In this paper, we demonstrate how PCA and ICA can be used for embedding graphs in pattern-spaces. Graph spectral feature vectors are calculated from the leading eigenvalues and eigenvectors of the unweighted graph adjacency matrix. The vectors are then embedded in a lower dimensional pattern space using both the PCA and ICA decomposition methods. Synthetic and real sequences are tested using th...

Neutrinos in a core-collapse supernova undergo coherent flavor transformations in their own background. We explore this phenomenon during the cooling stage of the explosion. Our three-flavor calculations reveal qualitatively new effects compared to a two-flavor analysis. These effects are especially clearly seen for the inverted mass hierarchy: we find a different pattern of spectral "swaps" in...

A paradoxical two-tone pattern is explored, which is heard as ascending when played in one key but as descending when played in a different key. The pattern thus provides a striking counterexample to the principle of invariance under transposition. In addition, the pattern in any one key is heard as ascending by some listeners but as descending by others. This study examines the effects of spec...

An innovative approach to spectral pattern recognition for multispectral images based on Genetic Programming is introduced. The problem is faced in terms of unsupervised pixel classification. The system is tested on a multispectral image with 31 spectral bands and 256 · 256 pixels. A good quality clustered output image is obtained.

A broad-bandwidth persistent spectral hole-burning organic material is used as a fast optical processor that compares an input temporal-frequency profile with a recorded reference spectral shape. This patternrecognition procedure relies on a subpicosecond temporal cross-correlation process. The size of the phaseencoding spectral interval exceeds 1 THz. The storage material, the spectral encoder...

Selected combinations from three or more light excitation wavelengths are serially impinged on a sample based on an encoding pattern. A plurality of spectra is detected from the sample responsive to respective ones of the selected combinations of light excitation wavelengths. A shifted Raman excitation spectral component and a nonshifted spectral component characteristic of the sample are ident...

Clustering is a fundamental problem in machine learning with numerous important applications in statistical signal processing, pattern recognition, and computer vision, where unsupervised analysis of data classification structures are required. The current stateof-the-art in clustering is widely accepted to be the socalled spectral clustering. Spectral clustering, based on pairwise affinities o...