73,440 research outputs found
Learning fast, accurate, and stable closures of a kinetic theory of an active fluid
Important classes of active matter systems can be modeled using kinetic
theories. However, kinetic theories can be high dimensional and challenging to
simulate. Reduced-order representations based on tracking only low-order
moments of the kinetic model serve as an efficient alternative, but typically
require closure assumptions to model unrepresented higher-order moments. In
this study, we present a learning framework based on neural networks that
exploit rotational symmetries in the closure terms to learn accurate closure
models directly from kinetic simulations. The data-driven closures demonstrate
excellent a-priori predictions comparable to the state-of-the-art Bingham
closure. We provide a systematic comparison between different neural network
architectures and demonstrate that nonlocal effects can be safely ignored to
model the closure terms. We develop an active learning strategy that enables
accurate prediction of the closure terms across the entire parameter space
using a single neural network without the need for retraining. We also propose
a data-efficient training procedure based on time-stepping constraints and a
differentiable pseudo-spectral solver, which enables the learning of stable
closures suitable for a-posteriori inference. The coarse-grained simulations
equipped with data-driven closure models faithfully reproduce the mean velocity
statistics, scalar order parameters, and velocity power spectra observed in
simulations of the kinetic theory. Our differentiable framework also
facilitates the estimation of parameters in coarse-grained descriptions
conditioned on data
The Jones polynomial: quantum algorithms and applications in quantum complexity theory
We analyze relationships between quantum computation and a family of
generalizations of the Jones polynomial. Extending recent work by Aharonov et
al., we give efficient quantum circuits for implementing the unitary
Jones-Wenzl representations of the braid group. We use these to provide new
quantum algorithms for approximately evaluating a family of specializations of
the HOMFLYPT two-variable polynomial of trace closures of braids. We also give
algorithms for approximating the Jones polynomial of a general class of
closures of braids at roots of unity. Next we provide a self-contained proof of
a result of Freedman et al. that any quantum computation can be replaced by an
additive approximation of the Jones polynomial, evaluated at almost any
primitive root of unity. Our proof encodes two-qubit unitaries into the
rectangular representation of the eight-strand braid group. We then give
QCMA-complete and PSPACE-complete problems which are based on braids. We
conclude with direct proofs that evaluating the Jones polynomial of the plat
closure at most primitive roots of unity is a #P-hard problem, while learning
its most significant bit is PP-hard, circumventing the usual route through the
Tutte polynomial and graph coloring.Comment: 34 pages. Substantial revision. Increased emphasis on HOMFLYPT,
greatly simplified arguments and improved organizatio
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Revisiting Numerical Pattern Mining with Formal Concept Analysis
In this paper, we investigate the problem of mining numerical data in the
framework of Formal Concept Analysis. The usual way is to use a scaling
procedure --transforming numerical attributes into binary ones-- leading either
to a loss of information or of efficiency, in particular w.r.t. the volume of
extracted patterns. By contrast, we propose to directly work on numerical data
in a more precise and efficient way, and we prove it. For that, the notions of
closed patterns, generators and equivalent classes are revisited in the
numerical context. Moreover, two original algorithms are proposed and used in
an evaluation involving real-world data, showing the predominance of the
present approach
Symblicit algorithms for optimal strategy synthesis in monotonic Markov decision processes
When treating Markov decision processes (MDPs) with large state spaces, using
explicit representations quickly becomes unfeasible. Lately, Wimmer et al. have
proposed a so-called symblicit algorithm for the synthesis of optimal
strategies in MDPs, in the quantitative setting of expected mean-payoff. This
algorithm, based on the strategy iteration algorithm of Howard and Veinott,
efficiently combines symbolic and explicit data structures, and uses binary
decision diagrams as symbolic representation. The aim of this paper is to show
that the new data structure of pseudo-antichains (an extension of antichains)
provides another interesting alternative, especially for the class of monotonic
MDPs. We design efficient pseudo-antichain based symblicit algorithms (with
open source implementations) for two quantitative settings: the expected
mean-payoff and the stochastic shortest path. For two practical applications
coming from automated planning and LTL synthesis, we report promising
experimental results w.r.t. both the run time and the memory consumption.Comment: In Proceedings SYNT 2014, arXiv:1407.493
Quantum Knitting
We analyze the connections between the mathematical theory of knots and
quantum physics by addressing a number of algorithmic questions related to both
knots and braid groups.
Knots can be distinguished by means of `knot invariants', among which the
Jones polynomial plays a prominent role, since it can be associated with
observables in topological quantum field theory.
Although the problem of computing the Jones polynomial is intractable in the
framework of classical complexity theory, it has been recently recognized that
a quantum computer is capable of approximating it in an efficient way. The
quantum algorithms discussed here represent a breakthrough for quantum
computation, since approximating the Jones polynomial is actually a `universal
problem', namely the hardest problem that a quantum computer can efficiently
handle.Comment: 29 pages, 5 figures; to appear in Laser Journa
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