From Point to Set: Extend the Learning of Distance Metrics

Most of the current metric learning methods are proposed for point-to-point distance (PPD) based classification. In many computer vision tasks, however, we need to measure the point-to-set distance (PSD) and even set-to-set distance (SSD) for classification. In this paper, we extend the PPD based Mahalanobis distance metric learning to PSD and SSD based ones, namely point-to-set distance metric learning (PSDML) and set-to-set distance metric learning (SSDML), and solve them under a unified optimization framework. First, we generate positive and negative sample pairs by computing the PSD and SSD between training samples. Then, we characterize each sample pair by its covariance matrix, and propose a covariance kernel based discriminative function. Finally, we tackle the PSDML and SSDML problems by using standard support vector machine solvers, making the metric learning very efficient for multiclass visual classification tasks. Experiments on gender classification, digit recognition, object categorization and face recognition show that the proposed metric learning methods can effectively enhance the performance of PSD and SSD based classification. 1

10-Hertz quantum light source generation on the cesium D2 line using single photon modulation

Generation of quantum light source is a promising technique to overcome the standard quantum limit in precision measurement. Here, we demonstrate an experimental generation of quadrature squeezing resonating on the cesium D2 line down to 10 Hz for the first time. The maximum squeezing in audio frequency band is 5.57 dB. Moreover, we have presented a single-photon modulation locking to control the squeezing angle, while effectively suppressing the influence of laser noise on low-frequency squeezing. The whole system operates steadily for hours. The generated low-frequency quantum light source can be applied in quantum metrology,light-matter interaction investigation and quantum memory in the audio frequency band and even below

(2,2′-Bipyridine-κ2 N,N′){N-[(2-oxidonaphthalen-1-yl-κO)methyl­idene]-l-valinato-κO}copper(II) trihydrate

In the title complex, [Cu(C16H15NO3)(C10H8N2)]·3H2O, the CuII atom is five coordinated by O,N,O′-donor atoms of the Schiff base ligand and by two N atoms of the 2,2′-bipyridine ligand in a distorted square-pyramidal geometry. In the crystal, mol­ecules are linked into a two-dimensional network parallel to (011) by O—H⋯O hydrogen bonds