12,079 research outputs found
Robust detail-preserving signal extraction
We discuss robust filtering procedures for signal extraction from noisy time series. Particular attention is paid to the preservation of relevant signal details like abrupt shifts. moving averages and running medians are widely used but have shortcomings when large spikes (outliers) or trends occur. Modifications like modified trimmed means and linear median hybrid filters combine advantages of both approaches, but they do not completely overcome the difficulties. Better solutions can be based on robust regression techniques, which even work in real time because of increased computational power and faster algorithms. Reviewing previous work we present filters for robust signal extraction and discuss their merits for preserving trends, abrupt shifts and local extremes as well as for the removal of outliers. --
Analysis-of-marginal-Tail-Means (ATM): a robust method for discrete black-box optimization
We present a new method, called Analysis-of-marginal-Tail-Means (ATM), for
effective robust optimization of discrete black-box problems. ATM has important
applications to many real-world engineering problems (e.g., manufacturing
optimization, product design, molecular engineering), where the objective to
optimize is black-box and expensive, and the design space is inherently
discrete. One weakness of existing methods is that they are not robust: these
methods perform well under certain assumptions, but yield poor results when
such assumptions (which are difficult to verify in black-box problems) are
violated. ATM addresses this via the use of marginal tail means for
optimization, which combines both rank-based and model-based methods. The
trade-off between rank- and model-based optimization is tuned by first
identifying important main effects and interactions, then finding a good
compromise which best exploits additive structure. By adaptively tuning this
trade-off from data, ATM provides improved robust optimization over existing
methods, particularly in problems with (i) a large number of factors, (ii)
unordered factors, or (iii) experimental noise. We demonstrate the
effectiveness of ATM in simulations and in two real-world engineering problems:
the first on robust parameter design of a circular piston, and the second on
product family design of a thermistor network
Tensor network states in time-bin quantum optics
The current shift in the quantum optics community towards large-size
experiments -- with many modes and photons -- necessitates new classical
simulation techniques that go beyond the usual phase space formulation of
quantum mechanics. To address this pressing demand we formulate linear quantum
optics in the language of tensor network states. As a toy model, we extensively
analyze the quantum and classical correlations of time-bin interference in a
single fiber loop. We then generalize our results to more complex time-bin
quantum setups and identify different classes of architectures for
high-complexity and low-overhead boson sampling experiments
An extensive English language bibliography on graph theory and its applications
Bibliography on graph theory and its application
An evolutionary algorithm with double-level archives for multiobjective optimization
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed
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