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Linear theory for single and double flap wavemakers
In this paper, we are concerned with deterministic wave generation in a hydrodynamic laboratory. A linear wavemaker theory is developed based on the fully dispersive water wave equations. The governing field equation is the Laplace equation for potential flow with several boundary conditions: the dynamic and kinemati...
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Image-domain multi-material decomposition for dual-energy CT based on correlation and sparsity of material images
Dual energy CT (DECT) enhances tissue characterization because it can produce images of basis materials such as soft-tissue and bone. DECT is of great interest in applications to medical imaging, security inspection and nondestructive testing. Theoretically, two materials with different linear attenuation coefficient...
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A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model
In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines. One method to reduce wake losses is by misaligning the rotor with the incoming flow using its yaw actuator, thereby laterally deflecting the wake away from downstream turbine...
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UCB Exploration via Q-Ensembles
We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experim...
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Towards a Deep Improviser: a prototype deep learning post-tonal free music generator
Two modest-sized symbolic corpora of post-tonal and post-metric keyboard music have been constructed, one algorithmic, the other improvised. Deep learning models of each have been trained and largely optimised. Our purpose is to obtain a model with sufficient generalisation capacity that in response to a small quanti...
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Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data
Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method...
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Avoiding a Tragedy of the Commons in the Peer Review Process
Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process a...
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Partially Recursive Acceptance Rejection
Generating random variates from high-dimensional distributions is often done approximately using Markov chain Monte Carlo. In certain cases, perfect simulation algorithms exist that allow one to draw exactly from the stationary distribution, but most require $O(n \ln(n))$ time, where $n$ measures the size of the inpu...
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Reifenberg Flatness and Oscillation of the Unit Normal Vector
We show (under mild topological assumptions) that small oscillation of the unit normal vector implies Reifenberg flatness. We then apply this observation to the study of chord-arc domains and to a quantitative version of a two-phase free boundary problem for harmonic measure previously studied by Kenig-Toro.
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Stability and optimality of distributed secondary frequency control schemes in power networks
We present a systematic method for designing distributed generation and demand control schemes for secondary frequency regulation in power networks such that stability and an economically optimal power allocation can be guaranteed. A dissipativity condition is imposed on net power supply variables to provide stabilit...
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A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism
Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic t...
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OVI 6830Å Imaging Polarimetry of Symbiotic Stars
I present here the first results from an ongoing pilot project with the 1.6 m telescope at the OPD, Brasil, aimed at the detection of the OVI $\lambda$6830 line via linear polarization in symbiotic stars. The main goal is to demonstrate that OVI imaging polarimetry is an efficient technique for discovering new symbio...
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MMGAN: Manifold Matching Generative Adversarial Network
It is well-known that GANs are difficult to train, and several different techniques have been proposed in order to stabilize their training. In this paper, we propose a novel training method called manifold-matching, and a new GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds representing the ...
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Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts
Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats. Predicting infection from symptom data is critical, though using symptom data from varied studies in aggregate is challenging because the data is collected in di...
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Constrained empirical Bayes priors on regression coefficients
Under model uncertainty, empirical Bayes (EB) procedures can have undesirable properties such as extreme estimates of inclusion probabilities (Scott & Berger, 2010) or inconsistency under the null model (Liang et al., 2008). To avoid these issues, we define empirical Bayes priors with constraints that ensure that the...
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A finite field analogue for Appell series F_3
In this paper we introduce a finite field analogue for the Appell series F_3 and give some reduction formulae and certain generating functions for this function over finite fields.
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Two-way Two-tape Automata
In this article we consider two-way two-tape (alternating) automata accepting pairs of words and we study some closure properties of this model. Our main result is that such alternating automata are not closed under complementation for non-unary alphabets. This improves a similar result of Kari and Moore for picture ...
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Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing
360$^{\circ}$ video requires human viewers to actively control "where" to look while watching the video. Although it provides a more immersive experience of the visual content, it also introduces additional burden for viewers; awkward interfaces to navigate the video lead to suboptimal viewing experiences. Virtual ci...
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Zero-Shot Learning by Generating Pseudo Feature Representations
Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel classes without any training instances. In this paper we present a simple but high-performance ZSL approach by generating pseudo feature representations (GPFR). Given the dataset of seen classes and side information of unseen classes (e.g. att...
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Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Speaker change detection (SCD) is an important task in dialog modeling. Our paper addresses the problem of text-based SCD, which differs from existing audio-based studies and is useful in various scenarios, for example, processing dialog transcripts where speaker identities are missing (e.g., OpenSubtitle), and enhan...
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Bivariate Discrete Generalized Exponential Distribution
In this paper we develop a bivariate discrete generalized exponential distribution, whose marginals are discrete generalized exponential distribution as proposed by Nekoukhou, Alamatsaz and Bidram ("Discrete generalized exponential distribution of a second type", Statistics, 47, 876 - 887, 2013). It is observed that ...
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Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings
Over 150,000 new people in the United States are diagnosed with colorectal cancer each year. Nearly a third die from it (American Cancer Society). The only approved noninvasive diagnosis tools currently involve fecal blood count tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five minutes and are ...
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More or Less? Predict the Social Influence of Malicious URLs on Social Media
Users of Online Social Networks (OSNs) interact with each other more than ever. In the context of a public discussion group, people receive, read, and write comments in response to articles and postings. In the absence of access control mechanisms, OSNs are a great environment for attackers to influence others, from ...
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Path-Following through Control Funnel Functions
We present an approach to path following using so-called control funnel functions. Synthesizing controllers to "robustly" follow a reference trajectory is a fundamental problem for autonomous vehicles. Robustness, in this context, requires our controllers to handle a specified amount of deviation from the desired tra...
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Tunable Anomalous Andreev Reflection and Triplet Pairings in Spin Orbit Coupled Graphene
We theoretically study scattering process and superconducting triplet correlations in a graphene junction comprised of ferromagnet-RSO-superconductor in which RSO stands for a region with Rashba spin orbit interaction. Our results reveal spin-polarized subgap transport through the system due to an anomalous equal-spi...
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Conditional Model Selection in Mixed-Effects Models with cAIC4
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R-package cAIC4 that allows for the computation of the conditional Akaike Information Criterion (cAIC). Computation o...
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Inequalities for the lowest magnetic Neumann eigenvalue
We study the ground state energy of the Neumann magnetic Laplacian on planar domains. For a constant magnetic field we consider the question whether, under an assumption of fixed area, the disc maximizes this eigenvalue. More generally, we discuss old and new bounds obtained on this problem.
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Automated optimization of large quantum circuits with continuous parameters
We develop and implement automated methods for optimizing quantum circuits of the size and type expected in quantum computations that outperform classical computers. We show how to handle continuous gate parameters and report a collection of fast algorithms capable of optimizing large-scale quantum circuits. For the ...
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Testing the simplifying assumption in high-dimensional vine copulas
Testing the simplifying assumption in high-dimensional vine copulas is a difficult task because tests must be based on estimated observations and amount to checking constraints on high-dimensional distributions. So far, corresponding tests have been limited to single conditional copulas with a low-dimensional set of ...
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TSP With Locational Uncertainty: The Adversarial Model
In this paper we study a natural special case of the Traveling Salesman Problem (TSP) with point-locational-uncertainty which we will call the {\em adversarial TSP} problem (ATSP). Given a metric space $(X, d)$ and a set of subsets $R = \{R_1, R_2, ... , R_n\} : R_i \subseteq X$, the goal is to devise an ordering of ...
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Exact solutions to three-dimensional generalized nonlinear Schrodinger equations with varying potential and nonlinearities
It is shown that using the similarity transformations, a set of three-dimensional p-q nonlinear Schrodinger (NLS) equations with inhomogeneous coefficients can be reduced to one-dimensional stationary NLS equation with constant or varying coefficients, thus allowing for obtaining exact localized and periodic wave sol...
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A Study of Reinforcement Learning for Neural Machine Translation
Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world systems where deep models and large datasets are leveraged. I...
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Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification
Artificial neural network (ANN) is a very useful tool in solving learning problems. Boosting the performances of ANN can be mainly concluded from two aspects: optimizing the architecture of ANN and normalizing the raw data for ANN. In this paper, a novel method which improves the effects of ANN by preprocessing the r...
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Maximum likelihood estimation of determinantal point processes
Determinantal point processes (DPPs) have wide-ranging applications in machine learning, where they are used to enforce the notion of diversity in subset selection problems. Many estimators have been proposed, but surprisingly the basic properties of the maximum likelihood estimator (MLE) have received little attenti...
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Orthogonal Machine Learning: Power and Limitations
Double machine learning provides $\sqrt{n}$-consistent estimates of parameters of interest even when high-dimensional or nonparametric nuisance parameters are estimated at an $n^{-1/4}$ rate. The key is to employ Neyman-orthogonal moment equations which are first-order insensitive to perturbations in the nuisance par...
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Numerical investigation of supersonic shock-wave/boundary-layer interaction in transitional and turbulent regime
We perform direct numerical simulations of shock-wave/boundary-layer interactions (SBLI) at Mach number M = 1.7 to investigate the influence of the state of the incoming boundary layer on the interaction properties. We reproduce and extend the flow conditions of the experiments performed by Giepman et al., in which a...
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Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations
Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may ...
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Asymptotics for high-dimensional covariance matrices and quadratic forms with applications to the trace functional and shrinkage
We establish large sample approximations for an arbitray number of bilinear forms of the sample variance-covariance matrix of a high-dimensional vector time series using $ \ell_1$-bounded and small $\ell_2$-bounded weighting vectors. Estimation of the asymptotic covariance structure is also discussed. The results hol...
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Bayesian Paragraph Vectors
Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words. Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such...
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Subset Synchronization in Monotonic Automata
We study extremal and algorithmic questions of subset and careful synchronization in monotonic automata. We show that several synchronization problems that are hard in general automata can be solved in polynomial time in monotonic automata, even without knowing a linear order of the states preserved by the transition...
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Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification
The selection of West Java governor is one event that seizes the attention of the public is no exception to social media users. Public opinion on a prospective regional leader can help predict electability and tendency of voters. Data that can be used by the opinion mining process can be obtained from Twitter. Becaus...
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Ray: A Distributed Framework for Emerging AI Applications
The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray---a distributed system...
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A blockchain-based Decentralized System for proper handling of temporary Employment contracts
Temporary work is an employment situation useful and suitable in all occasions in which business needs to adjust more easily and quickly to workload fluctuations or maintain staffing flexibility. Temporary workers play therefore an important role in many companies, but this kind of activity is subject to a special fo...
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Valley polarized relaxation and upconversion luminescence from Tamm-Plasmon Trion-Polaritons with a MoSe2 monolayer
Transition metal dichalcogenides represent an ideal testbed to study excitonic effects, spin-related phenomena and fundamental light-matter coupling in nanoscopic condensed matter systems. In particular, the valley degree of freedom, which is unique to such direct band gap monolayers with broken inversion symmetry, a...
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Machine learning based localization and classification with atomic magnetometers
We demonstrate identification of position, material, orientation and shape of objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic induction imaging supported by machine learning. Machine learning maximizes the information extracted from the images created by the magnetometer, demonstrating t...
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On the difficulty of finding spines
We prove that the set of symplectic lattices in the Siegel space $\mathfrak{h}_g$ whose systoles generate a subspace of dimension at least 3 in $\mathbb{R}^{2g}$ does not contain any $\mathrm{Sp}(2g,\mathbb{Z})$-equivariant deformation retract of $\mathfrak{h}_g$.
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Distributed Decoding of Convolutional Network Error Correction Codes
A Viterbi-like decoding algorithm is proposed in this paper for generalized convolutional network error correction coding. Different from classical Viterbi algorithm, our decoding algorithm is based on minimum error weight rather than the shortest Hamming distance between received and sent sequences. Network errors m...
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Temperature induced phase transition from cycloidal to collinear antiferromagnetism in multiferroic Bi$_{0.9}$Sm$_{0.1}$FeO$_3$ driven by $f$-$d$ induced magnetic anisotropy
In multiferroic BiFeO$_3$ a cycloidal antiferromagnetic structure is coupled to a large electric polarization at room temperature, giving rise to magnetoelectric functionality that may be exploited in novel multiferroic-based devices. In this paper, we demonstrate that by substituting samarium for 10% of the bismuth ...
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POSEYDON - Converting the DAFNE Collider into a double Positron Facility: a High Duty-Cycle pulse stretcher and a storage ring
This project proposes to reuse the DAFNE accelerator complex for producing a high intensity (up to 10^10), high-quality beam of high-energy (up to 500 MeV) positrons for HEP experiments, mainly - but not only - motivated by light dark particles searches. Such a facility would provide a unique source of ultra-relativi...
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Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations
Understanding planetary interiors is directly linked to our ability of simulating exotic quantum mechanical systems such as hydrogen (H) and hydrogen-helium (H-He) mixtures at high pressures and temperatures. Equations of State (EOSs) tables based on Density Functional Theory (DFT), are commonly used by planetary sci...
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Identifying exogenous and endogenous activity in social media
The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in Online social networks (OSNs). Here we develop a method for extrac...
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Water flow in Carbon and Silicon Carbide nanotubes
In this work the conduction of ion-water solution through two discrete bundles of armchair carbon and silicon carbide nanotubes, as useful membranes for water desalination, is studied. In order that studies on different types of nanotubes be comparable, the chiral vectors of C and Si-C nanotubes are selected as (7,7)...
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Multidimensional extremal dependence coefficients
Extreme values modeling has attracting the attention of researchers in diverse areas such as the environment, engineering, or finance. Multivariate extreme value distributions are particularly suitable to model the tails of multidimensional phenomena. The analysis of the dependence among multivariate maxima is useful...
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A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas
Systems subject to uncertain inputs produce uncertain responses. Uncertainty quantification (UQ) deals with the estimation of statistics of the system response, given a computational model of the system and a probabilistic model of its inputs. In engineering applications it is common to assume that the inputs are mut...
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Pachinko Prediction: A Bayesian method for event prediction from social media data
The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee...
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A Digital Hardware Fast Algorithm and FPGA-based Prototype for a Novel 16-point Approximate DCT for Image Compression Applications
The discrete cosine transform (DCT) is the key step in many image and video coding standards. The 8-point DCT is an important special case, possessing several low-complexity approximations widely investigated. However, 16-point DCT transform has energy compaction advantages. In this sense, this paper presents a new 1...
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Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
It is well known that the initialization of weights in deep neural networks can have a dramatic impact on learning speed. For example, ensuring the mean squared singular value of a network's input-output Jacobian is $O(1)$ is essential for avoiding the exponential vanishing or explosion of gradients. The stronger con...
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Singular perturbation for abstract elliptic equations and application
Boundary value problem for complete second order elliptic equation is considered in Banach space. The equation and boundary conditions involve a small and spectral parameter. The uniform L_{p}-regularity properties with respect to space variable and parameters are established. Here, the explicit formula for the solut...
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Pruning and Nonparametric Multiple Change Point Detection
Change point analysis is a statistical tool to identify homogeneity within time series data. We propose a pruning approach for approximate nonparametric estimation of multiple change points. This general purpose change point detection procedure `cp3o' applies a pruning routine within a dynamic program to greatly redu...
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Context encoding enables machine learning-based quantitative photoacoustics
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases. While photoacoustic (PA) imaging is a novel modality with great potential to measure optical absorption...
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Simulating the interaction between a falling super-quadric object and a soap film
The interaction that occurs between a light solid object and a horizontal soap film of a bamboo foam contained in a cylindrical tube is simulated in 3D. We vary the shape of the falling object from a sphere to a cube by changing a single shape parameter as well as varying the initial orientation and position of the o...
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Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach
Compared with relational database (RDB), graph database (GDB) is a more intuitive expression of the real world. Each node in the GDB is a both storage and logic unit. Since it is connected to its neighboring nodes through edges, and its neighboring information could be easily obtained in one-step graph traversal. It ...
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A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix invers...
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Sub-Nanometer Channels Embedded in Two-Dimensional Materials
Two-dimensional (2D) materials are among the most promising candidates for next-generation electronics due to their atomic thinness, allowing for flexible transparent electronics and ultimate length scaling. Thus far, atomically-thin p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have been ...
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Emotion in Reinforcement Learning Agents and Robots: A Survey
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, compu...
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Tensor Networks in a Nutshell
Tensor network methods are taking a central role in modern quantum physics and beyond. They can provide an efficient approximation to certain classes of quantum states, and the associated graphical language makes it easy to describe and pictorially reason about quantum circuits, channels, protocols, open systems and ...
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Cheryl's Birthday
We present four logic puzzles and after that their solutions. Joseph Yeo designed 'Cheryl's Birthday'. Mike Hartley came up with a novel solution for 'One Hundred Prisoners and a Light Bulb'. Jonathan Welton designed 'A Blind Guess' and 'Abby's Birthday'. Hans van Ditmarsch and Barteld Kooi authored the puzzlebook 'O...
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Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
Exploration bonus derived from the novelty of the states in an environment has become a popular approach to motivate exploration for deep reinforcement learning agents in the past few years. Recent methods such as curiosity-driven exploration usually estimate the novelty of new observations by the prediction errors o...
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Lenient Multi-Agent Deep Reinforcement Learning
Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep...
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A New Framework for Synthetic Aperture Sonar Micronavigation
Synthetic aperture imaging systems achieve constant azimuth resolution by coherently summating the observations acquired along the aperture path. At this aim, their locations have to be known with subwavelength accuracy. In underwater Synthetic Aperture Sonar (SAS), the nature of propagation and navigation in water m...
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Emotionalism within People-Oriented Software Design
In designing most software applications, much effort is placed upon the functional goals, which make a software system useful. However, the failure to consider emotional goals, which make a software system pleasurable to use, can result in disappointment and system rejection even if utilitarian goals are well impleme...
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Quantifying the Estimation Error of Principal Components
Principal component analysis is an important pattern recognition and dimensionality reduction tool in many applications. Principal components are computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$ that approximates a population covariance $\Sigma$, and these eigenvectors are often used to ...
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On Convex Programming Relaxations for the Permanent
In recent years, several convex programming relaxations have been proposed to estimate the permanent of a non-negative matrix, notably in the works of Gurvits and Samorodnitsky. However, the origins of these relaxations and their relationships to each other have remained somewhat mysterious. We present a conceptual f...
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Many cubic surfaces contain rational points
Building on recent work of Bhargava--Elkies--Schnidman and Kriz--Li, we produce infinitely many smooth cubic surfaces defined over the field of rational numbers that contain rational points.
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Representation learning of drug and disease terms for drug repositioning
Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existe...
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Analysis of a remarkable singularity in a nonlinear DDE
In this work we investigate the dynamics of the nonlinear DDE (delay-differential equation) x''(t)+x(t-T)+x(t)^3=0 where T is the delay. For T=0 this system is conservative and exhibits no limit cycles. For T>0, no matter how small, an infinite number of limit cycles exist, their amplitudes going to infinity in the l...
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Characterization of Lipschitz functions in terms of variable exponent Lebesgue spaces
Our aim is to characterize the Lipschitz functions by variable exponent Lebesgue spaces. We give some characterizations of the boundedness of the maximal or nonlinear commutators of the Hardy-Littlewood maximal function and sharp maximal function in variable exponent Lebesgue spaces when the symbols $b$ belong to the...
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Superconductivity Induced by Interfacial Coupling to Magnons
We consider a thin normal metal sandwiched between two ferromagnetic insulators. At the interfaces, the exchange coupling causes electrons within the metal to interact with magnons in the insulators. This electron-magnon interaction induces electron-electron interactions, which, in turn, can result in p-wave supercon...
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Privacy Assessment of De-identified Opal Data: A report for Transport for NSW
We consider the privacy implications of public release of a de-identified dataset of Opal card transactions. The data was recently published at this https URL. It consists of tap-on and tap-off counts for NSW's four modes of public transport, collected over two separate week-long periods. The data has been further tr...
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On the Importance of Correlations in Rational Choice: A Case for Non-Nashian Game Theory
The Nash equilibrium paradigm, and Rational Choice Theory in general, rely on agents acting independently from each other. This note shows how this assumption is crucial in the definition of Rational Choice Theory. It explains how a consistent Alternate Rational Choice Theory, as suggested by Jean-Pierre Dupuy, can b...
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Reliable estimation of prediction uncertainty for physico-chemical property models
The predictions of parameteric property models and their uncertainties are sensitive to systematic errors such as inconsistent reference data, parametric model assumptions, or inadequate computational methods. Here, we discuss the calibration of property models in the light of bootstrapping, a sampling method akin to...
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Rethinking Split Manufacturing: An Information-Theoretic Approach with Secure Layout Techniques
Split manufacturing is a promising technique to defend against fab-based malicious activities such as IP piracy, overbuilding, and insertion of hardware Trojans. However, a network flow-based proximity attack, proposed by Wang et al. (DAC'16) [1], has demonstrated that most prior art on split manufacturing is highly ...
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Unsupervised robotic sorting: Towards autonomous decision making robots
Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation. Usually, to decide where to sort an object, the system needs to solve either an instance retrieval (known object) or a supervised classification (predefined set of classes) problem...
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Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis
The Whittle likelihood is widely used for Bayesian nonparametric estimation of the spectral density of stationary time series. However, the loss of efficiency for non-Gaussian time series can be substantial. On the other hand, parametric methods are more powerful if the model is well-specified, but may fail entirely ...
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A consistent approach to unstructured mesh generation for geophysical models
Geophysical model domains typically contain irregular, complex fractal-like boundaries and physical processes that act over a wide range of scales. Constructing geographically constrained boundary-conforming spatial discretizations of these domains with flexible use of anisotropically, fully unstructured meshes is a ...
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Metric Map Merging using RFID Tags & Topological Information
A map merging component is crucial for the proper functionality of a multi-robot system performing exploration, since it provides the means to integrate and distribute the most important information carried by the agents: the explored-covered space and its exact (depending on the SLAM accuracy) morphology. Map mergin...
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Learning Local Feature Aggregation Functions with Backpropagation
This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem). To achieve that, we compose the local feature aggregati...
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Sub-harmonic Injection Locking in Metronomes
In this paper, we demonstrate sub-harmonic injection locking (SHIL) in mechanical metronomes. To do so, we first formulate metronome's physical compact model, focusing on its nonlinear terms for friction and the escapement mechanism. Then we analyze metronomes using phase-macromodel-based techniques and show that the...
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Spin Seebeck effect in a polar antiferromagnet $α$-Cu$_{2}$V$_{2}$O$_{7}$
We have studied the longitudinal spin Seebeck effect in a polar antiferromagnet $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ in contact with a Pt film. Below the antiferromagnetic transition temperature of $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$, spin Seebeck voltages whose magnetic field dependence is similar to that reported in antifer...
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Mackey algebras which are Gorenstein
We complete the picture available in the literature by showing that the integral Mackey algebra is Gorenstein if and only if the group order is square-free, in which case it must have Gorenstein dimension one. We illustrate this result by looking in details at the examples of the cyclic group of order four and the Kl...
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Efficient acquisition rules for model-based approximate Bayesian computation
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate mo...
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Active Learning for Regression Using Greedy Sampling
Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label th...
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Centroid estimation based on symmetric KL divergence for Multinomial text classification problem
We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids. Experiments on several standard data sets indicate that the new method achieves substantial improvements over the traditional clas...
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Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering
Calculation of near-neighbor interactions among high dimensional, irregularly distributed data points is a fundamental task to many graph-based or kernel-based machine learning algorithms and applications. Such calculations, involving large, sparse interaction matrices, expose the limitation of conventional data-and-...
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Evolution and Recent Developments of the Gaseous Photon Detectors Technologies
The evolution and the present status of the gaseous photon detectors technologies are reviewed. The most recent developments in several branches of the field are described, in particular the installation and commissioning of the first large area MPGD-based detectors of single photons on COMPASS RICH-1. Investigation ...
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Superconducting Qubit-Resonator-Atom Hybrid System
We propose a hybrid quantum system, where an $LC$ resonator inductively interacts with a flux qubit and is capacitively coupled to a Rydberg atom. Varying the external magnetic flux bias controls the flux-qubit flipping and the flux qubit-resonator interface. The atomic spectrum is tuned via an electrostatic field, m...
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Heroes and Zeroes: Predicting the Impact of New Video Games on Twitch.tv
Video games and the playing thereof have been a fixture of American culture since their introduction in the arcades of the 1980s. However, it was not until the recent proliferation of broadband connections robust and fast enough to handle live video streaming that players of video games have transitioned from a conte...
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Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study
Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing the performance of a classifier. For speech emotion recognition tasks, generating effective features is crucial. Currently, handcrafted features are mostly used for speech emo...
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Insense: Incoherent Sensor Selection for Sparse Signals
Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number ...
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Discrete Distribution for a Wiener Process Range and its Properties
We introduce the discrete distribution of a Wiener process range. Rather than finding some basic distributional properties including hazard rate function, moments, Stress-strength parameter and order statistics of this distribution, this work studies some basic properties of the truncated version of this distribution...
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