N–Dimensional Orthogonal Tile Sizing Problem

AMS subject classification: 68Q22, 90C90

Unfolding kernel embeddings of graphs:enhancing class separation through manifold learning

In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behavior that suggests that long range distances are not estimated accurately, resulting in an increased curvature of the embedding space. Hence, we propose to use a number of manifold learning techniques to compute a low-dimensional embedding of the graphs in an attempt to unfold the embedding manifold, and increase the class separation. We perform an extensive experimental evaluation on a number of standard graph datasets using the shortest-path (Borgwardt and Kriegel, 2005), graphlet (Shervashidze et al., 2009), random walk (Kashima et al., 2003) and Weisfeiler-Lehman (Shervashidze et al., 2011) kernels. We observe the most significant improvement in the case of the graphlet kernel, which fits with the observation that neglecting the locational information of the substructures leads to a stronger curvature of the embedding manifold. On the other hand, the Weisfeiler-Lehman kernel partially mitigates the locality problem by using the node labels information, and thus does not clearly benefit from the manifold learning. Interestingly, our experiments also show that the unfolding of the space seems to reduce the performance gap between the examined kernels.

A Method for Solving a Class of Multiple-Criteria Analysis Problems

AMS subject classification: 90C29.

Monte Carlo Study of Particle Transport Problem in Air Pollution

2002 Mathematics Subject Classification: 65C05.

On The Cauchy Problem for Non Effectively Hyperbolic Operators, The Ivrii-Petkov-Hörmander Condition and the Gevrey Well Posedness

2000 Mathematics Subject Classification: 35L15, Secondary 35L30.

Class Number Two for Real Quadratic Fields of Richaud-Degert Type

2000 Mathematics Subject Classification: Primary: 11D09, 11A55, 11C08, 11R11, 11R29; Secondary: 11R65, 11S40; 11R09.

Class Size Reduction for the State of Florida: Is This the Solution for a Better Education?

Smaller class sizes have a positive impact on student achievement but Florida struggles with the problem of how to achieve smaller classes. Through a review of the literature, this paper discusses some of the programs currently used across the US, with the focus on Florida. Conclusions and implications are presented.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

The 1994 BFA Graduating Class Departmrnt of Visual Arts

I find it hard to believe that already we are graduating our third group of students. It seem like our department was founded only a few months ago. Time Flies! Over the years I've come to respect the energy,Principles, commitment, talent and diversity of my colleagues in visual arts. I have never worked with such an outstanding group of people, and I firmly believe we deliver a University experience second to none. Having said that, I also believe our success would be diminished were it not for the fact that we have had the priviledge of working with students who were equally outstanding. Newfoundlanders can be justly proud of the Sir wilfred Grenfell College graduates from the many communities throughout the province who will represent Newfoundland and Labrador admirably on regional, national and international levels in years to come. This years catalogue records part of the many accomplishments of the class of 1994. Once again it is evident that discovering, nuturing and developing individual expression yields artwork of substance and consequence. It also confirms our belief that students should independent, resourceful and creative problem-solvers with a firm belief in their own view-points and the wherewithall to express those viewpoints through personal visual expression.

The 1995 BFA Graduating Class Department of Visual Arts

As you consider this catalogue of works, reflect upon the variety of tasks- intellectual, emotional and technical- that have led to this visible record of ability and expression. the interdisciplinary rigors of the visual arts are present in these pages, and the breadth of the skill, ability, problem-solving and communication that have been developed and refined during the years of study are portrayed an in this presentation of accomplishment. The Graduates represented in the following pages will go on to a variety of careers-teaching, making art, starting businesses,or following any number of diverse paths that they have prepared during their undergraduate years. The work they have chosen to present here is merely a synopsis of the broad spectrum of skills and abilities they have gained during their years at grenfell college.

THE EFFECT OF PURPOSEFUL MATHEMATICS DISCOURSE IN THE CLASSROOM ON STUDENTS’ MATHEMATICS LANGUAGE IN THE CONTEXT OF PROBLEM SOLVING

This study examines how one secondary school teacher’s use of purposeful oral mathematics language impacted her students’ language use and overall communication in written solutions while working with word problems in a grade nine academic mathematics class. Mathematics is often described as a distinct language. As with all languages, students must develop a sense for oral language before developing social practices such as listening, respecting others ideas, and writing. Effective writing is often seen by students that have strong oral language skills. Classroom observations, teacher and student interviews, and collected student work served as evidence to demonstrate the nature of both the teacher’s and the students’ use of oral mathematical language in the classroom, as well as the effect the discourse and language use had on students’ individual written solutions while working on word problems. Inductive coding for themes revealed that the teacher’s purposeful use of oral mathematical language had a positive impact on students’ written solutions. The teacher’s development of a mathematical discourse community created a space for the students to explore mathematical language and concepts that facilitated a deeper level of conceptual understanding of the learned material. The teacher’s oral language appeared to transfer into students written work albeit not with the same complexity of use of the teacher’s oral expression of the mathematical register. Students that learn mathematical language and concepts better appear to have a growth mindset, feel they have ownership over their learning, use reorganizational strategies, and help develop a discourse community.

Climbing Up the Leaderboard: An Empirical Study of Applying Gamification Techniques to a Computer Programming Class

Conventional taught learning practices often experience difficulties in keeping students motivated and engaged. Video games, however, are very successful at sustaining high levels of motivation and engagement through a set of tasks for hours without apparent loss of focus. In addition, gamers solve complex problems within a gaming environment without feeling fatigue or frustration, as they would typically do with a comparable learning task. Based on this notion, the academic community is keen on exploring methods that can deliver deep learner engagement and has shown increased interest in adopting gamification – the integration of gaming elements, mechanics, and frameworks into non-game situations and scenarios – as a means to increase student engagement and improve information retention. Its effectiveness when applied to education has been debatable though, as attempts have generally been restricted to one-dimensional approaches such as transposing a trivial reward system onto existing teaching materials and/or assessments. Nevertheless, a gamified, multi-dimensional, problem-based learning approach can yield improved results even when applied to a very complex and traditionally dry task like the teaching of computer programming, as shown in this paper. The presented quasi-experimental study used a combination of instructor feedback, real time sequence of scored quizzes, and live coding to deliver a fully interactive learning experience. More specifically, the “Kahoot!” Classroom Response System (CRS), the classroom version of the TV game show “Who Wants To Be A Millionaire?”, and Codecademy’s interactive platform formed the basis for a learning model which was applied to an entry-level Python programming course. Students were thus allowed to experience multiple interlocking methods similar to those commonly found in a top quality game experience. To assess gamification’s impact on learning, empirical data from the gamified group were compared to those from a control group who was taught through a traditional learning approach, similar to the one which had been used during previous cohorts. Despite this being a relatively small-scale study, the results and findings for a number of key metrics, including attendance, downloading of course material, and final grades, were encouraging and proved that the gamified approach was motivating and enriching for both students and instructors.

The problem posed by exam choice on the comparability of results in the Finnish matriculation examination

The article focuses on the upper secondary matriculation examination in Finland as a school leaving and university entrance examination. The presented research addresses the question of whether increased choice of the subject-specific examinations has the potential to undermine the comparability of examination results and to direct students’ choices not only in the examination but already beforehand at school. The authors refer to Finland’s tradition of more than 160 years of a national examination connecting the academic track of upper secondary schools with universities. The authors explain the Finnish system by describing the adoption of a course-based (vs. class- or year-based) curriculum for the three-year upper secondary education and the subsequent reforms in the matriculation examination. This increases students’ choices considerably with regard to the subject-specific exams included in the examination (a minimum of four). As a result, high-achieving students compete against each other in the more demanding subjects while the less able share the same normal distribution of grades in the less demanding subjects. As a consequence, students tend to strategic exam-planning, which in turn affects their study choices at school, often to the detriment of the more demanding subjects and, subsequently, of students’ career opportunities, endangering the traditional national objective of an all-round pre-academic upper secondary education. This contribution provides an overview of Finnish upper secondary education and of the matriculation examination (cf. Klein, 2013) while studying three separate but related issues by using data from several years of Finnish matriculation results: the relation of the matriculation examination and the curriculum; the problems of comparability vis-à-vis university entry due to the increased choice within the examination; the relations between students’ examination choices and their course selection and achievement during upper secondary school. (DIPF/Orig.)

A class of domain decomposition preconditioners for hp-discontinuous Galerkin finite element methods

In this article we address the question of efficiently solving the algebraic linear system of equations arising from the discretization of a symmetric, elliptic boundary value problem using hp-version discontinuous Galerkin finite element methods. In particular, we introduce a class of domain decomposition preconditioners based on the Schwarz framework, and prove bounds on the condition number of the resulting iteration operators. Numerical results confirming the theoretical estimates are also presented.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.