19,503 research outputs found
Minimal inference from incomplete 2x2-tables
Estimates based on 2x2 tables of frequencies are widely used in statistical
applications. However, in many cases these tables are incomplete in the sense
that the data required to compute the frequencies for a subset of the cells
defining the table are unavailable. Minimal inference addresses those
situations where this incompleteness leads to target parameters for these
tables that are interval, rather than point, identifiable. In particular, we
develop the concept of corroboration as a measure of the statistical evidence
in the observed data that is not based on likelihoods. The corroboration
function identifies the parameter values that are the hardest to refute, i.e.,
those values which, under repeated sampling, remain interval identified. This
enables us to develop a general approach to inference from incomplete 2x2
tables when the additional assumptions required to support a likelihood-based
approach cannot be sustained based on the data available. This minimal
inference approach then provides a foundation for further analysis that aims at
making sharper inference supported by plausible external beliefs
Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents
Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment
Rent Shifts in American Rental Housing Markets, 2000-2009: Directional Heterogeneity in Distance Decay Patterns
Please note: Abstract uploaded as a pdf document (like a full paper)
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents
Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment
Effects of Neuropeptide Y on Adipocyte Metabolism
Recently, we have shown that neuropeptide Y (NPY) is produced and upregulated in visceral adipose tissue of an early-life programmed rat model of central obesity. Moreover, we have demonstrated that NPY contributes to the pathogenesis of obesity. However, the role of NPY in regulating adipocyte metabolism is poorly understood. The present study examined the effects of NPY on adipocyte metabolism using 3T3-L1 adipocytes. We found that NPY potentiated isoproterenol (P-adrenergic agonist) stimulated lipolysis. This potentiation occurred upstream of adenylyl cyclase, since NPY did not enhance forskolin (direct activator of adenylyl cyclase) stimulated lipolysis. The potentiation was mediated by increased phosphorylation of hormone sensitive lipase. In contrast, NPY did not alter the expression of several key lipolytic and lipogenic enzymes/proteins or glucose uptake. Our results revealed a novel cross talk between the NPY and 3-adrenergic signaling pathways in regulating lipolysis and added a new dimension to the role NPY plays in regulating energy balanc
Autonomous Demand Side Management Based on Energy Consumption Scheduling and Instantaneous Load Billing: An Aggregative Game Approach
In this paper, we investigate a practical demand side management scenario
where the selfish consumers compete to minimize their individual energy cost
through scheduling their future energy consumption profiles. We propose an
instantaneous load billing scheme to effectively convince the consumers to
shift their peak-time consumption and to fairly charge the consumers for their
energy consumption. For the considered DSM scenario, an aggregative game is
first formulated to model the strategic behaviors of the selfish consumers. By
resorting to the variational inequality theory, we analyze the conditions for
the existence and uniqueness of the Nash equilibrium (NE) of the formulated
game. Subsequently, for the scenario where there is a central unit calculating
and sending the real-time aggregated load to all consumers, we develop a one
timescale distributed iterative proximal-point algorithm with provable
convergence to achieve the NE of the formulated game. Finally, considering the
alternative situation where the central unit does not exist, but the consumers
are connected and they would like to share their estimated information with
others, we present a distributed agreement-based algorithm, by which the
consumers can achieve the NE of the formulated game through exchanging
information with their immediate neighbors.Comment: 11 pages, 7 figure
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