November 2018 arXiv papers — page 88

Kowshik Thopalli, Rushil Anirudh, Jayaraman J. Thiagarajan, Pavan Turaga

We present a novel unsupervised domain adaptation (DA) method for cross-domain visual recognition. Though subspace methods have found success in DA, their performance is often limited due to the assumption of approximating an entire dataset using a single low-dimensional subspace. Instead, we develop a method to effectively represent the source and target da

Travis Robson, Neil J. Cornish

The Laser Interferometer Space Antenna (LISA) will open a rich discovery space in the milli-Hertz gravitational wave band. In addition to the anticipated signals from many millions of binary systems, this band may contain new and previously un-imagined sources for which we currently have no models. To detect unmodeled and unexpected signals we need to be abl

Čukić Milena, Stokić Miodrag, Radenković Slavoljub, Ljubisavljević Miloš

Biomarkers of Major Depressive Disorder(MDD), its phases and forms have long been sought. Research indicates that the complexity measures of the cortical electrical activity (EEG) might be candidates for this role. To examine whether the complexity of EEG activity, measured by Higuchi fractal dimension (HFD) and sample entropy (SampEn), differs between healt

D. C. Marinescu, Pirmin J. Weigele, J. Carlos Egues, Dominik Zumbühl

We derive a closed-form expression for the weak localization (WL) corrections to the magnetoconductivity of a 2D electron system with arbitrary Rashba $α$ and Dresselhaus $β$ (linear) and $β_3$ (cubic) spin-orbit interaction couplings, in a perpendicular magnetic field geometry. In a system of reference with an in-plane $\hat{z}$ axis chosen as the high spin

Eduardo Guendelman, Emil Nissimov, Svetlana Pacheva

We discuss a new extended gravity model in ordinary $D=4$ spacetime dimensions, where an additional term in the action involving Gauss-Bonnet topological density is included without the need to couple it to matter fields unlike the case of ordinary D=4 Gauss-Bonnet gravity models. Avoiding the Gauss-Bonnet density becoming a total derivative is achieved by e

Ulderico Fugacci, Federico Iuricich, Leila De Floriani

We consider the problem of efficiently computing a discrete Morse complex on simplicial complexes of arbitrary dimension and very large size. Based on a common graph-based formalism, we analyze existing data structures for simplicial complexes, and we define an efficient encoding for the discrete Morse gradient on the most compact of such representations. We

R. W. R. Darling, Mark L. Velednitsky

Binary classification problems can be naturally modeled as bipartite graphs, where we attempt to classify right nodes based on their left adjacencies. We consider the case of labeled bipartite graphs in which some labels and edges are not trustworthy. Our goal is to reduce noise by identifying and fixing these labels and edges. We first propose a geometric t

Alexey A. Melnikov, Aleksandr P. Alodjants, Leonid E. Fedichkin

Quantum particles are known to be faster than classical when they propagate stochastically on certain graphs. A time needed for a particle to reach a target node on a distance, the hitting time, can be exponentially less for quantum walks than for classical random walks. It is however not known how fast would interacting quantum particles propagate on differ

Sakil Barbhuiya, Zafeirios Papazachos, Peter Kilpatrick, Dimitrios S. Nikolopoulos

Cybersecurity attacks in Cloud data centres are increasing alongside the growth of the Cloud services market. Existing research proposes a number of anomaly detection systems for detecting such attacks. However, these systems encounter a number of challenges, specifically due to the unknown behaviour of the attacks and the occurrence of genuine Cloud workloa

Vahid Noroozi, Sara Bahaadini, Lei Zheng, Sihong Xie

While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied. We introduce a semi-supervised neural network model, named Multi-view Discrimin

D. E. Tsurikov, A. M. Yafyasov

Searching of optimal parameters of nanoelectronic devices is a primal problem in their modeling. We solve this problem on example of the electron ballistic switch in quantum network model. For this purpose, we use a computing scheme in which closed channels are taking into account. It allows calculating correctly a scattering matrix of the switch and, conseq

Anit Kumar Sahu, Shaunak Mishra, Narayan Bhamidipati

Real time bidding (RTB) enables demand side platforms (bidders) to scale ad campaigns across multiple publishers affiliated to an RTB ad exchange. While driving multiple campaigns for mobile app install ads via RTB, the bidder typically has to: (i) maintain each campaign's efficiency (i.e., meet advertiser's target cost-per-install), (ii) be sensitiv

Andreas Kaloudis, Dimitrios Tsolis

The major perspective of this paper is to provide more evidence regarding how "quickly", in different macroeconomic states, companies adjust their capital structure to their leverage targets. This study extends the empirical research on the topic of capital structure by focusing on a quantile regression method to investigate the behavior of firm-spec

Zhen Li, Nicholas J. Meyer, Eric B. Laber, Robert Brigantic

Pursuit-evasion is a multi-agent sequential decision problem wherein a group of agents known as pursuers coordinate their traversal of a spatial domain to locate an agent trying to evade them. Pursuit evasion problems arise in a number of import application domains including defense and route planning. Learning to optimally coordinate pursuer behaviors so as

Krzysztof Dȩbicki, Enkelejd Hashorva, Zbigniew Michna

The ruin probability in the classical Brownian risk model can be explicitly calculated for both finite and infinite-time horizon. This is not the case for the simultaneous ruin probability in two-dimensional Brownian risk model. Resorting on asymptotic theory, we derive in this contribution approximations of both simultaneous ruin probability and simultaneou

Artem Roenko, Konstantin Sveshnikov

The effective interaction of the electron magnetic moment anomaly with the Coulomb field of superheavy nuclei is investigated by taking into account its dynamical screening at small distances. The shift of the electronic levels, caused by this interaction, is considered for H-like atoms and for compact nuclear quasi-molecules, non-perturbatively both in $Zα$

O. Sergijenko

For dynamical dark energy with the barotropic equation of state we determine the mean values of parameters and their confidence ranges together with other cosmological parameters on the basis of different combined datasets. The used observations include Planck data on CMB temperature anisotropy, E-mode polarization and lensing, BICEP2/Keck Array data on B-mo

Torsten Asselmeyer-Maluga

4-manifolds have special topological properties which can be used to get a different view on quantum mechanics. One important property (connected with exotic smoothness) is the natural appearance of 3-manifold wild embeddings (Alexanders horned sphere) which can be interpreted as quantum states. This relation can be confirmed by using the Turaev-Drinfeld qua

Kanza Hamid, Amina Asif, Wajid Abbasi, Durre Sabih

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model a

H. Xu, X. H. Yang, J. Liu, M. Borghesi

The influence of high-Z dopant (Bromine) in low-Z foam (polystyrene) target on laser-driven fast electron propagation is studied by the 3D hybrid particle-in-cell (PIC)/fluid code HEETS.It is found that the fast electrons are better confined in doped targets due to the increasing resistivity of the target, which induces a stronger resistive magnetic field wh

Madeleine S. Kotzagiannidis, Mike E. Davies

In this work, we present a theoretical study of signals with sparse representations in the vertex domain of a graph, which is primarily motivated by the discrepancy arising from respectively adopting a synthesis and analysis view of the graph Laplacian matrix. Sparsity on graphs and, in particular, the characterization of the subspaces of signals which are s

Tatiana Latychevskaia, Hans-Werner Fink

Coherent diffraction imaging (CDI) allows the retrieval of the structure of an isolated object, such as a macromolecule, from its diffraction pattern. CDI requires the fulfilment of two conditions: the imaging radiation must be coherent and the object must be isolated. We discuss that it is possible to directly retrieve the molecular structure from its diffr

Mark Sh. Levin

The article describes a special time-interval balancing in multi-processor scheduling of composite modular jobs. This scheduling problem is close to just-in-time planning approach. First, brief literature surveys are presented on just-in-time scheduling and due-data/due-window scheduling problems. Further, the problem and its formulation are proposed for the

Milan Aggarwal, Nupur Kumari, Ayush Bansal, Balaji Krishnamurthy

Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP. Automatically generating paraphrases has its utility in many NLP tasks like question answering, information retrieval, conversational systems to name a few. In this paper, we introduce iterative refinement of generated

Botond Fazeka, Alexander Schindler, Thomas Lidy, Andreas Rauber

We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples and metadata as input. The audio is fed into a Convolutional Neural Network (CNN) using four convolutional layers. The additionally provided metadata is processed using fully connected layers. The flattened convolutional l

Adam da Silva, Halyson Baltazar

The aim of this paper is to classify three dimensional compact Riemannian manifolds $(M^{3},g)$ that admits a non-constant solution to the equation $$-Δf g+Hess f-fRic=μRic+λg,$$ for some special constants $(μ, λ)$, under assumption that the manifold has cyclic parallel Ricci tensor. Namely, the structures that we will study here will be: positive static tri

Yuzhi Zhou, Huajie Chen, Aihui Zhou

We propose a novel numerical algorithm for computing the electronic structure related eigenvalue problem of incommensurate systems. Unlike the conventional practice that approximates the system by a large commensurate supercell, our algorithm directly discretizes the eigenvalue problem under the framework of a plane wave method. The emerging ergodicity and t

Diego Guedes-Sobrinho, Weiqi Wang, Ian Hamilton, Juarez L. F. Da Silva

Gold nanoclusters have been the focus of numerous computational studies but an atomistic understanding of their structural and dynamical properties at finite temperature is far from satisfactory. To address this deficiency, we investigate gold nanoclusters via ab initio molecular dynamics, in a range of sizes where a core-shell morphology is observed. We ana

Bo Zhou, Randolph Crawford, Belma Dogdas, Gregory Goldmacher

Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large-scale use. For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In So

V. Cikojević, L. Vranješ Markić, G. E. Astrakharchik, J. Boronat

We have studied dilute Bose-Bose mixtures of atoms with attractive interspecies and repulsive intraspecies interactions using quantum Monte Carlo methods at $T=0$. Using a number of models for interactions, we determine the range of validity of the universal equation of state of the symmetric liquid mixture as a function of two parameters: the $s$-wave scatt

J. M. Enriquez-Salamanca, Maria J. Gonzalez

We prove the corona theorem for domains whose boundary lies in certain smooth quasicircles. These curves, which are not necessarily Dini-smooth, are defined by quasiconformal mappings whose complex dilatation verifies certain conditions. Most importantly we do not assume any "thickness" condition on the boundary domain. In this sense, our results com

Vadim E. Levit, David Tankus

A graph $G$ is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function $w$ is defined on its vertices. Then $G$ is $w$-well-covered if all maximal independent sets are of the same weight. For every graph $G$, the set of weight functions $w$ such that $G$ is $w$-well-covered is a vector space, denoted as $WC

Vadim E. Levit, David Tankus

A graph $G$ is well-covered if all its maximal independent sets are of the same cardinality. Assume that a weight function $w$ is defined on its vertices. Then $G$ is $w$-well-covered if all maximal independent sets are of the same weight. For every graph $G$, the set of weight functions $w$ such that $G$ is $w$-well-covered is a vector space, denoted $WCW(G

Bolun Chen, Jan R. Engelbrecht, Renato Mirollo

We study the dynamics of a generalized version of the famous Kuramoto-Sakaguchi coupled oscillator model. In the classic version of this system, all oscillators are governed by the same ODE, which depends on the order parameter of the oscillator configuration. The order parameter is the arithmetic mean of the configuration of complex oscillator phases, multi

Talya Eden, Dana Ron, C. Seshadhri

Given query access to an undirected graph $G$, we consider the problem of computing a $(1\pmε)$-approximation of the number of $k$-cliques in $G$. The standard query model for general graphs allows for degree queries, neighbor queries, and pair queries. Let $n$ be the number of vertices, $m$ be the number of edges, and $n_k$ be the number of $k$-cliques. Pre

Abdul Obeid, Peter D. Bruza, Peter Wittek

This article explores how probabilistic programming can be used to simulate quantum correlations in an EPR experimental setting. Probabilistic programs are based on standard probability which cannot produce quantum correlations. In order to address this limitation, a hypergraph formalism was programmed which both expresses the measurement contexts of the EPR

Xiaojin Zhu

I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training-data

Wangyu Luo, Wael Alghamdi, Yue M. Lu

We present the optimal design of a spectral method widely used to initialize nonconvex optimization algorithms for solving phase retrieval and other signal recovery problems. Our work leverages recent results that provide an exact characterization of the performance of the spectral method in the high-dimensional limit. This characterization allows us to map

Alexander Schindler, Thomas Lidy, Andreas Rauber

In this paper we present a Deep Neural Network architecture for the task of acoustic scene classification which harnesses information from increasing temporal resolutions of Mel-Spectrogram segments. This architecture is composed of separated parallel Convolutional Neural Networks which learn spectral and temporal representations for each input resolution. T

Nikolaos S. Papageorgiou, Vicenţiu D. Rădulescu, Dušan D. Repovš

We consider perturbations of nonlinear eigenvalue problems driven by a nonhomogeneous differential operator plus an indefinite potential. We consider both sublinear and superlinear perturbations and we determine how the set of positive solutions changes as the real parameter $λ$ varies. We also show that there exists a minimal positive solution $\overline{u}

Kyle Augustson, Stéphane Mathis

The purpose of this short paper is to give a summary of the derivation of a perturbative method to assess the impact of an arbitrary 3D magnetic field configuration, whose energy is everywhere small relative to the gravitational binding energy, on the form and value of the eigenfrequencies and eigenfunctions of stellar oscillations.

Kyle Augustson, Stéphane Mathis

A simplified model for stellar and planetary convection is derived for the magnitude of the rms velocity, degree of superadiabaticity, and characteristic length scale with Rossby number as well as with thermal and viscous diffusivities. Integrating the convection model into a linearization of the dynamics in the transition region between convectively unstabl

Haiyan Fan, Baizhan Xia, Liang Tong, Shengjie Zheng

Topologically gapless edge states, characterized by topological invariants and Berry's phases of bulk energy bands, provide amazing techniques to robustly control the reflectionless propagation of electrons, photons and phonons. Recently, a new family of topological phases, dictated by the bulk polarization, has been observed, leading to the discovery of

Kin Ming Hui, Soojung Kim

Let $n\geq 3$, $0< m<\frac{n-2}{n}$ and $T>0$. We construct positive solutions to the fast diffusion equation $u_t=Δu^m$ in $\mathbb{R}^n\times(0,T)$, which vanish at time $T$. By introducing a scaling parameter $β$ inspired by \cite{DKS}, we study the second-order asymptotics of the self-similar solutions associated with $β$ at spatial infinity. We also inv

V. Nestoridis, A. G. Siskakis, A. Stavrianidi, S. Vlachos

For a function space $X(\OO)$ satisfying weak assumptions we prove that the generic function in $X(\OO)$ is totally unbounded, hence non-extendable. We provide several examples of such spaces; they are mainly localized versions of classical function spaces and intersections of them.

Qian Bin, Xin-You Lü, Shang-Wu Bin, Ying Wu

We investigate theoretically the model of a cavity-quantum-electrodynamics (QED) system that consists of two two-level atoms coupled to a single-mode cavity in the weak coupling regime, where the system is driven by quantum light. The dynamics behavior of the entire system is tackled in the framework of a cascaded quantum system. We find that the two-photon

Tahereh Eftekhari

In this paper, we produce an interval extension of the three-step Kung and Traub&#39;s method for solving nonlinear equations. Furthermore, the convergence analysis of the new method is discussed and this method is compared to already present methods.

Li-Li Zheng, Qian Bin, Zhi-Ming Zhan, Sha Li

We investigate theoretically the dynamics of the system that consists of a cascade three-level emitter interacting with a single-mode resonator in the deep-strong-coupling regime. We show that the dynamical evolution of the system can only occur in a certain parity chain decided by the initial state, in which the photon population and the initial state proba

Kirill Kovalenko, Nikita Derevyanko

The purpose of the present paper is to provide a general overview of a variety of results related to a category of cotangent sums which have been proven to be associated to the so-called Nyman-Beurling criterion for the Riemann Hypothesis. These sums are also related to the Estermann Zeta function.

Ravi Yadav, Rajyavardhan Ray, Mohamed S. Eldeeb, Satoshi Nishimoto

Very recently a quantum liquid was reported to form in H$_3$LiIr$_2$O$_6$, an iridate proposed to be a close realization of the Kitaev honeycomb model. To test this assertion we perform detailed quantum chemistry calculations to determine the magnetic interactions between Ir moments. We find that weakly bond dependent ferromagnetic Kitaev exchange dominates

Jiatao Jiang, Zhen Cui, Chunyan Xu, Jian Yang

Learning representation on graph plays a crucial role in numerous tasks of pattern recognition. Different from grid-shaped images/videos, on which local convolution kernels can be lattices, however, graphs are fully coordinate-free on vertices and edges. In this work, we propose a Gaussian-induced convolution (GIC) framework to conduct local convolution filt

Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user&#39;s profile with the items that the user has consumed, ICF recommends items that are similar to the user&#39;s profile. With the prevalence o

Carlo Cenedese, Yu Kawano, Sergio Grammatico, Ming Cao

Distributed decision making in multi-agent networks has recently attracted significant research attention thanks to its wide applicability, e.g. in the management and optimization of computer networks, power systems, robotic teams, sensor networks and consumer markets. Distributed decision-making problems can be modeled as inter-dependent optimization proble

Shin-ichi Matsumura

The purpose of this paper is to give two supplements for vanishing theorems: One is a relative version of the Kawamata-Viehweg-Nadel type vanishing theorem, which is obtained from an observation for the variation of the numerical dimension of singular hermitian line bundles. The other is an analytic injectivity theorem for log canonical pairs on surfaces, wh

Dariush Hassani, Shahnoosh Rafibakhsh

In this paper, we present a parallel algorithm for Monte Carlo simulation of the 2D Ising Model to perform efficiently on a cluster computer using MPI. We use C++ programming language to implement the algorithm. In our algorithm, every process creates a sub-lattice and the energy is calculated after each Monte Carlo iteration. Each process communicates with

Nadezhda Aleksandrova

Under numerical investigation is propagation of surface pendulum waves in 3D block medium. The medium is modeled by 3D lattice of masses connected with elastic springs and viscous dampers. The surface vertical pulsed concentrated loading is considered. The displacements and velocities of the surface masses are calculated. The numerical results obtained for t

Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov

Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and most of the previously proposed architectur

Saada Hamouda

In this paper, we will give estimates for the logarithmic derivative $ \left\vert \frac{f^{\left( k\right) }\left( z\right) }{f\left( z\right) } \right\vert $ where $f$ is a meromorphic function in a region of the form $ D\left( 0,R\right) =\left\{ z\in \mathbb{C} :0<\left\vert z\right\vert <R\right\} .$ Some applications on the growth of solutions of linear

Tanmayee Narendra, Anush Sankaran, Deepak Vijaykeerthy, Senthil Mani

Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex. In order to establish trust for their widespread commercial use, it is important to formalize a principled framework to reason over these models. In this work, we use ideas from causal infer

Alexander Schindler, Thomas Lidy, Stephan Karner, Matthias Hecker

We present an empirical study of applying deep Convolutional Neural Networks (CNN) to the task of fashion and apparel image classification to improve meta-data enrichment of e-commerce applications. Five different CNN architectures were analyzed using clean and pre-trained models. The models were evaluated in three different tasks person detection, product a

Soham Saha, Girish Varma, C. V. Jawahar

We address the visual relocalization problem of predicting the location and camera orientation or pose (6DOF) of the given input scene. We propose a method based on how humans determine their location using the visible landmarks. We define anchor points uniformly across the route map and propose a deep learning architecture which predicts the most relevant a

Izzeddin Gur, Dilek Hakkani-Tur, Gokhan Tur, Pararth Shah

We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first enco

Joao Marcos do Ó, Jacques Giacomoni, Pawan Kumar Mishra

In this paper, we study the following nonlocal nonautonomous Hamiltonian system on whole $\mathbb R$ $$ \left\{\begin{array}{ll} (-Δ)^\frac12~ u +u=Q(x) g(v)&\quad\mbox{in } \mathbb R,\\ (-Δ)^\frac12~ v+v = P(x)f(u)&\quad\mbox{in } \mathbb R, \end{array}\right. $$ where $(-Δ)^\frac12$ is {the} square root Laplacian operator. We assume that the nonlinearities

Neri Merhav, Asaf Cohen

Consider the problem of guessing the realization of a random vector $\textbf{X}$ by repeatedly submitting queries (guesses) of the form &#34;Is $\textbf{X}$ equal to $\textbf{x}$?&#34; until an affirmative answer is obtained. In this setup, a key figure of merit is the number of queries required until the right vector is identified, a number that is termed t

Qi Xuan, Xincheng Shu, Zhongyuan Ruan, Jinbao Wang

In this big data era, more and more social activities are digitized thereby becoming traceable, and thus the studies of social networks attract increasing attention from academia. It is widely believed that social networks play important role in the process of information diffusion. However, the opposite question, i.e., how does information diffusion process

Ahmed ElSayed, Elif Kongar, Ausif Mahmood, Tarek Sobh

Using heterogeneous depth cameras and 3D scanners in 3D face verification causes variations in the resolution of the 3D point clouds. To solve this issue, previous studies use 3D registration techniques. Out of these proposed techniques, detecting points of correspondence is proven to be an efficient method given that the data belongs to the same individual.

Bryan Wang, Yi-Hsuan Yang

Music creation is typically composed of two parts: composing the musical score, and then performing the score with instruments to make sounds. While recent work has made much progress in automatic music generation in the symbolic domain, few attempts have been made to build an AI model that can render realistic music audio from musical scores. Directly synth

Hyekyoung Lee, Moo K. Chung, Hongyoon Choi, Hyejin Kang

Persistent homology has been applied to brain network analysis for finding the shape of brain networks across multiple thresholds. In the persistent homology, the shape of networks is often quantified by the sequence of $k$-dimensional holes and Betti numbers.The Betti numbers are more widely used than holes themselves in topological brain network analysis.

Xinsong Zhang, Pengshuai Li, Weijia Jia, Hai Zhao

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle w

Tuhina Mukherjee, K. Sreenadh

This article deals with a survey of recent developments and results on Choquard equations where we focus on the existence and multiplicity of solutions of the partial differential equations which involve the nonlinearity of convolution type. Because of its nature, these equations are categorized under the nonlocal problems. We give a brief survey on the work

Chao Zhang, Min-Hsiu Hsieh, Dacheng Tao

The vicinal risk minimization (VRM) principle, first proposed by \citet{vapnik1999nature}, is an empirical risk minimization (ERM) variant that replaces Dirac masses with vicinal functions. Although there is strong numerical evidence showing that VRM outperforms ERM if appropriate vicinal functions are chosen, a comprehensive theoretical understanding of VRM

John Yang, Gyujeong Lee, Minsung Hyun, Simyung Chang

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible to foresee all unwanted outcomes and pe

Peng Liu, Brian Paden, Umit Ozguner

Motion planning for autonomous vehicles requires spatio-temporal motion plans (i.e. state trajectories) to account for dynamic obstacles. This requires a trajectory tracking control process which faithfully tracks planned trajectories. In this paper, a control scheme is presented which first optimizes a planned trajectory and then tracks the optimized trajec

Irfan Khan, Vikram Bhattacharjee, Yinliang Xu, Soummya Kar

This paper presents an improved secondary voltage control (SVC) methodology incorporating compressive sensing (CS) for a multi-area power system. SVC minimizes the voltage deviation of the load buses while CS deals with the problem of the limited bandwidth capacity of the communication channel by reducing the size of massive data output from phasor measureme

Vishal Agarwal

Face image quality is an important factor in facial recognition systems as its verification and recognition accuracy is highly dependent on the quality of image presented. Rejecting low quality images can significantly increase the accuracy of any facial recognition system. In this project, a simple approach is presented to train a deep convolutional neural

Ishan Jindal, Zhiwei Qin, Xuewen Chen, Matthew Nokleby

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi tr

Rohitash Chandra, Konark Jain, Ratneel V. Deo, Sally Cripps

Bayesian neural learning feature a rigorous approach to estimation and uncertainty quantification via the posterior distribution of weights that represent knowledge of the neural network. This not only provides point estimates of optimal set of weights but also the ability to quantify uncertainty in decision making using the posterior distribution. Markov ch

Alvaro Alvarez-Parrilla, Martín Eduardo Frías-Armenta, Carlos Yee-Romero

We classify the rational differential 1-forms with simple poles and simple zeros on the Riemann sphere according to their isotropy group; when the 1-form has exactly two poles the isotropy group is isomorphic to $\mathbb{C}^{*}$, namely $\{z\mapsto az\ \vert\ a\in\mathbb{C}, a\neq0\}$, and when the 1-form has $k\geq 3$ poles the isotropy group is finite. In

Sreehari H., Nirmal Iyer, Radhika D., Anuj Nandi

We carried out spectro-temporal analysis of the archived data from multiple outbursts spanning over the last two decades from the black hole X-ray binary GX 339-4. In this paper, the mass of the compact object in the X-ray binary system GX 339-4 is constrained based on three indirect methods. The first method uses broadband spectral modelling with a two comp

Sahand Khakabimamaghani, Masood Masjoody, Ladislav Stacho

In this paper, we provide an algorithm for traversing geometric graphs which visits all vertices, and reports every vertex and edge exactly once. To achieve this, we combine a given geometric graph $G$ with the integer lattice, seen as a graph, in such a way that the resulting hypothetical graph can be traversed using the algorithm in \cite{Chavez}. To overc

Yuxiao Guo, Ben Niu

In this paper, we present an algorithm for deriving the normal forms of Bautin bifurcations in reaction-diffusion systems with time delays and Neumann boundary conditions. On the center manifold near a Bautin bifurcation, the first and second Lyapunov coefficients are calculated explicitly, which completely determine the dynamical behavior near the bifurcati

Jared Marx-Kuo, Vaughan McDonald, John M. O&#39;Brien, Alexander Vetter

For an arbitrary finite monoid $M$ and subgroup $K$ of the unit group of $M$, we prove that there is a bijection between irreducible representations of $M$ with nontrivial $K$-fixed space and irreducible representations of $\mathcal{H}_K$, the convolution algebra of $K\times K$-invariant functions from $M$ to $F$, where $F$ is a field of characteristic not d

Misha Gromov

We introduce a notion of Hilbertian n-volume in metric spaces with Besicovitch-type inequalities built-in into the definitions. which, ultimately, may turn useful for an approach to singular spaces with positive scalar curvature

Gianlorenzo D&#39;Angelo, Martin Olsen, Lorenzo Severini

Centrality metrics are among the main tools in social network analysis. Being central for a user of a network leads to several benefits to the user: central users are highly influential and play key roles within the network. Therefore, the optimization problem of increasing the centrality of a network user recently received considerable attention. Given a ne

Jaewon Kim

The SYK model proposed by Sachdev, Ye, and Kitaev consists of Majorana fermions that interact randomly four at a time. The model develops a dense spectrum above the ground state, due to which the model becomes nearly conformal. This suggests that a holographic dual may exist, which makes the SYK model interesting in the study of quantum gravity. It has been

Javier Olmedo, Emanuele Alesci

We study the dynamics and predictions of a new emergent-universe model recently derived within Quantum Reduced Loop Gravity and based on the so-called statistical regularization scheme. These effective geometries show a dynamical transition from a stationary spacetime, with nearly constant scale factor at very early times, to a late-time semiclassical phase

Sumeet Singh, Vikas Sindhwani, Jean-Jacques E. Slotine, Marco Pavone

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, which guarantees that the learned system can be accompanied by a robust controller capable of stabilizing any

Gregory Beylkin, Lucas Monzon, Xinshuo Yang

We consider fast deterministic algorithms to identify the &#34;best&#34; linearly independent terms in multivariate mixtures and use them to compute, up to a user-selected accuracy, an equivalent representation with fewer terms. One algorithm employs a pivoted Cholesky decomposition of the Gram matrix constructed from the terms of the mixture to select what

Arvind K. Saibaba

This paper is concerned with the analysis of the randomized subspace iteration for the computation of low-rank approximations. We present three different kinds of bounds. First, we derive both bounds for the canonical angles between the exact and the approximate singular subspaces. Second, we derive bounds for the low-rank approximation in any unitarily inva

Bozena Czerny, Mohammad-Hassan Naddaf

We review the current status of the understanding how the accretion onto the central black hole proceeds in Active Galaxies. Standard accretion disk is a key element in all relatively bright active galaxies like Seyferts and quasars, although it is not present in very low luminosity sources, like Sgr A*. However, the standard disk does not explain the broad

Rishi Maiti, Rohit A. Hemnani, Rubab Amin, Zhizhen Ma

Atomically thin two-dimensional (2D) materials provide a wide range of basic building blocks with unique properties, making them ideal for heterogeneous integration with a mature chip platform for advances in optical communication technology. Control and understanding of the precise value of the optical index of these materials, however, is challenging, due

Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu

Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is usual

Ryosuke Furuta, Naoto Inoue, Toshihiko Yamasaki

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing appli

Mohammad Fereydounian, Hamed Hassani, Mohammad Vahid Jamali, Hessam Mahdavifar

Low-capacity scenarios have become increasingly important in the technology of the Internet of Things (IoT) and the next generation of wireless networks. Such scenarios require efficient and reliable transmission over channels with an extremely small capacity. Within these constraints, the state-of-the-art coding techniques may not be directly applicable. Mo

Eung Jin Chun, Arindam Das, Jinsu Kim, Jongkuk Kim

Standard Model may allow an extended gauge sector with anomaly-free flavored gauge symmetries, such as $L_{i} - L_{j}$, $B_{i} - L_{j}$, and $B - 3L_{i}$, where $i,j=1,2,3$ are flavor indices. We investigate phenomenological implications of the new flavored gauge boson $Z^{\prime}$ in the above three classes of gauge symmetries. Focusing on the gauge boson m

Ronen Tamari, Hiroyuki Shindo, Dafna Shahaf, Yuji Matsumoto

Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds. We focus on the challenging real-world problem of action-graph extraction from material science papers, where language is highly specialized and data annotation is expensive and scarce. We propose a novel approach, Text2Quest, where procedural text is inte

Misha Gromov

We present several problems and results relating the scalar curvatures of manifolds with mean curvatures of their boundaries

Catherine Bénéteau, Oleg Ivrii, Myrto Manolaki, Daniel Seco

Let $E$ be a closed subset of the unit circle of measure zero. Recently, Beise and Müller showed the existence of a function in the Hardy space $H^2$ for which the partial sums of its Taylor series approximate any continuous function on $E$. In this paper, we establish an analogue of this result in a non-linear setting where we consider optimal polynomial ap

Sergei M. Kopeikin

The present paper studies the post-Newtonian dynamics of N bodies in general relativity. We derive covariant equations of translational and rotational motion of N extended bodies having arbitrary distribution of mass and velocity of matter by employing the set of global and local coordinate charts on curved spacetime manifold of N-body system along with the

Omid Zabeti

Suppose $G$ is a locally solid lattice group. It is known that there are non-equivalent classes of bounded homomorphisms on $G$ which have topological structures. In this paper, our attempt is to assign lattice structures on them. More precisely, we use of a version of the remarkable Riesz-Kantorovich formulae and Fatou property for bounded order bounded hom