Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

SwarmPS: Rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that ∼104–105 particle projections are required to attain a 3 Å resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present SwarmPS, a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (∼102), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. SwarmPS is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

Mapping the language of science and science teaching practices : a case study of early childhood school science

Concerns raised in educational reports about school science in terms of students. outcomes and attitudes, as well as science teaching practices prompted investigation into science learning and teaching practices at the foundational level of school science. Without science content and process knowledge, understanding issues of modern society and active participation in decision-making is difficult. This study contended that a focus on the development of the language of science could enable learners to engage more effectively in learning science and enhance their interest and attitudes towards science. Furthermore, it argued that explicit teaching practices where science language is modelled and scaffolded would facilitate the learning of science by young children at the beginning of their formal schooling. This study aimed to investigate science language development at the foundational level of school science learning in the preparatory-school with students aged five and six years. It focussed on the language of science and science teaching practices in early childhood. In particular, the study focussed on the capacity for young students to engage with and understand science language. Previous research suggests that students have difficulty with the language of science most likely because of the complexities and ambiguities of science language. Furthermore, literature indicates that tensions transpire between traditional science teaching practices and accepted early childhood teaching practices. This contention prompted investigation into means and models of pedagogy for learning foundational science language, knowledge and processes in early childhood. This study was positioned within qualitative assumptions of research and reported via descriptive case study. It was located in a preparatory-school classroom with the class teacher, teacher-aide, and nineteen students aged four and five years who participated with the researcher in the study. Basil Bernstein.s pedagogical theory coupled with Halliday.s Systemic Functional Linguistics (SFL) framed an examination of science pedagogical practices for early childhood science learning. Students. science learning outcomes were gauged by focussing a Hallydayan lens on their oral and reflective language during 12 science-focussed episodes of teaching. Data were collected throughout the 12 episodes. Data included video and audio-taped science activities, student artefacts, journal and anecdotal records, semi-structured interviews and photographs. Data were analysed according to Bernstein.s visible and invisible pedagogies and performance and competence models. Additionally, Halliday.s SFL provided the resource to examine teacher and student language to determine teacher/student interpersonal relationships as well as specialised science and everyday language used in teacher and student science talk. Their analysis established the socio-linguistic characteristics that promoted science competencies in young children. An analysis of the data identified those teaching practices that facilitate young children.s acquisition of science meanings. Positive indications for modelling science language and science text types to young children have emerged. Teaching within the studied setting diverged from perceived notions of common early childhood practices and the benefits of dynamic shifting pedagogies were validated. Significantly, young students demonstrated use of particular specialised components of school-science language in terms of science language features and vocabulary. As well, their use of language demonstrated the students. knowledge of science concepts, processes and text types. The young students made sense of science phenomena through their incorporation of a variety of science language and text-types in explanations during both teacher-directed and independent situations. The study informs early childhood science practices as well as practices for foundational school science teaching and learning. It has exposed implications for science education policy, curriculum and practices. It supports other findings in relation to the capabilities of young students. The study contributes to Systemic Functional Linguistic theory through the development of a specific resource to determine the technicality of teacher language used in teaching young students. Furthermore, the study contributes to methodology practices relating to Bernsteinian theoretical perspectives and has demonstrated new ways of depicting and reporting teaching practices. It provides an analytical tool which couples Bernsteinian and Hallidayan theoretical perspectives. Ultimately, it defines directions for further research in terms of foundation science language learning, ongoing learning of the language of science and learning science, science teaching and learning practices, specifically in foundational school science, and relationships between home and school science language experiences.

Street-level drug law enforcement : a meta-analytical review

Our paper presents the results of a meta-analytical review of street level drug law enforcement. We conducted a series of meta-analyses to compare and contrast the effectiveness of four types of drug law enforcement approaches, including community-wide policing, problem-oriented/ partnership approaches that were geographically focused, hotspots policing and standard, unfocused law enforcement efforts. We examined the relative impact of these different crime control tactics on streetlevel drug problems as well as associated problems such as property crime, disorder and violent crime. The results of the meta-analyses, together with examination of forest plots, reveal that problem-oriented policing and geographically-focused interventions involving cooperative partnerships between police and third parties tend to be more effective at controlling drug problems than community-wide policing efforts that are unfocused and spread out across a community. But geographically focused and community-wide drug law enforcement interventions that leverage partnerships are more effective at dealing with drug problems than traditional, law enforcement-only interventions. Our results suggest that the key to successful drug law enforcement lies in the capacity of the police to forge productive partnerships with third parties rather than simply increasing police presence or intervention (e.g., arrests) at drug hotspots.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

An analytical solution for diffusion and nonlinear uptake of oxygen in a spherical cell

We develop a new analytical solution for a reactive transport model that describes the steady-state distribution of oxygen subject to diffusive transport and nonlinear uptake in a sphere. This model was originally reported by Lin (Journal of Theoretical Biology, 1976 v60, pp449–457) to represent the distribution of oxygen inside a cell and has since been studied extensively by both the numerical analysis and formal analysis communities. Here we extend these previous studies by deriving an analytical solution to a generalized reaction-diffusion equation that encompasses Lin’s model as a particular case. We evaluate the solution for the parameter combinations presented by Lin and show that the new solutions are identical to a grid-independent numerical approximation.

An analytical and experimental study of the vibration response of a clamped ribbed plate

An analytical solution is presented in this paper for the vibration response of a ribbed plate clamped on all its boundary edges by employing a travelling wave solution. A clamped ribbed plate test rig is also assembled in this study for the experimental investigation of the ribbed plate response and to provide verification results to the analytical solution. The dynamic characteristics and mode shapes of the ribbed plate are measured and compared to those obtained from the analytical solution and from finite element analysis (FEA). General good agreements are found between the results. Discrepancies between the computational and experimental results at low and high frequencies are also discussed. Explanations are offered in the study to disclose the mechanism causing the discrepancies. The dependency of the dynamic response of the ribbed plate on the distance between the excitation force and the rib is also investigated experimentally. It confirms the findings disclosed in a previous analytical study [T. R. Lin and J. Pan, A closed form solution for the dynamic response of finite ribbed plates. Journal of the Acoustical Society of America 119 (2006) 917-925] that the vibration response of a clamped ribbed plate due to a point force excitation is controlled by the plate stiffness when the source is more than a quarter plate bending wavelength away from the rib and from the plate boundary. The response is largely affected by the rib stiffness when the source location is less than a quarter bending wavelength away from the rib.

Boundary weaving : the social structure and processes of organizational boundaries

Boundaries are an important field of study because they mediate almost every aspect of organizational life. They are becoming increasingly more important as organizations change more frequently and yet, despite the endemic use of the boundary metaphor in common organizational parlance, they are poorly understood. Organizational boundaries are under-theorized and researchers in related fields often simply assume their existence, without defining them. The literature on organizational boundaries is fragmented with no unifying theoretical basis. As a result, when it is recognized that an organizational boundary is "dysfunctional". there is little recourse to models on which to base remediating action. This research sets out to develop just such a theoretical model and is guided by the general question: "What is the nature of organizational boundaries?" It is argued that organizational boundaries can be conceptualised through elements of both social structure and of social process. Elements of structure include objects, coupling, properties and identity. Social processes include objectification, identification, interaction and emergence. All of these elements are integrated by a core category, or basic social process, called boundary weaving. An organizational boundary is a complex system of objects and emergent properties that are woven together by people as they interact together, objectifying the world around them, identifying with these objects and creating couplings of varying strength and polarity as well as their own fragmented identity. Organizational boundaries are characterised by the multiplicity of interconnections, a particular domain of objects, varying levels of embodiment and patterns of interaction. The theory developed in this research emerged from an exploratory, qualitative research design employing grounded theory methodology. The field data was collected from the training headquarters of the New Zealand Army using semi-structured interviews and follow up observations. The unit of analysis is an organizational boundary. Only one research context was used because of the richness and multiplicity of organizational boundaries that were present. The model arose, grounded in the data collected, through a process of theoretical memoing and constant comparative analysis. Academic literature was used as a source of data to aid theory development and the saturation of some central categories. The final theory is classified as middle range, being substantive rather than formal, and is generalizable across medium to large organizations in low-context societies. The main limitation of the research arose from the breadth of the research with multiple lines of inquiry spanning several academic disciplines, with some relevant areas such as the role of identity and complexity being addressed at a necessarily high level. The organizational boundary theory developed by this research replaces the typology approaches, typical of previous theory on organizational boundaries and reconceptualises the nature of groups in organizations as well as the role of "boundary spanners". It also has implications for any theory that relies on the concept of boundaries, such as general systems theory. The main contribution of this research is the development of a holistic model of organizational boundaries including an explanation of the multiplicity of boundaries . no organization has a single definable boundary. A significant aspect of this contribution is the integration of aspects of complexity theory and identity theory to explain the emergence of higher-order properties of organizational boundaries and of organizational identity. The core category of "boundary weaving". is a powerful new metaphor that significantly reconceptualises the way organizational boundaries may be understood in organizations. It invokes secondary metaphors such as the weaving of an organization's "boundary fabric". and provides managers with other metaphorical perspectives, such as the management of boundary friction, boundary tension, boundary permeability and boundary stability. Opportunities for future research reside in formalising and testing the theory as well as developing analytical tools that would enable managers in organizations to apply the theory in practice.

Analysis of productive performance of crop and animal production systems : an integrated analytical framework

This article presents a two-stage analytical framework that integrates ecological crop (animal) growth and economic frontier production models to analyse the productive efficiency of crop (animal) production systems. The ecological crop (animal) growth model estimates "potential" output levels given the genetic characteristics of crops (animals) and the physical conditions of locations where the crops (animals) are grown (reared). The economic frontier production model estimates "best practice" production levels, taking into account economic, institutional and social factors that cause farm and spatial heterogeneity. In the first stage, both ecological crop growth and economic frontier production models are estimated to calculate three measures of productive efficiency: (1) technical efficiency, as the ratio of actual to "best practice" output levels; (2) agronomic efficiency, as the ratio of actual to "potential" output levels; and (3) agro-economic efficiency, as the ratio of "best practice" to "potential" output levels. Also in the first stage, the economic frontier production model identifies factors that determine technical efficiency. In the second stage, agro-economic efficiency is analysed econometrically in relation to economic, institutional and social factors that cause farm and spatial heterogeneity. The proposed framework has several important advantages in comparison with existing proposals. Firstly, it allows the systematic incorporation of all physical, economic, institutional and social factors that cause farm and spatial heterogeneity in analysing the productive performance of crop and animal production systems. Secondly, the location-specific physical factors are not modelled symmetrically as other economic inputs of production. Thirdly, climate change and technological advancements in crop and animal sciences can be modelled in a "forward-looking" manner. Fourthly, knowledge in agronomy and data from experimental studies can be utilised for socio-economic policy analysis. The proposed framework can be easily applied in empirical studies due to the current availability of ecological crop (animal) growth models, farm or secondary data, and econometric software packages. The article highlights several directions of empirical studies that researchers may pursue in the future.

Developing financial decision support for highway infrastructure sustainability

The development of highway infrastructure typically requires major capital input over a long period. This often causes serious financial constraints for investors. The push for sustainability has added new dimensions to the complexity in the evaluation of highway projects, particularly on the cost front. This makes the determination of long-term viability even more a precarious exercise. Life-cycle costing analysis (LCCA) is generally recognised as a valuable tool for the assessment of financial decisions on construction works. However to date, existing LCCA models are deficient in dealing with sustainability factors, particularly for infrastructure projects due to their inherent focus on the economic issues alone. This research probed into the major challenges of implementing sustainability in highway infrastructure development in terms of financial concerns and obligations. Using results of research through literature review, questionnaire survey of industry stakeholders and semi-structured interview of senior practitioners involved in highway infrastructure development, the research identified the relative importance of cost components relating to sustainability measures and on such basis, developed ways of improving existing LCCA models to incorporate sustainability commitments into long-term financial management. On such a platform, a decision support model incorporated Fuzzy Analytical Hierarchy Process and LCCA for the evaluation of the specific cost components most concerned by infrastructure stakeholders. Two real highway infrastructure projects in Australia were then used for testing, application and validation, before the decision support model was finalised. Improved industry understanding and tools such as the developed model will lead to positive sustainability deliverables while ensuring financial viability over the lifecycle of highway infrastructure projects.