring depend on the 6D phase-space distribution of the electron bunch. This effect is related to the interference of fields radiated by different electrons. Changes in the relative electron positions and velocities inside the bunch result in fluctuations in the total emitted ener

Matthew J. Dolan, Ayodele Ore

Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety. We develop a varian

Xiao-Feng Shi, Dong-Jun Ma, Song-lin Dang, Zong-Qiang Ma

The photon Doppler velocimetry (PDV) spectrum is investigated in an attempt to reveal the particle parameters of ejecta from shock-loaded samples in a vacuum. A GPU-accelerated Monte-Carlo algorithm, which considers the multiple-scattering effects of light, is applied to reconstruct the light field of the ejecta and simulate the corresponding PDV spectrum. T

Wanchun Liu, Daniel E. Quevedo, Yonghui Li, Branka Vucetic

We investigate a novel anytime control algorithm for wireless networked control with random dropouts. The controller computes sequences of tentative future control commands using time-varying (Markovian) computational resources. The sensor-controller and controller-actuator channel states are spatial- and time-correlated, and are modeled as a multi-state Mar

Hong-Hsi Lee, Els Fieremans, Dmitry S Novikov

Background: Monte Carlo simulations of diffusion are commonly used as a model validation tool as they are especially suitable for generating the diffusion MRI signal in complicated tissue microgeometries. New method: Here we describe the details of implementing Monte Carlo simulations in three-dimensional (3d) voxelized segmentations of cells in microscopy i

Gwo Dong Lin, Xiaoling Dou

It is well known that the Laplace-Stieltjes transform of a nonnegative random variable (or random vector) uniquely determines its distribution function. We extend this uniqueness theorem by using the Muntz-Szasz Theorem and the identity for the Laplace-Stieltjes and Laplace-Carson transforms of a distribution function. The latter appears for the first time t

Milutin Brankovic, Joachim Gudmundsson, André van Renssen

Solomon and Elkin constructed a shortcutting scheme for weighted trees which results in a 1-spanner for the tree metric induced by the input tree. The spanner has logarithmic lightness, logarithmic diameter, a linear number of edges and bounded degree (provided the input tree has bounded degree). This spanner has been applied in a series of papers devoted to

Randy Lemons, Nicole Neveu, Joseph Duris, Agostino Marinelli

A long sought-after goal for photocathode electron sources has been to improve performance by temporally shaping the incident exitation laser pulse. The narrow bandwidth, short wavelength, and picosecond pulse duration make it challenging to employ conventional spectral pulse shaping techniques. We present a novel and efficient intensity-envelope shaping tec

Nicolau S. Aiex, Han Hong

We prove that the space of free boundary CMC surfaces of bounded topology, bounded area and bounded boundary length is compact in the $C^k$ graphical sense away from a finite set of points. This is a CMC version of a result for minimal surfaces by Fraser-Li \cite{fraser.a-li.m2014}.

Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig

Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to provide appropriate answers in many cases. In this paper, we ask the question "how can we know when language models know, with confidence, the answer to a particular query?" W

Xupeng Wei, Achilleas Anastasopoulos

We consider a demand management problem of an energy community, in which several users obtain energy from an external organization such as an energy company, and pay for the energy according to pre-specified prices that consist of a time-dependent price per unit of energy, as well as a separate price for peak demand. Since users' utilities are their priv

Yarong Luo, Mengyuan Wang, Chi Guo

Currently state estimation is very important for the robotics, and the uncertainty representation based Lie group is natural for the state estimation problem. It is necessary to exploit the geometry and kinematic of matrix Lie group sufficiently. Therefore, this note gives a detailed derivation of the recently proposed matrix Lie group $SE_K(3)$ for the firs

Zackery Benson, Anton Peshkov, Derek C. Richardson, Wolfgang Losert

We numerically study the effect of inter-particle friction coefficient on the response to cyclical pure shear of spherical particles in three dimensions. We focus on the rotations and translations of grains and look at the spatial distribution of these displacements as well as their probability distribution functions. We find that with increasing friction, t

Qi Zhang, Antoni B. Chan

Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. However, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large

Yu Li, Ming Liu, Yaling Yi, Qince Li

Removing undesired reflection from an image captured through a glass surface is a very challenging problem with many practical application scenarios. For improving reflection removal, cascaded deep models have been usually adopted to estimate the transmission in a progressive manner. However, most existing methods are still limited in exploiting the result i

Zhebin Wu, Tianchi Liao, Chuan Chen, Cong Liu

Tensor data often suffer from missing value problem due to the complex high-dimensional structure while acquiring them. To complete the missing information, lots of Low-Rank Tensor Completion (LRTC) methods have been proposed, most of which depend on the low-rank property of tensor data. In this way, the low-rank component of the original data could be recov

Xiangqiang Gao, Rongke Liu, Aryan Kaushik

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from a IoT user can be divided into several parts and cooperatively accomplished by multiple satel

Gaoting Lin, Jaehong Jeong, Chaebin Kim, Yao Wang

Spin-orbit coupled honeycomb magnets with the Kitaev interaction have received a lot of attention due to their potential of hosting exotic quantum states including quantum spin liquids. Thus far, the most studied Kitaev systems are 4d/5d-based honeycomb magnets. Recent theoretical studies predicted that 3d-based honeycomb magnets, including Na2Co2TeO6 (NCTO)

Hyungjun Kim, Jihoon Park, Changhun Lee, Jae-Joon Kim

Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices. However, Binary Neural Networks (BNNs) tend to suffer from severe accuracy degradation compared to the full-precision counterpart model. Several techniques were proposed to impro

Baiyang Chen, Xiaoliang Chen

User identity linkage (UIL) across social networks has recently attracted an increasing amount of attention due to its significant research challenges and practical value. Most of the existing methods use a single method to express different types of attribute features. However, the simplex pattern can neither cover the entire set of different attribute feat

Shuxiao Chen, Sifan Liu, Zongming Ma

In network applications, it has become increasingly common to obtain datasets in the form of multiple networks observed on the same set of subjects, where each network is obtained in a related but different experiment condition or application scenario. Such datasets can be modeled by multilayer networks where each layer is a separate network itself while dif

Zhongxin Bai, Xiao-Lei Zhang

Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. However, there is lack of comprehensive reviews on the exciting progress. In this paper, we review several major subtasks of speaker recognition, including speaker verification, identification, diarization, and

Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu

Properties of molecules are indicative of their functions and thus are useful in many applications. With the advances of deep learning methods, computational approaches for predicting molecular properties are gaining increasing momentum. However, there lacks customized and advanced methods and comprehensive tools for this task currently. Here we develop a su

T. Mithun, A. Maluckov, B. M. Manda, Ch. Skokos

The Salerno model constitutes an intriguing interpolation between the integrable Ablowitz-Ladik (AL) model and the more standard (non-integrable) discrete nonlinear Schr{ö}dinger (DNLS) one. The competition of local on-site nonlinearity and nonlinear dispersion governs the thermalization of this model. Here, we investigate the statistical mechanics of the Sa

Eric R. Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they o

Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu

Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has been made in estimating hand and object separately with deep learning methods, simultaneous HO pose estimation and contact modeling has not yet been fully explored. In this paper, we

C. Neill, T. McCourt, X. Mi, Z. Jiang

A promising approach to study condensed-matter systems is to simulate them on an engineered quantum platform. However, achieving the accuracy needed to outperform classical methods has been an outstanding challenge. Here, using eighteen superconducting qubits, we provide an experimental blueprint for an accurate condensed-matter simulator and demonstrate how

Tobias K. Fritz, Lee Patrick, Anja Feldmeier-Krause, Rainer Schödel

In the central few degrees of the bulge of the Milky Way there is a flattened structure of gas, dust and stars (the central molecular zone) similar to nuclear disks in other galaxies. Due to extreme foreground extinction we possess only sparse information about the (mostly old) stellar population of the nuclear disc. Here we present our KMOS spectroscopic su

J. E. Horvath, P. H. R. S. Moraes

The detection of an unexpected $\sim 2.5 M_{\odot}$ component in the gravitational wave event GW190814 has puzzled the community of High-Energy astrophysicists, since in the absence of further information it is not clear whether this is the heaviest "neutron star" ever detected or either the lightest black hole known, of a kind absent in the local ne

Miljenko Čemeljić, Feng Yuan, Hai Yang

Three-dimensional numerical simulations of a hot accretion flow around a supermassive black hole are performed using the general relativity magneto-hydrodynamic (GRMHD) code Athena++. We focus on the case of SANE, with the initial magnetic field consisting of multiple loops with oppositely directed poloidal magnetic field in the torus. Using the simulation d

Laura Colmenarejo, Alejandro H. Morales, Greta Panova

The chromatic symmetric function (CSF) of Dyck paths of Stanley and its Shareshian-Wachs $q$-analogue have important connections to Hessenberg varieties, diagonal harmonics, and LLT polynomials. In the case of, so-called, abelian Dyck paths they are also curiously related to placements of non-attacking rooks by results of Stanley-Stembridge (1993) and Guay-P

Yuan Meng, Sanmukh Kuppannagari, Rajgopal Kannan, Viktor Prasanna

Most of the existing work on FPGA acceleration of Convolutional Neural Network (CNN) focus on employing a single strategy (algorithm, dataflow, etc.) across all the layers. Such an approach does not achieve optimal latency on complex and deep CNNs. Emerging CNNs have diverse per-layer computation characteristics including parallelism, arithmetic intensity, l

Yaguan Qian, Jiamin Wang, Bin Wang, Shaoning Zeng

The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted more attention. Many algorithms have been proposed to craft powerful adversarial examples. However, most of these algorithms modified the global or local region of pixels without taking network explanations into account. Hence, the perturbations are redundant, which are ea

Ruobing Bai, Eric Ocegueda, Kaushik Bhattacharya

The emergent photoactive materials through photochemistry make it possible to directly convert photon energy to mechanical work. There is much recent work in developing appropriate materials and a promising new system is semi-crystalline polymers of the photoactive molecule azobenzene. We develop a phase field model with two order parameters for the crystal-

Ken Dykema, Amudhan Krishnaswamy-Usha

Given a DT-operator $Z$ whose Brown measure is radially symmetric and has a certain concentration property, it is shown that $Z$ is not spectral in the sense of Dunford. This is accomplished by showing that the angles between certain complementary Haagerup-Schultz projections of $Z$ are zero. New estimates on norms and traces of powers of algebra-valued circ

Shibo Li, Robert M. Kirby, Shandian Zhe

Many applications, such as in physical simulation and engineering design, demand we estimate functions with high-dimensional outputs. The training examples can be collected with different fidelities to allow a cost/accuracy trade-off. In this paper, we consider the active learning task that identifies both the fidelity and input to query new training example

J. Sanjay, M. V. S. Ramarao, R. Mahesh, Sandip Ingle

The Centre for Climate Change Research (CCCR;http://cccr.tropmet.res.in) at the Indian Institute of Tropical Meteorology (IITM; http://www.tropmet.res.in), Pune, launched in 2009 with the support of the Ministry of Earth Sciences (MoES), Government of India, focuses on the development of new climate modelling capabilities in India and South Asia to address i

J. Sanjay, R. Krishnan, M. V. S. Ramarao, R. Mahesh

Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn

Hamza Varikoden, Bhupendra Bahadur Singh, K. P. Sooraj, Manish K. Joshi

During 2015, the southwest monsoon (SWM) rainfall over the country remained deficient with seasonal rainfall of about 86% of the long period average (Table 1.1). Last year, the seasonal rainfall deficiency over the country as a whole was 12% (www.imd.gov.in). Thus, this is a fourth episode of two consecutive years, with deficient monsoon, similar to 1904-05,

Elisa Ferracane, Sandeep Konam

As machine learning algorithms are more widely deployed in healthcare, the question of algorithmic fairness becomes more critical to examine. Our work seeks to identify and understand disparities in a deployed model that classifies doctor-patient conversations into sections of a medical SOAP note. We employ several metrics to measure disparities in the class

Priya S. Nayak, Rhythm Girdhar, Shreekanth M. Prabhu

Traditionally linguists have organized languages of the world as language families modelled as trees. In this work we take a contrarian approach and question the tree-based model that is rather restrictive. For example, the affinity that Sanskrit independently has with languages across Indo-European languages is better illustrated using a network model. We c

Ashif A. Fathnan, Toufiq M. Hossain, Dadin Mahmudin, Yusuf N. Wijayanto

We report the characterization of an achromatic focusing metasurface at oblique incident angles. We show that in addition to the inherent off-axis aberrations that occurs due to the hyperbolic phase profile of the metasurface, the focusing performance is significantly degraded due to the meta-atoms' angular dispersion. To obtain insights into how the ang

H. Jafarian, A. H. Taghavi, A. Javaheri, R. Rawassizadeh

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. Th

Weidi Wang, Zeyuan Wang, Yinghui Zhang, Bo Sun

The ability to recognize dynamical phenomena (e.g., dynamical phases) and dynamical processes in physical events from videos, then to abstract physical concepts and reveal physical laws, lies at the core of human intelligence. The main purposes of this paper are to use neural networks for classifying the dynamical phases of some videos and to demonstrate tha

M. Norouzisadeh, G. Cerchiari, F. Croccolo

Differential Dynamic Microscopy (DDM) is the combination of optical microscopy to statistical analysis to obtain information about the dynamical behaviour of a variety of samples spanning from soft matter physics to biology. In DDM, the dynamical evolution of the samples is investigated separately at different length scales and extracted from a set of images

M. V. Gorbatenko, V. P. Neznamov

We consider interactions of scalar particles, photons, and fermions in Schwarzschild, Reissner-Nordström, Kerr, and Kerr-Newman gravitational and electromagnetic fields with a zero and nonzero cosmological constant. We also consider interactions of scalar particles, photons, and fermions with nonextremal rotating charged black holes in a minimal five-dimensi

Asher Yahalom

The general theory of relativity (GR) is symmetric under smooth coordinate transformations, also known as diffeomorphisms. The general coordinate transformation group has a linear subgroup denoted as the Lorentz group of symmetry, which is also maintained in the weak field approximation to GR. The dominant operator in the weak field equation of GR is thus th

Vladimír Lukeš, Eduard Rohan

The two-scale computational homogenization method is proposed for modelling of locally periodic fluid-saturated media subjected a to large deformation induced by quasistatic loading. The periodic heterogeneities are relevant to the mesoscopic scale at which a double porous medium constituted by hyperelastic skeleton and an incompressible viscous fluid is fea

Mireille Tixier, J Pouget

Ionic electro-active polymer (Nafion for example) can be used as sensor or actuator. To this end, a thin film of the water-saturated material is sandwiched between two electrodes. Water saturation causes a quasi-complete dissociation of the polymer and the release of small cations. The application of an electric field across the thickness results in the bend

Muhammad Nadeem, Iolanda Di Bernardo, Xiaolin Wang, Michael S. Fuhrer

The sub-threshold swing is the fundamental critical parameter determining the operation of a transistor in low-power applications such as switches. It determines the fraction of dissipation due to the gate capacitance used for turning the device on and off, and in a conventional transistor it is limited by Boltzmann's tyranny to kTln(10)/q, or 60 mV per

Ali Raza, Faaiq Waqar, Arni Sturluson, Cory Simon

Metal-organic framework (MOFs) are nanoporous materials that could be used to capture carbon dioxide from the exhaust gas of fossil fuel power plants to mitigate climate change. In this work, we design and train a message passing neural network (MPNN) to predict simulated CO$_2$ adsorption in MOFs. Towards providing insights into what substructures of the MO

Jing-Hui Huang, Xue-Ying Duan, Xiang-Yun Hu, Huan Chen

In this paper, we present two feasible and efficient methods to numerically solve the quark's Dyson-Schwinger (qDSE), the qDSE is mathematical systems of nonlinear integral equations of the second kind with high degrees of freedom. It is difficult to analytically solve the qDSE due to its non-linearity and the singularity. Normally we discrete the singul

Felix Grezes, Zhaoheng Ni, Viet Anh Trinh, Michael Mandel

Recurrent neural networks using the LSTM architecture can achieve significant single-channel noise reduction. It is not obvious, however, how to apply them to multi-channel inputs in a way that can generalize to new microphone configurations. In contrast, spatial clustering techniques can achieve such generalization, but lack a strong signal model. This pape

Hong Xiong

The automatic search performance of search engines has become an essential part of measuring the difference in user experience. An efficient automatic search system can significantly improve the performance of search engines and increase user traffic. Hadoop has strong data integration and analysis capabilities, while R has excellent statistical capabilities

Jonathan D. Cohen

This note describes a simple score to indicate the effectiveness of mitigation against infections of COVID-19 as observed by new case counts. The score includes normalization, making comparisons across jurisdictions possible. The smoothing employed provides robustness in the face of reporting vagaries while retaining salient features of evolution, enabling a

Siamak Tafazoli

The general objective of this Ph.D. thesis is to study the dynamics and control of rigid and flexible spacecraft supported by a high-fidelity numerical simulation environment. The demand for greater attitude pointing precision, attitude maneuvering or recovery with the increased use of lightweight and flexible materials necessitates the consideration of flex

Ce Xu

In this paper we present some new identities for multiple polylogarithms (abbr. MPLs) and multiple harmonic star sums (abbr. MHSSs) by using the methods of iterated integral computations of logarithm functions. Then, by applying these formulas obtained, we establish some explicit relations between Kaneko-Yamamoto type multiple zeta values (abbr. K-Y MZVs), m

M. Farasat Shamir, I. Fayyaz

Motivated by recent proposals of possible wormhole shape functions, we construct a wormhole shape function by employing the Karmarkar condition for static traversable wormhole geometry. The proposed shape function generates wormhole geometry that connects two asymptotically flat regions of spacetime and satisfies the required conditions. Further, we discuss

Gaurav Bhardwaj, Utkarsh A. Mishra, N. Sukavanam, R. Balasubramanian

A novel efficient downstairs trajectory is proposed for a 9 link biped robot model with toe-foot. Brachistochrone is the fastest descent trajectory for a particle moving only under the influence of gravity. In most situations, while climbing downstairs, human hip also follow brachistochrone trajectory for a more responsive motion. Here, an adaptive trajector

Yao Wu, Jian Cao, Guandong Xu

An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead to unsatisfactory recommendation. This problem can be solved by limiting the number of users who receive the recommendation for a service, but this may be viewed as unfair. In this

Eric L. Manibardo, Ibai Laña, Javier Del Ser

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of

Raoul Nigmatullin, Semyon Dorokhin, Alexander Ivchenko

This paper demonstrates that highly accurate radiometric identification is possible using CAPoNeF feature engineering method. We tested basic ML classification algorithms on experimental data gathered by SDR. The statistical and correlational properties of suggested features were analyzed first with the help of Point Biserial and Pearson Correlation Coeffici

Zhaoheng Ni, Felix Grezes, Viet Anh Trinh, Michael I. Mandel

Spatial clustering techniques can achieve significant multi-channel noise reduction across relatively arbitrary microphone configurations, but have difficulty incorporating a detailed speech/noise model. In contrast, LSTM neural networks have successfully been trained to recognize speech from noise on single-channel inputs, but have difficulty taking full ad

Daniel Bauer, Lars Kuhnert, Lutz Eckstein

With the recent boost in autonomous driving, increased attention has been paid on radars as an input for occupancy mapping. Besides their many benefits, the inference of occupied space based on radar detections is notoriously difficult because of the data sparsity and the environment dependent noise (e.g. multipath reflections). Recently, deep learning-based

Thierry Njougouo, Victor Camargo, Patrick Louodop, Fernando Fagundes Ferreira

We study the dynamics of a multilayer network of chaotic oscillators subject to an amplification. Previous studies have proven that multilayer networks present phenomena such as synchronization, cluster and chimera states. Here we consider a network with two layers of Roessler chaotic oscillators as well as applications to multilayer networks of chaotic jerk

Jalal Arabneydi, Aditya Mahajan

A decentralized control system with linear dynamics, quadratic cost, and Gaussian disturbances is considered. The system consists of a finite number of subsystems whose dynamics and per-step cost function are coupled through their mean-field (empirical average). The system has mean-field sharing information structure, i.e., each controller observes the state

Jalal Arabneydi, Aditya Mahajan

In this paper, we are interested in systems with multiple agents that wish to collaborate in order to accomplish a common task while a) agents have different information (decentralized information) and b) agents do not know the model of the system completely i.e., they may know the model partially or may not know it at all. The agents must learn the optimal

Jun Li, Yumeng Shao, Ming Ding, Chuan Ma

Federated learning (FL), as a distributed machine learning approach, has drawn a great amount of attention in recent years. FL shows an inherent advantage in privacy preservation, since users' raw data are processed locally. However, it relies on a centralized server to perform model aggregation. Therefore, FL is vulnerable to server malfunctions and ext

Xiaoyu Du, Chris Hargreaves, John Sheppard, Felix Anda

Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe. Digital forensic investigators are overloaded with the volume of cases requiring their expertise compounded by the volume of data to be processed. Artificial intelligence is often seen as the solution to many big data problems. This paper summarises

Mahmoud M. Badr, Mohamed I. Ibrahem, Mohamed Mahmoud, Mostafa M. Fouda

In smart grid, malicious customers may compromise their smart meters (SMs) to report false readings to achieve financial gains illegally. Reporting false readings not only causes hefty financial losses to the utility but may also degrade the grid performance because the reported readings are used for energy management. This paper is the first work that inves