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Even denominator fractional quantum Hall states at an isospin transition in monolayer graphene
Magnetic fields quench the kinetic energy of two dimensional electrons, confining them to highly degenerate Landau levels. In the absence of disorder, the ground state at partial Landau level filling is determined only by Coulomb interactions, leading to a variety of correlation-driven phenomena. Here, we realize a q...
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Reverse approximation of gradient flows as Minimizing Movements: a conjecture by De Giorgi
We consider the Cauchy problem for the gradient flow \begin{equation} \label{eq:81} \tag{$\star$} u'(t)=-\nabla\phi(u(t)),\quad t\ge 0;\quad u(0)=u_0, \end{equation} generated by a continuously differentiable function $\phi:\mathbb H \to \mathbb R$ in a Hilbert space $\mathbb H$ and study the reverse approximation of...
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Linking de novo assembly results with long DNA reads by dnaasm-link application
Currently, third-generation sequencing techniques, which allow to obtain much longer DNA reads compared to the next-generation sequencing technologies, are becoming more and more popular. There are many possibilities to combine data from next-generation and third-generation sequencing. Herein, we present a new applic...
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ReBNet: Residual Binarized Neural Network
This paper proposes ReBNet, an end-to-end framework for training reconfigurable binary neural networks on software and developing efficient accelerators for execution on FPGA. Binary neural networks offer an intriguing opportunity for deploying large-scale deep learning models on resource-constrained devices. Binariz...
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Prime geodesic theorem for the modular surface
Under the generalized Lindelöf hypothesis, the exponent in the error term of the prime geodesic theorem for the modular surface is reduced to $\frac{5}{8}+\varepsilon $ outside a set of finite logarithmic measure.
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Multiple regimes and coalescence timescales for massive black hole pairs ; the critical role of galaxy formation physics
We discuss the latest results of numerical simulations following the orbital decay of massive black hole pairs in galaxy mergers. We highlight important differences between gas-poor and gas-rich hosts, and between orbital evolution taking place at high redshift as opposed to low redshift. Two effects have a huge impa...
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The Molecular Gas Environment in the 20 km s$^{-1}$ Cloud in the Central Molecular Zone
We recently reported a population of protostellar candidates in the 20 km s$^{-1}$ cloud in the Central Molecular Zone of the Milky Way, traced by H$_2$O masers in gravitationally bound dense cores. In this paper, we report high-angular-resolution ($\sim$3'') molecular line studies of the environment of star formatio...
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Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal information. Although pre-training from ANN or direct training based on backpropagation (BP) makes the supervised training of SNNs possib...
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Inverse of a Special Matrix and Application
The matrix inversion is an interesting topic in algebra mathematics. However, to determine an inverse matrix from a given matrix is required many computation tools and time resource if the size of matrix is huge. In this paper, we have shown an inverse closed form for an interesting matrix which has much applications...
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Incompressible fluid problems on embedded surfaces: Modeling and variational formulations
Governing equations of motion for a viscous incompressible material surface are derived from the balance laws of continuum mechanics. The surface is treated as a time-dependent smooth orientable manifold of codimension one in an ambient Euclidian space. We use elementary tangential calculus to derive the governing eq...
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Gaiotto's Lagrangian subvarieties via loop groups
The purpose of this note is to give a simple proof of the fact that a certain substack, defined in [2], of the moduli stack $T^{\ast}Bun_G(\Sigma)$ of Higgs bundles over a curve $\Sigma$, for a connected, simply connected semisimple group $G$, possesses a Lagrangian structure. The substack, roughly speaking, consists...
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Geohyperbolic Routing and Addressing Schemes
The key requirement to routing in any telecommunication network, and especially in Internet-of-Things (IoT) networks, is scalability. Routing must route packets between any source and destination in the network without incurring unmanageable routing overhead that grows quickly with increasing network size and dynamic...
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Conical: an extended module for computing a numerically satisfactory pair of solutions of the differential equation for conical functions
Conical functions appear in a large number of applications in physics and engineering. In this paper we describe an extension of our module CONICAL for the computation of conical functions. Specifically, the module includes now a routine for computing the function ${\rm R}^{m}_{-\frac{1}{2}+i\tau}(x)$, a real-valued ...
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Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
We present an approach towards convex optimization that relies on a novel scheme which converts online adaptive algorithms into offline methods. In the offline optimization setting, our derived methods are shown to obtain favourable adaptive guarantees which depend on the harmonic sum of the queried gradients. We fur...
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Kondo Length in Bosonic Lattices
Motivated by the fact that the low-energy properties of the Kondo model can be effectively simulated in spin chains, we study the realization of the effect with bond impurities in ultracold bosonic lattices at half-filling. After presenting a discussion of the effective theory and of the mapping of the bosonic chain ...
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RVP-FLMS : A Robust Variable Power Fractional LMS Algorithm
In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the e...
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Dichotomy for Digraph Homomorphism Problems (two algorithms)
Update : An issue has been found in the correctness of our algorithm, and we are working to resolve the issue. Until a resolution is found, we retract our main claim that our approach gives a combinatorial solution to the CSP conjecture. We remain hopeful that we can resolve the issues. We thank Ross Willard for care...
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Macro-molecular data storage with petabyte/cm^3 density, highly parallel read/write operations, and genuine 3D storage capability
Digital information can be encoded in the building-block sequence of macro-molecules, such as RNA and single-stranded DNA. Methods of "writing" and "reading" macromolecular strands are currently available, but they are slow and expensive. In an ideal molecular data storage system, routine operations such as write, re...
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Design and implementation of lighting control system using battery-less wireless human detection sensor networks
Artificial lighting is responsible for a large portion of total energy consumption and has great potential for energy saving. This paper designs an LED light control algorithm based on users' localization using multiple battery-less binary human detection sensors. The proposed lighting control system focuses on reduc...
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Independence in generic incidence structures
We study the theory $T_{m,n}$ of existentially closed incidence structures omitting the complete incidence structure $K_{m,n}$, which can also be viewed as existentially closed $K_{m,n}$-free bipartite graphs. In the case $m = n = 2$, this is the theory of existentially closed projective planes. We give an $\forall\e...
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Propagation of regularity for the MHD system in optimal Sobolev space
We study the problem of propagation of regularity of solutions to the incompressible viscous non-resistive magneto-hydrodynamics system. According to scaling, the Sobolev space $H^{\frac n2-1}(\mathbb R^n)\times H^{\frac n2}(\mathbb R^n)$ is critical for the system. We show that if a weak solution $(u(t),b(t))$ is in...
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On the link between atmospheric cloud parameters and cosmic rays
We herewith attempt to investigate the cosmic rays behavior regarding the scaling features of their time series. Our analysis is based on cosmic ray observations made at four neutron monitor stations in Athens (Greece), Jung (Switzerland) and Oulu (Finland), for the period 2000 to early 2017. Each of these datasets w...
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To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-constrained computing devices. Model compression techniques can address the computation issue of deep inference on embedded devices. This technique i...
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Universal Scaling Laws for Correlation Spreading in Quantum Systems with Short- and Long-Range Interactions
We study the spreading of information in a wide class of quantum systems, with variable-range interactions. We show that, after a quench, it generally features a double structure, whose scaling laws are related to a set of universal microscopic exponents that we determine. When the system supports excitations with a ...
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Examples of plane rational curves with two Galois points in positive characteristic
We present four new examples of plane rational curves with two Galois points in positive characteristic, and determine the number of Galois points for three of them. Our results are related to a problem on projective linear groups.
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Optimal Kullback-Leibler Aggregation in Mixture Density Estimation by Maximum Likelihood
We study the maximum likelihood estimator of density of $n$ independent observations, under the assumption that it is well approximated by a mixture with a large number of components. The main focus is on statistical properties with respect to the Kullback-Leibler loss. We establish risk bounds taking the form of sha...
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Non-asymptotic theory for nonparametric testing
We consider nonparametric testing in a non-asymptotic framework. Our statistical guarantees are exact in the sense that Type I and II errors are controlled for any finite sample size. Meanwhile, one proposed test is shown to achieve minimax optimality in the asymptotic sense. An important consequence of this non-asym...
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Classification of out-of-time-order correlators
The space of n-point correlation functions, for all possible time-orderings of operators, can be computed by a non-trivial path integral contour, which depends on how many time-ordering violations are present in the correlator. These contours, which have come to be known as timefolds, or out-of-time-order (OTO) conto...
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Hierarchical Reinforcement Learning: Approximating Optimal Discounted TSP Using Local Policies
In this work, we provide theoretical guarantees for reward decomposition in deterministic MDPs. Reward decomposition is a special case of Hierarchical Reinforcement Learning, that allows one to learn many policies in parallel and combine them into a composite solution. Our approach builds on mapping this problem into...
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End-to-End Musical Key Estimation Using a Convolutional Neural Network
We present an end-to-end system for musical key estimation, based on a convolutional neural network. The proposed system not only out-performs existing key estimation methods proposed in the academic literature; it is also capable of learning a unified model for diverse musical genres that performs comparably to exis...
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Approximate Optimal Designs for Multivariate Polynomial Regression
We introduce a new approach aiming at computing approximate optimal designs for multivariate polynomial regressions on compact (semi-algebraic) design spaces. We use the moment-sum-of-squares hierarchy of semidefinite programming problems to solve numerically the approximate optimal design problem. The geometry of th...
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Determinants of public cooperation in multiplex networks
Synergies between evolutionary game theory and statistical physics have significantly improved our understanding of public cooperation in structured populations. Multiplex networks, in particular, provide the theoretical framework within network science that allows us to mathematically describe the rich structure of ...
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Variational Encoding of Complex Dynamics
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged co-variate models to study suc...
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Accelerating Imitation Learning with Predictive Models
Sample efficiency is critical in solving real-world reinforcement learning problems, where agent-environment interactions can be costly. Imitation learning from expert advice has proved to be an effective strategy for reducing the number of interactions required to train a policy. Online imitation learning, which int...
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Neural Networks for Predicting Algorithm Runtime Distributions
Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance. Knowledge about the resulting runtime distributions (RTDs) of algorithms on given problem instances can...
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Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild
Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Lab...
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Small Hankel operators on generalized Fock spaces
We consider Fock spaces $F^{p,\ell}_{\alpha}$ of entire functions on ${\mathbb C}$ associated to the weights $e^{-\alpha |z|^{2\ell}}$, where $\alpha>0$ and $\ell$ is a positive integer. We compute explicitly the corresponding Bergman kernel associated to $F^{2,\ell}_{\alpha}$ and, using an adequate factorization of ...
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Efficient enumeration of solutions produced by closure operations
In this paper we address the problem of generating all elements obtained by the saturation of an initial set by some operations. More precisely, we prove that we can generate the closure of a boolean relation (a set of boolean vectors) by polymorphisms with a polynomial delay. Therefore we can compute with polynomial...
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Compressive Sensing via Convolutional Factor Analysis
We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned {\em in situ} from the compressed measurements. An alternating direction method of multipliers (ADMM) paradigm for compressive sensing inversion based on convolutional factor analysis is develo...
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Exact Dimensionality Selection for Bayesian PCA
We present a Bayesian model selection approach to estimate the intrinsic dimensionality of a high-dimensional dataset. To this end, we introduce a novel formulation of the probabilisitic principal component analysis model based on a normal-gamma prior distribution. In this context, we exhibit a closed-form expression...
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Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model
Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network. In this work, we look at one such sequence-to-sequence model, na...
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Exponential convergence of testing error for stochastic gradient methods
We consider binary classification problems with positive definite kernels and square loss, and study the convergence rates of stochastic gradient methods. We show that while the excess testing loss (squared loss) converges slowly to zero as the number of observations (and thus iterations) goes to infinity, the testin...
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What is a hierarchically hyperbolic space?
The first part of this survey is a heuristic, non-technical discussion of what an HHS is, and the aim is to provide a good mental picture both to those actively doing research on HHSs and to those who only seek a basic understanding out of pure curiosity. It can be read independently of the second part, which is a de...
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What makes a gesture a gesture? Neural signatures involved in gesture recognition
Previous work in the area of gesture production, has made the assumption that machines can replicate "human-like" gestures by connecting a bounded set of salient points in the motion trajectory. Those inflection points were hypothesized to also display cognitive saliency. The purpose of this paper is to validate that...
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Neural networks and rational functions
Neural networks and rational functions efficiently approximate each other. In more detail, it is shown here that for any ReLU network, there exists a rational function of degree $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close, and similarly for any rational function there exists a ReLU network of size $O(\t...
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Local Structure Theorems for Erdos Renyi Graphs and their Algorithmic Application
We analyze some local properties of sparse Erdos-Renyi graphs, where $d(n)/n$ is the edge probability. In particular we study the behavior of very short paths. For $d(n)=n^{o(1)}$ we show that $G(n,d(n)/n)$ has asymptotically almost surely (a.a.s.~) bounded local treewidth and therefore is a.a.s.~nowhere dense. We al...
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DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate...
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Automatic Pill Reminder for Easy Supervision
In this paper we present a working model of an automatic pill reminder and dispenser setup that can alleviate irregularities in taking prescribed dosage of medicines at the right time dictated by the medical practitioner and switch from approaches predominantly dependent on human memory to automation with negligible ...
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Chaotic dynamics of movements stochastic instability and the hypothesis of N.A. Bernstein about "repetition without repetition"
The registration of tremor was performed in two groups of subjects (15 people in each group) with different physical fitness at rest and at a static loads of 3N. Each subject has been tested 15 series (number of series N=15) in both states (with and without physical loads) and each series contained 15 samples (n=15) ...
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Deep Learning for Real Time Crime Forecasting
Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramati...
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Unpredictable sequences and Poincaré chaos
To make research of chaos more friendly with discrete equations, we introduce the concept of an unpredictable sequence as a specific unpredictable function on the set of integers. It is convenient to be verified as a solution of a discrete equation. This is rigorously proved in this paper for quasilinear systems, and...
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Scaling of the Detonation Product State with Reactant Kinetic Energy
This submissions has been withdrawn by arXiv administrators because the submitter did not have the right to agree to our license.
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Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes
Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information spreading. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appe...
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Almost automorphic functions on the quantum time scale and applications
In this paper, we first propose two types of concepts of almost automorphic functions on the quantum time scale. Secondly, we study some basic properties of almost automorphic functions on the quantum time scale. Then, we introduce a transformation between functions defined on the quantum time scale and functions def...
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Stochastic Game in Remote Estimation under DoS Attacks
This paper studies remote state estimation under denial-of-service (DoS) attacks. A sensor transmits its local estimate of an underlying physical process to a remote estimator via a wireless communication channel. A DoS attacker is capable to interfere the channel and degrades the remote estimation accuracy. Consider...
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VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems. While deep learning has enabled recent breakthroughs across a wide spectrum of scene understanding tasks, its applicability to state estimation tasks has been limited due t...
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On Decidability of the Ordered Structures of Numbers
The ordered structures of natural, integer, rational and real numbers are studied here. It is known that the theories of these numbers in the language of order are decidable and finitely axiomatizable. Also, their theories in the language of order and addition are decidable and infinitely axiomatizable. For the langu...
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Finite $p$-groups of conjugate type $\{ 1, p^3 \}$
We classify finite $p$-groups, upto isoclinism, which have only two conjugacy class sizes $1$ and $p^3$. It turns out that the nilpotency class of such groups is $2$.
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Betting on Quantum Objects
Dutch book arguments have been applied to beliefs about the outcomes of measurements of quantum systems, but not to beliefs about quantum objects prior to measurement. In this paper, we prove a quantum version of the probabilists' Dutch book theorem that applies to both sorts of beliefs: roughly, if ideal beliefs are...
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Casimir-Polder size consistency -- a constraint violated by some dispersion theories
A key goal in quantum chemistry methods, whether ab initio or otherwise, is to achieve size consistency. In this manuscript we formulate the related idea of "Casimir-Polder size consistency" that manifests in long-range dispersion energetics. We show that local approximations in time-dependent density functional theo...
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A combinatorial proof of Bass's determinant formula for the zeta function of regular graphs
We give an elementary combinatorial proof of Bass's determinant formula for the zeta function of a finite regular graph. This is done by expressing the number of non-backtracking cycles of a given length in terms of Chebychev polynomials in the eigenvalues of the adjacency operator of the graph.
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Electron-Muon Ranger: hardware characterization
The Electron-Muon Ranger (EMR) is a fully-active tracking-calorimeter in charge of the electron background rejection downstream of the cooling channel at the international Muon Ionization Cooling Experiment. It consists of 2832 plastic scintillator bars segmented in 48 planes in an X-Y arrangement and uses particle r...
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Photometric Redshifts for Hyper Suprime-Cam Subaru Strategic Program Data Release 1
Photometric redshifts are a key component of many science objectives in the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). In this paper, we describe and compare the codes used to compute photometric redshifts for HSC-SSP, how we calibrate them, and the typical accuracy we achieve with the HSC five-band photom...
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Shallow Updates for Deep Reinforcement Learning
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or p...
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Survey on Additive Manufacturing, Cloud 3D Printing and Services
Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the esta...
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Barnacles and Gravity
Theories with more than one vacuum allow quantum transitions between them, which may proceed via bubble nucleation; theories with more than two vacua posses additional decay modes in which the wall of a bubble may further decay. The instantons which mediate such a process have $O(3)$ symmetry (in four dimensions, rat...
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A note on some constants related to the zeta-function and their relationship with the Gregory coefficients
In this paper new series for the first and second Stieltjes constants (also known as generalized Euler's constant), as well as for some closely related constants are obtained. These series contain rational terms only and involve the so-called Gregory coefficients, which are also known as (reciprocal) logarithmic numb...
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Thresholding Bandit for Dose-ranging: The Impact of Monotonicity
We analyze the sample complexity of the thresholding bandit problem, with and without the assumption that the mean values of the arms are increasing. In each case, we provide a lower bound valid for any risk $\delta$ and any $\delta$-correct algorithm; in addition, we propose an algorithm whose sample complexity is o...
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Spin - Phonon Coupling in Nickel Oxide Determined from Ultraviolet Raman Spectroscopy
Nickel oxide (NiO) has been studied extensively for various applications ranging from electrochemistry to solar cells [1,2]. In recent years, NiO attracted much attention as an antiferromagnetic (AF) insulator material for spintronic devices [3-10]. Understanding the spin - phonon coupling in NiO is a key to its func...
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A Univariate Bound of Area Under ROC
Area under ROC (AUC) is an important metric for binary classification and bipartite ranking problems. However, it is difficult to directly optimizing AUC as a learning objective, so most existing algorithms are based on optimizing a surrogate loss to AUC. One significant drawback of these surrogate losses is that the...
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On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system, with potential impact in its final performance. Despite its importance, text preprocessing has not received much attention in the deep learning literature. In this paper we investigate the impact of simple text p...
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Evidences against cuspy dark matter halos in large galaxies
We develop and apply new techniques in order to uncover galaxy rotation curves (RC) systematics. Considering that an ideal dark matter (DM) profile should yield RCs that have no bias towards any particular radius, we find that the Burkert DM profile satisfies the test, while the Navarro-Frenk-While (NFW) profile has ...
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A Hybrid Framework for Multi-Vehicle Collision Avoidance
With the recent surge of interest in UAVs for civilian services, the importance of developing tractable multi-agent analysis techniques that provide safety and performance guarantees have drastically increased. Hamilton-Jacobi (HJ) reachability has successfully provided these guarantees to small-scale systems and is ...
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On the zeros of Riemann $Ξ(z)$ function
The Riemann $\Xi(z)$ function (even in $z$) admits a Fourier transform of an even kernel $\Phi(t)=4e^{9t/2}\theta''(e^{2t})+6e^{5t/2}\theta'(e^{2t})$. Here $\theta(x):=\theta_3(0,ix)$ and $\theta_3(0,z)$ is a Jacobi theta function, a modular form of weight $\frac{1}{2}$. (A) We discover a family of functions $\{\Phi_...
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On Topologized Fundamental Groups with Small Loop Transfer Viewpoints
In this paper, by introducing some kind of small loop transfer spaces at a point, we study the behavior of topologized fundamental groups with the compact-open topology and the whisker topology, $\pi_{1}^{qtop}(X,x_{0})$ and $\pi_{1}^{wh}(X,x_{0})$, respectively. In particular, we give necessary or sufficient conditi...
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The boundary value problem for Yang--Mills--Higgs fields
We show the existence of Yang--Mills--Higgs (YMH) fields over a Riemann surface with boundary where a free boundary condition is imposed on the section and a Neumann boundary condition on the connection. In technical terms, we study the convergence and blow-up behavior of a sequence of Sacks-Uhlenbeck type $\alpha$-Y...
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Polish Topologies for Graph Products of Groups
We give strong necessary conditions on the admissibility of a Polish group topology for an arbitrary graph product of groups $G(\Gamma, G_a)$, and use them to give a characterization modulo a finite set of nodes. As a corollary, we give a complete characterization in case all the factor groups $G_a$ are countable.
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Iterated failure rate monotonicity and ordering relations within Gamma and Weibull distributions
Stochastic ordering of distributions of random variables may be defined by the relative convexity of the tail functions. This has been extended to higher order stochastic orderings, by iteratively reassigning tail-weights. The actual verification of those stochastic orderings is not simple, as this depends on inverti...
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Quantum depletion of a homogeneous Bose-Einstein condensate
We have measured the quantum depletion of an interacting homogeneous Bose-Einstein condensate, and confirmed the 70-year old theory of N.N. Bogoliubov. The observed condensate depletion is reversibly tuneable by changing the strength of the interparticle interactions. Our atomic homogeneous condensate is produced in ...
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A Deep Generative Model for Graphs: Supervised Subset Selection to Create Diverse Realistic Graphs with Applications to Power Networks Synthesis
Creating and modeling real-world graphs is a crucial problem in various applications of engineering, biology, and social sciences; however, learning the distributions of nodes/edges and sampling from them to generate realistic graphs is still challenging. Moreover, generating a diverse set of synthetic graphs that al...
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Analytic Combinatorics in Several Variables: Effective Asymptotics and Lattice Path Enumeration
The field of analytic combinatorics, which studies the asymptotic behaviour of sequences through analytic properties of their generating functions, has led to the development of deep and powerful tools with applications across mathematics and the natural sciences. In addition to the now classical univariate theory, r...
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Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
While strong progress has been made in image captioning over the last years, machine and human captions are still quite distinct. A closer look reveals that this is due to the deficiencies in the generated word distribution, vocabulary size, and strong bias in the generators towards frequent captions. Furthermore, hu...
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A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries
This paper presents a novel framework for accurate pedestrian intent prediction at intersections. Given some prior knowledge of the curbside geometry, the presented framework can accurately predict pedestrian trajectories, even in new intersections that it has not been trained on. This is achieved by making use of th...
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Generating GraphQL-Wrappers for REST(-like) APIs
GraphQL is a query language and thereupon-based paradigm for implementing web Application Programming Interfaces (APIs) for client-server interactions. Using GraphQL, clients define precise, nested data-requirements in typed queries, which are resolved by servers against (possibly multiple) backend systems, like data...
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GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discr...
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The growth of carbon chains in IRC+10216 mapped with ALMA
Linear carbon chains are common in various types of astronomical molecular sources. Possible formation mechanisms involve both bottom-up and top-down routes. We have carried out a combined observational and modeling study of the formation of carbon chains in the C-star envelope IRC+10216, where the polymerization of ...
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Thermoelectric Transport Coefficients from Charged Solv and Nil Black Holes
In the present work we study charged black hole solutions of the Einstein-Maxwell action that have Thurston geometries on its near horizon region. In particular we find solutions with charged Solv and Nil geometry horizons. We also find Nil black holes with hyperscaling violation. For all our solutions we compute the...
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A unified method for maximal truncated Calderón-Zygmund operators in general function spaces by sparse domination
In this note we give simple proofs of several results involving maximal truncated Caldeón-Zygmund operators in the general setting of rearrangement invariant quasi-Banach function spaces by sparse domination. Our techniques allow us to track the dependence of the constants in weighted norm inequalities; additionally,...
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Evaluating Quality of Chatbots and Intelligent Conversational Agents
Chatbots are one class of intelligent, conversational software agents activated by natural language input (which can be in the form of text, voice, or both). They provide conversational output in response, and if commanded, can sometimes also execute tasks. Although chatbot technologies have existed since the 1960s a...
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Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images
Predictive coding is attractive for compression of hyperspecral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent schemes. In this letter we propose a rate control algorithm and integrate it in a lossy extension to the CCSDS-123 lossless compression recom...
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Quantum-continuum calculation of the surface states and electrical response of silicon in solution
A wide range of electrochemical reactions of practical importance occur at the interface between a semiconductor and an electrolyte. We present an embedded density-functional theory method using the recently released self-consistent continuum solvation (SCCS) approach to study these interfaces. In this model, a quant...
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Simulation study of signal formation in position sensitive planar p-on-n silicon detectors after short range charge injection
Segmented silicon detectors (micropixel and microstrip) are the main type of detectors used in the inner trackers of Large Hadron Collider (LHC) experiments at CERN. Due to the high luminosity and eventual high fluence, detectors with fast response to fit the short shaping time of 20 ns and sufficient radiation hardn...
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Re-DPoctor: Real-time health data releasing with w-day differential privacy
Wearable devices enable users to collect health data and share them with healthcare providers for improved health service. Since health data contain privacy-sensitive information, unprotected data release system may result in privacy leakage problem. Most of the existing work use differential privacy for private data...
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Efficient Computation of the Stochastic Behavior of Partial Sum Processes
In this paper the computational aspects of probability calculations for dynamical partial sum expressions are discussed. Such dynamical partial sum expressions have many important applications, and examples are provided in the fields of reliability, product quality assessment, and stochastic control. While these prob...
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Laser electron acceleration on curved surfaces
Electron acceleration by relativistically intense laser beam propagating along a curved surface allows to split softly the accelerated electron bunch and the laser beam. The presence of a curved surface allows to switch an adiabatic invariant of electrons in the wave instantly leaving the gained energy to the particl...
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A Physicist's view on Chopin's Études
We propose the use of specific dynamical processes and more in general of ideas from Physics to model the evolution in time of musical structures. We apply this approach to two Études by F. Chopin, namely op.10 n.3 and op.25 n.1, proposing some original description based on concepts of symmetry breaking/restoration a...
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Thermalization after holographic bilocal quench
We study thermalization in the holographic (1+1)-dimensional CFT after simultaneous generation of two high-energy excitations in the antipodal points on the circle. The holographic picture of such quantum quench is the creation of BTZ black hole from a collision of two massless particles. We perform holographic compu...
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A mean-field approach to Kondo-attractive-Hubbard model
With the purpose of investigating coexistence between magnetic order and superconductivity, we consider a model in which conduction electrons interact with each other, via an attractive Hubbard on-site coupling $U$, and with local moments on every site, via a Kondo-like coupling, $J$. The model is solved on a simple ...
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Endpoint Sobolev and BV continuity for maximal operators
In this paper we investigate some questions related to the continuity of maximal operators in $W^{1,1}$ and $BV$ spaces, complementing some well-known boundedness results. Letting $\widetilde M$ be the one-dimensional uncentered Hardy-Littlewood maximal operator, we prove that the map $f \mapsto \big(\widetilde Mf\bi...
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Quantum models as classical cellular automata
A synopsis is offered of the properties of discrete and integer-valued, hence "natural", cellular automata (CA). A particular class comprises the "Hamiltonian CA" with discrete updating rules that resemble Hamilton's equations. The resulting dynamics is linear like the unitary evolution described by the Schrödinger e...
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