Very Special Relativity

By Very Special Relativity (VSR) we mean descriptions of nature whose space-time symmetries are certain proper subgroups of the Poincar\'e group. These subgroups contain space-time translations together with at least a 2-parameter subgroup of the Lorentz group isomorphic to that generated by $K_{x}+J_{y}$ and $K_{y}-J_{x}$. We find that VSR implies special relativity (SR) in the context of local quantum field theory or of CP conservation. Absent both of these added hypotheses, VSR provides a simulacrum of SR for which most of the consequences of Lorentz invariance remain wholly or essentially intact, and for which many sensitive searches for departures from Lorentz invariance must fail. Several feasible experiments are discussed for which Lorentz-violating effects in VSR may be detectable.Comment: 3 pages, revte

Preliminary Evaluation of Kenaf as a Structural Material

Kenaf (Hibiscus cannabinus L.) is an annual fiber plant that is kin to cotton and okra and native to east-central Africa, though it is currently grown in numerous locations around the globe. The plant\u27s apparent high strength and light weight along with its environmental and sustainability advantages makes it a good candidate for use in structural materials. The goal of this study was to design a kenaf product that resembled parallel strand lumber and required minimal processing of the kenaf. The mechanical properties of the two main components of kenaf, the bast fibers and the core, were evaluated using experimental techniques. To supplement components testing, nine 1.2 in. x 2.3 in. x 12 in. kenaf beams were fabricated using strands of Whitten kenaf and a urea formaldehyde resin. The beams were loaded to failure in 3-point bending to characterize strength and stiffness. The kenaf beams had an average bending strength and average horizontal shear strength that were 26.3% and 6.8% respectively of the same properties of southern yellow pine lumber. The average elastic modulus was 7.8% of that of southern yellow pine. A limiting factor of the beams was the fact that the adhesive formed cracks throughout the beams while curing. A linear-elastic analytical model was produced in the form of a calculations spreadsheet to describe the initial load-displacement behavior of the kenaf beams. This model validated the experimental observation that the adhesive did not carry flexural stresses. It also showed that the lower bound strength values found in the component testing correlated with the properties of the materials in the beam. This preliminary study laid the groundwork for future development of whole-stalk kenaf as a structural material. Suggestions for future investigation are discussed at the conclusion of this thesis

Bethe Projections for Non-Local Inference

Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programming, collective graphical models, and forms of semi-supervised learning such as posterior regularization. We present a method to discriminatively learn broad families of inference objectives, capturing powerful non-local statistics of the latent variables, while maintaining tractable and provably fast inference using non-Euclidean projected gradient descent with a distance-generating function given by the Bethe entropy. We demonstrate the performance and flexibility of our method by (1) extracting structured citations from research papers by learning soft global constraints, (2) achieving state-of-the-art results on a widely-used handwriting recognition task using a novel learned non-convex inference procedure, and (3) providing a fast and highly scalable algorithm for the challenging problem of inference in a collective graphical model applied to bird migration.Comment: minor bug fix to appendix. appeared in UAI 201