<p>Cumulon is a system aimed at simplifying the development and deployment of statistical analysis of big data in public clouds. Cumulon allows users to program in their familiar language of matrices and linear algebra, without worrying about how to map data and computation to specific hardware and cloud software platforms. Given user-specified requirements in terms of time, monetary cost, and risk tolerance, Cumulon automatically makes intelligent decisions on implementation alternatives, execution parameters, as well as hardware provisioning and configuration settings -- such as what type of machines and how many of them to acquire. Cumulon also supports clouds with auction-based markets: it effectively utilizes computing resources whose availability varies according to market conditions, and suggests best bidding strategies for them. Cumulon explores two alternative approaches toward supporting such markets, with different trade-offs between system and optimization complexity. Experimental study is conducted to show the efficiency of Cumulon's execution engine, as well as the optimizer's effectiveness in finding the optimal plan in the vast plan space.</p> / Dissertation

In this thesis we address a long-term maintainability problem in Haskell type class system. In particular we study a possibility of backward-compatible changes in existing class hierarchies. In the first part of the thesis we give a brief overview of the language. The following part summarizes current proposed solutions to the problem and analyzes their properties. Based on this analysis we derive our own language extension proposal. In the penultimate chapter we present several possible applications of the language extension and compare the extension to other solutions. As a part of the thesis we also give a proof-of-concept implementation of the extension for the GHC compiler, which is briefly described in the last part of this thesis. Powered by TCPDF (www.tcpdf.org)

<p>The European Court of Justice, the ECJ, has the governing function in the Union as a guardian of law and justice. Even today, with a Union of 15 memberstates, statistics show that the ECJ and the Court of First Instance have increasing difficulties in fulfilling their tasks. This situation is chiefly a cause of an increasing number of cases raised. Due to this, profound changes have to be made in order to preserve common lawagreements in a future expansion of the Union. The purpose of this paper is to exam which changes the Courts have to make in order to meet an increased amount of cases that an enlarged Union would mean.</p>

We study four measures of problem instance behavior that might account for the observed differences in interior-point method (IPM) iterations when these methods are used to solve semidefinite programming (SDP) problem instances: (i) an aggregate geometry measure related to the primal and dual feasible regions (aspect ratios) and norms of the optimal solutions, (ii) the (Renegar-) condition measure C(d) of the data instance, (iii) a measure of the near-absence of strict complementarity of the optimal solution, and (iv) the level of degeneracy of the optimal solution. We compute these measures for the SDPLIB suite problem instances and measure the correlation between these measures and IPM iteration counts (solved using the software SDPT3) when the measures have finite values. Our conclusions are roughly as follows: the aggregate geometry measure is highly correlated with IPM iterations (CORR = 0.896), and is a very good predictor of IPM iterations, particularly for problem instances with solutions of small norm and aspect ratio. The condition measure C(d) is also correlated with IPM iterations, but less so than the aggregate geometry measure (CORR = 0.630). The near-absence of strict complementarity is weakly correlated with IPM iterations (CORR = 0.423). The level of degeneracy of the optimal solution is essentially uncorrelated with IPM iterations.

The European Court of Justice, the ECJ, has the governing function in the Union as a guardian of law and justice. Even today, with a Union of 15 memberstates, statistics show that the ECJ and the Court of First Instance have increasing difficulties in fulfilling their tasks. This situation is chiefly a cause of an increasing number of cases raised. Due to this, profound changes have to be made in order to preserve common lawagreements in a future expansion of the Union. The purpose of this paper is to exam which changes the Courts have to make in order to meet an increased amount of cases that an enlarged Union would mean.

Recent studies show that when words are correlated with the colours they are printed in (e.g., MOVE is presented 75% of the time in blue), colour identification is faster when the word is presented in its expected colour (MOVE in blue) than in an unexpected colour (MOVE in green). The present series of experiments explored the possible mechanisms involved in this colour-word contingency learning effect. Experiment 1 demonstrated that the effect was already present after 18 learning trials. During subsequent unlearning, the effect extinguished equally rapidly, suggesting that only a handful of the most recently encountered trials are used to predict responses. Two reanalyses of data from Schmidt, Crump, Cheesman, and Besner (2007) ruled out an account of the effect in terms of stimulus repetitions. Experiments 2 and 3 demonstrated that participants who carry a memory load do not show a contingency effect, supporting the hypothesis that limited-capacity resources are used to retrieve a small number of trial memories in order to prepare a response. Experiment 4 demonstrated that memory resources are required for both storage and retrieval processes.

Recent studies show that when words are correlated with the colours they are printed in (e.g., MOVE is presented 75% of the time in blue), colour identification is faster when the word is presented in its expected colour (MOVE in blue) than in an unexpected colour (MOVE in green). The present series of experiments explored the possible mechanisms involved in this colour-word contingency learning effect. Experiment 1 demonstrated that the effect was already present after 18 learning trials. During subsequent unlearning, the effect extinguished equally rapidly, suggesting that only a handful of the most recently encountered trials are used to predict responses. Two reanalyses of data from Schmidt, Crump, Cheesman, and Besner (2007) ruled out an account of the effect in terms of stimulus repetitions. Experiments 2 and 3 demonstrated that participants who carry a memory load do not show a contingency effect, supporting the hypothesis that limited-capacity resources are used to retrieve a small number of trial memories in order to prepare a response. Experiment 4 demonstrated that memory resources are required for both storage and retrieval processes.

The objective of the present work is to employ a multiple-instance learning image retrieval system by incorporating a spatial similarity measure. Multiple-Instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an image database. The degree of similarity between two spatial relations is linked to the distance between the associated nodes in an Interval Neighbor Group (ING). The shorter the distance, the higher degree of similarity, while a longer one, a lower degree of similarity. Once all the pairwise similarity values are derived, an ensemble similarity measure will then integrate these pairwise similarity assessments and give an overall similarity value between two images. Therefore, images in a database can be quantitatively ranked according to the degree of ensemble similarity with the query image. Similarity retrieval method evaluates the ensemble similarity based on the spatial relations and common objects present in the maximum common subimage between the query and a database image are considered. Therefore, reliable spatial relation features extracted from the image, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable retrieval results that better match user¡¦s expectation. In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, the proposed RSS-ING scheme v.s. 2D Be-string similarity method, and single-instance vs. multiple-instance learning. The performance in terms of similarity curves, execution time and memory space requirement show favorably for the proposed multiple-instance spatial similarity-based approach.

abstract: Diabetic retinopathy (DR) is a common cause of blindness occurring due to prolonged presence of diabetes. The risk of developing DR or having the disease progress is increasing over time. Despite advances in diabetes care over the years, DR remains a vision-threatening complication and one of the leading causes of blindness among American adults. Recent studies have shown that diagnosis based on digital retinal imaging has potential benefits over traditional face-to-face evaluation. Yet there is a dearth of computer-based systems that can match the level of performance achieved by ophthalmologists. This thesis takes a fresh perspective in developing a computer-based system aimed at improving diagnosis of DR images. These images are categorized into three classes according to their severity level. The proposed approach explores effective methods to classify new images and retrieve clinically-relevant images from a database with prior diagnosis information associated with them. Retrieval provides a novel way to utilize the vast knowledge in the archives of previously-diagnosed DR images and thereby improve a clinician's performance while classification can safely reduce the burden on DR screening programs and possibly achieve higher detection accuracy than human experts. To solve the three-class retrieval and classification problem, the approach uses a multi-class multiple-instance medical image retrieval framework that makes use of spectrally tuned color correlogram and steerable Gaussian filter response features. The results show better retrieval and classification performances than prior-art methods and are also observed to be of clinical and visual relevance. / Dissertation/Thesis / M.S. Electrical Engineering 2012

Constraint Programming (CP) is a powerful technique for solving a variety of combinatorial problems. Automated modelling using a refinement based approach abstracts over modelling decisions in CP by allowing users to specify their problem in a high level specification language such as ESSENCE. This refinement process produces many models resulting from different choices that can be selected, each with their own strengths. A parameterised specification represents a problem class where the parameters of the class define the instance of the class we wish to solve. Since each model has different performance characteristics the model chosen is crucial to be able to solve the instance effectively. This thesis presents a method to generate instances automatically for the purpose of choosing a subset of the available models that have superior performance across the instance space. The second contribution of this thesis is a framework to automate the testing of a toolchain for automated modelling. This process includes a generator of test cases that covers all aspects of the ESSENCE specification language. This process utilises our first contribution namely instance generation to generate parameterised specifications. This framework can detect errors such as inconsistencies in the model produced during the refinement process. Once we have identified a specification that causes an error, this thesis presents our third contribution; a method for reducing the specification to a much simpler form, which still exhibits a similar error. Additionally this process can generate a set of complementary specifications including specifications that do not cause the error to help pinpoint the root cause.