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1. Historical road network statistics for core-based statistical areas in the U.S. (1900 - 2010)

   https://doi.org/10.6084/m9.figshare.19584088.v1  

   Burghardt, Keith; Uhl, Johannes H. (January 2022, figshare)

   Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing.  Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Keith A. Burghardt, USC Information Sciences Institute, USA Codebook: these CBSA statistics are stratified by degree of aggregation. - CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015 - CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015 - CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. - Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015 - Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics. The statistics are the following: msaid: CBSA codeid: (if patch statistics) arbitrary int unique to each patch within the CBSA that yearyear: year of statisticspop: population within all CBSA countiespatch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA)patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA)patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA)all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patchesall_bupl: Same as above but for all data in 2015 regardless of whether properties were in patchesall_bua: Same as above but for all data in 2015 regardless of whether properties were in patchesnum_nodes: number of nodes (intersections)num_edges: number of edges (roads between intersections)distance: total road length in kmk_mean: mean number of undirected roads per intersectionk1: fraction of nodes with degree 1k4plus: fraction of nodes with degree 4+bearing: histogram of different bearings between intersectionsentropy: entropy of bearing histogrammean_local_gridness: Griddedness used in textmean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles) Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road Network Evolution in the Urban and Rural  United States Since 1900. Computers, Environment and Urban Systems. 

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2. Bounds for exit times of Brownian motion and the first Dirichlet eigenvalue for the Laplacian

   https://doi.org/10.1090/tran/8903  

   Bañuelos, Rodrigo; Mariano, Phanuel; Wang, Jing (August 2023, Transactions of the American Mathematical Society)

   For domains in R d \mathbb {R}^d , d ≥ 2 d\geq 2 , we prove universal upper and lower bounds on the product of the bottom of the spectrum for the Laplacian to the power p > 0 p>0 and the supremum over all starting points of the p p -moments of the exit time of Brownian motion. It is shown that the lower bound is sharp for integer values of p p and that for p ≥ 1 p \geq 1 , the upper bound is asymptotically sharp as d → ∞ d\to \infty . For all p > 0 p>0 , we prove the existence of an extremal domain among the class of domains that are convex and symmetric with respect to all coordinate axes. For this class of domains we conjecture that the cube is extremal. 

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3. Foliar nutrient concentrations of six northern hardwood species responded to nitrogen and phosphorus fertilization but did not predict tree growth

   https://doi.org/10.7717/peerj.13193  

   Hong, Daniel S.; Gonzales, Kara E.; Fahey, Timothy J.; Yanai, Ruth D. (January 2022, PeerJ)

   Foliar chemistry can be useful for diagnosing soil nutrient availability and plant nutrient limitation. In northern hardwood forests, foliar responses to nitrogen (N) addition have been more often studied than phosphorus (P) addition, and the interactive effects of N and P addition have rarely been described. In the White Mountains of central New Hampshire, plots in ten forest stands of three age classes across three sites were treated annually beginning in 2011 with 30 kg N ha −1 y −1 or 10 kg P ha −1 y −1 or both or neither–a full factorial design. Green leaves of American beech ( Fagus grandifolia Ehrh.), pin cherry ( Prunus pensylvanica L.f.), red maple ( Acer rubrum L.), sugar maple ( A. saccharum Marsh.), white birch ( Betula papyrifera Marsh.), and yellow birch ( B. alleghaniensis Britton) were sampled pre-treatment and 4–6 years post-treatment in two young stands (last cut between 1988–1990), four mid-aged stands (last cut between 1971–1985) and four mature stands (last cut between 1883–1910). In a factorial analysis of species, stand age class, and nutrient addition, foliar N was 12% higher with N addition ( p < 0.001) and foliar P was 45% higher with P addition ( p < 0.001). Notably, P addition reduced foliar N concentration by 3% ( p = 0.05), and N addition reduced foliar P concentration by 7% ( p = 0.002). When both nutrients were added together, foliar P was lower than predicted by the main effects of N and P additions ( p = 0.08 for N × P interaction), presumably because addition of N allowed greater use of P for growth. Foliar nutrients did not differ consistently with stand age class ( p  ≥ 0.11), but tree species differed ( p  ≤ 0.01), with the pioneer species pin cherry having the highest foliar nutrient concentrations and the greatest responses to nutrient addition. Foliar calcium (Ca) and magnesium (Mg) concentrations, on average, were 10% ( p < 0.001) and 5% lower ( p = 0.01), respectively, with N addition, but were not affected by P addition ( p = 0.35 for Ca and p = 0.93 for Mg). Additions of N and P did not affect foliar potassium (K) concentrations ( p = 0.58 for N addition and p = 0.88 for P addition). Pre-treatment foliar N:P ratios were high enough to suggest P limitation, but trees receiving N ( p = 0.01), not P ( p = 0.64), had higher radial growth rates from 2011 to 2015. The growth response of trees to N or P addition was not explained by pre-treatment foliar N, P, N:P, Ca, Mg, or K. 

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4. Gravity Spy Machine Learning Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b

   https://doi.org/10.5281/zenodo.5649211  

   Coughlin, Scott; Zevin, Michael; Bahaadini, Sara; Rohani, Neda; Allen, Sara; Berry, Christopher; Crowston, Kevin; Harandi, Mabi; Jackson, Corey; Kalogera, Vicky; et al (January 2021, Zenodo)

   {"Abstract":["This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (O1, O2 and O3, where O3 is split into O3a and O3b). Gravity Spy classified all noise events identified by the Omicron trigger pipeline in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made Omega scans of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.<\/p>\n\nThere are 22 classes used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are two additional classes used to classify O3 data.<\/p>\n\nFor O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle<\/p>\n\nFor O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Blip_Low_Frequency<\/strong>, Chirp, Extremely_Loud, Fast_Scattering<\/strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle<\/p>\n\nIf you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool GWpy. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command<\/p>\n\n```conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy```<\/p>\n\nAfter downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.<\/p>\n\n```<\/p>\n\nfrom gwpy.table import GravitySpyTable<\/p>\n\nH1_O2 = GravitySpyTable.read('H1_O2.csv')<\/p>\n\nH1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)]<\/p>\n\nH1_O2[0:4].download(nproc=1)<\/p>\n\n```<\/p>\n\nEach of the columns in the CSV files are taken from various different inputs: <\/p>\n\n[\u2018event_time\u2019, \u2018ifo\u2019, \u2018peak_time\u2019, \u2018peak_time_ns\u2019, \u2018start_time\u2019, \u2018start_time_ns\u2019, \u2018duration\u2019, \u2018peak_frequency\u2019, \u2018central_freq\u2019, \u2018bandwidth\u2019, \u2018channel\u2019, \u2018amplitude\u2019, \u2018snr\u2019, \u2018q_value\u2019] contain metadata about the signal from the Omicron pipeline. <\/p>\n\n[\u2018gravityspy_id\u2019] is the unique identifier for each glitch in the dataset. <\/p>\n\n[\u20181400Ripples\u2019, \u20181080Lines\u2019, \u2018Air_Compressor\u2019, \u2018Blip\u2019, \u2018Chirp\u2019, \u2018Extremely_Loud\u2019, \u2018Helix\u2019, \u2018Koi_Fish\u2019, \u2018Light_Modulation\u2019, \u2018Low_Frequency_Burst\u2019, \u2018Low_Frequency_Lines\u2019, \u2018No_Glitch\u2019, \u2018None_of_the_Above\u2019, \u2018Paired_Doves\u2019, \u2018Power_Line\u2019, \u2018Repeating_Blips\u2019, \u2018Scattered_Light\u2019, \u2018Scratchy\u2019, \u2018Tomte\u2019, \u2018Violin_Mode\u2019, \u2018Wandering_Line\u2019, \u2018Whistle\u2019] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). <\/p>\n\n[\u2018ml_label\u2019, \u2018ml_confidence\u2019] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. <\/p>\n\n[\u2018url1\u2019, \u2018url2\u2019, \u2018url3\u2019, \u2018url4\u2019] are the links to the publicly-available Omega scans for each glitch. \u2018url1\u2019 shows the glitch for a duration of 0.5 seconds, \u2018url2\u2019 for 1 seconds, \u2018url3\u2019 for 2 seconds, and \u2018url4\u2019 for 4 seconds.<\/p>\n\n```<\/p>\n\nFor the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.<\/p>\n\nFor detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. <\/p>"]} 

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5. Counting points on smooth plane quartics

   https://doi.org/10.1007/s40993-022-00397-8  

   Costa, Edgar; Harvey, David; Sutherland, Andrew V. (March 2023, Research in Number Theory)

   Abstract We present efficient algorithms for counting points on a smooth plane quartic curve X modulo a prime p . We address both the case where X is defined over  $${\mathbb {F}}_p$$ F p and the case where X is defined over $${\mathbb {Q}}$$ Q and p is a prime of good reduction. We consider two approaches for computing $$\#X({\mathbb {F}}_p)$$ # X ( F p ) , one which runs in $$O(p\log p\log \log p)$$ O ( p log p log log p ) time using $$O(\log p)$$ O ( log p ) space and one which runs in $$O(p^{1/2}\log ^2p)$$ O ( p 1 / 2 log 2 p ) time using $$O(p^{1/2}\log p)$$ O ( p 1 / 2 log p ) space. Both approaches yield algorithms that are faster in practice than existing methods. We also present average polynomial-time algorithms for $$X/{\mathbb {Q}}$$ X / Q that compute $$\#X({\mathbb {F}}_p)$$ # X ( F p ) for good primes $$p\leqslant N$$ p ⩽ N in $$O(N\log ^3 N)$$ O ( N log 3 N ) time using O ( N ) space. These are the first practical implementations of average polynomial-time algorithms for curves that are not cyclic covers of $${\mathbb {P}}^1$$ P 1 , which in combination with previous results addresses all curves of genus $$g\leqslant 3$$ g ⩽ 3 . Our algorithms also compute Cartier–Manin/Hasse–Witt matrices that may be of independent interest. 

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