pwc-archive/papers-with-abstracts huggingface dataset:
Optimal transport through a toll
Authors: Arthur Stephanovitch, Anqi Dong, Tryphon T. Georgiou
Date: 2022-08-06 00:00:00

We address the problem of optimal transport with a quadratic cost functional and a constraint on the flux through a constriction along the path. The constriction, conceptually represented by a toll station, limits the flow rate across. We provide a precise formulation which, in addition, is amenable to generalization in higher dimensions. We work out in detail the case of transport in one dimension by proving existence and uniqueness of solution. Under suitable regularity assumptions we give an explicit construction of the transport plan. Generalization of flux constraints to higher dimensions and possible extensions of the theory are discussed.
Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions
Authors: Siddharth H. Nair, Vijay Govindarajan, Theresa Lin, Yan Wang, Eric H. Tseng, Francesco Borrelli
Date: 2022-08-06 00:00:00
Tasks: Autonomous Driving

We propose a Stochastic MPC (SMPC) approach for autonomous driving which incorporates multi-modal, interaction-aware predictions of surrounding vehicles. For each mode, vehicle motion predictions are obtained by a control model described using a basis of fixed features with unknown weights. The proposed SMPC formulation finds optimal controls which serves two purposes: 1) reducing conservatism of the SMPC by optimizing over parameterized control laws and 2) prediction and estimation of feature weights used in interaction-aware modeling using Kalman filtering. The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems
Authors: Abolfazl Lavaei, Mateo Perez, Milad Kazemi, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Majid Zamani
Date: 2022-08-06 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning (RL). The approach is based on implicitly abstracting each subsystem in the network with a finite Markov decision process with unknown transition probabilities, synthesizing a strategy for each abstract model in an assume-guarantee fashion using RL, and then mapping the results back over the original network with approximate optimality guarantees. We provide lower bounds on the satisfaction probability of the overall network based on those over individual subsystems. A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems. We consider finite-horizon properties expressed in the syntactically co-safe fragment of linear temporal logic. These properties can readily be converted into automata-based reward functions, providing scalar reward signals suitable for RL. Since such reward functions are often sparse, we supply a potential-based reward shaping technique to accelerate learning by producing dense rewards. The effectiveness of the proposed approaches is demonstrated via two physical benchmarks including regulation of a room temperature network and control of a road traffic network.
Safety Barrier Certificates for Stochastic Hybrid Systems
Authors: Abolfazl Lavaei, Sadegh Soudjani, Emilio Frazzoli
Date: 2022-08-06 00:00:00

This work is concerned with the safety controller synthesis of stochastic hybrid systems, in which continuous evolutions are described by stochastic differential equations with both Brownian motions and Poisson processes, and instantaneous jumps are governed by stochastic difference equations with additive noises. Our proposed framework leverages the notion of control barrier certificates (CBC), as a discretization-free approach, to synthesize safety controllers for stochastic hybrid systems while providing safety guarantees in finite time horizons. In our proposed scheme, we first provide an augmented framework to characterize each stochastic hybrid system containing continuous evolutions and instantaneous jumps with a unified system covering both scenarios. We then introduce an augmented control barrier certificate (ACBC) for augmented systems and propose sufficient conditions to construct an ACBC based on CBC of original hybrid systems. By utilizing the constructed ACBC, we quantify upper bounds on the probability that the stochastic hybrid system reaches certain unsafe regions in a finite time horizon. The proposed approach is verified over a nonlinear case study.
Compositional Controller Synthesis for Interconnected Stochastic Systems with Markovian Switching
Authors: Abolfazl Lavaei, Emilio Frazzoli
Date: 2022-08-06 00:00:00

In this work, we propose a compositional scheme for the safety controller synthesis of interconnected discrete-time stochastic systems with Markovian switching signals. Our proposed approach is based on a notion of so-called control storage certificates computed for individual subsystems, by leveraging which, one can synthesize state-feedback controllers for interconnected systems to enforce safety specifications over finite time horizons. To do so, we employ a sum-of-squares (SOS) optimization approach to search for multiple storage certificates of each switching subsystem while synthesizing its corresponding safety controller. We then utilize dissipativity theory to compositionally construct barrier certificates for interconnected systems based on storage certificates of individual subsystems. The proposed dissipativity-type compositional conditions can leverage the structure of the interconnection topology and be fulfilled independently of the number or gains of subsystems. We eventually employ the constructed barrier certificate and quantify upper bounds on the probability that the interconnected system reaches certain unsafe regions in a finite time horizon. We apply our results to a room temperature network of 200 rooms with Markovian switching signals while accepting multiple storage certificates. We compositionally synthesize safety controllers to maintain the temperature of each room in a comfort zone for a bounded time horizon.
Trust-Aware Control of Automated Vehicles in Car-Following Interactions with Human Drivers
Authors: Mehmet Fatih Ozkan, Yao Ma
Date: 2022-08-05 00:00:00
Tasks: Decision Making

Trust is essential for automated vehicles (AVs) to promote and sustain technology acceptance in human-dominated traffic scenarios. However, computational trust dynamic models describing the interactive relationship between the AVs and surrounding human drivers in traffic rarely exist. This paper aims to fill this gap by developing a quantitative trust dynamic model of the human driver in the car-following interaction with the AV and incorporating the proposed trust dynamic model into the AV's control design. The human driver's trust level is modeled as a plan evaluation metric that measures the explicability of the AV's plan from the human driver's perspective, and the explicability score of the AV's plan is integrated into the AV's decision-making process. With the proposed approach, trust-aware AVs generate explicable plans by optimizing both predefined plans and explicability of the plans in the car-following interactions with the following human driver. The results collectively demonstrate that the trust-aware AV can generate more explicable plans and achieve a higher trust level for the human driver compared to trust-unaware AV in human-AV interactions.
Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives
Authors: Khaled Alrfou, Amir Kordijazi, Tian Zhao
Date: 2022-07-29 00:00:00
Tasks: object-detection, Object Detection

Finding quantitative descriptors representing the microstructural features of a given material is an ongoing research area in the paradigm of Materials-by-Design. Historically, microstructural analysis mostly relies on qualitative descriptions. However, to build a robust and accurate process-structure-properties relationship, which is required for designing new advanced high-performance materials, the extraction of quantitative and meaningful statistical data from the microstructural analysis is a critical step. In recent years, computer vision (CV) methods, especially those which are centered around convolutional neural network (CNN) algorithms have shown promising results for this purpose. This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks, including classification, object detection, segmentation, feature extraction, and reconstruction. Additionally, we identified the main challenges with regard to the application of these methods to materials science research. Finally, we discussed some possible future directions of research in this area. In particular, we emphasized the application of transformer-based models and their capabilities to improve the microstructural analysis of materials.
One person's modus ponens...: Comment on "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja and colleagues (2021)
Authors: Maxwell J. D Ramstead
Date: 2022-08-08 00:00:00
Tasks: valid

In this comment on "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja and colleagues (2021) in Physics of Life Reviews, I argue that the argument presented by the authors is valid; however, I claim that the argument contains a flawed premise, which undermines their conclusions. In addition, I argue that work on the FEP that has appeared since the target paper was published underwrites a cogent response to the issues that are raised by Raja and colleagues.
Recurrence measures and transitions in stock market dynamics
Authors: Krishnadas M., K. P. Harikrishnan, G. Ambika
Date: 2022-08-06 00:00:00
Tasks: Time Series, Time Series Analysis

The financial markets are understood as complex dynamical systems whose dynamics is analysed mostly using nonstationary and brief data sets that usually come from stock markets. For such data sets, a reliable method of analysis is based on recurrence plots and recurrence networks, constructed from the data sets over the period of study. In this study, we do a comprehensive analysis of the complexity of the underlying dynamics of 26 markets around the globe using recurrence based measures. We also examine trends in the nature of transitions as revealed from these measures by the sliding window analysis along the time series during the global financial crisis of 2008 and compare that with changes during the most recent pandemic related lock down. We show that the measures derived from recurrence patterns can be used to capture the nature of transitions in stock market dynamics. Our study reveals that the changes around 2008 indicate stochasticity driven transition, which is different from the transition during the pandemic.
Finite Tests from Functional Characterizations
Authors: Charles Gauthier, Raghav Malhotra, Agustin Troccoli Moretti
Date: 2022-08-07 00:00:00

Classically, testing whether decision makers belong to specific preference classes involves two main approaches. The first, known as the functional approach, assumes access to an entire demand function. The second, the revealed preference approach, constructs inequalities to test finite demand data. This paper bridges these methods by using the functional approach to test finite data through preference learnability results. We develop a computationally efficient algorithm that generates tests for choice data based on functional characterizations of preference families. We provide these restrictions for various applications, including homothetic and weakly separable preferences, where the latter's revealed preference characterization is provably NP-Hard. We also address choice under uncertainty, offering tests for betweenness preferences. Lastly, we perform a simulation exercise demonstrating that our tests are effective in finite samples and accurately reject demands not belonging to a specified class.
Quantile Random-Coefficient Regression with Interactive Fixed Effects: Heterogeneous Group-Level Policy Evaluation
Authors: Ruofan Xu, Jiti Gao, Tatsushi Oka, Yoon-Jae Whang
Date: 2022-08-07 00:00:00
Tasks: quantile regression, regression

We propose a quantile random-coefficient regression with interactive fixed effects to study the effects of group-level policies that are heterogeneous across individuals. Our approach is the first to use a latent factor structure to handle the unobservable heterogeneities in the random coefficient. The asymptotic properties and an inferential method for the policy estimators are established. The model is applied to evaluate the effect of the minimum wage policy on earnings between 1967 and 1980 in the United States. Our results suggest that the minimum wage policy has significant and persistent positive effects on black workers and female workers up to the median. Our results also indicate that the policy helps reduce income disparity up to the median between two groups: black, female workers versus white, male workers. However, the policy is shown to have little effect on narrowing the income gap between low- and high-income workers within the subpopulations.
Partial Identification of Personalized Treatment Response with Trial-reported Analyses of Binary Subgroups
Authors: Sheyu Li, Valentyn Litvin, Charles F. Manski
Date: 2022-08-05 00:00:00
Tasks: Articles

Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically into binary categories. Given this reporting practice, we study the problem of inference on long mean treatment outcomes E[y(t)|x], where t is a treatment, y(t) is a treatment outcome, and the covariate vector x has length K, each component being a binary variable. The available data are estimates of {E[y(t)|xk = 0], E[y(t)|xk = 1], P(xk)}, k = 1, . . . , K reported in journal articles. We show that reported trial findings partially identify {E[y(t)|x], P(x)}. Illustrative computations demonstrate that the summaries of trial findings in journal articles may imply only wide bounds on long mean outcomes. One can realistically tighten inferences if one can combine reported trial findings with credible assumptions having identifying power, such as bounded-variation assumptions.
Experience of the COVID-19 pandemic in Wuhan leads to a lasting increase in social distancing
Authors: Darija Barak, Edoardo Gallo, Ke Rong, Ke Tang, Wei Du
Date: 2022-08-08 00:00:00

On 11th Jan 2020, the first COVID-19 related death was confirmed in Wuhan, Hubei. The Chinese government responded to the outbreak with a lockdown that impacted most residents of Hubei province and lasted for almost three months. At the time, the lockdown was the strictest both within China and worldwide. Using an interactive web-based experiment conducted half a year after the lockdown with participants from 11 Chinese provinces, we investigate the behavioral effects of this `shock' event experienced by the population of Hubei. We find that both one's place of residence and the strictness of lockdown measures in their province are robust predictors of individual social distancing behavior. Further, we observe that informational messages are effective at increasing compliance with social distancing throughout China, whereas fines for noncompliance work better within Hubei province relative to the rest of the country. We also report that residents of Hubei increase their propensity to social distance when exposed to social environments characterized by the presence of a superspreader, while the effect is not present outside of the province. Our results appear to be specific to the context of COVID-19, and are not explained by general differences in risk attitudes and social preferences.
Selection on moral hazard in the Swiss market for mandatory health insurance: Empirical evidence from Swiss Household Panel data
Authors: Francetic Igor
Date: 2022-08-07 00:00:00

Selection on moral hazard represents the tendency to select a specific health insurance coverage depending on the heterogeneity in utilisation ''slopes''. I use data from the Swiss Household Panel and from publicly available regulatory data to explore the extent of selection on slopes in the Swiss managed competition system. I estimate responses in terms of (log) doctor visits to lowest and highest deductible levels using Roy-type models, identifying marginal treatment effects with local instrumental variables. The response to high coverage plans (i.e. plans with the lowest deductible level) among high moral hazard types is 25-35 percent higher than average.
Potterian Economics
Authors: Daniel Levy, Avichai Snir
Date: 2022-08-06 00:00:00

Recent studies in psychology and neuroscience offer systematic evidence that fictional works exert a surprisingly strong influence on readers and have the power to shape their opinions and worldviews. Building on these findings, we study what we term Potterian economics, the economic ideas, insights, and structure, found in Harry Potter books, to assess how the books might affect economic literacy. A conservative estimate suggests that more than 7.3 percent of the world population has read the Harry Potter books, and millions more have seen their movie adaptations. These extraordinary figures underscore the importance of the messages the books convey. We explore the Potterian economic model and compare it to professional economic models to assess the consistency of the Potterian economic principles with the existing economic models. We find that some of the principles of Potterian economics are consistent with economists models. Many other principles, however, are distorted and contain numerous inaccuracies, contradicting professional economists views and insights. We conclude that Potterian economics can teach us about the formation and dissemination of folk economics, the intuitive notions of naive individuals who see market transactions as a zero-sum game, who care about distribution but fail to understand incentives and efficiency, and who think of prices as allocating wealth but not resources or their efficient use.
Gacha Game: When Prospect Theory Meets Optimal Pricing
Authors: Tan Gan
Date: 2022-08-07 00:00:00

I study the optimal pricing process for selling a unit good to a buyer with prospect theory preferences. In the presence of probability weighting, the buyer is dynamically inconsistent and can be either sophisticated or naive about her own inconsistency. If the buyer is naive, the uniquely optimal mechanism is to sell a ``loot box'' that delivers the good with some constant probability in each period. In contrast, if the buyer is sophisticated, the uniquely optimal mechanism introduces worst-case insurance: after successive failures in obtaining the good from all previous loot boxes, the buyer can purchase the good at full price.
On the Distributional Robustness of Finite Rational Inattention Models
Authors: Emerson Melo
Date: 2022-08-05 00:00:00
Tasks: Sensitivity

In this paper we study a rational inattention model in environments where the decision maker faces uncertainty about the true prior distribution over states. The decision maker seeks to select a stochastic choice rule over a finite set of alternatives that is robust to prior ambiguity. We fully characterize the distributional robustness of the rational inattention model in terms of a tractable concave program. We establish necessary and sufficient conditions to construct robust consideration sets. Finally, we quantify the impact of prior uncertainty, by introducing the notion of \emph{Worst-Case Sensitivity}.
Self-supervised Enhanced Radar Imaging Based on Deep-Learning-Assisted Compressed Sensing
Authors: Shaoyin Huang
Date: 2022-08-08 00:00:00
Tasks: compressed sensing

Traditional radar imaging methods suffer from the problems of low resolution and poor noise suppression. We propose a new radar imaging method based on Self-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). The original radar image as the input of net. The net is trained to learn the mapping function between the original radar image and the high quality radar image. However, the high quality radar image cant be obtained. We solve this problem by used the sparsity of radar image. The original radar image and image with the zeros value as the reference of net. Ours net dont need a lot of data to train. Real radar data are used to evaluate the performance of the proposed method. The experimental results demonstrate the superiority of the proposed method
Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images
Authors: Fubao Zhu, Guojie Wang, Chen Zhao, Saurabh Malhotra, Min Zhao, Zhuo He, Jianzhou Shi, Zhixin Jiang, Weihua Zhou
Date: 2022-08-07 00:00:00
Tasks: Model Optimization, Translation

Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpretation and quantification. It is essential to reorient the reconstructed transaxial SPECT MPI into standard SA slices. This study is aimed to develop a deep-learning-based approach for automatic reorientation of MPI. Methods: A total of 254 patients were enrolled, including 228 stress SPECT MPIs and 248 rest SPECT MPIs. Five-fold cross-validation with 180 stress and 201 rest MPIs was used for training and internal validation; the remaining images were used for testing. The rigid transformation parameters (translation and rotation) from manual reorientation were annotated by an experienced operator and used as the ground truth. A convolutional neural network (CNN) was designed to predict the transformation parameters. Then, the derived transform was applied to the grid generator and sampler in spatial transformer network (STN) to generate the reoriented image. A loss function containing mean absolute errors for translation and mean square errors for rotation was employed. A three-stage optimization strategy was adopted for model optimization: 1) optimize the translation parameters while fixing the rotation parameters; 2) optimize rotation parameters while fixing the translation parameters; 3) optimize both translation and rotation parameters together.
Intelligent MIMO Detection Using Meta Learning
Authors: Haomiao Huo, Jindan Xu, Gege Su, Wei Xu, Ning Wang
Date: 2022-08-08 00:00:00
Tasks: Meta-Learning

In a K-best detector for multiple-input-multiple-output(MIMO) systems, the value of K needs to be sufficiently large to achieve near-maximum-likelihood (ML) performance. By treating K as a variable that can be adjusted according to a fitting function of some learnable coefficients, an intelligent MIMO detection network based on deep neural networks (DNN) is proposed to reduce complexity of the detection algorithm with little performance degradation. In particular, the proposed intelligent detection algorithm uses meta learning to learn the coefficients of the fitting function for K to circumvent the problem of learning K directly. The idea of network fusion is used to combine the learning results of the meta learning component networks. Simulation results show that the proposed scheme achieves near-ML detection performance while its complexity is close to that of linear detectors. Besides, it also exhibits strong ability of fast training.