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.

Ecological restoration as a strategy for mitigating and adapting to climate change: lessons and challenges from Brazil.

Abstract: Climate change is a global phenomenon that affects biophysical systems and human well-being. The Paris Agreement of the United Nations Framework Convention on Climate Change entered into force in 2016 with the objective of strengthening the global response to climate change by keeping global temperature rise this century well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to 1.5 °C. The agreement requires all Parties to submit their “nationally determined contributions” (NDCs) and to strengthen these efforts in the years ahead. Reducing carbon emissions from deforestation and forest degradation is an important strategy for mitigating climate change, particularly in developing countries with large forests. Extensive tropical forest loss and degradation have increased awareness at the international level of the need to undertake large-scale ecological restoration, highlighting the need to identify cases in which restoration strategies can contribute to mitigation and adaptation. Here we consider Brazil as a case study to evaluate the benefits and challenges of implementing large-scale restoration programs in developing countries. The Brazilian NDC included the target of restoring and reforesting 12 million hectares of forests for multiple uses by 2030. Restoration of native vegetation is one of the foundations of sustainable rural development in Brazil and should consider multiple purposes, from biodiversity and ecosystem services conservation to social and economic development. However, ecological restoration still presents substantial challenges for tropical and mega-diverse countries, including the need to develop plans that are technically and financially feasible, as well as public policies and monitoring instruments that can assess effectiveness. The planning, execution, and monitoring of restoration efforts strongly depend on the context and the diagnosis of the area with respect to reference ecosystems (e.g., forests, savannas, grasslands, wetlands). In addition, poor integration of climate change policies at the national and subnational levels and with other sectorial policies constrains the large-scale implementation of restoration programs. The case of Brazil shows that slowing deforestation is possible; however, this analysis highlights the need for increased national commitment and international support for actions that require large-scale transformations of the forest sector regarding ecosystem restoration efforts. Scaling up the ambitions and actions of the Paris Agreement implies the need for a global framework that recognizes landscape restoration as a cost-effective nature-based solution and that supports countries in addressing their remaining needs, challenges, and barriers.

Latency measurement in resource requests

Abstract: Systems and method for the management and processing of resource requests by a service provider, such as a content delivery network (“CDN”) service provider, on behalf of a content provider are provided. The CDN service provider can measure the performance associated with the delivery of resources to a requesting client computing devices from various computing devices associated with the CDN service provider. In one embodiment, a client computing device can execute code, such as scripts, that cause the client computing device to transmit requests to different computing devices associated with the CDN service provider's domain. Information associated with the processing of the responses can be used to measure CDN service provider latencies.

Content generation revenue sharing

Abstract: Techniques are described that facilitate generating useful content based on user interactions, such as by providing an answer-providing service that facilitates interactions between users who supply questions and users who supply responses to the questions of other users, as well as using the generated content in various ways. In some situations, users are compensated for participating in interactions with the answer-providing service in various ways, including by sharing a portion of an ongoing revenue stream generated from an answer to a question with users who provided responses that are used as part of the answer. In some situations, the sharing of an ongoing revenue stream related to an answer may be split between the users who provided the responses for the answer in various manners, including based on assessed levels of expertise of those users.

How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures

Abstract: With recent advances in network architectures for Neural Machine Translation (NMT) recurrent models have effectively been replaced by either convolutional or self-attentional approaches, such as in the Transformer. While the main innovation of the Transformer architecture is its use of self-attentional layers, there are several other aspects, such as attention with multiple heads and the use of many attention layers, that distinguish the model from previous baselines. In this work we take a fine-grained look at the different architectures for NMT. We introduce an Architecture Definition Language (ADL) allowing for a flexible combination of common building blocks. Making use of this language we show in experiments that one can bring recurrent and convolutional models very close to the Transformer performance by borrowing concepts from the Transformer architecture, but not using self-attention. Additionally, we find that self-attention is much more important on the encoder side than on the decoder side, where it can be replaced by a RNN or CNN without a loss in performance in most settings. Surprisingly, even a model without any target side self-attention performs well.

Formal reasoning about the security of amazon web services

Abstract: We report on the development and use of formal verification tools within Amazon Web Services (AWS) to increase the security assurance of its cloud infrastructure and to help customers secure themselves. We also discuss some remaining challenges that could inspire future research in the community.