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.

Sparse Convolutional Neural Networks

Abstract: Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and intra-channel redundancy, with a fine-tuning step that minimize the recognition loss caused by maximizing sparsity. This procedure zeros out more than 90% of parameters, with a drop of accuracy that is less than 1% on the ILSVRC2012 dataset. We also propose an efficient sparse matrix multiplication algorithm on CPU for Sparse Convolutional Neural Networks (SCNN) models. Our CPU implementation demonstrates much higher efficiency than the off-the-shelf sparse matrix libraries, with a significant speedup realized over the original dense network. In addition, we apply the SCNN model to the object detection problem, in conjunction with a cascade model and sparse fully connected layers, to achieve significant speedups.

Rain forest fragmentation and the dynamics of amazonian tree communities

Abstract: Few studies have assessed effects of habitat fragmentation on tropical forest dynamics. We describe results from an 18-yr experimental study of the effects of rain forest fragmentation on tree-community dynamics in central Amazonia. Tree communities were assessed in 39 permanent, 1-ha plots in forest fragments of 1, 10, or 100 ha in area, and in 27 plots in nearby continuous forest. Repeated censuses of >56000 marked trees (≥10 cm diameter at breast height) were used to generate annualized estimates of tree mortality, damage, and turnover in fragmented and continuous forest. On average, forest fragments exhibited markedly elevated dynamics, apparently as a result of increased windthrow and microclimatic changes near forest edges. Mean mortality, damage, and turnover rates were much higher within 60 m of edges (4.01, 4.10, and 3.16%, respectively) and moderately higher within 60–100 m of edges (2.40, 1.96, and 2.05%) than in forest interiors (1.27, 1.48, and 1.15%). Less-pronounced changes in mortality and turnover rates were apparently detectable up to ∼300 m from forest edges. Edge aspect had no significant effect on forest dynamics. Tree mortality and damage rates did not vary significantly with fragment age, suggesting that increased dynamics are not merely transitory effects that occur immediately after fragmentation, while turnover rates increased with age in most (8/9) fragments. These findings reveal that fragmentation causes important changes in the dynamics of Amazonian forests, especially within ∼100 m of habitat edges. A mathematical “core-area model” incorporating these data predicted that edge effects will increase rapidly in importance once fragments fall below ∼100–400 ha in area, depending on fragment shape. Accelerated dynamics in fragments will alter forest structure, floristic composition, biomass, and microclimate and are likely to exacerbate effects of fragmentation on disturbance-sensitive species.

Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning.

Abstract: Previous studies have shown that the method of hydrogen mass repartitioning (HMR) is a potentially useful tool for accelerating molecular dynamics (MD) simulations. By repartitioning the mass of heavy atoms into the bonded hydrogen atoms, it is possible to slow the highest-frequency motions of the macromolecule under study, thus allowing the time step of the simulation to be increased by up to a factor of 2. In this communication, we investigate further how this mass repartitioning allows the simulation time step to be increased in a stable fashion without significantly increasing discretization error. To this end, we ran a set of simulations with different time steps and mass distributions on a three-residue peptide to get a comprehensive view of the effect of mass repartitioning and time step increase on a system whose accessible phase space is fully explored in a relatively short amount of time. We next studied a 129-residue protein, hen egg white lysozyme (HEWL), to verify that the observed behavior extends to a larger, more-realistic, system. Results for the protein include structural comparisons from MD trajectories, as well as comparisons of pKa calculations via constant-pH MD. We also calculated a potential of mean force (PMF) of a dihedral rotation for the MTS [(1-oxyl-2,2,5,5-tetramethyl-pyrroline-3-methyl)methanethiosulfonate] spin label via umbrella sampling with a set of regular MD trajectories, as well as a set of mass-repartitioned trajectories with a time step of 4 fs. Since no significant difference in kinetics or thermodynamics is observed by the use of fast HMR trajectories, further evidence is provided that long-time-step HMR MD simulations are a viable tool for accelerating MD simulations for molecules of biochemical interest.

Long-term decline of the Amazon carbon sink

Abstract: Atmospheric carbon dioxide records indicate that the land surface has acted as a strong global carbon sink over recent decades, with a substantial fraction of this sink probably located in the tropics, particularly in the Amazon. Nevertheless, it is unclear how the terrestrial carbon sink will evolve as climate and atmospheric composition continue to change. Here we analyse the historical evolution of the biomass dynamics of the Amazon rainforest over three decades using a distributed network of 321 plots. While this analysis confirms that Amazon forests have acted as a long-term net biomass sink, we find a long-term decreasing trend of carbon accumulation. Rates of net increase in above-ground biomass declined by one-third during the past decade compared to the 1990s. This is a consequence of growth rate increases levelling off recently, while biomass mortality persistently increased throughout, leading to a shortening of carbon residence times. Potential drivers for the mortality increase include greater climate variability, and feedbacks of faster growth on mortality, resulting in shortened tree longevity. The observed decline of the Amazon sink diverges markedly from the recent increase in terrestrial carbon uptake at the global scale, and is contrary to expectations based on models.