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.

Methane release below a tropical hydroelectric dam

Abstract: [1] Tropical reservoirs upstream from hydroelectric dams are known to release significant amounts of methane to the atmosphere. Here we demonstrate that methane emissions downstream from hydroelectric dams can also be large. Emissions of CH4 downstream of Balbina reservoir in the central Amazon basin (Brazil) were calculated from regular measurements of degassing in the outflow of the turbines and downstream diffusive losses. Annual emissions from the reservoir surface and downstream from the dam were 34 and 39 Gg C, respectively. The downstream emission alone represented the equivalent of 3% of all methane released from central Amazon floodplain.

Method and system for transporting inventory items

Abstract: A system for transporting inventory items includes an inventory holder capable of storing inventory items and a mobile drive unit. The mobile drive unit is capable of moving to a first point with the inventory holder at least one of coupled to and supported by the mobile drive unit. The mobile drive unit is additionally capable of determining a location of the inventory holder and calculating a difference between the location of the inventory holder and the first point. The mobile drive unit is then capable of determining whether the difference is greater than a predetermined tolerance. In response to determining that the difference is greater than the predetermined tolerance, the mobile drive unit is also capable of moving to a second point based on the location of the inventory holder, docking with the inventory holder, and moving the mobile drive unit and the inventory holder to the first point.

Compositional response of Amazon forests to climate change

Abstract: Most of the planet's diversity is concentrated in the tropics, which includes many regions undergoing rapid climate change. Yet, while climate‐induced biodiversity changes are widely documented elsewhere, few studies have addressed this issue for lowland tropical ecosystems. Here we investigate whether the floristic and functional composition of intact lowland Amazonian forests have been changing by evaluating records from 106 long‐term inventory plots spanning 30 years. We analyse three traits that have been hypothesized to respond to different environmental drivers (increase in moisture stress and atmospheric CO2 concentrations): maximum tree size, biogeographic water‐deficit affiliation and wood density. Tree communities have become increasingly dominated by large‐statured taxa, but to date there has been no detectable change in mean wood density or water deficit affiliation at the community level, despite most forest plots having experienced an intensification of the dry season. However, among newly recruited trees, dry‐affiliated genera have become more abundant, while the mortality of wet‐affiliated genera has increased in those plots where the dry season has intensified most. Thus, a slow shift to a more dry‐affiliated Amazonia is underway, with changes in compositional dynamics (recruits and mortality) consistent with climate‐change drivers, but yet to significantly impact whole‐community composition. The Amazon observational record suggests that the increase in atmospheric CO2 is driving a shift within tree communities to large‐statured species and that climate changes to date will impact forest composition, but long generation times of tropical trees mean that biodiversity change is lagging behind climate change.

Detecting item interaction and movement

Abstract: This disclosure describes a system for tracking removal or placement of items at inventory locations with a materials handling facility. In some instances, a user may remove an item from an inventory location and the inventory management system may detect that removal and update a user item list associated with the user to include an item identifier representative of the removed item. Likewise, if the user places an item at an inventory location, the inventory management system may detect that placement and update the user item list to remove an item identifier representative of the placed item.

Histograms of Sparse Codes for Object Detection

Abstract: Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features. Can we go beyond gradients and do better than HOG? We provide an affirmative answer by proposing and investigating a sparse representation for object detection, Histograms of Sparse Codes (HSC). We compute sparse codes with dictionaries learned from data using K-SVD, and aggregate per-pixel sparse codes to form local histograms. We intentionally keep true to the sliding window framework (with mixtures and parts) and only change the underlying features. To keep training (and testing) efficient, we apply dimension reduction by computing SVD on learned models, and adopt supervised training where latent positions of roots and parts are given externally e.g. from a HOG-based detector. By learning and using local representations that are much more expressive than gradients, we demonstrate large improvements over the state of the art on the PASCAL benchmark for both root-only and part-based models.