The Threshold Learning Outcome on self-management for the Bachelor of Laws degree : a proposed focus for teaching strategies in the first year law curriculum

The Australian Learning and Teaching Council (ALTC) Discipline Scholars for Law, Professors Sally Kift and Mark Israel, articulated six Threshold Learning Outcomes (TLOs) for the Bachelor of Laws degree as part of the ALTC’s 2010 project on Learning and Teaching Academic Standards. One of these TLOs promotes the learning, teaching and assessment of self-management skills in Australian law schools. This paper explores the concept of self-management and how it can be relevantly applied in the first year of legal education. Recent literature from the United States (US) and Australia provides insights into the types of issues facing law students, as well as potential antidotes to these problems. Based on these findings, I argue that designing a pedagogical framework for the first year law curriculum that promotes students’ connection with their intrinsic interests, values, motivations and purposes will facilitate student success in terms of their personal well-being, ethical dispositions and academic engagement.

Supervised latent dirichlet allocation models for efficient activity representation

Local spatio-temporal features with a Bag-of-visual words model is a popular approach used in human action recognition. Bag-of-features methods suffer from several challenges such as extracting appropriate appearance and motion features from videos, converting extracted features appropriate for classification and designing a suitable classification framework. In this paper we address the problem of efficiently representing the extracted features for classification to improve the overall performance. We introduce two generative supervised topic models, maximum entropy discrimination LDA (MedLDA) and class- specific simplex LDA (css-LDA), to encode the raw features suitable for discriminative SVM based classification. Unsupervised LDA models disconnect topic discovery from the classification task, hence yield poor results compared to the baseline Bag-of-words framework. On the other hand supervised LDA techniques learn the topic structure by considering the class labels and improve the recognition accuracy significantly. MedLDA maximizes likelihood and within class margins using max-margin techniques and yields a sparse highly discriminative topic structure; while in css-LDA separate class specific topics are learned instead of common set of topics across the entire dataset. In our representation first topics are learned and then each video is represented as a topic proportion vector, i.e. it can be comparable to a histogram of topics. Finally SVM classification is done on the learned topic proportion vector. We demonstrate the efficiency of the above two representation techniques through the experiments carried out in two popular datasets. Experimental results demonstrate significantly improved performance compared to the baseline Bag-of-features framework which uses kmeans to construct histogram of words from the feature vectors.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

A modified Density-Based Scanning Algorithm with Noise for spatial travel pattern analysis from Smart Card AFC data

Smart Card Automated Fare Collection (AFC) data has been extensively exploited to understand passenger behavior, passenger segment, trip purpose and improve transit planning through spatial travel pattern analysis. The literature has been evolving from simple to more sophisticated methods such as from aggregated to individual travel pattern analysis, and from stop-to-stop to flexible stop aggregation. However, the issue of high computing complexity has limited these methods in practical applications. This paper proposes a new algorithm named Weighted Stop Density Based Scanning Algorithm with Noise (WS-DBSCAN) based on the classical Density Based Scanning Algorithm with Noise (DBSCAN) algorithm to detect and update the daily changes in travel pattern. WS-DBSCAN converts the classical quadratic computation complexity DBSCAN to a problem of sub-quadratic complexity. The numerical experiment using the real AFC data in South East Queensland, Australia shows that the algorithm costs only 0.45% in computation time compared to the classical DBSCAN, but provides the same clustering results.

Numerical algorithms for time-fractional subdiffusion equation with second-order accuracy

This article aims to fill in the gap of the second-order accurate schemes for the time-fractional subdiffusion equation with unconditional stability. Two fully discrete schemes are first proposed for the time-fractional subdiffusion equation with space discretized by finite element and time discretized by the fractional linear multistep methods. These two methods are unconditionally stable with maximum global convergence order of $O(\tau+h^{r+1})$ in the $L^2$ norm, where $\tau$ and $h$ are the step sizes in time and space, respectively, and $r$ is the degree of the piecewise polynomial space. The average convergence rates for the two methods in time are also investigated, which shows that the average convergence rates of the two methods are $O(\tau^{1.5}+h^{r+1})$. Furthermore, two improved algorithms are constrcted, they are also unconditionally stable and convergent of order $O(\tau^2+h^{r+1})$. Numerical examples are provided to verify the theoretical analysis. The comparisons between the present algorithms and the existing ones are included, which show that our numerical algorithms exhibit better performances than the known ones.

Maximum principle and numerical method for the multi-term time–space Riesz–Caputo fractional differential equations

The maximum principle for the space and time–space fractional partial differential equations is still an open problem. In this paper, we consider a multi-term time–space Riesz–Caputo fractional differential equations over an open bounded domain. A maximum principle for the equation is proved. The uniqueness and continuous dependence of the solution are derived. Using a fractional predictor–corrector method combining the L1 and L2 discrete schemes, we present a numerical method for the specified equation. Two examples are given to illustrate the obtained results.

Variations in proximity as a tool for audience engagement

The tool proximity and ways in which variations in audience-performer proximity can engage audiences of contemporary dance in a different way is discussed. The key aspects and features of the Voyeur, created by the author in 2009, a dance work that tested these theories in action and looked at how specifically changes in the traditional presentation paradigm affected engagement are highlighted.

Terra Preta (Agriculture+Art+Algorithm)

Terra Preta is a site-specific bio-energy project which aims to create a synergy between the public and the pre-existing engineered landscape of Freshkills Park on Staten Island, New York. The project challenges traditional paradigms of public space by proposing a dynamic and ever-changing landscape. The initiative allows the publuc to self-organise the landscape and to engage in 'algorithmic processes' of growth, harvest and space creation.

The Australian Qualifications Framework for bachelor degree in property economics

The Australian Qualifications Framework (AQF) requires every course in Australia to be reviewed and compliant by 2015. This paper compares the difference between AQF level 7 and level 8 and outlines the paradigm shift in course development, improvement and quality assurance. The AQF requires an outcome oriented process which influences the development, monitoring and implementation of AQF courses. Firstly the graduate profile is defined to underscore the direction of the property course development. Required graduate attributes are then defined, together with course learning outcomes. Each unit/subject assessment is then designed to reflect the desired learning outcomes, and then finally the unit/subject content is backfilled. This reverse engineered process will ensure that all students have been taught and assessed on the graduate attributes which will form the graduate profile. Therefore, monitoring the inclusion of learning outcomes on unit/subject level during course restructure and development is crucial to achieve the course learning outcomes. This paper recommends that further evaluation needs to be conducted in the course development phases by involving professional accreditation bodies, industry representatives, students and recent graduates in this course development process. It also discusses challenges for developing an undergraduate property course.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

Different approaches of applying single-objective binary genetic algorithm on the wind farm design

This paper discusses three different ways of applying the single-objective binary genetic algorithm into designing the wind farm. The introduction of different applications is through altering the binary encoding methods in GA codes. The first encoding method is the traditional one with fixed wind turbine positions. The second involves varying the initial positions from results of the first method, and it is achieved by using binary digits to represent the coordination of wind turbine on X or Y axis. The third is the mixing of the first encoding method with another one, which is by adding four more binary digits to represent one of the unavailable plots. The goal of this paper is to demonstrate how the single-objective binary algorithm can be applied and how the wind turbines are distributed under various conditions with best fitness. The main emphasis of discussion is focused on the scenario of wind direction varying from 0° to 45°. Results show that choosing the appropriate position of wind turbines is more significant than choosing the wind turbine numbers, considering that the former has a bigger influence on the whole farm fitness than the latter. And the farm has best performance of fitness values, farm efficiency, and total power with the direction between 20°to 30°.

Electromyography and sonomyography analysis of the tibialis anterior: a cross sectional study

Background Foot dorsiflexion plays an essential role in both controlling balance and human gait. Electromyography (EMG) and sonomyography (SMG) can provide information on several aspects of muscle function. The aim was to establish the relationship between the EMG and SMG variables during isotonic contractions of foot dorsiflexors. Methods Twenty-seven healthy young adults performed the foot dorsiflexion test on a device designed ad hoc. EMG variables were maximum peak and area under the curve. Muscular architecture variables were muscle thickness and pennation angle. Descriptive statistical analysis, inferential analysis and a multivariate linear regression model were carried out. The confidence level was established with a statistically significant p-value of less than 0.05. Results The correlation between EMG variables and SMG variables was r = 0.462 (p < 0.05). The linear regression model to the dependent variable “peak normalized tibialis anterior (TA)” from the independent variables “pennation angle and thickness”, was significant (p = 0.002) with an explained variance of R2 = 0.693 and SEE = 0.16. Conclusions There is a significant relationship and degree of contribution between EMG and SMG variables during isotonic contractions of the TA muscle. Our results suggest that EMG and SMG can be feasible tools for monitoring and assessment of foot dorsiflexors. TA muscle parameterization and assessment is relevant in order to know that increased strength accelerates the recovery of lower limb injuries.

Ability to discriminate between healthy and low back pain sufferers using ultrasound during maximum lumbar extension

Objective To analyze the ability to discriminate between healthy individuals and individuals with chronic nonspecific low back pain (CNLBP) by measuring the relation between patient-reported outcomes and objective clinical outcome measures of the erector spinae (ES) muscles using an ultrasound during maximal isometric lumbar extension. Design Cross-sectional study with screening and diagnostic tests with no blinded comparison. Setting University laboratory. Participants Healthy individuals (n=33) and individuals with CNLBP (n=33). Interventions Each subject performed an isometric lumbar extension. With the variables measured, a discriminate analysis was performed using a value ≥6 in the Roland and Morris disability questionnaire (RMDQ) as the grouping variable. Then, a logistic regression with the functional and architectural variables was performed. A new index was obtained from each subject value input in the discriminate multivariate analysis. Main Outcome Measures Morphologic muscle variables of the ES muscle were measured through ultrasound images. The reliability of the measures was calculated through intraclass correlation coefficients (ICCs). The relation between patient-reported outcomes and objective clinical outcome measures was analyzed using a discriminate function from standardized values of the variables and an analysis of the reliability of the ultrasound measurement. Results The reliability tests show an ICC value >.95 for morphologic and functional variables. The independent variables included in the analysis explained 42% (P=.003) of the dependent variable variance. Conclusions The relation between objective variables (electromyography, thickness, pennation angle) and a subjective variable (RMDQ ≥6) and the capacity of this relation to identify CNLBP within a group of healthy subjects is moderate. These results should be considered by clinicians when treating this type of patient in clinical practice.

Statistically assisted fluid registration algorithm - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.

Tensor-based analysis of genetic influences on brain integrity using DTI in 100 twins

Information from the full diffusion tensor (DT) was used to compute voxel-wise genetic contributions to brain fiber microstructure. First, we designed a new multivariate intraclass correlation formula in the log-Euclidean framework. We then analyzed used the full multivariate structure of the tensor in a multivariate version of a voxel-wise maximum-likelihood structural equation model (SEM) that computes the variance contributions in the DTs from genetic (A), common environmental (C) and unique environmental (E) factors. Our algorithm was tested on DT images from 25 identical and 25 fraternal twin pairs. After linear and fluid registration to a mean template, we computed the intraclass correlation and Falconer's heritability statistic for several scalar DT-derived measures and for the full multivariate tensors. Covariance matrices were found from the DTs, and inputted into SEM. Analyzing the full DT enhanced the detection of A and C effects. This approach should empower imaging genetics studies that use DTI.