Representation Learning with Fine-grained Patterns

With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most of existing algorithms on benchmark data sets. Many efforts have been devoted to studying the mechanism of deep learning. One important observation is that deep learning can learn the discriminative patterns from raw materials directly in a task-dependent manner. Therefore, the representations obtained by deep learning outperform hand-crafted features significantly. However, those patterns are often learned from super-class labels due to a limited availability of fine-grained labels, while fine-grained patterns are desired in many real-world applications such as visual search in online shopping. To mitigate the challenge, we propose an algorithm to learn the fine-grained patterns sufficiently when only super-class labels are available. The effectiveness of our method can be guaranteed with the theoretical analysis. Extensive experiments on real-world data sets demonstrate that the proposed method can significantly improve the performance on target tasks corresponding to fine-grained classes, when only super-class information is available for training

A NEW METHOD FOR MEASURING SEGMENT MASS & SEGMENT CENTER OF MASS LOCATION OF HUMAN BODY

The purpose of this study was to introduce a new method for measuring segment mass & segment center of mass of human body, and determine whether valid measures of segment inertial properties can be generated from using this new method. In first place, we introduced the principles of two types of instruments used in this new method, one for measuring segment moment of mass (mb x rb), and the other for measuring segment center of mass (rb), and then we obtained segment mass (mb)' We measured 9 subjects using the above two types of instruments, and these segments measured included one forearm-hand, one upper limb, one shank-foot and one lower limb. There is no significance discrepancy between the calculations of database provided by Xiuyuan Zheng using eT method and ours, which showed that the new method is a valid method

A Bayesian Approach to Estimate the Size and Structure of the Broad-line Region in Active Galactic Nuclei Using Reverberation Mapping Data

This is the first paper in a series devoted to systematic study of the size and structure of the broad-line region (BLR) in active galactic nuclei (AGNs) using reverberation mapping (RM) data. We employ a recently developed Bayesian approach that statistically describes the variabibility as a damped random walk process and delineates the BLR structure using a flexible disk geometry that can account for a variety of shapes, including disks, rings, shells, and spheres. We allow for the possibility that the line emission may respond non-linearly to the continuum, and we detrend the light curves when there is clear evidence for secular variation. We use a Markov Chain Monte Carlo implementation based on Bayesian statistics to recover the parameters and uncertainties for the BLR model. The corresponding transfer function is obtained self-consistently. We tentatively constrain the virial factor used to estimate black hole masses; more accurate determinations will have to await velocity-resolved RM data. Application of our method to RM data with Hbeta monitoring for about 40 objects shows that the assumed BLR geometry can reproduce quite well the observed emission-line fluxes from the continuum light curves. We find that the Hbeta BLR sizes obtained from our method are on average ~20% larger than those derived from the traditional cross-correlation method. Nevertheless, we still find a tight BLR size-luminosity relation with a slope of alpha=0.55\pm0.03 and an intrinsic scatter of ~0.18 dex. In particular, we demonstrate that our approach yields appropriate BLR sizes for some objects (such as Mrk 142 and PG 2130+099) where traditional methods previously encountered difficulties.Comment: 17 pages, 10 figures, 2 tables; minor reversion to match the published versio