Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

Towards a CRM and SCM Benefits Measurement Model

Organizations invest heavily in Customer Relationship Management (CRM) and Supply Chain Management (SCM) systems, and their related infrastructure, presumably expecting positive benefits to the organization. Assessing the benefits of such systems is an important aspect of managing such systems. Given the substantial differences between CRM and SCM systems with traditional intra-organizational applications, existing Information Systems benefits measurement models and frameworks are ill-suited to gauge CRM and SCM benefits. This paper reports the preliminary findings of a research that seeks to develop a measurement model to assess benefits of CRM and SCM applications. The a-priori benefits measurement model is developed reviewing the 55 academic studies and 40 practitioner papers. The review of related literature yielded 606 benefits, which were later synthesized into 74 mutually exclusive benefit measures of CRM and SCM applications arranged under five dimensions.

Theorising a model for teaching and assessing reflective learning in higher education

The importance of reflection in higher education, and across disciplinary fields is widely recognised; it is generally included in university graduate attributes, professional standards and program objectives. Furthermore, reflection is commonly embedded into assessment requirements in higher education subjects, often without necessary scaffolding or clear expectations for students. Despite the rhetoric around the importance of reflection for ongoing learning, there is scant literature on any systematic, developmental approach to teaching reflective learning across higher education programs/courses. Given that professional or academic reflection is not intuitive, and requires specific pedagogic intervention to do well, a program/course-wide approach is essential. This paper draws on current literature to theorise a new, transferable and customisable model for teaching and assessing reflective learning across higher education, which foregrounds and explains the pedagogic field of higher education as a multi-dimensional space. We argue that explicit and strategic pedagogic intervention, supported by dynamic resources, is necessary for successful, broad-scale approaches to reflection in higher education.

Melanoma mouse model implicates metabotropic glutamate signaling in melanocytic neoplasia

To gain insight into melanoma pathogenesis, we characterized an insertional mouse mutant, TG3, that is predisposed to develop multiple melanomas. Physical mapping identified multiple tandem insertions of the transgene into intron 3 of Grm1 (encoding metabotropic glutamate receptor 1) with concomitant deletion of 70 kb of intronic sequence. To assess whether this insertional mutagenesis event results in alteration of transcriptional regulation, we analyzed Grm1 and two flanking genes for aberrant expression in melanomas from TG3 mice. We observed aberrant expression of only Grm1. Although we did not detect its expression in normal mouse melanocytes, Grm1 was ectopically expressed in the melanomas from TG3 mice. To confirm the involvement of Grm1 in melanocytic neoplasia, we created an additional transgenic line with Grm1 expression driven by the dopachrome tautomerase promoter. Similar to the original TG3, the Tg(Grm1)EPv line was susceptible to melanoma. In contrast to human melanoma, these transgenic mice had a generalized hyperproliferation of melanocytes with limited transformation to fully malignant metastasis. We detected expression of GRM1 in a number of human melanoma biopsies and cell lines but not in benign nevi and melanocytes. This study provides compelling evidence for the importance of metabotropic glutamate signaling in melanocytic neoplasia.

Hypothetical model of hydrophilic lubrication in synovial joints

This paper presents a new insight into the mechanism of biolubrication of articulating mammalian joints that includes the function of surface-active phospholipids (SAPLs). SAPLs can be adsorbed on surface of cartilage membranes as a hydrophobic monolayer (H-phobic-M Madel or Hills' Model) or as a newly proposed hydrophilic bilayer (H-philic-B Model). With respect to the synovial joint's frictionless work, three processes are identified namely: monolayer/bilayer phospholipids binding to cartilage with lubricin interaction; influence of induced-pressure on interaction of hyaluronan with phospholipids; and biolubrication arising from two gliding articular hydrophilic surfaces acting as reverse micelle. Lubricin is considered to play critical role as a supplier of phospholipids, which overlay the articular surface of articular cartilage. Hyaluronic acid is considered to play a critical mediating role in the interaction between the hydrophilic part of phospholipids, the articular surface and water (hydration) in facilitating the lubrication process. Tivo models of frictionless lubrication processes, namely hydrophobic (H-phobic-M Model) and our conceptual hydrophilic (H-philic-B Model), are compared. © Institution of Engineers Australia, 2008.

The influence of sensitivity to reward and punishment, propensity for sensation seeking, depression, and anxiety on the risky behaviour of novice drivers : a path model

Young novice drivers are significantly more likely to be killed or injured in car crashes than older, experienced drivers. Graduated driver licensing (GDL), which allows the novice to gain driving experience under less-risky circumstances, has resulted in reduced crash incidence; however, the driver's psychological traits are ignored. This paper explores the relationships between gender, age, anxiety, depression, sensitivity to reward and punishment, sensation-seeking propensity, and risky driving. Participants were 761 young drivers aged 17–24 (M= 19.00, SD= 1.56) with a Provisional (intermediate) driver's licence who completed an online survey comprising socio-demographic questions, the Impulsive Sensation Seeking Scale, Kessler's Psychological Distress Scale, the Sensitivity to Punishment and Sensitivity to Reward Questionnaire, and the Behaviour of Young Novice Drivers Scale. Path analysis revealed depression, reward sensitivity, and sensation-seeking propensity predicted the self-reported risky behaviour of the young novice drivers. Gender was a moderator; and the anxiety level of female drivers also influenced their risky driving. Interventions do not directly consider the role of rewards and sensation seeking, or the young person's mental health. An approach that does take these variables into account may contribute to improved road safety outcomes for both young and older road users.

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.

SAP webflow made configurable: unifying workflow templates into a configurable model

To facilitate the implementation of workflows, enterprise and workflow system vendors typically provide workflow templates for their software. Each of these templates depicts a variant of how the software supports a certain business process, allowing the user to save the effort of creating models and links to system components from scratch by selecting and activating the appropriate template. A combination of the strengths from different templates is however only achievable by manually adapting the templates which is cumbersome. We therefore suggest in this paper to combine different workflow templates into a single configurable workflow template. Using the workflow modeling language of SAP’s WebFlow engine, we show how such a configurable workflow modeling language can be created by identifying the configurable elements in the original language. Requirements imposed on configurations inhibit invalid configurations. Based on a default configuration such configurable templates can be used as easy as the traditional templates. The suggested approach is also applicable to other workflow modeling languages

A continuous time model for election timing

We consider a continuous time model for election timing in a Majoritarian Parliamentary System where the government maintains a constitutional right to call an early election. Our model is based on the two-party-preferred data that measure the popularity of the government and the opposition over time. We describe the poll process by a Stochastic Differential Equation (SDE) and use a martingale approach to derive a Partial Differential Equation (PDE) for the government’s expected remaining life in office. A comparison is made between a three-year and a four-year maximum term and we also provide the exercise boundary for calling an election. Impacts on changes in parameters in the SDE, the probability of winning the election and maximum terms on the call exercise boundaries are discussed and analysed. An application of our model to the Australian Federal Election for House of Representatives is also given.

A mathematical model for genetic regulation of the lactose operon

In this paper we construct a mathematical model for the genetic regulatory network of the lactose operon. This mathematical model contains transcription and translation of the lactose permease (LacY) and a reporter gene GFP. The probability of transcription of LacY is determined by 14 binding states out of all 50 possible binding states of the lactose operon based on the quasi-steady-state assumption for the binding reactions, while we calculate the probability of transcription for the reporter gene GFP based on 5 binding states out of 19 possible binding states because the binding site O2 is missing for this reporter gene. We have tested different mechanisms for the transport of thio-methylgalactoside (TMG) and the effect of different Hill coefficients on the simulated LacY expression levels. Using this mathematical model we have realized one of the experimental results with different LacY concentrations, which are induced by different concentrations of TMG.