pwc-archive/papers-with-abstracts huggingface dataset:
Instantaneous Power Theory Revisited with Classical Mechanics
Authors: Federico Milano, Georgios Tzounas, Ioannis Dassios
Date: 2024-01-26 00:00:00

The paper revisits the concepts of instantaneous active and reactive powers and provides a novel definition for basic circuit elements based on quantities utilized in classical mechanics, such as absolute and relative velocity, momentum density, angular momentum and apparent forces. The discussion leverages from recent publications by the authors that interpret the voltage and current as velocities in generalized Lagrangian coordinates. The main result of the paper is a general and compact expression for the instantaneous active and reactive power of inductances, capacitances and resistances as a multivector proportional to the generalized kinetic energy and the geometric frequency multivector. Several numerical examples considering stationary and transient sinusoidal and non-sinusoidal conditions are discussed in the case study.
A novel ANROA based control approach for grid-tied multi-functional solar energy conversion system
Authors: Dinanath Prasad, Narendra Kumar, Rakhi Sharma, Hasmat Malik, Fausto Pedro García Márquez, Jesús María Pinar Pérez
Date: 2024-01-26 00:00:00
Tasks: Unity

An adaptive control approach for a three-phase grid-interfaced solar photovoltaic system based on the new Neuro-Fuzzy Inference System with Rain Optimization Algorithm (ANROA) methodology is proposed and discussed in this manuscript. This method incorporates an Adaptive Neuro-fuzzy Inference System (ANFIS) with a Rain Optimization Algorithm (ROA). The ANFIS controller has excellent maximum tracking capability because it includes features of both neural and fuzzy techniques. The ROA technique is in charge of controlling the voltage source converter switching. Avoiding power quality problems including voltage fluctuations, harmonics, and flickers as well as unbalanced loads and reactive power usage is the major goal. Besides, the proposed method performs at zero voltage regulation and unity power factor modes. The suggested control approach has been modeled and simulated, and its performance has been assessed using existing alternative methods. A statistical analysis of proposed and existing techniques has been also presented and discussed. The results of the simulations demonstrate that, when compared to alternative approaches, the suggested strategy may properly and effectively identify the best global solutions. Furthermore, the system's robustness has been studied by using MATLAB/SIMULINK environment and experimentally by Field Programmable Gate Arrays Controller (FPGA)-based Hardware-in-Loop (HLL).
Emergence of cooperation under punishment: A reinforcement learning perspective
Authors: Chenyang Zhao, Guozhong Zheng, Chun Zhang, Jiqiang Zhang, Li Chen
Date: 2024-01-29 00:00:00
Tasks: Imitation Learning, Q-Learning, reinforcement-learning, Reinforcement Learning

Punishment is a common tactic to sustain cooperation and has been extensively studied for a long time. While most of previous game-theoretic work adopt the imitation learning where players imitate the strategies who are better off, the learning logic in the real world is often much more complex. In this work, we turn to the reinforcement learning paradigm, where individuals make their decisions based upon their past experience and long-term returns. Specifically, we investigate the Prisoners' dilemma game with Q-learning algorithm, and cooperators probabilistically pose punishment on defectors in their neighborhood. Interestingly, we find that punishment could lead to either continuous or discontinuous cooperation phase transitions, and the nucleation process of cooperation clusters is reminiscent of the liquid-gas transition. The uncovered first-order phase transition indicates that great care needs to be taken when implementing the punishment compared to the continuous scenario.
The dynamics of casual groups can keep free-riders at bay
Authors: José F. Fontanari, Mauro Santos
Date: 2024-01-28 00:00:00

Understanding the conditions for maintaining cooperation in groups of unrelated individuals despite the presence of non-cooperative members is a major research topic in contemporary biological, sociological, and economic theory. The $N$-person snowdrift game models the type of social dilemma where cooperative actions are costly, but there is a reward for performing them. We study this game in a scenario where players move between play groups following the casual group dynamics, where groups grow by recruiting isolates and shrink by losing individuals who then become isolates. This describes the size distribution of spontaneous human groups and also the formation of sleeping groups in monkeys. We consider three scenarios according to the probability of isolates joining a group. We find that for appropriate choices of the cost-benefit ratio of cooperation and the aggregation-disaggregation ratio in the formation of casual groups, free-riders can be completely eliminated from the population. If individuals are more attracted to large groups, we find that cooperators persist in the population even when the mean group size diverges. We also point out the remarkable similarity between the replicator equation approach to public goods games and the trait group formulation of structured demes.
Choroidal thinning assessment through facial video analysis
Authors: Qinghua He, Yi Zhang, Mengxi Shen, Giovanni Gregori, Philip J. Rosenfeld, Ruikang K. Wang
Date: 2024-01-29 00:00:00
Tasks: Diagnostic

Different features of skin are associated with various medical conditions and provide opportunities to evaluate and monitor body health. This study created a strategy to assess choroidal thinning through the video analysis of facial skin. Videos capturing the entire facial skin were collected from 48 participants with age-related macular degeneration (AMD) and 12 healthy individuals. These facial videos were analyzed using video-based trans-angiosomes imaging photoplethysmography (TaiPPG) to generate facial imaging biomarkers that were correlated with choroidal thickness (CT) measurements. The CT of all patients was determined using swept-source optical coherence tomography (SS-OCT). The results revealed the relationship between relative blood pulsation amplitude (BPA) in three typical facial angiosomes (cheek, side-forehead and mid-forehead) and the average macular CT (r = 0.48, p < 0.001; r = -0.56, p < 0.001; r = -0.40, p < 0.01). When considering a diagnostic threshold of 200{\mu}m, the newly developed facial video analysis tool effectively distinguished between cases of choroidal thinning and normal cases, yielding areas under the curve of 0.75, 0.79 and 0.69. These findings shed light on the connection between choroidal blood flow and facial skin hemodynamics, which suggests the potential for predicting vascular diseases through widely accessible skin imaging data.
Limited data on infectious disease distribution exposes ambiguity in epidemic modeling choices
Authors: Laura Di Domenico, Eugenio Valdano, Vittoria Colizza
Date: 2024-01-26 00:00:00

Traditional disease transmission models assume that the infectious period is exponentially distributed with a recovery rate fixed in time and across individuals. This assumption provides analytical and computational advantages, however it is often unrealistic. Efforts in modeling non-exponentially distributed infectious periods are either limited to special cases or lead to unsolvable models. Also, the link between empirical data (infectious period distribution) and the modeling needs (corresponding recovery rates) lacks a clear understanding. Here we introduce a mapping of an arbitrary distribution of infectious periods into a distribution of recovery rates. We show that the same infectious period distribution at the population level can be reproduced by two modeling schemes -- host-based and population-based -- depending on the individual response to the infection, and aggregated empirical data cannot easily discriminate the correct scheme. Besides being conceptually different, the two schemes also lead to different epidemic trajectories. Although sharing the same behavior close to the disease-free equilibrium, the host-based scheme deviates from the expected epidemic when reaching the endemic equilibrium of an SIS transmission model, while the population-based scheme turns out to be equivalent to assuming a homogeneous recovery rate. We show this through analytical computations and stochastic epidemic simulations on a contact network, using both generative network models and empirical contact data. It is therefore possible to reproduce heterogeneous infectious periods in network-based transmission models, however the resulting prevalence is sensitive to the modeling choice for the interpretation of the empirically collected data on infection duration. In absence of higher resolution data, studies should acknowledge such deviations in the epidemic predictions.
Partial Identification of Binary Choice Models with Misreported Outcomes
Authors: Orville Mondal, Rui Wang
Date: 2024-01-30 00:00:00

This paper provides partial identification of various binary choice models with misreported dependent variables. We propose two distinct approaches by exploiting different instrumental variables respectively. In the first approach, the instrument is assumed to only affect the true dependent variable but not misreporting probabilities. The second approach uses an instrument that influences misreporting probabilities monotonically while having no effect on the true dependent variable. Moreover, we derive identification results under additional restrictions on misreporting, including bounded/monotone misreporting probabilities. We use simulations to demonstrate the robust performance of our approaches, and apply the method to study educational attainment.
Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity
Authors: Yike Wang, Chris Gu, Taisuke Otsu
Date: 2024-01-29 00:00:00
Tasks: Causal Inference, counterfactual

This paper presents a novel application of graph neural networks for modeling and estimating network heterogeneity. Network heterogeneity is characterized by variations in unit's decisions or outcomes that depend not only on its own attributes but also on the conditions of its surrounding neighborhood. We delineate the convergence rate of the graph neural networks estimator, as well as its applicability in semiparametric causal inference with heterogeneous treatment effects. The finite-sample performance of our estimator is evaluated through Monte Carlo simulations. In an empirical setting related to microfinance program participation, we apply the new estimator to examine the average treatment effects and outcomes of counterfactual policies, and to propose an enhanced strategy for selecting the initial recipients of program information in social networks.
csranks: An R Package for Estimation and Inference Involving Ranks
Authors: Denis Chetverikov, Magne Mogstad, Pawel Morgen, Joseph Romano, Azeem Shaikh, Daniel Wilhelm
Date: 2024-01-26 00:00:00

This article introduces the R package csranks for estimation and inference involving ranks. First, we review methods for the construction of confidence sets for ranks, namely marginal and simultaneous confidence sets as well as confidence sets for the identities of the tau-best. Second, we review methods for estimation and inference in regressions involving ranks. Third, we describe the implementation of these methods in csranks and illustrate their usefulness in two examples: one about the quantification of uncertainty in the PISA ranking of countries and one about the measurement of intergenerational mobility using rank-rank regressions.
Determinants of well-being
Authors: Cristina Pereira, Hermínia Gonçalves, Teresa Sequeira
Date: 2024-01-30 00:00:00

Traditionally, European social policies have focused on material well-being and social justice, neglecting subjective indicators. This review systematically examines the scientific understanding of well-being, its indicators, and its relationship with governance. It suggests that political systems and institutions significantly impact well-being, and that subjective indicators should be incorporated into public policy decisions. The findings advocate for a more holistic approach to well-being measurement, encompassing both objective and subjective dimensions.
Enhancing Urban Traffic Safety: An Evaluation on Taipei's Neighborhood Traffic Environment Improvement Program
Authors: Frank Y. Huang, Po-Chun Huang
Date: 2024-01-30 00:00:00

In densely populated urban areas, where interactions between pedestrians, vehicles, and motorcycles are frequent and complex, traffic safety is a critical concern. This paper evaluates the Neighborhood Traffic Environment Improvement Program in Taipei, which involved painting green pedestrian paths, adjusting no-parking red/yellow lines, and painting speed limit and stop/slow signs on lanes and alleys. Exploiting staggered rollout of policy implementation and administrative traffic accident data, we found that the program reduced daytime traffic accidents by 5 percent and injuries by 8 percent, while having no significant impact on nighttime incidents. The effectiveness of the program during the day is mainly attributed to the painted green sidewalks, with adequate sunlight playing a part in the program's success. Our findings indicate that cost-effective strategies like green pedestrian lanes can be effective in areas with dense populations and high motorcycle traffic, as they improve safety by encouraging pedestrians to use marked areas and deterring vehicles from these zones.
Does green innovation crowd out other innovation of firms? Based on the extended CDM model and unconditional quantile regressions
Authors: Yi Jiang, Richard S. J. Tol
Date: 2024-01-29 00:00:00
Tasks: quantile regression

In the era of sustainability, firms grapple with the decision of how much to invest in green innovation and how it influences their economic trajectory. This study employs the Crepon, Duguet, and Mairesse (CDM) framework to examine the conversion of R&D funds into patents and their impact on productivity, effectively addressing endogeneity by utilizing predicted dependent variables at each stage to exclude unobservable factors. Extending the classical CDM model, this study contrasts green and non-green innovations' economic effects. The results show non-green patents predominantly drive productivity gains, while green patents have a limited impact in non-heavy polluting firms. However, in high-pollution and manufacturing sectors, both innovation types equally enhance productivity. Using unconditional quantile regression, I found green innovation's productivity impact follows an inverse U-shape, unlike the U-shaped pattern of non-green innovation. Significantly, in the 50th to 80th productivity percentiles of manufacturing and high-pollution firms, green innovation not only contributes to environmental sustainability but also outperforms non-green innovation economically.
The WTP-WTA Gap for Public Goods: New Insights from Compensating and Equivalent Variation Closed-Form Solutions
Authors: Daniel H. Karney, Khyati Malik
Date: 2024-01-27 00:00:00
Tasks: Form

This study finds exact closed-form solutions for compensating variation (CV) and equivalent variation (EV) for both marginal and non-marginal changes in public goods given homothetic utility. The parameters for these solutions are recoverable from observable data in empirical applications as a single sufficient statistic summarizes consumer preferences. The closed-form CV and EV expressions identify three economic mechanisms that determine the magnitudes of CV and EV. One of these mechanisms, the relative preference effect, helps explain the disparity between willingness to pay (WTP) and willingness to accept (WTA) for public goods.
Robust Performance Evaluation of Independent and Identical Agents
Authors: Ashwin Kambhampati
Date: 2024-01-29 00:00:00

A principal provides nondiscriminatory incentives for independent and identical agents. The principal cannot observe the agents' actions, nor does she know the entire set of actions available to them. It is shown, very generally, that any worst-case optimal contract is nonaffine in performances. In addition, each agent's pay must depend on the performance of another. In the case of two agents and binary output, existence of a worst-case optimal contract is established and it is proven that any such contract exhibits joint performance evaluation -- each agent's pay is strictly increasing in the performance of the other. The analysis identifies a fundamentally new channel leading to the optimality of nonlinear team-based incentive pay.
A mathematical theory of power
Authors: Daniele De Luca
Date: 2024-01-29 00:00:00

This paper proposes a new approach to power in Game Theory. Cooperation and conflict are simulated with a mechanism of payoff alteration, called F-game. Using convex combinations of preferences, an F-game can measure players' attitude to cooperate. We can then define actual and potential power as special relations between different states of the system.
X-ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case Study
Authors: Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, K. Joost Batenburg
Date: 2024-01-30 00:00:00
Tasks: Image Generation

X-ray imaging can be efficiently used for high-throughput in-line inspection of industrial products. However, designing a system that satisfies industrial requirements and achieves high accuracy is a challenging problem. The effect of many system settings is application-specific and difficult to predict in advance. Consequently, the system is often configured using empirical rules and visual observations. The performance of the resulting system is characterized by extensive experimental testing. We propose to use computational methods to substitute real measurements with generated images corresponding to the same experimental settings. With this approach, it is possible to observe the influence of experimental settings on a large amount of data and to make a prediction of the system performance faster than with conventional methods. We argue that a high accuracy of the image generator may be unnecessary for an accurate performance prediction. We propose a quantitative methodology to characterize the quality of the generation model using POD curves. The proposed approach can be adapted to various applications and we demonstrate it on the poultry inspection problem. We show how a calibrated image generation model can be used to quantitatively evaluate the effect of the X-ray exposure time on the performance of the inspection system.
A Point Cloud Enhancement Method for 4D mmWave Radar Imagery
Authors: Qingmian Wan, Hongli Peng, Xing Liao, Kuayue Liu, Junfa Mao
Date: 2024-01-30 00:00:00

A point cloud enhancement method for 4D mmWave radar imagery is proposed in this paper. Based on the patch antenna and MIMO array theories, the MIMO array with small redundancy and high SNR is designed to provide the probability of high angular resolution and detection rate. The antenna array is deployed using a ladder shape in vertical direction to decrease the redundancy and improve the resolution in horizontal direction with the constrains of physical factors. Considering the complicated environment of the real world with non-uniform distributed clutters, the dynamic detection method is used to solve the weak target sensing problem. The window size of CFAR detector is assumed variant to be determined using optimization method, making it adaptive to different environments especially when weak targets exist. The angular resolution increase using FT-based DOA method and the designed antenna array is described, which provides the basis of accurate detection and dense point cloud. To verify the performance of the proposed method, experiments of simulations and practical measurements are carried out, whose results show that the accuracy and the point cloud density are improved with comparison of the original manufacturer mmWave radar of TI AWR2243.
Assessment of the area measurement on Cartosat-1 image
Authors: Joanna Pluto-Kossakowska, David Grandgirard, Rafal Zielinski, Simon Kay
Date: 2024-01-29 00:00:00

The goal of this study was the evaluation of agriculture parcel area measurement accuracy on Cartosat-1 imagery, and the determination of the technical tolerance appropriate for measurement using photointerpretation techniques. A further objective was to find out the influence of image type, land cover or parcel size on the area measurement variability. In our experiment, five independent operators measured 185 parcels, 3 times, on each image. Next, the buffer width, calculated as the difference between measured and reference parcel area, was derived and was the subject of statistical analysis. Prior to verifying the normality of the buffer widths, a detection of anomalous measurements is recommended. This detection of outliers within each group of observations (i.e. parcels) was made using the Jacknife distance test on each type of imagery (Cartosat Aft, Cartosat Fore). Then, the General Linear Model procedure to identify major significant effects and interactions was followed by analysis of variance to ease the interpretation of the variability observed of the area measurement. Finally, two different parameters, reproducibility limit and critical difference, were calculated to make comparison with other sensors like digital aerial orthophoto in this study possible. The repeatability limits gave the acceptability difference between two operators when measuring the same parcel. For orthophoto this value reached 2.86m, on Cartosat-1 5.17m and 8.76m for Aft and Fore image respectively.
Moment-based metrics for molecules computable from cryo-EM images
Authors: Andy Zhang, Oscar Mickelin, Joe Kileel, Eric J. Verbeke, Nicholas F. Marshall, Marc Aurèle Gilles, Amit Singer
Date: 2024-01-26 00:00:00
Tasks: Cryogenic Electron Microscopy (cryo-EM)

Single particle cryogenic electron microscopy (cryo-EM) is an imaging technique capable of recovering the high-resolution 3-D structure of biological macromolecules from many noisy and randomly oriented projection images. One notable approach to 3-D reconstruction, known as Kam's method, relies on the moments of the 2-D images. Inspired by Kam's method, we introduce a rotationally invariant metric between two molecular structures, which does not require 3-D alignment. Further, we introduce a metric between a stack of projection images and a molecular structure, which is invariant to rotations and reflections and does not require performing 3-D reconstruction. Additionally, the latter metric does not assume a uniform distribution of viewing angles. We demonstrate uses of the new metrics on synthetic and experimental datasets, highlighting their ability to measure structural similarity.
Channel Characterization of UAV-RIS-aided Systems with Adaptive Phase-shift Configuration
Authors: Thanh Luan Nguyen, Georges Kaddoum, Tri Nhu Do, Zygmunt J. Haas
Date: 2024-01-30 00:00:00

This letter considers a UAV aiding communication between a ground transmitter and a ground receiver in the presence of co-channel interference. A discrete-time Markov process is adopted to model the complex nature of the Air-to-Ground (A2G) channel, including the occurrence of Line-of-Sight, Non-Line-of-Sight, and blockage events. Moreover, an adaptive phase-shift-enabled Reconfigurable Intelligent Surface (RIS) is deployed to combat A2G blockage events. Novel frameworks based on the shadowed Rician distribution are proposed to derive closed-form expressions for Ground-to-Air/A2G SINR' distributions. Numerical results show that RISs with large numbers of elements, e.g., 256 RIS elements, improve end-to-end Outage Probability (OP) and reduce blockages.