558 research outputs found

    Differential KO-theory: constructions, computations, and applications

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    We provide a systematic and detailed treatment of differential refinements of KO-theory. We explain how various flavors capture geometric aspects in different but related ways, highlighting the utility of each. While general axiomatics exist, no explicit constructions seem to have appeared before. This fills a gap in the literature in which K-theory is usually worked out leaving KO-theory essentially untouched, with only scattered partial information in print. We compare to the complex case, highlighting which constructions follow analogously and which are much more subtle. We construct a pushforward and differential refinements of genera, leading to a Riemann-Roch theorem for KO^\widehat{\rm KO}-theory. We also construct the corresponding Atiyah-Hirzebruch spectral sequence (AHSS) and explicitly identify the differentials, including ones which mix geometric and topological data. This allows us to completely characterize the image of the Pontrjagin character. Then we illustrate with examples and applications, including higher tangential structures, Adams operations, and a differential Wu formula.Comment: 105 pages, very minor changes, comments welcom

    Primary operations in differential cohomology

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    We characterize primary operations in differential cohomology via stacks, and illustrate by differentially refining Steenrod squares and Steenrod powers explicitly. This requires a delicate interplay between integral, rational, and mod p cohomology, as well as cohomology with U(1) coefficients and differential forms. Along the way we develop computational techniques in differential cohomology, including a K\"unneth decomposition, that should also be useful in their own right, and point to applications to higher geometry and mathematical physics.Comment: 36 pages, minor correction

    GPU Based Path Integral Control with Learned Dynamics

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    We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path integral form of the optimal control. The resulting algorithm runs in a receding-horizon fashion in realtime, and is subject to no restrictive assumptions about costs, constraints, or dynamics. A simple change to the path integral control formulation allows the algorithm to take model uncertainty into account during planning, and we demonstrate its performance on a quadrotor navigation task. In addition to this novel adaptation of path integral control, this is the first time that a receding-horizon implementation of iterative path integral control has been run on a real system.Comment: 6 pages, NIPS 2014 - Autonomously Learning Robots Worksho

    Deformation classes of invertible field theories and the Freed--Hopkins conjecture

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    We prove a conjecture of Freed and Hopkins, which relates deformation classes of reflection positive, invertible, dd-dimensional extended field theories with fixed symmetry type to a certain generalized cohomology of a Thom spectrum. Along the way, we establish several results, including the construction of a smooth variant of the Brown--Comenetz dual of the sphere spectrum and a calculation of the "deformation type" of the extended geometric bordism category.Comment: 47 page