pwc-archive/papers-with-abstracts huggingface dataset:
The cooperation-defection evolution on social networks
Authors: Bijan Sarkar
Date: 2020-07-20 00:00:00

Without contributing, defectors take more benefit from social resources than cooperators which is the reflection of a specific character of individuals. However, natural physical mechanisms of our society promote cooperation. Thus, in the long run, the evolution about genetic variation is something more than the social evolution about fitness. The loci of evolutionary paths of the cooperation and the defection are correlated, but not a full complement of each other. Yet, the only single specific mechanism which is operated by some rules explains the enhancement of cooperation where the independent analysis of defect evolutionary mechanism is ignored. Moreover, the execution of a particular evolutionary rule through algorithm method over the long time encounters highly sensitive influence of the model parameters. Theoretically, biodiversity of two types relatively persists rarely. Here I describe the evolutionary outcome in the demographic fluctuation. Using both analytical procedure and algorithm method the article concludes that the intratype fitness of individual species is the key factor for not only surviving, but thriving. In consideration of the random drift, the experimental outcomes show that dominant enhancement of cooperation over defection is qualitatively independent of environmental scenario. Collectively, the set of the rules becomes an evolutionary principle to cooperation enhancement.
A Do-It-Yourself (DIY) Light-Wave Sensing and Communication Project: Low-Cost, Portable, Effective, and Fun
Authors: Sabit Ekin, John F. O'Hara, Emrah Turgut, Nicole Colston, Jeffrey L. Young
Date: 2020-07-20 00:00:00

A do-it-yourself (DIY) light-wave sensing (LWS) and communication project was developed to generate interest and clarify basic electromagnetic (EM) and wireless communication concepts among students at different education levels from middle school to undergraduate. This paper demonstrates the nature of the project and its preliminary effectiveness. Wireless sensing/communication concepts are generally considered hard to comprehend being underpinned only by theoretical coursework and occasional simulations. Further, K-12 schools and small academic institutions may not have the resources necessary to produce tangible demonstrations for clarification. The consequent lack of affordable hands-on experiences fails to motivate and engage students. The DIY-LWS is intended to make wireless concepts more understandable and less esoteric by linking fundamental concepts with familiar technologies such as solar cells, visible lights, and smartphones. It is also intended to pique student interest by allowing them to personally assemble, operate, and explore a light-based wireless communication system. A preliminary assessment is used to determine the student base knowledge level and enthusiasm for wireless and related core topics. Students are instructed to assemble and test their own DIY-LWS hardware to provide a hands-on experience and stimulate further exploration. Short lectures are given to link conceptual ideas to the real-world phenomena. Finally, students are re-assessed to quantify any change in conceptual understanding. The DIY-LWS kits have been used in multiple events with students at different levels from secondary to high schools to college. Pre- and post-assessments revealed pronounced improvements (the number of correct answers doubled) in student understanding of EM concepts. Instructors observed tremendous interest and excitement among the students during and after the experiments.
Social capital may mediate the relationship between social distance and COVID-19 prevalence
Date: 2020-07-21 00:00:00

The threat of the new coronavirus (COVID-19) is increasing. Regarding the
difference in the infection rate observed in each region, in addition to
studies seeking the cause due to differences in the social distance (population
density), there is an increasing trend toward studies seeking the cause due to
differences in social capital. However, studies have not yet been conducted on
whether social capital could influence the infection rate even if it controls
the effect of population density. Therefore, in this paper, we analyzed the
relationship between infection rate, population density, and social capital
using statistical data for each prefecture. Statistical analysis showed that
social capital not only correlates with infection rates and population
densities but still has a negative correlation with infection rates controlling
for the effects of population density. Besides, controlling the relationship
between variables by mean age showed that social capital had a greater
correlation with infection rate than population density. In other words, social
capital mediates the correlation between population density and infection
rates. This means that social distance alone is not enough to deter coronavirus
infection, and social capital needs to be recharged.
Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
Authors: Vinícius Oliari, Sebastiaan Goossens, Christian Häger, Gabriele Liga, Rick M. Bütler, Menno van den Hout, Sjoerd van der Heide, Henry D. Pfister, Chigo Okonkwo, Alex Alvarado
Date: 2020-07-20 00:00:00
Tasks: BIG-bench Machine Learning

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch''. One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance--complexity trade-offs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work.
Consensus-Based Current Sharing and Voltage Balancing in DC Microgrids with Exponential Loads
Date: 2020-07-17 00:00:00

In this work, we present a novel consensus-based secondary control scheme for
current sharing and voltage balancing in DC microgrids, composed of distributed
generation units, dynamic RLC lines, and nonlinear ZIE (constant impedance,
constant current, and exponential) loads. Situated atop a primary voltage
control layer, our secondary controllers have a distributed structure, and
utilize information exchanged over a communication network to compute necessary
control actions. Besides showing that the desired objectives are always
attained in steady state, we deduce sufficient conditions for the existence and
uniqueness of an equilibrium point for constant power loads -- E loads with
zero exponent. Our control design hinges only on the local parameters of the
generation units, facilitating plug-and-play operations. We provide a voltage
stability analysis, and illustrate the performance and robustness of our
designs via simulations. All results hold for arbitrary, albeit connected,
microgrid and communication network topologies.
Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks
Authors: Zhipeng Lin, Tiejun Lv, Wei Ni, J. Andrew Zhang, Ren Ping Liu
Date: 2020-07-20 00:00:00

Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost- and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This paper presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramer-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.
Inter-Mobile-Device Distance Estimation using Network Localization Algorithms for Digital Contact Logging Applications
Authors: Lillian Clark, Alan Papalia, Jônata Tyska Carvalho, Luca Mastrostefano, Bhaskar Krishnamachari
Date: 2020-07-20 00:00:00

Mobile applications are being developed for automated logging of contacts via Bluetooth to help scale up digital contact tracing efforts in the context of the ongoing COVID-19 pandemic. A useful component of such applications is inter-device distance estimation, which can be formulated as a network localization problem. We survey several approaches and evaluate the performance of each on real and simulated Bluetooth Low Energy (BLE) measurement datasets with respect to both distance estimate accuracy and the proximity detection problem. We investigate the effects of obstructions like pockets, differences between device models, and the environment (i.e. indoors or outdoors) on performance. We conclude that while direct estimation can provide the best proximity detection when Received Signal Strength Indicator (RSSI) measurements are available, network localization algorithms like Isomap, Local Linear Embedding, and the spring model outperform direct estimation in the presence of missing or very noisy measurements. The spring model consistently achieves the best distance estimation accuracy.
"Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals
Authors: Amir Weiss, Boaz Nadler
Date: 2020-07-20 00:00:00

We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linear Wiener-like filter as if the unknown deterministic signal were known. This leads to a threshold-type regularized estimator, where the threshold at each frequency is determined in a data-driven manner. We perform a theoretical analysis of our proposed estimator, and derive approximate formulas for its Mean Squared Error (MSE) at both low and high Signal-to-Noise Ratio (SNR) regimes. We show that in the low SNR regime our method provides enhanced noise suppression, and in the high SNR regime it approaches the optimal unrealizable solution. Further, as we demonstrate in simulations, our solution is highly suitable for (approximately) bandlimited or frequency-domain sparse signals, and provides a significant gain of several dBs relative to other methods in the resulting MSE.
A Control Theoretical Adaptive Human Pilot Model: Theory and Experimental Validation
Date: 2020-07-20 00:00:00

This paper proposes an adaptive human pilot model that is able to mimic the
crossover model in the presence of uncertainties. The proposed structure is
based on the model reference adaptive control, and the adaptive laws are
obtained using the Lyapunov-Krasovskii stability criteria. The model can be
employed for human-in-the-loop stability and performance analyses incorporating
different types of controllers and plant types. For validation purposes, an
experimental setup is employed to collect data and a statistical analysis is
conducted to measure the predictive power of the pilot model.
Punctuated equilibrium as the default mode of evolution of large populations on fitness landscapes dominated by saddle points in the weak-mutation limit
Date: 2020-07-20 00:00:00

Punctuated equilibrium is a mode of evolution in which phenetic change occurs
in rapid bursts that are separated by much longer intervals of stasis during
which mutations accumulate but no major phenotypic change occurs. Punctuated
equilibrium has been originally proposed within the framework of paleobiology,
to explain the lack of transitional forms that is typical of the fossil record.
Theoretically, punctuated equilibrium has been linked to self-organized
criticality (SOC), a model in which the size of avalanches in an evolving
system is power-law distributed, resulting in increasing rarity of major
events. We show here that, under the weak-mutation limit, a large population
would spend most of the time in stasis in the vicinity of saddle points in the
fitness landscape. The periods of stasis are punctuated by fast transitions, in
lnNe time (Ne, effective population size), when a new beneficial mutation is
fixed in the evolving population, which moves to a different saddle, or on much
rarer occasions, from a saddle to a local peak. Thus, punctuated equilibrium is
the default mode of evolution under a simple model that does not involve SOC or
other special conditions.
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data
Authors: Nasser Kazemi
Date: 2020-07-20 00:00:00
Tasks: Compressive Sensing, Deep Learning

In the past decade, deep learning algorithms gained a remarkable interest in the signal processing community. The availability of big datasets and advanced computational resources resulted in developing efficient algorithms. However, such algorithms are biased towards the training dataset. Thus, the transferability of deep-learning-based operators are challenging, especially when the goal is to apply the learned operator on a new dataset/domain. Lack of transferability of learned operator across domains hinders the applicability of deep learning algorithms in processing seismic data. Unlike camera images, the comprehensively labeled seismic datasets are not available. Moreover, from one task to another, the training parameters should be tuned. To remedy this shortcoming, we have developed a workflow that transfers the learned operator from the camera images to the seismic domain, without modifying its training parameters. The similarities in the algorithms and optimization methods in camera and seismic data processing allow us to do so. Accordingly, by incorporating feed-forward de-noising convolutional neural networks (DnCNN) in regularization by de-noising regularizer, we formulate two transferable optimization problems for de-noising and compressive sensing recovery of seismic data. Simulated and real-world data examples show the efficiency of our proposed workflow.
Inference of COVID-19 epidemiological distributions from Brazilian hospital data
Date: 2020-08-24 00:00:00

Knowing COVID-19 epidemiological distributions, such as the time from patient
admission to death, is directly relevant to effective primary and secondary
care planning, and moreover, the mathematical modelling of the pandemic
generally. We determine epidemiological distributions for patients hospitalised
with COVID-19 using a large dataset ($N=21{,}000-157{,}000$) from the Brazilian
Sistema de Informa\c{c}\~ao de Vigil\^ancia Epidemiol\'ogica da Gripe database.
A joint Bayesian subnational model with partial pooling is used to
simultaneously describe the 26 states and one federal district of Brazil, and
shows significant variation in the mean of the symptom-onset-to-death time,
with ranges between 11.2-17.8 days across the different states, and a mean of
15.2 days for Brazil. We find strong evidence in favour of specific probability
density function choices: for example, the gamma distribution gives the best
fit for onset-to-death and the generalised log-normal for
onset-to-hospital-admission. Our results show that epidemiological
distributions have considerable geographical variation, and provide the first
estimates of these distributions in a low and middle-income setting. At the
subnational level, variation in COVID-19 outcome timings are found to be
correlated with poverty, deprivation and segregation levels, and weaker
correlation is observed for mean age, wealth and urbanicity.
The Historical Impact of Anthropogenic Climate Change on Global Agricultural Productivity
Authors: Ariel Ortiz-Bobea, Toby R. Ault, Carlos M. Carrillo, Robert G. Chambers, David B. Lobell
Date: 2020-07-20 00:00:00
Tasks: counterfactual

Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that anthropogenic climate change has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 9 years of productivity growth. The effect is substantially more severe (a reduction of ~30-33%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.
Treatment Effects with Targeting Instruments
Authors: Sokbae Lee, Bernard Salanié
Date: 2020-07-20 00:00:00
Tasks: counterfactual, Selection bias

Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We illustrate the usefulness of our framework by applying it to data from the Head Start Impact Study. Under a plausible positive selection assumption, we derive informative bounds that suggest less beneficial effects of Head Start expansions than the parametric estimates of Kline and Walters (2016).
Zero-Error Tracking for Autonomous Vehicles through Epsilon-Trajectory Generation
Date: 2020-07-20 00:00:00
Tasks: Autonomous Vehicles

This paper presents a control method and trajectory planner for vehicles with
first-order nonholonomic constraints that guarantee asymptotic convergence to a
time-indexed trajectory. To overcome the nonholonomic constraint, a fixed point
in front of the vehicle can be controlled to track a desired trajectory, albeit
with a steady-state error. To eliminate steady state error, a sufficiently
smooth trajectory is reformulated for the new reference point such that, when
tracking the new trajectory, the vehicle asymptotically converges to the
original trajectory. The resulting zero-error tracking law is demonstrated
through a novel framework for creating time-indexed Clothoids. The Clothoids
can be planned to pass through arbitrary waypoints using traditional methods
yet result in trajectories that can be followed with zero steady-state error.
The results of the control method and planner are illustrated in simulation
wherein zero-error tracking is demonstrated.
Deep Local Volatility
Authors: Marc Chataigner, Stéphane Crépey, Matthew Dixon
Date: 2020-07-20 00:00:00
Tasks: Deep Learning, Experimental Design

Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks. However, many of these approaches do not enforce any no-arbitrage conditions, and the subsequent local volatility surface is never considered. In this article, we develop a deep learning approach for interpolation of European vanilla option prices which jointly yields the full surface of local volatilities. We demonstrate the modification of the loss function or the feed forward network architecture to enforce (hard constraints approach) or favor (soft constraints approach) the no-arbitrage conditions and we specify the experimental design parameters that are needed for adequate performance. A novel component is the use of the Dupire formula to enforce bounds on the local volatility associated with option prices, during the network fitting. Our methodology is benchmarked numerically on real datasets of DAX vanilla options.
The expected number of viable autocatalytic sets in chemical reaction systems
Authors: Stuart Kauffman, Mike Steel
Date: 2020-07-20 00:00:00

The emergence of self-sustaining autocatalytic networks in chemical reaction systems has been studied as a possible mechanism for modelling how living systems first arose. It has been known for several decades that such networks will form within systems of polymers (under cleavage and ligation reactions) under a simple process of random catalysis, and this process has since been mathematically analysed. In this paper, we provide an exact expression for the expected number of self-sustaining autocatalytic networks that will form in a general chemical reaction system, and the expected number of these networks that will also be uninhibited (by some molecule produced by the system). Using these equations, we are able to describe the patterns of catalysis and inhibition that maximise or minimise the expected number of such networks. We apply our results to derive a general theorem concerning the trade-off between catalysis and inhibition, and to provide some insight into the extent to which the expected number of self-sustaining autocatalytic networks coincides with the probability that at least one such system is present.
Power Allocation for Coexisting Multicarrier Radar and Communication Systems in Cluttered Environments
Authors: Fangzhou Wang, Hongbin Li
Date: 2020-07-21 00:00:00

In this paper, power allocation is examined for the coexistence of a radar and a communication system that employ multicarrier waveforms. We propose two designs for the considered spectrum sharing problem by maximizing the output signal-to-interference-plus-noise ratio (SINR) at the radar receiver while maintaining certain communication throughput and power constraints. The first is a joint design where the subchannel powers of both the radar and communication systems are jointly optimized. Since the resulting problem is highly nonconvex, we introduce a reformulation by combining the power variables of both systems into a single stacked variable, which allows us to bypass a conventional computationally intensive alternating optimization procedure. The resulting problem is then solved via a quadratic transform method along with a sequential convex programming (SCP) technique. The second is a unilateral design which optimizes the radar transmission power with fixed communication power. The unilateral design is suitable for cases where the communication system pre-exists while the radar occasionally joins the channel as a secondary user. The problem is solved by a Taylor expansion based iterative SCP procedure. Numerical results are presented to demonstrate the effectiveness of the proposed joint and unilateral designs in comparison with a subcarrier allocation based method.