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Robust 3D Distributed Formation Control with Application to Quadrotors
We present a distributed control strategy for a team of quadrotors to autonomously achieve a desired 3D formation. Our approach is based on local relative position measurements and does not require global position information or inter-vehicle communication. We assume that quadrotors have a common sense of direction, ...
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Discrete Extremes
Our contribution is to widen the scope of extreme value analysis applied to discrete-valued data. Extreme values of a random variable $X$ are commonly modeled using the generalized Pareto distribution, a method that often gives good results in practice. When $X$ is discrete, we propose two other methods using a discr...
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Rapid Adaptation with Conditionally Shifted Neurons
We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons. We apply this mechanism in the framework of metalearning, where the aim is to replicate some of the flexibility of huma...
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JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and ha...
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Inflationary Primordial Black Holes as All Dark Matter
Following a new microlensing constraint on primordial black holes (PBHs) with $\sim10^{20}$--$10^{28}\,\mathrm{g}$~[1], we revisit the idea of PBH as all Dark Matter (DM). We have shown that the updated observational constraints suggest the viable mass function for PBHs as all DM to have a peak at $\simeq 10^{20}\,\m...
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Condition number and matrices
It is well known the concept of the condition number $\kappa(A) = \|A\|\|A^{-1}\|$, where $A$ is a $n \times n$ real or complex matrix and the norm used is the spectral norm. Although it is very common to think in $\kappa(A)$ as "the" condition number of $A$, the truth is that condition numbers are associated to prob...
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Computational Tools in Weighted Persistent Homology
In this paper, we study further properties and applications of weighted homology and persistent homology. We introduce the Mayer-Vietoris sequence and generalized Bockstein spectral sequence for weighted homology. For applications, we show an algorithm to construct a filtration of weighted simplicial complexes from a...
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Dirac fermions in borophene
Honeycomb structures of group IV elements can host massless Dirac fermions with non-trivial Berry phases. Their potential for electronic applications has attracted great interest and spurred a broad search for new Dirac materials especially in monolayer structures. We present a detailed investigation of the \beta 12 ...
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Resolving the notorious case of conical intersections for coupled cluster dynamics
The motion of electrons and nuclei in photochemical events often involve conical intersections, degeneracies between electronic states. They serve as funnels for nuclear relaxation - on the femtosecond scale - in processes where the electrons and nuclei couple nonadiabatically. Accurate ab initio quantum chemical mod...
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Benchmarking gate-based quantum computers
With the advent of public access to small gate-based quantum processors, it becomes necessary to develop a benchmarking methodology such that independent researchers can validate the operation of these processors. We explore the usefulness of a number of simple quantum circuits as benchmarks for gate-based quantum co...
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Determinant structure for tau-function of holonomic deformation of linear differential equations
In our previous works, a relationship between Hermite's two approximation problems and Schlesinger transformations of linear differential equations has been clarified. In this paper, we study tau-functions associated with holonomic deformations of linear differential equations by using Hermite's two approximation pro...
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Transfer Operator Based Approach for Optimal Stabilization of Stochastic System
In this paper we develop linear transfer Perron Frobenius operator-based approach for optimal stabilization of stochastic nonlinear system. One of the main highlight of the proposed transfer operator based approach is that both the theory and computational framework developed for the optimal stabilization of determin...
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CryptoDL: Deep Neural Networks over Encrypted Data
Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions f...
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Data-driven modeling of collaboration networks: A cross-domain analysis
We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22.000 R&D alliances between 14.500 firms, and science, specifically 300.000 co-authorship relations between 95.000 scientists. Considering the different domains of the data sets, we address two questions: (a) to...
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Tunnelling in Dante's Inferno
We study quantum tunnelling in Dante's Inferno model of large field inflation. Such a tunnelling process, which will terminate inflation, becomes problematic if the tunnelling rate is rapid compared to the Hubble time scale at the time of inflation. Consequently, we constrain the parameter space of Dante's Inferno mo...
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Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
Highly automated robot ecologies (HARE), or societies of independent autonomous robots or agents, are rapidly becoming an important part of much of the world's critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in...
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Some theoretical results on tensor elliptical distribution
The multilinear normal distribution is a widely used tool in tensor analysis of magnetic resonance imaging (MRI). Diffusion tensor MRI provides a statistical estimate of a symmetric 2nd-order diffusion tensor, for each voxel within an imaging volume. In this article, tensor elliptical (TE) distribution is introduced ...
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Applying Text Mining to Protest Stories as Voice against Media Censorship
Data driven activism attempts to collect, analyze and visualize data to foster social change. However, during media censorship it is often impossible to collect such data. Here we demonstrate that data from personal stories can also help us to gain insights about protests and activism which can work as a voice for th...
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Cost-Effective Training of Deep CNNs with Active Model Adaptation
Deep convolutional neural networks have achieved great success in various applications. However, training an effective DNN model for a specific task is rather challenging because it requires a prior knowledge or experience to design the network architecture, repeated trial-and-error process to tune the parameters, an...
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Flipping growth orientation of nanographitic structures by plasma enhanced chemical vapor deposition
Nanographitic structures (NGSs) with multitude of morphological features are grown on SiO2/Si substrates by electron cyclotron resonance - plasma enhanced chemical vapor deposition (ECR-PECVD). CH4 is used as source gas with Ar and H2 as dilutants. Field emission scanning electron microscopy, high resolution transmis...
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Topological thermal Hall effect due to Weyl magnons
We present the first theoretical evidence of zero magnetic field topological (anomalous) thermal Hall effect due to Weyl magnons. Here, we consider Weyl magnons in stacked noncoplanar frustrated kagomé antiferromagnets recently proposed by Owerre, [arXiv:1708.04240]. The Weyl magnons in this system result from macros...
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Numerical prediction of the piezoelectric transducer response in the acoustic nearfield using a one-dimensional electromechanical finite difference approach
We present a simple electromechanical finite difference model to study the response of a piezoelectric polyvinylidenflourid (PVDF) transducer to optoacoustic (OA) pressure waves in the acoustic nearfield prior to thermal relaxation of the OA source volume. The assumption of nearfield conditions, i.e. the absence of a...
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A free energy landscape of the capture of CO2 by frustrated Lewis pairs
Frustrated Lewis pairs (FLPs) are known for its ability to capture CO2. Although many FLPs have been reported experimentally and several theoretical studies have been carried out to address the reaction mechanism, the individual roles of Lewis acids and bases of FLP in the capture of CO2 is still unclear. In this stu...
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Improved Point Source Detection in Crowded Fields using Probabilistic Cataloging
Cataloging is challenging in crowded fields because sources are extremely covariant with their neighbors and blending makes even the number of sources ambiguous. We present the first optical probabilistic catalog, cataloging a crowded (~0.1 sources per pixel brighter than 22nd magnitude in F606W) Sloan Digital Sky Su...
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Local Convergence of Proximal Splitting Methods for Rank Constrained Problems
We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint. For this, we show conditions under which the proximal operator of a function involving the rank constraint is locally identical to the proximal operator of its convex envelope, h...
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On the boundary between qualitative and quantitative measures of causal effects
Causal relationships among variables are commonly represented via directed acyclic graphs. There are many methods in the literature to quantify the strength of arrows in a causal acyclic graph. These methods, however, have undesirable properties when the causal system represented by a directed acyclic graph is degene...
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How Could Polyhedral Theory Harness Deep Learning?
The holy grail of deep learning is to come up with an automatic method to design optimal architectures for different applications. In other words, how can we effectively dimension and organize neurons along the network layers based on the computational resources, input size, and amount of training data? We outline pr...
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Optimal top dag compression
It is shown that for a given ordered node-labelled tree of size $n$ and with $s$ many different node labels, one can construct in linear time a top dag of height $O(\log n)$ and size $O(n / \log_\sigma n) \cap O(d \cdot \log n)$, where $\sigma = \max\{ 2, s\}$ and $d$ is the size of the minimal dag. The size bound $O...
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Synthesizing SystemC Code from Delay Hybrid CSP
Delay is omnipresent in modern control systems, which can prompt oscillations and may cause deterioration of control performance, invalidate both stability and safety properties. This implies that safety or stability certificates obtained on idealized, delay-free models of systems prone to delayed coupling may be err...
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Analysis of Annual Cyclone Frequencies over Bay of Bengal: Effect of 2004 Indian Ocean Tsunami
This paper discusses the time series trend and variability of the cyclone frequencies over Bay of Bengal, particularly in order to conclude if there is any significant difference in the pattern visible before and after the disastrous 2004 Indian ocean tsunami based on the observed annual cyclone frequency data obtain...
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Load Thresholds for Cuckoo Hashing with Overlapping Blocks
Dietzfelbinger and Weidling [DW07] proposed a natural variation of cuckoo hashing where each of $cn$ objects is assigned $k = 2$ intervals of size $\ell$ in a linear (or cyclic) hash table of size $n$ and both start points are chosen independently and uniformly at random. Each object must be placed into a table cell ...
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Evaluating the hot hand phenomenon using predictive memory selection for multistep Markov Chains: LeBron James' error correcting free throws
Consider the problem of modeling memory for discrete-state random walks using higher-order Markov chains. This Letter introduces a general Bayesian framework under the principle of minimizing prediction error to select, from data, the number of prior states of recent history upon which a trajectory is statistically d...
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Closed almost-Kähler 4-manifolds of constant non-negative Hermitian holomorphic sectional curvature are Kähler
We show that a closed almost Kähler 4-manifold of globally constant holomorphic sectional curvature $k\geq 0$ with respect to the canonical Hermitian connection is automatically Kähler. The same result holds for $k<0$ if we require in addition that the Ricci curvature is J-invariant. The proofs are based on the obser...
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Pretest and Stein-Type Estimations in Quantile Regression Model
In this study, we consider preliminary test and shrinkage estimation strategies for quantile regression models. In classical Least Squares Estimation (LSE) method, the relationship between the explanatory and explained variables in the coordinate plane is estimated with a mean regression line. In order to use LSE, th...
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Outcrop fracture characterization on suppositional planes cutting through digital outcrop models (DOMs)
Conventional fracture data collection methods are usually implemented on planar surfaces or assuming they are planar; these methods may introduce sampling errors on uneven outcrop surfaces. Consequently, data collected on limited types of outcrop surfaces (mainly bedding surfaces) may not be a sufficient representati...
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Exploiting routinely collected severe case data to monitor and predict influenza outbreaks
Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available an...
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Consistent Inter-Model Specification for Time-Homogeneous SPX Stochastic Volatility and VIX Market Models
This paper shows how to recover stochastic volatility models (SVMs) from market models for the VIX futures term structure. Market models have more flexibility for fitting of curves than do SVMs, and therefore they are better-suited for pricing VIX futures and derivatives. But the VIX itself is a derivative of the S&P...
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Optimal Task Scheduling in Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality
Mobile edge computing (MEC) is expected to be an effective solution to deliver 360-degree virtual reality (VR) videos over wireless networks. In contrast to previous computation-constrained MEC framework, which reduces the computation-resource consumption at the mobile VR device by increasing the communication-resour...
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Infinitary generalizations of Deligne's completeness theorem
Given a regular cardinal $\kappa$ such that $\kappa^{<\kappa}=\kappa$, we study a class of toposes with enough points, the $\kappa$-separable toposes. These are equivalent to sheaf toposes over a site with $\kappa$-small limits that has at most $\kappa$ many objects and morphisms, the (basis for the) topology being g...
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes...
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Saxion Cosmology for Thermalized Gravitino Dark Matter
In all supersymmetric theories, gravitinos, with mass suppressed by the Planck scale, are an obvious candidate for dark matter; but if gravitinos ever reached thermal equilibrium, such dark matter is apparently either too abundant or too hot, and is excluded. However, in theories with an axion, a saxion condensate is...
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One-step and Two-step Classification for Abusive Language Detection on Twitter
Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecti...
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Massive data compression for parameter-dependent covariance matrices
We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated datasets that are required to estimate the covariance matrix required for the analysis of gaussian-distributed data. This is relevant when the covariance matrix cannot be calculated directly...
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A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure
Electronic medical records (EMR) contain longitudinal information about patients that can be used to analyze outcomes. Typically, studies on EMR data have worked with established variables that have already been acknowledged to be associated with certain outcomes. However, EMR data may also contain hitherto unrecogni...
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Knotted solutions for linear and nonlinear theories: electromagnetism and fluid dynamics
We examine knotted solutions, the most simple of which is the "Hopfion", from the point of view of relations between electromagnetism and ideal fluid dynamics. A map between fluid dynamics and electromagnetism works for initial conditions or for linear perturbations, allowing us to find new knotted fluid solutions. K...
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Implicit Media Tagging and Affect Prediction from video of spontaneous facial expressions, recorded with depth camera
We present a method that automatically evaluates emotional response from spontaneous facial activity recorded by a depth camera. The automatic evaluation of emotional response, or affect, is a fascinating challenge with many applications, including human-computer interaction, media tagging and human affect prediction...
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Improving Legal Information Retrieval by Distributional Composition with Term Order Probabilities
Legal professionals worldwide are currently trying to get up-to-pace with the explosive growth in legal document availability through digital means. This drives a need for high efficiency Legal Information Retrieval (IR) and Question Answering (QA) methods. The IR task in particular has a set of unique challenges tha...
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Modeling Study of Laser Beam Scattering by Defects on Semiconductor Wafers
Accurate modeling of light scattering from nanometer scale defects on Silicon wafers is critical for enabling increasingly shrinking semiconductor technology nodes of the future. Yet, such modeling of defect scattering remains unsolved since existing modeling techniques fail to account for complex defect and wafer ge...
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Combinatorial and Asymptotical Results on the Neighborhood Grid
In 2009, Joselli et al introduced the Neighborhood Grid data structure for fast computation of neighborhood estimates in point clouds. Even though the data structure has been used in several applications and shown to be practically relevant, it is theoretically not yet well understood. The purpose of this paper is to...
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On the structure of join tensors with applications to tensor eigenvalue problems
We investigate the structure of join tensors, which may be regarded as the multivariable extension of lattice-theoretic join matrices. Explicit formulae for a polyadic decomposition (i.e., a linear combination of rank-1 tensors) and a tensor-train decomposition of join tensors are derived on general join semilattices...
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Adversarial Variational Optimization of Non-Differentiable Simulators
Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often difficult, as simulators rarely admit a tractable density or likelihood function. We introduce Adversarial Variational...
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Learning Light Transport the Reinforced Way
We show that the equations of reinforcement learning and light transport simulation are related integral equations. Based on this correspondence, a scheme to learn importance while sampling path space is derived. The new approach is demonstrated in a consistent light transport simulation algorithm that uses reinforce...
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Posterior Asymptotic Normality for an Individual Coordinate in High-dimensional Linear Regression
We consider the sparse high-dimensional linear regression model $Y=Xb+\epsilon$ where $b$ is a sparse vector. For the Bayesian approach to this problem, many authors have considered the behavior of the posterior distribution when, in truth, $Y=X\beta+\epsilon$ for some given $\beta$. There have been numerous results ...
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Subdeterminant Maximization via Nonconvex Relaxations and Anti-concentration
Several fundamental problems that arise in optimization and computer science can be cast as follows: Given vectors $v_1,\ldots,v_m \in \mathbb{R}^d$ and a constraint family ${\cal B}\subseteq 2^{[m]}$, find a set $S \in \cal{B}$ that maximizes the squared volume of the simplex spanned by the vectors in $S$. A motivat...
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On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments
This paper considers channel estimation and uplink achievable rate of the coarsely quantized massive multiple-input multiple-output (MIMO) system with radio frequency (RF) impairments. We utilize additive quantization noise model (AQNM) and extended error vector magnitude (EEVM) model to analyze the impacts of low-re...
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On The Complexity of Enumeration
We investigate the relationship between several enumeration complexity classes and focus in particular on problems having enumeration algorithms with incremental and polynomial delay (IncP and DelayP respectively). We show that, for some algorithms, we can turn an average delay into a worst case delay without increas...
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On $p$-degree of elliptic curves
In this note we investigate the $p$-degree function of elliptic curves over the field $\mathbb{Q}_p$ of $p$-adic numbers. The $p$-degree measures the least complexity of a non-zero $p$-torsion point on an elliptic curve. We prove some properties of this function and compute it explicitly in some special cases.
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Combinatorial formulas for Kazhdan-Lusztig polynomials with respect to W-graph ideals
In \cite{y1} Yin generalized the definition of $W$-graph ideal $E_J$ in weighted Coxeter groups and introduced the weighted Kazhdan-Lusztig polynomials $ \left \{ P_{x,y} \mid x,y\in E_J\right \}$, where $J$ is a subset of simple generators $S$. In this paper, we study the combinatorial formulas for those polynomials...
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Arithmetic statistics of modular symbols
Mazur, Rubin, and Stein have recently formulated a series of conjectures about statistical properties of modular symbols in order to understand central values of twists of elliptic curve $L$-functions. Two of these conjectures relate to the asymptotic growth of the first and second moments of the modular symbols. We ...
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Approximation Techniques for Stochastic Analysis of Biological Systems
There has been an increasing demand for formal methods in the design process of safety-critical synthetic genetic circuits. Probabilistic model checking techniques have demonstrated significant potential in analyzing the intrinsic probabilistic behaviors of complex genetic circuit designs. However, its inability to s...
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Characterizing time-irreversibility in disordered fermionic systems by the effect of local perturbations
We study the effects of local perturbations on the dynamics of disordered fermionic systems in order to characterize time-irreversibility. We focus on three different systems, the non-interacting Anderson and Aubry-André-Harper (AAH-) models, and the interacting spinless disordered t-V chain. First, we consider the e...
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A Lower Bound for the Number of Central Configurations on H^2
We study the indices of the geodesic central configurations on $\H^2$. We then show that central configurations are bounded away from the singularity set. With Morse's inequality, we get a lower bound for the number of central configurations on $\H^2$.
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Scattering dominated high-temperature phase of 1T-TiSe2: an optical conductivity study
The controversy regarding the precise nature of the high-temperature phase of 1T-TiSe2 lasts for decades. It has intensified in recent times when new evidence for the excitonic origin of the low-temperature charge-density wave state started to unveil. Here we address the problem of the high-temperature phase through ...
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Motional Ground State Cooling Outside the Lamb-Dicke Regime
We report Raman sideband cooling of a single sodium atom to its three-dimensional motional ground state in an optical tweezer. Despite a large Lamb-Dicke parameter, high initial temperature, and large differential light shifts between the excited state and the ground state, we achieve a ground state population of $93...
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Quantifying the uncertainties in an ensemble of decadal climate predictions
Meaningful climate predictions must be accompanied by their corresponding range of uncertainty. Quantifying the uncertainties is non-trivial, and different methods have been suggested and used in the past. Here, we propose a method that does not rely on any assumptions regarding the distribution of the ensemble membe...
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A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks
Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications lik...
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Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities ...
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Universal locally univalent functions and universal conformal metrics with constant curvature
We prove Runge-type theorems and universality results for locally univalent holomorphic and meromorphic functions. Refining a result of M. Heins, we also show that there is a universal bounded locally univalent function on the unit disk. These results are used to prove that on any hyperbolic simply connected plane do...
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Towards a New Interpretation of Separable Convolutions
In recent times, the use of separable convolutions in deep convolutional neural network architectures has been explored. Several researchers, most notably (Chollet, 2016) and (Ghosh, 2017) have used separable convolutions in their deep architectures and have demonstrated state of the art or close to state of the art ...
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Numerical Simulations of Regolith Sampling Processes
We present recent improvements in the simulation of regolith sampling processes in microgravity using the numerical particle method smooth particle hydrodynamics (SPH). We use an elastic-plastic soil constitutive model for large deformation and failure flows for dynamical behaviour of regolith. In the context of proj...
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Lexical analysis of automated accounts on Twitter
In recent years, social bots have been using increasingly more sophisticated, challenging detection strategies. While many approaches and features have been proposed, social bots evade detection and interact much like humans making it difficult to distinguish real human accounts from bot accounts. For detection syste...
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The effect of the virial state of molecular clouds on the influence of feedback from massive stars
A set of Smoothed Particle Hydrodynamics simulations of the influence of photoionising radiation and stellar winds on a series of 10$^{4}$M$_{\odot}$ turbulent molecular clouds with initial virial ratios of 0.7, 1.1, 1.5, 1.9 and 2.3 and initial mean densities of 136, 1135 and 9096\,cm$^{-3}$ are presented. Reduction...
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A contract-based method to specify stimulus-response requirements
A number of formal methods exist for capturing stimulus-response requirements in a declarative form. Someone yet needs to translate the resulting declarative statements into imperative programs. The present article describes a method for specification and verification of stimulus-response requirements in the form of ...
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Estimation of the covariance structure of heavy-tailed distributions
We propose and analyze a new estimator of the covariance matrix that admits strong theoretical guarantees under weak assumptions on the underlying distribution, such as existence of moments of only low order. While estimation of covariance matrices corresponding to sub-Gaussian distributions is well-understood, much ...
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Deep Reinforcement Learning for De-Novo Drug Design
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately...
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Batch Data Processing and Gaussian Two-Armed Bandit
We consider the two-armed bandit problem as applied to data processing if there are two alternative processing methods available with different a priori unknown efficiencies. One should determine the most effective method and provide its predominant application. Gaussian two-armed bandit describes the batch, and poss...
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Estimating Under Five Mortality in Space and Time in a Developing World Context
Accurate estimates of the under-5 mortality rate (U5MR) in a developing world context are a key barometer of the health of a nation. This paper describes new models to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is, wishing to estimate U5MR across...
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Cyclotomic Construction of Strong External Difference Families in Finite Fields
Strong external difference family (SEDF) and its generalizations GSEDF, BGSEDF in a finite abelian group $G$ are combinatorial designs raised by Paterson and Stinson [7] in 2016 and have applications in communication theory to construct optimal strong algebraic manipulation detection codes. In this paper we firstly p...
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Acoustic double negativity induced by position correlations within a disordered set of monopolar resonators
Using a Multiple Scattering Theory algorithm, we investigate numerically the transmission of ultrasonic waves through a disordered locally resonant metamaterial containing only monopolar resonators. By comparing the cases of a perfectly random medium with its pair correlated counterpart, we show that the introduction...
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Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to sl...
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Strichartz estimates for non-degenerate Schrödinger equations
We consider Schrödinger equation with a non-degenerate metric on the Euclidean space. We study local in time Strichartz estimates for the Schrödinger equation without loss of derivatives including the endpoint case. In contrast to the Riemannian metric case, we need the additional assumptions for the well-posedness o...
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On the Diophantine equation $\sum_{j=1}^kjF_j^p=F_n^q$
Let $F_n$ denote the $n^{th}$ term of the Fibonacci sequence. In this paper, we investigate the Diophantine equation $F_1^p+2F_2^p+\cdots+kF_{k}^p=F_{n}^q$ in the positive integers $k$ and $n$, where $p$ and $q$ are given positive integers. A complete solution is given if the exponents are included in the set $\{1,2\...
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Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks
Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty exp...
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The diffusion equation with nonlocal data
We study the diffusion (or heat) equation on a finite 1-dimensional spatial domain, but we replace one of the boundary conditions with a "nonlocal condition", through which we specify a weighted average of the solution over the spatial interval. We provide conditions on the regularity of both the data and weight for ...
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Exploring Neural Transducers for End-to-End Speech Recognition
In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 b...
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Information Theory of Data Privacy
By combining Shannon's cryptography model with an assumption to the lower bound of adversaries' uncertainty to the queried dataset, we develop a secure Bayesian inference-based privacy model and then in some extent answer Dwork et al.'s question [1]: "why Bayesian risk factors are the right measure for privacy loss"....
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A combined entropy and utility based generative model for large scale multiple discrete-continuous travel behaviour data
Generative models, either by simple clustering algorithms or deep neural network architecture, have been developed as a probabilistic estimation method for dimension reduction or to model the underlying properties of data structures. Although their apparent use has largely been limited to image recognition and classi...
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Phase Transitions of Spectral Initialization for High-Dimensional Nonconvex Estimation
We study a spectral initialization method that serves a key role in recent work on estimating signals in nonconvex settings. Previous analysis of this method focuses on the phase retrieval problem and provides only performance bounds. In this paper, we consider arbitrary generalized linear sensing models and present ...
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Understanding GANs: the LQG Setting
Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. In this paper, we aim to provide an understanding of some of the basic issues surrounding GANs including their formulation, generalization and stability on a simple benchmark where the data has a high-dimension...
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A Rosenau-type approach to the approximation of the linear Fokker--Planck equation
{The numerical approximation of the solution of the Fokker--Planck equation is a challenging problem that has been extensively investigated starting from the pioneering paper of Chang and Cooper in 1970. We revisit this problem at the light of the approximation of the solution to the heat equation proposed by Rosenau...
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Analysis of Footnote Chasing and Citation Searching in an Academic Search Engine
In interactive information retrieval, researchers consider the user behavior towards systems and search tasks in order to adapt search results by analyzing their past interactions. In this paper, we analyze the user behavior towards Marcia Bates' search stratagems such as 'footnote chasing' and 'citation search' in a...
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Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to ...
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You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data. The main focus is on two substantial issues of the video surveillance scenario: (1) the walkers do not cooperate in providing learning data to establish their identities and (2) the data are ofte...
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Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, b...
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Characterization of catastrophic instabilities: Market crashes as paradigm
Catastrophic events, though rare, do occur and when they occur, they have devastating effects. It is, therefore, of utmost importance to understand the complexity of the underlying dynamics and signatures of catastrophic events, such as market crashes. For deeper understanding, we choose the US and Japanese markets f...
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Discovery of Intrinsic Quantum Anomalous Hall Effect in Organic Mn-DCA Lattice
The quantum anomalous Hall (QAH) phase is a novel topological state of matter characterized by a nonzero quantized Hall conductivity without an external magnetic field. The realizations of QAH effect, however, are experimentally challengeable. Based on ab initio calculations, here we propose an intrinsic QAH phase in...
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Temporal Convolution Networks for Real-Time Abdominal Fetal Aorta Analysis with Ultrasound
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consistin...
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The Role of Data Analysis in the Development of Intelligent Energy Networks
Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data ana...
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Quantification of the memory effect of steady-state currents from interaction-induced transport in quantum systems
Dynamics of a system in general depends on its initial state and how the system is driven, but in many-body systems the memory is usually averaged out during evolution. Here, interacting quantum systems without external relaxations are shown to retain long-time memory effects in steady states. To identify memory effe...
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Geometrical optimization approach to isomerization: Models and limitations
We study laser-driven isomerization reactions through an excited electronic state using the recently developed Geometrical Optimization procedure [J. Phys. Chem. Lett. 6, 1724 (2015)]. The goal is to analyze whether an initial wave packet in the ground state, with optimized amplitudes and phases, can be used to enhan...
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