An investigation was made into the sensitivity of cells in the macaque superior temporal sulcus (STS) to the sight of different perspective views of the head. This allowed assessment of (a) whether coding was 'viewer-centred' (view specific) or 'object-centred' (view invariant) and (b) whether viewer-centred cells were preferentially tuned to 'characteristic' views of the head. The majority of cells (110) were found to be viewer-centred and exhibited unimodal tuning to one view. 5 cells displayed object-centred coding responding equally to all views of the head. A further 5 cells showed 'mixed' properties, responding to all views of the head but also discriminating between views. 6 out of 56 viewer and object-centred cells exhibited selectivity for face identity or species. Tuning to view varied in sharpness. For most (54/73) cells the angle of perspective rotation reducing response to half maximal was 45-70 degrees but for 19/73 it was greater than 90 degrees. More cells were optimally tuned to characteristic views of the head (the full face or profile) than to other views. Some cells were, however, found tuned to intermediate views throughout the full 360 degree range. This coding of many distinct head views may have a role in the analysis of social signals based on the interpretation of the direction of other individuals' attention.

In this study, an improved and robust one-dimensional human arterial network model is presented. The one-dimensional blood flow equations are solved using the Taylor-locally conservative Galerkin finite element method. The model improvements are carried out by adopting parts of the physical models from different authors to establish an accurate baseline model. The predicted pressure-flow waveforms at various monitoring positions are compared against in vivo measurements from published works. The results obtained show that wave shapes predicted are similar to that of the experimental data and exhibit a good overall agreement with measured waveforms. Finally, computational studies on the influence of an abdominal aortic aneurysm are presented. The presence of aneurysms results in a significant change in the waveforms throughout the network.

A variety of cell types exist in the temporal cortex providing high-level visual descriptions of bodies and their movements. We have investigated the sensitivity of such cells to different viewing conditions to determine the frame(s) of reference utilized in processing. The responses of the majority of cells in the upper bank of the superior temporal sulcus (areas TPO and PGa) found to be sensitive to static and dynamic information about the body were selective for one perspective view (e.g. right profile, reaching right or walking left). These cells can be considered to provide viewer-centred descriptions because they depend on the observer's vantage point. Viewer-centred descriptions could be used in guiding behaviour. They could also be used as an intermediate step for establishing view-independent responses of other cell types which responded to many or all perspective views selectively of the same object (e.g. head) or movement. These cells have the properties of object-centred descriptions, where the object viewed provides the frame of reference for describing the disposition of object parts and movements (e.g. head on top of shoulders, reaching across the body, walking forward ‘following the nose’). For some cells in the lower bank of the superior temporal sulcus (area TEa) the responses to body movements were related to the object or goal of the movements (e.g. reaching for or walking towards a specific place). This goal-centred sensitivity to interaction allowed the cells to be selectively activated in situations where human subjects would attribute causal and intentional relationships.

Machine learning for Non-Destructive Evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This paper demonstrates how an efficient, hybrid finite element and ray-based simulation can be used to train a Convolutional Neural Network (CNN) to characterize real defects. To demonstrate this methodology, an inline-pipe inspection application is considered. This uses four plane wave images from two arrays, and is applied to the characterization of cracks of length 1-5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6 dB drop method, is used as a comparison point. For the 6 dB drop method the average absolute error in length and angle prediction is ±1.1 mm, ±8.6° while the CNN is almost four times more accurate at ±0.29 mm, ±2.9°. To demonstrate the adaptability of the deep-learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed the 6 dB drop method has an average error of ±1.5 mm, ±12° while the CNN has ±0.45 mm, ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing.