  We consider two classes of stream-based computations which admit taking
linear combinations of execution runs: probabilistic sampling and generalized
animation. The dataflow architecture is a natural platform for programming with
streams. The presence of linear combinations allows us to introduce the notion
of almost continuous transformation of dataflow graphs. We introduce a new
approach to higher-order dataflow programming: a dynamic dataflow program is a
stream of dataflow graphs evolving by almost continuous transformations. A
dynamic dataflow program would typically run while it evolves. We introduce
Fluid, an experimental open source system for programming with dataflow graphs
and almost continuous transformations.
