November 2018 arXiv papers — page 17

Irina A. Mizeva, Elena V. Potapova, Viktor V. Dremin, Evgeny A. Zherebtsov

The variation of blood flow characteristics caused by the probe pressure during noninvasive studies is of particular interest within the context of fundamental and applied research. It has been shown previously that the weak local pressure induces vasodilation, whereas the increased pressure is able to stop the blood flow in the compressed area, as well as t

Chethan N Gowdigere, Jagannath Santara, Sumedha

We study the tricritical Ising universality class using conformal bootstrap techniques. By studying bootstrap constraints originating from multiple correlators on the CFT data of multiple OPEs, we are able to determine the scaling dimension of the spin field $\Delta_\sigma$ in various non-integer dimensions $2 \le d \le 3$. $\Delta_{\sigma}$ is connected to

Sujoy Paul, Jeroen van Baar

Deep Reinforcement Learning has shown tremendous success in solving several games and tasks in robotics. However, unlike humans, it generally requires a lot of training instances. Trajectories imitating to solve the task at hand can help to increase sample-efficiency of deep RL methods. In this paper, we present a simple approach to use such trajectories, ap

M. Baak, R. Koopman, H. Snoek, S. Klous

A prescription is presented for a new and practical correlation coefficient, $\phi_K$, based on several refinements to Pearson's hypothesis test of independence of two variables. The combined features of $\phi_K$ form an advantage over existing coefficients. First, it works consistently between categorical, ordinal and interval variables. Second, it captures

Sara Pourjamal, Mikko Kataja, Nicolò Maccaferri, Paolo Vavassori

We present a systematic study on the optical and magneto-optical properties of Ni/SiO$_2$/Au dimer lattices. By considering the excitation of orthogonal dipoles in the Ni and Au nanodisks, we analytically demonstrate that the magnetoplasmonic response of dimer lattices is governed by a complex interplay of near- and far-field interactions. Near-field couplin

Natalia Garcia-Colin, Dimitri Leemans

A graph ${\mathcal G}$ is locally X if the graphs induced on the neighbours of every vertex of ${\mathcal G}$ are isomorphic to the graph $X$. We prove that the infinite family of incidence graphs of the $r$-rank incidence geometries, $\Gamma(KG(n,k),r)$, constructed using the Kneser graphs $KG(n,k)$, are locally $X$ with $X$ being the incidence graphs of th

Peng-Lu Zhao, An-Min Wang

We investigate the mutual influence of tilt, disorder, and Coulomb interaction in a type-I Dirac semimetal (DSM) with $x$-direction tilt by performing a renormalization group analysis. The interplay between disorder and ordinary tilt generates an effective tilt along the $x$-direction, which is the physically observable one. There exist two types of disorder

Sang-Ki Ko, Chang Jo Kim, Hyedong Jung, Choongsang Cho

We propose a sign language translation system based on human keypoint estimation. It is well-known that many problems in the field of computer vision require a massive amount of dataset to train deep neural network models. The situation is even worse when it comes to the sign language translation problem as it is far more difficult to collect high-quality tr

Chiaki Sakama, Hien D. Nguyen, Taisuke Sato, Katsumi Inoue

In this paper, we introduce methods of encoding propositional logic programs in vector spaces. Interpretations are represented by vectors and programs are represented by matrices. The least model of a definite program is computed by multiplying an interpretation vector and a program matrix. To optimize computation in vector spaces, we provide a method of par

Zhi-hao Pan, Si-you Guo, B. C. Li

In this paper, we use the Gaussian distribution to analyze transverse momentum spectra of K_0^S and K^(*0) at midrapidity in d +Au, Cu+Cu and p+p Collisions at 200 GeV. The two-component Gaussian distribution can approximately agree with the experimental data and the three-component distribution can perfectly agree with the data in the given range. Moreover,

Veta Ghenescu

The FCAL collaboration has performed a design study for luminometers at future electronpositron colliders. Compact sampling calorimeters with precisely positioned silicon sensors and a fast readout will reach the necessary performance even in the presence of background from beamstrahlung and two-photon processes. A prototype calorimeter has been built with s

Sachin Mehta, Mohammad Rastegari, Linda Shapiro, Hannaneh Hajishirzi

We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2, for modeling visual and sequential data. Our network uses group point-wise and depth-wise dilated separable convolutions to learn representations from a large effective receptive field with fewer FLOPs and parameters. The performance of our network is ev

Junki Ohmura, Maxine Eskenazi

Dialog response ranking is used to rank response candidates by considering their relation to the dialog history. Although researchers have addressed this concept for open-domain dialogs, little attention has been focused on task-oriented dialogs. Furthermore, no previous studies have analyzed whether response ranking can improve the performance of existing d

Mohammad Naseri Tehrani, Shahrokh Farahmand

Recently, non-orthogonal codes have been advocated for IoT massive access. Activity detection has been demonstrated to entail common support recovery in a jointly sparse multiple measurement vector (MMV) problem and MMV algorithms have been successfully applied offering various degrees of complexity-performance trade-off. Targeting the small measurement per

François Laudenbach, Carlos Moraga Ferrandiz

Let M be a closed n-dimensional manifold, n > 2, whose first real cohomology group H 1 (M ; R) is non-zero. We present a general method for constructing a Morse 1-form $\alpha$ on M , closed but non-exact, and a pseudo-gradient X such that the differential $\partial$ X of the Novikov complex of the pair ($\alpha$, X) has at least one incidence coefficient wh

Ragunathan Mariappan, Vaibhav Rajan

Learning by integrating multiple heterogeneous data sources is a common requirement in many tasks. Collective Matrix Factorization (CMF) is a technique to learn shared latent representations from arbitrary collections of matrices. It can be used to simultaneously complete one or more matrices, for predicting the unknown entries. Classical CMF methods assume

Maciej Zamorski, Maciej Zięba

In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN model. This representation can be further

Ying Yang, Yiming Pan

In this article, we developed a generalized coupled-mode theory for mixing an isolated state with the gapped continuum to unify the engineering mechanism of gap-protected zero modes, Fano resonance and Tamm surface states, even though those phenomena are diverse in numerous branches of physics and optics. By tuning the on-site potential and coupling strength

Leon Andretti Abdillah

Smartphones as one of information technology products have been affected higher education in various aspects. This article explains the useness of smartphones in facilitating online examination in information systems and computer science students. The research objective to be achieved by the researchers through the research, are as follows: 1) Utilizing smar

Yutong Feng, Yifan Feng, Haoxuan You, Xibin Zhao

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on

Corouge Isabelle, Corentin Vallée, Pierre Maurel, Isabelle Corouge

Resting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily practice and academic research stay discreet compared to resting-state BOLD. However, by giving direct access to cerebral blood flow maps, rs-fASL leads to significant clinical subject scaled application as CBF can be considered as a biomarker in common neuropathology. Our work here

Saïd Abdeddaïm, Sylvestre Vimard, Lina Fatima Soualmia

Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural n

Abhisek Das, Satanik Panda, Suman Datta, Soumitra Naskar

In this paper authors are going to present a Markov Decision Process (MDP) based algorithm in Industrial Internet of Things (IIoT) as a safety compliance layer for human in loop system. Though some industries are moving towards Industry 4.0 and attempting to automate the systems as much as possible by using robots, still human in loop systems are very common

Dimitris Gkoumas, Dawei Sogn

Fusing and ranking multimodal information remains always a challenging task. A robust decision-level fusion method should not only be dynamically adaptive for assigning weights to each representation but also incorporate inter-relationships among different modalities. In this paper, we propose a quantum-inspired model for fusing and ranking visual and textua

Nikolas Porz, Martin Hanke, Manuel Baumgartner, Peter Spichtinger

The representation of cloud processes in weather and climate models is crucial for their feedback on atmospheric flows. Since there is no general macroscopic theory of clouds, the parameterization of clouds in corresponding simulation software depends crucially on the underlying modeling assumptions. In this study we present a new model of inter-mediate comp

Heinz H. Bauschke, Hui Ouyang, Xianfu Wang

We introduce the circumcenter mapping induced by a set of (usually nonexpansive) operators. One prominent example of a circumcenter mapping is the celebrated Douglas--Rachford splitting operator. Our study is motivated by the Circumcentered--Douglas--Rachford method recently introduced by Behling, Bello Cruz, and Santos in order to accelerate the Douglas--Ra

Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick Oliveira

We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large teleh

Emilie Kaufmann, Wouter Koolen

This paper presents new deviation inequalities that are valid uniformly in time under adaptive sampling in a multi-armed bandit model. The deviations are measured using the Kullback-Leibler divergence in a given one-dimensional exponential family, and may take into account several arms at a time. They are obtained by constructing for each arm a mixture marti

Juliane Rosemeier, Manuel Baumgartner, Peter Spichtinger

Clouds are important components of the atmosphere. Since it is usually not possible to treat them as ensembles of huge numbers of particles, parameterizations on the basis of averaged quantities (mass and/or number concentration) must be derived. Since no first-principles derivations of such averaged schemes are available today, many alternative approximatin

Shaddin Dughmi, David Kempe, Ruixin Qiang

To select a subset of samples or "winners" from a population of candidates, order sampling [Rosen 1997] and the k-unit Myerson auction [Myerson 1981] share a common scheme: assign a (random) score to each candidate, then select the k candidates with the highest scores. We study a generalization of both order sampling and Myerson's allocation rule, called win

Qiang Sun

In this study, molecular dynamic simulations are employed to investigate the nucleation of NaCl crystal in solutions. According to the simulations, the dissolved behaviors of NaCl in water are dependent on ion concentrations. With increasing NaCl concentrations, the dissolved Na$^+$ and Cl$^-$ ions tend to be aggregated in solutions. In combination with our

Manoj Kumar Singh, Lakhwinder Singh, Mehmet Agartioglu, Vivek Sharma

We report results from searches of pseudoscalar and vector bosonic super-weakly interacting massive particles (super-WIMP) in the TEXONO experiment at the Kuo-Sheng Nuclear Power Station, using 314.15 kg days of data from $n$-type Point-Contact Germanium detector. The super-WIMPs are absorbed and deposit total energy in the detector, such that the experiment

Mohd Suhail Rizvi

The transportation of the cargoes in biological cells is primarily driven by the motor proteins on filamentous protein tracks. The stochastic nature of the motion of motor protein often leads to its spontaneous detachment from the track. Using the available experimental data, we demonstrate a tradeoff between the speed of the motor and its rate of spontaneou

Ola Amara-Omari, Mary Schaps

We show that a vertex in the reduced crystal is i-external for a residue i if the defect is less than the absolute value of the i-component of the hub. We demonstrate the existence of a bound on the degree after which all vertices of a given defect d are external in at least one i-string. Combining this with the Chuang-Rouquier categorification for the simpl

Shijin Ding, Quanrong Li

This paper is concerned with the validity of the Prandtl boundary layer theory in the inviscid limit of the steady incompressible Navier-Stokes equations, which is an extension of the pioneer paper (Y. Guo et al., 2017, Ann. PDE) from a domain of $[0,L]\times\mathbb{R}_+$ to $[0,L]\times[0,2]$. Under the symmetry assumption, we establish the validity of the

Dinesh Pandey, Kamal Lochan Patra

For a graph $G,$ we denote the number of connected subgraphs of $G$ by $F(G)$. For a tree $T$, $F(T)$ has been studied extensively and it has been observed that $F(T)$ has a reverse correlation with Wiener index of $T$. Based on that, we call $F(G),$ the core index of $G$. In this paper, we characterize the graphs which extremize the core index among all gra

Sharareh Sayyad, Naoto Tsuji, Abolhassan Vaezi, Massimo Capone

We study the dynamical behavior of doped electronic systems subject to a global ramp of the repulsive Hubbard interaction. We start with formulating a real-time generalization of the fluctuation-exchange approximation. Implementing this numerically, we investigate the weak-coupling regime of the Hubbard model both in the electron-doped and hole-doped regimes

Kui Hou, Jizi Lin, Chengjie Zhu, Yaping Yang

Manipulating photons is an essential technique in quantum communication and computation. Combining the Raman electromagnetically induced transparency technology, we show that the photon blockade behavior can be actively controlled by using an external control field in a two atoms cavity-QED system. As a result, a versatile photon gateway can be achieved in t

James A. D. Diacoumis, Yvonne. Y. Y. Wong

We explore the issue of prior dependence in the context of one-sided constraints on the dark matter-photon and dark matter-neutrino elastic scattering cross-sections derived from cosmic microwave background (CMB) anisotropies measurements. Testing in particular the linear flat, Jeffreys, and logarithmic flat priors, we find that the former two yield upper li

Pascal Baseilhac, Satoshi Tsujimoto, Luc Vinet, Alexei Zhedanov

The Heun-Askey-Wilson algebra is introduced through generators $\{\boX,\boW\}$ and relations. These relations can be understood as an extension of the usual Askey-Wilson ones. A central element is given, and a canonical form of the Heun-Askey-Wilson algebra is presented. A homomorphism from the Heun-Askey-Wilson algebra to the Askey-Wilson one is identified.

Kathleen Campbell Garwood, Ph. D., Arpit Arun Dhobale

When considering answering important questions with data, unsupervised data offers extensive insight opportunity and unique challenges. This study considers student survey data with a specific goal of clustering students into like groups with underlying concept of identifying different poverty levels. Fuzzy logic is considered during the data cleaning and or

Jizi Lin, Kui Hou, Chengjie Zhu, Yaping Yang

We present a study of manipulating the multiphoton blockade phenomenon in a single mode cavity with two ladder-type three-level atoms. Combining the cavity QED with electromagnetically induced transparency technique, we show that it is possible to actively manipulate the photon blockade when two atoms are in phase radiations. As a result, the two-photon bloc

Chuanchen Luo, Yuntao Chen, Naiyan Wang, Zhaoxiang Zhang

With the surge of deep learning techniques, the field of person re-identification has witnessed rapid progress in recent years. Deep learning based methods focus on learning a feature space where samples are clustered compactly according to their corresponding identities. Most existing methods rely on powerful CNNs to transform the samples individually. In c

Daqiang Bao, Chengjie Zhu, Yaping Yang, G. S. Agarwal

We investigate the cavity excitation spectrum and the photon number distribution in a cavity QED system driven by a broadband squeezed vacuum. In an empty cavity, we show that only states with even number of photons can be measured under resonant condition since the squeezed vacuum consists of states with even number of photons only. When a single atom is tr

Jie Liu, Zhen Yan, Jian-Ping Yuan, Ru-Shuang Zhao

One large glitch was detected in PSR B1737$-$30 using data spanning from MJD 57999 to 58406 obtained with the newly built Shanghai Tian Ma Radio Telescope (TMRT). The glitch took place at the time around MJD 58232.4 when the pulsar underwent an increase in the rotation frequency of $\Delta \nu$ about 1.38$\times 10^{-6}$ Hz, corresponding to a fractional ste

Siddique Latif, Rajib Rana, Junaid Qadir

Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems. However, recent research on adversarial examples poses enormous challenges on the robustness of SER systems by showing the susceptibility of deep neural networks to adversarial examples as they rely only on small and

Grigory Vekstein

The present paper provides comprehensive description of various regimes involved in the two-fluid model of the resistive tearing instability. These include two novel regimes of this instability, which correspond to the long-wave modes that can develop in a highly-elongated current sheet. This issue is relevant to the study of fast magnetic reconnection and m

Dianbo Liu, Timothy Miller, Raheel Sayeed, Kenneth D. Mandl

Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained e

Cindy Tsang

Let $L/K$ be a finite Galois extension whose Galois group $G$ is non-abelian and characteristically simple. Using tools from graph theory, we shall give a closed formula for the number of Hopf-Galois structures on $L/K$ with associated group isomorphic to $G$.

Toshihiro Noda, Yasuhiro Hieida, Shin-ichi Tadaki

On highways with two lanes, cars are requested to run on the slow lane and are allowed to run on the fast lane for overtaking. However, on real two-lane highways, we daily observe a phenomenon called "reverse lane usage", in which the flow on the fast lane exceeds that on the slow lane. The origins of the phenomenon have not been discussed clearly. In this p

Li Ding, Chen Feng

We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame. We use DNNs to model the highly non-convex mapping process that traditionally involves hand-crafted data association, sensor pose initialization, and global refinement. Our

Andrey Sayanskiy, Anton S. Kupriianov, Su Xu, Polina Kapitanova

We reveal peculiarities of the trapped (dark) mode excitation in a polarization-insensitive all-dielectric metasurface, whose unit super-cell is constructed by particularly arranging four cylindrical dielectric particles. Involving group-theoretical description we discuss in detail the effect of different orientations of particles within the super-cell on ch

Ippei Nagamachi

Consider a morphism between connected locally Noetherian normal schemes. In this paper, we discuss when the sequence of the etale fundamental groups associated to the morphism is exact. Moreover, we give a characterization of when the kernel of the induced homomorphism between their fundamental groups is topologically finitely generated, for the morphism fro

Shao-Meng Qin

C-dismantling (CD) problem aims at finding the minimum vertex set D of a graph G(V,E) after removing which the remaining graph will break into connected components with the size not larger than C. In this paper, we introduce a spin-glass model with C+1 integer-value states into the CD problem and then study the properties of this spin-glass model by the beli

Tatsuki Koga, Naoki Nonaka, Jun Sakuma, Jun Seita

Deep learning has significant potential for medical imaging. However, since the incident rate of each disease varies widely, the frequency of classes in a medical image dataset is imbalanced, leading to poor accuracy for such infrequent classes. One possible solution is data augmentation of infrequent classes using synthesized images created by Generative Ad

Baoyi Chen

This work studies the thermal production of $J/\psi$ and $\psi(2S)$ with Boltzmann transport model in the Quark Gluon Plasma (QGP) produced in $\sqrt{s_{NN}}=5.02$ TeV Pb-Pb collisions. $J/\psi$ nuclear modification factors are studied in details with the mechanisms of primordial production and the recombination of charm and anti-charm quarks in the thermal

Atsufumi Honda, Kentaro Saji, Keisuke Teramoto

A mixed type surface is a connected regular surface in a Lorentzian 3-manifold with non-empty spacelike and timelike point sets. The induced metric of a mixed type surface is a signature-changing metric, and their lightlike points may be regarded as singular points of such metrics. In this paper, we investigate the behavior of Gaussian curvature at a non-deg

Ziran Zhang, Zixuan Yao, Qinbo Sun, Huihuan Qian

Autonomous Surface Vehicles (ASVs) provide an effective way to actualize applications such as environment monitoring, search and rescue, and scientific researches. However, the conventional ASVs depends overly on the stored energy. Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an auxiliary propulsion system. The electric energy

Ramtin Zand, Kerem Y. Camsari, Supriyo Datta, Ronald F. DeMara

Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restricted Boltzmann machines (RBMs) to form deep belief networks (DBNs). The embedded MRAM-based neuron is modeled using precise physics equations. The simulation results exhibit the de

Bo Zhao, Lili Meng, Weidong Yin, Leonid Sigal

Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges,

Pavel Tolmachev, Rishi R. Dhingra, Michael Pauley, Mathias Dutschmann

Computational models of the respiratory central pattern generator (rCPG) are usually based on biologically-plausible Hodgkin Huxley neuron models. Such models require numerous parameters and thus are prone to overfitting. The HH approach is motivated by the assumption that the biophysical properties of neurons determine the network dynamics. Here, we impleme

Longlong Jing, Xiaodong Yang, Jingen Liu, Yingli Tian

The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections. To alleviate this problem, in this paper, we propose 3DRotNet: a fully self-supervised approach to learn spatiotemporal features from unlabeled videos. A set of rotations are applie

Chandrashekar Murugeshan, Virginia Kilborn, Danail Obreschkow, Karl Glazebrook

The neutral atomic hydrogen (HI) content of spiral galaxies has been observed to vary with environment, with more HI-deficient spirals residing in high density environments. This can be attributed to environmental effects such as ram pressure stripping and tidal interactions, which remove HI from the discs of galaxies. However, some spirals in low-density en

Ian Miller, Jon Wallace

A key limitation of current multi-robot systems is a lack of relative localization, particularly in environments without GPS or motion capture systems. This article presents a centralized method for relatively localizing a 2D swarm using sensors and beacons on the robots themselves. The UKF-based algorithm as well as the requisite novel and cost-effective se

Tatsuo Kobayashi, Shio Tamba

We study modular transformation of holomorphic Yukawa couplings in magnetized D-brane models. It is found that their products are modular forms, which are non-trivial representations of finite modular subgroups, e.g. $S_3$, $S_4$, $\Delta(96)$ and $\Delta(384)$.

M. T. Bell, B. Doucot, M. E. Gershenson, L. B. Ioffe

We propose a novel platform for the study of quantum phase transitions in one dimension (1D QPT). The system consists of a specially designed chain of asymmetric SQUIDs; each SQUID contains several Josephson junctions with one junction shared between the nearest-neighbor SQUIDs.We develop the theoretical description of the low energy part of the spectrum. In

Xueying Li, Yanqi Luo, Martin Holt, Zhonghou Cai

Using nanoprobe X-ray diffraction microscopy, we investigate the relationship between residual strains from crystal growth in CsPbBr$_3$ thin film crystals, their stability, and local bandgap. We find that out-of-plane compressive strain that arises from cooldown from crystallization is detrimental to material stability under X-ray irradiation. We also find

Tattwamasi Amrutam

We show that for a large class of actions $\Gamma \curvearrowright \mathcal{A}$ of $C^*$-simple groups $\Gamma$ on unital $C^*$-algebras $\mathcal{A}$, including any non-faithful action of a hyperbolic group with trivial amenable radical, every intermediate $C^*$-subalgebra $\mathcal{B}$, $C_{\lambda}^*(\Gamma)\subseteq \mathcal{B} \subseteq \mathcal{A}\rtim

C. H. Jeffrey Pang

This work builds on our recent work on a distributed optimization algorithm for graphs with directed unreliable communications. We show its linear convergence when we take either the proximal of each function or an affine minorant for when the function is smooth.

Anindya Goswami, Omkar Manjarekar, Anjana R

This paper presents the solution to a European option pricing problem by considering a regime-switching jump diffusion model of the underlying financial asset price dynamics. The regimes are assumed to be the results of an observed pure jump process, driving the values of interest rate and volatility coefficient. The pure jump process is assumed to be a semi

Jiaming Liang, Renato D. C. Monteiro

This paper describes and establishes the iteration-complexity of a doubly accelerated inexact proximal point (D-AIPP) method for solving the nonconvex composite minimization problem whose objective function is of the form $f+h$ where $f$ is a (possibly nonconvex) differentiable function whose gradient is Lipschitz continuous and $h$ is a closed convex functi

Wei Wei, Megan Barry, Thomas Klähn, Prashanth Jaikumar

Compact stars containing quark matter may masquerade as neutron stars in the range of measured mass and radius, making it difficult to draw firm conclusions on the phase of matter inside the star. The sensitivity of core $g$-mode oscillations to the presence of a mixed phase may alleviate this difficulty. In hybrid stars that admit quark matter in a mixed ph

Andrew Hassell, Pierre Portal, Jan Rozendaal

We define a scale of Hardy spaces $\mathcal{H}^{p}_{FIO}(\mathbb{R}^{n})$, $p\in[1,\infty]$, that are invariant under suitable Fourier integral operators of order zero. This builds on work by Smith for $p=1$. We also introduce a notion of off-singularity decay for kernels on the cosphere bundle of $\mathbb{R}^{n}$, and we combine this with wave packet transf

Dabiao Ma, Zhiba Su, Yuhao Lu, Wenxuan Wang

Neural networks with Auto-regressive structures, such as Recurrent Neural Networks (RNNs), have become the most appealing structures for acoustic modeling of parametric text to speech synthesis (TTS) in ecent studies. Despite the prominent capacity to capture long-term dependency, these models consist of massive sequential computations that cannot be fully p

Peng Lu, Hangyu Lin, Yanwei Fu, Shaogang Gong

Sketch has been employed as an effective communicative tool to express the abstract and intuitive meanings of object. Recognizing the free-hand sketch drawing is extremely useful in many real-world applications. While content-based sketch recognition has been studied for several decades, the instance-level Sketch-Based Image Retrieval (SBIR) tasks have attra

Ming Yan, Jiangnan Xia, Chen Wu, Bin Bi

A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection a

Panagiotis Kouvaros, Alessio Lomuscio

We address the problem of verifying neural-based perception systems implemented by convolutional neural networks. We define a notion of local robustness based on affine and photometric transformations. We show the notion cannot be captured by previously employed notions of robustness. The method proposed is based on reachability analysis for feed-forward neu

Suvendu Rakshit, Ansu Johnson, C. S. Stalin, Poshak Gandhi

We present the color and flux variability analysis at 3.4 {\mu}m (W1-band) and 4.6 {\mu}m (W 2-band) of 492 narrow-line Seyfert 1 (NLSy1) galaxies using archival data from the Wide-field Infrared Survey Explorer (WISE). In the WISE color-color, (W1 - W2) versus (W2 - W3) diagram, ~58% of the NLSy1 galaxies of our sample lie in the region occupied by the blaz

Majid Hashemi, Gholamhossein Haghighat

In this study, we investigate observability of the neutral scalar ($H$) and pseudoscalar ($A$) Higgs bosons in the framework of the Type-I 2HDM at SM-like scenario at a linear collider operating at $\sqrt s=$ 500 and 1000 GeV. The signal process chain $e^- e^+ \rightarrow A H \rightarrow ZHH\rightarrow jj b\bar{b}b\bar{b}$ where $jj$ is a di-jet resulting fr

Stephen Deterding

Let $U$ be a bounded open subset of the complex plane. Let $0<\alpha<1$ and let $A_{\alpha}(U)$ denote the space of functions that satisfy a Lipschitz condition with exponent $\alpha$ on the complex plane, are analytic on $U$ and are such that for each $\epsilon >0$, there exists $\delta >0$ such that for all $z$, $w \in U$, $|f(z)-f(w)| \leq \epsilon |z-w|^

K. Vasudevan, K. Madhu, Shivani Singh

Single user massive multiple input multiple output (MIMO) can be used to increase the spectral efficiency, since the data is transmitted simultaneously from a large number of antennas located at both the base station and mobile. It is feasible to have a large number of antennas in the mobile, in the millimeter wave frequencies. However, the major drawback of

Xi Chen, Weidong Liu, Yichen Zhang

This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical

Fayçal Hammad, Étienne Massé, Patrick Labelle

The fate of black hole thermodynamics under Weyl transformations is investigated by going back to the laws of black hole mechanics. It is shown that the transformed surface gravity, that one would identify with the black hole temperature in the conformal frame, as well as the black hole entropy, that one would identify with the horizon area, cannot be invari

Jun Ho Son, Jing-Yuan Chen, S. Raghu

We generalize our previous lattice construction of the abelian bosonization duality in $2+1$ dimensions to the entire web of dualities as well as the $N_f=2$ self-duality, via the lattice implementation of a set of modular transformations in the theory space. The microscopic construction provides explicit operator mappings, and allows the manifestation of so

Injo Hur, Darren C. Ong

The KdV hierarchy is a family of evolutions on a Schr\"odinger operator that preserves its spectrum. Canonical systems are a generalization of Schr\"odinger operators, that nevertheless share many features with Schr\"odinger operators. Since this is a very natural generalization, one would expect that it would also be straightforward to build a hierarchy of

Yuanhang Su, Kai Fan, Nguyen Bach, C. -C. Jay Kuo

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performan

Kabir Chakravarti, Sumanta Chakraborty, Sukanta Bose, Soumitra SenGupta

We calculate the tidal Love numbers of black holes and neutron stars in the presence of higher dimensions. The perturbation equations around an arbitrary static and spherically symmetric metric for the even parity modes are presented in the context of an effective four-dimensional theory on the brane. This subsequently leads to the sought expression for the

Q. M. Zhang, D. Li, Y. Huang

In this paper, we report our multiwavelength imaging observations of chromospheric evaporation in a C5.5 circular-ribbon flare (CRF) on 2014 August 24. The flare was observed by the Atmospheric Imaging Assembly (AIA) on board the \textit{Solar Dynamics Observatory} (\textit{SDO}), X-ray Telescope (XRT) on board the \textit{Hinode} spacecraft, and ground-base

Campo Elías González Pineda, Sandra Milena García

In this article we calculate the length of the golden spiral, and we study the golden rectangles. We calculate some measures of interest. We also show that the only rectangles that can be subdivided or that generate sub rectangles indefinitely are the golden rectangles. We emphasize that when subdividing a rectangle into sub rectangles, the Fibonacci sequenc

Hidetomo Nagai, Yasuhiro Ohta, Ryogo Hirota

We propose new type of discrete and ultradiscrete soliton equations, which admit extended soliton solution called periodic phase soliton solution. The discrete equation is derived from the discrete DKP equation and the ultradiscrete one is obtained by applying the ultradiscrete limit. The soliton solutions have internal freedom and change their shape periodi

Ying Zhang

Convolutions of independent random variables often arise in a natural way in many applied problems. In this article, we compare convolutions of two sets of gamma (negative binomial) random variables in the convolution order and the usual stochastic order in a unified set-up, when the shape and scale parameters satisfy a partial order called reverse-coupled m

David Warde-Farley, Tom Van de Wiele, Tejas Kulkarni, Catalin Ionescu

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent simultaneously learns a goal-conditioned

Suhani Vora, Reza Mahjourian, Soeren Pirk, Anelia Angelova

Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. Working towards this capability, we address the task of predicting future frame segmentation from a stream of monocular video by leveraging the 3D str

Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci

We introduce the Metropolis-Hastings generative adversarial network (MH-GAN), which combines aspects of Markov chain Monte Carlo and GANs. The MH-GAN draws samples from the distribution implicitly defined by a GAN's discriminator-generator pair, as opposed to standard GANs which draw samples from the distribution defined only by the generator. It uses the di

Drew Linsley, Junkyung Kim, David Berson, Thomas Serre

Recent successes in deep learning have started to impact neuroscience. Of particular significance are claims that current segmentation algorithms achieve "super-human" accuracy in an area known as connectomics. However, as we will show, these algorithms do not effectively generalize beyond the particular source and brain tissues used for training -- severely

Zhong-Xiao Man, Yun-Jie Xia, Rosario Lo Franco

We study the validity of Landauer principle in the non-Markovian regime by means of collision models where the intracollisions inside the reservoir cause memory effects generating system-environment correlations. We adopt the system-environment correlations created during the dynamical process to assess the effect of non-Markovianity on the Landauer principl

Samuel Bladwell

In this paper, I present a simple method of obtaining spin-polarised current from a QPC with a large Rashba interaction. The origin of this spin polarisation is the adiabatic evolution of spin "up" of the first QPC sub-band, into spin "down" of the second QPC sub-band. Unique experimental signatures of this effect can be obtained using a magnetic focusing se

Alex G. C. de Sá, Cristiano G. Pimenta, Gisele L. Pappa, Alex A. Freitas

This supplementary material aims to describe the proposed multi-label classification (MLC) search spaces based on the MEKA and WEKA softwares. First, we overview 26 MLC algorithms and meta-algorithms in MEKA, presenting their main characteristics, such as hyper-parameters, dependencies and constraints. Second, we review 28 single-label classification (SLC) a

Xiongfei Wang, Bo Li, Yuanning Gao, Xinchou Lou

We perform a helicity amplitude analysis for the processes of $e^+e^-$$\rightarrow$$J/\psi,\psi(2S)\rightarrow\Xi(1530)\bar\Xi(1530)\rightarrow\pi\pi\Xi\bar\Xi\rightarrow\Lambda\bar\Lambda4\pi\rightarrow p\bar{p}6\pi$. The joint angular distribution for these processes are obtained, which allows a proper estimation of the detection efficiency using helicity

Xiangcun Meng, Jiao Li

Although type Ia supernovae (SNe Ia) are so important in many astrophysical fields, a debate on their progenitor model is still endless. Searching the surviving companion in a supernova remnant (SNR) may distinguish different progenitor models, since a companion still exists in the remnant for the single-degenerate (SD) model, but does not for the double deg