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A critical analysis of string APIs: The case of Pharo
Most programming languages, besides C, provide a native abstraction for character strings, but string APIs vary widely in size, expressiveness, and subjective convenience across languages. In Pharo, while at first glance the API of the String class seems rich, it often feels cumbersome in practice; to improve its usa...
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Power Maxwell distribution: Statistical Properties, Estimation and Application
In this article, we proposed a new probability distribution named as power Maxwell distribution (PMaD). It is another extension of Maxwell distribution (MaD) which would lead more flexibility to analyze the data with non-monotone failure rate. Different statistical properties such as reliability characteristics, mome...
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Deep Graphs
We propose an algorithm for deep learning on networks and graphs. It relies on the notion that many graph algorithms, such as PageRank, Weisfeiler-Lehman, or Message Passing can be expressed as iterative vertex updates. Unlike previous methods which rely on the ingenuity of the designer, Deep Graphs are adaptive to t...
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On the optimal investment-consumption and life insurance selection problem with an external stochastic factor
In this paper, we study a stochastic optimal control problem with stochastic volatility. We prove the sufficient and necessary maximum principle for the proposed problem. Then we apply the results to solve an investment, consumption and life insurance problem with stochastic volatility, that is, we consider a wage ea...
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An Introduction to Adjoints and Output Error Estimation in Computational Fluid Dynamics
In recent years, the use of adjoint vectors in Computational Fluid Dynamics (CFD) has seen a dramatic rise. Their utility in numerous applications, including design optimization, data assimilation, and mesh adaptation has sparked the interest of both researchers and practitioners alike. In many of these fields, the c...
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Incremental Sharpe and other performance ratios
We present a new methodology of computing incremental contribution for performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling ratios. Using Euler's homogeneous function theorem, we are able to decompose these performance ratios as a linear combination of individual modified performance ratios. This...
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Saliency Detection by Forward and Backward Cues in Deep-CNNs
As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not. In this paper, we propose a top-do...
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Active sorting of orbital angular momentum states of light with cascaded tunable resonators
Light carrying orbital angular momentum (OAM) has been shown to be of use in a disparate range of fields ranging from astronomy to optical trapping, and as a promising new dimension for multiplexing signals in optical communications and data storage. A challenge to many of these applications is a reliable and dynamic...
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Factorization tests and algorithms arising from counting modular forms and automorphic representations
A theorem of Gekeler compares the number of non-isomorphic automorphic representations associated with the space of cusp forms of weight $k$ on $\Gamma_0(N)$ to a simpler function of $k$ and $N$, showing that the two are equal whenever $N$ is squarefree. We prove the converse of this theorem (with one small exception...
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ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems
Model-free policy learning has enabled robust performance of complex tasks with relatively simple algorithms. However, this simplicity comes at the cost of requiring an Oracle and arguably very poor sample complexity. This renders such methods unsuitable for physical systems. Variants of model-based methods address t...
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Learning the Kernel for Classification and Regression
We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform some numerical experiments of polynomial kernels with regression and classification tasks on different datasets.
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Critical system involving fractional Laplacian
In this paper, we study the following critical system with fractional Laplacian: \begin{equation*} \begin{cases} (-\Delta)^{s}u= \mu_{1}|u|^{2^{\ast}-2}u+\frac{\alpha\gamma}{2^{\ast}}|u|^{\alpha-2}u|v|^{\beta} \ \ \ \text{in} \ \ \mathbb{R}^{n}, (-\Delta)^{s}v= \mu_{2}|v|^{2^{\ast}-2}v+\frac{\beta\gamma}{2^{\ast}}|u|...
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Skin Lesion Classification Using Hybrid Deep Neural Networks
Skin cancer is one of the major types of cancers and its incidence has been increasing over the past decades. Skin lesions can arise from various dermatologic disorders and can be classified to various types according to their texture, structure, color and other morphological features. The accuracy of diagnosis of sk...
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Nice derivations over principal ideal domains
In this paper we investigate to what extent the results of Z. Wang and D. Daigle on nice derivations of the polynomial ring in three variables over a field k of characteristic zero extend to the polynomial ring over a PID R, containing the field of rational numbers. One of our results shows that the kernel of a nice ...
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Cooperative Estimation via Altruism
A novel approach, based on the notion of altruism, is presented to cooperative estimation in a system comprising two information-sharing estimators. The underlying assumption is that the system's global mission can be accomplished even if only one of the estimators achieves satisfactory performance. The notion of alt...
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Multi-GPU maximum entropy image synthesis for radio astronomy
The maximum entropy method (MEM) is a well known deconvolution technique in radio-interferometry. This method solves a non-linear optimization problem with an entropy regularization term. Other heuristics such as CLEAN are faster but highly user dependent. Nevertheless, MEM has the following advantages: it is unsuper...
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Green function for linearized Navier-Stokes around a boundary layer profile: away from critical layers
In this paper, we construct the Green function for the classical Orr-Sommerfeld equations, which are the linearized Navier-Stokes equations around a boundary layer profile. As an immediate application, we derive uniform sharp bounds on the semigroup of the linearized Navier-Stokes problem around unstable profiles in ...
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SETI in vivo: testing the we-are-them hypothesis
After it was proposed that life on Earth might descend from seeding by an earlier civilization, some authors noted that this alternative offers a testable aspect: the seeds could be supplied with a signature that might be found in extant organisms. In particular, it was suggested that the optimal location for such an...
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Explicit Time Integration of Transient Eddy Current Problems
For time integration of transient eddy current problems commonly implicit time integration methods are used, where in every time step one or several nonlinear systems of equations have to be linearized with the Newton-Raphson method due to ferromagnetic materials involved. In this paper, a generalized Schur-complemen...
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Transversality for local Morse homology with symmetries and applications
We prove the transversality result necessary for defining local Morse chain complexes with finite cyclic group symmetry. Our arguments use special regularized distance functions constructed using classical covering lemmas, and an inductive perturbation process indexed by the strata of the isotropy set. A global exist...
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On the maximum principle for the Riesz transform
Let $\mu$ be a measure in $\mathbb R^d$ with compact support and continuous density, and let $$ R^s\mu(x)=\int\frac{y-x}{|y-x|^{s+1}}\,d\mu(y),\ \ x,y\in\mathbb R^d,\ \ 0<s<d. $$ We consider the following conjecture: $$ \sup_{x\in\mathbb R^d}|R^s\mu(x)|\le C\sup_{x\in\text{supp}\,\mu}|R^s\mu(x)|,\quad C=C(d,s). $$ Th...
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The complex case of Schmidt's going-down Theorem
In 1967, Schmidt wrote a seminal paper [10] on heights of subspaces of R n or C n defined over a number field K, and diophantine approximation problems. The going-down Theorem -- one of the main theorems he proved in his paper -- remains valid in two cases depending on whether the embedding of K in the complex field ...
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Optimal rates of estimation for multi-reference alignment
In this paper, we establish optimal rates of adaptive estimation of a vector in the multi-reference alignment model, a problem with important applications in fields such as signal processing, image processing, and computer vision, among others. We describe how this model can be viewed as a multivariate Gaussian mixtu...
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Distribution System Voltage Control under Uncertainties using Tractable Chance Constraints
Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this paper, we focus on reactive power compensation to control voltage in the presen...
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Overcomplete Frame Thresholding for Acoustic Scene Analysis
In this work, we derive a generic overcomplete frame thresholding scheme based on risk minimization. Overcomplete frames being favored for analysis tasks such as classification, regression or anomaly detection, we provide a way to leverage those optimal representations in real-world applications through the use of th...
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A Hybrid Approach for Trajectory Control Design
This work presents a methodology to design trajectory tracking feedback control laws, which embed non-parametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysi...
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Achieving and Managing Availability SLAs with ITIL Driven Processes, DevOps, and Workflow Tools
System and application availability continues to be a fundamental characteristic of IT services. In recent years the IT Operations team at Wolters Kluwer CT Corporation has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective mai...
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Foresight: Rapid Data Exploration Through Guideposts
Current tools for exploratory data analysis (EDA) require users to manually select data attributes, statistical computations and visual encodings. This can be daunting for large-scale, complex data. We introduce Foresight, a visualization recommender system that helps the user rapidly explore large high-dimensional d...
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Transport by Lagrangian Vortices in the Eastern Pacific
Rotationally coherent Lagrangian vortices (RCLVs) are identified from satellite-derived surface geostrophic velocities in the Eastern Pacific (180$^\circ$-130$^\circ$ W) using the objective (frame-invariant) finite-time Lagrangian-coherent-structure detection method of Haller et al. (2016) based on the Lagrangian-ave...
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Online Adaptive Methods, Universality and Acceleration
We present a novel method for convex unconstrained optimization that, without any modifications, ensures: (i) accelerated convergence rate for smooth objectives, (ii) standard convergence rate in the general (non-smooth) setting, and (iii) standard convergence rate in the stochastic optimization setting. To the best ...
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A Note on a Quantitative Form of the Solovay-Kitaev Theorem
The problem of finding good approximations of arbitrary 1-qubit gates is identical to that of finding a dense group generated by a universal subset of $SU(2)$ to approximate an arbitrary element of $SU(2)$. The Solovay-Kitaev Theorem is a well-known theorem that guarantees the existence of a finite sequence of 1-qubi...
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Learning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model's prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. I...
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Stochastic Multi-armed Bandits in Constant Space
We consider the stochastic bandit problem in the sublinear space setting, where one cannot record the win-loss record for all $K$ arms. We give an algorithm using $O(1)$ words of space with regret \[ \sum_{i=1}^{K}\frac{1}{\Delta_i}\log \frac{\Delta_i}{\Delta}\log T \] where $\Delta_i$ is the gap between the best arm...
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Max K-armed bandit: On the ExtremeHunter algorithm and beyond
This paper is devoted to the study of the max K-armed bandit problem, which consists in sequentially allocating resources in order to detect extreme values. Our contribution is twofold. We first significantly refine the analysis of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and next propo...
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Comparing Different Models for Investigating Cascading Failures in Power Systems
This paper centers on the comparison of three different models that describe cascading failures of power systems. Specifically, these models are different in characterizing the physical properties of power networks and computing the branch power flow. Optimal control approach is applied on these models to identify th...
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Initial-boundary value problems in a rectangle for two-dimensional Zakharov-Kuznetsov equation
Initial-boundary value problems in a bounded rectangle with different types of boundary conditions for two-dimensional Zakharov-Kuznetsov equation are considered. Results on global well-posedness in the classes of weak and regular solution are established. As applications of the developed technique results on boundar...
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Two-dimensional Fermi gases near a p-wave resonance: effect of quantum fluctuations
We study the stability of p-wave superfluidity against quantum fluctuations in two-dimensional Fermi gases near a p-wave Feshbach resonance . An analysis is carried out in the limit when the interchannel coupling is strong. By investigating the effective potential for the pairing field via the standard loop expansion...
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Optimal Strong Rates of Convergence for a Space-Time Discretization of the Stochastic Allen-Cahn Equation with multiplicative noise
The stochastic Allen-Cahn equation with multiplicative noise involves the nonlinear drift operator ${\mathscr A}(x) = \Delta x - \bigl(\vert x\vert^2 -1\bigr)x$. We use the fact that ${\mathscr A}(x) = -{\mathcal J}^{\prime}(x)$ satisfies a weak monotonicity property to deduce uniform bounds in strong norms for solut...
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A generalized quantum Slepian-Wolf
In this work we consider a quantum generalization of the task considered by Slepian and Wolf [1973] regarding distributed source compression. In our task Alice, Bob, Charlie and Reference share a joint pure state. Alice and Bob wish to send a part of their respective systems to Charlie without collaborating with each...
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The rational SPDE approach for Gaussian random fields with general smoothness
A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form $L^{\beta}u = \mathcal{W}$, where $\mathcal{W}$ is Gaussian white noise, $L$ is a second-order differential operator, and $\beta>0$ i...
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An Analog of the Neumann Problem for the $1$-Laplace Equation in the Metric Setting: Existence, Boundary Regularity, and Stability
We study an inhomogeneous Neumann boundary value problem for functions of least gradient on bounded domains in metric spaces that are equipped with a doubling measure and support a Poincaré inequality. We show that solutions exist under certain regularity assumptions on the domain, but are generally nonunique. We als...
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Activating spin-forbidden transitions in molecules by the highly localized plasmonic field
Optical spectroscopy has been the primary tool to study the electronic structure of molecules. However the strict spin selection rule has severely limited its ability to access states of different spin multiplicities. Here we propose a new strategy to activate spin-forbidden transitions in molecules by introducing sp...
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On the Essential Spectrum of Schrödinger Operators on Trees
It is known that the essential spectrum of a Schrödinger operator $H$ on $\ell^{2}\left(\mathbb{N}\right)$ is equal to the union of the spectra of right limits of $H$. The natural generalization of this relation to $\mathbb{Z}^{n}$ is known to hold as well. In this paper we generalize the notion of right limits to ge...
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The equilibrium of over-pressurised polytropes
We investigate the impact of an external pressure on the structure of self-gravitating polytropes for axially symmetric ellipsoids and rings. The confinement of the fluid by photons is accounted for through a boundary condition on the enthalpy $H$. Equilibrium configurations are determined numerically from a generali...
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Charge and pairing dynamics in the attractive Hubbard model: mode coupling and the validity of linear-response theory
Pump-probe experiments have turned out as a powerful tool in order to study the dynamics of competing orders in a large variety of materials. The corresponding analysis of the data often relies on standard linear-response theory generalized to non-equilibrium situations. Here we examine the validity of such an approa...
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From voids to filaments: environmental transformations of galaxies in the SDSS
We investigate the impact of filament and void environments on galaxies, looking for residual effects beyond the known relations with environment density. We quantified the host environment of galaxies as the distance to the spine of the nearest filament, and compared various galaxy properties within 12 bins of this ...
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Distributed Algorithms Made Secure: A Graph Theoretic Approach
In the area of distributed graph algorithms a number of network's entities with local views solve some computational task by exchanging messages with their neighbors. Quite unfortunately, an inherent property of most existing distributed algorithms is that throughout the course of their execution, the nodes get to le...
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Interpretation of Neural Networks is Fragile
In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predictions. For example, if an algorithm classifies a given pathology image to be a malignant tumor, then the doctor may need to know which part...
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Dipolar phonons and electronic screening in monolayer FeSe on SrTiO$_3$
Monolayer films of FeSe grown on SrTiO$_3$ substrates exhibit significantly higher superconducting transition temperatures than those of bulk FeSe. Interaction of electrons in the FeSe layer with dipolar SrTiO$_3$ phonons has been suggested as the cause of the enhanced transition temperature. In this paper we systema...
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A Supervised Approach to Extractive Summarisation of Scientific Papers
Automatic summarisation is a popular approach to reduce a document to its main arguments. Recent research in the area has focused on neural approaches to summarisation, which can be very data-hungry. However, few large datasets exist and none for the traditionally popular domain of scientific publications, which open...
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Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems
Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, previous work on multiuser MIMO has shown that the codebook size for channel feedback should scale exponentially with the number of base station (BS) antennas, which is greatly inc...
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Thermalization in simple metals: The role of electron-phonon and phonon-phonon scatterings
We study the electron and phonon thermalization in simple metals excited by a laser pulse. The thermalization is investigated numerically by solving the Boltzmann transport equation taking into account all the relevant scattering mechanism: the electron-electron, electron-phonon (e-ph), phonon-electron (ph-e), and ph...
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Convergence of the free Boltzmann quadrangulation with simple boundary to the Brownian disk
We prove that the free Boltzmann quadrangulation with simple boundary and fixed perimeter, equipped with its graph metric, natural area measure, and the path which traces its boundary converges in the scaling limit to the free Boltzmann Brownian disk. The topology of convergence is the so-called Gromov-Hausdorff-Prok...
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Canonical quantization of nonlinear sigma models with theta term, with applications to symmetry-protected topological phases
We canonically quantize $O(D+2)$ nonlinear sigma models (NLSMs) with theta term on arbitrary smooth, closed, connected, oriented $D$-dimensional spatial manifolds $\mathcal{M}$, with the goal of proving the suitability of these models for describing symmetry-protected topological (SPT) phases of bosons in $D$ spatial...
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A Nonparametric Bayesian Approach to Copula Estimation
We propose a novel Dirichlet-based Pólya tree (D-P tree) prior on the copula and based on the D-P tree prior, a nonparametric Bayesian inference procedure. Through theoretical analysis and simulations, we are able to show that the flexibility of the D-P tree prior ensures its consistency in copula estimation, thus ab...
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Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone
In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocit...
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A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs
Predicting the cheapest sample size for the optimal stratification in multivariate survey design is a problem in cases where the population frame is large. A solution exists that iteratively searches for the minimum sample size necessary to meet accuracy constraints in partitions of atomic strata created by the Carte...
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Murmur Detection Using Parallel Recurrent & Convolutional Neural Networks
In this article, we propose a novel technique for classification of the Murmurs in heart sound. We introduce a novel deep neural network architecture using parallel combination of the Recurrent Neural Network (RNN) based Bidirectional Long Short-Term Memory (BiLSTM) & Convolutional Neural Network (CNN) to learn visua...
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Time-of-Flight Electron Energy Loss Spectroscopy by Longitudinal Phase Space Manipulation with Microwave Cavities
The possibility to perform high-resolution time-resolved electron energy loss spectroscopy has the potential to impact a broad range of research fields. Resolving small energy losses with ultrashort electron pulses, however, is an enormous challenge due to the low average brightness of a pulsed beam. In this letter, ...
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Learning across scales - A multiscale method for Convolution Neural Networks
In this work we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted as a time-dependent nonlinear differential equation and learning as controlling the parameters of the differential equation such that the...
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Capacitated Covering Problems in Geometric Spaces
In this article, we consider the following capacitated covering problem. We are given a set $P$ of $n$ points and a set $\mathcal{B}$ of balls from some metric space, and a positive integer $U$ that represents the capacity of each of the balls in $\mathcal{B}$. We would like to compute a subset $\mathcal{B}' \subsete...
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Irregular Oscillatory-Patterns in the Early-Time Region of Coherent Phonon Generation in Silicon
Coherent phonon (CP) generation in an undoped Si crystal is theoretically investigated to shed light on unexplored quantum-mechanical effects in the early-time region immediately after the irradiation of ultrashort laser pulse. One examines time signals attributed to an induced charge density of an ionic core, placin...
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Energy-Efficient Hybrid Stochastic-Binary Neural Networks for Near-Sensor Computing
Recent advances in neural networks (NNs) exhibit unprecedented success at transforming large, unstructured data streams into compact higher-level semantic information for tasks such as handwriting recognition, image classification, and speech recognition. Ideally, systems would employ near-sensor computation to execu...
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Position-based coding and convex splitting for private communication over quantum channels
The classical-input quantum-output (cq) wiretap channel is a communication model involving a classical sender $X$, a legitimate quantum receiver $B$, and a quantum eavesdropper $E$. The goal of a private communication protocol that uses such a channel is for the sender $X$ to transmit a message in such a way that the...
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Geometric vulnerability of democratic institutions against lobbying: a sociophysics approach
An alternative voting scheme is proposed to fill the democratic gap between a president elected democratically via universal suffrage (deterministic outcome, the actual majority decides), and a president elected by one person randomly selected from the population (probabilistic outcome depending on respective support...
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Strongly correlated double Dirac fermions
Double Dirac fermions have recently been identified as possible quasiparticles hosted by three-dimensional crystals with particular non-symmorphic point group symmetries. Applying a combined approach of ab-initio methods and dynamical mean field theory, we investigate how interactions and double Dirac band topology c...
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A Phase Variable Approach for Improved Volitional and Rhythmic Control of a Powered Knee-Ankle Prosthesis
Although there has been recent progress in control of multi-joint prosthetic legs for periodic tasks such as walking, volitional control of these systems for non-periodic maneuvers is still an open problem. In this paper, we develop a new controller that is capable of both periodic walking and common volitional leg m...
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New estimates for the $n$th prime number
In this paper we establish a new explicit upper and lower bound for the $n$-th prime number, which improve the currently best estimates given by Dusart in 2010. As the main tool we use some recently obtained explicit estimates for the prime counting function. A further main tool is the usage of estimates concerning t...
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Unbiased Markov chain Monte Carlo for intractable target distributions
Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. Markov chain Monte Carlo (MCMC) algorithms have been proposed for this setting, such as t...
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An Observer for an Occluded Reaction-Diffusion System With Spatially Varying Parameters
Spatially dependent parameters of a two-component chaotic reaction-diffusion PDE model describing ocean ecology are observed by sampling a single species. We estimate model parameters and the other species in the system by autosynchronization, where quantities of interest are evolved according to misfit between model...
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D4M 3.0
The D4M tool is used by hundreds of researchers to perform complex analytics on unstructured data. Over the past few years, the D4M toolbox has evolved to support connectivity with a variety of database engines, graph analytics in the Apache Accumulo database, and an implementation using the Julia programming languag...
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Human-in-the-Loop SLAM
Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic a...
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What drives gravitational instability in nearby star-forming spirals? The impact of CO and HI velocity dispersions
The velocity dispersion of cold interstellar gas, sigma, is one of the quantities that most radically affect the onset of gravitational instabilities in galaxy discs, and the quantity that is most drastically approximated in stability analyses. Here we analyse the stability of a large sample of nearby star-forming sp...
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An improved belief propagation algorithm for detecting meso-scale structure in complex networks
The framework of statistical inference has been successfully used to detect the meso-scale structures in complex networks, such as community structure, core-periphery (CP) structure. The main principle is that the stochastic block model (SBM) is used to fit the observed network and the learnt parameters indicate the ...
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On Least Squares Linear Regression Without Second Moment
If X and Y are real valued random variables such that the first moments of X, Y, and XY exist and the conditional expectation of Y given X is an affine function of X, then the intercept and slope of the conditional expectation equal the intercept and slope of the least squares linear regression function, even though ...
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Combinatorics of involutive divisions
The classical involutive division theory by Janet decomposes in the same way both the ideal and the escalier. The aim of this paper, following Janet's approach, is to discuss the combinatorial properties of involutive divisions, when defined on the set of all terms in a fixed degree D, postponing the discussion of id...
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Reducing asynchrony to synchronized rounds
Synchronous computation models simplify the design and the verification of fault-tolerant distributed systems. For efficiency reasons such systems are designed and implemented using an asynchronous semantics. In this paper, we bridge the gap between these two worlds. We introduce a (synchronous) round-based computati...
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A wearable general-purpose solution for Human-Swarm Interaction
Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. Controlling the motion and behavior of these swarms presents unique challenges for human operators, who cannot yet effectively convey their high-level intentions to a group of robots in application....
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Numerical simulation of oxidation processes in a cross-flow around tube bundles
An oxidation process is simulated for a bundle of metal tubes in a cross-flow. A fluid flow is governed by the incompressible Navier-Stokes equations. To describe the transport of oxygen, the corresponding convection-diffusion equation is applied. The key point of the model is related to the description of oxidation ...
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Design and optimization of a portable LQCD Monte Carlo code using OpenACC
The present panorama of HPC architectures is extremely heterogeneous, ranging from traditional multi-core CPU processors, supporting a wide class of applications but delivering moderate computing performance, to many-core GPUs, exploiting aggressive data-parallelism and delivering higher performances for streaming co...
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NodeTrix Planarity Testing with Small Clusters
We study the NodeTrix planarity testing problem for flat clustered graphs when the maximum size of each cluster is bounded by a constant $k$. We consider both the case when the sides of the matrices to which the edges are incident are fixed and the case when they can be arbitrarily chosen. We show that NodeTrix plana...
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Exact results for directed random networks that grow by node duplication
We present exact analytical results for the degree distribution and for the distribution of shortest path lengths (DSPL) in a directed network model that grows by node duplication. Such models are useful in the study of the structure and growth dynamics of gene regulatory and scientific citation networks. Starting fr...
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Investigating the past history of EXors: the cases of V1118 Ori, V1143 Ori, and NY Ori
EXor objects are young variables that show episodic variations of brightness commonly associated to enhanced accretion outbursts. With the aim of investigating the long-term photometric behaviour of a few EXor sources, we present here data from the archival plates of the Asiago Observatory, showing the Orion field wh...
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Some estimates for $θ$-type Calderón-Zygmund operators and linear commutators on certain weighted amalgam spaces
In this paper, we first introduce some new kinds of weighted amalgam spaces. Then we discuss the strong type and weak type estimates for a class of Calderón--Zygmund type operators $T_\theta$ in these new weighted spaces. Furthermore, the strong type estimate and endpoint estimate of linear commutators $[b,T_{\theta}...
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A Cluster Elastic Net for Multivariate Regression
We propose a method for estimating coefficients in multivariate regression when there is a clustering structure to the response variables. The proposed method includes a fusion penalty, to shrink the difference in fitted values from responses in the same cluster, and an L1 penalty for simultaneous variable selection ...
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Achieving robust and high-fidelity quantum control via spectral phase optimization
Achieving high-fidelity control of quantum systems is of fundamental importance in physics, chemistry and quantum information sciences. However, the successful implementation of a high-fidelity quantum control scheme also requires robustness against control field fluctuations. Here, we demonstrate a robust optimizati...
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Help Me Find a Job: A Graph-based Approach for Job Recommendation at Scale
Online job boards are one of the central components of modern recruitment industry. With millions of candidates browsing through job postings everyday, the need for accurate, effective, meaningful, and transparent job recommendations is apparent more than ever. While recommendation systems are successfully advancing ...
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Bundle Optimization for Multi-aspect Embedding
Understanding semantic similarity among images is the core of a wide range of computer vision applications. An important step towards this goal is to collect and learn human perceptions. Interestingly, the semantic context of images is often ambiguous as images can be perceived with emphasis on different aspects, whi...
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Glitch Classification and Clustering for LIGO with Deep Transfer Learning
The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian noise transients known as glitches, since their high occurrence rate in...
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Provable Smoothness Guarantees for Black-Box Variational Inference
Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable convergence guarantees require structural properties of the objective. This paper shows that for location-scale family approximations, if the ta...
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A k-means procedure based on a Mahalanobis type distance for clustering multivariate functional data
This paper proposes a clustering procedure for samples of multivariate functions in $(L^2(I))^{J}$, with $J\geq1$. This method is based on a k-means algorithm in which the distance between the curves is measured with a metrics that generalizes the Mahalanobis distance in Hilbert spaces, considering the correlation an...
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Tidal Dissipation in WASP-12
WASP-12 is a hot Jupiter system with an orbital period of $P= 1.1\textrm{ day}$, making it one of the shortest-period giant planets known. Recent transit timing observations by Maciejewski et al. (2016) and Patra et al. (2017) find a decreasing period with $P/|\dot{P}| = 3.2\textrm{ Myr}$. This has been interpreted a...
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Label Sanitization against Label Flipping Poisoning Attacks
Many machine learning systems rely on data collected in the wild from untrusted sources, exposing the learning algorithms to data poisoning. Attackers can inject malicious data in the training dataset to subvert the learning process, compromising the performance of the algorithm producing errors in a targeted or an i...
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DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction
Chemical-chemical interaction (CCI) plays a key role in predicting candidate drugs, toxicity, therapeutic effects, and biological functions. In various types of chemical analyses, computational approaches are often required due to the amount of data that needs to be handled. The recent remarkable growth and outstandi...
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Intervals between numbers that are sums of two squares
In this paper, we improve the moment estimates for the gaps between numbers that can be represented as a sum of two squares of integers. We consider certain sum of Bessel functions and prove the upper bound for its weighted mean value. This bound provides estimates for the $\gamma$-th moments of gaps for all $\gamma\...
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On a family of Caldero-Chapoton algebras that have the Laurent phenomenon
We realize a family of generalized cluster algebras as Caldero-Chapoton algebras of quivers with relations. Each member of this family arises from an unpunctured polygon with one orbifold point of order 3, and is realized as a Caldero-Chapoton algebra of a quiver with relations naturally associated to any triangulati...
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Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network
Ricean channel model is widely used in wireless communications to characterize the channels with a line-of-sight path. The Ricean K factor, defined as the ratio of direct path and scattered paths, provides a good indication of the link quality. Most existing works estimate K factor based on either maximum-likelihood ...
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Augmented lagrangian two-stage algorithm for LP and SCQP
In this paper, we consider a framework of projected gradient iterations for linear programming (LP) and an augmented lagrangian two-stage algorithm for strongly convex quadratic programming (SCQP). Based on the framework of projected gradient, LP problem is transformed to a finite number of SCQP problems. Furthermore...
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Exact relations between homoclinic and periodic orbit actions in chaotic systems
Homoclinic and unstable periodic orbits in chaotic systems play central roles in various semiclassical sum rules. The interferences between terms are governed by the action functions and Maslov indices. In this article, we identify geometric relations between homoclinic and unstable periodic orbits, and derive exact ...
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Subexponentially growing Hilbert space and nonconcentrating distributions in a constrained spin model
Motivated by recent experiments with two-component Bose-Einstein condensates, we study fully-connected spin models subject to an additional constraint. The constraint is responsible for the Hilbert space dimension to scale only linearly with the system size. We discuss the unconventional statistical physical and ther...
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