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.

Deployment and adjustment of airborne unmanned aerial vehicles

Abstract: This disclosure describes a power unmanned aerial vehicle (UAV) that may generate a current from a magnetic field of an overhead power line. In various implementations, while the power UAV is flying, the power UAV may receive another UAV at a platform. A control element of the power UAV may generate signals to cause the power UAV to fly to a location of a conductor of the power line. The control element may also determine a position of the secondary coil with respect to the power line and generate control signals to adjust the position of the secondary coil based on the determined position of the secondary coil, a determined safety distance, and/or a determined threshold distance for efficient current generation. A shielding substrate may also be provided to shield electronics of the power UAV or other UAVs from magnetic fields.

Extended Kernel Recursive Least Squares Algorithm

Abstract: This paper presents a kernelized version of the extended recursive least squares (EX-KRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS), or equivalently a general nonlinear state model in the input space. The center piece of this development is a reformulation of the well known extended recursive least squares (EX-RLS) algorithm in RKHS which only requires inner product operations between input vectors, thus enabling the application of the kernel property (commonly known as the kernel trick). The first part of the paper presents a set of theorems that shows the generality of the approach. The EX-KRLS is preferable to 1) a standard kernel recursive least squares (KRLS) in applications that require tracking the state-vector of general linear state-space models in the kernel space, or 2) an EX-RLS when the application requires a nonlinear observation and state models. The second part of the paper compares the EX-KRLS in nonlinear Rayleigh multipath channel tracking and in Lorenz system modeling problem. We show that the proposed algorithm is able to outperform the standard KRLS and EX-RLS in both simulations.

Distributed live auction

Abstract: A method for distributing a live auction over the Internet to remote bidders. A human proxy attends the live auction in order to monitor the auction and compose status updates that are distributed to remote bidders via the Internet in real time to allow the remote bidders to follow the auction. Remote bidders may place bids for items that are transmitted via the Internet to the human proxy, who may then submit the bids to the auctioneer, components that facilitate distribution of the live auction over the Internet include: an auction console, an auction sever, collector/redistributor nodes, and client programs.

Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations.

Abstract: Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spanning several domains effectively when conversing with humans who have their own world knowledge. Existing knowledge-grounded conversation datasets are primarily stylized with explicit roles for conversation partners. These datasets also do not explore depth or breadth of topical coverage with transitions in conversations. We introduce Topical-Chat, a knowledge-grounded humanhuman conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don’t have explicitly defined roles, to help further research in opendomain conversational AI. We also train several state-of-theart encoder-decoder conversational models on Topical-Chat and perform automated and human evaluation for benchmarking.

GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Abstract: We propose a new network architecture, Gated Attention Networks (GaAN), for learning on graphs. Unlike the traditional multi-head attention mechanism, which equally consumes all attention heads, GaAN uses a convolutional sub-network to control each attention head's importance. We demonstrate the effectiveness of GaAN on the inductive node classification problem. Moreover, with GaAN as a building block, we construct the Graph Gated Recurrent Unit (GGRU) to address the traffic speed forecasting problem. Extensive experiments on three real-world datasets show that our GaAN framework achieves state-of-the-art results on both tasks.