Recognizing objects by piecing together the Segmentation Puzzle

We present an algorithm that recognizes objects of a given category using a small number of hand segmented images as references. Our method first over segments an input image into superpixels, and then finds a shortlist of optimal combinations of superpixels that best fit one of template parts, under affine transformations. Second, we develop a contextual interpretation of the parts, gluing image segments using top-down fiducial points, and checking overall shape similarity. In contrast to previous work, the search for candidate superpixel combinations is not exponential in the number of segments, and in fact leads to a very efficient detection scheme. Both the storage and the detection of templates only require space and time proportional to the length of the template boundary, allowing us to store potentially millions of templates, and to detect a template anywhere in a large image in roughly 0.01 seconds. We apply our algorithm on the Weizmann horse database, and show our method is comparable to the state of the art while offering a simpler and more efficient alternative compared to previous work

Tuning Mesoscopic Self-Assembly Behavior via Nano Building-Block Interactions and Architecture.

Using molecular dynamics (MD) computer simulations we show that a variety of complex, technologically relevant phases emerge from tuning aspects of nanoparticle architecture and interactions. In doing so, we demonstrate that nanoparticles can be thought of as building-blocks in larger scale assemblies over which we can tune nearly every aspect of the structure for specific applications such as photonics, photovoltaics, or catalysis. We highlight three specific case studies - polymer/nanoparticle composite building-block assemblies, star polymer microdroplets, and amino-acid coated nanoparticles with embedded dipoles that form rods of preferred chirality. In all cases predictions from simulations are used to either guide building-block assembly or to offer detailed insight into structures that were not previously understood. Additionally, we establish general, domain-agnostic mesophase behavior, as well as hypothesize synthesis and assembly strategies to target highly specific structures for any given application.PhDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113306/1/rmarson_1.pd

From Cellular Components to Living Cells (and Back): Evolution of Function in Biological Networks

Network models pervade modern biology. From ecosystems down to molecular interactions in cells, they provide abstraction and explanation for biological processes. Thus, the relation between structure and function of networks is central to any comprehensive attempt for a theoretical understanding of life. Just as any living system, biological networks are shaped by evolutionary processes. In reverse, artificial evolution can be employed to reconstruct networks and to study their evolution. To this end, I have implemented an evolutionary algorithm specifically designed for the evolution of network models. With the developed evolutionary framework, a study of the evolution of information-processing networks was performed. It is shown that selection favours an organisational structure that is related to function, such that computations can be visualised as transitions between organisations. Furthermore, mathematical modelling is applied to extract reaction-kinetic constants from fluorescence microscopy data, and the model is presented and discussed in detail. Using this approach, a detailed quantitative model of exchange dynamics at PML nuclear bodies (NBs) is created, showing that PML NB components exhibit highly individual exchange kinetics. The FRAP data for PML NBs is additionally used as a test-case for automatic model inference using evolutionary methods, and a set of necessary and sufficient criteria for a good model fit is revealed. In the last part of this thesis, a stochastic analysis of the genetic regulatory system of DEF-like and GLO-like class B floral homeotic genes provides an explanation for their intricate regulatory wiring. The different potential regulatory architectures are investigated using Monte Carlo simulation, a simplified master-equation model, and fixedpoint analysis. It is shown that a positive autoregulatory loop via obligate heterodimerisation of transcription factor proteins reduces noise in cell-fate organ identity decisions.Netzwerkmodelle sind weit verbreitet in der modernen Biologie. In allen Teilgebieten - von der Ökologie bis hin zur Molekularbiologie - bieten sie die Möglichkeit, untersuchte Prozesse und Phänomene zu abstrahieren und damit auf theoretischer Ebene zugänglich zu machen. Es wird ein evolutionärer Algorithmus vorgestellt, der speziell für die Erzeugung von Netzwerkmodellen angepasst ist. Dafür wurde eine Genetische Programmierung der Netzwerkstruktur mit einer Evolutionsstrategie auf den kinetischen Parametern verknüpft. Mit dem neu entwickelten Evolutionären Algorithmus wurde dann eine Studie zur Evolution von informationsverarbeitenden Netzwerken durchgeführt. Selektion erzeugt eine funktionale Organisationsstruktur, in welcher eine Berechnung als Transition zwischen Organisationen abgebildet werden kann. Desweiteren wurden mathematische Modellierungsmethoden verwendet, um kinetische Reaktionskonstanten aus fluoreszenz-mikroskopischen Daten zu gewinnen. Die verwendete Methode wird im Detail vorgestellt und diskutiert. Auf diese Weise entstand ein detailliertes Modell des Proteinaustauschs an PML nuclear bodies (NBs), in welchem die Komponenten der PML NBs sehr differenzierte Austauschverhalten zeigen. Darüber hinaus werden die gewonnenen Daten genutzt, um die automatische Evolution von Netzwerkmodellen in einer realistischen Fallstudie zu testen. Zum Schluss wird eine stochastische Analyse des Zusammenspiels der DEF- und GLO-Gene in der Blütenentwicklung gezeigt, welche eine Erklärung für ihre überraschend komplexe Verschaltung liefert. Die verschiedenen möglichen Regulationsmechanismen werden mithilfe von Monte-Carlo-Simulation, einem Master-Equation-Ansatz und der Fixpunktanalyse verglichen. Es wird gezeigt, dass positive Autoregulation durch obligatorische Heterodimerisierung den Einfluss des Zufalls auf die Organidentität reduziert

Constructivist Artificial Intelligence With Genetic Programming

Learning is an essential attribute of an intelligent system. A proper understanding of the process of learning in terms of knowledge-acquisition, processing and its effective use has been one of the main goals of artificial intelligence (AI). AI, in order to achieve the desired flexibility, performance levels and wide applicability should explore and exploit a variety of learning techniques and representations. Evolutionary algorithms, in recent years, have emerged as powerful learning methods employing task-independent approaches to problem solving and are potential candidates for implementing adaptive computational models. These algorithms, due to their attractive features such as implicit and explicit parallelism, can also be powerful meta-leaming tools for other learning systems such as connectionist networks. These networks, also known as artificial neural networks, offer a paradigm for learning at an individual level that provide an extremely rich landscape of learning mechanisms which AI should exploit. The research proposed in this thesis investigates the role of genetic programming (GP) in connectionism, a learning paradigm that, despite being extremely powerful has a number of limitations. The thesis, by systematically identifying the reasons for these limitations has argued as to why connectionism should be approached with a new perspective in order to realize its true potentialities. With genetic-based designs the key issue has been the encoding strategy. That is, how to encode a neural network within a genotype so as to achieve an optimum network structure and/ or an efficient learning that can best solve a given problem. This in turn raises a number of key questions such as: 1. Is the representation (that is the genotype) that the algorithms employ sufficient to express and explore the vast space of network architectures and learning mechanisms? 2. Is the representation capable of capturing the concepts of hierarchy and modularity that are vital and so naturally employed by humans in problem-solving? 3. Are some representations better in expressing these? If so, how to exploit the strengths that are inherent to these representations? 4. If the aim is really to automate the design process what strategies should be employed so as to minimize the involvement of a designer in the design loop? 5. Is the methodology or the approach able to overcome at least some of the limitations that are commonly seen in connectionist networks? 6. Most importantly, how effective is the approach in problem-solving? These issues are investigated through a novel approach that combines genetic programming and a self-organizing neural network which provides a framework for the simulations. Through the powerful notions of constructivism and micro-macro dynamics the approach provides a way of exploiting the potential features (such as the hierarchy and modularity) that are inherent to the representation that GP employs. By providing a general definition for learning and by imposing a single potential constraint within the representation the approach demonstrates that genetic programming, if used for construction and optimization, could be extremely creative. The method also combines the bottom-up and top-down strategies that are key to evolve ALife-like systems. A comparison with earlier methods is drawn to identify the merits of the proposed approach. A pattern recognition task is considered for illustration. Simulations suggest that genetic- programming can be a powerful meta-leaming tool for implementing useful network architectures and flexible learning mechanisms for self-organizing neural networks while interacting with a given task environment. It appears that it is possible to extend the novel approach further to other types of networks. Finally the role of flexible learning in implementing adaptive AI systems is discussed. A number of potential applications domain is identified