Bayesian clustering in decomposable graphs

In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis is placed on a particular prior distribution which derives its motivation from the class of product partition models; the properties of this prior relative to existing priors is examined through theory and simulation. We then demonstrate the use of graphical models in the field of agriculture, showing how the proposed prior distribution alleviates the inflexibility of previous approaches in properly modeling the interactions between the yield of different crop varieties.Comment: 3 figures, 1 tabl

Semi Automated Partial Credit Grading of Programming Assignments

The grading of student programs is a time consuming process. As class sizes continue to grow, especially in entry level courses, manually grading student programs has become an even more daunting challenge. Increasing the difficulty of grading is the needs of graphical and interactive programs such as those used as part of the UNH Computer Science curriculum (and various textbooks). There are existing tools that support the grading of introductory programming assignments (TAME and Web-CAT). There are also frameworks that can be used to test student code (JUnit, Tester, and TestNG). While these programs and frameworks are helpful, they have little or no no support for programs that use real data structures or that have interactive or graphical features. In addition, the automated tests in all these tools provide only “all or nothing” evaluation. This is a significant limitation in many circumstances. Moreover, there is little or no support for dynamic alteration of grading criteria, which means that refactoring of test classes after deployment is not easily done. Our goal is to create a framework that can address these weaknesses. This framework needs to: 1. Support assignments that have interactive and graphical components. 2. Handle data structures in student programs such as lists, stacks, trees, and hash tables. 3. Be able to assign partial credit automatically when the instructor can predict errors in advance. 4. Provide additional answer clustering information to help graders identify and assign consistent partial credit for incorrect output that was not predefined. Most importantly, these tools, collectively called RPM (short for Rapid Program Management), should interface effectively with our current grading support framework without requiring large amounts of rewriting or refactoring of test code

Three-dimensional topological lattice models with surface anyons

We study a class of three dimensional exactly solvable models of topological matter first put forward by Walker and Wang [arXiv:1104.2632v2]. While these are not models of interacting fermions, they may well capture the topological behavior of some strongly correlated systems. In this work we give a full pedagogical treatment of a special simple case of these models, which we call the 3D semion model: We calculate its ground state degeneracies for a variety of boundary conditions, and classify its low-lying excitations. While point defects in the bulk are confined in pairs connected by energetic strings, the surface excitations are more interesting: the model has deconfined point defects pinned to the boundary of the lattice, and these exhibit semionic braiding statistics. The surface physics is reminiscent of a $\nu=1/2$ bosonic fractional quantum Hall effect in its topological limit, and these considerations help motivate an effective field theoretic description for the lattice models as variants of $bF$ theories. Our special example of the 3D semion model captures much of the behavior of more general `confined Walker-Wang models'. We contrast the 3D semion model with the closely related 3D version of the toric code (a lattice gauge theory) which has deconfined point excitations in the bulk and we discuss how more general models may have some confined and some deconfined excitations. Having seen that there exist lattice models whose surfaces have the same topological order as a bosonic fractional quantum Hall effect on a confining bulk, we construct a lattice model whose surface has similar topological order to a fermionic quantum hall effect. We find that in these models a fermion is always deconfined in the three dimensional bulk

Development of a client interface for a methodology independent object-oriented CASE tool : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University

The overall aim of the research presented in this thesis is the development of a prototype CASE Tool user interface that supports the use of arbitrary methodology notations for the construction of small-scale diagrams. This research is part of the larger CASE Tool project, MOOT (Massey's Object Oriented Tool). MOOT is a meta-system with a client-server architecture that provides a framework within which the semantics and syntax of methodologies can be described. The CASE Tool user interface is implemented in Java so it is as portable as possible and has a consistent look and feel. It has been designed as a client to the rest of the MOOT system (which acts as a server). A communications protocol has been designed to support the interaction between the CASE Tool client and a MOOT server. The user interface design of MOOT must support all possible graphical notations. No assumptions about the types of notations that a software engineer may use can be made. MOOT therefore provides a specification language called NDL for the definition of a methodology's syntax. Hence, the MOOT CASE Tool client described in this thesis is a shell that is parameterised by NDL specifications. The flexibility provided by such a high level of abstraction presents significant challenges in terms of designing effective human-computer interaction mechanisms for the MOOT user interface. Functional and non-functional requirements of the client user interface have been identified and applied during the construction of the prototype. A notation specification that defines the syntax for Coad and Yourdon OOA/OOD has been written in NDL and used as a test case. The thesis includes the iterative evaluation and extension of NDL resulting from the prototype development. The prototype has shown that the current approach to NDL is efficacious, and that the syntax and semantics of a methodology description can successfully be separated. The developed prototype has shown that it is possible to build a simple, non-intrusive, and efficient, yet flexible, useable, and helpful interface for meta-CASE tools. The development of the CASE Tool client, through its generic, methodology independent design, has provided a pilot with which future ideas may be explored

Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena

Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is further complicated by many theoretical issues, such as the I-equivalence among different structures. In this work, we focus on a specific subclass of BNs, named Suppes-Bayes Causal Networks (SBCNs), which include specific structural constraints based on Suppes' probabilistic causation to efficiently model cumulative phenomena. Here we compare the performance, via extensive simulations, of various state-of-the-art search strategies, such as local search techniques and Genetic Algorithms, as well as of distinct regularization methods. The assessment is performed on a large number of simulated datasets from topologies with distinct levels of complexity, various sample size and different rates of errors in the data. Among the main results, we show that the introduction of Suppes' constraints dramatically improve the inference accuracy, by reducing the solution space and providing a temporal ordering on the variables. We also report on trade-offs among different search techniques that can be efficiently employed in distinct experimental settings. This manuscript is an extended version of the paper "Structural Learning of Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018 International Conference on Computational Science

SV-map between Type I and Heterotic Sigma Models

The scattering amplitudes of gauge bosons in heterotic and open superstring theories are related by the single-valued projection which yields heterotic amplitudes by selecting a subset of multiple zeta value coefficients in the $\alpha'$ (string tension parameter) expansion of open string amplitudes. In the present work, we argue that this relation holds also at the level of low-energy expansions (or individual Feynman diagrams) of the respective effective actions, by investigating the beta functions of two-dimensional sigma models describing world-sheets of open and heterotic strings. We analyze the sigma model Feynman diagrams generating identical effective action terms in both theories and show that the heterotic coefficients are given by the single-valued projection of the open ones. The single-valued projection appears as a result of summing over all radial orderings of heterotic vertices on the complex plane representing string world-sheet.Comment: 28 page