Secrecy and Intelligence: Introduction

The catalyst for this special issue of Secrecy and Society stems from a workshop titled “Secrecy and Intelligence: Opening the Black Box” at North Carolina State University, April, 2016. This workshop brought together interested scholars, intelligence practitioners, and civil society members from the United States and Europe to discuss how different facets of secrecy and other practices shape the production of knowledge in intelligence work. This dialogue aimed to be reflective on how the closed social worlds of intelligence shape what intelligence actors and intelligence analysts, who include those within the intelligence establishment and those on the outside, know about security threats and the practice of intelligence. The papers in this special issue reflect conversations that occurred during and after the workshop

Fast Bunch Integrators at Fermilab During Run II

The Fast Bunch Integrator is a bunch intensity monitor designed around the measurements made from Resistive Wall Current Monitors. During the Run II period these were used in both Tevatron and Main Injector for single and multiple bunch intensity measurements. This paper presents an overview of the design and use of these systems during this period.Comment: 6 p

An NMF-Based Building Block for Interpretable Neural Networks With Continual Learning

Existing learning methods often struggle to balance interpretability and predictive performance. While models like nearest neighbors and non-negative matrix factorization (NMF) offer high interpretability, their predictive performance on supervised learning tasks is often limited. In contrast, neural networks based on the multi-layer perceptron (MLP) support the modular construction of expressive architectures and tend to have better recognition accuracy but are often regarded as black boxes in terms of interpretability. Our approach aims to strike a better balance between these two aspects through the use of a building block based on NMF that incorporates supervised neural network training methods to achieve high predictive performance while retaining the desirable interpretability properties of NMF. We evaluate our Predictive Factorized Coupling (PFC) block on small datasets and show that it achieves competitive predictive performance with MLPs while also offering improved interpretability. We demonstrate the benefits of this approach in various scenarios, such as continual learning, training on non-i.i.d. data, and knowledge removal after training. Additionally, we show examples of using the PFC block to build more expressive architectures, including a fully-connected residual network as well as a factorized recurrent neural network (RNN) that performs competitively with vanilla RNNs while providing improved interpretability. The PFC block uses an iterative inference algorithm that converges to a fixed point, making it possible to trade off accuracy vs computation after training but also currently preventing its use as a general MLP replacement in some scenarios such as training on very large datasets. We provide source code at https://github.com/bkvogel/pfcComment: 42 pages, 13 figure

Transmission and performance of taiko in Edo Bayashi, Hachijo, and modern kumi-daiko styles

This document is a study of the history, instruments, transmission method and performance practices of three types of Japanese taiko drumming. Included are transcriptions of representative pieces, several of which have never been written down in Western notation, as taiko is generally an orally transmitted musical form. Field research was done throughout the summers of 2007 and 2008 with renowned taiko artist Kenny Endo at the Taiko Center of the Pacific in Honolulu, Hawaii