Kolmogorov Complexity in perspective. Part II: Classification, Information Processing and Duality

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts published in a same volume. Part II is dedicated to the relation between logic and information system, within the scope of Kolmogorov algorithmic information theory. We present a recent application of Kolmogorov complexity: classification using compression, an idea with provocative implementation by authors such as Bennett, Vitanyi and Cilibrasi. This stresses how Kolmogorov complexity, besides being a foundation to randomness, is also related to classification. Another approach to classification is also considered: the so-called "Google classification". It uses another original and attractive idea which is connected to the classification using compression and to Kolmogorov complexity from a conceptual point of view. We present and unify these different approaches to classification in terms of Bottom-Up versus Top-Down operational modes, of which we point the fundamental principles and the underlying duality. We look at the way these two dual modes are used in different approaches to information system, particularly the relational model for database introduced by Codd in the 70's. This allows to point out diverse forms of a fundamental duality. These operational modes are also reinterpreted in the context of the comprehension schema of axiomatic set theory ZF. This leads us to develop how Kolmogorov's complexity is linked to intensionality, abstraction, classification and information system.Comment: 43 page

Bioinformatics framework for genotyping microarray data analysis

Functional genomics is a flourishing science enabled by recent technological breakthroughs in high-throughput instrumentation and microarray data analysis. Genotyping microarrays establish the genotypes of DNA sequences containing single nucleotide polymorphisms (SNPs), and can help biologists probe the functions of different genes and/or construct complex gene interaction networks. The enormous amount of data from these experiments makes it infeasible to perform manual processing to obtain accurate and reliable results in daily routines. Advanced algorithms as well as an integrated software toolkit are needed to help perform reliable and fast data analysis. The author developed a MatlabTM based software package, called TIMDA (a Toolkit for Integrated Genotyping Microarray Data Analysis), for fully automatic, accurate and reliable genotyping microarray data analysis. The author also developed new algorithms for image processing and genotype-calling. The modular design of TIMDA allows satisfactory extensibility and maintainability. TIMDA is open source (URL: http://timda.SF.net and can be easily customized by users to meet their particular needs. The quality and reproducibility of results in image processing and genotype-calling and the ease of customization indicate that TIMDA is a useful package for genomics research

Distance based heterogeneous volume modelling.

Natural objects, such as bones and watermelons, often have a heterogeneous composition and complex internal structures. Material properties inside the object can change abruptly or gradually, and representing such changes digitally can be problematic. Attribute functions represent physical properties distribution in the volumetric object. Modelling complex attributes within a volume is a complex task. There are several approaches to modelling attributes, but distance functions have gained popularity for heterogeneous object modelling because, in addition to their usefulness, they lead to predictability and intuitiveness. In this thesis, we consider a unified framework for heterogeneous volume modelling, specifically using distance fields. In particular, we tackle various issues associated with them such as the interpolation of volumetric attributes through time for shape transformation and intuitive and predictable interpolation of attributes inside a shape. To achieve these results, we rely on smooth approximate distance fields and interior distances. This thesis deals with outstanding issues in heterogeneous object modelling, and more specifically in modelling functionally graded materials and structures using different types of distances and approximation thereof. We demonstrate the benefits of heterogeneous volume modelling using smooth approximate distance fields with various applications, such as adaptive microstructures, morphological shape generation, shape driven interpolation of material properties through time and shape conforming interpolation of properties. Distance based modelling of attributes allows us to have a better parametrization of the object volume and design gradient properties across an object. This becomes more important nowadays with the growing interest in rapid prototyping and digital fabrication of heterogeneous objects and can find practical applications in different industries

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

RADseq and mate choice assays reveal unidirectional gene flow among three lamprey ecotypes despite weak assortative mating: Insights into the formation and stability of multiple ecotypes in sympatry

Adaptive divergence with gene flow often results in complex patterns of variation within taxa exhibiting substantial ecological differences among populations. One example where this may have occurred is the parallel evolution of freshwater‐resident nonparasitic lampreys from anadromous‐parasitic ancestors. Previous studies have focused on transitions between these two phenotypic extremes, but here, we considered more complex evolutionary scenarios where an intermediate freshwater form that remains parasitic is found sympatrically with the other two ecotypes. Using population genomic analysis (restriction‐associated DNA sequencing), we found that a freshwater‐parasitic ecotype was highly distinct from an anadromous‐parasitic form (Qlake‐P = 96.8%, Fst = 0.154), but that a freshwater‐nonparasitic form was almost completely admixed in Loch Lomond, Scotland. Demographic reconstructions indicated that both freshwater populations likely derived from a common freshwater ancestor. However, while the nonparasitic ecotype has experienced high levels of introgression from the anadromous‐parasitic ecotype (Qanad‐P = 37.7%), there is no evidence of introgression into the freshwater‐parasitic ecotype. Paradoxically, mate choice experiments predicted high potential for gene flow: Males from all ecotypes were stimulated to spawn with freshwater‐parasitic females, which released gametes in response to all ecotypes. Differentially fixed single nucleotide polymorphisms identified genes associated with growth and development, which could possibly influence the timing of metamorphosis, resulting in significant ecological differences between forms. This suggests that multiple lamprey ecotypes can persist in sympatry following shifts in adaptive peaks, due to environmental change during their repeated colonization of post‐glacial regions, followed by periods of extensive gene flow among such diverging populations

Neural Diversity in the Drosophila Olfactory Circuitry: A Dissertation

Different neurons and glial cells in the Drosophila olfactory circuitry have distinct functions in olfaction. The mechanisms to generate most of diverse neurons and glial cells in the olfactory circuitry remain unclear due to the incomprehensive study of cell lineages. To facilitate the analyses of cell lineages and neural diversity, two independent binary transcription systems were introduced into Drosophila to drive two different transgenes in different cells. A technique called ‘dual-expression-control MARCM’ (mosaic analysis with a repressible cell marker) was created by incorporating a GAL80-suppresible transcription factor LexA::GAD (GAL4 activation domain) into the MARCM. This technique allows the induction of UAS- and lexAop- transgenes in different patterns among the GAL80-minus cells. Dual-expression-control MARCM with a ubiquitous driver tubP-LexA::GAD and various subtype-specific GAL4s which express in antennal lobe neurons (ALNs) allowed us to characterize diverse ALNs and their lineage relationships. Genetic studies showed that ALN cell fates are determined by spatial identities rooted in their precursor cells and temporal identities based on their birth timings within the lineage, and then finalized through cell-cell interactions mediated by Notch signaling. Glial cell lineage analyses by MARCM and dual-expression-control MARCM show that diverse post-embryonic born glial cells are lineage specified and independent of neuronal lineage. Specified glial lineages expand their glial population by symmetrical division and do not further diversify glial cells. Construction of a GAL4-insensitive transcription factor LexA::VP16 (VP16 acidic activation domain) allows the independent induction of lexAop transgenes in the entire mushroom body (MB) and labeling of individual MB neurons by MARCM in the same organism. A computer algorithm is developed to perform morphometric analysis to assist the study of MB neuron diversity