dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation To evaluate our results we created the ontologies we expect as results manually for each data set. We therefore consider a manual annotation by a non-expert human being as our gold standard. We compare the manually created ontologies to the automatically generated ontologies using a point based scoring system as follows: Agreement Score: Award one point for: • Each correct assigned range for a property • The correctly assigned class for the dataset In addition we award a correct assignment if a class we received in the generated result is a subclass of the class we expect from the gold standard. We also award a fraction of a point for non-recognized classes which have a similarity score according to the measure of Resnik. (Resnik, 1995)  If classes we have found are semantically similar but not depicted in the ontology we are using for classification, then we will add the resnik measure to our agreement score.
Interpretation LogMap is based on a repair matching in order to link two ontologies. Table  2  presents a comparison of the matching result with DBpedia between LogMap and our automatic approach. The result of LogMap with DBpedia has obtained a low matching, only few concepts have been detected with the dataset. This low matching illustrates the difficulty to identify concepts from the value of datasets and explains the low matching between both results. Our automatic approach has identified more concepts than Logmap. Although LogMap was not specified in the interpretation of the meaning, but rather in the matching, we use it to compare the part of our approach which allows to match a column name with a concept. So we can say that the step of feature value analysis obtains a good result which is due to its combination of several steps of matching based on the natural language processing.
We have also, tried the matching with Wikidata, but LogMap has found no matching. In comparison with DBpedia, Wikidata has the particularity to have a URI with an identifier which is not a string similar to a label. We assume this particularity can imply some difficulties for LogMap. Moreover, LogMap uses also the hierarchy of the ontology, but with this type of data, there is no hierarchical information which is another problem with a tool for ontology matching. Our advantage is that our approach considers and combines several types of information to identify a concept as for example a label or a comment. In the case of Wikidata, taking into account the label of the concept during the analysis, allows for obtaining a good result.
Comparison with Manual ontologies We present our evaluation results in table  3. This table presents in the first column the comparison between our automatic generated ontology (AGO) and the first manual ontology (MO1). The second column presents the same comparison but with the second manual ontology (MO2). The last column shows the result of comparison between the two manual ontologies. The process has been applied two times for each data set: one time to link concepts to DBPedia (DB) and one time with Wikidata (WD).
Interpretation Thanks to the comparison between two manual ontologies, we can see that two different persons can create two very different ontologies from a same dataset, since we have obtained an average of similarity of 35,4%. When we compare our automatic approach with the two manual ontologies, we can observe that the result is close to the first ontology but is very distant to the second manual ontology. As the first manual ontology has been created with the same way of reasoning than the automatic process, we obtain an average of 71,48% of similarity. However, we can say our approach is very influenced by our way to create an ontology. The creation of an ontology is generally influenced by its creators because it depends directly of their approach of building and the meaning which they want to represent.
That is why, two ontologies creates for the same goal but by two different people can be very different. Moreover, we can say that we obtain a better result with Wikidata than with DBpedia which taxonomy is a more developed. DBpedia is very rich in individuals, but a very flat class taxonomy compared to Wikidata concerning our Dataset. It also needs to be considered, that because of ambigious ways column names and values are defined in the ontology (e. g. abbreviations, shortened names etc. ) misunderstandings of the human annotators about their meanings have been occuring and could often only partially resolved by extensive web research on their part.
Conclusion In this paper we presented a new approach to integrate geospatial data into the semantic web using a fully automated way. By focusing on the geometry as a new central point for concept detection, we can build a local ontology structured around this main concept which allows to gather all information about it. We encountered that mapping our datasets using the Wikidata Ontology we can achieve on average better results than using DBpedia, due to a lack of a developed class hierarchy in DBPedia. Overall the results for our tested datasets seem very close to at least one of our annotators and we believe that our results depict one kind of interpretation of the datasets quite well. However, we as well experienced a lack of detection of several attributes of the datasets such as double values or meaningful integer values.
Future Work In our future work we want to explore how we can better resolve conflicts in our datasets. We therefore intend to create statistical profiles of typical geometries for the classes we are about to encounter and therefore create plausability criteria for the relations between classes representing geometrical objects both type and possibly area-based. Having discovered typical relations between typical together occuring geometry concepts we may as well achieve a more powerful basis for later reasoning experiments. In addition we may be able to deduce the class of geometries by their environment once we have been conducting an in-depth statistical analysis of geometry relations. This prospect could even be extended to be applied on moving objects that might be in a dangerzone of some kind.
Sample The present study was conducted using a cross-section and ex post facto design  [50] , with non-probability sampling for convenience. A total of 446 family caregivers of children with chronic diseases hospitalized in the Children's Hospital of Mexico, Federico Gómez, in Mexico City were recruited as subjects. The sample included women (82. 3%) and men (17. 7%) ranging in age from 18 to 63 years, with a mean age of 32. 2 (SD = 8. 7). The majority of the participants were married (40. 1%) or in a free union (37. 4%), with housewife as their main occupation (65. 5%). The highest percentage of participants (60. 5%) reported income of up to US $132 per month and primary and secondary education (63%). The inclusion criteria were 1) being 18 years of age or older, 2) being the father or mother or family caregiver of children hospitalized due to the chronic disease for which data were collected, and 3) having read and signed an informed consent form.
Procedure Family caregivers were contacted by the research team in the hospitalization rooms of the Hospital Infantil de México Federico Gómez, where their children received treatment. Team members then asked caregivers to participate in the study, explaining the objectives of the study and clarifying any doubts the caregivers may have. Caregivers who agreed to participate were required to sign an informed consent form and subsequently answer the RESI-M and a sociodemographic data questionnaire. The instruments were applied individually, with subjects answering questions on their own in a single session.
Self-assessment instruments Socio-demographic data questionnaire. Twenty items were used to assess sociodemographic variables related to the individual, the family, and the context of the caregiver. These variables were age, gender, marital status, years of marriage, level of studies, religion, number of children, occupation, place of origin, parental role, type of family, life cycle of the family, social support networks, and monthly family income. In addition, the questionnaire was used to obtain information related to the child: gender, age, diagnosis, medical service, time of hospitalization, and time since diagnosis. The Measurement Scale of Resilience in Mexicans (RESI-M;  [26] ). This self-evaluation report comprises 43 four-point Likert-type items, each ranging from 1 (strongly disagree) to 4 (completely agree), distributed across five factors: 1) Strength and Self-Confidence, items 1-19, α = 0.
Cronbach's alpha coefficient is considered to indicate high internal consistency for values ≥. 80, adequate consistency for values ≥. 70, and low consistency for values <. 60  [53]. Finally, Student's t-test was performed to compare the means of the RESI-M total score and its factors between both sexes. Statistical calculations were performed using LISREL (version 6. 1), the SPSS statistical package (version 22), and Excel 2007. Missing values were replaced by the arithmetic mean. The percentage of non-response was low, i. e. , lower than 3% per item, with a maximum of two responses per participant.
Aerobic stability After 90 days of ensiling, silos were opened and approximately 7. 5% of the wet weight in each silo was subsampled. These samples were placed into separate 4-L insulated containers (3 replicates per treatment), covered with two layers of cheesecloth and stored at 20 °C for 14 days. Two Dallas Thermochron iButtons (Embedded Data Systems, Lawrenceburg, KY, USA) were embedded in the silage near the bottom and centre of each container with the temperature in the storage room recorded every 15 min. Ambient temperature and the temperature in each container were simultaneously monitored for 14 days. The contents of each container were thoroughly mixed and sampled after 14 days of aerobic exposure for chemical (15 g) and microbial analyses (10 g) and for DNA extraction (30 g). Aerobic stability was calculated according to Teller et al.
Bioinformatic analysis Bacterial sequences were scanned for contaminants and unpaired reads, trimmed to a fixed length of 250 bp and paired-end assembled using FLASH software  [35]. Read counts per sample were checked to confirm no sample failed to be amplified or sequenced. Reads were clustered at 97% identity using DNACLUST  [36]  and clusters showing abundances higher than 3 were then scanned for chimeras with UCHIME denovo and UCHIME reference in order to estimate final operational taxonomic units (OTUs)  [37]. OTUs were analyzed for taxonomic distribution with the RDP classifier software using the complete Greengenes database (http://greengenes. second genome. com/downloads) supplemented with eukaryotic sequences from the Silva databases and a customized set of mitochondria and chloroplasts 16S sequences.
Each OTUs taxonomic depth with a RDP classifier score below 0. 5 was kept to reconstruct the final lineage. Diversity metrics were obtained by aligning OTU sequences to a Greengenes core reference alignment  [38]  using the PyNAST aligner  [39]. Alpha diversity index (Chao1, Shannon-Weiner indexes, and rarefaction curves), and taxonomic classifications were then computed using QIIME software  [40, 41]. Fungal sequences were analyzed following the MiSeq standard operating procedure for MOTHUR analysis  [42]  using a process adapted for 18S rDNA gene sequences. Reference and taxonomy alignments were performed using the updated SILVA reference file v119  [43] , containing the full length sequences and taxonomic references for 51,533 SSU sequences from Eukaryota, 464,618 sequences from Bacteria and 18,797 sequences from Archaea.
Briefly, the reads from each sample were randomly subsampled down to 15,000 using HTSeq and Python  [44]. The sequences were then assembled, trimmed and singletons were removed. Reads with homopolymers greater than eight bases and sequences with one or more ambiguous bases were removed from the data set. Remaining sequences were aligned and run against the SILVA reference database. Before being classified into OTUs, sequences were pre-clustered at 97% identity and chimeras were scanned and removed using UCHIME  [37]. Final OTUs were obtained and analyzed for taxonomic distribution with the SILVA database. Alpha diversity indexes and taxonomic classifications were then computed in a manner similar to that of bacterial sequences. Determination of the core microbiome was accomplished by comparing samples from all crops across time.
Calculations and statistical analysis Microbial populations were estimated as colony forming unit (CFU)/g of forage or silage DM and were log transformed prior to statistical analysis. Ensiling parameters, aerobic stability and microbial data were assessed using a randomized complete block design considering 4 crops (barley, oats, triticale and intercrop) at 3 times of sampling (0, 90 and 104 days). An analysis of variance using the crops and time was performed to determine significant differences in diversity indexes. Significance was defined as a P value of less than 0. 05 using SAS  [45].
Core microbiome during ensiling of small grain cereal forages The number of different OTUs identified were higher for fungal than for the bacterial core microbiome, a result that can be explained by differences in the databases and analyzes used for taxonomic assignments. Bacterial taxonomic assignments relied on the Greengenes database and a RDP classifier with a depth taxonomic OTU threshold and similarity cut off for taxonomic clustering. As the RDP classifier is not designed to process 18S rDNA sequences, the Silva database was used for fungal identification and only a similarity cut off was applied for taxonomic assignment. It might be possible that OTUs belonging to the same lineage (i. e. , one hit identified at the family level and the other at the genus level for example) were identified as different OTUs through 18S rDNA analysis, increasing the final number of estimated fungal OTUs, a bias that likely did not occur during bacterial analysis.
To have a conservative approach, we decided to take the 20% inner part (10% of both sides along the midline) as the ROI in this study (Figure  1(c) ). The mean HU of the pixels in the ROI was then calculated using MATLAB (MathWorks, Natick, MA, USA). For each studied rib, 10 equally spaced cross-sectional images from 5% to 95% (vertebrae to sternum) of its length were selected to calculate a mean HU. Then, the mean HU was converted into equivalent K 2 HPO 4 densities (mg K 2 HPO 4 /cm 3 ) using the pad-like calibration phantom. Then, two steps were performed in order to estimate the BMD of child rib cortical bones. First, the Model 3CT calibration Phantom (in mg K 2 HPO 4 /cm 3 , Mindways Software, USA) and the CIRS Phantom 062M (in mg HA/cm 3 , CIRS Inc. , Virginia, USA) were imaged simultaneously to establish the relationship between the two units.
Among them, Berteau et al. [4]  determined the rib elasticity (Young's modulus in transversal direction) of two teenage subjects (aged 15 and 17 years) using ultrasonic measurements, but due to the difference in orientation, this data cannot be compared to that of the current study. Dynamic tensile tests (strain rate 0. 5/s) have been performed on cortical bone coupons harvested from the anterior, lateral, and posterior regions of the ribs  [9]. This study on adult specimens included one 18-year-old subject. The mean Young's modulus of 10. 0 GPa for that subject is very similar to that obtained in the current study (Figure  5 ). In another study, quasi-static (loading speed 0. 042 mm/s) three-point bending tests on 44 rib sections collected from 12 subjects aged from 5 months to 9 years were performed  [3].
Conclusion This study is the first to estimate the elasticity of pediatric rib cortical bones in vivo. Young's modulus was estimated for subjects aged from 1 to 18 years using the calibrated QCT images collected clinically. The elasticity values estimated from the QCT images are similar to those from the direct measurements on pediatric samples. The estimated elasticity of the rib cortical bone increases with age (from 1 to 18 years old). This data can be useful for sensitivity studies using rib models. This noninvasive method overcame the drawback of the paucity of pediatric tissues for biomechanical research. This study paves the way for the estimation of mechanical properties of pediatric cortical bones in vivo.
Acknowledgments This work was performed within the framework of the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). The authors would like to acknowledge  M. Benjamin Aubert  and  M. Thomas Joubert  for their technical contributions to the BMD calculations. The authors also would like to acknowledge  GDR Stic-Santé  for a travel grant. Y. Zhu is supported by a grant from the  Région Rhône-Alpes. This research was partly funded by the  Clusters-ARC  from the  Région Rhône-Alpes  and by the  ParisTech BiomecAM chair program  on subject-specific musculoskeletal modeling.
PET Six Wistar rats and seven SHRs were randomized to undergo MCAo under either control (n = 3 and 4 per strain, respectively) or hyperoxia (n = 3 per strain) conditions. These sample sizes were not based on a power calculation given the absence of literature data on expected effect size (i. e. , % reduction in FMISO uptake from hyperoxia). They were considered large enough to test our straightforward hypothesis that hyperoxia would markedly reduce FMISO uptake in ischemic regions (see Introduction), also taking into consideration the aforementioned ethical requirements. Immediately after clip placement on the MCA, the animal was positioned in the PET scanner (microPET P4, Concorde Microsystems, Knoxville, USA) with the brain located centrally in the field of view. Under the oxygen condition of the scan (control or hyperoxia), within 5mins of MCAo FMISO (77 ± 9 MBq) was administered in the tail vein over 30secs  [10] , with FMISO PET acquisition starting simultaneously with the injection.
Dynamic PET data were acquired for 3 hours. The energy and coincidence windows were 350-650keV and 6nsec, respectively, and the voxel size 0. 5x0. 5x0. 5mm. Venous blood samples were taken at 120, 150 and 180 mins post-injection, and plasma radioactivity concentration measured in a well counter to provide values for least squares scaling of a pre-existing arterial plasma input function  [32]. Transmission scanning with a rotating 68 Ge/ 68 Ga point source was conducted at the end of the FMISO scan, with geometrically-windowed coincidence mode used to provide accurate attenuation information in the presence of the residual FMISO radioactivity. The list-mode data were binned into sinograms for the following time frames: 8 × 15s, 6 × 30s, 15 × 1min, 5 × 2min, 30 × 5min. For each sinogram, an image was reconstructed using the PROMIS 3D filtered backprojection algorithm  [51] , with a Hann window applied to result in a reconstructed image resolution of 2.
European Union  Grant (https://europa. eu)EUSTROKE Health-F2-2008-2022131 provided funding for experiments, consumables, and salaries for  S. Ejaz  and  S. Sitnikov. Medical Research Council  (www. mrc. ac. uk) collaborative grant ( G0600986 ) provided funding for  DJ Williamson salary, and Deutsche Forschungsgemeinschaft  (www. dfg. de):grant  Je 598/1-1  provided funding for  Ulf Jensen-Kondering  fellowship. We acknowledge the financial support by the  state of Schleswig-Holstein  within the  funding programme "Open Access Publikationsfonds ".
Acknowledgements We thank the team of the ImagoSeine facility at the  Institut Jacques Monod  for their help and support, the  France-Bio-Imaging national research structure  ( ANR-10-INBS-04 ),  Renata Basto ,  Maria Rujano ,  Delphine Gogendeau ,  Nasser Rusan , the  Bloomington Drosophila Stock Center  and the  Developmental Studies Hybridoma Bank  for fly stocks and reagents. We are grateful to  Nicolas Borghi ,  Veronique Brodu ,  Julien Dumont ,  Nicolas Minc ,  Lionel Pintard  for critical comments on the manuscript. We are grateful to the laboratory members for helpful discussions. We thank  Laetitia Besse ,  Edouard Jaumouille ,  Mathilde Lediuzet ,  Jean-Yannick Petit ,  Fabrice Licata  for technical assistance, and  Thomas Rubin  for help with the colcemid experiments. NT is a fellow of  France-Bio-Imaging.
There are several variations in the architectural design, and explaining this is beyond the scope of the present article. The important aspects for our purposes are the SIMD restriction for groups of threads and the limited size of fast access private memory per thread, mentioned earlier. To take advantage of new parallel-computing architectures, and account for their specificity and constraints, algorithms and software in general, and for RNGs in particular, must be adapted and sometimes radically redesigned. Feeding all the PEs with a single source of random numbers is generally unacceptable, first because it would create too much overhead and a large bottleneck. In highly-parallel systems, one may need thousands or even millions of virtual RNGs. They can be either different RNGs or copies of the same RNG starting from different states, that run in parallel without exchanging data between one another, and behave from the user's viewpoint just like independent RNGs.
To achieve this, one would use a separate stream for each required source of random numbers in the system, and use one substream for each of the n independent runs  [24, 32, 33, 36, 41]. Reliable software facilities that provide RNGs with multiple "independent" streams and substreams have been available for some time for that purpose  [33, 37, 45, 38]. Most were initially designed primarily for simulations over a single processor, for synchronizing CRNs, but they can be used as well to provide multiple streams for parallel processing. In particular, the RngStreams package of  [45]  has been incorporated in the parallel package which supports parallel programming in R  [70]. RngStreams can also be used directly in OpenMP, which is the most standard interface for parallel programming in shared-memory environments, and in MPI, a popular parallel programming interface based on message passing.
A p very close to 0 usually indicates strong departure from uniformity, whereas p very close to 1 indicates excessive uniformity, which also represents departure from randomness. For poor RNGs, p-values smaller than 10 -15 (and often much smaller) are often returned  [43] , and it is then very clear that the RNG fails the tests. Collections of statistical tests for RNGs can be found in  [23, 43, 52] , for example. The most extensive collection and software is TestU01  [43]. Among other things, it includes predefined batteries of tests called SmallCrush, Crush, and BigCrush, which have become very popular. RNGs that pass all tests in those batteries are called Crush-resistant  [74]. However, it is only possible to apply a finite (small) number of statistical tests in practice, and this can never prove that a RNG is flawless.
These values can then be generated in any order, they are easy to replicate, and it is easy to split the entire sequence into long disjoint streams. Since g is a bijection, the period is 2 k where k is the number of bits used to represent the counter. This k will usually be a multiple of 32, say in the range from 128 to 1024. Good encryption algorithms satisfy the avalanche criterion, which says that changing a single bit in the counter changes approximately half the bits in the output, on average. One drawback is that they are typically slower than popular RNGs when implemented in software. But they can be simplified and still remain good enough for many simulation applications. Salmon et al. [74]  propose counter-based RNGs that use simplifications of the AES and Threefish algorithms, named ARS and Threefry, which trade security for speed, and a new counter-based method called Philox.