November 2018 arXiv papers — page 128

Vineet Goyal, Julien Grand-Clément

We consider a robust approach to address uncertainty in model parameters in Markov Decision Processes (MDPs), which are widely used to model dynamic optimization in many applications. Most prior works consider the case where the uncertainty on transitions related to different states is uncoupled and the adversary is allowed to select the worst possible reali

Richard Garner

The Vietoris monad on the category of compact Hausdorff spaces is a topological analogue of the power-set monad on the category of sets. Exploiting Manes' characterisation of the compact Hausdorff spaces as algebras for the ultrafilter monad on sets, we give precise form to the above analogy by exhibiting the Vietoris monad as induced by a weak distribut

Carlos Cardona, Sunny Guha, Surya Kiran Kanumilli, Kallol Sen

We generalize the computation of anomalous dimension and correction to OPE coefficients at finite conformal spin considered recently in \cite{arXiv:1806.10919, arXiv:1808.00612} to arbitrary space-time dimensions. By using the inversion formula of Caron-Huot and the integral (Mellin) representation of conformal blocks, we show that the contribution from indi

Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representatio

Abdus Salam Sarkar, Aamir Mushtaq, Dushyant Kushavah, Suman Kalyan Pal

Two-dimensional (2D) tin(II) monosulfide (SnS) with strong structural anisotropy has been proven to be a phosphorene analogue. However, difficulty in isolating very thin layer of SnS pose challenges in practical utilization. Here, we prepare ultrathin SnS via liquid phase exfoliation. With transmission electron microscopy, we identify the buckled structure o

Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for tra

Zhuliang Yao, Shijie Cao, Wencong Xiao, Chen Zhang

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficien

Jackson Loper, Guangyao Zhou, Stuart Geman

We propose an approach for estimating the probability that a given small target, among many, will be the first to be reached in a molecular dynamics simulation. Reaching small targets out of a vast number of possible configurations constitutes an entropic barrier. Experimental evidence suggests that entropic barriers are ubiquitous in biomolecular systems, a

Kiran S. Kedlaya

Let $X$ be a smooth scheme over a finite field of characteristic $p$. Consider the coefficient objects of locally constant rank on $X$ in $\ell$-adic Weil cohomology: these are lisse Weil sheaves in \'etale cohomology when $\ell \neq p$, and overconvergent $F$-isocrystals in rigid cohomology when $\ell=p$. Using the Langlands correspondence for global functi

Yisu Jia, Stefanos Kechagias, James Livsey, Robert Lund

This paper develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any pre-specified marginal distribution, including the classical Poisson, gen

Yinzheng Gu, Chuanpeng Li, Jinbin Xie

It has been shown that image descriptors extracted by convolutional neural networks (CNNs) achieve remarkable results for retrieval problems. In this paper, we apply attention mechanism to CNN, which aims at enhancing more relevant features that correspond to important keypoints in the input image. The generated attention-aware features are then aggregated b

Pranay Mukherjee, Abhirup Das, Ayan Kumar Bhunia, Partha Pratim Roy

Can we ask computers to recognize what we see from brain signals alone? Our paper seeks to utilize the knowledge learnt in the visual domain by popular pre-trained vision models and use it to teach a recurrent model being trained on brain signals to learn a discriminative manifold of the human brain's cognition of different visual object categories in re

Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh, Branislav Kveton

Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is unlikely to examine the entire recommended list, partial feedback arises naturally. At the same time, diverse recommendations are important because it is challenging to model all tas

Hui Liu, Qingyu Yin, William Yang Wang

Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions. Existing approaches for explainable machine learning systems tend to focus on interpreting the outputs or the connections between inputs and outputs. However, the fine-grained in

Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang

The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex tr

Xiaoyu Lai, Enping Zhou, Renxin Xu

The fundamental strong interaction determines the nature of pulsar-like compact stars which are essentially in the form of bulk strong matter. From an observational point of view, it is proposed that bulk strong matter could be composed of strangeons, i.e. quark-clusters with there-light-flavor symmetry of quarks, and therefore pulsar-like compact objects co

Umang Mathur, P. Madhusudan, Mahesh Viswanathan

We study the problem of completely automatically verifying uninterpreted programs---programs that work over arbitrary data models that provide an interpretation for the constants, functions and relations the program uses. The verification problem asks whether a given program satisfies a postcondition written using quantifier-free formulas with equality on th

Yi Gu, Lei Zhang

Let $(M,g)$ be a compact Riemann surface with no boundary and $u=(u_1,...,u_n)$ be a solution of the following singular Liouville system: \begin{equation*} Δ_g u_i+\sum_{j=1}^na_{ij}ρ_j(\frac{h_je^{u_j}}{\int_M h_j e^{u_j}dV_g}-\frac{1}{vol_g(M)})=\sum_{t=1}^N4πγ_t( δ_{p_t}-\frac{1}{vol_g(M)}), \end{equation*} where $i=1,...,n$, $h_1,...,h_n$ are positive sm

Wei Dai, Guolin Qin

In this paper, we are concerned with the critical order H\'{e}non-Lane-Emden type equations with Navier boundary condition on a half space $\mathbb{R}^n_+$: \begin{equation}\label{NPDE0}\\\begin{cases} (-\Delta)^{\frac{n}{2}} u(x)=f(x,u(x)),\ u(x)\geq0,\ x\in\mathbb{R}^{n}_+, \\ u=(-\Delta)u = \cdots = (-\Delta)^{\frac{n}{2}-1}u = 0,\ x\in\partial\mathbb{R}^

Jiayang Liu, Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto

In this study, we propose a new methodology to control how user's data is recognized and used by AI via exploiting the properties of adversarial examples. For this purpose, we propose reversible adversarial example (RAE), a new type of adversarial example. A remarkable feature of RAE is that the image can be correctly recognized and used by the AI model spec

Hungchong Kim, K. S. Kim, Myung-Ki Cheoun, Daisuke Jido

In this work, we investigate additional signatures to support the tetraquark mixing framework that has been recently proposed as a possible structure for the two nonets, namely $a_0 (980)$, $K_0^* (800)$, $f_0 (500)$, $f_0 (980)$ in the light nonet, $a_0 (1450)$, $K_0^* (1430)$, $f_0 (1370)$, $f_0 (1500)$ in the heavy nonet. First, we advocate that the two n

Akaki Tikaradze

We show that given two smooth affine varieties over $\mathbb{C}$ such that their rings of differential operators are Morita equivalent, then corresponding cotangent bundles are isomorphic as symplectic varieties.

Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias

Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the at

Yi An, Liangjin Huang, Jun Li, Jinyong Leng

We introduce deep learning technique to perform complete mode decomposition for few-mode optical fiber for the first time. Our goal is to learn a fast and accurate mapping from near-field beam profiles to the complete mode coefficients, including both modal amplitudes and phases. We train the convolutional neural network with simulated beam patterns, and eva

Tan Nguyen, Nhat Ho, Ankit Patel, Anima Anandkumar

Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture. The DGM uses a CNN to design the prior distribution in the probabilistic model. Furthermor

Zhengyi Zhou

We introduce group actions on polyfolds and polyfold bundles. We prove quotient theorems for polyfolds, when the group action has finite isotropy. We prove that the sc-Fredholm property is preserved under quotient if the base polyfold is infinite dimensional. The quotient construction is the main technical tool in the construction of equivariant fundamental

Ivan De Oliveira Nunes, Karim Eldefrawy, Norrathep Rattanavipanon, Michael Steiner

In this work, we take the first step towards formal verification of Remote Attestation (RA) by designing and verifying an architecture called VRASED: Verifiable Remote Attestation for Simple Embedded Devices. VRASED instantiates a hybrid (HW/SW) RA co-design aimed at low-end embedded systems, e.g., simple IoT devices. VRASED provides a level of security comp

Shuangting Liu, Jiaqi Zhang, Yuxin Chen, Yifan Liu

Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image. Unbalanced semantic label distribution could have a negative influence on segmentation accuracy. In this paper, we investigate using data augmentation approach to balance the semantic label distribution in order to improve segmen

Zhengdao Chen, Baranidharan Raman, Ari Stern

Motivated by the Hodgkin-Huxley model of neuronal dynamics, we study explicit numerical integrators for "conditionally linear" systems of ordinary differential equations. We show that splitting and composition methods, when applied to the Van der Pol oscillator and to the Hodgkin-Huxley model, do a better job of preserving limit cycles of these syste

Fei Li, Facheng Jin, Yujun Yang, Jing Chen

By using the B-spline numerical method, we investigate a two-photon double-ionization (TPDI) process of helium in a high-frequency laser field with its frequency ranging from 1.6~a.u. to 3.0~a.u. and the pulse duration ranging from 75 to 160~attoseconds. We found that there exists a characteristic time $t_{c}$ for a TPDI process, such that the pattern of ene

Chad Giusti, Darrick Lee

One of the core advantages topological methods for data analysis provide is that the language of (co)chains can be mapped onto the semantics of the data, providing a natural avenue for human understanding of the results. Here, we describe such a semantic structure on Chen's classical iterated integral cochain model for paths in Euclidean space. Specifica

Michael Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi

Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities. We present in this paper a framework named Flux that shows how further refinement of the core ideas of machine learning, built upon the foundation of the Julia pr

Deng Zhang

We study the stochastic nonlinear Schroedinger equations with linear multiplicative noise, particularly in the defocusing mass-critical and energy-critical cases. For general initial data, we prove the global existence and uniqueness of solutions in both cases. When the quadratic variation of noise is globally bounded, we also obtain the rescaled scattering

SNO Collaboration, B. Aharmim, S. N. Ahmed, A. E. Anthony

Experimental tests of Lorentz symmetry in systems of all types are critical for ensuring that the basic assumptions of physics are well-founded. Data from all phases of the Sudbury Neutrino Observatory, a kiloton-scale heavy water Cherenkov detector, are analyzed for possible violations of Lorentz symmetry in the neutrino sector. Such violations would appear

Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm

Counterfactual Regret Minimization (CFR) is the leading framework for solving large imperfect-information games. It converges to an equilibrium by iteratively traversing the game tree. In order to deal with extremely large games, abstraction is typically applied before running CFR. The abstracted game is solved with tabular CFR, and its solution is mapped ba

Kai Liu, Hui Feng, Tao Yang, Bo Hu

In this paper, a novel robust beamforming scheme is proposed in three dimensional multi-input multi-output (3D-MIMO) systems. As one of the typical deployments of massive MIMO, a 3D-MIMO system owns sparse channels in angular domain. Thus, various of sparse channel estimation algorithms produce sparse channel estimation errors which can be utilized to narrow

Stefano Galli, Ju Liu, Guanxi Zhang

We study an old but unconventional waveguiding technique based on the use of overhead bare metal Medium Voltage (MV) power lines as open waveguides and not as transmission lines. This technique can be used in support of 5G applications such as backhauling and broadband access in the mmW frequency range or above. Although the analysis of open waveguides dates

Mohammad Movahednasab, Naeimeh Omidvar, Mohammad Reza Pakravan, Tommy Svensson

In a wireless powered communication network (WPCN), an energy access point supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store the received energy in their batteries for their future data transmission. In this paper, we propose an online stochastic policy that jointly controls energy transmission from

Qianlan Bai, Xinyan Zhou, Xing Wang, Yuedong Xu

This paper studies a fundamental problem regarding the security of blockchain on how the existence of multiple misbehaving pools influences the profitability of selfish mining. Each selfish miner maintains a private chain and makes it public opportunistically for the purpose of acquiring more rewards incommensurate to his Hashrate. We establish a novel Marko

BV Divyashree, Amarnath R, Naveen M, G Hemantha Kumar

Locating region of interest for breast cancer masses in the mammographic image is a challenging problem in medical image processing. In this research work, the keen idea is to efficiently extract suspected mass region for further examination. In particular to this fact breast boundary segmentation on sliced rgb image using modified intensity based approach f

Dmytro O. Terletskyi

This paper contains analysis of main modern approaches to dynamic code generation, in particular generation of new classes of objects during program execution. The main attention was paid to universal exploiters of homogeneous classes of objects, which were proposed as a part of such knowledge representation model as object-oriented dynamic networks, as the

Ahmed Al-Dallal

Senior project is a typical essential course in computing educational programs. The course involves the selection of a project problem, the submission of various documents, and intensive communication among the project team members and between them and the course instructors. To facilitate all these tasks, we introduce the senior project management system (S

Xiaolei Ma, Yi Li, Zhiyong Cui, Yinhai Wang

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to ext

Shengwei Chen, Yanyan Shen, Yanmin Zhu

Cloud services have grown rapidly in recent years, which provide high flexibility for cloud users to fulfill their computing requirements on demand. To wisely allocate computing resources in the cloud, it is inevitably important for cloud service providers to be aware of the potential utilization of various resources in the future. This paper focuses on pred

M. Ross Tagaras, Jian Weng, Roland E. Allen

Cavalleri and coworkers have discovered evidence of light-induced superconductivity and related phenomena in several different materials. Here we suggest that some features may be naturally interpreted using a time-dependent Ginzburg-Landau model. In particular, we focus on the lifetime of the transient state in La$_{1.675}$Eu$_{0.2}$Sr$_{0.125}$CuO$_4$ (LES

Kana Iwakuni, Thinh Q. Bui, Justin Niedermeyer, Takashi Sukegawa

We have developed a dispersive spectrometer using a compact immersion grating for direct frequency comb spectroscopy in the long-wave infrared region of 8-10 μm. A frequency resolution of 463 MHz is achieved, which is the highest reported in this wavelength region with a dispersive direct frequency comb spectrometer. We also demonstrate individual mode-resol

Ryan G. James, Jeffrey Emenheiser, James P. Crutchfield

The partial information decomposition (PID) is a promising framework for decomposing a joint random variable into the amount of influence each source variable Xi has on a target variable Y, relative to the other sources. For two sources, influence breaks down into the information that both X0 and X1 redundantly share with Y, what X0 uniquely shares with Y, w

Tachporn Sanguanpuak, Nandana Rajatheva, Dusit Niyato, Matti Latva-aho

We model the scenarios of network slicing allocation for the micro-operator (MO) network. The MO creates the slices "as a service" of wireless resource and then allocates these slices to multiple mobile network operators (MNOs). We propose the slice allocation problem of multiple MNOs with the goal of maximizing the social welfare of the network defi

Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti

In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, eac

Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti

In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic ensemble selection framework using meta-l

Nicole M. Lloyd-Ronning

We investigate the expected radio emission from the reverse shock of short GRBs, using the afterglow parameters derived from the observed short GRB light curves. In light of recent results suggesting that in some cases the radio afterglow is due to emission from the reverse shock, we examine the extent to which this component is detectable for short GRBs. In

Jeff Johnson

Reducing hardware overhead of neural networks for faster or lower power inference and training is an active area of research. Uniform quantization using integer multiply-add has been thoroughly investigated, which requires learning many quantization parameters, fine-tuning training or other prerequisites. Little effort is made to improve floating point relat

Migran N. Gevorkyan, Anastasia V. Demidova, Anna V. Korolkova, Dmitry S. Kulyabov

This paper discusses stochastic numerical methods of Runge-Kutta type with weak and strong convergences for systems of stochastic differential equations in Itô form. At the beginning we give a brief overview of the stochastic numerical methods and information from the theory of stochastic differential equations. Then we motivate the approach to the implement

Faizuddin Ahmed, Farook Rahaman, Susmita Sarkar

In this article, we discuss four dimensional non-static space-times in the background of de-Sitter and anti-de Sitter spaces with the matter-energy sources a stiff fluid, anisotropic fluid, and an electromagnetic field. Under various parameter conditions the solutions may represent models of naked singularity and/or black holes. Finally, the energy-momentum

Emmanouil Theodosis, Petros Maragos

The problem of spoofing attacks is increasingly relevant as digital systems are becoming more ubiquitous. Thus the detection of such attacks and the localisation of attackers have been objects of recent study. After an attack has been detected, various algorithms have been proposed in order to localise the attacker. In this work we propose a new adaptive pru

B. F. L. Ward

Working in the context of the Planck scale cosmology formulation of Bonanno and Reuter, we use our resummed quantum gravity approach to Einstein's general theory of relativity to estimate the value of the cosmological constant as $ρ_Λ=(0.0024 eV)^4$. We show that susy GUT models are constrained by the closeness of this estimate to experiment. We also add

E. F. Keane

The field of Fast Radio Burst (FRB) science is currently thriving and growing rapidly. The lines of active investigation include theoretical and observational aspects of these enigmatic millisecond radio signals. These pursuits are for the most part intertwined so that each keeps the other in check, characteristic of the healthy state of the field. The immed

Yves Aurégan

A concept of ultra-thin low frequency perfect sound absorber is proposed and demonstrated experimentally. To minimize non-linear effects, an high ratio of active area to total area is used to avoid large localized amplitudes. The absorber consists of three elements: a mass supported by a very flexible membrane, a cavity and a resistive layer. The resonance f

James Large, Paul Southam, Anthony Bagnall

tl;dr: no, it cannot, at least not on average on the standard archive problems. We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier approximation and a recursive median sieve) could be automatically applied to time series classification problems as a preprocessing step to im

Issaku Kanamori, Hideo Matsufuru

We investigate implementation of lattice Quantum Chromodynamics (QCD) code on the Intel AVX-512 architecture. The most time consuming part of the numerical simulations of lattice QCD is a solver of linear equation for a large sparse matrix that represents the strong interaction among quarks. To establish widely applicable prescriptions, we examine rather gen

A. I. Volokitin

We study the resonances in Casimir friction, radiative heat transfer and heat generation for two plates sliding relative to each other. Resonances have a different origin in the frequency range of the \textit{normal} (NDE) and \textit{anomalous} (ADE) Doppler effect. In the frequency range of NDE, resonances are associated with resonant photon tunnelling bet

Bin Liu, Shuai Nie, Yaping Zhang, Shan Liang

LSTM-based speaker verification usually uses a fixed-length local segment randomly truncated from an utterance to learn the utterance-level speaker embedding, while using the average embedding of all segments of a test utterance to verify the speaker, which results in a critical mismatch between testing and training. This mismatch degrades the performance of

Prashant Kumar, Martin Schmelzer, Richard P. Dwight

A multilevel Monte Carlo (MLMC) method for quantifying model-form uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS) simulations is presented. Two, high-dimensional, stochastic extensions of the RANS equations are considered to demonstrate the applicability of the MLMC method. The first approach is based on global perturbation of the ba

Adel Rahimi, Mohammad Bahrani

In this paper, we propose a new method for query expansion, which uses FarsNet (Persian WordNet) to find similar tokens related to the query and expand the semantic meaning of the query. For this purpose, we use synonymy relations in FarsNet and extract the related synonyms to query words. This algorithm is used to enhance information retrieval systems and i

M. Seiler, M. Seiß, H. Hoffmann, F. Spahn

The observation of the non-Keplerian behavior of propeller structures in Saturn's outer A ring (Tiscareno et al. 2010, Seiler et al. 2017, Spahn et al. 2018) raises the question, how the propeller responds to the wandering of the central embedded moonlet. Here, we study numerically how the induced propeller is changing for a librating moonlet. It turns o

Luciano Ponzellini Marinelli, Nahuel Caruso, Margarita Portapila

Many local integral methods are based on an integral formulation over small and heavilly overlapping stencils with local RBF interpolations. These functions have become an extremely effective tool for interpolation on scattered node sets, however the ill-conditioning of the interpolation matrix -- when the RBF shape parameter tends to zero corresponding to b

Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti

In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence i

Andrew C. Cullen, Simon R. Clarke

We present an algorithm for constructing numerical solutions to one--dimensional nonlinear, variable coefficient boundary value problems. This scheme is based upon applying the Homotopy Analysis Method (HAM) to decompose a nonlinear differential equation into a series of linear differential equations that can be solved using a sparse, spectrally accurate Geg

Daniel E. Gilbert, Martin T. Wells

Game theory is the study of tractable games which may be used to model more complex systems. Board games, video games and sports, however, are intractable by design, so "ludological" theories about these games as complex phenomena should be grounded in empiricism. A first "ludometric" concern is the empirical measurement of the amount of luck

R. L. Smart, B. Bucciarelli, H. R. A. Jones, F. Marocco

We present new results from the Parallaxes of Southern Extremely Cool dwarfs program to measure parallaxes, proper motions and multi-epoch photometry of L and early T dwarfs. The observations were made on 108 nights over the course of 8 years using the Wide Field Imager on the ESO 2.2m telescope. We present 118 new parallaxes of L \& T dwarfs of which 52 hav

Roland E. Allen

Since neither supersymmetry nor dark matter WIMPs have yet been observed, pessimism about their reality has been growing. Here we discuss a new supersymmetric theory and a new dark matter candidate which are naturally consistent with current experimental results, but which imply a plethora of new phenomena awaiting discovery within the foreseeable future.

Rafael M. O. Cruz, George D. C. Cavalcanti, Tsang Ing Ren

Dynamic classifier selection systems aim to select a group of classifiers that is most adequate for a specific query pattern. This is done by defining a region around the query pattern and analyzing the competence of the classifiers in this region. However, the regions are often surrounded by noise which can difficult the classifier selection. This fact make

Katherine C. McCormick, Jonas Keller, David J. Wineland, Andrew C. Wilson

Coherently displaced harmonic oscillator number states of a harmonically bound ion can be coupled to two internal states of the ion by a laser-induced motional sideband interaction. The internal states can subsequently be read out in a projective measurement via state-dependent fluorescence, with near-unit fidelity. This leads to a rich set of line shapes wh

Loren I. Matilsky, Bradley W. Hindman, Juri Toomre

The advent of helioseismology has determined in detail the average rotation rate of the Sun as a function of radius and latitude. These data immediately reveal two striking boundary layers of shear in the solar convection zone (CZ): a tachocline at the base, where the differential rotation of the CZ transitions to solid-body rotation in the radiative zone, a

Salvatore D. Pace, David K. Campbell

We investigate numerically the existence and stability of higher-order recurrences (HoRs), including super-recurrences, super-super-recurrences, etc., in the alpha and beta Fermi-Pasta-Ulam-Tsingou (FPUT) lattices for initial conditions in the fundamental normal mode. Our results represent a considerable extension of the pioneering work of Tuck and Menzel on

Deren Lei, Zichen Sun, Yijun Xiao, William Yang Wang

Deep neural networks with remarkably strong generalization performances are usually over-parameterized. Despite explicit regularization strategies are used for practitioners to avoid over-fitting, the impacts are often small. Some theoretical studies have analyzed the implicit regularization effect of stochastic gradient descent (SGD) on simple machine learn

Ankur Chowdhary, Adel Alshamrani, Dijiang Huang

Multi-tenant cloud networks have various security and monitoring service functions (SFs) that constitute a service function chain (SFC) between two endpoints. SF rule ordering overlaps and policy conflicts can cause increased latency, service disruption and security breaches in cloud networks. Software Defined Network (SDN) based Network Function Virtualizat

Jeremy Mould, Gisella Clementini, Gary Da Costa

Indications from Gaia data release 2 (DR2) are that the tip of the red giant branch (TRGB, a population II standard candle related to the helium flash in low mass stars) is close to -4 in absolute I magnitude in the Cousins photometric system. Our sample is high latitude southern stars from the thick disk and inner halo, and our result is consistent with lon

Maohua Le

Let $m$, $n$ be positive integers such that $m>n$, $\gcd(m,n)=1$ and $m \not\equiv n \bmod 2$. In 1956, L. Jeśmanowicz \cite{Jes} conjectured that the equation $(m^2 - n^2)^x + (2mn)^y = (m^2+n^2)^z$ has only the positive integer solution $(x,y,z) = (2,2,2)$. This problem is not yet solved. In this paper, combining a lower bound for linear forms in two logar

Ankur Chowdhary, Dijiang Huang

Network function virtualization (NFV) based service function chaining (SFC) allows the provisioning of various security and traffic engineering applications in a cloud network. Inefficient deployment of network functions can lead to security violations and performance overhead. In an OpenFlow enabled cloud, the key problem with current mechanisms is that sev

Nadja Brouns, Samir Tata, Heiko Ludwig, E. Serral Asensio

This research report presents an analysis of the state of the art of modeling Internet of Things (IoT)-aware business processes. IOT links the physical world to the digital world. Traditionally, we would find information about events and processes in the physical world in the digital world entered by humans and humans using this information to control the ph

Ankur Chowdhary, Sailik Sengupta, Adel Alshamrani, Dijiang Huang

Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion De

James Tuite, Grahame Erskine

The undirected degree/diameter and degree/girth problems and their directed analogues have been studied for many decades in the search for efficient network topologies. Recently such questions have received much attention in the setting of mixed graphs, i.e. networks that admit both undirected \emph{edges} and directed \emph{arcs}. The degree/diameter proble

Satyan B. Sreenath, Julia S. Kimbell, Saikat Basu, Andrew J. Coniglio

Objective: Minimal literature exists investigating changes in inflammation with respect to the main nasal cavity (MNC) and paranasal sinuses (PS) before and after maximal medical therapy (MMT) for chronic rhinosinusitis (CRS). We hypothesized that MMT produces a differential level of change in the volume of air space in the MNC and PS, and that resolution of

Yubo Sun, Zhiguang Zhou, Reza Asadpour, Muhammad A. Alam

Photovoltaic (PV) cells have become one of the most promising renewable energy technologies. To make PV more competitive with incumbent technologies, higher power output densities are needed. One promising approach is to add bifaciality to existing monofacial PV devices, allowing more output power from the additional reflection of sunlight from the ground (a

Zainab Farzal, Saikat Basu, Alyssa Burke, Olulade O. Fasanmade

Introduction: Topical intranasal drugs are widely prescribed for Chronic Rhinosinusitis (CRS), although delivery can vary with device type and droplet size. The study objective was to compare nebulized and sprayed droplet deposition in the paranasal sinuses and ostiomeatal complex (OMC) across multiple droplet sizes in CRS patients using computational fluid

Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit Dhillon

Deep learning models have become state of the art for natural language processing (NLP) tasks, however deploying these models in production system poses significant memory constraints. Existing compression methods are either lossy or introduce significant latency. We propose a compression method that leverages low rank matrix factorization during training,to

E. Babaei, I. V. Evstigneev, K. R. Schenk-Hoppé

The classical multidimensional version of Fatou's lemma (Schmeidler 1970) originally obtained for unconditional expectations and the standard non-negative cone in a finite-dimensional linear space is extended to conditional expectations and general closed pointed cones.

Alexander Shekhovtsov, Boris Flach

In this work we investigate the reasons why Batch Normalization (BN) improves the generalization performance of deep networks. We argue that one major reason, distinguishing it from data-independent normalization methods, is randomness of batch statistics. This randomness appears in the parameters rather than in activations and admits an interpretation as a

Brandon Tran, Jerry Li, Aleksander Madry

A recent line of work has uncovered a new form of data poisoning: so-called \emph{backdoor} attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary. In this paper, we identi

Ankur Chowdhary, Dijiang Huang, Adel Alshamrani, Abdulhakim Sabur

SDN provides a programmable command and control networking system in a multi-tenant cloud network using control and data plane separation. However, separating the control and data planes make it difficult for incorporating some security services (e.g., firewalls) into SDN framework. Most of the existing solutions use SDN switches as packet filters and rely o

Bojan Petrovski, Ignacio Aguado, Andreea Hossmann, Michael Baeriswyl

Most of the world's data is stored in relational databases. Accessing these requires specialized knowledge of the Structured Query Language (SQL), putting them out of the reach of many people. A recent research thread in Natural Language Processing (NLP) aims to alleviate this problem by automatically translating natural language questions into SQL queri

Michael Kruse, Hal Finkel

The LLVM compiler framework supports a selection of loop transformations such as vectorization, distribution and unrolling. Each transformation is carried-out by specialized passes that have been developed independently. In this paper we propose an integrated approach to loop optimizations: A single dedicated pass that mutates a Loop Structure DAG. Each tran

Kevin Keating

Let $K$ be a local field and let $L/K$ be a totally ramified Galois extension of degree $p^n$. Being semistable and possessing a Galois scaffold are two conditions which facilitate the computation of the additive Galois module structure of $L/K$. In this note we show that $L/K$ is semistable if and only if $L/K$ has a Galois scaffold. We also give sufficient

Andrey E. Shishkov, Yevgeniia A. Yevgenieva

Regimes with a singular peaking for a wide class of quasilinear second order parabolic equations are studied. On the basis of energy methods, precise estimates of a final profile of a weak solution in a neighborhood of the peaking time are established depending on the rate of increase of a global energy of this solution. Key words: quasilinear parabolic equa

Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge

Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery. A main ingredient required for machine learning is a training dataset consisting of molecular feature

Agnes Backhausz, Balazs Szegedy

We present a new approach to graph limit theory which unifies and generalizes the two most well developed directions, namely dense graph limits (even the more general $L^p$ limits) and Benjamini--Schramm limits (even in the stronger local-global setting). We illustrate by examples that this new framework provides a rich limit theory with natural limit object

Jiaao Chen, Jianshu Chen, Zhou Yu

The ability to select an appropriate story ending is the first step towards perfect narrative comprehension. Story ending prediction requires not only the explicit clues within the context, but also the implicit knowledge (such as commonsense) to construct a reasonable and consistent story. However, most previous approaches do not explicitly use background c

Michael Kruse, Hal Finkel

Directives for the compiler such as pragmas can help programmers to separate an algorithm's semantics from its optimization. This keeps the code understandable and easier to optimize for different platforms. Simple transformations such as loop unrolling are already implemented in most mainstream compilers. We recently submitted a proposal to add generali

C. O. Lopez, J. E. Beasley

In this paper we consider the problem of packing a fixed number of identical circles inside the unit circle container, where the packing is complicated by the presence of fixed size circular prohibited areas. Here the objective is to maximise the radius of the identical circles. We present a heuristic for the problem based upon formulation space search. Comp