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QuanFuzz: Fuzz Testing of Quantum Program
Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from traditional program make this issue hard to resolve. In this paper, we present QuanFuzz, a search-based test input generator for quantum program...
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Option Pricing Models Driven by the Space-Time Fractional Diffusion: Series Representation and Applications
In this paper, we focus on option pricing models based on space-time fractional diffusion. We briefly revise recent results which show that the option price can be represented in the terms of rapidly converging double-series and apply these results to the data from real markets. We focus on estimation of model parame...
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Anticipation: an effective evolutionary strategy for a sub-optimal population in a cyclic environment
We built a two-state model of an asexually reproducing organism in a periodic environment endowed with the capability to anticipate an upcoming environmental change and undergo pre-emptive switching. By virtue of these anticipatory transitions, the organism oscillates between its two states that is a time $\theta$ ou...
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Unravelling Airbnb Predicting Price for New Listing
This paper analyzes Airbnb listings in the city of San Francisco to better understand how different attributes such as bedrooms, location, house type amongst others can be used to accurately predict the price of a new listing that optimal in terms of the host's profitability yet affordable to their guests. This model...
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Finding, Hitting and Packing Cycles in Subexponential Time on Unit Disk Graphs
We give algorithms with running time $2^{O({\sqrt{k}\log{k}})} \cdot n^{O(1)}$ for the following problems. Given an $n$-vertex unit disk graph $G$ and an integer $k$, decide whether $G$ contains (1) a path on exactly/at least $k$ vertices, (2) a cycle on exactly $k$ vertices, (3) a cycle on at least $k$ vertices, (4)...
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The self-referring DNA and protein: a remark on physical and geometrical aspects
All known life forms are based upon a hierarchy of interwoven feedback loops, operating over a cascade of space, time and energy scales. Among the most basic loops are those connecting DNA and proteins. For example, in genetic networks, DNA genes are expressed as proteins, which may bind near the same genes and there...
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Optimal input design for system identification using spectral decomposition
The aim of this paper is to design a band-limited optimal input with power constraints for identifying a linear multi-input multi-output system. It is assumed that the nominal system parameters are specified. The key idea is to use the spectral decomposition theorem and write the power spectrum as $\phi_{u}(j\omega)=...
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Creating a Web Analysis and Visualization Environment
Due to the rapid growth of the World Wide Web, resource discovery becomes an increasing problem. As an answer to the demand for information management, a third generation of World-Wide Web tools will evolve: information gathering and processing agents. This paper describes WAVE (Web Analysis and Visualization Environ...
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Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
Supervised learning has been very successful for automatic segmentation of images from a single scanner. However, several papers report deteriorated performances when using classifiers trained on images from one scanner to segment images from other scanners. We propose a transfer learning classifier that adapts to di...
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Service Providers of the Sharing Economy: Who Joins and Who Benefits?
Many "sharing economy" platforms, such as Uber and Airbnb, have become increasingly popular, providing consumers with more choices and suppliers a chance to make profit. They, however, have also brought about emerging issues regarding regulation, tax obligation, and impact on urban environment, and have generated hea...
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The generalized Fermat equation with exponents 2, 3, n
We study the Generalized Fermat Equation $x^2 + y^3 = z^p$, to be solved in coprime integers, where $p \ge 7$ is prime. Using modularity and level lowering techniques, the problem can be reduced to the determination of the sets of rational points satisfying certain 2-adic and 3-adic conditions on a finite set of twis...
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On the image of the almost strict Morse n-category under almost strict n-functors
In an earlier work, we constructed the almost strict Morse $n$-category $\mathcal X$ which extends Cohen $\&$ Jones $\&$ Segal's flow category. In this article, we define two other almost strict $n$-categories $\mathcal V$ and $\mathcal W$ where $\mathcal V$ is based on homomorphisms between real vector spaces and $\...
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Short-term Memory of Deep RNN
The extension of deep learning towards temporal data processing is gaining an increasing research interest. In this paper we investigate the properties of state dynamics developed in successive levels of deep recurrent neural networks (RNNs) in terms of short-term memory abilities. Our results reveal interesting insi...
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Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge
We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or p...
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Neutron Stars in Screened Modified Gravity: Chameleon vs Dilaton
We consider the scalar field profile around relativistic compact objects such as neutron stars for a range of modified gravity models with screening mechanisms of the chameleon and Damour-Polyakov types. We focus primarily on inverse power law chameleons and the environmentally dependent dilaton as examples of both m...
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Spin pumping into superconductors: A new probe of spin dynamics in a superconducting thin film
Spin pumping refers to the microwave-driven spin current injection from a ferromagnet into the adjacent target material. We theoretically investigate the spin pumping into superconductors by fully taking account of impurity spin-orbit scattering that is indispensable to describe diffusive spin transport with finite s...
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Evidence of Eta Aquariid Outbursts Recorded in the Classic Maya Hieroglyphic Script Using Orbital Integrations
No firm evidence has existed that the ancient Maya civilization recorded specific occurrences of meteor showers or outbursts in the corpus of Maya hieroglyphic inscriptions. In fact, there has been no evidence of any pre-Hispanic civilization in the Western Hemisphere recording any observations of any meteor showers ...
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Degenerations of NURBS curves while all of weights approaching infinity
NURBS curve is widely used in Computer Aided Design and Computer Aided Geometric Design. When a single weight approaches infinity, the limit of a NURBS curve tends to the corresponding control point. In this paper, a kind of control structure of a NURBS curve, called regular control curve, is defined. We prove that t...
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A Convex Parametrization of a New Class of Universal Kernel Functions for use in Kernel Learning
We propose a new class of universal kernel functions which admit a linear parametrization using positive semidefinite matrices. These kernels are generalizations of the Sobolev kernel and are defined by piecewise-polynomial functions. The class of kernels is termed "tessellated" as the resulting discriminant is defin...
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Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Large batch size training of Neural Networks has been shown to incur accuracy loss when trained with the current methods. The exact underlying reasons for this are still not completely understood. Here, we study large batch size training through the lens of the Hessian operator and robust optimization. In particular,...
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Sparse-Group Bayesian Feature Selection Using Expectation Propagation for Signal Recovery and Network Reconstruction
We present a Bayesian method for feature selection in the presence of grouping information with sparsity on the between- and within group level. Instead of using a stochastic algorithm for parameter inference, we employ expectation propagation, which is a deterministic and fast algorithm. Available methods for featur...
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A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography
Brain CT has become a standard imaging tool for emergent evaluation of brain condition, and measurement of midline shift (MLS) is one of the most important features to address for brain CT assessment. We present a simple method to estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS over the ...
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Anisotropic Dielectric Relaxation in Single Crystal H$_{2}$O Ice Ih from 80-250 K
Three properties of the dielectric relaxation in ultra-pure single crystalline H$_{2}$O ice Ih were probed at temperatures between 80-250 K; the thermally stimulated depolarization current, static electrical conductivity, and dielectric relaxation time. The measurements were made with a guarded parallel-plate capacit...
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Design of Improved Quasi-Cyclic Protograph-Based Raptor-Like LDPC Codes for Short Block-Lengths
Protograph-based Raptor-like low-density parity-check codes (PBRL codes) are a recently proposed family of easily encodable and decodable rate-compatible LDPC (RC-LDPC) codes. These codes have an excellent iterative decoding threshold and performance across all design rates. PBRL codes designed thus far, for both lon...
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Comparing the Finite-Time Performance of Simulation-Optimization Algorithms
We empirically evaluate the finite-time performance of several simulation-optimization algorithms on a testbed of problems with the goal of motivating further development of algorithms with strong finite-time performance. We investigate if the observed performance of the algorithms can be explained by properties of t...
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On the Constituent Attributes of Software and Organisational Resilience
Our societies are increasingly dependent on services supplied by computers & their software. New technology only exacerbates this dependence by increasing the number, performance, and degree of autonomy and inter-connectivity of software-empowered computers and cyber-physical "things", which translates into unprecede...
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Borcherds-Bozec algebras, root multiplicities and the Schofield construction
Using the twisted denominator identity, we derive a closed form root multiplicity formula for all symmetrizable Borcherds-Bozec algebras and discuss its applications including the case of Monster Borcherds-Bozec algebra. In the second half of the paper, we provide the Schofield constuction of symmetric Borcherds-Boze...
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Pressure-induced spin pairing transition of Fe$^{3+}$ in oxygen octahedra
High pressure can provoke spin transitions in transition metal-bearing compounds. These transitions are of high interest not only for fundamental physics and chemistry, but also may have important implications for geochemistry and geophysics of the Earth and planetary interiors. Here we have carried out a comparative...
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LSH on the Hypercube Revisited
LSH (locality sensitive hashing) had emerged as a powerful technique in nearest-neighbor search in high dimensions [IM98, HIM12]. Given a point set $P$ in a metric space, and given parameters $r$ and $\varepsilon > 0$, the task is to preprocess the point set, such that given a query point $q$, one can quickly decide ...
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A Novel Metamaterial-Inspired RF-coil for Preclinical Dual-Nuclei MRI
In this paper we propose, design and test a new dual-nuclei RF-coil inspired by wire metamaterial structures. The coil operates due to resonant excitation of hybridized eigenmodes in multimode flat periodic structures comprising several coupled thin metal strips. It was shown that the field distribution of the coil (...
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A biofilm and organomineralisation model for the growth and limiting size of ooids
Ooids are typically spherical sediment grains characterised by concentric layers encapsulating a core. There is no universally accepted explanation for ooid genesis, though factors such as agitation, abiotic and/or microbial mineralisation and size limitation have been variously invoked. We develop a mathematical mod...
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Spectral Filtering for General Linear Dynamical Systems
We give a polynomial-time algorithm for learning latent-state linear dynamical systems without system identification, and without assumptions on the spectral radius of the system's transition matrix. The algorithm extends the recently introduced technique of spectral filtering, previously applied only to systems with...
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Markov $L_2$-inequality with the Laguerre weight
Let $w_\alpha(t) := t^{\alpha}\,e^{-t}$, where $\alpha > -1$, be the Laguerre weight function, and let $\|\cdot\|_{w_\alpha}$ be the associated $L_2$-norm, $$ \|f\|_{w_\alpha} = \left\{\int_{0}^{\infty} |f(x)|^2 w_\alpha(x)\,dx\right\}^{1/2}\,. $$ By $\mathcal{P}_n$ we denote the set of algebraic polynomials of degre...
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Intrusion Prevention and Detection in Grid Computing - The ALICE Case
Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount o...
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Evolution-Preserving Dense Trajectory Descriptors
Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets. This paper improves their performance by applying rank pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajecto...
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Multilingual and Cross-lingual Timeline Extraction
In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of multilingual and cross-lingual data sources. Based on the assumption that event-related information can be recovered from different documents written in different languages, we extend the...
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Mixture modeling on related samples by $ψ$-stick breaking and kernel perturbation
There has been great interest recently in applying nonparametric kernel mixtures in a hierarchical manner to model multiple related data samples jointly. In such settings several data features are commonly present: (i) the related samples often share some, if not all, of the mixture components but with differing weig...
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Optimal segmentation of directed graph and the minimum number of feedback arcs
The minimum feedback arc set problem asks to delete a minimum number of arcs (directed edges) from a digraph (directed graph) to make it free of any directed cycles. In this work we approach this fundamental cycle-constrained optimization problem by considering a generalized task of dividing the digraph into D layers...
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Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
This paper describes our approach for the triple scoring task at the WSDM Cup 2017. The task required participants to assign a relevance score for each pair of entities and their types in a knowledge base in order to enhance the ranking results in entity retrieval tasks. We propose an approach wherein the outputs of ...
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A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation
In this paper, we present an efficient computational framework with the purpose of generating weighted pseudo-measurements to improve the quality of Distribution System State Estimation (DSSE) and provide observability with Advanced Metering Infrastructure (AMI) against unobservable customers and missing data. The pr...
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Nonsparse learning with latent variables
As a popular tool for producing meaningful and interpretable models, large-scale sparse learning works efficiently when the underlying structures are indeed or close to sparse. However, naively applying the existing regularization methods can result in misleading outcomes due to model misspecification. In particular,...
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The Role of Network Analysis in Industrial and Applied Mathematics
Many problems in industry --- and in the social, natural, information, and medical sciences --- involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. The study of networks is concerned explicitly with connectivity betwee...
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Automated Detection, Exploitation, and Elimination of Double-Fetch Bugs using Modern CPU Features
Double-fetch bugs are a special type of race condition, where an unprivileged execution thread is able to change a memory location between the time-of-check and time-of-use of a privileged execution thread. If an unprivileged attacker changes the value at the right time, the privileged operation becomes inconsistent,...
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Fano resonances and fluorescence enhancement of a dipole emitter near a plasmonic nanoshell
We analytically study the spontaneous emission of a single optical dipole emitter in the vicinity of a plasmonic nanoshell, based on the Lorenz-Mie theory. We show that the fluorescence enhancement due to the coupling between optical emitter and sphere can be tuned by the aspect ratio of the core-shell nanosphere and...
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Unoriented Spectral Triples
Any oriented Riemannian manifold with a Spin-structure defines a spectral triple, so the spectral triple can be regarded as a noncommutative Spin-manifold. Otherwise for any unoriented Riemannian manifold there is the two-fold covering by oriented Riemannian manifold. Moreover there are noncommutative generalizations...
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Gradient-enhanced kriging for high-dimensional problems
Surrogate models provide a low computational cost alternative to evaluating expensive functions. The construction of accurate surrogate models with large numbers of independent variables is currently prohibitive because it requires a large number of function evaluations. Gradient-enhanced kriging has the potential to...
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Contagion dynamics of extremist propaganda in social networks
Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group...
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Estimate exponential memory decay in Hidden Markov Model and its applications
Inference in hidden Markov model has been challenging in terms of scalability due to dependencies in the observation data. In this paper, we utilize the inherent memory decay in hidden Markov models, such that the forward and backward probabilities can be carried out with subsequences, enabling efficient inference ov...
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Fabrication of antenna-coupled KID array for Cosmic Microwave Background detection
Kinetic Inductance Detectors (KIDs) have become an attractive alternative to traditional bolometers in the sub-mm and mm observing community due to their innate frequency multiplexing capabilities and simple lithographic processes. These advantages make KIDs a viable option for the $O(500,000)$ detectors needed for t...
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The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R
Penalized regression models such as the lasso have been extensively applied to analyzing high-dimensional data sets. However, due to memory limitations, existing R packages like glmnet and ncvreg are not capable of fitting lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are increasingly see...
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Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers
Many optimization algorithms converge to stationary points. When the underlying problem is nonconvex, they may get trapped at local minimizers and occasionally stagnate near saddle points. We propose the Run-and-Inspect Method, which adds an "inspect" phase to existing algorithms that helps escape from non-global sta...
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Hierarchical Adversarially Learned Inference
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract repre...
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Demonstration of a quantum key distribution network in urban fibre-optic communication lines
We report the results of the implementation of a quantum key distribution (QKD) network using standard fibre communication lines in Moscow. The developed QKD network is based on the paradigm of trusted repeaters and allows a common secret key to be generated between users via an intermediate trusted node. The main fe...
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ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural ne...
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UntrimmedNets for Weakly Supervised Action Recognition and Detection
Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models fro...
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Flatness of Minima in Random Inflationary Landscapes
We study the likelihood which relative minima of random polynomial potentials support the slow-roll conditions for inflation. Consistent with renormalizability and boundedness, the coefficients that appear in the potential are chosen to be order one with respect to the energy scale at which inflation transpires. Inve...
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Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives. To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (generator), providing...
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Magneto-elastic coupling model of deformable anisotropic superconductors
We develop a magneto-elastic (ME) coupling model for the interaction between the vortex lattice and crystal elasticity. The theory extends the Kogan-Clem's anisotropic Ginzburg-Landau (GL) model to include the elasticity effect. The anisotropies in superconductivity and elasticity are simultaneously considered in the...
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Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis
We consider the problem of high-dimensional misspecified phase retrieval. This is where we have an $s$-sparse signal vector $\mathbf{x}_*$ in $\mathbb{R}^n$, which we wish to recover using sampling vectors $\textbf{a}_1,\ldots,\textbf{a}_m$, and measurements $y_1,\ldots,y_m$, which are related by the equation $f(\lef...
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Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing
We consider the problem of minimizing a convex objective function $F$ when one can only evaluate its noisy approximation $\hat{F}$. Unless one assumes some structure on the noise, $\hat{F}$ may be an arbitrary nonconvex function, making the task of minimizing $F$ intractable. To overcome this, prior work has often fo...
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Online $^{222}$Rn removal by cryogenic distillation in the XENON100 experiment
We describe the purification of xenon from traces of the radioactive noble gas radon using a cryogenic distillation column. The distillation column is integrated into the gas purification loop of the XENON100 detector for online radon removal. This enabled us to significantly reduce the constant $^{222}$Rn background...
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The Kite Graph is Determined by Its Adjacency Spectrum
The Kite graph $Kite_{p}^{q}$ is obtained by appending the complete graph $K_{p}$ to a pendant vertex of the path $P_{q}$. In this paper, the kite graph is proved to be determined by the spectrum of its adjacency matrix.
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Matchability of heterogeneous networks pairs
We consider the problem of graph matchability in non-identically distributed networks. In a general class of edge-independent networks, we demonstrate that graph matchability is almost surely lost when matching the networks directly, and is almost perfectly recovered when first centering the networks using Universal ...
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Visual Progression Analysis of Student Records Data
University curriculum, both on a campus level and on a per-major level, are affected in a complex way by many decisions of many administrators and faculty over time. As universities across the United States share an urgency to significantly improve student success and success retention, there is a pressing need to be...
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A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists h...
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Capacity Releasing Diffusion for Speed and Locality
Diffusions and related random walk procedures are of central importance in many areas of machine learning, data analysis, and applied mathematics. Because they spread mass agnostically at each step in an iterative manner, they can sometimes spread mass "too aggressively," thereby failing to find the "right" clusters....
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Two-term spectral asymptotics for the Dirichlet pseudo-relativistic kinetic energy operator on a bounded domain
Continuing the series of works following Weyl's one-term asymptotic formula for the counting function $N(\lambda)=\sum_{n=1}^\infty(\lambda_n{-}\lambda)_-$ of the eigenvalues of the Dirichlet Laplacian and the much later found two-term expansion on domains with highly regular boundary by Ivrii and Melrose, we prove a...
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Exact Good-Turing characterization of the two-parameter Poisson-Dirichlet superpopulation model
Large sample size equivalence between the celebrated {\it approximated} Good-Turing estimator of the probability to discover a species already observed a certain number of times (Good, 1953) and the modern Bayesian nonparametric counterpart has been recently established by virtue of a particular smoothing rule based ...
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Uniform deviation and moment inequalities for random polytopes with general densities in arbitrary convex bodies
We prove an exponential deviation inequality for the convex hull of a finite sample of i.i.d. random points with a density supported on an arbitrary convex body in $\R^d$, $d\geq 2$. When the density is uniform, our result yields rate optimal upper bounds for all the moments of the missing volume of the convex hull, ...
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On the Efficiency of Connection Charges---Part II: Integration of Distributed Energy Resources
This two-part paper addresses the design of retail electricity tariffs for distribution systems with distributed energy resources (DERs). Part I presents a framework to optimize an ex-ante two-part tariff for a regulated monopolistic retailer who faces stochastic wholesale prices on the one hand and stochastic demand...
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Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) parameters. In this work, we present empirical comparison between the standard LSTM recurrent neural network architecture and three new parameter...
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Transition Jitter in Heat Assisted Magnetic Recording by Micromagnetic Simulation
In this paper we apply an extended Landau-Lifschitz equation, as introduced by Baňas et al. for the simulation of heat-assisted magnetic recording. This equation has similarities with the Landau-Lifshitz-Bloch equation. The Baňas equation is supposed to be used in a continuum setting with sub-grain discretization by ...
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Complexity of human response delay in intermittent control: The case of virtual stick balancing
Response delay is an inherent and essential part of human actions. In the context of human balance control, the response delay is traditionally modeled using the formalism of delay-differential equations, which adopts the approximation of fixed delay. However, experimental studies revealing substantial variability, a...
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Algebraic cycles on some special hyperkähler varieties
This note contains some examples of hyperkähler varieties $X$ having a group $G$ of non-symplectic automorphisms, and such that the action of $G$ on certain Chow groups of $X$ is as predicted by Bloch's conjecture. The examples range in dimension from $6$ to $132$. For each example, the quotient $Y=X/G$ is a Calabi-Y...
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On recurrence in G-spaces
We introduce and analyze the following general concept of recurrence. Let $G$ be a group and let $X$ be a G-space with the action $G\times X\longrightarrow X$, $(g,x)\longmapsto gx$. For a family $\mathfrak{F}$ of subset of $X$ and $A\in \mathfrak{F}$, we denote $\Delta_{\mathfrak{F}}(A)=\{g\in G: gB\subseteq A$ for ...
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Deep adversarial neural decoding
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning. Our approach first inverts the linear transformation from latent features to brain responses with maximum a posteriori estimation and then inverts th...
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Optimally Guarding 2-Reflex Orthogonal Polyhedra by Reflex Edge Guards
We study the problem of guarding an orthogonal polyhedron having reflex edges in just two directions (as opposed to three) by placing guards on reflex edges only. We show that (r - g)/2 + 1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus. This bound is...
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Strongly regular decompositions and symmetric association schemes of a power of two
For any positive integer $m$, the complete graph on $2^{2m}(2^m+2)$ vertices is decomposed into $2^m+1$ commuting strongly regular graphs, which give rise to a symmetric association scheme of class $2^{m+2}-2$. Furthermore, the eigenmatrices of the symmetric association schemes are determined explicitly. As an applic...
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Resilient Non-Submodular Maximization over Matroid Constraints
The control and sensing of large-scale systems results in combinatorial problems not only for sensor and actuator placement but also for scheduling or observability/controllability. Such combinatorial constraints in system design and implementation can be captured using a structure known as matroids. In particular, t...
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Tests for comparing time-invariant and time-varying spectra based on the Anderson-Darling statistic
Based on periodogram-ratios of two univariate time series at different frequency points, two tests are proposed for comparing their spectra. One is an Anderson-Darling-like statistic for testing the equality of two time-invariant spectra. The other is the maximum of Anderson-Darling-like statistics for testing the eq...
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Temperley-Lieb and Birman-Murakami-Wenzl like relations from multiplicity free semi-simple tensor system
In this article we consider conditions under which projection operators in multiplicity free semi-simple tensor categories satisfy Temperley-Lieb like relations. This is then used as a stepping stone to prove sufficient conditions for obtaining a representation of the Birman-Murakami-Wenzl algebra from a braided mult...
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A Nash Type result for Divergence Parabolic Equation related to Hormander's vector fields
In this paper we consider the divergence parabolic equation with bounded and measurable coefficients related to Hormander's vector fields and establish a Nash type result, i.e., the local Holder regularity for weak solutions. After deriving the parabolic Sobolev inequality, (1,1) type Poincaré inequality of Horman...
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Wick order, spreadability and exchangeability for monotone commutation relations
We exhibit a Hamel basis for the concrete $*$-algebra $\mathfrak{M}_o$ associated to monotone commutation relations realised on the monotone Fock space, mainly composed by Wick ordered words of annihilators and creators. We apply such a result to investigate spreadability and exchangeability of the stochastic process...
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Visualizing Time-Varying Particle Flows with Diffusion Geometry
The tasks of identifying separation structures and clusters in flow data are fundamental to flow visualization. Significant work has been devoted to these tasks in flow represented by vector fields, but there are unique challenges in addressing these tasks for time-varying particle data. The unstructured nature of pa...
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Factorization tricks for LSTM networks
We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and st...
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Pure Rough Mereology and Counting
The study of mereology (parts and wholes) in the context of formal approaches to vagueness can be approached in a number of ways. In the context of rough sets, mereological concepts with a set-theoretic or valuation based ontology acquire complex and diverse behavior. In this research a general rough set framework ca...
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Relaxation of nonlinear elastic energies involving deformed configuration and applications to nematic elastomers
We start from a variational model for nematic elastomers that involves two energies: mechanical and nematic. The first one consists of a nonlinear elastic energy which is influenced by the orientation of the molecules of the nematic elastomer. The nematic energy is an Oseen--Frank energy in the deformed configuration...
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A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing
Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is ...
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Toward Incorporation of Relevant Documents in word2vec
Recent advances in neural word embedding provide significant benefit to various information retrieval tasks. However as shown by recent studies, adapting the embedding models for the needs of IR tasks can bring considerable further improvements. The embedding models in general define the term relatedness by exploitin...
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Randomized Load Balancing on Networks with Stochastic Inputs
Iterative load balancing algorithms for indivisible tokens have been studied intensively in the past. Complementing previous worst-case analyses, we study an average-case scenario where the load inputs are drawn from a fixed probability distribution. For cycles, tori, hypercubes and expanders, we obtain almost matchi...
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The classification of Lagrangians nearby the Whitney immersion
The Whitney immersion is a Lagrangian sphere inside the four-dimensional symplectic vector space which has a single transverse double point of self-intersection index $+1.$ This Lagrangian also arises as the Weinstein skeleton of the complement of a binodal cubic curve inside the projective plane, and the latter Wein...
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Simulation study of energy resolution, position resolution and $π^0$-$γ$ separation of a sampling electromagnetic calorimeter at high energies
A simulation study of energy resolution, position resolution, and $\pi^0$-$\gamma$ separation using multivariate methods of a sampling calorimeter is presented. As a realistic example, the geometry of the calorimeter is taken from the design geometry of the Shashlik calorimeter which was considered as a candidate for...
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Multi-Round Influence Maximization (Extended Version)
In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round. MRIM problem m...
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Generalisation dynamics of online learning in over-parameterised neural networks
Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient ...
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Nonparametric Testing for Differences in Electricity Prices: The Case of the Fukushima Nuclear Accident
This work is motivated by the problem of testing for differences in the mean electricity prices before and after Germany's abrupt nuclear phaseout after the nuclear disaster in Fukushima Daiichi, Japan, in mid-March 2011. Taking into account the nature of the data and the auction design of the electricity market, we ...
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Dynamic coupling of ferromagnets via spin Hall magnetoresistance
The synchronized magnetization dynamics in ferromagnets on a nonmagnetic heavy metal caused by the spin Hall effect is investigated theoretically. The direct and inverse spin Hall effects near the ferromagnetic/nonmagnetic interface generate longitudinal and transverse electric currents. The phenomenon is known as th...
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Exact Combinatorial Inference for Brain Images
The permutation test is known as the exact test procedure in statistics. However, often it is not exact in practice and only an approximate method since only a small fraction of every possible permutation is generated. Even for a small sample size, it often requires to generate tens of thousands permutations, which c...
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Laser annealing heals radiation damage in avalanche photodiodes
Avalanche photodiodes (APDs) are a practical option for space-based quantum communications requiring single-photon detection. However, radiation damage to APDs significantly increases their dark count rates and reduces their useful lifetimes in orbit. We show that high-power laser annealing of irradiated APDs of thre...
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Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification
We are interested in the development of surrogate models for uncertainty quantification and propagation in problems governed by stochastic PDEs using a deep convolutional encoder-decoder network in a similar fashion to approaches considered in deep learning for image-to-image regression tasks. Since normal neural net...
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A symmetric monoidal and equivariant Segal infinite loop space machine
In [MMO] (arXiv:1704.03413), we reworked and generalized equivariant infinite loop space theory, which shows how to construct $G$-spectra from $G$-spaces with suitable structure. In this paper, we construct a new variant of the equivariant Segal machine that starts from the category $\scr{F}$ of finite sets rather th...
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