Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California

Accurate water-balance measurements in the seasonal, snow-dominated Sierra Nevada are important for forest and downstream water management. However, few sites in the southern Sierra offer detailed records of the spatial and temporal patterns of snowpack and soil-water storage and the fluxes affecting them, i.e., precipitation as rain and snow, snowmelt, evapotranspiration, and runoff. To explore these stores and fluxes we instrumented the Wolverton basin (2180-2750 m) in Sequoia National Park with distributed, continuous sensors. This 2006-2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. Clustered sensors record lateral differences with regards to aspect and canopy cover at approximately 2250 and 2625 m in elevation, where two meteorological stations are installed. Meteorological stations record air temperature, relative humidity, radiation, precipitation, wind speed and direction, and snow depth. Data are available at hourly intervals by water year (1 October-30 September) in non-proprietary formats from online data repositories (https://doi.org/10.6071/M3S94T)

Climate Change and Land Management Impact Rangeland Condition and Sage-Grouse Habitat in Southeastern Oregon

Contemporary pressures on sagebrush steppe from climate change, exotic species, wildfire, and land use change threaten rangeland species such as the greater sage-grouse (Centrocercus urophasianus). To effectively manage sagebrush steppe landscapes for long-term goals, managers need information about the potential impacts of climate change, disturbances, and management activities. We integrated information from a dynamic global vegetation model, a sage-grouse habitat climate envelope model, and a state-and-transition simulation model to project broad-scale vegetation dynamics and potential sage-grouse habitat across 23.5 million acres in southeastern Oregon. We evaluated four climate scenarios, including continuing current climate and three scenarios of global climate change, and three management scenarios, including no management, current management and a sage-grouse habitat restoration scenario. All climate change scenarios projected expansion of moist shrub steppe and contraction of dry shrub steppe, but climate scenarios varied widely in the projected extent of xeric shrub steppe, where hot, dry summer conditions are unfavorable for sage-grouse. Wildfire increased by 26% over the century under current climate due to exotic grass encroachment, and by two- to four-fold across all climate change scenarios as extreme fire years became more frequent. Exotic grasses rapidly expanded in all scenarios as large areas of the landscape initially in semi-degraded condition converted to exotic-dominated systems. Due to the combination of exotic grass invasion, juniper encroachment, and climatic unsuitability for sage-grouse, projected sage-grouse habitat declined in the first several decades, but increased in area under the three climate change scenarios later in the century, as moist shrub steppe increased and rangeland condition improved. Management activities in the model were generally unsuccessful in controlling exotic grass invasion but were effective in slowing woodland expansion. Current levels of restoration treatments were insufficient to prevent some juniper expansion, but increased treatment rates under the restoration scenario maintained juniper near initial levels in priority treatment areas. Our simulations indicate that climate change may have both positive and negative implications for maintaining sage-grouse habitat

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR

Automatic estimation of harmonic tension by distributed representation of chords

The buildup and release of a sense of tension is one of the most essential aspects of the process of listening to music. A veridical computational model of perceived musical tension would be an important ingredient for many music informatics applications. The present paper presents a new approach to modelling harmonic tension based on a distributed representation of chords. The starting hypothesis is that harmonic tension as perceived by human listeners is related, among other things, to the expectedness of harmonic units (chords) in their local harmonic context. We train a word2vec-type neural network to learn a vector space that captures contextual similarity and expectedness, and define a quantitative measure of harmonic tension on top of this. To assess the veridicality of the model, we compare its outputs on a number of well-defined chord classes and cadential contexts to results from pertinent empirical studies in music psychology. Statistical analysis shows that the model's predictions conform very well with empirical evidence obtained from human listeners.Comment: 12 pages, 4 figures. To appear in Proceedings of the 13th International Symposium on Computer Music Multidisciplinary Research (CMMR), Porto, Portuga

The Composition of M-type asteroids II: Synthesis of spectroscopic and radar observations

This work updates and expands on results of our long-term radar-driven observational campaign of main-belt asteroids (MBAs) focused on Bus-DeMeo Xc- and Xk-type objects (Tholen X and M class asteroids) using the Arecibo radar and NASA Infrared Telescope Facilities (Ockert-Bell et al. 2008; 2010; Shepard et al. 2008; 2010). Eighteen of our targets were near-simultaneously observed with radar and those observations are described in Shepard et al. (2010). We combine our near-infrared data with available visible wavelength data for a more complete compositional analysis of our targets. Compositional evidence is derived from our target asteroid spectra using two different methods, a \c{hi}2 search for spectral matches in the RELAB database and parametric comparisons with meteorites. We present four new methods of parametric comparison, including discriminant analysis. Discriminant analysis identifies meteorite type with 85% accuracy. This paper synthesizes the results of these two analog search algorithms and reconciles those results with analogs suggested from radar data (Shepard et al. 2010). We have observed 29 asteroids, 18 in conjunction with radar observations. For eighteen out of twenty-nine objects observed (62%) our compositional predictions are consistent over two or more methods applied. We find that for our Xc and Xk targets the best fit is an iron meteorite for 34% of the samples. Enstatite Chondrites were best fits for 6 of our targets (21%). Stony-iron meteorites were best fits for 2 of our targets (7%). A discriminant analysis suggests that asteroids with no absorption band can be compared to iron meteorites and asteroids with both a 0.9 and 1.9 {\mu}m absorption band can be compared to stony-iron meteorites.Comment: 30 pages, 5 figures, 10 table

Using eye-tracking in applied linguistics and second language research

With eye-tracking technology the eye is thought to give researchers a window into the mind. Importantly, eye-tracking has significant advantages over traditional online processing measures: chiefly that it allows for more ‘natural’ processing as it does not require a secondary task, and that it provides a very rich moment-to-moment data source. In recognition of the technology’s benefits, an ever increasing number of researchers in applied linguistics and second language research are beginning to use it. As eye-tracking gains traction in the field, it is important to ensure that it is established in an empirically sound fashion. To do this it is important for the field to come to an understanding about what eye-tracking is, what eye-tracking measures tell us, what it can be used for, and what different eye-tracking systems can and cannot do. Further, it is important to establish guidelines for designing sound research studies using the technology. The goal of the current review is to begin to address these issues

The future of enterprise groupware applications

This paper provides a review of groupware technology and products. The purpose of this review is to investigate the appropriateness of current groupware technology as the basis for future enterprise systems and evaluate its role in realising, the currently emerging, Virtual Enterprise model for business organisation. It also identifies in which way current technological phenomena will transform groupware technology and will drive the development of the enterprise systems of the future

Development of Hydrocephalus in Mice Expressing the Gi-Coupled GPCR Ro1 RASSL Receptor in Astrocytes

We developed a transgenic mouse line that expresses the