dataesr/scientific-paragraphs-categorization huggingface dataset:
A second benefit of the assignment rules is the possibility to provide recommendations to team members on which criteria they need to improve and on which they should be careful not to worsen. Let us consider the illustrative example presented in Fig. 13. We use, in this case, the assignment rules extracted for DM2, as well as a team member who was evaluated by DM2 as neutral in commitment (first criterion), bad in working with other and produced code quality (the following two criteria), very bad in his or her vision on the whole project (fourth criterion), but very good in documentation and testing (last criterion). We notice that this contributor is evaluated by DM2 as neutral, since none of the assignment rules from the Good category are validated, nor any from the Bad category. The plots, however, give insights into how the contributor may improve in order to become a good contributor.
With respect to the first assignment rule, he/she could improve slightly in produced code quality (g 3 ) in order to become good on this criterion and in the overall vision of the project (g 4 ) in order to not be very bad. The second assignment rule requires more drastic changes on the fourth criterion and will not likely be taken into account by DM2. The third rule requires the same changes as the first, but also an improvement from bad to good in teamwork skills. The fourth rule tells us that slight improvements in commitment (g 1 ) from neutral to good and in documentation and testing from good to very good may also make this contributor a good one. Finally, only improving in teamwork, however quite radically (from bad to very good) could also be proposed. While DM2 would naturally ask all contributors to strive to improve on all criteria, ideally wishing all contributors to be very good on all of them, the previous recommendations may serve as the paths of least resistance, allowing contributors to become globally good quickly and without too much effort, As the recommendations with respect to the second and third assignment rules include those for the first rule, DM2 would consider recommending only the latter as well as those from rules 4 and 5.
and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA. MAN also consults for Illumina Inc. , the Michael J. Fox Foundation, and the University of California Healthcare. MAN also has commercial affiliation with Data Tecnica International, Glen Echo, MD, USA. Mark J. Caulfield (MJC) has commercial affiliation and is Chief Scientist for Genomics England, a UK government company. OHF is supported by grants from Metagenics (on women's health and epigenetics) and from Nestle (on child health). Peter S. Sever (PSS) is financial supported from several pharmaceutical companies which manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees. Paul W. Franks (PWF) has been a paid consultant in the design of a personalized nutrition trial (PREDICT) as part of a private-public partnership at Kings College London, UK, and has received research support from several pharmaceutical companies as part of European Union Innovative Medicines Initiative (IMI) projects.
Meta-analysis and quality control We employed a modified METAL software  [24]  to perform 2 degrees of freedom joint metaanalysis, using the inverse-variance weighted fixed-effects approach. We applied multiple steps of QC, both at cohort association analysis and at meta-analysis level, implemented with EasyQC, an R package  [75]. They included filtering of markers with imputation quality < 0. 5; with minor allele frequency < 1%; minor allele count 10; if alleles were mismatched when comparing the cohort's alleles with the 1000 Genomes cosmopolitan panel; and/or if the allele frequencies were different from those of the 1000 Genomes. In addition, a cohort participated in the meta-analysis if it had more than 50 individuals consuming alcohol. The meta-analysis results were reported if they had more than 5,000 individuals and if at least two studies for each SNV contributed to the analysis.
Correlated meta-analysis The genome (millions of SNPs) are under the null hypothesis of no genotype-phenotype association, which is only mildly contaminated with a relatively smaller set of SNVs that are under the alternative. The correlated meta-analysis  [25, 26]  performs a large sampling of genome and produces the polychoric correlation estimator (using SAS PROC FREQ). The estimator measures the relation degree of any non-independence between scans. The correlated meta-analysis corrects the inference for it, retaining the proper type I error structure. The correlated meta-analysis  [25, 26]  uses the Fisher's 1925 method by combining P-values at each location of the genome. This technique uses the fact that for number of scans, sum of -2 ln (p i ), approximately chi-square (X 2 ) with two degrees of freedom.
In the case of correlated GWAS, this sum is no longer distributed as a simple X 2. Instead, the correlated meta-analysis method  [25, 26]  uses an inverse-normal transform, Z i = θ -1 (p i ) forming the N dimensional vector Z of all Z i s. Then, the method applies the basic theorem of multidimensional statistics for the matrix D, if Z~N(O, E) then DZ~N(O, E∑D'). In particular, when D is a 1×N vector of all 1's, SUM(Z) = D Z ~N(0, SUM(∑)), whose tail probability gives the Z meta-analysis P-value. In this case, for estimating ∑, the SNV P-values are dichotomized across the genome as (P 0. 5; P > 0. 5). The software was developed in SAS.
Bioinformatics analyses The annotation of variants was sourced from NCBI dbSNP build 138 (hg19) during the analyses and updated to dbSNP build 150 (hg38) for reporting results. Our candidate SNVs for BP were questioned if they resided in any of regulatory marks, analyzing information from the NCBI Entrez gene, dbSNP, Encyclopedia of DNA Elements Consortium (ENCODE) project and the Roadmap Epigenomics Mapping Consortium (ROADMAP), as summarized by Hap-loReg  [28, 29] , and RegulomeDB  [30, 31]. HaploReg (v. 4. 1) queries were used to identify functional annotations including the chromatin state segmentation on the Roadmap reference epigenomes, conserved regions by GERP and SiPhy, the experiments of DNAse hypersensitivity and ChIP-seq experiments from ENCODE. UCSC Genome Browser and GENCODE were used for gene annotations.
We calculated the proximity of each variant to a gene. RegulomeDB (v. 1. 1, accessed on 06. 15. 2017) provided regulatory information of gene expression via ChIP factors, DNase sensitivity, and transcription factor (TF) binding sites from ENCODE. RegulomeDB uses the Position-Weight Matrix for TF binding, and databases JASPAR CORE, TRANSFAC and UniPROBE  [79]. RegulomeDB reported Chromatin States from ROADMAP, eQTLs from several tissue types, DNase footprinting  [80, 81] , differentially methylated regions  [82] , manually curated regions and validated functional SNVs. GWAS3D  [33]  (accessed on 03. 15. 2017) was used to analyze genetic variants that may affect regulatory elements, by integrating annotations from cell type-specific chromatin states, epigenetic modifications, sequence motifs and cross-species conservation.
The regulatory elements are inferred from the genome-wide chromosome interaction data, chromatin marks in different cell types measured by high-throughput chromosome conformation capture technologies (5C, ChIA-PET and Hi-C) from ENCODE, Gene Expression Omnibus (GEO) database, published resources and regulatory factor motifs. We gathered also evidence for eQTLs based on GTEx (v. 7), GRASP software and special gene expression reported results  [83, 84]. The importance of our novel and potential novel BP genes (Tables  1 2 3 4 5 ) were mined by means of four methods: enrichment analysis, protein-protein interactions (PPI), analytical gene expression cis-regulation, and analytical gene expression trans-regulation. The GeneGO and Literature Lab of ACUMENTA software (accessed on 03. 15. 2017) were used for enrichment analysis.
We tested if novel genes were significantly enriched among prespecified gene sets defined in pathways, or by shared roles in particular diseases or biological processes from Gene Ontology. The GeneGO enrichment analysis consists of matching unique gene symbols of possible targets for the "common", "similar" and "unique" sets with gene symbols in functional ontologies. The probability of a random intersection between a set of gene symbols, the size of target list with ontology entities, is estimated by P-value of a hypergeometric intersection. The lower P-value means higher relevance of the entity to the dataset, which shows in higher rating for the entity. Literature Lab is an interface between experimentally-derived gene lists and scientific literature in a curated vocabulary of 24,000 biological and biochemical terms.
It employs statistical and clustering analysis on over 17. 5 million PubMed abstracts (from 01. 01. 1990 to the present) to identify pathways (809 pathways), diseases, compounds, cell biology and other areas of biology and biochemistry. The analysis engine compares statistically the submitted gene set to 1,000 random gene sets generated in the analysis to identify term relationships that are associated with the gene set more than by chance alone. The BP candidate genes were assessed via PPI of databases from Biological General Repository for Interaction Datasets (BioGrid), Escherichia coli K-12 (EcoCyc), and Human Protein Database (HPRD) as summarized by the National Center for Biotechnology Information (NCBI, accessed on 02. 28. 2017). The gene list from PPI was evaluated using igraph package  [85].
Ã RegulomeDB scoring has classes defined as 1b, 1d and 1f: likely to affect binding and linked to expression of a gene target, with details: 1b (eQTL + TF binding + any motif + DNase footprint + DNase peak); 1d (eQTL + TF binding + any motif + DNase peak); 1f (eQTL + TF binding/DNase peak), 2a and 2b: likely to affect binding, 3a: less likely to affect binding, 4, 5, and 6: minimal binding evidence, and 7: no data. This software was accessed on 06. 15. 2017. Regulatory function measured by Promoter histone marks, Enhancer histone marks, DNase (DNAse hypersensitivity), Proteins bound, Motifs changed. (XLSX) S15 Table. Data analysis tools and databases. (DOCX).
The following authors declare commercial private and/or governmental affiliations:  Bruce M. Psaty  ( BMP ) serves on the DSMB of a clinical trial funded by  Zoll Lifecor  and on the Steering Committee of the  Yale Open Data Access Project  funded by  Johnson & Johnson. Barbara V. National Heart, Lung, and Blood Institute (NHLBI). Brenda W. J. H. Penninx  (BWJHP) has received research funding (non-related to the work reported here) from  Jansen Research and Boehringer Ingelheim. Mike A. Nalls (MAN)  is supported by a consulting contract between Data Tecnica International LLC and the  National Institute on Aging (NIA) ,  National Institutes of Health (NIH) ,  Bethesda, MD, USA. MAN also consults for  Illumina Inc. , the  Michael J. Fox Foundation , and the  University of California Healthcare.
MAN also has commercial affiliation with Data Tecnica International,  Glen Echo, MD ,  USA. Mark J. Caulfield  (MJC) has commercial affiliation and is  Chief Scientist for Genomics England , a  UK government company. Oscar H Franco (OHF)  is supported by grants from  Metagenics  (on women's health and epigenetics) and from  Nestle  (on child health). Peter S. Sever  (PSS) is financial supported from several pharmaceutical companies which manufacture either blood pressure lowering or lipid lowering agents, or both, and consultancy fees. Paul W. Franks  (PWF) has been a paid consultant in the design of a personalized nutrition trial (PREDICT) as part of a private-public partnership at  Kings College London, UK , and has received research support from several pharmaceutical companies as part of  European Union Innovative Medicines Initiative (IMI)  projects.
1. 2). In addition to the AFT age, the track length distribution is often used to determine the nature of the cooling paths and allows the reconstruction of the time-temperature history of the basement rocks by thermal history modelling  (Ketcham et al. , 1999 (Ketcham et al. , , 2007;; Gallagher, 2012). Where possible, a minimum of 100 horizontal confined tracks for each basement sample were measured on 1250x magnification with a KONTRON-MOP-AMO3 digitizing tablet to construct M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 11 length-frequency distributions. For some samples, no length data was available due to low spontaneous track densities and/or a low number of suitable grains. Thermal history modelling was performed on basement samples with a sufficient number (~50 or ideally 100) of lengths using the QTQt software from Gallagher (2012), the  Ketcham et al.
0 ± 0. 5 s at 20. 0 ± 0. 5 °C to expose the spontaneous fission tracks and subsequently imaged on a Zeiss AXIO Imager M2m Autoscan System at the University of Adelaide. Fission track densities were measured using the FastTracks software and AFT ages were calculated using U concentrations, obtained simultaneously with the apatite U-Pb data acquisition. More details on the methodology are described in  Gleadow et al. (2015) ,  Gillespie et al. (2017) ,  Glorie et al. (in press ).
Detrital AFT dating application In a sedimentary rock sample, different AFT age populations can be present because the sample possibly contains material originating from source terranes that experienced different t-T histories. The obtained single grain-age distributions for the detrital samples in this study are visualised by an abanico plot, which combines a radial plot with a Kernel Density Estimation  (Galbraith and Green, 1990) , using the RadialPlotter software  (Vermeesch, 2009). M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 12 (KDE) plot Central ages were calculated and a minimum age model was performed as well with the RadialPlotter software  (Vermeesch, 2009). The homogeneity of the single-grain ages can be tested with a chi-squared test (χ 2  ; Galbraith 1981; Green 1981).
Data reduction was performed using the Iolite software  (Paton et al. , 2011)  following the procedures described in  Chew et al. (2014). A 207 Pb corrected overall weighted mean 238 U/ 206 Pb age was calculated, which represents the best estimate of the apatite U-Pb cooling age. More details on the methodology can be found in  Chew et al. (2011 Chew et al. ( , 2014)).
Basement thermochronology: AFT, ZHe and apatite U-Pb results The apparent AFT ages of the twenty basement samples range from early Cretaceous to early Eocene and can be interpreted in terms of episodic basement cooling and protracted   2 ). Fourteen Ordovician-Silurian granitoid samples and one Carboniferous meta-sandstone (KB-122) from in the NTS, four Carboniferous-Early Permian granitoid samples from the MTS and one Carboniferous-Early Permian granite from in the Alai Range in the STS (Figure  2 ; Table  1 ) were analysed. All basement samples pass the chi-squared test (>5%) (Table  2 ). In total, 12 thermal history models are reconstructed using the QTQt software (Gallagher, 2012) based on samples with a sufficient to acceptable number of horizontal confined tracks (Fig. 3 ; Table  2 ).
Cenozoic The most recent reactivation of the Tien Shan initiates in the Oligocene-early Miocene, during which the India-Eurasia convergence caused major tectonic reactivation in large swathes of Central Asia  (Molnar and Tapponier, 1975; Aitchison et al. , 2007; De Grave et al. , 2007; Bande et al. , 2015b). During this late Cenozoic reactivation, the TFF accumulated approximately 60 km of slip  (Burtman et al. , 1996; Alexeiev et al. , 2017) , which was accommodated by the development of mountain building along the TFF and a clockwise rotation of the Fergana Basin  (Thomas et al. , 1993; Bande et al. , 2015b). The late Cenozoic reactivation is supported by a large dataset of basement and detrital AFT studies in the Kyrgyz Tien Shan  (Bullen et al. , 2001; Sobel et al. , 2006a, b; Glorie et al.
AFT ages are reported as zeta-age t(ζ) (in Ma), calculated with the zeta calibration factor that is based on Durango and Fish Canyon Tuff age standard analyses. AFT length results are reported as mean track length (l m in µm) with standard deviation σ (in µm), obtained from the measurement of an amount (n l ) of natural, horizontal confined tracks. For some samples, no AFT length data is reported due to low spontaneous track densities and/or a low number of suitable grains. Length data and subsequent thermal history modelling of samples in Zircon (U-Th)/He analysis results. For each sample, four single grain ages (aliquots) were determined. Analyses in italic were discarded for the calculation of the sample's average age, because of anomalous values possibly caused by inclusions or Helium trapping in the zircon crystals (see 3.
P(χ 2 ) is the chi-squared probability that the dated grains have a constant ρ s /ρ i -ratio  (Galbraith, 1981). The AFT central age t(c) (in Ma), age dispersion (%) and minimum age (Min. age) are calculated using the RadialPlotter software  (Vermeesch, 2009). Mean track length (l m ), standard deviation (σ) and the number of track lengths (n l )are measured for the highest quality samples in which it was possible to measure a significant number of horizontal confined tracks. AFT analyst is indicated in the last.
This elegant framework was reused for slicing extended finite state machines  [3]  and probabilistic programs  [4]. In both works, an algorithm computing weak control-closure, working differently from ours, was designed and integrated in a rather efficient slicing algorithm. While there exist efficient algorithms to compute the dominator tree in a graph  [8, 10, 16, 19] , and even certified ones  [15] , and thus efficient algorithms computing control dependence when defined in terms of post-dominance, algorithms in the general case  [2, 11, 23]  are at least cubic. Mechanized verification of control dependence computation was done in formalizations of program slicing. Wasserrab  [26]  formalized language-independent slicing in Isabelle/HOL, but did not provide an algorithm. Blazy et al. [7]  and our previous work  [18]  formalized control dependence in Coq, respectively, for an intermediate language of the CompCert C compiler  [20]  and on a WHILE language with possible errors.
Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.
Consequently, a learnt predicate embedding, given its position in the se-mantic space, can be expressed in an varied number of different ways in the text. Training data for NLG models is not always readily available; this applies to the context of Semantic Web as well. The difficulty is that data that is available in knowledge bases needs to be aligned with the corresponding texts. Existing solutions for data-to-text generation either focus mainly on creating a small, domain-specific corpus where data and text are manually aligned by a small group of experts, such as the WeatherGov  [7]  and RoboCup  [8]  datasets, or rely heavily on crowdsourcing  [9] , which makes them costly to apply for large domains. Our second contribution is an automatic approach for building a large datato-text corpus of rich linguistic variability.
Conventional systems based on neural networks when employed on NLG tasks, such as Machine Translation  [6]  or Question Generation  [11]  are incapable of learning high quality vector representation for the infrequent tokens (i. e. either words or entities) in their training dataset. Inspired by  [12, 11] , we address this problem by adapting a multi-placeholder method that enables the model to emit special tokens that map a rare entity in the text to its corresponding triple in the input set. We use perplexity, and the BLEU and ROUGE metrics in order to automatically evaluate our approach's ability of predicting the Wikipedia summary that corresponds to a set of unknown triples showing substantial improvement over our baselines. Furthermore, we evaluate a set of generated summaries against human evaluation criteria.
Based on the average rating across our selected criteria, we conclude that our approach is able to generate coherent textual summaries that address most of the information that is encoded in the input triples. Lastly, we demonstrate our method's capability to successfully infer semantic relationships among entities by computing the nearest neighbours of the learned embeddings of respective entities in our datasets. The structure of the paper is as follows. Section 2 discusses existing approaches to NLG and the Semantic Web, and relates them to our model. Section 3 presents the components of our approach. Section 4 describes the construction of our datasets. Section 5 presents experiments and an evaluation of the model. Section 6 summarises the contributions of this work and outlines future plans.
Datasets In order to address the training needs of our proposed model, we build two datasets of aligned knowledge base triples with texts. For the first dataset, we leverage the intrinsic alignment of DBpedia and Wikipedia in order to create a corpus of loosely aligned triples and textual summaries. In our second scenario, following [Elsahar et al. 2017], we align Wikipedia summaries with the communitycurated triples of Wikidata. Inspired by Lebret et al. , we chose a corpus about biographies. Biographies represent one of the largest single domains in Wikipedia, providing us with a substantial amount of training data [cite?]. We believe they offer the necessary diversity and linguistic variability that will allow us to explore effectively the generative ability of our systems. In addition, the fact that the biographies tend to adopt a limited number of structural paradigms, pro-vides us with the opportunity to better understand the limitations of our approach.
By using PetScan 6  , we collected a list of 1, 479, 170 Wikipedia articles that have been detected by the WikiProject Biography 7. We then extracted the Wikipedia summaries and their corresponding DBpedia triples from the Mapping-based Objects 8  and Literals 8 DBpedia dataset, retaining only articles for which an infobox exists. For the Wikidata version of the dataset, we used the Wikidata truthy dumps  9  and we kept only items for which Wikidata triples exist. In addition to the above datasets, we also leverage two DBpedia datasets:(i) the Instance Types 8 and (ii) the Genders 8 datasets. The first one is used to provide us with special tokens for the entities that occur infrequently in our aligned datasets, and the second in order for us to append a gender-related triple to the DBpedia triples that have been already allocated to an article.
In order to enable our model to process date-related triples and learn how their information is lexicalised in the text, we decompose them into two different triples. The first one is used to represent the month as it has been identified in the original triple, and the second one to represent the year. The object of the latter is subsequently mapped to the special <year> token. Table  2  presents an example of our date encoding approach. For each entity that has been identified in a Wikipedia summary using DBpedia Spotlight, we extracted its corresponding triples from the Mapping-based Objects dataset in the DBpedia's case, and the Wikidata truthy dump in Wikidata's case. We assume that the subjects or objects of a set of triples are consistent with the main subject of the corresponding Wikipedia summary.