title: Dynamic planar magnetic reconnection solutions for incompressible plasmas

authors: Craig, Ian J.D.

subjects: MHD-plasmas-waves

abstract: The planar magnetic reconnection problem for viscous, resistive plasmas is addressed. We show that solutions can be developed by superposing transient nonlinear disturbances onto quiescent “background” fields. The disturbance fields are unrestricted in form, but the spatial part of the background field must satisfy ∇2K= -λK. This decomposition allows previous analytic reconnection solutions, based on one-dimensional disturbance fields of “plane wave” form, to be recovered as special cases. However, we point out that planar disturbance fields must be fully two-dimensional to avoid the pressure problem associated with analytic merging models, that is, to avoid unbounded current sheet pressures in the limit of small plasma resistivities. The details of the reconnection problem are then illustrated using cellular background field simulations in doubly periodic geometries. The flux pile-up rate is shown to saturate when the pressure of the current sheet exceeds the hydromagnetic pressure of the background field. Although the presaturation regime is well described by one-dimensional current sheet theory, the nonlinear postsaturation regime remains poorly understood. Preliminary evidence suggests that, although after saturation the early evolution of the field can be described by slow Sweet-Parker scalings, the first implosion no longer provides the bulk of the energy release.

text: Dynamic planar magnetic reconnection solutions for incompressible plasmas The planar magnetic reconnection problem for viscous, resistive plasmas is addressed. We show that solutions can be developed by superposing transient nonlinear disturbances onto quiescent “background” fields. The disturbance fields are unrestricted in form, but the spatial part of the background field must satisfy ∇2K= -λK. This decomposition allows previous analytic reconnection solutions, based on one-dimensional disturbance fields of “plane wave” form, to be recovered as special cases. However, we point out that planar disturbance fields must be fully two-dimensional to avoid the pressure problem associated with analytic merging models, that is, to avoid unbounded current sheet pressures in the limit of small plasma resistivities. The details of the reconnection problem are then illustrated using cellular background field simulations in doubly periodic geometries. The flux pile-up rate is shown to saturate when the pressure of the current sheet exceeds the hydromagnetic pressure of the background field. Although the presaturation regime is well described by one-dimensional current sheet theory, the nonlinear postsaturation regime remains poorly understood. Preliminary evidence suggests that, although after saturation the early evolution of the field can be described by slow Sweet-Parker scalings, the first implosion no longer provides the bulk of the energy release.

year: 1999-05

title: Interactive concept learning for end-user applications

authors: Maulsby, David

subjects: computer science

abstract: Personalizable software agents will learn new tasks from their users. This implies being able to learn from instructions users might give: examples, yes/no responses, and ambiguous, incomplete hints. Agents should also exploit background knowledge customized for applications such as drawing, word processing and form-filling. The task models that agents learn describe data, actions and their context. Learning about data from examples and hints is the subject of this paper. The Cima learning system combines evidence from examples, task knowledge and user hints to form Disjunctive Normal Form (DNF) rules for classifying, generating or modifying data. Cima's dynamic bias manager generates candidate features (attribute values, functions or relations), from which its DNF learning algorithm selects relevant features and forms the rules. The algorithm is based on a classic greedy method, with two enhancements. First, the standard learning criterion, correct classification, is augmented with a set of utility and instructional criteria. Utility criteria ensure that descriptions are properly formed for use in actions, whether to classify, search for, generate or modify data. Instructional criteria ensure that descriptions include features that users suggest and avoid those that users reject. The second enhancement is to augment the usual statistical metric for selecting relevant attributes with a set of heuristics, including beliefs based on user suggestions and application-specific background knowledge. Using multiple heuristics increases the justification for selecting features; more important, it helps the learner choose among alternative interpretations of hints. When tested on dialogues observed in a prior user study on a simulated interface agent, the learning algorithm achieves 95% of the learning efficiency standard established in that study.

text: Interactive concept learning for end-user applications Personalizable software agents will learn new tasks from their users. This implies being able to learn from instructions users might give: examples, yes/no responses, and ambiguous, incomplete hints. Agents should also exploit background knowledge customized for applications such as drawing, word processing and form-filling. The task models that agents learn describe data, actions and their context. Learning about data from examples and hints is the subject of this paper. The Cima learning system combines evidence from examples, task knowledge and user hints to form Disjunctive Normal Form (DNF) rules for classifying, generating or modifying data. Cima's dynamic bias manager generates candidate features (attribute values, functions or relations), from which its DNF learning algorithm selects relevant features and forms the rules. The algorithm is based on a classic greedy method, with two enhancements. First, the standard learning criterion, correct classification, is augmented with a set of utility and instructional criteria. Utility criteria ensure that descriptions are properly formed for use in actions, whether to classify, search for, generate or modify data. Instructional criteria ensure that descriptions include features that users suggest and avoid those that users reject. The second enhancement is to augment the usual statistical metric for selecting relevant attributes with a set of heuristics, including beliefs based on user suggestions and application-specific background knowledge. Using multiple heuristics increases the justification for selecting features; more important, it helps the learner choose among alternative interpretations of hints. When tested on dialogues observed in a prior user study on a simulated interface agent, the learning algorithm achieves 95% of the learning efficiency standard established in that study.

year: 1995-02

title: The intergenerational perpetuation of achievement messages in whānau

authors: Southey, Kim Marie

subjects: Maori

abstract: The current research is an open exploration of achievement messages that are carried from generation to generation in whānau, in relation to the mainstream education system of Aotearoa. Participant groups comprised of two to three generations within each whānau. A maximum of eight whānau will be asked to participate. A series of continuous conversations will be held with each member of the whānau, reminiscent of the narrative approach, but most recently described as a methodology that allows for ensuring that data is collected in its fullest form.
Differing views about achievement and how it is negotiated within whānau will be explored; along with issues on what, and who, within whānau are the most predominant indicators of achievement views. Detracting from an ethnocentric view of scholastic ability (a common marker for achievement) is done through abandoning mainstream ideals. This research favours an open exploration approach allowing for differing values about what constitutes achievement, and what context achievement is based in. Outcomes of the research are intended to show patterns within whānau and among Māori on the mainstream school system, and how this system has contributed to achievement messages. The positive or negative nature of the contribution from mainstream education is highly important and relevant to further research goals, including the ability to use outcomes to suggest social change in education provision for Māori.

text: The intergenerational perpetuation of achievement messages in whānau The current research is an open exploration of achievement messages that are carried from generation to generation in whānau, in relation to the mainstream education system of Aotearoa. Participant groups comprised of two to three generations within each whānau. A maximum of eight whānau will be asked to participate. A series of continuous conversations will be held with each member of the whānau, reminiscent of the narrative approach, but most recently described as a methodology that allows for ensuring that data is collected in its fullest form.
Differing views about achievement and how it is negotiated within whānau will be explored; along with issues on what, and who, within whānau are the most predominant indicators of achievement views. Detracting from an ethnocentric view of scholastic ability (a common marker for achievement) is done through abandoning mainstream ideals. This research favours an open exploration approach allowing for differing values about what constitutes achievement, and what context achievement is based in. Outcomes of the research are intended to show patterns within whānau and among Māori on the mainstream school system, and how this system has contributed to achievement messages. The positive or negative nature of the contribution from mainstream education is highly important and relevant to further research goals, including the ability to use outcomes to suggest social change in education provision for Māori.

year: 2003