Resistivity testing of piled foundations

SIGLEAvailable from British Library Document Supply Centre- DSC:D37120/81 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Mushroom Poisoning in Rhodesia.

A CAJM article on mushroom poisoning in Rhodesia (now Zimbabwe.

Mathematical Analysis of Convolutional Neural Networks

In this thesis, the main topic is convolution as a mathematical operation and Convolutional Neural Networks (CNN’s). While convolution is classically defined as a function, it can also be defined as an operator from Lp(R) to itself for 1 ≤ p ≤ 2 where Tw(f ) = f ∗ w given some w ∈ L1(R). CNN’s use convolution in its convolutional layers. Defining a neural network to be the composition of layer maps, we find that the neural network is, by necessity, Lipschitz. While CNN’s can be very powerful for image classification, slight changes to an image can completely fool the network. By augmenting our training data with these modifications, the network’s ability to correctly classify images with these modifications significantly increases

The Kernel Density Integral Transformation

Feature preprocessing continues to play a critical role when applying machine learning and statistical methods to tabular data. In this paper, we propose the use of the kernel density integral transformation as a feature preprocessing step. Our approach subsumes the two leading feature preprocessing methods as limiting cases: linear min-max scaling and quantile transformation. We demonstrate that, without hyperparameter tuning, the kernel density integral transformation can be used as a simple drop-in replacement for either method, offering protection from the weaknesses of each. Alternatively, with tuning of a single continuous hyperparameter, we frequently outperform both of these methods. Finally, we show that the kernel density transformation can be profitably applied to statistical data analysis, particularly in correlation analysis and univariate clustering.Comment: Published in Transactions on Machine Learning Research (10/2023

Counselor Influences on College Decision Making

Although numerous studies have been devoted to understanding the role of the counselor in schools, few studies have been conducted to understand the specific influence that counselors have on girls throughout their college admissions process (Bryan, Farmer-Hinton, Rawls, & Woods, 2017; Bryan, Holcomb-McCoy, Moore-Thomas, & Day-Vines, 2009; Bryan, Moore‐Thomas, Day‐Vines, & Holcomb‐McCoy, 2011). The purpose of this study was to examine the interactions between girls and their high school college admissions counselors and the resources and programmatic offerings of the college counseling office that girls experience. Perna’s (2006) proposed conceptual model of college student choice served as the conceptual framework for this narrative inquiry study of five female undergraduate students at a small, liberal arts college in the southeastern United States. The qualitative data was collected through semi-structured interviews. The data was examined using the lens of the study’s three research questions and six themes emerged from the open coding process: outside factors and influences, college-level factors, positive counselor interactions, ineffective counselor, resource for college admissions, and girls’ own college admissions journey. While each girl came from a different high school background, they each expressed a need for emotional support and resources from their college counselor. This study found that the availability of resources did not impact a girl’s connection to the college counselor. Rather, a higher level of emotional support was correlated with greater satisfaction with the counselor, regardless of resources available