847 research outputs found
Semi-supervised Eigenvectors for Large-scale Locally-biased Learning
In many applications, one has side information, e.g., labels that are
provided in a semi-supervised manner, about a specific target region of a large
data set, and one wants to perform machine learning and data analysis tasks
"nearby" that prespecified target region. For example, one might be interested
in the clustering structure of a data graph near a prespecified "seed set" of
nodes, or one might be interested in finding partitions in an image that are
near a prespecified "ground truth" set of pixels. Locally-biased problems of
this sort are particularly challenging for popular eigenvector-based machine
learning and data analysis tools. At root, the reason is that eigenvectors are
inherently global quantities, thus limiting the applicability of
eigenvector-based methods in situations where one is interested in very local
properties of the data.
In this paper, we address this issue by providing a methodology to construct
semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these
locally-biased eigenvectors can be used to perform locally-biased machine
learning. These semi-supervised eigenvectors capture
successively-orthogonalized directions of maximum variance, conditioned on
being well-correlated with an input seed set of nodes that is assumed to be
provided in a semi-supervised manner. We show that these semi-supervised
eigenvectors can be computed quickly as the solution to a system of linear
equations; and we also describe several variants of our basic method that have
improved scaling properties. We provide several empirical examples
demonstrating how these semi-supervised eigenvectors can be used to perform
locally-biased learning; and we discuss the relationship between our results
and recent machine learning algorithms that use global eigenvectors of the
graph Laplacian
Microwave Temperature Profiler Mounted in a Standard Airborne Research Canister
Many atmospheric research aircraft use a standard canister design to mount instruments, as this significantly facilitates their electrical and mechanical integration and thereby reduces cost. Based on more than 30 years of airborne science experience with the Microwave Temperature Profiler (MTP), the MTP has been repackaged with state-of-the-art electronics and other design improvements to fly in one of these standard canisters. All of the controlling electronics are integrated on a single 4 ~5-in. (.10 ~13- cm) multi-layer PCB (printed circuit board) with surface-mount hardware. Improved circuit design, including a self-calibrating RTD (resistive temperature detector) multiplexer, was implemented in order to reduce the size and mass of the electronics while providing increased capability. A new microcontroller-based temperature controller board was designed, providing better control with fewer components. Five such boards are used to provide local control of the temperature in various areas of the instrument, improving radiometric performance. The new stepper motor has an embedded controller eliminating the need for a separate controller board. The reference target is heated to avoid possible emissivity (and hence calibration) changes due to moisture contamination in humid environments, as well as avoiding issues with ambient targets during ascent and descent. The radiometer is a double-sideband heterodyne receiver tuned sequentially to individual oxygen emission lines near 60 GHz, with the line selection and intermediate frequency bandwidths chosen to accommodate the altitude range of the aircraft and mission
The Lived Experience of the Adolescent Listening to Preferred Music
The purpose of this phenomenological research study was to gain knowledge about the lived experience of normal adolescents listening to their favorite music. This was an initial inquiry into the music listening experiences of four normal teenagers from the Philadelphia, PA area, with the purpose of providing information for clinicians about how a teen client’s favorite music can be incorporated into clinical treatment and what sort of information this can yield about the music listener. Through open-ended interviews, co-researchers disclosed thoughts, feelings, body responses and other responses, revealing that the each participant has a music listening experience particular to him or herself. There were similarities among the participants as well, though these should not be considered trends across the general adolescent population. While listening to favorite music, all participants enjoyed fond memories of friends and family while considering their values and feeling an improvement in mood. All participants projected their own feelings onto the music, and used descriptions for the music that reflected this. Notably, all the participants in this study used the lyrics in different ways from each other. Recommendations were made for future research to which knowledge from this study could be applied to developing a system for classifying music listening types. This system could be used by music therapists to improve the quality of treatment provided to teen clients.M.A., Creative Arts in Therapy -- Drexel University, 201
Differential Epidemiology: IQ, Neuroticism, And Chronic Disease By The 50 U.S. States
Current research shows that geo-political units (e.g., the 50 U.S. states) vary meaningfully on psychological dimensions like intelligence (IQ) and neuroticism (N). A new scientific discipline has also emerged, differential epidemiology, focused on how psychological variables affect health. We integrate these areas by reporting large correlations between aggregate-level IQ and N (measured for the 50 U.S. states) and state differences in rates of chronic disease (e.g., stroke, heart disease). Controlling for health-related behaviors (e.g., smoking, exercise) reduced but did not eliminate these effects. Strong relationships also existed between IQ, N, disease, and a host of other state-level variables (e.g., income, crime, education). The nexus of inter-correlated state variables could reflect a general fitness factor hypothesized by cognitive epidemiologists, although valid inferences about causality will require more research.
Mapping historical forest biomass for stock-change assessments at parcel to landscape scales
Understanding historical forest dynamics, specifically changes in forest
biomass and carbon stocks, has become critical for assessing current forest
climate benefits and projecting future benefits under various policy,
regulatory, and stewardship scenarios. Carbon accounting frameworks based
exclusively on national forest inventories are limited to broad-scale
estimates, but model-based approaches that combine these inventories with
remotely sensed data can yield contiguous fine-resolution maps of forest
biomass and carbon stocks across landscapes over time. Here we describe a
fundamental step in building a map-based stock-change framework: mapping
historical forest biomass at fine temporal and spatial resolution (annual, 30m)
across all of New York State (USA) from 1990 to 2019, using freely available
data and open-source tools.
Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA)
data, and off-the-shelf LiDAR collections we developed three modeling
approaches for mapping historical forest aboveground biomass (AGB): training on
FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps
(indirect), and an ensemble averaging predictions from the direct and indirect
models. Model prediction surfaces (maps) were tested against FIA estimates at
multiple scales. All three approaches produced viable outputs, yet tradeoffs
were evident in terms of model complexity, map accuracy, saturation, and
fine-scale pattern representation. The resulting map products can help identify
where, when, and how forest carbon stocks are changing as a result of both
anthropogenic and natural drivers alike. These products can thus serve as
inputs to a wide range of applications including stock-change assessments,
monitoring reporting and verification frameworks, and prioritizing parcels for
protection or enrollment in improved management programs.Comment: Manuscript: 24 pages, 7 figures; Supplements: 12 pages, 5 figures;
Submitted to Forest Ecology and Managemen
Combined Spatially Resolved Optical Emission Imaging and Modeling Studies of Microwave-Activated H<sub>2</sub>/Ar and H<sub>2</sub>/Kr Plasmas Operating at Powers and Pressures Relevant for Diamond Chemical Vapor Deposition
Microwave
(MW) activated H2/Ar (and H2/Kr)
plasmas operating under powers and pressures relevant to diamond chemical
vapor deposition have been investigated experimentally and by 2-D
modeling. The experiments return spatially and wavelength resolved
optical emission spectra of electronically excited H2 molecules
and H and ArÂ(/Kr) atoms for a range of H2/noble gas mixing
ratios. The self-consistent 2-DÂ(r, z) modeling of different H2/Ar gas mixtures includes calculations
of the MW electromagnetic fields, the plasma chemistry and electron
kinetics, heat and species transfer and gas–surface interactions.
Comparison with the trends revealed by the spatially resolved optical
emission measurements and their variations with changes in process
conditions help guide identification and refinement of the dominant
plasma (and plasma emission) generation mechanisms and the more important
Ar–H, Ar–H2, and H–H2 coupling
reactions. Noble gas addition is shown to encourage radial expansion
of the plasma, and thus to improve the uniformity of the H atom concentration
and the gas temperature just above the substrate. Noble gas addition
in the current experiments is also found to enhance (unwanted) sputtering
of the copper base plate of the reactor; the experimentally observed
increase in gas phase Cu* emission is shown to correlate with the
near substrate ArH+ (and KrH+) ion concentrations
returned by the modeling, rather than with the relatively more abundant
H3+ (and H3O+) ions
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