The Massie-Kahahawai case of 1931-1932 shook the Territory of Hawai'i to its very core. Thalia Massie, a young Navy wife, alleged that she had been kidnapped and raped by "some Hawaiian boys" in Waik?k?. A few days later, five young men stood accused of her rape. Mishandling of evidence and contradictory testimony led to?a mistrial, but before a second trial could be convened, one of the accused, Horace Ida, was kidnapped and beaten by a group of Navy men and a second, Joseph Kahahawai, lay dead from a gunshot wound. Thalia's husband, Thomas Massie; her mother, Grace Fortescue; and two Navy men were convicted of manslaughter despite witnesses who saw them kidnap Kahahawai and the later dis- covery of Kahahawai's body in Massie's car. Under pressure from Congress and the Navy, territorial governor Lawrence McCully Judd commuted their sentences. After spending only an hour in the governor's office at 'Iolani Palace, the four were set free. Local Story is a close examination of how Native Hawaiians, Asian immigrants, and others responded to challenges posed by the military and federal government during the case's investigation and aftermath. In addition to providing a concise account?of events as they unfolded, the book shows how this historical narrative has been told and retold in later decades to affirm a local identity among descendants of working-class Native Hawaiians, Asians, and others-in fact, this understanding of the term "local" in the islands dates from the Massie-Kahahawai case. The Massie-Kahahawai case revealed racial and sexual tensions in pre-World War II Hawai'i that kept local men and white women apart. And this tension coexisted with the uneasy relationship between federal and military officials and territorial administrators.

This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices' sizes to circuits' performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices' sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit's performances as input features and devices' sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.