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: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.

Touch-screen user interface

Abstract: A user interface for a touch-screen display of a dedicated handheld electronic book reader device is described. The user interface detects human gestures manifest as pressure being applied by a finger or stylus to regions on the touch-screen display. In one implementation, the touch-screen user interface enables a user to turn one or more pages in response to applying a force or pressure to the touch-screen display. In another implementation, the touch-screen user interface is configured to bookmark a page temporarily by applying a pressure to the display, then allowing a user to turn pages to a new page, but reverting back to a previously-displayed page when the pressure is removed. In another implementation, the touch-screen user interface identifies and filters electronic books based on book size and/or a time available to read a book. In another implementation, the touch-screen user interface converts text to speech in response to a user touching the touch-screen display.

Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?

Abstract: This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section.

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

Abstract: We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection Our method is essentially as fast as an equivalent one which selects the "support" patterns at random, yet it can outperform random selection on hard curve fitting tasks More importantly, it leads to a sufficiently stable approximation of the log marginal likelihood of the training data, which can be optimised to adjust a large number of hyperparameters automatically We demonstrate the model selection capabilities of the algorithm in a range of experiments In line with the development of our method, we present a simple view on sparse approximations for GP models and their underlying assumptions and show relations to other methods

Brazil's Soy Moratorium

Abstract: Brazil's Soy Moratorium (SoyM) was the first voluntary zero-deforestation agreement implemented in the tropics and set the stage for supply-chain governance of other commodities, such as beef and palm oil [supplementary material (SM)]. In response to pressure from retailers and nongovernmental organizations (NGOs), major soybean traders signed the SoyM, agreeing not to purchase soy grown on lands deforested after July 2006 in the Brazilian Amazon. The soy industry recently extended the SoyM to May 2016, by which time they assert that Brazil's environmental governance, such as the increased enforcement and national implementation of the Rural Environmental Registry of private properties (Portuguese acronym CAR) mandated by the Forest Code (FC) ( 1 ), will be robust enough to justify ending the agreement ( 2 ). We argue that a longer-term commitment is needed to help maintain deforestation-free soy supply chains, as full compliance and enforcement of these regulations is likely years away. Ending the SoyM prematurely would risk a return to deforestation for soy expansion at a time when companies are committing to zero-deforestation supply chains ( 3 ).

OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations

Abstract: We present a novel model called OCGAN for the classical problem of one-class novelty detection, where, given a set of examples from a particular class, the goal is to determine if a query example is from the same class. Our solution is based on learning latent representations of in-class examples using a de-noising auto-encoder network. The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class. In order to accomplish this goal, firstly, we force the latent space to have bounded support by introducing a tanh activation in the encoder's output layer. Secondly, using a discriminator in the latent space that is trained adversarially, we ensure that encoded representations of in-class examples resemble uniform random samples drawn from the same bounded space. Thirdly, using a second adversarial discriminator in the input space, we ensure all randomly drawn latent samples generate examples that look real. Finally, we introduce a gradient-descent based sampling technique that explores points in the latent space that generate potential out-of-class examples, which are fed back to the network to further train it to generate in-class examples from those points. The effectiveness of the proposed method is measured across four publicly available datasets using two one-class novelty detection protocols where we achieve state-of-the-art results.