pwc-archive/papers-with-abstracts huggingface dataset:
On Linear Time-Invariant Systems Analysis via A Single Trajectory: A Linear Programming Approach
Authors: Hassan Abdelraouf, Fahad Albalawi, Eric Feron
Date: 2021-09-21 00:00:00

In this note, a novel methodology that can extract a number of analysis results for linear time-invariant systems (LTI) given only a single trajectory of the considered system is proposed. The superiority of the proposed technique relies on the fact that it provides an automatic and formal way to obtain valuable information about the controlled system by only having access to a single trajectory over a finite period of time (i.e., the system dynamics is assumed to be unknown). At first, we characterize the stability region of LTI systems given only a single trajectory dataset by constructing the associated Lyapunov function of the system. The Lyapunov function is found by formulating and solving a linear programming (LP) problem. Then, we extend the same methodology to a variety of essential analysis results for LTI systems such as deriving bounds on the output energy, deriving bounds on output peak, deriving $\mathbf{L}_2$ and RMS gains. To illustrate the efficacy of the proposed data-driven paradigm, a comparison analysis between the learned LTI system metrics and the true ones is provided.
Intelligent Traffic Control System by Using Image Information
Authors: Zong-Ming Lin, Cheng-Yang Chang, Chin-Yu Hu, Yung-Yuan Chen
Date: 2021-09-21 00:00:00
Tasks: Traffic Signal Control

This paper implements a traffic signal control system by using real-time traffic flow feedback. This system is designed to deal with two-lane intersections. We construct an experiment field similar to the roads and drivers in Taiwan using an autonomous simulation software called Virtual Test Drive (VTD) released by MSC Software. We erect four cameras on the side of the roads to get the image of the intersection, then transfer the image information into traffic flow information. Analyze the traffic information in each lane by using Greenshields traffic flow model. Control the traffic signals by using Webster's method to increase the performance and soothe the traffic.
On Net Energy Metering X: Optimal Prosumer Decisions, Social Welfare, and Cross-Subsidies
Authors: Ahmed S. Alahmed, Lang Tong
Date: 2021-09-21 00:00:00

We introduce NEM X, an inclusive retail tariff model that captures features of existing net energy metering (NEM) policies. It is shown that the optimal prosumer decision has three modes: (a) the net-consuming mode where the prosumer consumes more than its behind-the-meter distributed energy resource (DER) production when the DER production is below a predetermined lower threshold, (b) the net-producing mode where the prosumer consumes less than its DER production when the DER production is above a predetermined upper threshold, and (c) the net-zero energy mode where the prosumer's consumption matches to its DER generation when its DER production is between the lower and upper thresholds. Both thresholds are obtained in closed-form. Next, we analyze the regulator's rate-setting process that determines NEM X parameters such as retail/sell rates, fixed charges, and price differentials in time-of-use tariffs' on and off-peak periods. A stochastic Ramsey pricing program that maximizes social welfare subject to the revenue break-even constraint for the regulated utility is formulated. Performance of several NEM X policies is evaluated using real and synthetic data to illuminate impacts of NEM policy designs on social welfare, cross-subsidies of prosumers by consumers, and payback time of DER investments that affect long-run DER adoptions.
Identifiability of Chemical Reaction Networks with Intrinsic and Extrinsic Noise from Stationary Distributions
Authors: Theodore W. Grunberg, Domitilla Del Vecchio
Date: 2021-09-21 00:00:00

Many biological systems can be modeled as a chemical reaction network with unknown parameters. Data available to identify these parameters are often in the form of a stationary distribution, such as that obtained from measurements of a cell population. In this work, we introduce a framework for analyzing the identifiability of the reaction rate coefficients of chemical reaction networks from stationary distribution data. Working with the linear noise approximation, which is a diffusive approximation to the chemical master equation, we give a computational procedure to certify global identifiability based on Hilbert's Nullstellensatz. We present a variety of examples that show the applicability of our method to chemical reaction networks of interest in systems and synthetic biology, including discrimination between possible molecular mechanisms for the interaction between biochemical species.
Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems
Authors: Lin Song, Neng Wan, Aditya Gahlawat, Chuyuan Tao, Naira Hovakimyan, Evangelos A. Theodorou
Date: 2021-09-21 00:00:00

We propose a unified framework to fast generate a safe optimal control action for a new task from existing controllers on Multi-Agent Systems (MASs). The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task. Instead of sophisticatedly tuning the cost parameters and other hyper-parameters for safe and reliable behavior in the optimal control framework, the safety of each single task solution is guaranteed using the control barrier functions (CBFs) for high-degree stochastic systems, which constrains the system state within a known safe operation region where it originates from. Linearity of CBF constraints in control enables the control action composition. The discussed framework can immediately provide reliable solutions to new tasks by taking a weighted mixture of solved component-task actions and filtering on some CBF constraints, instead of performing an extensive sampling to achieve a new controller. Our results are verified and demonstrated on both a single UAV and two cooperative UAV teams in an environment with obstacles.
A Data-Driven Democratized Control Architecture for Regional Transmission Operators
Authors: Xiaoyuan Fan, Daniel Moscovitz, Liang Du, Walid Saad
Date: 2021-09-20 00:00:00

As probably the most complicated and critical infrastructure system, U.S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users. This paper proposes a data-driven democratized control architecture considering two democratization pathways to assist transmission system operators, with a targeted use case of developing online proactive islanding strategies. Detailed discussions on load capability profiling at transmission buses and disaggregation of DER generations are provided and illustrated with real-world utility data. By Combining network and operational constraints, transmission system operators can be equipped with new tools built on top of this architecture, to derive accurate, proactive, and strategic islanding decisions to incorporate the wide range of dynamic portfolios and needs when facing extreme events or unseen grid contingencies.
Robust economic model predictive control with zone tracking
Authors: Benjamin Decardi-Nelson, Jinfeng Liu
Date: 2021-09-20 00:00:00
Tasks: model, Model Predictive Control

This paper presents a robust economic model predictive control (EMPC) formulation with zone tracking for discrete-time uncertain nonlinear systems. The proposed design ensures that the zone tracking objective is achieved in finite steps and at the same time optimizes the economic performance. In the proposed design, instead of tracking the original target zone, a robust control invariant set within the target zone is determined and is used as the actual zone tracked in the proposed EMPC. This approach ensures that the zone tracking objective is achieved within finite steps and once the zone tracking objective is achieved (the system state enters the robust control invariant set), the system state does not come out of the target zone anymore. To optimize the economic performance within the zone in the presence of disturbances, we introduce the notion of risk factor in the controller design. An algorithm to determine the economic zone to be tracked is provided. The risk factor determines the conservativeness of the controller and provides a way to tune the EMPC for better economic performance. A nonlinear chemical example is presented to demonstrate the performance of the proposed formulation.
Life in a random universe: Sciama's argument reconsidered
Authors: Zhi-Wei Wang, Samuel L. Braunstein
Date: 2021-09-10 00:00:00

Random sampling in high dimensions has successfully been applied to phenomena as diverse as nuclear resonances, neural networks and black hole evaporation. Here we revisit an elegant argument by the British physicist Dennis Sciama, which demonstrated that were our universe random, it would almost certainly have a negligible chance for life. Under plausible assumptions, we show that a random universe can masquerade as `intelligently designed,' with the fundamental constants instead appearing to be fined tuned to be achieve the highest probability for life to occur. For our universe, this mechanism may only require there to be around a dozen currently unknown fundamental constants. We speculate on broader applications for the mechanism we uncover.
Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States
Authors: Mohit Batham, Soudeh Mirghasemi, Mohammad Arshad Rahman, Manini Ojha
Date: 2021-09-21 00:00:00
Tasks: Discrete Choice Models

This chapter presents an overview of a specific form of limited dependent variable models, namely discrete choice models, where the dependent (response or outcome) variable takes values which are discrete, inherently ordered, and characterized by an underlying continuous latent variable. Within this setting, the dependent variable may take only two discrete values (such as 0 and 1) giving rise to binary models (e.g., probit and logit models) or more than two values (say $j=1,2, \ldots, J$, where $J$ is some integer, typically small) giving rise to ordinal models (e.g., ordinal probit and ordinal logit models). In these models, the primary goal is to model the probability of responses/outcomes conditional on the covariates. We connect the outcomes of a discrete choice model to the random utility framework in economics, discuss estimation techniques, present the calculation of covariate effects and measures to assess model fitting. Some recent advances in discrete data modeling are also discussed. Following the theoretical review, we utilize the binary and ordinal models to analyze public opinion on marijuana legalization and the extent of legalization -- a socially relevant but controversial topic in the United States. We obtain several interesting results including that past use of marijuana, belief about legalization and political partisanship are important factors that shape the public opinion.
Cryptocurrencies and the Future of Money
Authors: Matheus R. Grasselli, Alexander Lipton
Date: 2021-09-21 00:00:00

We review different classes of cryptocurrencies with emphasis on their economic properties. Pure-asset coins such as Bitcoin, Ethereum and Ripple are characterized by not being a liability of any economic agent and most resemble commodities such as gold. Central bank digital currencies, at the other end of the economic spectrum, are liabilities of a Central Bank and most resemble cash. In between, there exist a range of so-called stable coins, with varying degrees of economic complexity. We use balance sheet operations to highlight the properties of each class of cryptocurrency and their potential uses. In addition, we propose the basic structure for a macroeconomic model incorporating all the different types of cryptocurrencies under consideration.
Evolution of topics in central bank speech communication
Authors: Magnus Hansson
Date: 2021-09-21 00:00:00
Tasks: Topic Models

This paper studies the content of central bank speech communication from 1997 through 2020 and asks the following questions: (i) What global topics do central banks talk about? (ii) How do these topics evolve over time? I turn to natural language processing, and more specifically Dynamic Topic Models, to answer these questions. The analysis consists of an aggregate study of nine major central banks and a case study of the Federal Reserve, which allows for region specific control variables. I show that: (i) Central banks address a broad range of topics. (ii) The topics are well captured by Dynamic Topic Models. (iii) The global topics exhibit strong and significant autoregressive properties not easily explained by financial control variables.
Overinference from Weak Signals and Underinference from Strong Signals
Authors: Ned Augenblick, Eben Lazarus, Michael Thaler
Date: 2021-09-20 00:00:00
Tasks: Informativeness

When people receive new information, sometimes they revise their beliefs too much, and sometimes too little. In this paper, we show that a key driver of whether people overinfer or underinfer is the strength of the information. Based on a model in which people know which direction to update in, but not exactly how much to update, we hypothesize that people will overinfer from weak signals and underinfer from strong signals. We then test this hypothesis across four different environments: abstract experiments, a naturalistic experiment, sports betting markets, and financial markets. In each environment, our consistent and robust finding is overinference from weak signals and underinference from strong signals. Our framework and findings can help harmonize apparently contradictory results from the experimental and empirical literatures.
Knowledge Accumulation, Privacy, and Growth in a Data Economy
Authors: Lin William Cong, Danxia Xie, Longtian Zhang
Date: 2021-09-21 00:00:00

We build an endogenous growth model with consumer-generated data as a new key factor for knowledge accumulation. Consumers balance between providing data for profit and potential privacy infringement. Intermediate good producers use data to innovate and contribute to the final good production, which fuels economic growth. Data are dynamically nonrival with flexible ownership while their production is endogenous and policy-dependent. Although a decentralized economy can grow at the same rate (but are at different levels) as the social optimum on the Balanced Growth Path, the R&D sector underemploys labor and overuses data -- an inefficiency mitigated by subsidizing innovators instead of direct data regulation. As a data economy emerges and matures, consumers' data provision endogenously declines after a transitional acceleration, allaying long-run privacy concerns but portending initial growth traps that call for interventions.
Endogenous Growth Under Multiple Uses of Data
Authors: Lin William Cong, Wenshi Wei, Danxia Xie, Longtian Zhang
Date: 2021-09-21 00:00:00

We model a dynamic data economy with fully endogenous growth where agents generate data from consumption and share them with innovation and production firms. Different from other productive factors such as labor or capital, data are nonrival in their uses across sectors which affect both the level and growth of economic outputs. Despite the vertical nonrivalry, the innovation sector dominates the production sector in data usage and contribution to growth because (i) data are dynamically nonrival and add to knowledge accumulation, and (ii) innovations "desensitize" raw data and enter production as knowledge, which allays consumers' privacy concerns. Data uses in both sectors interact to generate spillover of allocative distortion and exhibit an apparent substitutability due to labor's rivalry and complementarity with data. Consequently, growth rates under a social planner and a decentralized equilibrium differ, which is novel in the literature and has policy implications. Specifically, consumers' failure to fully internalize knowledge spillover when bearing privacy costs, combined with firms' market power, underprice data and inefficiently limit their supply, leading to underemployment in the innovation sector and a suboptimal long-run growth. Improving data usage efficiency is ineffective in mitigating the underutilization of data, but interventions in the data market and direct subsidies hold promises.
Ultrasound Domain Adaptation Using Frequency Domain Analysis
Authors: Mostafa Sharifzadeh, Ali K. Z. Tehrani, Habib Benali, Hassan Rivaz
Date: 2021-09-21 00:00:00
Tasks: Domain Adaptation, Lesion Segmentation

A common issue in exploiting simulated ultrasound data for training neural networks is the domain shift problem, where the trained models on synthetic data are not generalizable to clinical data. Recently, Fourier Domain Adaptation (FDA) has been proposed in the field of computer vision to tackle the domain shift problem by replacing the magnitude of the low-frequency spectrum of a synthetic sample (source) with a real sample (target). This method is attractive in ultrasound imaging given that two important differences between synthetic and real ultrasound data are caused by unknown values of attenuation and speed of sound (SOS) in real tissues. Attenuation leads to slow variations in the amplitude of the B-mode image, and SOS mismatch creates aberration and subsequent blurring. As such, both domain shifts cause differences in the low-frequency components of the envelope data, which are replaced in the proposed method. We demonstrate that applying the FDA method to the synthetic data, simulated by Field II, obtains an 3.5\% higher Dice similarity coefficient for a breast lesion segmentation task.
HAPS-assisted Hybrid RF-FSO Multicast Communications: Error and Outage Analysis
Authors: Olfa Ben Yahia, Eylem Erdogan, Gunes Karabulut Kurt
Date: 2021-09-21 00:00:00

In this work, we study the performance of multiple-hop mixed frequency (RF)/free-space optical (FSO) communication-based decode-and-forward protocol for multicast networks. So far, serving a large number of users is considered a promising approach for real-time applications to address the massive data traffic demands. In this regard, we propose two practical use-cases. In the former model, we propose a high altitude platform station (HAPS)-aided mixed RF/FSO/RF communication scheme where a terrestrial ground station intends to communicate with a cluster of nodes through two stratospheric HAPS systems. In the latter model, we assume that the line of sight connectivity is inaccessible between the two HAPS systems due to high attenuation caused by large propagation distances. Thereby, we propose a low Earth orbit satellite-aided mixed RF/FSO/FSO/RF communication. For the proposed scenarios, closed-form expressions of outage probability (OP) and bit error rate are derived. In addition, to illustrate the asymptotic behavior of the proposed models, diversity gains are obtained. Furthermore, ergodic capacity and energy efficiency (EE) are provided for both scenarios. Finally, the simulation results are provided to validate the theoretical derivations. The results show that satellite-aided mixed RF/FSO/FSO/RF scenarios achieve better OP, whereas HAPS-aided mixed RF/FSO/RF scenario can achieve higher EE.
Estimation of the Scatterer Size Distributions in Quantitative Ultrasound Using Constrained Optimization
Authors: Noushin Jafarpisheh, Ivan M. Rosado-Mendez, Timothy J. Hall, Hassan Rivaz
Date: 2021-09-21 00:00:00
Tasks: Form

Quantitative ultrasound (QUS) parameters such as the effective scatterer diameter (ESD) reveal tissue properties by analyzing ultrasound backscattered echo signal. ESD can be attained through parametrizing backscatter coefficient using form factor models. However, reporting a single scatterer size cannot accurately characterize a tissue, particularly when the media contains scattering sources with a broad range of sizes. Here we estimate the probability of contribution of each scatterer size by modeling the measured form factor as a linear combination of form factors from individual sacatterer sizes. We perform the estimation using two novel techniques. In the first technique, we cast scatterer size distribution as an optimization problem, and efficiently solve it using a linear system of equations. In the second technique, we use the solution of this system of equations to constrain the optimization function, and solve the constrained problem. The methods are evaluated in simulated backscattered coefficients using Faran theory. We evaluate the robustness of the proposed techniques by adding Gaussian noise. The results show that both methods can accurately estimate the scatterer size distribution, and that the second method outperforms the first one.
Load Estimation for Electric Power Distribution Networks
Authors: Chiebuka Eyisi, Saeed Lotfifard
Date: 2021-09-20 00:00:00
Tasks: Management

Distribution Load Estimation (DLE) is a key function of Distribution Management System (DMS). In this paper a novel method for presenting historical load data in the form of Representative Load Curves (RLC) is presented. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) is used in this regard to estimate the RLC. Accurate RLCs provide better pseudo-measurements for real-time load estimation in distribution networks. The performance of the proposed method is demonstrated on an 11kV radial distribution network with the aid of the MATLAB software. The mean absolute percent error (MAPE) criterion is used to quantify the accuracy of the estimated RLC.
Variational Embedding Multiscale Sample Entropy:complexity-based analysis for multichannel systems
Authors: Hongjian Xiao, Danilo P. Mandic
Date: 2021-09-20 00:00:00
Tasks: Diversity

To quantify the complexity of a system, entropy-based methods have received considerable critical attentions in real-world data analysis. Among numerous entropy algorithms, amplitude-based formulas, represented by Sample Entropy, suffer from a limitation of data length especially when it comes to practical scenarios. And this shortcoming is further highlighted by involving coarse graining procedure in multi-scale process. The unbalance between embedding dimension and data size will undoubtedly result in inaccurate and undefined estimation. To that cause, Variational Embedding Multiscale Sample Entropy is proposed in this paper, which assigns signals from various channels with distinct embedding dimensions. And this algorithm is tested by both stimulated and real signals. Furthermore, the performance of the new entropy is investigated and compared with Multivariate Multiscale Sample Entropy and Variational Embedding Multiscale Diversity Entropy. Two real-world database, wind data sets with varying regimes and physiological database recorded from young and elderly people, were utilized. As a result, the proposed algorithm gives an improved separation for both situations.