Biases from incorrect reflectance convolution

DIFFUSE REFLECTANCE MEASUREMENTS FROM DIFFERENT SURFACES

Relative diffuse reflection measurements from the original and ground surfaces using a He-Ne laser are reported. The intensity measurements for the metallic, transparent dielectric, matte plastic surfaces and colored papers are presented. Our results indicate that among metallic surfaces tested, aluminum has the highest diffuse reflectance; while the original stainless steel surface shows the l...

Incorrect predictions, not incorrect statistics!

Page 1 of 1 (page number not for citation purposes) In a recent study on nurses’ ability to predict volume status after subarachnoid haemorrhage, we found very low predictive values [1]. In a letter to the editor, Dane [2] argued that an error in statistics ‘makes any reasonable conclusions impossible’. Because Dr Dane was the statistical reviewer of our manuscript, both the authors and the edi...

Recovery from Incorrect knowledge in Soar

Incorrect knowledge can be a problem for any intelligent system. Soar is a proposal for the underlying architecture that supports intelligence. It has a single representation of long-term memory and a single learning mechanism called chunking. This paper investigates the problem of recovery from incorrect knowledge in Soar. Recovery is problematic in Soar because of the simplicity of chunking: ...

Combining learning from instruction with recovery from incorrect knowledge

diffuse reflectance measurements from different surfaces

relative diffuse reflection measurements from the original and ground surfaces using a he-ne laser are reported. the intensity measurements for the metallic, transparent dielectric, matte plastic surfaces and colored papers are presented. our results indicate that among metallic surfaces tested, aluminum has the highest diffuse reflectance; while the original stainless steel surface shows the l...