Unsupervised Network Pretraining via Encoding Human Design

Over the years, computer vision researchers have spent an immense amount of effort on designing image features for the visual object recognition task. We propose to incorporate this valuable experience to guide the task of training deep neural networks. Our idea is to pretrain the network through the task of replicating the process of hand-designed feature extraction. By learning to replicate the process, the neural network integrates previous research knowledge and learns to model visual objects in a way similar to the hand-designed features. In the succeeding finetuning step, it further learns object-specific representations from labeled data and this boosts its classification power. We pretrain two convolutional neural networks where one replicates the process of histogram of oriented gradients feature extraction, and the other replicates the process of region covariance feature extraction. After finetuning, we achieve substantially better performance than the baseline methods.Comment: 9 pages, 11 figures, WACV 2016: IEEE Conference on Applications of Computer Visio

Tuning the Conductance of Monatomic Carbon Chain

Ab initio calculations show that the conductance of short monatomic carbon chain can be dramatically modified by adhering a single H, N, or O atom to the chain. For example, the conductance of the pristine chain gets about two orders of magnitude smaller if an H atom is adhered to the chain. By a statistical model, the structure of the carbon chain with the single atom adhered is found to be quite stable at room temperature, indicating that the method can be used to tune the conductance of monatomic carbon chain.Comment: 11pages, 6figure

Sequential optimization for efficient high-quality object proposal generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster

Entanglement Property and Monogamy Relation of Gerneralized Mixed W

We introduce a new class of multipartite entangled mixed states with pure state decompositions of generalized W states, similar to Schmidt-correlated states having generalized GHZ states in the pure state decomposition. The entanglement and separability properties are studied according to PPT operations. Monogamy relations linked to these states are also investigated.Comment: 8 page

Amending coherence-breaking channels via unitary operations

The coherence-breaking channels play a significant role in quantum information theory. We study the coherence-breaking channels and give a method to amend the coherence-breaking channels by applying unitary operations. For given incoherent channel $\Phi$, we give necessary and sufficient conditions for the channel to be a coherence-breaking channel and amend it via unitary operations. For qubit incoherent channels $\Phi$ that are not coherence-breaking ones, we consider the mapping $\Phi\circ\Phi$ and present the conditions for coherence-breaking and channel amendment as well.Comment: 8 page