Liberated Presentism

(The version now posted is a revision of what was posted earlier. Final version now published.) The article gives a novel argument to show that there is sense of 'exists' suitable for posing a substantive issue between presentists and eternalists. It then seeks to invigorate a neglected variety of presentism. There are seven doctrines, widely accepted even among presentists, that create problems for presentism. Without distinguishing existence and being, presentists can comfortably reject all seven. Doing so would dispose of the majority of presentism’s problems. Further, it would enable presentists to reduce A-judgments to B-judgments, thereby insulating presentism from doubts about the intelligibility of A-theories. For reasons indicated very briefly, it might also make presentism less difficult to reconcile with special relativity, though the point is not pursued here

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.Comment: 25 pages, Technical report, related to Burke and Lasenby, AMDO 2014 conference paper. Code sample: https://github.com/mgb45/SignerBodyPose Video: https://www.youtube.com/watch?v=dJMTSo7-uF

m-Reading: Fiction reading from mobile phones

Mobile phones are reportedly the most rapidly expanding e-reading device worldwide. However, the embodied, cognitive and affective implications of smartphone-supported fiction reading for leisure (m-reading) have yet to be investigated empirically. Revisiting the theoretical work of digitization scholar Anne Mangen, we argue that the digital reading experience is not only contingent on patterns of embodied reader–device interaction (Mangen, 2008 and later) but also embedded in the immediate environment and broader situational context. We call this the situation constraint. Its application to Mangen’s general framework enables us to identify four novel research areas, wherein m-reading should be investigated with regard to its unique affordances. The areas are reader–device affectivity, situated embodiment, attention training and long-term immersion

A new paradigm for the U.S. economy?

Economic conditions - United States ; Business cycles

Improving Scientist Productivity, Architecture Portability, and Performance in ParFlow

Legacy scientific applications represent significant investments by universities, engineers, and researchers and contain valuable implementations of key scientific computations. Over time hardware architectures have changed. Adapting existing code to new architectures is time consuming, expensive, and increases code complexity. The increase in complexity negatively affects the scientific impact of the applications. There is an immediate need to reduce complexity. We propose using abstractions to manage and reduce code complexity, improving scientific impact of applications. This thesis presents a set of abstractions targeting boundary conditions in iterative solvers. Many scientific applications represent physical phenomena as a set of partial differential equations (PDEs). PDEs are structured around steady state and boundary condition equations, starting from initial conditions. The proposed abstractions separate architecture specific implementation details from the primary computation. We use ParFlow to demonstrate the effectiveness of the abstractions. ParFlow is a hydrologic and geoscience application that simulates surface and subsurface water flow. The abstractions have enabled ParFlow developers to successfully add new boundary conditions for the first time in 15 years, and have enabled an experimental OpenMP version of ParFlow that is transparent to computational scientists. This is achieved without requiring expensive rewrites of key computations or major codebase changes; improving developer productivity, enabling hardware portability, and allowing transparent performance optimizations