pwc-archive/papers-with-abstracts huggingface dataset:
Analysis and Prevention of MCAS-Induced Crashes
Authors: Noah T. Curran, Thomas W. Kennings, Kang G. Shin
Date: 2023-01-20 00:00:00

Semi-autonomous (SA) systems face the challenge of determining which source to prioritize for control, whether it's from the human operator or the autonomous controller, especially when they conflict with each other. While one may design an SA system to default to accepting control from one or the other, such design choices can have catastrophic consequences in safety-critical settings. For instance, the sensors an autonomous controller relies upon may provide incorrect information about the environment due to tampering or natural fault. On the other hand, the human operator may also provide erroneous input. To better understand the consequences and resolution of this safety-critical design choice, we investigate a specific application of an SA system that failed due to a static assignment of control authority: the well-publicized Boeing 737-MAX Maneuvering Characteristics Augmentation System (MCAS) that caused the crashes of Lion Air Flight 610 and Ethiopian Airlines Flight 302. First, using a representative simulation, we analyze and demonstrate the ease by which the original MCAS design could fail. Our analysis reveals the most robust public analysis of aircraft recoverability under MCAS faults, offering bounds for those scenarios beyond the original crashes. We also analyze Boeing's updated MCAS and show how it falls short of its intended goals and continues to rely upon on a fault-prone static assignment of control priority. Using these insights, we present Semi-Autonomous MCAS (SA-MCAS), a new MCAS that both meets the intended goals of MCAS and avoids the failure cases that plague both MCAS designs. We demonstrate SA-MCAS's ability to make safer and timely control decisions of the aircraft, even when the human and autonomous operators provide conflicting control inputs.
A Free Industry-grade Education Tool for Bulk Power System Reliability Assessment
Authors: Yongli Zhu, Chanan Singh
Date: 2023-01-20 00:00:00

A free industry-grade education tool is developed for bulk-power-system reliability assessment. The software architecture is illustrated using a high-level flowchart. Three main algorithms of this tool, i.e., sequential Monte Carlo simulation, unit preventive maintenance schedule, and optimal-power-flow-based load shedding, are introduced. The input and output formats are described in detail, including the roles of different data cards and results categorization. Finally, an example case study is conducted on a five-area system to demonstrate the effectiveness and efficiency of this tool.
Power Supply Compensation for Capacitive Loads
Authors: Jonathan L. Fasig, Barry K. Gilbert, Erik S. Daniel
Date: 2023-01-17 00:00:00

As ASIC supply voltages approach one volt, the source-impedance goals for power distribution networks are driven ever lower as well. One approach to achieving these goals is to add decoupling capacitors of various values until the desired impedance profile is obtained. An unintended consequence of this approach can be reduced power supply stability and even oscillation. In this paper, we present a case study of a system design which encountered these problems and we describe how these problems were resolved. Time-domain and frequency-domain analysis techniques are discussed and measured data is presented.
A Multi-stack Power-to-Hydrogen Load Control Framework for the Power Factor-Constrained Integration in Volatile Peak Shaving Conditions
Authors: Jiarong Li
Date: 2023-01-14 00:00:00

Large-scale power-to-hydrogen (P2H) systems formed by multi-stack are potentially powerful peak-shaving resources of power systems. However, due to the research gap in connecting the grid-side performance with the inherent operation control, the continuous operation of P2H loads is limited by the PF assessment under volatile conditions when integrating into the grid. This paper first fills the gap in proposing the analytical models of active and reactive power of P2H loads with a typical power converter interface topology. On this basis, the all-condition PF characteristics of multi-stack P2H loads are captured as functions of unified current and temperature control variables. Then, a PF-constrained multi-timescale control framework is constructed to evaluate flexibility, PF, production, and security comprehensively. A two-level nexus, including a model-based hour-ahead robust model predictive controller and a rule-based real-time increment correction algorithm, is proposed to guarantee the control accuracy and tractability. Case studies verify an intrinsic control tradeoff between PF and production, resulting in an unequal-split allocation strategy compared to the traditional production-oriented control. The significance of the extended PF and security dimensions is verified to improve the flexibility. Furthermore, five typical operating modes respectively corresponding to low, medium, and high load levels at the cluster level are concluded for industrial application.
Phase transitions in in vivo or in vitro populations of spiking neurons belong to different universality classes
Authors: Braden A. W. Brinkman
Date: 2023-01-23 00:00:00

The "critical brain hypothesis" posits that neural circuitry may be tuned close to a "critical point" or "phase transition" -- a boundary between different operating regimes of the circuit. The renormalization group and theory of critical phenomena explain how systems tuned to a critical point display scale invariance due to fluctuations in activity spanning a wide range of time or spatial scales. In the brain this scale invariance has been hypothesized to have several computational benefits, including increased collective sensitivity to changes in input and robust propagation of information across a circuit. However, our theoretical understanding of critical phenomena in neural circuitry is limited because standard renormalization group methods apply to systems with either highly organized or completely random connections. Connections between neurons lie between these extremes, and may be either excitatory (positive) or inhibitory (negative), but not both. In this work we develop a renormalization group method that applies to models of spiking neural populations with some realistic biological constraints on connectivity, and derive a scaling theory for the statistics of neural activity when the population is tuned to a critical point. We show that the scaling theories differ for models of in vitro versus in vivo circuits -- they belong to different "universality classes" -- and that both may exhibit "anomalous" scaling at a critical balance of inhibition and excitation. We verify our theoretical results on simulations of neural activity data, and discuss how our scaling theory can be further extended and applied to real neural data.
Ordinal methods for a characterization of evolving functional brain networks
Authors: Klaus Lehnertz
Date: 2023-01-13 00:00:00
Tasks: Time Series, Time Series Analysis

Ordinal time series analysis is based on the idea to map time series to ordinal patterns, i.e., order relations between the values of a time series and not the values themselves, as introduced in 2002 by C. Bandt and B. Pompe. Despite a resulting loss of information, this approach captures meaningful information about the temporal structure of the underlying system dynamics as well as about properties of interactions between coupled systems. This - together with its conceptual simplicity and robustness against measurement noise - makes ordinal time series analysis well suited to improve characterization of the still poorly understood spatial-temporal dynamics of the human brain. This minireview briefly summarizes the state-of-the-art of uni- and bivariate ordinal time-series-analysis techniques together with applications in the neurosciences. It will highlight current limitations to stimulate further developments which would be necessary to advance characterization of evolving functional brain networks.
Accurately summarizing an outbreak using epidemiological models takes time
Authors: B. K. M. Case, Jean-Gabriel Young, Laurent Hébert-Dufresne
Date: 2023-01-20 00:00:00

Recent outbreaks of monkeypox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic Susceptible-Infectious-Recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing, and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations, and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.
Processes analogous to ecological interactions and dispersal shape the dynamics of economic activities
Authors: Victor Boussange, Didier Sornette, Heike Lischke, Loïc Pellissier
Date: 2023-01-23 00:00:00

The processes of ecological interactions, dispersal and mutations shape the dynamics of biological communities, and analogous eco-evolutionary processes acting upon economic entities have been proposed to explain economic change. This hypothesis is compelling because it explains economic change through endogenous mechanisms, but it has not been quantitatively tested at the global economy level. Here, we use an inverse modelling technique and 59 years of economic data covering 77 countries to test whether the collective dynamics of national economic activities can be characterised by eco-evolutionary processes. We estimate the statistical support of dynamic community models in which the dynamics of economic activities are coupled with positive and negative interactions between the activities, the spatial dispersal of the activities, and their transformations into other economic activities. We find strong support for the models capturing positive interactions between economic activities and spatial dispersal of the activities across countries. These results suggest that processes akin to those occurring in ecosystems play a significant role in the dynamics of economic systems. The strength-of-evidence obtained for each model varies across countries and may be caused by differences in the distance between countries, specific institutional contexts, and historical contingencies. Overall, our study provides a new quantitative, biologically inspired framework to study the forces shaping economic change.
Revisiting Panel Data Discrete Choice Models with Lagged Dependent Variables
Authors: Christopher R. Dobronyi, Fu Ouyang, Thomas Tao Yang
Date: 2023-01-23 00:00:00
Tasks: Discrete Choice Models

This paper revisits the identification and estimation of a class of semiparametric (distribution-free) panel data binary choice models with lagged dependent variables, exogenous covariates, and entity fixed effects. We provide a novel identification strategy, using an "identification at infinity" argument. In contrast with the celebrated Honore and Kyriazidou (2000), our method permits time trends of any form and does not suffer from the "curse of dimensionality". We propose an easily implementable conditional maximum score estimator. The asymptotic properties of the proposed estimator are fully characterized. A small-scale Monte Carlo study demonstrates that our approach performs satisfactorily in finite samples. We illustrate the usefulness of our method by presenting an empirical application to enrollment in private hospital insurance using the Household, Income and Labour Dynamics in Australia (HILDA) Survey data.
Labor Income Risk and the Cross-Section of Expected Returns
Authors: Mykola Pinchuk
Date: 2023-01-22 00:00:00
Tasks: Sensitivity

This paper explores asset pricing implications of unemployment risk from sectoral shifts. I proxy for this risk using cross-industry dispersion (CID), defined as a mean absolute deviation of returns of 49 industry portfolios. CID peaks during periods of accelerated sectoral reallocation and heightened uncertainty. I find that expected stock returns are related cross-sectionally to the sensitivities of returns to innovations in CID. Annualized returns of the stocks with high sensitivity to CID are 5.9% lower than the returns of the stocks with low sensitivity. Abnormal returns with respect to the best factor model are 3.5%, suggesting that common factors can not explain this return spread. Stocks with high sensitivity to CID are likely to be the stocks, which benefited from sectoral shifts. CID positively predicts unemployment through its long-term component, consistent with the hypothesis that CID is a proxy for unemployment risk from sectoral shifts.
Inference for Two-stage Experiments under Covariate-Adaptive Randomization
Authors: Jizhou Liu
Date: 2023-01-21 00:00:00
Tasks: Experimental Design, Vocal Bursts Valence Prediction

This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either control or treatment groups, based on individual-level covariates. Under the homogeneous partial interference assumption, I establish conditions under which the proposed difference-in-``average of averages'' estimators are consistent and asymptotically normal for the corresponding average primary and spillover effects and develop consistent estimators of their asymptotic variances. Combining these results establishes the asymptotic validity of tests based on these estimators. My findings suggest that ignoring covariate information in the design stage can result in efficiency loss, and commonly used inference methods that ignore or improperly use covariate information can lead to either conservative or invalid inference. Then, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator. Finally, I discuss covariate adjustment, which incorporates additional baseline covariates not used for treatment assignment. The practical relevance of the theoretical results is illustrated through a simulation study and an empirical application.
Composite distributions in the social sciences: A comparative empirical study of firms' sales distribution for France, Germany, Italy, Japan, South Korea, and Spain
Authors: Arturo Ramos, Till Massing, Atushi Ishikawa, Shouji Fujimoto, Takayuki Mizuno
Date: 2023-01-20 00:00:00

We study 17 different statistical distributions for sizes obtained {}from the classical and recent literature to describe a relevant variable in the social sciences and Economics, namely the firms' sales distribution in six countries over an ample period. We find that the best results are obtained with mixtures of lognormal (LN), loglogistic (LL), and log Student's $t$ (LSt) distributions. The single lognormal, in turn, is strongly not selected. We then find that the whole firm size distribution is better described by a mixture, and there exist subgroups of firms. Depending on the method of measurement, the best fitting distribution cannot be defined by a single one, but as a mixture of at least three distributions or even four or five. We assess a full sample analysis, an in-sample and out-of-sample analysis, and a doubly truncated sample analysis. We also provide the formulation of the preferred models as solutions of the Fokker--Planck or forward Kolmogorov equation.
Approximations of multi-period liability values by simple formulas
Authors: Nils Engler, Filip Lindskog
Date: 2023-01-23 00:00:00

This paper is motivated by computational challenges arising in multi-period valuation in insurance. Aggregate insurance liability cashflows typically correspond to stochastic payments several years into the future. However, insurance regulation requires that capital requirements are computed for a one-year horizon, by considering cashflows during the year and end-of-year liability values. This implies that liability values must be computed recursively, backwards in time, starting from the year of the most distant liability payments. Solving such backward recursions with paper and pen is rarely possible, and numerical solutions give rise to major computational challenges. The aim of this paper is to provide explicit and easily computable expressions for multi-period valuations that appear as limit objects for a sequence of multi-period models that converge in terms of conditional weak convergence. Such convergence appears naturally if we consider large insurance portfolios such that the liability cashflows, appropriately centered and scaled, converge weakly as the size of the portfolio tends to infinity.
Gender-Segmented Labor Markets and Foreign Demand Shocks
Authors: Carlos Góes, Gladys Lopez-Acevedo, Raymond Robertson
Date: 2023-01-23 00:00:00
Tasks: Segmentation

Gender segmentation in labor markets shapes the local effects of international trade. We develop a theory that combines exports with gender-segmented labor markets and show that, in this framework, foreign demand shocks may either increase or decrease the female-to-male employment ratio. If a foreign demand shock happens in a female-intensive (male-intensive) sector, the model predicts that the female-to-male employment ratio should increase (decrease). We then use plausibly exogenous variation in the exposure of Tunisian local labor markets to foreign demand shocks and show that the empirical results are consistent with the theoretical prediction. In Tunisia, a developing country with a high degree of gender segmentation in labor markets, foreign-demand shocks have been relatively larger in male-intensive sectors. This induced a decrease in the female-to-male employment ratio, with households likely substituting female for male labor supply.
Decarbonization of financial markets: a mean-field game approach
Authors: Pierre Lavigne, Peter Tankov
Date: 2023-01-22 00:00:00

We build a model of a financial market where a large number of firms determine their dynamic emission strategies under climate transition risk in the presence of both green-minded and neutral investors. The firms aim to achieve a trade-off between financial and environmental performance, while interacting through the stochastic discount factor, determined in equilibrium by the investors' allocations. We formalize the problem in the setting of mean-field games and prove the existence and uniqueness of a Nash equilibrium for firms. We then present a convergent numerical algorithm for computing this equilibrium and illustrate the impact of climate transition risk and the presence of green-minded investors on the market decarbonization dynamics and share prices. We show that uncertainty about future climate risks and policies leads to higher overall emissions and higher spreads between share prices of green and brown companies. This effect is partially reversed in the presence of environmentally concerned investors, whose impact on the cost of capital spurs companies to reduce emissions.
Learning Production Process Heterogeneity Across Industries: Implications of Deep Learning for Corporate M&A Decisions
Authors: Jongsub Lee, Hayong Yun
Date: 2023-01-21 00:00:00

Using deep learning techniques, we introduce a novel measure for production process heterogeneity across industries. For each pair of industries during 1990-2021, we estimate the functional distance between two industries' production processes via deep neural network. Our estimates uncover the underlying factors and weights reflected in the multi-stage production decision tree in each industry. We find that the greater the functional distance between two industries' production processes, the lower are the number of M&As, deal completion rates, announcement returns, and post-M&A survival likelihood. Our results highlight the importance of structural heterogeneity in production technology to firms' business integration decisions.
When do Default Nudges Work?
Authors: Carl Bonander, Mats Ekman, Niklas Jakobsson
Date: 2023-01-20 00:00:00

Nudging is a burgeoning topic in science and in policy, but evidence on the effectiveness of nudges among differentially-incentivized groups is lacking. This paper exploits regional variations in the roll-out of the Covid-19 vaccine in Sweden to examine the effect of a nudge on groups whose intrinsic incentives are different: 16-17-year-olds, for whom Covid-19 is not dangerous, and 50-59-year-olds, who face a substantial risk of death or severe dis-ease. We find a significantly stronger response in the younger group, consistent with the theory that nudges are more effective for choices that are not meaningful to the individual.
Corporate Culture and Organizational Fragility
Authors: Matthew Elliott, Benjamin Golub, Matthieu V. Leduc
Date: 2023-01-21 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Complex organizations accomplish tasks through many steps of collaboration among workers. Corporate culture supports collaborations by establishing norms and reducing misunderstandings. Because a strong corporate culture relies on costly, voluntary investments by many workers, we model it as an organizational public good, subject to standard free-riding problems, which become severe in large organizations. Our main finding is that voluntary contributions to culture can nevertheless be sustained, because an organization's equilibrium productivity is endogenously highly sensitive to individual contributions. However, the completion of complex tasks is then necessarily fragile to small shocks that damage the organization's culture.
DELTA-MRI: Direct deformation Estimation from LongiTudinally Acquired k-space data
Authors: Jens Renders, Banafshe Shafieizargar, Marleen Verhoye, Jan De Beenhouwer, Arnold J. den Dekker, Jan Sijbers
Date: 2023-01-23 00:00:00
Tasks: Change Detection, compressed sensing, Diagnostic, Image Reconstruction

Longitudinal MRI is an important diagnostic imaging tool for evaluating the effects of treatment and monitoring disease progression. However, MRI, and particularly longitudinal MRI, is known to be time consuming. To accelerate imaging, compressed sensing (CS) theory has been applied to exploit sparsity, both on single image as on image sequence level. State-of-the-art CS methods however, are generally focused on image reconstruction, and consider analysis (e.g., alignment, change detection) as a post-processing step. In this study, we propose DELTA-MRI, a novel framework to estimate longitudinal image changes {\it directly} from a reference image and subsequently acquired, strongly sub-sampled MRI k-space data. In contrast to state-of-the-art longitudinal CS based imaging, our method avoids the conventional multi-step process of image reconstruction of subsequent images, image alignment, and deformation vector field computation. Instead, the set of follow-up images, along with motion and deformation vector fields that describe their relation to the reference image, are estimated in one go. Experiments show that DELTA-MRI performs significantly better than the state-of-the-art in terms of the normalized reconstruction error.