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.

Automated profiling of resource usage

Abstract: Operating profiles for consumers of computing resources may be automatically determined based on an analysis of actual resource usage measurements and other operating metrics. Measurements may be taken while a consumer, such as a virtual machine instance, uses computing resources, such as those provided by a host. A profile may be dynamically determined based on those measurements. Profiles may be generalized such that groups of consumers with similar usage profiles are associated with a single profile. Assignment decisions may be made based on the profiles, and computing resources may be reallocated or oversubscribed if the profiles indicate that the consumers are unlikely to fully utilize the resources reserved for them. Oversubscribed resources may be monitored, and consumers may be transferred to different resource providers if contention for resources is too high.

System and method for obtaining information relating to an item of commerce using a portable imaging device

Abstract: A method, system, and apparatus are provided for allowing users to readily obtain information associated with a selected item from a remote location. More specifically, a user at the location of the first entity operates a portable imaging device to capture an image of identifying data, such as a barcode, that identifies a selected item. The captured image is then communicated to a server operated by a second entity that is different than the first entity to obtain item information (e.g., price, availability, etc.) associated with the selected item. The item information is communicated back to the portable imaging device for display to the user while the user remains at the location of the first entity. In other embodiments, the information extracted from the captured image may also be used to forecast future purchasing activity for the selected item.

A crisis in the making: responses of Amazonian forests to land use and climate change.

Abstract: At least three global-change phenomena are having major impacts on Amazonian forests: (1) accelerating deforestation and logging; (2) rapidly changing patterns of forest loss; and (3) interactions between human land-use and climatic variability. Additional alterations caused by climatic change, rising concentrations of atmospheric carbon dioxide, mining, overhunting and other large-scale phenomena could also have important effects on the Amazon ecosystem. Consequently, decisions regarding Amazon forest use in the next decade are crucial to its future existence.

Explainable Deep Learning: A Field Guide for the Uninitiated

Abstract: Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks. Yet, due to its black-box nature, it is inherently difficult to understand which aspects of the input data drive the decisions of the network. There are various real-world scenarios in which humans need to make actionable decisions based on the output DNNs. Such decision support systems can be found in critical domains, such as legislation, law enforcement, etc. It is important that the humans making high-level decisions can be sure that the DNN decisions are driven by combinations of data features that are appropriate in the context of the deployment of the decision support system and that the decisions made are legally or ethically defensible. Due to the incredible pace at which DNN technology is being developed, the development of new methods and studies on explaining the decision-making process of DNNs has blossomed into an active research field. A practitioner beginning to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field is taking. This complexity is further exacerbated by the general confusion that exists in defining what it means to be able to explain the actions of a deep learning system and to evaluate a system's "ability to explain". To alleviate this problem, this article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field.

Did Ranchers and Slaughterhouses Respond to Zero-Deforestation Agreements in the Brazilian Amazon?

Abstract: New supply chain interventions offer promise to reduce deforestation from expansion of commercial agriculture, as more multinational companies agree to stop sourcing from farms with recent forest clearing. We analyzed the zero-deforestation cattle agreements signed by major meatpacking companies in the Brazilian Amazon state of Para using property-level data on beef supply chains. Our panel analysis of daily purchases by slaughterhouses before and after the agreements demonstrates that they now avoid purchasing from properties with deforestation, which was not the case prior to the agreements. Supplying ranchers registered their properties in a public environmental registry nearly 2 years before surrounding non-supplying properties, and 85% of surveyed ranchers indicated that the agreements were the driving force. In addition, supplying properties had significantly reduced deforestation rates following the agreements. Our results demonstrate important changes in the beef supply chain, but the agreements’ narrow scope and implementation diminish outcomes for forest conservation.