The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.

By adjusting the design of the ISA and enabling circuit timing-sensitive optimizations in a compiler, we can more effectively exploit timing speculation. While there has been growing interest in systems that leverage circuit-level timing speculation to improve the performance and power-efficiency of processors, most of the innovation has been at the microarchitectural level. We make the observation that some code sequences place greater demand on circuit timing deadlines than others. Furthermore, by selectively replacing these codes with instruction sequences which are semantically equivalent but reduce activity on timing critical circuit paths, we can trigger fewer timing errors and hence reduce recovery costs.