Alignment-free characterization of 2D gratings

Modeling, measuring, and using BRDFs: significant French contributions

The scattering of light by a surface is described by the bidirectional reflectance distribution function (BRDF). Unfortunately, this function cannot be straightforwardly acquired or modeled. French researchers have proposed interesting contributions to the field, with several models and accurate experimental systems. For instance, the National Metrological Institute (LNE-CNAM) has implemented the best angular resolution goniospectrophotometer (0.015°). Modeling the BRDF has also been deeply studied in France, especially with the microfacet theory in recent years, a better understanding of the shadowing-masking function, new general distribution functions, visible normals, interfaced Lambertian microfacets, and analysis concerning light multiple reflections. This paper presents the state of the art regarding some significant French contributions in these fields.

SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform

BACKGROUND

Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.

RESULTS

A new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k-mers from a sequence, then maps each k-mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification.

CONCLUSIONS

Using two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.