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.

Request routing processing

Abstract: Generally described, the present disclosure is directed to managing request routing functionality corresponding to resource requests for one or more resources associated with a content provider. The processing of the DNS requests by the service provider can include the selective filtering of DNS queries associated with a DNS query-based attack. A service provider can assign DNS servers corresponding to a distributed set of network addresses, or portions of network addresses, such that DNS queries exceeding a threshold, such as in DNS query-based attacks, can be filtered in a manner that can mitigate performance impact on for the content provider or service provider.

Metabolic adjustments in two Amazonian cichlids exposed to hypoxia and anoxia

Abstract: The effects of graded hypoxia on the physiological and biochemical responses were examined in two closely related species of cichlids of the Amazon: Astronotus crassipinnis and Symphysodon aequifasciatus. Ten fish of each species were exposed to graded hypoxia for 8 h in seven oxygen concentrations (5.92, 3.15, 1.54, 0.79, 0.60, 0.34, and 0.06 mg O2 L− 1), with the aim to evaluate hypoxia tolerance and metabolic adjustments, where plasma glucose and lactate levels, hepatic and muscle glycogen contents, and maximum enzyme activities (PK, LDH, MDH and CS) in skeletal and cardiac muscles were measured. Another experimental set was done to quantify oxygen consumption (MO2) and opercular movements in two oxygen concentrations. Hypoxia tolerance differed between the two species. Astronotus crassipinnis was able to tolerate anoxia for 178 min while S. aequifasciatus was able to withstand 222 min exposure in deep hypoxia (0.75 mg O2 L− 1). Suppressed MO2 was observed during exposure to 0.34 (A. crassipinnis) and 0.79 mg O2 L− 1 (S. aequifasciatus), while opercular movements increased in both species exposed to hypoxia. Higher levels of muscle and liver glycogen and larger hypoxia-induced increases in plasma glucose and lactate were observed in A. crassipinnis, which showed a higher degree of hypoxia tolerance. Changes in enzyme levels were tissue-specific and differed between species suggesting differential abilities in down-regulating oxidative pathways and increasing anaerobic metabolism. Based on the present data, we conclude that these animals are good anaerobes and highly adapted to their environment, which is allowed by their abilities to regulate metabolic pathways and adjust their enzyme levels.

System and method for selecting rows from dimensional databases

Abstract: A system and method selects rows from a fact table in a dimensional database containing a fact table, a time dimension table and other dimension tables. The other dimension tables each contain rows containing a time invariant attribute to identify an item described by the row, an effective time attribute for the row, and other attributes. If an attribute for an item changes, a new row is added to the dimension table containing the time-invariant attribute for the item and current attributes for the item, without deleting or overwriting any existing rows for that item. Such dimension tables can be selected or used to create other tables using one or more time attributes of the dimension tables. The tables created can be selected or used to create still other tables using one or more time attributes of those tables. One or more of the resulting tables or one or more tables created from a resulting table are then joined to a table in the dimensional database or a table created from a table in the dimensional database.

Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

Abstract: How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and semantic search. In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. While a number of approaches have been developed to address this problem for general graphs, they do not fully utilize information available in KGs, or lack flexibility needed to model complex relationship between entities and their importance. To address these limitations, we explore supervised machine learning algorithms. In particular, building upon recent advancement of graph neural networks (GNNs), we develop GENI, a GNN-based method designed to deal with distinctive challenges involved with predicting node importance in KGs. Our method performs an aggregation of importance scores instead of aggregating node embeddings via predicate-aware attention mechanism and flexible centrality adjustment. In our evaluation of GENI and existing methods on predicting node importance in real-world KGs with different characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.