This paper describes an open-source simulator for cyberphysical systems called CyPhySim that is based on Ptolemy II. This simulator supports classical (Runge-Kutta) and quantized-state simulation of ordinary differential equations, modal models (hybrid systems), discrete-event models, the Functional Mockup Interface (FMI) for model-exchange and co-simulation, discrete-time (periodic) systems, and algebraic loop solvers. CyPhySim provides a graphical editor, an XML file syntax for models, and an open API for programmatic construction of models. It includes an innovation called "smooth tokens," which allow for a blend of numerical and symbolic computation, and for certain kinds of system models, dramatically reducing the computation required for simulation.

Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proofof-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.

Abstract. We present a new and more efficient technique for computing the route that maximizes the probability of on-time arrival in stochastic networks, also known as the path-based stochastic on-time arrival (SOTA) problem. Our primary contribution is a pathfinding algorithm that uses the solution to the policy-based SOTA problem-which is of pseudopolynomial-time complexity in the time budget of the journey-as a search heuristic for the optimal path. In particular, we show that this heuristic can be exceptionally efficient in practice, effectively making it possible to solve the path-based SOTA problem as quickly as the policybased SOTA problem. Our secondary contribution is the extension of policy-based preprocessing to path-based preprocessing for the SOTA problem. In the process, we also introduce Arc-Potentials, a more efficient generalization of Stochastic Arc-Flags that can be used for both policy-and path-based SOTA. After developing the pathfinding and preprocessing algorithms, we evaluate their performance on two different real-world networks. To the best of our knowledge, these techniques provide the most efficient computation strategy for the path-based SOTA problem for general probability distributions, both with and without preprocessing.

Programmable Logic Controllers (PLCs) are an established platform, widely used throughout industrial automation but poorly understood among researchers. This paper gives an overview of the state of the practice, explaining why this settled technology persists throughout industry and presenting a critical analysis of the strengths and weaknesses of the dominant programming styles for today's PLC-based automation systems. We describe the software execution patterns that are standardized loosely in IEC 61131-3. We identify opportunities for improvements that would enable increasingly complex industrial automation applications while strengthening safety and reliability. Specifically, we propose deterministic, distributed programming models that embrace explicit timing, event-triggered computation, and improved security.