Managing Madness: The ethics of identifying and treating mental illness

This essay analyzes different contemporary models for defining mental illness and offers a new framework that promotes the use of normative values during the clinical diagnostic process. Although ethic centric models for identifying mental illness do currently exist, these accounts are limited. Specifically, these accounts acknowledge the relationship between mental illness labels and implied responsibility in making their argument to support a normative framework, yet do not explain what capacities are necessary for an agent to have full responsibility. Recognizing this shortcoming, this paper provides an enriched model for identifying mental illness by marrying a normative conception of psychiatric dysfunction to a differential model for assigning moral responsibility. To end this paper looks at how this model may be applied in clinical practice by working through a specific case study of Alcohol Dependence Disorder. Ultimately, I conclude that a normative conception of mental illness married to an account of responsibility is a more appropriate and comprehensive model for recognizing and treating psychiatric dysfunction in clinical practice. By carefully balancing normative and pragmatic considerations, physicians can create more effective and just therapeutic regimens that are tailored to individual patients’ circumstances and needs

Stress concentration at fillets, holes, and keyways as found by the plaster-model method

Bibliography: p. 31-32

Situated cognition and the culture of learning

Includes bibliographical references (p. 16-17

A process-oriented language for describing aspects of reading comprehension

Includes bibliographical references (p. 36-38)The research described herein was supported in part by the National Institute of Education under Contract No. MS-NIE-C-400-76-011

On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here

Markerless View Independent Gait Analysis with Self-camera Calibration

We present a new method for viewpoint independent markerless gait analysis. The system uses a single camera, does not require camera calibration and works with a wide range of directions of walking. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and use of marker based technology. Tests on more than 200 video sequences with subjects walking freely along different walking directions have been performed. The obtained results show that markerless gait analysis can be achieved without any knowledge of internal or external camera parameters and that the obtained data that can be used for gait biometrics purposes. The performance of the proposed method is particularly encouraging for its appliance in surveillance scenarios