Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

Efficient Methods for Continuous and Discrete Shape Analysis

When interpreting an image of a given object, humans are able to abstract from the presented color information in order to really see the presented object. This abstraction is also known as shape. The concept of shape is not defined exactly in Computer Vision and in this work, we use three different forms of these definitions in order to acquire and analyze shapes. This work is devoted to improve the efficiency of methods that solve important applications of shape analysis. The most important problem in order to analyze shapes is the problem of shape acquisition. To simplify this very challenging problem, numerous researchers have incorporated prior knowledge into the acquisition of shapes. We will present the first approach to acquire shapes given a certain shape knowledge that computes always the global minimum of the involved functional which incorporates a Mumford-Shah like functional with a certain class of shape priors including statistic shape prior and dynamical shape prior. In order to analyze shapes, it is not only important to acquire shapes, but also to classify shapes. In this work, we follow the concept of defining a distance function that measures the dissimilarity of two given shapes. There are two different ways of obtaining such a distance function that we address in this work. Firstly, we model the set of all shapes as a metric space induced by the shortest path on an orbifold. The shortest path will provide us with a shape morphing, i.e., a continuous transformation from one shape into another. Secondly, we address the problem of shape matching that finds corresponding points on two shapes with respect to a preselected feature. Our main contribution for the problem of shape morphing lies in the immense acceleration of the morphing computation. Instead of solving partial resp. ordinary differential equations, we are able to solve this problem via a gradient descent approach that subsequently shortens the length of a path on the given manifold. During our runtime test, we observed a run-time acceleration of up to a factor of 1000. Shape matching is a classical discrete problem. If each shape is discretized by N shape points, most Computer Vision methods needed a cubic run-time. We will provide two approaches how to reduce this worst-case complexity to O(N2 log(N)). One approach exploits the planarity of the involved graph in order to efficiently compute N shortest path in a graph of O(N2) vertices. The other approach computes a minimal cut in a planar graph in O(N log(N)). In order to make this approach applicable to shape matching, we improved the run-time of a recently developed graph cut approach by an empirical factor of 2–4

Fundamental Study of Photoluminescence-Shape Relationship of Fluorescent Nanodiamonds using Machine Learning Assisted Correlative Transmission Electron Microscopy and Photoluminescence Microscopy Method

Luminescent nanoparticles have shown wide applications ranging from lighting, display, sensors, and biomedical diagnostics and imaging. Among these, fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) color centers are posed as emerging materials particularly in biomedical and biological imaging applications due to their room-temperature emission, excellent photo- and chemical- stability, high bio-compatibility, and versatile functionalization potentials. The shape variation of nanoparticles has a decisive influence on their fluorescence. However, current relative studies are limited by the lack of reliable statistical analysis of nanoparticle shape and the difficulty of achieving a precise correlation between shape/structure and optical measurements of large numbers of individual nanoparticles. Therefore, new methods are urgently needed to overcome these challenges to assist in nanoparticle synthesis control and fluorescence performance optimization. In this thesis a new correlative TEM and photoluminescence (PL) microscopy (TEMPL) method has been developed that combines the measurements of the optical properties and the materials structure at the exact same particle and sample area, so that accurate correlation can be established to statistically study the FND morphology/structure and PL properties, at the single nanoparticle level. Moreover, machine learning based methods have been developed for categorizing the 2D and 3D shapes of a large number of nanoparticles generated in TEMPL method. This ML-assisted TEMPL method has been applied to understand the PL correlation with the size and shape of FNDs at the single particle level. In this thesis, a strong correlation between particle morphology and NV fluorescence in FND particles has been revealed: thin, flake-like particles produce enhanced fluorescence. The robustness of this trend is proven in FND with different surface oxidation treatments. This finding offers guidance for fluorescence-optimized sensing applications of FND, by controlling the shape of the particles in fabrication. Overall the TEMPL methodology developed in the thesis provides a versatile and general way to study the shape and fluorescence relationship of various nanoparticles and opens up the possibility of correlation methods between other characterisation techniques

Development of anFPGA-based Data Reduction System for the Belle II DEPFET Pixel Detector

The innermost two layers of the Belle II detector at the KEKB collider in Tsukuba, Japan will be covered by highly granular DEPFET pixel sensors. The large number of pixels lead to a maximum data rate of 256 Gbps, which has to be significantly reduced by the Data Acquisition System. For data reduction, the hit information of the silicon-strip vertex detector surrounding the pixel detector is used to define so-called Regions of Interest (ROI) in the pixel detector. Only hit information of the pixels located inside these ROIs are saved. The ROIs for the pixel detector are computed by reconstructing track segments from strip data and extrapolation to the pixel detector. The goal is to achieve a reduction factor of up to 10 with this ROI selection. All the necessary processing stages, the receiving, decoding and multiplexing of SVD data on 48 optical fibers, the track reconstruction and the definition of the ROIs, will be performed by the DATCON system, developed in the scope of this thesis. The planned hardware design is based on a distributed set of Advanced Mezzanine Cards (AMC), each equipped with a Field Programmable Gate Array (FPGA) and four optical transceivers. An algorithm is developed based on a Hough Transformation, a commonly used pattern recognition method in image processing to identify the track segments in the strip detector and calculation of the track parameters. Using simulations, the performance of the developed algorithms are evaluated. For use in the DATCON system the Hough track reconstruction is implemented on FPGAs. Several tests of the modules required to create the ROIs are performed in a simulation environment and tested on the AMC hardware. After a line of successful tests, the DATCON prototype was used in two test beam campaigns to verify the concept and practice the integration with the other detector systems. The developed track reconstruction algorithm shows a high reconstruction efficiency down to low track momenta. A higher data reduction than originally intended was achieved within the limits of the available processing time. The FPGA track reconstruction algorithm is found to be even three times faster than demanded by the trigger rate of the experiment. The used concepts and developed algorithms are not specifically designed for the Belle II vertex detector only, but can be used in different experiments. It was successfully tested on the low-level trigger for Belle II, using drift chamber information and showed a comparably good track reconstruction performance

The Hyper Suprime-Cam Software Pipeline

In this paper, we describe the optical imaging data processing pipeline developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The HSC Pipeline builds on the prototype pipeline being developed by the Large Synoptic Survey Telescope's Data Management system, adding customizations for HSC, large-scale processing capabilities, and novel algorithms that have since been reincorporated into the LSST codebase. While designed primarily to reduce HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline for reducing general-observer HSC data. The HSC pipeline includes high level processing steps that generate coadded images and science-ready catalogs as well as low-level detrending and image characterizations.Comment: 39 pages, 21 figures, 2 tables. Submitted to Publications of the Astronomical Society of Japa

Doctor of Philosophy

dissertationAdvances in computer hardware have enabled routine MD simulations of systems with tens of thousands of atoms for up to microseconds (soon milliseconds). The key limiting factor in whether these simulations can advance hypothesis testing in active research is the accuracy of the force fields. In many ways, force fields for RNA are less mature than those for proteins. Yet even the current generation of force fields offers benefits to researchers as we demonstrate with our re-refinement effort on two RNA hairpins. Additionally, our simulation study of the binding of 2-aminobenzimidazole inhibitors to hepatitis C RNA offers a computational perspective on which of the two rather different published structures (one NMR, the other X-ray) is a more reasonable structure for future CADD efforts as well as which free energy methods are suited to these highly charged complexes. Finally, further effort on force field improvement is critical. We demonstrate an effective method to determine quantitative conformational population analysis of small RNAs using enhanced sampling methods. These efforts are allowing us to uncover force field pathologies and quickly test new modifications. In summary, this research serves to strengthen communication between experimental and theoretical methods in order produce mutual benefit

Geometric Structure Extraction and Reconstruction

Geometric structure extraction and reconstruction is a long-standing problem in research communities including computer graphics, computer vision, and machine learning. Within different communities, it can be interpreted as different subproblems such as skeleton extraction from the point cloud, surface reconstruction from multi-view images, or manifold learning from high dimensional data. All these subproblems are building blocks of many modern applications, such as scene reconstruction for AR/VR, object recognition for robotic vision and structural analysis for big data. Despite its importance, the extraction and reconstruction of a geometric structure from real-world data are ill-posed, where the main challenges lie in the incompleteness, noise, and inconsistency of the raw input data. To address these challenges, three studies are conducted in this thesis: i) a new point set representation for shape completion, ii) a structure-aware data consolidation method, and iii) a data-driven deep learning technique for multi-view consistency. In addition to theoretical contributions, the algorithms we proposed significantly improve the performance of several state-of-the-art geometric structure extraction and reconstruction approaches, validated by extensive experimental results

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used