Scan Integration as a Labeling Problem

Integration is a crucial step in the reconstruction of complete 3D surface model from multiple scans. Ever-present registration errors and scanning noise make integration a nontrivial problem. In this paper, we propose a novel method for multi-view scan integration where we solve it as a labelling problem. Unlike previous methods, which have been based on various merging schemes, our labelling-based method is essentially a selection strategy. The overall surface model is composed of surface patches from selected input scans. We formulate the labelling via a higher-order Markov Random Field (MRF) which assigns a label representing an index of some input scan to every point in a base surface. Using a higherorder MRF allows us to more effectively capture spatial relations between 3D points. We employ belief propagation to infer this labelling and experimentally demonstrate that this integration approach provides significantly improved integration via both qualitative and quantitative comparisons

Evolvable Integration of Activities with Statecharts

The dynamic behavior of a system can be specified in statecharts,\ud and the activities of the system can be implemented in terms of\ud functions in the C programming language. Later, the statecharts\ud and the activities can be integrated to realize the system that\ud fulfils a given set of requirements.\ud \ud After the integration, the statecharts, the activities, and the\ud requirements are subject to change due to emerging necessities\ud such as bug fixes. Any change to any of these artifacts has a cost\ud in terms of effort, and risk of errors.\ud \ud In this paper, we provide a rigorous analysis of a relevant subset\ud of possible changes to activities, and their associated costs. In\ud addition, we present the overview of our solution to reduce these\ud costs.\u

Correlation functions of integrable models: a description of the ABACUS algorithm

Recent developments in the theory of integrable models have provided the means of calculating dynamical correlation functions of some important observables in systems such as Heisenberg spin chains and one-dimensional atomic gases. This article explicitly describes how such calculations are generally implemented in the ABACUS C++ library, emphasizing the universality in treatment of different cases coming as a consequence of unifying features within the Bethe Ansatz.Comment: 30 pages, 8 figures, Proceedings of the CRM (Montreal) workshop on Integrable Quantum Systems and Solvable Statistical Mechanics Model

Investigation of Statistical and Imaging Methods for Luminescence Detection of Irradiated Ingredients

This project investigated two potential approaches to improving the reliability of lumines-cence methods for detecting minor irradiated ingredients in foods. Whereas in the 1980’s there were no validated methods for laboratory detection of irradiated foods, work conducted in the UK and elsewhere by the mid 1990’s had resulted in the development of a series of physical, chemical and biological methods capable of detecting a range of irradiated food classes. Of these the luminescence methods embodied in EN1788 (Thermoluminescence) and EN13751 (Photostimulated luminescence) standards have been applied to detection of a vari-ety of products including herbs and spices, and seafood. In common with the other EN stan-dard methods almost all validation work had been originally conducted using pure irradiated or unirradiated ingredients. Yet application experience had shown the presence of mixed products containing both irradiated and unirradiated ingredients. A short study was commis-sioned by MAFF to investigate the impact of blending on standard EN1788 methods, and on the provisional draft EN13751 (the standard having been published in the meantime) method. This showed the impact of dilution of irradiated material between 10% and 0.1% concentra-tions on detection rates, which unsurprisingly are reduced by extreme dilution. UK labelling regulation, both before and after adoption of the European Directive on Food Irradiation, call for labelling of all irradiated ingredients regardless of concentration or origin within the final product. This study was therefore motivated by the recognition of the long term need for im-proved methods to improve reliability at low concentrations. Two complementary approaches were investigated. The project first examined whether TL data collected using the EN1788 method could be enhanced using advanced statistical proce-dures. Data sets from the SURRC TL archive, and from project CSA4790 were used both to define the characteristics of irradiated and unirradiated end members, and to assess classifica-tion methods using the controlled blending experimental data sets of CSA 4790. Multivariate analyses, based on principal components analysis and discriminant analysis of glow curve data; kinetic deconvolution approaches coupled to PCA and DA, and neural analyses were investigated and compared with detection rates achieved using expert visual classification. To complement this experiments were undertaken to explore the potential of using focussed laser stimulation to produce spatially resolved measurements from mineral grains separated from foods. Two systems were evaluated based on IR and visible band lasers. Work was under-taken to explore sample presentation and to assess the ability of this approach to distinguish mixtures of irradiated and unirradiated grains. The statistical work was successful in developing three approaches which could be used for objective identification of irradiated materials. Pure irradiated and unirradiated data sets from 150 sample pairs were obtained having searched the SUERC archive of more than 3500 lu-minescence analyses. These were used to set up multivariate analyses based on the ap-proaches outlined above. Performance in recognising irradiated ingredients using these meth-ods was then assessed with data drawn from the MAFF blending investigation, comprising 160 permutations of irradiated and unirradiated herbs and spices at 10%, 1% and 0.1% con-centrations. It was possible to achieve good detection rates with alatistical approaches, the best approaches inigated being the use of glow curve deconvolution coupwith li discrimination, and the use of neural appros. The absolute performance achieved matched that opert visual clfication utilising the revised EN1788 criterwhich were adopted within the international standauring course of this project. The use of ad-vancedtistical methods, while not adding performance, can pde objective support to visual classifications. During performance assessment it was aloted that theformance of all methods wasficiently close to infer that detections rates are most dependent on the statistical presence or absence of irradiated grains within the extracted samples used for TL analysis. This raises practical suggestions for improving detection rates at low concentrations based on the use of larger samples and more specific mineral separation approaches. These may be worth investigating further. Laser scanning approaches were also investigated using highly focussed laser beams to stimulated luminescence sequentially from different parts of separated mineral samples. Work was conducted using a system which had been developed in earlier work at SUERC, and then followed by additional investigation using an improved instrument built during the project. Initial work confirmed the feasibility of using laser scanning approaches to obtain spatially resolved luminescence data at or near the dimensions of individual mineral grains. Practical obstacles included the recognition that laser scattering from surfaces coated with mineral grains introduced an element of cross-talk between different parts of the sample, and difficulties in accurate re-positioning of the sample using the first generation prototype in-strument. Work was conducted to investigate a series of different sample presentation media to improve the former, and to incorporate high precision mechanical and optoelectronic means of re-positioning samples between initial measurements, external irradiation, and sub-sequent re-measurement. Both IR and visible band semiconductor lasers were investigated with successful production of single grain images. The short and medium term reliability of the lasers used was acceptable. The lasers used both however eventually failed, which sug-gests that long term lifetime may be an issue for further work. Of the two lasers the IR laser in particular gave a good signal to background ratio for discriminating between irradiated and unirradiated grains. Quantitative analysis of the grain resolved images confirms the potential of this approach in identifying minor irradiated components. The overall conclusions of the work are that both statistical approaches and imaging instru-ments are able to enhance current methods. The observation that visual classification can match the performance even of deconvolution or neural approaches suggests that future effort should be directed more towards improvement of grain statistics in conventional measure-ments, and in further development and investigation of imaging approaches. In these ways it can anticipated that the performance of standard luminescence methods for detecting dilute mixtures of irradiated and unirradiated food ingredients could be significantly improved. To do so would further enhance work conducted by FSA and other bodies to ensure that regula-tions governing the use of irradiation in food processing and the labelling of imported foods are followed

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo.

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing

SecDec-3.0: numerical evaluation of multi-scale integrals beyond one loop

SecDec is a program which can be used for the factorization of dimensionally regulated poles from parametric integrals, in particular multi-loop integrals, and the subsequent numerical evaluation of the finite coefficients. Here we present version 3.0 of the program, which has major improvements compared to version 2: it is faster, contains new decomposition strategies, an improved user interface and various other new features which extend the range of applicability.Comment: 46 pages, version to appear in Comput.Phys.Com

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Two-photon imaging and analysis of neural network dynamics

The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behaviour. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so called 'microcircuits') remains comparably poor. In large parts, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near- millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress in Physic