126,407 research outputs found
Extremal Aging For Trap Models
In the seminal work [5], Ben Arous and \v{C}ern\'y give a general
characterization of aging for trap models in terms of -stable
subordinators with . Some of the important examples that fall
into this universality class are Random Hopping Time (RHT) dynamics of Random
Energy Model (REM) and -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 -stable aging to the
case . 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 -spin
models [6, 9], and biased random walk on critical Galton-Watson trees
conditioned to survive [11]
Biasing MCTS with Features for General Games
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
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
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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