935 research outputs found
Tasks for Hong Kong\u27s Economy in the New Era - Shift to a Service-Oriented Economy and the Introduction of a Comprehensive Competition Policy
Feature tracking and geometrical priors counteract illusory non-rigidities from outputs of motion-energy cells
Diffusion of Silicon and Manganese in Liquid Iron. I : Diffusion in Liquid Iron Saturated with Carbon
Diffusion coefficients of silicon and manganese in liquid iron (carbon-saturated) were determined in temperature range between 1, 300°and 1, 600°by the method of so-called semi-infinite medium. Blank values accompanied with the measurement of diffusion in liquid state were examined and the following results were obtained : (i) Diffusion coefficients of silicon in Fe-C (saturated)-Si(1.5%) alloys can be expressed as follows : log D (cm^2sec^) = -3.62 - 0.179 × 10^4/T, activation energy Q = 8.2 kcal/g・atom. (ii) Diffusion coefficients of manganese in Fe-C(saturated)-Mn(2.5%) alloys can be expressed as follows : log D (cm^2sec^) = -3.71 - 0.127 × 10^/ T, activation energy Q = 5.8 kcal/g・atom
Object Rigidity: Competition and cooperation between motion-energy and feature-tracking mechanisms and shape-based priors
Why do moving objects appear rigid when projected retinal images are deformed
nonrigidly? We used rotating rigid objects that can appear rigid or non-rigid
to test whether shape features contribute to rigidity perception. When two
circular rings were rigidly linked at an angle and jointly rotated at moderate
speeds, observers reported that the rings wobbled and were not linked rigidly
but rigid rotation was reported at slow speeds. When gaps, paint or vertices
were added, the rings appeared rigidly rotating even at moderate speeds. At
high speeds, all configurations appeared non-rigid. Salient features thus
contribute to rigidity at slow and moderate speeds, but not at high speeds.
Simulated responses of arrays of motion-energy cells showed that motion flow
vectors are predominantly orthogonal to the contours of the rings, not parallel
to the rotation direction. A convolutional neural network trained to
distinguish flow patterns for wobbling versus rotation, gave a high probability
of wobbling for the motion-energy flows. However, the CNN gave high
probabilities of rotation for motion flows generated by tracking features with
arrays of MT pattern-motion cells and corner detectors. In addition, circular
rings can appear to spin and roll despite the absence of any sensory evidence,
and this illusion is prevented by vertices, gaps, and painted segments, showing
the effects of rotational symmetry and shape. Combining CNN outputs that give
greater weight to motion energy at fast speeds and to feature tracking at slow,
with the shape-based priors for wobbling and rolling, explained rigid and
nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate
how cooperation and competition between different neuronal classes lead to
specific states of visual perception and to transitions between the states.Comment: 36 pages, 11 figures (10 main figures and 1 appendix figure
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