Learning to Navigate the Energy Landscape

In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimization of the reconstruction error which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization. To make the RGB camera relocalization problem particularly challenging, we introduce a new dataset of 3D environments which are significantly larger than those found in other publicly-available datasets. Our experiments reveal that the proposed method is able to achieve state-of-the-art camera relocalization results. We also demonstrate the generalizability of our approach on Hand Pose Estimation and Image Retrieval tasks

AccuSyn: Using Simulated Annealing to Declutter Genome Visualizations

We apply Simulated Annealing, a well-known metaheuristic for obtaining near-optimal solutions to optimization problems, to discover conserved synteny relations (similar features) in genomes. The analysis of synteny gives biologists insights into the evolutionary history of species and the functional relationships between genes. However, as even simple organisms have huge numbers of genomic features, syntenic plots initially present an enormous clutter of connections, making the structure difficult to understand. We address this problem by using Simulated Annealing to minimize link crossings. Our interactive web-based synteny browser, AccuSyn, visualizes syntenic relations with circular plots of chromosomes and draws links between similar blocks of genes. It also brings together a huge amount of genomic data by integrating an adjacent view and additional tracks, to visualize the details of the blocks and accompanying genomic data, respectively. Our work shows multiple ways to manually declutter a synteny plot and then thoroughly explains how we integrated Simulated Annealing, along with human interventions as a human-in-the-loop approach, to achieve an accurate representation of conserved synteny relations for any genome. The goal of AccuSyn was to make a fairly complete tool combining ideas from four major areas: genetics, information visualization, heuristic search, and human-in-the-loop. Our results contribute to a better understanding of synteny plots and show the potential that decluttering algorithms have for syntenic analysis, adding more clues for evolutionary development. At this writing, AccuSyn is already actively used in the research being done at the University of Saskatchewan and has already produced a visualization of the recently-sequenced Wheat genome

Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement

Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge

This paper describes the winning entry to the IJCNN 2011 Social Network Challenge run by Kaggle.com. The goal of the contest was to promote research on real-world link prediction, and the dataset was a graph obtained by crawling the popular Flickr social photo sharing website, with user identities scrubbed. By de-anonymizing much of the competition test set using our own Flickr crawl, we were able to effectively game the competition. Our attack represents a new application of de-anonymization to gaming machine learning contests, suggesting changes in how future competitions should be run. We introduce a new simulated annealing-based weighted graph matching algorithm for the seeding step of de-anonymization. We also show how to combine de-anonymization with link prediction---the latter is required to achieve good performance on the portion of the test set not de-anonymized---for example by training the predictor on the de-anonymized portion of the test set, and combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201

Coding Strategies for Genetic Algorithms and Neural Nets

The interaction between coding and learning rules in neural nets (NNs), and between coding and genetic operators in genetic algorithms (GAs) is discussed. The underlying principle advocated is that similar things in "the world" should have similar codes. Similarity metrics are suggested for the coding of images and numerical quantities in neural nets, and for the coding of neural network structures in genetic algorithms. A principal component analysis of natural images yields receptive fields resembling horizontal and vertical edge and bar detectors. The orientation sensitivity of the "bar detector" components is found to match a psychophysical model, suggesting that the brain may make some use of principal components in its visual processing. Experiments are reported on the effects of different input and output codings on the accuracy of neural nets handling numeric data. It is found that simple analogue and interpolation codes are most successful. Experiments on the coding of image data demonstrate the sensitivity of final performance to the internal structure of the net. The interaction between the coding of the target problem and reproduction operators of mutation and recombination in GAs are discussed and illustrated. The possibilities for using GAs to adapt aspects of NNs are considered. The permutation problem, which affects attempts to use GAs both to train net weights and adapt net structures, is illustrated and methods to reduce it suggested. Empirical tests using a simulated net design problem to reduce evaluation times indicate that the permutation problem may not be as severe as has been thought, but suggest the utility of a sorting recombination operator, that matches hidden units according to the number of connections they have in common. A number of experiments using GAs to design network structures are reported, both to specify a net to be trained from random weights, and to prune a pre-trained net. Three different coding methods are tried, and various sorting recombination operators evaluated. The results indicate that appropriate sorting can be beneficial, but the effects are problem-dependent. It is shown that the GA tends to overfit the net to the particular set of test criteria, to the possible detriment of wider generalisation ability. A method of testing the ability of a GA to make progress in the presence of noise, by adding a penalty flag, is described

Automatic sound synthesizer programming: techniques and applications

The aim of this thesis is to investigate techniques for, and applications of automatic sound synthesizer programming. An automatic sound synthesizer programmer is a system which removes the requirement to explicitly specify parameter settings for a sound synthesis algorithm from the user. Two forms of these systems are discussed in this thesis: tone matching programmers and synthesis space explorers. A tone matching programmer takes at its input a sound synthesis algorithm and a desired target sound. At its output it produces a configuration for the sound synthesis algorithm which causes it to emit a similar sound to the target. The techniques for achieving this that are investigated are genetic algorithms, neural networks, hill climbers and data driven approaches. A synthesis space explorer provides a user with a representation of the space of possible sounds that a synthesizer can produce and allows them to interactively explore this space. The applications of automatic sound synthesizer programming that are investigated include studio tools, an autonomous musical agent and a self-reprogramming drum machine. The research employs several methodologies: the development of novel software frameworks and tools, the examination of existing software at the source code and performance levels and user trials of the tools and software. The main contributions made are: a method for visualisation of sound synthesis space and low dimensional control of sound synthesizers; a general purpose framework for the deployment and testing of sound synthesis and optimisation algorithms in the SuperCollider language sclang; a comparison of a variety of optimisation techniques for sound synthesizer programming; an analysis of sound synthesizer error surfaces; a general purpose sound synthesizer programmer compatible with industry standard tools; an automatic improviser which passes a loose equivalent of the Turing test for Jazz musicians, i.e. being half of a man-machine duet which was rated as one of the best sessions of 2009 on the BBC's 'Jazz on 3' programme

Towards The Deep Semantic Learning Machine Neuroevolution Algorithm: An exploration on the CIFAR-10 problem task

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsSelecting the topology and parameters of Convolutional Neural Network (CNN) for a given supervised machine learning task is a non-trivial problem. The Deep Semantic Learning Machine (Deep-SLM) deals with this problem by automatically constructing CNNs without the use of the Backpropagation algorithm. The Deep-SLM is a novel neuroevolution technique and functions as stochastic semantic hill-climbing algorithm searching over the space of CNN topologies and parameters. The geometric semantic properties of the Deep-SLM induce a unimodel error space and eliminate the existence of local optimal solutions. This makes the Deep-SLM potentially favorable in terms of search efficiency and effectiveness. This thesis provides an exploration of a variant of the Deep-SLM algorithm on the CIFAR-10 problem task, and a validation of its proof of concept. This specific variant only forms mutation node ! mutation node connections in the non-convolutional part of the constructed CNNs. Furthermore, a comparative study between the Deep-SLM and the Semantic Learning Machine (SLM) algorithms was conducted. It was observed that sparse connections can be an effective way to prevent overfitting. Additionally, it was shown that a single 2D convolution layer initialized with random weights does not result in well-generalizing features for the Deep-SLM directly, but, in combination with a 2D max-pooling down sampling layer, effective improvements in performance and generalization of the Deep-SLM could be achieved. These results constitute to the hypothesis that convolution and pooling layers can improve performance and generalization of the Deep-SLM, unless the components are properly optimized.Selecionar a topologia e os parâmetros da Rede Neural Convolucional (CNN) para uma tarefa de aprendizado automático supervisionada não é um problema trivial. A Deep Semantic Learning Machine (Deep-SLM) lida com este problema construindo automaticamente CNNs sem recorrer ao uso do algoritmo de Retro-propagação. A Deep-SLM é uma nova técnica de neuroevolução que funciona enquanto um algoritmo de escalada estocástico semântico na pesquisa de topologias e de parâmetros CNN. As propriedades geométrico-semânticas da Deep-SLM induzem um unimodel error space que elimina a existência de soluções ótimas locais, favorecendo, potencialmente, a Deep-SLM em termos de eficiência e eficácia. Esta tese providencia uma exploração de uma variante do algoritmo da Deep-SLM no problemo de CIFAR-10, assim como uma validação do seu conceito de prova. Esta variante específica apenas forma conexões nó de mutação!nó de mutação na parte non convolucional da CNN construída. Mais ainda, foi conduzido um estudo comparativo entre a Deep-SLM e o algoritmo da Semantic Learning Machine (SLM). Tendo sido observado que as conexões esparsas poderão tratar-se de uma forma eficiente de prevenir o overfitting. Adicionalmente, mostrou-se que uma singular camada de convolução 2D, iniciada com valores aleatórios, não resulta, directamente, em características generalizadas para a Deep-SLM, mas, em combinação com uma camada de 2D max-pooling, melhorias efectivas na performance e na generalização da Deep-SLM poderão ser concretizadas. Estes resultados constituem, assim, a hipótese de que as camadas de convolução e pooling poderão melhorar a performance e a generalização da Deep-SLM, a não ser que os componentes sejam adequadamente otimizados

Automatic detection and classification of leukaemia cells

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)

A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner

Simple low cost causal discovery using mutual information and domain knowledge

PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the insights they can provide regarding the situation under study. Their use of prior knowledge and the causal knowledge produced by the learners are examined. Current causal learning algorithms are discussed in terms of their strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN that operates with a polynomial time complexity in both the number of variables and records examined. It makes no prior assumptions about the form of the relationships and is capable of making extensive use of available domain information. This learner is compared to a number of current learning algorithms and it is shown to be competitive with them