pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Electricity Production Networks Using Mathematical and Simulation Method in Photovoltaic Power Generations
Authors: Mohsen Momenitabar, Zhila Dehdari Ebrahimi, Mohammad Arani
Date: 2020-04-09 00:00:00

Currently waste of energy is one of the most important issues, power plants are encountered all over the world. In this study, two mathematical programming models are presented to formulate solar power plants pertinent to the design of a lean manufacturing system. We propose an approach consisting of a combination of mathematical and economic models used for distribution systems of electricity transmission networks. Besides the system, diverse costs and the rate of energy waste during the transmission are considered as two criteria for the comparison. The main approach is to employ a simulation method for both models and draw a comparison between the results of simulation and optimization models run by Lingo software. Finally, analyzing the test results lead the system to a remarkable cost reduction of the country energy demand in the distribution system of electricity transmission networks.
Analyzing the spatial distribution of acute coronary syndrome cases using synthesized data on arterial hypertension prevalence
Date: 2020-04-10 00:00:00

In the current study, the authors demonstrate the method aimed at analyzing
the distribution of acute coronary syndrome (ACS) cases in Saint Petersburg
using the synthetic population approach and a statistical model for arterial
hypertension prevalence. The cumulative number of emergency services calls in a
separate geographical area (a grid cell of a map) associated with ACS is
matched with the assessed number of dwellers and individuals with arterial
hypertension, which makes it possible to find locations with excessive ACS
incidence. The proposed method is implemented in Python programming language,
the visualization results are shown using QGIS open software. Three categories
of locations are proposed based on the analysis results. The demonstrated
method might be applied for using the statistical assessments of hidden health
conditions in the population to categorize spatial distributions of their
visible consequences.
Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
Authors: Charles F. Manski, Francesca Molinari
Date: 2020-04-13 00:00:00
Tasks: Anatomy

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
Fault Location Using the Natural Frequency of Oscillation of Current Discharge in MTdc Networks
Authors: Bhaskar Mitra, Suman Debanth, Badrul Chowdhury
Date: 2020-04-13 00:00:00

This paper discusses a novel fault location approach using single ended measurement. The natural dissipation of the circuit parameters are considered for fault location. A relationship between the damped natural frequency of oscillation of the transmission line current and fault location is established in this paper. The hybrid dc circuit breaker (dcCB) interrupts the fault current and the line current attenuates under the absence of any driving voltage source. The line capacitance discharges into the fault at a specific frequency of oscillation and rate of attenuation. Utilizing this information, the fault location in a multi-terminal direct current (MTdc) network can be predicted. A three terminal radial model of a MTdc is used for performance evaluation of the proposed method using Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).
Dual State-Space Model of Market Liquidity: The Chinese Experience 2009-2010
Authors: P. B. Lerner
Date: 2020-04-13 00:00:00

This paper proposes and motivates a dynamical model of the Chinese stock market based on a linear regression in a dual state space connected to the original state space of correlations between the volume-at-price buckets by a Fourier transform. We apply our model to the price migration of executed orders by the Chinese brokerages in 2009-2010. Regulatory brokerage tapes were used to conduct a natural experiment assuming that tapes correspond to randomly assigned, informed and uninformed traders. Our analysis demonstrated that customers' orders were tightly correlated--in a highly nonlinear sense of the neural networks--with the Chinese market sentiment index, significantly correlated with the stock returns and exhibited no correlation with the bellwether bond of the Bank of China. We did not notice any spike of illiquidity transmitting from the US Flash Crash in May 2010 to trading in China.
Embedded model discrepancy: A case study of Zika modeling
Date: 2020-04-13 00:00:00
Tasks: Epidemiology

Mathematical models of epidemiological systems enable investigation of and
predictions about potential disease outbreaks. However, commonly used models
are often highly simplified representations of incredibly complex systems.
Because of these simplifications, the model output, of say new cases of a
disease over time, or when an epidemic will occur, may be inconsistent with
available data. In this case, we must improve the model, especially if we plan
to make decisions based on it that could affect human health and safety, but
direct improvements are often beyond our reach. In this work, we explore this
problem through a case study of the Zika outbreak in Brazil in 2016. We propose
an embedded discrepancy operator---a modification to the model equations that
requires modest information about the system and is calibrated by all relevant
data. We show that the new enriched model demonstrates greatly increased
consistency with real data. Moreover, the method is general enough to easily
apply to many other mathematical models in epidemiology.
A Lightweight Solution of Industrial Computed Tomography with Convolutional Neural Network
Date: 2020-04-14 00:00:00
Tasks: Image Reconstruction

As an advanced non-destructive testing and quality control technique,
industrial computed tomography (ICT) has found many applications in smart
manufacturing. The existing ICT devices are usually bulky and involve mass data
processing and transmission. It results in a low efficiency and cannot keep
pace with smart manufacturing. In this paper, with the support from Internet of
things (IoT) and convolutional neural network (CNN), we proposed a lightweight
solution of ICT devices for smart manufacturing. It consists of efforts from
two aspects: distributed hardware allocation and data reduction. At the first
aspect, ICT devices are separated into four functional units: data acquisition,
cloud storage, computing center and control terminals. They are distributed and
interconnected by IoT. Only the data acquisition unit still remains in the
production lines. This distribution not only slims the ICT device, but also
permits the share of the same functional units. At the second aspect, in the
data acquisition unit, sparse sampling strategy is adopted to reduce the raw
data and singular value decomposition (SVD) is used to compress these data.
They are then transmitted to the cloud storage. At the computing center, an ICT
image reconstruction algorithm and a CNN are applied to these compressed sparse
sampling data to obtain high quality CT images. The experiments with practical
ICT data have been executed to demonstrate the validity of the proposed
solution. The results indicate that this solution can achieve a drastic data
reduction, a storage space save and an efficiency improvement without
significant image degradation. The presented work has been helpful to push the
applications of ICT in smart manufacturing.
Modeling, state estimation, and optimal control for the US COVID-19 outbreak
Date: 2020-04-18 00:00:00
Tasks: Decision Making, State Estimation

The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an
unprecedented spread and continue to cause an increasing number of fatalities
worldwide. While vaccines are still under development, social distancing,
extensive testing, and quarantining of confirmed infected subjects remain the
most effective measures to contain the pandemic. These measures carry a
significant socioeconomic cost. In this work, we introduce a novel
optimization-based decision-making framework for managing the COVID-19 outbreak
in the US. This includes modeling the dynamics of affected populations,
estimating the model parameters and hidden states from data, and an optimal
control strategy for sequencing social distancing and testing events such that
the number of infections is minimized. The analysis of our extensive
computational efforts reveals that social distancing and quarantining are most
effective when implemented early, with quarantining of confirmed infected
subjects having a much higher impact. Further, we find that "on-off" policies
alternating between strict social distancing and relaxing such restrictions can
be effective at "flattening" the curve while likely minimizing social and
economic cost.
A High Coverage Camera Assisted Received Signal Strength Ratio Algorithm for Indoor Visible Light Positioning
Date: 2020-04-14 00:00:00

In this paper, a high coverage algorithm termed enhanced camera assisted
received signal strength ratio (eCA-RSSR) positioning algorithm is proposed for
visible light positioning (VLP) systems. The basic idea of eCA-RSSR is to
utilize visual information captured by the camera to estimate the incidence
angles of visible lights first. Based on the incidence angles, eCA-RSSR
utilizes the received signal strength ratio (RSSR) calculated by the photodiode
(PD) to estimate the ratios of the distances between the LEDs and the receiver.
Based on an Euclidean plane geometry theorem, eCA-RSSR transforms the ratios of
the distances into the absolute values. In this way, eCA-RSSR only requires 3
LEDs for both orientation-free 2D and 3D positioning, implying that eCA-RSSR
can achieve high coverage. Based on the absolute values of the distances, the
linear least square method is employed to estimate the position of the
receiver. Therefore, for the receiver having a small distance between the PD
and the camera, the accuracy of eCA-RSSR does not depend on the starting values
of the non-linear least square method and the complexity of eCA-RSSR is low.
Furthermore, since the distance between the PD and camera can significantly
affect the performance of eCA-RSSR, we further propose a compensation algorithm
for eCA-RSSR based on the single-view geometry. Simulation results show that
eCA-RSSR can achieve centimeter-level accuracy over 80% indoor area for both
the receivers having a small and a large distance between the PD and the
camera.
An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification
Date: 2020-04-19 00:00:00
Tasks: Speaker Verification, Speech Synthesis

Anti-spoofing for automatic speaker verification is now a well established
area of research, with three competitive challenges having been held in the
last 6 years. A great deal of research effort over this time has been invested
into the development of front-end representations tailored to the spoofing
detection task. One such approach known as constant Q cepstral coefficients
(CQCCs) have been shown to be especially effective in detecting attacks
implemented with a unit selection based speech synthesis algorithm. Despite
their success, they largely fail in detecting other forms of spoofing attack
where more traditional front-end representations give substantially better
results. Similar differences were also observed in the most recent, 2019
edition of the ASVspoof challenge series. This paper reports our attempts to
help explain these observations. The explanation is shown to lie in the level
of attention paid by each front-end to different sub-band components of the
spectrum. Thus far, surprisingly little has been learned about what artefacts
are being detected by spoofing countermeasures. Our work hence aims to shed
light upon signal or spectrum level artefacts that serve to distinguish
different forms of spoofing attack from genuine, bone fide speech. With a
better understanding of these artefacts we will be better positioned to design
more reliable countermeasures.
Molecular docking and binding mode analysis of selected FDA approved drugs against COVID-19 selected key protein targets: An effort towards drug repurposing to identify the combination therapy to combat COVID-19
Date: 2020-04-14 00:00:00
Tasks: Drug Discovery, Molecular Docking

The emergence of COVID-19 has severely compromised the arsenal of antiviral
and antibiotic drugs. Drug discovery is a multistep process with a high failure
rate, high cost and it takes approximately 10-12 years for the development of
new molecules into the clinical candidate. On the other side, drug repurposing
also called old drugs for new uses, is an attractive alternative approach for a
new application of marketed FDA approved or investigational drugs. In the
current pandemic situation raised due to COVID-19, repurposing of existing FDA
approved drugs are emerging as the first line of the treatment. The causative
viral agent of this highly contagious disease and acute respiratory syndrome
coronavirus (SARS-CoV) shares high nucleotide similarity. Therefore, many
existing viral targets are structurally expected to be similar to SARS-CoV and
likely to be inhibited by the same compounds. Here, we selected three viral key
proteins based on their vital role in viral life cycle: ACE2 (helps in entry
into the human host), viral nonstructural proteins RNA-dependent RNA polymerase
(RdRp) NSP12, and NSP16 which helps in replication, and viral latency (invasion
from immunity). The FDA approved drugs chloroquine (CQ), hydroxychloroquine
(HCQ), remdesivir (RDV) and arbidol (ABD) are emerging as promising agents to
combat COVID-19. Our hypothesis behind the docking studies is to determine the
binding affinities of these drugs and identify the key amino acid residues
playing a key role in their mechanism of action. The docking studies were
carried out through Autodock and online COVID-19 docking server. Further
studies on a broad range of FDA approved drugs including few more protein
targets, molecular dynamics studies, in-vitro and in-vivo biological evaluation
are required to identify the combination therapy targeting various stages of
the viral life cycle.
Role of a Habitat's Air Humidity in Covid-19 Mortality
Authors: Irina V. Biktasheva
Date: 2020-04-14 00:00:00

Transient local over-dry environment might be a contributor and an explanation for the observed asynchronous local rises in Covid-19 mortality. We propose that a habitat's air humidity negatively correlate with Covid-19 morbidity and mortality, and support this hypothesis on the example of publicly available data from German federal states.
A Malaria Control Model Using Mobility Data: An Early Explanation of Kedougou's Case in Senegal
Date: 2020-04-12 00:00:00
Tasks: Sociology

Studies in malaria control cover many areas such as medicine, sociology,
biology, mathematic, physic, computer science and so forth. Researches in the
realm of mathematic are conducted to predict the occurrence of the disease and
to support the eradication process. Basically, the modeling methodology is
predominantly deterministic and differential equation based while selecting
clinical and biological features that seem to be important. Yet, if the
individual characteristics matter when modeling the disease, the overall
estimation of the malaria is not done based on the health status of each
individual but in a non-specified percentage of the global population. The goal
of this paper is to propose a model that relies on a daily evolution of the
individual's state, which depends on their mobility and the characteristics of
the area they visit. Thus, the mobility data of a single person moving from one
area to another, gathered thanks to mobile networks, is the essential building
block to predict the outcome of the disease. We implement our solution and
demonstrate its effectiveness through empirical experiments. The results show
how promising the model is in providing possible insights into the failure of
the disease control in the Kedougou region.
Prospects and limits of SIR-type Mathematical Models to Capture the COVID-19 Pandemic
Date: 2020-04-13 00:00:00
Tasks: parameter estimation

For the description of a pandemic mathematical models could be interesting.
Both for physicians and politicians as a base for decisions to treat the
disease. The responsible estimation of parameters is a main issue of
mathematical pandemic models. Especially a good choice of $\beta$ as the number
of others that one infected person encounters per unit time (per day)
influences the adequateness of the results of the model. For the actual
COVID-19 pandemic some aspects of the parameter choice will be discussed.
Because of the incompatibility of the data of the Johns-Hopkins-University to
the data of the German Robert-Koch-Institut we use the COVID-19 data of the
European Centre for Disease Prevention and Control (ECDC) as a base for the
parameter estimation. Two different mathematical methods for the data analysis
will be discussed in this paper and possible sources of trouble will be shown.
As example of the parameter choice serve the data of the USA and the UK. The
resulting parameters will be used estimated and used in W.\,O. Kermack and
A.\,G. McKendrick's SIR model. Strategies for the commencing and ending of
social and economic shutdown measures are discussed. The numerical solution of
the ordinary differential equation system of the modified SIR model is being
done with a Runge-Kutta integration method of fourth order. At the end the
applicability of the SIR model could be shown essentially. Suggestions about
appropriate points in time at which to commence with lockdown measures based on
the acceleration rate of infections conclude the paper.
Cross-Country Age Disparities in COVID-19 Cases with Hospitalization, ICU Usage, and Morbidity
Date: 2020-04-12 00:00:00
Tasks: Attribute

In this paper, we examine cross-country differences, in terms of the age
distribution of symptomatic cases, hospitalizations, intensive care unit (ICU)
cases, and fatalities due to the novel COVID-19. By calculating conditional
probabilities, we bridge country-level incidence data gathered from different
countries and attribute the variability in data to country demographics. We
then provide case, hospitalization, ICU, and fatality estimates for a
comprehensive list of countries using the existing data from a variety of
countries.
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack
Date: 2020-04-25 00:00:00
Tasks: Adversarial Attack

Consider a consensus-driven multi-agent dynamic system. The interaction
range, which defines the set of neighbors for each agent, plays a key role in
influencing connectivity of the underlying network. In this paper, we assume
the system is under attack by a predator and explore the question of finding
the optimal interaction range that facilitates the most-efficient escape
trajectories for the group of agents. We find that for many cases of interest
the optimal interaction range is one that forces the network to break up into a
handful of disconnected graphs, each containing a subset of agents, thus
outperforming the two extreme cases corresponding to fully-connected and
fully-disconnected networks. In other words, the results indicate that some
connectivity among the agents is helpful because information is effectively
transmitted from the agents closest to the predator to others slightly farther
away, but also that too much connectivity can be detrimental to the agility of
the group, thus hampering efficient and rapid escape.
Potential in the Schrodinger equation: estimation from empirical data
Authors: J. L. Subias
Date: 2020-03-17 00:00:00

A recent model for the stock market calculates future price distributions of a stock as a wave function of a quantum particle confined in an infinite potential well. In such a model the question arose as to how to estimate the classical potential needed for solving the Schrodinger equation. In the present article the method used in that work for evaluating the potential is described, in the simplest version to implement, and more sophisticated implementations are suggested later.
Information Token Driven Machine Learning for Electronic Markets: Performance Effects in Behavioral Financial Big Data Analytics
Authors: Jim Samuel
Date: 2020-03-30 00:00:00
Tasks: BIG-bench Machine Learning, Math

Conjunct with the universal acceleration in information growth, financial services have been immersed in an evolution of information dynamics. It is not just the dramatic increase in volumes of data, but the speed, the complexity and the unpredictability of big-data phenomena that have compounded the challenges faced by researchers and practitioners in financial services. Math, statistics and technology have been leveraged creatively to create analytical solutions. Given the many unique characteristics of financial bid data (FBD) it is necessary to gain insights into strategies and models that can be used to create FBD specific solutions. Behavioral finance data, a subset of FBD, is seeing exponential growth and this presents an unprecedented opportunity to study behavioral finance employing big data analytics methodologies. The present study maps machine learning (ML) techniques and behavioral finance categories to explore the potential for using ML techniques to address behavioral aspects in FBD. The ontological feasibility of such an approach is presented and the primary purpose of this study is propositioned- ML based behavioral models can effectively estimate performance in FBD. A simple machine learning algorithm is successfully employed to study behavioral performance in an artificial stock market to validate the propositions. Keywords: Information; Big Data; Electronic Markets; Analytics; Behavior
The interdependency structure in the Mexican stock exchange: A network approach
Authors: Erick Treviño Aguilar
Date: 2020-04-14 00:00:00

Our goal in this paper is to study and characterize the interdependency structure of the Mexican Stock Exchange (mainly stocks from BMV) in the period 2000-2019 and provide visualizations which in a one shot provide a big-picture panorama. To this end, we estimate correlation/concentration matrices from different models and then compute metrics from network theory including eigencentralities and network modularity
Combined servo error pre-compensation and feedrate optimization -- with application to a 3D printer and precision motion stage
Date: 2020-04-15 00:00:00

Servo error pre-compensation and feedrate optimization are often performed
independently to improve the accuracy and speed of manufacturing machines.
However, this independent approach leads to unnecessary trade-offs between
productivity and quality in manufacturing. This paper proposes a novel linear
programming approach for combined servo error pre-compensation and feedrate
optimization, subject to contour error (tolerance) and kinematic constraints.
The incorporation of servo error pre-compensation into feedrate optimization
allows for faster motions without violating tolerance constraints. Experiments
carried out on a 3D printer and precision motion stage are respectively used to
demonstrate up to 43% and 47% reduction in cycle time without compromising part
quality using the proposed compared to the independent approach.