Search for: All records

Data collected from the Hubbard Brook Flux Tower starting in August 2016 that is also uploaded to the Ameriflux website under site name US-HBK. These data are from a suite of sensors installed on the 110 ft. tower and in the ground below the tower. Fast data is collected at 10Hz and is processed into 30min time steps using Licor’s Eddy Pro software. Slow data are averaged at 30 minute intervals and are included with this dataset. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

In exterior calculus on smooth manifolds, the exterior derivative and wedge products are natural with respect to smooth maps between manifolds, that is, these operations commute with pullback. In discrete exterior calculus (DEC), simplicial cochains play the role of discrete forms, the coboundary operator serves as the discrete exterior derivative, and an antisymmetrized cup-like product provides a discrete wedge product. We show that these discrete operations in DEC are natural with respect to abstract simplicial maps. A second contribution is a new averaging interpretation of the discrete wedge product in DEC. We also show that this wedge product is the same as Wilson’s cochain product defined using Whitney and de Rham maps. 

Data collected from the Hubbard Brook Flux Tower starting in August 2016 that is also uploaded to the Ameriflux website under site name US-HBK. These data are from a suite of sensors installed on the 110 ft. tower and in the soil adjacent to the tower. Flux data are collected at 10 Hz and are processed into 30-minute time steps using Licor’s Eddy Pro software. Supporting micro-meteorological data are averaged at 30 minute intervals and are included with this data set. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Abstract To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems. 

Data collected from the Hubbard Brook Flux Tower starting in August 2016 that is also uploaded to the Ameriflux website under site name US-HBK. These data are from a suite of sensors installed on the 110 ft. tower and in the soil adjacent to the tower. Flux data are collected at 10 Hz and are processed into 30-minute time steps using Licor’s Eddy Pro software. Supporting micro-meteorological data are averaged at 30 minute intervals and are included with this data set.