pwc-archive/papers-with-abstracts huggingface dataset:
Inertia Estimation Through Covariance Matrix
Authors: Federico Bizzarri, Davide del Giudice, Samuele Grillo, Daniele Linaro, Angelo Brambilla, Federico Milano
Date: 2022-06-28 00:00:00

This work presents a technique to estimate on-line the inertia of a power system based on ambient measurements. The proposed technique utilizes the covariance matrix of these measurements and solves an optimization problem that fits such measurements to the synchronous machine classical model. We show that the proposed technique is adequate to accurately estimate the actual inertia of synchronous machines and also the virtual inertia provided by the controllers of converter-interfaced generators that emulate the behavior of synchronous machines. We also show that the proposed approach is able to estimate the equivalent damping of the classical synchronous machine model. This feature is exploited to estimate the droop of grid-following converters, which has a similar effect of the swing equation equivalent damping. The technique is comprehensively tested on a modified version of the IEEE 39-bus system as well as on a dynamic 1479-bus model of the all-island Irish transmission system.
Influence of a Medium on Capacitive Power Transfer Capability
Authors: Cedric Lecluyse, Ben Minnaert, Simon Ravyts, Michael Kleemann
Date: 2022-06-28 00:00:00

Despite the advantages of capacitive power transfer (CPT), inductive power transfer (IPT) is still preferred. The reason: IPT systems have a gap power density in air that is 400 times greater. Conclusively, IPT can transmit more power than CPT over greater distances in air, but what about other media? This paper gives an answer on how media, different from air, influence the power transfer over different distances. First, we analyze theoretically the capacitive coupling with different media in the gap. Next, we simulate the CPT system using finite element software and compared it with the theoretical analysis. Finally, we employ the results of the finite element simulation in a power electronic simulation to examine the influence of the medium on the electrical power transfer.
Multivariable Grid-Forming Converters with Direct States Control
Authors: Meng Chen, Dao Zhou, Frede Blaabjerg
Date: 2022-06-28 00:00:00

A multi-input multi-output based grid-forming (MIMO-GFM) converter has been proposed using multivariable feedback control, which has been proven as a superior and robust system using low-order controllers. However, the original MIMO-GFM control is easily affected by the high-frequency components especially for the converter without inner cascaded voltage and current loops and when it is connected into a strong grid. This paper proposes an improved MIMO-GFM control method, where the frequency and internal voltage are chosen as state variables to be controlled directly. In this way, the impact of high-frequency components is eliminated without increasing the complexity of the control system. The H-infinity synthesis is used to tune the parameters to obtain an optimized performance. Experimental results verify the effectiveness of the proposed method.
Optimisation models for the day-ahead energy and reserve scheduling of a hybrid wind-battery virtual power plant
Authors: Daniel Fernández-Muñoz, Juan I. Pérez-Díaz
Date: 2022-06-28 00:00:00
Tasks: Scheduling

This work presents a suite of two optimisation models for the short-term scheduling and redispatch of a virtual power plant (VPP) composed of a wind farm and a Li-ion battery, that participates in the day-ahead energy and secondary regulation reserve markets of the Iberian electricity market. First, a two-stage stochastic mixed-integer linear programming model is used to obtain the VPP's generation and reserve schedule and the opportunity cost of the energy stored in the battery. The model has an hourly resolution and a look-ahead period beyond the markets' scheduling horizon and considers the hourly battery degradation costs as a function of both the depth of discharge and the discharge rate. Different strategies are evaluated to forecast the real-time use of the committed secondary regulation reserves. Second, a deterministic MILP model is used to determine the redispatch of the VPP using as input the generation and reserve schedule and the VPP's storage opportunity cost provided by the former model and is executed on an hourly rolling basis. The results obtained show that the proposed models are effective for the short-term scheduling and redispatch of the VPP used with a low computational time, making them tractable and practical for their daily use.
Assessment of U.S. Department of Transportation Lane-Level Map for Connected Vehicle Applications
Authors: Wang Hu, David Oswald, Guoyuan Wu, Jay A. Farrell
Date: 2022-06-28 00:00:00

High-definition (Hi-Def) digital maps are an indispensable automated driving technology that is developing rapidly. There are various commercial or governmental map products in the market. It is notable that the U.S. Department of Transportation (USDOT) map tool allows the user to create MAP and Signal Phase and Timing (SPaT) messages with free access. However, an analysis of the accuracy of this map tool is currently lacking in the literature. This paper provides such an analysis. The analysis manually selects 39 feature points within about 200 meters of the verified point and 55 feature points over longer distances from the verified point. All feature locations are surveyed using GNSS and mapped using the USDOT tool. Different error sources are evaluated to allow assessment of the USDOT map accuracy. In this investigation, The USDOT map tool is demonstrated to achieve 17 centimeters horizontal accuracy, which meets the lane-level map requirement. The maximum horizontal map error is less than 30 centimeters.
Design and control analysis of a deployable clustered hyperbolic paraboloid cable net
Authors: Shuo Ma, Kai Lu, Muhao Chen, Robert E. Skelton
Date: 2022-06-26 00:00:00
Tasks: Experimental Design

This paper presents an analytical and experimental design and deployment control analysis of a hyperbolic paraboloid cable net based on clustering actuation strategies. First, the dynamics and statics for clustered tensegrity structures (CTS) are given. Then, we propose the topology design of the deployable hyperbolic paraboloid cable net. The deployability of the cable net is achieved by using clustered cables. It is shown that the clustered cables significantly reduce the number of actuators required for control. The deployment trajectory and actuation prestress in the cables are designed to ensure the tensions are feasible during the deployment process. Then, we compare the deployment analysis's open-loop and closed-loop control strategies. Finally, a lab-scale model is constructed to validate the actuation laws. We test the static performance and deployment process of the experimental model. Results show that the closed-loop control approach is more stable and smoother than the open-loop one in the deployment process. The approaches developed in this paper can also be used for various deployable tensegrity structures.
Stochastic compartmental models of COVID-19 pandemic must have temporally correlated uncertainties
Authors: Konstantinos Mamis, Mohammad Farazmand
Date: 2022-06-23 00:00:00
Tasks: Epidemiology

Compartmental models are an important quantitative tool in epidemiology, enabling us to forecast the course of a communicable disease. However, the model parameters, such as the infectivity rate of the disease, are riddled with uncertainties, which has motivated the development and use of stochastic compartmental models. Here, we first show that a common stochastic model, which treats the uncertainties as white noise, is fundamentally flawed since it erroneously implies that greater parameter uncertainties will lead to the eradication of the disease. Then, we present a principled modeling of the uncertainties based on reasonable assumptions on the contacts of each individual. Using the central limit theorem and Doob's theorem on Gaussian Markov processes, we prove that the correlated Ornstein-Uhlenbeck process is the appropriate tool for modeling uncertainties in the infectivity rate. We demonstrate our results using a compartmental model of the COVID-19 pandemic and the available US data from the Johns Hopkins University COVID-19 database. In particular, we show that the white noise stochastic model systematically underestimates the severity of the Omicron variant of COVID-19, whereas the Ornstein-Uhlenbeck model correctly forecasts the course of this variant. Moreover, using an SIS model of sexually transmitted disease, we derive an exact closed-form solution for the asymptotic distribution of infected individuals. This analytic result shows that the white noise model underestimates the severity of the pandemic because of unrealistic noise-induced transitions. Our results strongly support the need for temporal correlations in modeling of uncertainties in compartmental models of infectious disease.
Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition
Authors: Saulius Jokubaitis, Dmitrij Celov
Date: 2022-06-28 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

This paper elaborates on the sectoral-regional view of the business cycle synchronization in the EU -- a necessary condition for the optimal currency area. We argue that complete and tidy clustering of the data improves the decision maker's understanding of the business cycle and, by extension, the quality of economic decisions. We define the business cycles by applying a wavelet approach to drift-adjusted gross value added data spanning over 2000Q1 to 2021Q2. For the application of the synchronization analysis, we propose the novel soft-clustering approach, which adjusts hierarchical clustering in several aspects. First, the method relies on synchronicity dissimilarity measures, noting that, for time series data, the feature space is the set of all points in time. Then, the ``soft'' part of the approach strengthens the synchronization signal by using silhouette measures. Finally, we add a probabilistic sparsity algorithm to drop out the most asynchronous ``noisy'' data improving the silhouette scores of the most and less synchronous groups. The method, hence, splits the sectoral-regional data into three groups: the synchronous group that shapes the EU business cycle; the less synchronous group that may hint at cycle forecasting relevant information; the asynchronous group that may help investors to diversify through-the-cycle risks of the investment portfolios. The results support the core-periphery hypothesis.
Estimating the Currency Composition of Foreign Exchange Reserves
Authors: Matthew Ferranti
Date: 2022-06-28 00:00:00

Central banks manage about \$12 trillion in foreign exchange reserves, influencing global exchange rates and asset prices. However, some of the largest holders of reserves report minimal information about their currency composition, hindering empirical analysis. I describe a Hidden Markov Model to estimate the composition of a central bank's reserves by relating the fluctuation in the portfolio's valuation to the exchange rates of major reserve currencies. I apply the model to China and Singapore, two countries that collectively hold about \$3.4 trillion in reserves and conceal their composition. I find that both China's reserve composition likely resembles the global average, while Singapore probably holds fewer US dollars.
Misspecification and Weak Identification in Asset Pricing
Authors: Frank Kleibergen, Zhaoguo Zhan
Date: 2022-06-27 00:00:00

The widespread co-existence of misspecification and weak identification in asset pricing has led to an overstated performance of risk factors. Because the conventional Fama and MacBeth (1973) methodology is jeopardized by misspecification and weak identification, we infer risk premia by using a double robust Lagrange multiplier test that remains reliable in the presence of these two empirically relevant issues. Moreover, we show how the identification, and the resulting appropriate interpretation, of the risk premia is governed by the relative magnitudes of the misspecification J-statistic and the identification IS-statistic. We revisit several prominent empirical applications and all specifications with one to six factors from the factor zoo of Feng, Giglio, and Xiu (2020) to emphasize the widespread occurrence of misspecification and weak identification.
Dissecting the dot-com bubble in the 1990s NASDAQ
Authors: Yuchao Fan
Date: 2022-06-28 00:00:00

In this paper, I revisit Phillips, Wu and Yu's seminal 2011 paper on testing for the dot-com bubble. I apply recent advancements of their methods to individual Nasdaq stocks and use a novel specification for fundamentals. To address a divide in the literature, I generate a detailed sectoral breakdown of the dot-com bubble. I find that it comprised multiple overlapping episodes of exuberance and that there were indeed two starting dates for internet exuberance.
Competing for Attention -- The Effect of Talk Radio on Elections and Political Polarization in the US
Authors: Ashani Amarasinghe, Paul A. Raschky
Date: 2022-06-28 00:00:00

This paper studies the effects of talk radio, specifically the Rush Limbaugh Show, on electoral outcomes and attitude polarization in the U.S. We propose a novel identification strategy that considers the radio space in each county as a market where multiple stations are competing for listeners' attention. Our measure of competition is a spatial Herfindahl-Hirschman Index (HHI) in radio frequencies. To address endogeneity concerns, we exploit the variation in competition based on accidental frequency overlaps in a county, conditional on the overall level of radio frequency competition. We find that counties with higher exposure to the Rush Limbaugh Show have a systematically higher vote share for Donald Trump in the 2016 and 2020 U.S. presidential elections. Combining our county-level Rush Limbaugh Show exposure measure with individual survey data reveals that self-identifying Republicans in counties with higher exposure to the Show express more conservative political views, while self-identifying Democrats in these same counties express more moderate political views. Taken together, these findings provide some of the first insights on the effects of contemporary talk radio on political outcomes, both at the aggregate and individual level.
Reducing Polarization on Abortion, Guns and Immigration: An Experimental Study
Authors: Michele Belot, Guglielmo Briscese
Date: 2022-06-27 00:00:00

We study individuals' willingness to engage with others who hold opposite views on polarizing policies. A representative sample of 2,507 Americans are given the opportunity to listen to recordings of fellow countrymen and women expressing their views on immigration, abortion laws and gun ownership laws. We find that most Americans (more than two-thirds) are willing to listen to a view opposite to theirs, and a fraction (ten percent) reports changing their views as a result. We also test whether emphasizing having common grounds with those who think differently helps bridging views. We identify principles the vast majority of people agree upon: (1) a set of fundamental human rights, and (2) a set of simple behavioral etiquette rules. A random subsample of people are made explicitly aware they share common views, either on human rights (one-third of the sample) or etiquette rules (another one-third of the sample), before they have the opportunity to listen to different views. We find that the treatments induce people to adjust their views towards the center on abortion and immigration, relative to a control group, thus reducing polarization.
The role of unobservable characteristics in friendship network formation
Authors: Pablo Brañas-Garza, Lorenzo Ductor, Jaromír Kovárík
Date: 2022-06-27 00:00:00
Tasks: Attribute

Inbreeding homophily is a prevalent feature of human social networks with important individual and group-level social, economic, and health consequences. The literature has proposed an overwhelming number of dimensions along which human relationships might sort, without proposing a unified empirically-grounded framework for their categorization. We exploit rich data on a sample of University freshmen with very similar characteristic - age, race and education- and contrast the relative importance of observable vs. unobservables characteristics in their friendship formation. We employ Bayesian Model Averaging, a methodology explicitly designed to target model uncertainty and to assess the robustness of each candidate attribute while predicting friendships. We show that, while observable features such as assignment of students to sections, gender, and smoking are robust key determinants of whether two individuals befriend each other, unobservable attributes, such as personality, cognitive abilities, economic preferences, or socio-economic aspects, are largely sensible to the model specification, and are not important predictors of friendships.
Artificial Intelligence based Video Codec (AIVC) for CLIC 2022
Authors: Théo Ladune, Gordon Clare, Pierrick Philippe, Félix Henri
Date: 2022-06-28 00:00:00

This paper presents the AIVC submission to the CLIC 2022 video track. AIVC is a fully-learned video codec based on conditional autoencoders. The flexibility of the AIVC models is leveraged to implement rate allocation and frame structure competition to select the optimal coding configuration per-sequence. This competition yields compelling compression performance, offering a rate reduction of -26 % compared with the absence of competition.
Fast Low Rank column-wise Compressive Sensing for Accelerated Dynamic MRI
Authors: Silpa Babu, Sajan Goud Lingala, Namrata Vaswani
Date: 2022-06-27 00:00:00
Tasks: Compressive Sensing

This work develops a fast, memory-efficient, and general algorithm for accelerated/undersampled dynamic MRI by assuming an approximate LR model on the matrix formed by the vectorized images of the sequence. By general, we mean that our algorithm can be used for multiple accelerated dynamic MRI applications and multiple sampling rates (acceleration rates) and patterns with a single choice of parameters (no parameter tuning). We show that our proposed algorithms, alternating Gradient Descent (GD) and minimization for MRI (altGDmin-MRI and altGDmin-MRI2), outperform many existing approaches while also being faster than all of them, on average. This claim is based on comparisons on 8 different retrospectively undersampled single- or multi-coil dynamic MRI applications, undersampled using either 1D Cartesian or 2D pseudo-radial undersampling at multiple sampling rates. All comparisons used the same set of algorithm parameters. Our second contribution is a mini-batch and a fully online extension that can process new measurements and return reconstructions either as soon as measurements of a new image frame arrive, or after a short delay.
Bi-Static Sensing for Near-Field RIS Localization
Authors: Reza Ghazalian, Kamran Keykhosravi, Hui Chen, Henk Wymeersch, Riku Jäntti
Date: 2022-06-28 00:00:00
Tasks: Position

We address the localization of a reconfigurable intelligent surface (RIS) for a single-input single-output multi-carrier system using bi-static sensing between a fixed transmitter and a fixed receiver. Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work. We first derive the Cramer-Rao bounds (CRBs) on the estimation error of the RIS position and orientation and the time of arrival (TOA) for the path transmitter-RIS-receiver. We propose a multi-stage low-complexity estimator for RIS localization purposes. In this proposed estimator, we first perform a line search to estimate the TOA. Then, we use the far-field approximation of the NF signal model to implicitly estimate the angle of arrival and the angle of departure at the RIS center. Finally, the RIS position and orientation estimate are refined via a quasi-Newton method. Simulation results reveal that the proposed estimator can attain the CRBs. We also investigate the effects of several influential factors on the accuracy of the proposed estimator like the RIS size, transmitted power, system bandwidth, and RIS position and orientation.
Robust utility maximization with nonlinear continuous semimartingales
Authors: David Criens, Lars Niemann
Date: 2022-06-28 00:00:00

In this paper we study a robust utility maximization problem in continuous time under model uncertainty. The model uncertainty is governed by a continuous semimartingale with uncertain local characteristics. Here, the differential characteristics are prescribed by a set-valued function that depends on time and path. We show that the robust utility maximization problem is in duality with a conjugate problem, and we study the existence of optimal portfolios for logarithmic, exponential and power utilities.
Fast sequence to graph alignment using the graph wavefront algorithm
Authors: Haowen Zhang, Shiqi Wu, Srinivas Aluru, Heng Li
Date: 2022-06-27 00:00:00

Motivation: A pan-genome graph represents a collection of genomes and encodes sequence variations between them. It is a powerful data structure for studying multiple similar genomes. Sequence-to-graph alignment is an essential step for the construction and the analysis of pan-genome graphs. However, existing algorithms incur runtime proportional to the product of sequence length and graph size, making them inefficient for aligning long sequences against large graphs. Results: We propose the graph wavefront alignment algorithm (Gwfa), a new method for aligning a sequence to a sequence graph. Although the worst-case time complexity of Gwfa is the same as the existing algorithms, it is designed to run faster for closely matching sequences, and its runtime in practice often increases only moderately with the edit distance of the optimal alignment. On four real datasets, Gwfa is up to four orders of magnitude faster than other exact sequence-to-graph alignment algorithms. We also propose a graph pruning heuristic on top of Gwfa, which can achieve an additional $\sim$10-fold speedup on large graphs. Availability: Gwfa code is accessible at https://github.com/lh3/gwfa.
Minimal Morphoelastic Models of Solid Tumour Spheroids: A Tutorial
Authors: Benjamin J. Walker, Giulia L. Celora, Alain Goriely, Derek E. Moulton, Helen M. Byrne
Date: 2022-06-28 00:00:00

Tumour spheroids have been the focus of a variety of mathematical models, ranging from Greenspan's classical study of the 1970s through to contemporary agent-based models. Of the many factors that regulate spheroid growth, mechanical effects are perhaps some of the least studied, both theoretically and experimentally, though experimental enquiry has established their significance to tumour growth dynamics. In this tutorial, we formulate a hierarchy of mathematical models of increasing complexity to explore the role of mechanics in spheroid growth, all the while seeking to retain desirable simplicity and analytical tractability. Beginning with the theory of morphoelasticity, which combines solid mechanics and growth, we successively refine our assumptions to develop a somewhat minimal model of mechanically regulated spheroid growth that is free from many unphysical and undesirable behaviours. In doing so, we will see how iterating upon simple models can provide rigorous guarantees of emergent behaviour, which are often precluded by existing, more complex modelling approaches. Perhaps surprisingly, we also demonstrate that the final model considered in this tutorial agrees favourably with classical experimental results, highlighting the potential for simple models to provide mechanistic insight whilst also serving as mathematical examples.