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  1. A new framework for advanced machine learning-based analysis of hyperspectral datasets HSKL was built using the well-known package scikit-learn. In this paper, we describe HSKL’s structure and basic usage. We also showcase the diversity of models supported by the package by applying 17 classification algorithms and measure their baseline performance in segmenting objects with highly similar spectral properties.
    Free, publicly-accessible full text available July 19, 2022
  2. Advanced algorithms used in geospatial imaging were adopted for biomedical application to analyze hyperspectral datasets. To demonstrate the effectiveness, endmember extractions method was applied for delineating tumors in animal models of cancer.
  3. Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entiremore »software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features.« less
    Free, publicly-accessible full text available July 19, 2022
  4. High-quality temperature data at a finer spatio-temporal scale is critical for analyzing the risk of heat exposure and hazards in urban environments. The variability of urban landscapes makes cities a challenging environment for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts; first, the spatio-temporal granularities are too coarse, and second, the inability to track the ambient air temperature (AAT) instead of land surface temperature (LST). Overcoming these limitations requires developing models for mapping the variability in heat exposure in urban environments. We investigated an integrated approach for mapping urban heat hazards bymore »harnessing a diverse set of high-resolution measurements, including both ground-based and satellite-based temperature data. We mounted vehicle-borne mobile sensors on city buses to collect high-frequency temperature data throughout 2018 and 2019. Our research also incorporated key biophysical parameters and Landsat 8 LST data into Random Forest regression modeling to map the hyperlocal variability of heat hazard over areas not covered by the buses. The vehicle-borne temperature sensor data showed large temperature differences within the city, with the largest variations of up to 10 °C and morning-afternoon diurnal changes at a magnitude around 20 °C. Random Forest modeling on noontime (11:30 am – 12:30 pm) data to predict AAT produced accurate results with a mean absolute error of 0.29 °C and successfully showcased the enhanced granularity in urban heat hazard mapping. These maps revealed well-defined hyperlocal variabilities in AAT, which were not evident with other research approaches. Urban core and dense residential areas revealed larger than 5 °C AAT differences from their nearby green spaces. The sensing framework developed in this study can be easily implemented in other urban areas, and findings from this study will be beneficial in understanding the heat vulnerabilities of individual communities. It can be used by the local government to devise targeted hazard mitigation efforts such as increasing green space, developing better heatsafety policies, and exposure warning for workers.« less
  5. Free, publicly-accessible full text available June 1, 2023
  6. Free, publicly-accessible full text available October 1, 2022
  7. A bstract A search for long-lived particles decaying into muon pairs is performed using proton-proton collisions at a center-of-mass energy of 13 TeV, collected by the CMS experiment at the LHC in 2017 and 2018, corresponding to an integrated luminosity of 101 fb − 1 . The data sets used in this search were collected with a dedicated dimuon trigger stream with low transverse momentum thresholds, recorded at high rate by retaining a reduced amount of information, in order to explore otherwise inaccessible phase space at low dimuon mass and nonzero displacement from the primary interaction vertex. No significant excessmore »of events beyond the standard model expectation is found. Upper limits on branching fractions at 95% confidence level are set on a wide range of mass and lifetime hypotheses in beyond the standard model frameworks with the Higgs boson decaying into a pair of long-lived dark photons, or with a long-lived scalar resonance arising from a decay of a b hadron. The limits are the most stringent to date for substantial regions of the parameter space. These results can be also used to constrain models of displaced dimuons that are not explicitly considered in this paper.« less
    Free, publicly-accessible full text available April 1, 2023
  8. A bstract The top quark pair production cross section is measured in proton-proton collisions at a center-of-mass energy of 5.02 TeV. The data were collected in a special LHC low-energy and low-intensity run in 2017, and correspond to an integrated luminosity of 302 pb − 1 . The measurement is performed using events with one electron and one muon of opposite charge, and at least two jets. The measured cross section is 60 . 7 ± 5 . 0 (stat) ± 2 . 8 (syst) ± 1 . 1 (lumi) pb. A combination with the result in the single leptonmore »+ jets channel, based on data collected in 2015 at the same center-of-mass energy and corresponding to an integrated luminosity of 27.4 pb − 1 , is then performed. The resulting measured value is 63 . 0 ± 4 . 1 (stat) ± 3 . 0 (syst+lumi) pb, in agreement with the standard model prediction of $$ {66.8}_{-3.1}^{+2.9} $$ 66.8 − 3.1 + 2.9 pb.« less
    Free, publicly-accessible full text available April 1, 2023
  9. A bstract A search for a heavy resonance decaying into a top quark and a W boson in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV is presented. The data analyzed were recorded with the CMS detector at the LHC and correspond to an integrated luminosity of 138 fb − 1 . The top quark is reconstructed as a single jet and the W boson, from its decay into an electron or muon and the corresponding neutrino. A top quark tagging technique based on jet clustering with a variable distance parameter and simultaneous jet grooming is used tomore »identify jets from the collimated top quark decay. The results are interpreted in the context of two benchmark models, where the heavy resonance is either an excited bottom quark b ∗ or a vector-like quark B. A statistical combination with an earlier search by the CMS Collaboration in the all-hadronic final state is performed to place upper cross section limits on these two models. The new analysis extends the lower range of resonance mass probed from 1.4 down to 0.7 TeV. For left-handed, right-handed, and vector-like couplings, b ∗ masses up to 3.0, 3.0, and 3.2 TeV are excluded at 95% confidence level, respectively. The observed upper limits represent the most stringent constraints on the b ∗ model to date.« less
    Free, publicly-accessible full text available April 1, 2023