Microarray is the gene expression data that represent gene in different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. Significance analysis of microarrays (SAM) is a statistical technique for determining whether changes in gene expression are statistically significant. During the SAM procedure permutation ...

A frame decimation scheme is proposed that makes automatic extraction of Structure and Motion (SaM) from handheld sequences more practical. Decimation of the number of frames used for the actual SaM calculations keeps the size of the problem manageable, regardless of the input frame rate. The proposed preprocessor is based upon global motion estimation between frames and a sharpness measure. Wi...

Interpolation of field data from unstructured meshes requires the potentially expensive identification of the finite element or volume within which the interpolating point lies. A number of geometric search algorithms have been proposed to reduce this expense at the cost of setting up and storing additional search tables. Using tetrahedral finite element models we show that a structured auxilia...

The aim of this study is to validate the Spectral Angle Mapper (SAM) algorithm for geological mapping in Central Jebilet Morocco and compare the results between high and medium spatial resolution sensors, such as Quickbird and Landsat TM respectively. The geology of the study area is dominated by the Sahrlef schist, which is intruded by numerous bodies of acidic and basic magmatic rocks and len...

The sparse additive model for text modeling involves the sum-of-exp computing, whose cost is consuming for large scales. Moreover, the assumption of equal background across all classes/topics may be too strong. This paper extends to propose sparse additive model with low rank background (SAM-LRB) and obtains simple yet efficient estimation. Particularly, employing a double majorization bound, w...

Data Mining in non-stationary data streams is particularly relevant in the context of the Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) ...

Protecting software and data becomes more and more important, especially, when sensitive or expensive software is executed on remote hosts. This protection includes copy protection, prevention of disassembling, prevention of altering the program flow and protection of processed data. For personal computers protection is more focused on copy protection. However, providing extended security to pr...

The sparse additive model for text modeling involves the sum-of-exp computing, whose cost is consuming for large scales. Moreover, the assumption of equal background across all classes/topics may be too strong. This paper extends to propose sparse additive model with low rank background (SAM-LRB) and obtains simple yet efficient estimation. Particularly, employing a double majorization bound, w...

Human Face Verification by Robust 3D Surface Alignment By Dirk Joel Luchini Colbry Traditional 2D face recognition systems are not tolerant to changes in pose, lighting and expression. This dissertation explores the use of 3D data to improve face recognition by accounting for these variations. A two step, fully automatic, 3D surface alignment algorithm is developed to correlate the surfaces of ...

Software Asset Management (SAM) is essential for a large company with centralized software distribution system. Unfortunately, the operationalization of SAM has various problems. These problems become even more complicated when it comes to managing big data. This research proposes Pareto Principle reduce data dimensions solve problem sizes without losing dataset characteristics before conductin...