pwc-archive/papers-with-abstracts huggingface dataset:
Estimation of Infection Rate and Prediction of Initial Infected Individuals of COVID-19
Date: 2020-04-27 00:00:00

We consider the pandemic spreading of COVID-19 for some selected countries
after the outbreak of the coronavirus in Wuhan City, China. We estimated the
infection rate and the initial infected individuals of COVID-19 by using the
officially reported data at the early stage of the epidemic for the susceptible
(S), infectable (I), quarantined (Q), and the cofirmed recovered (Rk)
population model, so called SIQRk model. In the reported data we know the
quarantined cases and the recovered cases. We can not know the recovered cases
from the asymptomatic cases. In the SIQRk model we can estimated the model
parameters and the initial infecting cases (confirmed ans asymtomatic cases)
from the data fits. We obtained the infection rate in the range between 0.233
and 0.462, the basic reproduction number Ro in the range between 1.8 and 3.5,
and the initial number of infected individuals in the range betwee 10 and 8409
for some selected countries. By using fitting parameters we estimated the
maximum time of the infection for Germany when the government are performing
the quarantine policy. The disease is undergoing to the calm state about six
months after first patients were identified.
Evolutionary dynamics and eigenspectrum of confluent Heun equation
Date: 2020-04-27 00:00:00

We consider a biological population evolving under the joint action of
selection, mutation and random genetic drift. The evolutionary dynamics are
described by a one-dimensional Fokker-Planck equation whose eigenfunctions obey
a confluent Heun equation. These eigenfunctions are expanded in an infinite
series of orthogonal Jacobi polynomials and the expansion coefficients are
found to obey a three-term recursion equation. Using scaling ideas, we obtain
an expression for the expansion coefficients and an analytical estimate of the
number of terms required in the series for an accurate determination of the
eigenfunction. The eigenvalue spectrum is studied using a perturbation theory
for weak selection and numerically for strong selection. In the latter case, we
find that the eigenvalue for the first excited state exhibits a sharp
transition: for mutation rate below one, the eigenvalue increases linearly with
increasing mutation rate and then remains a constant; higher eigenvalues are
found to display a more complex behavior.
Bank financial stability, bank valuation and international oil prices: Evidence from listed Russian public banks
Authors: Claudiu Albulescu
Date: 2020-04-27 00:00:00

Using data on 17 listed public banks from Russia over the period 2008 to 2016, we analyze whether international oil prices affect the bank stability in an oil-dependent country. We posit that a decrease in international oil prices has a negative long-run macroeconomic impact for an oil-exporting country, which further deteriorates the bank financial stability. More specifically, a decrease in international oil prices leads for an oil-exporting country as Russia to a currency depreciation and to a deterioration of the fiscal stance. In addition, given the positive correlation of oil and stock prices documented by numerous previous studies, a decrease in international oil prices represents a negative signal for the stock markets investors, negatively affecting banks' share prices and thus, their capacity to generate sustainable earnings. In this context, the bank financial stability can be menaced. With a focus on public listed banks and using a Pool Mean Group (PMG) estimator, we show that an increase in international oil prices and in the price to book value ratio has a long-run positive effect on Russian public banks stability, and conversely. While positive oil-price shocks contribute to bank stability in the long run, an opposite effect is recorded for negative shocks. However, no significant impact is documented in the short run. Our findings are robust to different bank stability specifications, different samples and control variables.
Discussion of "Design of Controllers with Arbitrary Convergence Time" (Automatica 112 (2020) 108710)
Date: 2020-07-14 00:00:00

This note corrects some technical inaccuracies in a recently published paper
on predefined-time convergence (Automatica 112 (2020) 108710) and discusses
implementation issues of the presented control algorithm.
Measuring wage inequality under right censoring
Date: 2020-04-27 00:00:00

In this paper we investigate potential changes which may have occurred over
the last two decades in the probability mass of the right tail of the wage
distribution, through the analysis of the corresponding tail index. In
specific, a conditional tail index estimator is introduced which explicitly
allows for right tail censoring (top-coding), which is a feature of the widely
used current population survey (CPS), as well as of other surveys. Ignoring the
top-coding may lead to inconsistent estimates of the tail index and to under or
over statements of inequality and of its evolution over time. Thus, having a
tail index estimator that explicitly accounts for this sample characteristic is
of importance to better understand and compute the tail index dynamics in the
censored right tail of the wage distribution. The contribution of this paper is
threefold: i) we introduce a conditional tail index estimator that explicitly
handles the top-coding problem, and evaluate its finite sample performance and
compare it with competing methods; ii) we highlight that the factor values used
to adjust the top-coded wage have changed over time and depend on the
characteristics of individuals, occupations and industries, and propose
suitable values; and iii) we provide an in-depth empirical analysis of the
dynamics of the US wage distribution's right tail using the public-use CPS
database from 1992 to 2017.
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots
Authors: Liam J. Ruske, Jochen Kursawe, Anestis Tsakiridis, Valerie Wilson, Alexander G. Fletcher, Richard A. Blythe, Linus J. Schumacher
Date: 2020-04-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Experimental Design, Model Selection

The deluge of single-cell data obtained by sequencing, imaging and epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between different states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and differentiation in a well-mixed population. These models describe dynamics where differentiation and division are coupled or uncoupled. For each model, we derive analytic expressions for each subpopulation's mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of differentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an \textit{in vitro} dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or differentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.
Analyzing COVID-19 pandemic with a new growth model for population ecology
Date: 2020-04-27 00:00:00
Tasks: Form

We have proposed a new form of growth rate for population ecology. Generally,
the growth rate is dependent on the size of the population at that particular
epoch. We have introduced an alternative time-dependent form of growth rate.
This form satisfies essential conditions to represent population growth and can
be an alternative form for growth models to analyze population dynamics. We
have employed the generalized Richards model as a guideline to compare our
results. Further, we have applied our model in the case of epidemics. To check
the efficacy of our model, we have verified the 2003 SARS data. This model has
estimated the final epidemic size with good accuracy. Thereafter, we intend to
describe the present COVID-2019 pandemic. We have performed our analysis with
data for Italy, Spain, and Germany. Following, we have tried to predict the
number of COVID-19 cases and the turning point for the USA, UK, and India.
Comparison of Windowed-Decoder Configurations for Spatially Coupled LDPC Codes Under Equal-Complexity Constraints
Date: 2020-04-27 00:00:00
Tasks: Decoder

Spatially Coupled Low-Density Parity-Check (SC-LDPC) codes offer excellent
decoding performance and can be elegantly decoded with a Windowed Decoder (WD).
We determine an efficient WD configuration with low control overhead. For fair
comparisons, we normalize all configurations to the same maximal computational
complexity, which is an important measure of the decoding effort in
packet-based data communication systems. We determine an optimized
configuration from a joint evaluation of the window size, the window update
strategy, and parity check-based Early Termination (ET). Firstly, we use a
variable node-centered update strategy, which omits updates of messages in some
parts of the decoding window. With the complexity normalization, the window
size can be increased compared to a check node-centered update strategy, which
uniformly updates all messages in the decoding window. Secondly, we only
require the satisfaction of the top-most parity-check equations in each window
to move to the next position more quickly. Using a surprisingly large window
size, the resulting WD halves the average decoding complexity of the block
decoder while maintaining a rather small gap in the decoding performance.
Econophysics Approach and Model on Mixed Economy
Authors: Ion Spanulescu, Anca Gheorghiu
Date: 2020-04-25 00:00:00
Tasks: model

In this paper the general principles and categories of mixed economy that currently exist in almost all countries of the world are presented. The paper also presents an Advanced Model of Mixed Economy with Threshold (AMMET), which is characterized by a reduced value (approx. 10-15%) of the State and public sector participation in the national economy and proposes and analyzes an econophysics model for the mixed economy.
Scale-free Protocol Design for Output Synchronization of Heterogeneous Multi-agent subject to Unknown, Non-uniform and Arbitrarily Large Input Delays
Date: 2020-04-26 00:00:00

This paper studies output synchronization problems for heterogeneous networks
of continuous- or discrete-time right-invertible linear agents in presence of
unknown, non-uniform and arbitrarily large input delay based on localized
information exchange. It is assumed that all the agents are introspective,
meaning that they have access to their own local measurements. Universal linear
protocols are proposed for each agent to achieve output synchronizations.
Proposed protocols are designed solely based on the agent models using no
information about communication graph and the number of agents or other agent
models information. Moreover, the protocols can tolerate arbitrarily large
input delays.
Control with uncertain data of socially structured compartmental epidemic models
Authors: G. Albi, L. Pareschi, M. Zanella
Date: 2020-04-27 00:00:00

The adoption of containment measures to reduce the amplitude of the epidemic peak is a key aspect in tackling the rapid spread of an epidemic. Classical compartmental models must be modified and studied to correctly describe the effects of forced external actions to reduce the impact of the disease. The importance of social structure, such as the age dependence that proved essential in the recent COVID-19 pandemic, must be considered, and in addition, the available data are often incomplete and heterogeneous, so a high degree of uncertainty must be incorporated into the model from the beginning. In this work we address these aspects, through an optimal control formulation of a socially structured epidemic model in presence of uncertain data. After the introduction of the optimal control problem, we formulate an instantaneous approximation of the control that allows us to derive new feedback controlled compartmental models capable of describing the epidemic peak reduction. The need for long-term interventions shows that alternative actions based on the social structure of the system can be as effective as the more expensive global strategy. The timing and intensity of interventions, however, is particularly relevant in the case of uncertain parameters on the actual number of infected people. Simulations related to data from the first wave of the recent COVID-19 outbreak in Italy are presented and discussed.
A Sparse Learning Approach to the Design of Radar Tunable Architectures with Enhanced Selectivity Properties
Date: 2020-04-27 00:00:00
Tasks: Sparse Learning

This paper considers the design of tunable decision schemes capable of
rejecting with high probability mismatched signals embedded in Gaussian
interference with unknown covariance matrix. To this end, a sparse recovery
technique is exploited to enhance the resolution at which the target angle of
arrival is estimated with the objective to obtain high-selective detectors. The
outcomes of this estimation procedure are used to devise detection
architectures relying on either the twostage design paradigm or heuristic
design procedures based upon the generalized likelihood ratio test. Remarkably,
the new decision rules exhibit a bounded-constant false alarm rate property and
allow for a tradeoff between the matched detection performance and the
rejection of undesired signals by tuning a design parameter. At the analysis
stage, the performance of the newly proposed detectors is assessed also in
comparison with existing selective competitors. The results show that the new
detectors can outperform the considered counterparts in terms of rejection of
unwanted signals, while retaining reasonable detection performance of matched
signals.
Avoiding zero probability events when computing Value at Risk contributions
Authors: Takaaki Koike, Yuri F. Saporito, Rodrigo S. Targino
Date: 2020-04-28 00:00:00

This paper is concerned with the process of risk allocation for a generic multivariate model when the risk measure is chosen as the Value-at-Risk (VaR). We recast the traditional Euler contributions from an expectation conditional on an event of zero probability to a ratio involving conditional expectations whose conditioning events have strictly positive probability. We derive an analytical form of the proposed representation of VaR contributions for various parametric models. Our numerical experiments show that the estimator using this novel representation outperforms the standard Monte Carlo estimator in terms of bias and variance. Moreover, unlike the existing estimators, the proposed estimator is free from hyperparameters under a parametric setting.
Leveraging Spatial Uncertainty for Online Error Compensation in EMT
Date: 2020-04-28 00:00:00

Purpose: Electromagnetic Tracking (EMT) can potentially complement
fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due
to the susceptibility to external distortions, systematic error in EMT needs to
be compensated algorithmically. Compensation algorithms for EMT in guidewire
procedures are only practical in an online setting.
  Methods: We collect positional data and train a symmetric Artificial Neural
Network (ANN) architecture for compensating navigation error. The results are
evaluated in both online and offline scenarios and are compared to polynomial
fits. We assess spatial uncertainty of the compensation proposed by the ANN.
Simulations based on real data show how this uncertainty measure can be
utilized to improve accuracy and limit radiation exposure in hybrid navigation.
  Results: ANNs compensate unseen distortions by more than 70%, outperforming
polynomial regression. Working on known distortions, ANNs outperform
polynomials as well. We empirically demonstrate a linear relationship between
tracking accuracy and model uncertainty. The effectiveness of hybrid tracking
is shown in a simulation experiment.
  Conclusion: ANNs are suitable for EMT error compensation and can generalize
across unseen distortions. Model uncertainty needs to be assessed when spatial
error compensation algorithms are developed, so that training data collection
can be optimized. Finally, we find that error compensation in EMT reduces the
need for x-ray images in hybrid navigation.
Complex-order Derivative and Integral Filters and its Applications
Date: 2020-04-28 00:00:00

In this paper, complex-order derivative and integral filters are proposed,
which are consistent with the filters with fractional derivative and integral
orders. Compared with the filters designed only with real orders, complex order
filters can reveal more details of input signals, and this can benefit a large
number of tasks. The tremendous effect of the proposed complex-order filters,
has been verified by several image processing examples. Especially, for the
challenging problem indicating the diseased regions in prostate TRUS images,
the proposed complex order filters look very promising. It is very astonishing
that, indicating the diseased regions is fulfilled by complex-order integral,
not by derivative. This is completely against the traditional views that using
derivative operators to achieve the goal.
Privacy Protection via Joint Real and Reactive Load Shaping in Smart Grids
Authors: Cihan Emre Kement, Marija Ilic, Hakan Gultekin, Cihan Tugrul Cicek, Bulent Tavli
Date: 2020-04-28 00:00:00
Tasks: Management

Frequent metering of electricity consumption is crucial for demand side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer appliance usage, which reveals information about consumers' private lives. Existing load shaping techniques for privacy primarily focus only on altering metered real power, whereas smart meters collect reactive power consumption data as well for various purposes. This study addresses consumer privacy preservation via load shaping in a demand response scheme, considering both real and reactive power. We build a multi-objective optimization framework that enables us to characterize the interplay between privacy maximization, user cost minimization, and user discomfort minimization objectives. Our results reveal that minimizing information leakage due to a single component, e.g., real power, would suffer from overlooking information leakage due to the other component, e.g., reactive power, causing sub-optimal decisions. In fact, joint shaping of real and reactive power components results in the best possible privacy preservation performance, which leads to more than a twofold increase in privacy in terms of mutual information.
Benchmarking LiDAR Sensors for Development and Evaluation of Automotive Perception
Date: 2020-04-28 00:00:00
Tasks: Benchmarking, Systematic Literature Review

Environment perception and representation are some of the most critical tasks
in automated driving. To meet the stringent needs of safety standards such as
ISO 26262 there is a need for efficient quantitative evaluation of the
perceived information. However, to use typical methods of evaluation, such as
comparing using annotated data, is not scalable due to the manual effort
involved.
  There is thus a need to automate the process of data annotation. This paper
focuses on the LiDAR sensor and aims to identify the limitations of the sensor
and provides a methodology to generate annotated data of a measurable quality.
The limitations with the sensor are analysed in a Systematic Literature Review
on available academic texts and refined by unstructured interviews with
experts.
  The main contributions are 1) the SLR with related interviews to identify
LiDAR sensor limitations and 2) the associated methodology which allows us to
generate world representations.
A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations
Date: 2020-04-28 00:00:00

The outbreak and propagation of COVID-19 have posed a considerable challenge
to modern society. In particular, the different restrictive actions taken by
governments to prevent the spread of the virus have changed the way humans
interact and conceive interaction. Due to geographical, behavioral, or economic
factors, different sub-groups among a population are more (or less) likely to
interact, and thus to spread/acquire the virus. In this work, we present a
general multi-group SEIRA model for representing the spread of COVID-19 among a
heterogeneous population and test it in a numerical case of study. By
highlighting its applicability and the ease with which its general formulation
can be adapted to particular studies, we expect our model to lead us to a
better understanding of the evolution of this pandemic and to better
public-health policies to control it.
How do online consumers review negatively?
Date: 2020-04-28 00:00:00

Negative reviews on e-commerce platforms, mainly in the form of texts, are
posted by online consumers to express complaints about unsatisfactory
experiences, providing a proxy of big data for sellers to consider
improvements. However, the exact knowledge that lies beyond the negative
reviewing still remains unknown. Aimed at a systemic understanding of how
online consumers post negative reviews, using 1, 450, 000 negative reviews from
JD.com, the largest B2C platform in China, the behavioral patterns from
temporal, perceptional and emotional perspectives are comprehensively explored
in the present study. Massive consumers behind these reviews across four
sectors in the most recent 10 years are further split into five levels to
reveal group discriminations at a fine resolution. Circadian rhythms of
negative reviewing after making purchases were found, and the periodic
intervals suggest stable habits in online consumption and that consumers tend
to negatively review at the same hour of the purchase. Consumers from lower
levels express more intensive negative feelings, especially on product pricing
and seller attitudes, while those from upper levels demonstrate a stronger
momentum of negative emotion. The value of negative reviews from higher-level
consumers is thus unexpectedly highlighted because of less emotionalization and
less biased narration, while the longer-lasting characteristic of these
consumers' negative responses also stresses the need for more attention from
sellers. Our results shed light on implementing distinguished proactive
strategies in different buyer groups to help mitigate the negative impact due
to negative reviews.
Introduction of flexible production bids and combined package-price bids in a framework of integrated power-reserve market coupling
Date: 2020-04-27 00:00:00

In this article we propose a multi-zonal integrated energy-reserve market
model. We assume that bidders may submit their demand and supply bids on the
one hand in the form of conventional hourly step bids and block bids, which are
cleared and paid according to market clearing prices (MCPs). On the other hand,
suppliers may submit so called flexible production bids, while both suppliers
and consumers may submit fill-or-kill type package-priced combined bids --
these bids are accepted if their acceptance implies an improvement in the
resulting total social welfare, which the market clearing algorithm aims to
optimize. The model includes network constraints for the nominal case (if no
reserves are activated) and also for perturbed cases when the allocated
reserves are activated.