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1. NEON Tree Crowns Dataset

   https://doi.org/10.5281/zenodo.3765871  

   Weinstein, Ben ; Marconi, Sergio ; Zare, Alina ; Bohlman, Stephanie ; Graves, Sarah ; Singh, Aditya ; White, Ethan ( January 2020 , Zenodo)

   Abstract

   The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. 

   How can I see the data?

   A web server to look through predictions is available through idtrees.org

   Dataset Organization

   The shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: https://data.neonscience.org/data-products/explore?search=camera. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see https://www.neonscience.org/field-sites/field-sites-map. 

   The predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available:

   Height: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001)

   Area: The crown area in m² of the rectangular bounding box.

   Label: All data in this release are "Tree".

   Score: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)

   How were predictions made?

   The DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.

   How were predictions validated?

   Please see

   Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics, 56, 101061.

   Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv.

   Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing 11.11 (2019): 1309.

   Were any sites removed?

   Several sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request.

   # Contact

   We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. 

   Gordon and Betty Moore Foundation: GBMF4563 

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2. Inclusive and differential cross section measurements of single top quark production in association with a Z boson in proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV

   https://doi.org/10.1007/JHEP02(2022)107  

   Tumasyan, A. ; Adam, W. ; Andrejkovic, J. W. ; Bergauer, T. ; Chatterjee, S. ; Damanakis, K. ; Dragicevic, M. ; Escalante Del Valle, A. ; Frühwirth, R. ; Jeitler, M. ; et al ( February 2022 , Journal of High Energy Physics)

   A bstract Inclusive and differential cross sections of single top quark production in association with a Z boson are measured in proton-proton collisions at a center-of-mass energy of 13 TeV with a data sample corresponding to an integrated luminosity of 138 fb − 1 recorded by the CMS experiment. Events are selected based on the presence of three leptons, electrons or muons, associated with leptonic Z boson and top quark decays. The measurement yields an inclusive cross section of $$ {87.9}_{-7.3}^{+7.5}{\left(\mathrm{stat}\right)}_{-6.0}^{+7.3}\left(\mathrm{syst}\right) $$ 87.9 − 7.3 + 7.5 stat − 6.0 + 7.3 syst fb for a dilepton invariant mass greater than 30 GeV, in agreement with standard model (SM) calculations and represents the most precise determination to date. The ratio between the cross sections for the top quark and the top antiquark production in association with a Z boson is measured as $$ {2.37}_{-0.42}^{+0.56}{\left(\mathrm{stat}\right)}_{-0.13}^{+0.27}\left(\mathrm{syst}\right) $$ 2.37 − 0.42 + 0.56 stat − 0.13 + 0.27 syst . Differential measurements at parton and particle levels are performed for the first time. Several kinematic observables are considered to study the modeling of the process. Results are compared to theoretical predictions with different assumptions on the source of the initial-state b quark and found to be in agreement, within the uncertainties. Additionally, the spin asymmetry, which is sensitive to the top quark polarization, is determined from the differential distribution of the polarization angle at parton level to be 0 . 54 ± 0 . 16 (stat) ± 0 . 06 (syst), in agreement with SM predictions. 

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3. DIMC: 2219TOPS/W 2569F2/b Digital In-Memory Computing Macro in 28nm Based on Approximate Arithmetic Hardware

   https://doi.org/10.1109/ISSCC42614.2022.9731659  

   Wang, Dewei ; Lin, Chuan-Tung ; Chen, Gregory K. ; Knag, Phil ; Krishnamurthy, Ram K. ; Seok, Mingoo ( February 2022 , 2022 IEEE International Solid- State Circuits Conference (ISSCC))

   In-memory-computing (IMC) SRAM architecture has gained significant attention as it achieves high energy efficiency for computing a convolutional neural network (CNN) model [1]. Recent works investigated the use of analog-mixed-signal (AMS) hardware for high area and energy efficiency [2], [3]. However, AMS hardware output is well known to be susceptible to process, voltage, and temperature (PVT) variations, limiting the computing precision and ultimately the inference accuracy of a CNN. We reconfirmed, through the simulation of a capacitor-based IMC SRAM macro that computes a 256D binary dot product, that the AMS computing hardware has a significant root-mean-square error (RMSE) of 22.5% across the worst-case voltage, temperature (Fig. 16.1.1 top left) and 3-sigma process variations (Fig. 16.1.1 top right). On the other hand, we can implement an IMC SRAM macro using robust digital logic [4], which can virtually eliminate the variability issue (Fig. 16.1.1 top). However, digital circuits require more devices than AMS counterparts (e.g., 28 transistors for a mirror full adder [FA]). As a result, a recent digital IMC SRAM shows a lower area efficiency of 6368F2/b (22nm, 4b/4b weight/activation) [5] than the AMS counterpart (1170F2/b, 65nm, 1b/1b) [3]. In light of this, we aim to adopt approximate arithmetic hardware to improve area and power efficiency and present two digital IMC macros (DIMC) with different levels of approximation (Fig. 16.1.1 bottom left). Also, we propose an approximation-aware training algorithm and a number format to minimize inference accuracy degradation induced by approximate hardware (Fig. 16.1.1 bottom right). We prototyped a 28nm test chip: for a 1b/1b CNN model for CIFAR-10 and across 0.5-to-1.1V supply, the DIMC with double-approximate hardware (DIMC-D) achieves 2569F2/b, 932-2219TOPS/W, 475-20032GOPS, and 86.96% accuracy, while for a 4b/1b CNN model, the DIMC with the single-approximate hardware (DIMC-S) achieves 3814F2/b, 458-990TOPS/W 

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4. Measurements of Higgs boson production cross-sections in the H → τ+τ− decay channel in pp collisions at $$ \sqrt{s} $$ = 13 TeV with the ATLAS detector

   https://doi.org/10.1007/JHEP08(2022)175  

   Aad, G. ; Abbott, B. ; Abbott, D. C. ; Abed Abud, A. ; Abeling, K. ; Abhayasinghe, D. K. ; Abidi, S. H. ; Aboulhorma, A. ; Abramowicz, H. ; Abreu, H. ; et al ( August 2022 , Journal of High Energy Physics)

   A bstract Measurements of the production cross-sections of the Standard Model (SM) Higgs boson ( H ) decaying into a pair of τ -leptons are presented. The measurements use data collected with the ATLAS detector from pp collisions produced at the Large Hadron Collider at a centre-of-mass energy of $$ \sqrt{s} $$ s = 13 TeV, corresponding to an integrated luminosity of 139 fb − 1 . Leptonic ( τ → ℓν ℓ ν τ ) and hadronic ( τ → hadrons ν τ ) decays of the τ -lepton are considered. All measurements account for the branching ratio of H → ττ and are performed with a requirement |y H | < 2 . 5, where y H is the true Higgs boson rapidity. The cross-section of the pp → H → ττ process is measured to be 2 . 94 ± $$ 0.21{\left(\mathrm{stat}\right)}_{-0.32}^{+0.37} $$ 0.21 stat − 0.32 + 0.37 (syst) pb, in agreement with the SM prediction of 3 . 17 ± 0 . 09 pb. Inclusive cross-sections are determined separately for the four dominant production modes: 2 . 65 ± $$ 0.41{\left(\mathrm{stat}\right)}_{-0.67}^{+0.91} $$ 0.41 stat − 0.67 + 0.91 (syst) pb for gluon-gluon fusion, 0 . 197 ± $$ 0.028{\left(\mathrm{stat}\right)}_{-0.026}^{+0.032} $$ 0.028 stat − 0.026 + 0.032 (syst) pb for vector-boson fusion, 0 . 115 ± $$ 0.058{\left(\mathrm{stat}\right)}_{-0.040}^{+0.042} $$ 0.058 stat − 0.040 + 0.042 (syst) pb for vector-boson associated production, and 0 . 033 ± $$ 0.031{\left(\mathrm{stat}\right)}_{-0.017}^{+0.022} $$ 0.031 stat − 0.017 + 0.022 (syst) pb for top-quark pair associated production. Measurements in exclusive regions of the phase space, using the simplified template cross-section framework, are also performed. All results are in agreement with the SM predictions. 

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5. Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

   Wu, Siqi ; Resnick, Paul ( May 2021 , Proceedings of the Fifteenth International AAAI Conference on Web and Social Media)

   Budak, Ceren ; Cha, Meeyoung ; Quercia, Daniele ; Xie, Lexing (Ed.)

   We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf. 

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