Satellite-aided mobile communications limited operational test in the trucking industry

An experiment with NASA's ATS-6 satellite, that demonstrates the practicality of satellite-aided land mobile communications is described. Satellite communications equipment for the experiment was designed so that it would be no more expensive, when mass produced, than conventional two-way mobile radio equipment. It embodied the operational features and convenience of present day mobile radios. Vehicle antennas 75 cm tall and 2 cm in diameter provided good commercial quality signals to and from trucks and jeeps. Operational applicability and usage data were gathered by installing the radio equipment in five long-haul tractor-trailer trucks and two Air Force search and rescue jeeps. Channel occupancy rates are reported. Air Force personnel found the satellite radio system extremely valuable in their search and rescue mission during maneuvers and actual rescue operations. Propagation data is subjectively analyzed and over 4 hours of random data is categorized and graded as to signal quality on a second by second basis. Trends in different topographic regions are reported. An overall communications reliability of 93% was observed despite low satellite elevation angles ranging from 9 to 24 degrees

Neural Machine Translation for Code Generation

Neural machine translation (NMT) methods developed for natural language processing have been shown to be highly successful in automating translation from one natural language to another. Recently, these NMT methods have been adapted to the generation of program code. In NMT for code generation, the task is to generate output source code that satisfies constraints expressed in the input. In the literature, a variety of different input scenarios have been explored, including generating code based on natural language description, lower-level representations such as binary or assembly (neural decompilation), partial representations of source code (code completion and repair), and source code in another language (code translation). In this paper we survey the NMT for code generation literature, cataloging the variety of methods that have been explored according to input and output representations, model architectures, optimization techniques used, data sets, and evaluation methods. We discuss the limitations of existing methods and future research directionsComment: 33 pages, 1 figur

Automating the Formal Verification of Software

Formally verified correctness is one of the most desirable properties of software systems. Despite great progress made toward verification via interactive proof assistants, such as Coq and Isabelle/HOL, such verification remains one of the most effort-intensive (and often prohibitively difficult) software development activities. Recent work has created tools that automatically synthesize proofs either through reasoning using precomputed facts or using machine learning to model proofs and then perform biased search through the proof space. However, models in existing tools fail to capture the richness present in proofs, such as the information the programmer has access to when writing proofs and the natural language contained within variable names. Furthermore, these prior models do not make use of variations in the learning process and advances in large language models. In this dissertation, I develop tools to improve proof synthesis and to enable fully automating more verification. I first present TacTok, a proof-synthesis tool that models proofs using both the partial proof written thus far and the semantics of the proof state. I then present Diva, a proof-synthesis tool that controls the learning process to produce a diverse set of models and, due to the unique nature of proof synthesis (the existence of the theorem prover, an oracle that infallibly judges a proof’s correctness), efficiently combines these models to improve the overall proving power. I then present Passport, a proof-synthesis tool that systematically explores different ways of encoding identifiers in proofs to improve synthesis. Finally, I present Baldur, a proof-synthesis tool that uses transformer-based pretrained large language models fine-tuned on proofs to generate and repair whole proofs at once, rather than one step at a time. This dissertation contributes new ideas for improving automated proof synthesis and empirically demonstrates that the improvement is significant on large benchmarks consisting of open-source software projects

Framework programmable platform for the advanced software development workstation. Integration mechanism design document

The Framework Programmable Software Development Platform (FPP) is a project aimed at combining effective tool and data integration mechanisms with a model of the software development process in an intelligent integrated software development environment. Guided by this model, this system development framework will take advantage of an integrated operating environment to automate effectively the management of the software development process so that costly mistakes during the development phase can be eliminated

Lecture Notes on Network Information Theory

These lecture notes have been converted to a book titled Network Information Theory published recently by Cambridge University Press. This book provides a significantly expanded exposition of the material in the lecture notes as well as problems and bibliographic notes at the end of each chapter. The authors are currently preparing a set of slides based on the book that will be posted in the second half of 2012. More information about the book can be found at http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201