pwc-archive/papers-with-abstracts huggingface dataset:
Inverse Mechano-Electrical Reconstruction of Cardiac Excitation Wave Patterns from Mechanical Deformation using Deep Learning
Authors: Jan Christoph, Jan Lebert
Date: 2020-07-31 00:00:00

The inverse mechano-electrical problem in cardiac electrophysiology is the attempt to reconstruct electrical excitation or action potential wave patterns from the heart's mechanical deformation that occurs in response to electrical excitation. Because heart muscle cells contract upon electrical excitation due to the excitation-contraction coupling mechanism, the resulting deformation of the heart should reflect macroscopic action potential wave phenomena. However, whether the relationship between macroscopic electrical and mechanical phenomena is well-defined and furthermore unique enough to be utilized for an inverse imaging technique, in which mechanical activation mapping is used as a surrogate for electrical mapping, has yet to be determined. Here, we provide a numerical proof-of-principle that deep learning can be used to solve the inverse mechano-electrical problem in phenomenological two- and three-dimensional computer simulations of the contracting heart wall, or in elastic excitable media, with muscle fiber anisotropy. We trained a convolutional autoencoder neural network to learn the complex relationship between electrical excitation, active stress, and tissue deformation during both focal or reentrant chaotic wave activity, and consequently used the network to succesfully estimate or reconstruct electrical excitation wave patterns from mechanical deformation in sheets and bulk-shaped tissues, even in the presence of noise and at low spatial resolutions. We demonstrate that even complicated three-dimensional electrical excitation wave phenomena, such as scroll waves and their vortex filaments, can be computed with very high reconstruction accuracies of about 95% from mechanical deformation using autoencoder neural networks, and we provide a comparison with results that were obtained previously with a physics- or knowledge-based approach.
Game-theoretic modeling of collective decision-making during epidemics
Authors: Mengbin Ye, Lorenzo Zino, Alessandro Rizzo, Ming Cao
Date: 2020-08-05 00:00:00
Tasks: Decision Making

The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model.
A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces
Authors: Parinaz Naseri, Sean V. Hum
Date: 2020-07-28 00:00:00
Tasks: BIG-bench Machine Learning, Multiobjective Optimization

The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the thickness of the separating substrates. Here, we propose a generative machine learning (ML)-based approach to solve this one-to-many mapping and automate the inverse design of dual- and triple-layer metasurfaces. Using this approach, it is possible to solve multiobjective optimization problems by synthesizing thin structures composed of potentially brand-new scatterer designs, in cases where the inter-layer coupling between the layers is non-negligible and synthesis by traditional methods becomes cumbersome. Various examples to provide specific magnitude and phase responses of $x$- and $y$-polarized scattering coefficients across a frequency range as well as mask-based responses for different metasurface applications are presented to verify the practicality of the proposed method.
The impact of financial risks on economic growth in EU-15
Authors: Ionut Jianu, Laura-Madalina Pirscoveanu, Maria-Daniela Tudorache
Date: 2020-07-20 00:00:00

This paper examines the impact of financial risks on economic growth in the first 15 Member States of the European Union, considering 1995-2014 period and aims to lay down a new explanatory model of economic growth, based mainly on the behavioral reactivity of the financial disruptions mentioned above. The model was estimated through the panel estimated generalized least squares method and included additional control variables in order to strengthen the research conducted. Our goal consists in the examination of the financial risks in the European Union and in the estimation of their impact on economic growth.
Predicting Crack Growth and Fatigue Life with Surrogate Models
Authors: Simon Pfingstl, Jose Ignacio Rios, Horst Baier, Markus Zimmermann
Date: 2020-08-05 00:00:00
Tasks: regression

Fatigue-induced damage is still one of the most uncertain failures in structural systems. Prognostic health monitoring together with surrogate models can help to predict the fatigue life of a structure. This paper demonstrates how to combine data from previously observed crack evolutions with data from the currently observed structure in order to predict crack growth and the total fatigue life. We show the application of one physics-based model, which is based on Paris' law, and four mathematical surrogate models: recurrent neural networks, Gaussian process regression, k-nearest neighbors, and support vector regression. For a coupon test, we predict the time to failure and the crack growth with confidence intervals. Moreover, we compare the performance of all proposed models by the mean absolute error, coefficient of determination, mean of log-likelihood, and their confidence intervals. The results show that the best mathematical surrogate models are Gaussian process regression and recurrent neural networks. Furthermore, this paper shows that the mathematical surrogate models tend to have conservative confidence intervals, whereas the physics-based model exhibits overly optimistic (too small) confidence intervals.
Deep-Learning Based Adaptive Ultrasound Imaging from Sub-Nyquist Channel Data
Authors: Alon Mamistvalov, Ariel Amar, Naama Kessler, Yonina C. Eldar
Date: 2020-08-06 00:00:00
Tasks: Decoder

Traditional beamforming of medical ultrasound images relies on sampling rates significantly higher than the actual Nyquist rate of the received signals. This results in large amounts of data to store and process, imposing hardware and software challenges on the development of ultrasound machinery and algorithms, and impacting the resulting performance. In light of the capabilities demonstrated by deep learning methods over the past years across a variety of fields, including medical imaging, it is natural to consider their ability to recover high-quality ultrasound images from partial data. Here, we propose an approach for deep-learning based reconstruction of B-mode images from temporally and spatially sub-sampled channel data. We begin by considering sub-Nyquist sampled data, time-aligned in the frequency domain and transformed back to the time domain. The data is further sampled spatially, so that only a subset of the received signals is acquired. The partial data is used to train an encoder-decoder convolutional neural network, using as targets minimum-variance (MV) beamformed signals that were generated from the original, fully-sampled data. Our approach yields high-quality B-mode images, with higher resolution than previously proposed reconstruction approaches (NESTA) from compressed data as well as delay-and-sum beamforming (DAS) of the fully-sampled data. In terms of contrast to noise ratio, our results are comparable to MV beamforming of the fully-sampled data, thus enabling better and more efficient imaging than is mostly used in clinical practice today.
Developing a real estate yield investment deviceusing granular data and machine learning
Authors: Monica Azqueta-Gavaldon, Gonzalo Azqueta-Gavaldon, Inigo Azqueta-Gavaldon, Andres Azqueta-Gavaldon
Date: 2020-06-20 00:00:00
Tasks: BIG-bench Machine Learning, Descriptive

This project aims at creating an investment device to help investors determine which real estate units have a higher return to investment in Madrid. To do so, we gather data from Idealista.com, a real estate web-page with millions of real estate units across Spain, Italy and Portugal. In this preliminary version, we present the road map on how we gather the data; descriptive statistics of the 8,121 real estate units gathered (rental and sale); build a return index based on the difference in prices of rental and sale units(per neighbourhood and size) and introduce machine learning algorithms for rental real estate price prediction.
Best Practices for Alchemical Free Energy Calculations
Authors: Antonia S. J. S. Mey, Bryce Allen, Hannah E. Bruce Macdonald, John D. Chodera, Maximilian Kuhn, Julien Michel, David L. Mobley, Levi N. Naden, Samarjeet Prasad, Andrea Rizzi, Jenke Scheen, Michael R. Shirts, Gary Tresadern, Huafeng Xu
Date: 2020-08-07 00:00:00

Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing \emph{alchemical} intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations, including relative and absolute small molecule binding free energy calculations to biomolecular targets.
Pricing foreseeable and unforeseeable risks in insurance portfolios
Authors: Weihong Ni, Corina Constantinescu, Alfredo Egídio dos Reis, Véronique Maume-Deschamps
Date: 2020-07-15 00:00:00

In this manuscript we propose a method for pricing insurance products that cover not only traditional risks, but also unforeseen ones. By considering the Poisson process parameter to be a mixed random variable, we capture the heterogeneity of foreseeable and unforeseeable risks. To illustrate, we estimate the weights for the two risk streams for a real dataset from a Portuguese insurer. To calculate the premium, we set the frequency and severity as distributions that belong to the linear exponential family. Under a Bayesian setup , we show that when working with a finite mixture of conjugate priors, the premium can be estimated by a mixture of posterior means, with updated parameters, depending on claim histories. We emphasise the riskiness of the unforeseeable trend, by choosing heavy-tailed distributions. After estimating distribution parameters involved using the Expectation-Maximization algorithm, we found that Bayesian premiums derived are more reactive to claim trends than traditional ones.
On the Potential of Extending Aircraft Service Time Using a Fatigue Damage Index
Authors: Simon Pfingstl, Dominik Steinweg, Markus Zimmermann, Mirko Hornung
Date: 2020-08-07 00:00:00

Aircraft structures experience various kinds of loads over their entire lifetime, typically leading to fatigue and ultimately structural failure. In order to avoid structural failure during operation, the maximum number of flight cycles and flight hours is regulated by laws ensuring continued airworthiness. However, since every flight impacts the aircraft differently, not all airframes are equally stressed at the time of decommissioning. This paper proposes a new retirement criterion based on the so-called fatigue damage index (FDI). The criterion takes into account that aircraft are differently operated and thus enables an individual decommissioning of aircraft without compromising its safety. Based on aircraft sample data covering 95% of the Airbus A320 fleet over two years, the enhanced decommissioning criterion is estimated to significantly extend the average aircraft service life. The impact varies within the fleet, depending on the experienced seat load factors, cruise altitudes, and taxi times considered for the individual aircraft during operation. While seat load factors and flight altitudes significantly affect the defined FDI, the influence of taxi times is only minor. Based on the estimated increase in aircraft service life, the paper argues that for service life extensions, the FDI shall be considered as the limit of validity in the regulatory framework governing the decommissioning of aircraft.
Privacy-Preserving Dynamic Average Consensus via State Decomposition: Case Study on Multi-Robot Formation Control
Date: 2020-09-11 00:00:00
Tasks: Privacy Preserving

Dynamic average consensus is a decentralized control/estimation framework
where a group of agents cooperatively track the average of local time-varying
reference signals. In this paper, we develop a novel state decomposition-based
privacy preservation scheme to protect the privacy of agents when sharing
information with neighboring agents. Specifically, we first show that an
external eavesdropper can successfully wiretap the reference signals of all
agents in a conventional dynamic average consensus algorithm. To protect
privacy against the eavesdropper, a state decomposition scheme is developed
where the original state of each agent is decomposed into two sub-states: one
succeeds the role of the original state in inter-node interactions, while the
other sub-state only communicates with the first one and is invisible to other
neighboring agents. Rigorous analyses are performed to show that 1) the
proposed privacy scheme preserves the convergence of the average consensus; and
2) the privacy of the agents is protected such that an eavesdropper cannot
discover the private reference signals with any guaranteed accuracy. The
developed privacy-preserving dynamic average consensus framework is then
applied to the formation control of multiple non-holonomic mobile robots, in
which the efficacy of the scheme is demonstrated. Numerical simulation is
provided to illustrate the effectiveness of the proposed approach.
A mathematical model of the COVID-19 pandemic dynamics with dependent variable infection rate: Application to the Republic of Korea
Authors: Aycil Cesmelioglu, Kenneth L. Kuttler, Meir Shillor, Anna M. Spagnuolo
Date: 2020-07-27 00:00:00

This work constructs, analyzes, and simulates a new compartmental SEIR-type model for the dynamics and potential control of the current COVID-19 pandemic. The novelty in this work is two-fold. First, the population is divided according to its compliance with disease control directives (lockdown, shelter-in-place, masks/face coverings, physical distancing, etc.) into those who fully comply and those who follow the directives partially, or are necessarily mobile (such as medical staff). This split, indirectly, reflects on the quality and consistency of these measures. This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread. Second, the adequate contact rate, which directly affects the infection rate, is one of the model unknowns, as it keeps track of the changes in the population behavior and the effectiveness of various disease treatment modalities via a differential inclusion. Existence, uniqueness and positivity results are proved using a nonstandard convex analysis-based approach. As a case study, the pandemic outbreak in the Republic of Korea (South Korea) is simulated. The model parameters were found by minimizing the deviation of the model prediction from the reported data over the first 100 days of the pandemic in South Korea.The simulations show that the model captures accurately the pandemic dynamics in the subsequent 75 days, which provides confidence in the model predictions and its future use. In particular, the model predicts that about 40% of the infections were not documented, which implies that asymptomatic infections contribute silently but substantially to the spread of the disease indicating that more widespread asymptomatic testing is necessary.
2$\theta$-burster for rhythm-generating circuits
Date: 2020-07-29 00:00:00
Tasks: Rhythm

We propose and demonstrate the use of a minimal 2$\theta$ model for
endogenous bursters coupled in 3-cell neural circuits. This 2$\theta$ model
offers the benefit of simplicity of designing larger neural networks along with
an acute reduction on the computation cost.
Interspecies evolutionary dynamics mediated by public goods in bacterial quorum sensing
Date: 2020-08-11 00:00:00

Bacterial quorum sensing is the communication that takes place between
bacteria as they secrete certain molecules into the intercellular medium that
later get absorbed by the secreting cells themselves and by others. Depending
on cell density, this uptake has the potential to alter gene expression and
thereby affect global properties of the community. We consider the case of
multiple bacterial species coexisting, referring to each one of them as a
genotype and adopting the usual denomination of the molecules they collectively
secrete as public goods. A crucial problem in this setting is characterizing
the coevolution of genotypes as some of them secrete public goods (and pay the
associated metabolic costs) while others do not but may nevertheless benefit
from the available public goods. We introduce a network model to describe
genotype interaction and evolution when genotype fitness depends on the
production and uptake of public goods. The model comprises a random graph to
summarize the possible evolutionary pathways the genotypes may take as they
interact genetically with one another, and a system of coupled differential
equations to characterize the behavior of genotype abundance in time. We study
some simple variations of the model analytically and more complex variations
computationally. Our results point to a simple trade-off affecting the
long-term survival of those genotypes that do produce public goods. This
trade-off involves, on the producer side, the impact of producing and that of
absorbing the public good. On the non-producer side, it involves the impact of
absorbing the public good as well, now compounded by the molecular
compatibility between the producer and the non-producer. Depending on how these
factors turn out, producers may or may not survive.
Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation
Authors: Hasan Albinsaid, Keshav Singh, Sudip Biswas, Chih-Peng Li, Mohamed-Slim Alouini
Date: 2020-08-08 00:00:00

Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter-channel interference among the active antennas is a challenge in GSM systems and is the focus of this letter. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (B-DNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub-DNNs. After N-ordinary DNN detection, the Euclidean distance-based soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.
Multistability of Small Reaction Networks
Authors: Xiaoxian Tang, Hao Xu
Date: 2020-08-10 00:00:00

For three typical sets of small reaction networks (networks with two reactions, one irreversible and one reversible reaction, or two reversible-reaction pairs), we completely answer the challenging question: what is the smallest subset of all multistable networks such that any multistable network outside of the subset contains either more species or more reactants than any network in this subset?
How Efficient is Contact Tracing in Mitigating the Spread of Covid-19? A Mathematical Modeling Approach
Authors: T. A. Biala, Y. O. Afolabi, A. Q. M. Khaliq
Date: 2020-08-10 00:00:00

Contact Tracing (CT) is one of the measures taken by government and health officials to mitigate the spread of the novel coronavirus. In this paper, we investigate its efficacy by developing a compartmental model for assessing its impact on mitigating the spread of the virus. We describe the impact on the reproduction number ${\mathcal{R}_0}$ of COVID-19. In particular, we discuss the importance and relevance of parameters of the model such as the number of reported cases, effectiveness of tracking and monitoring policy, and the transmission rates to contact tracing. We describe the terms "perfect tracking", "perfect monitoring" and "perfect reporting" to indicate that traced contacts will be tracked while incubating, tracked contacts are efficiently monitored so that they do not cause secondary infections, and all infected persons are reported, respectively. We consider three special scenarios: (1) perfect monitoring and perfect tracking of contacts of a reported case, (2) perfect reporting of cases and perfect monitoring of tracked reported cases and (3) perfect reporting and perfect tracking of contacts of reported cases. Furthermore, we gave a lower bound on the proportion of contacts to be traced to ensure that the effective reproduction, ${\mathcal{R}_c}$, is below one and describe ${\mathcal{R}_c}$ in terms of observable quantities such as the proportion of reported and traced cases. Model simulations using the COVID-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission of COVID-19 and reduce peak hospitalizations and deaths.