The Future of Civil War History

In March 2013, hundreds of academics, preservationists, consultants, historical interpreters, museum professionals, living historians, students, K-12 teachers, and new media specialists gathered in Gettysburg, Pennsylvania to assess the state and potential future of the study of the American Civil War. The essays in this special issue build on the themes of that conference: embracing the democratic and civic potential of historical thinking; reaffirming the power of place and the importance of specific, focused stories; integrating military, political, social, cultural, and gender history; and encouraging collaboration among historians working in different settings. Our three guest editors offer their own thoughts about the state and potential future of Civil War history. [excerpt

Multimedia retrieval in MultiMatch: The impact of speech transcript errors on search behaviour

This study discusses the findings of an evaluation study on the performance of a multimedia multimodal information access sub-system (MIAS), incorporating automatic speech recognition technology (ASR) to automatically transcribe the speech content of video soundtracks. The study’s results indicate that an information-rich but minimalist graphical interface is preferred. It was also discovered that users tend to have a misplaced confidence in the accuracy of ASR-generated speech transcripts, thus they are not inclined to conduct a systematic auditory inspection (their usual search behaviour) of a video’s soundtrack if the query term does not appear in the transcript. In order to alert the user to the possibility that a search term may be incorrectly recognised as some other word, a matching algorithm is proposed that searches for word sequences of similar phonemic structure to the query term

Foreign Attachment Power Constrained-An End to Quasi In Rem Jurisdiction?

Pennsylvania\u27s foreign attachment procedures were held to be an unconstitutional violation of due process by the United States Court of Appeals for the Third Circuit. This note analyzes the evolving concept of due process in its relation to summary seizures and places the instant decision within that framework. The author attempts to evaluate the court\u27s attack of the attachment statute\u27s constitutionality based on a balancing test for procedural due process as well as the concurring opinion\u27s contention that a minimum contacts test should replace quasi in rem jurisdiction

Alien Registration- Carmichael, James R. (Baldwin, Cumberland County)

https://digitalmaine.com/alien_docs/32883/thumbnail.jp

Application of Conventional Feedforward and Deep Neural Networks to Power Distribution System State Estimation and State Forecasting

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the autocorrelation of the dataset being predicted. The performance of MLPs will be presented in terms of root-mean-square error (RMSE) between actual and predicted voltage magnitude and voltage phase angles and training execution time for distribution system state estimation (DSSE). The performance of CNNs, and LSTMs will be presented in terms of RMSE between actual and predicted real power demand and execution time when performing distribution system state forecasting (DSSF). Additionally, Bayesian Optimization with Gaussian Processes are used to optimize MLPs for regression. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting respectively

Foreign Attachment Power Constrained-An End to Quasi In Rem Jurisdiction?

Pennsylvania\u27s foreign attachment procedures were held to be an unconstitutional violation of due process by the United States Court of Appeals for the Third Circuit. This note analyzes the evolving concept of due process in its relation to summary seizures and places the instant decision within that framework. The author attempts to evaluate the court\u27s attack of the attachment statute\u27s constitutionality based on a balancing test for procedural due process as well as the concurring opinion\u27s contention that a minimum contacts test should replace quasi in rem jurisdiction

E-Commerce and Equivalence: Defining the Proper Scope of Internet Patents--Foreword

The diverse expression of views provided in the following papers provides a rich foundation for consideration of the issues surrounding the scope of Internet-type patents. On behalf of the Symposium writers and sponsors we invite you to continue consideration of the legal rules and policy implications surrounding this interesting and important subject

Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks (LSTMs) to mitigate the aforementioned challenges in power distribution systems. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the correlation of the dataset being predicted. The performance of MLPS, CNNs, and LSTMs to perform state estimation and state forecasting will be presented in terms of average root-mean square error (RMSE) and training execution time. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting