Query-Sensitive Embeddings

A common problem in many types of databases is retrieving the most similar matches to a query object. Finding those matches in a large database can be too slow to be practical, especially in domains where objects are compared using computationally expensive similarity (or distance) measures. This paper proposes a novel method for approximate nearest neighbor retrieval in such spaces. Our method is embedding-based, meaning that it constructs a function that maps objects into a real vector space. The mapping preserves a large amount of the proximity structure of the original space, and it can be used to rapidly obtain a short list of likely matches to the query. The main novelty of our method is that it constructs, together with the embedding, a query-sensitive distance measure that should be used when measuring distances in the vector space. The term "query-sensitive" means that the distance measure changes depending on the current query object. We report experiments with an image database of handwritten digits, and a time-series database. In both cases, the proposed method outperforms existing state-of-the-art embedding methods, meaning that it provides significantly better trade-offs between efficiency and retrieval accuracy.

Learning Embeddings for Indexing, Retrieval, and Classification, with Applications to Object and Shape Recognition in Image Databases

Nearest neighbor retrieval is the task of identifying, given a database of objects and a query object, the objects in the database that are the most similar to the query. Retrieving nearest neighbors is a necessary component of many practical applications, in fields as diverse as computer vision, pattern recognition, multimedia databases, bioinformatics, and computer networks. At the same time, finding nearest neighbors accurately and efficiently can be challenging, especially when the database contains a large number of objects, and when the underlying distance measure is computationally expensive. This thesis proposes new methods for improving the efficiency and accuracy of nearest neighbor retrieval and classification in spaces with computationally expensive distance measures. The proposed methods are domain-independent, and can be applied in arbitrary spaces, including non-Euclidean and non-metric spaces. In this thesis particular emphasis is given to computer vision applications related to object and shape recognition, where expensive non-Euclidean distance measures are often needed to achieve high accuracy. The first contribution of this thesis is the BoostMap algorithm for embedding arbitrary spaces into a vector space with a computationally efficient distance measure. Using this approach, an approximate set of nearest neighbors can be retrieved efficiently - often orders of magnitude faster than retrieval using the exact distance measure in the original space. The BoostMap algorithm has two key distinguishing features with respect to existing embedding methods. First, embedding construction explicitly maximizes the amount of nearest neighbor information preserved by the embedding. Second, embedding construction is treated as a machine learning problem, in contrast to existing methods that are based on geometric considerations. The second contribution is a method for constructing query-sensitive distance measures for the purposes of nearest neighbor retrieval and classification. In high-dimensional spaces, query-sensitive distance measures allow for automatic selection of the dimensions that are the most informative for each specific query object. It is shown theoretically and experimentally that query-sensitivity increases the modeling power of embeddings, allowing embeddings to capture a larger amount of the nearest neighbor structure of the original space. The third contribution is a method for speeding up nearest neighbor classification by combining multiple embedding-based nearest neighbor classifiers in a cascade. In a cascade, computationally efficient classifiers are used to quickly classify easy cases, and classifiers that are more computationally expensive and also more accurate are only applied to objects that are harder to classify. An interesting property of the proposed cascade method is that, under certain conditions, classification time actually decreases as the size of the database increases, a behavior that is in stark contrast to the behavior of typical nearest neighbor classification systems. The proposed methods are evaluated experimentally in several different applications: hand shape recognition, off-line character recognition, online character recognition, and efficient retrieval of time series. In all datasets, the proposed methods lead to significant improvements in accuracy and efficiency compared to existing state-of-the-art methods. In some datasets, the general-purpose methods introduced in this thesis even outperform domain-specific methods that have been custom-designed for such datasets.

NETEMBED: A Network Resource Mapping Service for Distributed Applications

Emerging configurable infrastructures such as large-scale overlays and grids, distributed testbeds, and sensor networks comprise diverse sets of available computing resources (e.g., CPU and OS capabilities and memory constraints) and network conditions (e.g., link delay, bandwidth, loss rate, and jitter) whose characteristics are both complex and time-varying. At the same time, distributed applications to be deployed on these infrastructures exhibit increasingly complex constraints and requirements on resources they wish to utilize. Examples include selecting nodes and links to schedule an overlay multicast file transfer across the Grid, or embedding a network experiment with specific resource constraints in a distributed testbed such as PlanetLab. Thus, a common problem facing the efficient deployment of distributed applications on these infrastructures is that of "mapping" application-level requirements onto the network in such a manner that the requirements of the application are realized, assuming that the underlying characteristics of the network are known. We refer to this problem as the network embedding problem. In this paper, we propose a new approach to tackle this combinatorially-hard problem. Thanks to a number of heuristics, our approach greatly improves performance and scalability over previously existing techniques. It does so by pruning large portions of the search space without overlooking any valid embedding. We present a construction that allows a compact representation of candidate embeddings, which is maintained by carefully controlling the order via which candidate mappings are inserted and invalid mappings are removed. We present an implementation of our proposed technique, which we call NETEMBED – a service that identify feasible mappings of a virtual network configuration (the query network) to an existing real infrastructure or testbed (the hosting network). We present results of extensive performance evaluation experiments of NETEMBED using several combinations of real and synthetic network topologies. Our results show that our NETEMBED service is quite effective in identifying one (or all) possible embeddings for quite sizable queries and hosting networks – much larger than what any of the existing techniques or services are able to handle.

Cerebellar Learning in an Opponent Motor Controller for Adaptive Load Compensation and Synergy Formation

This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes load compensation while also allowing both coactivation and reciprocal inhibition of sets of antagonist muscles. In particular, we show how synaptic long term depression guided by feedback from muscle stretch receptors can lead to trans-cerebellar gain changes that are load-compensating. It is argued that the same processes help to adaptively discover multi-joint synergies. Simulations of rapid single joint rotations under load illustrates design feasibility and stability.

Health care and neoliberalism in Ireland: withholding the gift and corrupting the social contract

The health of a nation tells much about the nature of a social contract between citizen and state. The way that health care is organised, and the degree to which it is equitably accessible, constitutes a manifestation of the effects of moments and events in that country's history. Using four case studies, this thesis uses a historical genealogical approach to explain the evolution of Ireland's particular version of health care provision. The total social fact of the gift relationship, central to all human relations, will be used to form a theoretical and conceptual framework on which to build an analysis of Ireland's health and welfare conditions. Additionally, social contract theory will enable an examination of the role of solidarity in relation to social expectations around health care provision. Through the analysis of these cases, the complex matrix of the influential forces that have shaped current conditions are exposed and revealed, enabling a critical understanding of the extent of acquiescence to the inequitable system that arguably exists. The vulnerability of citizens in need of care to the external and global effects of market forces and neoliberalism, therefore, becomes central to any argument for state-provided health and welfare. The hegemony of such forces can be seen to influence the manner in which the idea of individual self-reliance, in place of collective solidarity, is conceptualised and subsequently infiltrated into a range of aspects of the social world. For example, the particular discourse of the market and of economic concerns succeeds in shaping understandings of responsibilities around central areas of health and welfare. Similarly the 'possessor principle' can be seen to be misplaced within the context of health and social care, but yet has become normalised within this discourse. Within this matrix of complex influencing factors, the welfare state struggles to impose a balance between market values and social values. Responsibilities of the state to support and compensate its citizens for the ills of the market have become devalued, as the core values of classical liberalism have become distorted beyond recognition, leaving instead bare neoliberal concerns. This thesis traces the genealogical origins of this transition within the recent history of Irish health care and thereby reveals the embedding of individualism in place of solidarity, the on going reneging of the social contract and the corruption of the gift relationship.

Oxidation-reduction potential and its influence on Cheddar cheese quality

Oxidation-reduction (redox) potential is a fundamental physicochemical parameter that affects the growth of microorganisms in dairy products and contributes to a balanced flavour development in cheese. Even though redox potential has an important impact on the quality of dairy products, it is not usually monitored in dairy industry. The aims of this thesis were to develop practical methods for measuring redox potential in cheese, to provide detailed information on changes in redox potential during the cheesemaking and cheese ripening and how this parameter is influenced by starter systems and to understand the relationship between redox potential and cheese quality. Methods were developed for monitoring redox potential during cheesemaking and early in ripening. Changes in redox potential during laboratory scale manufacture of Cheddar, Gouda, Emmental, and Camembert cheeses were determined. Distinctive kinetics of reduction in redox potential during cheesemakings were observed, and depended on the cheese technology and starter culture utilised. Redox potential was also measured early in ripening by embedding electrodes into Cheddar cheese at moulding together with the salted curd pieces. Using this approach it was possible to monitor redox potential during the pressing stage. The redox potential of Emmental cheese was also monitored during ripening. Moreover, since bacterial growth drives the reduction in redox potential during cheese manufacture and ripening, the ability of Lactococcus lactis strains to affect redox potential was studied. Redox potential of a Cheddar cheese extract was altered by bacterial growth and there were strain-specific differences in the nature of the redox potential/time curves obtained. Besides, strategies to control redox potential during cheesemaking and ripening were developed. Oxidizing or reducing agents were added to the salted curd before pressing and results confirmed that a negative redox potential is essential for the development of sulfur compounds in Cheddar cheese. Overall, the studies described in this thesis gave an evidence of the importance of the redox potential on the quality of dairy products. Redox potential could become an additional parameter used to select microorganisms candidate as starters in fermented dairy products. Moreover, it has been demonstrated that the redox potential influences the development of flavour component. Thus, measuring continuously changes in redox potential of a product and controlling, and adjusting if necessary, the redox potential values during manufacture and ripening could be important in the future of the dairy industry.

Human Vascular Microphysiological System for in vitro Drug Screening.

In vitro human tissue engineered human blood vessels (TEBV) that exhibit vasoactivity can be used to test human toxicity of pharmaceutical drug candidates prior to pre-clinical animal studies. TEBVs with 400-800 μM diameters were made by embedding human neonatal dermal fibroblasts or human bone marrow-derived mesenchymal stem cells in dense collagen gel. TEBVs were mechanically strong enough to allow endothelialization and perfusion at physiological shear stresses within 3 hours after fabrication. After 1 week of perfusion, TEBVs exhibited endothelial release of nitric oxide, phenylephrine-induced vasoconstriction, and acetylcholine-induced vasodilation, all of which were maintained up to 5 weeks in culture. Vasodilation was blocked with the addition of the nitric oxide synthase inhibitor L-N(G)-Nitroarginine methyl ester (L-NAME). TEBVs elicited reversible activation to acute inflammatory stimulation by TNF-α which had a transient effect upon acetylcholine-induced relaxation, and exhibited dose-dependent vasodilation in response to caffeine and theophylline. Treatment of TEBVs with 1 μM lovastatin for three days prior to addition of Tumor necrosis factor - α (TNF-α) blocked the injury response and maintained vasodilation. These results indicate the potential to develop a rapidly-producible, endothelialized TEBV for microphysiological systems capable of producing physiological responses to both pharmaceutical and immunological stimuli.

Polymeric photosensitizer-embedded self-expanding metal stent for repeatable endoscopic photodynamic therapy of cholangiocarcinoma

Photodynamic therapy (PDT) is a new therapeutic approach for the palliative treatment of malignant bile duct obstruction. In this study, we designed photosensitizer-embedded self-expanding nonvascular metal stent (PDT-stent) which allows repeatable photodynamic treatment of cholangiocarcinoma without systemic injection of photosensitizer. Polymeric photosensitizer (pullulan acetate-conjugated pheophorbide A; PPA) was incorporated in self-expanding nonvascular metal stent. Residence of PPA in the stent was estimated in buffer solution and subcutaneous implantation on mouse. Photodynamic activity of PDT-stent was evaluated through laserexposure on stent-layered tumor cell lines, HCT-116 tumor-xenograft mouse models and endoscopic intervention of PDT-stent on bile duct of mini pigs. Photo-fluorescence imaging of the PDT-stent demonstrated homogeneous embedding of polymeric Pheo-A (PPA) on stent membrane. PDT-stent sustained its photodynamic activities at least for 2 month. And which implies repeatable endoscopic PDT is possible after stent emplacement. The PDT-stent after light exposure successfully generated cytotoxic singlet oxygen in the surrounding tissues, inducing apoptotic degradation of tumor cells and regression of xenograft tumors on mouse models. Endoscopic biliary in-stent photodynamic treatments on minipigs also suggested the potential efficacy of PDT-stent on cholangiocarcinoma. In vivo and in vitro studies revealed our PDT-stent, allows repeatable endoscopic biliary PDT, has the potential for the combination therapy (stent plus PDT) of cholangiocarcinoma. © 2014 Elsevier Ltd.

SMARTFIRE: an integrated computational fluid dynamics code and expert system for fire field modelling

SMARTFIRE, an open architecture integrated CFD code and knowledge based system attempts to make fire field modeling accessible to non-experts in Computational Fluid Dynamics (CFD) such as fire fighters, architects and fire safety engineers. This is achieved by embedding expert knowledge into CFD software. This enables the 'black-art' associated with the CFD analysis such as selection of solvers, relaxation parameters, convergence criteria, time steps, grid and boundary condition specification to be guided by expert advice from the software. The user is however given the option of overriding these decisions, thus retaining ultimate control. SMARTFIRE also makes use of recent developments in CFD technology such as unstructured meshes and group solvers in order to make the CFD analysis more efficient. This paper describes the incorporation within SMARTFIRE of the expert fire modeling knowledge required for automatic problem setup and mesh generation as well as the concept and use of group solvers for automatic and manual dynamic control of the CFD code.

Parallel performance in multi-physics simulation

A comprehensive simulation of solidification/melting processes requires the simultaneous representation of free surface fluid flow, heat transfer, phase change, non-linear solid mechanics and, possibly, electromagnetics together with their interactions in what is now referred to as "multi-physics" simulation. A 3D computational procedure and software tool, PHYSICA, embedding the above multi-physics models using finite volume methods on unstructured meshes (FV-UM) has been developed. Multi-physics simulations are extremely compute intensive and a strategy to parallelise such codes has, therefore, been developed. This strategy has been applied to PHYSICA and evaluated on a range of challenging multi-physics problems drawn from actual industrial cases.

Dynamic fluid–structure interaction using finite volume unstructured mesh procedures

A three-dimensional finite volume, unstructured mesh (FV-UM) method for dynamic fluid–structure interaction (DFSI) is described. Fluid structure interaction, as applied to flexible structures, has wide application in diverse areas such as flutter in aircraft, wind response of buildings, flows in elastic pipes and blood vessels. It involves the coupling of fluid flow and structural mechanics, two fields that are conventionally modelled using two dissimilar methods, thus a single comprehensive computational model of both phenomena is a considerable challenge. Until recently work in this area focused on one phenomenon and represented the behaviour of the other more simply. More recently, strategies for solving the full coupling between the fluid and solid mechanics behaviour have been developed. A key contribution has been made by Farhat et al. [Int. J. Numer. Meth. Fluids 21 (1995) 807] employing FV-UM methods for solving the Euler flow equations and a conventional finite element method for the elastic solid mechanics and the spring based mesh procedure of Batina [AIAA paper 0115, 1989] for mesh movement. In this paper, we describe an approach which broadly exploits the three field strategy described by Farhat for fluid flow, structural dynamics and mesh movement but, in the context of DFSI, contains a number of novel features: • a single mesh covering the entire domain, • a Navier–Stokes flow, • a single FV-UM discretisation approach for both the flow and solid mechanics procedures, • an implicit predictor–corrector version of the Newmark algorithm, • a single code embedding the whole strategy.

Dynamic fluid-structure interactions using finite volume unstructured mesh procedures

A three-dimensional finite volume, unstructured mesh (FV-UM) method for dynamic fluid–structure interaction (DFSI) is described. Fluid structure interaction, as applied to flexible structures, has wide application in diverse areas such as flutter in aircraft, wind response of buildings, flows in elastic pipes and blood vessels. It involves the coupling of fluid flow and structural mechanics, two fields that are conventionally modelled using two dissimilar methods, thus a single comprehensive computational model of both phenomena is a considerable challenge. Until recently work in this area focused on one phenomenon and represented the behaviour of the other more simply. More recently, strategies for solving the full coupling between the fluid and solid mechanics behaviour have been developed. A key contribution has been made by Farhat et al. [Int. J. Numer. Meth. Fluids 21 (1995) 807] employing FV-UM methods for solving the Euler flow equations and a conventional finite element method for the elastic solid mechanics and the spring based mesh procedure of Batina [AIAA paper 0115, 1989] for mesh movement. In this paper, we describe an approach which broadly exploits the three field strategy described by Farhat for fluid flow, structural dynamics and mesh movement but, in the context of DFSI, contains a number of novel features: a single mesh covering the entire domain, a Navier–Stokes flow, a single FV-UM discretisation approach for both the flow and solid mechanics procedures, an implicit predictor–corrector version of the Newmark algorithm, a single code embedding the whole strategy.

Dynamic fluid-structure interaction using finite volume unstructured mesh procedures

A three dimensional finite volume, unstructured mesh method for dynamic fluid-structure interation is described. The broad approach is conventional in that the fluid and structure are solved sequentially. The pressure and viscous stresses from the flow algorithm provide load conditions for the solid algorithm, whilst at the fluid structure interface the deformed structure provides boundary condition from the structure to the fluid. The structure algorithm also provides the necessary mesh adaptation for the flow field, the effect of which is accounted for in the flow algorithm. The procedures described in this work have several novel features, namely: * a single mesh covering the entire domain. * a Navier Stokes flow. * a single FV-UM discretisation approach for both the flow and solid mechanics procedures. * an implicit predictor-corrector version of the Newmark algorithm. * a single code embedding the whole strategy. The procedure is illustrated for a three dimensional loaded cantilever in fluid flow.

Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.

Flexible self-adjustment of task deployment in dynamic environments

This paper presents an investigation into dynamic self-adjustment of task deployment and other aspects of self-management, through the embedding of multiple policies. Non-dedicated loosely-coupled computing environments, such as clusters and grids are increasingly popular platforms for parallel processing. These abundant systems are highly dynamic environments in which many sources of variability affect the run-time efficiency of tasks. The dynamism is exacerbated by the incorporation of mobile devices and wireless communication. This paper proposes an adaptive strategy for the flexible run-time deployment of tasks; to continuously maintain efficiency despite the environmental variability. The strategy centres on policy-based scheduling which is informed by contextual and environmental inputs such as variance in the round-trip communication time between a client and its workers and the effective processing performance of each worker. A self-management framework has been implemented for evaluation purposes. The framework integrates several policy-controlled, adaptive services with the application code, enabling the run-time behaviour to be adapted to contextual and environmental conditions. Using this framework, an exemplar self-managing parallel application is implemented and used to investigate the extent of the benefits of the strategy