High-dimensional data take place from digital image processing, gene expression micro arrays, neuronal population activities to financial time series. Dimensionality Reduction - extracting low dimensional structure from high dimension - is a key problem in many areas like information processing, machine learning, data mining, information retrieval and pattern recognition, where we find some data reduction techniques. In this thesis we will give a survey about modern data reduction techniques, representing the state-of-the-art of theory, methods and application, by introducing the language of mathematics there. This needs a special care concerning the questions of, e.g., how to understand discrete structures as manifolds, to identify their structure, preparing the dimension reduction, and to face complexity in the algorithmically methods. A special emphasis will be paid to Principal Component Analysis, Locally Linear Embedding and Isomap Algorithms. These algorithms are studied by a research group from Vilnius, Lithuania and Zeev Volkovich, from Software Engineering Department, ORT Braude College of Engineering, Karmiel, and others. The main purpose of this study is to compare the results of the three of the algorithms. While the comparison is beeing made we will focus the results and duration.

Ductal carcinoma in situ (DCIS); is thought directly to precede invasive breast cancer (IBC). Screening mammography has driven the incidence of this key precursor lesion to >65,000 cases per year. However, little is known about the factors controlling the natural history or risk for recurrence following treatment of a particular patients DCIS. Though the heterogeneity of the disease is well established, no histologic or demographic criteria have been able to stratify DCIS for treatment. We hypothesize that at initial diagnosis there exist biologically distinct subsets of DCIS with associated prognoses that may be recognized by molecular markers. Molecular approaches have been limited by technical design issues related to the types of tissue available for analysis, namely degraded formalin-fixed paraffin embedded (FFPE) specimens and small core biopsy samples. However, new technologies promise to overcome these issues. In the first phase of our investigation, we aimed a) to pilot feasibility studies on the use of FFPE DCIS for molecular analyses including gene expression microarray and b) to pilot feasibility study of selective, high throughput sequencing through the use of "exon capture" on small input material that simulated expected DCIS core biopsy amounts. The results of this work offer specific technical guidelines for the molecular study of DCIS. Moreover, they have enabled the initiation of the second phase of this study, which aims to assess molecular profiles of DCIS recurrence and progression.

A quandle is a set with a binary operation that satisfies three axioms that corresponds to the three Reidemeister moves on knot diagrams. Homology and cohomology theories of quandles were introduced in 1999 by Carter, Jelsovsky,Kamada, Langford, and Saito as a modification of the rack (co)homology theory defined by Fenn, Rourke, and Sanderson. Cocycles of the quandle (co)homology, along with quandle colorings of knot diagrams, were used to define a new invariant called the quandle cocycle invariants, defined in a state-sum form. This invariant is constructed using a finite quandle and a cocyle, and it has the advantage that it can distinguish some knots from their mirror images, and orientations of knotted surfaces. To compute the quandle cocycle invariant for a specific knot, we need to find a quandle that colors the given knot non-trivially, and find a cocycle of the quandle. It is not easy to find cocycles,since the cocycle conditions form a large, over-determined system of linear equations. At first the computations relied on cocycles found by computer calculations. We have seen significant progress in computations after Mochizuki discovered a family of 2- and 3-cocycles for dihedral and other linear Alexander quandles written by polynomial expressions. In this dissertation, following the method of the construction by Mochizuki, a variety of n-cocycles for n >̲ 2 are constructed for some Alexander quandles, given by polynomial expressions. As an application, these cocycles are used to compute the invariants for (2,n)-torus knots, twist knots and their r-twist spins. The calculations in the case of (2,n)-torus knots resulted in formulas that involved the derivative of the Alexander polynomial. Non-triviality of some quandle homology groups is also proved using these cocycles. Another application is given for tangle embeddings. The quandle cocycle invariants are used as obstructions to embedding tangles in links. The formulas for the cocycle invariants of tangles are obtained using polynomial cocycles, and by comparing the invariant values, information is obtained on which tangles do not embed in which knots. Tangles and knots in the tables are examined, and concrete examples are listed.

The network representation of the interactions between proteins and genes allows for a holistic perspective of the complex machinery underlying the living cell. However, the large number of interacting entities within the cell makes network construction a daunting and arduous task, prone to errors and missing information. Fortunately, the structure of biological networks is not different from that of other complex systems, such as social networks, the world-wide web or power grids, for which growth models have been proposed to better understand their structure and function. This means that we can design tools based on these models in order to exploit the topology of biological interactomes with the aim to construct more complete and reliable maps of the cell. In this work, we propose three novel and powerful approaches for the prediction of interactions in biological networks and conclude that it is possible to mine the topology of these complex system representations and produce reliable and biologically meaningful information that enriches the datasets to which we have access today.

Η συνεχής μείωση των διαστάσεων των ψηφιακών κυκλωμάτων σε συνδυασμό με την ολοένα αυξανόμενη πολυπλοκότητά τους, έχει οδηγήσει στην απαίτηση για αξιοπιστία και συνεπώς στην εφαρμογή τεχνικών ελέγχου για την εξασφάλιση της ορθής λειτουργίας τους. Οι βασικοί τρόποι εφαρμογής του ελέγχου σε ένα κύκλωμα μετά την κατασκευή του και την τοποθέτησή του στη συσκευασία είναι ο εξωτερικός (off-chip – εξολοκλήρου χρήση εξωτερικού ελεγκτή ATE), ο BIST (Built-In Self Test – μηδενική χρήση ATE) και ο ενσωματωμένος (embedded – συνδυασμός χρήσης ATE με ενσωματωμένες δομές ελέγχου). Η συγκεκριμένη διπλωματική εργασία επικεντρώνεται στη χρήση του ενσωματωμένου ελέγχου και συγκεκριμένα σε μια κατηγορία αυτού που ονομάζεται εμφώλευση συνόλου δοκιμής (test set embedding) στην οποία το σύνολο δοκιμής ενσωματώνεται σε μια μεγαλύτερη ακολουθία καταστάσεων ενός κυκλώματος παραγωγής διανυσμάτων δοκιμής. Σε αυτή τη διπλωματική εργασία προτείνεται μια νέα μέθοδος για ενσωματωμένο έλεγχο που κάνει χρήση της ανατροφοδότησης (reseeding) για έλεγχο με χρήση ολισθητή γραμμικής ανάδρασης (LFSR). Η μέθοδος αυτή χρησιμοποιείται είτε σε απλές αρχιτεκτονικές ελέγχου με LFSR, είτε σε πολυφασικές αρχιτεκτονικές, πάντα για κυκλώματα με πολλαπλές αλυσίδες. Στην πολυφασική αρχιτεκτονική εκμεταλλευόμαστε τις ακολουθίες από bits που εξάγονται από διάφορες βαθμίδες ενός LFSR, το οποίο χρησιμοποιείται για την παραγωγή διανυσμάτων δοκιμής, για να κωδικοποιήσουμε το σετ ελέγχου της υπό δοκιμή λειτουργικής μονάδας. Παρουσιάζεται ένας νέος αλγόριθμος, ο οποίος περιλαμβάνει τέσσερα κριτήρια για την αποδοτική επιλογή νέων αρχικών καταστάσεων και των βαθμίδων του LFSR. Τέλος παρουσιάζεται και μια μεθοδολογία μείωσης του μήκους της παραγόμενης ακολουθίας δοκιμής. Στη συνέχεια και για να συγκρίνουμε τα αποτελέσματα που εξάγονται από την παραπάνω μέθοδο υλοποιήθηκε μια νέα τεχνική που έχει προταθεί πρόσφατα στη βιβλιογραφία. Η μέθοδος αυτή καλείται REusing Scan chains for test Pattern decompressIoN (RESPIN) και έχει κύριο χαρακτηριστικό την εμφώλευση του συνόλου δοκιμής. Σύμφωνα με τη μέθοδο αυτή η αποσυμπίεση των διανυσμάτων που ελέγχουν μια λειτουργική μονάδα γίνεται με τη χρήση αλυσίδων ελέγχου μιας δεύτερης λειτουργικής μονάδας που βρίσκεται μέσα στο chip και που τη στιγμή του ελέγχου είναι σε αδρανή κατάσταση. Έπειτα από εκτενή σύγκριση των δυο προαναφερθέντων τεχνικών καθώς και άλλων τεχνικών που αναφέρονται στη βιβλιογραφία καταλήξαμε στο συμπέρασμα ότι ο συνδυασμός του αλγόριθμου επιλογής νέων αρχικών καταστάσεων ενός LFSR με την τεχνική μείωσης των ακολουθιών ελέγχου αποτελεί ελκυστική λύση και παρέχει καλύτερα αποτελέσματα τόσο ως προς το πλήθος των δεδομένων που αποθηκεύονται στο ΑΤΕ, όσο και ως προς το μήκος των ακολουθιών δοκιμής. / The continual reduction of digital systems’ size in combination to the increase of their complexity, leads to the need of reliability. Consequently it is necessary to apply testing techniques in order to ensure the right functionality. The ways to apply the testing in an in package circuit is the external (off-chip – total use of the external ATE), the BIST (Built-In Self Test – no use of ATE) and the embedded (use of external ATE in combination to embedded test structures). This diploma thesis focus in the embedded testing and particular in test set embedding. In this technique the test set is embodied in a larger state sequence of a test pattern generator circuit. In this diploma thesis we suggest a new method of embedded testing which uses the reseeding of a LFSR. This method is used either in simple architectures with LFSR, or in multiphase architectures, always for circuits with multiple scan chains. In the multiphase architecture we take advantage of the sequence of bits that are driven by the various stages of a LFSR, which is used to generate test patterns, in order to embody the test set of the circuit under test. We present a new algorithm, which include four standards for the efficient selection of new seeds and states of the LFSR. Finally, we present a new method for test sequence length reduction. After that and in order to compare the results of the above method we implement a new technique, which has been suggested recently in the bibliography. This method is called REusing Scan chains for test Pattern decompressIoN (RESPIN) and its main characteristic is the test set embedding. According to this method, the decompression of test patterns is accomplished using the scan chains of another on-chip module, which is in idle state during the test. After a thorough comparison of these two techniques we conclude that the combination of the seed selection algorithm with the test sequence length reduction technique comprise an attractive solution and gives better results for the amount of data to be stored in the external ATE and for the test sequence length.

La capture du mouvement est un défi majeur dans le cadre de la modélisation d'objets articulés. Ce problème implique la recherche de correspondances entre objets vus dans des images différentes. On propose trois approches pour résoudre ce problème basé sur des techniques de vision par ordinateur et la théorie spectrale des graphes. La première consiste à modéliser une scène 3D à l'aide d'une collection de points. On propose deux extensions de l'algorithme de Lucas-Kanade pour tracker des caractéristiques de manière efficace et pour estimer le "scene-flow". La deuxième approche basée sur la théorie spectrale des graphes cherche à établir des correspondances entre des objets représentés par des graphes. Finalement on s'intéresse au problème de segmentation qui soit cohérente dans le temps et notre approche est basée sur une méthode de clustering spectral appliquée à une séquence temporelle.

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.

This study explores how higher education can enable the management accounting curriculum to include sustainability content and learning outcomes to encourage future accountants and leaders to use such information in organizational decision-making. It examines current systems thinking theories, and studies how the leverage points available through the management accounting function may assist organizations to embed sustainability into daily practice. To support this transformation, the research reviews the knowledge-base, activities, and tools of management accounting and suggests how to incorporate sustainability principles and criteria into the curriculum within a community college in British Columbia (BC) that has established sustainability as a strategic goal. Action research interviews explore how the management accounting curriculum within the school of business could be enhanced to support organizations - and by extension society - in embedding sustainability into practice, and identifies recommendations for curriculum re-design at the department level, and key elements of change-making to enable it.

Interference makes reasoning about imperative programs difficult but it can be controlled syntactically by a language's type system, such as Syntactic Control of Interference (SCI). Haskell is a purely-functional, statically-typed language with a rich type system including algebraic datatypes and type classes. It is popular as a defining language for definitional interpreters of domain-specific languages, making it an ideal candidate for implementation of definitional interpreters for SCI and Syntactic Control of Interference Revisited (SCIR), a variant that improves on SCI. Inference rules and denotational semantics functions are presented for PCF, IA, SCI, and SCIR. An extension to Haskell98 is used to define Haskell functions for those languages' semantics and to define type constructions to statically check their syntax. The results in applied programming language theory demonstrate the suitability and techniques of Haskell for definitional interpretation of languages with rich type systems. / Thesis (Master, Computing) -- Queen's University, 2008-06-10 21:23:33.291