4 November 2017:
We made a lot of progress this week, completing 3 big papers. I apologize for becoming pretty badtempered during this time... I was pretty stressed. But now I'm happy, because we have a lot to show the folks coming to our special AMS session tomorrow!
1) Adam has been working on Hamiltonian and Lagrangian mechanics from an "open systems" point of view, and this week he put this paper on ths arXiv:
Abstract. Using the framework of category theory, we formalize the heuristic principles that physicists employ in constructing the Hamiltonians for open classical systems as sums of Hamiltonians of subsystems. First we construct a category where the objects are symplectic manifolds and the morphisms are spans whose legs are surjective Poisson maps. Using a slight variant of Fong's theory of "decorated" cospans, we then decorate the apices of our spans with Hamiltonians. This gives a category where morphisms are open classical systems, and composition allows us to build these systems from smaller pieces.
He also gets a functor from a category of Lagrangian open systems to this category of Hamiltonian systems.
2) Kenny and I have been continuing Blake and Brendan's work on open Markov processes, bringing 2morphisms into the game. We put this on the arXiv:
Abstract. Coarsegraining is a standard method of extracting a simple Markov process from a more complicated one by identifying states. Here we extend coarsegraining to open Markov processes. An "open" Markov process is one where probability can flow in or out of certain states called "inputs" and "outputs". One can build up an ordinary Markov process from smaller open pieces in two basic ways: composition, where we identify the outputs of one open Markov process with the inputs of another, and tensoring, where we set two open Markov processes side by side. In previous work, Fong, Pollard and the first author showed that these constructions make open Markov processes into the morphisms of a symmetric monoidal category. Here we go further by constructing a symmetric monoidal double category where the 2morphisms are ways of coarsegraining open Markov processes. We also extend the already known "blackboxing" functor from the category of open Markov processes to our double category. Blackboxing sends any open Markov process to the linear relation between input and output data that holds in steady states, including nonequilibrium steady states where there is a nonzero flow of probability through the process. To extend blackboxing to a functor between double categories, we need to prove that blackboxing is compatible with coarsegraining.
3) Our project with DARPA has finally given birth to a paper! I hope this is just the first; it starts laying down the theoretical groundwork for designing networked systems. John is here now and we're coming up with a bunch of new ideas:

John Baez, John Foley, Joseph Moeller and Blake Pollard, Network models.
Abstract. Networks can be combined in many ways, such as overlaying one on top of another or setting two side by side. We introduce "network models" to encode these ways of combining networks. Different network models describe different kinds of networks. We show that each network model gives rise to an operad, whose operations are ways of assembling a network of the given kind from smaller parts. Such operads, and their algebras, can serve as tools for designing networks. Technically, a network model is a lax symmetric monoidal functor from the free symmetric monoidal category on some set to Cat, and the construction of the corresponding operad proceeds via a symmetric monoidal version of the Grothendieck construction.
I blogged about this last one here:
See you at our special session at 9 am in Room 268 of the HUB on Saturday! Registration starts around 7:30 on the 3rd floor of the same building! Anyone who wants to should join us for dinner Saturday and Sunday night.