pwc-archive/papers-with-abstracts huggingface dataset:
Pointing out the Convolution Problem of Stochastic Aggregation Methods for the Determination of Flexibility Potentials at Vertical System Interconnections
Authors: Johannes Gerster, Marcel Sarstedt, Eric MSP Veith, Sebastian Lehnhoff, Lutz Hofmann
Date: 2021-02-05 00:00:00

The increase of generation capacity in the area of responsibility of the distribution system operator (DSO) requires strengthening of coordination between transmission system operator (TSO) and DSO in order to prevent conflicting or counteracting use of flexibility options. For this purpose, methods for the standardized description and identification of the aggregated flexibility potential of distribution grids (DGs) are developed. Approaches for identifying the feasible operation region (FOR) of DGs can be categorized into two main classes: Data-driven/stochastic approaches and optimization based approaches. While the latter have the advantage of working in real-world scenarios where no full grid models exist, when relying on naive sampling strategies, they suffer from poor coverage of the edges of the FOR. To underpin the need for improved sampling strategies for data-driven approaches, in this paper we point out and analyse the shortcomings of naive sampling strategies with focus on the problem of leptocurtic distribution of resulting interconnection power flows (IPFs). We refer to this problem as convolution problem, as it can be traced back to the fact that the probability density function (PDF) of the sum of two or more independent random variables is the convolution of their respective PDFs. To demonstrate the convolution problem, we construct a series of synthetic 0.4 kV feeders, which are characterized by an increasing number of nodes and apply a sampling strategy to them that draws set-values for the controllable distributed energy resources (DERs) from independent uniform distributions. By calculating the power flow for each sample in each feeder, we end up with a collapsing IPF point cloud clearly indicating the convolution problem.
Infrastructure Assisted Constrained Connected Automated Vehicle Trajectory Optimization on Curved Roads: A Spatial Formulation on a Curvilinear Coordinate
Authors: Ran Yi, Yang Zhou, Xin Wang, Zhiyuan Liu, Xiaotian Li, Bin Ran
Date: 2021-03-01 00:00:00
Tasks: Model Predictive Control

Vehicle trajectory optimization is essential to ensure vehicles travel efficiently and safely. This paper presents an infrastructure assisted constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads. This paper systematically formulates the problem based on a curvilinear coordinate which is flexible to model complex road geometries. Further, to deal with the spatial varying road obstacles, traffic regulations, and geometric characteristics, two-dimensional vehicle kinematics is given in a spatial formulation with exact road information provided by the infrastructure. Consequently, we applied a multi-objective model predictive control (MPC) approach to optimize the trajectories in a rolling horizon while satisfying the collision avoidances and vehicle kinematics constraints. To verify the efficiency of our method, a numerical simulation is conducted. As the results suggest, the proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations, which is robust to road geometries and disturbances.
Robust Data-Enabled Predictive Control: Tractable Formulations and Performance Guarantees
Authors: Linbin Huang, Jianzhe Zhen, John Lygeros, Florian Dörfler
Date: 2021-05-15 00:00:00

We introduce a general framework for robust data-enabled predictive control (DeePC) for linear time-invariant (LTI) systems. The proposed framework enables us to obtain model-free optimal control for LTI systems based on noisy input/output data. More specifically, robust DeePC solves a min-max optimization problem to compute the optimal control sequence that is resilient to all possible realizations of the uncertainties in the input/output data within a prescribed uncertainty set. We present computationally tractable reformulations of the min-max problem with various uncertainty sets. Furthermore, we show that even though an accurate prediction of the future behavior is unattainable in practice due to inaccessibility of the perfect input/output data, the obtained robust optimal control sequence provides performance guarantees for the actually realized input/output cost. We further show that the robust DeePC generalizes and robustifies the regularized DeePC (with quadratic regularization or 1-norm regularization) proposed in the literature. Finally, we demonstrate the performance of the proposed robust DeePC algorithm on high-fidelity, nonlinear, and noisy simulations of a grid-connected power converter system.
Impacts of Time-of-Use Rate Changes on the Electricity Bills of Commercial Consumers
Authors: Lane D. Smith, Daniel S. Kirschen
Date: 2021-05-15 00:00:00

Changes in the profile of prices in wholesale electricity markets prompt utilities to redesign their tariffs and adjust their time-of-use periods to ensure a more adequate cost recovery. However, changing the rate structures could adversely affect commercial consumers by increasing their electricity bills and hindering their ability to reduce costs using techniques like net energy metering. As time-of-use periods are adjusted, consumers will need to rely on the flexibility of distributed energy resources to achieve cost reductions. This paper explores the effect that Pacific Gas and Electric Company's redesigned rates have on the electricity bills of consumers with different demand profiles. Sensitivity analyses are conducted to examine the effect of asset sizing on reducing costs under each tariff.
Iterative Algorithms for Assessing Network Resilience Against Structured Perturbations
Authors: Shenyu Liu, Sonia Martinez, Jorge Cortes
Date: 2021-05-14 00:00:00

This paper studies network resilience against structured additive perturbations to its topology. We consider dynamic networks modeled as linear time-invariant systems subject to perturbations of bounded energy satisfying specific sparsity and entry-wise constraints. Given an energy level, the structured pseudospectral abscissa captures the worst-possible perturbation an adversary could employ to de-stabilize the network, and the structured stability radius is the maximum energy in the structured perturbation that the network can withstand without becoming unstable. Building on a novel characterization of the worst-case structured perturbation, we propose iterative algorithms that efficiently compute the structured pseudospectral abscissa and structured stability radius. We provide theoretical guarantees of the local convergence of the algorithms and illustrate their efficacy and accuracy on several network examples.
Transactive Framework for Dynamic Energy Storage Allocation for Critical Load Management
Authors: Arnab Dey, Vivek Khatana, Ankur Mani, Murti V. Salapaka
Date: 2021-01-14 00:00:00
Tasks: energy management, Management

Increased penetration of Distributed Energy Resources (DER) and Renewable Energy Systems (RES) transforming the conventional distribution grid into a transactive framework supervised by a distribution system operator (DSO). Although the emerging transactive energy management techniques improve the grid reliability, the inherent uncertainty of RES poses a challenge in meeting the power demand of the critical infrastructure in the microgrid unless sufficient battery energy storage is maintained. However, maintaining expensive battery storage increases the operating cost of the DSO. In this article, we propose a cost-effective dynamic resource allocation strategy to optimize the battery reserve requirement while ensuring the critical demand is met with a provable guarantee. Our proposed scheme enables the DSO to optimize the RES and battery reserve allocation to eliminate the risk of over or underproduction. We present numerical simulations under three different scenarios of multiple microgrids with uncertain renewable generation. The simulation results demonstrate the efficacy of the proposed transactive stochastic control algorithm.
Wealth rheology
Authors: Zdzislaw Burda, Malgorzata J. Krawczyk, Krzysztof Malarz, Malgorzata Snarska
Date: 2021-05-17 00:00:00

We study wealth rank correlations in a simple model of macro-economy. To quantify rank correlations between wealth rankings at different times, we use Kendall's $\tau$ and Spearman's $\rho$, Goodman--Kruskal's $\gamma$, and the lists' overlap ratio. We show that the dynamics of wealth flow and the speed of reshuffling in the ranking list depend on parameters of the model controlling the wealth exchange rate and the wealth growth volatility. As an example of the rheology of wealth in real data, we analyze the lists of the richest people in Poland, Germany, the USA and the world.
An appeal for an open scientific debate about the proximal origin of SARS-CoV-2
Authors: Jacques van Helden, Colin D Butler, Bruno Canard, Guillaume Achaz, François Graner, Rossana Segreto, Yuri Deigin, Fabien Colombo, Serge Morand, Didier Casane, Dan Sirotkin, Karl Sirotkin, Etienne Decroly José Halloy
Date: 2021-05-13 00:00:00

One year after the onset of the COVID-19 pandemic, the origin of SARS-CoV-2 still eludes humanity. Early publications firmly stated that the virus was of natural origin, and the possibility that the virus might have escaped from a lab was discarded in most subsequent publications. However, based on a re-analysis of the initial arguments, highlighted by the current knowledge about the virus, we show that the natural origin is not supported by conclusive arguments, and that a lab origin cannot be formally discarded. We call for an opening of peer-reviewed journals to a rational, evidence-based and prejudice-free evaluation of all the reasonable hypotheses about the virus' origin. We advocate that this debate should take place in the columns of renowned scientific journals, rather than being left to social media and newspapers.
Uniform Inference on High-dimensional Spatial Panel Networks
Authors: Victor Chernozhukov, Chen Huang, Weining Wang
Date: 2021-05-16 00:00:00

We propose employing a debiased-regularized, high-dimensional generalized method of moments (GMM) framework to perform inference on large-scale spatial panel networks. In particular, network structure with a flexible sparse deviation, which can be regarded either as latent or as misspecified from a predetermined adjacency matrix, is estimated using debiased machine learning approach. The theoretical analysis establishes the consistency and asymptotic normality of our proposed estimator, taking into account general temporal and spatial dependency inherent in the data-generating processes. A primary contribution of our study is the development of uniform inference theory that enables hypothesis testing on the parameters of interest, including zero or non-zero elements in the network structure. Additionally, the asymptotic properties for the estimator are derived for both linear and nonlinear moments. Simulations demonstrate superior performance of our proposed approach. Lastly, we apply our methodology to investigate the spatial network effect of stock returns.
What shapes climate change perceptions in Africa? A random forest approach
Authors: Juan B Gonzalez, Alfonso Sanchez
Date: 2021-05-17 00:00:00

Climate change perceptions are fundamental for adaptation and environmental policy support. Although Africa is one of the most vulnerable regions to climate change, little research has focused on how climate change is perceived in the continent. Using random forest methodology, we analyse Afrobarometer data (N = 45,732), joint with climatic data, to explore what shapes climate change perceptions in Africa. We include 5 different dimensions of climate change perceptions: awareness, belief in its human cause, risk perception, need to stop it and self-efficacy. Results indicate that perceived agriculture conditions are crucial for perceiving climate change. Country-level factors and long-term changes in local weather conditions are among the most important predictors. Moreover, education level, access to information, poverty, authoritarian values, and trust in institutions shape individual climate change perceptions. Demographic effects -- including religion -- seem negligible. These findings suggest policymakers and environmental communicators how to frame climate change in Africa to raise awareness, gather public support and induce adaptation.
Attention elasticities and invariant information costs
Authors: Dániel Csaba
Date: 2021-05-17 00:00:00
Tasks: Sensitivity, Unity

We consider a generalization of rational inattention problems by measuring costs of information through the information radius (Sibson, 1969; Verd\'u, 2015) of statistical experiments. We introduce a notion of attention elasticity measuring the sensitivity of attention strategies with respect to changes in incentives. We show how the introduced class of cost functions controls attention elasticities while the Shannon model restricts attention elasticity to be unity. We explore further differences and similarities relative to the Shannon model in relation to invariance, posterior separability, consideration sets, and the ability to learn events with certainty. Lastly, we provide an efficient alternating minimization method -- analogous to the Blahut-Arimoto algorithm -- to obtain optimal attention strategies.
Spatial Measures of Socioeconomic Deprivation: An Application to Four Midwestern Industrial Cities
Authors: Scott W. Hegerty
Date: 2021-05-13 00:00:00

Decades of economic decline have led to areas of increased deprivation in a number of U.S. inner cities, which can be linked to adverse health and other outcomes. Yet the calculation of a single "deprivation" index, which has received wide application in Britain and elsewhere in the world, involves a choice of variables and methods that have not been directly compared in the American context. This study creates four related measures--using two sets of variables and two weighting schemes--to create such indices for block groups in Buffalo, Cleveland, Detroit, and Milwaukee. After examining the indices' similarities, we then map concentrations of high deprivation in each city and analyze their relationships to income, racial makeup, and transportation usage. Overall, we find certain measures to have higher correlations than others, but that all show deprivation to be linked with lower incomes and a higher nonwhite population.
Acquisitive Crimes, Time of Day, and Multiunit Housing in the City of Milwaukee
Authors: Scott W. Hegerty
Date: 2021-05-13 00:00:00
Tasks: regression

According to "Social Disorganization" theory, criminal activity increases if the societal institutions that might be responsible for maintaining order are weakened. Do large apartment buildings, which often have fairly transient populations and low levels of community involvement, have disproportionately high rates of crime? Do these rates differ during the daytime or nighttime, depending when residents are present, or away from their property? This study examines four types of "acquisitive" crime in Milwaukee during 2014. Overall, nighttime crimes are shown to be more dispersed than daytime crimes. A spatial regression estimation finds that the density of multiunit housing is positively related to all types of crime except burglaries, but not for all times of day. Daytime robberies, in particular, increase as the density of multiunit housing increases.
Bank Density, Population Density, and Economic Deprivation Across the United States
Authors: Scott W. Hegerty
Date: 2021-05-13 00:00:00

Recent research on the geographic locations of bank branches in the United States has identified thresholds below which a given area can be considered to be a "banking desert." Thus far, most analyses of the country as a whole have tended to focus on minimum distances from geographic areas to the nearest bank, while a recent density-based analysis focused only on the city of Chicago. As such, there is not yet a nationwide study of bank densities for the entire United States. This study calculates banks per square mile for U.S. Census tracts over ten different ranges of population density. One main finding is that bank density is sensitive to the measurement radius used (for example, density in urban areas can be calculated as the number of banks within two miles, while some rural areas require a 20-mile radius). This study then compiles a set of lower 5- and 10-percent thresholds that might be used to identify "banking deserts" in various urban, suburban, and rural areas; these largely conform to the findings of previous analyses. Finally, adjusting for population density using regression residuals, this paper examines whether an index of economic deprivation is significantly higher in the five percent of "desert" tracts than in the remaining 95 percent. The differences are largest -- and highly significant -- in the densest tracts in large urban areas.
Are the Spatial Concentrations of Core-City and Suburban Poverty Converging in the Rust Belt?
Authors: Scott W. Hegerty
Date: 2021-05-13 00:00:00

Decades of deindustrialization have led to economic decline and population loss throughout the U.S. Midwest, with the highest national poverty rates found in Detroit, Cleveland, and Buffalo. This poverty is often confined to core cities themselves, however, as many of their surrounding suburbs continue to prosper. Poverty can therefore be highly concentrated at the MSA level, but more evenly distributed within the borders of the city proper. One result of this disparity is that if suburbanites consider poverty to be confined to the central city, they might be less willing to devote resources to alleviate it. But due to recent increases in suburban poverty, particularly since the 2008 recession, such urban-suburban gaps might be shrinking. Using Census tract-level data, this study quantifies poverty concentrations for four "Rust Belt" MSAs, comparing core-city and suburban concentrations in 2000, 2010, and 2015. There is evidence of a large gap between core cities and outlying areas, which is closing in the three highest-poverty cities, but not in Milwaukee. A set of four comparison cities show a smaller, more stable city-suburban divide in the U.S. "Sunbelt," while Chicago resembles a "Rust Belt" metro.
When to sell an indivisible object: Optimal timing with Markovian buyers
Authors: Kiho Yoon
Date: 2021-05-17 00:00:00
Tasks: Object

We study the problem of when to sell an indivisible object. There is a monopolistic seller who owns an indivisible object and plans to sell it over a given span of time to the set of potential buyers whose valuations for the object evolve over time. We formulate the seller's problem as a dynamic mechanism design problem. We provide a procedure for finding the optimal solution and show how to check incentive compatibility. We also discuss the threshold rule from the perspective of optimal stopping.
Identifying Wisdom (of the Crowd): A Regression Approach
Authors: Jonathan Libgober
Date: 2021-05-14 00:00:00
Tasks: regression

Experts in a population hold (a) beliefs over a state (call these state beliefs), as well as (b) beliefs over the distribution of beliefs in the population (call these hypothetical beliefs). If these are generated via updating a common prior using a fixed information structure, then the information structure can (generically) be derived by regressing hypothetical beliefs on state beliefs, provided there are at least as many signals as states. In addition, the prior solves an eigenvector equation derived from a matrix determined by the state beliefs and the hypothetical beliefs. Thus, the ex-ante informational environment (i.e., how signals are generated) can be determined using ex-post data (i.e., the beliefs in the population). I discuss implications of this finding, as well as what is identified when there are more states than signals.
ADMM and Spectral Proximity Operators in Hyperspectral Broadband Phase Retrieval for Quantitative Phase Imaging
Authors: Vladimir Katkovnik, Igor Shevkunov, Karen Egiazarian
Date: 2021-05-14 00:00:00
Tasks: Object, Retrieval

A novel formulation of the hyperspectral broadband phase retrieval is developed for the scenario where both object and modulation phase masks are spectrally varying. The proposed algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and Spectral Proximity Operators (SPO) derived for Gaussian and Poissonian observations. Computations for these operators are reduced to the solution of sets of cubic (for Gaussian) and quadratic (for Poissonian) algebraic equations. These proximity operators resolve two problems. Firstly, the complex domain spectral components of signals are extracted from the total intensity observations calculated as sums of the signal spectral intensities. In this way, the spectral analysis of the total intensities is achieved. Secondly, the noisy observations are filtered, compromising noisy intensity observations and their predicted counterparts. The ability to resolve the hyperspectral broadband phase retrieval problem and to find the spectrum varying object are essentially defined by the spectral properties of object and image formation operators. The simulation tests demonstrate that the phase retrieval in this formulation can be successfully resolved.
56 GBaud PAM-4 100 km Transmission System with Photonic Processing Schemes
Authors: Irene Estébanez, Shi Li, Janek Schwind, Ingo Fischer, Stephan Pachnicke, Apostolos Argyris
Date: 2021-05-17 00:00:00

Analog photonic computing has been proposed and tested in recent years as an alternative approach for data recovery in fiber transmission systems. Photonic reservoir computing, performing nonlinear transformations of the transmitted signals and exhibiting internal fading memory, has been found advantageous for this kind of processing. In this work, we show that the effectiveness of the internal fading memory depends significantly on the properties of the signal to be processed. Specifically, we demonstrate two experimental photonic post-processing schemes for a 56 GBaud PAM-4 experimental transmission system, with 100 km uncompensated standard single-mode fiber and direct detection. We show that, for transmission systems with significant chromatic dispersion, the contribution of a photonic reservoir's fading memory to the computational performance is limited. In a comparison between the data recovery performances between a reservoir computing and an extreme learning machine fiber-based configuration, we find that both offer equivalent data recovery. The extreme learning machine approach eliminates the necessity of external recurrent connectivity, which simplifies the system and increases the computation speed. Above 31 dB OSNR, the photonics-based equalization exhibits a lower BER than the respective offline DSP-based KK receiver.
Optimal Spatial Signal Design for mmWave Positioning under Imperfect Synchronization
Authors: Musa Furkan Keskin, Fan Jiang, Florent Munier, Gonzalo Seco-Granados, Henk Wymeersch
Date: 2021-05-17 00:00:00
Tasks: Position, Robust Design

We consider the problem of spatial signal design for multipath-assisted mmWave positioning under limited prior knowledge on the user's location and clock bias. We propose an optimal robust design and, based on the low-dimensional precoder structure under perfect prior knowledge, a codebook-based heuristic design with optimized beam power allocation. Through numerical results, we characterize different position-error-bound (PEB) regimes with respect to clock bias uncertainty and show that the proposed low-complexity codebook-based designs outperform the conventional directional beam codebook and achieve near-optimal PEB performance for both analog and digital architectures.