pwc-archive/papers-with-abstracts huggingface dataset:
Determination of an Optimal Control Strategy for Vaccine Administration in COVID-19 Pandemic Treatment
Date: 2020-04-20 00:00:00

During decades, mathematical models have been used to predict the behavior of
physical and biologic systems, and to define strategies aiming the minimization
of the effects regarding different types of diseases. In the present days, the
development of mathematical models to simulate the dynamic behavior of novel
coronavirus disease (COVID-19) is considered an important theme due to the
quantity of infected people worldwide. In this work, the aim is to determine an
optimal control strategy for vaccine administration in COVID-19 pandemic
treatment considering real data from China. For this purpose, an inverse
problem is formulated and solved in order to determine the parameters of the
compartmental SIR (Susceptible-Infectious-Recovered) model. To solve such
inverse problem, the Differential Evolution (DE) algorithm is employed. After
this step, two optimal control problems (mono- and multi-objective) to
determine the optimal strategy for vaccine administration in COVID-19 pandemic
treatment are proposed. The first consists of minimizing the quantity of
infected individuals during the treatment. The second considers minimizing
together the quantity of infected individuals and the prescribed vaccine
concentration during the treatment, i.e., a multi-objective optimal control
problem. The solution of each optimal control problems is obtained using DE and
Multi-Objective Differential Evolution (MODE) algorithms, respectively. The
results regarding the proposed multi-objective optimal control problem provides
a set of evidences from which an optimal strategy for vaccine administration
can be chosen, according to a given criterion.
Leaderless Consensus of a Hierarchical Cyber-Physical System
Authors: Xiao Chen, Yanjun Li, Arman Goudarzi, Ji Xiang
Date: 2020-04-16 00:00:00

This paper models a class of hierarchical cyber-physical systems and studies its associated consensus problem. The model has a pyramid structure, which reflects many realistic natural or human systems. By analyzing the spectrum of the coupling matrix, it is shown that all nodes in the physical layer can reach a consensus based on the proposed distributed protocols without interlayer delays. Then, the result is extended to the case with interlayer delays. A necessary and sufficient condition for consensus-seeking is derived from the frequency domain perspective, which describes a permissible range of the delay. Finally, the application of the proposed model in the power-sharing problem is simulated to demonstrate the effectiveness and significance of the analytic results.
Signaling activations through G-protein-coupled-receptor aggregations
Authors: Masaki Watabe, Hideaki Yoshimura, Satya N. V. Arjunan, Kazunari Kaizu, Koichi Takahashi
Date: 2020-04-16 00:00:00

Eukaryotic cells transmit extracellular signal information to cellular interiors through the formation of a ternary complex made up of a ligand (or agonist), G-protein, and G-protein coupled receptor (GPCR). Previously formalized theories of ternary complex formation have mainly assumed that observable states of receptors can only take the form of monomers. Here, we propose a multiary complex model of GPCR signaling activations via the vector representation of various unobserved aggregated receptor states. Our results from model simulations imply that receptor aggregation processes can govern cooperative effects in a regime inaccessible by previous theories. In particular, we show how the affinity of ligand-receptor binding can be largely varied by various oligomer formations in the low concentration range of G-protein stimulus.
Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large scale agent-based model
Authors: Kirill S. Glavatskiy, Mikhail Prokopenko, Adrian Carro, Paul Ormerod, Michael Harre
Date: 2020-04-16 00:00:00

Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.
Cellular velocity, electrical persistence and sensing in developed and vegetative cells during electrotaxis
Authors: Isabella Guido, Douglas Diehl, Nora Aleida Olszok, Eberhard Bodenschatz
Date: 2020-04-16 00:00:00

Cells have the ability to detect electric fields and respond to them with directed migratory movement. Investigations identified genes and proteins that play important roles in defining the migration efficiency. Nevertheless, the sensing and transduction mechanisms underlying directed cell migration are still under discussion. We use Dictyostelium discoideum cells as model system for studying eukaryotic cell migration in DC electric fields. We have defined the temporal electric persistence to characterize the memory that cells have in a varying electric field. In addition to imposing a directional bias, we observed that the electric field influences the cellular kinematics by accelerating the movement of cells along their paths. Moreover, the study of vegetative and briefly starved cells provided insight into the electrical sensing of cells. We found evidence that conditioned medium of starved cells was able to trigger the electrical sensing of vegetative cells that would otherwise not orient themselves in the electric field. This observation may be explained by the presence of the conditioned medium factor (CMF), a protein secreted by the cells, when they begin to starve. The results of this study give new insights into understanding the mechanism that triggers the electrical sensing and transduces the external stimulus into directed cell migration. Finally, the observed increased mobility of cells over time in an electric field could offer a novel perspective towards wound healing assays.
Root Distribution in Pad\'e Approximants and its Effect on Holomorphic Embedding Method Convergence
Date: 2020-04-16 00:00:00

The requirement for solving nonlinear algebraic equations is ubiquitous in
the field of electric power system simulations. While Newton-based methods have
been used to advantage, they sometimes do not converge, leaving the user
wondering whether a solution exists. In addition to improved robustness, one
advantage of holomorphic embedding methods (HEM) is that, even when they do not
converge, roots plots of the Pad\'e approximants (PAs) to the functions in the
inverse-$\alpha$ plane can be used to determine whether a solution exist. The
convergence factor (CF) of the near-diagonal PAs applied to functions expanded
about the origin is determined by the logarithmic capacity of the associated
branch cut (BC) and the distance of the evaluation point from the origin.
However the underlying mechanism governing this rate has been obscure. We prove
that the ultimate distribution of the PA roots on the BC in the complex plane
converges weakly to the equilibrium distribution of electrostatic charges on a
2-D conductor system with the same topology in a physical setting. This, along
with properties of the Maclaurin series can be used to explain the structure of
the CF equation We demonstrate the theoretical convergence behavior, with
numerical experiments.
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown
Date: 2020-04-16 00:00:00
Tasks: Time Series, Time Series Analysis

Spectral analysis characterises oscillatory time series behaviours such as
cycles, but accurate estimation requires reasonable numbers of observations.
Current COVID-19 time series for many countries are short: pre- and
post-lockdown series are shorter still. Accurate estimation of potentially
interesting cycles within such series seems beyond reach. We solve the problem
of obtaining accurate estimates from short time series by using recent Bayesian
spectral fusion methods. Here we show that transformed new daily COVID-19 cases
for many countries generally contain three cycles operating at wavelengths of
around 2.7, 4.1 and 6.7 days (weekly). We show that the shorter cycles are
suppressed after lockdown. The pre- and post lockdown differences suggest that
the weekly effect is at least partly due to non-epidemic factors, whereas the
two shorter cycles seem intrinsic to the epidemic. Unconstrained, new cases
grow exponentially, but the internal cyclic structure causes periodic falls in
cases. This suggests that lockdown success might only be indicated by four or
more daily falls in cases. Spectral learning for epidemic time series
contributes to the understanding of the epidemic process, helping evaluate
interventions and assists with forecasting. Spectral fusion is a general
technique that is able to fuse spectra recorded at different sampling rates,
which can be applied to a wide range of time series from many disciplines.
Computing all identifiable functions of parameters for ODE models
Authors: Alexey Ovchinnikov, Anand Pillay, Gleb Pogudin, Thomas Scanlon
Date: 2020-04-16 00:00:00
Tasks: All

Parameter identifiability is a structural property of an ODE model for recovering the values of parameters from the data (i.e., from the input and output variables). This property is a prerequisite for meaningful parameter identification in practice. In the presence of nonidentifiability, it is important to find all functions of the parameters that are identifiable. The existing algorithms check whether a given function of parameters is identifiable or, under the solvability condition, find all identifiable functions. However, this solvability condition is not always satisfied, which presents a challenge. Our first main result is an algorithm that computes all identifiable functions without any additional assumptions, which is the first such algorithm as far as we know. Our second main result concerns the identifiability from multiple experiments (with generically different inputs and initial conditions among the experiments). For this problem, we prove that the set of functions identifiable from multiple experiments is what would actually be computed by input-output equation-based algorithms (whether or not the solvability condition is fulfilled), which was not known before. We give an algorithm that not only finds these functions but also provides an upper bound for the number of experiments to be performed to identify these functions. We provide an implementation of the presented algorithms.
Privacy-Preserving Policy Synthesis in Markov Decision Processes
Date: 2020-04-16 00:00:00
Tasks: Decision Making, Privacy Preserving

In decision-making problems, the actions of an agent may reveal sensitive
information that drives its decisions. For instance, a corporation's investment
decisions may reveal its sensitive knowledge about market dynamics. To prevent
this type of information leakage, we introduce a policy synthesis algorithm
that protects the privacy of the transition probabilities in a Markov decision
process. We use differential privacy as the mathematical definition of privacy.
The algorithm first perturbs the transition probabilities using a mechanism
that provides differential privacy. Then, based on the privatized transition
probabilities, we synthesize a policy using dynamic programming. Our main
contribution is to bound the "cost of privacy," i.e., the difference between
the expected total rewards with privacy and the expected total rewards without
privacy. We also show that computing the cost of privacy has time complexity
that is polynomial in the parameters of the problem. Moreover, we establish
that the cost of privacy increases with the strength of differential privacy
protections, and we quantify this increase. Finally, numerical experiments on
two example environments validate the established relationship between the cost
of privacy and the strength of data privacy protections.
Trajectory Planning and Control for Automatic Docking of ASVs with Full-Scale Experiments
Date: 2020-04-16 00:00:00
Tasks: Position, Trajectory Planning

We propose a method for performing automatic docking of a small autonomous
surface vehicle (ASV) by interconnecting an optimization-based trajectory
planner with a dynamic positioning (DP) controller for trajectory tracking. The
trajectory planner provides collision-free trajectories by considering a map
with static obstacles, and produces feasible trajectories through inclusion of
a mathematical model of the ASV and its actuators. The DP controller tracks the
time-parametrized position, velocity and acceleration produced by the
trajectory planner using proportional-integral-derivative feedback with
velocity and acceleration feed forward. The method's performance is tested on a
small ASV in confined waters in Trondheim, Norway. The ASV performs
collision-free docking maneuvers with respect to static obstacles when tracking
the generated reference trajectories and achieves successful docking.
Backward Propagation
Date: 2020-04-15 00:00:00

The document covers the fundamental algorithm of backward propagation from
the point of view of reconstructing the wavefield captured by a "screen" in an
imaging system. Owing to a property of the Helmholtz equation, wavefields have
an inherent propagation symmetry which can be exploited for image formation. In
traditional presentations of this topic the link between this "reciprocity" and
the reconstruction procedure are motivated only at an intuitive level. I
propose to make this more rigorous by using a technique called "plane-to-plane"
propagation which is also known as "beam propagation method" in more advanced
settings.
Knowledge-and-Data-Driven Amplitude Spectrum Prediction for Hierarchical Neural Vocoders
Date: 2020-05-18 00:00:00
Tasks: text-to-speech, Text to Speech

In our previous work, we have proposed a neural vocoder called HiNet which
recovers speech waveforms by predicting amplitude and phase spectra
hierarchically from input acoustic features. In HiNet, the amplitude spectrum
predictor (ASP) predicts log amplitude spectra (LAS) from input acoustic
features. This paper proposes a novel knowledge-and-data-driven ASP (KDD-ASP)
to improve the conventional one. First, acoustic features (i.e., F0 and
mel-cepstra) pass through a knowledge-driven LAS recovery module to obtain
approximate LAS (ALAS). This module is designed based on the combination of
STFT and source-filter theory, in which the source part and the filter part are
designed based on input F0 and mel-cepstra, respectively. Then, the recovered
ALAS are processed by a data-driven LAS refinement module which consists of
multiple trainable convolutional layers to get the final LAS. Experimental
results show that the HiNet vocoder using KDD-ASP can achieve higher quality of
synthetic speech than that using conventional ASP and the WaveRNN vocoder on a
text-to-speech (TTS) task.
Accurate closed-form solution of the SIR epidemic model
Date: 2020-04-16 00:00:00
Tasks: Form, Math

An accurate closed-form solution is obtained to the SIR Epidemic Model through the use of Asymptotic Approximants (Barlow et. al, 2017, Q. Jl Mech. Appl. Math, 70 (1), 21-48). The solution is created by analytically continuing the divergent power series solution such that it matches the long-time asymptotic behavior of the epidemic model. The utility of the analytical form is demonstrated through its application to the COVID-19 pandemic.
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future
Authors: Hiteshi Tandon, Prabhat Ranjan, Tanmoy Chakraborty, Vandana Suhag
Date: 2020-04-16 00:00:00
Tasks: Time Series, Time Series Analysis

COVID-19, a novel coronavirus, is currently a major worldwide threat. It has infected more than a million people globally leading to hundred-thousands of deaths. In such grave circumstances, it is very important to predict the future infected cases to support prevention of the disease and aid in the healthcare service preparation. Following that notion, we have developed a model and then employed it for forecasting future COVID-19 cases in India. The study indicates an ascending trend for the cases in the coming days. A time series analysis also presents an exponential increase in the number of cases. It is supposed that the present prediction models will assist the government and medical personnel to be prepared for the upcoming conditions and have more readiness in healthcare systems.
On the Performance of Non-Orthogonal Multiple Access over Composite Fading Channels
Date: 2020-04-16 00:00:00

This paper analyzes the performance of a cooperative relaying non-orthogonal
multiple access (NOMA) network over Fisher-Snedecor F composite fading
channels. Specifically, one base station (BS) is assumed to communicate with
two receiving mobile users with one acting also as a decode-and-forward (DF)
relay. To highlight the achievable performance gains of the NOMA scheme,
conventional relaying with orthogonal multiple access (OMA) is also analyzed.
For the two systems under consideration, we derive novel exact closed-form
expressions for the ergodic capacity along with the corresponding asymptotic
representations. The derived expressions are then used to assess the influence
of various system parameters, including the fading and shadowing parameters, on
the performance of both the NOMA and OMA systems. Monte-Carlo simulation are
provided throughout to verify the accuracy of our analysis. Results reveal that
the NOMA system can considerably outperform the OMA approach when the power
allocation factor is carefully selected. It is also shown that as the fading
and/or shadowing parameters are increased, the ergodic capacity performance
enhances.
A Heuristics-based Home Energy Management System for Demand Response
Date: 2020-04-16 00:00:00
Tasks: energy management, Management

The so-called Internet of Things (IoT) and advanced communication
technologies have already demonstrated a great potential to manage residential
energy resources via demand-side management. This work presents a home energy
management system in that focused on the energy reallocation problem where
consumers shall shift their energy consumption patterns away from peak periods
and/or high electricity prices. Our solution differentiates residential loads
into two categories: (i) fixed power appliances and (ii) flexible ones.
Therefrom, we formulate our problem as a constraint optimization problem, which
is non-linear and cannot be mathematically solved in closed-form. We then
employ and compare two well-known heuristics, the genetic algorithm (GA) and
the harmony search algorithm (HSA), to minimize electricity expense and peak to
average ratio. These two approaches are compared to the case where no
reallocation happens. Our numerical results show that both methods; GAand HSA
can effectively reduce the electricity cost by 0.9%, 3.98%, and PAR by 15%,
5.8%, respectively
Identification of a class of index models: A topological approach
Date: 2020-04-16 00:00:00

We establish nonparametric identification in a class of so-called index
models using a novel approach that relies on general topological results. Our
proof strategy requires substantially weaker conditions on the functions and
distributions characterizing the model compared to existing strategies; in
particular, it does not require any large support conditions on the regressors
of our model. We apply the general identification result to additive random
utility and competing risk models.
Biophysically detailed mathematical models of multiscale cardiac active mechanics
Authors: Francesco Regazzoni, Luca Dedè, Alfio Quarteroni
Date: 2020-04-16 00:00:00
Tasks: Computational Efficiency

We propose four novel mathematical models, describing the microscopic mechanisms of force generation in the cardiac muscle tissue, which are suitable for multiscale numerical simulations of cardiac electromechanics. Such models are based on a biophysically accurate representation of the regulatory and contractile proteins in the sarcomeres. Our models, unlike most of the sarcomere dynamics models that are available in the literature and that feature a comparable richness of detail, do not require the time-consuming Monte Carlo method for their numerical approximation. Conversely, the models that we propose only require the solution of a system of PDEs and/or ODEs (the most reduced of the four only involving 20 ODEs), thus entailing a significant computational efficiency. By focusing on the two models that feature the best trade-off between detail of description and identifiability of parameters, we propose a pipeline to calibrate such parameters starting from experimental measurements available in literature. Thanks to this pipeline, we calibrate these models for room-temperature rat and for body-temperature human cells. We show, by means of numerical simulations, that the proposed models correctly predict the main features of force generation, including the steady-state force-calcium and force-length relationships, the length-dependent prolongation of twitches and increase of peak force, the force-velocity relationship. Moreover, they correctly reproduce the Frank-Starling effect, when employed in multiscale 3D numerical simulation of cardiac electromechanics.
Using single-cell entropy to describe the dynamics of reprogramming and differentiation of induced pluripotent stem cells
Authors: Yusong Ye, Zhuoqin Yang, Jinzhi Lei
Date: 2020-04-16 00:00:00

Induced pluripotent stem cells (iPSCs) provide a great model to study the process of reprogramming and differentiation of stem cells. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the cell fate change process, which shows increasing during differentiation, and decreasing upon reprogramming. Moreover, based on the scEntropy dynamics, we construct a phenomenological stochastic differential equation model and the corresponding Fokker-Plank equation for cell state transitions during iPSC differentiation, which provide insights to infer cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to the biological process of iPSC, and suggests that the scEntropy can provide a suitable quantify to describe cell fate transition in differentiation and reprogramming of stem cells.