pwc-archive/papers-with-abstracts huggingface dataset:
Emergence and Synchronization in Chaotic Oscillators and in the Human Cortical Network
Authors: Nir Lahav
Date: 2021-05-22 00:00:00

When we look at the world around us, we see complex physical systems and emergent phenomena. Emergence occurs when a system is observed to have properties that its parts do not have on their own. These properties or behaviors emerge only when the parts interact in a wider whole. Examples of emergence can vary from the synchronization of pendulum clocks hanging on the same wall to the phenomenon of life as an emergent property of chemistry. One of the most complex systems that exist in nature is the human brain. It contains on average 100 to 200 billion neurons and about 100 trillion synapses connecting them. From this vast neuronal dynamics, the ability to learn and store memory emerges as well as the ability to have complex cognitive skills, conscious experience and a sense of self. In this work, we investigated how complex systems like the human brain and chaotic systems create emergent properties. In order to do so, we used network theory (paper 1), chaos and synchronization theory (paper 2 and 3).
Modeling viral mutations in the spread of epidemics
Authors: Vitor M. Marquioni, Marcus A. M. de Aguiar
Date: 2021-05-24 00:00:00

Although traditional models of epidemic spreading focus on the number of infected, susceptible and recovered individuals, a lot of attention has been devoted to integrate epidemic models with population genetics. Here we develop an individual-based model for epidemic spreading on networks in which viruses are explicitly represented by finite chains of nucleotides that can mutate inside the host. Under the hypothesis of neutral evolution we compute analytically the average pairwise genetic distance between all infecting viruses over time. We also derive a mean-field version of this equation that can be added directly to compartmental models such as SIR or SEIR to estimate the genetic evolution. We compare our results with the inferred genetic evolution of SARS-CoV-2 at the beginning of the epidemic in China and found good agreement with the analytical solution of our model. Finally, using genetic distance as a proxy for different strains, we use numerical simulations to show that the lower the connectivity between communities, e.g., cities, the higher the probability of reinfection.
A Mathematical Model of COVID-19 Transmission
Authors: R. Jayatilaka, R. Patel, M. Brar, Y. Tang, N. M. Jisrawi, F. Chishtie, J. Drozd, S. R. Valluri
Date: 2021-05-25 00:00:00
Tasks: Epidemiology, model

Disease transmission is studied through disciplines like epidemiology, applied mathematics, and statistics. Mathematical simulation models for transmission have implications in solving public and personal health challenges. The SIR model uses a compartmental approach including dynamic and nonlinear behavior of transmission through three factors: susceptible, infected, and removed (recovered and deceased) individuals. Using the Lambert W Function, we propose a framework to study solutions of the SIR model. This demonstrates the applications of COVID-19 transmission data to model the spread of a real-world disease. Different models of disease including the SIR, SIRmp and SEIRpqr model are compared with respect to their ability to predict disease spread. Physical distancing impacts and personal protection equipment use are discussed with relevance to the COVID-19 spread.
Adaptive and optimized COVID-19 vaccination strategies across geographical regions and age groups
Authors: Jeta Molla, Alejandro Ponce de León Chávez, Takayuki Hiraoka, Tapio Ala-Nissila, Mikko Kivelä, Lasse Leskelä
Date: 2021-05-24 00:00:00

We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could potentially be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering.
Measuring Financial Advice: aligning client elicited and revealed risk
Authors: John R. J. Thompson, Longlong Feng, R. Mark Reesor, Chuck Grace, Adam Metzler
Date: 2021-05-25 00:00:00

Financial advisors use questionnaires and discussions with clients to determine a suitable portfolio of assets that will allow clients to reach their investment objectives. Financial institutions assign risk ratings to each security they offer, and those ratings are used to guide clients and advisors to choose an investment portfolio risk that suits their stated risk tolerance. This paper compares client Know Your Client (KYC) profile risk allocations to their investment portfolio risk selections using a value-at-risk discrepancy methodology. Value-at-risk is used to measure elicited and revealed risk to show whether clients are over-risked or under-risked, changes in KYC risk lead to changes in portfolio configuration, and cash flow affects a client's portfolio risk. We demonstrate the effectiveness of value-at-risk at measuring clients' elicited and revealed risk on a dataset provided by a private Canadian financial dealership of over $50,000$ accounts for over $27,000$ clients and $300$ advisors. By measuring both elicited and revealed risk using the same measure, we can determine how well a client's portfolio aligns with their stated goals. We believe that using value-at-risk to measure client risk provides valuable insight to advisors to ensure that their practice is KYC compliant, to better tailor their client portfolios to stated goals, communicate advice to clients to either align their portfolios to stated goals or refresh their goals, and to monitor changes to the clients' risk positions across their practice.
Climate, Agriculture and Food
Authors: Ariel Ortiz-Bobea
Date: 2021-05-25 00:00:00

Agriculture is arguably the most climate-sensitive sector of the economy. Growing concerns about anthropogenic climate change have increased research interest in assessing its potential impact on the sector and in identifying policies and adaptation strategies to help the sector cope with a changing climate. This chapter provides an overview of recent advancements in the analysis of climate change impacts and adaptation in agriculture with an emphasis on methods. The chapter provides an overview of recent research efforts addressing key conceptual and empirical challenges. The chapter also discusses practical matters about conducting research in this area and provides reproducible R code to perform common tasks of data preparation and model estimation in this literature. The chapter provides a hands-on introduction to new researchers in this area.
How residence permits affect the labor market attachment of foreign workers: Evidence from a migration lottery in Liechtenstein
Authors: Berno Buechel, Selina Gangl, Martin Huber
Date: 2021-05-25 00:00:00

We analyze the impact of obtaining a residence permit on foreign workers' labor market and residential attachment. To overcome the usually severe selection issues, we exploit a unique migration lottery that randomly assigns access to otherwise restricted residence permits in Liechtenstein (situated between Austria and Switzerland). Using an instrumental variable approach, our results show that lottery compliers (whose migration behavior complies with the assignment in their first lottery) raise their employment probability in Liechtenstein by on average 24 percentage points across outcome periods (2008 to 2018) as a result of receiving a permit. Relatedly, their activity level and employment duration in Liechtenstein increase by on average 20 percentage points and 1.15 years, respectively, over the outcome window. These substantial and statistically significant effects are mainly driven by individuals not (yet) working in Liechtenstein prior to the lottery rather than by previous cross-border commuters. Moreover, we find both the labor market and residential effects to be persistent even several years after the lottery with no sign of fading out. These results suggest that granting resident permits to foreign workers can be effective to foster labor supply even beyond the effect of cross-border commuting from adjacent regions.
Perception of corruption influences entrepreneurship inside established companies
Authors: F. Javier Sanchez-Vidal, Camino Ramon-Llorens
Date: 2021-05-25 00:00:00
Tasks: Relation, Sand

Based on the Global Entrepreneurship Monitor (GEM) surveys and conducting a panel data estimation to test our hypothesis, this paper examines whether corruption perceptions might sand or grease the wheels for entrepreneurship inside companies or intrapreneurship in a sample of 92 countries for the period 2012 to 2019. Our results find that the corruption perception sands the wheel for intrapreneurship. There is evidence of a quadratic relation, but this relation is only clear for the less developed countries, which sort of moderate the very negative effect of corruption for these countries. The results also confirm that corruption influences differently on intrapreneurship depending on the level of development of the country.
Environmental Kuznets Curve & Effectiveness of International Policies: Evidence from Cross Country Carbon Emission Analysis
Authors: Elvan Ece Satici, Bayram Cakir
Date: 2021-05-25 00:00:00

In this article, we are presenting the relationship between environmental pollution and the income level of the selected twenty-four countries. We implemented a data-based research analysis where, for each country, we analyzed the related data for fifty-six years, from 1960 to 2016, to assess the relationship between the carbon emission and income level. After performing the related data analysis for each country, we concluded whether the results for that country were in line with the Environmental Kuznets Curve (EKC) hypothesis. The EKC hypothesis suggests that the carbon emission per capita starts a declining trend when the country-specific high level of income is reached. The results of our data analyses show that the EKC hypothesis is valid for high-income countries and the declining trends of carbon emission are clearly observed when the income level reaches a specific high enough level. On the other hand, for the non-high income countries, our analysis results show that it is too early to make an assessment at this growth stage of their economies because they have not reached their related high-enough income per capita levels yet. Furthermore, we performed two more additional analyses on high-income countries. First, we analyzed the related starting years of their carbon emission declining trends. The big variance in the starting years of the carbon emission declining trends shows that the international policies are clearly ineffective in initiating the declining trend in carbon emission. In addition, for the high-income countries, we explained the differences in their carbon emission per capita levels in 2014 with their SGI indices and their dependence on high-carbon emission energy production.
Impact of Financial Inclusion on the Socio-Economic Status of Rural and Urban Households of Vulnerable Sections in Karnataka
Authors: Manohar Serrao, Aloysius Sequeira, K. V. M. Varambally
Date: 2021-05-25 00:00:00

Financial inclusion and inclusive growth are the buzzwords today. Inclusive growth empowers people belonging to vulnerable sections. This in turn depends upon a variety of factors, the most important being financial inclusion, which plays a strategic role in promoting inclusive growth and helps in reducing poverty by providing regular and reliable sources of finance to the vulnerable sections. In this direction, the Government of India in its drive for financial inclusion has taken several measures to increase the access to and availing of formal financial services by unbanked households. The purpose of this paper is to assess the nature and extent of financial inclusion and its impact on the socio-economic status of households belonging to vulnerable sections focusing on inclusive growth. This has been analyzed with the theoretical background on financial access and economic growth, and by analyzing the primary data collected from the Revenue Divisions of Karnataka. The results show that there is a disparity in nature and extent of financial inclusion. Access to, availing of formal banking services pave the way to positive changes in the socio-economic status of households belonging to vulnerable sections which are correlated, leading to inclusive growth based on which the paper proposes a model to make the financial system more inclusive and pro-poor.
The Giving Game
Authors: Peter Weijland
Date: 2021-05-25 00:00:00
Tasks: Distributed Computing

This paper describes a basic model of a gift economy in the shape of a Giving Game and reveals the fundamental structure of such a game. Main result is that the game shows a community effect in that a small subgroup of players eventually keeps all circulating goods for themselves. Example applications are where computers are sharing processing power for complex calculations, or when commodity traders are making transactions in some professional community. The Giving Game may equally well be viewed as a basic model of clientelism or corruption. Keywords in this paper are giving, gift economy, community effect, stabilization, computational complexity, corruption, micro-economics, game theory, stock trading, distributed computing, crypto currency, blockchain.
Generalized Hessian-Schatten Norm Regularization for Image Reconstruction
Authors: Manu Ghulyani, Deepak G Skariah, Muthuvel Arigovindan
Date: 2021-05-25 00:00:00
Tasks: Image Reconstruction

Regularization plays a crucial role in reliably utilizing imaging systems for scientific and medical investigations. It helps to stabilize the process of computationally undoing any degradation caused by physical limitations of the imaging process. In the past decades, total variation regularization, especially second-order total variation (TV-2) regularization played a dominant role in the literature. Two forms of generalizations, namely Hessian-Schatten norm (HSN) regularization, and total generalized variation (TGV) regularization, have been recently proposed and have become significant developments in the area of regularization for imaging inverse problems owing to their performance. Here, we develop a novel regularization for image recovery that combines the strengths of these well-known forms. We achieve this by restricting the maximization space in the dual form of HSN in the same way that TGV is obtained from TV-2. We name the new regularization as the generalized Hessian-Schatten norm regularization (GHSN), and we develop a novel optimization method for image reconstruction using the new form of regularization based on the well-known framework called alternating direction method of multipliers (ADMM). We demonstrate the strength of the GHSN using some reconstruction examples.
A Fast MR Fingerprinting Simulator for Direct Error Estimation and Sequence Optimization
Authors: Siyuan Hu, Stephen Jordan, Rasim Boyacioglu, Ignacio Rozada, Matthias Troyer, Mark Griswold, Debra McGivney, Dan Ma
Date: 2021-05-25 00:00:00

MR Fingerprinting is a novel quantitative MR technique that could simultaneously provide multiple tissue property maps. When optimizing MRF scans, modeling undersampling errors and field imperfections in cost functions will make the optimization results more practical and robust. However, this process is computationally expensive and impractical for sequence optimization algorithms when MRF signal evolutions need to be generated for each optimization iteration. Here, we introduce a fast MRF simulator to simulate aliased images from actual scan scenarios including undersampling and system imperfections, which substantially reduces computational time and allows for direct error estimation and efficient sequence optimization. By constraining the total number of tissues present in a brain phantom, MRF signals from highly undersampled scans can be simulated as the product of the spatial response functions based on sampling patterns and sequence-dependent temporal functions. During optimization, the spatial response function is independent of sequence design and does not need to be recalculated. We evaluate the performance and computational speed of the proposed approach by simulations and in vivo experiments. We also demonstrate the power of applying the simulator in MRF sequence optimization. The simulation results from the proposed method closely approximate the signals and MRF maps from in vivo scans, with 158 times shorter processing time than the conventional simulation method using Non-uniform Fourier transform. Incorporating the proposed simulator in the MRF optimization framework makes direct estimation of undersampling errors during the optimization process feasible, and provide optimized MRF sequences that are robust against undersampling factors and system inhomogeneity.
A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase
Authors: Romain Fara, Philippe Ratajczak, Dinh-Thuy Phan Huy, Abdelwaheb Ourir, Marco Di Renzo, Julien de Rosny
Date: 2021-05-25 00:00:00
Tasks: continuous-control, Continuous Control

With the development of the next generation of mobile networks, new research challenges have emerged, and new technologies have been proposed to face them. On the one hand, the reconfigurable intelligent surface (RIS) technology is being investigated for partially controlling the wireless channels. The RIS is a promising technology for improving the signal quality by controlling the scattering of the electromagnetic waves in a nearly passive manner. On the other hand, ambient backscatter communications (AmBC) is another promising technology that is tailored for addressing the energy efficiency requirements for the Internet of Things (IoT). This technique enables low-power communications by backscattering ambient signals and, thus, reusing existing electromagnetic waves for communications. RIS technology can be utilized in the context of AmBC for improving the system performance. In this paper, we report a prototype of an RIS that offers the capability of controlling the phase shift of the reflected waves in a continuous manner, and we characterize its characteristics by using full-wave simulations and through experimental measurements. Specifically, we introduce a phase shift model for predicting the signal reflected by the RIS prototype. We apply the proposed model for optimizing an RISassisted AmBC system and we demonstrate that the use of an RIS can significantly improve the system performance.
Distortions Characterization for Dynamic Carrier Allocation in Ultra High-Throughput Satellites
Authors: Tony Colin, Thomas Delamotte, Andreas Knopp
Date: 2021-05-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

A novel analytical formula for the characterization of linear and nonlinear distortions in future ultra high-throughput communication payloads is proposed in this work. In this context, the carrier-to-interference ratio related to single-carrier and multicarrier signals is derived. Through the analysis of its behavior valuable insights are created, especially regarding the interaction between linear and nonlinear intersymbol interference. Furthermore, the principle of dynamic carrier allocation optimization is highlighted in a realistic scenario. Within the presented framework, it is proven that a significant gain can be achieved even with a limited number of carriers. Finally, a complexity and accuracy analysis emphasizes the practicality of the proposed approach.
The perturbed prox-preconditioned spider algorithm: non-asymptotic convergence bounds
Authors: Gersende Fort, E Moulines
Date: 2021-05-25 00:00:00

A novel algorithm named Perturbed Prox-Preconditioned SPIDER (3P-SPIDER) is introduced. It is a stochastic variancereduced proximal-gradient type algorithm built on Stochastic Path Integral Differential EstimatoR (SPIDER), an algorithm known to achieve near-optimal first-order oracle inequality for nonconvex and nonsmooth optimization. Compared to the vanilla prox-SPIDER, 3P-SPIDER uses preconditioned gradient estimators. Preconditioning can either be applied "explicitly" to a gradient estimator or be introduced "implicitly" as in applications to the EM algorithm. 3P-SPIDER also assumes that the preconditioned gradients may (possibly) be not known in closed analytical form and therefore must be approximated which adds an additional degree of perturbation. Studying the convergence in expectation, we show that 3P-SPIDER achieves a near-optimal oracle inequality O(n^(1/2) /epsilon) where n is the number of observations and epsilon the target precision even when the gradient is estimated by Monte Carlo methods. We illustrate the algorithm on an application to the minimization of a penalized empirical loss.
Matched Illumination Waveforms using Multi-Tone Sinusoidal Frequency Modulation
Authors: Kaushallya Adhikari, David A. Hague
Date: 2021-05-24 00:00:00
Tasks: Time Series, Time Series Analysis

This paper explores the design of constant modulus Matched-Illumination (MI) waveforms using the Multi-Tone Sinusoidal Frequency Modulation (MTSFM) waveform model. MI waveforms are optimized for detecting targets in known noise and clutter Power Spectral Densities (PSDs). There exist well-defined information theoretic methods that describe the design of MI waveforms for a myriad of target/noise/clutter models. However, these methods generally only produce the magnitude square of the MI waveform's spectrum. Additionally, the waveform's time-series is not guaranteed to be constant modulus. The MTSFM is a constant modulus waveform model with a discrete set of design coefficients. The coefficients are adjusted to synthesize constant modulus waveforms that approximate the ideal MI waveform's spectrum. Simulations demonstrate that the MTSFM's detection performance closely approximates an ideal MI waveform spectrum and generally outperforms flat spectrum waveforms across a range of transmit energies when the noise and clutter PSDs vary greatly across the operational band.
The simple emergence of complex molecular function
Authors: Susanna Manrubia
Date: 2021-05-25 00:00:00

At odds with a traditional view of molecular evolution that seeks a descent-with-modification relationship between functional sequences, new functions can emerge {\it de novo} with relative ease. At early times of molecular evolution, random polymers could have sufficed for the appearance of incipient chemical activity, while the cellular environment harbors a myriad of proto-functional molecules. The emergence of function is facilitated by several mechanisms intrinsic to molecular organization, such as redundant mapping of sequences into structures, phenotypic plasticity, modularity, or cooperative associations between genomic sequences. It is the availability of niches in the molecular ecology that filters new potentially functional proposals. New phenotypes and subsequent levels of molecular complexity could be attained through combinatorial explorations of currently available molecular variants. Natural selection does the rest.