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We present a mathematical programming-based method for model predictive control of cyber-physical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of a receding horizon control framework. We prove correctness of our algorithms, and present experimental results for controller synthesis for building energy and climate control.

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System-level design issues become critical as implementation technology evolves toward increasingly complex integrated circuits and the time-to-market pressure continues relentlessly. To cope with these issues, new methodologies that emphasize re-use at all levels of abstraction are a "must", and this is a major focus of our work in the Gigascale Silicon Research Center. We present some important concepts for system design that are likely to provide at least some of the gains in productivity postulated above. In particular, we focus on a method that separates parts of the design process and makes them nearly independent so that complexity could be mastered. In this domain, architecture-function co-design and communication-based design are introduced and motivated. Platforms are essential elements of this design paradigm. We define system platforms and we argue about their use and relevance. Then we present an application of the design methodology to the design of wireless systems. Finally, we present a new approach to platform-based design called modern embedded systems, compilers, architectures and languages, based on highly concurrent and software-programmable architectures and associated design tools.

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We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is trained without any data of target domains and tested on the unseen target domains. To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability. First, we propose to randomize the synthetic images with the styles of real images in terms of visual appearances using auxiliary datasets, in order to effectively learn domain-invariant representations. Second, we further enforce pyramid consistency across different "stylized" images and within an image, in order to learn domaininvariant and scale-invariant features, respectively. Extensive experiments are conducted on the generalization from GTA and SYNTHIA to Cityscapes, BDDS and Mapillary; and our method achieves superior results over the stateof-the-art techniques. Remarkably, our generalization results are on par with or even better than those obtained by state-of-the-art simulation-to-real domain adaptation methods, which access the target domain data at training time.