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Stochastic comparisons of series and parallel systems with heterogeneous components
In this paper, we discuss stochastic comparisons of parallel systems with independent heterogeneous exponentiated Nadarajah-Haghighi (ENH) components in terms of the usual stochastic order, dispersive order, convex transform order and the likelihood ratio order. In the presence of the Archimedean copula, we study sto...
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Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in epidemiology and other related areas of population science. In this paper, we study statistical inference in the increasingly popular two-sa...
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Edgeworth correction for the largest eigenvalue in a spiked PCA model
We study improved approximations to the distribution of the largest eigenvalue $\hat{\ell}$ of the sample covariance matrix of $n$ zero-mean Gaussian observations in dimension $p+1$. We assume that one population principal component has variance $\ell > 1$ and the remaining `noise' components have common variance $1$...
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On one nearly everywhere continuous and nowhere differentiable function, that defined by automaton with finite memory
This paper is devoted to the investigation of the following function $$ f: x=\Delta^{3}_{\alpha_{1}\alpha_{2}...\alpha_{n}...}{\rightarrow} \Delta^{3}_{\varphi(\alpha_{1})\varphi(\alpha_{2})...\varphi(\alpha_{n})...}=f(x)=y, $$ where $\varphi(i)=\frac{-3i^{2}+7i}{2}$, $ i \in N^{0}_{2}=\{0,1,2\}$, and $\Delta^{3}_{\a...
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Hochschild cohomology for periodic algebras of polynomial growth
We describe the dimensions of low Hochschild cohomology spaces of exceptional periodic representation-infinite algebras of polynomial growth. As an application we obtain that an indecomposable non-standard periodic representation-infinite algebra of polynomial growth is not derived equivalent to a standard self-injec...
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Fracton Models on General Three-Dimensional Manifolds
Fracton models, a collection of exotic gapped lattice Hamiltonians recently discovered in three spatial dimensions, contain some 'topological' features: they support fractional bulk excitations (dubbed fractons), and a ground state degeneracy that is robust to local perturbations. However, because previous fracton mo...
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Topic Modeling on Health Journals with Regularized Variational Inference
Topic modeling enables exploration and compact representation of a corpus. The CaringBridge (CB) dataset is a massive collection of journals written by patients and caregivers during a health crisis. Topic modeling on the CB dataset, however, is challenging due to the asynchronous nature of multiple authors writing a...
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On the Taylor coefficients of a subclass of meromorphic univalent functions
Let $\mathcal{V}_p(\lambda)$ be the collection of all functions $f$ defined in the unit disc $\ID$ having a simple pole at $z=p$ where $0<p<1$ and analytic in $\ID\setminus\{p\}$ with $f(0)=0=f'(0)-1$ and satisfying the differential inequality $|(z/f(z))^2 f'(z)-1|< \lambda $ for $z\in \ID$, $0<\lambda\leq 1$. Each $...
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Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact connected by simple curves over which training and test accuracy are nearly constant. We introduce a training procedure to discover these h...
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On the scaling patterns of infectious disease incidence in cities
Urban areas with larger and more connected populations offer an auspicious environment for contagion processes such as the spread of pathogens. Empirical evidence reveals a systematic increase in the rates of certain sexually transmitted diseases (STDs) with larger urban population size. However, the main drivers of ...
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Homological subsets of Spec
We investigate homological subsets of the prime spectrum of a ring, defined by the help of the Ext-family $\{\Ext^i_R(-,R)\}$. We extend Grothendieck's calculation of $\dim(\Ext^g_R(M,R))$. We compute support of $\Ext^i_R(M,R)$ in many cases. Also, we answer a low-dimensional case of a problem posed by Vasconcelos on...
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Mean field repulsive Kuramoto models: Phase locking and spatial signs
The phenomenon of self-synchronization in populations of oscillatory units appears naturally in neurosciences. However, in some situations, the formation of a coherent state is damaging. In this article we study a repulsive mean-field Kuramoto model that describes the time evolution of n points on the unit circle, wh...
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Signal tracking beyond the time resolution of an atomic sensor by Kalman filtering
We study causal waveform estimation (tracking) of time-varying signals in a paradigmatic atomic sensor, an alkali vapor monitored by Faraday rotation probing. We use Kalman filtering, which optimally tracks known linear Gaussian stochastic processes, to estimate stochastic input signals that we generate by optical pu...
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Case Study: Explaining Diabetic Retinopathy Detection Deep CNNs via Integrated Gradients
In this report, we applied integrated gradients to explaining a neural network for diabetic retinopathy detection. The integrated gradient is an attribution method which measures the contributions of input to the quantity of interest. We explored some new ways for applying this method such as explaining intermediate ...
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6.2-GHz modulated terahertz light detection using fast terahertz quantum well photodetectors
The fast detection of terahertz radiation is of great importance for various applications such as fast imaging, high speed communications, and spectroscopy. Most commercial products capable of sensitively responding the terahertz radiation are thermal detectors, i.e., pyroelectric sensors and bolometers. This class o...
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Inference via low-dimensional couplings
We investigate the low-dimensional structure of deterministic transformations between random variables, i.e., transport maps between probability measures. In the context of statistics and machine learning, these transformations can be used to couple a tractable "reference" measure (e.g., a standard Gaussian) with a t...
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A unified thermostat scheme for efficient configurational sampling for classical/quantum canonical ensembles via molecular dynamics
We show a unified second-order scheme for constructing simple, robust and accurate algorithms for typical thermostats for configurational sampling for the canonical ensemble. When Langevin dynamics is used, the scheme leads to the BAOAB algorithm that has been recently investigated. We show that the scheme is also us...
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Wide Bandwidth, Frequency Modulated Free Electron Laser
It is shown via theory and simulation that the resonant frequency of a Free Electron Laser may be modulated to obtain an FEL interaction with a frequency bandwidth which is at least an order of magnitude greater than normal FEL operation. The system is described in the linear regime by a summation over exponential ga...
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A Transformation-Proximal Bundle Algorithm for Solving Large-Scale Multistage Adaptive Robust Optimization Problems
This paper presents a novel transformation-proximal bundle algorithm to solve multistage adaptive robust mixed-integer linear programs (MARMILPs). By explicitly partitioning recourse decisions into state decisions and local decisions, the proposed algorithm applies affine decision rule only to state decisions and all...
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Dual SVM Training on a Budget
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-...
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AWAKE readiness for the study of the seeded self-modulation of a 400\,GeV proton bunch
AWAKE is a proton-driven plasma wakefield acceleration experiment. % We show that the experimental setup briefly described here is ready for systematic study of the seeded self-modulation of the 400\,GeV proton bunch in the 10\,m-long rubidium plasma with density adjustable from 1 to 10$\times10^{14}$\,cm$^{-3}$. % W...
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Exploring the Psychological Basis for Transitions in the Archaeological Record
In lieu of an abstract here is the first paragraph: No other species remotely approaches the human capacity for the cultural evolution of novelty that is accumulative, adaptive, and open-ended (i.e., with no a priori limit on the size or scope of possibilities). By culture we mean extrasomatic adaptations--including ...
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Well quasi-orders and the functional interpretation
The purpose of this article is to study the role of Gödel's functional interpretation in the extraction of programs from proofs in well quasi-order theory. The main focus is on the interpretation of Nash-Williams' famous minimal bad sequence construction, and the exploration of a number of much broader problems which...
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A local limit theorem for Quicksort key comparisons via multi-round smoothing
As proved by Régnier and Rösler, the number of key comparisons required by the randomized sorting algorithm QuickSort to sort a list of $n$ distinct items (keys) satisfies a global distributional limit theorem. Fill and Janson proved results about the limiting distribution and the rate of convergence, and used these ...
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On the Computation of Kantorovich-Wasserstein Distances between 2D-Histograms by Uncapacitated Minimum Cost Flows
In this work, we present a method to compute the Kantorovich distance, that is, the Wasserstein distance of order one, between a pair of two-dimensional histograms. Recent works in Computer Vision and Machine Learning have shown the benefits of measuring Wasserstein distances of order one between histograms with $N$ ...
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Efficient anchor loss suppression in coupled near-field optomechanical resonators
Elastic dissipation through radiation towards the substrate is a major loss channel in micro- and nanomechanical resonators. Engineering the coupling of these resonators with optical cavities further complicates and constrains the design of low-loss optomechanical devices. In this work we rely on the coherent cancell...
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Searching for previously unknown classes of objects in the AKARI-NEP Deep data with fuzzy logic SVM algorithm
In this proceedings application of a fuzzy Support Vector Machine (FSVM) learning algorithm, to classify mid-infrared (MIR) sources from the AKARI NEP Deep field into three classes: stars, galaxies and AGNs, is presented. FSVM is an improved version of the classical SVM algorithm, incorporating measurement errors int...
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Stability of laminar Couette flow of compressible fluids
Cylindrical Couette flow is a subject where the main focus has long been on the onset of turbulence or, more precisely, the limit of stability of the simplest laminar flow. The theoretical framework of this paper is a recently developed action principle for hydrodynamics. It incorporates Euler-Lagrange equations that...
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A Hierarchical Max-infinitely Divisible Process for Extreme Areal Precipitation Over Watersheds
Understanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable...
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The dependence of cluster galaxy properties on the central entropy of their host cluster
We present a study of the connection between brightest cluster galaxies (BCGs) and their host galaxy clusters. Using galaxy clusters at $0.1<z<0.3$ from the Hectospec Cluster Survey (HeCS) with X-ray information from the Archive of {\it Chandra} Cluster Entropy Profile Tables (ACCEPT), we confirm that BCGs in low cen...
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Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
We propose a novel architecture for $k$-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In o...
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Uncertainty principle and geometry of the infinite Grassmann manifold
We study the pairs of projections $$ P_If=\chi_If ,\ \ Q_Jf= \left(\chi_J \hat{f}\right)\check{\ } , \ \ f\in L^2(\mathbb{R}^n), $$ where $I, J\subset \mathbb{R}^n$ are sets of finite Lebesgue measure, $\chi_I, \chi_J$ denote the corresponding characteristic functions and $\hat{\ } , \check{\ }$ denote the Fourier-Pl...
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Existence and symmetry of solutions for critical fractional Schrödinger equations with bounded potentials
This paper is concerned with the following fractional Schrödinger equations involving critical exponents: \begin{eqnarray*} (-\Delta)^{\alpha}u+V(x)u=k(x)f(u)+\lambda|u|^{2_{\alpha}^{*}-2}u\quad\quad \mbox{in}\ \mathbb{R}^{N}, \end{eqnarray*} where $(-\Delta)^{\alpha}$ is the fractional Laplacian operator with $\alph...
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Early Detection of Promoted Campaigns on Social Media
Social media expose millions of users every day to information campaigns --- some emerging organically from grassroots activity, others sustained by advertising or other coordinated efforts. These campaigns contribute to the shaping of collective opinions. While most information campaigns are benign, some may be depl...
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Discovery of statistical equivalence classes using computer algebra
Discrete statistical models supported on labelled event trees can be specified using so-called interpolating polynomials which are generalizations of generating functions. These admit a nested representation. A new algorithm exploits the primary decomposition of monomial ideals associated with an interpolating polyno...
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Can Boltzmann Machines Discover Cluster Updates ?
Boltzmann machines are physics informed generative models with wide applications in machine learning. They can learn the probability distribution from an input dataset and generate new samples accordingly. Applying them back to physics, the Boltzmann machines are ideal recommender systems to accelerate Monte Carlo si...
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Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to deco...
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Weak subsolutions to complex Monge-Ampère equations
We compare various notions of weak subsolutions to degenerate complex Monge-Ampère equations, showing that they all coincide. This allows us to give an alternative proof of mixed Monge-Ampère inequalities due to Kolodziej and Dinew.
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Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction
Efficient assessment of convolved hidden Markov models is discussed. The bottom-layer is defined as an unobservable categorical first-order Markov chain, while the middle-layer is assumed to be a Gaussian spatial variable conditional on the bottom-layer. Hence, this layer appear as a Gaussian mixture spatial variable...
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On the post-Keplerian corrections to the orbital periods of a two-body system and their application to the Galactic Center
Detailed numerical analyses of the orbital motion of a test particle around a spinning primary are performed. They aim to investigate the possibility of using the post-Keplerian (pK) corrections to the orbiter's periods (draconitic, anomalistic and sidereal) as a further opportunity to perform new tests of post-Newto...
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Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time
Bottom-up and top-down, as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analysing their influence over time. For this purpose we d...
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Sharp estimates for oscillatory integral operators via polynomial partitioning
The sharp range of $L^p$-estimates for the class of Hörmander-type oscillatory integral operators is established in all dimensions under a positive-definite assumption on the phase. This is achieved by generalising a recent approach of the first author for studying the Fourier extension operator, which utilises polyn...
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Inhomogeneous Heisenberg Spin Chain and Quantum Vortex Filament as Non-Holonomically Deformed NLS Systems
Through the Hasimoto map, various dynamical systems can be mapped to different integrodifferential generalizations of Nonlinear Schrodinger (NLS) family of equations some of which are known to be integrable. Two such continuum limits, corresponding to the inhomogeneous XXX Heisenberg spin chain [Balakrishnan, J. Phys...
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An Information-Theoretic Analysis of Deduplication
Deduplication finds and removes long-range data duplicates. It is commonly used in cloud and enterprise server settings and has been successfully applied to primary, backup, and archival storage. Despite its practical importance as a source-coding technique, its analysis from the point of view of information theory i...
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A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception
While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition...
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Triangulum II: Not Especially Dense After All
Among the Milky Way satellites discovered in the past three years, Triangulum II has presented the most difficulty in revealing its dynamical status. Kirby et al. (2015a) identified it as the most dark matter-dominated galaxy known, with a mass-to-light ratio within the half-light radius of 3600 +3500 -2100 M_sun/L_s...
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Improving the upper bound on the length of the shortest reset words
We improve the best known upper bound on the length of the shortest reset words of synchronizing automata. The new bound is slightly better than $114 n^3 / 685 + O(n^2)$. The Černý conjecture states that $(n-1)^2$ is an upper bound. So far, the best general upper bound was $(n^3-n)/6-1$ obtained by J.-E.~Pin and P.~F...
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Graphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised lear...
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Characteristic classes in general relativity on a modified Poincare curvature bundle
Characteristic classes in space-time manifolds are discussed for both even- and odd-dimensional spacetimes. In particular, it is shown that the Einstein--Hilbert action is equivalent to a second Chern-class on a modified Poincare bundle in four dimensions. Consequently, the cosmological constant and the trace of an e...
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Observation of a 3D magnetic null point
We describe high resolution observations of a GOES B-class flare characterized by a circular ribbon at chromospheric level, corresponding to the network at photospheric level. We interpret the flare as a consequence of a magnetic reconnection event occurred at a three-dimensional (3D) coronal null point located above...
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3D spatial exploration by E. coli echoes motor temporal variability
Unraveling bacterial strategies for spatial exploration is crucial to understand the complexity of the organi- zation of life. Currently, a cornerstone for quantitative modeling of bacterial transport, is their run-and-tumble strategy to explore their environment. For Escherichia coli, the run time distribution was r...
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Unsupervised Latent Behavior Manifold Learning from Acoustic Features: audio2behavior
Behavioral annotation using signal processing and machine learning is highly dependent on training data and manual annotations of behavioral labels. Previous studies have shown that speech information encodes significant behavioral information and be used in a variety of automated behavior recognition tasks. However,...
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Test map characterizations of local properties of fundamental groups
Local properties of the fundamental group of a path-connected topological space can pose obstructions to the applicability of covering space theory. A generalized covering map is a generalization of the classical notion of covering map defined in terms of unique lifting properties. The existence of generalized coveri...
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Adaptive channel selection for DOA estimation in MIMO radar
We present adaptive strategies for antenna selection for Direction of Arrival (DoA) estimation of a far-field source using TDM MIMO radar with linear arrays. Our treatment is formulated within a general adaptive sensing framework that uses one-step ahead predictions of the Bayesian MSE using a parametric family of We...
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Photometric characterization of the Dark Energy Camera
We characterize the variation in photometric response of the Dark Energy Camera (DECam) across its 520~Mpix science array during 4 years of operation. These variations are measured using high signal-to-noise aperture photometry of $>10^7$ stellar images in thousands of exposures of a few selected fields, with the tel...
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Tough self-healing elastomers by molecular enforced integration of covalent and reversible networks
Self-healing polymers crosslinked by solely reversible bonds are intrinsically weaker than common covalently crosslinked networks. Introducing covalent crosslinks into a reversible network would improve mechanical strength. It is challenging, however, to apply this design concept to dry elastomers, largely because re...
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SYK Models and SYK-like Tensor Models with Global Symmetry
In this paper, we study an SYK model and an SYK-like tensor model with global symmetry. First, we study the large $N$ expansion of the bi-local collective action for the SYK model with manifest global symmetry. We show that the global symmetry is enhanced to a local symmetry at strong coupling limit, and the correspo...
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Which Stars are Ionizing the Orion Nebula ?
The common assumption that Theta-1-Ori C is the dominant ionizing source for the Orion Nebula is critically examined. This assumption underlies much of the existing analysis of the nebula. In this paper we establish through comparison of the relative strengths of emission lines with expectations from Cloudy models an...
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FLaapLUC: a pipeline for the generation of prompt alerts on transient Fermi-LAT $γ$-ray sources
The large majority of high energy sources detected with Fermi-LAT are blazars, which are known to be very variable sources. High cadence long-term monitoring simultaneously at different wavelengths being prohibitive, the study of their transient activities can help shedding light on our understanding of these objects...
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A network approach to topic models
One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical structure of a collection of documents. Despite their success --- in particular of its most widely ...
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CRPropa 3.1 -- A low energy extension based on stochastic differential equations
The propagation of charged cosmic rays through the Galactic environment influences all aspects of the observation at Earth. Energy spectrum, composition and arrival directions are changed due to deflections in magnetic fields and interactions with the interstellar medium. Today the transport is simulated with differe...
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Isometries in spaces of Kähler potentials
The space of Kähler potentials in a compact Kähler manifold, endowed with Mabuchi's metric, is an infinite dimensional Riemannian manifold. We characterize local isometries between spaces of Kähler potentials, and prove existence and uniqueness for such isometries.
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On the Statistical Challenges of Echo State Networks and Some Potential Remedies
Echo state networks are powerful recurrent neural networks. However, they are often unstable and shaky, making the process of finding an good ESN for a specific dataset quite hard. Obtaining a superb accuracy by using the Echo State Network is a challenging task. We create, develop and implement a family of predictab...
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Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
In this paper, we prove that some Gaussian structural equation models with dependent errors having equal variances are identifiable from their corresponding Gaussian distributions. Specifically, we prove identifiability for the Gaussian structural equation models that can be represented as Andersson-Madigan-Perlman c...
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Smart Contract SLAs for Dense Small-Cell-as-a-Service
The disruptive power of blockchain technologies represents a great opportunity to re-imagine standard practices of telecommunication networks and to identify critical areas that can benefit from brand new approaches. As a starting point for this debate, we look at the current limits of infrastructure sharing, and spe...
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On Kiguradze theorem for linear boundary value problems
We investigate the limiting behavior of solutions of nonhomogeneous boundary value problems for the systems of linear ordinary differential equations. The generalization of Kiguradze theorem (1987) on passage to the limit is obtained.
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Fidelity Lower Bounds for Stabilizer and CSS Quantum Codes
In this paper we estimate the fidelity of stabilizer and CSS codes. First, we derive a lower bound on the fidelity of a stabilizer code via its quantum enumerator. Next, we find the average quantum enumerators of the ensembles of finite length stabilizer and CSS codes. We use the average quantum enumerators for obtai...
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Cross-validation improved by aggregation: Agghoo
Cross-validation is widely used for selecting among a family of learning rules. This paper studies a related method, called aggregated hold-out (Agghoo), which mixes cross-validation with aggregation; Agghoo can also be related to bagging. According to numerical experiments, Agghoo can improve significantly cross-val...
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MAT: A Multimodal Attentive Translator for Image Captioning
In this work we formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from most existing work where the whole image is represented by convolutional ne...
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A Semi-Supervised and Inductive Embedding Model for Churn Prediction of Large-Scale Mobile Games
Mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. One critical challenge for these platforms and services is to understand user churn behavior in mobile games. Accurate churn prediction will benefit many ...
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Sequential rerandomization
The seminal work of Morgan and Rubin (2012) considers rerandomization for all the units at one time. In practice, however, experimenters may have to rerandomize units sequentially. For example, a clinician studying a rare disease may be unable to wait to perform an experiment until all the experimental units are recr...
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Neural SLAM: Learning to Explore with External Memory
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments. To achieve this, we embed procedures mimicking that of traditional Simultaneous Localization and Mapping (SLAM) into the soft attention based addressing of external memory archite...
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Information Storage and Retrieval using Macromolecules as Storage Media
To store information at extremely high-density and data-rate, we propose to adapt, integrate, and extend the techniques developed by chemists and molecular biologists for the purpose of manipulating biological and other macromolecules. In principle, volumetric densities in excess of 10^21 bits/cm^3 can be achieved wh...
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Morgan type uncertainty principle and unique continuation properties for abstract Schrödinger equations
In this paper, Morgan type uncertainty principle and unique continuation properties of abstract Schrödinger equations with time dependent potentials in vector-valued classes are obtained. The equation involves a possible linear operators considered in the Hilbert spaces. So, by choosing the corresponding spaces H and...
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Can the Journal Impact Factor Be Used as a Criterion for the Selection of Junior Researchers? A Large-Scale Empirical Study Based on ResearcherID Data
Early in researchers' careers, it is difficult to assess how good their work is or how important or influential the scholars will eventually be. Hence, funding agencies, academic departments, and others often use the Journal Impact Factor (JIF) of where the authors have published to assess their work and provide reso...
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Deep MIMO Detection
In this paper, we consider the use of deep neural networks in the context of Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose a modern neural network architecture suitable for this detection task. First, we consider the case in which the MIMO channel is consta...
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Cross-View Image Matching for Geo-localization in Urban Environments
In this paper, we address the problem of cross-view image geo-localization. Specifically, we aim to estimate the GPS location of a query street view image by finding the matching images in a reference database of geo-tagged bird's eye view images, or vice versa. To this end, we present a new framework for cross-view ...
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Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow
In recent years, deep learning based on artificial neural network (ANN) has achieved great success in pattern recognition. However, there is no clear understanding of such neural computational models. In this paper, we try to unravel "black-box" structure of Ann model from network flow. Specifically, we consider the ...
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Excitation of multiple 2-mode parametric resonances by a single driven mode
We demonstrate autoparametric excitation of two distinct sub-harmonic mechanical modes by the same driven mechanical mode corresponding to different drive frequencies within its resonance dispersion band. This experimental observation is used to motivate a more general physical picture wherein multiple mechanical mod...
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Solar system science with the Wide-Field InfraRed Survey Telescope (WFIRST)
We present a community-led assessment of the solar system investigations achievable with NASA's next-generation space telescope, the Wide Field InfraRed Survey Telescope (WFIRST). WFIRST will provide imaging, spectroscopic, and coronagraphic capabilities from 0.43-2.0 $\mu$m and will be a potential contemporary and e...
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Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition
Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or learning discriminative models using the discriminative clustering algorithm. In this paper, we pro...
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Near-Infrared Knots and Dense Fe Ejecta in the Cassiopeia A Supernova Remnant
We report the results of broadband (0.95--2.46 $\mu$m) near-infrared spectroscopic observations of the Cassiopeia A supernova remnant. Using a clump-finding algorithm in two-dimensional dispersed images, we identify 63 "knots" from eight slit positions and derive their spectroscopic properties. All of the knots emit ...
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On the conjecture of Jeśmanowicz
We give a survey on some results covering the last 60 years concerning Jeśmanowicz' conjecture. Moreover, we conclude the survey with a new result by showing that the special Diophantine equation $$(20k)^x+(99k)^y=(101k)^z$$ has no solution other than $(x,y,z)=(2,2,2)$.
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Lattice implementation of Abelian gauge theories with Chern-Simons number and an axion field
Real time evolution of classical gauge fields is relevant for a number of applications in particle physics and cosmology, ranging from the early Universe to dynamics of quark-gluon plasma. We present a lattice formulation of the interaction between a $shift$-symmetric field and some $U(1)$ gauge sector, $a(x)\tilde{F...
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II-FCN for skin lesion analysis towards melanoma detection
Dermoscopy image detection stays a tough task due to the weak distinguishable property of the object.Although the deep convolution neural network signifigantly boosted the performance on prevelance computer vision tasks in recent years,there remains a room to explore more robust and precise models to the problem of l...
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Zero-Shot Visual Imitation
The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill polic...
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Spreading in kinetic reaction-transport equations in higher velocity dimensions
In this paper, we extend and complement previous works about propagation in kinetic reaction-transport equations. The model we study describes particles moving according to a velocity-jump process, and proliferating according to a reaction term of monostable type. We focus on the case of bounded velocities, having di...
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Matching of orbital integrals (transfer) and Roche Hecke algebra isomorphisms
Let $F$ be a non-Archimedan local field, $G$ a connected reductive group defined and split over $F$, and $T$ a maximal $F$-split torus in $G$. Let $\chi_0$ be a depth zero character of the maximal compact subgroup $\mathcal{T}$ of $T(F)$. It gives by inflation a character $\rho$ of an Iwahori subgroup $\mathcal{I}$ o...
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Testing atomic collision theory with the two-photon continuum of astrophysical nebulae
Accurate rates for energy-degenerate l-changing collisions are needed to determine cosmological abundances and recombination. There are now several competing theories for the treatment of this process, and it is not possible to test these experimentally. We show that the H I two-photon continuum produced by astrophys...
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Metrologically useful states of spin-1 Bose condensates with macroscopic magnetization
We study theoretically the usefulness of spin-1 Bose condensates with macroscopic magnetization in a homogeneous magnetic field for quantum metrology. We demonstrate Heisenberg scaling of the quantum Fisher information for states in thermal equilibrium. The scaling applies to both antiferromagnetic and ferromagnetic ...
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The Final Chapter In The Saga Of YIG
The magnetic insulator Yttrium Iron Garnet can be grown with exceptional quality, has a ferrimagnetic transition temperature of nearly 600 K, and is used in microwave and spintronic devices that can operate at room temperature. The most accurate prior measurements of the magnon spectrum date back nearly 40 years, but...
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Modeling and Analysis of HetNets with mm-Wave Multi-RAT Small Cells Deployed Along Roads
We characterize a multi tier network with classical macro cells, and multi radio access technology (RAT) small cells, which are able to operate in microwave and millimeter-wave (mm-wave) bands. The small cells are assumed to be deployed along roads modeled as a Poisson line process. This characterization is more real...
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Ermakov-Painlevé II Symmetry Reduction of a Korteweg Capillarity System
A class of nonlinear Schrödinger equations involving a triad of power law terms together with a de Broglie-Bohm potential is shown to admit symmetry reduction to a hybrid Ermakov-Painlevé II equation which is linked, in turn, to the integrable Painlevé XXXIV equation. A nonlinear Schrödinger encapsulation of a Kortew...
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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do...
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Value Asymptotics in Dynamic Games on Large Horizons
This paper is concerned with two-person dynamic zero-sum games. Let games for some family have common dynamics, running costs and capabilities of players, and let these games differ in densities only. We show that the Dynamic Programming Principle directly leads to the General Tauberian Theorem---that the existence o...
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Classical counterparts of quantum attractors in generic dissipative systems
In the context of dissipative systems, we show that for any quantum chaotic attractor a corre- sponding classical chaotic attractor can always be found. We provide with a general way to locate them, rooted in the structure of the parameter space (which is typically bidimensional, accounting for the forcing strength a...
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Marginal likelihood based model comparison in Fuzzy Bayesian Learning
In a recent paper [1] we introduced the Fuzzy Bayesian Learning (FBL) paradigm where expert opinions can be encoded in the form of fuzzy rule bases and the hyper-parameters of the fuzzy sets can be learned from data using a Bayesian approach. The present paper extends this work for selecting the most appropriate rule...
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Position-sensitive propagation of information on social media using social physics approach
The excitement and convergence of tweets on specific topics are well studied. However, by utilizing the position information of Tweet, it is also possible to analyze the position-sensitive tweet. In this research, we focus on bomb terrorist attacks and propose a method for separately analyzing the number of tweets at...
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The application of Monte Carlo methods for learning generalized linear model
Monte Carlo method is a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other mathematical methods. Basically, many statisticians have been...
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Language Bootstrapping: Learning Word Meanings From Perception-Action Association
We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The ...
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