Lary, David J.

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David Lary is a Professor in the Physics Department. His research area is Applied Physics for Societal Benefit. He uses computational discovery to support decision-making in such varied areas as weather forecasting, climate data records, agriculture, tornado prediction, disaster response, famine relief, health systems, and online fraud.

Learn more about Dr. Lary on his home and Research Explorer pages.


Recent Submissions

Now showing 1 - 7 of 7
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    Estimating the Daily Pollen Concentration in the Atmosphere Using Machine Learning and NEXRAD Weather Radar Data
    (Springer International Publishing, 2019-06-07) Zewdie, Gebreab K.; Lary, David J.; Liu, Xun; Wu, Daji; Levetin, E.; Zewdie, Gebreab K.; Lary, David J.; Liu, Xun; Wu, Daji
    Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen depend on the ambient weather conditions. The temperature, rainfall, humidity, cloud cover, and wind are known to affect the amount of pollen in the atmosphere. In the past, various regression techniques have been applied to estimate and forecast the daily pollen concentration in the atmosphere based on the weather conditions. In this research, machine learning methods were applied to the Next Generation Weather Radar (NEXRAD) data to estimate the daily Ambrosia pollen over a 300 km × 300 km region centered on a NEXRAD weather radar. The Neural Network and Random Forest machine learning methods have been employed to develop separate models to estimate Ambrosia pollen over the region. A feasible way of estimating the daily pollen concentration using only the NEXRAD radar data and machine learning methods would lay the foundation to forecast daily pollen at a fine spatial resolution nationally. © 2019, Springer Nature Switzerland AG.
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    Applying Deep Neural Networks and Ensemble Machine Learning Methods To Forecast Airborne Ambrosia Pollen
    (MDPI AG, 2019-06-04) Zewdie, Gebreab K.; Lary, David J.; Levetin, E.; Garuma, G. F.; Zewdie, Gebreab K.; Lary, David J.
    Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a method for the robust estimation of the concentration of airborne Ambrosia pollen using a suite of machine learning approaches including deep learning and ensemble learners. Each of these machine learning approaches utilize data from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric weather and land surface reanalysis. The machine learning approaches used for developing a suite of empirical models are deep neural networks, extreme gradient boosting, random forests and Bayesian ridge regression methods for developing our predictive model. The training data included twenty-four years of daily pollen concentration measurements together with ECMWF weather and land surface reanalysis data from 1987 to 2011 is used to develop the machine learning predictive models. The last six years of the dataset from 2012 to 2017 is used to independently test the performance of the machine learning models. The correlation coefficients between the estimated and actual pollen abundance for the independent validation datasets for the deep neural networks, random forest, extreme gradient boosting and Bayesian ridge were 0.82, 0.81, 0.81 and 0.75 respectively, showing that machine learning can be used to effectively forecast the concentrations of airborne pollen.
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    Phytophthora Megakarya and Phytophthora Palmivora, Closely Related Causal Agents of Cacao Black Pod Rot, Underwent Increases in Genome Sizes and Gene Numbers by Different Mechanisms
    (2017-03-01) Ali, Shahin S.; Shao, Jonathan; Lary, David J.; Kronmiller, Brent A.; Shen, Danyu; Strem, Mary D.; Amoako-Attah, Ishmael; Akrofi, Andrew Yaw; Begoude, B. A. Didier; ten Hoopen, G. Martijn; Coulibaly, Klotioloma; Kebe, Boubacar Ismael; Melnick, Rachel L.; Guiltinan, Mark J.; Tyler, Brett M.; Meinhardt, Lyndel W.; Bailey, Bryan A.; Lary, David J.
    Phytophthora megakarya (Pmeg) and Phytophthora palmivora (Ppal) are closely related species causing cacao black pod rot. Although Ppal is a cosmopolitan pathogen, cacao is the only known host of economic importance for Pmeg. Pmeg is more virulent on cacao than Ppal. We sequenced and compared the Pmeg and Ppal genomes and identified virulence-related putative gene models (PGeneM) that may be responsible for their differences in host specificities and virulence. Pmeg and Ppal have estimated genome sizes of 126.88 and 151.23Mb and PGeneM numbers of 42,036 and 44,327, respectively. The evolutionary histories of Pmeg and Ppal appear quite different. Postspeciation, Ppal underwent whole-genome duplication whereas Pmeg has undergone selective increases in PGeneM numbers, likely through accelerated transposable element-driven duplications. Many PGeneMs in both species failed to match transcripts and may represent pseudogenes or cryptic genetic reservoirs. Pmeg appears to have amplified specific gene families, some of which are virulence-related. Analysis of mycelium, zoospore, and in planta transcriptome expression profiles using neural network self-organizing map analysis generated 24 multivariate and nonlinear self-organizing map classes. Many members of the RxLR, necrosis-inducing phytophthora protein, and pectinase genes families were specifically induced in planta. Pmeg displays a diverse virulence-related gene complement similar in size to and potentially of greater diversity than Ppal but it remains likely that the specific functions of the genes determine each species' unique characteristics as pathogens.
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    Holistics 3.0 for Health
    (MDPI AG, 2014-07-24) Lary, David J.; Woolf, Steven; Faruque, Fazlay; LePage, James P.; Lary, David J.
    Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions.
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    Using Remote Control Aerial Vehicles to Study Variability of Airborne Particulates
    (Libertas Academica Ltd, 2015-08-04) Harrison, William A.; Lary, David J.; Nathan, Brian J.; Moore, Alec G.; Harrison, William A.; Lary, David J.; Nathan, Brian J.; Moore, Alec G.
    Airborne particulates play a significant role in the atmospheric radiative balance and impact human health. To characterize this impact, global-scale observations and data products are needed. Satellite products allow for this global coverage but require in situ validations. This study used a remote-controlled aerial vehicle to look at the horizontal, vertical, and temporal variability of airborne particulates within the first 150 m of the atmosphere. Four flights were conducted on December 4, 2014, between 12:00 pm and 5:00 pm local time. The first three flights flew a pattern of increasing altitude up to 140 m. The fourth flight was conducted at a near-constant altitude of 60 m. The mean PM_{2.5} concentration for the three flights with varying altitude was 36.3 μg/m³, with the highest concentration occurring below 10 m altitude. The overall vertical variation was very small with a standard deviation of only 3.6 μg/m³. PM_{2.5} concentration also did not change much throughout the day with mean concentrations for the altitude-varying flights of 35.1, 37.2, and 36.8 μg/m³. The fourth flight, flown at a near-constant altitude, had a lower concentration of 23.5 μg/m3. © 2015, the authors, publisher and licensee Libertas Academica Limited.
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    Survey on the Estimation of Mutual Information Methods as a Measure of Dependency Versus Correlation Analysis
    (2015-02) Gencaga, Deniz; Malakar, Nabin K.; Lary, David J.; Gencaga, Deniz; Malakar, Nabin K.; Lary, David J.
    In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyse general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, we use a piece-wise constant Bayesian methodology using a general Dirichlet prior. In this estimation method, we use a two-stage approach where we approximate the probability distribution first and then calculate the marginal and joint entropies. Here, we demonstrate the performance of this Bayesian approach versus the others for computing the dependency between different variables. We also compare these with linear correlation analysis. Finally, we apply MI and correlation analysis to the identification of the bias in the determination of the aerosol optical depth (AOD) by the satellite based Moderate Resolution Imaging Spectroradiometer (MODIS) and the ground based AErosol RObotic NETwork (AERONET). Here, we observe that the AOD measurements by these two instruments might be different for the same location. The reason of this bias is explored by quantifying the dependencies between the bias and 15 other variables including cloud cover, surface reflectivity and others.
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    Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles
    (2012-05-09) Khan, Amir; Schaefer, David; Tao, Lei; Miller, David J.; Sun, Kang; Zondlo, Mark A.; Harrison, William A.; Roscoe, Bryan; Lary, David J.; Khan, Amir; Schaefer, David; Harrison, William A.; Roscoe, Bryan; Lary, David J.
    We demonstrate compact, low power, lightweight laser-based sensors for measuring trace gas species in the atmosphere designed specifically for electronic unmanned aerial vehicle (UAV) platforms. The sensors utilize non-intrusive optical sensing techniques to measure atmospheric greenhouse gas concentrations with unprecedented vertical and horizontal resolution (similar to 1 m) within the planetary boundary layer. The sensors are developed to measure greenhouse gas species including carbon dioxide, water vapor and methane in the atmosphere. Key innovations are the coupling of very low power vertical cavity surface emitting lasers (VCSELs) to low power drive electronics and sensitive multi-harmonic wavelength modulation spectroscopic techniques. The overall mass of each sensor is between 1-2 kg including batteries and each one consumes less than 2 W of electrical power. In the initial field testing, the sensors flew successfully onboard a T-Rex Align 700E robotic helicopter and showed a precision of 1% or less for all three trace gas species. The sensors are battery operated and capable of fully automated operation for long periods of time in diverse sensing environments. Laser-based trace gas sensors for UAVs allow for high spatial mapping of local greenhouse gas concentrations in the atmospheric boundary layer where land/atmosphere fluxes occur. The high-precision sensors, coupled to the ease-of-deployment and cost effectiveness of UAVs, provide unprecedented measurement capabilities that are not possible with existing satellite-based and suborbital aircraft platforms.

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