pwc-archive/papers-with-abstracts huggingface dataset:
Tradespace analysis of GNSS Space Segment Architectures
Authors: Filipe Pereira, Daniel Selva
Date: 2020-07-06 00:00:00

Global Navigation Satellite Systems (GNSS) provide ubiquitous, continuous and reliable positioning, navigation and timing information around the world. However, GNSS space segment design decisions have been based on the precursor Global Positioning System (GPS), which was designed in the 70s. This paper revisits those early design decisions in view of major technological advancements and new GNSS environmental threats. The rich tradespace between User Navigation Error (UNE) performance and constellation deployment costs is explored and conclusions are complemented by sensitivity analysis and association rule mining. This study finds that constellations at an orbit altitude of ~2 Earth radii can outperform existing GNSS in terms of cost, robustness and UNE. These insights should be taken into account when designing future generations of GNSS.
Optimal Decision Rules for Weak GMM
Authors: Isaiah Andrews, Anna Mikusheva
Date: 2020-07-08 00:00:00

This paper studies optimal decision rules, including estimators and tests, for weakly identified GMM models. We derive the limit experiment for weakly identified GMM, and propose a theoretically-motivated class of priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status, and we recommend quasi-Bayes for settings where identification is a concern. We further propose weighted average power-optimal identification-robust frequentist tests and confidence sets, and prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak identification.
A hybrid discrete-continuum approach to model Turing pattern formation
Authors: Fiona R Macfarlane, Mark AJ Chaplain, Tommaso Lorenzi
Date: 2020-07-08 00:00:00

Since its introduction in 1952, Turing's (pre-)pattern theory ("the chemical basis of morphogenesis") has been widely applied to a number of areas in developmental biology. The related pattern formation models normally comprise a system of reaction-diffusion equations for interacting chemical species ("morphogens"), whose heterogeneous distribution in some spatial domain acts as a template for cells to form some kind of pattern or structure through, for example, differentiation or proliferation induced by the chemical pre-pattern. Here we develop a hybrid discrete-continuum modelling framework for the formation of cellular patterns via the Turing mechanism. In this framework, a stochastic individual-based model of cell movement and proliferation is combined with a reaction-diffusion system for the concentrations of some morphogens. As an illustrative example, we focus on a model in which the dynamics of the morphogens are governed by an activator-inhibitor system that gives rise to Turing pre-patterns. The cells then interact with morphogens in their local area through either of two forms of chemically-dependent cell action: chemotaxis and chemically-controlled proliferation. We begin by considering such a hybrid model posed on static spatial domains, and then turn to the case of growing domains. In both cases, we formally derive the corresponding deterministic continuum limit and show that that there is an excellent quantitative match between the spatial patterns produced by the stochastic individual-based model and its deterministic continuum counterpart, when sufficiently large numbers of cells are considered. This paper is intended to present a proof of concept for the ideas underlying the modelling framework, with the aim to then apply the related methods to the study of specific patterning and morphogenetic processes in the future.
The Statistical Characteristics of Power-Spectrum Subband Energy Ratios under Additive Gaussian White Noise
Authors: Han Li, Yanzhu Hu, Song Wang, Zhen Meng
Date: 2020-07-08 00:00:00
Tasks: valid

The power-spectrum subband energy ratio (PSER) has been applied in a variety of fields, but reports on its statistical properties have been limited. As such, this study investigates these characteristics in the presence of additive Gaussian white noise for both pure noise and mixed signals. By analyzing the probability and independence of power spectrum bins, and the relationship between the F and beta distributions, we develop a probability distribution for the PSER. Results showed that in the case of pure noise, the PSER follows a beta distribution. In addition, the probability density function and the quantile exhibited no relationship with the noise variance, only with the number of lines in the power spectrum, that is, PSER is not affected by noise. When Gaussian white noise was mixed with the known signal, the resulting PSER followed a doubly non-central beta distribution. In this case, it was difficult to identify the quantile, as the probability density and cumulative distribution functions were represented by infinite series. However, when a spectral bin did not contain the power spectrum of the known signal, an approximated quantile was found. This quantile is strictly proved to be in agreement with the quantile in the case of pure noise and offers a convenient methodology for identifying valid signals.
An Adaptive and Explicit Fourth Order Runge-Kutta-Fehlberg Method Coupled with Compact Finite Differencing for Pricing American Put Options
Authors: Chinonso Nwankwo, Weizhong Dai
Date: 2020-07-08 00:00:00

We propose an adaptive and explicit fourth-order Runge-Kutta-Fehlberg method coupled with a fourth-order compact scheme to solve the American put options problem. First, the free boundary problem is converted into a system of partial differential equations with a fixed domain by using logarithm transformation and taking additional derivatives. With the addition of an intermediate function with a fixed free boundary, a quadratic formula is derived to compute the velocity of the optimal exercise boundary analytically. Furthermore, we implement an extrapolation method to ensure that at least, a third-order accuracy in space is maintained at the boundary point when computing the optimal exercise boundary from its derivative. As such, it enables us to employ fourth-order spatial and temporal discretization with Dirichlet boundary conditions for obtaining the numerical solution of the asset option, option Greeks, and the optimal exercise boundary. The advantage of the Runge-Kutta-Fehlberg method is based on error control and the adjustment of the time step to maintain the error at a certain threshold. By comparing with some existing methods in the numerical experiment, it shows that the present method has a better performance in terms of computational speed and provides a more accurate solution.
Structural Gaussian mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks
Authors: Savi Virolainen
Date: 2020-07-09 00:00:00

A structural Gaussian mixture vector autoregressive model is introduced. The shocks are identified by combining simultaneous diagonalization of the reduced form error covariance matrices with constraints on the time-varying impact matrix. This leads to flexible identification conditions, and some of the constraints are also testable. The empirical application studies asymmetries in the effects of the U.S. monetary policy shock and finds strong asymmetries with respect to the sign and size of the shock and to the initial state of the economy. The accompanying CRAN distributed R package gmvarkit provides a comprehensive set of tools for numerical analysis.
A Bivariate Compound Dynamic Contagion Process for Cyber Insurance
Authors: Jiwook Jang, Rosy Oh
Date: 2020-06-12 00:00:00
Tasks: Management

As corporates and governments become more digital, they become vulnerable to various forms of cyber attack. Cyber insurance products have been used as risk management tools, yet their pricing does not reflect actual risk, including that of multiple, catastrophic and contagious losses. For the modelling of aggregate losses from cyber events, in this paper we introduce a bivariate compound dynamic contagion process, where the bivariate dynamic contagion process is a point process that includes both externally excited joint jumps, which are distributed according to a shot noise Cox process and two separate self-excited jumps, which are distributed according to the branching structure of a Hawkes process with an exponential fertility rate, respectively. We analyse the theoretical distributional properties for these processes systematically, based on the piecewise deterministic Markov process developed by Davis (1984) and the univariate dynamic contagion process theory developed by Dassios and Zhao (2011). The analytic expression of the Laplace transform of the compound process and its moments are presented, which have the potential to be applicable to a variety of problems in credit, insurance, market and other operational risks. As an application of this process, we provide insurance premium calculations based on its moments. Numerical examples show that this compound process can be used for the modelling of aggregate losses from cyber events. We also provide the simulation algorithm for statistical analysis, further business applications and research.
Extremum Power Seeking Control of A Hybrid Wind-Solar-Storage DC Power System
Date: 2020-07-15 00:00:00
Tasks: Point Tracking

This paper presents a combined power system with a common dc bus that
contains solar power, wind power, battery storage, and a constant dc load
(CDL). In wind system, an AC-DC uncontrolled rectifier is used at the first
stage and the DC-DC converter is controlled by a maximum power point tracker
(MPPT) at second stage. In the solar system, two cascaded boost converters are
controlled through a sliding mode controller (SMC) to regulate the power flow
to the load. A supervisory control strategy is also introduced to maximize the
simultaneous energy harvesting from both renewable sources and balance the
energy between the sources, battery, and the load. According to the level of
power generation available at each renewable energy source, the state of charge
in the battery, and the load requirement, the controller results in four
contingencies. Simulation results show the accurate operation of the
supervisory controller and functionality of the maximum power point tracking
algorithm for solar and for wind power.
Comprehensive assessment of error correction methods for high-throughput sequencing data
Authors: Yun Heo, Gowthami Manikandan, Anand Ramachandran, Deming Chen
Date: 2020-07-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The advent of DNA and RNA sequencing has revolutionized the study of genomics and molecular biology. Next generation sequencing (NGS) technologies like Illumina, Ion Torrent, SOLiD sequencing etc. have brought about a quick and cheap way to sequence genomes. Recently, third generation sequencing (TGS) technologies like PacBio and Oxford Nanopore Technology (ONT) have also been developed. Different technologies use different underlying methods for sequencing and are prone to different error rates. Though many tools exist for error correction of sequencing data from NGS and TGS methods, no standard method is available yet to evaluate the accuracy and effectiveness of these error-correction tools. In this study, we present a Software Package for Error Correction Tool Assessment on nuCLEic acid sequences (SPECTACLE) providing comprehensive algorithms to evaluate error-correction methods for DNA and RNA sequencing, for NGS and TGS platforms. We also present a compilation of sequencing datasets for Illumina, PacBio and ONT platforms that present challenging scenarios for error-correction tools. Using these datasets and SPECTACLE, we evaluate the performance of 23 different error-correction tools and present unique and helpful insights into their strengths and weaknesses. We hope that our methodology will standardize the evaluation of DNA and RNA error-correction tools in the future.
Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices
Authors: Pengxia Wu, Julian Cheng
Date: 2020-07-10 00:00:00
Tasks: Compressive Sensing

For massive multiple-input multiple-output (MIMO) systems operating in frequency-division duplex mode, downlink channel state information (CSI) acquisition will incur large overhead. This overhead is substantially reduced when sparse channel estimation techniques are employed, owing to the channel sparsity in the angular domain. When a sparse channel estimation method is implemented, the measurement matrix, which is related to the pilot matrix, is essential to the channel estimation performance. Existing sparse channel estimation schemes widely adopt random measurement matrices, which have been criticized for their suboptimal reconstruction performance. This paper proposes novel data-driven solutions to design the measurement matrix. Model-based autoencoders are customized to optimize the measurement matrix by unfolding the classical basis pursuit algorithm. The obtained data-driven measurement matrices are applied to existing sparse reconstruction algorithms, leading to flexible hybrid data-driven implementations for sparse channel estimation. Numerical results show that the proposed data-driven measurement matrices can achieve more accurate reconstructions and use fewer measurements than the existing random matrices, thereby leading to a higher achievable rate for CSI acquisition. Moreover, compared with existing pure deep learning-based sparse reconstruction methods, the proposed hybrid data-driven scheme, which uses the novel data-driven measurement matrices with conventional sparse reconstruction algorithms, can achieve higher reconstruction accuracy.
A Unifying Framework for Adaptive Radar Detection in the Presence of Multiple Alternative Hypotheses
Authors: Pia Addabbo, Sudan Han, Fillippo Biondi, Gaetano Giunta, Danilo Orlando
Date: 2020-07-10 00:00:00

In this paper, we develop a new elegant framework relying on the Kullback-Leibler Information Criterion to address the design of one-stage adaptive detection architectures for multiple hypothesis testing problems. Specifically, at the design stage, we assume that several alternative hypotheses may be in force and that only one null hypothesis exists. Then, starting from the case where all the parameters are known and proceeding until the case where the adaptivity with respect to the entire parameter set is required, we come up with decision schemes for multiple alternative hypotheses consisting of the sum between the compressed log-likelihood ratio based upon the available data and a penalty term accounting for the number of unknown parameters. The latter rises from suitable approximations of the Kullback-Leibler Divergence between the true and a candidate probability density function. Interestingly, under specific constraints, the proposed decision schemes can share the constant false alarm rate property by virtue of the Invariance Principle. Finally, we show the effectiveness of the proposed framework through the application to examples of practical value in the context of radar detection also in comparison with two-stage competitors. This analysis highlights that the architectures devised within the proposed framework represent an effective means to deal with detection problems where the uncertainty on some parameters leads to multiple alternative hypotheses.
Cotranscriptional kinetic folding of RNA secondary structures including pseudoknots
Authors: Vo Hong Thanh, Dani Korpela, Pekka Orponen
Date: 2020-07-10 00:00:00

Computational prediction of RNA structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA secondary structure formation, including pseudoknots, that takes into account the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.
Multiscale modeling of glioma pseudopalisades: contributions from the tumor microenvironment
Authors: Pawan Kumar, Jing Li, Christina Surulescu
Date: 2020-07-10 00:00:00
Tasks: Prognosis

Gliomas are primary brain tumors with a high invasive potential and infiltrative spread. Among them, glioblastoma multiforme (GBM) exhibits microvascular hyperplasia and pronounced necrosis triggered by hypoxia. Histological samples showing garland-like hypercellular structures (so-called pseudopalisades) centered around the occlusion site of a capillary are typical for GBM and hint on poor prognosis of patient survival. We propose a multiscale modeling approach in the kinetic theory of active particles framework and deduce by an upscaling process a reaction-diffusion model with repellent pH-taxis. We prove existence of a unique global bounded classical solution for a version of the obtained macroscopic system and investigate the asymptotic behavior of the solution. Moreover, we study two different types of scaling and compare the behavior of the obtained macroscopic PDEs by way of simulations. These show that patterns1 (including pseudopalisades) can be formed for some parameter ranges, in accordance with the tumor grade. This is true when the PDEs are obtained via parabolic scaling (undirected tissue), while no such patterns are observed for the PDEs arising by a hyperbolic limit (directed tissue). This suggests that brain tissue might be undirected - at least as far as glioma migration is concerned. We also investigate two different ways of including cell level descriptions of response to hypoxia and the way they are related.
Control Hardware-in-the-loop for Voltage Controlled Inverters with Unbalanced and Non-linear Loads in Stand-alone Photovoltaic(PV) Islanded Microgrids
Date: 2020-09-14 00:00:00

Unbalanced and nonlinear loads connected to microgrids (MG) with local
distributed energy resources (DERs) are two of the leading causes of power
quality problems. Nonlinearloads introduce voltage and current harmonics, and
single-phase loads can cause voltage and current imbalances in a three-phase
network. This paper presents a hierarchical control scheme for
voltage-controlled photovoltaic (PV) inverters with unbalanced and nonlinear
loads in micro-grids. The hierarchical control consists of primary control and
voltage compensation control(VCC) and a DC voltage regulator (VR). The primary
control scheme controls active and reactive power-sharing and the VCCregulates
the unbalanced voltage and harmonics distortion. The effectiveness of the
scheme is verified using Opal-RT real-time simulation and experimentally using
control hardware-in-the-loop. The voltage distortion at the point of common
coupling (PCC)decreased from 6.38 percent to 1.91 percent after compensation,
while the unbalanced and harmonic loads are shared proportionally among the DG
units.
Monitoring of Critical Infrastructures by Micro-Motion Estimation: the Mosul Dam Destabilization
Authors: Filippo Biondi, Pia Addabbo, Carmine Clemente, Silvia Liberata Ullo, Danilo Orlando
Date: 2020-07-10 00:00:00
Tasks: Motion Estimation

In this paper, authors propose a new procedure to provide a tool for monitoring critical infrastructures. Particularly, through the analysis of COSMO-SkyMed satellite data, a detailed and updated survey is provided, for monitoring the accelerating destabilization process of the Mosul dam, that represents the largest hydraulic facility of Iraq and is located on the Tigris river. The destructive potential of the wave that would be generated, in the event of the dam destruction, could have serious consequences. If the concern for human lives comes first, the concern for cultural heritage protection is not negligible, since several archaeological sites are located around the Mosul dam. The proposed procedure is an in-depth modal assessment based on the micro-motion estimation, through a Doppler sub-apertures tracking and a Multi-Chromatic Analysis (MCA). The method is based initially on the Persistent Scatterers Interferometry (PSI) that is also discussed for completeness and validation. The modal analysis has detected the presence of several areas of resonance that could mean the presence of cracks, and the results have shown that the dam is still in a strong destabilization. Moreover, the dam appears to be divided into two parts: the northern part is accelerating rapidly while the southern part is decelerating and a main crack in this north-south junction is found. The estimated velocities through the PS-InSAR technique show a good agreement with the GNSS in-situ measurements, resulting in a very high correlation coefficient and showing how the proposed procedure works efficiently.
DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids
Date: 2020-07-10 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition

Developing and selecting hearing aids is a time consuming process which is simplified by using objective models. Previously, the framework for auditory discrimination experiments (FADE) accurately simulated benefits of hearing aid algorithms with root mean squared prediction errors below 3 dB. One FADE simulation requires several hours of (un)processed signals, which is obstructive when the signals have to be recorded. We propose and evaluate a data-reduced FADE version (DARF) which facilitates simulations with signals that cannot be processed digitally, but that can only be recorded in real-time. DARF simulates one speech recognition threshold (SRT) with about 30 minutes of recorded and processed signals of the (German) matrix sentence test. Benchmark experiments were carried out to compare DARF and standard FADE exhibiting small differences for stationary maskers (1 dB), but larger differences with strongly fluctuating maskers (5 dB). Hearing impairment and hearing aid algorithms seemed to reduce the differences. Hearing aid benefits were simulated in terms of speech recognition with three pairs of real hearing aids in silence ($\geq$8 dB), in stationary and fluctuating maskers in co-located (stat. 2 dB; fluct. 6 dB), and spatially separated speech and noise signals (stat. $\geq$8 dB; fluct. 8 dB). The simulations were plausible in comparison to data from literature, but a comparison with empirical data is still open. DARF facilitates objective SRT simulations with real devices with unknown signal processing in real environments. Yet, a validation of DARF for devices with unknown signal processing is still pending since it was only tested with three similar devices. Nonetheless, DARF could be used for improving as well as for developing or model-based fitting of hearing aids.