September 2018 arXiv papers — page 12

Wenxi Liu, Yuanlong Yu, Chun-Yang Zhang, Genggeng Liu

In recent years, crowd analysis is important for applications such as smart cities, intelligent transportation system, customer behavior prediction, and visual surveillance. Understanding the characteristics of the individual motion in a crowd can be beneficial for social event detection and abnormal detection, but it has rarely been studied. In this paper,

Balint Radics

For a class of precision CPT-invariance test measurements using antihydrogen, a deficit in the data indicates the presence of the signal. The construction of classical confidence intervals for the properties of the antiatoms from measurements may pose a challenge due to the limited statistics experimentally available. We use the Feldman-Cousins method to est

Luis Badesa, Fei Teng, Goran Strbac

The reduced level of system inertia in low-carbon power grids increases the need for alternative frequency services. However, simultaneously optimising the provision of these services in the scheduling process, subject to significant uncertainty, is a complex task given the challenge of linking the steady-state optimisation with frequency dynamics. This pape

Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou

Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance status. Little work exists that addresses

Hidayet Aksu, A. Selcuk Uluagac, Elizabeth S. Bentley

With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive

Fabrizio Canfora, Marcela Lagos, Seung Hun Oh, Julio Oliva

We show that one can reduce the coupled system of seven field equations of the (3+1)-dimensional gauged Skyrme model to the Heun equation (which, for suitable choices of the parameters, can be further reduced to the Whittaker-Hill equation) in two non-trivial topological sectors. Hence, one can get a complete analytic description of gauged solitons in (3+1)

Shiming Hu, Lei Liu, Yizheng Huang, Wenwen Liu

In this paper, a compact and low-cost structured illumination microscope (SIM) based on a 2X2 fiber coupler is presented. Fringe illumination is achieved by placing two output fiber tips at a conjugate Fourier plane of the sample plane as the point sources. Raw structured illumination (SI) images in different pattern orientations are captured when rotating t

Li-Xia Dai, Hao Pan

We prove that $$ \sum_{n \leq x} φ([x/n])\leq\bigg(\frac{1380}{4009}+\frac{2629}{4009}\cdot\frac1{ζ(2)}+o(1)\bigg)x\log x $$ as $x\to\infty$, where $φ$ denotes the Euler totient function and $[x]$ denotes the integer part of $x$.

Saman Karimi, Mohammad R. Garousi

It is known that D$_p$-brane effective action at the leading order of $α'$ in flat space-time which is given by DBI action, transforms to D$_{p-1}$-brane effective action under standard T-duality transformations of the open string gauge bosons and transverse scalar fields. Extending this duality to order $α'$, one may find corrections to the DBI acti

Andrew K. Lampinen, Surya Ganguli

Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across tasks, so that multi-task learning can i

Mikyoung Lim, Graeme W. Milton

For certain shapes of inclusions embedded in a body, the field inside the inclusion is uniform for some boundary condition. We provide a construction scheme for inclusions of general shapes having such a uniformity property in two dimensions based on the conformal mapping technique for the potential problem. Using this complex analysis method, we also design

Zeev Dvir, Sivakanth Gopi, Yuzhou Gu, Avi Wigderson

We introduce a simple logical inference structure we call a $\textsf{spanoid}$ (generalizing the notion of a matroid), which captures well-studied problems in several areas. These include combinatorial geometry, algebra (arrangements of hypersurfaces and ideals), statistical physics (bootstrap percolation) and coding theory. We initiate a thorough investigat

Madieyna Diouf

The Legendre conjecture has resisted analysis over a century, even under assumption of the Riemann Hypothesis. We present, a significant improvement on previous results by greatly reducing the assumption to a more modest statement called the Parity conjecture. Let $p_n$ and $p_{n+1}$ be two consecutive odd primes, let $m$ be their midpoint fixed once for all

K. Zhu, M. Victoria, T. Brown, G. B. Andresen

Ambitious targets for renewable energy and CO2 taxation both represent political instruments for decarbonisation of the energy system. We model a high number of coupled electricity and heating systems, where the primary sources of CO2 neutral energy are from variable renewable energy sources (VRES), i.e., wind and solar generators. The model includes hourly

Artyom Smirnov, Alexey Zaytsev

Let $\mathcal{A}$ be an abelian variety over a number field, with a good reduction at a prime ideal containing a prime number $p$. Denote by ${\rm A}$ an abelian variety over a finite field of characteristic $p$, obtained by the reduction of $\mathcal{A}$ at the prime ideal. In this paper we derive an algorithm which allows to decompose the group scheme ${\r

Francisco Presas, Sushmita Venugopalan

We classify symplectically foliated fillings of certain foliated manifolds with a contact structure on the leaves. We show that for the foliated sphere cotangent bundle of the Reeb foliation on the three-sphere, the corresponding foliated disk cotangent bundle is the unique strong symplectic foliated filling up to blowups and symplectic deformation equivalen

Nguyen Thanh Phuc, Akihito Ishizaki

Manipulation of quantum systems is the basis for many promising quantum technologies. However, how quantum mechanical principles can be used to manipulate the dynamics of quantum dissipative systems remains unanswered because of strong decoherence effects arising from interaction with the surrounding environment. In this work, we demonstrate that electron tr

Songze Li, Mingchao Yu, Chien-Sheng Yang, A. Salman Avestimehr

Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling

Zu-Cheng Chen, Fan Huang, Qing-Guo Huang

The advent of gravitational wave (GW) and multi-messenger astronomy has stimulated the research on the formation mechanisms of binary black holes (BBHs) observed by LIGO/Virgo. In literature, the progenitors of these BBHs could be stellar-origin black holes (sBHs) or primordial black holes (PBHs). In this paper we calculate the Stochastic Gravitational-Wave

Sajad Daei, Arash Amini, Farzan Haddadi

Matrix sensing is the problem of reconstructing a low-rank matrix from a few linear measurements. In many applications such as collaborative filtering, the famous Netflix prize problem, and seismic data interpolation, there exists some prior information about the column and row spaces of the ground-truth low-rank matrix. In this paper, we exploit this prior

Eunji Sim, Suhwan Song, Kieron Burke

We argue that any general mathematical measure of density error, no matter how reasonable, is too arbitrary to be of universal use. However the energy functional itself provides a universal relevant measure of density errors. For the self-consistent density of any Kohn-Sham calculation with an approximate functional, the theory of density-corrected density f

Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patc

Colin Hagemeyer

Given a semisimple Lie algebra $\mathfrak{g}$, we can represent invariants of tensor products of fundamental representations of the quantum enveloping algebra $U_q(\mathfrak{g})$ using particular directed graphs called webs. In particular webs are trivalent graphs (with leaves) whose edges are labeled by fundamental representations. Picking generating morphi

Wolfgang Schreyer, TUCAN collaboration

The TRIUMF ultracold advanced neutron (TUCAN) collaboration is a transpacific collaboration with the objective to measure the neutron electric dipole moment. We aim for an unprecedented sensitivity of $10^{-27}\,e$cm, an improvement by a factor of 30 over the current upper limit for this elusive quantity. To achieve this goal, we are planning a next-generati

F. F. Karpeshin, M. B. Trzhaskovskaya

Recent experimental data are analyzed, concerning the half-lives of the 229mTh isomer in neutral atoms and various ions. Calculation is performed on the united platform of interplay of traditional and subthreshold resonance conversion. General agreement with experiment is obtained in the cases of Th I and Th III, a prediction is made concerning half-life in

Tao Ma

Financial forecasting is challenging and attractive in machine learning. There are many classic solutions, as well as many deep learning based methods, proposed to deal with it yielding encouraging performance. Stock time series forecasting is the most representative problem in financial forecasting. Due to the strong connections among stocks, the informatio

Sachiko Nakamura, Yudai Iida, Yuta Murotani, Ryusuke Matsunaga

Higgs mode in superconductors, i.e. the collective amplitude mode of the order parameter does not associate with charge nor spin fluctuations, therefore it does not couple to the electromagnetic field in the linear response regime. On the contrary to this common understanding, here, we demonstrate that, if the dc supercurrent is introduced into the supercond

Dale Koenig, Anastasiia Tsvietkova

We prove that certain problems naturally arising in knot theory are NP--hard or NP--complete. These are the problems of obtaining one diagram from another one of a link in a bounded number of Reidemeister moves, determining whether a link has an unlinking or splitting number $k$, finding a $k$-component unlink as a sublink, and finding a $k$-component altern

Yiqi Zhu, Ji Ma, Jiangbin Su, Lei Zhou

In this work, the instabilities at the nanoscale (i.e. nanoinstabilities) of triangular pyramids-like Cu2O porous nanostructured films (PNFs) are studied by heating treatments under different atmosphere and temperature. It is found that the nanoscale building triangular-pyramids turn round preferentially at the sharp angles and/or coalesce with their contact

Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson

The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches such as the score function method. Although there is overwhelming empirical evidence in the literature showing its success, there is relatively little research exploring why the rep

Natalia Zuniga-Garcia, Mauricio Tec, James G. Scott, Natalia Ruiz-Juri

This paper considers the problem of measuring spatial and temporal variation in driver productivity on ride-sourcing trips. This variation is especially important from a driver's perspective: if a platform's drivers experience systematic disparities in earnings because of variation in their riders' destinations, they may perceive the pricing mode

Tobias Columbus, Frank Herrlich, Bjoern Muetzel, Gabriela Weitze-Schmithüsen

In this paper we investigate the systolic landscape of translation surfaces for fixed genus and fixed angles of their cone points. We furthermore study how the systoles of a translation surface relate to the systoles of its graph of saddle connections. This allows us to develop an algorithm to compute the systolic ratio of origamis in the stratum $\mathcal{H

Yunhao Tang, Shipra Agrawal

We propose to improve trust region policy search with normalizing flows policy. We illustrate that when the trust region is constructed by KL divergence constraints, normalizing flows policy generates samples far from the 'center' of the previous policy iterate, which potentially enables better exploration and helps avoid bad local optima. Through ex

Yan Jin, Elchanan Mossel, Govind Ramnarayan

We consider a variation of the problem of corruption detection on networks posed by Alon, Mossel, and Pemantle '15. In this model, each vertex of a graph can be either truthful or corrupt. Each vertex reports about the types (truthful or corrupt) of all its neighbors to a central agency, where truthful nodes report the true types they see and corrupt nod

Xiuying Chen, Shen Gao, Chongyang Tao, Yan Song

In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once

Chul-hee Lee

The Gross-Keating invariant of a half-integral matrix over a $p$-adic integer ring is a fundamental concept in the study of quadratic forms, and has important applications to Siegel modular forms and arithmetic geometry. We introduce the Mathematica package computeGK, a computer program for calculating the Gross-Keating invariant and the Siegel series of a h

Bailin Song

The space of the global sections of chiral de Rham complex on a compact Ricci-flat Kähler manifold is calculated and it is expressed as an invariant subspace of a $βγ-bc$ system under the action of certain Lie algebra.

Junichiro Fukuyama

A sunflower with $k$ petals, or $k$-sunflower, is a family of $k$ sets every two of which have a common intersection. Known since 1960, the sunflower conjecture states that a family ${\mathcal F}$ of sets each of cardinality $m$ includes a $k$-sunflower if $|{\mathcal F}| \ge c_k^m$ for some $c_k \in {\mathbb R}_{>0}$ depending only on $k$. The case $k=3$ of

Toshikazu Ebisuzaki, Hidetoshi Katori, Jun'ichiro Makino, Atsushi Noda

The new technique of measuring frequency by optical lattice clocks now approaches to the relative precision of $(Δf/f)=O(10^{-18})$. We propose to place such precise clocks in space and to use Doppler tracking method for detecting low-frequency gravitational wave below 1 Hz. Our idea is to locate three spacecrafts at one A.U. distance (say at L1, L4 & L5 of

Punit K. Jha, So Hirata

Low-order perturbation corrections to the electronic grand potential, internal energy, chemical potential, and entropy of a gas of noninteracting, identical molecules at a nonzero temperature are determined numerically as the $λ$-derivatives of the respective quantity calculated exactly (by thermal full configuration interaction) with a perturbation-scaled H

Jingfeng Zhang, Laura Wynter

Recent work has studied the reasons for the remarkable performance of deep neural networks in image classification. We examine batch normalization on the one hand and the dynamical systems view of residual networks on the other hand. Our goal is in understanding the notions of stability and smoothness of the inter-layer propagation of ResNets so as to explai

Mhafuzul Islam, Mahsrur Chowdhury, Hongda Li, Hongxin Hu

Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems

H. W. Joo, H. S. Park, J. H. Kim, S. K. Kim

Scintillation crystals are commonly used for direct detection of weakly interacting massive particles (WIMPs), which are suitable candidates for a particle dark matter. It is well known that the scintillation light yields are different for electron recoil and nuclear recoil. To calibrate the energies of WIMP-induced nuclear recoil signals, the quenching fact

Stephen F. King, Ye-Ling Zhou

We discuss the breaking of $SO(3)$ down to finite family symmetries such as $A_4$, $S_4$ and $A_5$ using supersymmetric potentials for the first time. We analyse in detail the case of supersymmetric $A_4$ and its finite subgroups $Z_3$ and $Z_2$. We then propose a supersymmetric $A_4$ model of leptons along these lines, originating from $SO(3)\times U(1)$, w

Nazanin Takbiri, Ramin Soltani, Dennis L. Goeckel, Amir Houmansadr

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are i

Leopoldo Bertossi

We propose a generic numerical measure of the inconsistency of a database with respect to a set of integrity constraints. It is based on an abstract repair semantics. In particular, an inconsistency measure associated to cardinality-repairs is investigated in detail. More specifically, it is shown that it can be computed via answer-set programs, but sometime

Klaus W. Becker, Holger Fehske, Van-Nham Phan

We present an extended version of the projector-based renormalization method that can be used to address not only equilibrium but also non-equilibrium situations in coupled fermion-boson systems. The theory is applied to interacting electrons, holes and photons in a semiconductor microcavity, where the loss of cavity photons into vacuum is of particular impo

Artyom Yurov, Valerian Yurov

In this article we take a close look at three types of transformations usable in the Schwarzschild black hole perturbation theory: a standard (DT), a binary (BDT) and a generalized (GDT) Darboux transformations. In particular, we discuss the absolutely crucial property of {\em isospectrality} of the aforementioned transformations which guarantees that the qu

Marco Merchand, Marc Sher, Keith Thrasher

In 2013, Geller, Bar-Shalom and Soni (GBS) proposed a Randall-Sundrum model with a bulk SU(2) doublet. They found that the doublet could both stabilize the extra dimension and break the electroweak symmetry; the radion and the Higgs boson were then one and the same. This alternative to the Goldberger-Wise mechanism contained enhanced Higgs couplings to gluon

Dominic J. Williamson, Zhen Bi, Meng Cheng

In this work we explore the interplay between global symmetry and the mobility of quasiparticle excitations. We show that fractonic matter naturally appears in a three dimensional U(1) gauge theory, enriched by global U(1) and translational symmetries, via the mechanism of anyonic spin-orbital coupling. We develop a systematic understanding of such symmetry-

D. A. Kaltsas, G. N. Throumoulopoulos

Motivated by a geometric method employed for the derivation of the Nambu bracket for ideal two-dimensional incompressible hydrodynamics, we reconstruct the reduced magnetohydrodynamic (RMHD) model by a priori imposition of its conservation laws. It turns out that there exists a more general model that conserves the same quantities with RMHD. The noncanonical

Tim Austin

Total correlation (`TC') and dual total correlation (`DTC') are two classical ways to quantify the correlation among an $n$-tuple of random variables. They both reduce to mutual information when $n=2$. The first part of this paper sets up the theory of TC and DTC for general random variables, not necessarily finite-valued. This generality has not bee

Yanjun Zhang, Tie Li, Guangyu Na, Guoqing Li

A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finall

Uyoata Uyoata, Mqhele Dlodlo

Enabling technologies for 5G and future wireless communication have attracted the interest of industry and research communities. One of such technologies is Device-to-Device (D2D) communication which exploits user proximity to offer spectral efficiency, energy efficiency and increased throughput. Data offloading, public safety communication, context aware co

Markus Ring, Daniel Schlör, Dieter Landes, Andreas Hotho

Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can

Li Guo, Gavin Sim, Bogdan Matuszewski

The recent advances in ECG sensor devices provide opportunities for user self-managed auto-diagnosis and monitoring services over the internet. This imposes the requirements for generic ECG classification methods that are inter-patient and device independent. In this paper, we present our work on using the densely connected convolutional neural network (Dens

Nicoló Savioli, Miguel Silva Vieira, Pablo Lamata, Giovanni Montana

The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function. This work addresses the fully automatic and robust access to one of the key RV biomarkers, its ejection fraction, from the gold standard imaging modality, MRI. The problem becomes the accurate segmentati

Yang Gao, Swarup Chandra, Zhuoyi Wang, Latifur Khan

When performing data classification over a stream of continuously occurring instances, a key challenge is to develop an open-world classifier that anticipates instances from an unknown class. Studies addressing this problem, typically called novel class detection, have considered classification methods that reactively adapt to such changes along the stream.

Aniket Bera, Dinesh Manocha

We present an algorithm for realtime anomaly detection in low to medium density crowd videos using trajectory-level behavior learning. Our formulation combines online tracking algorithms from computer vision, non-linear pedestrian motion models from crowd simulation, and Bayesian learning techniques to automatically compute the trajectory-level pedestrian be

V. S. Varadarajan, Robert C. Dalang

This article discusses the life and work of Professor Srishti Dhar Chatterji, who passed away on September 28, 2017, in Lausanne, Switzerland, most suddenly and unexpectedly, after a very brief illness. Complete bibliographical information is included

RB Yadav, Srikanth KV

In this article we define a metric on C(S1; S1). Also, we give some density results in C(S^1; S^1).

István Gaál, László Remete, Tí mea Szabó

In our recent paper we gave an efficient algorithm to calculate "small" solutions of relative Thue equations (where "small" means an upper bound of type $10^{500}$ for the sizes of solutions). Here we apply this algorithm to calculating power integral bases in sextic fields with an imaginary quadratic subfield and to calculating relative powe

István Gaál, László Remete

We consider binomial Thue equations of type $x^n-my^n=\pm 1$ in $x,y\in Z$. Optimizing the method of Peth\H o we perform an extensive calculation by a high performance computer to determine all solutions with $\max(|x|,|y|)<10^{500}$ of binomial Thue equations for $m<10^7$ for exponents $n=3,4,5,7,11,13,17,19,23,29$.

Sergey P. Shary

In this paper, we consider the solution of ill-conditioned systems of linear algebraic equations that can be determined imprecisely. To improve the stability of the solution process, we &#34;immerse&#34; the original imprecise linear system in an interval system of linear algebraic equations of the same structure and then consider its tolerable solution set.

Tower Wang

By fractional relativity we mean a theoretical framework to study physics with the dispersion relation $E^α=m^αc^{2α}+p^αc^α$, which recovers special relativity at $α=2$. One such framework is established in a particular curved energy-momentum space. It is shown that the fractional Schrödinger equation arises as a nonrelativistic limit of the Klein-Gordon eq

Radin Dardashti, Richard Dawid, Sean Gryb, Karim Thébault

We provide an analysis of the empirical consequences of the AdS/CFT duality with reference to the application of the duality in a fundamental theory, effective theory and instrumental context. Analysis of the first two contexts is intended to serve as a guide to the potential empirical and ontological status of gauge/gravity dualities as descriptions of actu

Miljan Dašić, Konstantinos Gkagkas, Igor Stanković

We present a molecular dynamics study of the effects of confinement on the lubrication and flow properties of ionic liquids. We use a coarse-grained salt model description of ionic liquid as a lubricant confined between finite solid plates and subjected to two dynamic regimes: shear and cyclic loading. The impact of confinement on the ion arrangement and mec

Shahab Jalalvand, Andrej Ljolje, Srinivas Bangalore

Spoken language understanding (SLU) systems are widely used in handling of customer-care calls.A traditional SLU system consists of an acoustic model (AM) and a language model (LM) that areused to decode the utterance and a natural language understanding (NLU) model that predicts theintent. While AM can be shared across different domains, LM and NLU models n

István Gaál

Diophantine equations can often be reduced to various types of classical Thue equations. These equations usually have only very small solutions, on the other hand to compute all solutions (i.e. to prove the non-existence of large solutions) is a time consuming procedure. Therefore it is very practical to have a fast algorithm to calculate the &#34;small&#34;

István Gaál, László Remete

It is a classical problem in algebraic number theory to decide if a number field admits power integral bases and further to calculate all generators of power integral bases. This problem is especially delicate to consider in an infinite parametric family of number fields. In the present paper we investigate power integral bases in the infinite parametric fam

István Gaál, Tí mea Szabó

We consider infinite parametric families of octic fields, that are quartic extensions of quadratic fields. We describe all relative power integral bases of the octic fields over the quadratic subfields.

István Gaál, Gábor Petrányi

It is a classical problem in algebraic number theory to decide if a number field is monogeneous, that is if it admits power integral bases. It is especially interesting to consider this question in an infinite parametric familiy of number fields. In this paper we consider the infinite parametric family of simplest quartic fields $K$ generated by a root $ξ$ o

Romain Noël, Laurent Navarro, Guy Courbebaisse

The traffic modelling often keeps the mesoscopic scale in the theoretical sphere because the integro-differential nature of its equations. In the present work we suggest to use the lattice Boltzmann method to overcome these difficulties. In particular, the method has a strong theoretical foundation. An improved version of the lattice Boltzmann method for mul

Ziwei Zhang

Spectral clustering and Singular Value Decomposition (SVD) are both widely used technique for analyzing graph data. In this note, I will present their connections using simple linear algebra, aiming to provide some in-depth understanding for future research.

Negin Hayatbini, Kuo-lin Hsu, Soroosh Sorooshian, Yunji Zhang

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to

Timo S. Hänninen, Igor E. Verbitsky

Let $σ$ and $ω$ be locally finite Borel measures on $\mathbb{R}^d$, and let $p\in(1,\infty)$ and $q\in(0,\infty)$. We study the two-weight norm inequality $$ \lVert T(fσ) \rVert_{L^q(ω)}\leq C \lVert f \rVert_{L^p(σ)}, \quad \text{for all} \, \, f \in L^p(σ), $$ for both the positive summation operators $T=T_λ(\cdot σ)$ and positive maximal operators $T=M_λ(

Zhao Zhang, Lei Huang, Uri Manor, Linjing Fang

Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate the metadata and data service and negatively impact other users. In this paper, we present FanStore, a transient runtime file system that optimizes DL I/O on existing hardware/sof

Sulabh Kumra, Ferat Sahin

In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy from demonstrations performed by teacher. RLfD enables a human user to add new capabilities to a robot in an intuitive manner, without explicitly reprogramming it. In this work, we pr

Vinicius Bischoff

Although feature models are widely used in practice, for example, representing variability in software product lines, their integration is still a challenge. Many integration techniques have been proposed, although none of these have proven to be fully effective. Integrating feature models becomes a difficult, costly, error-prone task. Since their transition

Zhe Zhang, Xiang Chen, Zhi Tian

Deep neural networks (DNNs) have found applications in diverse signal processing (SP) problems. Most efforts either directly adopt the DNN as a black-box approach to perform certain SP tasks without taking into account of any known properties of the signal models, or insert a pre-defined SP operator into a DNN as an add-on data processing stage. This paper p

Christiane Goergen, Manuele Leonelli

The accuracy of probability distributions inferred using machine-learning algorithms heavily depends on data availability and quality. In practical applications it is therefore fundamental to investigate the robustness of a statistical model to misspecification of some of its underlying probabilities. In the context of graphical models, investigations of rob

Kyle G. Leach, Jason D. Holt

Superallowed $β$-decay studies provide some of the best constraints on the possibility of additional quark generations, as well as limits on exotic currents in the weak interaction. The three experimental quantities that are required for performing these tests using $0^+\rightarrow0^+$ nuclear decays (branching ratio, half-life, and $Q$-value) can all be mea

Saad Bin Ahmed, Saeeda Naz, Muhammad Imran Razzak, Rubiyah Yusof

The camera captured images have various aspects to investigate. Generally, the emphasis of research depends on the interesting regions. Sometimes the focus could be on color segmentation, object detection or scene text analysis. The image analysis, visibility and layout analysis are the tasks easier for humans as suggested by behavioral trait of humans, but

Razieh Nabi, Phyllis Kanki, Ilya Shpitser

The goal of personalized decision making is to map a unit&#39;s characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway may be of interest as well. For

Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang

Using synthetic data for training deep neural networks for robotic manipulation holds the promise of an almost unlimited amount of pre-labeled training data, generated safely out of harm&#39;s way. One of the key challenges of synthetic data, to date, has been to bridge the so-called reality gap, so that networks trained on synthetic data operate correctly w

Jonathan Juett, Benjamin Kuipers

The young infant explores its body, its sensorimotor system, and the immediately accessible parts of its environment, over the course of a few months creating a model of peripersonal space useful for reaching and grasping objects around it. Drawing on constraints from the empirical literature on infant behavior, we present a preliminary computational model o

Mark F. Demers

The use of anisotropic Banach spaces has provided a wealth of new results in the study of hyperbolic dynamical systems in recent years, yet their application to specific systems is often technical and difficult to access. The purpose of this note is to provide an introduction to the use of these spaces in the study of hyperbolic maps and to highlight the imp

Timur Takhtaganov, Juliane Müller

We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adaptively construct training designs by maxi

Steven Clontz

Often, a given selection game studied in the literature has a known dual game. In dual games, a winning strategy for a player in either game may be used to create a winning strategy for the opponent in the dual. For example, the Rothberger selection game involving open covers is dual to the point-open game. This extends to a general theorem: if $\{\operatorn

J. I. Brown, C. D. C. DeGagne

Assume that the vertices of a graph $G$ are always operational, but the edges of $G$ fail independently with probability $q \in[0,1]$. The \emph{all-terminal reliability} of $G$ is the probability that the resulting subgraph is connected. The all-terminal reliability can be formulated into a polynomial in $q$, and it was conjectured \cite{BC1} that all the r

Victor Eijkhout

We present an experimental investigation of the performance of MPI derived datatypes. For messages up to the megabyte range most schemes perform comparably to each other and to manual copying into a regular send buffer. However, for large messages the internal buffering of MPI causes differences in efficiency. The optimal scheme is a combination of packing a

Gokhan Sagirlar, Barbara Carminati, Elena Ferrari

In general, a botnet is a collection of compromised internet computers, controlled by attackers for malicious purposes. To increase attacks&#39; success chance and resilience against defence mechanisms, modern botnets have often a decentralized P2P structure. Here, IoT devices are playing a critical role, becoming one of the major tools for malicious parties

Vassiliy Lubchenko, Arkady Kurnosov

We present a microscopic picture rationalizing the surprisingly steep decrease of the band gap with temperature in insulators, crystalline or otherwise. The gap narrowing largely results from fluctuations of long-wavelength optical phonons---when the latter are present---or their disordered analogs, if the material is amorphous. We elaborate on this notion t

Hayk Hakobyan, Alexander Philippov, Anatoly Spitkovsky

High energy radiation from nonthermal particles accelerated in relativistic magnetic reconnection is thought to be important in many astrophysical systems, ranging from blazar jets and black hole accretion disk coronae to pulsars and magnetar flares. The presence of a substantial density of high energy photons ($>$MeV) in these systems can make two-photon pa

Yifu Zhang, Jorge Cortes

This paper proposes a control strategy for power systems with a two-layer structure that achieves global stabilization and, at the same time, delimits the transient frequencies of targeted buses to a desired safe interval. The first layer is a model predictive control that, in a receding horizon fashion, optimally allocates the power resources while softly r

Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu

The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services. However, with the tremendous increase of users and POIs, POI recommender systems still face several challenging problems: (1) the hardness of modeling non-linear user-POI intera

Patrick Draper

Electric flux may be screened by pair nucleation of heavy charges, a process that has a simple description in terms of a worldline instanton. When flux is wrapped around a small compact spatial dimension, worldline instantons still induce flux dissipation, but the leading process does not create real charged pairs. Instead, dissipation can be described in ef

Matjaž Kovše, Rasila V A, Ambat Vijayakumar

Let $S$ be a set of vertices of a connected graph $G$. The Steiner distance of $S$ is the minimum size of a connected subgraph of $G$ containing all the vertices of $S$. The sum of all Steiner distances on sets of size $k$ is called the Steiner $k$-Wiener index, hence for $k=2$ we get the Wiener index. The modular graphs are graphs in which every three verti

Pavel Chebotarev, Yana Tsodikova, Anton Loginov, Zoya Lezina

In this paper, we study the efficiency of egoistic and altruistic strategies within the model of social dynamics determined by voting in a stochastic environment (the ViSE model) using two criteria: maximizing the average capital increment and minimizing the number of bankrupt participants. The proposals are generated stochastically; three families of the co

Ying Liu, Cheng Zheng

A new statistical procedure (Model-X \cite{candes2018}) has provided a way to identify important factors using any supervised learning method controlling for FDR. This line of research has shown great potential to expand the horizon of machine learning methods beyond the task of prediction, to serve the broader needs in scientific researches for interpretabl