Class library ranlip for multivariate nonuniform random variate generation

This paper describes generation of nonuniform random variates from Lipschitz-continuous densities using acceptance/rejection, and the class library _ranlip which implements this method. It is assumed that the required distribution has Lipschitz-continuous density, which is either given analytically or as a black box. The algorithm builds a piecewise constant upper approximation to the density (the hat function), using a large number of its values and subdivision of the domain into hyperrectangles. The class library _ranlip provides very competitive preprocessing and generation times, and yields small rejection constant, which is a measure of efficiency of the generation step. It exhibits good performance for up to five variables, and provides the user with a black box nonuniform random variate generator for a large class of distributions, in particular, multimodal distributions. It will be valuable for researchers who frequently face the task of sampling from unusual distributions, for which specialized random variate generators are not available.

Parameter optimization of kernel-based one-class classifier on imbalance learning

Compared with conventional two-class learning schemes, one-class classification simply uses a single class in the classifier training phase. Applying one-class classification to learn from unbalanced data set is regarded as the recognition based learning and has shown to have the potential of achieving better performance. Similar to twoclass learning, parameter selection is a significant issue, especially when the classifier is sensitive to the parameters. For one-class learning scheme with the kernel function, such as one-class Support Vector Machine and Support Vector Data Description, besides the parameters involved in the kernel, there is another one-class specific parameter: the rejection rate v. In this paper, we proposed a general framework to involve the majority class in solving the parameter selection problem. In this framework, we first use the minority target class for training in the one-class classification stage; then we use both minority and majority class for estimating the generalization performance of the constructed classifier. This generalization performance is set as the optimization criteria. We employed the Grid search and Experiment Design search to attain various parameter settings. Experiments on UCI and Reuters text data show that the parameter optimized one-class classifiers outperform all the standard one-class learning schemes we examined.

"As long as it doesn't spill over into class": harms arising from students` alcohol use, and the role of policy in reducing them

While there is considerable evidence about the prevalence of student alcohol use, little empirical work has examined the range and level of exposure to alcohol-related risks facing student populations, and the views of key stakeholders about these. This study was conducted at a large multi-campus university in order to gauge the scale and severity of students’ alcohol-related problems, and ways in which these may be mitigated. Student perspectives on campus based policy making with respect to alcohol were also canvassed. This study utilised a range of evaluative instruments, including standardised questionnaire protocols, structured interviews and focus groups. Data gained from students showed a large level of exposure to alcohol-related harm, and staff informants reported student harms such as drink-driving, interpersonal aggression, social nuisance, inadequate security, sexually risky behaviour, and physical malaise. As a group, students seem receptive to campus-based policies that have a harm reduction focus, but are less supportive of institutionalised measures aimed at the student body. Given the divergence of views about the harms arising from student drinking, and a general repudiation of institutional policy measures which may lessen these, the development of harm-reducing policy on alcohol remains challenging.

A new class of proton-conducting ionic plastic crystals based on organic cations and dihydrogen phosphate

Choline dihydrogen phosphate ([N_1.1.1.2OH]DHP) and 1-butyl-3-methylimidazolium dihydrogen phosphate ([C₄mim]DHP) were synthesized as a new class of proton-conducting ionic plastic crystals. Both [N_1.1.1.2OH]DHP and [C₄mim]DHP showed solid–solid phase transition(s) and showed a final entropy of fusion lower than 20 J K⁻¹ mol⁻¹ which is consistent with Timmerman’s criterion for molecular plastic crystals. The ionic conductivity of [N_1.1.1.2OH]DHP was in the range of 10⁻⁶ S cm⁻¹–10⁻³ S cm⁻¹ in the plastic crystalline phase. On the other hand, the ionic conductivity of [C₄mim]DHP showed about 10⁻⁵ S cm⁻¹ in the plastic crystalline phase. [N_1.1.1.2OH]DHP showed one order of magnitude higher ionic conductivity than [C₄mim]DHP in the temperature range where the plastic phase is stable.

Scandium-doped AlN 1D hexagonal nanoprisms : a class of room-temperature ferromagnetic materials

Pretty vacancy: The formation energy of Al vacancies in aluminum nitride is decreased by doping with nonmagnetic scandium ions. These vacancies are shown to be the cause of the room-temperature ferromagnetism in the resulting 1D hexagonal nanoprisms of AlN:Sc, a result that is confirmed by first-principles calculations. The doping approach provides a new route to dilute magnetic semiconductor materials.