103 research outputs found
Compositional Verification of Interacting Systems Using Event Monads
No full textLarge software systems are usually divided into multiple components that interact with each other. How to verify interacting components in a modular way is one of the major problems in formal verification. In many cases, interaction between components can be modeled asynchronously, where events are sent without requiring a response in order to continue with execution of the component. In this paper, we propose a lightweight, event-based framework for verification of components with asynchronous interaction. We define event monads and event systems, and a Hoare logic-style calculus for reasoning about them. The framework is implemented in Isabelle and applied to several case studies, including models for distributed computing, cache-coherence protocols, and verification of partition scheduling in a real-time operating system
A general framework for combining traffic flow modelling and Bayesian network for traffic parameters estimation based on probe vehicle trajectory data
No full textThis research focuses on traffic parameters estimation based on trajectory data considering an arterial with several signalized intersections. We develop a general framework which can combine various traffic flow models with Bayesian Network (BN) for traffic parameters estimation. The BN is formulated to establish connections between the traffic arrival process, traffic states, traffic flow model parameters and observed vehicle trajectories. More specifically, given traffic arrivals and fundamental diagram parameters (e.g., capacity, jam density, and free flow speed), vehicle trajectories are derived or simulated based on traffic flow modelling (e.g., shockwave analysis (SA), Cell Transmission Model (CTM), and the microscopic traffic simulation model VISSIM). By combining a dynamic traffic flow model with Bayesian inference, we develop a framework to establish the learning process for traffic parameters estimation, such as traffic arrivals, traffic states as well as traffic flow model parameters. The expectation-maximization (EM) algorithm is introduced to solve the parameters learning process. The proposed framework is then used to formulate different estimation models by combining different traffic flow models. Specifically, we formulate the SA-BN, CTM-BN and VISSIM-BN models and analyze them accordingly. Additionally, a novel (Deep Neural Network) DNN-BN model has also been developed by replacing the CTM in the conventional CTM-BN model with the DNN. A series of numerical experiments are conducted using these models. It is shown that the performances of all these models are promising. It can be concluded that this framework has the following two features: (1) It is flexible in that any dynamic traffic flow model can be incorporated; (2) by combining the model-based approach with the data-based approach, even with a low penetration of vehicle trajectory data, good accuracy can be achieved for both estimating the traffic parameters as well as the traffic dynamics around signalized intersections along an arterial</p
TRAFFIC PARAMETERS ESTIMATION WITH AN ITERATIVE PARTIAL BACKPROPAGATION MAXIMUM LIKELIHOOD ESTIMATION (IPB-MLE) FRAMEWORK
No full textThis study proposes a numerical framework for traffic parameters estimation, namely Iterative Partial Backpropagation Maximum Likelihood Estimation (IPB-MLE). This framework utilizes analytical continuations to avoid the discrete nature of the Poisson distribution, hence providing more accurate convergences. Backpropagation introduced in this framework contributes flexibility and possibilities of introducing customizations to the problem formulation. In the experiment, the IPB-MLE framework yields better accuracy and robustness in satisfactory computational times. Estimation takes on average 2 seconds for 10% penetration rate, and the computational complexity increases only linearly with the amount of data and number of variables. Therefore, the IPB-MLE framework offers a great potential for modeling more complex but realistic situations.</p
RETRACTED ARTICLE: Lycium barbarum polysaccharide alleviates oxygen glucose deprivation-induced PC-12 cells damage by up-regulating miR-24
No full textWe, the Editors and Publisher of the journal Artificial Cells, Nanomedicine, and Biotechnology, have retracted the following article:Shiqing Song, Faliang Lin, Pengyan Zhu, Changyan Wu, Shuling Zhao, Qiao Han & Xiaomei Li (2019) Lycium barbarum polysaccharide alleviates oxygen glucose deprivation-induced PC-12 cells damage by up-regulating miR-24. Artificial Cells, Nanomedicine, and Biotechnology, 47(1), 3994–4000, DOI: 10.1080/21691401.2019.1673767Since publication, concerns have been raised about the integrity of the data in the article. When approached for an explanation, the authors have been unable to verify their original data. We also have concerns regarding the integrity of the authorship, as one author has stated they did not consent to being listed as an author. We are therefore retracting this article and the corresponding author listed in this publication have been informed.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’
Iterative Backpropagation Method for Efficient Gradient Estimation in Bilevel Network Equilibrium Optimization Problems
No full textNetwork optimization or network design with an embedded traffic assignment (TA) to model user equilibrium principle, sometimes expressed as bilevel problems or mathematical programs with equilibrium constraints (MPEC), is at the heart of transportation planning and operations. For applications to large-scale multimodal networks with high dimensional decision variables, the problem is nontrivial, to say the least. General-purpose algorithms and problem-specific bilevel formulations have been proposed in the past to solve small problems for demonstration purposes. Research gap, however, exists in developing efficient solution methods for large-scale problems in both static and dynamic contexts. This paper proposes an efficient gradient estimation method called Iterative Backpropagation (IB) for network optimization problems with an embedded static TA model. IB exploits the iterative structure of the TA solution procedure and simultaneously calculates the gradients while the TA process converges. IB does not require any additional function evaluation and consequently scales very well with higher dimensions. We apply the proposed approach to origin-destination (OD) estimation, an MPEC problem, of the Hong Kong multimodal network with 49,806 decision variables, 8,797 nodes, 18,207 links, 2,684 transit routes, and 165,509 OD pairs. The calibrated model performs well in matching the link counts. Specifically, the IB-gradient based optimization technique reduces the link volume squared error by 98%, mean absolute percentage error (MAPE) from 95.29% to 21.23%, and the average GEH statistics from 24.18 to 6.09 compared with the noncalibrated case. The framework, even though applied to OD estimation in this paper, is applicable to a wide variety of optimization problems with an embedded TA model, opening up an efficient way to solve large-scale MPEC or bilevel problems.</p
A general framework of combining traffic flow models and Bayesian inference for traffic parameters estimation
No full textA hybrid approach to traffic volume and residual queue estimation for signalized intersections
No full textTraffic Parameters Estimation with An Iterative Partial Backpropagation Maximum Likelihood Estimation (IPB-MLE) Framework
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