Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

/pdf/what-is-emotional-intelligence-4cdho6tdum.pdf

What is emotional intelligence

Mother Nature knows best--How engineered organizations of the future will resemble natural-born systems.

Persuasive Technology: Using Computers to Change What We Think and Do

In 2000, the Cohn-Kanade (CK) database was released for the purpose of promoting research into automatically detecting individual facial expressions. Since then, the CK database has become one of the most widely used test-beds for algorithm development and evaluation. During this period, three limitations have become apparent: 1) While AU codes are well validated, emotion labels are not, as they refer to what was requested rather than what was actually performed, 2) The lack of a common performance metric against which to evaluate new algorithms, and 3) Standard protocols for common databases have not emerged. As a consequence, the CK database has been used for both AU and emotion detection (even though labels for the latter have not been validated), comparison with benchmark algorithms is missing, and use of random subsets of the original database makes meta-analyses difficult. To address these and other concerns, we present the Extended Cohn-Kanade (CK+) database. The number of sequences is increased by 22% and the number of subjects by 27%. The target expression for each sequence is fully FACS coded and emotion labels have been revised and validated. In addition to this, non-posed sequences for several types of smiles and their associated metadata have been added. We present baseline results using Active Appearance Models (AAMs) and a linear support vector machine (SVM) classifier using a leave-one-out subject cross-validation for both AU and emotion detection for the posed data. The emotion and AU labels, along with the extended image data and tracked landmarks will be made available July 2010.

/pdf/the-extended-cohn-kanade-dataset-ck-a-complete-dataset-for-3ui317eamh.pdf

The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression

Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain.

A major factor hindering the deployment of a fully functional automatic facial expression detection system is the lack of representative data. A solution to this is to narrow the context of the target application, so enough data is available to build robust models so high performance can be gained. Automatic pain detection from a patient's face represents one such application. To facilitate this work, researchers at McMaster University and University of Northern British Columbia captured video of participant's faces (who were suffering from shoulder pain) while they were performing a series of active and passive range-of-motion tests to their affected and unaffected limbs on two separate occasions. Each frame of this data was AU coded by certified FACS coders, and self-report and observer measures at the sequence level were taken as well. This database is called the UNBC-McMaster Shoulder Pain Expression Archive Database. To promote and facilitate research into pain and augment current datasets, we have publicly made available a portion of this database which includes: 1) 200 video sequences containing spontaneous facial expressions, 2) 48,398 FACS coded frames, 3) associated pain frame-by-frame scores and sequence-level self-report and observer measures, and 4) 66-point AAM landmarks. This paper documents this data distribution in addition to describing baseline results of our AAM/SVM system. This data will be available for distribution in March 2011.

Painful data: The UNBC-McMaster shoulder pain expression archive database

A number of facial actions have been found to be associated with pain. However, the consistency with which these actions occur during pain of different types has not been examined. This paper focuses on the consistency of facial expressions during pain induced by several modalities of nociceptive stimulation. Forty-one subjects were exposed to pain induced by electric shock, cold, pressure and ischemia. Facial actions during painful and pain-free periods were measured with the Facial Action Coding System. Four actions showed evidence of a consistent association with pain, increasing in likelihood, intensity or duration across all modalities: brow lowering, tightening and closing of the eye lids and nose wrinkling/upper lip raising. Factor analyses suggested that the facial actions reflected a general factor with a reasonably consistent pattern across modalities which could be combined into a sensitive single measure of pain expression. The findings suggest that the 4 actions identified carry the bulk of facial information about pain. They also provide evidence for the existence of a universal facial expression of pain. Implications of the findings for the measurement of pain expression are discussed.

The consistency of facial expressions of pain: a comparison across modalities

The present study examined psychometric properties of facial expressions of pain. A diverse sample of 129 people suffering from shoulder pain underwent a battery of active and passive range-of-motion tests to their affected and unaffected limbs. The same tests were repeated on a second occasion. Participants rated the maximum pain induced by each test on three self-report scales. Facial actions were measured with the Facial Action Coding System. Several facial actions discriminated painful from non-painful movements; however, brow-lowering, orbit tightening, levator contraction and eye closing appeared to constitute a distinct, unitary action. An index of pain expression based on these actions demonstrated test-retest reliability and concurrent validity with self-reports of pain. The findings support the concept of a core pain expression with desirable psychometric properties. They are also consistent with the suggestion of individual differences in pain expressiveness. Reasons for varying reports of relations between pain expression and self-reports in previous studies are discussed.

Kenneth M. Prkachin

Papers

Viewing facial expressions of pain engages cortical areas involved in the direct experience of pain.

Painful data: The UNBC-McMaster shoulder pain expression archive database

The consistency of facial expressions of pain: a comparison across modalities

The structure, reliability and validity of pain expression: evidence from patients with shoulder pain.

The painful face - Pain expression recognition using active appearance models