The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Christian S Jensen - One of the best experts on this subject based on the ideXlab platform.
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towards context aware search and analysis on social media data
Extending Database Technology, 2013Co-Authors: Leon Derczynski, Bin Yang, Christian S JensenAbstract:Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology. A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a Constant Stream of data points, each containing text with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally, for each context, emerging applications and further avenues for investigation are discussed.
Leon Derczynski - One of the best experts on this subject based on the ideXlab platform.
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towards context aware search and analysis on social media data
Extending Database Technology, 2013Co-Authors: Leon Derczynski, Bin Yang, Christian S JensenAbstract:Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology. A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a Constant Stream of data points, each containing text with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally, for each context, emerging applications and further avenues for investigation are discussed.
Bin Yang - One of the best experts on this subject based on the ideXlab platform.
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towards context aware search and analysis on social media data
Extending Database Technology, 2013Co-Authors: Leon Derczynski, Bin Yang, Christian S JensenAbstract:Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology. A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a Constant Stream of data points, each containing text with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally, for each context, emerging applications and further avenues for investigation are discussed.
Conway, Bevil R. - One of the best experts on this subject based on the ideXlab platform.
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Temporal dynamics of the neural representation of hue and luminance contrast
2020Co-Authors: Hermann, Katherine L., Singh, Shridhar R., Rosenthal, Isabelle A., Pantazis Dimitrios, Conway, Bevil R.Abstract:Hue and luminance contrast are the most basic visual features, emerging in early layers of convolutional neural networks trained to perform object categorization. In human vision, the timing of the neural computations that extract these features, and the extent to which they are determined by the same or separate neural circuits, is unknown. We addressed these questions using multivariate analyses of human brain responses measured with magnetoencephalography. We report four discoveries. First, it was possible to decode hue tolerant to changes in luminance contrast, and luminance contrast tolerant to changes in hue, consistent with the existence of separable neural mechanisms for these features. Second, the decoding time course for luminance contrast peaked 16-24 ms before hue and showed a more prominent secondary peak corresponding to decoding of stimulus cessation. These results are consistent with the idea that the brain uses luminance contrast as an updating signal to separate events within the Constant Stream of visual information. Third, neural representations of hue generalized to a greater extent across time, providing a neural correlate of the preeminence of hue over luminance contrast in perceptual grouping and memory. Finally, decoding of luminance contrast was more variable across participants for hues associated with daylight (orange and blue) than for anti-daylight (green and pink), suggesting that color-constancy mechanisms reflect individual differences in assumptions about natural lighting
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Temporal dynamics of the neural representation of hue and luminance contrast
2020Co-Authors: Hermann, Katherine L., Singh, Shridhar R., Rosenthal, Isabelle A., Pantazis Dimitrios, Conway, Bevil R.Abstract:Hue and luminance contrast are the most basic visual features, emerging in early layers of convolutional neural networks trained to perform object categorization. In human vision, the timing of the neural computations that extract these features, and the extent to which they are determined by the same or separate neural circuits, is unknown. We addressed these questions using multivariate analyses of human brain responses measured with magnetoencephalography. We report four discoveries. First, it was possible to decode hue tolerant to changes in luminance contrast, and luminance contrast tolerant to changes in hue, consistent with the existence of separable neural mechanisms for these features. Second, the decoding time course for luminance contrast peaked 16-24 ms before hue and showed a more prominent secondary peak corresponding to decoding of stimulus cessation. These results support the idea that the brain uses luminance contrast as an updating signal to separate events within the Constant Stream of visual information. Third, neural representations of hue generalized to a greater extent across time, providing a neural correlate of the preeminence of hue over luminance contrast in perceptual grouping and memory. Finally, decoding of luminance contrast was more variable across participants for hues associated with daylight (orange and blue) than for anti-daylight (green and pink), suggesting that color-constancy mechanisms reflect individual differences in assumptions about natural lighting
Hermann, Katherine L. - One of the best experts on this subject based on the ideXlab platform.
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Temporal dynamics of the neural representation of hue and luminance contrast
2020Co-Authors: Hermann, Katherine L., Singh, Shridhar R., Rosenthal, Isabelle A., Pantazis Dimitrios, Conway, Bevil R.Abstract:Hue and luminance contrast are the most basic visual features, emerging in early layers of convolutional neural networks trained to perform object categorization. In human vision, the timing of the neural computations that extract these features, and the extent to which they are determined by the same or separate neural circuits, is unknown. We addressed these questions using multivariate analyses of human brain responses measured with magnetoencephalography. We report four discoveries. First, it was possible to decode hue tolerant to changes in luminance contrast, and luminance contrast tolerant to changes in hue, consistent with the existence of separable neural mechanisms for these features. Second, the decoding time course for luminance contrast peaked 16-24 ms before hue and showed a more prominent secondary peak corresponding to decoding of stimulus cessation. These results are consistent with the idea that the brain uses luminance contrast as an updating signal to separate events within the Constant Stream of visual information. Third, neural representations of hue generalized to a greater extent across time, providing a neural correlate of the preeminence of hue over luminance contrast in perceptual grouping and memory. Finally, decoding of luminance contrast was more variable across participants for hues associated with daylight (orange and blue) than for anti-daylight (green and pink), suggesting that color-constancy mechanisms reflect individual differences in assumptions about natural lighting
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Temporal dynamics of the neural representation of hue and luminance contrast
2020Co-Authors: Hermann, Katherine L., Singh, Shridhar R., Rosenthal, Isabelle A., Pantazis Dimitrios, Conway, Bevil R.Abstract:Hue and luminance contrast are the most basic visual features, emerging in early layers of convolutional neural networks trained to perform object categorization. In human vision, the timing of the neural computations that extract these features, and the extent to which they are determined by the same or separate neural circuits, is unknown. We addressed these questions using multivariate analyses of human brain responses measured with magnetoencephalography. We report four discoveries. First, it was possible to decode hue tolerant to changes in luminance contrast, and luminance contrast tolerant to changes in hue, consistent with the existence of separable neural mechanisms for these features. Second, the decoding time course for luminance contrast peaked 16-24 ms before hue and showed a more prominent secondary peak corresponding to decoding of stimulus cessation. These results support the idea that the brain uses luminance contrast as an updating signal to separate events within the Constant Stream of visual information. Third, neural representations of hue generalized to a greater extent across time, providing a neural correlate of the preeminence of hue over luminance contrast in perceptual grouping and memory. Finally, decoding of luminance contrast was more variable across participants for hues associated with daylight (orange and blue) than for anti-daylight (green and pink), suggesting that color-constancy mechanisms reflect individual differences in assumptions about natural lighting
