pwc-archive/papers-with-abstracts huggingface dataset:
Callas or the trajectory of the meteor
Authors: Jean-Philippe Epron, Jocelyne Sarfati, Nathalie Henrich Bernardoni
Date: 2020-04-23 00:00:00

The lyric career of Maria Callas, though exceptional, is also noteworthy for its brevity. The first signs of downturn appeared at the age of 36 and her voice fell silent at only 40. Though the literature has massively commented on this premature worsening, few analyses of its characteristics have been made public so far. The purpose of our study was to realise a perceptual and acoustical analysis of recorded arias by the artist at the climax to the fall. The audible impairments were first verbally described, and then compared to acoustical observations based on spectrographic analyses and fundamental-frequency measurements.
Antimalarial Artefenomel Inhibits Human SARS-CoV-2 Replication in Cells while Suppressing the Receptor ACE2
Date: 2020-04-28 00:00:00

The steep climbing of victims caused by the new coronavirus disease 2019 (COVID-19) throughout the planet is sparking an unprecedented effort to identify effective therapeutic regimens to tackle the pandemic. The SARS-CoV-2 virus is known to gain entry into various cell types through the binding of one of its surface proteins (spike) to the host Angiotensin-Converting Enzyme 2 (ACE2). Thus, spike-ACE2 interaction represents a major target for vaccines and antiviral drugs. A novel method has been recently described by some of the authors to pharmacologically downregulate the expression of target proteins at the post-translational level. This technology builds on computational advancements in the simulation of folding mechanisms to rationally block protein expression by targeting folding intermediates, hence hampering the folding process. Here, we report the all-atom simulations of the entire sequence of events underlying the folding pathway of ACE2. Our data revealed the existence of a folding intermediate showing two druggable pockets hidden in the native conformation. Both pockets were targeted by a virtual screening repurposing campaign aimed at quickly identifying drugs capable to decrease the expression of ACE2. We identified four compounds capable of lowering ACE2 expression in Vero cells in a dose-dependent fashion. All these molecules were found to inhibit the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein. Importantly, the antiviral activity has been tested against live SARS-CoV-2 (MEX-BC2/2020 strain). One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing antiviral activity against SARS-CoV-2. Ongoing studies are further evaluating the possibility of repurposing these drugs for the treatment of COVID-19.
Mapping Coupled Time-series Onto Complex Network
Authors: Jamshid Ardalankia, Jafar Askari, Somaye Sheykhali, Emmanuel Haven, G. Reza Jafari
Date: 2020-04-28 00:00:00
Tasks: Time Series, Time Series Analysis

In order to extract hidden joint information from two possibly uncorrelated time-series, we explored the measures of network science. Alongside common methods in time-series analysis of the economic markets, mapping the joint structure of two time-series onto a network provides insight into hidden aspects embedded in the couplings. We discretize the amplitude of two time-series and investigate relative simultaneous locations of those amplitudes. Each segment of a discretized amplitude is considered as a node. The simultaneity of the amplitudes of the two time-series is considered as the edges in the network. The frequency of occurrences forms the weighted edges. In order to extract information, we need to measure that to what extent the coupling deviates from the coupling of two uncoupled series. Also, we need to measure that to what extent the couplings inherit their characteristics from a Gaussian distribution or a non-Gaussian distribution. We mapped the network from two surrogate time-series. The results show that the couplings of markets possess some features which diverge from the same features of the network mapped from white noise, and from the network mapped from two surrogate time-series. These deviations prove that there exist joint information and cross-correlation therein. By applying the network's topological and statistical measures and the deformation ratio in the joint probability distribution, we distinguished basic structures of cross-correlation and coupling of cross-markets. It was discovered that even two possibly known uncorrelated markets may possess some joint patterns with each other. Thereby, those markets should be examined as coupled and \textit{weakly} coupled markets.
A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions
Date: 2020-05-13 00:00:00

Objectives. Public health officials need tools to assist with anticipating
the healthcare resources required to confront the SARS-COV-2 pandemic. We built
a modeling tool to aid practicing public health officials with estimating
healthcare demand from the pandemic in their jurisdictions and to evaluate the
potential impacts of population-wide social-distancing interventions. Methods.
The tool uses a SEIR compartmental model to project the local spread of the
pandemic. Users input case counts, healthcare resources, and select
intervention strategies to evaluate. Outputs include the number of infections
and deaths with and without intervention, and the demand for hospital and
critical care beds and ventilators relative to existing capacity. We illustrate
the tool using data from three regions of Chile. Results. Our scenarios
indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by
June, peaking in July or August at 6 to 50 times the current supply of beds and
ventilators. A lockdown strategy or combination of case isolation, home
quarantine, social distancing of individuals greater than 70 years, and
telework interventions may keep treatment demand below capacity. Conclusions.
Aggressive interventions can avert substantial morbidity and mortality from
COVID-19. Our tool permits rapid evaluation of locally-applicable policy
scenarios and updating of results as new data become available.
Neural Network and Particle Filtering: A Hybrid Framework for Crack Propagation Prediction
Date: 2020-04-27 00:00:00
Tasks: Prediction

Crack detection, length estimation, and Remaining Useful Life (RUL)
prediction are among the most studied topics in reliability engineering.
Several research efforts have studied physics of failure (PoF) of different
materials, along with data-driven approaches as an alternative to the
traditional PoF studies. To bridge the gap between these two techniques, we
propose a novel hybrid framework for fatigue crack length estimation and
prediction. Physics-based modeling is performed on the fracture mechanics
degradation data by estimating parameters of the Paris Law, including the
associated uncertainties. Crack length estimations are inferred by feeding
manually extracted features from ultrasonic signals to a Neural Network (NN).
The crack length prediction is then performed using the Particle Filter (PF)
approach, which takes the Paris Law as a move function and uses the NN's output
as observation to update the crack growth path. This hybrid framework combines
machine learning, physics-based modeling, and Bayesian updating with promising
results.
Kalman Filter and Wavelet Cross-correlation for VHF Broadband Interferometer Lightning Mapping
Authors: Ammar Alammari, Ammar Alkahtani, Mohd Riduan, Fuad Noman, Mona Riza Mohd Esa, Muhammad Haziq Mohamad Sabri, Sulaiman Ali Mohammad, Ahmed Salih Al-Khaleefa, Zen Kawasaki, Vassilios Agelidis
Date: 2020-04-25 00:00:00
Tasks: Denoising

A lightning mapping system based on perpendicular crossed baseline interferometer (ITF) technology has been developed rapidly in recent years. Several processing methods have been proposed to estimate the temporal location and spatial map of lightning strikes. In this paper, a single very high frequency (VHF) interferometer is used to simulate and augment the lightning maps. We perform a comparative study of using different processing techniques and procedures to enhance the localization and mapping of lightning VHF radiation. The benchmark environment involves the use of different noise reduction and cross-correlation methods. Moreover, interpolation techniques are introduced to smoothen the correlation peaks for more accurate lightning localization. A positive narrow bipolar event (NBE) lightning discharge is analyzed and the mapping procedure is confirmed using both simulated and measured lightning signals. The results indicate that a good estimation of lightning radiation sources is achieved when using wavelet denoising and cross-correlations in wavelet-domain (CCWD) with a minimal error of 3.46 (degrees). The investigations carried out in this study confirm that the ITF mapping system could effectively map the lightning VHF radiation source.
Method for Estimation of Nonlinear $\lambda$-I Relation of Single Phase Power Transformer Core by Using On-line Measurable Parameters
Date: 2020-04-28 00:00:00
Tasks: Relation

In design of new power transformers, reliable and efficient tools are
required to expedite research and development processes. Some of these tools
are used to interpret the data obtained from the transformer tests for better
judgement about the response of the transformer. In this paper a clear
procedure is proposed to estimate the nonlinear relation between flux and
excitation current of power transformers by using online measurable data, which
is the main reason of the input current harmonics. It is shown that the
procedure computes this relation with very high precision and leads to
identical results to that applied in simulation.
Utilizing Pileup Effect and Intermittently Nonlinear Filtering in Synthesis of Covert and Hard-to-Intercept Communication Links
Date: 2020-04-28 00:00:00

We outline an approach to physical-layer steganography where the transmitted
low-power stego messages are statistically indistinguishable from the Gaussian
component of the channel noise (e.g. the thermal noise) observed in the same
spectral band, and thus the channel noise itself serves as an effective cover
signal. We also demonstrate how the apparent spectral and temporal properties
of transmitted additional, higher-power cover signals (including those using
the existing communication protocols) can be made to match those of the
low-power stego payload and the Gaussian noise, providing extra layers of
obfuscation for both the cover and the stego messages. We further illustrate
how a specific combination of linear and nonlinear filtering can be used for
effective separation of the cover, payload, and/or "friendly jamming" signals
even when all transmissions have essentially the same spectral characteristics
as well as temporal and amplitude structures, and when there are no explicit
differences in the spectral and/or temporal allocations for the cover and the
stego messages.
COVID-19 causes record decline in global CO2 emissions
Date: 2020-06-15 00:00:00

The considerable cessation of human activities during the COVID-19 pandemic
has affected global energy use and CO2 emissions. Here we show the
unprecedented decrease in global fossil CO2 emissions from January to April
2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when
compared with the period last year. In addition other emerging estimates of
COVID impacts based on monthly energy supply or estimated parameters, this
study contributes to another step that constructed the near-real-time daily CO2
emission inventories based on activity from power generation (for 29
countries), industry (for 73 countries), road transportation (for 406 cities),
aviation and maritime transportation and commercial and residential sectors
emissions (for 206 countries). The estimates distinguished the decline of CO2
due to COVID-19 from the daily, weekly and seasonal variations as well as the
holiday events. The COVID-related decreases in CO2 emissions in road
transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to
2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%),
residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2,
-15%). Regionally, decreases in China were the largest and earliest (234.5 Mt
CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S.
(162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional
nitrogen oxides concentrations observed by satellites and ground-based
networks, but the calculated signal of emissions decreases (about 1Gt CO2) will
have little impacts (less than 0.13ppm by April 30, 2020) on the overserved
global CO2 concertation. However, with observed fast CO2 recovery in China and
partial re-opening globally, our findings suggest the longer-term effects on
CO2 emissions are unknown and should be carefully monitored using multiple
measures.
SARS-COV-2 Pandemic: Understanding the Impact of Lockdown in the Most Affected States of India
Date: 2020-04-28 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

SARS-COV-2 has stopped the world in its footsteps and a third of the
population has been forced to stay at home. Here we present a comparative study
of the performance of states of India, in curbing the spread of the disease,
that are most affected by the pandemic. We have done so based on the data
collected between 14th March, 2020 to 17th April, 2020. We do this by comparing
the smoothened time series and percentage changes along with change point
detection of the daily confirmed cases. We also discuss the different policies
and strategies adopted by the states in curbing the disease and the ground
level implementation. We have developed ARIMA(p,d,q) models where (p,d,q) are
obtained by minimising the AIK(Akaike Information Criterion). These models are
used to make forecasts for the country which show that by 7th May, 2020, India
would have around 50,237 confirmed cases and a doubling rate of 11 days. On the
basis of the performance, of each state, we argue that a local level strategy
which is based on the demographics of that particular region be developed
instead of a central and uniform one.
Maximising the Benefits of an Acutely Limited Number of COVID-19 Tests
Date: 2020-04-28 00:00:00

We propose a novel testing and containment strategy in order to contain the
spread of SARS-CoV2 while permitting large parts of the population to resume
social and economic activity. Our approach recognises the fact that testing
capacities are severely constrained in many countries. In this setting, we show
that finding the best way to utilise this limited number of tests during a
pandemic can be formulated concisely as an allocation problem. Our problem
formulation takes into account the heterogeneity of the population and uses
pooled testing to identify and isolate individuals while prioritising key
workers and individuals with a higher risk of spreading the disease. In order
to demonstrate the efficacy of our testing and containment mechanism, we
perform simulations using a network-based SIR model. Our simulations indicate
that applying our mechanism on a population of $100,000$ individuals with only
$16$ tests per day reduces the peak number of infected individuals by
approximately $20\%$, when compared to the scenario where no intervention is
implemented.
Classical Option Pricing and Some Steps Further
Date: 2020-04-27 00:00:00

This paper considers the asset price p as relations C=pV between the value C and the volume V of the executed transactions and studies the consequences of this definition for the option pricing equations. We show that the classical BSM model implicitly assumes that value C and volume V of transactions follow identical Brownian processes. Violation of this identity leads to 2-dimensional BSM-like equation with two constant volatilities. We show that agents expectations those approve execution of transactions can further increase the dimension of the BSM model. We study the case when agents expectations may depend on the option price data and show that such assumption can lead to the nonlinear BSM-like equations. We reconsider the Heston stochastic volatility model for the price determined by the value and the volume and derive 3-dimensional BSM-like model with stochastic value volatility and constant volume volatility. Variety of the BSM-like equations states the problem of reasonable balance between the accuracy and the complexity of the option pricing equations.
A Method for Vehicle Collision Risk Assessment through Inferring Driver's Braking Actions in Near-Crash Situations
Date: 2020-04-28 00:00:00
Tasks: Attribute, Collision Avoidance

Driving information and data under potential vehicle crashes create
opportunities for extensive real-world observations of driver behaviors and
relevant factors that significantly influence the driving safety in emergency
scenarios. Furthermore, the availability of such data also enhances the
collision avoidance systems (CASs) by evaluating driver's actions in near-crash
scenarios and providing timely warnings. These applications motivate the need
for heuristic tools capable of inferring relationship among driving risk,
driver/vehicle characteristics, and road environment. In this paper, we
acquired amount of real-world driving data and built a comprehensive dataset,
which contains multiple "driver-vehicle-road" attributes. The proposed method
works in two steps. In the first step, a variable precision rough set (VPRS)
based classification technique is applied to draw a reduced core subset from
field driving dataset, which presents the essential attributes set most
relevant to driving safety assessment. In the second step, we design a decision
strategy by introducing mutual information entropy to quantify the significance
of each attribute, then a representative index through accumulation of weighted
"driver-vehicle-road" factors is calculated to reflect the driving risk for
actual situation. The performance of the proposed method is demonstrated in an
offline analysis of the driving data collected in field trials, where the aim
is to infer the emergency braking actions in next short term. The results
indicate that our proposed model is a good alternative for providing improved
warnings in real-time because of its high prediction accuracy and stability.
Conditional Spoken Digit Generation with StyleGAN
Date: 2020-09-15 00:00:00
Tasks: Image Generation, Speech Synthesis

This paper adapts a StyleGAN model for speech generation with minimal or no
conditioning on text. StyleGAN is a multi-scale convolutional GAN capable of
hierarchically capturing data structure and latent variation on multiple
spatial (or temporal) levels. The model has previously achieved impressive
results on facial image generation, and it is appealing to audio applications
due to similar multi-level structures present in the data. In this paper, we
train a StyleGAN to generate mel-frequency spectrograms on the Speech Commands
dataset, which contains spoken digits uttered by multiple speakers in varying
acoustic conditions. In a conditional setting our model is conditioned on the
digit identity, while learning the remaining data variation remains an
unsupervised task. We compare our model to the current unsupervised
state-of-the-art speech synthesis GAN architecture, the WaveGAN, and show that
the proposed model outperforms according to numerical measures and subjective
evaluation by listening tests.
Modelling the role of media induced fear conditioning in mitigating post-lockdown COVID-19 pandemic: perspectives on India
Date: 2020-05-25 00:00:00

Several countries that have been successful in constraining the severity of
COVID-19 pandemic via "lockdown" are now considering to slowly end it, mainly
because of enormous socio-economic side-effects. An abrupt ending of lockdown
can increase the basic reproductive number and undo everything; therefore,
carefully designed exit strategies are needed to sustain its benefits post
upliftment. To study the role of fear conditioning on mitigating the spread of
COVID-19 in post-lockdown phase, in this work, we propose an age- and social
contact- structures dependent Susceptible, Feared, Exposed, Infected and
Recovered (SFEIR) model. Simulating the SFEIR model on Indian population with
fear conditioning via mass media (like, television, community radio, internet
and print media) along with positive reinforcement, it is found that increase
in fraction of feared people results in the significant decrease in the growth
of infected population. The present study suggests that, during post-lockdown
phase, media induced fear conditioning in conjunction with closure of schools
for about one more year can serve as an important non-pharmaceutical
intervention to substantially mitigate this pandemic in India. The proposed
SFEIR model, by quantifying the influence of media in inducing fear
conditioning, underlies the importance of community driven changes in country
specific mitigation of COVID-19 spread in post-lockdown phase.
Convolutional Neural Networks vs. Deformable Image Registration For Medical Slice Interpolation
Authors: Dilip Kumar Verma, Ahmadreza Baghaie
Date: 2020-04-28 00:00:00
Tasks: Image Registration, Object, Video Frame Interpolation

Medical image slice interpolation is an active field of research. The methods for this task can be categorized into two broad groups: intensity-based and object-based interpolation methods. While intensity-based methods are generally easier to perform and less computationally expensive, object-based methods are capable of producing more accurate results and account for deformable changes in the objects within the slices. In this paper, performance of two well-known object-based interpolation methods is analyzed and compared. Here, a deformable registration-based method specifically designed for medical applications and a learning-based method, trained for video frame interpolation, are considered. While the deformable registration-based technique is capable of accurate modeling of the changes in the shapes of the objects within slices, the learning-based method is able to produce results with similar accuracy, but with a much sharper appearance in a fraction of the time. This is despite the fact that the learning-based approach is not trained on medical images and rather is trained using regular video footage. However, experiments show that the method is capable of accurate slice interpolation results.
A Stochastic LQR Model for Child Order Placement in Algorithmic Trading
Authors: Jackie Jianhong Shen
Date: 2020-04-28 00:00:00
Tasks: Algorithmic Trading, Form

Modern Algorithmic Trading ("Algo") allows institutional investors and traders to liquidate or establish big security positions in a fully automated or low-touch manner. Most existing academic or industrial Algos focus on how to "slice" a big parent order into smaller child orders over a given time horizon. Few models rigorously tackle the actual placement of these child orders. Instead, placement is mostly done with a combination of empirical signals and heuristic decision processes. A self-contained, realistic, and fully functional Child Order Placement (COP) model may never exist due to all the inherent complexities, e.g., fragmentation due to multiple venues, dynamics of limit order books, lit vs. dark liquidity, different trading sessions and rules. In this paper, we propose a reductionism COP model that focuses exclusively on the interplay between placing passive limit orders and sniping using aggressive takeout orders. The dynamic programming model assumes the form of a stochastic linear-quadratic regulator (LQR) and allows closed-form solutions under the backward Bellman equations. Explored in detail are model assumptions and general settings, the choice of state and control variables and the cost functions, and the derivation of the closed-form solutions.