Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.

We present Success weighted by Completion Time (SCT), a new metric for evaluating navigation performance for mobile robots. Several related works on navigation have used Success weighted by Path Length (SPL) as the primary method of evaluating the path an agent makes to a goal location, but SPL is limited in its ability to properly evaluate agents with complex dynamics. In contrast, SCT explicitly takes the agent's dynamics model into consideration, and aims to accurately capture how well the agent has approximated the fastest navigation behavior afforded by its dynamics. While several embodied navigation works use point-turn dynamics, we focus on unicycle-cart dynamics for our agent, which better exemplifies the dynamics model of popular mobile robotics platforms (e.g., LoCoBot, TurtleBot, Fetch, etc.). We also present RRT*-Unicycle, an algorithm for unicycle dynamics that estimates the fastest collision-free path and completion time from a starting pose to a goal location in an environment containing obstacles. We experiment with deep reinforcement learning and reward shaping to train and compare the navigation performance of agents with different dynamics models. In evaluating these agents, we show that in contrast to SPL, SCT is able to capture the advantages in navigation speed a unicycle model has over a simpler point-turn model of dynamics. Lastly, we show that we can successfully deploy our trained models and algorithms outside of simulation in the real world. We embody our agents in an real robot to navigate an apartment, and show that they can generalize in a zero-shot manner.

Autonomous drones are cautious when navigating the unknown. Now MIT researchers have developed a trajectory-planning model that helps drones fly at high speeds through previously unexplored areas, while staying safe. The model -- aptly named "FASTER" -- estimates the quickest possible path from a starting point to a destination point across all areas the drone can and can't see, with no regard for safety. But, as the drone flies, the model continuously logs collision-free "back-up" paths that slightly deviate from that fast flight path. When the drone is unsure about a particular area, it detours down the back-up path and replans its path. The drone can thus cruise at high speeds along the quickest trajectory while occasionally slowing down slightly to ensure safety.

HyperLabel--a new desktop data labeling application for Machine Learning (ML) just announced by Sixgill, LLC--offers the fastest path to creating high-quality labeled datasets for better ML models. With HyperLabel, there's no need to upload files to an external service. Users retain complete ownership, privacy and control of their data, while accelerating project onboarding and completion with quick and easy usability anchored on the desktop. It's all cloud-free, highly scalable and locally installed. HyperLabel is designed to be fast, easy and accurate, from setup to label export.

Upgrade Android Studio (I have version 3.3). Download Bazel just as Google tells you to. However, you don't need MSYS2 if you already have other things like Git Shell -- or maybe I already have MinGW somewhere, or who knows. Upgrade Android Studio (I have version 3.3). Download Bazel just as Google tells you to.

Fastest-path queries between two points in a very large road map is an increasingly important primitive in modern transportation and navigation systems, thus very efficient computation of these paths is critical for system performance and throughput. We present a method to compute an effective heuristic for the fastest path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths. Our method is based on two hierarchical sets of separators of the map represented by two binary trees. A preprocessing step computes a short vector of values per road junction based on the separator trees, which is then stored with the map and used to efficiently compute the heuristic at the online query stage. We demonstrate experimentally that this method scales well to any map size, providing a better quality heuristic, thus more efficient A* search, for fastest path queries between points at all distances - especially small and medium range - relative to other known heuristics.

Learning Deep Learning can be confusing and often very frustrating. In this talk, Sam will set out a roadmap to go from knowing nothing to being fluent in Deep Learning in the fastest way possible. He will highlight courses, frameworks, math, methods, and strategies to get you started and set you on the path to being able to use Deep Learning for real worlds problems and apps. EVENT: FOSSASIA 2018 SPEAKER: Sam Witteveen, Machine Learning Developer Expert Google PERMISSIONS: The original video was published with the Creative Commons Attribution license (reuse allowed).

In this paper we propose a Multi-Objective Ant Colony Optimization (MOACO) algorithm called CHAC, which has been designed to solve the problem of finding the path on a map (corresponding to a simulated battlefield) that minimizes resources while maximizing safety. CHAC has been tested with two different state transition rules: an aggregative function that combines the heuristic and pheromone information of both objectives and a second one that is based on the dominance concept of multiobjective optimization problems. These rules have been evaluated in several different situations (maps with different degree of difficulty), and we have found that they yield better results than a greedy algorithm (taken as baseline) in addition to a military behaviour that is also better in the tactical sense. The aggregative function, in general, yields better results than the one based on dominance.