Virtual to Real Reinforcement Learning for Autonomous Driving

Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data

Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation

In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural networks; (2) an architecture that can be end-to-end trained; (3) a practical strategy to initialize weights for very deep networks under unbalanced training data distribution. ST allows us to extract highly robust features under various circumstances, and in order to address the computation redundance hidden in these features we develop a principled and efficient inference scheme named physics inspired optimization (PIO). PIO's basic idea is to formulate some phenomena observed in ST features into mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the proposed method is more accurate than state-of-the-art methods.Comment: To appear in CVPR 2017. Project Page: https://sites.google.com/view/st-pio

RON: Reverse Connection with Objectness Prior Networks for Object Detection

We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully convolutional architecture, RON mainly focuses on two fundamental problems: (a) multi-scale object localization and (b) negative sample mining. To address (a), we design the reverse connection, which enables the network to detect objects on multi-levels of CNNs. To deal with (b), we propose the objectness prior to significantly reduce the searching space of objects. We optimize the reverse connection, objectness prior and object detector jointly by a multi-task loss function, thus RON can directly predict final detection results from all locations of various feature maps. Extensive experiments on the challenging PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO benchmarks demonstrate the competitive performance of RON. Specifically, with VGG-16 and low resolution 384X384 input size, the network gets 81.3% mAP on PASCAL VOC 2007, 80.7% mAP on PASCAL VOC 2012 datasets. Its superiority increases when datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. With 1.5G GPU memory at test phase, the speed of the network is 15 FPS, 3X faster than the Faster R-CNN counterpart.Comment: Project page will be available at https://github.com/taokong/RON, and formal paper will appear in CVPR 201

Gas Fluidization and Pneumatic Conveying in Confined Beds: A Numerical Study

The fluidization phenomena for gas-fine particle two-phase flow is numerically simulated in confined fluidized bed based on two-fluid model, applying body-fitted coordination for the irregular geometry zone in this work. The simulation was performed either with a partially packed fluidized bed in the bottom part applying various superficial gas velocities in bubbling region, which is used to study the bubbles formation and the average porosity in the packed zone, or with a whole packed fluidized bed applying various solid flux rates in pneumatic region, which is used to investigate the mechanism in the packed bed. The void fraction of the fine-particles confined bed as a function of flow velocity is compared with experimental results by G. Donsi etc.(1). The numerical pressure drop in the fully packed bed is compared with experimental results by Yulong Ding etc. (2)

A novel testing model for opportunistic screening of pre-diabetes and diabetes among U.S. adults.

ObjectiveThe study aim was to evaluate the performance of a novel simultaneous testing model, based on the Finnish Diabetes Risk Score (FINDRISC) and HbA1c, in detecting undiagnosed diabetes and pre-diabetes in Americans.Research design and methodsThis cross-sectional analysis included 3,886 men and women (≥ 20 years) without known diabetes from the U.S. National Health and Nutrition Examination Survey (NHANES) 2005-2010. The FINDRISC was developed based on eight variables (age, BMI, waist circumference, use of antihypertensive drug, history of high blood glucose, family history of diabetes, daily physical activity and fruit & vegetable intake). The sensitivity, specificity, and the receiver operating characteristic (ROC) curve of the testing model were calculated for undiagnosed diabetes and pre-diabetes, determined by oral glucose tolerance test (OGTT).ResultsThe prevalence of undiagnosed diabetes was 7.0% and 43.1% for pre-diabetes (27.7% for isolated impaired fasting glucose (IFG), 5.1% for impaired glucose tolerance (IGT), and 10.3% for having both IFG and IGT). The sensitivity and specificity of using the HbA1c alone was 24.2% and 99.6% for diabetes (cutoff of ≥6.5%), and 35.2% and 86.4% for pre-diabetes (cutoff of ≥5.7%). The sensitivity and specificity of using the FINDRISC alone (cutoff of ≥9) was 79.1% and 48.6% for diabetes and 60.2% and 61.4% for pre-diabetes. Using the simultaneous testing model with a combination of FINDRISC and HbA1c improved the sensitivity to 84.2% for diabetes and 74.2% for pre-diabetes. The specificity for the simultaneous testing model was 48.4% of diabetes and 53.0% for pre-diabetes.ConclusionsThis simultaneous testing model is a practical and valid tool in diabetes screening in the general U.S. population

Evaluation of Finnish Diabetes Risk Score in screening undiagnosed diabetes and prediabetes among U.S. adults by gender and race: NHANES 1999-2010.

ObjectiveTo evaluate the performance of Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes and prediabetes among U.S. adults by gender and race.MethodsThis cross-sectional analysis included participants (aged ≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999-2010. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and the optimal cutoff points for identifying undiagnosed diabetes and prediabetes were calculated for FINDRISC by gender and race/ethnicity.ResultsAmong the 20,633 adults (≥20 years), 49.8% were women and 53.0% were non-Hispanic White. The prevalence of undiagnosed diabetes and prediabetes was 4.1% and 35.6%, respectively. FINDRISC was positively associated with the prevalence of diabetes (OR = 1.48 for 1 unit increase, p<0.001) and prediabetes (OR = 1.15 for 1 unit increase, p<0.001). The area under ROC for detecting undiagnosed diabetes was 0.75 for total population, 0.74 for men and 0.78 for women (p = 0.04); 0.76 for White, 0.76 for Black and 0.72 for Hispanics (p = 0.03 for White vs. Hispanics). The area under ROC for detecting prediabetes was 0.67 for total population, 0.66 for men and 0.70 for women (p<0.001); 0.68 for White, 0.67 for Black and 0.65 for Hispanics (p<0.001 for White vs. Hispanics). The optimal cutoff point was 10 (sensitivity = 0.75) for men and 12 (sensitivity = 0.72) for women for detecting undiagnosed diabetes; 9 (sensitivity = 0.61) for men and 10 (sensitivity = 0.69) for women for detecting prediabetes.ConclusionsFINDRISC is a simple and non-invasive screening tool to identify individuals at high risk for diabetes in the U.S. adults

A new framework for consensus for discrete-time directed networks of multi-agents with distributed delays

Copyright @ 2012 Taylor & FrancisIn this article, the distributed consensus problem is considered for discrete-time delayed networks of dynamic agents with fixed topologies, where the networks under investigation are directed and the time-delays involved are distributed time delays including a single or multiple time delay(s) as special cases. By using the invariance principle of delay difference systems, a new unified framework is established to deal with the consensus for the discrete-time delayed multi-agent system. It is shown that the addressed discrete-time network with arbitrary distributed time delays reaches consensus provided that it is strongly connected. A numerical example is presented to illustrate the proposed methods.This work was supported in part by City University of Hong Kong under Grant 7008114, the Royal Society of the UK, the National Natural Science Foundation of China under Grants 60774073 and 61074129, and the Natural Science Foundation of Jiangsu Province of China under Grant BK2010313