dataesr/scientific-paragraphs-categorization huggingface dataset:
Libraries were finally sequenced in paired-end mode (2 × 50 bp) on Illumina HiSeq2500 platform. Idx RT primers: TTTTTTTTTTTTTTTTTTTTN = poly-T allowing matching with mRNA poly-A tail, NNNN = 4 bases UMI (randomly generated), XXXXXX = 6 bases cell barcode. The rest of the sequence consists of a PCR adaptor and a T7 promoter sequence for further IVT amplification. P5N6 XXX: NNNNNN = random hexamer allowing the capture of the fragmented IVT amplified RNA, XXXX = 4 bases "plate barcode". The rest of the sequence consists of a PCR adaptor. P5. Rd1/P7. Rd2: P5 and P7 Illumina sequencing adaptors. using UMAP representation (package umap v0. 2. 8. 0) and colored the cells either according to their time of collection or their CALISTA cluster affiliation. Bulk ATAC-seq analysis. Raw data processing: Tn5 adapters sequences were first trimmed with the following command: < cutadapt -q 20 -g "AGATGTGTATAAGAGACAG; max_error_rate = 0.
1-keep-dup all > In order to retain only significant accessibility peaks across samples, each list of peaks used in advanced analysis has been defined as the intersection between peaks of the 3 donors tested at the same time point. Peak annotation: Peaks were assigned to genomic regions thanks to a homemade script based on the FindOverlap function from the R package "GenomicRanges"  [72]. Genomic elements positions (exons, introns, CpG islands, and CTCF) were retrieved from UCSC database (hg19). As for the RNA-seq analysis, promoter regions were retrieved from the online database FANTOM5  [70]. Intergenic category was defined as the exclusion of all other defined categories. No priority has been set across the different genomic elements. Therefore, peaks overlapping several genomic features are counted multiple times, resulting in a total number of peaks across elements exceeding the total number of peaks detected at each time point.
Peak differential analysis: DEseq2 tool was used to calculate difference in read count between peaks in 2 consecutive time points  [73]. More precisely, the region considered is defined as the interval formed by the union of 2 overlapping peaks at t2 and t1. Motif enrichment: Peak motif enrichment analysis was conducted with the tool "findMo-tifsGenome. pl" from the HOMER software tool suite  [74]. Background file was generated using an autogenerated list of random regions across the genome (hg19). Motifs were scanned using the total length of our peaks by providing the option <size given>. scATAC-seq analysis and bulk ATAC-seq comparison. Fastq files generated by Imagine Institute platform were aligned to the hg38 reference genome using Cell Ranger software to obtain count matrix, which was further imported into R (v4.
1. 2). Using Seurat (v4. 1. 1) and Signac (v1. 6. 0) R packages, we integrated the data into a ChromatinAssay object and attributed a genomic annotation using FANTOM5 database for promoters and biomaRt (v2. 52. 0) for other annotations. Nonstandard chromosomes were removed from the analysis as the number of corresponding peaks was insignificant (<50). TF-IDF normalization followed by singular value decomposition (SVD) were performed on the top features shared by more than 90% of the cells. Dimensions 2 to 30 of LSI reduction were used for cluster analysis. Regarding comparison with the bulk ATAC-seq datasets, aligned reads were shifted from hg38 to hg19 reference genome thanks to Lift Genome Annotations tool. FindOverlap function from GenomicRanges R package (v1. 48. 0) was used to test the overlap between the accessible regions detected in bulk ATAC-seq and scATAC-seq datasets.
ATAC-seq and scRNA-seq combined analysis (accessibility-expression). Identification of promoters that have configurational changes: In an effort to identify promoter regions that are affected (and not affected) by configurational changes of the chromatin, we employed the R Bioconductor package "GenomicRanges"  [72]. By comparing the peaks overlapping the promoters between 2 time points (0 h to 5 h, 5h to 24 h, and 24 h to 48 h), we grouped promoters into 4 possible chromatin accessibility configurations: "open-open", "open-closed", "closed-open", and "closed-closed". We then used the CAGE-defined promoters to gene isoform mapping from the Regulatory Circuits resource  [46, 70]  to identify promoters that overlap with the peaks of ATAC-seq and their corresponding target genes. Differential gene expression of single-cell RNA sequencing: We computed Z-scores for every gene in each of the 2 donors between 2 different time points using the mean and standard deviation of the UMI counts of approximately 100 single cells.
Details about WGNCA are given in Materials and methods. Note that category 1 reproduces the best overall dynamic pattern observed for genes showing detectable expression in single cells in (B). Category 1 = 5,194 genes (donor1) and 5,518 genes (donor2), category 2 = 3,977 genes (donor1) and 2,602 (donor2), category 3 = 1,089 genes (donor1) and 609 genes (donor2). (Numerical values available in scRNA-seq repository GSE156734: "GSE156734_Spread_MARSseq_data_all_filters_20200728. csv") scATAC-seq, single-cell ATAC sequencing; scRNA-seq, single-cell RNA sequencing; WGCNA, weighted correlation network analysis.
(Numerical values available in scRNA-seq repository GSE156734: "GSE156734_CALISTA_results_d1csv. gz" "GSE156734_Spread_MARSseq_data_all_filters_20200728. csv" "GSE156734_CALISTA_results_d2. csv. gz") CALISTA, Clustering And Lineage Inference in Single-cell Transcriptional Analysis; PCA, principal component analysis; UMAP, Uniform Manifold Approximation and Projection. https://doi. org/10. 1371/journal. pbio. 3001849. g002.
, 2021). Here we present a software tool to assess recreational UGS supply and demand to facilitate the incorporation of recreation service in UGS planning. This tool is available on a web-platform and is designed to be implemented as the "Urban Nature Access model" in InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs)-a free, open-source software suite that models multiple ES delivered by nature  (Hamel et al. , 2021; Sharp et al. , 2020). The model is easy to use and allows users to rapidly assess recreational UGS supply, demand and the supply-demand balance with flexible data requirements. Based on our review of the literature (Section 2), the tool application illustrated in this article improves on existing options to support decision making in several ways: (i) it allows for rapid calculation of recreational UGS supply and demand to aid assessments based on commonly available data; (ii) it is compatible with both a "UGS standard" approach and needs-based UGS assessments; (iii) it is supported by an online calculation and visualization platform that facilitates comparison and communication of impacts of different UGS planning scenarios.
Existing recreation service modelling tools Multiple reviews on ES assessment tools have discussed recreation service modelling  (Bagstad et al. , 2013; Brown and Fagerholm, 2015; Carter et al. , 2012; Grêt-Regamey et al. , 2017). Among the most popular models, Social Values for Ecosystem Services (SolVES) relies on a survey on public values and preference for locations to predict and map recreation value in landspape. SolVES can reveal heterogenous preferences for recreation but is problematic to transfer the results to unstudied area. The current InVEST Recreation model ("Visitation: Recreation and tourism", v3. 8) model approximates visitation using Flickr photos and builds a regression model with environmental attributes layer  (Sharp et al. , 2020). However, that model is not suitable for quantifying daily recreation in UGS, as it relies on a dataset with a bias towards highly attractive areas.
Urban greenspace We derived UGS data from an existing dataset for 2017 with 81 land use types  (MOS, 2017). UGS considered in the analysis include: (1) forests (MOS land cover code 1-4, including wood or forests, sections or clearings in the forest, poplar, open spaces with shrub or herbaceous vegetation); (2) grassland (MOS 7); (3) water banks (MOS 5, banks of waterways without harbour or storage activities); (4) public parks and gardens (MOS 13 and 25, parks and gardens, animal parks, zoos, amusement parks); (5) free slots for camping and caravanning (MOS 24). We did not include private gardens, outdoor sports fields, and golf course due to their restricted access. The total UGS area is 3859 km 2 , equating to 31% of the study area.
Distances were double-checked with the stated travel time declared by respondents. We assumed a 3. 6 km/h speed by foot, 16 km/h by bike, and 60 km/h by car and public transport. To obtain 2 According to the Green View Index (GVI) developed by the Massachusetts Institute of Technology, calculated using Google Street View panoramas, showing the percentage coverage of the canopy of a pixel: http://senseable. mit. edu/treepedia/cities/paris. 3 The IRIS perimeters are joined with socioeconomic data from a large dataset on localized social and tax file (FiLoSoFi) provided by the national statistics institute (INSEE). The median available income corresponds to the median income (among residents in the IRIS) actually available to a household to consume or save, that is the primary income + transfer income -compulsory taxes.
H. Liu et al. the search radii for different age groups, a Poisson regression was applied to the stated number of visits. The count data models such as the Poisson or negative binomial are commonly used to analyse visitation data, as this type of models is particularly accurate when the dependent variable is an integer that takes few different values, such as visitor trips to a destination site  (Shaw, 1988; Englin and Shonkwiler, 1995; Baerenklau et al. , 2010; Roussel et al. , 2016; Tardieu and Tuffery, 2019). When plotting the data, we found that the Poisson function best described the decay of visitation against travelled distance to greenspace in our dataset. This is confirmed by likelihood ratio test on alpha, representing the dispersion parameter in our regression, which showed that our dataset was not overdispersed, justifying here the use of a Poisson model over a negative binomial model.
Acknowledgements This research was supported by the following projects:  National Science Foundation for Young Scientists of China  (Project No. 41801224 ). Research and Development Program of Guangdong Province  (Project No. 2020B1111530004 ). Science and technology plan project of  Guangzhou  (Project No. 201904010159 ). Science Foundation of Guangdong Province ,  General  project (Project No. 2018A0303130077 ). IDEFESE  project funded by the  French Ministry for an Ecological and Solidarity Transition (MTES/PUCA) ,  Environmental and Energy management agency (ADEME)  and  AgroParisTech/Université Paris Saclay  (Project No. 1716C0007  and  2201137986 ). PH acknowledges the  Singapore National Research Foundation ,  Prime Minister's Office  ( NRF-NRFF12-2020-0009 ); PH and RPR acknowledge the  Marianne and Marcus Wallenberg Foundation , the  Gordon and Betty Moore Foundation , and the  LuEsther T.
We revisit parallel-innermost term rewriting as a model of parallel computation on inductive data structures and provide a corresponding notion of runtime complexity parametric in the size of the start term. We propose automatic techniques to derive both upper and lower bounds on parallel complexity of rewriting that enable a direct reuse of existing techniques for sequential complexity. The applicability and the precision of the method are demonstrated by the relatively light effort in extending the program analysis tool AProVE and by experiments on numerous benchmarks from the literature.
e. , UCspecific test cases. These test cases only support a network-level performance evaluation using synthetic traffic flows, and include scenarios with limited traffic as well as stressing conditions. Differently, UC-specific test cases are defined based on the operation of a specific CCAM application. In these cases, network traffic is generated by the applications to assess the impact of 5G solutions and deployment options on the CCAM application. The next UC categories are considered: advanced driving, focused on manoeuvres to enable autonomous driving while assuring safety; vehicle platooning, which particularly considers convoy formation and management; extended sensors, to gather and manage data from the road network to enhance autonomous operation; remote driving, which aims at operating vehicles through a network connection; and vehicle QoS support, which tests on-board high-bandwidth multimedia services.
); level 1 (transport) studying network capabilities (throughput, delay, etc. ); and level 2 (application), focused on obtaining relevant measurement data at application level (E2E latency, user experienced data rate, reliability). Per network segment measurements These are intended to observe performance in a hop-by-hop basis. The network segments considered are: UE/OBU -gNodeB (gNB), gNB -Packet Gateway (PGW), and PGW-MEC or PGW -cloud. Control or data plane Measure the impact of control plane operation on the E2E performance, attending to UE/network signalling.
Additionally, two cross-layer data groups are gathered, the run-time conditions, about terminal settings and positioning, and handover events, with details about base station transitions and other 5G radio and network signalling. The stage about Extract, Transform and Load in Fig. 2  deals with raw data handling to enable further data processing and calculation of the KPIs. It includes the translation of raw data into the Common Data Format (CDF), building log files that ensure comparability and consistency across different testing sites. The CDF considers different files to be created for each stack level previously described: access, transport, application and, additionally, handover events. All data contained for the first three are aggregated in one-second time windows, for the sake of space and processing performance, while handover events are saved asynchronously.
The Data Builder generates an XML description file of the test and a data quality report, which are joined to the archive, as it is shown in Fig. 3. The CTS includes a Web application to show the CDF files and sanity status of each test. Upon the arrival of testing archives to the CTS, a Python script tool (C S V + X M L parser) is used to assure conformance with data format and coherence, such as all measurements are within the same time frame and geographical coordinates of the referred test. Then, the tool populates a relational database implemented in PostGreSQL with CDF fields from logs and metadata, enabling programmatic access to the data. This way, further data analytics can be performed easily. A first processing of data is carried out in the same CTS, calculating KPIs directly observable from measurements in the form of descriptive statistics for the user.
Measurements and data management A variety of tools were selected and integrated into the vehicles to log CAM data at the different layers. At application layer, the software developed to exchange the messages was modified to dump CAM messages into a JSON file in the OBUs of both vehicles. At network layer, the traffic is dumped performing a filter on the network interfaces and ports involved in the connection. At access layer, a Qualcomm tool was used to gather RAN data. These raw logs are the basis to build the CDF files based on the flows between the PCOs (in this example, the three OBUs and the MEC server). All CDF files include positioning information coming from the GPS receiver. A Python script was developed to translate data from the Qualcomm tool and integrate all measurements taken at all levels.
The script also matches the information between the vehicles and MEC, based on the payload of the messages, and calculates the aggregated measurements on the data flow between them to finally output the CDF files. The CDF files are used as data logs to be uploaded to the CTS platform using the Data Builder application. After the data quality check is passed using the DQCT, both logs and meta data are uploaded by the software to the CTS, where it is checked for consistency. This process has been carried out successfully with the measurements used for this paper and, hence, test data have been finally inserted in the CTS database and statistics are calculated, without detecting any error. From these, the network latency and packet loss rate (PLR) between the vehicles and the MEC server are particularly evaluated, extracting these data from the source e2elatenc y and packetlossrate fields, respectively, from the CDF file that belongs to the network level (see Table  5 ).
The statistics calculated by the CTS are used here as the Net work latenc y (KPI #4) and Net work reliabilit y (KPI #7) in Table  2. The acceleration of the autonomous vehicle is also evaluated taking the data from the CTS, which comes from the source speed field from the CDF file of the application level (see Table  5 ). The acceleration is calculated in post-processing, accessing the data from the CTS database; hence, the CCAM KPI Instantaneous accelerations in Table  3  is computed. Since each overtaking trial lasts around 60 seconds and the CDF files aggregate results in a per-second basis, each trial accessible in the CTS contains around 60 records. Each of these records contains cumulative, mean or most recent values for each parameter measured during tests. For the particular case of latency and network reliability, all packets transmitted and received within the one-second window are considered to get an averaged latency and packet loss rate during the second.
A data processing infrastructure has been deployed to make easier data management through a common platform that automatises data quality assurance, management of test reports, calculation of statistics for overall comparison, and calculation of final KPIs. A reference test case is described in detail, which validate the evaluation platform under a real scenario about autonomous overtaking, presenting an innovative experimental campaign with a 5G-NR network deployment with autonomous vehicles in an European border. Results are gathered from the evaluation platform, after covering the stages of measurements, logging, aggregation of data, storage in database, and calculation of statistics. An analysis of the network performance in terms of latency and PLR has been carried out, highlighting the effect of handovers and their potential impact on final services.
Acknowledgement This work has been supported by the grants  EC 825496  ( 5G-MOBIX ), funded by the  European Commission ;  PID2020-112675RBC41  ( ONOFRE-3 ), funded by  MCIN/AEI/10. 13039/501100011033 ;  RYC-2017-23823 , funded by  MCIN/AEI/10. 13039/501100011033  and by " ESF Investing in your future ";  PGE-MOVES-SING-2019-000104  ( MECANO ), funded by the  Spanish Ministry for the Ecological Transition and the Demographic Challenge ; and  RED2018-102585-T (Go2Edge ), funded by the  Spanish Ministry of Science, Innovation and Universities.
In this vein, Shneor proposes a piece of software that evaluates the accuracy of a machine-tool for the purpose of 5-axis positional machining  [6]. The kinematic model of a machine structure is used to calculate an error matrix. The proposed model can be used to modify a theoretical machining path and deduce the standard errors on the machined surface. The model developed is relatively simple and generic and does not consider the specific kinematic behavior of the machine. The machining of a part reproduces a machining configuration but induces the appearance of defects #4 and #6. They may affect the quality of the machined part, making this quality a result of factors beyond geometric machine-tool behavior alone. Moreover, further defects can appear as a consequence, for example, of CAD modeling.
The fillet is composed of two free-form ruled surfaces A and B defined from fourth-order quasi-uniform rational B-splines (Fig. 1 ). The test result is therefore influenced by the accuracy of the CAD model, the CAM tool path computation, the measurement uncertainty of free form surface by a coordinate measurement machine (CMM), and the machine-tool geometric behavior  [15]. Sato's first study investigates the influence of the CAM software and geometrical machine defects on machined S-shape part defects  [16]. In Sato's work, the study of CAM software is limited to setting up trajectory computation tolerance. This paper therefore aims to study the influence of each step of the machining process on the part defect in order to improve the acceptance testing process of machine tools. The idea is to separate all geometrical defects occurring in the machining process including modeling parts, calculating the tool path, and the kinematic behavior of the machine tool.
In so doing, by measuring the machined S-shape test part, we can be sure to identify the behavior that is detrimental to geometric quality. After an introduction of the S-shape test part, a first analytical study is undertaken in order to identify overcut and undercut resulting from tool path computation. A second study shows the influence of CAM software with an analysis of the tool path computation process. In a third step, the influence of the kinematic behavior of the machine tool is studied. Finally, an S-shape test part is machined, and the measurement results are presented. In the light of both these measurements and the findings of previous studies, we propose correction actions to improve the accuracy of machining of part similar to S-shape test pieces. These correction actions should ensure the improvement of machine-tool accuracy during the 5-axis machining of free-form surfaces.
B-spline tool path computation Numerical controls currently offer two interesting features: polynomial interpolation; and space correction of the tool geometry. Thus, it is possible to calculate the tool path without using a CAM system. The problem of calculating the tangent position of the tool and the orientation of the tool axis is directly dealt with by the numerical control without any a priori control. For the Siemens 840D CNC, using the CUT3DC function gives the program named TP5C1 (Table  1 ). It is also possible to calculate mathematically, using equation (  1 ), the equations of the two measurement curves located at Z11 and Z25 (program TP6C2). The equations of these curves are exact and there are no approximations. The program of Table 2 is obtained. These two programs present the opportunity to preclude the defect induced by CAM software.
The processing includes corrections related to the TP1S1, TP3CR, and TP4S1 deviations are very similar. The kinematic behavior of the machine tool is predominant compared to the error profile generated by the tool path. By contrast, in the case of TP2S2, the kinematic error profile is very close to the geometrical error profile. In this case, the error of the tool path calculation is predominant. The behavior of the machine-tool numerical controller can reach 0. 034 mm. It is predominant compared to the error profile generated by the tool path computed by CAM software. Programs TP5C1 and TP6C2 are also tested. The kinematic behavior of these programs is measured on the machine with the following setting of Cycle 832 (Sinumerik 840D): Ori_Finish, path tolerance = 0. 03 mm, angle orientation tolerance = 0.
Conclusion In this paper, we studied the evolution of the geometry of the S-shape test part during each step of machining in order to quantify the associated impact on the final machined part. As the S-shape test part is defined from two non-developable ruled surfaces, an overcut appears that can reach 0. 0387 mm. This is joined by a defect resulting from CAM software tool path computation. The maximum computed deviation is 0. 01 mm. The movement of the machine-tool axis is imposed by the numerical controller. This step generates a supplementary maximum deviation of 0. 034 mm. Finally, we analyzed the influence of inverse transformation errors resulting from the gap between the geometric model implemented in the numerical controller and the real geometric behavior of the machine-tool studied.