Unified Universal Quantum Cloning Machine and Fidelities

We present a unified universal quantum cloning machine, which combines several different existing universal cloning machines together including the asymmetric case. In this unified framework, the identical pure states are projected equally into each copy initially constituted by input and one half of the maximally entangled states. We show explicitly that the output states of those universal cloning machines are the same. One importance of this unified cloning machine is that the cloning procession is always the symmetric projection which reduces dramatically the difficulties for implementation. Also it is found that this unified cloning machine can be directly modified to the general asymmetric case. Besides the global fidelity and the single-copy fidelity, we also present all possible arbitrary-copy fidelities.Comment: 4 pages, 2 figure

Influence of the Optical Multi-Film Thickness on the Saturation of the Structural Color Displayed

In this paper, it is demonstrated that saturation of the structure color exhibited by the multi-film systems can be determined by the thickness parameter of the layers of multi-films. To prove this principle, the multi-film of 1-quarter film system and 3-quarter film system with the central wavelength of 650nm, exhibiting a color red, are fabricated by deposition method. Simulation was done base on the light interference principle, and both simulation and experiment results show that the reflective spectra of the 1-quarter film system have a wider bandwidth. Saturations of the color from different systems are calculated separately by the CIE colorimetry method, to prove that the 3-quarter film system produces colors with higher saturation. Keywords: saturation; structural color; multi-film; thin film thickness; interference principl

Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation

Singing voice detection is the task to identify the frames which contain the singer vocal or not. It has been one of the main components in music information retrieval (MIR), which can be applicable to melody extraction, artist recognition, and music discovery in popular music. Although there are several methods which have been proposed, a more robust and more complete system is desired to improve the detection performance. In this paper, our motivation is to provide an extensive comparison in different stages of singing voice detection. Based on the analysis a novel method was proposed to build a more efficiently singing voice detection system. In the proposed system, there are main three parts. The first is a pre-process of singing voice separation to extract the vocal without the music. The improvements of several singing voice separation methods were compared to decide the best one which is integrated to singing voice detection system. And the second is a deep neural network based classifier to identify the given frames. Different deep models for classification were also compared. The last one is a post-process to filter out the anomaly frame on the prediction result of the classifier. The median filter and Hidden Markov Model (HMM) based filter as the post process were compared. Through the step by step module extension, the different methods were compared and analyzed. Finally, classification performance on two public datasets indicates that the proposed approach which based on the Long-term Recurrent Convolutional Networks (LRCN) model is a promising alternative.Comment: 15 page