Parallel bio-inspired methods for model optimization and pattern recognition

Nature based computational models are usually inherently parallel. The collaborative intelligence in those models emerges from the simultaneous instruction processing by simple independent units (neurons, ants, swarm members, etc...). This dissertation investigates the benefits of such parallel models in terms of efficiency and accuracy. First, the viability of a parallel implementation of bio-inspired metaheuristics for function optimization on consumer-level graphic cards is studied in detail. Then, in an effort to expose those parallel methods to the research community, the metaheuristic implementations were abstracted and grouped in an open source parameter/function optimization library libCudaOptimize. The library was verified against a well known benchmark for mathematical function minimization, and showed significant gains in both execution time and minimization accuracy. Crossing more into the application side, a parallel model of the human neocortex was developed. This model is able to detect, classify, and predict patterns in time-series data in an unsupervised way. Finally, libCudaOptimize was used to find the best parameters for this neocortex model, adapting it to gesture recognition within publicly available datasets

Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction

Coherent diffraction imaging methods enable imaging beyond lens-imposed resolution limits. In these methods, the object can be recovered by minimizing an error metric that quantifies the difference between diffraction patterns as observed, and those calculated from a present guess of the object. Efficient minimization methods require analytical calculation of the derivatives of the error metric, which is not always straightforward. This limits our ability to explore variations of basic imaging approaches. In this paper, we propose to substitute analytical derivative expressions with the automatic differentiation method, whereby we can achieve object reconstruction by specifying only the physics-based experimental forward model. We demonstrate the generality of the proposed method through straightforward object reconstruction for a variety of complex ptychographic experimental models.Comment: 23 pages (including references and supplemental material), 19 externally generated figure file

On Finslerized Absolute Parallelism spaces

The aim of the present paper is to construct and investigate a Finsler structure within the framework of a Generalized Absolute Parallelism space (GAP-space). The Finsler structure is obtained from the vector fields forming the parallelization of the GAP-space. The resulting space, which we refer to as a Finslerized Parallelizable space, combines within its geometric structure the simplicity of GAP-geometry and the richness of Finsler geometry, hence is potentially more suitable for applications and especially for describing physical phenomena. A study of the geometry of the two structures and their interrelation is carried out. Five connections are introduced and their torsion and curvature tensors derived. Some special Finslerized Parallelizable spaces are singled out. One of the main reasons to introduce this new space is that both Absolute Parallelism and Finsler geometries have proved effective in the formulation of physical theories, so it is worthy to try to build a more general geometric structure that would share the benefits of both geometries.Comment: Some references added and others removed, PACS2010, Typos corrected, Amendemrnts and revisions performe