6,826 research outputs found
Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007
This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p
Fairplay or Greed: Mandating University Responsibility Toward Student Inventors
Over twenty years have passed since the enactment of The Patent and Trademark Law Amendments Act (Bayh-Dole Act) and universities continue to struggle with their technology transfer infrastructures. Lost in that struggle are those who could be considered the backbone of university research: the students. Graduate and undergraduate students remain baffled by the patent assignment and technology transfer processes within their various institutions. Efforts should be undertaken by universities to clarify the student\u27s position in the creative process
Seeing things
This paper is concerned with the problem of attaching meaningful symbols to aspects of the visible environment in machine and biological vision. It begins with a review of some of the arguments commonly used to support either the 'symbolic' or the 'behaviourist' approach to vision. Having explored these avenues without arriving at a satisfactory conclusion, we then present a novel argument, which starts from the question : given a functional description of a vision system, when could it be said to support a symbolic interpretation? We argue that to attach symbols to a system, its behaviour must exhibit certain well defined regularities in its response to its visual input and these are best described in terms of invariance and equivariance to transformations which act in the world and induce corresponding changes of the vision system state. This approach is illustrated with a brief exploration of the problem of identifying and acquiring visual representations having these symmetry properties, which also highlights the advantages of using an 'active' model of vision
Can parametric statistical methods be trusted for fMRI based group studies?
The most widely used task fMRI analyses use parametric methods that depend on
a variety of assumptions. While individual aspects of these fMRI models have
been evaluated, they have not been evaluated in a comprehensive manner with
empirical data. In this work, a total of 2 million random task fMRI group
analyses have been performed using resting state fMRI data, to compute
empirical familywise error rates for the software packages SPM, FSL and AFNI,
as well as a standard non-parametric permutation method. While there is some
variation, for a nominal familywise error rate of 5% the parametric statistical
methods are shown to be conservative for voxel-wise inference and invalid for
cluster-wise inference; in particular, cluster size inference with a cluster
defining threshold of p = 0.01 generates familywise error rates up to 60%. We
conduct a number of follow up analyses and investigations that suggest the
cause of the invalid cluster inferences is spatial auto correlation functions
that do not follow the assumed Gaussian shape. By comparison, the
non-parametric permutation test, which is based on a small number of
assumptions, is found to produce valid results for voxel as well as cluster
wise inference. Using real task data, we compare the results between one
parametric method and the permutation test, and find stark differences in the
conclusions drawn between the two using cluster inference. These findings speak
to the need of validating the statistical methods being used in the
neuroimaging field
Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
Methodological research rarely generates a broad interest, yet our work on
the validity of cluster inference methods for functional magnetic resonance
imaging (fMRI) created intense discussion on both the minutia of our approach
and its implications for the discipline. In the present work, we take on
various critiques of our work and further explore the limitations of our
original work. We address issues about the particular event-related designs we
used, considering multiple event types and randomisation of events between
subjects. We consider the lack of validity found with one-sample permutation
(sign flipping) tests, investigating a number of approaches to improve the
false positive control of this widely used procedure. We found that the
combination of a two-sided test and cleaning the data using ICA FIX resulted in
nominal false positive rates for all datasets, meaning that data cleaning is
not only important for resting state fMRI, but also for task fMRI. Finally, we
discuss the implications of our work on the fMRI literature as a whole,
estimating that at least 10% of the fMRI studies have used the most problematic
cluster inference method (P = 0.01 cluster defining threshold), and how
individual studies can be interpreted in light of our findings. These
additional results underscore our original conclusions, on the importance of
data sharing and thorough evaluation of statistical methods on realistic null
data
Water and energy savings from greywater reuse: a modelling scheme using disaggregated consumption data
Municipal drinking water supplies are under great stress globally, and one way to mitigate the problems is the reutilization of wastewater in various settings. In this paper, a greywater reuse scheme and the impact of system design and configuration on water and energy savings are investigated. The objective of the paper was to investigate the impact of hydraulic design and performance of a greywater treatment and reuse system on water and energy savings. A simulation model was created based on real, disaggregated water consumption data that predicts the reuse potential. Three scenarios were investigated; (1) greywater collection from the bathroom and reuse for toilet flushing, (2) greywater collection from bathroom sinks and showers, and reuse as hot water for sinks and showers, and (3) a combination of (1) and (2) where greywater collection from bathroom sinks and showers is used for toilet flushing, sinks and shower. The results indicate hot water reductions between 55.6 and 58.2%, while cold water reductions ranged from 5.8 to 30.6%. Reductions in energy for producing hot water between 43.5 and 46.8% were observed. Recommendations per connected user for hydraulic design ranged from 0.033 to 0.1\ua0dm3\ua0min−1, 3\ua0dm3, and 0.7–10\ua0dm3\ua0for treatment capacity, collection and holding tank volume
Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests
One-sided t-tests are commonly used in the neuroimaging field, but two-sided
tests should be the default unless a researcher has a strong reason for using a
one-sided test. Here we extend our previous work on cluster false positive
rates, which used one-sided tests, to two-sided tests. Briefly, we found that
parametric methods perform worse for two-sided t-tests, and that non-parametric
methods perform equally well for one-sided and two-sided tests
Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
We propose to use Gaussian process regression to accurately estimate the
diffusion MRI signal at arbitrary locations in q-space. By estimating the
signal on a grid, we can do synthetic diffusion spectrum imaging:
reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier
transform. We also propose an alternative reconstruction method guaranteeing a
nonnegative EAP that integrates to unity. The reconstruction is validated on
data simulated from two Gaussians at various crossing angles. Moreover, we
demonstrate on non-uniformly sampled in vivo data that the method is far
superior to linear interpolation, and allows a drastic undersampling of the
data with only a minor loss of accuracy. We envision the method as a potential
replacement for standard diffusion spectrum imaging, in particular when
acquistion time is limited.Comment: 5 page
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