35 research outputs found
Generating descriptive text from functional brain images
Recent work has shown that it is possible to take brain images of a subject acquired while they saw a scene and reconstruct an approximation of that scene from the images. Here we show that it is also possible to generate _text_ from brain images. We began with images collected as participants read names of objects (e.g., ``Apartment'). Without accessing information about the object viewed for an individual image, we were able to generate from it a collection of semantically pertinent words (e.g., "door," "window"). Across images, the sets of words generated overlapped consistently with those contained in articles about the relevant concepts from the online encyclopedia Wikipedia. The technique described, if developed further, could offer an important new tool in building human computer interfaces for use in clinical settings
Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments
AbstractIn this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study by Mitchell et al. (2008) [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that these features can outperform those in Mitchell et al. (2008) [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects
Generating Text from Functional Brain Images
Recent work has shown that it is possible to take brain images acquired during viewing of a scene and reconstruct an approximation of the scene from those images. Here we show that it is also possible to generate text about the mental content reflected in brain images. We began with images collected as participants read names of concrete items (e.g., âApartmentââ) while also seeing line drawings of the item named. We built a model of the mental semantic representation of concrete concepts from text data and learned to map aspects of such representation to patterns of activation in the corresponding brain image. In order to validate this mapping, without accessing information about the items viewed for left-out individual brain images, we were able to generate from each one a collection of semantically pertinent words (e.g., âdoor,â âwindowâ for âApartmentââ). Furthermore, we show that the ability to generate such words allows us to perform a classification task and thus validate our method quantitatively
Recommended implementation of arterial spinâlabeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109790/1/mrm25197.pd
The Science Performance of JWST as Characterized in Commissioning
This paper characterizes the actual science performance of the James Webb
Space Telescope (JWST), as determined from the six month commissioning period.
We summarize the performance of the spacecraft, telescope, science instruments,
and ground system, with an emphasis on differences from pre-launch
expectations. Commissioning has made clear that JWST is fully capable of
achieving the discoveries for which it was built. Moreover, almost across the
board, the science performance of JWST is better than expected; in most cases,
JWST will go deeper faster than expected. The telescope and instrument suite
have demonstrated the sensitivity, stability, image quality, and spectral range
that are necessary to transform our understanding of the cosmos through
observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures;
https://iopscience.iop.org/article/10.1088/1538-3873/acb29
The James Webb Space Telescope Mission
Twenty-six years ago a small committee report, building on earlier studies,
expounded a compelling and poetic vision for the future of astronomy, calling
for an infrared-optimized space telescope with an aperture of at least .
With the support of their governments in the US, Europe, and Canada, 20,000
people realized that vision as the James Webb Space Telescope. A
generation of astronomers will celebrate their accomplishments for the life of
the mission, potentially as long as 20 years, and beyond. This report and the
scientific discoveries that follow are extended thank-you notes to the 20,000
team members. The telescope is working perfectly, with much better image
quality than expected. In this and accompanying papers, we give a brief
history, describe the observatory, outline its objectives and current observing
program, and discuss the inventions and people who made it possible. We cite
detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space
Telescope Overview, 29 pages, 4 figure
Running Head: How Inhibitory Oscillations Can Train Neural Networks and Punish Competitors NC MS 3099 How Inhibitory Oscillations Can Train Neural Networks and Punish Competitors
The first two authors contributed equally to this researc