360,843 research outputs found
Background matching in the brown shrimp Crangon crangon : adaptive camouflage and behavioural-plasticity
A combination of burrowing behaviour and very efficient background matching makes the brown shrimp Crangon crangon almost invisible to potential predators and preys. This raises questions on how shrimp succeed in concealing themselves in the heterogeneous and dynamic estuarine habitats they inhabit and what type of environmental variables and behavioural factors affect their colour change abilities. Using a series of behavioural experiments, we show that the brown shrimp is capable of repeated fast colour adaptations (20% change in dark pigment cover within one hour) and that its background matching ability is mainly influenced by illumination and sediment colour. Novel insights are provided on the occurrence of non-adaptive (possibly stress) responses to background changes after long-time exposure to a constant background colour or during unfavourable conditions for burying. Shrimp showed high levels of intra- and inter-individual variation, demonstrating a complex balance between behavioural-plasticity and environmental adaptation. As such, the study of crustacean colour changes represents a valuable opportunity to investigate colour adaptations in dynamic habitats and can help us to identify the mayor environmental and behavioural factors influencing the evolution of animal background matching
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201
New Insights into History Matching via Sequential Monte Carlo
The aim of the history matching method is to locate non-implausible regions
of the parameter space of complex deterministic or stochastic models by
matching model outputs with data. It does this via a series of waves where at
each wave an emulator is fitted to a small number of training samples. An
implausibility measure is defined which takes into account the closeness of
simulated and observed outputs as well as emulator uncertainty. As the waves
progress, the emulator becomes more accurate so that training samples are more
concentrated on promising regions of the space and poorer parts of the space
are rejected with more confidence. Whilst history matching has proved to be
useful, existing implementations are not fully automated and some ad-hoc
choices are made during the process, which involves user intervention and is
time consuming. This occurs especially when the non-implausible region becomes
small and it is difficult to sample this space uniformly to generate new
training points. In this article we develop a sequential Monte Carlo (SMC)
algorithm for implementation which is semi-automated. Our novel SMC approach
reveals that the history matching method yields a non-implausible distribution
that can be multi-modal, highly irregular and very difficult to sample
uniformly. Our SMC approach offers a much more reliable sampling of the
non-implausible space, which requires additional computation compared to other
approaches used in the literature
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