pwc-archive/papers-with-abstracts huggingface dataset:
Universal Wireless Power Transfer for Energy Security, Availability and Convenience
Authors: Dinh Hoa Nguyen, Andrew Chapman
Date: 2020-08-28 00:00:00

This article proposes a novel system concept named universal wireless power transfer, in which power can be wirelessly transferred between different entities (e.g. vehicles, robots, homes, grid facilities, consumer electronic devices, etc.) equipped with proper energy transmitters and receivers, whether stationary or in motion. This concept generalizes individually existing wireless power transfer systems, where a specific wireless power transfer technology is used, and where the wireless energy transmitter or receiver is fixed. As a result, energy mobility, flexibility, and convenience are significantly improved by the proposed universal wireless power transfer concept in this study. Moreover, factors relevant to system energy efficiency are analyzed according to each utilized wireless power transfer technology. Necessary market mechanisms for such a concept to be successfully deployed are also introduced, along with an analysis of the benefits engendered in terms of improving energy systems, the environment, human comfort, and convenience. Finally, a discussion of the proposed concept, policy implications and recommendations for future research directions which will underpin universal wireless power transfer systems are presented.
Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion
Date: 2020-08-28 00:00:00
Tasks: Voice Conversion

The voice conversion challenge is a bi-annual scientific event held to
compare and understand different voice conversion (VC) systems built on a
common dataset. In 2020, we organized the third edition of the challenge and
constructed and distributed a new database for two tasks, intra-lingual
semi-parallel and cross-lingual VC. After a two-month challenge period, we
received 33 submissions, including 3 baselines built on the database. From the
results of crowd-sourced listening tests, we observed that VC methods have
progressed rapidly thanks to advanced deep learning methods. In particular,
speaker similarity scores of several systems turned out to be as high as target
speakers in the intra-lingual semi-parallel VC task. However, we confirmed that
none of them have achieved human-level naturalness yet for the same task. The
cross-lingual conversion task is, as expected, a more difficult task, and the
overall naturalness and similarity scores were lower than those for the
intra-lingual conversion task. However, we observed encouraging results, and
the MOS scores of the best systems were higher than 4.0. We also show a few
additional analysis results to aid in understanding cross-lingual VC better.
A Method with Lower-than-ML Threshold for Frequency Estimation of Multiple Sinusoids
Authors: P. Vishnu, C. S. Ramalingam
Date: 2020-08-28 00:00:00

Estimating the frequencies of multiple sinusoids in the presence of AWGN and when the data record is short is commonly accomplished by subspace-based methods such as ESPRIT, MUSIC, Min-Norm, etc. These methods do not assume that the data are zero outside the observation interval. If we assume otherwise, the threshold SNR is lowered significantly, but the price paid is unacceptable bias. Among all known unbiased estimators, the maximum-likelihood estimator (MLE) has the lowest threshold, but is computationally the most expensive. We propose a new algorithm that carries out, when needed, (i) zero-padding, and (ii) removal and re-estimation. These added steps result in a threshold SNR that is lower than that of the MLE for the examples considered herein, viz., noisy signals containing sinusoids with random parameters and up to five components. The maximum improvement in threshold was 10 dB for the two-sinusoid case. The bias of the estimates is also either equal to or lower than MLE's. Unlike the MLE, the proposed method is very much computationally feasible.
Vehicle Telematics Via Exteroceptive Sensors: A Survey
Authors: Fernando Molano Ortiz, Matteo Sammarco, Luís Henrique M. K. Costa, Marcin Detyniecki
Date: 2020-08-27 00:00:00
Tasks: Survey

Whereas a very large number of sensors are available in the automotive field, currently just a few of them, mostly proprioceptive ones, are used in telematics, automotive insurance, and mobility safety research. In this paper, we show that exteroceptive sensors, like microphones or cameras, could replace proprioceptive ones in many fields. Our main motivation is to provide the reader with alternative ideas for the development of telematics applications when proprioceptive sensors are unusable for technological issues, privacy concerns, or lack of availability in commercial devices. We first introduce a taxonomy of sensors in telematics. Then, we review in detail all exteroceptive sensors of some interest for vehicle telematics, highlighting advantages, drawbacks, and availability in off-the-shelf devices. Successively, we present a list of notable telematics services and applications in research and industry like driving profiling or vehicular safety. For each of them, we report the most recent and important works relying on exteroceptive sensors, as long as the available datasets. We conclude showing open challenges using exteroceptive sensors both for industry and research.
A comprehensive spatial-temporal infection model
Date: 2020-08-28 00:00:00
Tasks: Chemical Process, model

Motivated by analogies between the spreading of human-to-human infections and
of chemical processes, we develop a comprehensive model that accounts both for
infection and for transport. In this analogy, the three different populations
of infection models correspond to three chemical species. Areal densities
emerge as the key variables, thus capturing the effect of spatial density. We
derive expressions for the kinetics of the infection rates and for the
important parameter R0, that include areal density and its spatial
distribution. Coupled with mobility the model allows the study of various
effects. We first present results for a batch reactor, the chemical process
equivalent of the SIR model. Because density makes R0 a decreasing function of
the process extent, the infection curves are different and smaller than for the
standard SIR model. We show that the effect of the initial conditions is
limited to the onset of the epidemic. We derive effective infection curves for
a number of cases, including a back-and-forth commute between regions of low
and high R0 environments. We then consider spatially distributed systems. We
show that diffusion leads to traveling waves, which in 1-D geometries propagate
at a constant speed and with a constant shape, both of which are sole functions
of R0. The infection curves are slightly different than for the batch problem,
as diffusion mitigates the infection intensity, thus leading to an effective
lower R0. The dimensional wave speed is found to be proportional to the product
of the square root of the diffusivity and of an increasing function of R0,
confirming the importance of restricting mobility in arresting the propagation
of infection. We examine the interaction of infection waves under various
conditions and scenarios, and extend the wave propagation analysis to 2-D
heterogeneous systems.
The Identity Fragmentation Bias
Authors: Tesary Lin, Sanjog Misra
Date: 2020-08-28 00:00:00

Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same consumer. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias, referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We then compare several corrective measures, and discuss their respective advantages and caveats.
Laplacian Matrices for Extremely Balanced and Unbalanced Phylogenetic Trees
Date: 2020-08-28 00:00:00

Phylogenetic trees are important tools in the study of evolutionary
relationships between species. Measures such as the index of Sackin, Colless,
and Total Cophenetic have been extensively used to quantify tree balance, one
key property of phylogenies. Recently a new proposal has been introduced, based
on the spectrum of the Laplacian matrix associated with the tree. In this work,
we calculate the Laplacian matrix analytically for two extreme cases,
corresponding to fully balanced and fully unbalanced trees. For maximally
balanced trees no closed form for the Laplacian matrix was derived, but we
present an algorithm to construct it. We show that Laplacian matrices of fully
balanced trees display self-similar patterns that result in highly degenerated
eigenvalues. Degeneracy is the main signature of this topology, since it is
totally absent in fully unbalanced trees. We also establish some analytical and
numerical results about the largest eigenvalue of Laplacian matrices for these
topologies.
Development and Assessment of a Nearly Autonomous Management and Control System for Advanced Reactors
Authors: Linyu Lin, Paridhi Athe, Pascal Rouxelin, Robert Youngblood, Abhinav Gupta, Jeffrey Lane, Maria Avramova, Nam Dinh
Date: 2020-08-29 00:00:00
Tasks: Management

This paper develops a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. The development process of NAMAC is characterized by a three layer-layer architecture: knowledge base, the Digital Twin (DT) developmental layer, and the NAMAC operational layer. The DT is described as a knowledge acquisition system from the knowledge base for intended uses in the NAMAC system. A set of DTs with different functions is developed with acceptable performance and assembled according to the NAMAC operational workflow to furnish recommendations to operators. To demonstrate the capability of the NAMAC system, a case study is designed, where a baseline NAMAC is implemented for operating a simulator of the Experimental Breeder Reactor II during a single loss of flow accident. When NAMAC is operated in the training domain, it can provide reasonable recommendations that prevent the peak fuel centerline temperature from exceeding a safety criterion.
Implication of Natal Care and Maternity Leave on Child Morbidity: Evidence from Ghana
Date: 2020-08-29 00:00:00

Failure to receive post-natal care within first week of delivery causes a 3%
increase in the possibility of Acute Respiratory Infection in children under
five. Mothers with unpaid maternity leave put their children at a risk of 3.9%
increase in the possibility of ARI compared to those with paid maternity leave.
Sparse High-Order Portfolios via Proximal DCA and SCA
Authors: Jinxin Wang, Zengde Deng, Taoli Zheng, Anthony Man-Cho So
Date: 2020-08-29 00:00:00
Tasks: Portfolio Optimization, Vocal Bursts Intensity Prediction

In this paper, we aim at solving the cardinality constrained high-order portfolio optimization, i.e., mean-variance-skewness-kurtosis model with cardinality constraint (MVSKC). Optimization for the MVSKC model is of great difficulty in two parts. One is that the objective function is non-convex, the other is the combinational nature of the cardinality constraint, leading to non-convexity as well dis-continuity. Based on the observation that cardinality constraint has the difference-of-convex (DC) property, we transform the cardinality constraint into a penalty term and then propose three algorithms including the proximal difference of convex algorithm (pDCA), pDCA with extrapolation (pDCAe) and the successive convex approximation (SCA) to handle the resulting penalized MVSK (PMVSK) formulation. Moreover, theoretical convergence results of these algorithms are established respectively. Numerical experiments on the real datasets demonstrate the superiority of our proposed methods in obtaining high utility and sparse solutions as well as efficiency in terms of time usage.
Darwinian Evolution of Taste
Authors: J. C. Phillips
Date: 2020-08-29 00:00:00

What is life. Schrodingers question is discussed here for a specific protein, villin, which builds cells in tissues that detect taste and sound. Villin is represented by a sequence of 827 amino acids bound to a peptide backbone chain. We focus attention on a limited problem, the Darwinian evolution of villin sequences from chickens to humans. This biophysical problem is analyzed using a new physicical method based on thermodynamic domain scaling, a technique that bridges the gap between physical concepts, self-organized criticality, and conventional biostructural practice. It turns out that the evolutionary changes can be explained by Darwinian selection, which is not generally accepted by biologists at the protein level. The presentation is self-contained, and requires no prior experience with proteins at the molecular level.
On Microstructure Estimation Using Flatbed Scanners for Paper Surface Based Authentication
Authors: Runze Liu, Chau-Wai Wong
Date: 2020-08-29 00:00:00

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a "fingerprint" of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch.
Initialization Process of a Power System Transient Simulation Scheme for Stability Studies
Date: 2020-08-29 00:00:00

The initialization process of a novel power system transient simulation
scheme for stability studies is put forward, by further developing a
"time-domain harmonic power-flow algorithm". The initialization process is
formulated as an algebraic problem to ensure that the power system under study
is in steady state and operated at a specified operating point, at the
beginning of a transient simulation run. The algebraic problem is then solved
efficiently by a preconditioned finite difference Newton-GMRES method. Case
studies verify the validity and efficiency of the initialization process. The
proposed initialization process is general-purpose and can be applied to other
power system transient simulation schemes.
Secure Recovery Procedure for Manufacturing Systems using Synchronizing Automata and Supervisory Control Theory
Date: 2020-08-29 00:00:00

Manufacturing systems may be subject to external attacks and failures, so it
is important to deal with the recovery of the system after these situations.
This paper deals with the problem of recovering a manufacturing system, modeled
as a Discrete Event System (DES) using the Supervisory Control Theory (SCT),
when the control structure, called supervisor, desynchronizes from the physical
plant. The desynchronization may be seen as plant and supervisor being in
uncorresponding states. The recovery of the system may be attained if there is
a word, the synchronizing word, that regardless the state of each one of them,
brings the system and supervisor back to a known state. The concepts of
synchronizing automata are used to do so. In this paper we show under what
conditions a set of synchronizing plants and specifications leads to a
synchronizing supervisor obtained by the Supervisory Control Theory. The
problem is extended to cope with multiple supervisors, proposing a local
recovery when possible. We also present a simple way to model problems,
composed of machines and buffers, as synchronizing automata such that it is
always possible do restore synchronization between the control (supervisor) and
the plant.
Nonparametric Predictive Inference for Asian options
Authors: Ting He
Date: 2020-08-30 00:00:00

Asian option, as one of the path-dependent exotic options, is widely traded in the energy market, either for speculation or hedging. However, it is hard to price, especially the one with the arithmetic average price. The traditional trading procedure is either too restrictive by assuming the distribution of the underlying asset or less rigorous by using the approximation. It is attractive to infer the Asian option price with few assumptions of the underlying asset distribution and adopt to the historical data with a nonparametric method. In this paper, we present a novel approach to price the Asian option from an imprecise statistical aspect. Nonparametric Predictive Inference (NPI) is applied to infer the average value of the future underlying asset price, which attempts to make the prediction reflecting more uncertainty because of the limited information. A rational pairwise trading criterion is also proposed in this paper for the Asian options comparison, as a risk measure. The NPI method for the Asian option is illustrated in several examples by using the simulation techniques or the empirical data from the energy market.
Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals
Authors: Amir Weiss, Arie Yeredor
Date: 2020-08-30 00:00:00

An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and Kailath's (P-K's) classical approach in exploiting the special (Toeplitz) structure of the observations' covariance. However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in order to obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS) estimates of the sensors gains and phases offsets with asymptotically optimal weighting, based only on the empirical covariance matrix of the measurements. Moreover, we prove that our resulting estimates are also asymptotically optimal w.r.t. the raw data, and can therefore be deemed equivalent to the ML Estimates (MLE), which are otherwise obtained by joint ML estimation of all the unknown model parameters. After deriving computationally convenient expressions of the respective Cram\'er-Rao lower bounds, we also show that our estimates offer improved performance when applied to non-Gaussian signals (and/or noise) as quasi-MLE in a similar setting. The optimal performance of our estimates is demonstrated in simulation experiments, with a considerable improvement (reaching an order of magnitude and more) in the resulting mean squared errors w.r.t. P-K's ordinary LS estimates. We also demonstrate the improved accuracy in a multiple-sources directions-of-arrivals estimation task.
Blind Determination of the Number of Sources Using Distance Correlation
Authors: Amir Weiss, Arie Yeredor
Date: 2020-08-30 00:00:00

A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Sz\'ekely et al.'s distance correlation measure, we define the Sources' Dependency Criterion (SDC), from which our estimate arises. Unlike most previously proposed estimates, the SDC estimate exploits the full independence of the sources and noise, as well as the non-Gaussianity of the sources (as opposed to the Gaussianity of the noise), via implicit use of high-order statistics. This leads to a more robust, resilient and stable estimate w.r.t. the mixing matrix and the noise covariance structure. Empirical simulation results demonstrate these virtues, on top of superior performance in comparison with current state of the art estimates.
Enhanced Time-Frequency Representation and Mode Decomposition
Authors: Haijian Zhang, Guang Hua
Date: 2020-08-30 00:00:00

Time-frequency representation (TFR) allowing for mode reconstruction plays a significant role in interpreting and analyzing the nonstationary signal constituted of various modes. However, it is difficult for most previous methods to handle signal modes with closely-spaced or spectrally-overlapped instantaneous frequencies (IFs) especially in adverse environments. To address this issue, we propose an enhanced TFR and mode decomposition (ETFR-MD) method, which is particularly adapted to represent and decompose multi-mode signals with close or crossing IFs under low signal-to-noise ratio (SNR) conditions. The emphasis of the proposed ETFR-MD is placed on accurate IF and instantaneous amplitude (IA) extraction of each signal mode based on short-time Fourier transform (STFT). First, we design an initial IF estimation method specifically for the cases involving crossing IFs. Further, a low-complexity mode enhancement scheme is proposed so that enhanced IFs better fit underlying IF laws. Finally, the IA extraction from signal's STFT coefficients combined with the enhanced IFs enables us to reconstruct each signal mode. In addition, we derive mathematical expressions that reveal optimal window lengths and separation interference of our method. The proposed ETFR-MD is compatible with previous related methods, thus can be regarded as a step toward a more general time-frequency representation and decomposition method. Experimental results confirm the superior performance of the ETFR-MD when compared to a state-of-the-art benchmark.