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A visual search engine for Bangladeshi laws
Browsing and finding relevant information for Bangladeshi laws is a challenge faced by all law students and researchers in Bangladesh, and by citizens who want to learn about any legal procedure. Some law archives in Bangladesh are digitized, but lack proper tools to organize the data meaningfully. We present a text ...
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Minors of two-connected graphs of large path-width
Let $P$ be a graph with a vertex $v$ such that $P\backslash v$ is a forest, and let $Q$ be an outerplanar graph. We prove that there exists a number $p=p(P,Q)$ such that every 2-connected graph of path-width at least $p$ has a minor isomorphic to $P$ or $Q$. This result answers a question of Seymour and implies a con...
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Computable Operations on Compact Subsets of Metric Spaces with Applications to Fréchet Distance and Shape Optimization
We extend the Theory of Computation on real numbers, continuous real functions, and bounded closed Euclidean subsets, to compact metric spaces $(X,d)$: thereby generically including computational and optimization problems over higher types, such as the compact 'hyper' spaces of (i) nonempty closed subsets of $X$ w.r....
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Siamese Capsule Networks
Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class t...
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Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition
An adversarial attack is an exploitative process in which minute alterations are made to natural inputs, causing the inputs to be misclassified by neural models. In the field of speech recognition, this has become an issue of increasing significance. Although adversarial attacks were originally introduced in computer...
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Revisiting the cavity-method threshold for random 3-SAT
A detailed Monte Carlo-study of the satisfiability threshold for random 3-SAT has been undertaken. In combination with a monotonicity assumption we find that the threshold for random 3-SAT satisfies $\alpha_3 \leq 4.262$. If the assumption is correct, this means that the actual threshold value for $k=3$ is lower than...
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An improved Krylov eigenvalue strategy using the FEAST algorithm with inexact system solves
The FEAST eigenvalue algorithm is a subspace iteration algorithm that uses contour integration in the complex plane to obtain the eigenvectors of a matrix for the eigenvalues that are located in any user-defined search interval. By computing small numbers of eigenvalues in specific regions of the complex plane, FEAST...
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Planar magnetic structures in coronal mass ejection-driven sheath regions
Planar magnetic structures (PMSs) are periods in the solar wind during which interplanetary magnetic field vectors are nearly parallel to a single plane. One of the specific regions where PMSs have been reported are coronal mass ejection (CME)-driven sheaths. We use here an automated method to identify PMSs in 95 CME...
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Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks
The feature map obtained from the denoising autoencoder (DAE) is investigated by determining transportation dynamics of the DAE, which is a cornerstone for deep learning. Despite the rapid development in its application, deep neural networks remain analytically unexplained, because the feature maps are nested and par...
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Weighted Contrastive Divergence
Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of terms involved in computing the partition function. In this way one has to resort to approximation schemes for the evaluation of the gradien...
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Small-Scale Challenges to the $Λ$CDM Paradigm
The dark energy plus cold dark matter ($\Lambda$CDM) cosmological model has been a demonstrably successful framework for predicting and explaining the large-scale structure of Universe and its evolution with time. Yet on length scales smaller than $\sim 1$ Mpc and mass scales smaller than $\sim 10^{11} M_{\odot}$, th...
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Alternating minimization, scaling algorithms, and the null-cone problem from invariant theory
Alternating minimization heuristics seek to solve a (difficult) global optimization task through iteratively solving a sequence of (much easier) local optimization tasks on different parts (or blocks) of the input parameters. While popular and widely applicable, very few examples of this heuristic are rigorously show...
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Positivity of denominator vectors of cluster algebras
In this paper, we prove that positivity of denominator vectors holds for any skew-symmetric cluster algebra.
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Primordial Black Holes and Slow-Roll Violation
For primordial black holes (PBH) to be the dark matter in single-field inflation, the slow-roll approximation must be violated by at least ${\cal O}(1)$ in order to enhance the curvature power spectrum within the required number of efolds between CMB scales and PBH mass scales. Power spectrum predictions which rely o...
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Using Optimal Ratio Mask as Training Target for Supervised Speech Separation
Supervised speech separation uses supervised learning algorithms to learn a mapping from an input noisy signal to an output target. With the fast development of deep learning, supervised separation has become the most important direction in speech separation area in recent years. For the supervised algorithm, trainin...
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Volumetric parametrization from a level set boundary representation with PHT Splines
A challenge in isogeometric analysis is constructing analysis-suitable volumetric meshes which can accurately represent the geometry of a given physical domain. In this paper, we propose a method to derive a spline-based representation of a domain of interest from voxel-based data. We show an efficient way to obtain ...
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On the Compressive Power of Deep Rectifier Networks for High Resolution Representation of Class Boundaries
This paper provides a theoretical justification of the superior classification performance of deep rectifier networks over shallow rectifier networks from the geometrical perspective of piecewise linear (PWL) classifier boundaries. We show that, for a given threshold on the approximation error, the required number of...
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Absolute spectroscopy near 7.8 μm with a comb-locked extended-cavity quantum-cascade-laser
We report the first experimental demonstration of frequency-locking of an extended-cavity quantum-cascade-laser (EC-QCL) to a near-infrared frequency comb. The locking scheme is applied to carry out absolute spectroscopy of N2O lines near 7.87 {\mu}m with an accuracy of ~60 kHz. Thanks to a single mode operation over...
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Some remarks on upper bounds for Weierstrass primary factors and their application in spectral theory
We study upper bounds on Weierstrass primary factors and discuss their application in spectral theory. One of the main aims of this note is to draw attention to works of Blumenthal and Denjoy from 1910, but we also provide some new results and some numerical computations of our own.
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Topology of polyhedral products over simplicial multiwedges
We prove that certain conditions on multigraded Betti numbers of a simplicial complex $K$ imply existence of a higher Massey product in cohomology of a moment-angle-complex $\mathcal Z_K$, which contains a unique element (a strictly defined product). Using the simplicial multiwedge construction, we find a family $\ma...
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Improved Representation Learning for Predicting Commonsense Ontologies
Recent work in learning ontologies (hierarchical and partially-ordered structures) has leveraged the intrinsic geometry of spaces of learned representations to make predictions that automatically obey complex structural constraints. We explore two extensions of one such model, the order-embedding model for hierarchic...
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Mesoporous Silica as a Carrier for Amorphous Solid Dispersion
In the past decade, the discovery of active pharmaceutical substances with high therapeutic value but poor aqueous solubility has increased, thus making it challenging to formulate these compounds as oral dosage forms. The bioavailability of these drugs can be increased by formulating these drugs as an amorphous drug...
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A Probability Monad as the Colimit of Finite Powers
We define and study a probability monad on the category of complete metric spaces and short maps. It assigns to each space the space of Radon probability measures on it with finite first moment, equipped with the Kantorovich-Wasserstein distance. This monad is analogous to the Giry monad on the category of Polish spa...
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Robust Optimal Design of Energy Efficient Series Elastic Actuators: Application to a Powered Prosthetic Ankle
Design of robotic systems that safely and efficiently operate in uncertain operational conditions, such as rehabilitation and physical assistance robots, remains an important challenge in the field. Current methods for the design of energy efficient series elastic actuators use an optimization formulation that typica...
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Subwavelength phononic bandgap opening in bubbly media
The aim of this paper is to show both analytically and numerically the existence of a subwavelength phononic bandgap in bubble phononic crystals. The key is an original formula for the quasi-periodic Minnaert resonance frequencies of an arbitrarily shaped bubble. The main findings in this paper are illustrated with a...
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Hybrid integration of solid-state quantum emitters on a silicon photonic chip
Scalable quantum photonic systems require efficient single photon sources coupled to integrated photonic devices. Solid-state quantum emitters can generate single photons with high efficiency, while silicon photonic circuits can manipulate them in an integrated device structure. Combining these two material platforms...
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Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
Tasks such as search and recommendation have become increas- ingly important for E-commerce to deal with the information over- load problem. To meet the diverse needs of di erent users, person- alization plays an important role. In many large portals such as Taobao and Amazon, there are a bunch of di erent types of s...
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Phase-diagram and dynamics of Rydberg-dressed fermions in two-dimensions
We investigate the ground-state properties and the collective modes of a two-dimensional two-component Rydberg-dressed Fermi liquid in the dipole-blockade regime. We find instability of the homogeneous system toward phase separated and density ordered phases, using the Hartree-Fock and random-phase approximations, re...
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Ambient noise correlation-based imaging with moving sensors
Waves can be used to probe and image an unknown medium. Passive imaging uses ambient noise sources to illuminate the medium. This paper considers passive imaging with moving sensors. The motivation is to generate large synthetic apertures, which should result in enhanced resolution. However Doppler effects and lack o...
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Anderson localization in the Non-Hermitian Aubry-André-Harper model with physical gain and loss
We investigate the Anderson localization in non-Hermitian Aubry-André-Harper (AAH) models with imaginary potentials added to lattice sites to represent the physical gain and loss during the interacting processes between the system and environment. By checking the mean inverse participation ratio (MIPR) of the system,...
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Towards de Sitter from 10D
Using a 10D lift of non-perturbative volume stabilization in type IIB string theory we study the limitations for obtaining de Sitter vacua. Based on this we find that the simplest KKLT vacua with a single Kahler modulus stabilized by a gaugino condensate cannot be uplifted to de Sitter. Rather, the uplift flattens ou...
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Incorporating genuine prior information about between-study heterogeneity in random effects pairwise and network meta-analyses
Background: Pairwise and network meta-analyses using fixed effect and random effects models are commonly applied to synthesise evidence from randomised controlled trials. The models differ in their assumptions and the interpretation of the results. The model choice depends on the objective of the analysis and knowled...
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Wasserstein Distributional Robustness and Regularization in Statistical Learning
A central question in statistical learning is to design algorithms that not only perform well on training data, but also generalize to new and unseen data. In this paper, we tackle this question by formulating a distributionally robust stochastic optimization (DRSO) problem, which seeks a solution that minimizes the ...
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InfiniteBoost: building infinite ensembles with gradient descent
In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas. Two notable ensemble methods widely used in practice are gradient boosting and random forests. In this paper we present InfiniteBoost - a novel algorithm, which combines important properties of these t...
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On-demand microwave generator of shaped single photons
We demonstrate the full functionality of a circuit that generates single microwave photons on demand, with a wave packet that can be modulated with a near-arbitrary shape. We achieve such a high tunability by coupling a superconducting qubit near the end of a semi-infinite transmission line. A dc superconducting quan...
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Fast and Stable Pascal Matrix Algorithms
In this paper, we derive a family of fast and stable algorithms for multiplying and inverting $n \times n$ Pascal matrices that run in $O(n log^2 n)$ time and are closely related to De Casteljau's algorithm for Bézier curve evaluation. These algorithms use a recursive factorization of the triangular Pascal matrices a...
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Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in com...
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Dynamic adaptive procedures that control the false discovery rate
In the multiple testing problem with independent tests, the classical linear step-up procedure controls the false discovery rate (FDR) at level $\pi_0\alpha$, where $\pi_0$ is the proportion of true null hypotheses and $\alpha$ is the target FDR level. Adaptive procedures can improve power by incorporating estimates ...
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Compression-Based Regularization with an Application to Multi-Task Learning
This paper investigates, from information theoretic grounds, a learning problem based on the principle that any regularity in a given dataset can be exploited to extract compact features from data, i.e., using fewer bits than needed to fully describe the data itself, in order to build meaningful representations of a ...
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Isomorphism between Differential and Moment Invariants under Affine Transform
The invariant is one of central topics in science, technology and engineering. The differential invariant is essential in understanding or describing some important phenomena or procedures in mathematics, physics, chemistry, biology or computer science etc. The derivation of differential invariants is usually difficu...
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Throughput Analysis for Wavelet OFDM in Broadband Power Line Communications
Windowed orthogonal frequency-division multiplexing (OFDM) and wavelet OFDM have been proposed as medium access techniques for broadband communications over the power line network by the standard IEEE 1901. Windowed OFDM has been extensively researched and employed in different fields of communication, while wavelet ...
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Simple And Efficient Architecture Search for Convolutional Neural Networks
Neural networks have recently had a lot of success for many tasks. However, neural network architectures that perform well are still typically designed manually by experts in a cumbersome trial-and-error process. We propose a new method to automatically search for well-performing CNN architectures based on a simple h...
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Quantum estimation of detection efficiency with no-knowledge quantum feedback
We investigate that no-knowledge measurement-based feedback control is utilized to obtain the estimation precision of the detection efficiency. For the feedback operators that concern us, no-knowledge measurement is the optimal way to estimate the detection efficiency. We show that the higher precision can be achieve...
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Intermittent Granular Dynamics at a Seismogenic Plate Boundary
Earthquakes at seismogenic plate boundaries are a response to the differential motions of tectonic blocks embedded within a geometrically complex network of branching and coalescing faults. Elastic strain is accumulated at a slow strain rate of the order of $10^{-15}$ s$^{-1}$, and released intermittently at interval...
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Optimal continuous-time ALM for insurers: a martingale approach
We study a continuous-time asset-allocation problem for a firm in the insurance industry that backs up the liabilities raised by the insurance contracts with the underwriting profits and the income resulting from investing in the financial market. Using the martingale approach and convex duality techniques we charact...
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Combating Fake News: A Survey on Identification and Mitigation Techniques
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploitin...
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A causal approach to analysis of censored medical costs in the presence of time-varying treatment
There has recently been a growing interest in the development of statistical methods to compare medical costs between treatment groups. When cumulative cost is the outcome of interest, right-censoring poses the challenge of informative missingness due to heterogeneity in the rates of cost accumulation across subjects...
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Inferring the parameters of a Markov process from snapshots of the steady state
We seek to infer the parameters of an ergodic Markov process from samples taken independently from the steady state. Our focus is on non-equilibrium processes, where the steady state is not described by the Boltzmann measure, but is generally unknown and hard to compute, which prevents the application of established ...
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What Does a TextCNN Learn?
TextCNN, the convolutional neural network for text, is a useful deep learning algorithm for sentence classification tasks such as sentiment analysis and question classification. However, neural networks have long been known as black boxes because interpreting them is a challenging task. Researchers have developed sev...
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The Wisdom of Polarized Crowds
As political polarization in the United States continues to rise, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diversity of individual perspectives typically leads to superior team performance on complex tasks, strong political perspectives have been associated wit...
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Probabilistic Database Summarization for Interactive Data Exploration
We present a probabilistic approach to generate a small, query-able summary of a dataset for interactive data exploration. Departing from traditional summarization techniques, we use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate query answ...
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GP-GAN: Towards Realistic High-Resolution Image Blending
Recent advances in generative adversarial networks (GANs) have shown promising potentials in conditional image generation. However, how to generate high-resolution images remains an open problem. In this paper, we aim at generating high-resolution well-blended images given composited copy-and-paste ones, i.e. realist...
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Relational Autoencoder for Feature Extraction
Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experiment...
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Sentiment and Sarcasm Classification with Multitask Learning
Sentiment classification and sarcasm detection are both important NLP tasks. We show that these two tasks are correlated, and present a multi-task learning-based framework using deep neural network that models this correlation to improve the performance of both tasks in a multi-task learning setting.
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Context in Neural Machine Translation: A Review of Models and Evaluations
This review paper discusses how context has been used in neural machine translation (NMT) in the past two years (2017-2018). Starting with a brief retrospect on the rapid evolution of NMT models, the paper then reviews studies that evaluate NMT output from various perspectives, with emphasis on those analyzing limita...
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Manipulation of type-I and type-II Dirac points in PdTe2 superconductor by external pressure
A pair of type-II Dirac cones in PdTe$_2$ was recently predicted by theories and confirmed in experiments, making PdTe$_2$ the first material that processes both superconductivity and type-II Dirac fermions. In this work, we study the evolution of Dirac cones in PdTe$_2$ under hydrostatic pressure by the first-princi...
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A fast Metropolis-Hastings method for generating random correlation matrices
We propose a novel Metropolis-Hastings algorithm to sample uniformly from the space of correlation matrices. Existing methods in the literature are based on elaborated representations of a correlation matrix, or on complex parametrizations of it. By contrast, our method is intuitive and simple, based the classical Ch...
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Compact Groups analysis using weak gravitational lensing
We present a weak lensing analysis of a sample of SDSS Compact Groups (CGs). Using the measured radial density contrast profile, we derive the average masses under the assumption of spherical symmetry, obtaining a velocity dispersion for the Singular Isothermal Spherical model, $\sigma_V = 270 \pm 40 \rm ~km~s^{-1}$,...
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An Applied Knowledge Framework to Study Complex Systems
The complexity of knowledge production on complex systems is well-known, but there still lacks knowledge framework that would both account for a certain structure of knowledge production at an epistemological level and be directly applicable to the study and management of complex systems. We set a basis for such a fr...
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Classifying subcategories in quotients of exact categories
We classify certain subcategories in quotients of exact categories. In particular, we classify the triangulated and thick subcategories of an algebraic triangulated category, i.e. the stable category of a Frobenius category.
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Dealing with Rational Second Order Ordinary Differential Equations where both Darboux and Lie Find It Difficult: The $S$-function Method
Here we present a new approach to search for first order invariants (first integrals) of rational second order ordinary differential equations. This method is an alternative to the Darbouxian and symmetry approaches. Our procedure can succeed in many cases where these two approaches fail. We also present here a Maple...
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Urban Vibrancy and Safety in Philadelphia
Statistical analyses of urban environments have been recently improved through publicly available high resolution data and mapping technologies that have been adopted across industries. These technologies allow us to create metrics to empirically investigate urban design principles of the past half-century. Philadelp...
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Exponential quadrature rules without order reduction
In this paper a technique is suggested to integrate linear initial boundary value problems with exponential quadrature rules in such a way that the order in time is as high as possible. A thorough error analysis is given for both the classical approach of integrating the problem firstly in space and then in time and ...
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New Derivatives for the Functions with the Fractal Tartan Support
In this manuscript, we generalize F-calculus to apply it on fractal Tartan spaces. The generalized standard F-calculus is used to obtain the integral and derivative of the functions on the fractal Tartan with different dimensions. The generalized fractional derivatives have local properties that make it more useful i...
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Learning Navigation Behaviors End to End
A longstanding goal of behavior-based robotics is to solve high-level navigation tasks using end to end navigation behaviors that directly map sensors to actions. Navigation behaviors, such as reaching a goal or following a path without collisions, can be learned from exploration and interaction with the environment,...
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Bounded Information Rate Variational Autoencoders
This paper introduces a new member of the family of Variational Autoencoders (VAE) that constrains the rate of information transferred by the latent layer. The latent layer is interpreted as a communication channel, the information rate of which is bound by imposing a pre-set signal-to-noise ratio. The new constraint...
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Wasan geometry with the division by 0
Results in Wasan geometry of tangents circles can still be considered in a singular case by the division by 0.
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New insights into non-central beta distributions
The beta family owes its privileged status within unit interval distributions to several relevant features such as, for example, easyness of interpretation and versatility in modeling different types of data. However, its flexibility at the unit interval endpoints is poor enough to prevent from properly modeling the ...
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Mean Curvature Flows of Closed Hypersurfaces in Warped Product Manifolds
We investigate the mean curvature flows in a class of warped product manifolds with closed hypersurfaces fibering over $\mathbb{R}$. In particular, we prove that under natural conditions on the warping function and Ricci curvature bound for the ambient space, there exists a large class of closed initial hypersurfaces...
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Optimising finite-difference methods for PDEs through parameterised time-tiling in Devito
Finite-difference methods are widely used in solving partial differential equations. In a large problem set, approximations can take days or weeks to evaluate, yet the bulk of computation may occur within a single loop nest. The modelling process for researchers is not straightforward either, requiring models with di...
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An Ontology to support automated negotiation
In this work we propose an ontology to support automated negotiation in multiagent systems. The ontology can be connected with some domain-specific ontologies to facilitate the negotiation in different domains, such as Intelligent Transportation Systems (ITS), e-commerce, etc. The specific negotiation rules for each ...
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Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness...
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On the Upward/Downward Closures of Petri Nets
We study the size and the complexity of computing finite state automata (FSA) representing and approximating the downward and the upward closure of Petri net languages with coverability as the acceptance condition. We show how to construct an FSA recognizing the upward closure of a Petri net language in doubly-expone...
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Semantic Similarity from Natural Language and Ontology Analysis
Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -- most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likenes...
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The Hardness of Synthesizing Elementary Net Systems from Highly Restricted Inputs
Elementary net systems (ENS) are the most fundamental class of Petri nets. Their synthesis problem has important applications in the design of digital hardware and commercial processes. Given a labeled transition system (TS) $A$, feasibility is the NP-complete decision problem whether $A$ can be equivalently synthesi...
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Exceptional Lattice Green's Functions
The three exceptional lattices, $E_6$, $E_7$, and $E_8$, have attracted much attention due to their anomalously dense and symmetric structures which are of critical importance in modern theoretical physics. Here, we study the electronic band structure of a single spinless quantum particle hopping between their neares...
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Optimal design with EGM approach in conjugate natural convection with surface radiation in a two-dimensional enclosure
Analysis of conjugate natural convection with surface radiation in a two-dimensional enclosure is carried out in order to search the optimal location of the heat source with entropy generation minimization (EGM) approach and conventional heat transfer parameters. The air as an incompressible fluid and transparent med...
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Convex cocompactness in pseudo-Riemannian hyperbolic spaces
Anosov representations of word hyperbolic groups into higher-rank semisimple Lie groups are representations with finite kernel and discrete image that have strong analogies with convex cocompact representations into rank-one Lie groups. However, the most naive analogy fails: generically, Anosov representations do not...
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A Closer Look at Memorization in Deep Networks
We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differ...
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Hierarchical 3D fully convolutional networks for multi-organ segmentation
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and p...
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An evolutionary game model for behavioral gambit of loyalists: Global awareness and risk-aversion
We study the phase diagram of a minority game where three classes of agents are present. Two types of agents play a risk-loving game that we model by the standard Snowdrift Game. The behaviour of the third type of agents is coded by {\em indifference} w.r.t. the game at all: their dynamics is designed to account for ...
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The Minimum Euclidean-Norm Point on a Convex Polytope: Wolfe's Combinatorial Algorithm is Exponential
The complexity of Philip Wolfe's method for the minimum Euclidean-norm point problem over a convex polytope has remained unknown since he proposed the method in 1974. The method is important because it is used as a subroutine for one of the most practical algorithms for submodular function minimization. We present th...
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Constraints on a possible evolution of mass density power-law index in strong gravitational lensing from cosmological data
In this work, by using strong gravitational lensing (SGL) observations along with Type Ia Supernovae (Union2.1) and gamma ray burst data (GRBs), we propose a new method to study a possible redshift evolution of $\gamma(z)$, the mass density power-law index of strong gravitational lensing systems. In this analysis, we...
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The Strong Cell-based Hydrogen Peroxide Generation Triggered by Cold Atmospheric Plasma
Hydrogen peroxide (H2O2) is an important signaling molecule in cancer cells. However, the significant secretion of H2O2 by cancer cells have been rarely observed. Cold atmospheric plasma (CAP) is a near room temperature ionized gas composed of neutral particles, charged particles, reactive species, and electrons. Her...
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Theory of circadian metabolism
Many organisms repartition their proteome in a circadian fashion in response to the daily nutrient changes in their environment. A striking example is provided by cyanobacteria, which perform photosynthesis during the day to fix carbon. These organisms not only face the challenge of rewiring their proteome every 12 h...
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Rare-earth/transition-metal magnetic interactions in pristine and (Ni,Fe)-doped YCo5 and GdCo5
We present an investigation into the intrinsic magnetic properties of the compounds YCo5 and GdCo5, members of the RETM5 class of permanent magnets (RE = rare earth, TM = transition metal). Focusing on Y and Gd provides direct insight into both the TM magnetization and RE-TM interactions without the complication of s...
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Optimum weight chamber examples of moduli spaces of stable parabolic bundles in genus 0
We present an explicit construction of the moduli spaces of rank 2 stable parabolic bundles of parabolic degree 0 over the Riemann sphere, corresponding to "optimum" open weight chambers of parabolic weights in the weight polytope. The complexity of the different moduli space' weight chambers is understood in terms o...
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Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model
We report the development and validation of a data-driven real-time risk score that provides timely assessments for the clinical acuity of ward patients based on their temporal lab tests and vital signs, which allows for timely intensive care unit (ICU) admissions. Unlike the existing risk scoring technologies, the p...
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X-View: Graph-Based Semantic Multi-View Localization
Global registration of multi-view robot data is a challenging task. Appearance-based global localization approaches often fail under drastic view-point changes, as representations have limited view-point invariance. This work is based on the idea that human-made environments contain rich semantics which can be used t...
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Local Hardy spaces with variable exponents associated to non-negative self-adjoint operators satisfying Gaussian estimates
In this paper we introduce variable exponent local Hardy spaces associated with a non-negative self-adjoint operator L. We define them by using an area square integral involving the heat semigroup associated to L. A molecular characterization is established and as an aplication of the molecular characterization we pr...
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Provable Inductive Robust PCA via Iterative Hard Thresholding
The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse components, is a well-studied problem in machine learning. One natural question that arises is that, as in the inductive setting, if features a...
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The Topological Period-Index Problem over 8-Complexes
We study the Postnikov tower of the classifying space of a compact Lie group P(n,mn), which gives obstructions to lifting a topological Brauer class of period $n$ to a PU_{mn}-torsor, where the base space is a CW complex of dimension 8. Combined with the study of a twisted version of Atiyah-Hirzebruch spectral sequen...
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The Rise of Jihadist Propaganda on Social Networks
Using a dataset of over 1.9 million messages posted on Twitter by about 25,000 ISIS members, we explore how ISIS makes use of social media to spread its propaganda and to recruit militants from the Arab world and across the globe. By distinguishing between violence-driven, theological, and sectarian content, we trace...
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Simulated performance of the production target for the Muon g-2 Experiment
The Muon g-2 Experiment plans to use the Fermilab Recycler Ring for forming the proton bunches that hit its production target. The proposed scheme uses one RF system, 80 kV of 2.5 MHz RF. In order to avoid bunch rotations in a mismatched bucket, the 2.5 MHz is ramped adiabatically from 3 to 80 kV in 90 ms. In this st...
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Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of simulators as the training platform. Unfortunately, the reality gap between synthetic and real visual data prohibits direct migration of the...
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On Fienup Methods for Regularized Phase Retrieval
Alternating minimization, or Fienup methods, have a long history in phase retrieval. We provide new insights related to the empirical and theoretical analysis of these algorithms when used with Fourier measurements and combined with convex priors. In particular, we show that Fienup methods can be viewed as performing...
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Coupling the reduced-order model and the generative model for an importance sampling estimator
In this work, we develop an importance sampling estimator by coupling the reduced-order model and the generative model in a problem setting of uncertainty quantification. The target is to estimate the probability that the quantity of interest (QoI) in a complex system is beyond a given threshold. To avoid the prohibi...
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Anomalous dynamical phase in quantum spin chains with long-range interactions
The existence or absence of non-analytic cusps in the Loschmidt-echo return rate is traditionally employed to distinguish between a regular dynamical phase (regular cusps) and a trivial phase (no cusps) in quantum spin chains after a global quench. However, numerical evidence in a recent study [J. C. Halimeh and V. Z...
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Cold keV dark matter from decays and scatterings
We explore ways of creating cold keV-scale dark matter by means of decays and scatterings. The main observation is that certain thermal freeze-in processes can lead to a cold dark matter distribution in regions with small available phase space. In this way the free-streaming length of keV particles can be suppressed ...
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Solving high-dimensional partial differential equations using deep learning
Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the "curse of dimensionality". This paper introduces a deep learning-based approach that can handle general high-dimensiona...
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