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Local asymptotic equivalence of pure quantum states ensembles and quantum Gaussian white noise
Quantum technology is increasingly relying on specialised statistical inference methods for analysing quantum measurement data. This motivates the development of "quantum statistics", a field that is shaping up at the overlap of quantum physics and "classical" statistics. One of the less investigated topics to date i...
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Training DNNs with Hybrid Block Floating Point
The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full precision floating-point arithmetic to maximize performance per area. Ongoing research efforts seek...
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Unified Spectral Clustering with Optimal Graph
Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to sev...
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The Effect of Mixing on the Observed Metallicity of the Smith Cloud
Measurements of high-velocity clouds' metallicities provide important clues about their origins, and hence on whether they play a role in fueling ongoing star formation in the Galaxy. However, accurate interpretation of these measurements requires compensating for the galactic material that has been mixed into the cl...
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Semi-Supervised Generation with Cluster-aware Generative Models
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster-aware Generative M...
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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
We present a generalization bound for feedforward neural networks in terms of the product of the spectral norm of the layers and the Frobenius norm of the weights. The generalization bound is derived using a PAC-Bayes analysis.
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Tidal viscosity of Enceladus
In the preceding paper (Efroimsky 2017), we derived an expression for the tidal dissipation rate in a homogeneous near-spherical Maxwell body librating in longitude. Now, by equating this expression to the outgoing energy flux due to the vapour plumes, we estimate the mean tidal viscosity of Enceladus, under the assu...
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MOROCO: The Moldavian and Romanian Dialectal Corpus
In this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at this https URL. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, po...
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Linearized Einstein's field equations
From the Einstein field equations, in a weak-field approximation and for speeds small compared to the speed of light in vacuum, the following system is obtained \begin{align*} \nabla \times \overrightarrow{E_g} & = -\frac{1}{c} \frac{\partial \overrightarrow{B_g}}{\partial t}, \nabla \cdot \overrightarrow{E_g} \;\; &...
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Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations
We propose a novel tree classification system called Treelogy, that fuses deep representations with hand-crafted features obtained from leaf images to perform leaf-based plant classification. Key to this system are segmentation of the leaf from an untextured background, using convolutional neural networks (CNNs) for ...
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Simulation and stability analysis of oblique shock wave/boundary layer interactions at Mach 5.92
We investigate flow instability created by an oblique shock wave impinging on a Mach 5.92 laminar boundary layer at a transitional Reynolds number. The adverse pressure gradient of the oblique shock causes the boundary layer to separate from the wall, resulting in the formation of a recirculation bubble. For sufficie...
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Notes on rate equations in nonlinear continuum mechanics
The paper gives an introduction to rate equations in nonlinear continuum mechanics which should obey specific transformation rules. Emphasis is placed on the geometrical nature of the operations involved in order to clarify the different concepts. The paper is particularly concerned with common classes of constitutiv...
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Streaming PCA and Subspace Tracking: The Missing Data Case
For many modern applications in science and engineering, data are collected in a streaming fashion carrying time-varying information, and practitioners need to process them with a limited amount of memory and computational resources in a timely manner for decision making. This often is coupled with the missing data p...
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Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data
In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse th...
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Introduction to the declination function for gerrymanders
The declination is a quantitative method for identifying possible partisan gerrymanders by analyzing vote distributions. In this expository note we explain and motivate the definition of the declination. The minimal computer code required for computing the declination is included. We end by computing its value on sev...
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TFDASH: A Fairness, Stability, and Efficiency Aware Rate Control Approach for Multiple Clients over DASH
Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet and adopted in the industry. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients and suffers from lackluster performance with respect to a number of desirable p...
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Predicting language diversity with complex network
Evolution and propagation of the world's languages is a complex phenomenon, driven, to a large extent, by social interactions. Multilingual society can be seen as a system of interacting agents, where the interaction leads to a modification of the language spoken by the individuals. Two people can reach the state of ...
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The paradox of Vito Volterra's predator-prey model
This article is dedicated to the late Giorgio Israel. R{é}sum{é}. The aim of this article is to propose on the one hand a brief history of modeling starting from the works of Fibonacci, Robert Malthus, Pierre Francis Verhulst and then Vito Volterra and, on the other hand, to present the main hypotheses of the very fa...
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Distributive Minimization Comprehensions and the Polynomial Hierarchy
A categorical point of view about minimization in subrecursive classes is presented by extending the concept of Symmetric Monoidal Comprehension to that of Distributive Minimization Comprehension. This is achieved by endowing the former with coproducts and a finality condition for coalgebras over the endofunctor send...
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On the differentiability of hairs for Zorich maps
Devaney and Krych showed that for the exponential family $\lambda e^z$, where $0<\lambda <1/e$, the Julia set consists of uncountably many pairwise disjoint simple curves tending to $\infty$. Viana proved that these curves are smooth. In this article we consider a quasiregular counterpart of the exponential map, the ...
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On the Real-time Vehicle Placement Problem
Motivated by ride-sharing platforms' efforts to reduce their riders' wait times for a vehicle, this paper introduces a novel problem of placing vehicles to fulfill real-time pickup requests in a spatially and temporally changing environment. The real-time nature of this problem makes it fundamentally different from o...
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Energy Harvesting Communication Using Finite-Capacity Batteries with Internal Resistance
Modern systems will increasingly rely on energy harvested from their environment. Such systems utilize batteries to smoothen out the random fluctuations in harvested energy. These fluctuations induce highly variable battery charge and discharge rates, which affect the efficiencies of practical batteries that typicall...
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A Novel Algorithm for Optimal Electricity Pricing in a Smart Microgrid Network
The evolution of smart microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly de...
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A Reinforcement Learning Approach to Jointly Adapt Vehicular Communications and Planning for Optimized Driving
Our premise is that autonomous vehicles must optimize communications and motion planning jointly. Specifically, a vehicle must adapt its motion plan staying cognizant of communications rate related constraints and adapt the use of communications while being cognizant of motion planning related restrictions that may b...
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Differentially Private High Dimensional Sparse Covariance Matrix Estimation
In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions. We propose a new method, called DP-Thresholding, to achieve a non-trivial $\ell_2$-norm based error bound, which is significantly b...
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Stochastic Functional Gradient Path Planning in Occupancy Maps
Planning safe paths is a major building block in robot autonomy. It has been an active field of research for several decades, with a plethora of planning methods. Planners can be generally categorised as either trajectory optimisers or sampling-based planners. The latter is the predominant planning paradigm for occup...
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MM2RTB: Bringing Multimedia Metrics to Real-Time Bidding
In display advertising, users' online ad experiences are important for the advertising effectiveness. However, users have not been well accommodated in real-time bidding (RTB). This further influences their site visits and perception of the displayed banner ads. In this paper, we propose a novel computational framewo...
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Generic coexistence of Fermi arcs and Dirac cones on the surface of time-reversal invariant Weyl semimetals
The hallmark of Weyl semimetals is the existence of open constant-energy contours on their surface -- the so-called Fermi arcs -- connecting Weyl points. Here, we show that for time-reversal symmetric realizations of Weyl semimetals these Fermi arcs in many cases coexist with closed Fermi pockets originating from sur...
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Superfluid Field response to Edge dislocation motion
We study the dynamic response of a superfluid field to a moving edge dislocation line to which the field is minimally coupled. We use a dissipative Gross-Pitaevskii equation, and determine the initial conditions by solving the equilibrium version of the model. We consider the subsequent time evolution of the field fo...
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Koopman Operator Spectrum and Data Analysis
We examine spectral operator-theoretic properties of linear and nonlinear dynamical systems with equilibrium and quasi-periodic attractors and use such properties to characterize a class of datasets and introduce a new notion of the principal dimension of the data.
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Exploiting Physical Dynamics to Detect Actuator and Sensor Attacks in Mobile Robots
Mobile robots are cyber-physical systems where the cyberspace and the physical world are strongly coupled. Attacks against mobile robots can transcend cyber defenses and escalate into disastrous consequences in the physical world. In this paper, we focus on the detection of active attacks that are capable of directly...
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Unsupervised Neural Machine Translation
In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs. There have been several proposals to alleviate this issue with, for instance, triangulation and semi-supervised learning techniques, bu...
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Geometric Methods for Robust Data Analysis in High Dimension
Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quantities of data gathered from automated sensors and user input. Furthermore, the dimensionality of many datasets is extreme: more details a...
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Modules Over the Ring of Ponderation functions with Applications to a Class of Integral Operators
In this paper we introduce new modules over the ring of ponderation functions, so we recover old results in harmonic analysis from the side of ring theory. Moreover, we prove that Laplace transform, Fourier transform and Hankel transform generate some kind of modules over the ring of ponderation functions.
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Strict monotonicity of principal eigenvalues of elliptic operators in $\mathbb{R}^d$ and risk-sensitive control
This paper studies the eigenvalue problem on $\mathbb{R}^d$ for a class of second order, elliptic operators of the form $\mathscr{L} = a^{ij}\partial_{x_i}\partial_{x_j} + b^{i}\partial_{x_i} + f$, associated with non-degenerate diffusions. We show that strict monotonicity of the principal eigenvalue of the operator ...
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Best Practices for Applying Deep Learning to Novel Applications
This report is targeted to groups who are subject matter experts in their application but deep learning novices. It contains practical advice for those interested in testing the use of deep neural networks on applications that are novel for deep learning. We suggest making your project more manageable by dividing it ...
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Precision Interfaces
Building interactive tools to support data analysis is hard because it is not always clear what to build and how to build it. To address this problem, we present Precision Interfaces, a semi-automatic system to generate task-specific data analytics interfaces. Precision Interface can turn a log of executed programs i...
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Gaussian Approximation of a Risk Model with Stationary Hawkes Arrivals of Claims
We consider a classical risk process with arrival of claims following a stationary Hawkes process. We study the asymptotic regime when the premium rate and the baseline intensity of the claims arrival process are large, and claim size is small. The main goal of this article is to establish a diffusion approximation b...
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Data Modelling for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms
To conduct a more realistic evaluation on Virtualized Network Functions resource allocation algorithms, researches needed data on: (1) potential NFs chains (policies), (2) traffic flows passing through these NFs chains, (3) how the dynamic traffic changes affect the NFs (scale out/in) and (4) different data center ar...
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Understanding Negations in Information Processing: Learning from Replicating Human Behavior
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understandi...
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A study of sliding motion of a solid body on a rough surface with asymmetric friction
Recent studies show interest in materials with asymmetric friction forces. We investigate terminal motion of a solid body with circular contact area. We assume that friction forces are asymmetric orthotropic. Two cases of pressure distribution are analyzed: Hertz and Boussinesq laws. Equations for friction force and ...
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Fisher information matrix of binary time series
A common approach to analyzing categorical correlated time series data is to fit a generalized linear model (GLM) with past data as covariate inputs. There remain challenges to conducting inference for short time series length. By treating the historical data as covariate inputs, standard errors of estimates of GLM p...
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A Fourier analytic approach to inhomogeneous Diophantine approximation
In this paper, we study inhomogeneous Diophantine approximation with rational numbers of reduced form. The central object to study is the set $W(f,\theta)$ as follows, \begin{eqnarray*} \left\{x\in [0,1]:\left |x-\frac{m+\theta(n)}{n}\right|<\frac{f(n)}{n}\text{ for infinitely many coprime pairs of numbers } m,n\righ...
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Screening in perturbative approaches to LSS
A specific value for the cosmological constant, \Lambda, can account for late-time cosmic acceleration. However, motivated by the so-called cosmological constant problem(s), several alternative mechanisms have been explored. To date, a host of well-studied dynamical dark energy and modified gravity models exists. Goi...
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Thin films with precisely engineered nanostructures
Synthesis of rationally designed nanostructured materials with optimized mechanical properties, e.g., high strength with considerable ductility, requires rigorous control of diverse microstructural parameters including the mean size, size dispersion and spatial distribution of grains. However, currently available syn...
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End-to-End Network Delay Guarantees for Real-Time Systems using SDN
We propose a novel framework that reduces the management and integration overheads for real-time network flows by leveraging the capabilities (especially global visibility and management) of software-defined networking (SDN) architectures. Given the specifications of flows that must meet hard real-time requirements, ...
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Efficient cold outflows driven by cosmic rays in high redshift galaxies and their global effects on the IGM
We present semi-analytical models of galactic outflows in high redshift galaxies driven by both hot thermal gas and non-thermal cosmic rays. Thermal pressure alone may not sustain a large scale outflow in low mass galaxies (i.e $M\sim 10^8$~M$_\odot$), in the presence of supernovae (SNe) feedback with large mass load...
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On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers
Learning algorithms that learn linear models often have high representation bias on real-world problems. In this paper, we show that this representation bias can be greatly reduced by discretization. Discretization is a common procedure in machine learning that is used to convert a quantitative attribute into a quali...
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Kustaanheimo-Stiefel transformation with an arbitrary defining vector
Kustaanheimo-Stiefel (KS) transformation depends on the choice of some preferred direction in the Cartesian 3D space. This choice, seldom explicitly mentioned, amounts typically to the direction of the first or the third coordinate axis in celestial mechanics and atomic physics, respectively. The present work develop...
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A dynamic game approach to distributionally robust safety specifications for stochastic systems
This paper presents a new safety specification method that is robust against errors in the probability distribution of disturbances. Our proposed distributionally robust safe policy maximizes the probability of a system remaining in a desired set for all times, subject to the worst possible disturbance distribution i...
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Entity Linking for Queries by Searching Wikipedia Sentences
We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-pr...
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Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest
Reinforcement Learning AI commonly uses reward/penalty signals that are objective and explicit in an environment -- e.g. game score, completion time, etc. -- in order to learn the optimal strategy for task performance. However, Human-AI interaction for such AI agents should include additional reinforcement that is im...
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Relational recurrent neural networks
Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods. It is unclear, however, whether they also have an ability to perform complex relational reasoning with the information they remember. Here, we first confirm our intuitions that standard memory architect...
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A Scalable Discrete-Time Survival Model for Neural Networks
There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation...
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A driven-dissipative spin chain model based on exciton-polariton condensates
An infinite chain of driven-dissipative condensate spins with uniform nearest-neighbor coherent coupling is solved analytically and investigated numerically. Above a critical occupation threshold the condensates undergo spontaneous spin bifurcation (becoming magnetized) forming a binary chain of spin-up or spin-down ...
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Projected Primal-Dual Gradient Flow of Augmented Lagrangian with Application to Distributed Maximization of the Algebraic Connectivity of a Network
In this paper, a projected primal-dual gradient flow of augmented Lagrangian is presented to solve convex optimization problems that are not necessarily strictly convex. The optimization variables are restricted by a convex set with computable projection operation on its tangent cone as well as equality constraints. ...
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A Short Note on Almost Sure Convergence of Bayes Factors in the General Set-Up
Although there is a significant literature on the asymptotic theory of Bayes factor, the set-ups considered are usually specialized and often involves independent and identically distributed data. Even in such specialized cases, mostly weak consistency results are available. In this article, for the first time ever, ...
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Rainbow matchings in properly-coloured multigraphs
Aharoni and Berger conjectured that in any bipartite multigraph that is properly edge-coloured by $n$ colours with at least $n + 1$ edges of each colour there must be a matching that uses each colour exactly once. In this paper we consider the same question without the bipartiteness assumption. We show that in any mu...
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Recursive computation of the invariant distribution of Markov and Feller processes
This paper provides a general and abstract approach to approximate ergodic regimes of Markov and Feller processes. More precisely, we show that the recursive algorithm presented in Lamberton & Pages (2002) and based on simulation algorithms of stochastic schemes with decreasing step can be used to build invariant mea...
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Secondary resonances and the boundary of effective stability of Trojan motions
One of the most interesting features in the libration domain of co-orbital motions is the existence of secondary resonances. For some combinations of physical parameters, these resonances occupy a large fraction of the domain of stability and rule the dynamics within the stable tadpole region. In this work, we presen...
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Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network
The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems, while return-based algorithms have advantages in making use of sample trajectories. In this paper, we propose a general framework to comb...
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Probabilistic Constraints on the Mass and Composition of Proxima b
Recent studies regarding the habitability, observability, and possible orbital evolution of the indirectly detected exoplanet Proxima b have mostly assumed a planet with $M \sim 1.3$ $M_\oplus$, a rocky composition, and an Earth-like atmosphere or none at all. In order to assess these assumptions, we use previous stu...
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Explicit Salem sets, Fourier restriction, and metric Diophantine approximation in the $p$-adic numbers
We exhibit the first explicit examples of Salem sets in $\mathbb{Q}_p$ of every dimension $0 < \alpha < 1$ by showing that certain sets of well-approximable $p$-adic numbers are Salem sets. We construct measures supported on these sets that satisfy essentially optimal Fourier decay and upper regularity conditions, an...
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Tunneling of the hard-core model on finite triangular lattices
We consider the hard-core model on finite triangular lattices with Metropolis dynamics. Under suitable conditions on the triangular lattice dimensions, this interacting particle system has three maximum-occupancy configurations and we investigate its high-fugacity behavior by studying tunneling times, i.e., the first...
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Spectral State Compression of Markov Processes
Model reduction of the Markov process is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a finite-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we stu...
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Multi-band characterization of the hot Jupiters: WASP-5b, WASP-44b and WASP-46b
We have carried out a campaign to characterize the hot Jupiters WASP-5b, WASP-44b and WASP-46b using multiband photometry collected at the Observatório do Pico Dos Dias in Brazil. We have determined the planetary physical properties and new transit ephemerides for these systems. The new orbital parameters and physica...
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Non-Fermi liquid at the FFLO quantum critical point
When a 2D superconductor is subjected to a strong in-plane magnetic field, Zeeman polarization of the Fermi surface can give rise to inhomogeneous FFLO order with a spatially modulated gap. Further increase of the magnetic field eventually drives the system into a normal metal state. Here, we perform a renormalizatio...
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Unsupervised Ensemble Regression
Consider a regression problem where there is no labeled data and the only observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many samples $x_j$. With no knowledge on the accuracy of the experts, is it still possible to accurately estimate the unknown responses $y_{j}$? Can one still detect the le...
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Accurate approximation of the distributions of the 3D Poisson-Voronoi typical cell geometrical features
Although Poisson-Voronoi diagrams have interesting mathematical properties, there is still much to discover about the geometrical properties of its grains. Through simulations, many authors were able to obtain numerical approximations of the moments of the distributions of more or less all geometrical characteristics...
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Flux-Stabilized Majorana Zero Modes in Coupled One-Dimensional Fermi Wires
One promising avenue to study one-dimensional ($1$D) topological phases is to realize them in synthetic materials such as cold atomic gases. Intriguingly, it is possible to realize Majorana boundary modes in a $1$D number-conserving system consisting of two fermionic chains coupled only by pair-hopping processes. It ...
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Nonlinear Kalman Filtering for Censored Observations
The use of Kalman filtering, as well as its nonlinear extensions, for the estimation of system variables and parameters has played a pivotal role in many fields of scientific inquiry where observations of the system are restricted to a subset of variables. However in the case of censored observations, where measureme...
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Advances in Atomic Resolution In Situ Environmental Transmission Electron Microscopy and 1 Angstrom Aberration Corrected In Situ Electron Microscopy
Advances in atomic resolution in situ environmental transmission electron microscopy for direct probing of gas-solid reactions, including at very high temperatures are described. In addition, recent developments of dynamic real time in situ studies at the Angstrom level using a hot stage in an aberration corrected en...
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An improved high order finite difference method for non-conforming grid interfaces for the wave equation
This paper presents an extension of a recently developed high order finite difference method for the wave equation on a grid with non-conforming interfaces. The stability proof of the existing methods relies on the interpolation operators being norm-contracting, which is satisfied by the second and fourth order opera...
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Refined estimates for simple blow-ups of the scalar curvature equation on S^n
In their work on a sharp compactness theorem for the Yamabe problem, Khuri, Marques and Schoen apply a refined blow-up analysis (what we call `second order blow-up argument' in this article) to obtain highly accurate approximate solutions for the Yamabe equation. As for the conformal scalar curvature equation on S^n ...
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The discrete logarithm problem over prime fields: the safe prime case. The Smart attack, non-canonical lifts and logarithmic derivatives
In this brief note we connect the discrete logarithm problem over prime fields in the safe prime case to the logarithmic derivative.
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DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model
Convolutional neural networks (CNNs) can be applied to graph similarity matching, in which case they are called graph CNNs. Graph CNNs are attracting increasing attention due to their effectiveness and efficiency. However, the existing convolution approaches focus only on regular data forms and require the transfer o...
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Profile Estimation for Partial Functional Partially Linear Single-Index Model
This paper studies a \textit{partial functional partially linear single-index model} that consists of a functional linear component as well as a linear single-index component. This model generalizes many well-known existing models and is suitable for more complicated data structures. However, its estimation inherits ...
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Optimal $k$-Coverage Charging Problem
Wireless rechargeable sensor networks, consisting of sensor nodes with rechargeable batteries and mobile chargers to replenish their batteries, have gradually become a promising solution to the bottleneck of energy limitation that hinders the wide deployment of wireless sensor networks (WSN). In this paper, we focus ...
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Facebook's gender divide
Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media in particular are prone to g...
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Knowing the past improves cooperation in the future
Cooperation is the cornerstone of human evolutionary success. Like no other species, we champion the sacrifice of personal benefits for the common good, and we work together to achieve what we are unable to achieve alone. Knowledge and information from past generations is thereby often instrumental in ensuring we kee...
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BMO estimate of lacunary Fourier series on nonabelian discrete groups
We show that the classical equivalence between the BMO norm and the $L^2$ norm of a lacunary Fourier series has an analogue on any discrete group $G$ equipped with a conditionally negative function.
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Multi-objective training of Generative Adversarial Networks with multiple discriminators
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary. Such methods perform single-objective optimization on some simple consolidation of the losses,...
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Application of Surface Coil for Nuclear Magnetic Resonance Studies of Semi-conducting Thin Films
We conduct a comprehensive set of tests of performance of surface coils used for nuclear magnetic resonance (NMR) study of quasi 2-dimensional samples. We report ${^{115} \rm{In}}$ and ${^{31} \rm{P}}$ NMR measurements on InP, semi-conducting thin substrate samples. Surface coils of both zig-zag meander-line and conc...
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A universal coarse K-theory
In this paper, we construct an equivariant coarse homology theory with values in the category of non-commutative motives of Blumberg, Gepner and Tabuada, with coefficients in any small additive category. Equivariant coarse K-theory is obtained from the latter by passing to global sections. The present construction ex...
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Parameters of Three Selected Model Galactic Potentials Based on the Velocities of Objects at Distances up to 200 kpc
This paper is a continuation of our recent paper devoted to refining the parameters of three component (bulge, disk, halo) axisymmetric model Galactic gravitational potentials differing by the expression for the dark matter halo using the velocities of distant objects. In all models the bulge and disk potentials are ...
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Giant Thermal Conductivity Enhancement in Multilayer MoS2 under Highly Compressive Strain
Multilayer MoS2 possesses highly anisotropic thermal conductivities along in-plane and cross-plane directions that could hamper heat dissipation in electronics. With about 9% cross-plane compressive strain created by hydrostatic pressure in a diamond anvil cell, we observed about 12 times increase in the cross-plane ...
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Interrogation of spline surfaces with application to isogeometric design and analysis of lattice-skin structures
A novel surface interrogation technique is proposed to compute the intersection of curves with spline surfaces in isogeometric analysis. The intersection points are determined in one-shot without resorting to a Newton-Raphson iteration or successive refinement. Surface-curve intersection requires usually the solution...
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On recursive computation of coprime factorizations of rational matrices
We propose general computational procedures based on descriptor state-space realizations to compute coprime factorizations of rational matrices with minimum degree denominators. Enhanced recursive pole dislocation techniques are developed, which allow to successively place all poles of the factors into a given "good"...
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On Dark Matter Interactions with the Standard Model through an Anomalous $Z'$
We study electroweak scale Dark Matter (DM) whose interactions with baryonic matter are mediated by a heavy anomalous $Z'$. We emphasize that when the DM is a Majorana particle, its low-velocity annihilations are dominated by loop suppressed annihilations into the gauge bosons, rather than by p-wave or chirally suppr...
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Spin diffusion from an inhomogeneous quench in an integrable system
Generalised hydrodynamics predicts universal ballistic transport in integrable lattice systems when prepared in generic inhomogeneous initial states. However, the ballistic contribution to transport can vanish in systems with additional discrete symmetries. Here we perform large scale numerical simulations of spin dy...
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Morphological Simplification of Archaeological Fracture Surfaces
We propose to employ scale spaces of mathematical morphology to hierarchically simplify fracture surfaces of complementarily fitting archaeological fragments. This representation preserves contact and is insensitive to different kinds of abrasion affecting the exact complementarity of the original fragments. We prese...
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A note on a separating system of rational invariants for finite dimensional generic algebras
The paper deals with a construction of a separating system of rational invariants for finite dimensional generic algebras. In the process of dealing an approach to a rough classification of finite dimensional algebras is offered by attaching them some quadratic forms.
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A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convol...
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Protein Pattern Formation
Protein pattern formation is essential for the spatial organization of many intracellular processes like cell division, flagellum positioning, and chemotaxis. A prominent example of intracellular patterns are the oscillatory pole-to-pole oscillations of Min proteins in \textit{E. coli} whose biological function is to...
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What do we need to build explainable AI systems for the medical domain?
Artificial intelligence (AI) generally and machine learning (ML) specifically demonstrate impressive practical success in many different application domains, e.g. in autonomous driving, speech recognition, or recommender systems. Deep learning approaches, trained on extremely large data sets or using reinforcement le...
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Targeted matrix completion
Matrix completion is a problem that arises in many data-analysis settings where the input consists of a partially-observed matrix (e.g., recommender systems, traffic matrix analysis etc.). Classical approaches to matrix completion assume that the input partially-observed matrix is low rank. The success of these metho...
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Coresets for Dependency Networks
Many applications infer the structure of a probabilistic graphical model from data to elucidate the relationships between variables. But how can we train graphical models on a massive data set? In this paper, we show how to construct coresets -compressed data sets which can be used as proxy for the original data and ...
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Relative stability of a ferroelectric state in (Na0.5Bi0.5)TiO3-based compounds under substitutions: Role of a tolerance factor in expansion of the temperature interval of stable ferroelectric state
The influence of the B-site ion substitutions in (1-x)(Bi1/2Na1/2)TiO3-xBaTiO3 system of solid solutions on the relative stability of the ferroelectric and antiferroelectric phases has been studied. The ions of zirconium, tin, along with (In0.5Nb0.5), (Fe0.5Nb0.5), (Al0.5V0.5) ion complexes have been used as substitu...
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Data-driven modelling and validation of aircraft inbound-stream at some major European airports
This paper presents an exhaustive study on the arrivals process at eight important European airports. Using inbound traffic data, we define, compare, and contrast a data-driven Poisson and PSRA point process. Although, there is sufficient evidence that the interarrivals might follow an exponential distribution, this ...
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Exact evolution equation for the effective potential
We derive a new exact evolution equation for the scale dependence of an effective action. The corresponding equation for the effective potential permits a useful truncation. This allows one to deal with the infrared problems of theories with massless modes in less than four dimensions which are relevant for the high ...
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