dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses were carried out at a microscopic scale using small and high magnification SEM images such as 2000, 50,000 and 100,000 magnifications to investigate γ′ morphology. The average dimensions and volume fractions of the γ′ precipitates were quantified using digitized images and ImageJ analysis software. The area of each particle was measured using the ImageJ software in view to measure γ′ phase size. The particle radius was then calculated as the radius of a circle whose surface area equaled that of the corresponding particle.
Thermo-calc® Simulations The Thermo-calc® software is widely used for simulating phase transformations processes under equilibrium conditions. Fig. 3  shows a simulation of the amounts of phases, in moles, between 400 and 1400 °C for the AD730 TM superalloy under equilibrium conditions on two different scales. On the first scale, phases like γ′, γ, liquid and σ are shown. The simulation results predict that the γ′ solvus, solidus, and liquidus temperatures are respectively 1090, 1220 and 1350 °C. The secondary scale displays MC and M 23 C 6 carbides, M 3 B 2 borides and τ phases. The MC carbide is stable and does not dissolve until 1300 °C. It is well-known that, in Ni-based superalloys, the principal purpose of the homogenization process is to dissolve the irregular-shaped primary γ′ precipitates that are formed during the casting process in view of fine particles re-precipitation during cooling  [14].
Dissolution Kinetics Model for Multiple Precipitates A dissolution model based on Johnson-Mehl-Avrami-Kolmogorov (JMAK)  [39]  is proposed to study the precipitation evolution in AD730 TM. More specifically, the γ′ area fraction on various samples from different solution heat treatments is measured and used as a basis to estimate dissolution kinetics. The γ′ area fraction ( ) can be written as a function of heat treatment time:  (12)  where F Slim is the γ′ area fraction at thermodynamic equilibrium, F 1 the difference between the γ′ area fraction at t=0 seconds and F Slim , a constant dissolution time and F Slim a material parameter. An optimization procedure was developed, using Origin and Matlab 2013, in order to find the values for the preceding factors for each solution heat treatment temperature.
The calculated value is close to reported activation energies for the diffusion of either Ti (272 kJ. mol -1 ) or that of Cr in a Ni matrix (286 kJ. mol -1 )  [40]. Values for are given in Table  II. values were determined for different elements of solute in a γ matrix at various heat treatment temperatures by Thermo-calc® software. Fig. 12  shows the composition of solute elements in the γ matrix and γ′ precipitates at different temperatures. The coarsening kinetics of γ′ particles in AD730 TM can be predicted using Eqs. (  20 ), (  21 ) and (  23 ):  (24)  According to the data presented in Fig. 12 , the mass percentages of Ti and Cr in the γ matrix were determined to be 3. 2% and 16. 1% respectively. Furthermore, and were calculated to be 3. 34 and 0. 09 respectively. Since the fluctuation of is negligible for temperatures around 1080 °C and 1100 °C, using previous data and Eq.
O-MI and O-DF Standards Primary goal of bIoTope is to enable companies to easily create new IoT systems and rapidly harness available information using advanced Systemsof-Systems (SoS) capabilities for Connected Smart Objects. To this end, bIoTope takes full advantage of messaging standards developed and officially published by The Open Group, notably the Open Messaging Interface (O-MI) and Open Data Format (O-DF)  [3, 4]  standards. Those standards emerged out of past EU FP6-FP7 projects, where reallife industrial applications required the collection and management of product instance-level information for many domains involving heavy and personal vehicles, household equipment, etc. Based on the needs of those real-life applications, and as no existing standards could be identified that would fulfil those requirements without extensive modification or extensions, the partner consortia started the specification of new IoT interoperability standards.
Vicinity VICINITY -"Interoperability as a Service for the IoT: a bottom-up approach"  [20]  The VICINITY project will build and demonstrate a platform and ecosystem for IoT infrastructures that will offer "Interoperability as a Service". The platform aims to be device and standard agnostic, and will rely on a decentralised and user-centric approach. VICINITY aims to retain full control of the ownership and distribution of data across the different IoT domains. VICINITY introduced the concept of virtual neighbourhood, where users can share the access to their smart objects without losing the control over them. A virtual neighbourhood will be a part of an IoT infrastructure that offers decentralised interoperability and will release the vendor locks that are present in the current IoT ecosystems.
VICINITY Solution VICINITY presents a virtual neighbourhood concept. A decentralized approach resembling a social network will be used. The users are allowed to configure installations and integrate standards according to the preferred services, as well as being able to fully control their desired level of privacy. Data exchange between different devices is handled through the VICINITY open interoperability gateway, which reduce the need for having a technical background in order to exploit to the VICINITY ecosystem. An API will allow for easy development of an adapter to the platform. Once an IoT infrastructure is integrated, its owner can simply manage the access to his/her IoT data and controls using the VICINITY neighbourhood manager (VNM). Connecting to detected IoT infrastructures is handled by the open VICIN-ITY auto discovery device.
The third use case will be eHealth (GNOMON, GR) which looks at the specific needs and constraints of eHealth. Value-added services will include the detection of abnormal events, and the possible finding and clustering of similar patients based on data mining. The final use case will show how a large number of data sources from different domains can be combined in an intelligent parking application (Hafenstrom, NO). This will use data from booking, heating management and health status while considering how users canbe incentivised to use the application. VICINTY is open and welcomes the participation of further interested consumers, integrators and developers of value-added services.
Datasets To evaluate performance, we used three standard, publicly available datasets featuring documents of various types and sizes. Figure  3  shows the distributions of document size and manually assigned keywords for each dataset. The Hulth2003 1  (Hulth, 2003)  dataset contains abstracts drawn from the Inspec database of physics and engineering papers. Following our baselines, we used the 500 documents in the validation set and the "uncontrolled" keywords assigned by human annotators. The mean document size is 120 words and on average, 21 keywords (in terms of unigrams) are available for each document. We also used the training set of Marujo2012 1 , containing 450 web news stories of about 440 words on average, covering 10 different topics from art and culture to business, sport, and technology  (Marujo et al.
, 2012). For each story, the keyphrases assigned by at least 9 out of 10 Amazon Mechanical Turkers are provided as gold standard. After splitting the keyphrases into unigrams, this makes for an average of 68 keywords per document, which is much higher than for the two other datasets, even the one comprising long documents (Semeval, see next). The Semeval 2 dataset (Kim et al. , 2010) offers parsed scientific papers collected from the ACM Digital Library. More precisely, we used the 100 articles in the test set and the corresponding authorand-reader-assigned keyphrases. Each document is approximately 1,860 words in length and is associated with about 24 keywords. Notes. In Marujo2012, the keywords were assigned in an extractive manner, but many of them are verbs. In the two other datasets, keywords were freely chosen by human coders in an abstractive way and as such, some of them are not present in the original text.
Implementation Before constructing the graphs-of-words and passing them to the keyword extraction methods, we performed the following pre-processing steps: Stopwords removal. Stopwords from the 1 https://github. com/snkim/ AutomaticKeyphraseExtraction 2 https://github. com/boudinfl/centrality_ measures_ijcnlp13/tree/master/data SMART information retrieval system 3  were discarded. Part-of-Speech tagging and screening using the openNLP  (Hornik, 2015 ) R (R Core Team, 2015) implementation of the Apache OpenNLP Maxent POS tagger. Then, following  (Mihalcea and Tarau, 2004) , only nouns and adjectives were kept. For Marujo2012, as many gold standard keywords are verbs, this step was skipped (note that we did experiment with and without POS-based screening but got better results in the second case).
Stemming with the R SnowballC package (Bouchet-Valat, 2014) (Porter's stemmer). Gold standard keywords were also stemmed. After pre-processing, graphs-of-words (as described in Section 2) were constructed for each document and various window sizes (from 3, increasing by 1, until a plateau in scores was reached). We used the R igraph package  (Csardi and Nepusz, 2006)  to write graph building and weighted k-core implementation code. For K-truss, we used the C++ implementation offered by  (Wang and Cheng, 2012). Finally, for TRP and CRP, we retained the top 33% keywords on Hulth2003 and Marujo2012 (short and medium size documents), whereas on Semeval (long documents), we retained the top 15 keywords. This is consistent with our baselines. Indeed, the number of manually assigned keywords increases with document size up to a certain point, and stabilizes afterwards.
The code of the implementation and the datasets can be found here 4. Truss nu q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0 5000 10000 20000 3 4 5 6 7 8 9 10 11 12 13 14 7 Experimental Results.
Precision/Recall trade-off As shown in Figure  5 , our methods dens and inf outperform the baselines by a wide margin on the datasets containing small and medium size documents (Hulth2003 and Marujo2012). As expected, this superiority is gained from a drastic improvement in recall, for a comparatively lower precision loss. TR and main exhibit higher precision than recall, which is in accordance with  (Rousseau and Vazirgiannis, 2015). The same observation can be made for our CR method. For TR, the unbalance is more severe on the Hulth2003 and Marujo2012 datasets (short/medium documents) when the elbow method is used (TRE), probably because it tends to retain only a few nodes. However, on Semeval (long documents), using the elbow method (TRE) gives the best trade-off between precision and recall.
Best models comparison For each dataset, we retained the degeneracy technique and window size giving the absolute best performance in F1-score, and compared all methods under these settings (see Tables  1 2 3 ). We tested for statistical significance in macro-averaged F1 scores using the non-parametric version of the t-test, the Mann-Whitney U test 5. On Hulth2003 and Marujo2012 (short and medium size documents), our methods dens and inf strongly and significantly outperform all baselines, with respective absolute improvements of more than 5. 5% with respect to the best performing baseline PRECISION q q q q q q q q q q q q 4 6 8 10 12 14 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 q q q q q q q q q q q q 4 6 8 10 12 14 0. 40 0. 45 0. 50 0. 55 0. 60 window size q q q q q q q q q q q q k-truss.
Accuracy of ABC estimation and relative importance of summary statistics In order to optimize our ABC estimation procedure and to evaluate its average performance, we first applied it to a large number of genomic samples simulated under random population size histories. These pseudo-observed datasets (PODs) included 25 diploid genomes and 100 independent 2Mb-long regions. For each POD, population sizes were estimated by ABC, using 450,000 simulated datasets of the same size. These estimated values were compared with their true values for different tolerance rates and different ABC adjustment approaches to process the accepted histories. We found that the best procedure was to accept simulated histories with a tolerance rate of 0. 005, to adjust their parameter values using a non linear neural network regression  [32] , and to summarize the resulting posterior distribution by its median.
In the following, LD statistics were always computed from genotype data at SNPs with a MAF above 20%, unless otherwise specified. Accuracy of ABC estimation and relative importance of the summary statistics. Prediction error for the estimated population size in each time window (left) and standard deviation of this error (right), evaluated from 2,000 random population size histories. Summary statistics considered in the ABC analysis included different combinations of (i) the AFS (possibly without the overall proportion of SNPs) and (ii) the average zygotic LD for several distance bins. These statistics were computed from n = 25 diploid individuals, using all SNPs for AFS statistics and only those with a MAF above 20% for LD statistics. The posterior distribution of each parameter was obtained by neural network regression  [32] , with a tolerance rate of 0.
For the analysis of observed data sets with a genome length above 200Mb, we thus considered the alternative strategy consisting in comparing observed statistics computed from the full genome (which is computationally very easy) with simulated statistics computed from a subset of the genome. We may think about these simulated summary statistics as an approximation of the genome-wide simulated statistics. To evaluate this strategy, we assumed that the genome length was 100 2Mb-long segments in the observed sample and 10 2Mb-long segments in the simulated samples (S7 Fig). Credible intervals were only slightly improved compared to using a genome length of 10 2Mb-long segments in both simulated and observed datasets, but PEs and their variance between scenarios were reduced, especially for the most recent and the oldest time windows, reaching values almost as low as those obtained when using 100 2Mb-long segments in both simulated and observed datasets.
Comparison with MSMC For each scenario of  Fig 3,  we also analyzed five simulated samples with MSMC  [10] , using two, four or eight of the haplotypes from each sample. When applied to two haplotypes, MSMC is an improved version of PSMC  [9] , a software that has been used to estimate population size history in many different species within the last few years  [35] [36] [37] [38]. In our simulations, MSMC based on two haplotypes provided a very accurate estimation of the population size history within a time window starting between a few hundreds and a few thousands generations BP, depending on the scenario, and finishing after the expected TMRCA of 50 haplotypes (  Fig 4). Within this time window, estimations obtained by MSMC from the five replicates were all very close to the true history, even more than ABC estimations.
Application to NGS samples in cattle We applied our ABC approach to estimate the population size history in four cattle breeds, using large samples of diploid genomes recently published by the 1,000 bull genomes project  [40]. An important issue when analyzing NGS data is the potential influence of sequencing and phasing errors on the estimations. To investigate this question, we first evaluated how these errors affect the summary statistics considered in our ABC approach. We considered a set of 12 Holstein animals for which the haplotypes inferred from NGS data within the 1,000 bull genomes project could be compared with those inferred from 800K SNP chip data obtained independently from another project. Assuming that 800K data are free of genotyping errors, we computed the summary statistics from these data and checked whether similar values could be obtained from NGS data at the same positions (S20 Fig).
Computing LD from genotypic data, we obtained more realistic results, with a recent population size of 7,000. However, there was a great difference between the estimation obtained when computing AFS statistics from all SNPs, and that obtained when computing these statistics only from SNPs with a MAF above 10% ( In contrast, there was little difference between the estimations obtained when computing AFS statistics with a MAF threshold of 10 or 20%, which strongly suggests that these strategies are both robust against sequencing errors, at least for this particular dataset. To be conservative, we used a MAF threshold of 20% for the final analysis of the four breeds. Summary statistics considered in the ABC analysis were (i) the AFS and (ii) the average LD for several distance bins. LD statistics were computed either from haplotypes or from genotypes, using SNPs with a MAF above 20%.
Moreover, the SNP data handled by our method can be unpolarized, i. e. it is not necessary to know which of the alleles at a given SNP is ancestral. Using polarized data would probably improve the estimations, as this would allow computing the unfolded rather than folded AFS. However, inferring ancestral alleles is not always possible and is prone to errors, so we chose to focus on statistics computable for all datasets. Finally, based on the analysis of NGS data in cattle, we showed that our approach can easily be made robust to sequencing errors by computing summary statistics only from SNPs with common alleles (MAF ! 10 or 20%, Fig  5 ). This modification is expected to increase the population size prediction errors and the width of credible intervals if the dataset contains no sequencing errors (S9 Fig), but this seems by far preferable to the large biases caused by sequencing errors, as illustrated by our study and several previous ones  [9, 12].
The ABC approach Assume we observe a dataset D, from which we want to estimate the parameters θ of a given model. In a Bayesian framework, this involves computing the posterior probability Pðy j DÞ for any possible parameter value. In many situations, and in particular in population genetics, this posterior cannot be derived because of the model complexity and even numerical evaluations are impossible due to the high dimensionality of the observed data space. The idea of ABC  [20]  is to replace in this context the full dataset D by a vector of summary statistics S capturing most information contained in the data and to estimate model parameters based on the approximate posterior Pðy j SÞ. The estimating procedure consists in sampling a very large number of parameter values from a prior distribution, simulating datasets from these parameter values, and accepting the parameter values leading to summary statistics that are sufficiently similar to those of the observed dataset.
Model and priors Here the observed data D is a set of n diploid genomes sampled from a single panmictic population, and the model assumed to have generated these data is the coalescent with mutation and recombination  [16]. We assume that effective population size varied according to a piecewise constant process. Following  [9, 10] , we considered a fixed number of time windows, whose size increased exponentially from recent to old periods. More precisely, we used I = 21 windows of the form [t i , t i+1 ], where t i = exp(log(1 + aT)i/(I -1)) -1)/a generations BP for i from 0 to I -1, with T = 130,000 and a = 0. 06, and t I = +1. These specific values of T and a were chosen to capture important periods of cattle history. Modifying T would allow population size changes to occur on a longer or shorter period in the past, and modifying a would allow to describe more precisely one specific part of the history, playing on the ratio between the length of recent versus old time windows.
For the recombination rate, we used an uniform prior between 1e-9 and 1e-8, consistent with recent estimations in cattle  [66]. For the mutation rate we considered a fixed value, in order to compare our estimation approach with other recent ones making the same hypothesis  [9, 10, 12] , but it would be straightforward to use a prior distribution instead. This value was taken equal to 1e-8, as in  [12]. For the real data set, a total of 234 phased bull genomes were obtained from the 1,000 bull genomes project, Run II  [40]. These included 129 Holstein (125 Black and 4 Red), 43 Fleckvieh, 47 Angus and 15 Jersey animals. Holstein animals came from various flocks with distinct geographical origins. In order to study homogeneous groups, we thus focused on the 52 Holstein animals from Australia (other geographical origins had significantly lower sample sizes).
We further selected 25 unrelated animals within each breed with the following procedure: first, we removed all animals that were either extremely inbred or extremely related to another sampled animal, based on the genomic relationship matrix computed from GCTA  [69]. Then, we sampled 25 animals at random among the remaining ones. For the Jersey breed, as only 15 animals were available and as they were found to be all unrelated to each other, we kept them all. The summary statistics described above were computed using the same python script for both simulated and cattle samples. Since the length of cattle chromosomes was much larger than that of simulated segments (2Mb), we first cut each cattle chromosome into consecutive but non-overlapping 2Mb-long segments. To keep the approach computationally efficient, the average LD for a given distance bin was not evaluated from all SNP pairs satisfying the distance condition, but from a random subset of these pairs.
Software and data availability Python and R scripts for the PopSizeABC method can be found at https://forge-dga. jouy. inra. fr/projects/popsizeabc/. Simulated and observed summary statistics used in this study are also provided on this web page. Middle: Bias for the estimated population size in each time window. Bottom: Empirical coverage (left) and width (right) of the 90% credible interval for the population size in each time window. These quantiles were evaluated from 2,000 random population size histories. For each of these histories, one POD of n = 25 diploid genomes was simulated, where each genome consisted in 100 independent 2Mb-long segments. Population size history was estimated from this POD by ABC, using 450,000 simulated datasets with the same sample size and genome length. Summary statistics considered in the ABC analysis were (i) the AFS and (ii) the average zygotic LD for several distance bins.
Acknowledgments Data analyzes were performed on the computer cluster of the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées  (www. bioinfo. genotoul. fr). The cattle genomes were obtained from the 1,000 bull genomes project (www. 1000bullgenomes. com). The 12 Holstein bull genotypes for the bovine 800K SNP chip were obtained from the  GEMBAL  project, which is funded by the  Agence Nationale de la Recherche  ( ANR-10-GENM-0014 ),  APISGENE ,  Races de France  and  INRA "AIP Bioressources ". Preliminar analyzes were performed by  Stanislas Sochacki , who was funded by the  Projets Exploratoires Pluridisciplinaires  ( PEPS 2012 Bio-Maths-Info ). Flora Jay's position was funded by the  Agence Nationale de la Recherche   Demochips  project ( ANR-12-BSV7-0012 ). We would like to thank  Lounès Chikhi ,  Olivier Mazet ,  Simona Grusea ,  Bertrand Servin ,  Didier Boichard  and all members of the  Demochips  project for their useful comments on this study.
Funding: SB was funded by the grant  ANR-10-GENM-0014  from  Agence Nationale de la Recherche  (http://www. agence-nationale-recherche. fr) and the grant PEPS 2012  Bio-Maths-Info  from  Université de Toulouse  (http://www. univ-toulouse. fr). FJ, SM and FA were funded by the grant  ANR-12-BSV7-0012  from  Agence Nationale de la Recherche  had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.