Letter of recomendation for Howard Pearson

This is a letter of recommendation from the Southern California Music Company commending Howard for his good work

Certificate of promotion for Howard Pearson

This is a certificate of promotion from the eighth grade issued to Howard Pearson from the LA School department

Clopper-Pearson Bounds from HEP Data Cuts

For the measurement of $N_s$ signals in $N$ events rigorous confidence bounds on the true signal probability $p_{\rm exact}$ were established in a classical paper by Clopper and Pearson [Biometrica 26, 404 (1934)]. Here, their bounds are generalized to the HEP situation where cuts on the data tag signals with probability $P_s$ and background data with likelihood $P_b<P_s$. The Fortran program which, on input of $P_s$, $P_b$, the number of tagged data $N^Y$ and the total number of data $N$, returns the requested confidence bounds as well as bounds on the entire cumulative signal distribution function, is available on the web. In particular, the method is of interest in connection with the statistical analysis part of the ongoing Higgs search at the LEP experiments

Pennington to Cecil Pearson, August 26, 1947

Pennington writing to Cecil Pearson about her dissertation and student loans, wishing that Pacific College could offer more and suggests places where a student could find such funding.https://digitalcommons.georgefox.edu/levi_pennington/1191/thumbnail.jp

Modules identification in gene positive networks of hepatocellular carcinoma using pearson agglomerative method and Pearson cohesion coupling modularity

In this study, a gene positive network is proposed based on a weighted undirected graph, where the weight represents the positive correlation of the genes. A Pearson agglomerative clustering algorithm is employed to build a clustering tree, where dotted lines cut the tree from bottom to top leading to a number of subsets of the modules. In order to achieve better module partitions, the Pearson correlation coefficient modularity is addressed to seek optimal module decomposition by selecting an optimal threshold value. For the liver cancer gene network under study, we obtain a strong threshold value at 0.67302, and a very strong correlation threshold at 0.80086. On the basis of these threshold values, fourteen strong modules and thirteen very strong modules are obtained respectively. A certain degree of correspondence between the two types of modules is addressed as well. Finally, the biological significance of the two types of modules is analyzed and explained, which shows that these modules are closely related to the proliferation and metastasis of liver cancer. This discovery of the new modules may provide new clues and ideas for liver cancer treatment

Foreground detection enhancement using Pearson correlation filtering

Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech