Amazon.com

About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Computer science & Service (business). The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Unmanned aerial vehicle delivery system

Abstract: This disclosure describes an unmanned aerial vehicle ("UAV") configured to autonomously deliver items of inventory to various destinations. The UAV may receive inventory information and a destination location and autonomously retrieve the inventory from a location within a materials handling facility, compute a route from the materials handling facility to a destination and travel to the destination to deliver the inventory.

Movement recognition as input mechanism

Abstract: The detection of relative motion or orientation between a user and a computing device can be used to control aspects of the device. For example, the computing device can include an imaging element and software for locating positions, shapes, separations, and/or other aspects of a user's facial features relative to the device, such that an orientation of the device relative to the user can be determined. A user then can provide input to the device by performing actions such as tilting the device, moving the user's head, making a facial expression, or otherwise altering an orientation of at least one aspect of the user with respect to the device. Such an approach can be used in addition to, or as an alternative to, conventional input devices such as keypads and touch screens.

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Abstract: We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundary-aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model are publicly available at https://wywu.github.io/projects/LAB/LAB.html

Endemism centres, refugia and botanical collection density in Brazilian Amazonia.

Abstract: HERBARIUM specimen collecting in the Brazilian Amazon has been concentrated in widely scattered collecting centres associated with some proposed centres of substrate-independent endemism1–3, suggesting that these may be sampling artefacts. Furthermore, many Amazonian plant species are uncommon, so the more intensely a local flora is studied, the more it will seem to be unique. This weakens the botanical argument for a dry Pleistocene Amazon4 and the associated forest refuge theory for the origin of Amazonian plant diversity1–3, because modern endemism centres are used as evidence to define past isolated forest patches—sites of allopatric speciation in a supposedly dry climate (Fig. 1). With a detailed map of botanical collection density, it is possible to recognize the true concentrations of plant endemism, which is important for selecting priority conservation areas to guarantee preservation of unique species.

Eventually Consistent: Building reliable distributed systems at a worldwide scale demands trade-offs?between consistency and availability.

Abstract: At the foundation of Amazon’s cloud computing are infrastructure services such as Amazon’s S3 (Simple Storage Service), SimpleDB, and EC2 (Elastic Compute Cloud) that provide the resources for constructing Internet-scale computing platforms and a great variety of applications. The requirements placed on these infrastructure services are very strict; they need to score high marks in the areas of security, scalability, availability, performance, and cost effectiveness, and they need to meet these requirements while serving millions of customers around the globe, continuously.