pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets
Authors: Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
Date: 2022-10-16 00:00:00
Tasks: Management

Due to the ever-increasing popularity of ride-hailing services and the indisputable shift towards alternative fuel vehicles, the intersection of the ride-hailing market and smart electric mobility provides an opportunity to trade different services to achieve societal optimum. In this work, we present a hierarchical, game-based, control mechanism for balancing the simultaneous charging of multiple ride-hailing fleets. The mechanism takes into account sometimes conflicting interests of the ride-hailing drivers, the ride-hailing company management, and the external agents such as power-providing companies or city governments that will play a significant role in charging management in the future. The upper-level control considers charging price incentives and models the interactions between the external agents and ride-hailing companies as a Reverse Stackelberg game with a single leader and multiple followers. The lower-level control motivates the revenue-maximizing drivers to follow the company operator's requests through surge pricing and models the interactions as a single leader, multiple followers Stackelberg game. We provide a pricing mechanism that ensures the existence of a unique Nash equilibrium of the upper-level game that minimizes the external agent's objective at the same time. We provide theoretical and experimental robustness analysis of the upper-level control with respect to parameters whose values depend on sensitive information that might not be entirely accessible to the external agent. For the lower-level algorithm, we combine the Nash equilibrium of the upper-level game with a quadratic mixed integer optimization problem to find the optimal surge prices. Finally, we illustrate the performance of the control mechanism in a case study based on real taxi data from the city of Shenzhen in China.
Direct Computing on Control Capability for Linear Continuous-time Systems Based on Hurwitz Matrix
Authors: Mingwang Zhao
Date: 2022-10-16 00:00:00

In this paper, based on the controllable canonical form and the Hurwitz matrix of the Hurwitz stability criterion, an analytical volume computing method for the smooth controllability zonotope for the linear continuous-time(LCT) systems, without of help of the eigenvalue computing of the systems, is presented. And then, the computing method is generlized to the volume computing of the controllability ellipsoid of the LCT systems. Because the controllability zonotope and ellipsoid are directly related to control capability and their volumes are the main index describing the control capability, the new volume computing methods proposed in this paper can help greatly the computing, analysis and optimization of the control capability of LCT systems.
Analytical Volume Analysis for the Finite-time Controllable Region of the Linear Discrete-time Systems
Authors: Mingwang Zhao
Date: 2022-10-16 00:00:00

In this paper, the works on the analytical volume analysis for the controllable regions of the linear discrete-time (LDT) systems in papers \cite{zhaomw202001} and \cite {zhaomw202004} are discussed further and a new theorem on the analytical computing for the finite-time controllability zonotope (controllable region) of LDT systems are proven. And then, three analytical factors describing the control capability of the systems are deconstructed successfully from the analytical volume expression of the controllable region. Finally, the theorem is generalized to three cases: the narrow controllable region, the matrix $A$ with $n$ negative eigenvalues, the linear continuous-time systems.
Optimal Controller Tuning Technique for a First-Order Process with Time Delay
Authors: Clinton Enwerem, Ihechiluru Okoro
Date: 2022-10-15 00:00:00

We present a controller tuning strategy for first-order plus time delay (FOPTD) processes, where the time delay in the model is approximated using the Pad\'e function. Using Routh-Hurwitz stability analysis, we derive the gain that gives rise to desirable PID controller settings. The resulting PID controller, now correctly tuned, produces satisfactory closed-loop behavior and stabilizes the first-order plant. Our proposed technique eliminates the dead-time component in the model and results in a minimum-phase system with all of its poles and zeros in the left-half $s$-plane. To demonstrate the effectiveness of our approach, we present control simulation results from an in-depth performance comparison between our technique and other established model-based strategies used for the control of time-delayed systems. These results prove that, for the FOPTD model, Pad\'e approximation eliminates the undesirable effects of the time delay and promises a faster tracking performance superior to conventional model-based controllers.
Model of rough surfaces with Gaussian processes
Authors: Arsalan Jawaid, Jörg Seewig
Date: 2022-10-15 00:00:00
Tasks: Contact mechanics, Gaussian Processes, model

Surface roughness plays a critical role and has effects in, e.g. fluid dynamics or contact mechanics. For example, to evaluate fluid behavior at different roughness properties, real-world or numerical experiments are performed. Numerical simulations of rough surfaces can speed up these studies because they can help collect more relevant information. However, it is hard to simulate rough surfaces with deterministic or structured components in current methods. In this work, we present a novel approach to simulate rough surfaces with a Gaussian process (GP) and a noise model because GPs can model structured and periodic elements. GPs generalize traditional methods and are not restricted to stationarity so they can simulate a wider range of rough surfaces. In this paper, we summarize the theoretical similarities of GPs with auto-regressive moving-average processes and introduce a linear process view of GPs. We also show examples of ground and honed surfaces simulated by a predefined model. The proposed method can also be used to fit a model to measurement data of a rough surface. In particular, we demonstrate this to model turned profiles and surfaces that are inherently periodic.
Oscillatory cooperation prevalence emerges from misperception
Authors: Jing Zhang, Zhao Li, Jiqiang Zhang, Lin Ma, Guozhong Zheng, Li Chen
Date: 2022-10-17 00:00:00

Oscillatory behaviors are ubiquitous in nature and the human society. However, most previous works fail to reproduce them in the two-strategy game-theoretical models. Here we show that oscillatory behaviors naturally emerge if incomplete information is incorporated into the cooperation evolution of a non-Markov model. Specifically, we consider a population playing prisoner's dilemma game, where each individual can only probabilistically get access to their neighbors' payoff information and store them within their memory with a given length. They make their decisions based upon these memories. Interestingly, we find that the level of cooperation generally cannot stabilize but render quasi-periodic oscillation, and this observation is strengthened for a longer memory and a smaller information acquisition probability. The mechanism uncovered shows that there are misperceived payoffs about the player's neighborhood, facilitating the growth of cooperators and defectors at different stages that leads to oscillatory behaviors as a result. Our findings are robust to the underlying structure of the population. Given the omnipresence of incomplete information, our findings may provide a plausible explanation for the phenomenon of oscillatory behaviors in the real world.
Circuit Theory for Chemical Reaction Networks
Authors: Francesco Avanzini, Nahuel Freitas, Massimiliano Esposito
Date: 2022-10-14 00:00:00

We lay the foundation of a circuit theory for chemical reaction networks. Chemical reactions are grouped into chemical modules solely characterized by their current-concentration characteristic, as electrical devices by their current-voltage (I-V) curve in electronic circuit theory. This, combined with the chemical analog of Kirchhoff's current and voltage laws, provides a powerful tool to predict reaction currents and dissipation across complex chemical networks. The theory can serve to build accurate reduced models of complex networks as well as to design networks performing desired tasks.
Dynamic Topological Data Analysis of Functional Human Brain Networks
Authors: Moo K. Chung, Soumya Das, Hernando Ombao
Date: 2022-10-17 00:00:00
Tasks: Time Series, Time Series Analysis, Topological Data Analysis

Developing reliable methods to discriminate different transient brain states that change over time is a key neuroscientific challenge in brain imaging studies. Topological data analysis (TDA), a novel framework based on algebraic topology, can handle such a challenge. However, existing TDA has been somewhat limited to capturing the static summary of dynamically changing brain networks. We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks. We construct a Wasserstein distance based inference procedure to discriminate between time series of networks. The method is applied to the resting-state functional magnetic resonance images of human brain. We demonstrate that our proposed dynamic-TDA approach can distinctly discriminate between the topological patterns of male and female brain networks. MATLAB code for implementing this method is available at https://github.com/laplcebeltrami/PH-STAT.
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets
Authors: Yanbing Liu, Wei Li, Kun Cheng, Xun Liu, Wei Yang
Date: 2022-10-05 00:00:00
Tasks: Classification, image-classification, Image Classification

In recent years, the optical 4f system has drawn much attention in building high-speed and ultra-low-power optical neural networks (ONNs). Most optical systems suffer from the misalignment of the optical devices during installment. The performance of ONN based on the optical 4f system (4f-ONN) is considered sensitive to the misalignment in the optical path introduced. In order to comprehensively investigate the influence caused by the misalignment, we proposed a method for estimating the performance of a 4f-ONN in response to various misalignment in the context of the image classification task.The misalignment in numerical simulation is estimated by manipulating the optical intensity distributions in the fourth focus plane in the 4f system. Followed by a series of physical experiments to validate the simulation results. Using our method to test the impact of misalignment of 4f system on the classification accuracy of two popular image classification datasets, MNIST and Quickdraw16. On both datasets, we found that the performances of 4f-ONN generally degraded dramatically as the positioning error increased. Different positioning error tolerance in the misalignment orientations was observed over the two datasets. Classification performance could be preserved by positioning errors up to 200 microns in a specific direction.
Modified Wilcoxon-Mann-Whitney tests of stochastic dominance
Authors: Brendan K. Beare, Jackson D. Clarke
Date: 2022-10-17 00:00:00
Tasks: valid

Given independent samples from two univariate distributions, one-sided Wilcoxon-Mann-Whitney statistics may be used to conduct rank-based tests of stochastic dominance. We broaden the scope of applicability of such tests by showing that the bootstrap may be used to conduct valid inference in a matched pairs sampling framework permitting dependence between the two samples. Further, we show that a modified bootstrap incorporating an implicit estimate of a contact set may be used to improve power. Numerical simulations indicate that our test using the modified bootstrap effectively controls the null rejection rates and can deliver more or less power than that of the Donald-Hsu test. In the course of establishing our results we obtain a weak approximation to the empirical ordinance dominance curve permitting its population density to diverge to infinity at zero or one at arbitrary rates.
Inference on Extreme Quantiles of Unobserved Individual Heterogeneity
Authors: Vladislav Morozov
Date: 2022-10-16 00:00:00
Tasks: Prediction Intervals

We develop a methodology for conducting inference on extreme quantiles of unobserved individual heterogeneity (e.g., heterogeneous coefficients, treatment effects) in panel data and meta-analysis settings. Inference is challenging in such settings: only noisy estimates of heterogeneity are available, and central limit approximations perform poorly in the tails. We derive a necessary and sufficient condition under which noisy estimates are informative about extreme quantiles, along with sufficient rate and moment conditions. Under these conditions, we establish an extreme value theorem and an intermediate order theorem for noisy estimates. These results yield simple optimization-free confidence intervals for extreme quantiles. Simulations show that our confidence intervals have favorable coverage and that the rate conditions matter for the validity of inference. We illustrate the method with an application to firm productivity differences between denser and less dense areas.
A New Method for Generating Random Correlation Matrices
Authors: Ilya Archakov, Peter Reinhard Hansen, Yiyao Luo
Date: 2022-10-15 00:00:00

We propose a new method for generating random correlation matrices that makes it simple to control both location and dispersion. The method is based on a vector parameterization, gamma = g(C), which maps any distribution on R^d, d = n(n-1)/2 to a distribution on the space of non-singular nxn correlation matrices. Correlation matrices with certain properties, such as being well-conditioned, having block structures, and having strictly positive elements, are simple to generate. We compare the new method with existing methods.
Exploring the stability of solar geoengineering agreements
Authors: Niklas V. Lehmann
Date: 2022-10-17 00:00:00

A simple model is introduced to study the cooperative behavior of nations regarding solar geoengineering. The results of this model are explored through numerical methods. A general finding is that cooperation and coordination between nations on solar geoengineering is very much incentivized. Furthermore, the stability of solar geoengineering agreements between nations crucially depends on the perceived riskiness of solar geoengineering. If solar geoengineering is perceived as riskier, the stability of the most stable solar geoengineering agreements is reduced. However, the stability of agreements is completely independent of countries preferences.
Large gender and age differences in hand disinfection behavior during the COVID-19 pandemic: Field data from Swiss retail stores
Authors: Frauke von Bieberstein, Anna-Corinna Kulle, Stefanie Schumacher
Date: 2022-10-17 00:00:00

Hand hygiene is one of the key low-cost measures proposed by the World Health Organization (WHO) to contain the spread of COVID-19. In a field study conducted during the pandemic in June and July 2020 in Switzerland, we captured the hand disinfection behavior of customers in five stores of a large retail chain (n = 8,245). The study reveals considerable differences with respect to gender and age: Women were 8.3 percentage points more likely to disinfect their hands compared to men. With respect to age, we identified a steep increase across age groups, with people age 60 years and older disinfecting their hands significantly more often than younger adults (>+16.7 percentage points) and youth (>+ 31.7 percentage points). A validation study conducted in December 2020 (n = 1,918) confirmed the gender and age differences at a later point in the pandemic. In sum, the differences between gender and age groups are substantial and should be considered in the design of protective measures to ensure clean and safe hands.
Climate uncertainty, financial frictions and constrained efficient carbon taxation
Authors: Felix Kübler
Date: 2022-10-17 00:00:00

In this paper, I consider a simple heterogeneous agents model of a production economy with uncertain climate change and examine constrained efficient carbon taxation. If there are frictionless, complete financial markets, the simple model predicts a unique Pareto-optimal level of carbon taxes and abatement. In the presence of financial frictions, however, the optimal level of abatement cannot be defined without taking a stand on how abatement costs are distributed among individuals. I propose a simple linear cost-sharing scheme that has several desirable normative properties. I use calibrated examples of economies with incomplete financial markets and/or limited market participation to demonstrate that different schemes to share abatement costs can have large effects on optimal abatement levels and that the presence of financial frictions can increase optimal abatement by a factor of three relative to the case of frictionless financial market.
Welfare estimations from imagery. A test of domain experts ability to rate poverty from visual inspection of satellite imagery
Authors: Wahab Ibrahim, Ola Hall
Date: 2022-10-17 00:00:00

The present study uses domain experts to estimate welfare levels and indicators from high-resolution satellite imagery. We use the wealth quintiles from the 2015 Tanzania DHS dataset as ground truth data. We analyse the performance of the visual estimation of relative wealth at the cluster level and compare these with wealth rankings from the DHS survey of 2015 for that country using correlations, ordinal regressions and multinomial logistic regressions. Of the 608 clusters, 115 received the same ratings from human experts and the independent DHS rankings. For 59 percent of the clusters, experts ratings were slightly lower. On the one hand, significant positive predictors of wealth are the presence of modern roofs and wider roads. For instance, the log odds of receiving a rating in a higher quintile on the wealth rankings is 0.917 points higher on average for clusters with buildings with slate or tile roofing compared to those without. On the other hand, significant negative predictors included poor road coverage, low to medium greenery coverage, and low to medium building density. Other key predictors from the multinomial regression model include settlement structure and farm sizes. These findings are significant to the extent that these correlates of wealth and poverty are visually readable from satellite imagery and can be used to train machine learning models in poverty predictions. Using these features for training will contribute to more transparent ML models and, consequently, explainable AI.
A Group Public Goods Game with Position Uncertainty
Authors: Chowdhury Mohammad Sakib Anwar, Jorge Bruno, Sonali SenGupta
Date: 2022-10-15 00:00:00
Tasks: Position

We model a dynamic public good contribution game, where players are (naturally) formed into groups. The groups are exogenously placed in a sequence, with limited information available to players about their groups' position in the sequence. Contribution decisions are made by players simultaneously and independently, and the groups' total contribution is made sequentially. We try to capture both inter and intra-group behaviors and analyze different situations where players observe partial history about total contributions of their predecessor groups. Given this framework, we show that even when players observe a history of defection (no contribution), a cooperative outcome is achievable. This is particularly interesting in the situation when players observe only their immediate predecessor groups' contribution, where we observe that players play an important role in motivating others to contribute.
Setting Interim Deadlines to Persuade
Authors: Maxim Senkov
Date: 2022-10-15 00:00:00

A principal funds a multistage project and retains the right to cut the funding if it stagnates at some point. An agent wants to convince the principal to fund the project as long as possible, and can design the flow of information about the progress of the project in order to persuade the principal. If the project is sufficiently promising ex ante, then the agent commits to providing only the good news that the project is accomplished. If the project is not promising enough ex ante, the agent persuades the principal to start the funding by committing to provide not only good news but also the bad news that a project milestone has not been reached by an interim deadline. I demonstrate that the outlined structure of optimal information disclosure holds irrespective of the agent's profit share, benefit from the flow of funding, and the common discount rate.
Approximate optimality and the risk/reward tradeoff in a class of bandit problems
Authors: Zengjing Chen, Larry G. Epstein, Guodong Zhang
Date: 2022-10-14 00:00:00

This paper studies a sequential decision problem where payoff distributions are known and where the riskiness of payoffs matters. Equivalently, it studies sequential choice from a repeated set of independent lotteries. The decision-maker is assumed to pursue strategies that are approximately optimal for large horizons. By exploiting the tractability afforded by asymptotics, conditions are derived characterizing when specialization in one action or lottery throughout is asymptotically optimal and when optimality requires intertemporal diversification. The key is the constancy or variability of risk attitude. The main technical tool is a new central limit theorem.
Breathing Pattern Monitoring using Remote Sensors
Authors: Janosch Kunczik, Kerstin Hubbermann, Lucas Mösch, Andreas Follmann, Michael Czaplik, Carina Barbosa Pereira
Date: 2022-10-17 00:00:00

Breathing is one of the most important body functions because it provides it with oxygen, which is vital for energy production. In addition, the removal of carbon dioxide actively regulates the acid-base level, which is essential for the physiological function of the body. Due to its close connection with many other body functions, respiration can also be used as an indicator for a wide spectrum of medical conditions, which at first glance have little to do with breathing. Neurological, cardiological, inflammatory, metabolic, and even psychological conditions symptomatically show up in breathing patterns. Hence, being able to classify them automatically and unobtrusively, can allow cost-effective monitoring systems to continuously assess the health of a patient. In this work, multiple respiratory signal-extraction algorithms for thermal and RGB cameras are presented and compared. A novel algorithm for the extraction of multiple respiratory features is presented and evaluated. Using a one vs. one multiclass support vector machine, these features were used to classify a wide range of respiratory patterns with an accuracy of up to 95.79%.