Search

It is crucial to understand the current pattern of urban park visitation to achieve environmental justice. Current discussions of environmental equity of parks mainly focus on the inequality provision measured by park accessibility, park area, park quality, and park congestion, ignoring the inequity of social benefits through interactions among mixed-income groups. Based on fine-grained mobile phone location data at the census block group level in 2018 and 2019, we explored visitation patterns and the mixed-income levels of visitors in urban parks in New York City. The visitors were divided into five income groups, with the “income entropy” used as a measure of the mixed level of the visitors in terms of income groups. We answered an important question: what factors affect the visitation intensity and the mixed level of income groups in urban park visitors? Our results revealed that the time of year and season, the parks’ characteristics, the built environment, and the socioeconomic characteristics of the park’s surrounding neighborhoods have strong associations with park use patterns. We also offered implications for urban planning and urban design to promote park visitation and income-diverse park use and improve social inclusion and environmental equity.

A new time-varying adaptive dynamic programming (ADP) algorithm is developed for finite-horizon optimal control problems for a class of discrete-time affine nonlinear systems in this paper. Inspired by the pseudo-linear method, the nonlinear system can be approximated by a series of time-varying linear systems. In each iteration of the time-varying ADP algorithm, the optimal control law for the time-varying linear system is obtained. Convergence analysis is provided for the time-varying ADP algorithm. For an arbitrary initial state, it is proven that states of the time-varying linear systems converge to the states of discrete-time affine nonlinear systems if the convergence criterion is satisfied. It is shown that the iterative value functions and the iterative control laws converge to the optimal value function and the optimal control law, respectively. Finally, numerical results are presented to verify the effectiveness of the present method.

In this article, an online adaptive optimal control algorithm based on adaptive dynamic programming is developed to solve the multiplayer nonzero-sum game (MP-NZSG) for discrete-time unknown nonlinear systems. First, a model-free coupled globalized dual-heuristic dynamic programming (GDHP) structure is designed to solve the MP-NZSG problem, in which there is no model network or identifier. Second, in order to relax the requirement of systems dynamics, an online adaptive learning algorithm is developed to solve the Hamilton-Jacobi equation using the system states of two adjacent time steps. Third, a series of critic networks and action networks are used to approximate value functions and optimal policies for all players. All the neural network (NN) weights are updated online based on real-time system states. Fourth, the uniformly ultimate boundedness analysis of the NN approximation errors is proved based on the Lyapunov approach. Finally, simulation results are given to demonstrate the effectiveness of the developed scheme.

This paper tests a multi-factor asset pricing model that does not assume that the return’s beta coefficients are constants. This is done by estimating the generalized arbitrage pricing theory (GAPT) using price differences. An implication of the GAPT is that when using price differences instead of returns, the beta coefficients are constant. We employ the adaptive multi-factor (AMF) model to test the GAPT utilizing a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded exchange-traded funds. We compare the performance of the AMF model with the Fama–French 5-factor (FF5) model. For nearly all time periods less than six years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as five years) is more consistent with realized asset returns than is the FF5 model.

This paper investigates popular market microstructure measures for stock returns prediction and builds a clustering model for them to study their correlation and the best measures to use as representatives. Using high-dimensional statistical methods, we build the clustering dendrogram and select 20 representatives from all measures. Furthermore, we provide several interesting insights of the market microstructure measures from our clustering results. We found that the time-weighting technique is only useful for Herfindahl-Hirschman Index (HHI) related measures. HHI measures on the number of trades are always redundant. However, the HHI measures on quotes are very important. Also, we find a strong relationship between quote prices and quote shares.

In this paper, a stable value iteration (SVI) algorithm is developed to solve discrete-time two-player zero-sum game (TP-ZSG) for nonlinear systems based on adaptive dynamic programming (ADP). In the SVI algorithm, both optimality and stability of nonlinear systems are considered with proofs given. First, an iterative ADP algorithm is presented to obtain the approximate optimal solutions by solving Hamilton–Jacobi–Isaacs (HJI) equation. Second, a range of the discount factor is shown, which guarantees HJI equation serving as a Lyapunov equation. Moreover, we prove that if the iteration number reaches a given number, then the iterative control inputs make the closed-loop system asymptotic stable. Third, in order to improve the practicability of the developed stability condition, a simple criteria is established based on Lyapunov stability theory. Neural networks (NNs) are used to approximate the system states, the value function, the control and disturbance inputs. Finally, simulation results are given to illustrate the performance of the developed optimal control method.

Based on adaptive dynamic programming &#x0028 ADP &#x0029, the fixed-point tracking control problem is solved by a value iteration &#x0028 VI &#x0029 algorithm. First, a class of discrete-time &#x0028 DT &#x0029 nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value, and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks &#x0028 NNs &#x0029 are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.

This paper has proposed a new baseline deep learning model of more benefits for image classification. Different from the convolutional neural network(CNN) practice where filters are trained by back propagation to represent different patterns of an image, we are inspired by a method called "PCANet" in "PCANet: A Simple Deep Learning Baseline for Image Classification?" to choose filter vectors from basis vectors in frequency domain like Fourier coefficients or wavelets without back propagation. Researchers have demonstrated that those basis in frequency domain can usually provide physical insights, which adds to the interpretability of the model by analyzing the frequencies selected. Besides, the training process will also be more time efficient, mathematically clear and interpretable compared with the "black-box" training process of CNN.

The purpose of this paper is to test the time-invariance of the beta coefficients estimated by the Adaptive Multi-Factor (AMF) model. The AMF model is implied by the generalized arbitrage pricing theory (GAPT), which implies constant beta coefficients. The AMF model utilizes a Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded ETFs. We compare the AMF model with the Fama-French 5-factor (FF5) model. We show that for nearly all time periods with length less than 6 years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model. This implies that the AMF model with a rolling window (such as 5 years) is more consistent with realized asset returns than is the FF5 model.

The paper provides a new explanation of the low-volatility anomaly. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different factors, indicating that volatility is not an independent risk, but that it's related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors. The AMF model outperforms the Fama-French 5-factor model both in-sample and out-of-sample., 29 pages, 11 figures, 10 tables

By employing the mean-field theory and hydrodynamic scheme, we study the gap solitons of superfluid Fermi gas in Fourier-Synthesized(FS) optical lattices. By means of numerical methods and variational approximation, the atomic interaction, the chemical potential, the potential depth of the lattice and relative phase of the Fermi system are derived along the Bose-Enstein condensation(BEC)side to the Bardeen-Cooper-Schrieffer (BCS)side. It means that the condition exciting gap solitons is obtained. Moreover, we analyze the fundamental gap soltions of the superfluid Fermi gas. It is found that the relative phase α impacts greatly on the properties of fundamental gap solitons for superfluid Fermi gas. Especially, the nonlinearity interaction term g decreases with α. Add, due to Fermi pressure, curvature changes of g in the BEC limit(γ = 1, here, γ is a function of an interaction parameter) is larger than that at unitary (γ = 2/3). Spatial distribution of gap solitons exhibit very obvious different when the system transit from the BEC side to BCS side.

In this paper, an optimal and stable iteration learning scheme is developed for two-player zero-sum game (ZSG) in the discrete-time nonlinear systems. In the implementation of developed algorithm, the optimal and stable solution of Hamilton-Jacobi-Isaacs can be obtained based on adaptive dynamic programming (ADP). First, in order to obtain the optimal control policies, value iteration approach is employed with the proof of convergence given. Second, based on Lyapunov theory, an easy condition is proposed to get the stable control laws. Third, neural networks are used to implement the developed algorithm. Finally, a simulation example is included to verify the present method.

The paper proposes a new algorithm for the high-dimensional financial data — the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama–French 5-factor model.

The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

In view of the existing problem of mixed-model assembly line balancing, a mathematical model is proposed based on two factors, which are integrated including the workstation number and the assembly line efficiency. Then a new hybrid genetic algorithm is developed for finding optimal solution of the problems. To prevent the premature convergence problem and enhance the globe-optimization capability, GA (genetic algorithms) is combined with SA (simulated annealing algorithms). The results of the simulation indicated that the hybrid algorithm has better efficiency an optimization performance.

Based on the study of the process of Agent federation-based rapid design and Petri nets (PN), the modeling and analysis of task conflict, design conflict and resources conflict in each stage of rapid design are set up, and then the detailed modeling of the whole process of agent federation-based rapid design is presented, which provides a design frame from the assigns to the design result for rapid design.