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.

Dynamic partitioning techniques for data streams

Abstract: A partitioning policy, comprising an indication of an initial mapping of data records of a stream to a plurality of partitions, is selected to distribute data records of a data stream among a plurality of nodes of a stream management service. Data ingestion nodes and storage nodes are configured according to the initial mapping. In response to a determination that a triggering criterion for dynamically repartitioning the data stream has been met, a modified mapping is generated, and a different set of ingestion and storage nodes are configured. For at least some time during which arriving data records are stored in accordance with the modified mapping, data records stored at the first set of storage nodes in accordance with the initial mapping are retained.

Systems and methods to facilitate human/robot interaction

Abstract: Short range transmissions are used to identify potential interactions between warehouse workers and warehouse robots in automated warehouses The robot can be equipped with one or more short range transmission tags, such as radio frequency identification (RFID) tags, while the warehouse worker can be equipped with a short range transmission reader, such as an RFID reader The robot can detect a warehouse worker that is within range when the RFID tags on the robot are written to by the RFID reader The warehouse robots and warehouse workers can also be equipped with one or more cameras to identify fiducials in the automated warehouse and to report their positions A central control or interaction server can ensure that warehouse robots and warehouse workers are routed appropriately to avoid incidents

Muscodor albus E-6, an endophyte of Guazuma ulmifolia making volatile antibiotics: isolation, characterization and experimental establishment in the host plant.

Abstract: Muscodor albus is an endophytic fungus, represented by a number of isolates from tropical tree and vine species in several of the world's rainforests, that produces volatile organic compounds (VOCs) with antibiotic activity. A new isolate, E-6, of this organism, with unusual biochemical and biological properties, has been obtained from the branches of a mature Guazuma ulmifolia (Sterculiaceae) tree growing in a dry tropical forest in SW Ecuador. This unique organism produces many VOCs not previously observed in other M. albus isolates, including butanoic acid, 2-methyl-; butanoic acid, 3-methyl-; 2-butenal, 2-methyl-; butanoic acid, 3-methylbutyl ester; 3-buten-1-ol, 3-methyl; guaiol; 1-octene, 3-ethyl-; formamide, N-(1-methylpropyl); and certain azulene and naphthalene derivatives. Some compounds usually seen in other M. albus isolates also appeared in the VOCs of isolate E-6, including caryophyllene; phenylethyl alcohol; acetic acid, 2-phenylethyl ester; bulnesene; and various propanoic acid, 2-methyl- derivatives. The biological activity of the VOCs of E-6 appears different from the original isolate of this fungus, CZ-620, since a Gram-positive bacterium was killed, and Sclerotinia sclerotiorum and Rhizoctonia solani were not. Scanning electron micrographs of the mycelium of isolate E-6 showed substantial intertwining of the hyphal strands. These strands seemed to be held together by an extracellular matrix accounting for the strong mat-like nature of the mycelium, which easily lifts off the agar surface upon transfer, unlike any other isolate of this fungus. The ITS-5.8S rDNA partial sequence data showed 99 % similarity to the original M. albus strain CZ-620. For the first time, successful establishment of M. albus into its natural host, followed by recovery of the fungus, was accomplished in seedlings of G. ulmifolia. Overall, isolates of M. albus, including E-6, have chemical, biological and structural characteristics that make them potentially useful in medicine, agricultural and industrial applications.

Improving Lexical Choice in Neural Machine Translation.

Abstract: We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a simple lexical module which is jointly trained with the rest of the model. We evaluate our approaches on eight language pairs with data sizes ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU, surpassing phrase-based translation in nearly all settings.