Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and extracted features please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda

OPTIMAL PORTFOLIO CONSTRUCTION BY MIXING HEDGE FUND

The returns of the hedge fund are declining in recent years, accompanying with the impact of the financial crisis in 2008. There will be a question that whether the hedge fund can still be used to blend in a conventional portfolio to improve the performance. Our paper focuses on the comparison analysis and does the basic asset allocation for the hedge fund and traditional portfolio. We analyze the risk-adjusted returns for conventional assets of US Equities, EAFE Equities, US Bonds and International Bonds as well as the hedge fund. Finally we find that, under current market condition, hedge fund is still an ideal alternative asset for the choice of the portfolio to increase the risk-adjusted return level

Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review

Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted

A Screening Strategy for Structured Optimization Involving Nonconvex ℓq,p\ell_{q,p} Regularization

In this paper, we develop a simple yet effective screening rule strategy to improve the computational efficiency in solving structured optimization involving nonconvex $\ell_{q,p}$ regularization. Based on an iteratively reweighted $\ell_1$ (IRL1) framework, the proposed screening rule works like a preprocessing module that potentially removes the inactive groups before starting the subproblem solver, thereby reducing the computational time in total. This is mainly achieved by heuristically exploiting the dual subproblem information during each iteration.Moreover, we prove that our screening rule can remove all inactive variables in a finite number of iterations of the IRL1 method. Numerical experiments illustrate the efficiency of our screening rule strategy compared with several state-of-the-art algorithms

A novel type of hybrid ultrasonic motor using longitudinal and torsional vibration modes with side panels

A novel type of hybrid ultrasonic motor using longitudinal and torsional vibration modes is presented, which has four side panels uniformly distributed along the circumference of the stator cylinder. There is rectangle piezoelectric ceramics (PZTs) based on d31 effect bonded on both sides of each side panels, which can be used to convert the first bending vibration mode of the side panels into the second torsional vibration mode of the stator when the exciting voltage is applied. Meanwhile, there are rectangle PZTs based on d31 effect bonded on the surfaces of the stator cylinder between every two side panels, which can be used to excite the first longitudinal vibration mode of the stator. The simulation results using finite element method (FEM) software Workbench reveals the suitable polarization arrangement of PZTs and the final designed structure of the motor. The appearance size of the prototype is 28.2 mm×28.2 mm×68 mm, while the outer diameter of the stator cylinder is 20 mm. The major vibration and mechanical characteristics of the prototype have been measured. The working frequency of the prototype measured in experiment is around 43.12 kHz, which is consistent with the numerical results. When operating voltage of 350 Vp-p is applied, the no-load speed of the prototype is 103 rpm and the stalling torque is 48 mN·m