Discovering a taste for the unusual: exceptional models for preference mining

Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge.     

Production, Growth and Business Cycles: Technical Appendix

The methods used in our two survey papers on real business cycles (King,Plosser and Rebelo, 1988a,b) are detailed in this document. Our presentationof the basic neoclassical model of growth and business cycles is broken intothree parts. First, we describe the model and its steady state, discussing:the structure of the environment including government policy rules; the natureof optimal individual decisions and the dynamic competitive equilibrium;technical restrictions to insure steady state growth; comparable restrictionson preferences and policy rules; stationary levels and ratios in the steadystate; and the nature of a transformed economy. Second, we detail methods forstudying near steady-state dynamics, considering: the linear approximationapproach; the rational expectations solution algorithm; the nature ofalternative solutions; and the special case of the fixed labor model. Third,we discuss the computation of simulations, moments and impulse responses.The objective of this appendix is to provide a detailed analysis of aneoclassical economy that is sufficiently flexible to permit: (a) exogenoussteady state growth; (b) distorting tax rules of various sorts; and (c) timevarying government spending. Although we do not focus on all of these issuesin the present discussion, other investigations in progress will utilize thisframework. The appendix is divided into three main parts. Part A describes theartificial economy under study and analyses its steady state, Part B developsmethods to study approximate dynamics around the steady state, and Part Cderives a set of formulas for generating population moments. This technicalappendix is designed to serve two functions. First, it develops thetheoretical material in Sections 2 and 3 of the main text in more depths.Second, it serves as a detailed guide to PC-MATLAB programs for computingdynamic equilibria, written by King and Rebelo in the Spring of 1987. Notationin programs and the technical appendix has been detailed as closely asfeasible.     

Wearable music in engaging technologies

We address the relationship between a music performer and her instrument as a possible model for re-thinking wearable technologies. Both musical instruments and textiles invite participation and by engaging with them we intuitively develop a sense of their malleability, resistance and fragility. In the action of touching we not only sense, but more importantly we react. We adjust the nature of our touch according to a particular material’s property. In this paper we draw on musical practice as it suggests attitudes of specificity rather than adaptability. This practice exposes the design of generalised “multi-use” devices, such as the all-in-one electronic organ, as rooted in utilitarian thinking. We argue that this tendency ignores the complexities of musical cultures and thus fails to provide technologies, which provoke creative action rather than aim for the promise of control and efficiency.     

EEG Data Classification for Mental State Analysis Using Wavelet Packet Transform and Gaussian Process Classifier

Stress is one of the most common problems that is faced by a majority of the students. Long-term stress can lead to serious health problems, for example, depression, heart disease, anxiety, and sleep disorder. This paper proposes an efficient stress level detection framework to detect the stress in students using Electroencephalogram (EEG) signals. The framework classifies stress into three levels; low stress, medium stress and high stress. In this experiment, EEG data is collected from six subjects by placing two electrodes in the prefrontal region. During each trial, the subject solves arithmetic questions under some time pressure. The EEG data is collected while the subject solves the question. The collected data is pre-processed using a band-pass filter to remove artefacts and appropriate features are extracted through the wavelet packet transform and PyEEG module. ReliefF feature selection method is used to select the best features for classification. The selected feature set is classified into three categories using Gaussian Classification. The proposed framework effectively classifies the level of stress with an accuracy of 94%.

     

Asymmetric septate uterus with unilateral menstrual retention. A rare uterine malformation

A case is reported of a patient with an asymmetric septate uterus and menstrual retention in the blind cavity localized on the right side of the septum. A review is made of the literature about this rare malformation first described by Robert, is made.     

A coupled analysis of viscoplastic deformation and heat transfer—I: Theoretical considerations

A thermodynamic theory of deformation using internal variables is adapted to rigid viscoplastic materials, and the formulations for finite element discretization are given specifically for metal forming problems. A scheme of numerical integration of heat-balance equations that leads to the coupled analysis of deformation and heat transfer is presented.     

Staff Detection with Stable Paths

The preservation of musical works produced in the past requires their digitalization and transformation into a machine-readable format. The processing of handwritten musical scores by computers remains far from ideal. One of the fundamental stages to carry out this task is the staff line detection. We investigate a general-purpose, knowledge-free method for the automatic detection of music staff lines based on a stable path approach. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.