Applying mesh conformation on shape analysis with missing data

A mesh conformation approach that makes use of deformable generic meshes has been applied to establishing correspondences between 3D shapes with missing data. Given a group of shapes with correspondences, we can build up a statistical shape model by applying principal component analysis (PCA). The conformation at first globally maps the generic mesh to the 3D shape based on manually located corresponding landmarks, and then locally deforms the generic mesh to clone the 3D shape. The local deformation is constrained by minimizing the energy of an elastic model. An algorithm was also embedded in the conformation process to fill missing surface data of the shapes. Using synthetic data, we demonstrate that the conformation preserves the configuration of the generic mesh and hence it helps to establish good correspondences for shape analysis. Case studies of the principal component analysis of shapes were presented to illustrate the successes and advantages of our approach

Hawking's radiation in non-stationary rotating de Sitter background

Hawking's radiation effect of Klein-Gordon scalar field, Dirac particles and Maxwell's electromagnetic field in the non-stationary rotating de Sitter cosmological space-time is investigated by using a method of generalized tortoise co-ordinates transformation. The locations and the temperatures of the cosmological horizons of the non-stationary rotating de Sitter model are derived. It is found that the locations and the temperatures of the rotating cosmological model depend not only on the time but also on the angle. The stress-energy regularization techniques are applied to the two dimensional analog of the de Sitter metrics and the calculated stress-energy tensor contains the thermal radiation effect.Comment: 13 pages, LaTex format, accepted for publication Astrophysics and Space Science, Springer; Journal ID: 10509, Article ID: 606, Date 2011-01-1

Quakes in Solid Quark Stars

A starquake mechanism for pulsar glitches is developed in the solid quark star model. It is found that the general glitch natures (i.e., the glitch amplitudes and the time intervals) could be reproduced if solid quark matter, with high baryon density but low temperature, has properties of shear modulus \mu = 10^{30~34} erg/cm^3 and critical stress \sigma_c = 10^{18~24} erg/cm^3. The post-glitch behavior may represent a kind of damped oscillations.Comment: 11 pages, 4 figures (but Fig.3 is lost), a complete version can be obtained by http://vega.bac.pku.edu.cn/~rxxu/publications/index_P.htm, a new version to be published on Astroparticle Physic

MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes

Attribute recognition, particularly facial, extracts many labels for each image. While some multi-task vision problems can be decomposed into separate tasks and stages, e.g., training independent models for each task, for a growing set of problems joint optimization across all tasks has been shown to improve performance. We show that for deep convolutional neural network (DCNN) facial attribute extraction, multi-task optimization is better. Unfortunately, it can be difficult to apply joint optimization to DCNNs when training data is imbalanced, and re-balancing multi-label data directly is structurally infeasible, since adding/removing data to balance one label will change the sampling of the other labels. This paper addresses the multi-label imbalance problem by introducing a novel mixed objective optimization network (MOON) with a loss function that mixes multiple task objectives with domain adaptive re-weighting of propagated loss. Experiments demonstrate that not only does MOON advance the state of the art in facial attribute recognition, but it also outperforms independently trained DCNNs using the same data. When using facial attributes for the LFW face recognition task, we show that our balanced (domain adapted) network outperforms the unbalanced trained network.Comment: Post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016 http://link.springer.com/chapter/10.1007%2F978-3-319-46454-1_