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: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Efficient reconciliation and flow control for anti-entropy protocols

Abstract: The paper shows that anti-entropy protocols can process only a limited rate of updates, and proposes and evaluates a new state reconciliation mechanism as well as a flow control scheme for anti-entropy protocols.

Providing gift clustering functionality to assist a user in ordering multiple items for a recipient

Abstract: A method and system for creating of gift clusters of multiple items in a client/server environment by users, and for the ordering of such user-defined gift clusters of multiple items. In particular, a user can specify multiple items to be associated together as a gift cluster, and can also specify a variety of descriptive information about the gift cluster. That user or another user can then order the gift cluster as a gift for themselves or for another recipient, and may also order the gift cluster for the same or different recipients multiple times. The descriptive information can provide various information about how the gift cluster is to be used, and can also assist the user or others in identifying when the gift cluster is appropriate for a recipient. When customers are later searching for appropriate gift clusters, the various categorization or other descriptive information can then assist is identifying appropriate gift clusters. The gift clustering functionality may be provided by and work in conjunction with a particular item ordering service, or instead may work independently from any particular item ordering service.

Monitoring of replicated data instances

Abstract: Replicated instances in a database environment provide for automatic failover and recovery. A monitoring component can obtain a lease enabling the component to periodically communicate with, and monitor, one or more data instances in the data environment, where the data instance can be a replicated instance including a primary and a secondary replica. For a large number of instances, the data environment can be partitioned such that each monitoring component can be assigned a partition of the workload. In the event of a failure of a monitoring component, the instances can be repartitioned and the remaining monitoring components can be assigned to the new partitions to substantially evenly distribute the workload.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

Abstract: The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials. A computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.

Detection of and responses to network attacks

Abstract: Disclosed are various embodiments for detecting and responding to attacks on a computer network. One embodiment of such a method describes monitoring data communications transmitted to a target class of first computing nodes; in response to detecting a non-legitimate data communication to a computing node in the target class, determining whether the non-legitimate data communication is a form of attack on a network to which the computing nodes are connected; and in response to determining that the network is under attack, implementing new security measures for second computing nodes that are not part of the target class to protect the second computing nodes against the attack on the network while the attack is ongoing.