Learn More
We propose a Generalized Dantzig Selector (GDS) for linear models, in which any norm encoding the parameter structure can be leveraged for estimation. We investigate both computational and statistical aspects of the GDS. Based on conjugate proximal operator, a flexible inexact ADMM framework is designed for solving GDS. Thereafter, non-asymptotic(More)
In this paper, we consider spectral approximation of fractional differential equations (FDEs). A main ingredient of our approach is to define a new class of generalized Jacobi functions (GJFs), which is intrinsically related to fractional calculus, and can serve as natural basis functions for properly designed spectral methods for FDEs. We establish(More)
—This contribution applies digital predistorter to compensate distortions caused by memory high power amplifiers (HPAs) which exhibit true output saturation characteristics. Particle swarm optimization is first implemented to identify the Wiener HPA's parameters. The estimated Wiener HPA model is then directly used to design the predistorter. The proposed(More)
Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle(More)
MOTIVATION The high redundancy of and high degree of cross-talk between biological pathways hint that a sub-pathway may respond more effectively or sensitively than the whole pathway. However, few current pathway enrichment analysis methods account for the sub-pathways or structures of the tested pathways. We present a sub-pathway-based enrichment approach(More)
This paper presents a new approach to tracking people in crowded scenes, where people are subject to long-term (partial) occlusions and may assume varying postures and articulations. In such videos, detection-based trackers give poor performance since detecting people occurrences is not reliable, and common assumptions about locally smooth trajectories do(More)
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regu-larized regression, such as Lasso, needs to consider four aspects: the norm, the loss function, the design matrix, and the noise model. This paper presents generalizations of such estimation error analysis on all four aspects compared to the existing(More)