dataesr/scientific-paragraphs-categorization huggingface dataset:
XP acknowledges support from  NSF   CAREER  grant  IOS-1552868. XP and AT acknowledge support from  NSF NeuroNex  grant  1707400. AT also acknowledges support from  NIMH  and  NINDS  under Award Number  U19MH114830. We thank  Stephan Saalfeld ,  Khaled Khairy  and  Eric Trautman  for help with the parameters for 2D stitching and rough alignment of the dataset. We thank  Garrett McGrath  for computer system administration, and  May Husseini  and  Larry  and  Janet Jackel  for project administration at  Princeton University. We thank  Brock Wester ,  William Gray-Roncal ,  Sandy Hider ,  Tim Gion ,  Daniel Xenes ,  Jordan Matelsky ,  Caitlyn Bishop ,  Derek Pryor ,  Dean Kleissas ,  Luis Rodriguez  and  Miller Wilt  from  John Hopkins University Applied Physics Lab  for providing data assessments on the neural circuit reconstruction and infrastructure through BOSSdb We would like to thank the  "Connectomics at Google" team  for developing Neuroglancer and computational resource donations, in particular  J.
Data and code availability EM image data and cellular segmentation can be viewed at https://www. microns-explorer. org/cortical-mm3/. The software tools used to stitch and rough align the dataset is available in the AllenInstitute github repository https://github. com/AllenInstitute/render-modules. The volume assembly process is entirely based on image metadata and transformations manipulations and is supported by the Render service (https://github. com/saalfeldlab/render). The code for defect detection and resin detection are available at https://github. com/jabae/detectEM/ and the code for running large-scale inference can be found at https://github. com/seung-lab/SEAMLeSS/ under the "alex-emdetector" branch. The code for alignment using convolutional nets is at https://github. com/seung-lab/SEAMLeSS.
The code for neuronal boundary detection is at https://github. com/seung-lab/DeepEM. Distributed inference code can be found at https://github. com/seung-lab/chunkflow. The distributed clustering algorithms for watershed and agglomeration are implemented in https://github. com/seung-lab/abiss, and the workflow management system based on Apache Airflow can be found at https://github. com/seung-lab/seuron. The code for synaptic partner assignment and synapse segmentation is available at https://github. com/seung-lab/Synaptor/. CloudVolume, Igneous, and Kimimaro are available at https://github. com/seung-lab/cloud-volume, https://github. com/seung-lab/igneous, and https://github. com/seung-lab/kimimaro.
The fold training set contained 3,830 samples of image and label pairs, while the crack training set contained 3,894 samples. The samples had size of 256 ✕ 256 px. Labels were manually annotated with VAST (Berger, Seung, and Lichtman 2018). Training augmentations included (1) flip & rotate and (2) brightness & contrast augmentations described in the Dataset Augmentation section below. Inference was distributed in chunks of 2048 ✕ 2048 across each section with 50% overlap between adjacent chunks in both X and Y.
Turner et al. 2020). This used 1660 μm 3 of total training data with 867 clefts, including data from other EM volumes (N. L. Turner et al. 2020; Sven Dorkenwald et al. 2019; Casey M. Schneider-Mizell et al. , 2020; Buchanan et  al. 2021). We also used the Residual Symmetric U-Net architecture for this task, using a model with 16, 20, and 40 feature maps, Instance Normalization (Ulyanov, Vedaldi, and Lempitsky 2016) , an Adam optimizer (Kingma and Ba 2014) , and a binary cross entropy loss with a static learning rate of 1 ✕ 10 -3 for 2. 71 million training updates. Training augmented the data using (1) flip & rotate, (2) missing section, (3) warping, (4) brightness & contrast, and (5) misalignment techniques described in the Dataset augmentation section below. This model produced perfect assignment results within our test set of 81 clefts (with 96% accuracy within the validation set of 110 clefts).
We evaluated our final cleft detection model by analyzing the overlap between the connected components of predictions and the labels. Matching how we planned to process the larger volume, we merged connected components together which had centroid coordinates within 700 nm from one another and were assigned to the same synaptic partners in each training volume. This yielded cleft detection performance estimates of 98. 2 precision and 98. 2 recall for our small volume test set with 56 clefts. Following the methods described in (J. Wu et al. 2021) , we predicted synaptic clefts on the entire aligned dataset. The input and output patch size of the convolutional network was 192 ✕ 192 ✕ 18. The image chunk overlap and patch overlap were both 26 ✕ 26 ✕ 4. The input image chunk size was 2516 ✕ 2516 ✕ 284, and the the output synaptic cleft map size for a single task in the cloud was 2464 ✕ 2464 ✕ 276.
g. ,  Anderson et al. , 1977; Lognonné & Johnson, 2007). In contrast to InSight, both seismometers were mounted on the lander. While the Viking 1 seis--2-manuscript submitted to ESS mometer did not work properly, the Viking 2 seismometer picked up mostly vibrations from the lander owing to operation and wind sensitivity. The lander vibrations and nonideal frequency-dependent sensitivity of the Viking SP sensor inhibited the detection of marsquakes and Viking did therefore fail to provide unambiguous data for Martian seismic activity and subsurface structure studies. SEIS has been deployed on the ground of the Homestead hollow, 1. 81 m south of the closest lander foot  (Stutzmann et al. , 2021). Homestead hollow is a degraded impact crater of about 27 m diameter in Elysium Planitia with a smooth sandy, granule-and pebble-rich surface with few rocks  (Golombek et al.
(2018). Here, we avoid amplitude normalization strategies by replacing the conventional cross-correlation and linear stack with the amplitude unbiased phase cross-correlation  (PCC, Schimmel (1999) ) and time-frequency phase weighted stack (tf-PWS,  Schimmel and Gallart (2007) ;  Schimmel et al. (2011) ). In the following we highlight the main methods used to determine the reflection response at the InSight landing site and describe our data selection and processing.
Data and Time Frame The VBB SEIS data are recorded on three oblique axes. Data from the three axes are needed to build the vertical and horizontal components  (Lognonne et al. , 2019). For this study we use vertical components, recorded from May 22, 2019 to June 30, 2020 at -7-manuscript submitted to ESS 20 samples-per-second and instrument corrected to ground velocity from 0. 01 to 10 Hz. The correction is performed for the three oblique axes before rotation to the vertical and horizontal components. The late starting date has been chosen to avoid the initial deployment and testing phase. Key dates related to the seismic part of the mission are listed in Table  1. As.
Data Segmentation and Selection We filter the seismic recordings to a broad frequency band, 1. 2 -9. 8 Hz, and determine the relative RMS variability for sliding data windows. A maximum RMS variability threshold is then defined to obtain a mask and to extract data segments with RMS variability below the threshold through a minimum time duration. We use this procedure to build subsidiary data sets which contain less data problems such as glitches and outlying amplitude events. The approach and the subsidiary data sets are described in the following. The moving window RMS r is determined for window i and length N using r i = 1 N i+N/2 n=i-N/2 a 2 n , (2) where n stands for the time index and a n for the seismic record. i marks the center sample of the sliding window. In a next step the relative variance s 2 of the RMS amplitudes is computed during a second moving window analysis with window length M > N.
The relative variance s 2 of window j is expressed as s 2 j = j+M/2 i=j-M/2 r i -R j 2 (M -1)R 2 j with R j = 1 M j+M/2 i=j-M/2 r i , (3) where R j is the non-zero mean RMS amplitude within the analysis window j. We define the relative variance s 2 as the variance divided by the square of the mean to obtain a dimensionless (scale invariant) measurement of variability. This definition permits to compare data segments with variable RMS mean and to adjust to different seismic background noise levels which may happen along the Sol. In a next step, data segments are determined with s 2 j < S 2 , where S 2 is a chosen maximum RMS variability. A new subsidiary data base is then built accepting only data chunks with time duration larger than a minimum time duration, say t min. Thus, the free parameters of this data selection and segmentation approach are the window length (N and M of eqs.
It can be seen that most of the selected data segments are from the evening and morning hours. This is expected since lander and wind activity are low during the night. Still, glitches and other aseismic abrupt signals may happen during night which makes our data adaptive selection and segmentation approach different from any time dependent (e. g. , day/night) selection. It is also seen that less data has been selected during the last 60 Sols owing to the increase of bad weather during this period. The data segment length distribution for the 30% subsidiary data set is shown in is the same for both data sets. The absolute RMS amplitude is not important here, but the comparison of both histograms shows that the 30% data set consists mostly of smallamplitude segments. The 100 % data set RMS amplitudes have been obtained for 2-hour windows which consist typically of small and high RMS amplitude variability.
The data have been band-pass filtered between 0. 8 and 9. 5 Hz before computation of the autocorrelations. Different spectral lines and bumps are visible in these spectra. The spectral lines at integer frequencies are related to the "tick noise" which is an aseismic signal caused by electrical coupling during the SEIS temperature acquisition at 1 sps (sam-ple per second). The spectral amplitude bumps are related to lander modes and/or resonance of the seismic subsurface structure (e. g. ,  Ceylan et al. , 2021). The main bumps have been labeled along the spectrum for the 100 % data set. Bump 1 and spectral lines at integer frequencies appear to be enhanced in the data sets with less RMS amplitude variability, i. e. , for the 30% and specially 3% data sets. This is partly due to the attenuation of other signals and noise with higher RMS amplitude variability.
Reflection Response and Interpretations Fig. 6  shows autocorrelations computed using PCC and stacked with the tf-PWS approach. The employed seismic recordings belong to the 30 % data set. We bandpass filtered these data from 1. 2 to 8. 9 Hz and applied the three band-rejection filters explained in the previous section. The autocorrelations have been stacked within non-overlapping 3-Sol windows and negative amplitudes are plotted in blue. Note that P-wave reflections at seismic discontinuities with an impedance increase are expected to have negative am- Indeed, the interpretation of autocorrelations without any further a priori information is difficult. However, receiver function studies of marsquakes (Knapmeyer-Endrun.
Without replacement means that no autocorrelogram is used more than one time in a draw. We repeated the random drawing 40 times to enable the determination of mean and standard deviation for the similarity to the reference waveform. A comparison of the similarities for the 100 %, 30 %, and 3% data sets is shown in Figs 8e,f and S5e,f. It can be seen that slowest signal convergence is achieved using the 100 % data set (black curves). This means that a high amplitude variability is not contributing significantly to the extraction of the EGF. Note that this result does not change when using only the data until Sol 410. I. e. , the increasing RMS variability after Sol 450 (Fig. 2 ) is not causing the slower convergence. The 3 % and 30 % data sets seem to provide more similar results. The 3 % and 30 % data sets have a total length of 151.
Acknowledgments French authors are supported by  ANR   MAGIS  ( ANR-19-CE31-0008-08 ) and by  CNES  for SEIS science support. MS thanks SANIMS ( RTI2018-095594-B-I00 ) and invitations for research stays at IPGP. marsquake inversion. Earth and Space Science, e2020EA001118. Efron, B. ,  & Tibshirani ,  R. ( 1986 ). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science,  54-75. Farra, V. ,  & Vinnik , L. (2000). Upper mantle stratification by p and s receiver func--24-manuscript submitted to ESS.
Data Availability Statement We acknowledge NASA, CNES, their partner agencies and Institutions (UKSA, SSO, DLR, JPL, IPGP-CNRS, ETHZ, IC, MPS-MPG) and the flight operations team at JPL, SISMOC, MSDS, IRIS-DMC and PDS for providing SEED SEIS data. Seis raw data are available at https://doi. org/10. 18715/SEIS. INSIGHT. XB 2016. Processed data corresponding to this article are in  Schimmel et al. (2021). PCC and tf-PWS software are available at http://diapiro. ictja. csic. es/gt/mschi/SCIENCE/tseries. html#software. Figures have been done with SAC and GMT  (Goldstein & Snoke, 2005;  Wessel et al. , 2019).
DustPedia To best constrain our chemical evolution models, it is key to have a sizable sample of galaxies for which we have reliable gas, stellar and dust masses, as well as Star Formation Rates (SFR), metallicities and Nitrogen-to-Oxygen ratios. Few samples have all this data available, and the largest which does is the DustPedia  (Davies et al. , 2017)  sample. DustPedia is a collaborative focused research project working towards a definitive understanding of dust in the local Universe, by capitalising on the legacy of Herschel. The full DustPedia sample consists of 875 nearby (v < 3000 km/s), extended (D25 > 1 ) galaxies that have been observed by Herschel and have a near-infrared (NIR) detected stellar component. These galaxies have excellent multi-wavelength aperture-matched photometry available (typically 25 bands;  Clark et al.
, 2019). PG16 Metallicities for HiGH and HAPLESS are taken from De  Vis et al. (2017b). Nitrogen-to-Oxygen ratios were calculated for this work following exactly the method used for DustPedia  (De Vis et al. , 2019). We have removed all the HiGH and HAPLESS sources that are already present in the DustPedia sample and all sources that do not have all the required data available. The remaining 18 HiGH sources and 5 HAPLESS sources are all LTGs and are added to our observational sample.
We also impose a strong limit so that no more than 50 per cent of the gas mass can be blown out of the galaxy in a single (30 Myr) timestep. To determine what happens to the outflows after they leave the galaxy, we use the outflow velocity distribution to determine the median velocity of each component in each bin. The mean velocity for each bin is then used together with the average total (baryon + dark matter) halo mass profile for each bin from the Illustris TNG100 simulation (extracted using the illustris python package;  Nelson et al. , 2019)  to determine the (friction-less) ballistic trajectory for each outflow component using a simple simulation. If the mean outflow velocity is larger than the escape velocity, the outflowing gas is lost into the IGM. However, a large fraction of the outflows fall back onto the galaxy after a time τrec determined by its ballistic trajectory.
DATA AVAILABILITY The DustPedia data used in this paper are available from the DustPedia Archive (http://dustpedia. astro. noa. gr/) and the HIGH and HAPLESS data are tabulated in De Vis et al. (2017a)  and  Clark et al. (2015)  respectively. The chemical evolution code used for producing all the chemical evolution models in this work is publicly available on Github (https://github. com/zemogle/chemevol).
The Senegal_2011 and Senegal_2015 strains also underwent depletion of host ribosomal RNA following a homemade protocol  24. cDNA was produced with random hexameric primers (Roche) using M-MLV (Invitrogen) or Superscript IV (Thermo Fisher Scientific) reverse transcriptase. After second-strand synthesis with Second-Strand Synthesis Buffer (New England BioLabs), dsDNA was used for library preparation using Nextera XT DNA Kit (Illumina) or NEBNext Ultra II RNA Library Prep kit (New England Biolabs) according to the manufacturer's instructions. The final libraries were checked on a Bioanalyzer (Agilent) and combined with other libraries from unrelated projects to be sequenced on an Illumina NextSeq 500 instrument (150 cycles, paired ends). Raw sequencing datasets were deposited to the European Nucleotide Archive database under accession number PRJEB39677.
The sequencing data were processed through a custom pipeline  94. Briefly, nucleotides with a quality score <30 were trimmed using Trimmomatic v0. 36 (ref. 95 ). Reads were filtered against the Aedes albopictus reference genome using Bowtie2 v2. 3. 4. 3 (ref. 96 ) and the remaining reads were subjected to de novo assembly with the Ray v2. 3. 1-mpi tool  97  or metaSPAdes  98. Scaffolds were subjected to a blastn search in the nucleotide NCBI database using BLAST v2. 2. 40 (ref. 99 ). The closest hit was used to produce a chimeric genome sequence that served as a reference to remap the filtered reads with Bowtie2 v2. 3. 4. 3 and generate a consensus sequence. Phylogenetic analyses. Genome sequences of ZIKV and Spondweni virus were retrieved from GenBank. The nucleotide sequences were aligned using MAFFT  100.
The phylogenetic analyses were performed based on nucleotide (open reading frame), amino-acid, and codon alignments using the maximum-likelihood method with substitution models (GTR + F + G4, FLU + G + R3, and SCHN05 + FU + R4, respectively) selected with ModelFinder  101. Support for the tree was assessed with 1000 ultrafast bootstrap replicates  102. The consensus trees were reconstructed with IQ-TREE v1. 6. 3 (ref. 103 ) and visualized in FigTree v1. 4. 4 (https://github. com/ rambaut/figtree/releases). The phylogenetic tree root position was in agreement among the nucleotide-based tree without an outgroup (midpoint), amino-acid or codon tree with Spondweni virus as an outgroup, as well as with previously published ZIKV phylogenetic trees  104. In addition to the seven genome sequences generated in this study, the alignment used to construct the tree included 37 sequences from GenBank (accession numbers: MK241416; MF574587; KX198135; KU647676; KY693679; KU497555; MF794971; MK829154; MH882540; KY014295; MH063262; KY631494; KY693677; MF434522; MF801378; KU758877; KU937936; KY693680; KU509998; KX806557; LC191864; KU963796; MF036115; LC219720; MN190155; KY241695; MH013290; MK238037; KX051562; EU545988; KX377336; MN025403; MF510857; KU963574; KF268948; MK105975; KY288905).
Epidemiological modeling overview. To assess the epidemiological potential of ZIKV strains from empirically observed variation in mosquito infection dynamics, stochastic agentbased simulations were performed using the R package nosoi 54 (available from https://github. com/slequime/nosoi). This modeling framework accounts for the influence of within-host infection dynamics on transmission probability during mosquito-human infectious contacts in a full epidemiological context  106. Virus transmission was assumed to only occur either between an infected human and an uninfected mosquito, or between an infected mosquito and an uninfected human. Sexual and vertical transmission modes were considered epidemiologically insignificant  107, 108  and ignored in the model, and so were superinfections.
The EIP value was used as a threshold for the individual probability of virus transmission over time. Transmission probability was 0% before the EIP and 100% after the EIP. Implementation. Epidemiological simulations were run in R v3. 6. 0 using the packages foreach (https://cran. r-project. org/web/packages/foreach/foreach. pdf), doParallel (https://cran. r-project. org/web/packages/doParallel/doParallel. pdf), and nosoi  54. The custom code is provided in Supplementary Software 1. Briefly, simulations were initiated with one infected human and zero infected mosquitoes and run for 400 days or until 100,000 humans or 1,000,000 mosquitoes were reached, whichever occurred first. A total of 100 independent replicate simulations were run for each ZIKV strain.
Acknowledgements We thank  Catherine Lallemand  for assistance with mosquito rearing, the  Institut Pasteur animal facility  staff for the breeding of Ifnar1 -/-mice. We thank  Diego Ayala ,  Christophe Paupy , and  Davy Jiolle  for initially providing the mosquito colony from Gabon and  Anubis Vega-Rúa  for initially providing the mosquito colony from Guadeloupe. We are grateful to the volunteers and the  ICAReB  staff for the human blood supply. We thank  Elke Maas ,  Carolien De Keyzer , and  Lindsey Bervoets  for technical assistance with the AG129 mouse experiments, the  KU Leuven Rega animal facility  staff for breeding the AG129 mice, and  Jelle Matthijnssens  and  Daan Jansen  for resequencing the ZIKV strains at the  KU Leuven Rega Institute. We thank  Alexandre Hego  and the  GIGA-Imaging  platform for their precious help and advice, and  Christian Alfano  and  Ivan Gladwyn-Ng  for their guidance with the vertical transmission experiments.
This work was primarily funded by the  European Union 's  Horizon 2020 research and innovation program  under ZikaPLAN grant agreement no. 734584  (to L. L. and J. N. ) and under ZIKAlliance grant agreement no. 734548  (to L. N. and J. N. ). This work was also supported by  Agence Nationale de la Recherche  (grants  ANR-16-CE35-0004-01 ,  ANR-17-ERC2-0016-01 , and  ANR-18-CE35-0003-01  to L. L. ), the  French Government 's  Investissement d'Avenir program Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID  to L. L. and X. M. ), the  Inception program  ( Investissement d'Avenir  grant  ANR-16-CONV-0005  to L. L. ), the  Research Foundation Flanders (FWO Ph. D. fellowship  1S21918N  to S. J. ), and the  Fonds de la Recherche Scientifique (FRIA Ph.
Data availability Raw sequence data generated in this study were deposited in the European Nucleotide Archive database under accession number PRJEB39677 (https://www. ebi. ac. uk/ena/ browser/view/PRJEB39677). Phylogenetic analysis of virus genomes used GenBank accession numbers MK241416, MF574587, KX198135, KU647676, KY693679, KU497555, MF794971, MK829154, MH882540, KY014295, MH063262, KY631494, KY693677, MF434522, MF801378, KU758877, KU937936, KY693680, KU509998, KX806557, LC191864, KU963796, MF036115, LC219720, MN190155, KY241695, MH013290, MK238037, KX051562, EU545988, KX377336, MN025403, MF510857, KU963574, KF268948, MK105975, and KY288905. Source data are provided with this paper.
The BIM model is often considered as a graphical database of a building, containing its geometric and semantic data, which allows access to data throughout its entire lifecycle directly from the equipment  [12] [13] [14]. This data access, and the fact that they can contain all the geometric and semantic data of the building, has led many researchers to try to use BIM as a basis to develop the DT of a building, for either existing buildings or those in the design stage  [15, 16]. The concept of Digital Twin first appeared in 2002 in a presentation by Grieves on the concept of PLM (Product Lifecycle Management) as an evolution of it, and it was then redefined in 2012 by Grieves and Vickers  [17]  as a digital representation of a physical asset and the automatic connections that bind them together.