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.

Secure method for communicating credit card data when placing an order on a non-secure network

Abstract: A method and system for placing an order charged to a credit card, over an unsecured network. The customer completing an order for goods or services enters information required for the order, such as the shipping and billing addresses and identification of the goods, but enters only a subset of the credit card account number to which the order is to be charged. The order is transmitted over the Internet or other network to a remote merchant location (32) from a customer's location (10). A computer (38) at the remote merchant location processes the order to extract the data provided by the customer for storage in a database (40). During a subsequent telephone call to the remote merchant location, the customer enters the complete credit card number, preferably on a touch-tone keypad (28). The touch-tone signals are processed by an automated attendant system (44) for input of the complete credit card number into the computer. Using the portion of the complete credit card number that corresponds to the subset entered by the customer on the order form, the computer identifies the order previously placed and inserts the complete credit card number in the order data stored on the database to finalize the order.

System and method for locating web-based product offerings

Abstract: A search engine system assists users in locating web pages from which user-specified products can be purchased. Web pages located by a crawler program are scored, based on a set of rules, according to likelihood of including an online product offering. A query server accesses an index of the scored web pages to locate pages that are both responsive to a user's search query and likely to include a product offering. In one embodiment, the responsive web pages are listed on a composite search results page together with products that satisfy the query.

Biodegradation of Polyester Polyurethane by Endophytic Fungi

Abstract: Bioremediation is an important approach to waste reduction that relies on biological processes to break down a variety of pollutants. This is made possible by the vast metabolic diversity of the microbial world. To explore this diversity for the breakdown of plastic, we screened several dozen endophytic fungi for their ability to degrade the synthetic polymer polyester polyurethane (PUR). Several organisms demonstrated the ability to efficiently degrade PUR in both solid and liquid suspensions. Particularly robust activity was observed among several isolates in the genus Pestalotiopsis, although it was not a universal feature of this genus. Two Pestalotiopsis microspora isolates were uniquely able to grow on PUR as the sole carbon source under both aerobic and anaerobic conditions. Molecular characterization of this activity suggests that a serine hydrolase is responsible for degradation of PUR. The broad distribution of activity observed and the unprecedented case of anaerobic growth using PUR as the sole carbon source suggest that endophytes are a promising source of biodiversity from which to screen for metabolic properties useful for bioremediation.

PoseTrack: A Benchmark for Human Pose Estimation and Tracking

Abstract: Existing systems for video-based pose estimation and tracking struggle to perform well on realistic videos with multiple people and often fail to output body-pose trajectories consistent over time. To address this shortcoming this paper introduces PoseTrack which is a new large-scale benchmark for video-based human pose estimation and articulated tracking. Our new benchmark encompasses three tasks focusing on i) single-frame multi-person pose estimation, ii) multi-person pose estimation in videos, and iii) multi-person articulated tracking. To establish the benchmark, we collect, annotate and release a new dataset that features videos with multiple people labeled with person tracks and articulated pose. A public centralized evaluation server is provided to allow the research community to evaluate on a held-out test set. Furthermore, we conduct an extensive experimental study on recent approaches to articulated pose tracking and provide analysis of the strengths and weaknesses of the state of the art. We envision that the proposed benchmark will stimulate productive research both by providing a large and representative training dataset as well as providing a platform to objectively evaluate and compare the proposed methods. The benchmark is freely accessible at https://posetrack.net/.

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

Abstract: Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.