Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

A Survey of Residual Cancer Risks Permitted by Health, Safety and Environmental Policy

The authors describe permitted U.S. residual cancer risks, focusing on numerical levels specifically and implicitly authorized by statute or regulation. They also discuss potential changes

Ensuring Urban Water Security in Water-Scarce Regions of the United States

On December 11-13, 2013, The Johnson Foundation at Wingspread, along with partner ReNUWit, convened experts from different parts of the country to discuss the implications of chronic and episodic water scarcity on our nation's water infrastructure -- with the goal of moving beyond the "case-by-case" conversation to one about how cities can transform their infrastructure and management strategies. The resulting report identifies key principles of water security and explores components of good strategy and innovative water supply options while building the case for transformation

Deep Network Uncertainty Maps for Indoor Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference on Humanoid Robots (Humanoids)

A Defense of Free-Roaming Cats from a Hedonist Account of Feline Well-being

There is a widespread belief that for their own safety and for the protection of wildlife, cats should be permanently kept indoors. Against this view, I argue that cat guardians have a duty to provide their feline companions with outdoor access. The argument is based on a sophisticated hedonistic account of animal well-being that acknowledges that the performance of species-normal ethological behavior is especially pleasurable. Territorial behavior, which requires outdoor access, is a feline-normal ethological behavior, so when a cat is permanently confined to the indoors, her ability to flourish is impaired. Since cat guardians have a duty not to impair the well-being of their cats, the impairment of cat flourishing via confinement signifies a moral failing. Although some cats assume significant risks and sometimes kill wild animals when roaming outdoors, these important considerations do not imply that all cats should be deprived of the opportunity to access the outdoors. Indeed, they do not, by themselves, imply that any cat should be permanently kept indoors

Use of GIS for planning visual surveillance installations

11-14 September, 2005, Denver, CO, USA. Visual Surveillance is now commonplace in modern societies. Generally, the layout of observers in artificial visual surveillance (e.g., CCTV camera) involves an iterative, manual and gut-feel process of trying various layouts until a satisfactory solution has been found. This paper proposes how a GIS, can be used to identify the optimal number and locations of observers, ensuring complete visual coverage using an automated technique, namely Rank and Overlap Elimination (ROPE). The ROPE technique is a greedy-search method, which iteratively selects the most visibly dominant observer with minimum overlapping vistas. The paper also proposes measurements to characterise the shape of open spaces, relevant in assessing natural surveillance. The paper demonstrates an extension, called Isovist Analyst, to the popular ArcView for planning artificial and natural surveillance in indoor and outdoor open spaces, with arbitrary geometry and topology