pwc-archive/papers-with-abstracts huggingface dataset:
Methods for forecasting the effect of exogenous risk on stock markets
Authors: Karina Arias-Calluari, Fernando Alonso-Marroquin, Morteza Nattagh-Najafi, Michael Harré
Date: 2020-05-08 00:00:00

Markets are subjected to both endogenous and exogenous risks that have caused disruptions to financial and economic markets around the globe, leading eventually to fast stock market declines. In the past, markets have recovered after any economic disruption. On this basis, we focus on the outbreak of COVID-19 as a case study of an exogenous risk and analyze its impact on the Standard and Poor's 500 (S\&P500) index. We assumed that the S\&P500 index reaches a minimum before rising again in the not-too-distant future. Here we present two cases to forecast the S\&P500 index. The first case uses an estimation of expected deaths released on 02/04/2020 by the University of Washington. For the second case, it is assumed that the peak number of deaths will occur 2-months since the first confirmed case occurred in the USA. The decline and recovery in the index were estimated for the following three months after the initial point of the predicted trend. The forecast is a projection of a prediction with stochastic fluctuations described by $q$-gaussian diffusion process with three spatio-temporal regimes. Our forecast was made on the premise that any market response can be decomposed into an overall deterministic trend and a stochastic term. The prediction was based on the deterministic part and for this case study is approximated by the extrapolation of the S\&P500 data trend in the initial stages of the outbreak. The stochastic fluctuations have the same structure as the one derived from the past 24 years. A reasonable forecast was achieved with 85\% of accuracy.
Cascade Control: Data-Driven Tuning Approach Based on Bayesian Optimization
Date: 2020-05-16 00:00:00
Tasks: Bayesian Optimization

Cascaded controller tuning is a multi-step iterative procedure that needs to
be performed routinely upon maintenance and modification of mechanical systems.
An automated data-driven method for cascaded controller tuning based on
Bayesian optimization is proposed. The method is tested on a linear axis drive,
modeled using a combination of first principles model and system
identification. A custom cost function based on performance indicators derived
from system data at different candidate configurations of controller parameters
is modeled by a Gaussian process. It is further optimized by minimization of an
acquisition function which serves as a sampling criterion to determine the
subsequent candidate configuration for experimental trial and improvement of
the cost model iteratively, until a minimum according to a termination
criterion is found. This results in a data-efficient procedure that can be
easily adapted to varying loads or mechanical modifications of the system. The
method is further compared to several classical methods for auto-tuning, and
demonstrates higher performance according to the defined data-driven
performance indicators. The influence of the training data on a cost prior on
the number of iterations required to reach optimum is studied, demonstrating
the efficiency of the Bayesian optimization tuning method.
Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms
Date: 2020-04-23 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Attempting to imitate the brain functionalities, researchers have bridged
between neuroscience and artificial intelligence for decades; however,
experimental neuroscience has not directly advanced the field of machine
learning. Here, using neuronal cultures, we demonstrate that increased training
frequency accelerates the neuronal adaptation processes. This mechanism was
implemented on artificial neural networks, where a local learning step-size
increases for coherent consecutive learning steps and tested on a simple
dataset of handwritten digits, MNIST. Based on our online learning results with
a few handwriting examples, success rates for brain-inspired algorithms
substantially outperform the commonly used machine learning algorithms. We
speculate this emerging bridge from slow brain function to machine learning
will promote ultrafast decision making under limited examples, which is the
reality in many aspects of human activity, robotic control, and network
optimization.
Wonders of chaos for communication
Authors: Murilo S. Baptista
Date: 2020-05-08 00:00:00

This work shows that chaotic signals with different power spectrum are robust to linear superposition, meaning that the superposition preserves Ergodic quantities (Lyapunov exponents) and the information content of the source signals, even after being transmitted over non-ideal physical medium. This wonderful property that chaotic signals have allows me to propose a novel communication system based on chaos, where information composed from and to multiple users each operating with different base frequencies and that is carried by chaotic wavesignals can be fully preserved after transmission in the open air wireless physical medium, and it can be trivially decoded with low probability of errors. This work tackles with great detail how chaotic signals and their information content are affected when travelling through medium that presents the non-ideal properties of multipath propagation, noise and chaotic interference (linear superposition), and how this impacts on the proposed communication system. Physical media with other non-ideal properties (dispersion and interference with periodic signals) are also discussed.
Utility maximization under endogenous pricing
Authors: Thai Nguyen, Mitja Stadje
Date: 2020-05-08 00:00:00

We study the expected utility maximization problem of a large investor who is allowed to make transactions on tradable assets in an incomplete financial market with endogenous permanent market impacts. The asset prices are assumed to follow a nonlinear price curve quoted in the market as the utility indifference curve of a representative liquidity supplier. We show that optimality can be fully characterized via a system of coupled forward-backward stochastic differential equations (FBSDEs) which corresponds to a non-linear backward stochastic partial differential equation (BSPDE). We show existence of solutions to the optimal investment problem and the FBSDEs in the case where the driver function of the representative market maker grows at least quadratically or the utility function of the large investor falls faster than quadratically or is exponential. Furthermore, we derive smoothness results for the existence of solutions of BSPDEs. Examples are provided when the market is complete or the utility function is exponential.
Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19 spread model
Date: 2020-05-21 00:00:00
Tasks: Epidemiology

In this article, we consider a dynamic epidemiology model for the spread of
the COVID-19 infection. Starting from the classical SEIR model, the model is
modified so as to better describe characteristic features of the underlying
pathogen and its infectious modes. In line with the large number of secondary
infections not related to contact with documented infectious individuals, the
model includes a cohort of asymptomatic or oligosymptomatic infectious
individuals, not accounted for in the data of new daily counts of infections. A
Bayesian particle filtering algorithm is used to update dynamically the
relevant cohort and simultaneously estimate the transmission rate as the new
data on the number of new infections and disease related death become
available. The underlying assumption of the model is that the infectivity rate
is dynamically changing during the epidemics, either because of a mutation of
the pathogen or in response to mitigation and containment measures. The
sequential Bayesian framework naturally provides a quantification of the
uncertainty in the estimate of the model parameters, including the reproduction
number, and of the size of the different cohorts. Moreover, we introduce a
dimensionless quantity, which is the equilibrium ratio between asymptomatic and
symptomatic cohort sizes, and propose a simple formula to estimate the
quantity. This ratio leads naturally to another dimensionless quantity that
plays the role of the basic reproduction number $R_0$ of the model. When we
apply the model and particle filter algorithm to COVID-19 infection data from
several counties in Northeastern Ohio and Southeastern Michigan we found the
proposed reproduction number $R_0$ to have a consistent dynamic behavior within
both states, thus proving to be a reliable summary of the success of the
mitigation measures.
A scaling law of multilevel evolution: how the balance between within- and among-collective evolution is determined
Authors: Nobuto Takeuchi, Namiko Mitarai, Kunihiko Kaneko
Date: 2020-05-09 00:00:00

Numerous living systems are hierarchically organised, whereby replicating components are grouped into reproducing collectives -- e.g., organelles are grouped into cells, and cells are grouped into multicellular organisms. In such systems, evolution can operate at two levels: evolution among collectives, which tends to promote selfless cooperation among components within collectives (called altruism), and evolution within collectives, which tends to promote cheating among components within collectives. The balance between within- and among-collective evolution thus exerts profound impacts on the fitness of these systems. Here, we investigate how this balance depends on the size of a collective (denoted by $N$) and the mutation rate of components ($m$) through mathematical analyses and computer simulations of multiple population genetics models. We first confirm a previous result that increasing $N$ or $m$ accelerates within-collective evolution relative to among-collective evolution, thus promoting the evolution of cheating. Moreover, we show that when within- and among-collective evolution exactly balance each other out, the following scaling relation generally holds: $Nm^{\alpha}$ is a constant, where scaling exponent $\alpha$ depends on multiple parameters, such as the strength of selection and whether altruism is a binary or quantitative trait. This relation indicates that although $N$ and $m$ have quantitatively distinct impacts on the balance between within- and among-collective evolution, their impacts become identical if $m$ is scaled with a proper exponent. Our results thus provide a novel insight into conditions under which cheating or altruism evolves in hierarchically-organised replicating systems.
Implication of Repatriating Migrant Workers on COVID-19 Spread and Transportation Requirements
Date: 2020-07-30 00:00:00

Nationwide lockdown for COVID-19 created an urgent demand for public
transportation among migrant workers stranded at different parts of India to
return to their native places. Arranging transportation could spike the number
of COVID-19 infected cases. Hence, this paper investigates the potential surge
in confirmed and active cases of COVID-19 infection and assesses the train and
bus fleet size required for the repatriating migrant workers. The expected to
repatriate migrant worker population was obtained by forecasting the 2011
census data and comparing it with the information reported in the news media. A
modified susceptible-exposed-infected-removed (SEIR) model was proposed to
estimate the surge in confirmed and active cases of COVID-19 patients in
India's selected states with high outflux of migrants. The developed model
considered combinations of different levels of the daily arrival rate of
migrant workers, total migrant workers in need of transportation, and the
origin of the trip dependent symptomatic cases on arrival. Reducing the daily
arrival rate of migrant workers for states with very high outflux of migrants
(i.e., Uttar Pradesh and Bihar) can help to lower the surge in confirmed and
active cases. Nevertheless, it could create a disparity in the number of days
needed to transport all repatriating migrant workers to the home states. Hence,
travel arrangements for about 100,000 migrant workers per day to Uttar Pradesh
and Bihar, about 50,000 per day to Rajasthan and Madhya Pradesh, 20,000 per day
to Maharashtra and less than 20,000 per day to other states of India was
recommended.
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration
Authors: Xingyao Wang, Chengyu Liu, Yuwen Li, Xianghong Cheng, Jianqing Li, Gari D. Clifford
Date: 2020-05-09 00:00:00
Tasks: Diagnostic, Rhythm, Segmentation, Time Series Analysis

Objective: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference for decoding the most likely state sequence. Methods: In contrast to previous state-of-the-art approaches, the TFAN-based method does not require any knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. The TFAN-based method was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent training and test databases (2099 recordings and 52180 beats). The databases for segmentation were separated into three levels of increasing difficulty (LEVEL-I, -II and -III) for performance reporting. Results: The TFAN-based method achieved a superior F1 score for all 12 databases except for `Test-B', with an average of 96.7%, compared to 94.6% for the state-of-the-art method. Moreover, the TFAN-based method achieved an overall F1 score of 99.2%, 94.4%, 91.4% on LEVEL-I, -II and -III data respectively, compared to 98.4%, 88.54% and 79.80% for the current state-of-the-art method. Conclusion: The TFAN-based method therefore provides a substantial improvement, particularly for more difficult cases, and on data sets not represented in the public training data. Significance: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.
Reaction-diffusion spatial modeling of COVID-19: Greece and Andalusia as case examples
Authors: P. G. Kevrekidis, J. Cuevas-Maraver, Y. Drossinos, Z. Rapti, G. A. Kevrekidis
Date: 2020-05-09 00:00:00

We examine the spatial modeling of the outbreak of COVID-19 in two regions: the autonomous community of Andalusia in Spain and the mainland of Greece. We start with a 0D compartmental epidemiological model consisting of Susceptible, Exposed, Asymptomatic, (symptomatically) Infected, Hospitalized, Recovered, and deceased populations. We emphasize the importance of the viral latent period and the key role of an asymptomatic population. We optimize model parameters for both regions by comparing predictions to the cumulative number of infected and total number of deaths via minimizing the $\ell^2$ norm of the difference between predictions and observed data. We consider the sensitivity of model predictions on reasonable variations of model parameters and initial conditions, addressing issues of parameter identifiability. We model both pre-quarantine and post-quarantine evolution of the epidemic by a time-dependent change of the viral transmission rates that arises in response to containment measures. Subsequently, a spatially distributed version of the 0D model in the form of reaction-diffusion equations is developed. We consider that, after an initial localized seeding of the infection, its spread is governed by the diffusion (and 0D model "reactions") of the asymptomatic and symptomatically infected populations, which decrease with the imposed restrictive measures. We inserted the maps of the two regions, and we imported population-density data into COMSOL, which was subsequently used to solve numerically the model PDEs. Upon discussing how to adapt the 0D model to this spatial setting, we show that these models bear significant potential towards capturing both the well-mixed, 0D description and the spatial expansion of the pandemic in the two regions. Veins of potential refinement of the model assumptions towards future work are also explored.
A Design of SDR-based Pseudo-Analog Wireless Video Transmission System
Authors: Xiao-Wei Tang, Xin-Lin Huang
Date: 2020-05-10 00:00:00

The pseudo-analog wireless video transmission technology can improve the effectiveness, reliability, and robustness of the conventional digital system in video broadcast scenarios. Although some prototypes of IEEE 802.11 series have been developed for researchers to do simulations and experiments, they are usually expensive and provide very limited access to the physical layer. More importantly, these prototypes cannot be used to verify the correctness of the new proposed pseudo-analog wireless video transmission algorithms directly due to limited modulation modes they can support. In this paper, we present a novel design of software radio platform (SDR)-based pseudo-analog wireless video transceiver which is completely transparent and allows users to learn all the implementation details. Firstly, we prove that the analog method can also achieve the optimal performance as the digital method from the perspective of the rate-distortion theory. Then, we describe the two hardware implementation difficulties existed in the designing process including the data format modification and the non-linear distortion. Next, we introduce the implementation details of the designed transceiver. Finally, we analyze the performance of the designed transceiver. Specifically, the results show that the designed system can work effectively in both simulations and experiments.
Lifetime Maximization for UAV-assisted Data Gathering Networks in the Presence of Jamming
Authors: Ali Rahmati, Seyyedali Hosseinalipour, Ismail Guvenc, Huaiyu Dai, Arupjyoti Bhuyan
Date: 2020-05-10 00:00:00

Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery. Since UAVs are powered by batteries, energy efficient communication is of paramount importance. In this paper, we investigate the problem of lifetime maximization of a UAV-assisted network in the presence of multiple sources of interference, where the UAVs are deployed to collect data from a set of wireless sensors. We demonstrate that the placement of the UAVs play a key role in prolonging the lifetime of the network since the required transmission powers of the UAVs are closely related to their locations in space. In the proposed scenario, the UAVs transmit the gathered data to a primary UAV called \textit{leader}, which is in charge of forwarding the data to the base station (BS) via a backhaul UAV network. We deploy tools from spectral graph theory to tackle the problem due to its high non-convexity. Simulation results demonstrate that our proposed method can significantly improve the lifetime of the UAV network.
Recognizing Individuals and Their Emotions Using Plants as Bio-Sensors through Electro-static Discharge
Authors: Buenyamin Oezkaya, Peter A. Gloor
Date: 2020-05-10 00:00:00

By measuring the electrostatic discharge of human bodies together with Mimosa Pudica and other plants in response to the human movement, we have been able to recognize (a) individuals based on their distinctive pattern of body movements with 66% accuracy as well as (b) positive or negative mood based on their gait characteristics with 85% accuracy. We use the Plant SpikerBox, a device that measures the electrical action potential while also measuring the electrostatic discharge between the electrode on the leaves of a plant and the capacitively coupled human body.
Novel Catchbond mediated oscillations in motor-microtubule complexes
Authors: Sougata Guha, Mithun K. Mitra, Ignacio Pagonabarraga, Sudipto Muhuri
Date: 2020-05-10 00:00:00

Generation of mechanical oscillation is ubiquitous to wide variety of intracellular processes. We show that catchbonding behaviour of motor proteins provides a generic mechanism of generating spontaneous oscillations in motor-cytoskeletal filament complexes. We obtain the phase diagram to characterize how this novel catch bond mediated mechanism can give rise to bistability and sustained limit cycle oscillations and results in very distinctive stability behaviour, including bistable and non-linearly stabilised in motor-microtubule complexes in biologically relevant regimes. Hitherto, it was thought that the primary functional role of the biological catchbond was to improve surface adhesion of bacteria and cell when subjected to external forces or flow field. Instead our theoretical study shows that the imprint of this catch bond mediated physical mechanism would have ramifications for whole gamut of intracellular processes ranging from oscillations in mitotic spindle oscillations to activity in muscle fibres.
Vector Control Algorithm Based on Different Current Control Switching Techniques for Ac Motor Drives
Date: 2020-05-10 00:00:00

A comparative analysis of vector control scheme based on different current
control switching pulses (HC, SPWM, DPWM and SVPWM) for the speed response of
motor drive is analysed in this paper. The control system using different
switching techniques, are comparatively simulated and analysed. Ac motor drives
are progressively used in high-performance application industries due to small
size, efficient performance, robust to torque response and high power to size
ratio. A mathematical model of ac motor drives is presented in order to explain
the numerical theory of motor drives. The vector control technique is utilized
for efficient speed control of ac motor drive based on independent torque and
air gap flux control. The study compares the total harmonic distortion contents
of phase currents of ac motor drive and speed response in each case. The
simulation result shows that total harmonic distortion across the phase current
in SVPWM is less as compared to other switching techniques while the rise time
in speed response across SVPWM technique is faster as compared to other
switching methods. The simulation result of ac motor drives speed control is
demonstrated in Matlab/Simulink 2018b.
Signal-to-Noise Ratio of Microwave Photonic Filter With an Interferometric Structure Based on an Incoherent Broadband Optical Source
Authors: Long Huang, Ruoming Li, Peng Xiang, Pan Dai, Wenxuan Wang, Mi Li, Xiangfei Chen, Yuechun Shi
Date: 2020-05-10 00:00:00

A comprehensive investigation of the signal-to-noise ratio (SNR) of the microwave photonic filter (MPF) with an interferometric structure based on an incoherent broadband optical source (IBOS) is presented from the time and frequency domains, respectively. The interferometric structure and the IBOS in the MPF result in a beneficial single-bandpass radiofrequency (RF) response. However, the IBOS adds a relatively large noise to the filtered RF signal. Therefore, the analysis of the SNR is of great importance for the design of this kind of MPFs. Theoretical analysis shows that the SNR is a function of the center frequency of the passband, the modulation index, the chromatic dispersion, and the shape of the IBOS. An experiment is performed to verify the theory, and experimental results agree well with the theoretical calculation.