Parity violating observables in radiative neutrino pair emission from metastable atoms

We report on a possibility of measuring parity violating effects in radiative neutrino pair emission from metastable atoms; asymmetric angular distribution of emitted photons from oriented atoms and emergent circular polarization. Their observation, along with the continuous photon energy spectrum which has 6 thresholds, may be interpreted as events being a combined weak and QED process, emission of $\gamma \nu_i \nu_j$ in the final state. The method may greatly help to perform neutrino mass spectroscopy using atoms, a systematic determination of the neutrino mass matrix.Comment: 9 pages, 4 figure

Stasistically constrained operator associated with additivity of communication channel

Additivity of quantum communication channel is discussed in terms of Poisson process to show it is additive in probability. Poisson process is shown to be responsible for entanglement which is a rare event.Comment: 6 pages, 2 figures, Quantum Optics and Application in Computation and Communication (Nov. 2004 Beijing

Novel Approximate Statistical Algorithm for Large Complex Datasets

In the field of pattern recognition, principal component analysis (PCA) is one of the most well-known feature extraction methods for reducing the dimensionality of high-dimensional datasets. Simple-PCA (SPCA), which is a faster version of PCA, performs effectively with iterative operated learning. However, SPCA might not be efficient when input data are distributed in a complex manner because it learns without using the class information in the dataset. Thus, SPCA cannot be said to be optimal from the perspective of feature extraction for classification. In this study, we propose a new learning algorithm that uses the class information in the dataset. Eigenvectors spanning the eigenspace of the dataset are produced by calculating the data variations within each class. We present our proposed algorithm and discuss the results of our experiments that used UCI datasets to compare SPCA and our proposed algorithm

Japanese sign language classification based on gathered images and neural networks

This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words