838 resultados para Teaching of Visual Arts. Teachers training. Experience.Seguir todas as regras


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Background: left ventricular wall motion on 2d echo (2de) is usually scored visually. we sought to examine the determinants of visually assessed wall motion scoring on 2de by comparison with myocardial thickening quantified on MRI. Methods: using a 16 segment model, we studied 287 segments in 30 patients aged 61+/ -11 years (6 female), with ischaemic LV dysfunction (defined by at least 2 segments dysfunctional on 2de). 2de was performed in 5 views and wall motion scores (WMS) assigned: 1 (normal) 103 segments, 2 (hypokinetic) 93 segments, 3 (akinetic) 87 segments. MRI was used to measure end systolic wall thickness (ESWT), end diastolic wall thickness (EDWT) and percentage systolic wall thickening (SWT%) in the plane of the 2de and to assess WMS in the same planes visually. No patient had a clinical ischemic event between the tests. Results: visual assessment of wall motion by 2de and MRI showed moderate agreement (kappa = 0.425). Resting 2de wall motion correlated significantly (p

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This study examined the discrimination of word-final stop contrasts (/p/-/t/, /p/-/k/, /t/-/k/) in English and Thai by 12 listeners who speak Vietnamese as their first language (L1). Vietnamese shares specific phonetic realization of stops with Thai, i.e., unreleased final stop and differs from English which allows both released and unreleased final stops. These 12 native Vietnamese (NV) listeners’ discrimination accuracy was compared to that of the two listener groups (Australian English (AE), native Thai (NT)) tested in previous studies. The NV group was less accurate than the native group in discriminating both English and Thai stop contrasts. In particular, for the Thai /t/-/k/ contrast, they were significantly less accurate than the AE listeners. The present findings suggest that experience with specific (i.e., unreleased) and native phonetic realization of sounds may be essential in accurate discrimination of final stop contrasts. The effect of L1 dialect on cross-language speech perception is discussed.

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Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.