20,724 research outputs found
Sharing deep generative representation for perceived image reconstruction from human brain activity
Decoding human brain activities via functional magnetic resonance imaging
(fMRI) has gained increasing attention in recent years. While encouraging
results have been reported in brain states classification tasks, reconstructing
the details of human visual experience still remains difficult. Two main
challenges that hinder the development of effective models are the perplexing
fMRI measurement noise and the high dimensionality of limited data instances.
Existing methods generally suffer from one or both of these issues and yield
dissatisfactory results. In this paper, we tackle this problem by casting the
reconstruction of visual stimulus as the Bayesian inference of missing view in
a multiview latent variable model. Sharing a common latent representation, our
joint generative model of external stimulus and brain response is not only
"deep" in extracting nonlinear features from visual images, but also powerful
in capturing correlations among voxel activities of fMRI recordings. The
nonlinearity and deep structure endow our model with strong representation
ability, while the correlations of voxel activities are critical for
suppressing noise and improving prediction. We devise an efficient variational
Bayesian method to infer the latent variables and the model parameters. To
further improve the reconstruction accuracy, the latent representations of
testing instances are enforced to be close to that of their neighbours from the
training set via posterior regularization. Experiments on three fMRI recording
datasets demonstrate that our approach can more accurately reconstruct visual
stimuli
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The Role of Information in Building Reputation in an Investment/Trust Game
This article analyses the role of information in building reputation in an investment/trust game. The model allows for information asymmetry in a finitely repeated sender-receiver game and solves for sequential equilibrium to show that if there are some trustworthy managers who always disclose their private information and choose to return a fair proportion of the firm's income as dividend to the investor, then a rational manager will mimic such behaviour in an attempt to earn a reputation for being trustworthy. The rational manager will mimic with probability 1 in the early periods of the game. The investor, too, will invest with probability 1 in these periods. However, in the later periods, the rational manager will mimic with a certain probability strictly less than 1. The probability will be such that it will make the investor indifferent between investing and not investing, and he, in turn, will invest with a probability (strictly less than 1) that will make the rational manager indifferent between mimicking and not mimicking; that is, the game will begin with pure-strategy play but will switch to mixed-strategy play. There is one exception, though: when the investor's ex ante beliefs about the manager's trustworthiness are exceptionally high, the game will continue in a pure strategy, and the switch to mixed-strategy play will never occur. Identical results obtain if the manager's choice of whether to share his private information with the investor is replaced by exogenously imposed information sharing. © 2013 Copyright European Accounting Association
Run-time risk management in adaptive ICT systems
We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers
Data Mining in Electronic Commerce
Modern business is rushing toward e-commerce. If the transition is done
properly, it enables better management, new services, lower transaction costs
and better customer relations. Success depends on skilled information
technologists, among whom are statisticians. This paper focuses on some of the
contributions that statisticians are making to help change the business world,
especially through the development and application of data mining methods. This
is a very large area, and the topics we cover are chosen to avoid overlap with
other papers in this special issue, as well as to respect the limitations of
our expertise. Inevitably, electronic commerce has raised and is raising fresh
research problems in a very wide range of statistical areas, and we try to
emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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