Lindsey Kuper

TL;DR: LVars is presented, a new model for deterministic-by-construction parallel programming that generalizes existing single-assignment models to allow multiple assignments that are monotonically increasing with respect to a user-specified lattice.

TL;DR: It is proved that in a language where communication takes place through these extended LVars, programs are at worst quasi-deterministic: on every run, they either produce the same answer or raise an error.

TL;DR: The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability, so work on mitigating this difficulty is given, by developing scalable verification techniques and identifying design choices that result in deep learning systems that are more amenable to verification.

TL;DR: In this article, the authors present a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with the autonomous vehicle network, a set of multiple processors including a general purpose processor and a general-purpose graphics processor.

TL;DR: LVish provides a Par monad for parallel computation that encapsulates determinism-preserving effects while allowing a more flexible form of communication between parallel tasks than previous guaranteed-deterministic models provided.