pwc-archive/papers-with-abstracts huggingface dataset:
An efficient explicit approach for predicting the Covid-19 spreading with undetected infectious: The case of Cameroon
Date: 2020-05-26 00:00:00

This paper considers an explicit numerical scheme for solving the
mathematical model of the propagation of Covid-19 epidemic with undetected
infectious cases. We analyze the stability and convergence rate of the new
approach in $L^{\infty}$-norm. The proposed method is less time consuming.
Furthermore, the method is stable, at least second-order convergent and can
serve as a robust tool for the integration of general systems of ordinary
differential equations. A wide set of numerical evidences which consider the
case of Cameroon are presented and discussed.
Identifying Key Sectors in the Regional Economy: A Network Analysis Approach Using Input-Output Data
Authors: Fernando DePaolis, Phil Murphy, M. Clara DePaolis Kaluza
Date: 2020-05-22 00:00:00

By applying network analysis techniques to large input-output system, we identify key sectors in the local/regional economy. We overcome the limitations of traditional measures of centrality by using random-walk based measures, as an extension of Blochl et al. (2011). These are more appropriate to analyze very dense networks, i.e. those in which most nodes are connected to all other nodes. These measures also allow for the presence of recursive ties (loops), since these are common in economic systems (depending to the level of aggregation, most firms buy from and sell to other firms in the same industrial sector). The centrality measures we present are well suited for capturing sectoral effects missing from the usual output and employment multipliers. We also develop an R package (xtranat) for the processing of data from IMPLAN(R) models and for computing the newly developed measures.
A De-biased Direct Question Approach to Measuring Consumers' Willingness to Pay
Date: 2020-05-22 00:00:00
Tasks: Decision Making

Knowledge of consumers' willingness to pay (WTP) is a prerequisite to
profitable price-setting. To gauge consumers' WTP, practitioners often rely on
a direct single question approach in which consumers are asked to explicitly
state their WTP for a product. Despite its popularity among practitioners, this
approach has been found to suffer from hypothetical bias. In this paper, we
propose a rigorous method that improves the accuracy of the direct single
question approach. Specifically, we systematically assess the hypothetical
biases associated with the direct single question approach and explore ways to
de-bias it. Our results show that by using the de-biasing procedures we
propose, we can generate a de-biased direct single question approach that is
accu-rate enough to be useful for managerial decision-making. We validate this
approach with two studies in this paper.
Adaptive Network Response to Line Failures in Power Systems
Authors: Chen Liang, Linqi Guo, Alessandro Zocca, Steven H. Low, Adam Wierman
Date: 2020-05-22 00:00:00

Transmission line failures in power systems propagate and cascade non-locally. In this work, we propose an adaptive control strategy that offers strong guarantees in both the mitigation and localization of line failures. Specifically, we leverage the properties of network bridge-block decomposition and a frequency regulation method called the unified control. If the balancing areas over which the unified control operates coincide with the bridge-blocks of the network, the proposed strategy drives the post-contingency system to a steady state where the impact of initial line outages is localized within the areas where they occurred whenever possible, stopping the cascading process. When the initial line outages cannot be localized, the proposed control strategy provides a configurable design that progressively involves and coordinates more balancing areas. We compare the proposed control strategy with the classical Automatic Generation Control (AGC) on the IEEE 118-bus and 2736-bus test networks. Simulation results show that our strategy greatly improves overall reliability in terms of the N-k security standard, and localizes the impact of initial failures in the majority of the simulated contingencies. Moreover, the proposed framework incurs significantly less load loss, if any, compared to AGC, in all our case studies.
Line Failure Localization of Power Networks Part II: Cut Set Outages
Authors: Linqi Guo, Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman
Date: 2020-05-22 00:00:00

Transmission line failure in power systems prop-agate non-locally, making the control of the resulting outages extremely difficult. In Part II of this paper, we continue the study of line failure localizability in transmission networks and characterize the impact of cut set outages. We establish a Simple Path Criterion, showing that the propagation pattern due to bridge outages, a special case of cut set failures, are fully determined by the positions in the network of the buses that participate in load balancing. We then extend our results to general cut set outages. In contrast to non-cut outages discussed in Part I whose subsequent line failures are contained within the original blocks, cut set outages typically impact the whole network, affecting the power flows on all remaining lines. We corroborate our analytical results in both parts using the IEEE 118-bus test system, in which the failure propagation patterns exhibit a clear block-diagonal structure predicted by our theory, even when using full AC power flow equations.
Mathematical analysis and potential therapeutic implications of a novel HIV-1 model of basal and activated transcription in T-cells and macrophages
Date: 2020-05-22 00:00:00
Tasks: Open-Ended Question Answering

HIV-1 affects tens of millions of people worldwide. Current treatments often
involve a cocktail of antiretroviral drugs, which are effective in reducing the
virus and extending life spans. However, there is currently no FDA-approved
HIV-1 transcription inhibitor. Furthermore, there have only been a few attempts
to model the transcription process in HIV-1. In this work, we extend a novel
three-state model of HIV-1 transcription introduced in DeMarino et al. (2020)
that has been developed and validated against experimental data. After fitting
this model to in vitro data, significant differences in the transcription
process of HIV-1 in T-cells and macrophages have been observed. In particular,
the activation of the HIV-1 promoter in T-cells appears to take place rapidly
as the Tat protein approaches a critical threshold. In contrast, the same
process occurs smoother in macrophages.
  In this work, we carry out systematic mathematical analyses of the model to
complement experimental data fitting and sensitivity analysis performed
earlier. We derive explicit solutions of the model to obtain exact
transcription process decay rates for the original model and then study the
effect of nonlinearity on the system behavior, including the existence and the
local and global stability of the positive equilibrium. We were able to show
the stability of the positive steady state in limiting cases, with the global
stability in the general case remaining an open question.
  By modeling the effect of transcription-inhibiting drug therapy, we provide a
nontrivial condition for it to be effective in reducing viral load. Moreover,
our numerical simulations and analysis point out that the effect of the
transcription-inhibitor can be enhanced by synchronizing with standard
treatments, such as combination antiretroviral therapy, to allow the reduction
of total dosages and toxicity.
Bat Optimized Watershed based Segmentation of Lamina Cribrosa
Date: 2020-05-21 00:00:00
Tasks: Segmentation, Sensitivity, Specificity, SSIM

The segmentation of Lamina Cribrosa(LC) is a challenging task to detect the
glaucomatous damage. In this paper a new method of segmenting the LC using bat
optimized Watershed segmentation is done. By using wavelet transform LC
structures are decomposed. Then, the decomposed image is optimized using Bat
algorithm and by applying histogram equalization the optimized image is
normalized. Watershed algorithm is used to segment the Lamina Cribrosa from its
outer layer. Using some parameters like PSNR, MSE, F-Measure, rand index,
sensitivity, specificity, SSIM and accuracy, the performance of the proposed
system is calculated. The results show that the proposed method provides higher
accuracy of 99.29%.
FPGA based design for online computation of Multivariate EMD (MEMD)
Authors: Sikender Gul, Muhammad Faisal Siddiqui, Naveed Ur Rehman
Date: 2020-05-22 00:00:00

Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to: i) their large size and multidimensional nature, thus requiring specialized algorithms and efficient hardware designs for on-line and real-time processing; ii) the nonstationary nature of data arising in many real life applications demanding new extensions of standard multiscale non-stationary signal processing tools. In this paper, we address the former issue by proposing a fully FPGA based hardware architecture of a popular multi-scale and multivariate signal processing algorithm, termed as multivariate empirical mode decomposition (MEMD). MEMD is a data-driven method that extends the functionality of standard empirical mode decomposition (EMD) algorithm to multichannel or multivariate data sets. Since its inception in 2010, the algorithm has found wide spread applications spanning different engineering related fields. Yet, no parallel FPGA based hardware design of the algorithm is available for its on-line and real-time processing. Our proposed architecture for MEMD uses fixed point operations and employs cubic spline interpolation within the sifting process. Finally, examples of decomposition of multivariate synthetic and real world biological signals are provided.
Implementing Non-Equilibrium Networks with Active Circuits of Duplex Catalysts
Authors: Antti Lankinen, Ismael Mullor Ruiz, Thomas E. Ouldridge
Date: 2020-05-23 00:00:00

DNA strand displacement (DSD) reactions have been used to construct chemical reaction networks in which species act catalytically at the level of the overall stoichiometry of reactions. These effective catalytic reactions are typically realised through one or more of the following: many-stranded gate complexes to coordinate the catalysis, indirect interaction between the catalyst and its substrate, and the recovery of a distinct ``catalyst'' strand from the one that triggered the reaction. These facts make emulation of the out-of-equilibrium catalytic circuitry of living cells more difficult. Here, we propose a new framework for constructing catalytic DSD networks: Active Circuits of Duplex Catalysts (ACDC). ACDC components are all double-stranded complexes, with reactions occurring through 4-way strand exchange. Catalysts directly bind to their substrates, and and the ``identity'' strand of the catalyst recovered at the end of a reaction is the same molecule as the one that initiated it. We analyse the capability of the framework to implement catalytic circuits analogous to phosphorylation networks in living cells. We also propose two methods of systematically introducing mismatches within DNA strands to avoid leak reactions and introduce driving through net base pair formation. We then combine these results into a compiler to automate the process of designing DNA strands that realise any catalytic network allowed by our framework.
Distributed remote estimation over the collision channel with and without local communication
Date: 2020-05-23 00:00:00
Tasks: parameter estimation

The emergence of the Internet-of-Things and cyber-physical systems
necessitates the coordination of access to limited communication resources in
an autonomous and distributed fashion. Herein, the optimal design of a wireless
sensing system with n sensors communicating with a fusion center via a
collision channel of limited capacity k (k < n) is considered. In particular,
it is shown that the problem of minimizing the mean-squared error subject to a
threshold-based strategy at the transmitters is quasi-convex. As such, low
complexity, numerical optimization methods can be applied. When coordination
among sensors is not possible, the performance of the optimal threshold
strategy is close to that of a centralized lower bound. The loss due to
decentralization is thoroughly characterized. Local communication among sensors
(using a sparsely connected graph), enables the on-line learning of unknown
parameters of the statistical model. These learned parameters are employed to
compute the desired thresholds locally and autonomously. Consensus-based
strategies are investigated and analyzed for parameter estimation. One strategy
approaches the performance of the decentralized approach with fast convergence
and a second strategy approaches the performance of the centralized approach,
albeit with slower convergence. A hybrid scheme that combines the best of both
approaches is proposed offering a fast convergence and excellent convergent
performance.
Fomite transmission and disinfection strategies for SARS-CoV-2 and related viruses
Authors: Nicolas Castaño, Seth Cordts, Myra Kurosu Jalil, Kevin Zhang, Saisneha Koppaka, Alison Bick, Rajorshi Paul, Sindy KY Tang
Date: 2020-05-23 00:00:00

Contaminated objects or surfaces, referred to as fomites, play a critical role in the spread of viruses, including SARS-CoV-2, the virus responsible for the COVID-19 pandemic. The long persistence of viruses (hours to days) on surfaces calls for an urgent need for surface disinfection strategies to intercept virus transmission and the spread of the disease. Elucidating the physicochemical processes and surface science underlying the adsorption and transfer of virus between surfaces, as well as their inactivation, are important in understanding how the disease is transmitted, and in developing effective interception strategies. This review aims to summarize the current knowledge and underlying physicochemical processes of virus transmission, in particular via fomites, and common disinfection approaches. Gaps in knowledge and needs for further research are also identified. The review focuses on SARS-CoV-2, but will supplement the discussions with related viruses.
An Automated, Cost-Effective Optical System for Accelerated Anti-microbial Susceptibility Testing (AST) using Deep Learning
Date: 2020-05-23 00:00:00

Antimicrobial susceptibility testing (AST) is a standard clinical procedure
used to quantify antimicrobial resistance (AMR). Currently, the gold standard
method requires incubation for 18-24 h and subsequent inspection for growth by
a trained medical technologist. We demonstrate an automated, cost-effective
optical system that delivers early AST results, minimizing incubation time and
eliminating human errors, while remaining compatible with standard phenotypic
assay workflow. The system is composed of cost-effective components and
eliminates the need for optomechanical scanning. A neural network processes the
captured optical intensity information from an array of fiber optic cables to
determine whether bacterial growth has occurred in each well of a 96-well
microplate. When the system was blindly tested on isolates from 33 patients
with Staphylococcus aureus infections, 95.03% of all the wells containing
growth were correctly identified using our neural network, with an average of
5.72 h of incubation time required to identify growth. 90% of all wells (growth
and no-growth) were correctly classified after 7 h, and 95% after 10.5 h. Our
deep learning-based optical system met the FDA-defined criteria for essential
and categorical agreements for all 14 antibiotics tested after an average of
6.13 h and 6.98 h, respectively. Furthermore, our system met the FDA criteria
for major and very major error rates for 11 of 12 possible drugs after an
average of 4.02 h, and 9 of 13 possible drugs after an average of 9.39 h,
respectively. This system could enable faster, inexpensive, automated AST,
especially in resource limited settings, helping to mitigate the rise of global
AMR.
Macroeconomic factors for inflation in Argentine 2013-2019
Date: 2020-05-23 00:00:00

The aim of this paper is to investigate the use of the Factor Analysis in
order to identify the role of the relevant macroeconomic variables in driving
the inflation. The Macroeconomic predictors that usually affect the inflation
are summarized using a small number of factors constructed by the principal
components. This allows us to identify the crucial role of money growth,
inflation expectation and exchange rate in driving the inflation. Then we use
this factors to build econometric models to forecast inflation. Specifically,
we use univariate and multivariate models such as classical autoregressive,
Factor models and FAVAR models. Results of forecasting suggest that models
which incorporate more economic information outperform the benchmark.
Furthermore, causality test and impulse response are performed in order to
examine the short-run dynamics of inflation to shocks in the principal factors.
Advanced Physical-Layer Technologies in VHF Data Link Communications
Authors: Hosseinali Jamal, David W. Matolak
Date: 2020-05-23 00:00:00

Due to the rapidly increasing number of flights and high usage of VHF channels, the VHF spectrum is becoming much more crowded, and use of analog waveforms will likely not maintain the required performance. Therefore, digital communications have been considered due to larger spectral efficiency. Notably, VHF data link modes 2 and 3 (VDL2 and 3) using D8PSK modulation has been proposed. VDL2 and 3 received attention due to its simplicity and more constant amplitude waveform, yielding lower PAPR. Recently, advanced VHF digital link (A-VDL) was proposed for VHF. This scheme enables use of essentially the same platform as VDL except for the physical layer processing, including modulation. The proposed A-VDL, following DVB-S2 standard, uses APSK modulation with higher modulation order than VDL, hence providing higher spectral efficiency than VDL. Compared to the widely used QAM, APSK is more resistant to amplifier amplitude and phase distortions. Thus, APSK has become of interest for satellite communications, as well as VHF communications in A-VDL. In this paper, we investigate other advanced technologies such as channel encoding technique low-density parity-check (LDPC), more efficient standardized voice encoders, as well as better pulse shaping filters than the classical square-root raised-cosine filter used in VDL and A-VDL. Via simulations and analysis, we compare the proposed scheme's link margin, PAPR, and spectral efficiency compared to VDL and A-VDL, which both use Reed Solomon (RS) encoding. In addition, as another way of generating the same VDL waveforms (or possibly other single-carrier aeronautical band waveforms), we investigate the single-carrier type waveform used in cellular LTE and 5G uplink communication links, the discrete Fourier transform-spread OFDM (DFT-s-OFDM), and discuss how we can take advantage of using the same LTE and 5G hardware resources.
Multicarrier Spectral Shaping for Non-White Interference Channels: Application to L-band Aviation Channels
Authors: Hosseinali Jamal, David W. Matolak
Date: 2020-05-23 00:00:00

In this paper, we investigate an algorithm to attain additive white Gaussian noise (AWGN) performance in spectrally non-white channels, using multicarrier communications, i.e., orthogonal frequency division multiplexing (OFDM) and filterbank multicarrier (FBMC). The non-white channel can be from non-white noise, or more commonly, interference. Our algorithm uses a simple optimization method to find usable subcarriers and assigns different power levels to the subcarriers to attain the equivalent AWGN channel bit error ratio (BER). Subcarriers that experience very high interference are assigned as null subcarriers. After describing our analysis, we show results for two non-white interference signal examples: the Gaussian pulse shaped distance measuring equipment (DME) pulses, and a classical rectangular-pulse interference signal. The DME example is pertinent for currently proposed aviation communication systems, where new multicarrier techniques, e.g., the L-band digital aviation communication systems (LDACS) have been designed as an inlay approach between the high-power DME channels in the L-band. Our results show how using this adaptive technique can improve performance and spectral efficiency, whereas fixed bandwidth schemes such as LDACS could suffer significant performance degradation. These results show the utility of this idea for future adaptive and cognitive radio applications for aviation or other non-white channels.
Robust Output Feedback Consensus for Networked Identical Nonlinear Negative-Imaginary Systems
Authors: Kanghong Shi, Igor G. Vladimirov, Ian R. Petersen
Date: 2020-05-23 00:00:00

A robust output feedback consensus problem for networked identical nonlinear negative-imaginary (NI) systems is investigated in this paper. Output consensus is achieved by applying identical linear output strictly negative-imaginary (OSNI) controllers to all the nonlinear NI plants in positive feedback through the network topology. First, we extend the definition of nonlinear NI systems from single-input single-output (SISO) systems to multiple-input multiple-output (MIMO) systems and also extend the definition of OSNI systems to nonlinear scenarios. Asymptotic stability is proved for the closed-loop interconnection of a nonlinear NI system and a nonlinear OSNI system under reasonable assumptions. Then, an NI property and an OSNI-like property are proved for networked identical nonlinear NI systems and networked identical linear OSNI systems, respectively. Output feedback consensus is proved for a network of identical nonlinear NI plants by investigating the stability of its closed-loop interconnection with a network of linear OSNI controllers. This closed-loop interconnection is proposed as a protocol to deal with the output consensus problem for networked identical nonlinear NI systems and is robust against uncertainty in the individual system's model.
Quickest Detection of Ecological Regimes for Natural Resource Management
Authors: Neha Deopa, Daniele Rinaldo
Date: 2020-05-23 00:00:00
Tasks: Management

We study the stochastic dynamics of natural resources under the threat of ecological regime shifts. We establish a Pareto optimal framework of regime shift detection under uncertainty that minimizes the delay with which economic agents become aware of the shift. We integrate ecosystem surveillance in the formation of optimal resource extraction policies. We fully solve the case of a profit-maximizing monopolist, study its response to regime shift detection and show the generality of our framework by extending our results to other decision makers and functional forms.We apply our framework to the case of the Cantareira water reservoir in S\~ao Paulo, Brazil, and study the events that led to its depletion and the consequent water supply crisis.