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Massively parallel lattice-Boltzmann codes on large GPU clusters
This paper describes a massively parallel code for a state-of-the art thermal lattice- Boltzmann method. Our code has been carefully optimized for performance on one GPU and to have a good scaling behavior extending to a large number of GPUs. Versions of this code have been already used for large-scale studies of con...
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A Large Dimensional Study of Regularized Discriminant Analysis Classifiers
This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number...
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Non-Spherical Szekeres models in the language of Cosmological Perturbations
We study the differences and equivalences between the non-perturbative description of the evolution of cosmic structure furnished by the Szekeres dust models (a non-spherical exact solution of Einstein's equations) and the dynamics of Cosmological Perturbation Theory (CPT) for dust sources in a $\Lambda$CDM backgroun...
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Smart materials and structures for energy harvesters
Vibrational energy harvesters capture mechanical energy from ambient vibrations and convert the mechanical energy into electrical energy to power wireless electronic systems. Challenges exist in the process of capturing mechanical energy from ambient vibrations. For example, resonant harvesters may be used to improve...
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DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently...
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A Single-Channel Architecture for Algebraic Integer Based 8$\times$8 2-D DCT Computation
An area efficient row-parallel architecture is proposed for the real-time implementation of bivariate algebraic integer (AI) encoded 2-D discrete cosine transform (DCT) for image and video processing. The proposed architecture computes 8$\times$8 2-D DCT transform based on the Arai DCT algorithm. An improved fast alg...
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More lessons from the six box toy experiment
Following a paper in which the fundamental aspects of probabilistic inference were introduced by means of a toy experiment, details of the analysis of simulated long sequences of extractions are shown here. In fact, the striking performance of probability-based inference and forecasting, compared to those obtained by...
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A Biomechanical Study on the Use of Curved Drilling Technique for Treatment of Osteonecrosis of Femoral Head
Osteonecrosis occurs due to the loss of blood supply to the bone, leading to spontaneous death of the trabecular bone. Delayed treatment of the involved patients results in collapse of the femoral head, which leads to a need for total hip arthroplasty surgery. Core decompression, as the most popular technique for tre...
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Output feedback exponential stabilization for 1-D unstable wave equations with boundary control matched disturbance
We study the output feedback exponential stabilization of a one-dimensional unstable wave equation, where the boundary input, given by the Neumann trace at one end of the domain, is the sum of the control input and the total disturbance. The latter is composed of a nonlinear uncertain feedback term and an external bo...
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Bio-inspired Tensegrity Soft Modular Robots
In this paper, we introduce a design principle to develop novel soft modular robots based on tensegrity structures and inspired by the cytoskeleton of living cells. We describe a novel strategy to realize tensegrity structures using planar manufacturing techniques, such as 3D printing. We use this strategy to develop...
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How proper are Bayesian models in the astronomical literature?
The well-known Bayes theorem assumes that a posterior distribution is a probability distribution. However, the posterior distribution may no longer be a probability distribution if an improper prior distribution (non-probability measure) such as an unbounded uniform prior is used. Improper priors are often used in th...
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Diffusive Tidal Evolution for Migrating hot Jupiters
I consider a Jovian planet on a highly eccentric orbit around its host star, a situation produced by secular interactions with its planetary or stellar companions. The tidal interactions at every periastron passage exchange energy between the orbit and the planet's degree-2 fundamental-mode. Starting from zero energy...
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Von Neumann dimension, Hodge index theorem and geometric applications
This note contains a reformulation of the Hodge index theorem within the framework of Atiyah's $L^2$-index theory. More precisely, given a compact Kähler manifold $(M,h)$ of even complex dimension $2m$, we prove that $$\sigma(M)=\sum_{p,q=0}^{2m}(-1)^ph_{(2),\Gamma}^{p,q}(M)$$ where $\sigma(M)$ is the signature of $M...
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Relativistic asymmetries in the galaxy cross-correlation function
We study the asymmetry in the two-point cross-correlation function of two populations of galaxies focusing in particular on the relativistic effects that include the gravitational redshift. We derive the cross-correlation function on small and large scales using two different approaches: General Relativistic and Newt...
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Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units
In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system. The subword units are obtained by the byte-pair encoding (BPE) compression algorithm. Compared to using words as modeling units, using characters or subword uni...
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Harmonic analysis and distribution-free inference for spherical distributions
Fourier analysis and representation of circular distributions in terms of their Fourier coefficients, is quite commonly discussed and used for model-free inference such as testing uniformity and symmetry etc. in dealing with 2-dimensional directions. However a similar discussion for spherical distributions, which are...
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Free-form modelling of galaxy clusters: a Bayesian and data-driven approach
A new method is presented for modelling the physical properties of galaxy clusters. Our technique moves away from the traditional approach of assuming specific parameterised functional forms for the variation of physical quantities within the cluster, and instead allows for a 'free-form' reconstruction, but one for w...
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Multi-focus Attention Network for Efficient Deep Reinforcement Learning
Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute...
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Weak lensing power spectrum reconstruction by counting galaxies.-- I: the ABS method
We propose an Analytical method of Blind Separation (ABS) of cosmic magnification from the intrinsic fluctuations of galaxy number density in the observed galaxy number density distribution. The ABS method utilizes the different dependences of the signal (cosmic magnification) and contamination (galaxy intrinsic clus...
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Optimal Output Consensus of High-Order Multi-Agent Systems with Embedded Technique
In this paper, we study an optimal output consensus problem for a multi-agent network with agents in the form of multi-input multi-output minimum-phase dynamics. Optimal output consensus can be taken as an extended version of the existing output consensus problem for higher-order agents with an optimization requireme...
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Equilibrium selection via Optimal transport
We propose a new dynamics for equilibrium selection of finite player discrete strategy games. The dynamics is motivated by optimal transportation, and models individual players' myopicity, greedy and uncertainty when making decisions. The stationary measure of the dynamics provides each pure Nash equilibrium a probab...
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Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks
This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clust...
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Representation Theorems for Solvable Sesquilinear Forms
New results are added to the paper [4] about q-closed and solvable sesquilinear forms. The structure of the Banach space $\mathcal{D}[||\cdot||_\Omega]$ defined on the domain $\mathcal{D}$ of a q-closed sesquilinear form $\Omega$ is unique up to isomorphism, and the adjoint of a sesquilinear form has the same propert...
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Using Continuous Power Modulation for Exchanging Local Channel State Information
This letter provides a simple but efficient technique, which allows each transmitter of an interference network, to exchange local channel state information with the other transmitters. One salient feature of the proposed technique is that a transmitter only needs measurements of the signal power at its intended rece...
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Spectral Efficient and Energy Aware Clustering in Cellular Networks
The current and envisaged increase of cellular traffic poses new challenges to Mobile Network Operators (MNO), who must densify their Radio Access Networks (RAN) while maintaining low Capital Expenditure and Operational Expenditure to ensure long-term sustainability. In this context, this paper analyses optimal clust...
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Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition
Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech recognition tasks. However, these models are computationally more expensive than N-gram LMs for decoding, and thus, challenging to integrate ...
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Vanishing of Littlewood-Richardson polynomials is in P
J. DeLoera-T. McAllister and K. D. Mulmuley-H. Narayanan-M. Sohoni independently proved that determining the vanishing of Littlewood-Richardson coefficients has strongly polynomial time computational complexity. Viewing these as Schubert calculus numbers, we prove the generalization to the Littlewood-Richardson polyn...
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Multi-task memory networks for category-specific aspect and opinion terms co-extraction
In aspect-based sentiment analysis, most existing methods either focus on aspect/opinion terms extraction or aspect terms categorization. However, each task by itself only provides partial information to end users. To generate more detailed and structured opinion analysis, we propose a finer-grained problem, which we...
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Discussion on Computationally Efficient Multivariate Spatio-Temporal Models for High-Dimensional Count-Valued Data by Bradley et al
I begin my discussion by summarizing the methodology proposed and new distributional results on multivariate log-Gamma derived in the paper. Then, I draw an interesting connection between their work with mean field variational Bayes. Lastly, I make some comments on the simulation results and the performance of the pr...
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Elucidation of the helical spin structure of FeAs
We present the results of resonant x-ray scattering measurements and electronic structure calculations on the monoarsenide FeAs. We elucidate details of the magnetic structure, showing the ratio of ellipticity of the spin helix is larger than previously thought, at 2.58(3), and reveal both a right-handed chirality an...
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A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications
Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by curr...
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The topography of the environment alters the optimal search strategy for active particles
In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during...
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A Neural Network Approach for Mixing Language Models
The performance of Neural Network (NN)-based language models is steadily improving due to the emergence of new architectures, which are able to learn different natural language characteristics. This paper presents a novel framework, which shows that a significant improvement can be achieved by combining different exi...
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Iron Intercalated Covalent-Organic Frameworks: First Crystalline Porous Thermoelectric Materials
Covalent-organic frameworks (COFs) are intriguing platforms for designing functional molecular materials. Here, we present a computational study based on van der Waals dispersion-corrected hybrid density functional theory calculations to analyze the material properties of boroxine-linked and triazine-linked intercala...
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DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Filters in a Convolutional Neural Network (CNN) contain model parameters learned from enormous amounts of data. In this paper, we suggest to decompose convolutional filters in CNN as a truncated expansion with pre-fixed bases, namely the Decomposed Convolutional Filters network (DCFNet), where the expansion coefficie...
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Symmetries, Invariants and Generating Functions: Higher-order Statistics of Biased Tracers
Gravitationally collapsed objects are known to be biased tracers of an underlying density contrast. Using symmetry arguments, generalised biasing schemes have recently been developed to relate the halo density contrast $\delta_h$ with the underlying density contrast $\delta$, divergence of velocity $\theta$ and their...
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Reversible Sequences of Cardinals, Reversible Equivalence Relations, and Similar Structures
A relational structure ${\mathbb X}$ is said to be reversible iff every bijective endomorphism $f:X\rightarrow X$ is an automorphism. We define a sequence of non-zero cardinals $\langle \kappa_i :i\in I\rangle$ to be reversible iff each surjection $f :I\rightarrow I$ such that $\kappa_j =\sum_{i\in f^{-1}[\{ j \}]}\k...
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A Longitudinal Study of Google Play
The difficulty of large scale monitoring of app markets affects our understanding of their dynamics. This is particularly true for dimensions such as app update frequency, control and pricing, the impact of developer actions on app popularity, as well as coveted membership in top app lists. In this paper we perform a...
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Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its app...
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Corpus-compressed Streaming and the Spotify Problem
In this work, we describe a problem which we refer to as the \textbf{Spotify problem} and explore a potential solution in the form of what we call corpus-compressed streaming schemes. Inspired by the problem of constrained bandwidth during use of the popular Spotify application on mobile networks, the Spotify problem...
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An intrinsic parallel transport in Wasserstein space
If M is a smooth compact connected Riemannian manifold, let P(M) denote the Wasserstein space of probability measures on M. We describe a geometric construction of parallel transport of some tangent cones along geodesics in P(M). We show that when everything is smooth, the geometric parallel transport agrees with ear...
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Factor Analysis for Spectral Estimation
Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stoc...
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Effects of Images with Different Levels of Familiarity on EEG
Evaluating human brain potentials during watching different images can be used for memory evaluation, information retrieving, guilty-innocent identification and examining the brain response. In this study, the effects of watching images, with different levels of familiarity, on subjects' Electroencephalogram (EEG) ha...
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Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion
Geophysical inversion should ideally produce geologically realistic subsurface models that explain the available data. Multiple-point statistics is a geostatistical approach to construct subsurface models that are consistent with site-specific data, but also display the same type of patterns as those found in a train...
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Graded super duality for general linear Lie superalgebras
We provide a new proof of the super duality equivalence between infinite-rank parabolic BGG categories of general linear Lie (super) algebras conjectured by Cheng and Wang and first proved by Cheng and Lam. We do this by establishing a new uniqueness theorem for tensor product categorifications motivated by work of B...
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On a Formal Model of Safe and Scalable Self-driving Cars
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- ...
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A Low-power Reversible Alkali Atom Source
An electrically-controllable, solid-state, reversible device for sourcing and sinking alkali vapor is presented. When placed inside an alkali vapor cell, both an increase and decrease of the rubidium vapor density by a factor of two are demonstrated through laser absorption spectroscopy on 10 to 15 s time scales. The...
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GoT-WAVE: Temporal network alignment using graphlet-orbit transitions
Global pairwise network alignment (GPNA) aims to find a one-to-one node mapping between two networks that identifies conserved network regions. GPNA algorithms optimize node conservation (NC) and edge conservation (EC). NC quantifies topological similarity between nodes. Graphlet-based degree vectors (GDVs) are a sta...
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Randomness-induced quantum spin liquid on honeycomb lattice
We present a quantu spin liquid state in a spin-1/2 honeycomb lattice with randomness in the exchange interaction. That is, we successfully introduce randomness into the organic radial-based complex and realize a random-singlet (RS) state. All magnetic and thermodynamic experimental results indicate the liquid-like b...
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Symmetric structure for the endomorphism algebra of projective-injective module in parabolic category
We show that for any singular dominant integral weight $\lambda$ of a complex semisimple Lie algebra $\mathfrak{g}$, the endomorphism algebra $B$ of any projective-injective module of the parabolic BGG category $\mathcal{O}_\lambda^{\mathfrak{p}}$ is a symmetric algebra (as conjectured by Khovanov) extending the resu...
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Cantor series and rational numbers
The article is devoted to the investigation of representation of rational numbers by Cantor series. Necessary and sufficient conditions for a rational number to be representable by a positive Cantor series are formulated for the case of an arbitrary sequence $(q_k)$ and some its corollaries are considered. Results of...
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PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and...
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Kähler metrics via Lorentzian Geometry in dimension four
Given a semi-Riemannian $4$-manifold $(M,g)$ with two distinguished vector fields satisfying properties determined by their shear, twist and various Lie bracket relations, a family of Kähler metrics $g_K$ is constructed, defined on an open set in $M$, which coincides with $M$ in many typical examples. Under certain c...
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z-Classes and Rational Conjugacy Classes in Alternating Groups
In this paper, we compute the number of z-classes (conjugacy classes of centralizers of elements) in the symmetric group S_n, when n is greater or equal to 3 and alternating group A_n, when n is greater or equal to 4. It turns out that the difference between the number of conjugacy classes and the number of z-classes...
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Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device
This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-lev...
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Martin David Kruskal: a biographical memoir
Martin David Kruskal was one of the most versatile theoretical physicists of his generation and is distinguished for his enduring work in several different areas, most notably plasma physics, a memorable detour into relativity, and his pioneering work in nonlinear waves. In the latter, together with Norman Zabusky, h...
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Unified theory for finite Markov chains
We provide a unified framework to compute the stationary distribution of any finite irreducible Markov chain or equivalently of any irreducible random walk on a finite semigroup $S$. Our methods use geometric finite semigroup theory via the Karnofsky-Rhodes and the McCammond expansions of finite semigroups with speci...
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A Simulator for Hedonic Games
Hedonic games are meant to model how coalitions of people form and break apart in the real world. However, it is difficult to run simulations when everything must be done by hand on paper. We present an online software that allows fast and visual simulation of several types of hedonic games. this http URL
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Estimating Large Precision Matrices via Modified Cholesky Decomposition
We introduce the $k$-banded Cholesky prior for estimating a high-dimensional bandable precision matrix via the modified Cholesky decomposition. The bandable assumption is imposed on the Cholesky factor of the decomposition. We obtained the P-loss convergence rate under the spectral norm and the matrix $\ell_{\infty}$...
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High order conformal symplectic and ergodic schemes for stochastic Langevin equation via generating functions
In this paper, we consider the stochastic Langevin equation with additive noises, which possesses both conformal symplectic geometric structure and ergodicity. We propose a methodology of constructing high weak order conformal symplectic schemes by converting the equation into an equivalent autonomous stochastic Hami...
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Crowd ideation of supervised learning problems
Crowdsourcing is an important avenue for collecting machine learning data, but crowdsourcing can go beyond simple data collection by employing the creativity and wisdom of crowd workers. Yet crowd participants are unlikely to be experts in statistics or predictive modeling, and it is not clear how well non-experts ca...
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Joins in the strong Weihrauch degrees
The Weihrauch degrees and strong Weihrauch degrees are partially ordered structures representing degrees of unsolvability of various mathematical problems. Their study has been widely applied in computable analysis, complexity theory, and more recently, also in computable combinatorics. We answer an open question abo...
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Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition
Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However,conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation ...
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A combinatorial model for the free loop fibration
We introduce the abstract notion of a closed necklical set in order to describe a functorial combinatorial model of the free loop fibration $\Omega Y\rightarrow \Lambda Y\rightarrow Y$ over the geometric realization $Y=|X|$ of a path connected simplicial set $X.$ In particular, to any path connected simplicial set $X...
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On the number of circular orders on a group
We give a classification and complete algebraic description of groups allowing only finitely many (left multiplication invariant) circular orders. In particular, they are all solvable groups with a specific semi-direct product decomposition. This allows us to also show that the space of circular orders of any group i...
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Preparation and Measurement in Quantum Memory Models
Quantum Cognition has delivered a number of models for semantic memory, but to date these have tended to assume pure states and projective measurement. Here we relax these assumptions. A quantum inspired model of human word association experiments will be extended using a density matrix representation of human memory...
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Detailed proof of Nazarov's inequality
The purpose of this note is to provide a detailed proof of Nazarov's inequality stated in Lemma A.1 in Chernozhukov, Chetverikov, and Kato (2017, Annals of Probability).
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TRPL+K: Thick-Restart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems
The Lanczos method is one of the standard approaches for computing a few eigenpairs of a large, sparse, symmetric matrix. It is typically used with restarting to avoid unbounded growth of memory and computational requirements. Thick-restart Lanczos is a popular restarted variant because of its simplicity and numerica...
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Behavior of l-bits near the many-body localization transition
Eigenstates of fully many-body localized (FMBL) systems are described by quasilocal operators $\tau_i^z$ (l-bits), which are conserved exactly under Hamiltonian time evolution. The algebra of the operators $\tau_i^z$ and $\tau_i^x$ associated with l-bits ($\boldsymbol{\tau}_i$) completely defines the eigenstates and ...
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ROPE: high-dimensional network modeling with robust control of edge FDR
Network modeling has become increasingly popular for analyzing genomic data, to aid in the interpretation and discovery of possible mechanistic components and therapeutic targets. However, genomic-scale networks are high-dimensional models and are usually estimated from a relatively small number of samples. Therefore...
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Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior
Automated vehicles can change the society by improved safety, mobility and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the high...
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e-Fair: Aggregation in e-Commerce for Exploiting Economies of Scale
In recent years, many new and interesting models of successful online business have been developed, including competitive models such as auctions, where the product price tends to rise, and group-buying, where users cooperate obtaining a dynamic price that tends to go down. We propose the e-fair as a business model f...
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Stable spike clusters for the precursor Gierer-Meinhardt system in R2
We consider the Gierer-Meinhardt system with small inhibitor diffusivity, very small activator diffusivity and a precursor inhomogeneity. For any given positive integer k we construct a spike cluster consisting of $k$ spikes which all approach the same nondegenerate local minimum point of the precursor inhomogeneity....
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Solvability of curves on surfaces
In this article, we study subloci of solvable curves in $\mathcal{M}_g$ which are contained in either a K3-surface or a quadric or a cubic surface. We give a bound on the dimension of such subloci. In the case of complete intersection genus $g$ curves in a cubic surface, we show that a general such curve is solvable....
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Scaling Limits for Super--replication with Transient Price Impact
We prove limit theorems for the super-replication cost of European options in a Binomial model with transient price impact. We show that if the time step goes to zero and the effective resilience between consecutive trading times remains constant then the limit of the super--replication prices coincide with the scali...
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Kernel-based Inference of Functions over Graphs
The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting -- and prevalent in several fields of study -- problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-ba...
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Noncommutative products of Euclidean spaces
We present natural families of coordinate algebras of noncommutative products of Euclidean spaces. These coordinate algebras are quadratic ones associated with an R-matrix which is involutive and satisfies the Yang-Baxter equations. As a consequence they enjoy a list of nice properties, being regular of finite global...
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X-ray and Optical Study of the Gamma-ray Source 3FGL J0838.8$-$2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar
We observed the field of the Fermi source 3FGL J0838.8-2829 in optical and X-rays, initially motivated by the cataclysmic variable (CV) 1RXS J083842.1-282723 that lies within its error circle. Several X-ray sources first classified as CVs have turned out to be gamma-ray emitting millisecond pulsars (MSPs). We find th...
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Adversarial Variational Inference and Learning in Markov Random Fields
Markov random fields (MRFs) find applications in a variety of machine learning areas, while the inference and learning of such models are challenging in general. In this paper, we propose the Adversarial Variational Inference and Learning (AVIL) algorithm to solve the problems with a minimal assumption about the mode...
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Geometric k-nearest neighbor estimation of entropy and mutual information
Nonparametric estimation of mutual information is used in a wide range of scientific problems to quantify dependence between variables. The k-nearest neighbor (knn) methods are consistent, and therefore expected to work well for large sample size. These methods use geometrically regular local volume elements. This pr...
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Resonant Electron Impact Excitation of 3d levels in Fe$^{14+}$ and Fe$^{15+}$
We present laboratory spectra of the $3p$--$3d$ transitions in Fe$^{14+}$ and Fe$^{15+}$ excited with a mono-energetic electron beam. In the energy dependent spectra obtained by sweeping the electron energy, resonant excitation is confirmed as an intensity enhancement at specific electron energies. The experimental r...
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Automated Top View Registration of Broadcast Football Videos
In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static mo...
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Reservoir Computing for Detection of Steady State in Performance Tests of Compressors
Fabrication of devices in industrial plants often includes undergoing quality assurance tests or tests that seek to determine some attributes or capacities of the device. For instance, in testing refrigeration compressors, we want to find the true refrigeration capacity of the compressor being tested. Such test (also...
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Fast swaption pricing in Gaussian term structure models
We propose a fast and accurate numerical method for pricing European swaptions in multi-factor Gaussian term structure models. Our method can be used to accelerate the calibration of such models to the volatility surface. The pricing of an interest rate option in such a model involves evaluating a multi-dimensional i...
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Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?
In this article, we extend the conventional framework of convolutional-Restricted-Boltzmann-Machine to learn highly abstract features among abitrary number of time related input maps by constructing a layer of multiplicative units, which capture the relations among inputs. In many cases, more than two maps are strong...
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Predicting how and when hidden neurons skew measured synaptic interactions
A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the "hidden" portion of the network. To properly interpret neu...
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Humanoid Robots as Agents of Human Consciousness Expansion
The "Loving AI" project involves developing software enabling humanoid robots to interact with people in loving and compassionate ways, and to promote people' self-understanding and self-transcendence. Currently the project centers on the Hanson Robotics robot "Sophia" -- specifically, on supplying Sophia with person...
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Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
In this paper we study leveraging confidence information induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model. A natural measure of confidence is $\|F({\bf x})\|_\infty$ (i.e. how confident $F$ is about its prediction?). We start by analyzing an adversarial traini...
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Stateless Puzzles for Real Time Online Fraud Preemption
The profitability of fraud in online systems such as app markets and social networks marks the failure of existing defense mechanisms. In this paper, we propose FraudSys, a real-time fraud preemption approach that imposes Bitcoin-inspired computational puzzles on the devices that post online system activities, such a...
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Multi-Period Flexibility Forecast for Low Voltage Prosumers
Near-future electric distribution grids operation will have to rely on demand-side flexibility, both by implementation of demand response strategies and by taking advantage of the intelligent management of increasingly common small-scale energy storage. The Home energy management system (HEMS), installed at low volta...
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Assessing the Economics of Customer-Sited Multi-Use Energy Storage
This paper presents an approach to assess the economics of customer-sited energy storage systems (ESSs) which are owned and operated by a customer. The ESSs can participate in frequency regulation and spinning reserve markets, and are used to help the customer consume available renewable energy and reduce electricity...
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A Hamiltonian approach for the Thermodynamics of AdS black holes
In this work we study the Thermodynamics of D-dimensional Schwarzschild-anti de Sitter (SAdS) black holes. The minimal Thermodynamics of the SAdS spacetime is briefly discussed, highlighting some of its strong points and shortcomings. The minimal SAdS Thermodynamics is extended within a Hamiltonian approach, by means...
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Dzyaloshinskii Moriya interaction across antiferromagnet / ferromagnet interface
The antiferromagnet (AFM) / ferromagnet (FM) interfaces are of central importance in recently developed pure electric or ultrafast control of FM spins, where the underlying mechanisms remain unresolved. Here we report the direct observation of Dzyaloshinskii Moriya interaction (DMI) across the AFM/FM interface of IrM...
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Pore cross-talk in colloidal filtration
Blockage of pores by particles is found in many processes, including filtration and oil extraction. We present filtration experiments through a linear array of ten channels with one dimension which is sub-micron, through which a dilute dispersion of Brownian polystyrene spheres flows under the action of a fixed press...
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Temporal Markov Processes for Transport in Porous Media: Random Lattice Networks
Monte Carlo (MC) simulations of transport in random porous networks indicate that for high variances of the log-normal permeability distribution, the transport of a passive tracer is non-Fickian. Here we model this non-Fickian dispersion in random porous networks using discrete temporal Markov models. We show that su...
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Defect Properties of Na and K in Cu2ZnSnS4 from Hybrid Functional Calculation
In-growth or post-deposition treatment of $Cu_{2}ZnSnS_{4}$ (CZTS) absorber layer had led to improved photovoltaic efficiency, however, the underlying physical mechanism of such improvements are less studied. In this study, the thermodynamics of Na and K related defects in CZTS are investigated from first principle a...
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Explicit minimisation of a convex quadratic under a general quadratic constraint: a global, analytic approach
A novel approach is introduced to a very widely occurring problem, providing a complete, explicit resolution of it: minimisation of a convex quadratic under a general quadratic, equality or inequality, constraint. Completeness comes via identification of a set of mutually exclusive and exhaustive special cases. Expli...
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Morphisms of open games
We define a notion of morphisms between open games, exploiting a surprising connection between lenses in computer science and compositional game theory. This extends the more intuitively obvious definition of globular morphisms as mappings between strategy profiles that preserve best responses, and hence in particula...
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U(1)$\times$SU(2) Gauge Invariance Made Simple for Density Functional Approximations
A semi-relativistic density-functional theory that includes spin-orbit couplings and Zeeman fields on equal footing with the electromagnetic potentials, is an appealing framework to develop a unified first-principles computational approach for non-collinear magnetism, spintronics, orbitronics, and topological states....
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L^2-Betti numbers of rigid C*-tensor categories and discrete quantum groups
We compute the $L^2$-Betti numbers of the free $C^*$-tensor categories, which are the representation categories of the universal unitary quantum groups $A_u(F)$. We show that the $L^2$-Betti numbers of the dual of a compact quantum group $G$ are equal to the $L^2$-Betti numbers of the representation category $Rep(G)$...
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