Development of a Translator from LLVM to ACL2

In our current work a library of formally verified software components is to be created, and assembled, using the Low-Level Virtual Machine (LLVM) intermediate form, into subsystems whose top-level assurance relies on the assurance of the individual components. We have thus undertaken a project to build a translator from LLVM to the applicative subset of Common Lisp accepted by the ACL2 theorem prover. Our translator produces executable ACL2 formal models, allowing us to both prove theorems about the translated models as well as validate those models by testing. The resulting models can be translated and certified without user intervention, even for code with loops, thanks to the use of the def::ung macro which allows us to defer the question of termination. Initial measurements of concrete execution for translated LLVM functions indicate that performance is nearly 2.4 million LLVM instructions per second on a typical laptop computer. In this paper we overview the translation process and illustrate the translators capabilities by way of a concrete example, including both a functional correctness theorem as well as a validation test for that example.

Mechanical Measurements of the ALMA Prototype Antennas

The specifications of the Atacama Large Millimeter Array (ALMA) have placed stringent requirements on the mechanical performance of its antennas. As part of the evaluation process of the VertexRSI and Alcatel EIE Consortium (AEC) ALMA prototype antennas, measurements of the path length, thermal, and azimuth bearing performance were made under a variety of weather conditions and observing modes. The results of mechanical measurements, reported here, are compared to the antenna specifications.

Dynamics and robustness of familiarity memory

When one is presented with an item or a face, one can sometimes have a sense of recognition without being able to recall where or when one has encountered it before. This sense of recognition is known as familiarity. Following previous computational models of familiarity memory we investigate the dynamical properties of familiarity discrimination, and contrast two different familiarity discriminators: one based on the energy of the neural network, and the other based on the time derivative of the energy. We show how the familiarity signal decays after a stimulus is presented, and examine the robustness of the familiarity discriminator in the presence of random fluctuations in neural activity. For both discriminators we establish, via a combined method of signal-to-noise ratio and mean field analysis, how the maximum number of successfully discriminated stimuli depends on the noise level.