pwc-archive/papers-with-abstracts huggingface dataset:
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles
Authors: Utku Ayvaz, Chih-Hong Cheng, Hao Shen
Date: 2023-05-28 00:00:00
Tasks: Autonomous Vehicles, counterfactual, Decision Making, Reinforcement Learning (RL)

While autonomous vehicles (AVs) may perform remarkably well in generic real-life cases, their irrational action in some unforeseen cases leads to critical safety concerns. This paper introduces the concept of collaborative reinforcement learning (RL) to generate challenging test cases for AV planning and decision-making module. One of the critical challenges for collaborative RL is the credit assignment problem, where a proper assignment of rewards to multiple agents interacting in the traffic scenario, considering all parameters and timing, turns out to be non-trivial. In order to address this challenge, we propose a novel potential-based reward-shaping approach inspired by counterfactual analysis for solving the credit-assignment problem. The evaluation in a simulated environment demonstrates the superiority of our proposed approach against other methods using local and global rewards.
Prediction of the 2023 Turkish Presidential Election Results Using Social Media Data
Authors: Aysun Bozanta, Fuad Bayrak, Ayse Basar
Date: 2023-05-28 00:00:00

Social media platforms influence the way political campaigns are run and therefore they have become an increasingly important tool for politicians to directly interact with citizens. Previous elections in various countries have shown that social media data may significantly impact election results. In this study, we aim to predict the vote shares of parties participating in the 2023 elections in Turkey by combining social media data from various platforms together with traditional polling data. Our approach is a volume-based approach that considers the number of social media interactions rather than content. We compare several prediction models across varying time windows. Our results show that for all time windows, the ARIMAX model outperforms the other algorithms.
Self-attention Dual Embedding for Graphs with Heterophily
Authors: Yurui Lai, Taiyan Zhang, Rui Fan
Date: 2023-05-28 00:00:00
Tasks: Informativeness, Node Classification

Graph Neural Networks (GNNs) have been highly successful for the node classification task. GNNs typically assume graphs are homophilic, i.e. neighboring nodes are likely to belong to the same class. However, a number of real-world graphs are heterophilic, and this leads to much lower classification accuracy using standard GNNs. In this work, we design a novel GNN which is effective for both heterophilic and homophilic graphs. Our work is based on three main observations. First, we show that node features and graph topology provide different amounts of informativeness in different graphs, and therefore they should be encoded independently and prioritized in an adaptive manner. Second, we show that allowing negative attention weights when propagating graph topology information improves accuracy. Finally, we show that asymmetric attention weights between nodes are helpful. We design a GNN which makes use of these observations through a novel self-attention mechanism. We evaluate our algorithm on real-world graphs containing thousands to millions of nodes and show that we achieve state-of-the-art results compared to existing GNNs. We also analyze the effectiveness of the main components of our design on different graphs.
Stochastic Model Predictive Control with Dynamic Chance Constraints
Authors: Maico Hendrikus Wilhelmus Engelaar, Sofie Haesaert, Mircea Lazar
Date: 2023-05-30 00:00:00
Tasks: model, Model Predictive Control, Stochastic Optimization

This work introduces a stochastic model predictive control scheme for dynamic chance constraints. We consider linear discrete-time systems affected by unbounded additive stochastic disturbance. To synthesize an optimal controller, we solve two subsequent stochastic optimization problems. The first problem concerns finding the maximal feasible probabilities of the dynamic chance constraints. After obtaining the probabilities, the second problem concerns finding an optimal controller using stochastic model predictive control. We solve both stochastic optimization problems by reformulating them into deterministic ones using probabilistic reachable tubes and constraint tightening. We prove that the developed algorithm is recursively feasible and yields closed-loop satisfaction of the dynamic chance constraints. In addition, we will introduce a novel implementation using zonotopes to describe the tightening analytically. Finally, we will end with an example illustrating the method's benefits.
Steady-state analysis of networked epidemic models
Authors: Sei Zhen Khong, Lanlan Su
Date: 2023-05-30 00:00:00
Tasks: Unity

Compartmental epidemic models with dynamics that evolve over a graph network have gained considerable importance in recent years but analysis of these models is in general difficult due to their complexity. In this paper, we develop two positive feedback frameworks that are applicable to the study of steady-state values in a wide range of compartmental epidemic models, including both group and networked processes. In the case of a group (resp. networked) model, we show that the convergence limit of the susceptible proportion of the population (resp. the susceptible proportion in at least one of the subgroups) is upper bounded by the reciprocal of the basic reproduction number (BRN) of the model. The BRN, when it is greater than unity, thus demonstrates the level of penetration into a subpopulation by the disease. Both non-strict and strict bounds on the convergence limits are derived and shown to correspond to substantially distinct scenarios in the epidemic processes, one in the presence of the endemic state and another without. Formulae for calculating the limits are provided in the latter case. We apply the developed framework to examining various group and networked epidemic models commonly seen in the literature to verify the validity of our conclusions.
Optimal Controller Identification for multivariable non-minimum phase systems
Authors: D. D. Huff, L. Campestrini, G. R. Gonçalves da Silva, A. S. Bazanella
Date: 2023-05-30 00:00:00

This paper extends the formulation of a data-driven control method - the Optimal Controller Identification (OCI) - to cope with non-minimum phase (NMP) systems, without a priori knowledge of the NMP transmission zero, i.e. without obtaining a prior model of the plant - as in any data-driven approach. The method is based on the Model Reference paradigm, in which the desired closed-loop performance is specified by means of a closed-loop transfer function - the reference model. Considering a convenient parametrization of the latter and a flexible performance criterion, it is possible to identify the NMP transmission zeros of the plant along with the optimal controller parameters, as it will be shown. Both diagonal and block-triangular reference model structures are treated in detail. Simulation examples show the effectiveness of the proposed approach.
Data Augmentation Methods of Dynamic Model Identification for Harbor Maneuvers using Feedforward Neural Network
Authors: Kouki Wakita, Yoshiki Miyauchi, Youhei Akimoto, Atsuo Maki
Date: 2023-05-30 00:00:00
Tasks: Data Augmentation

A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering and leaving the port. When the dynamic model is estimated by the system identification, a large number of tests or trials are required to measure the various motions of harbor maneuvers. However, the amount of data that can be obtained is limited due to the high costs and time-consuming nature of full-scale ship trials. In this paper, we improve the generalization performance of the dynamic model for the automatic berthing and unberthing controller by introducing data augmentation. This study used slicing and jittering as data augmentation methods and confirmed their effectiveness by numerical experiments using the free-running model tests. The dynamic model is represented by a neural network-based model in numerical experiments. Results of numerical experiments demonstrated that slicing and jittering are effective data augmentation methods but could not improve generalization performance for extrapolation states of the original dataset.
Design of the Reverse Logistics System for Medical Waste Recycling Part II: Route Optimization with Case Study under COVID-19 Pandemic
Authors: Chaozhong Xue, Yongqi Dong, Jiaqi Liu, Yijun Liao, Lingbo Li
Date: 2023-05-30 00:00:00

Medical waste recycling and treatment has gradually drawn concerns from the whole society, as the amount of medical waste generated is increasing dramatically, especially during the pandemic of COVID-19. To tackle the emerging challenges, this study designs a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the design of the route optimization module. In this module, a route optimization problem is designed considering transportation costs and multiple risk costs (e.g., environment risk, population risk, property risk, and other accident-related risks). The Analytic Hierarchy Process is employed to determine the weights for each risk element, and a customized genetic algorithm is developed to solve the route optimization problem. A case study under the COVID-19 pandemic is further provided to verify the proposed model. Limited by length, detailed descriptions of the whole system and the other modules can be found at https://shorturl.at/cdY59.
Turbidity Control in Sedimentation Columns by Direction Dependent Models
Authors: Jesus-Pablo Toledo-Zucco, Daniel Sbarbaro, Joao Manoel Gomes da Silva Jr
Date: 2023-05-29 00:00:00

Sedimentation is a crucial phenomenon in recovering water from slurries by separating solid-liquid. Thickeners and sedimentation columns are equipments widely used in the process industry to reclaim water from process slurries. This contribution addresses the problem of controlling the turbidity of the recovered water in a sedimentation column by manipulating the underflow. The phenomenological model describing the turbidity is too complex to be used in a control strategy, and it is difficult to identify its parameters using plant measurements. This work proposes an empirical piece-wise time-delay model for modeling the turbidity at the top of the column to circumvent these problems. A systematic design procedure is developed to tune a Proportional Integral controller guaranteeing closed-loop stability for systems modeled as a piece-wise time delay model. Experiments in a pilot plant validate the theoretical results and illustrate the control performance under various operational scenarios.
Extended Pacejka Tire Model for Enhanced Vehicle Stability Control
Authors: Kanwar Bharat Singh, Srikanth Sivaramakrishnan
Date: 2023-05-28 00:00:00

Despite their widespread use, current tire models have demonstrated a certain level of inaccuracy, primarily due to uncertainties related to unaccounted nonlinearity and disturbance effects resulting from tire operating conditions. Noteworthy factors such as tread depth, inflation pressure, tire temperature, and road surface condition significantly impact tire force and moment characteristics. These factors can vary considerably during tire operation and significantly affect both tire and vehicle performance. The improvement of tire models is crucial to enhance the effectiveness of advanced vehicle control systems, as accurate tire force characteristics are required for maintaining vehicle stability during demanding maneuvers. This paper investigates the impact of varying tire temperature, inflation pressure, and tread depth on steady-state tire force characteristics by analyzing the coefficients of the Pacejka 'magic formula' (MF) tire model. Based on this analysis, adaptation equations are proposed to compensate for the influence of these variables on the tire force curve. The advantages of using an adapted tire model are then demonstrated through simulation studies of a classical vehicle stability control system that can adapt to diverse operating conditions. A comparison is made between the adapted tire model-based controller and a controller based on a fixed reference model.
An Analytic End-to-End Deep Learning Algorithm based on Collaborative Learning
Authors: Sitan Li, Chien Chern Cheah
Date: 2023-05-26 00:00:00
Tasks: Deep Learning

In most control applications, theoretical analysis of the systems is crucial in ensuring stability or convergence, so as to ensure safe and reliable operations and also to gain a better understanding of the systems for further developments. However, most current deep learning methods are black-box approaches that are more focused on empirical studies. Recently, some results have been obtained for convergence analysis of end-to end deep learning based on non-smooth ReLU activation functions, which may result in chattering for control tasks. This paper presents a convergence analysis for end-to-end deep learning of fully connected neural networks (FNN) with smooth activation functions. The proposed method therefore avoids any potential chattering problem, and it also does not easily lead to gradient vanishing problems. The proposed End-to-End algorithm trains multiple two-layer fully connected networks concurrently and collaborative learning can be used to further combine their strengths to improve accuracy. A classification case study based on fully connected networks and MNIST dataset was done to demonstrate the performance of the proposed approach. Then an online kinematics control task of a UR5e robot arm was performed to illustrate the regression approximation and online updating ability of our algorithm.
Closed ecosystems extract energy through self-organized nutrient cycles
Authors: Akshit Goyal, Avi I. Flamholz, Alexander P. Petroff, Arvind Murugan
Date: 2023-05-30 00:00:00

Our planet is roughly closed to matter, but open to energy input from the sun. However, to harness this energy, organisms must transform matter from one chemical (redox) state to another. For example, photosynthetic organisms can capture light energy by carrying out a pair of electron donor and acceptor transformations (e.g., water to oxygen, CO$_2$ to organic carbon). Closure of ecosystems to matter requires that all such transformations are ultimately balanced, i.e., other organisms must carry out corresponding reverse transformations, resulting in cycles that are coupled to each other. A sustainable closed ecosystem thus requires self-organized cycles of matter, in which every transformation has sufficient thermodynamic favorability to maintain an adequate number of organisms carrying out that process. Here, we propose a new conceptual model that explains the self-organization and emergent features of closed ecosystems. We study this model with varying levels of metabolic diversity and energy input, finding that several thermodynamic features converge across ecosystems. Specifically, irrespective of their species composition, large and metabolically diverse communities self-organize to extract roughly 10% of the maximum extractable energy, or 100 fold more than randomized communities. Moreover, distinct communities implement energy extraction in convergent ways, as indicated by strongly correlated fluxes through nutrient cycles. As the driving force from light increases, however, these features -- fluxes and total energy extraction -- become more variable across communities, indicating that energy limitation imposes tight thermodynamic constraints on collective metabolism.
Optimization design of a micro-perforated panel absorber with 8.6 octave bands
Authors: Xiaoming Wang, Chen Liang, Yulin Mei
Date: 2023-04-23 00:00:00

In order to improve low-frequency characteristics of micro-perforated panel absorbers, sound absorption structures composed of micro-perforated panels and expansion chambers are design, and an optimization design method is constructed based on the transfer function model and the simulated annealing algorithm. First, a single-chamber structure composed of a micro-perforated panel and an expansion chamber is build, and the sound absorption curve is simulated by the finite element method. Second, for the sake of enlarging the continuous absorption bandwidth with absorption coefficients not less than 0.8, a three-chamber structure is designed, which has a sound absorption bandwidth of 1277Hz (27-1304Hz) covering 5.6 octave bands. Then, the transfer function model of the structure is established, and a series of theoretical formulae are derived to calculate the absorption coefficients. Subsequently, the sound absorption bandwidths calculated by the theoretical formulae and the finite element method are compared, and the relative error is 3.68%. Finally, an optimization design method is constructed by combining the transfer function model and the simulated annealing algorithm, where the optimization objective is to maximize the absorption bandwidth and the optimization variables are structural parameters of the three-chamber structure. The results show, after optimization, the three-chamber structure exhibits an excellent sound absorption performance, with a continuous bandwidth of 1591Hz (4-1595Hz), realizing 8.6 octave bands.
A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics
Authors: Tanishk Nandal, Vaibhav Fulara, Raj Kumar Singh
Date: 2023-05-28 00:00:00

In the realm of computational fluid dynamics (CFD), accurate prediction of aerodynamic behaviour plays a pivotal role in aerofoil design and optimization. This study proposes a novel approach that synergistically combines autoencoders and Generative Adversarial Networks (GANs) for the purpose of generating CFD results. Our innovative framework harnesses the intrinsic capabilities of autoencoders to encode aerofoil geometries into a compressed and informative 20-length vector representation. Subsequently, a conditional GAN network adeptly translates this vector into precise pressure-distribution plots, accounting for fixed wind velocity, angle of attack, and turbulence level specifications. The training process utilizes a meticulously curated dataset acquired from JavaFoil software, encompassing a comprehensive range of aerofoil geometries. The proposed approach exhibits profound potential in reducing the time and costs associated with aerodynamic prediction, enabling efficient evaluation of aerofoil performance. The findings contribute to the advancement of computational techniques in fluid dynamics and pave the way for enhanced design and optimization processes in aerodynamics.
Impulse Response Analysis of Structural Nonlinear Time Series Models
Authors: Giovanni Ballarin
Date: 2023-05-30 00:00:00
Tasks: Time Series

This paper proposes a semiparametric sieve approach to estimate impulse response functions of nonlinear time series within a general class of structural autoregressive models. We prove that a two-step procedure can flexibly accommodate nonlinear specifications while avoiding the need to choose fixed parametric forms. Sieve impulse responses are proven to be consistent by deriving uniform estimation guarantees, and an iterative algorithm makes it straightforward to compute them in practice. With simulations, we show that the proposed semiparametric approach proves effective against misspecification while suffering only from minor efficiency losses. In a U.S. monetary policy application, the pointwise sieve GDP response associated with an interest rate increase is larger than that of a linear model. Finally, in an analysis of interest rate uncertainty shocks, sieve responses indicate more substantial contractionary effects on production and inflation.
Playing the system: address manipulation and access to schools
Authors: Andreas Bjerre-Nielsen, Lykke Sterll Christensen, Mikkel Høst Gandil, Hans Henrik Sievertsen
Date: 2023-05-30 00:00:00

Strategic incentives may lead to inefficient and unequal provision of public services. A prominent example is school admissions. Existing research shows that applicants "play the system" by submitting school rankings strategically. We investigate whether applicants also play the system by manipulating their eligibility at schools. We analyze this applicant deception in a theoretical model and provide testable predictions for commonly-used admission procedures. We confirm these model predictions empirically by analyzing the implementation of two reforms. First, we find that the introduction of a residence-based school-admission criterion in Denmark caused address changes to increase by more than 100% before the high-school application deadline. This increase occurred only in areas where the incentive to manipulate is high-powered. Second, to assess whether this behavior reflects actual address changes, we study a second reform that required applicants to provide additional proof of place of residence to approve an address change. The second reform significantly reduced address changes around the school application deadline, suggesting that the observed increase in address changes mainly reflects manipulation. The manipulation is driven by applicants from more affluent households and their behavior affects non-manipulating applicants. Counter-factual simulations show that among students not enrolling in their first listed school, more than 25% would have been offered a place in the absence of address manipulation and their peer GPA is 0.2SD lower due to the manipulative behavior of other applicants. Our findings show that popular school choice systems give applicants the incentive to play the system with real implications for non-strategic applicants.
The Centralizing Effects of Private Order Flow on Proposer-Builder Separation
Authors: Tivas Gupta, Mallesh M Pai, Max Resnick
Date: 2023-05-30 00:00:00

The current Proposer-Builder Separation (PBS) equilibrium has several builders with different backgrounds winning blocks consistently. This paper considers how that equilibrium will shift when transactions are sold privately via order flow auctions (OFAs) rather than forwarded directly to the public mempool. We discuss a novel model that highlights the augmented value of private order flow for integrated builder searchers. We show that private order flow is complementary to top-of-block opportunities, and therefore integrated builder-searchers are more likely to participate in OFAs and outbid non integrated builders. They will then parlay access to these private transactions into an advantage in the PBS auction, winning blocks more often and extracting higher profits than non-integrated builders. To validate our main assumptions, we construct a novel dataset pairing post-merge PBS outcomes with realized 12-second volatility on a leading CEX (Binance). Our results show that integrated builder-searchers are more likely to win in the PBS auction when realized volatility is high, suggesting that indeed such builders have an advantage in extracting top-of-block opportunities. Our findings suggest that modifying PBS to disentangle the intertwined dynamics between top-of-block extraction and private order flow would pave the way for a fairer and more decentralized Ethereum.