pwc-archive/papers-with-abstracts huggingface dataset:
Evaluation of Linear Implicit Quantized State System method for analyzing mission performance of power systems
Authors: Navid Gholizadeh, Joseph M. Hood, Roger Dougal
Date: 2023-02-18 00:00:00
Tasks: Computational Efficiency, Quantization

The Linear Implicit Quantized State System (LIQSS) method has been evaluated for suitability in modeling and simulation of long-duration mission profiles of Naval power systems which are typically characterized by stiff, nonlinear, differential algebraic equations. A reference electromechanical system consists of an electric machine connected to a torque source on the shaft end and to an electric grid at its electrical terminals. The system is highly non-linear and has widely varying rate constants; at a typical steady state operating point, the electrical and electromechanical time constants differ by three orders of magnitude being 3.2 ms and 2.7 s respectively. Two important characteristics of the simulation, accuracy, and computational intensity both depend on the quantization size of the system state variables. At a quantization size of about 1 percent of a maximum value of a variable, results from the LIQSS1 method differed by less than 1 percent from results computed by well-known continuous system state space methods. The computational efficiency of the LIQSS1 method increased logarithmically with increasing quantization size, without significant loss of accuracy, up to some particular quantization size, beyond which the error increased rapidly. For the particular system under study, a sweet spot was found at a particular quantum size that yielded both high computational efficiency and good accuracy.
High Accuracy Distributed Kalman Filtering for Frequency and Phase Synchronization in Distributed Phased Arrays
Authors: Mohammed Rashid, Jeffrey A. Nanzer
Date: 2023-02-18 00:00:00

Precise frequency and phase synchronization are among the important aspects in a coherent distributed phased array antenna system, and are among the most challenging to achieve for microwave frequencies and above. We propose a high accuracy distributed Kalman filter (HA-DKF) in which the nodes in the array perform consensus averaging on their locally shared measurements, the innovation noise covariances, and the KF-predicted as well as the KF-updated estimates and error covariances, yielding improved electrical state synchronization compared to other works in the literature. We evaluate the algorithm through simulation and show that it converges in fewer iterations and yields better electrical state synchronization than other algorithms.
Semi-Analytical Electromagnetic Transient Simulation Using Differential Transformation
Authors: Min Xiong, Rui Yao, Yang Liu, Kai Sun, Feng Qiu
Date: 2023-02-18 00:00:00
Tasks: Numerical Integration

For electromagnetic transient (EMT) simulation of a power system, a state-space-based approach needs to solve state-space EMT equations by using numerical integration methods, e.g., the Euler method, Runge-Kutta methods, and trapezoidal-rule method, at small time steps. The simulation can be slow on a power system having multiple generators. To speed up state-space-based EMT simulations, this paper proposes a Differential Transformation based semi-analytical method that repeatedly utilizes a high-order semi-analytical solution of the EMT equations at longer time steps. The proposed semi-analytical method is tested on the detailed EMT model of a four-generator two-area system. Simulation results show the significant potential of the proposed method to accelerate EMT simulations of power systems compared with traditional numerical methods.
Multi-timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets
Authors: Sirui Wu, Jin Lin, Jiarong Li, Feng Liu, Yonghua Song, Yanhui Xu, Xiang Cheng, Zhipeng Yu
Date: 2023-02-18 00:00:00

Renewable power to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on the electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified framework. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC\&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of an RePtA VPP joining multiple markets over conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor's flexibility.
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits
Authors: Babak Abdolmaleki, John W. Simpson-Porco, Gilbert Bergna-Diaz
Date: 2023-02-18 00:00:00
Tasks: Distributed Optimization

Reactive power sharing and containment of voltages within limits for inverter-based resources (IBRs) are two important, yet coupled objectives in ac networks. In this article, we propose a distributed control technique to simultaneously achieve these objectives. Our controller consists of two components: a purely local nonlinear integral controller which adjusts the IBR voltage setpoint, and a distributed primal-dual optimizer that coordinates reactive power sharing between the IBRs. The controller prioritizes the voltage containment objective over reactive power sharing at all points in time; excluding the IBRs with saturated voltages, it provides reactive power sharing among all the IBRs. Considering the voltage saturation and the coupling between voltage and angle dynamics, a formal closed-loop stability analysis based on singular perturbation theory is provided, yielding practical tuning guidance for the overall control system. To validate the effectiveness of the proposed controller for different case studies, we apply it to a low-voltage microgrid and a microgrid adapted from the CIGRE medium-voltage network benchmark, both simulated in the MATLAB/Simulink environment.
Pulse in collapse: a game dynamics experiment
Authors: Wang Yijia, Wang Zhijian
Date: 2023-02-18 00:00:00

The collapse process is a constitutional sub-process in the full finding Nash equilibrium process. We conducted laboratory game experiments with human subjects to study this process. We observed significant pulse signals in the collapse process. The observations from the data support the completeness and the consistency of the game dynamics paradigm.
Nash equilibrium selection by eigenvalue control
Authors: Wang Zhijian
Date: 2023-02-17 00:00:00

People choose their strategies through a trial-and-error learning process in which they gradually discover that some strategies work better than others. The process can be modelled as an evolutionary game dynamics system, which may be controllable. In modern control theory, eigenvalue (pole) assignment is a basic approach to designing a full-state feedback controller, which can influence the outcome of a game. This study shows that, in a game with two Nash equilibria, the long-running strategy distribution can be controlled by pole assignment. We illustrate a theoretical workflow to design and evaluate the controller. To our knowledge, this is the first realisation of the control of equilibrium selection by design in the game dynamics theory paradigm. We hope the controller can be verified in a laboratory human subject game experiment.
Structural Controllability to Unveil Hidden Regulation Mechanisms in Unfolded Protein Response: the Role of Network Models
Authors: Nicole Luchetti, Alessandro Loppini, Margherita Anna Grazia Matarrese, Letizia Chiodo, Simonetta Filippi
Date: 2023-02-18 00:00:00

The Unfolded Protein Response is the cell mechanism for maintaining the balance of properly folded proteins in the endoplasmic reticulum , the specialized cellular compartment. Although it is largely studied from a biological point of view, much of the literature lacks a quantitative analysis of such a central signaling pathway. In this work, we aim to fill this gap by applying structural controllability analysis of complex networks to several Unfolded Protein Response networks to identify crucial nodes in the signaling flow. In particular, we first build different network models of the Unfolded Protein Response mechanism, relying on data contained in various protein-protein interaction databases. Then, we identify the driver nodes, essential for overall network control, i.e., the key proteins on which external stimulation may be optimally delivered to control network behavior. Our structural controllability analysis results show that the driver nodes commonly identified across databases match with known endoplasmic reticulum stress sensors. This potentially confirms that the theoretically identified drivers correspond to the biological key proteins associated with fundamental cellular activities and diseases. In conclusion, we prove that structural controllability is a reliable quantitative tool to investigate biological signaling pathways, and it can be potentially applied to networks more complex and less explored than Unfolded Protein Response.
Probabilistic model for spatially acquiring optical links in space under influence of band-limited beam jitter
Authors: Gerald Hechenblaikner
Date: 2023-02-18 00:00:00

An analytical model is derived for the probability of failure (P-fail) to spatially acquire an optical link with a jittering search beam. The analytical model accounts for an arbitrary jitter spectrum and considers the associated correlations between jitter excursions on adjacent tracks of the search spiral. An expression of P-fail in terms of basic transcendental functions is found by linearizing the exact analytical model with respect to the correlation strength. Predictions from the models indicate a strong decrease of P-fail with increasing correlation-strength, which is found to be in excellent agreement to results from Monte Carlo simulations. The dependency of P-fail on track-width and scan speed is investigated, confirming previous assumptions on the impact of correlations. Expressions and applicable constraints are derived for the limits of full and no correlations, and the optimal track width to minimize the acquisition time is computed for a range of scan speeds. The model is applicable to optical terminals equipped with a fast beam steering mirror, as often found for optical communication missions in space.
Identification-robust inference for the LATE with high-dimensional covariates
Authors: Yukun Ma
Date: 2023-02-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper presents an inference method for the local average treatment effect (LATE) in the presence of high-dimensional covariates, irrespective of the strength of identification. We propose a novel high-dimensional conditional test statistic with uniformly correct asymptotic size. We provide an easy-to-implement algorithm to infer the high-dimensional LATE by inverting our test statistic and employing the double/debiased machine learning method. Simulations indicate that our test is robust against both weak identification and high dimensionality concerning size control and power performance, outperforming other conventional tests. Applying the proposed method to railroad and population data to study the effect of railroad access on urban population growth, we observe that our methodology yields confidence intervals that are 49% to 92% shorter than conventional results, depending on specifications.
Clustered Covariate Regression
Authors: Abdul-Nasah Soale, Emmanuel Selorm Tsyawo
Date: 2023-02-18 00:00:00
Tasks: regression

High covariate dimensionality is increasingly occurrent in model estimation, and existing techniques to address this issue typically require sparsity or discrete heterogeneity of the \emph{unobservable} parameter vector. However, neither restriction may be supported by economic theory in some empirical contexts, leading to severe bias and misleading inference. The clustering-based grouped parameter estimator (GPE) introduced in this paper drops both restrictions and maintains the natural one that the parameter support be bounded. GPE exhibits robust large sample properties under standard conditions and accommodates both sparse and non-sparse parameters whose support can be bounded away from zero. Extensive Monte Carlo simulations demonstrate the excellent performance of GPE in terms of bias reduction and size control compared to competing estimators. An empirical application of GPE to estimating price and income elasticities of demand for gasoline highlights its practical utility.
What is essential is visible to the eye: Saliency in primary school ranking and its effect on academic achievements
Authors: Francois-Xavier Ladant, Julien Hedou, Paolo Sestito, Falco J. Bargagli-Stoffi
Date: 2023-02-20 00:00:00

We propose a new strategy to identify the impact of class rank, exploiting a "visible" primary school rank from teachers' exam grades, and an "invisible" rank from unreported standardized test scores. Leveraging a unique panel dataset on Italian students, we show that the visible rank has a substantial impact on students' perceptions, which affects subsequent academic performance. However, the effect of being surrounded by higher-SES or higher-achieving peers remains positive even accounting for the decrease in rank. Higher-ranked students self-select into high schools with higher average student achievements. Finally, exploiting an extensive survey, we identify psychological mechanisms channeling the rank effect.
Determinants of Performance in European ATM -- How to Analyze a Diverse Industry
Authors: Thomas Standfuss, Georg Hirte, Frank Fichert, Hartmut Fricke
Date: 2023-02-20 00:00:00
Tasks: Benchmarking, Management

Air traffic control is considered to be a bottleneck in European air traffic management. As a result, the performance of the air navigation service providers is critically examined and also used for benchmarking. Using quantitative methods, we investigate which endogenous and exogenous factors affect the performance of air traffic control units on different levels. The methodological discussion is complemented by an empirical analysis. Results may be used to derive recommendations for operators, airspace users, and policymakers. We find that efficiency depends significantly on traffic patterns and the decisions of airspace users, but changes in the airspace structure could also make a significant contribution to performance improvements.
The Globalization-Inequality Nexus: A Comparative Study of Developed and Developing Countries
Authors: Md Shah Naoaj
Date: 2023-02-19 00:00:00

This study examines the relationship between globalization and income inequality, utilizing panel data spanning from 1992 to 2020. Globalization is measured by the World Bank global-link indicators such as FDI, Remittance, Trade Openness, and Migration while income inequality is measured by Gini Coefficient and the median income of 50% of the population. The fixed effect panel data analysis provides empirical evidence indicating that globalization tends to reduce income inequality, though its impact varies between developed and developing countries. The analysis reveals a strong negative correlation between net foreign direct investment (FDI) inflows and inequality in developing countries, while no such relationship was found for developed countries.The relationship holds even if we consider an alternative measure of inequality. However, when dividing countries by developed and developing groups, no statistically significant relationship was observed. Policymakers can use these findings to support efforts to increase FDI, trade, tourism, and migration to promote growth and reduce income inequality.
Subsidizing agricultural inputs in Senegal: Comparative analysis of three modes of intervention using a farm household model
Authors: Aymeric Ricome, Kamel Louhichi, Sergio Gomez y Paloma
Date: 2023-02-02 00:00:00
Tasks: Survey

This report presents the results of an ex-ante impact assessment of several scenarios related to the farmer targeting of the input subsidy programme currently implemented in Senegal. This study has been achieved with the agricultural household model FSSIM-Dev, calibrated on a sample of 2 278 farm households from the ESPS-2 survey. The impacts on crop mix, fertilizer application, farm income and on the government cost are presented and discussed.
Effort Discrimination and Curvature of Contest Technology in Conflict Networks
Authors: Xiang Sun, Jin Xu, Junjie Zhou
Date: 2023-02-20 00:00:00
Tasks: Form

In a model of interconnected conflicts on a network, we compare the equilibrium effort profiles and payoffs under two scenarios: uniform effort (UE) in which each contestant is restricted to exert the same effort across all the battles she participates, and discriminatory effort (DE) in which such a restriction is lifted. When the contest technology in each battle is of Tullock form, a surprising neutrality result holds within the class of semi-symmetric conflict network structures: both the aggregate actions and equilibrium payoffs under two regimes are the same. We also show that, in some sense, the Tullock form is necessary for such a neutrality result. Moving beyond the Tullock family, we further demonstrate how the curvature of contest technology shapes the welfare and effort effects.
Legitimacy of collective decisions: a mechanism design approach
Authors: Kirneva Margarita, Núñez Matías
Date: 2023-02-19 00:00:00

We design two mechanisms that ensure that the majority preferred option wins in all equilibria. The first one is a simultaneous game where agents choose other agents to cooperate with on top of the vote for an alternative, thus overcoming recent impossibility results concerning the implementation of majority rule. The second one adds sequential ratification to the standard majority voting procedure allowing to reach the (correct) outcome in significantly fewer steps than the widely used roll call voting. Both mechanisms use off-equilibrium lotteries to incentivize truthful voting. We discuss different extensions, including the possibility for agents to abstain.
Multiplayer War of Attrition with Asymmetric Private Information
Authors: Hongcheng Li
Date: 2023-02-18 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper studies a war of attrition game in the setting of public good provision that combines three elements: (i) multiple players, (ii) incomplete information, and (iii) ex-ante asymmetry. In the unique equilibrium, asymmetry leads to a stratified behavior pattern where one player provides the public good instantly with a positive probability while each of the other players has a player-specific strict waiting time, before which even his highest type will not provide the good. Comparative statics show that a player with less patience, lower cost of provision, and higher reputation in value (expressed in a form of hazard rate) provides the good type-wise uniformly faster. In large societies, the cost of delay is mainly determined by the highest type of the instant-exit player.
Microfoundations of Expected Utility and Response Times
Authors: Valdes Salvador, Gonzalo ValdesEdwards
Date: 2023-02-18 00:00:00

This paper builds a rule for decisionmaking from the physical behavior of single neurons, the well established neural circuitry of mutual inhibition, and the evolutionary principle of natural selection. No axioms are used in the derivation of this rule. The paper provides a microfoundation to both Economics Choice Theory and Cognitive Psychologys Response Times Theory. The paper finds how classical expected utility should be modified to account for much neuroscientific evidence, and how neuroscientific correlates of choice should be linked to utility. In particular, the model implies the concept of utility is a network property and cannot be calculated as a function of frequencies in one layer of neurons alone; it requires understanding how different layers work together. The resulting rule is simple enough to model markets and games as is customary in the social sciences. Utility maximization and inaction are endogenous to the model, cardinality and independence of irrelevant alternatives, properties present in classical and random utility theories, are only met in limiting situations.
Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation
Authors: Mark Anderson, Tomi Kinnunen, Naomi Harte
Date: 2023-02-20 00:00:00
Tasks: Action Detection, Activity Detection, Sensitivity

While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic frontends learnt jointly with a backend. In this study, we focus specifically on learnable filterbanks. Prior studies have reported that in frontends using learnable filterbanks initialised to a mel scale, the learned filters do not differ substantially from their initialisation. Using a Gabor-based filterbank, we investigate the sensitivity of a learnable filterbank to its initialisation using several initialisation strategies on two audio tasks: voice activity detection and bird species identification. We use the Jensen-Shannon Distance and analysis of the learned filters before and after training. We show that although performance is overall improved, the filterbanks exhibit strong sensitivity to their initialisation strategy. The limited movement from initialised values suggests that alternate optimisation strategies may allow a learnable frontend to reach better overall performance.
An ASR-free Fluency Scoring Approach with Self-Supervised Learning
Authors: Wei Liu, Kaiqi Fu, Xiaohai Tian, Shuju Shi, Wei Li, Zejun Ma, Tan Lee
Date: 2023-02-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Pseudo Label, Self-Supervised Learning, speech-recognition, Speech Recognition

A typical fluency scoring system generally relies on an automatic speech recognition (ASR) system to obtain time stamps in input speech for either the subsequent calculation of fluency-related features or directly modeling speech fluency with an end-to-end approach. This paper describes a novel ASR-free approach for automatic fluency assessment using self-supervised learning (SSL). Specifically, wav2vec2.0 is used to extract frame-level speech features, followed by K-means clustering to assign a pseudo label (cluster index) to each frame. A BLSTM-based model is trained to predict an utterance-level fluency score from frame-level SSL features and the corresponding cluster indexes. Neither speech transcription nor time stamp information is required in the proposed system. It is ASR-free and can potentially avoid the ASR errors effect in practice. Experimental results carried out on non-native English databases show that the proposed approach significantly improves the performance in the "open response" scenario as compared to previous methods and matches the recently reported performance in the "read aloud" scenario.