dataesr/scientific-paragraphs-categorization huggingface dataset:
Most existing work on paraphrases focuses on lexical-, phrase-, sentenceand document level  (Burrows et al. , 2013). We primarily focus on contextualization of paraphrases based on existing paraphrase resources. Furthermore, we target multi-worded paraphrases, since single-word replacements are covered well in lexical substitution datasets, such as  (McCarthy and Navigli, 2007; Biemann, 2012). While these datasets contain multi-word substitution candidates, the substitution targets are strictly single words. Multi-word expressions are prevalent in text, constituting roughly as many entries as single words in a speaker's lexicon  (Sag et al. , 2002) , and are important for a number of NLP applications. For example, the work by  Finlayson and Kulkarni (2011)  shows that detection of multiword expressions improves the F-score of a word sense disambiguation task by 5 percent.
In this paper, we experiment with both MWE and single words and investigate the difficulty of the paraphrasing task for single words vs. MWEs, using the same contextual features. Our work, centered in assessing the effect of context for paraphrase ranking of humans and its automatic prediction, includes the following steps: 1) systematic combination of existing paraphrase resources to produce paraphrase candidates for single-and multi-word expressions, 2) collection of dataset for paraphrase ranking/selection annotation task using crowdsourcing, and 3) investigating different machine learning approaches for an automatic paraphrase ranking. 2 Related Work.
Paraphrase Resources and Machine Learning Approaches Paraphrasing consists of mainly two tasks, paraphrase generation and paraphrase identification. Paraphrase generation is the task of obtaining candidate paraphrases for a given target. Paraphrase identification estimates whether a given paraphrase candidate can replace a paraphrase target without changing the meaning in context. PPDB  (Pavlick et al. , 2015)  is one of the largest collections of paraphrase resources collected from bilingual parallel corpora. PPDB2 has recently been released with revised ranking scores. It is based on human judgments for 26,455 paraphrase pairs sampled from PPDB1. They apply ridge regression to rank paraphrases, using the features from PPDB1 and include word embeddings. The work of  (Kozareva and Montoyo, 2006 ) uses a dataset of paraphrases that were generated using monolingual machine translation.
In the dataset, sentence pairs are annotated as being paraphrases or not. For the binary classification, they use three machine learning algorithms (SVM, kNN and MaxEnt). As features they use word overlap features, n-grams ratios between targets and candidates, skip-grams longest common subsequences, POS tags and proper names. Connor and Roth (2007)  develop a global classifier that takes a word v and its context, along with a candidate word u, and determines whether u can replace v in the given context while maintaining the original meaning. Their work focuses on verb paraphrasing. Notions of context include: being either subject or object of the verb, named entities that appear as subject or object, all dependency links connected to the target, all noun phrases in sentences containing the target, or all of the above.
The work of  Brockett and Dolan (2005)  uses annotated datasets and Support Vector Machines (SVMs) to induce larger monolingual paraphrase corpora from a comparable corpus of news clusters found on the World Wide Web. Features in-clude morphological variants, WordNet synonyms and hypernyms, log-likelihood-based based word pairings dynamically obtained from baseline sentence alignments, and string features such as word-based edit distance  Bouamor et al. (2011)  introduce a targeted paraphrasing system, addressing the task of rewriting of subpart of a sentence to make the sentences easier for automatic translation. They report on experiments of rewriting sentences from Wikipedia edit history by contributors using existing paraphrase resources and web queries. An SVM classifier has been used for evaluation and an accuracy of 70% has been achieved.
Impact of Context on Paraphrasing In order to validate our intuitively plausible hypothesis that context has an impact on paraphrasing, we conduct experiments using the PPDB2 paraphrase database. PPDB2 is released with better paraphrase ranking than PPDB1  (Pavlick et al. , 2015)   platform. In the first annotation task, a total of 171 sentences are selected from the British Academic Written English (BAWE) corpus 1   (Alsop and Nesi, 2009) , with five paraphrase targets. The targets are selected in such a way that a) include MWEs as targets when it is possible (see Subection 3. 2 how we select targets), b) the candidates could bear more than one contextual meaning and, c) workers can select up to three paraphrases and have to supply their own paraphrase if none of the candidates match. To satisfy condition b), we have used the JoBimText DT database API  (Ruppert et al.
Paraphrase Dataset Collection using Crowdsourcing In this subsection, we present the processes carried out to collect datasets for the paraphrase ranking task. This includes selection of documents, identification of target paraphrases, and generation of candidate paraphrases from existing resources. We use 2. 8k essay sentences from the ANC 2  and BAWE corpora for the annotation task. Target detection and candidate generation: In order to explore the impact of contexts for paraphrasing, the first step is to determine possible targets for paraphrasing, as shown in Figure  1. As a matter of fact, every word or MWE in a sentence can be a target for paraphrasing. When prototyping the annotation setup, we found that five paraphrase targets are a reasonable amount to be completed in a single Human Intelligence Task (HIT), a single and self-contained unit of task to be completed and submitted by an annotator to receive a reward in a return 3.
Ranking is then performed by simply sorting predicted scores  (Li et al. , 2007). In the pairwise approach, the problem is regarded as a binary classification task where pairs are individually compared each other  (Freund et al. , 2003). Listwise ranking approaches learn a function by taking individual candidates as instances and optimizing a loss function defined on the predicted instances  (Xia et al. , 2008). We experiment with different learning-torank algorithms from the RankLib 6  Java package of the Lemur project 7. In this paper, we present the results obtained using LambdaMART. Lamb-daMART (Burges, 2010) uses gradient boosting to directly optimize learning-to-rank specific cost functions such as Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP).
Ngram features: A normalized frequency for a 2-5-gram context with the target and candidate phrases (F 7) based on Google Web 1T 5-Grams 10. Language model score: A normalized language model score using a sentence as context with the target and candidate phrases (F 8). An n-gram language model  (Pauls and Klein, 2011)  is built using the BNC and Wikipedia corpora. Also, we experimented with features that eventually did not improve results, such as the embeddings of the target's n = 5 most similar words, length and length ratios between target and candidate, most similar words and number of shared senses among target and candidate phrases based JoBimText DT  (Ruppert et al. , 2015) , and N-gram POS sequences and dependency labels of the tar-  get.
Correlation with PPDB2 Ranking As it can be seen from Table  1 , without contexts, a Spearman correlation of 0. 36 and 0. 25 is obtained by the workers against the PPDB2 default rankings for single and MWE annotations resp. However, when the contexts are provided to the workers, the ranking for the same items is lower with a Spearman correlation of 0. 32 and 0. 23 for single and MWE annotations resp. This indicates that the contexts provided has an impact on the ranking of paraphrases. Moreover, we observe that the correlation with PPDB2 ranking is considerably lower than the one reported by  Pavlick et al. (2015)  which is 0. 71. Data analysis revealed a lot of inconsistent scores within the PPDB2. For example, the word pairs (come in, sound) and (look at, okay) have a high correlation score (3.
Here, we have quantified the amount of context dependence on a new set of contextualized paraphrase judgments, which is -to our knowledgethe first dataset with multi-word targets 13. While our dataset seems of sufficient size to learn a high-quality context-aware paraphrase ranker, we would like to employ usage data from a semantic writing aid for further improving the quality, as well as for collecting domain-and userspecific paraphrase generation candidates.
High performance liquid chromatography (HPLC). HPLC of the tryptic peptides was performed on a C18 micro-column at a flow rate of 50 μ L min -1 with a linear gradient (10 to 80% in 60 min) of acetonitrile and 0. 1% formic acid. Fractionated peptides were analysed in triplicate with an electrospray ionisation quadripole time-of-flight (ESI-QqTOF) hybrid mass spectrometer (pulsar i, Applied Biosystems) using information dependent acquisition (IDA), which allows switching between MS and MS/MS experiments. Data were acquired and analysed with Analyst QS software (Version 1. 1). After 1 s acquisition of the MS spectrum, the two most intense multiple charged precursor ions (+ 2 to + 4) could be selected for 2 s-MS/MS spectral acquisitions. The mass-to-charge ratios of the precursor ions selected were excluded for 60 s to avoid re-analysis.
The minimum threshold intensity of the ion was set to 10 counts. The ion-spray potential and declustering potential were 5200 V and 50 V, respectively. The collision energy for the gas phase fragmentation of the precursor ions was determined automatically by the IDA based on their mass-to-charge ratio (m/z) values. Nucleotide and amino acid sequence identity. MS/MS data were pooled (from triplicates) and used for database searches using an in house version of Mascot (Matrix Science, London, UK; version 2. 1) and PEAKS (Bioinformatics solutions Inc. , Waterloo, Canada; version 7. 0) search engines against the previously published L. elliptica transcriptome  14. LC-MS/MS data was searched using carbamido-methylation as a fixed modification and methionine oxidation as a variable modification.
The peptide MS tolerance was set to 0. 5 Da and the MS/MS tolerance was set to 0. 5 Da. Protein sequence characterisation was carried out using BLAST sequence similarity searches against the UniProtKB/Swiss-Prot database (www. uniprot. org). Signal peptides were predicted using SignalP 4. 0 (www. cbs. dtu. dk/services/SignalP) and conserved domains from database models were predicted using external source database SMART (smart. embl-heidelberg. de). Quantification and localisation of putative biomineralisation transcripts. Seven putative biomineralisation genes were selected for tissue distribution expression profiling and in situ localisation (Supplementary Table  S1 ). Five were selected as they were present in the nacreous shell proteome and two were highly expressed in the previously published mantle transcriptome  12, 14.
These included a mix of well-characterised biomineralisation candidates such as Pif  16  and the Tyrosinases  [17] [18] [19]  (TyrA & TyrB), as well as less well-characterised biomineralisation candidates such as Mytilin  20  and also a novel nacre shell protein Zinc metallopeptidase and two completely novel genes which had either no annotation (Contig 01043), or only showed sequence similarity to specific domains ([Chitin-binding domain, concavalin-A and Lam-G] (Contig 01311)). Contig numbers refer to previously published transcriptome, assembled contig set available at: http://bit. ly/2cdR1eO and raw reads for assembly are available from the NCBI Short Read Archive Accession PRJNA79569. RNA extraction. Total RNA was extracted from tissues on ice using Tri-Reagent according to the manufacturer's instructions (Sigma-Aldrich, UK), and purified using RNeasy columns (QIAGEN, UK).
PCR amplicons were sequenced to confirm identity. cDNA was used as the template in PCR amplification for the seven candidate genes and the L. elliptica 18 s gene was used as a positive control and reference housekeeping gene for expression normalisation. Semi quantitative PCR (semi-qPCR) and normalised Integrated Density Value (IDV) calculations were carried out as per ref. 12 with minor temperature specific modifications for each primer set (Supplementary Table  S1 ). Gene expression data were checked for homogeneity of variance and normality using Levene's and Kolmogorov-Smirnov's tests respectively; all data met assumptions of homogeneity of variance and violated the assumption of normality. Data were transformed (Log10[X + 2]) but a normal distribution could not be achieved. Despite non-normal distribution of the transformed data, each tissue was compared using a General Linear Model Analysis of Variance (GLM-ANOVA) followed by post-hoc Tukey test.
Acknowledgements VAS was funded by a  NERC   DTG studentship  (Project Reference: NE/J500173/1) to the  British Antarctic Survey. MSC was financed by  NERC  core funding to the  British Antarctic Survey. We would like to thank  Jeremy Skepper  for advising us on TEM procedures and processing the mantle tissue samples prior to TEM,  Elizabeth M. Harper  for advice on Laternula elliptica mantle anatomy for the schematic,  Jamie Oliver  for technical help with digitising the schematic,  Deborah M. Power  for in situ hybridisation advice,  Valerie J. Smith  for advice on immunology and histology,  Lloyd S. Peck  for vesicle discussions and critical reading of the manuscript and the dive team at  Rothera research station, Antarctica  for support in animal collection. Diving oversight was provided by the  NERC National Facility for Scientific Diving, Oban.
ClinicalTrials. gov NCT01509508 Author Summary Why Was This Study Done? • A study in stable sexual partners in which one partner was HIV-positive and the other partner was HIV-negative (and both partners had disclosed to each other) showed that if the HIV-positive partner was on antiretroviral therapy, there was a 96% reduction in HIV transmission from the HIV-positive partner to the HIV-negative partner. • However, we do not know if antiretroviral therapy prescribed to HIV-positive individuals in the general population-and where individuals might not disclose their HIV status to sexual partners-would have a similar impact on HIV transmission. • It is important to determine whether prescribing antiretroviral therapy to all HIV-positive individuals is more effective at decreasing HIV transmission than starting individuals on antiretroviral therapy only once their HIV has progressed to the point at which local HIV treatment guidelines currently recommend that HIV-positive individuals start treatment.
Definition of Variables and Outcomes S1 Table presents the outcomes measured in phase 1 and presented here, and those that will be measured after trial completion. HIV prevalence was estimated on the basis of antibody test results from the DBSs collected during the first survey round only. We estimated ART coverage at the start of the trial (proportion of all HIV-positive individuals on ART) among those with positive DBS results (first survey round) using linked information from DoH clinics and pharmacy records (ARTemis and iDART databases). Matching between the three databases was based on first names, last name, date of birth, South African ID number, and cell phone numbers. Means and medians (interquartile ranges  [IQRs] ) for age at registration and other demographic characteristics were computed among individuals who completed at least one individual questionnaire at home.
Linkage to care within 6 mo was computed among individuals who were ascertained as HIV-positive at home, not previously in care (in DoH clinics in the study area), and observed at least 6 mo (taking into account population exits and the end date of data collection). Linkage to care was defined as having a first clinic visit either in a DoH clinic in the study area or a TasP clinic and was obtained by individual linkage in the databases as described above. ART uptake within 3 mo of the first clinic visit was computed in TasP clinics only, among participants not on ART at the first clinic visit, regardless of ART eligibility criteria. ART uptake was further stratified by CD4 count at first visit in the TasP clinics. Viral suppression was defined as having VL < 400 copies/ml on ART. We estimated the status within the HIV care cascade (diagnosed, ever on ART, ever virally suppressed during the trial follow-up) of trial participants who were contacted and identified to be HIV-positive (through DBS results and/or HIV rapid test) who linked to TasP or DoH clinics, using linked information from DoH clinics.
Randomisation Randomisation was performed by the trial statisticians before the start of the trial. 211 local areas were aggregated into 48 clusters. Initial sample size calculation showed that 34 clusters (2 × 17) would be required, and these were randomly allocated to the two arms, control and intervention. Randomisation was carried out within each stratum to derive an equal number of control and intervention communities per stratum. Random number generation and the randomisation procedure were performed in MapInfo version 11. 0. The sample size was subsequently amended to 2 × 11 with an increase in the duration of follow-up through a revision of the protocol for the main trial. For this initial phase, only ten (2 × 5) of the 22 clusters were used. To minimise the degree of between-cluster variation, clusters were stratified on the basis of predicted HIV prevalence, extrapolating from HIV surveillance data from the Africa Centre for Population Health's demographic surveillance area and data from antenatal clinics (six strata).
Acknowledgments We thank the study volunteers for allowing us into their homes and participating in this trial and the  Department of Health of South Africa  for its support of this study. We also thank  Jan Hontelez  for his contributions to the modelling work in the trial. Special thanks to  Professor Jean-François Delfraissy , Director of  ANRS. The members of the ANRS 12249 TasP trial group are as follows:  Marie-Louise Newell ,  Deenan Pillay ,  Collins Iwuji ,  Till Bärnighausen ,  Kobus Herbst ,  Thembisa Makowa ,  Kevi Naidu ,  Nonhlanhla Okesola ,  Tulio de Oliveira ,  Tamsen Rochat ,  Frank Tanser ,  Johannes Viljoen ,  Thembelile Zuma  ( Africa Centre for Population Health, University of KwaZulu-Natal, South Africa );  François Dabis ,  Joanna Orne-Gliemann ,  Rodolphe Thiébaut ,  Sophie Karcher ,  Eric Balestre ,  Melanie Plazy  ( ISPED, Centre INSERM U1219 Bordeaux Population Health, Université de Bordeaux, Bordeaux, France );  Sylvie Boyer ,  Laura March ,  Bruno Spire  ( INSERM, UMR912 SESSTIM, Marseille, France );  Alexandra Calmy  ( Service des Maladies Infectieuses, Hôpital Universitaire de Geneve, Geneva, Switzerland );  Joseph Larmarange  ( Centre Population et Développement UMR 196 ,  Université Paris Descartes ,  Institut de Recherche pour le Développement, Paris, France) ;  Vincent Calvez ,  Anne-Geneviève Marcelin  ( AP-HP, Virology, Hôpital Pitié-Salpêtrière, INSERM-Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Paris, France );  Rosemary Dray-Spira ,  France Lert ,  Kamal El Farouki  ( INSERM U1018 ,  CESP, Epidemiology of Occupational and Social Determinants of Health, Villejuif, France) ;  Richard Lessells  ( London School of Hygiene & Tropical Medicine, London, UK );  Kenneth Freedberg  ( Massachusetts General Hospital, Harvard Medical School, Boston, MA, US );  John Imrie  ( Futures Group, Johannesburg, South Africa );  Marie-Laure Chaix  ( EA 3620 ,  Université Paris-Descartes ,  Laboratoire de Virologie, Hôpital Necker-Enfants Malades, AP-HP, Paris, France );  Nuala McGrath ,  Colin Newell  ( Academic Unit of Primary Care and Population Sciences, and Department of Social Statistics and Demography, University of Southampton, Southampton, UK) ;  Brigitte Bazin ,  Claire Rekacewicz  (sponsor representatives).
ACKNOWLEDGEMENTS The authors are indebted to the institute  Carnot Ingénierie@Lyon  for its support and funding. This work was also supported by the  University of Lyon   1 , through its program « BQR Accueil EC 2015 ». The authors are grateful to  R. Checa  for technical assistance at the  « Centre de Magnétométrie de Lyon » , and to  J. Degouttes  and  N. Terrier  for their technical support at the  NanoLyon cleanroom facility.
He also used morphometry abundantly to compare present and past species. Although useful and also applicable to modern statoliths  [22, 24]  this was not possible here because specimens were often incompletely preserved. More recently, Malcolm B. Hart and collaborators  [23, 25]  have published data about fossil statoliths, making explicit reference to the continuation of Clarke's work. This paucity of studies means that the fossil record of statoliths is poorly documented. However, we believe that the publication of these fossil data is necessary for at least two reasons. First, this could further our understanding of coleoid evolution over time by adding new data to their fossil record. Second, knowledge of statoliths can only increase if micropaleontologists recognize and publish them. Here, we present new data about statoliths from the Middle Lutetian stage (Middle Eocene) of the Paris Basin.
This may be because micropaleontological samples are generally scarcer than those specifically searched for here, preventing any statolith discoveries. Another limitation would be that the Cretaceous "calcitic sea" poorly preserved aragonitic elements, thus preventing any statolith preservation  [64]. By contrast, most of the records are for the Cenozoic. During the Paleogene, statoliths are recorded only in the Middle Eocene (47. 8 to 37. 8 Mya), including the results of the present paper. Data come from North America (West and East coasts) and from the southern and eastern edges of the Paris Basin. Their taxonomic identification is easier due to their shapes, which resemble recent coleoid statoliths. Interestingly, the new species Loligo clarkei n. sp. presented here is also known from the east coast of North America coast at the same age (known as Loligo sp.
Acknowledgments We thank  Emilie Steimetz  for her help with SEM photographs,  Hervé Dineur  for providing data about other cephalopods from the studied outcrops,  Patrice Legrand  for his help in the field ("La cave aux coquillages" site), and  Paul G. K. Rodhouse  for biographical information on Malcolm  Roy Clarke  to whom one species is dedicated. The infographics (Figs 2 and 3 and S1-S4 Figs only) is by  Alexandre Lethiers  (UPMC). We thank  Malcom Hart  and  Dirk Fuchs  (reviewers) and  Steffen Kiel  (academic editor) for their valuable comments on the manuscript.
However, because of the low-temperature limitations of the measurements described in this work, the specific eutectic formulation and the associated eutectic temperature cannot be observed directly. 3. 2. Volumetric Properties. The density of the pure components and all mixtures was measured at atmospheric pressure from (293. 15-363. 15) K and the resulting data are presented in Tables  2 3 4. Excellent agreement was found between density data of each pure component measured herein with those available in the literature: that is, <0. 1% average deviation between experimental and literature values for [Pyrr 14 ][TFSI]  [37] [38] [39] [40] [41] [42] [43] [44] [45]  and ADN 11,46-52 and ca. [54] [55] [56] [57] [58] [59] [60]  The percentage deviations between experimental density values reported in this work for the neat components and those reported previously in the literature are shown in Figures S7 to S10.