June 2020 arXiv papers — page 15

Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang

Graph Convolutional Networks (GCNs) show promising results for semi-supervised learning tasks on graphs, thus become favorable comparing with other approaches. Despite the remarkable success of GCNs, it is difficult to train GCNs with insufficient supervision. When labeled data are limited, the performance of GCNs becomes unsatisfying for low-degree nodes. W

Jakub Kośmider

The aim of this paper is to study the Wold-type decomposition in the class of $m$-isometries. One of our main results establishes an equivalent condition for an analytic $m$-isometry to admit the Wold-type decomposition for $m\ge2$. In particular, we introduce the $k$-kernel condition which we use to characterize analytic $m$-isometric operators which are un

Huiying Mao, Ryan Martin, Brian Reich

Predicting the response at an unobserved location is a fundamental problem in spatial statistics. Given the difficulty in modeling spatial dependence, especially in non-stationary cases, model-based prediction intervals are at risk of misspecification bias that can negatively affect their validity. Here we present a new approach for model-free nonparametric

Nicholas C. Henderson, Ravi Varadhan, Thomas A. Louis

Shrinkage estimates of small domain parameters typically utilize a combination of a noisy "direct" estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage towards a poorly estimated

Akella Ravi Tej, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar

We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimation, despite suffering from high variance in the gradient estimates. On the other hand, more sample efficient alternatives like Bayesian quadrature methods have received little atte

Abdul Mueed Hafiz, Mahmoud Hassaballah

In multiclass deep network classifiers, the burden of classifying samples of different classes is put on a single classifier. As the result the optimum classification accuracy is not obtained. Also training times are large due to running the CNN training on single CPU/GPU. However it is known that using ensembles of classifiers increases the performance. Als

Heather D. Whitley, G. Elijah Kemp, Charles Yeamans, Zachary Walters

We examine the performance of pure boron, boron carbide, high density carbon, and boron nitride ablators in the polar direct drive exploding pusher (PDXP) platform. The platform uses the polar direct drive configuration at the National Ignition Facility to drive high ion temperatures in a room temperature capsule and has potential applications for plasma phy

Daniel Max Hoffmann, Anand Pillay

We discuss the role of weakly normal formulas in the theory of thorn forking, as part of a commentary on the paper "Thorn forking and stable forking" by Ealy and Onshuus (Rev. acad. colomb. cienc. exact. fis. nat. vol.40 no.157 Bogotá Oct./Dec. 2016). We also give a counterexample to Corollary 4.2 from that paper, and in the process discuss "theo

Toru Harada, Yoshiharu Hirabayashi

We study theoretically medium effects on $Ξ^-$ production in the ($K^-$, $K^+$) reaction, using the optimal Fermi-averaging procedure which describes the Fermi motion of a nucleon on the on-energy-shell $K^-p\to K^+Ξ^-$ reaction condition in nuclei. The result shows the strong energy and angular dependence of the in-medium $K^-p\to K^+Ξ^-$ cross section,whic

Abdul Mueed Hafiz

In this paper, a simple and efficient Hybrid Classifier is presented which is based on deep learning and reinforcement learning. Here, Q-Learning has been used with two states and 'two or three' actions. Other techniques found in the literature use feature map extracted from Convolutional Neural Networks and use these in the Q-states along with past

Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat

One-class CNNs have shown promise in novelty detection. However, very less work has been done on extending them to multiclass classification. The proposed approach is a viable effort in this direction. It uses one-class CNNs i.e., it trains one CNN per class, for multiclass classification. An ensemble of such one-class CNNs is used for multiclass classificat

Andres Colubri, Kailash Yadav, Abhishek Jha, Pardis C. Sabeti

The ongoing COVID-19 pandemic calls for a multi-faceted public health response comprising complementary interventions to control the spread of the disease while vaccines and therapies are developed. Many of these interventions need to be informed by epidemic risk predictions given available data, including symptoms, contact patterns, and environmental factor

Siyu Chen, Julia M. H. Schollick, Roel P. A. Dullens, Dirk G. A. L. Aarts

In this paper we study the process termed thermal regelation at single particle level. We experimentally extract the physical parameters that determine the interactions between ice and the silica particle for the regelation process. As the premelted layer around the particle plays a key role here, we use the extracted parameters to calculate the thickness of

She-Sheng Xue

Suppose that the early Universe starts with a cosmological $Λ$-term originating from quantum spacetime at the Planck scale. Dark energy drives inflation and reheating by reducing its value for massive particle-antiparticle pairs production and oscillation, resulting in a holographic and massive pair plasma state. The back-and-forth reaction of dark energy an

Krzysztof Malarz

We determine thresholds $p_c$ for random site percolation on a triangular lattice for neighbourhoods containing nearest (NN), next-nearest (2NN), next-next-nearest (3NN), next-next-next-nearest (4NN) and next-next-next-next-nearest (5NN) neighbours, and their combinations forming regular hexagons (3NN+2NN+NN, 5NN+4NN+NN, 5NN+4NN+3NN+2NN, 5NN+4NN+3NN+2NN+NN).

Ilia D. Mishev

We obtain extensions of classical hypergeometric identities of Bailey and Whipple that transform nearly-poised and very-well-poised series to Saalschützian series, Saalschützian series to Saalschützian series, and very-well-poised and nearly-poised series to very-well-poised series. We employ a method in which summations and transformations of lower-order se

Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat

We present a simple yet efficient Hybrid Classifier based on Deep Learning and Reinforcement Learning. Q-Learning is used with two Q-states and four actions. Conventional techniques use feature maps extracted from Convolutional Neural Networks (CNNs) and include them in the Qstates along with past history. This leads to difficulties with these approaches as

Li Li, Theodoros Pantelidis, Joseph Y. J. Chow, Saif Eddin Jabari

Real-time vehicle dispatching operations in traditional car-sharing systems is an already computationally challenging scheduling problem. Electrification only exacerbates the computational difficulties as charge level constraints come into play. To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that

Francisco Gonzalez Montoya, Stephen Wiggins

We study the phase space structures that control the transport in a classical Hamiltonian model for a chemical reaction. This model has been proposed to study the yield of products in an ultracold exothermic reaction. In the considered model, two elements determine the evolution of the system: a Van der Waals force and short-range force associated with the m

Sayan Banerjee, Shankar Bhamidi

We consider models of growing random trees $\{\mathcal{T}_f(n):n\geq 1\}$ with model dynamics driven by an attachment function $f:\mathbb{Z}_+\to \mathbb{R}_+$. At each stage a new vertex enters the system and connects to a vertex $v$ in the current tree with probability proportional to $f(\text{degree}(v))$. The main goal of this study is to understand the

Mingpu Qin

We propose an approach to study the ground state of quantum many-body systems in which Tensor Network States (TNS), specifically Projected Entangled Pair States (PEPS), and Green's function Monte Carlo (GFMC) are combined. PEPS, by design, encode the area law which governs the scaling of entanglement entropy in quantum systems with short range interactio

Youngwan Lee, Joong-won Hwang, Hyung-Il Kim, Kimin Yun

Since many safety-critical systems, such as surgical robots and autonomous driving cars operate in unstable environments with sensor noise and incomplete data, it is desirable for object detectors to take the localization uncertainty into account. However, there are several limitations of the existing uncertainty estimation methods for anchor-based object de

Héber H. Arcolezi, Willian R. B. M. Nunes, Rafael A. de Araujo, Selene Cerna

Neuromuscular electrical stimulation (NMES) has been effectively applied in many rehabilitation treatments of individuals with spinal cord injury (SCI). In this context, we introduce a novel, robust, and intelligent control-based methodology to closed-loop NMES systems. Our approach utilizes a robust control law to guarantee system stability and machine lear

Guolin Ke, Di He, Tie-Yan Liu

In this work, we investigate the positional encoding methods used in language pre-training (e.g., BERT) and identify several problems in the existing formulations. First, we show that in the absolute positional encoding, the addition operation applied on positional embeddings and word embeddings brings mixed correlations between the two heterogeneous informa

Yu Hu, David Soler Soneira, María Jesús Sánchez

Electrical energy storage is considered essential for the future energy systems. Among all the energy storage technologies, battery systems may provide flexibility to the power grid in a more distributed and decentralized way. In countries with deregulated electricity markets, grid-connected battery systems should be operated under the specific market design

Mokhtar Falek, Mustafa Moumni, Mahmoud Merad

We present the exact solution of the three-dimensional Duffin--Kemmer--Petiau oscillator for both spin 0 and spin 1 cases, with the presence of minimal uncertainty in momentum in anti--de Sitter model. We use the representation of vector spherical harmonics and the Nikiforov--Uvarov method to determine exactly the energy eigenvalues and the eigenfunctions in

Péter Kovács, Gergő Bognár, Christian Huber, Mario Huemer

We introduce VPNet, a novel model-driven neural network architecture based on variable projection (VP). Applying VP operators to neural networks results in learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet and presents experiments. The VPNet approach was eval

Viktoriia Kornich

Unconventional superconductivity is usually associated with symmetry breaking in the system. Here we consider a simple setup consisting of a solid state material with conduction electrons and an applied surface acoustic wave (SAW), that can break time and spatial translation symmetries. We study the symmetries of the possible SAW-induced order parameters, sh

Changjun Gao, Shuang Yu, Jianhui Qiu

The recently proposed regularized Lovelock tensors are kinetically coupled to the scalar field. The resulting equation of motion is second order. In particular, it is found that when the $p=3$ regularized Lovelock tensor is kinetically coupled to the scalar field, the scalar field is the potential candidate of cosmic dark energy.

Zdzislaw Burda

We develop an agent-based model to assess the cumulative number of deaths during hypothetical Covid-19-like epidemics for various non-pharmaceutical intervention strategies. We consider local and non-local modes of disease transmission. The first simulates transmission through social contacts in the vicinity of the place of residence while the second through

V. K. Sharma, H. Srinivasan, V. García Sakai, S. Mitra

Caffeine (1,3,7-trimethylxanthine), an ingredient of coffee, is used worldwide as a psychostimulant, antioxidant, and adjuvant analgesic. To gain insights into the action mechanism of caffeine, we report on its effects on the phase behaviour and microscopic dynamics of a dioctadecyldimethylammonium bromide (DODAB) lipid membrane, as studied quasielastic neut

Kun Xiao, Yao Meng, Xunhua Dai, Haotian Zhang

This paper presents a lifting-wing multirotor UAV that allows long-range flight. The UAV features a lifting wing in a special mounting angle that works together with rotors to supply lift when it flies forward, achieving a reduction in energy consumption and improvement of flight range compared to traditional multirotor UAVs. Its dynamic model is built accor

Siyu Liu, Wei Dai, Craig Engstrom, Jurgen Fripp

Data scarcity is common in deep learning models for medical image segmentation. Previous works proposed multi-dataset learning, either simultaneously or via transfer learning to expand training sets. However, medical image datasets have diverse-sized images and features, and developing a model simultaneously for multiple datasets is challenging. This work pr

Junqi Lin, Huixin Miao, Junjie Cao, Zhixun Su

Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches. The two-stage methods either suffer high computational redundancy for additional person detectors or group keypoints heuristically after predicting all the instance-free keypoints. The recently proposed single-s

Philip Bille, Inge Li Gørtz

We consider compact representations of collections of similar strings that support random access queries. The collection of strings is given by a rooted tree where edges are labeled by an edit operation (inserting, deleting, or replacing a character) and a node represents the string obtained by applying the sequence of edit operations on the path from the ro

Ronen Eldan, Dan Mikulincer, Hester Pieters

We make the first steps towards generalizing the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry. We consider a geometric random graph over a homogeneous metric space where the probability of two vertices to be connected is an arbitrary function of the dista

Xinzhe Luo, Xiahai Zhuang

Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training. However, intensity-based metrics can be misleading when the assumption of intensity class correspondence is viola

Keiji Saito, Masaru Hongo, Abhishek Dhar, Shin-ichi Sasa

The theory of fluctuating hydrodynamics has been an important tool for analyzing macroscopic behavior in nonlinear lattices. However, despite its practical success, its microscopic derivation is still incomplete. In this work, we provide the microscopic derivation of fluctuating hydrodynamics, using the coarse-graining and projection technique; the equivalen

Pablo Shmerkin

We prove that if $α\in (0,1/2]$, then the packing dimension of a set $E\subset\mathbb{R}^2$ for which there exists a set of lines of dimension $1$ intersecting $E$ in dimension $\ge α$ is at least $1/2+α+c(α)$ for some $c(α)>0$. In particular, this holds for $α$-Furstenberg sets, that is, sets having intersection of Hausdorff dimension $\geα$ with at least o

Tomasz Kuśmierczyk, Arto Klami

Variational approximations are increasingly based on gradient-based optimization of expectations estimated by sampling. Handling discrete latent variables is then challenging because the sampling process is not differentiable. Continuous relaxations, such as the Gumbel-Softmax for categorical distribution, enable gradient-based optimization, but do not defin

D. Koroliouk, V. S. Koroliuk

For multivariant Wright-Fisher models in population genetics, we introduce equilibrium states, expressed by fluctuations of probability ratio, in contrast to the traditionally used fluctuations, expressed by the difference between the current value of the random process and its equilibrium value. Then the drift component of the dynamic process of gene freque

Claudio Fontana, Wolfgang J. Runggaldier

In a discrete-time setting, we study arbitrage concepts in the presence of convex trading constraints. We show that solvability of portfolio optimization problems is equivalent to absence of arbitrage of the first kind, a condition weaker than classical absence of arbitrage opportunities. We center our analysis on this characterization of market viability an

Yujin Chen, Zhigang Tu, Di Kang, Ruizhi Chen

Accurate 3D reconstruction of the hand and object shape from a hand-object image is important for understanding human-object interaction as well as human daily activities. Different from bare hand pose estimation, hand-object interaction poses a strong constraint on both the hand and its manipulated object, which suggests that hand configuration may be cruci

Emanuele Dalsasso, Xiangli Yang, Loïc Denis, Florence Tupin

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN train

G. L. Klimchitskaya, V. M. Mostepanenko

The analytic asymptotic expressions for the Casimir free energy and entropy for two parallel graphene sheets possessing nonzero energy gap $Δ$ and chemical potential $μ$ are derived at arbitrarily low temperature. Graphene is described in the framework of thermal quantum field theory in the Matsubara formulation by means of the polarization tensor in (2+1)-d

Nadia Loy, Andrea Tosin

In this paper, we propose a Boltzmann-type kinetic description of mass-varying interacting multi-agent systems. Our agents are characterised by a microscopic state, which changes due to their mutual interactions, and by a label, which identifies a group to which they belong. Besides interacting within and across the groups, the agents may change label accord

Li Li, Victor Okoth, Saif Eddin Jabari

Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the ne

Chun-Hua Zeng, Jun-Xu Lu, En Wang, Ju-Jun Xie

We report on a theoretical study of the newly observed $Ω(2012)$ resonance in the nonleptonic weak decays of $Ω_c^0 \to π^+ \bar{K}Ξ^*(1530) (ηΩ) \to π^+ (\bar{K}Ξ)^-$ and $π^+ (\bar{K}Ξπ)^-$ via final-state interactions of the $\bar{K}Ξ^*(1530)$ and $ηΩ$ pairs. The weak interaction part is assumed to be dominated by the charm quark decay process: $c(ss) \to

Andrea Di Biagio, Carlo Rovelli

Facts happen at every interaction, but they are not absolute: they are relative to the systems involved in the interaction. Stable facts are those whose relativity can effectively be ignored. In this work, we describe how stable facts emerge in a world of relative facts and discuss their respective roles in connecting quantum theory and the world. The distin

C. C. Kwong, T. Wellens, K. Pandey, D. Wilkowski

We measure the resonant forward scattering of light by a highly saturated atomic medium through the flashes emitted immediately after an abrupt extinction of the probe beam. The experiment is done in a dilute regime where the phenomena are well captured using the independent scattering approximation. Comparing our measurements to a model based on Maxwell-Blo

Pierluigi Benevieri, Alessandro Calamai, Massimo Furi, Maria Patrizia Pera

We extend to the infinite dimensional context the link between two completely different topics recently highlighted by the authors: the classical eigenvalue problem for real square matrices and the Brouwer degree for maps between oriented finite dimensional real manifolds. Thanks to this extension, we solve a conjecture regarding global continuation in nonli

Eric Lantz, Mehdi Mabed, Fabrice Devaux

At first sight, the use of an everywhere positive Wigner function as a probability density to perform stochastic simulations in quantum optics seems equivalent to the introduction of local hidden variables, thus preventing any violation of Bell inequalities. However, because of the difference between symmetrically and normally ordered operators, some traject

Codruţ Grosu, Corina Grosu

We express Wronskian Hermite polynomials in the Hermite basis and obtain an explicit formula for the coefficients. From this we deduce an upper bound for the modulus of the roots in the case of partitions of length 2. We also derive a general upper bound for the modulus of the real and purely imaginary roots. These bounds are very useful in the study of irre

V. I. Tokar

A self-consistent (SC) renormalization group approach of the effective medium kind has been developed and applied to the solution of the Ising model (IM). A renormalization group equation in the local potential approximation (LPA) derived previously for spatially homogeneous systems has been extended to the lattice case and supplemented with a self-consisten

Kazunori Shinohara

A lemniscate is a curve defined by two foci, F1 and F2. If the distance between the focal points of F1 - F2 is 2a (a: constant), then any point P on the lemniscate curve satisfy the equation PF1 PF2 = a^2. Jacob Bernoulli first described the lemniscate in 1694. The Fagnano discovered the double angle formula of the lemniscate(1718). The Euler extended the Fa

Chunlong Luo, Tianqi Yu, Yufan Luo, Manqing Wang

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly int

Man-Chun Lee, Ka-Fai Li

In this work, we study the deformation of Hermitian metrics with Chern connection. By adapting the conformal perturbation method of Aubin and Ehrlich to Hermitian setting, we prove that Hermitian metrics with quasi-positive (resp. quasi-negative) second Chern-Ricci curvature can be deformed to one with positive (resp. negative) curvature.

Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian

As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and fast adaptation to new knowledge. In this paper, we concentrate on this "slow vs. fast" (SvF) dilemma to determine which knowledge components to be updated in a slow fashion

Anton A. Klyachko

According to Mazhuga's theorem, the fundamental group $H$ of any connected surface, possibly except for the Klein bottle, is a retract of each finitely generated group containing $H$ as a verbally closed subgroup. We prove that the Klein bottle group is indeed an exception but has a very close property.

Akshay Rangamani, Lorenzo Rosasco, Tomaso Poggio

We study the average $\mbox{CV}_{loo}$ stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm minimizes a bound on $\mbox{CV}_{loo}$ stability, which in turn is controlled by the condition number of the empirical kernel matrix. The latter can be characterized in the asymptotic

Arka Chatterjee, Broja G. Dutta, Prantik Nandi, Sandip K. Chakrabarti

Evolution of variability properties of Galactic transient sources is a diagnostic tool to understand various regimes of the accretion flow and its dynamics close to the central black hole. In this paper, we concentrate on the variability properties of the X-ray transient XTE J1650-500 and investigate the evolution of viscous delay, time lag, QPO frequency, a

Wenhui Yu, Zheng Qin

\textbf{G}raph \textbf{C}onvolutional \textbf{N}etwork (\textbf{GCN}) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage

Ying Yuan

Transverse momentum distributions of deuterons and anti-deuterons in Au+Au collisions at $\sqrt{S_{NN}}$=14.5, 62.4 and 200 GeV with different centrality are studied in the framework of the multisource thermal model. Transverse momentum spectra are conformably and approximately described by the Tsallis distribution. The dependence of parameters (average tran

Aqsa Nasrullah, Ishtiaq Ahmed, M. Jamil Aslam, Z. Asghar

The New Physics (NP) effects are studied in the rare baryonic decay $Λ_b \to Λ(\to pπ^-)\ell^{+}\ell^{-}$, with unpolarized $Λ_b$ using most general model independent approach by introducing new axial(vector), (pseudo)scalar and tensor operators in the weak effective Hamiltonian corresponding to $b\to s$ transitions. Recently, for $Λ_b \to Λ(\to pπ^-)μ^{+}μ^

Jeffrey M. Dudek, Moshe Y. Vardi

A promising new algebraic approach to weighted model counting makes use of tensor networks, following a reduction from weighted model counting to tensor-network contraction. Prior work has focused on analyzing the single-core performance of this approach, and demonstrated that it is an effective addition to the current portfolio of weighted-model-counting al

Panagioti E Georgoudis

The purpose of this paper is to introduce the unitary limit as applied in systems of cold atoms into collective states of heavy, even-even nuclei and to identify a related physical example. This is accompanied by the determination of observables of conformal symmetry in nuclear structure. A Hamiltonian is defined that governs the scattering process of an inc

Masahiro Hoshino

Recently, magnetic reconnection, where a relativistically hot plasma is confined by a strong magnetic field, has received great attention in relation to astrophysical objects (e.g., pulsar magnetosphere and magnetar). However, reconnection with relativistic high-speed drift current in the plasma sheet has not been investigated yet. Thus, from both the theore

Sebastián Reyes-Carocca

Let $p$ and $q$ be odd prime numbers. In this paper we study non-abelian pq-fold regular covers of the projective line, determine algebraic models for some special cases and provide a general isogeny decomposition of the corresponding Jacobian varieties. We also give a classification and description of the one-dimensional families of compact Riemann surfaces

Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang

Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings. The inner products of query and document embeddings are regarded as relevance scores. On MS MARCO Passage Ranking task

Yasuyuki Hatsuda, Masashi Kimura

We provide semi-analytic expressions for quasinormal mode frequencies of slowly rotating Kerr black holes to the quadratic order in the rotation parameter. We apply the parametrized black hole quasinormal mode ringdown formalism to the Chandrasekhar-Detweiler equation and the Sasaki-Nakamura equation instead of the Teukolsky equation, and compare our result

Ceyuan Yang, Yinghao Xu, Bo Dai, Bolei Zhou

Visual tempo, which describes how fast an action goes, has shown its potential in supervised action recognition. In this work, we demonstrate that visual tempo can also serve as a self-supervision signal for video representation learning. We propose to maximize the mutual information between representations of slow and fast videos via hierarchical contrastiv

D. B. Melrose, M. Z. Rafat, A. Mastrano

We propose and discuss an alternative pulsar radio emission mechanism that relies on rotation-driven plasma oscillations, rather than on a beam-driven instability, and suggest that it may be the generic radio emission mechanism for pulsars. We identify these oscillations as superluminal longitudinal waves in the pulsar plasma, and point out that these waves

Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed

We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment function containing two terms: (1) a unary term assigning query samples to the nearest class prototype, and (2) a pairwise Laplacian term encouraging nearby query samples to have co

Jiahui Chen, Xiaowen You, Youlong Wu, Shuai Ma

In this paper, we investigate the transmission delay of cache-aided broadcast networks with user cooperation. Novel coded caching schemes are proposed for both centralized and decentralized caching settings, by efficiently exploiting time and cache resources and creating parallel data delivery at the server and users. We derive a lower bound on the transmiss

Wenlong Hu

In this paper, we adopted a net liability model which assesses both market risk on the liability side and revenue risk on the asset side for a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts. Numeric solutions for net liabilities, fair rate of fees and Greeks of GMMB are obtained by a more accurate and fast Fourier Spac

Chao Huang, Rui Liu

Attracted by team scale and function diversity, a heterogeneous multi-robot system (HMRS), where multiple robots with different functions and numbers are coordinated to perform tasks, has been widely used for complex and large-scale scenarios, including disaster search and rescue, site surveillance, and social security. However, due to the variety of the tas

Loc Trinh, Michael Tsang, Sirisha Rambhatla, Yan Liu

In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations. Currently, most state-of-the-art deepfake detections are based on black-box models that process videos frame-by-frame for inference, and few closely examine their temporal inconsistencies. However, the exis

Zhaoyue Zhong, Xiangrong Wang, Cunquan Qu, Guanghui Wang

Community detection or clustering is a crucial task for understanding the structure of complex systems. In some networks, nodes are permitted to be linked by either "positive" or "negative" edges; such networks are called signed networks. Discovering communities in signed networks is more challenging than that in unsigned networks. In this st

Murat Kuscu, Hamideh Ramezani, Ergin Dinc, Shahab Akhavan

Bio-inspired molecular communications (MC), where molecules are used to transfer information, is the most promising technique to realise the Internet of Nano Things (IoNT), thanks to its inherent biocompatibility, energy-efficiency, and reliability in physiologically-relevant environments. Despite a substantial body of theoretical work concerning MC, the lac

Nivaldo A. Lemos

The variational problem of the least uncomfortable journey between two locations on a straight line is simplified by a choice of the dependent variable. It is shown that taking the position, instead of the velocity, as the optimal function of time to be determined does away with the isoperimetric constraint. The same results as those found with the velocity

Mohsin Kamal, Abdulah Aljohani, Eisa Alanazi

Due to the global pandemic of COVID-19, there is an urgent need to utilize existing technologies to their full potential. Internet of Things (IoT) is regarded as one of the most trending technologies with a great potential in fighting against the coronavirus outbreak. The IoT comprises of a scarce network in which the IoT devices sense the environment and se

Wenhui Yu, Zheng Qin

Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data is a challenging task since we can only observe positive (voted) samples and unvoted samples. It is difficult to distinguish between the negative samples and unlabeled positive

Wenhui Yu, Xiao Lin, Junfeng Ge, Wenwu Ou

Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples i

Sara Scaramuccia, Simon Nanty, Florent Masseglia

Understanding choices performed by online customers is a growing need in the travel industry. In many practical situations, the only available information is the flight search query performed by the customer with no additional profile knowledge. In general, customer flight bookings are driven by prices, duration, number of connections, and so on. However, no

Qingyin Ge, Yunuo Ma, Yuezhi Liao, Rongyu Li

With the good development in the financial industry, the market starts to catch people's eyes, not only by the diversified investing choices ranging from bonds and stocks to futures and options but also by the general "high-risk, high-reward" mindset prompting people to put money in the financial market. People are interested in reducing risk at

Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat

Object detection or localization is an incremental step in progression from coarse to fine digital image inference. It not only provides the classes of the image objects, but also provides the location of the image objects which have been classified. The location is given in the form of bounding boxes or centroids. Semantic segmentation gives fine inference

A. M. Hafiz

In this paper, a novel K-Nearest Neighbour and Support Vector Machine hybrid classification technique has been proposed that is simple and robust. It is based on the concept of discriminative nearest neighbourhood classification. The technique consists of using K-Nearest Neighbour Classification for test samples satisfying a proximity condition. The patterns

Longbing Cao

While data science has emerged as a contentious new scientific field, enormous debates and discussions have been made on it why we need data science and what makes it as a science. In reviewing hundreds of pieces of literature which include data science in their titles, we find that the majority of the discussions essentially concern statistics, data mining,

Longbing Cao

Data science is creating very exciting trends as well as significant controversy. A critical matter for the healthy development of data science in its early stages is to deeply understand the nature of data and data science, and to discuss the various pitfalls. These important issues motivate the discussions in this article.

Muhammad Nazrul Islam, A. K. M. Najmul Islam

The objective of this paper is to synthesize the digital interventions initiatives to fight against COVID-19 in Bangladesh and compare with other countries. In order to obtain our research objective, we conducted a systematic review of the online content. We first reviewed the digital interventions that have been used to fight against COVID-19 across the glo

A. V. Erofeeva, T. V. Zakharova

This article is devoted to EEG studying of connectivity cortical areas involved in keeping vision information in working memory. VAR-modeling was used for describing signals got from connected with working memory brain zones. Brain connections were estimated by based in Granger Causality Partial Directed Coherence (PDC) and then compared by Wilcoxon signed-r

Akram Seifi, Mohammad Ehteram, Vijay P. Singh, Amir Mosavi

In the present study, six meta-heuristic schemes are hybridized with artificial neural network (ANN), adaptive neuro-fuzzy interface system (ANFIS), and support vector machine (SVM), to predict monthly groundwater level (GWL), evaluate uncertainty analysis of predictions and spatial variation analysis. The six schemes, including grasshopper optimization algo

Hossein Eghbali Sarai, Kazem Haghnejad Azar, Ali Jabbari

In this paper, we study some cohomlogical properties of Banach algebras. For a Banach algebra $A$ and a Banach $A$-bimodule $B$, we investigate the vanishing of the first Hochschild cohomology groups $H^1(A^n,B^m)$ and $H_{w^*}^1(A^n,B^m)$, where $0\leq m,n\leq 3$. For amenable Banach algebra $A$, we show that there are Banach $A$-bimodules $C$, $D$ and elem

Shibasis Roy, Rahul Sinha, N. G. Deshpande

We consider charmless weak decays of beauty-baryons into decuplet baryons and pseudoscalar mesons in a general framework based on $SU(3)$-flavor decomposition of the decay amplitudes. The dynamical assumption independent $SU(3)$ analysis accounts for the effects of an arbitrarily broken $SU(3)$ symmetry in these decays. An alternative approach in terms of qu

Andrei O. Barvinsky, Alexander Yu. Kamenshchik

We review a number of issues in foundations of quantum theory and quantum cosmology including, in particular, the problem of the preferred basis in the many-worlds interpretation of quantum mechanics, the relation between this interpretation and the decoherence phenomenon, application of decoherence approach to quantum cosmology, the relation between the man

S. Ayik, B. Yilmaz, O. Yilmaz, A. S. Umar

Multinucleon transfer mechanism in the collision of ${}^{238}\text{U}+{}^{238}\text{U}$ system is investigated at $E_\text{c.m.} =833$ MeV in the framework of the quantal diffusion description based on the stochastic mean-field approach (SMF). Double cross-sections $σ(N,Z)$ as a function of the neutron and proton numbers, the cross-sections $σ(Z)$ and $σ(A)$

Nanda Gopala Kilingar

As X-FEM approximation does not need meshing of the crack, the method has garnered a lot of attention from industrial point of view. This thesis report summarises some of the concepts involved in Nitsche approach for resolving boundary conditions in embedded interfaces using XFEM. We consider here cases in which the jump of a field across the interface is gi

Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu

Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely expensive in the field of medical image analysis. Unlabeled data, on the other hand, is much easier to acquire. Semi-supervised learning and unsupervised domain adaptation both take

J. Gonzalez Sutter, A. Sarmiento Chávez, S. Soria, M. Granada

Artificially engineered superlattices were designed and fabricated to induce different growth mechanisms and structural characteristics. DC sputtering was used to grow ferromagnetic (La$_{0.8}$Ba$_{0.2}$MnO$_3$) / ferroelectric (Ba$_{0.25}$Sr$_{0.75}$TiO$_3$ or BaTiO$_3$) superlattices. We systematically modified the thickness of the ferromagnetic layer to a

Yongle Luo, Zhengpan Wang

Congruences on a graph inverse semigroup were recently described in terms of the underline graph. Based on such descriptions, we show that the lattice of congruences on a graph inverse semigroup is upper semimodular but not lower semimodular.