pwc-archive/papers-with-abstracts huggingface dataset:
The Effect of Population Size for Pathogen Transmission on Prediction of COVID-19 Pandemic Spread
Date: 2020-04-22 00:00:00

Extreme public health interventions play a critical role in mitigating the
local and global prevalence and pandemic potential of COVID-19. Here, we use
population size for pathogen transmission to measure the intensity of public
health interventions, which is a key characteristic variable for nowcasting and
forecasting of the epidemic. By formulating a hidden Markov dynamic system and
using nonlinear filtering theory, we have developed a stochastic epidemic
dynamic model under public health interventions. The model parameters and
states are estimated in time from internationally available public data by
combining an unscented filter and an interacting multiple model filter.
Moreover, we consider the computability of the population size and provide its
selection criterion. We estimate the mean of the basic reproductive number of
China and the rest of the globe except China (GEC) to be 2.46 (95% CI:
2.41-2.51) and 3.64 (95% CI: (3.55-3.72), respectively. We infer that the
number of latent infections of GEC is about 7.47*10^5 (95% CI:
7.32*10^5-7.62*10^5) as of April 2, 2020. We predict that the peak of
infections in hospitals of GEC may reach 3.00*10^6 on the present trajectory,
i.e., if the population size for pathogen transmission and epidemic parameters
remains unchanged. If the control intensity is strengthened, e.g., 50%
reduction or 75% reduction of the population size for pathogen transmission,
the peak would decline to 1.84*10^6, 1.27*10^6, respectively.
On the Impact of Different Voltage Unbalance Metrics in Distribution System Optimization
Date: 2020-04-20 00:00:00

With increasing penetrations of single-phase, rooftop solar PV installations,
the relative variations in per-phase loading and associated voltage unbalance
are expected to increase. High voltage unbalance may increase network losses
and lead to failure of three-phase equipment such as motor loads. However,
solar PV panels are connected to the grid through inverters, which can provide
reactive power support and may mitigate some of these negative effects. In this
paper, we utilize a three-phase AC optimal power flow (OPF) formulation to
minimize voltage unbalance using reactive power from solar PV inverters. When
considering actions to reduce voltage unbalance, it is important to recognize
that various organizations such as IEC, NEMA and IEEE provide different and
partially inconsistent definitions of voltage unbalance in their power quality
standards. This paper analyzes the impact of the different voltage unbalance
metrics using different combinations of voltage unbalance objectives and
constraints. For our analysis, the optimization scheme is tested on two
unbalanced low-voltage distribution networks. We observe that minimizing
voltage unbalance defined by one standard might actually increase voltage
unbalance as defined by another standard, potentially resulting in equipment
damage. We also observe that minimizing voltage unbalance does not always lead
to lower network losses. However, considerable reduction in voltage unbalance
with low network losses can be achieved by minimizing the losses while
simultaneously enforcing limits on multiple definitions of voltage unbalance.
Venturing the Definition of Green Energy Transition: A systematic literature review
Date: 2020-04-23 00:00:00
Tasks: Systematic Literature Review

The issue of climate change has become increasingly noteworthy in the past
years, the transition towards a renewable energy system is a priority in the
transition to a sustainable society. In this document, we explore the
definition of green energy transition, how it is reached, and what are the
driven factors to achieve it. To answer that firstly, we have conducted a
literature review discovering definitions from different disciplines, secondly,
gathering the key factors that are drivers for energy transition, finally, an
analysis of the factors is conducted within the context of European Union data.
Preliminary results have shown that household net income and governmental legal
actions related to environmental issues are potential candidates to predict
energy transition within countries. With this research, we intend to spark new
research directions in order to get a common social and scientific
understanding of green energy transition.
Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex
Authors: Annika Hagemann, Jens Wilting, Bita Samimizad, Florian Mormann, Viola Priesemann
Date: 2020-04-22 00:00:00

Epileptic seizures are characterized by abnormal and excessive neural activity, where cortical network dynamics seem to become unstable. However, most of the time, during seizure-free periods, cortex of epilepsy patients shows perfectly stable dynamics. This raises the question of how recurring instability can arise in the light of this stable default state. In this work, we examine two potential scenarios of seizure generation: (i) epileptic cortical areas might generally operate closer to instability, which would make epilepsy patients generally more susceptible to seizures, or (ii) epileptic cortical areas might drift systematically towards instability before seizure onset. We analyzed single-unit spike recordings from both the epileptogenic (focal) and the nonfocal cortical hemispheres of 20 epilepsy patients. We quantified the distance to instability in the framework of criticality, using a novel estimator, which enables an unbiased inference from a small set of recorded neurons. Surprisingly, we found no evidence for either scenario: Neither did focal areas generally operate closer to instability, nor were seizures preceded by a drift towards instability. In fact, our results from both pre-seizure and seizure-free intervals suggest that despite epilepsy, human cortex operates in the stable, slightly subcritical regime, just like cortex of other healthy mammalians.
Data-driven modeling reveals a universal dynamic underlying the COVID-19 pandemic under social distancing
Date: 2020-04-21 00:00:00

We show that the COVID-19 pandemic under social distancing exhibits universal
dynamics. The cumulative numbers of both infections and deaths quickly cross
over from exponential growth at early times to a longer period of power law
growth, before eventually slowing. In agreement with a recent statistical
forecasting model by the IHME, we show that this dynamics is well described by
the erf function. Using this functional form, we perform a data collapse across
countries and US states with very different population characteristics and
social distancing policies, confirming the universal behavior of the COVID-19
outbreak. We show that the predictive power of statistical models is limited
until a few days before curves flatten, forecast deaths and infections assuming
current policies continue and compare our predictions to the IHME models. We
present simulations showing this universal dynamics is consistent with disease
transmission on scale-free networks and random networks with non-Markovian
transmission dynamics.
Automated Contact Tracing: a game of big numbers in the time of COVID-19
Authors: Hyunju Kim, Ayan Paul
Date: 2020-04-22 00:00:00

One of the more widely advocated solutions for slowing down the spread of COVID-19 has been automated contact tracing. Since proximity data can be collected by personal mobile devices, the natural proposal has been to use this for automated contact tracing providing a major gain over a manual implementation. In this work, we study the characteristics of voluntary and automated contact tracing and its effectiveness for mapping the spread of a pandemic due to the spread of SARS-CoV-2. We highlight the infrastructure and social structures required for automated contact tracing to work. We display the vulnerabilities of the strategy to inadequate sampling of the population, which results in the inability to sufficiently determine significant contact with infected individuals. Of crucial importance will be the participation of a significant fraction of the population for which we derive a minimum threshold. We conclude that relying largely on automated contact tracing without population-wide participation to contain the spread of the SARS-CoV-2 pandemic can be counterproductive and allow the pandemic to spread unchecked. The simultaneous implementation of various mitigation methods along with automated contact tracing is necessary for reaching an optimal solution to contain the pandemic.
Control of fixation duration during visual search task execution
Date: 2020-04-22 00:00:00
Tasks: Decision Making

We study the ability of human observer to control fixation duration during
execution of visual search tasks. We conducted the eye-tracking experiments
with natural and synthetic images and found the dependency of fixation duration
on difficulty of the task and the lengths of preceding and succeeding saccades.
In order to explain it, we developed the novel control model of human
eye-movements that incorporates continuous-time decision making, observation
and update of belief state. This model is based on Partially Observable Markov
Decision Process with delay in observation and saccade execution that accounts
for a delay between eye and cortex. We validated the computational model
through comparison of statistical properties of simulated and experimental
eye-movement trajectories.
Optimum Location-Based Relay Selection in Wireless Networks
Authors: Hazer Inaltekin, Saman Atapattu, Jamie S. Evans
Date: 2020-04-23 00:00:00
Tasks: Point Processes

This paper studies the performance and key structural properties of the optimum location-based relay selection policy for wireless networks consisting of homogeneous Poisson distributed relays. The distribution of the channel quality indicator at the optimum relay location is obtained. A threshold-based distributed selective feedback policy is proposed for the discovery of the optimum relay location with finite average feedback load. It is established that the total number of relays feeding back obeys to a Poisson distribution and an analytical expression for the average feedback load is derived. The analytical expressions for the average rate and outage probability with and without selective feedback are also obtained for general path-loss models. It is shown that the optimum location-based relay selection policy outperforms other common relay selection strategies notably. It is also shown that utilizing location information from a small number of relays is enough to achieve almost the same performance with the infinite feedback load case. As generalizations, full-duplex relays, isotropic Poisson point processes, and heterogeneous source-to-relay and relay-to-destination links are also studied.
Optimal execution with liquidity risk in a diffusive order book market
Authors: Hyoeun Lee, Kiseop Lee
Date: 2020-04-23 00:00:00

We study the optimal order placement strategy with the presence of a liquidity cost. In this problem, a stock trader wishes to clear her large inventory by a predetermined time horizon $T$. A trader uses both limit and market orders, and a large market order faces an adverse price movement caused by the liquidity risk. First, we study a single period model where the trader places a limit order and/or a market order at the beginning. We show the behavior of optimal amount of market order, $m^*$, and optimal placement of limit order, $y^*$, under different market conditions. Next, we extend it to a multi-period model, where the trader makes sequential decisions of limit and market orders at multiple time points.
Joint Radar Target Detection and Parameter Estimation with MIMO OTFS
Date: 2020-04-23 00:00:00
Tasks: parameter estimation

Motivated by future automotive applications, we study the joint target
detection and parameter estimation problem using orthogonal time frequency
space (OTFS), a digital modulation format robust to time-frequency selective
channels. Assuming the transmitter is equipped with a mono-static MIMO radar,
we propose an efficient maximum likelihood based approach to detect targets and
estimate the corresponding delay, Doppler, and angle-of-arrival parameters. In
order to reduce the computational complexity associated to the high-dimensional
search, our scheme proceeds in two steps, i.e., target detection and coarse
parameter estimation followed by refined parameter estimation. Interestingly,
our numerical results demonstrate that the proposed scheme is able to identify
multiple targets if they are separated in at least one domain out of three
(delay, Doppler, and angle), while achieving the Cram\'er-Rao lower bound for
the parameter estimation.
Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control
Date: 2020-04-23 00:00:00
Tasks: Position

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is
employed to increase the communication capacity of millimeter-wave (mmWave)
networks. Large antenna arrays are equipped at the source node (SN),
destination node (DN), and FD-UAV relay to overcome the high path loss of
mmWave channels and to help mitigate the self-interference at the FD-UAV relay.
Specifically, we formulate a problem for maximization of the achievable rate
from the SN to the DN, where the UAV position, analog beamforming, and power
control are jointly optimized. Since the problem is highly non-convex and
involves high-dimensional, highly coupled variable vectors, we first obtain the
conditional optimal position of the FD-UAV relay for maximization of an
approximate upper bound on the achievable rate in closed form, under the
assumption of a line-of-sight (LoS) environment and ideal beamforming. Then,
the UAV is deployed to the position which is closest to the conditional optimal
position and yields LoS paths for both air-to-ground links. Subsequently, we
propose an alternating interference suppression (AIS) algorithm for the joint
design of the beamforming vectors and the power control variables. In each
iteration, the beamforming vectors are optimized for maximization of the
beamforming gains of the target signals and the successive reduction of the
interference, where the optimal power control variables are obtained in closed
form. Our simulation results confirm the superiority of the proposed
positioning, beamforming, and power control method compared to three benchmark
schemes. Furthermore, our results show that the proposed solution closely
approaches a performance upper bound for mmWave FD-UAV systems.
Adaptive evolution of hybrid bacteria by horizontal gene transfer
Authors: Jeffrey J. Power, Fernanda Pinheiro, Simone Pompei, Viera Kovacova, Melih Yüksel, Isabel Rathmann, Mona Förster, Michael Lässig, Berenike Maier
Date: 2020-04-23 00:00:00

Horizontal gene transfer is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer, and about its effects on the recipient organism's physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ~12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative and positive selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths.
Some Applications of Lie Groups in Theory of Technical Progress
Authors: Le Anh Vu, Duong Quang Hoa, Nguyen Minh Tri, Ha Van Hieu
Date: 2020-03-14 00:00:00

In recent decades, we have known some interesting applications of Lie theory in the theory of technological progress. Firstly, we will discuss some results of R. Saito in \cite{rS1980} and \cite{rS1981} about the application modeling of Lie groups in the theory of technical progress. Next, we will describe the result on Romanian economy of G. Zaman and Z. Goschin in \cite{ZG2010}. Finally, by using Sato's results and applying the method of G. Zaman and Z. Goschin, we give an estimation of the GDP function of Viet Nam for the 1995-2018 period and give several important observations about the impact of technical progress on economic growth of Viet Nam.
Optimizing the reliability of a bank with Logistic Regression and Particle Swarm Optimization
Authors: Vadlamani Ravi, Vadlamani Madhav
Date: 2020-03-31 00:00:00
Tasks: Electrical Engineering, regression

It is well-known that disciplines such as mechanical engineering, electrical engineering, civil engineering, aerospace engineering, chemical engineering and software engineering witnessed successful applications of reliability engineering concepts. However, the concept of reliability in its strict sense is missing in financial services. Therefore, in order to fill this gap, in a first-of-its-kind-study, we define the reliability of a bank/firm in terms of the financial ratios connoting the financial health of the bank to withstand the likelihood of insolvency or bankruptcy. For the purpose of estimating the reliability of a bank, we invoke a statistical and machine learning algorithm namely, logistic regression (LR). Once, the parameters are estimated in the 1st stage, we fix them and treat the financial ratios as decision variables. Thus, in the 1st stage, we accomplish the hitherto unknown way of estimating the reliability of a bank. Subsequently, in the 2nd stage, in order to maximize the reliability of the bank, we formulate an unconstrained optimization problem in a single-objective environment and solve it using the well-known particle swarm optimization (PSO) algorithm. Thus, in essence, these two stages correspond to predictive and prescriptive analytics respectively. The proposed 2-stage strategy of using them in tandem is beneficial to the decision-makers within a bank who can try to achieve the optimal or near-optimal values of the financial ratios in order to maximize the reliability which is tantamount to safeguarding their bank against solvency or bankruptcy.
Leveraging Hardware-Impaired Out-of-Band Information Through Deep Neural Networks for Robust Wireless Device Classification
Date: 2020-04-23 00:00:00
Tasks: Classification

Wireless device classification techniques play a key role in promoting
emerging wireless applications such as allowing spectrum regulatory agencies to
enforce their access policies and enabling network administrators to control
access and prevent impersonation attacks to their wireless networks. Leveraging
spectrum distortions of transmitted RF signals, caused by transceiver hardware
impairments created during manufacture and assembly stages, to provide device
classification has been the focus of many recent works. These prior works
essentially apply deep learning to extract features of the devices from their
hardware-impaired signals and rely on feature variations across the devices to
distinguish devices from one another. As technology advances, the manufacturing
impairment variations across devices are becoming extremely insignificant,
making these prior classification approaches inaccurate. This paper proposes a
novel, deep learning-based technique that provides scalable and highly accurate
classification of wireless devices, even when the devices exhibit insignificant
variation across their hardware impairments and have the same hardware,
protocol, and software configurations. The novelty of the proposed technique
lies in leveraging both the {\em in-band} and {\em out-of-band} signal
distortion information by oversampling the captured signals at the receiver and
feeding IQ samples collected from the RF signals to a deep neural network for
classification. Using a convolutional neural network (CNN) model, we show that
our proposed technique, when applied to high-end, high-performance devices with
minimally distorted hardware, doubles the device classification accuracy when
compared to existing approaches.
Trading characteristics of member firms on the Korea Exchange
Authors: Min-Young Lee, Woo-Sung Jung, Gabjin Oh
Date: 2020-04-19 00:00:00

In this paper, we study the characteristics of the member firms on the Korea Exchange. The member firms intermediate between the market participants and the exchange, and all the participants should trade stocks through members. To identify the characteristics of member firms, all member firms are categorized into three groups, such as the domestic members similar to individuals (DIMs), the domestic members similar to institutions (DSMs), and the foreign members (FRMs), in terms of the type of investor. We examine the dynamics of the member firms. The trading characteristics of members are revealed through the directionality and trend. While FRMs tend to trade one-way and move with the price change, DIMs are the opposite. In the market, DIMs and DSMs do herd and the herding moves in the opposite direction of the price change. One the other hand, FRMs do herd in the direction of the price change. The network analysis supports that the members are clustered into three groups similar to DIMs, DSMs, and FRMs. Finally, random matrix theory and a cross-sectional regression show that the inventory variation of members possesses significant information about stock prices and that member herding helps to price the stocks.
Beam Blockage in Optical Wireless Systems
Date: 2020-04-23 00:00:00
Tasks: Fairness

In this paper, we use the percentage blockage as a metric when an opaque disc
obstructs the Line-of-Sight link from the access point to the receiver in an
optical wireless indoor communication system. The effect of the different
parameters of the obstructing object are studied, these are the radius, the
height, and the horizontal distance from the receiver in the positive y
direction. The percentage of blocked room locations to the total number of room
locations when varying the disc parameters is studied assuming a single serving
link. It was found that depending on the dimensions of the obstructing object
and the distance from the receiver in addition to which access point is serving
the user, that blockage can vary between 0% up to 100%. Furthermore, the
service received by a user, in terms of beam blockage depends on the access
point they are connected to. The resulting fairness challenges will be
addressed in resource allocation optimization in future work.
Flexible framework for audio reconstruction
Date: 2020-07-29 00:00:00
Tasks: Audio inpainting, Quantization

The paper presents a unified, flexible framework for the tasks of audio
inpainting, declipping, and dequantization. The concept is further extended to
cover analogous degradation models in a transformed domain, e.g. quantization
of the signal's time-frequency coefficients. The task of reconstructing an
audio signal from degraded observations in two different domains is formulated
as an inverse problem, and several algorithmic solutions are developed. The
viability of the presented concept is demonstrated on an example where audio
reconstruction from partial and quantized observations of both the time-domain
signal and its time-frequency coefficients is carried out.