79,801 research outputs found
Health and Individual and Community Characteristics: A Research Protocol
Population health policies tend to target communities to enhance the health status of individuals. However, little is known about the effects of community or socio-economic environmental variables on individual health characteristics and behaviour patterns. This paper outlines procedures designed to examine the contribution of context in producing health.health policy
Context Trees: Augmenting Geospatial Trajectories with Context
Exposing latent knowledge in geospatial trajectories has the potential to
provide a better understanding of the movements of individuals and groups.
Motivated by such a desire, this work presents the context tree, a new
hierarchical data structure that summarises the context behind user actions in
a single model. We propose a method for context tree construction that augments
geospatial trajectories with land usage data to identify such contexts. Through
evaluation of the construction method and analysis of the properties of
generated context trees, we demonstrate the foundation for understanding and
modelling behaviour afforded. Summarising user contexts into a single data
structure gives easy access to information that would otherwise remain latent,
providing the basis for better understanding and predicting the actions and
behaviours of individuals and groups. Finally, we also present a method for
pruning context trees, for use in applications where it is desirable to reduce
the size of the tree while retaining useful information
Fighting with the Sparsity of Synonymy Dictionaries
Graph-based synset induction methods, such as MaxMax and Watset, induce
synsets by performing a global clustering of a synonymy graph. However, such
methods are sensitive to the structure of the input synonymy graph: sparseness
of the input dictionary can substantially reduce the quality of the extracted
synsets. In this paper, we propose two different approaches designed to
alleviate the incompleteness of the input dictionaries. The first one performs
a pre-processing of the graph by adding missing edges, while the second one
performs a post-processing by merging similar synset clusters. We evaluate
these approaches on two datasets for the Russian language and discuss their
impact on the performance of synset induction methods. Finally, we perform an
extensive error analysis of each approach and discuss prominent alternative
methods for coping with the problem of the sparsity of the synonymy
dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social
Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science
(LNCS
Investigation into the use of satellite remote sensing data products as part of a multi-modal marine environmental monitoring network
In this paper it is investigated how conventional in-situ sensor networks can be complemented by the satellite data streams available through numerous platforms orbiting the earth and the combined analyses products available through services such as MyOcean. Despite the numerous benefits associated with the use of satellite remote sensing data products, there are a number of limitations with their use in coastal zones. Here the ability of these data sources to provide contextual awareness, redundancy and increased efficiency to an in-situ sensor network is investigated. The potential use of a variety of chlorophyll and SST data products as additional data sources in the SmartBay monitoring network in Galway Bay, Ireland is analysed. The ultimate goal is to investigate the ability of these products to create a smarter marine monitoring network with increased efficiency. Overall it was found that while care needs to be taken in choosing these products, there was extremely promising performance from a number of these products that would be suitable in the context of a number of applications especially in relation to SST. It was more difficult to come to conclusive results for the chlorophyll analysis
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A shared neural ensemble links distinct contextual memories encoded close in time.
Recent studies suggest that a shared neural ensemble may link distinct memories encoded close in time. According to the memory allocation hypothesis, learning triggers a temporary increase in neuronal excitability that biases the representation of a subsequent memory to the neuronal ensemble encoding the first memory, such that recall of one memory increases the likelihood of recalling the other memory. Here we show in mice that the overlap between the hippocampal CA1 ensembles activated by two distinct contexts acquired within a day is higher than when they are separated by a week. Several findings indicate that this overlap of neuronal ensembles links two contextual memories. First, fear paired with one context is transferred to a neutral context when the two contexts are acquired within a day but not across a week. Second, the first memory strengthens the second memory within a day but not across a week. Older mice, known to have lower CA1 excitability, do not show the overlap between ensembles, the transfer of fear between contexts, or the strengthening of the second memory. Finally, in aged mice, increasing cellular excitability and activating a common ensemble of CA1 neurons during two distinct context exposures rescued the deficit in linking memories. Taken together, these findings demonstrate that contextual memories encoded close in time are linked by directing storage into overlapping ensembles. Alteration of these processes by ageing could affect the temporal structure of memories, thus impairing efficient recall of related information
Distilling Abstract Machines (Long Version)
It is well-known that many environment-based abstract machines can be seen as
strategies in lambda calculi with explicit substitutions (ES). Recently,
graphical syntaxes and linear logic led to the linear substitution calculus
(LSC), a new approach to ES that is halfway between big-step calculi and
traditional calculi with ES. This paper studies the relationship between the
LSC and environment-based abstract machines. While traditional calculi with ES
simulate abstract machines, the LSC rather distills them: some transitions are
simulated while others vanish, as they map to a notion of structural
congruence. The distillation process unveils that abstract machines in fact
implement weak linear head reduction, a notion of evaluation having a central
role in the theory of linear logic. We show that such a pattern applies
uniformly in call-by-name, call-by-value, and call-by-need, catching many
machines in the literature. We start by distilling the KAM, the CEK, and the
ZINC, and then provide simplified versions of the SECD, the lazy KAM, and
Sestoft's machine. Along the way we also introduce some new machines with
global environments. Moreover, we show that distillation preserves the time
complexity of the executions, i.e. the LSC is a complexity-preserving
abstraction of abstract machines.Comment: 63 page
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