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.

Mobile payments using payment tokens

Abstract: A user may request a payment token from a host. The payment token may be a unique one-time use identifier linked to one or more payment accounts associated with the user. The payment token may be subject to conditions of use. To redeem the payment token, the user device may generate an image code to visually present the payment token for access by a recipient's camera. The recipient may then record the image code. The user may also provide a security identifier to the recipient. The recipient may then transmit the image code and the security identifier to the host as a payment request. The host may verify the payment request and verify compliance with any associated conditions. When the payment token is valid, funds are available, and the conditions are satisfied, then the host may transfer the funds to an account of the recipient.

3D Face Morphable Models "In-the-Wild"

Abstract: 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions (in-the-wild). In this paper, we propose the first, to the best of our knowledge, in-the-wild 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an in-the-wild texture model. We show that the employment of such an in-the-wild texture model greatly simplifies the fitting procedure, because there is no need to optimise with regards to the illumination parameters. Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary images. Finally, we have captured the first 3D facial database with relatively unconstrained conditions and report quantitative evaluations with state-of-the-art performance. Complementary qualitative reconstruction results are demonstrated on standard in-the-wild facial databases.

Influence of habitat, litter type, and soil invertebrates on leaf-litter decomposition in a fragmented Amazonian landscape

Abstract: Amazonian forest fragments and second-growth forests often differ substantially from undisturbed forests in their microclimate, plant-species composition, and soil fauna. To determine if these changes could affect litter decomposition, we quantified the mass loss of two contrasting leaf-litter mixtures, in the presence or absence of soil macroinvertebrates, and in three forest habitats. Leaf-litter decomposition rates in second-growth forests (>10 years old) and in fragment edges ( 250 m from the edges of primary forests). In all three habitats, experimental exclusion of soil invertebrates resulted in slower decomposition rates. Faunal-exclosure effects were stronger for litter of the primary forest, composed mostly of leaves of old-growth trees, than for litter of second-growth forests, which was dominated by leaves of successional species. The latter had a significantly lower initial concentration of N, higher C:N and lignin:N ratios, and decomposed at a slower rate than did litter from forest interiors. Our results indicate that land-cover changes in Amazonia affect decomposition mainly through changes in plant species composition, which in turn affect litter quality. Similar effects may occur on fragment edges, particularly on very disturbed edges, where successional trees become dominant. The drier microclimatic conditions in fragment edges and second-growth forests (>10 years old) did not appear to inhibit decomposition. Finally, although soil invertebrates play a key role in leaf-litter decomposition, we found no evidence that differences in the abundance, species richness, or species composition of invertebrates between disturbed and undisturbed forests significantly altered decomposition rates.

Context Encoding for Semantic Segmentation

Abstract: Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpass the winning entry of COCO-Place Challenge in 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10 times more layers. The source code for the complete system are publicly available.

Outer approximation algorithms for separable nonconvex mixed-integer nonlinear programs

Abstract: A rigorous decomposition approach to solve separable mixed-integer nonlinear programs where the participating functions are nonconvex is presented. The proposed algorithms consist of solving an alternating sequence of Relaxed Master Problems (mixed-integer linear program) and two nonlinear programming problems (NLPs). A sequence of valid nondecreasing lower bounds and upper bounds is generated by the algorithms which converge in a finite number of iterations. A Primal Bounding Problem is introduced, which is a convex NLP solved at each iteration to derive valid outer approximations of the nonconvex functions in the continuous space. Two decomposition algorithms are presented in this work. On finite termination, the first yields the global solution to the original nonconvex MINLP and the second finds a rigorous bound to the global solution. Convergence and optimality properties, and refinement of the algorithms for efficient implementation are presented. Finally, numerical results are compared with currently available algorithms for example problems, illuminating the potential benefits of the proposed algorithm.