Measurement of the squeezed vacuum state by a bichromatic local oscillator

We present the experimental measurement of a squeezed vacuum state by means of a bichromatic local oscillator (BLO). A pair of local oscillators at $\pm$5 MHz around the central frequency $\omega_{0}$ of the fundamental field with equal power are generated by three acousto-optic modulators and phase-locked, which are used as a BLO. The squeezed vacuum light are detected by a phase-sensitive balanced-homodyne detection with a BLO. The baseband signal around $\omega_{0}$ combined with a broad squeezed field can be detected with the sensitivity below the shot-noise limit, in which the baseband signal is shifted to the vicinity of 5 MHz (the half of the BLO separation). This work has the important applications in quantum state measurement and quantum informatio

Multilabel Consensus Classification

In the era of big data, a large amount of noisy and incomplete data can be collected from multiple sources for prediction tasks. Combining multiple models or data sources helps to counteract the effects of low data quality and the bias of any single model or data source, and thus can improve the robustness and the performance of predictive models. Out of privacy, storage and bandwidth considerations, in certain circumstances one has to combine the predictions from multiple models or data sources to obtain the final predictions without accessing the raw data. Consensus-based prediction combination algorithms are effective for such situations. However, current research on prediction combination focuses on the single label setting, where an instance can have one and only one label. Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time. Direct applications of existing prediction combination methods to multilabel settings can lead to degenerated performance. In this paper, we address the challenges of combining predictions from multiple multilabel classifiers and propose two novel algorithms, MLCM-r (MultiLabel Consensus Maximization for ranking) and MLCM-a (MLCM for microAUC). These algorithms can capture label correlations that are common in multilabel classifications, and optimize corresponding performance metrics. Experimental results on popular multilabel classification tasks verify the theoretical analysis and effectiveness of the proposed methods