dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, supermatrix analyses were performed on concatenated nuclear gene alignments and concatenated plastid gene alignments compiled using previously described methods  72. All scripts used to perform analyses on the nuclear gene data are available at https://doi. org/10. 5281/zenodo. 3255100. Multiple sequence alignment and data filtering. We built a multiple sequence alignment based on predicted amino acid sequences of each gene and forced DNA sequences to conform to the amino acid alignment. We first divided sequences in each gene into two subsets, full-length and abnormal sequences, and then used PASTA  73  with default settings to align full-length sequences and UPP  74  to add abnormal sequences to the full-length alignment. We designated as abnormal any sequence that was 66% shorter or 66% longer than the median length of the full-length gene sequences.
Gene-tree estimation. To estimate gene trees, we used RAxML v. 8. 1. 17  76 , with one starting tree for building initial trees (used for long-branch filtering) and 10 different starting trees for final gene trees. Support was assessed with 100 replicates of bootstrapping. For DNA analyses, the GTR substitution model and the GAMMA-distributed site rates were used. For amino acid sequences, we used a Perl script adapted from the RAxML website to search among 16 different substitution models on a fixed starting tree per gene and chose the model with the highest likelihood ( JTT, JTTF or JTTDCMUT were selected for 349 out of 410 genes). For amino acid trees, we also used the GAMMA-distributed site rates. Species tree estimation. We used ASTRAL-II 15 v. 5. 0. 3 to estimate the species tree on the basis of all 410 genes; using 384 genes that each included at least half of the species changed only 3 low-support branches.
8. 1. 21  76. We then projected the maximumlikelihood parameter values for each gene into a two-dimensional plane using principal component analysis  88. We performed k-means clustering  89  in R  90  to group the codon positions into partitions, selecting k = 8, which accounted for 80% of the variation. Trees derived from nucleotide alignments can be found at https://doi. org/10. 5281/zenodo. 3255100). To examine the influence of the starting tree on the likelihood of the final tree, we performed preliminary analyses on an earlier version of our supermatrices. We generated nine different maximum-parsimony starting trees using RAxML v. 8. 1. 21 and one maximum-likelihood starting tree using FastTree-2 v. 2. 1. 5  91. We then ran ExaML on each of the starting trees, noting the final maximum-likelihood score.
We identified species with potential WGDs by comparing their paralogue age distribution to a simulated null using a Kolmogorov-Smirnov goodness of fit test  95. We then used mixture modelling and manual curation to identify significant peaks consistent with a potential WGD and to estimate their median paralogue K s values. Significant peaks were identified using a likelihood ratio test in the boot. comp function of the package mixtools in R  96. Estimating orthologous divergence. To place putative WGDs in relation to lineage divergence, we estimated the synonymous divergence of orthologues among pairs of species that may share a WGD based on their phylogenetic position and evidence from the within-species K s plots. We used the RBH Orthologue pipeline  17  to estimate the mean and median synonymous divergence of orthologues and compared those to the synonymous divergence of inferred paleopolyploid peaks.
MAPS analyses of WGDs from transcriptomes of multiple species. To infer and locate putative WGDs in our datasets, we used a gene-tree sorting and counting algorithm, the multi-taxon paleopolyploidy search (MAPS) tool  18. For each MAPS analysis, we selected at least two species that potentially share a WGD in their ancestry as well as representative species from lineages that may phylogenetically bracket the WGD. MAPS uses this given species tree to filter collections of nuclear gene trees for subtrees consistent with relationships at each node in the species tree. Using this filtered set of subtrees, MAPS identifies and records nodes with a gene duplication shared by descendant taxa. To infer and locate a potential WGD, we compared the number of duplications observed at each node to a null simulation of background gene birth and death rates  97, 98.
To maintain sufficient gene-tree numbers for each MAPS analysis, we used collections of gene-family phylogenies for six to eight taxa to infer ancient WGDs. For each MAPS analysis, the transcriptomes were translated into amino acid sequences using the TransPipe pipeline  17. Using these translations, we performed reciprocal protein BLAST (BLASTp) searches among datasets for the MAPS analysis using a cut-off of E = 1 × 10 -5. We clustered gene families from these BLAST results using OrthoFinder under the default parameters  102. Using a custom Perl script (https:// bitbucket. org/barkerlab/MAPS), we filtered for gene families that contained at least one gene copy from each taxon in a given MAPS analysis and discarded the remaining OrthoFinder clusters. We used PASTA  73  for automatic alignment and phylogeny reconstruction of gene families.
For each gene-family phylogeny, we ran PASTA until we reached three iterations without an improvement in likelihood score using a centroid breaking strategy. Within each iteration of PASTA, we constructed subset alignments using MAFFT  103  , used Muscle 104 for merging these subset alignments and RAxML 76 for tree estimation. The parameters for each software package were the default options for PASTA (https://bitbucket. org/barkerlab/1kp). We used the best-scoring PASTA tree for each multispecies nuclear gene family to collectively estimate the numbers of shared gene duplications on each branch of the given species. To generate null simulations, we first estimated the mean background gene duplication rate (λ) and gene loss rate (μ) with WGDgc 98 (Supplementary Tables  5, 11 ). Gene count data were obtained from OrthoFinder 102 clusters associated with each species tree (Supplementary Table  5 ).
λ and μ were estimated using only gene clusters that spanned the root of their respective species trees, which has been shown to reduce biases in the maximum-likelihood estimates  98  of λ and μ. We chose a maximum gene-family size of 100 for parameter estimation, which was necessary to provide an upper bound for numerical integration of node states  98. We provided a prior probability distribution on the number of genes at the root of each species tree, such that ancestral gene-family sizes followed a shifted geometric distribution with mean equal to the average number of genes per gene family across species (Supplementary Table  5 ). Gene trees were then simulated within each MAPS species trees using the GuestTreeGen program from GenPhyloData  105. For each species tree, we simulated 3,000 gene trees with at least one tip per species: 1,000 gene trees at the λ and μ maximum-likelihood estimates, 1,000 gene trees at half the estimated λ and μ, and 1,000 trees at three times λ and μ.
Loss of low abundance transcripts. To account for possible bias in the sampling of the gene space, all species that showed low levels of transcriptome completeness were removed. The lowest value of transcriptome completeness obtained from 30 annotated plant genomes was used as the lower exclusion limit. We removed all samples in which more than 42. 5% of BUSCO 31 sequences were missing using default settings and the eukaryotic dataset as the query database. Gene-family over-prediction. We clustered assembled protein sequences by sequence similarity and merged sequences that showed at least 99% identity. To check for the possibility of merging sequences that should be counted separately, different identity cut-offs were compared between the 1KP datasets and 32 annotated plant genomes. Extended Data Figure  3c , d shows the average gene-family sizes for 23 gene families and 13 clades obtained from 1KP samples and 32 annotated plant genomes.
These gene-family sizes show a high Pearson correlation (r = 0. 95) between 1KP samples and plant genomes, and therefore a linear relationship between the two approaches is indicated. The results from the 1KP dataset are on average smaller by a factor of 2. 3. Although this is a clear underestimate, the scale factor by which the estimate is too small is relatively consistent, especially as the gene-family sizes increase. Sequence clustering. We used cdhit v. 4. 5. 7  108, 109  to reduce the number of protein sequence duplications in the dataset. We assessed 100%, 99. 5%, 99%, 95% and 90% sequence identity thresholds. The percentage of remaining sequences for the 1KP samples and 32 reference genomes is displayed in Extended Data Fig. 3f. We chose 99% sequence identity as the value to use for this study.
Estimation of gene-family size. Gene-family experts provided the knowledge to classify protein sequences as members of gene families with profile HMMs. In total, 46 HMMs representing 23 large gene families  30  were used to estimate gene-family sizes in the analysed species. Classification rules and HMMs for 14 gene families that have been published previously  107  were converted to HMMER3 format and used in this study. Gene-family classification rules and HMMs for the remaining nine families can be found in Supplementary Table  8. HMMs were taken from the Pfam database (accessed 12 May 2016) or were provided by genefamily experts (Supplementary Table  8 ). HMMER  110 (v. 3. 1b2 ) was used to scan for matches in the filtered 1KP dataset. Where available, gathering thresholds were used; otherwise an E-value cut-off was applied to indicate domain presence.
Acknowledgements The  1KP  initiative was funded by the  Alberta Ministry of Advanced Education and Alberta Innovates AITF/iCORE Strategic Chair  ( RES0010334 ) to G. K. -S. W. ,  Musea Ventures , The  National Key Research and Development Program of China  ( 2016YFE0122000 ), The  Ministry of Science and Technology of the People's Republic of China  ( 2015BAD04B01/2015BAD04B03 ), the  State Key Laboratory of Agricultural Genomics  ( 2011DQ782025 ) and the  Guangdong Provincial Key Laboratory of core collection  of crop genetic resources research and application (2011A091000047). Sequencing activities at  BGI  were also supported by the  Shenzhen Municipal Government of China  ( CXZZ20140421112021913/JCYJ20150529150409546/JCYJ20150529150505656 ). Computation support was provided by the  China National GeneBank (CNGB) , the  Texas Advanced Computing Center (TACC) ,  WestGrid  and  Compute Canada ; considerable support, including personnel, computational resources and data hosting, was also provided by the iPlant Collaborative (CyVerse) funded by the  National Science Foundation  ( DBI-1265383 ),  National Science Foundation  grants  IOS 0922742  (to C.
Data availability All raw sequence reads have been posted in the NCBI SRA database under BioProject accession PRJEB4922. SRA entries for each assembly are listed in Supplementary Table  1. All sequence, gene tree and species tree data can be accessed through CyVerse Data Commons at https:// doi. org/10. 25739/8m7t-4e85. In addition, gene-family nucleotide and amino acid FASTA files can also be found at http://jlmwiki. plantbio. uga. edu/onekp/v2/; multiple sequence alignments, gene trees and species trees for single-copy nuclear genes included in phylogenomic analyses are also at https://doi. org/10. 5281/zenodo. 3255100; K s plots, alignments and trees used for WGD analyses can be found at https://bitbucket. org/ barkerlab/1kp; and data used for gene-family expansion analyses can be found at https://github.
Code availability Scripts used for phylogenomic species tree analyses are available at https://doi. org/10. 5281/zenodo. 3255100. Scripts used for MAPS analyses of WGDs are available at https://bitbucket. org/barkerlab/maps and scripts used for gene-family expansion analyses are available at https://github. com/GrosseLab/OneKP-gene-family-evo. All script files are also accessible through CyVerse Data Commons at https://doi.
The Concentrator Integrated Circuit (CIC) ASIC is a front-end chip for both Pixel-Strip (PS) and Strip-Strip (2S) modules of the future Phase-II CMS Outer Tracker upgrade at the High Luminosity LHC (HL-LHC). It collects the digital data coming from eight upstream front-end chips (either MPAs or CBCs, depending on the module type), formats the signal in data packets containing the trigger information from eight bunch crossings and the raw data from events passing the first trigger level, and finally transmits them to the LpGBT unit. The design and its implementation in a 65 nm CMOS technology of the first prototype that integrates all functionalities for system level operation are presented in this contribution.
Introduction The next generation of ASICs at HL-LHC will be exposed to the challenging conditions related to extremely high levels of radiation and particle rates  [1]. In the so-called phase-II upgrade, the CMS experiment needs a completely new tracker detector, requiring an higher granularity able to cope with the increased occupancy. One of the upgrade's main goal is to maintain the performances of the phase-I CMS detector at the corresponding pileup rates in the HL environment. In order to reach this goal, the new CMS Outer Tracker (OT) employs a novel technique for detecting high transverse momentum particles and provides this information at the 40 MHz Bunch Crossing (BX) rate to the L1-trigger system. In parallel, triggered events are transmitted to the Data Acquisition (DAQ) system at a nominal average trigger rate of 750 kHz.
ASIC architecture The CIC collects the digital data coming from 8 upstream FE chips (MPAs  [2]  in the case of PS, CBCs  [3]  in the case of 2S), it formats those data in packets containing the trigger information from 8 BXs and the raw data from events passing the first trigger level and it transmits them to the LpGBT chip  [4]. The CIC inputs are 6 differential bitlines for each of the 8 FE chip (1 bitline for L1 data and 5 bitlines for Trigger data) at 320 Mbps with a different data format for PS and 2S modules. The outputs are 7 differential bitlines (1 for L1 and 6 for Trigger data) at 320 Mbps (640 Mbps in the case of inner layers PS modules). All the bitlines between FE chips and CIC, and between CIC and LpGBT, are differential (sLVS). Due to the different balancing of the power distribution network between PS and 2S modules, the digital core of the CIC is powered at 1 V (for PS) and 1.
25 V (for 2S), while the custom sLVS drivers and receivers  [5]  are powered at 1. 25 V. Two different data streams are generated by the CIC to the Back-End (BE) and independently handled: the L1 and the Trigger data stream. L1 data is the stream of frames responding to the L1-accept trigger signal. Each frame aggregates the hit clusters data received from all the FE chips. The frame size is flexible and it depends on the number of hits in the module. The sustainable capacity (per CIC chip and per L1-accept event) is 254 clusters (127 Pixel-clusters plus 127 Strip-clusters for PS) and 127 clusters (2S). The CIC internal processing of the L1 data starts after reception of the L1-accept signal: the arrival of a new L1 data frame is detected and stored by the corresponding L1-FE Block into a FIFO (see Figure  1 ).
Each FIFO stores data from up to 16 L1 events with a fixed size per event for a maximum latency of 12. 6 μs and each L1-FE block handles frame reception independently from the others. Data stored in the eight FIFOs that correspond to the same L1-accept event are then merged by the L1 'output formatter' block. The Trigger data stream contains the necessary information for the L1-accept generation which is sent through 8 BX-long block-synchronous frames. Each data block includes the data aggregated from 8 FE input chips. The CIC provides to the readout chain an extra factor 10 of data reduction at 320 MHz (5 at 640 MHz), by grouping data over time (8 BX blocks) and space (8 input chips). Furthermore, the CIC can select and forward up to 40 stubs among the 192 potential input stubs. The CIC stores the trigger data incoming from the FE chips during 8 BXs, sorts them with regard to the information on the stub direction (bend info) and finally sends them out in 8 packets synchronous with the BX clock.
A word alignment procedure is required for the data received from the trigger input stream in order to identify the beginning and the end of the event payload and to align them with the 40 MHz clock. The blocks 'StubSelection' and 'StubOutputFormatter', shown in Figure  1 , collect the data produced by the 8 FE reconstruction blocks at a frequency of 40 MHz and store them into a stub register. A selection based on the stub bend is performed in the case when the stub occupancy doesn't fit the size of this register, giving priority to the smaller bends. The output stage is a serializer block, taking the packet register and serializing the data on the 6 trigger output bitlines. Whereas the elaboration of the trigger information is the same for both FE chips (CBC and MPA), a different treatment is required for L1 data, depending on the FE chip.
Data coming from CBCs are unsparsified, thus a data sparsification process is performed within the CIC. The allocated bandwidth for trigger and L1 frames is the same for MPA and CBC configuration. However, in order to handle the much higher occupancy in the tracker inner layers, trigger data from the MPA are sent in synchronous blocks of 2 clock cycles in order to average the stub rates. For 2S modules, trigger data transmission can be configured via 5 or 6 output lines. In the PS modules, all 6 trigger lines are always used. As for the 2S configuration, there is the possibility to transmit the trigger block without any bend information. The input data for the CIC are produced by eight different FE ASICs each one clocked by an external 320 MHz clock. A bitline phase alignment feature is required in order to resynchronize the CIC input bitlines for both datapaths with respect to the internal CIC system clock.
The reader, also protected by a waterproof box, had a memory capacity of about 1000 recordings and an interface that worked with Windows software. The minimum time between the detection of two successive PIT tags was 70 ms. For the toad experiment, the detectors were positioned on the ground of the tunnel. The first detector was positioned at a distance of 0. 5 m from the entrance, the second at 2. 5 m, the third at 4. 5 m and the last at 6. 5 m (or 0. 5 m from the exit). The distance between the first and last detector was thus about 6 m (Fig. 1 ). Preliminary observations showed that the toads easily crossed the detectors (thickness of 2. 5 cm) when moving in the tunnel. In the ground beetle experiment, the first detector was located 1 m from the entrance, the second at 3 m, the third at 5 m and the last at 7 m, i.
Acknowledgements We would like to thank  A. Conan ,  E. Garcia ,  A. Legrand ,  Q. Le Petitcorps ,  Fiona Pujalte  and  Victor Vergnes Pujalte  for their support in the field and  D. Picard  for student supervision. Many thanks to  Elise Bradbury  (elisebrad@gmail. com)for the text review and editing. This manuscript was improved by the reviewer's constructive comments.
Data and simulation This analysis is based on 36. 1 fb -1 of pp collisions recorded by the ATLAS experiment at the LHC at a center-of-mass energy of 13 TeV in 2015 and 2016. A number of quality criteria were imposed to ensure that the data were collected during stable beam conditions with the relevant detectors operational. Simulated signal and background events are used to optimize the event selection, to estimate the background contribution and to perform the hypothesis test of the benchmark signal models considered. The main backgrounds after applying criteria to enhance potential signals originate from SM t t and multijet production. The t t contribution and the related modeling uncertainties are evaluated using Monte Carlo (MC) simulated events, while the multijet contribution is estimated directly from data.
The large-R jets with the highest and the second-highest p T in the event are referred to as the leading and sub-leading jets, respectively. The leading jet has to satisfy p T > 500 GeV to ensure a nearly full trigger efficiency. The trigger efficiency is measured using a control sample in data and found to be approximately 100% in this p T range. The invariant mass m JJ of the two leading large-R jets is required to be m JJ > 1 TeV to avoid a kinematic bias caused by the jet p T requirements. The two leading jets are required to have an azimuthal angle difference larger than 1. 6. In addition, each jet is required to have at least one track-jet within ∆R = 1. 0 satisfying the loose b-tagging efficiency working point. The fraction of events with more 3 The rapidity is defined as y = 1  2 ln E+p z E-p z where E is the energy and p z is the longitudinal component of the momentum along the beam direction.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia;.
The data The data source used in this article is the Human Mortality Database (2018), which contains historical mortality data for 43 different countries and territories. HMD constitutes a reliable data source because it includes high-quality historical mortality data that was subject to a uniform set of procedures, guaranteeing the cross-national comparability of the information. In order to test and illustrate the performance of the method, we fit the model using life table death counts for the male population in England and Wales between 1960 and 2016. Additional results for various countries are presented in Appendix 2.
Thus, six models are evaluated. Four of them are targeting the log-transformed death rates, log m x , and the other two (Oeppen and MEM) are focusing on modeling the age-specific frequencies of the age-at-death distribution or mortality data in compositional format. We mention that the randomwalk model is chosen because of its simplicity, the Lee-Carter and Hyndman-Ullah methods are included in comparison because of their popularity and acceptance in the demographic and actuarial literature. The Renshaw-Haberman is a good example of how the projections can be improved by controlling for cohort effects and finally the Oeppen model is selected because of its similarity with the MEM model (mainly, the f (x) focus). Discussing the advantages and the features of the models used in comparison is beyond the scope of this article.
For more details about the models, the readers are referred to the original articles. The analysis is performed using the R programming language (R Core Team 2018). The Lee-Carter and the Hyndman-Ullah models are fitted and forecasted using the demography R package (  2017 ) and the Renshaw-Haberman implementation is adopted from the StMoMo software (2015). The source code of the other three models can be downloaded and installed in the form of an R software package from the authors' GitHub repository.