Imitating Driver Behavior with Generative Adversarial Networks

The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.Comment: 8 pages, 6 figure

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesity.

Most monogenic cases of obesity in humans have been linked to mutations in genes encoding members of the leptin-melanocortin pathway. Specifically, mutations in MC4R, the melanocortin-4 receptor gene, account for 3-5% of all severe obesity cases in humans1-3. Recently, ADCY3 (adenylyl cyclase 3) gene mutations have been implicated in obesity4,5. ADCY3 localizes to the primary cilia of neurons 6 , organelles that function as hubs for select signaling pathways. Mutations that disrupt the functions of primary cilia cause ciliopathies, rare recessive pleiotropic diseases in which obesity is a cardinal manifestation 7 . We demonstrate that MC4R colocalizes with ADCY3 at the primary cilia of a subset of hypothalamic neurons, that obesity-associated MC4R mutations impair ciliary localization and that inhibition of adenylyl cyclase signaling at the primary cilia of these neurons increases body weight. These data suggest that impaired signaling from the primary cilia of MC4R neurons is a common pathway underlying genetic causes of obesity in humans

What Counts in Economic Evaluations in Health? Benefit-cost Analysis Compared to Other Forms of Economic Evaluations

Economic evaluations are increasingly popular, both in the field of global health as well as in purely domestic settings. However, the proliferation and use of economic evaluations by members of multiple publics, many of whom are non-economists, creates misunderstandings as well as strategic opportunities. In this extended essay, Lauer and colleagues develop a critical analysis of economic evaluations that is intended to clarify concepts and terms, and thereby to enable a diverse community of users, performers, and commissioners of economic analyses in health to better understand and use such studies. The authors pay particular attention to cost-effectiveness analysis, long the mainstay of economic evaluations in health, and to benefit-cost analysis. The article starts by noting that economic evaluations in health (EEHs) take a number of typical forms, although all involve a comparison of inputs and outcomes, either of which may or may not be market-traded goods. They call a particular choice of inputs and outcomes a ‘table of accounts’. They argue that the notion of a table of accounts provides a useful way to understand the methodological diversity of EEHs, one which subsumes more established but also more restrictive terminology (e.g. the notion of ‘study perspective’). Lauer and colleagues present tables of account for a number of commonly used EEHs. They then discuss at length benefit-cost analysis, a distinctive form of EEH that has recently attracted substantial attention in the form of so-called ‘investment cases’ in healt

Panel 4 (Session A): OEM, Simulation, & Training Support

A mainstream function of NTAS is to serve as a quasi-trade show for collegiate and flight academy providers, updating them each year on the latest in equipment and training support; often focusing on time-critical support; as we did most recently on ADS-B Out implementation in 2015. Panelists are asked to present new aircraft features & capabilities, progress on ADS-B update for both new and retrofit application from NTAS 2015, new & novel equipment and administrative applications, and training support for ADS-B in academic, tablet, computer, and simulation delivery