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This is a report on computer programming undertaken to preprocess the 1960-1970 U. S. census public use samples to increase their accessibility. This includes wholesale recoding into positive integers from 1 to N, with 0 reserved for "not applicable", combining household and person records, sorting records into ten 1/10,000 samples, compacting binary codes to fit on a single reel of tape, and the production of a revised set of formatted tapes. [ABSTRACT FROM AUTHOR]

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.

Adaptation and reaction-time techniques were used to examine the role of different spatial-frequency channels in the perception of local and global structure. Subjects were shown figures consisting of a large C composed of smaller Cs and asked to identify the orientation of either the global C or its local elements. Prior to performing the task subjects were adapted to different spatial frequencies and the effect on subsequent performance was assessed. Two main results were found. First, the adapting frequency that most affected the global task was often lower than that most affecting the local task, suggesting that high and low frequencies independently code the structure of an image. Second, reaction time to global figures was often faster than to local figures at all levels of detectability, again suggesting a role of low-frequency channels in global processing.