126,407 research outputs found

    Extremal Aging For Trap Models

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    In the seminal work [5], Ben Arous and \v{C}ern\'y give a general characterization of aging for trap models in terms of α\alpha-stable subordinators with α∈(0,1)\alpha \in (0,1). Some of the important examples that fall into this universality class are Random Hopping Time (RHT) dynamics of Random Energy Model (REM) and pp-spin models observed on exponential time scales. In this paper, we explain a different aging mechanism in terms of {\it extremal processes} that can be seen as the extension of α\alpha-stable aging to the case α=0\alpha=0. We apply this mechanism to the RHT dynamics of the REM for a wide range of temperature and time scales. The other examples that exhibit extremal aging include the Sherrington Kirkpatrick (SK) model and pp-spin models [6, 9], and biased random walk on critical Galton-Watson trees conditioned to survive [11]

    Biasing MCTS with Features for General Games

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    This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games. This is unlikely to match the potential raw playing strength of DNNs, but has advantages in terms of generality, interpretability and resources (time and hardware) required for training. Features describing local patterns are used as inputs. The features are formulated in such a way that they are easily interpretable and applicable to a wide range of general games, and might encode simple local strategies. We gradually create new features during the same self-play training process used to learn feature weights. We evaluate the playing strength of an MCTS player biased by learnt features against a standard upper confidence bounds for trees (UCT) player in multiple different board games, and demonstrate significantly improved playing strength in the majority of them after a small number of self-play training games.Comment: Accepted at IEEE CEC 2019, Special Session on Games. Copyright of final version held by IEE

    Forest Inventories: Discrepancies and Uncertainties

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    Credits for sequestered carbon augment forests’ already considerable value as natural habitat and as producers of timber and biomass, making their accurate inventory more critical than ever before. This article examines discrepancies in inventories of forest attributes and their sources in four variables: area, timber volume per area, biomass per timber volume, and carbon concentration. Documented discrepancies range up to a multibillion-ton difference in the global stock of carbon in trees. Because the variables are multiplied together to estimate an attribute like carbon stock, more precise measurement of the most certain variable improves accuracy little, and a 10 percent error in biomass per timber levers a discrepancy as much as a mistake in millions of hectares. More precise measurements of, say, accessible stands cannot remedy inaccuracies from biased sampling of regional forests. The discrepancies and uncertainties documented here underscore the obligation to improve monitoring of global forests.forest monitoring, Forest Identity, forest carbon, remote sensing

    Nonparametric Methods in Astronomy: Think, Regress, Observe -- Pick Any Three

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    Telescopes are much more expensive than astronomers, so it is essential to minimize required sample sizes by using the most data-efficient statistical methods possible. However, the most commonly used model-independent techniques for finding the relationship between two variables in astronomy are flawed. In the worst case they can lead without warning to subtly yet catastrophically wrong results, and even in the best case they require more data than necessary. Unfortunately, there is no single best technique for nonparametric regression. Instead, we provide a guide for how astronomers can choose the best method for their specific problem and provide a python library with both wrappers for the most useful existing algorithms and implementations of two new algorithms developed here.Comment: 19 pages, PAS
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