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 H₂/Ar and H₂/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 Ar­(/Kr) atoms for a range of H2/noble gas mixing ratios. The self-consistent 2-D­(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