pwc-archive/papers-with-abstracts huggingface dataset:
Y-Configuration Active Bridge (YAB) Converter: A DAB-Type Single-Stage Isolated Three-Phase AC-DC Converter with Simple Sinusoidal Control
Authors: Mafu Zhang, Huanghaohe Zou, Saleh Farzamkia, Zibo Chen, Chen Chen, Alex Q. Huang
Date: 2024-10-28 00:00:00

This paper reviews commonly used three-phase isolated AC-DC converters and introduces a novel Y-configuration Active Bridge (YAB) converter for single-stage isolated AC-DC power conversion. The proposed YAB addresses the limitations of multi-stage designs by eliminating bulky electrolytic capacitor banks and input boost inductors, thereby enabling a simplified start-up process without the risk of inrush current. It retains the advantages of single-stage AC-DC Dual Active Bridge (AC-DC DAB) converters while significantly reducing control complexity. Across its entire operating range, the YAB achieves low total harmonic distortion, maintains relatively low current stress, and exhibits excellent soft-switching performance. The operating principle of the proposed converter is detailed, and an equivalent circuit model is presented. System performance is evaluated using a numerical Fast Fourier Transform (FFT) model. To validate the performance of the proposed converter, a three-phase 6 kW, 480 V YAB prototype is designed and tested in the laboratory. Experimental results demonstrate a maximum efficiency of 97.1\%.
Lecture Notes on Grid Modeling of Renewable Energy
Authors: Sohail Khan
Date: 2024-10-26 00:00:00

These lecture notes provide a comprehensive guide on Grid Modeling of Renewable Energy, offering a foundational overview of power system network modeling, power flow, and load flow algorithms critical for electrical and renewable energy engineering. Key topics include steady-state, dynamic, and frequency domain models, with a particular focus on renewable energy integration, simulation techniques, and their effects on grid stability and power quality. Practical examples using Matpower and Pandapower tools are included to reinforce concepts, ensuring that students gain hands-on experience in modeling and analyzing modern energy systems under variable conditions.
Spatio-temporal activity patterns induced by triadic interactions in an in silico neural medium
Authors: Ana P. Millán, Hanlin Sun, Joaquín J. Torres
Date: 2024-10-30 00:00:00

Triadic interactions are general mechanisms by which a node or neuron can regulate directly the link or synapse between other two neurons. The regulation takes place in a familiar way by either depressing or facilitating synaptic transmission. Such interactions are ubiquitous in neural systems, accounting for axo-axonic synapses and tripartite synapses mediated by astrocytes, for instance, and have been related to neuronal and synaptic processes at different time-scales, including short and long-term synaptic plasticity. In the field of network science, triadic interactions have been shown to produce complex spatio-temporal patterns of connectivity. Here, we investigate the emergent behavior of an in silico neural medium constituted by a population of leaky integrate-and-fire neurons with triadic interactions. We observe that, depending on relevant parameters defining triadic interactions, different activity patterns emerge. These include i) a silent phase, ii) a low-activity phase in which complex spatio-temporal patterns of low neuronal firing rate emerge that propagate through the medium, iii) a high-activity phase characterized by complex spatio-temporal patterns of high neuronal firing rate that propagate through the neural medium as waves of high firing activity over a bulk of low activity neurons, and iv) a pseudo-blinking phase in which the neural medium switches between high and low activity states, in a similar fashion to up/down state transitions. Here we analyse in depth the features of such patterns and relate our findings to the recently proposed model of triadic percolation.
MatExpert: Decomposing Materials Discovery by Mimicking Human Experts
Authors: Qianggang Ding, Santiago Miret, Bang Liu
Date: 2024-10-26 00:00:00
Tasks: Contrastive Learning, Retrieval

Material discovery is a critical research area with profound implications for various industries. In this work, we introduce MatExpert, a novel framework that leverages Large Language Models (LLMs) and contrastive learning to accelerate the discovery and design of new solid-state materials. Inspired by the workflow of human materials design experts, our approach integrates three key stages: retrieval, transition, and generation. First, in the retrieval stage, MatExpert identifies an existing material that closely matches the desired criteria. Second, in the transition stage, MatExpert outlines the necessary modifications to transform this material formulation to meet specific requirements outlined by the initial user query. Third, in the generation state, MatExpert performs detailed computations and structural generation to create new materials based on the provided information. Our experimental results demonstrate that MatExpert outperforms state-of-the-art methods in material generation tasks, achieving superior performance across various metrics including validity, distribution, and stability. As such, MatExpert represents a meaningful advancement in computational material discovery using langauge-based generative models.
Spatial moment dynamics and biomass density equations provide complementary, yet limited, descriptions of pattern formation in individual-based simulations
Authors: Anudeep Surendran, David Pinto-Ramos, Rafael Menezes, Ricardo Martinez-Garcia
Date: 2024-10-30 00:00:00

Spatial patterning is common in ecological systems and has been extensively studied via different modeling approaches. Individual-based models (IBMs) accurately describe nonlinear interactions at the organism level and the stochastic spatial dynamics that drives pattern formation, but their computational cost scales quickly with system complexity, limiting their practical use. Population-level approximations such as spatial moment dynamics (SMD) -- which describe the moments of organism distributions -- and coarse-grained biomass density models have been developed to address this limitation. However, the extent to which these approximated descriptions accurately capture the spatial patterns and population sizes emerging from individual-level simulations remains an open question. We investigate this issue considering a prototypical population dynamics IBM with long-range dispersal and intraspecific competition, for which we derive both its SMD and coarse-grained density approximations. We systematically compare the performance of these two approximations at predicting IBM population abundances and spatial patterns. Our results highlight that SMD and density-based approximations complement each other by correctly capturing these two population features within different parameter regimes. Importantly, we identify regions of the parameter space in which neither approximation performed well, which should encourage the development of more refined IBM approximation approaches.
CloudCast -- Total Cloud Cover Nowcasting with Machine Learning
Authors: Mikko Partio, Leila Hieta, Anniina Kokkonen
Date: 2024-10-27 00:00:00
Tasks: Optical Flow Estimation

Cloud cover plays a critical role in weather prediction and impacts several sectors, including agriculture, solar power generation, and aviation. Despite advancements in numerical weather prediction (NWP) models, forecasting total cloud cover remains challenging due to the small-scale nature of cloud formation processes. In this study, we introduce CloudCast, a convolutional neural network (CNN) based on the U-Net architecture, designed to predict total cloud cover (TCC) up to five hours ahead. Trained on five years of satellite data, CloudCast significantly outperforms traditional NWP models and optical flow methods. Compared to a reference NWP model, CloudCast achieves a 24% lower mean absolute error and reduces multi-category prediction errors by 46%. The model demonstrates strong performance, particularly in capturing the large-scale structure of cloud cover in the first few forecast hours, though later predictions are subject to blurring and underestimation of cloud formation. An ablation study identified the optimal input features and loss functions, with MAE-based models performing the best. CloudCast has been integrated into the Finnish Meteorological Institute's operational nowcasting system, where it improves cloud cover forecasts used by public and private sector clients. While CloudCast is limited by a relatively short skillful lead time of about three hours, future work aims to extend this through more complex network architectures and higher-resolution data. CloudCast code is available at https://github.com/fmidev/cloudcast.
Single cells can resolve graded stimuli
Authors: Mirna Kramar, Lauritz Hahn, Aleksandra M Walczak, Thierry Mora, Mathieu Coppey
Date: 2024-10-29 00:00:00

Cells use signalling pathways as windows into the environment to gather information, transduce it into their interior, and use it to drive behaviours. MAPK (ERK) is a highly conserved signalling pathway in eukaryotes, directing multiple fundamental cellular behaviours such as proliferation, migration, and differentiation, making it of few central hubs in the signalling circuitry of cells. Despite this versatility of behaviors, population-level measurements have reported low information content (\textless 1 bit) relayed through the ERK pathway, rendering the population barely able to distinguish the presence or absence of stimuli. Here, we contrast the information transmitted by a single cell and a population of cells. Using a combination of optogenetic experiments, data analysis based on information theory framework, and numerical simulations we quantify the amount of information transduced from the receptor to ERK, from responses to singular, brief and sparse input pulses. We show that single cells are indeed able to resolve between graded stimuli, yielding over 2 bit of information, however showing a large population heterogeneity.
Hybrid model of the kernel method for quantum computers
Authors: Jhordan Silveira de Borba, Jonas Maziero
Date: 2024-10-29 00:00:00
Tasks: Quantum Machine Learning

The field of quantum machine learning is a promising way to lead to a revolution in intelligent data processing methods. In this way, a hybrid learning method based on classic kernel methods is proposed. This proposal also requires the development of a quantum algorithm for the calculation of internal products between vectors of continuous values. In order for this to be possible, it was necessary to make adaptations to the classic kernel method, since it is necessary to consider the limitations imposed by the Hilbert space of the quantum processor. As a test case, we applied this new algorithm to learn to classify whether new points generated randomly, in a finite square located under a plane, were found inside or outside a circle located inside this square. It was found that the algorithm was able to correctly detect new points in 99% of the samples tested, with a small difference due to considering the radius slightly larger than the ideal. However, the kernel method was able to perform classifications correctly, as well as the internal product algorithm successfully performed the internal product calculations using quantum resources. Thus, the present work represents a contribution to the area, proposing a new model of machine learning accessible to both physicists and computer scientists.
Estimation and Inference in Dyadic Network Formation Models with Nontransferable Utilities
Authors: Ming Li, Zhentao Shi, Yapeng Zheng
Date: 2024-10-31 00:00:00

This paper studies estimation and inference in a dyadic network formation model with observed covariates, unobserved heterogeneity, and nontransferable utilities. With the presence of the high dimensional fixed effects, the maximum likelihood estimator is numerically difficult to compute and suffers from the incidental parameter bias. We propose an easy-to-compute one-step estimator for the homophily parameter of interest, which is further refined to achieve $\sqrt{N}$-consistency via split-network jackknife and efficiency by the bootstrap aggregating (bagging) technique. We establish consistency for the estimator of the fixed effects and prove asymptotic normality for the unconditional average partial effects. Simulation studies show that our method works well with finite samples, and an empirical application using the risk-sharing data from Nyakatoke highlights the importance of employing proper statistical inferential procedures.
Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
Authors: Facundo Argañaraz, Juan Carlos Escanciano
Date: 2024-10-31 00:00:00

Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE). Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012). We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined.
Moments by Integrating the Moment-Generating Function
Authors: Peter Reinhard Hansen, Chen Tong
Date: 2024-10-31 00:00:00

We introduce a novel method for obtaining a wide variety of moments of a random variable with a well-defined moment-generating function (MGF). We derive new expressions for fractional moments and fractional absolute moments, both central and non-central moments. The new moment expressions are relatively simple integrals that involve the MGF, but do not require its derivatives. We label the new method CMGF because it uses a complex extension of the MGF and can be used to obtain complex moments. We illustrate the new method with three applications where the MGF is available in closed-form, while the corresponding densities and the derivatives of the MGF are either unavailable or very difficult to obtain.
Forecasting Political Stability in GCC Countries
Authors: Mahdi Goldani
Date: 2024-10-28 00:00:00
Tasks: feature selection

Political stability is crucial for the socioeconomic development of nations, particularly in geopolitically sensitive regions such as the Gulf Cooperation Council Countries, Saudi Arabia, UAE, Kuwait, Qatar, Oman, and Bahrain. This study focuses on predicting the political stability index for these six countries using machine learning techniques. The study uses data from the World Banks comprehensive dataset, comprising 266 indicators covering economic, political, social, and environmental factors. Employing the Edit Distance on Real Sequence method for feature selection and XGBoost for model training, the study forecasts political stability trends for the next five years. The model achieves high accuracy, with mean absolute percentage error values under 10, indicating reliable predictions. The forecasts suggest that Oman, the UAE, and Qatar will experience relatively stable political conditions, while Saudi Arabia and Bahrain may continue to face negative political stability indices. The findings underscore the significance of economic factors such as GDP and foreign investment, along with variables related to military expenditure and international tourism, as key predictors of political stability. These results provide valuable insights for policymakers, enabling proactive measures to enhance governance and mitigate potential risks.
Economic Diversification and Social Progress in the GCC Countries: A Study on the Transition from Oil-Dependency to Knowledge-Based Economies
Authors: Mahdi Goldani, Soraya Asadi Tirvan
Date: 2024-10-28 00:00:00
Tasks: feature selection

The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GCC) countries in achieving economic diversification and social development, focusing on the Social Progress Index (SPI), which provides a broader measure of societal well-being beyond just economic growth. Using data from the World Bank, covering 2010 to 2023, the study employs the XGBoost machine learning model to forecast SPI values for the period of 2024 to 2026. Key components of the methodology include data preprocessing, feature selection, and the simulation of independent variables through ARIMA modeling. The results highlight significant improvements in education, healthcare, and women's rights, contributing to enhanced SPI performance across the GCC countries. However, notable challenges persist in areas like personal rights and inclusivity. The study further indicates that despite economic setbacks caused by global disruptions, including the COVID-19 pandemic and oil price volatility, GCC nations are expected to see steady improvements in their SPI scores through 2027. These findings underscore the critical importance of economic diversification, investment in human capital, and ongoing social reforms to reduce dependence on hydrocarbons and build knowledge-driven economies. This research offers valuable insights for policymakers aiming to strengthen both social and economic resilience in the region while advancing long-term sustainable development goals.
Job Loss and Political Entry
Authors: Laura Barros, Aiko Schmeißer
Date: 2024-10-31 00:00:00
Tasks: Causal Identification

The supply of politicians affects the quality of democratic institutions. Yet little is known about the economic motivations that drive individuals into politics. This paper examines how experiencing a job loss affects individuals' decisions to enter political life and explores its implications for political selection. Using highly granular administrative data linking individual records of political participation with comprehensive employer-employee data for all formal workers in Brazil, and leveraging mass layoffs for causal identification, we find that job loss significantly increases the likelihood of joining a political party and running for local office. Layoff-induced candidates are positively selected on various competence measures, suggesting that economic shocks improve the quality of political entrants. Further, we observe that the increase in candidacies is more pronounced among laid-off individuals with greater financial incentives from holding office and higher predicted income losses. Finally, using a regression discontinuity design, we find that eligibility for unemployment benefits increases the likelihood of becoming a party member and running for local office. These results are consistent with the reduction in private-sector opportunity costs and the increased time resources explaining the rise in political entry.
From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies
Authors: Federica Mascolo, Nora Bearth, Fabian Muny, Michael Lechner, Jana Mareckova
Date: 2024-10-30 00:00:00

Active labor market policies are widely used by the Swiss government, enrolling over half of all unemployed individuals. This paper evaluates the effectiveness of Swiss programs in improving employment and earnings outcomes using causal machine learning and rich administrative data on unemployed individuals in 2014 and 2015, including detailed labor market histories and other covariates. The findings for Swiss citizens and immigrants with permanent residency indicate a small positive average effect of a Temporary Wage Subsidy program on employment and earnings in the third year after program start. In contrast, Basic Courses, such as job application training, exhibit negative effects on both outcomes over the same period. No significant impacts are found for Employment Programs conducted outside the regular labor market or for Training Courses such as language or computer classes. The programs are most effective for individuals with a non-EU migration background, while Temporary Wage Subsidies also benefit those with lower educational attainment. Finally, shallow policy trees provide practical guidance for improving the targeting of program assignments.
Price Regulation, Technology and Provider Redistribution: Insights from Parity Laws
Authors: Piyush Akimitsu
Date: 2024-10-30 00:00:00

Do price regulations lead to inefficiencies and trade loss? The answer depends on the type of regulation, monetary and non-monetary factors influencing demand, technological factors affecting supply elasticities, difference between pre-regulation and expected post-regulation prices, and geographical area. State-level variations in telehealth parity laws provide a unique opportunity to study the effects of Price Controls and Cost Controls on healthcare service quantity, proxied by physician count, across metro and non-metro areas, with broadband as a technological mediator. At the micro level, Price Controls distort the input mix, causing production inefficiency and rotating the supply curve. Cost Controls change the consumption mix, causing consumption inefficiency and rotating the demand curve. This results in equilibrium quantity shifts causing allocative inefficiency in healthcare provision, signified by physician redistribution at the macro level. The micro-founded non-price competition models predicts shifts from unregulated to regulated equilibrium quantities under various combinations of Price Control and Cost Control types. Empirical analysis supports these predictions, showing that a Price Ceiling or Price Floor positively affects physician count in metro areas but negatively in non-metro areas. Conversely, Cost Parity has a negative effect in metro areas, while a Cost Ceiling positively impacts physician count in both regions. When examining combinations of different types of Price and Cost Controls, Cost Parity tends to dominate with a net negative effect, whereas a Cost Ceiling amplifies the positive effect of a Price Ceiling. The findings challenge the conventional narratives and underscore the role of regional disparities and technology in shaping policy outcomes.