pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Linear Time-Varying & Periodic Systems
Authors: Oren Fivel
Date: 2022-01-31 00:00:00
Tasks: Form

This thesis applies Floquet theory to analyze linear periodic time-varying (LPTV) systems, represented by a system of ordinary differential equations (ODEs) that depend on a time variable t and have a matrix of coefficients with period T>0. The transition matrix of an LPTV system represented by a square periodic-function matrix A(t)=A(t+T) can be expressed as the product of a square periodic function matrix P(t)=P(t+T) and an exponentiated square matrix of the form Rt, where R is a constant matrix (independent of t). Despite the validity of Floquet theory, it is difficult to find an analytical closed form for the matrices P(t) and R when the transition matrix {\Phi}_A (t,t_0 ) is unknown. In essence, it is difficult to find an analytical solution for an LPTV system (i.e., a closed form for its transition matrix). The research results show that for a given family of periodic matrices A(t), we can compare the powers of {\omega} that multiply the harmonics (i.e., {\omega} is part of the coefficients multiplying the cosine [sine] factors in even [odd] representations or of the exponential factors in complex representations) to determine the matrices P(t) and R. In addition, the results lead to relations between LPTV systems at frequency {\omega} and the associated linear time-invariant system, which is defined by having zero frequency ({\omega}=0).
Coordinated Frequency Control through Safe Reinforcement Learning
Authors: Yi Zhou, Liangcai Zhou, Di Shi, Xiaoying Zhao
Date: 2022-01-30 00:00:00
Tasks: AI Agent, Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

With widespread deployment of renewables, the electric power grids are experiencing increasing dynamics and uncertainties, with its secure operation being threatened. Existing frequency control schemes based on day-ahead offline analysis and minute-level online sensitivity calculations are difficult to adapt to rapidly changing system states. In particular, they are unable to facilitate coordinated control of system frequency and power flows. A refined approach and tools are urgently needed to assist system operators to make timely decisions. This paper proposes a novel model-free coordinated frequency control framework based on safe reinforcement learning, with multiple control objectives considered. The load frequency control problem is modeled as a constrained Markov decision process, which can be solved by an AI agent continuously interacting with the grid to achieve sub-second decision making. Extensive numerical experiments conducted at East China Power Grid demonstrate the effectiveness and promise of the proposed method.
How local antipredator response unbalances the rock-paper-scissors model
Authors: J. Menezes, S. Batista, M. Tenorio, E. A. Triaca, B. Moura
Date: 2022-01-31 00:00:00

Antipredator behaviour is a self-preservation strategy present in many biological systems, where individuals join the effort in a collective reaction to avoid being caught by an approaching predator. We study a nonhierarchical tritrophic system, whose predator-prey interactions are described by the rock-paper-scissors game rules. We performed a set of spatial stochastic simulations where organisms of one out of the species can resist predation in a collective strategy. The drop in predation capacity is local, which means that each predator faces a particular opposition depending on the prey group size surrounding it. Considering that the interference in a predator action depends on the prey's physical and cognitive ability, we explore the role of a conditioning factor that indicates the fraction of the species apt to perform the antipredator strategy. Because of the local unbalancing of the cyclic predator-prey interactions, departed spatial domains mainly occupied by a single species emerge. Unlike the rock-paper-scissors model with a weak species because a nonlocal reason, our findings show that if the predation probability of one species is reduced because individuals face local antipredator response, the species does not predominate. Instead, the local unbalancing of the rock-paper-scissors model results in the prevalence of the weak species' prey. Finally, the outcomes show that local unevenness may jeopardise biodiversity, with the coexistence being more threatened for high mobility.
A Metal Artifact Reduction Scheme For Accurate Iterative Dual-Energy CT Algorithms
Authors: Tao Ge, Maria Medrano, Rui Liao, Jeffrey F. Williamson, David G. Politte, Bruce R. Whiting, Joseph A. O'Sullivan
Date: 2022-01-31 00:00:00
Tasks: Diagnostic, Metal Artifact Reduction

CT images have been used to generate radiation therapy treatment plans for more than two decades. Dual-energy CT (DECT) has shown high accuracy in estimating electronic density or proton stopping-power maps used in treatment planning. However, the presence of metal implants introduces severe streaking artifacts in the reconstructed images, affecting the diagnostic accuracy and treatment performance. In order to reduce the metal artifacts in DECT, we introduce a metal-artifact reduction scheme for iterative DECT algorithms. An estimate is substituted for the corrupt data in each iteration. We utilize normalized metal-artifact reduction (NMAR) composed with image-domain decomposition to initialize the algorithm and speed up the convergence. A fully 3D joint statistical DECT algorithm, dual-energy alternating minimization (DEAM), with the proposed scheme is tested on experimental and clinical helical data acquired on a Philips Brilliance Big Bore scanner. We compared DEAM with the proposed method to the original DEAM and vendor reconstructions with and without metal-artifact reduction for orthopedic implants (O-MAR). The visualization and quantitative analysis show that DEAM with the proposed method has the best performance in reducing streaking artifacts caused by metallic objects.
Backcasting COVID-19: A Physics-Informed Estimate for Early Case Incidence
Authors: G. A. Kevrekidis, Z. Rapti, Y. Drossinos, P. G. Kevrekidis, M. A. Barmann, Q. Y. Chen, J. Cuevas-Maraver
Date: 2022-01-31 00:00:00

It is widely accepted that the number of reported cases during the first stages of the COVID-19 pandemic severely underestimates the number of actual cases. We leverage delay embedding theorems of Whitney and Takens and use Gaussian Process regression to estimate the number of cases during the first 2020 wave based on the second wave of the epidemic in several European countries, South Korea, and Brazil. We assume that the second wave was more accurately monitored and hence that it can be trusted. We then construct a manifold diffeomorphic to that of the implied original dynamical system, using fatalities or hospitalizations only. Finally, we restrict the diffeomorphism to the reported cases coordinate of the dynamical system. Our main finding is that in the European countries studied, the actual cases are under-reported by as much as 50\%. On the other hand, in South Korea -- which had an exemplary and proactive mitigation approach -- a far smaller discrepancy between the actual and reported cases is predicted, with an approximately 17\% predicted under-estimation. We believe that our backcasting framework is applicable to other epidemic outbreaks where (due to limited or poor quality data) there is uncertainty around the actual cases.
Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization
Authors: Juho Koistinen, Bernd Funovits
Date: 2022-02-01 00:00:00

We propose a new parametrization for the estimation and identification of the impulse-response functions (IRFs) of dynamic factor models (DFMs). The theoretical contribution of this paper concerns the problem of observational equivalence between different IRFs, which implies non-identification of the IRF parameters without further restrictions. We show how the previously proposed minimal identification conditions are nested in the new framework and can be further augmented with overidentifying restrictions leading to efficiency gains. The current standard practice for the IRF estimation of DFMs is based on principal components, compared to which the new parametrization is less restrictive and allows for modelling richer dynamics. As the empirical contribution of the paper, we develop an estimation method based on the EM algorithm, which incorporates the proposed identification restrictions. In the empirical application, we use a standard high-dimensional macroeconomic dataset to estimate the effects of a monetary policy shock. We estimate a strong reaction of the macroeconomic variables, while the benchmark models appear to give qualitatively counterintuitive results. The estimation methods are implemented in the accompanying R package.
Protection or Peril of Following the Crowd in a Pandemic-Concurrent Flood Evacuation
Authors: Elisa Borowski, Amanda Stathopoulos
Date: 2022-02-01 00:00:00
Tasks: Decision Making

The decisions of whether and how to evacuate during a climate disaster are influenced by a wide range of factors, including sociodemographics, emergency messaging, and social influence. Further complexity is introduced when multiple hazards occur simultaneously, such as a flood evacuation taking place amid a viral pandemic that requires physical distancing. Such multi-hazard events can necessitate a nuanced navigation of competing decision-making strategies wherein a desire to follow peers is weighed against contagion risks. To better understand these nuances, we distributed an online survey during a pandemic surge in July 2020 to 600 individuals in three midwestern and three southern states in the United States with high risk of flooding. In this paper, we estimate a random parameter logit model in both preference space and willingness-to-pay space. Our results show that the directionality and magnitude of the influence of peers' choices of whether and how to evacuate vary widely across respondents. Overall, the decision of whether to evacuate is positively impacted by peer behavior, while the decision of how to evacuate is negatively impacted by peers. Furthermore, an increase in flood threat level lessens the magnitude of these impacts. These findings have important implications for the design of tailored emergency messaging strategies. Specifically, emphasizing or deemphasizing the severity of each threat in a multi-hazard scenario may assist in: (1) encouraging a reprioritization of competing risk perceptions and (2) magnifying or neutralizing the impacts of social influence, thereby (3) nudging evacuation decision-making toward a desired outcome.
Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models
Authors: Christis Katsouris
Date: 2022-01-31 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Time Series Regression

We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time series regression models. Although, the particular comparisons of these two different modelling environments is done from the perspective of the partial-sum processes, it emphasizes that the presence of nuisance parameters can change the asymptotic behaviour of the functionals under consideration. Simulation experiments verify size distortions when testing for a break in nonstationary time series regressions which indicates that the Brownian bridge limit cannot provide a suitable asymptotic approximation in this case. Further research is required to establish the cause of size distortions under the null hypothesis of parameter stability.
Mapping intra firm trade in the automotive sector: a network approach
Authors: Matthew Smith, Yasaman Sarabi
Date: 2022-02-01 00:00:00

Intra-firm trade describes the trade between affiliated firms and is increasingly important as global production is fragmented. However, statistics and data on global intra-firm trade patterns are widely unavailable. This study proposes a novel multilevel approach combining firm and country level data to construct a set of country intra-firm trade networks for various segments of the automotive production chain. A multilevel network is constructed with a network of international trade at the macro level, a firm ownership network at the micro level and a firm-country affiliation network linking the two, at the meso level. A motif detection approach is used to filter these networks to extract potential intra-firm trade ties between countries, where the motif (or substructure) is two countries linked by trade, each affiliated with a firm, and these two firms linked by ownership. The motif detection is used to extract potential country level intra-firm trade ties. An Exponential Random Graph Model (ERGM) is applied to the country level intra-firm trade networks, one for each segment of the automotive production chain, to inform on the determinants of intra-firm trade at the country level.
Bankruptcy Shocks and Legal Labor Markets: Evidence from the Court Competition Era
Authors: Chad Brown, Jeronimo Carballo, Alessandro Peri
Date: 2022-01-31 00:00:00

We study how Chapter 11 bankruptcies affect local legal labor markets. We document that bankruptcy shocks increase county legal employment and corroborate this finding by exploiting a stipulation of the law known as Forum Shopping during the Court Competition Era (1991-1996). We quantify losses to local communities from firms forum shopping away from their local area as follows. First, we calculate the unrealized potential employment gains implied by our reduced-form results. Second, we structurally estimate a model of legal labor markets and quantify welfare losses. We uncover meaningful costs to local communities from lax bankruptcy venue laws.
Impact of Gold Prices on Stock Exchange: An Empirical Case Study of Nepal
Authors: Aneel Bhusal, Madhu Sudan Gautam
Date: 2022-01-30 00:00:00

The purpose of this study is to examine the long-run relationship between gold prices and Nepal Stock Exchange (NEPSE).
Impact of Information and Communication Technology on Individual Well-being
Authors: Aneel Bhusal
Date: 2022-01-30 00:00:00

This paper investigates the impact of information and communication technology (ICT) adoption on individual well-being.
Top Ten Behavioral Biases in Project Management: An Overview
Authors: Bent Flyvbjerg
Date: 2022-01-24 00:00:00
Tasks: Management

Behavioral science has witnessed an explosion in the number of biases identified by behavioral scientists, to more than 200 at present. This article identifies the 10 most important behavioral biases for project management. First, we argue it is a mistake to equate behavioral bias with cognitive bias, as is common. Cognitive bias is half the story; political bias the other half. Second, we list the top 10 behavioral biases in project management: (1) strategic misrepresentation, (2) optimism bias, (3) uniqueness bias, (4) the planning fallacy, (5) overconfidence bias, (6) hindsight bias, (7) availability bias, (8) the base rate fallacy, (9) anchoring, and (10) escalation of commitment. Each bias is defined, and its impacts on project management are explained, with examples. Third, base rate neglect is identified as a primary reason that projects underperform. This is supported by presentation of the most comprehensive set of base rates that exist in project management scholarship, from 2,062 projects. Finally, recent findings of power law outcomes in project performance are identified as a possible first stage in discovering a general theory of project management, with more fundamental and more scientific explanations of project outcomes than found in conventional theory.
Problems of Tax Administration and its Impact on Budget Revenues
Authors: Marika Ormotsadze
Date: 2022-01-23 00:00:00

The topic under study is of crucial importance, especially for developing countries. The aim of the present paper is to study the problems in revenue administration in terms of tax revenue in Georgia and analyze foreign experience in that respect. The main question arises here - What kind of tax rates should be implemented to be able to perform both functions of the fiscal and stimulating one. Liberal method of revenue seems an attractive one for taxpayers. According to the economic situation in Georgia, the best solution is to use the liberal method. This will help business to develop and people to find jobs. Taxation system will also benefit from that. Tax rate in Georgia amounts to 15% and is the same for everyone, regardless the size of the business. The taxation system is regarded to be proportional. As for the American and European countries, taxes there are progressive. I think the same practice should be implemented in Georgia, and not only in case of taxation.
The regulation methods of fiscal risk in the framework of the implementation of entrepreneurship support
Authors: Elena G. Demidova
Date: 2022-01-11 00:00:00

In this article, I consider the issues of financial resource shortage, the limited possibility of attracting bank loans, and business risk. All these problems constrain the development of entrepreneurship. Objectives I aim to determine a scientifically based financial mechanism of financing the priority directions of territories' development. In this study, I develop tools of financing the priority directions of the municipal economy. The proposed financial scheme allows to expand the volume of financing and ensure the access of businesses to financial support. The article proposes concrete financing mechanisms for investment with minimal risk for the budget and preferential conditions for business.
Measuring poverty in India with machine learning and remote sensing
Authors: Adel Daoud, Felipe Jordan, Makkunda Sharma, Fredrik Johansson, Devdatt Dubhashi, Sourabh Paul, Subhashis Banerjee
Date: 2021-12-27 00:00:00
Tasks: Deep Learning

In this paper, we use deep learning to estimate living conditions in India. We use both census and surveys to train the models. Our procedure achieves comparable results to those found in the literature, but for a wide range of outcomes.
On Market Design and Latency Arbitrage
Authors: Wolfgang Kuhle
Date: 2021-12-26 00:00:00

We argue that contemporary stock market designs are, due to traders' inability to fully express their preferences over the execution times of their orders, prone to latency arbitrage. In turn, we propose a new order type which allows traders to specify the time at which their orders are executed after reaching the exchange. Using this order type, traders can synchronize order executions across different exchanges, such that high-frequency traders, even if they operate at the speed of light, can no-longer engage in latency arbitrage.
Closing the sim-to-real gap in guided wave damage detection with adversarial training of variational auto-encoders
Authors: Ishan D. Khurjekar, Joel B. Harley
Date: 2022-01-26 00:00:00
Tasks: Deep Learning

Guided wave testing is a popular approach for monitoring the structural integrity of infrastructures. We focus on the primary task of damage detection, where signal processing techniques are commonly employed. The detection performance is affected by a mismatch between the wave propagation model and experimental wave data. External variations, such as temperature, which are difficult to model, also affect the performance. While deep learning models can be an alternative detection method, there is often a lack of real-world training datasets. In this work, we counter this challenge by training an ensemble of variational autoencoders only on simulation data with a wave physics-guided adversarial component. We set up an experiment with non-uniform temperature variations to test the robustness of the methods. We compare our scheme with existing deep learning detection schemes and observe superior performance on experimental data.
A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training
Authors: Ufuk Soylu, Michael L. Oelze
Date: 2022-02-01 00:00:00

Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquiring large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient deep learning training strategy, which we named \textit{Zone Training}. In \textit{Zone Training}, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of \textit{Zone Training} is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classified by a DL network. The results demonstrated that \textit{Zone Training} required a factor of 2-5 less training data to achieve similar classification accuracies compared to a conventional training strategy.
An FPGA-based System for Generalised Electron Devices Testing
Authors: Patrick Foster, Jinqi Huang, Alex Serb, Spyros Stathopoulos, Christos Papavassiliou, Themis Prodromakis
Date: 2022-02-01 00:00:00

Electronic systems are becoming more and more ubiquitous as our world digitises. Simultaneously, even basic components are experiencing a wave of improvements with new transistors, memristors, voltage/current references, data converters, etc, being designed every year by hundreds of R&D groups world-wide. To date, the workhorse for testing all these designs has been a suite of lab instruments including oscilloscopes and signal generators, to mention the most popular. However, as components become more complex and pin numbers soar, the need for more parallel and versatile testing tools also becomes more pressing. In this work, we describe and benchmark an FPGA system developed that addresses this need. This general purpose testing system features a 64-channel source-meter unit (SMU), and 2x banks of 32 digital pins for digital I/O. We demonstrate that this bench-top system can obtain $170 pA$ current noise floor, $40 ns$ pulse delivery at $\pm13.5 V$ and $12 mA$ maximum current drive/channel. We then showcase the instrument's use in performing a selection of three characteristic measurement tasks: a) current-voltage (IV) characterisation of a diode and a transistor, b) fully parallel read-out of a memristor crossbar array and c) an integral non-linearity (INL) test on a DAC. This work introduces a down-scaled electronics laboratory packaged in a single instrument which provides a shift towards more affordable, reliable, compact and multi-functional instrumentation for emerging electronic technologies.