dataesr/scientific-paragraphs-categorization huggingface dataset:
(2. 17) l = l+2 2 q=2 r,s ãr,s |β| 2q q! 1 2 q F (-ν, -q; 1; 2)δ l+2,2q + l+2 2 q=0 3≤n+2q≤l+2 r,s ãr,s |β| 2q n!q! 1 2 q β n ν! (ν -n)! F (-ν + n, -q; 1 + n; 2) Ãν-n l+2-n-2q + βn F (-ν, -q; 1 + n; 2) Ãν+n l+2-n-2q. (2. 18) From the recursion relation (2. 17) and the initial condition (2. 13) one also finds that Ãk l = 0 whenever k > 3l + ν. We have in fact programmed a function called BWDifference for Mathematica which computes the expansion coefficients Ãk l , l automatically and added it to the updated BenderWu package  [49]. Before we proceed to explain how the function can be used, we would like to make three claims here about the structure of the perturbative eigenenergies and wave-functions: (i) There is a unique perturbative solution (up to the normalization constant) of the form (2.
How to use the BWDifference function Here we present the BWDifference function which is incorporated into the updated BenderWu  [49]  package of Mathematica. This function solves perturbatively the difference equation of the form H(X, P )Ψ ν (x) = E ν Ψ ν (x) (2. 24) were ν is the level number and H(X, P ) is the "Hamiltonian" which depends on the momentum and coordinate displacement operators X = e for integer r and s (note that these can be negative as well). The. indicates an ordering of X and P. A conventional ordering which renders the operator H(X, P ) Hermitian is given by X r P s ≡ e √ (rp+sx). (2. 26) This ordering is assumed by the BWDifference function. Furthermore the BWDifference function assumes that at X = P = 1 (i. e. x = p = 0) the classical function H(X, P ) attains (at least a local) minimum.
The BWDifference function produces a perturbative expansion of "energy" E at level ν and an unnormalized wave-function Ψ(x), of the form given in (2. 9). As we have shown in the previous section, the energy is always in powers of , not √. This means that all E l in equation (2. 9) vanish whenever the l is odd. For this reason the code returns only even coefficients of E, i. e. returns E 2n. From now on when we talk about the "order" of the perturbative expansion we will mean the number n, rather than the order of √ , for which we reserve the letter l. Now note that n = -1 is the leading order (i. e. classical energy) which is identical to E -2 = H(X = 1, P = 1) and is of order 1/ in our convention. In order to access the BWDifference function, one must first install the BenderWu package bundled with this work.
The third element is not important for the user, and only serves for proper functioning of the function BWProcess, which was introduced in  [49]. Hence if we execute the command BWDifference[X+P+1/(XP),{X,P},5][[1]] we will get a list of perturbation series coefficients l , which in this case is with the same outcome as before. However a better way to use the code is to assign the output to a variable, and use the function BWProcess introduced already in the original BenderWu package  [49]  to control the output without having to recompute the expansion. In other words the benefit of using the BWProcess function is that one can make a computation to a high order once, and use the BWProcessto analyze the result without having to recompute the expansion. For example, if we call the line BW=BWDifference[X+P+1/(XP),{X,P},2,20]; it assigns the output of BWDifference to a variable BW, and hence contains all the perturbative information to order 20 in.
However from (A. 13), we can see that by defining β to be β = ξ β and appropriately rescaling of the coupling, the difference equations can be converted to involve only ξ 2 in the imaginary part of β. A little thought reveals that the difference equation can be setup in such a way that the real part of Ã-coefficients depend only on the even power of ξ 2 , while the imaginary part always contains an odd power of ξ 2. This means that the real part contains no irrational factors, for a choice of rational choice of all a r,s , and, if ξ 2 is irrational, the imaginary part of Ã will be always proportional to ξ 2 , multiplying a rational number. By splitting the difference equation for Ã into its real and imaginary parts, the irrational coefficients appear in a predictable manner, and such treatment of the difference equations whenever the imaginary part of Ã is non-vanishing, speeds up the Mathematica algorithm of the package significantly.
Acknowledgement We would like to thank  Gerald Dunne ,  Alba Grassi ,  Yasuyuki Hatsuda ,  Amir-Kian Kashani-Poor ,  Albrecht Klemm ,  Marcos Mariño , and  Mithat Ünsal  for useful discussions. We appreciate the careful reading of the manuscript by  Alba Grassi  and  Yasuyuki Hatsuda , and thank  Gerald Dunne  and  Marcos Mariño  for pointing out some important references. J. G. is supported by the grant  ANR-13-BS05-0001.
During a snapshot campaign in April 2013, hydraulic head measurement and water sampling were performed for 79 water wells. Heavy metal analyses for several elements were done. Based on the preliminary statistical characteristics (e. g. normal distribution) of elements and the absence of certain elements at certain wells, 111 Cd was chosen as the most suitable element to interpolate and for testing different interpolation methods; 38 data for 111 Cd were available for use. In order to get an approximately similar spatial distribution to the 111 Cd data, 42 data-points for hydraulic head were selected and analysed. Measured hydraulic head values were plotted against surface elevation with the objective of determining correlation (Fig. 2 ). High correlation between these parameters was expected for the alluvium aquifer, and for all of the measured 79 hydraulic head values, a highly correlation with 111 Cd was found (R 2 = 0.
The membership function μ A ranges between 0 and 1. If the value of the membership function is restricted to either 0 and 1, the fuzzy set is then reduced to a classical crisp set with a known boundary. As stated by Jang (1995), the fuzziness does not come from the randomness of the constituent members of the sets, but from the uncertain and imprecise nature of the abstract thoughts and concepts. In ANFIS the relationship between input and output is expressed in the form of If-Then rules. ANFIS used for the present work is based on the Sugeno fuzzy model  (Takagi and M. Sugeno, 1985)  which formalizes a systematic approach to generating fuzzy rules from an input-output dataset. A typical fuzzy rule in a Sugeno fuzzy model has the format: If x∊A and y∊B then: z = f (x, y) (5) where: A and B are fuzzy sets in the antecedent and f (x, y) is a crisp function in the consequent.
Implementation of EBK Exploratory data analysis, automatic variogram fitting and kriging were performed using ArcGis 10. 2 software. The EBK method is based on 3 main steps: Firstly, a semi-variogram model is estimated from the observed data set. Secondly, a new value is simulated at each of the observed data locations by using the semi-variogram estimated in the previous step. Thirdly, a new semi-variogram model is estimated from the newly simulated data from the second step. By using Bayes' rule, a weight for this semi-variogram model is calculated which shows how likely it is that the observed data can be generated from the semi-variogram. The second and third steps are repeated. This process creates a spectrum of semivariograms  (Pilz and Spöck, 2007). New parameters are also needed for EBK, such as subset size which defines the number of points in each subset, overlap factor which specifies the degree of overlap between subsets and number of simulation which specifies the number of semi-variograms that will be simulated for each subset.
Implementation of Geo-ANFIS The neuro-fuzzy model was developed using the ANFIS procedures of MATLAB  (Demuth and Beale, 2000). In this study, a code is written in Matlab 2012b for ANFIS using appropriate functions to calculate the best performance of the methods. Before using the model to interpolate unknown outputs (hydraulic head and 111 Cd), its actual predictive performance must be tested by comparing outputs estimated by calibrated models with known outputs. At each phase (training, validation and test), Geo-ANFIS performance is measured by the determination of the coefficient of goodness-of-fit (R 2 ) and the root mean square error (RMSE). RMSE = √ ________ E [ ( Z* (x) -Z(x) ) 2 ] (12) where: E, Z * and Z are previously defined. Input data are XY coordinates for the ANFIS XY. The data are pre-processed by elimination of unrealistic values to obtain a more stable dataset.
Geo-ANFIS model selection The Geo-ANFIS model selection is based on available data. Using these datasets at each phase (training, validation and test), the Geo-ANFIS performance is measured by the coefficient of goodness-of-fit (R 2 ) and root mean square error (RMSE). ANFIS XY is run up to 2 000 iterations with 100 random data simulations for 4 types of each membership function. 100 results for each type of membership function are analysed automatically to select the best ones. RMSE and R 2 values of training, validation and test subsets for the chosen types of membership functions for hydraulic head and 111 Cd are given in Table  1.
ACKNOWLEDGMENTS We thank  Sıtkı Koçman Foundation, French Embassy in Turkey ,  Muğla Sıtkı Koçman University-BAP  (Project no:  12/141 ) and  Tübitak & Eranetmed  (No:  115Y843   & Name: "GRECPIMA ") for their financial support. We would also like to thank  Res. Asst. Göksu Uslular  for his participation in the field.
Related Work Several games with a purpose (GWAPs) have been developed for gathering linguistic annotations for building resources and training systems  (Chamberlain et al. , 2013). Lafourcade (2007)  and  Fort et al. (2014)  developed games for defining semantic relations and dependency relations in French. Chamberlain et al. (2008)  created Phrase Detectives to annotate and validate things like co-reference. Jurgens and Navigli (2014)  recently proposed using video games to link Word- Net senses to images and perform word sense disambiguation. KnowYourNyms? gathers high quality semantic relationships between English words to increase the coverage of resources like Word-Net and assign a taxonomic structure to the Paraphrase Database  (Ganitkevitch et al. , 2013). Additionally, it provides rich data for training relation detection systems like LexNET  (Shwartz and Dagan, 2016) , up to now trained on small training datasets (BLESS  (Baroni and Lenci, 2011) , EVA-Lution  (Santus et al.
System Overview KnowYourNyms? is modeled after GWAPs like the ESP game or the Google Image Labeler, which use human-based computation to gather metadata to improve image recognition classifiers  (von Ahn and Dabbish, 2004). At a high level, the application is simple. Once a user creates an account, she may start a round of the game. For each round, the system selects a specific word (called the "base word") and asks the user to name as many semantic relationship pairs for that word as possible in a set time limit. After the allotted time expires, these named pairs are recorded in our database and serve as data points for possible semantic relationships. The user then sees a display of her scoring performance, which is primarily based on how many other users named the same relationships for the given base word.
Architecture The web application was built with the Django framework, using Python for all backend and database interaction and standard JavaScript, HTML, and CSS for the frontend, including the jQuery, d3. js, and Bootstrap JavaScript/CSS libraries. We used AWS Elastic Beanstalk, which deploys our Django web application to an AWS EC2 server. The application has multiple components that are important to the user experience, which are separated into three main views. Welcome Screen This screen gives information about the purpose of the game, what semantic relationships are, how to play, and a little about our team. When a user is signed in this screen displays some statistics about the player, including number of completed rounds, total score, and average score per round. Four checkboxes are displayed, one for each playable semantic relationship type (synonyms, antonyms, hyponyms, and meronyms).
Crowdsourced Approach To evaluate our game, we asked 160 crowdworkers to play KnowYourNyms? on Amazon Mechanical Turk for ten rounds each. Our intention was to seed the game with data so that normal users would receive scores based on words suggested by previous players. Although these workers were only asked to play ten rounds, many went on to play thirty, forty, or even a hundred rounds of the game. From these workers, we received over 15,000 user inputs. Table  3 : The number of relations (rels) learned at different confidence levels, where confidence is measured by the number of users (n) who named the relation. We compare this to the number of relations found in WordNet for the same base words. desirable-undesirable, similarity-difference, similarity-different, hitch-unhitch, immaturemature, wake-sleep, and sterile-dirty.
We hypothesized that abstract words (e. g. geology, sinking, dissolution) are more difficult to provide relations for than concrete words. An indicator of annotation difficulty for a word is the number of times users choose to skip it: if they cannot think of any good relationships, users can choose to pass to the next round. We calculate the correlation between word difficulty -measured as the ratio of the number of times the word was skipped to the number of times it was seen -and concreteness scores in the dataset built by  Brysbaert et al. (2014)  (hereafter CON-CRETE) which contains ratings for 37,058 English words and 2,896 two-word expressions. Words are ranked on a 5-point rating scale going from abstract words (low values) to words with concrete meaning (high values). We expect abstract words to be more difficult to handle and more frequently skipped by our users compared to concrete words.
Conclusions and Future Work KnowYourNyms? gamifies the process of gathering pairs of words holding specific semantic relationships that are not found in existing resources. While providing users with an entertaining experience, our application enables collection of large amounts of data that can be used to improve se-mantic relation classifiers and content analysis tools. This application offers exciting possibilities for further development. As the number of players grow, our lexical relation dataset will keep expanding. This will provide new opportunities for evaluation in full-blown applications and will richen our understanding of how people perceive word relations.
Acknowledgments We would like to thank  Ani Nenkova  and  Jonathan Smith  for the discussions and useful feedback on this project, and the Mechanical Turk workers who did the play testing. This material is based in part on research sponsored by  DARPA  under grant number  FA8750-13-2-0017  (the  DEFT program ). The U. S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of  DARPA  and the  U. S. Government. This work has also been supported by the  French National Research Agency  under project  ANR-16-CE33-0013.
We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.
Results Overview. In our procedure, any given molecule undergoes projection on twenty QSPR models for different biological properties: the results of these projections compose the ADME profile, and provide its molecular description. From this, we obtain a position onto the SOM map, so that the molecule can be assigned to a node, and all the properties previously assigned to that node are valid for the projected molecule, too. In silico models. Models from public data included the inhibition of membrane proteins responsible for drug active transport (efflux: Pgp, BCRP; and influx: OCT2, OATP1B1), as well as the recognition by Pgp and BCRP. Two additional properties were modelled starting from public data, the type of clearance (either renal or metabolic)  10  and the maximum recommended daily dose (MRDD)  26.
Models for implication or inhibition of CYPs were available at Servier (based on in-house data for specific CYP isoforms: implication of 1A2, 2D6, 3A4  11  and inhibition of 1A2, 2C9, 2D6, 3A4), in vitro metabolic stability in rat, mouse and human, intestinal absorption predictions from Caco2 experiments and brain permeability in rodents. In all these cases, the datasets counted hundreds of molecules, whereas larger datasets (thousands of molecules) developed with in-house Servier data, complete the list of in silico models.
Models development and validation. We used curated datasets from the scientific literature for all the properties (Pgp inhibition  13  , Pgp recognition  27  , OCT2 inhibition  28  , OATP1B1 inhibition  29  , Clearance 10 and MRDD  26  ) except for inhibition of and recognition by BCRP, for which we curated the collection by using data from several articles. Foundation of QSPR classification studies were the categorical classification of compounds, based on experimental measures as suggested by the authors of the curated collections (mostly IC 50 or percentage of inhibition). From the original collections, we also took the training/test sampling, in order to compare our results with those of the authors of the original models (see Methods section and Table  1 ). For public datasets, chemical structures were retrieved as SMILES from the original articles or, when not available, from PubChem 30 , whereas for private datasets structures are from the internal Servier database.
SMILES were converted to 3D structures (sdf format) with the software Marvin v6. 2. 1  31. Successively, the program MoKa  32, 33  was used to generate the most abundant tautomer and protomer at pH 7. 4 for each structure. With the software VolSurf+  31  we imported the molecular structures and created the X-matrix of molecular descriptors (detailed elsewhere)  [34] [35] [36] [37]  , which underwent the supervised classification procedures described below. For all the models, the response was of the type -1/0/+1 and several classifiers were applied. For public datasets, we tested various regression and classification methods, as implemented in scikit-learn (an open-source python library for data mining and analysis)  38. Different classifiers were combined in a way that only the consensus of the different methods gives the final classification.
For each model, an applicability domain was defined, and those compounds predicted out of the domain were assigned "0" as predicted class. Model performance was evaluated using a naïve external dataset and computing the accuracy for balances or Matthew's coefficient for unbalanced dataset. The most relevant details of the models (which methods were used for the consensus) are given in Table  1 , whereas models performance in external validation are shown in Fig. 1  and more technical details on the parameters used for each model can be found in Supplementary Information Tables S1-S7. In general, models are comparable with those developed in the original papers: if compared with the published performance, our results are at least as good in accuracy as the original ones. In several cases, the X-description was the same (VolSurf+) and we only changed the statistical treatment of the matrix, by combining classifiers and using the consensus of predictions.
The SOM algorithm has been recently applied (by some of us) to define an applicability domain for UPLC-MS retention time prediction  42  , but also to several (and different) research fields, including structural sub-cluster analysis  28  , ligand-based virtual screening  43  and docking-binding cavity analysis  44. Approximately 26,000 Servier molecules were extracted from an internal database, after filtering out compounds by molecular weight (retaining only if 100 < MW < 1000) and chemical composition (excluding those with elements other than C, H, N, O, P, S and halogens). A 50 × 50 map (2500 nodes) was built using the software "MaTCh" (Map The Chemicals), which is a Servier in-house implementation of the SOM algorithm. Though the low number of descriptors used, the high dimension (2500 possible positions) of the map is justified by the high number of molecules used for training.
e. optimal compound). In addition, it is also possible to project libraries of compounds, see where there are located, and realize which structural modification could get closer to the optimal compound. In conclusion, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery. Projecting NMEs on this map is a new way to explain their behaviour, to explore the space, to look for the most suitable ADME profile, to get warnings on potential ADME problems, and even to choose the proper in vitro experiments to carry out. A perspective of this work can be the addition of the pharmacological dimension, which is an essential aspect in the research of new drugs. Moreover, the same identical procedure could be applied to public data (for example to DrugBank molecules) to develop an ADME-Space with public molecules and descriptors.
Methods Experimental data curation: datasets from scientific literature. For P-glycoprotein inhibition, we used the dataset published by Broccatelli et al. 13  , who collected data for 1272 molecules from 61 articles. We used the data as originally conceived, with a training set of 772 molecules and a large test set of 503 molecules. The original model was based on a sequence of different "blocks", composed by the molecular description obtained with the software VolSurf+ and Flap, as well as PLS and LDA as regression/classification methods. Here, we simplified the description (we used only VolSurf+ over the entire training set) but we treated the descriptors X-matrix by combining several classifiers. For P-glycoprotein recognition, we used a dataset presented by Levatic et al. 27  , who classified compounds as P-glycoprotein substrates or nonsubstrates based on high-throughput data on different cancer cell lines.
We used their dataset for modelling, composed of 934 molecules. For the clearance model, we used the data collected by Lombardo F. and colleagues 10 , whose aim was to develop a computational model to predict the primary clearance mechanism, in order to guide further PK studies either in vitro or in vivo. They collected clearance data for 1028 molecules, but modelled a dataset of 469 with "clear quantitative data" for renal or metabolic clearance. They used a selection of VolSurf+ descriptors in combination with structural fragments, multivariate methods such as PCA and PLS, and an internal 5-fold cross-validation. We also limited the model to renal and metabolic pathways, but we used only VolSurf+ as descriptors and several supervised classifiers to build a composite model. Contrera et al.