Face Representations via Tensorfaces of Various Complexities

Neurons selective for faces exist in humans and monkeys. However, characteristics of face cell receptive fields are poorly understood. In this theoretical study, we explore the effects of complexity, defined as algorithmic information (Kolmogorov complexity) and logical depth, on possible ways that face cells may be organized. We use tensor decompositions to decompose faces into a set of components, called tensorfaces, and their associated weights, which can be interpreted as model face cells and their firing rates. These tensorfaces form a high-dimensional representation space in which each tensorface forms an axis of the space. A distinctive feature of the decomposition algorithm is the ability to specify tensorface complexity. We found that low-complexity tensorfaces have blob-like appearances crudely approximating faces, while high-complexity tensorfaces appear clearly face-like. Low-complexity tensorfaces require a larger population to reach a criterion face reconstruction error than medium- or high-complexity tensorfaces, and thus are inefficient by that criterion. Low-complexity tensorfaces, however, generalize better when representing statistically novel faces, which are faces falling beyond the distribution of face description parameters found in the tensorface training set. The degree to which face representations are parts based or global forms a continuum as a function of tensorface complexity, with low and medium tensorfaces being more parts based. Given the computational load imposed in creating high-complexity face cells (in the form of algorithmic information and logical depth) and in the absence of a compelling advantage to using high-complexity cells, we suggest face representations consist of a mixture of low- and medium-complexity face cells.

Welding Technologies Used in Assembling Recuperator of Microturbine System

Compact recuperator affects the thermal efficiency of the microturbine greatly and has been widely used in the microturbine system. Generally, a recuperator consists of a plurality of thin primary sheets, many relatively thick spacer bars and guide strips. The main steps of manufacturing a recuperator are joining a pair of sheets into a cell to form a high pressure air passage and sealing the cells together to form a high temperature gas passage. The key techniques are reducing the distortion of the sheets during jointing and keeping airproof performance of each passage. In this study, the special welding moulds and the corresponding assemble technologies for the split and annular recuperator are presented. Three problems during manufacturing a recuperator are solved: (1) Deformation of the cell during welding is effectively avoided by using the step-back welding method. (2) Modified primary sheet is presented. When making recuperators with this sheet, Resistance seam welding method can be adopted, and the number of spacer bars is significantly reduced. (3) With a special mould, an involute-curved-face cell is obtained synchronously with the welding process when making an annular recuperator. The welding methods introduced in this paper are devoted to recuperator manufacturing.

The role of the ‘face-cell’ area in the discrimination and recognition of faces by monkeys

Cortical neurons that are selectively sensitive to faces, parts of faces and particular facial expressions are concentrated in the banks and floor of the superior temporal sulcus in macaque monkeys. Their existence has prompted suggestions that it is damage to such a region in the human brain that leads to prosopagnosia: the inability to recognize faces or to discriminate between faces. This was tested by removing the face-cell area in a group of monkeys. The animals learned to discriminate between pictures of faces or inanimate objects, to select the odd face from a group, to inspect a face then select the matching face from a pair of faces after a variable delay, to discriminate between novel and familiar faces, and to identify specific faces. Removing the face-cell area produced no or little impairment which in the latter case was not specific for faces. In contrast, several prosopagnosic patients were impaired at several of these tasks. The animals were less able than before to discern the angle of regard in pictures of faces, suggesting that this area of the brain may be concerned with the perception of facial expression and bearing, which are important social signals in primates.