dataesr/scientific-paragraphs-categorization huggingface dataset:
AJ 95, 1278-1296 (1988) 32. Renard, S. , Thiébaut, E. , Malbet, F. : Image reconstruction in optical interferometry: benchmarking the regularization. A&A533, A64 (2011). DOI 10. 1051/0004-6361/201016263 33. Sanchez-Bermudez, J. : Gravity Chromatic Imaging of the Eta Car's Core. In: Imaging of Stellar Surfaces, p. 32 (2018). DOI 10. 5281/zenodo. 1220791 34. Sanchez-Bermudez, J. , Pott, J. U. , van Boekel, R. , Henning, T. , Baron, F. Carinae with a spectral resolution of 12 000. Studies of the primary star wind and innermost wind-wind collision zone". A&A594, A106 (2016).
5. GLib 2. 16 or later (https://developer. gnome. org/glib/) On Redhat/Fedora install the glib2-devel package; on Debian/Ubuntu install the libglib2. 0-dev package. If using MacPorts install the glib2 port. 6. OIFITSlib 2. 2. 0 or later (https://github. com/jsy1001/oifitslib/) 7. CMake 2. 8. 6 or later (https://cmake. org/) You will also require both C and Fortran 77 compilers to build BSMEM.
In order to use MiRA, it is necessary to install Yorick (version ≥ 2. 2. 04),Yeti (version ≥ 6. 3. 2) and OptimPackLegacy (version ≥ 1. 4. 0). It is also advisable to install YNFFT, a Yorick plug-in for the Nonequispaced Fast Fourier Transform, for faster operations. The aforementioned packages and software can be retrieved from the following links:• MiRA: https://github. com/emmt/MiRA•Yorick:http://dhmunro. github. io/yorick-doc/•OptimPackLegacy:https://github. com/emmt/OptimPackLegacy•Yeti:https://github. com/emmt/Yeti•YNFFT:https://github. com/emmt/ynfftYorick,Yeti,OptimPackLegacy,YNFFT and MiRA may be available as packages for your system or can be built from the source files. The source files can be downloaded either as Git repositories or as archive files. F. 2. 1 INSTALLATION OF PRECOMPILED PACKAGESOn Ubuntu based distributions, Yorick and MiRA can be installed using the apt terminal command, or by means of the synaptic package manager.
In case of the former, it is sufficient to write:sudo apt install yorick yorick-mira yorick-ynfftFor recent distributions, the previous command will also automatically install rlwrap, which is very handy to recall previous commands when running Yorick in interactive mode. The other possibility is to clone the Git repository of Yorick: git clone git@github. com:dhmunro/yorick. git• Go to the unpacked directory or to the Git repository and type: This will create a subdirectory. /relocate/ in the source tree. The Yorick executable is. /relocate/bin/yorick. You can move the. /relocate/directorywhereveryouwant(thename "relocate" is not important), but any changes in the relative locations of the files therein will prevent Yorick from starting correctly. You can add the path where the Yorick executable is to your $PATH variable, or create a softlink to the Yorick executable from wherever you like, or execute Yorick from a shell script outside its relocate/ directory.
AJ 95, 1278-1296 (1988) 32. Renard, S. , Thiébaut, E. , Malbet, F. : Image reconstruction in optical interferometry: benchmarking the regularization. A&A533, A64 (2011). DOI 10. 1051/0004-6361/201016263 33. Sanchez-Bermudez, J. : Gravity Chromatic Imaging of the Eta Car's Core. In: Imaging of Stellar Surfaces, p. 32 (2018). DOI 10. 5281/zenodo. 1220791 34. Sanchez-Bermudez, J. , Pott, J. U. , van Boekel, R. , Henning, T. , Baron, F. Carinae with a spectral resolution of 12 000. Studies of the primary star wind and innermost wind-wind collision zone". A&A594, A106 (2016) 8445, 84,451B (2012) 23. Monnier, J. D. , Zhao, M. , Pedretti, E. , Thureau, N. , Ireland, M. , Muir-head, P. , Berger, J. P. , Millan-Gabet, R. , Van Belle, G. , ten Brummelaar, T. , McAlister, H. , Ridgway, S. , Turner, N.
5, 3. 5], [5. 6, 1. 3]], // radi [[-6, 0], [5, 5. 5], [4, -7]], // centrs angs= [30, 0, -160]); the Gaussian disc was obtained with x= span(-10, 10, 500)(, -:1:500); The code can be found at https://github. com/NunonuN/ysil. It is open source under the GNU General Public License version 3, and it is in its final pre-release state. The following subsections briefly explain how to install and use YSIL. For further details, please visit the aforementioned website. B. 5. 1 INSTALLATIONIn order to use YSIL, it is necessary to install Yorick (see F. 2 , page 39 for details). Download YSIL to any desirable folder and include ysil. i in Yorick, as in, for example include, "ysil. i";B. 5. 2 EXAMPLESSome examples of simple structures or objects created with YSIL are illustrated in fig. 8. The uniform discs were created with the following command: disc= ysil_gaussian_disc(x,[4.
• Download the current version of the code from the host repository or use the following git command:If everything went well, the user should be able to execute squeeze from a terminal session. A simple test to identify any problem in the installation is to type the following command in the prompt: cd squeeze/build cmake. git clone https://github. com/fabienbaron/squeeze. git • Go inside the SQUEEZE main directory and update the OIFITSLIB module with the following command: git submodule update --init • Inside the main SQUEEZE directory use the following cmake commands to build the executable: make • Once the executable is created the user can add it to the global path. For a bash shell use: export PATH=/path/to/SQUEEZE main dir/bin $PATH For a tsch shell use: setenv PATH $PATH:/path/to/SQUEEZE main dir/bin.
5. GLib 2. 16 or later (https://developer. gnome. org/glib/) On Redhat/Fedora install the glib2-devel package; on Debian/Ubuntu install the libglib2. 0-dev package. If using MacPorts install the glib2 port. 6. OIFITSlib 2. 2. 0 or later (https://github. com/jsy1001/oifitslib/) 7. CMake 2. 8. 6 or later (https://cmake. org/) You will also require both C and Fortran 77 compilers to build BSMEM. Recommended compilers are discussed below. E. 2. 1. 1 BUILDING PGPLOT FROM SOURCE If you need to build PGPLOT from source:.
2. 2 INSTALLATION OF YORICK FROM SOURCE. F. 2. 2. 1 COMPILING AND INSTALLING FROM SOURCE In case you prefer or need to compile Yorick from scratch, then execute the following steps: 13 • From https://github. com/dhmunro/yorick get the latest version of Yorick. If you download an archive, unzip/untar it. Example: unzip dhmunro-yorick-y_2_2_04-6-gbc24b71. zip or tar xzvf dhmunro-yorick-y_2_2_04-6-gbc24b71. tar. gz where.
Methods: We simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman's model estimates. Results: With MNAR outcomes, only methods using Heckman's model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches. Conclusions: In the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure.
Then missing value are iteratively imputed until convergence (at least 10 cycles)  [17]. The theoretical properties of MICE are not well understood: except in simple cases, conditional imputation models do not correspond to any joint model  [25, 26]. However, it performs well in practice  [27, 28]. This procedure is realised in parallel to obtain several imputed datasets. Analyses and Rubin's rules are then applied to obtain the final estimations of the parameters of interest. We propose using Heckman's imputation model for MNAR outcomes and standard imputation regression models for missing predictors, such as linear models for continuous covariates and logistic models for binary covariates. In this framework, Galimard et al. [18]  proved that the missing data indicator of MNAR outcomes should be included in the imputation models of all other variables.
Thus, R 2 , the indicator of X 2 missingness, was defined by either: • P(R 2 = 1|X 1 , X 3 ) = (0. 25 + X 1 + X 3 ) • P(R 2 = 1|X 1 , Y ) = β R 2 0 + X 1 + Y β R 2 0 was fixed to 1. 10 and 0. 25 for binary and continuous outcomes, respectively. We obtained approximately 30% missing data for X 2. A total of N = 1000 independent datasets of size 500 were generated for each setting. The sample size was chosen to be similar to our motivating example.
Computational settings Simulations and analyses were performed using R statistical software, version 3. 3. 0 [30]. We computed the imputation procedure within the mice R package version 2. 25  [31]. Heckman's One-step model estimator was supplied by functions semiParBIV() and copulaSampleSel() of the GJRM R package version 0. 1-1, for binary and continuous cases respectively  [19, 32]. Our code is available in the supplementary materials (S1 for binary outcomes and S2 for continuous outcomes). Heckman's two-step model estimator was performed using the function heckit() of package sampleSelection version 1. 0-4  [14].
Missing data on outcome Y and covariate X 2 The results of the simulations that considered missing data on a binary outcome Y and on X 2 depending on X 1 and X 3 are presented in Table  5  (Fig. 1 ). Approximately 50% of the cases were analysed with CCA, while 70% were analysed with HEml and the entire dataset with MIHEml. Under a MAR mechanism for the missing outcome (ρ = 0), the biases for CCA, HEml and MIHEml range from 1. 0 to 2. 1%. The smallest standard error is obtained using CCA. If the missing mechanism is MNAR, then CCA is biased from 3. 8 to 8. 4%, whereas the biases of HEml and MIHEml remain less than 2. 5%. MIHEml provides lower standard errors than HEml notably because HEml uses only approximately 70% of the observations. The results for β 2 are similar (Additional file 1: Table  S10 ).
Application to illustrative examples The impact of treatment group on adherence has been assessed using a probit model adjusted on severity score. Adherence presented 115 (21%) missing data. There were 51 and 375 non-adherent and adherent patients, respectively. The missing data mechanism of adherence was strongly suspected to be MNAR. The severity score was missing for 114 (21%) patients, and its missing data mechanism was suspected to be MAR. Four methods were applied: CCA, HEml, MIHEml and MI. A standard MI approach was added using a MICE procedure with a linear imputation model for severity score and a probit imputation model for adherence. The aim of the latter model was to assess the performance of an available misspecified but widely used approach. The missing data mechanisms assumed by each method are presented in Table  7.
The HEml and MIHEml selection equations for adherence included treatment group, severity score and antibiotic treatment. The latter binary variable was chosen to fulfill the exclusion-restriction criterion. The MAR variables were imputed using linear and probit regression models for continuous and binary variables, respectively. Using MIHEml, the indicator of adherence missingness was included in the severity score imputation model. The MICE procedure was applied for 20 iterations, and m = 100 datasets were generated. Finally, Rubin's rules for small samples were applied. The results are presented in Table  7. The reference group for treatment is the combination group. The Severity score coefficient corresponds to an increase of We also challenged the MAR assumption concerning the missing mechanism associated with the severity score.
12 the bond length, chosen to be the same as the Lennard-Jones one. U FENE (r) =    -0. 5kR 2 0 ln 1-r R 0 2 r < R 0 ∞ r ≥ R 0 (2. 3) where k is the FENE bond stiffness and R 0 its cut-off radius. Here, classical values are used to avoid high frequency modes and chain crossing. k = 30 /σ 2 , R 0 = 1. 5σ and r c = 2. 5σ. and σ are set to 1 to simulate a generic polymer. Units are expressed as a function of and σ. For a parameter X, results are expressed as X * with x = x * σ, T = T * /k b , P = P * /σ 3 and t = t * √ m/ σ 2 = t * τ. The time step is dt = 5×10 -3 τ. Calculations are made with the software Lammps of Sandia labs  [34].
Software Installation and Numerical Examples 4. 1 Integration of the Fix rlp program in LAMMPS In order to use this patch code, you need to copy the two files "fix rlp. cpp" and "fix rlp. h" to the \src folder of LAMMPS and then compile them as you compile LAMMPS (see detailed instructions in the "README. txt" file). You will get an executable file (here lammps. exe) and you can run the job LAMMPS script (here test. txt), like this: mpirun -np 8. / lammps. exe < t e s t. t x t The "fix rlp" command is based on the "fix bond/create" from Lammps. It presents the following form (see detailed in the "fix rlp. txt" file): The optional keywords and their values are: • maxbound bmax. bmax is the maximal number of bonds of type bondtype an atom can have • prob proba seed. Create a bond with probability proba using seed for random number generation.
We observe that all systems are well equilibrated with this procedure. We can notice a sizable deformation of the characteristic ratio of the chain of N = 500 for n > 100, which is due to poor statistic for large n. Note that the final equilibration duration is reduced compared to the reference of Auhl et al, e. g t eq = 2. 6×10 6 τ for N = 350 versus t eq = 10 5 τ for N = 500 in our case, see table  1. Chain length N 10 50 200 500 t eq (units of τ) < 2×10 3 2. 5×10 3 2. 5×10 4  The topology of the melt was also characterized by calculating the average contour length, L pp , the entanglement length, N e , and the average number of interior kinks, Z using the Z1 code  [36]. The entanglement length N e was estimated by the classical S-coil formula, N e = (N -1) r 2 (N) / L pp 2. In table  2 , the values of L pp and L 2 pp , Z and N e are reported for N = 500 after t eq = 10 5 τ and compared to the brute force equilibrated melt.
[3]  distinguish two approaches to identify gene/protein names from literature: 1. Lexicon based approaches that are based on large terminological resources, e. g. resources generated from large databases like the UniProt Knowledgebase (UniProtKB)  [4]. 2. Machine Learning (ML) approaches such as conditional random fields  [5]  that is used in ABNER  [6]  and BANNER  [7]. The first approach has the benefit of normalisation (a. k. a. grounding)  [3, 8, 9] , i. e. the process of mapping a biological term (e. g. protein name) into a unique entry in a database of biological entities such as UniProtKB. Fundel and Zimmer  [10]  suggest a limitation that "the overlap of synonyms in different data sources is rather moderate" and thus, terms from other databases, such as the HUGO Gene Nomenclature Committee database  [11]  or Entrez Gene  [12] , are also needed to develop a more complete lexicon for gene and protein names.
This study investigates the suitability of the neural language models CBOW (Continuous Bag-of-Words) and Skip-gram of Mikolov et al. [16, 17]  to derive a list of acceptable alternative free-text terms (i. e. term variants) for genes/proteins mentioned in the biomedical literature. The study focuses on two research questions: 1. Is it possible to obtain a list of term variants for a gene/protein from CBOW and Skip-gram word embeddings? 2. Can an improved list of term variants for a gene/ protein be produced from the word embeddings by adding knowledge formalised in the CVDO about genes/proteins (i. e. providing more context to reduce ambiguity)? In this study, a term is a combination of one or more words/tokens, such as "Klf7(-/-)" with one token and "Annexin A4" with two tokens. Terms referring to a gene and its gene product (typically a protein) are likely to appear together as well as separately in the literature.
Although neural models are not new in DSMs, recent advances in NNs make feasible the derivation of words from corpora of billions of words, hence the growing interest in Deep Learning and the neural language models CBOW and Skip-gram  [16, 17]. CBOW and Skip-gram have gained popularity to the point of being the baseline for benchmarking word embeddings  [27]  and as baseline models for performance comparisons  [28]. CBOW and Skip-gram have already been trained to produce high-quality word embeddings from English Wikipedia  [27, 29]. Pyysalo et al. [30]  and Minarro-Gimenez et al. [19]  were the first to apply neural language models to PubMed corpora. Pyysalo et al. [30]  used Skip-gram with 22 M PubMed articles as well as more than 672 K PubMed Central Open Access full text articles. The main aim of Pyysalo et al.
's work was to make available word representations (1-to 5-grams) from the literature that could be reused. Minarro-Gimenez et al. [19]  used smaller datasets from PubMed as well as from other medical (i. e. Merck Manuals  [31] , Medscape  [32] ) and non-medical sources (i. e. Wikipedia  [33] ). Many later studies have created word embeddings with CBOW and Skip-gram using PubMed corpora. We describe some of these studies taking into account four tasks that focus on text words, concepts and their relations. At the end of this subsection, we include studies that combine ontologies with word embeddings.
Studies by Caviedes and Cimino  [38] , Pedersen et al. [34] , Hill et al. [35]  and Pakhomov et al. [36]  made available their datasets of word-pairs together with human judgments of relatedness/similarity. Hill et al. 's  [35]  dataset of 999 wordpairs, like the WordSimilarity-353 Test Collection  [39]  (353 word-pairs) and the MEN Test Collection  [40]  (3 K word-pairs), are common English words. These datasets can be regarded as gold standards for the evaluation of semantic models. Muneeb  [41]  et al. applied Skip-gram and CBOW to 1. 25 M PubMed articles and evaluated the quality of the word embeddings using the Pedersen et al. [34]  wordpairs. Muneeb  [41]  et al. concluded that Skip-gram is better suited than CBOW for semantic similarity and relatedness. Chiu et al. [42]  used the Pyysalo et al.
[30]  datasets and more than 10 M PubMed English abstracts from the BioASQ challenge  [43]  for an intrinsic evaluation of the Skip-gram and CBOW word embeddings with the Pakhomov et al. [36]  word-pairs. Chiu et al. [42]  conclude that Skip-gram shows overall better results for semantic similarity and relatedness than CBOW with different pre-processing.
Synonymy detection task Hill et al. [35]  interpret "relatedness" as "association" and the strongest similarity relation is synonymy. Two well-known datasets for evaluating synonymy are the 80 TOEFL (Test of English as a Foreign Language) synonym questions from  [21]  and the 50 ESL (English as a Second Language) synonym questions from  [44]. Both studies  [21]  and  [44]  consist of synonym questions with 4 options that require knowledge of common English words. It should be noted that the TOEFL synonym questions dataset is used in the paper that introduces LSA  [21]. To the best of our knowledge no gold standard of wordpairs together with human judgments for synonymy detection exists specific to the biomedical domain.
Name entity recognition (NER) and relation extraction tasks The BioCreative (Critical Assessment of Information Extraction systems in Biology) challenge  [45]  focuses on recognition of entities in text (i. e. NER) as well as relation extraction. For BioCreative II, Smith et al. [46]  mention three tasks: gene mention (GM), gene normalisation (GN), and protein-protein interaction (PPI); the first two are within the scope of NER, whilst the third is a relation extraction task that has NER as a subtask  [47]. Pyysalo et al. [30]  used Skip-gram to create word embeddings from three datasets: one based on all 22 M PubMed articles; a second based on more than 672 K PubMed Central Open Access full text articles; and a third combining the previous two. Pyysalo et al. [30]  clustered the word embeddings created using the wellknown K-means clustering algorithm  [48]  with k = 100.
Pyysalo et al. [30]  performed a set of NER experiments to assess the quality of both the word embeddings and the clusters created. The NER experiments rely on three biomedical domain corpora: GM using the BioCreative II dataset; anatomical entity recognition using the Anatomical Entity Mention corpus  [49] ; and disease recognition using the NCBI (National Center for Biotechnology Information) Disease corpus  [50]. More recently Chiu et al. [42]  performed an extrinsic evaluation of word embeddings created from CBOW and Skip-gram for NER using two biomedical domain corpora: GM using the BioCreative II dataset and the JNLPBA challenge corpus from Kim et al. [51]. The JNLPBA challenge is a NER task using an extended version of the GENIA corpus (version 3. 02)  [52]. The GENIA corpus is a manually annotated corpus of 2 K PubMed/MEDLINE abstracts selected from a search using Medical subject headings (MeSH)  [53]  terms "human", "blood cells", and "transcription factors".
Chiu et al. [42]  conclude that overall Skip-gram shows better results for NER using the datasets from  [46, 51]  than CBOW with different preprocessing. Li et al. [54]  used Skip-gram with 5. 33 M PubMed abstracts obtained from a search with "protein" as the keyword. Li et al. [54]  like Pyysalo et al. [30]  applied the K-means clustering algorithm to cluster word vectors. A difference to the Pyysalo et al. [30]  study is that Li et al. [54]  employed the Brown tree-building algorithm  [55] , which is intended for n-gram language models, after applying K-means clustering. To evaluate the PPI extraction performed, Li et al. [54]  relied on five publically annotated corpora that has been quantitatively analysed previously in a study by Pyysalo et al. [56]. Text categorisation (a. k. a. text classification, or topic spotting) Sebastiani  [15]  states that text categorization is "the activity of labeling natural language texts with thematic categories from a predefined set".
Data resources To find the genes/proteins mentioned within the 25 PubMed titles/abstracts, we followed Jensen et al. [65]  who divided the task into two: "first, the recognition of words that refer to entities and second, the unique identification of the entities in question". One curator manually annotated 105 terms related to gene/protein names from the 25 PubMed abstracts and titles. Corpus annotation requires at least two annotators and the development of annotation instructions, and thus, the small-annotated corpus cannot be considered a gold standard corpus as only one curator annotated the gene/ protein names and no detailed annotation guidelines were developed. For unique identification of genes/proteins we use UniProtKB identifiers. In the UniProtKB each protein entry has two identifiers  [66] : 1) an accession number (AC) that is assigned to each amino acid sequence upon inclusion into the UniProtKB; and 2) the "Entry name" (a.
The cardiovascular disease ontology (CVDO) CVDO provides the schema to integrate the 'omics data from multiple biological resources, such as the Uni-ProtKB, the miRBase  [68]  from EMBL-EBI, the Human Metabolome Database  [69]  and the data gathered from various scientific publications (e. g. 34 full-paper 'omics studies from the sysVASC systematic review and their auxiliary files). At the core of CVDO is the Ontology for Biomedical Investigations  [70]  along with other reference ontologies produced by the OBO Consortium, such as the Protein Ontology (PRO)  [71] , the Sequence Ontology (SO)  [72] , the three Gene Ontology (GO) sub-ontologies  [73] , the Chemical Entities of Biological Interest Ontology  [74] , the Cell Ontology  [75] , the Uber Anatomy Ontology  [76] , the Phenotypic Quality Ontology  [77] , and the Relationship Ontology  [78].