Deep Semantic Learning Machine Initial design and experiments

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsComputer vision is an interdisciplinary scientific field that allows the digital world to interact with the real world. It is one of the fastest-growing and most important areas of data science. Applications are endless, given various tasks that can be solved thanks to the advances in the computer vision field. Examples of types of tasks that can be solved thanks to computer vision models are: image analysis, object detection, image transformation, and image generation. Having that many applications is vital for providing models with the best possible performance. Although many years have passed since backpropagation was invented, it is still the most commonly used approach of training neural networks. A satisfactory performance can be achieved with this approach, but is it the best it can get? A fixed topology of a neural network that needs to be defined before any training begins seems to be a significant limitation as the performance of a network is highly dependent on the topology. Since there are no studies that would precisely guide scientists on selecting a proper network structure, the ability to adjust a topology to a problem seems highly promising. Initial ideas of the evolution of neural networks that involve heuristic search methods have provided encouragingly good results for the various reinforcement learning task. This thesis presents the initial experiments on the usage of a similar approach to solve image classification tasks. The new model called Deep Semantic Learning Machine is introduced with a new mutation method specially designed to solve computer vision problems. Deep Semantic Learning Machine allows a topology to evolve from a small network and adjust to a given problem. The initial results are pretty promising, especially in a training dataset. However, in this thesis Deep Semantic Learning Machine was developed only as proof of a concept and further improvements to the approach can be made

Intrinsically Disordered Proteins within the Genome

The hundreds of millions of DNA base pairs within eukaryotic cells are not found free but packed inside the micrometre-sized nuclei through the formation of a macromolecular structure known as chromatin. Chromatin consists of a chain of nucleosomes – nucleoprotein complexes where the DNA makes ∼1.75 turns around a protein octamer core composed of two copies each of H2A, H2B, H3 and H4 histones. A fifth histone H1 binds on the nucleosomal surface close to the entry/exit site of DNA, interacts with linker DNA and aids in chromatin compaction. Enabling the condensation of DNA to fit into the nucleus is however only one-half of chromatin’s role. The three-dimensional spatial organization of chromatin serves a second important role in allowing the capability to exert control over gene expression. The chromatin structure thus serves as an additional layer of complexity above the genome code and permits the transcription of different proteins varying with cell lineages/cycles. The proteins that makeup, modify and read the chromatin structure are particularly enriched in `Intrinsic Disorder’ – a class of proteins lacking a well-defined structure but existing as a dynamic ensemble of rapidly interchanging states. While folded proteins with well-defined structures are amenable to be characterized through standard methods of protein structure determination, the `plasticity’ of the disordered proteins challenges the use of such ensemble averaged techniques. In this thesis, Molecular Dynamics simulations are used to characterize the disordered regions of three proteins that form the core of chromatin structure: histones, linker histones (H1) and heterochromatin protein (HP1). The carboxy-terminal domain of H1 when within the nucleosome, adopts a compact but unstructured conformation that allows its positioning between the two linker DNA strands. In contrast, the amino-terminal domain of H1 undergoes a disorder-to-order transition to an amphiphilic helical conformation. The transition to the amphiphilic helix is however subtype-dependant with the degree of condensation varying with the subtypes' nucleosomal affinity. Finally, the simulations demonstrate that the affinity of HP1 subtypes for the H3 histone is caused by the synergetic effects of both the proteins' unstructured amino-terminal domain and the structured chromodomain

Evolutionary dataset optimisation: learning algorithm quality through evolution

In this paper we propose a new method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark data sets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the `best performing'. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well. These data sets can be studied to learn as to what attributes lead to a particular progress of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a number of numeric experiments are presented to show the performance of the method which we call Evolutionary Dataset Optimisation

A Longitudinal Study of Mammograms Utilizing the Automated Wavelet Transform Modulus Maxima Method

Breast cancer is a disease which predominatly affects women. About 1 in 8 women are diagnosed with breast cancer during their lifetime. Early detection is key to increasing the survival rate of breast cancer patients since the longer the tumor goes undetected, the more deadly it can become. The modern approach for diagnosing breast cancer relies on a combination of self-breast exams and mammography to detect the formation of tumors. However, this approach only accounts for tumors which are either detectable by touch or are large enough to be observed during a screening mammogram. For some individuals, by the time a tumor is detected, it has already progressed to a deadly stage. Unlike previous research, this paper focuses on the predetection of tumorous tissue. This novel approach sets out to examine changes in the breast microenvironment instead of locating and identifying tumors. The purpose of this paper is to explore whether it is possible to discover changes in the breast tissue microenvironment which later develop into breast cancer. We hypothesized that changes in the breast tissue would be detected by analyzing mammograms from the years prior to the discovery of tumorous tissue by a radiologist. We analyzed a set of time-series digital mammograms corresponding to 26 longitudinal cancer cases, obtained through a collaboration with Eastern Maine Medical Center (EMMC) in Bangor, Maine. We automated the Wavelet Transform Modulus Maxima (WTMM) method, a mathematical formalism that we used to perform a multifractal analysis. In particular, this automated WTMM (AWTMM) was used to calculate the Hurst exponent, a metric that is correlated with breast tissue density. The AWTMM allowed us to see with greater detail the changes in mammogram tissue, specifically concerning breast density. The results suggest that signs of malignancy can be observed as early as two years before standard radiological procedures. In this research, we identify a set of variables that show significance when classifying precancerous tissue

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

イ Xセン ジュウマン ゾウ ノ ディジタル ガゾウ カイセキ

京都大学0048新制・論文博士工学博士乙第5057号論工博第1610号新制||工||577(附属図書館)UT51-58-F220(主査)教授 桑原 道義, 教授 近藤 文治, 教授 長尾 眞学位規則第5条第2項該当Kyoto UniversityDFA

Energy Aware Algorithms for managing Wireless Sensor Networks

While the majority of the current Wireless Sensor Networks (WSNs) research has prioritized either the coverage of the monitored area or the energy efficiency of the network, it is clear that their relationship must be further studied in order to find optimal solutions that balance the two factors. Higher degrees of redundancy can be attained by increasing the number of active sensors monitoring a given area which results in better performance. However, this in turn increases the energy being consumed. In our research, we focus on attaining a solution that considers several optimization parameters such as the percentage of coverage, quality of coverage and energy consumption. The problem is modeled using a bipartite graph and employs an evolutionary algorithm to handle the activation and deactivation of the sensors. An accelerated version of the algorithm is also presented; this algorithm attempts to cleverly mutate the string being considered after analyzing the desired output conditions and performs a calculated crossover depending on the fitness of the parent strings. This results in a quicker convergence and a considerable reduction in the search time for attaining the desired solutions. Proficient cluster formation in wireless sensor networks reduces the total energy consumed by the network and prolongs the life of the network. There are various clustering approaches proposed, depending on the application and the objective to be attained. There are situations in which sensors are randomly dispersed over the area to be monitored. In our research, we also propose a solution for such scenarios using heterogeneous networks where a network has to self-organize itself depending on the physical allocations of sensors, cluster heads etc. The problem is modeled using a multi-stage graph and employs combinatorial algorithms to determine which cluster head a particular sensor would report to and which sink node a cluster head would report to. The solution proposed provides flexibility so that it can be applied to any network irrespective of density of resources deployed in the network. Finally we try to analyze how the modification of the sequence of execution of the two methods modifies the results. We also attempt to diagnose the reasons responsible for it and conclude by highlighting the advantages of each of the sequence

Automated grazing management

Management-intensive grazing is a grazing management strategy that relies on careful monitoring of animals at pasture and frequent relocation of animals between various regions of the pasture, or paddocks, in order to maximize the nutrients and sustenance the animals obtain through grazing. When applied successfully, this approach to grazing management yields higher animal production, while cutting feed costs; however, a great deal of overhead is introduced in monitoring and moving the animals throughout the pasture, making this approach very difficult to implement at a large scale. Recent successes in the field of dynamic virtual fencing have demonstrated the feasibility of automatically restraining and moving cattle within a pasture, and without the need to build or move fences. This technology may be used to reduce the physical overhead of management-intensive grazing, but it does not address the decision-making aspects. This thesis proposes a decision-making system to be used in conjunction with dynamic virtual fence technology to implement a fully-automated intensive grazing management strategy. The system has been implemented and tested in simulation, and the results thereof are presented and analyzed