pwc-archive/papers-with-abstracts huggingface dataset:
Smooth Robustness Measures for Symbolic Control Via Signal Temporal Logic
Authors: Shirantha Welikala, Hai Lin, Panos J. Antsaklis
Date: 2023-05-16 00:00:00
Tasks: Computational Efficiency

Symbolic control problems aim to synthesize control policies for dynamical systems under complex temporal specifications. For such problems, Signal Temporal Logic (STL) is increasingly used as the formal specification language due to its rich expressiveness. Moreover, the degree of satisfaction of STL specifications can be evaluated using ``STL robust semantics'' as a scalar robustness measure. This capability of STL enables transforming a symbolic control problem into an optimization problem that optimizes the corresponding robustness measure. However, since these robustness measures are non-smooth and non-convex, exact solutions can only be computed using computationally inefficient mixed-integer programming techniques that do not scale well. Therefore, recent literature has focused on using smooth approximations of these robustness measures to apply scalable and computationally efficient gradient-based methods to find local optima solutions. In this paper, we first generalize two recently established smooth robustness measures (SRMs) and two new ones and discuss their strengths and weaknesses. Next, we propose ``STL error semantics'' to characterize the approximation errors associated with different SRMs under different parameter configurations. This allows one to sensibly select an SRM (to optimize) along with its parameter values. We then propose ``STL gradient semantics'' to derive explicit gradients of SRMs leading to improve computational efficiency as well as accuracy compared to when using numerically estimated gradients. Finally, these contributions are highlighted using extensive simulation results.
Analysis, Control, and State Estimation for the Networked Competitive Multi-Virus SIR Model
Authors: Ciyuan Zhang, Sebin Gracy, Tamer Basar, Philip E. Pare
Date: 2023-05-15 00:00:00
Tasks: State Estimation

This paper proposes a novel discrete-time multi-virus susceptible-infected-recovered (SIR) model that captures the spread of competing epidemics over a population network. First, we provide sufficient conditions for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. Third, we present a sufficient condition for the model to be strongly locally observable, assuming that the network has only infected or recovered individuals. Fourth, we propose a Luenberger observer for estimating the states of our system. We prove that the estimation error of our proposed estimator converges to zero asymptotically with the observer gain. Finally, we present a distributed feedback controller which guarantees that each virus dies out at an exponential rate. We then show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out.
Scalable Adaptive Traffic Light Control Over a Traffic Network Including Transit Delays
Authors: Yingqing Chen, Christos G. Cassandras
Date: 2023-05-15 00:00:00

We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next. The goal is to minimize the overall mean waiting time and improve the ``green wave'' properties in such systems. Using a stochastic hybrid system model with parametric traffic light controllers, we use Infinitesimal Perturbation Analysis (IPA) to derive a data-driven cost gradient estimator with respect to these parameters. We then iteratively adjust them through an online gradient-based algorithm. We show that the event-driven nature of the IPA estimators driving the controllers leads to scalable computationally efficient controllers as the dimensionality of the traffic network increases.
Equilibria of Fully Decentralized Learning in Networked Systems
Authors: Yan Jiang, Wenqi Cui, Baosen Zhang, Jorge Cortés
Date: 2023-05-15 00:00:00

Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder their applicability to practical systems. To overcome this, we identify a structure that is simple to check for linear dynamical system, where each player learns in a fully decentralized fashion to minimize its cost. We first establish the existence of pure strategy Nash equilibria in the resulting noncooperative game. We then conjecture that the Nash equilibrium is unique provided that the system satisfies an additional requirement on its structure. We also introduce a decentralized mechanism based on projected gradient descent to have agents learn the Nash equilibrium. Simulations on a $5$-player game validate our results.
Forecast-based charging strategy to prolong the lifetime of lithium-ion batteries in standalone PV battery systems in Sub-Saharan Africa
Authors: Jonathan Schulte, Jan Figgener, Philipp Woerner, Hendrik Broering, Dirk Uwe Sauer
Date: 2023-05-15 00:00:00

Standalone PV battery systems have great potential to power the one billion people worldwide who lack access to electricity. Due to remoteness and poverty, durable and inexpensive systems are required for a broad range of applications. However, todays PV battery systems do not yet fully meet this requirement. Especially batteries still prove to be a hindrance, as they represent the most expensive and fastest aging component in a PV battery system. This work aims to address this by prolonging battery life. For this purpose, a forecast-based charging strategy was developed. As lithium-ion batteries age slower in a low state of charge, the goal of the operation strategy is to only charge the battery as much as needed. The impact of the proposed charging strategy is examined in a case study using one year of historical data of 14 standalone systems in Nigeria. It was found that the proposed operation strategy could reduce the average battery state of charge by around 20 percent without causing power outages for the mini-grids. This would significantly extend the life of the battery and ultimately lead to a more durable and cheaper operation of standalone PV battery systems.
Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reduction
Authors: Pei Tan, Christopher E Miles
Date: 2023-05-16 00:00:00
Tasks: Clustering, Dimensionality Reduction, Disentanglement

Collective motion of locally interacting agents is found ubiquitously throughout nature. The inability to probe individuals has driven longstanding interest in the development of methods for inferring the underlying interactions. In the context of heterogeneous collectives, where the population consists of individuals driven by different interactions, existing approaches require some knowledge about the heterogeneities or underlying interactions. Here, we investigate the feasibility of identifying the identities in a heterogeneous collective without such prior knowledge. We numerically explore the behavior of a heterogeneous Vicsek model and find sufficiently long trajectories intrinsically cluster in a PCA-based dimensionally reduced model-agnostic description of the data. We identify how heterogeneities in each parameter in the model (interaction radius, noise, population proportions) dictate this clustering. Finally, we show the generality of this phenomenon by finding similar behavior in a heterogeneous D'Orsogna model. Altogether, our results establish and quantify the intrinsic model-agnostic statistical disentanglement of identities in heterogeneous collectives.
Graph-Based Deep Learning for Sea Surface Temperature Forecasts
Authors: Ding Ning, Varvara Vetrova, Karin R. Bryan
Date: 2023-04-03 00:00:00
Tasks: Deep Learning

Sea surface temperature (SST) forecasts help with managing the marine ecosystem and the aquaculture impacted by anthropogenic climate change. Numerical dynamical models are resource intensive for SST forecasts; machine learning (ML) models could reduce high computational requirements and have been in the focus of the research community recently. ML models normally require a large amount of data for training. Environmental data are collected on regularly-spaced grids, so early work mainly used grid-based deep learning (DL) for prediction. However, both grid data and the corresponding DL approaches have inherent problems. As geometric DL has emerged, graphs as a more generalized data structure and graph neural networks (GNNs) have been introduced to the spatiotemporal domains. In this work, we preliminarily explored graph re-sampling and GNNs for global SST forecasts, and GNNs show better one month ahead SST prediction than the persistence model in most oceans in terms of root mean square errors.
Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging
Authors: Xuebao Cai, Yuhang Tan, Ting Su, Dong Liang, Hairong Zheng, Jinyou Xu, Peiping Zhu, Yongshuai Ge
Date: 2023-05-14 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Image Reconstruction

The low-density imaging performance of a zone plate based nano-resolution hard X-ray computed tomography (CT) system can be significantly improved by incorporating a grating-based Lau interferometer. Due to the diffraction, however, the acquired nano-resolution phase signal may suffer splitting problem, which impedes the direct reconstruction of phase contrast CT (nPCT) images. To overcome, a new model-driven nPCT image reconstruction algorithm is developed in this study. In it, the diffraction procedure is mathematically modeled into a matrix B, from which the projections without signal splitting can be generated invertedly. Furthermore, a penalized weighed least-square model with total variation (PWLS-TV) is employed to denoise these projections, from which nPCT images with high accuracy are directly reconstructed. Numerical and physical experiments demonstrate that this new algorithm is able to work with phase projections having any splitting distances. Results also reveal that nPCT images with higher signal-to-noise-ratio (SNR) would be reconstructed from projections with larger signal splittings. In conclusion, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nano-resolution phase contrast imaging.
Monitoring multicountry macroeconomic risk
Authors: Dimitris Korobilis, Maximilian Schröder
Date: 2023-05-16 00:00:00
Tasks: Time Series

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis.
Grenander-type Density Estimation under Myerson Regularity
Authors: Haitian Xie
Date: 2023-05-15 00:00:00
Tasks: Density Estimation

This study presents a novel approach to the density estimation of private values from second-price auctions, diverging from the conventional use of smoothing-based estimators. We introduce a Grenander-type estimator, constructed based on a shape restriction in the form of a convexity constraint. This constraint corresponds to the renowned Myerson regularity condition in auction theory, which is equivalent to the concavity of the revenue function for selling the auction item. Our estimator is nonparametric and does not require any tuning parameters. Under mild assumptions, we establish the cube-root consistency and show that the estimator asymptotically follows the scaled Chernoff's distribution. Moreover, we demonstrate that the estimator achieves the minimax optimal convergence rate.
Semiparametrically Optimal Cointegration Test
Authors: Bo Zhou
Date: 2023-05-13 00:00:00

This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam's theory of limit experiment, which in this context takes the form of Locally Asymptotically Brownian Functional (LABF). By leveraging the structural version of LABF, an Ornstein-Uhlenbeck experiment, we develop the asymptotic power envelopes of asymptotically invariant tests for both cases with and without a time trend. We propose feasible tests based on a nonparametrically estimated density and demonstrate that their power can achieve the semiparametric power envelopes, making them semiparametrically optimal. We validate the theoretical results through large-sample simulations and illustrate satisfactory size control and excellent power performance of our tests under small samples. In both cases with and without time trend, we show that a remarkable amount of additional power can be obtained from non-Gaussian distributions.
Health Impacts of Public Pawnshops in Industrializing Tokyo
Authors: Tatsuki Inoue
Date: 2023-05-16 00:00:00
Tasks: Nutrition

This study is the first to investigate whether pawnshops, financial institutions for low-income populations, have contributed to the decline in mortality in the early twentieth century. Using ward-level panel data from Tokyo City, this study revealed that the popularity of public pawnshops was associated with a 4% and 5% decrease in infant mortality and fetal death rates, respectively, during 1927-1935. The historical context implies that the potential channels of the relationships were improving nutrition and hygiene and covering childbirth costs. Moreover, a cost-benefit calculation highlighted that the establishment of public pawnshops was a cost-effective public investment for better public health. Contrarily, for-profit private pawnshops showed no significant association with health improvements.
Kites and Quails: Monetary Policy and Communication with Strategic Financial Markets
Authors: Giampaolo Bonomi, Ali Uppal
Date: 2023-05-15 00:00:00

We propose a model to study the consequences of including financial stability among the central bank's objectives when market players are strategic, and surprises compromise their stability. In this setup, central banks underreact to economic shocks, a prediction consistent with the Federal Reserve's behavior during the 2023 banking crisis. Moreover, policymakers' stability concerns bias investors' choices, inducing inefficiency. If the central bank has private information about its policy intentions, the equilibrium forward guidance entails an information loss, highlighting a trade-off between stabilizing markets through policy and communication. A "kitish" central banker, who puts less weight on stability, reduces these inefficiencies.
Dissertation on Applied Microeconomics of Freemium Pricing Strategies in Mobile App Market
Authors: Naixin Zhu
Date: 2023-04-22 00:00:00

In my dissertation, I will analyze how the product market position of a mobile app affects its pricing strategies, which in turn impacts an app's monetization process. Using natural language processing and k-mean clustering on apps' text descriptions, I created a new variable that measures the distinctiveness of an app as compared to its peers. I created four pricing variables, price, cumulative installs, and indicators of whether an app contains in-app ads and purchases. I found that the effect differs for successful apps and less successful apps. I measure the success here using cumulative installs and the firms that developed the apps. Based on third-party rankings and the shape of the distribution of installs, I set two thresholds and divided apps into market-leading and market-follower apps. The market-leading sub-sample consists of apps with high cumulative installs or developed by prestigious firms, and the market-follower sub-sample consists of the rest of the apps. I found that the impact of being niche is smaller in the market-leading apps because of their relatively higher heterogeneity. In addition, being niche also impact utility apps differently from hedonic apps or apps with two-sided market characteristics. For the special gaming category, being niche has some effect but is smaller than in the market follower sub-sample. My research provides novel empirical evidence of digital products to various strands of theoretical research, including the optimal distinctiveness theory, product differentiation, price discrimination in two or multi-sided markets, and consumer psychology.
A Theory of Auditability for Allocation Mechanisms
Authors: Aram Grigoryan, Markus Möller
Date: 2023-05-16 00:00:00

In centralized mechanisms and platforms, participants do not fully observe each others' type reports. Hence, if there is a deviation from the promised mechanism, participants may be unable to detect it. We formalize a notion of auditabilty that captures how easy or hard it is to detect deviations from a mechanism. We find a stark contrast between the auditabilities of prominent mechanisms. We also provide tight characterizations of maximally auditable classes of allocation mechanisms.
Complete Conditional Type Structures
Authors: Nicodemo De Vito
Date: 2023-05-15 00:00:00

Hierarchies of conditional beliefs (Battigalli and Siniscalchi 1999) play a central role for the epistemic analysis of solution concepts in sequential games. They are modelled by type structures, which allow the analyst to represent the players' hierarchies without specifying an infinite sequence of conditional beliefs. Here, we study type structures that satisfy a "richness" property, called completeness. This property is defined on the type structure alone, without explicit reference to hierarchies of beliefs or other type structures. We provide sufficient conditions under which a complete type structure represents all hierarchies of conditional beliefs. In particular, we present an extension of the main result in Friedenberg (2010) to type structures with conditional beliefs. KEYWORDS: Conditional probability systems, hierarchies of beliefs, type structures, completeness, terminality. JEL: C72, D80
COWPEA (Candidates Optimally Weighted in Proportional Election using Approval voting)
Authors: Toby Pereira
Date: 2023-04-03 00:00:00

This paper describes COWPEA (Candidates Optimally Weighted in Proportional Election using Approval voting), a method of proportional representation that uses approval voting, also known as random priority, though underdeveloped in the literature. COWPEA optimally elects an unlimited number of candidates with potentially different weights to a body, rather than giving a fixed number equal weight. A non-deterministic Approval-Based Committee (ABC) version that elects a fixed a number of candidates with equal weight is known as COWPEA Lottery. This is the only method known to pass the criteria of monotonicity, strong candidate Pareto efficiency, Independence of Irrelevant Ballots, and Independence of Unanimously Approved Candidates. It is also possible to convert COWPEA and COWPEA Lottery to score or graded voting methods. COWPEA and COWPEA Lottery are also compared against Optimal PAV and Optimal PAV Lottery.
TG-Critic: A Timbre-Guided Model for Reference-Independent Singing Evaluation
Authors: Xiaoheng Sun, Yuejie Gao, Hanyao Lin, Huaping Liu
Date: 2023-05-16 00:00:00
Tasks: Attribute

Automatic singing evaluation independent of reference melody is a challenging task due to its subjective and multi-dimensional nature. As an essential attribute of singing voices, vocal timbre has a non-negligible effect and influence on human perception of singing quality. However, no research has been done to include timbre information explicitly in singing evaluation models. In this paper, a data-driven model TG-Critic is proposed to introduce timbre embeddings as one of the model inputs to guide the evaluation of singing quality. The trunk structure of TG-Critic is designed as a multi-scale network to summarize the contextual information from constant-Q transform features in a high-resolution way. Furthermore, an automatic annotation method is designed to construct a large three-class singing evaluation dataset with low human-effort. The experimental results show that the proposed model outperforms the existing state-of-the-art models in most cases.
Tightly Integrated Motion Classification and State Estimation in Foot-Mounted Navigation Systems
Authors: Isaac Skog, Gustaf Hendeby, Manon Kok
Date: 2023-05-16 00:00:00
Tasks: Position, State Estimation

A framework for tightly integrated motion mode classification and state estimation in motion-constrained inertial navigation systems is presented. The framework uses a jump Markov model to describe the navigation system's motion mode and navigation state dynamics with a single model. A bank of Kalman filters is then used for joint inference of the navigation state and the motion mode. A method for learning unknown parameters in the jump Markov model, such as the motion mode transition probabilities, is also presented. The application of the proposed framework is illustrated via two examples. The first example is a foot-mounted navigation system that adapts its behavior to different gait speeds. The second example is a foot-mounted navigation system that detects when the user walks on flat ground and locks the vertical position estimate accordingly. Both examples show that the proposed framework provides significantly better position accuracy than a standard zero-velocity aided inertial navigation system. More importantly, the examples show that the proposed framework provides a theoretically well-grounded approach for developing new motion-constrained inertial navigation systems that can learn different motion patterns.