Joint power and admission control via p norm minimization deflation

In an interference network, joint power and admission control aims to support a maximum number of links at their specified signal to interference plus noise ratio (SINR) targets while using a minimum total transmission power. In our previous work, we formulated the joint control problem as a sparse $\ell_0$-minimization problem and relaxed it to a $\ell_1$-minimization problem. In this work, we propose to approximate the $\ell_0$-optimization problem to a p norm minimization problem where $0<p<1$, since intuitively p norm will approximate 0 norm better than 1 norm. We first show that the $\ell_p$-minimization problem is strongly NP-hard and then derive a reformulation of it such that the well developed interior-point algorithms can be applied to solve it. The solution to the $\ell_p$-minimization problem can efficiently guide the link's removals (deflation). Numerical simulations show the proposed heuristic outperforms the existing algorithms.Comment: 2013 IEEE International Conference on Acoustics, Speech, and Signal Processin

Interpretation of the unprecedentedly long-lived high-energy emission of GRB 130427A

High energy photons (>100 MeV) are detected by the Fermi/LAT from GRB 130427A up to almost one day after the burst, with an extra hard spectral component being discovered in the high-energy afterglow. We show that this hard spectral component arises from afterglow synchrotron-self Compton emission. This scenario can explain the origin of >10 GeV photons detected up to ~30000s after the burst, which would be difficult to be explained by synchrotron radiation due to the limited maximum synchrotron photon energy. The lower energy multi-wavelength afterglow data can be fitted simultaneously by the afterglow synchrotron emission. The implication of detecting the SSC emission for the circumburst environment is discussed.Comment: 4 pages, 2 figures, ApJL in pres

CASENet: Deep Category-Aware Semantic Edge Detection

Boundary and edge cues are highly beneficial in improving a wide variety of vision tasks such as semantic segmentation, object recognition, stereo, and object proposal generation. Recently, the problem of edge detection has been revisited and significant progress has been made with deep learning. While classical edge detection is a challenging binary problem in itself, the category-aware semantic edge detection by nature is an even more challenging multi-label problem. We model the problem such that each edge pixel can be associated with more than one class as they appear in contours or junctions belonging to two or more semantic classes. To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features. We then propose a multi-label loss function to supervise the fused activations. We show that our proposed architecture benefits this problem with better performance, and we outperform the current state-of-the-art semantic edge detection methods by a large margin on standard data sets such as SBD and Cityscapes.Comment: Accepted to CVPR 201