dataesr/scientific-paragraphs-categorization huggingface dataset:
Access via Web services An HTTP(S) server has been written as a custom C# [7] library that can be added to any Common Language Runtime (CLR) [8] executable, so its source code is completely under KM3NeT control. It is normally run with the Mono [9] CLR on Linux, but it works with all major operating systems as well without recompilation. The Web server that provides the main access point to the database is based on the HTTP(S) server, but does much more than just providing static or dynamic Web pages for graphical user interface: it has indeed been conceived rather as an Application Web server with data management and distribution tasks. For instance, if a large (GB-size) dataset is sent for insertion into the database, a deferred insertion job is started and the result (including possible errors) is mailed later to the user that posted the dataset.
Commonly accessed datasets might be automatically cached as files on disk, ready to be served to incoming requests. The cache management algorithm may consider both data size and request frequency to maximize the throughput in a proactive way. Several IT services access the Web server via server pages (i. e. pages not intended for human usage) to read and write data from the database. Data can be exchanged in XML or JSON format. SQL queries are replaced by HTTP GET requests with query parameters in the URL. Simple scripting tools such as wget and cURL can also be used, as well as any Web client libraries ready for inclusion in programs that need access to database information. In the shore station, access to the database goes via the Database Interface, providing access to specific datasets through HTTP GET requests and returning data in XML format.
Study design and patients The methodology of the study has been detailed in a recent article  [30]. We conducted a randomized, single-blind, placebo-controlled clinical trial in the University Oncology Hospital, Clermont-Ferrand, France. The study has been approved in December 2011 by the regional Ethics committee (CPP Sud-Est, France, number AU917) and registered on 16 February 2012 at http://www. clinicaltrials. gov (NCT01536314). The protocol for this trial and supporting CONSORT checklist (S1 and S2 Files) are available as supporting information. Women were eligible if they were at least 18 years old, with a diagnosis of breast cancer, programmed for mastectomy with or without axillary dissection, able to understand and willing to follow the study protocol. Exclusion criteria comprised contraindications for memantine and hypertension, severe cardiac insufficiency or diabetes (Type I and II), alcohol addiction and treatment with specific drugs (amantadine, ketamine, dextromethorphan, L-Dopa, dopaminergic, anticholinergic agonists, barbituric, neuroleptic, IMAO, antispastic agents, dantrolen or baclofen, phenytoin, cimetidine, ranitidine, procainamide, quinidine, quinine, nicotine, hydrochlorothiazide, warfarine).
Statistical analysis Statistical analyses were performed with Stata software (version 13; StataCorp, College Station, US). The tests were two-sided, with a type I error set at α = 0. 05. Concerning the primary outcome, comparison between the randomized groups was performed using an analysis of covariance with baseline score as a covariate as suggested by Vickers and Altman  [43]. Concerning the secondary outcomes, the comparisons between the randomized groups were carried out using Student's t-test or Mann-Whitney test when appropriate (normality verified by Shapiro-Wilk and homoscedasticity by Fisher-Snedecor tests) for quantitative parameters and using chisquared or Fisher's exact tests. For the primary endpoint, results were expressed as effect-size and 95% confidence interval. Concerning the analysis of repeated measures, random-effect models were considered, as it was usually proposed, to study fixed effects (group, time-points and interaction group × time) and taking into account between and within subject variability.
Results The investigators pre-screened 207 patients; 104 women refused to participate in the study, 60 did not meet the inclusion criteria and 43 gave written informed consent. These were randomized into the memantine or placebo group. Two patients withdrew before starting treatment and one interrupted the trial because surgery was postponed due to advanced disease. Demographic and clinical data of the 40 participants, including age, previous chemotherapy, type of anticancer agents and axillary dissection are presented in Table  1. At M3 post-mastectomy, a significant difference in the primary outcome, NRS pain intensity, was recorded in the memantine group compared with the placebo group (placebo: 1. 3 ± 1. 8; memantine: 0. 2 ± 0. 4; p = 0. 017) while at M6 post-mastectomy no significant difference was observed (placebo: 0.
Introduction Knowledge bases (KBs) are crucial tools to deal with the rise of data, since they provide ways to organize, manage and retrieve all digital knowledge. These repositories can cover any kind of area, from specific domains like biological processes, for example in GeneOntology  (Ashburner, Ball, Blake, Botstein, Butler, Cherry, Davis, Dolinski, Dwight, Eppig, et al. , 2000) , to very generic purposes. Freebase  (Bollacker, Evans, Paritosh, Sturge, & Taylor, 2008) , a huge collaborative KB which belongs to the Google Knowledge Graph, is an example of the latter kind which provides expert/common-level knowledge and capabilities to its users. An example of a knowledge engine is WolframAlpha (Wolfram  Research, 2009) , an engine which answers to any natural language question, like how far is Saturn from the sun?, with human-readable answers (1,492 ×10 9 km) using an internal KB.
As a result, diverse problems coexist in the same database. Another property of relationships that can have a big impact on the performance is the typing of their arguments. On FB15k, some relationships are very strongly typed like /sports/sports team/location, where one always expects a football team as head and a location as tail, and some are far less precise such as /common/webpage/category where one expects only web page addresses as tail but pretty much everything else as head. A link prediction algorithm should be able to adapt to these different settings. Though there exists (pseudo-) symbolic approaches for link prediction based on Markovlogic networks  (Kok & Domingos, 2007)  or random walks  (Lao et al. , 2011) , learning latent features representations of KB constituents -the so-called embedding methods -have recently proved to be more efficient for performing link prediction in KBs, (e.
Related Work In this section, we discuss the state-of-the-art of modeling large multi-relational databases, with a particular focus on embedding methods for knowledge base completion. One of the simplest and most successful 2-way models is TransE  (Bordes et al. , 2013b). In that model, relationships are represented as translations in the embedding space: if (h, , t) holds, then the embedding of the tail t should be close to the embedding of head h plus some vector that depends on the label. This is a natural approach to model hierarchical and asymmetric relationships, which are common in knowledge bases such as Freebase. Several modifications to TransE have been proposed recently, TransH  (Wang et al. , 2014b)  and TransR  (Lin et al. , 2015). In TransH, the embeddings of the entities are projected onto a hyperplane that depends on before the translation.
The second algorithm, TransR, follows the same idea, except that the projection operator is a matrix that is more general than an orthogonal projection to a hyperplane. As we shall see in the next section, TransE corresponds to our Bigram model with additional constraints on the parameters. While 2-way models were shown to have very good performances on some KB datasets, they have limited expressiveness and they can fail dramatically on harder datasets. In contrast, 3-way models perform some form of low-rank tensor factorization, and in that respect can have extremely high expressiveness depending on the rank constraints. In the context of link prediction for multi-relational data, RESCAL  (Nickel, Tresp, & Kriegel, 2011)  follows natural modeling assumptions. Similarly to TransE, RESCAL learns one low-dimensional embedding for each entity.
RDF is a standard model for data interchange on the Web. It is at the core of the Linked Data initiative  (Bizer, Heath, Idehen, & Berners-Lee, 2008 ) that aims to extend the linking structure of the Web to use URIs to name the relationship between things as well as the two ends of the link. This linking structure forms a directed, labeled graph, where the edges represent the named link between two objects. Thus such data is essentially made of triples. In RDF-terminology a triple is defined as (subject, predicate, object). The Freebase dump is available in this format. Figure  2  shows an example of a RDF file of Freebase, where m. 02mjmr is an identifier for the resource representing Barack Obama. This identifier has several predicates as ns:influence. influence node. influenced by or ns:people.
Datasets and Metrics Our experimental settings and evaluation metrics are borrowed from previous works, so as to allow for result comparisons. UMLS/Kinships Kinships  (Denham, 1973)  is a KB expressing the relational structure of the kinship system of the Australian tribe Alyawarra, and UMLS  (McCray, 2003)  is a KB of biomedical high-level concepts like diseases or symptoms connected by verbs like complicates, affects or causes. For these data sets, the whole set of possible triples, positive or negative, is observed. We used the area under the precision-recall curve as metric. The dataset was split in 10-folds for cross-validation: 8 for training, 1 for validation and the last one for test. Since the number of available negative triples is much bigger than the number of positive triples, the positive ones of each fold are replicated to match the number of negative ones.
Tatec models were validated every 10 epochs using the AUC under the precision-recall curve as validation criterion over 1,000 randomly chosen validation triples -keeping the same proportion of negative and positive triples. For TransE, which we ran as baseline, we validated every 10 epochs as well. FB15k Introduced by  Bordes et al. (2013b) , this data set is a subset of Freebase, a very large database of generic facts gathering more than 1. 2 billion triples and 80 million entities. For evaluation on it, we used a ranking metric. The head of each test triple is replaced by each of the entities of the dictionary in turn, and the score is computed for each of them. These scores are sorted in descending order and the rank of the correct entity is stored. The same procedure is repeated when removing the tail instead of the head.
This corruption approach implements the prior knowledge that unobserved triples are likely to be invalid, and has been widely used in previous work when learning embeddings of knowledge bases or words in the context of language models. These negative triples correspond to the second setting of negative examples of Section 4. 1. We ran 500 training epochs for both TransE, Bigram, Trigram and Tatec, and using the final filtered mean rank as validation criterion. If several models statistically have similar filtered mean ranks, we take the hits@10 as secondary validation criterion. 4 Since for this dataset, training, validation and test sets are fixed, to give a confidence interval to our results, we randomly split the test set into 4 subsets before computing the evaluation metrics. We do this 5 times, and finally we compute the mean and the standard deviation over these 20 values for mean rank and hits@10.
SVO SVO is a database of nouns connected by verbs through subject-verb-direct object relations and extracted from Wikipedia articles. It has been introduced by  Jenatton et al. (2012). For this database we perform a verb prediction task, where one has to assign the correct verb given two nouns acting as subject and direct object; in other words, we present results of ranking label given head and tail. As for FB15k, two ranking metrics are computed, the mean rank and the hits@5%, which is the proportion of predictions for which the correct verb is ranked in the top 5% of the total number of verbs, that is within the top 5% of 4,547 ≈ 227. We use the raw setting for SVO. Due to the different kind of task (predicting label instead of predicting head /tail ), the negative triples have been generated by replacing the label by a random verb.
Baselines Variants We performed breakdown experiments with 2 different versions of Tatec to assess the impact of its various aspects. These variants are: • Tatec-ft-no-pretrain: Tatec-ft without pre-training s 1 (h, l, t) and s 2 (h, l, t). • Tatec-ft-shared: Tatec-ft but sharing the entities embeddings between s 1 (h, l, t) and s 2 (h, l, t) and without pre-training. The experiments with these 3 versions of Tatec have been performed in the soft regularization setting. Their hyperparameters were chosen using the same grid as above. Previous Models We retrained TransE ourselves with the same hyperparameter grid as for Tatec and used it as a running baseline on all datasets, using either soft or hard regularization. In addition, we display the results of the best performing methods of the literature on each dataset, with values extracted from the original papers.
UMLS and Kinships The results for these two knowledge bases are provided in Table  3. In UMLS, most models are performing well. The combination of the Bigram and Trigram models is slightly better than the Trigram alone but it is not significant. It seems that the constituents of Tatec, Bigram and Trigram, do not encode very complementary information and their combination does not bring much improvement. Basically, on this dataset, many methods are somewhat as efficient as the best one, LFM. The difference between TransE and Bigram on this dataset illustrates the potential impact of the diagonal matrix D, which does not constrain embeddings of both head and tail entities of a triple to be similar. Regarding Kinships, there is a big gap between 2-way models like TransE and 3way models like RESCAL.
e. Tatec-ft) allows to get the best of both; however, if one of them is consistently performing worse on most relationships as it seems to happen for Kinships and SVO, then Tatec-lc is a good choice since it can cancel out any influence of the bad model. Table  7  depicts training times of various models on FB15k, presenting the relative time w. r. t. TransE for one training epoch. To speedup training, we could follow one or several of the following strategies: • use adaptive learning rates in order to make convergence faster; • train the model on GPUs, which is quite usual in the deep learning community when working with large datasets; • parallellize training with Hogwild  (Recht, Re, Wright, & Niu, 2011). We could also speed up the validation. For example, since the entities and relationships are usually strongly typed (i.
e. given a relationship, only a subset of entities are real candidates for both the subject and object), we might consider only entities of the suitable type for a given relationship and role. Nevertheless, given that scalability is not a major issue on the datasets used in this paper we did not look for any speed optimization here. (Bordes et al. , 2013b) , the algorithm is shown to perform well on FB15k and on a dataset extracted from the KB WordNet  (Miller, 1995) : we suspect that the WordNet dataset also contains symmetrical counterparts of test triples in the training set (such as hyperonym vs hyponym, meronym vs holonym). Tatec can also make use of this information and is, as expected, much better on relations with symmetrical counterparts in train: on FB15k, the mean rank of Tatec-ft-soft is of 17.
Relative training times with respect to TransE on FB15k for running one epoch on a single core. TransE and Symmetrical Relationships TransE has a peculiar behavior: it performs very well on FB15k but quite poorly on all the other datasets. Looking in detail at FB15k, we noticed that this database is made up of a lot of pairs of symmetrical relationships such as /film/film/subjects and /film/film subject/films, or /music/album/genre and /music/genre/albums. The simplicity of the translation model of TransE works well when, for predicting the validity of an unknown triple, the model can make use of its symmetrical counterpart if it was present in the training set. Specifically, 45,817 out of 59,071 test triples of FB15k have a symmetrical triple in the training set. If we split the test triples into two subsets, one containing the test triples for which a symmetrical triple has been used in the learning stage and the other containing those ones for which a symmetrical triple does not exist in the training set, the overall mean rank of TransE of 50.
Acknowledgments This work was carried out in the framework of the  Labex MS2T  ( ANR-11-IDEX-0004-02 ), and was funded by the  French National Agency for Research  ( EVEREST-12-JS02-005-01 ). Part of this work was done while  Nicolas Usunier  was with  Sorbonne universités ,  Université de technologie de Compiègne, CNRS, Heudiasyc   UMR 7253.
Site and instrumental description The PUY atmospheric station is located in central France (45 • 46 N, 2 • 57 E) and composed of a high-altitude site (at the top of the Puy de Dôme, 1465 m a. s. l. ) and a low-altitude site (Cezeaux, 410 m a. s. l. ). It is part of the global GAW (Global Atmospheric Watch) network and one of the 43 AC-TRIS (Aerosol, Cloud and Trace gases Research Infrastructure Network) stations for monitoring climate-relevant atmo-spheric variables. Meteorological parameters (temperature, RH, wind speed and direction) are continuously monitored at both sites (Puy de Dôme and Cezeaux). At the PUY station, a large data set of parameters of the gas phase (O 3 , CO, CO 2 , NO, NO 2 , CH 4 , N 2 O, SF 6 , volatile organic compounds, Rd) and of the particulate phase (scattering, absorption, elemental and organic carbon (EC/OC) on filter, inorganic ions on filter, condensation nuclei (CN) > 10 nm, CN > 1.
Impact of a multilayer atmosphere In order to exclude such cases of multilayer aerosol transport, the STRAT algorithm was applied to filter them out from the database. After filtering the multilayer cases, only 116 cases (33 %) remain from the original data set. Due to this lower number of data points, the correlation between the in situ and sun photometer extinction is lower when multilayer cases are excluded (r 2 = 0. 59) (Fig. 5 ). Under these particular atmospheric conditions, the in situ extinction coefficient at the PUY station ranges from 0 to 0. 04 km -1 while the sun photometer extinction ranges from 0 to 0. 2 km -1. The sun photometer values higher than 0. 2 km -1 observed in Fig. 4  but not in Fig. 5  were indeed due to heterogeneous cases, thus probably corresponding to dust or marine aerosol transported at higher altitudes than the PUY station.
Acknowledgements. The authors would like to acknowledge the  OPGC  and its staff and  INSU-CNRS  for their contribution to establishing and maintaining the PUY measurement site. This work was performed with the financial support of the  French ORAURE SOERE , the  French national program SNO-CLAP , the  European infrastructure projects ACTRIS  (grant no. 262254 ) and  ACTRIS-2  (grant no. 654109 ) and the  CNES   EECLAT  project. Edited by: A. Kokhanovsky Reviewed by: three anonymous referees.
Moreover, the IFLS (fourth wave) is one of the rare datasets from a developing country to provide cognitive test scores (recall tests), as well as questions that allow us to proxy whether each worker's occupation requires cognitive, physical or social skills. This set of variables provides a unique chance to unveil the nature of the height premium and notably the role played, directly or through the mediation of occupational sorting, by cognitive/physical/social skills and human capital. Our results can be summarized as follows. Focusing our analysis on adult working men, in order to avoid issues of selection into work, we …nd a height earnings premium of around 2% per centimeter of height. This is larger than estimates for developed countries (e. g. ,  Case and Paxson, 2011) , which is consistent with higher variability in childhood conditions in low income countries (see  Strauss and Thomas, 1998; Vogl 2014 ).
Data Our data comes from the Indonesia Family Life Survey (IFLS) conducted by RAND in collaboration with the University of Indonesia. IFLS was …rst …elded in 1993 by sampling approximately 22; 000 individuals of 7; 224 households living in 13 of the 26 provinces of Indonesia, which, in total, represent 83% of the Indonesian population. Latter waves were conducted in 1997, 2000 and late 2007, with a high re-contact rate of over 90:6% of the original IFLS1 households, including those relocated and the 'split-o¤'households (the longitudinal survey tracks a large fraction of household members who have moved out of their original households and re-interview them in their new households in the follow-up surveys). As a result, the IFLS has the appealing characteristic of having very low attrition rates compared to standard longitudinal datasets (see  Thomas et al.
, 2001 , about the …rst three waves and  Strauss et al. , 2009 , about the fourth one). The analysis in this paper uses the latest wave for year 2007 (IFLS4), the only wave to provide individual data on all the life outcomes required for our investigation. Data from other waves is also used in order to construct variables in relation to childhood conditions and socio-economic background. Given the large rate of formal inactivity among women, we restrict our analysis to working men in order to avoid issues related to endogenous selection into labor market participation. The initial sample size consists of 13; 820 men. We also avoid selection into education or retirement by focusing on men aged 25-65 (which excludes 29:4% of the initial sample). We then take out men with no declared occupation, in education, disabled or in military service (5%).
A Preliminary Look at the Data Descriptive statistics are reported in the Appendix Table A. 1. On average, workers in our selected sample are 39 years old, are 1:62 meter tall (standard deviation of 6:5 cm) and score 4:9 on the 10-point cognition scale. Average annual earnings is roughly $PPP 640, mean hourly wage is $PPP 0:6, and around 8% (6%) of the selected Indonesian males declare to be happy or very happy (very unhappy) in their life. A majority of workers are in the informal sector (55%) and a third in formal private employment. Around 63% of workers are in "brawn" occupations, characterized as requiring "lots of physical e¤ort", while only 8% are in occupations that require to work with a computer. Figure  1  illustrates the association between height and key variables, either adult life outcomes, like earnings and happiness, or codetermined human capital factors (cognitive skills, education and health).
Second, the test is taken in adulthood and may overstate the e¤ect of early life conditions if early cognitive abilities have been endogenously reinforced over the lifecycle (i. e. , if individuals with higher skills have further developed their abilities in "brainy" occupations). With the data at hand, it is not possible to quantify these opposite biases. The next columns of Table  1  directly investigate the role of background characteristics. We include the set of individual variables related to early life conditions as previously discussed (parents'education, place of birth and sanitation in the dwelling during childhood). The coe¢ cients on these childhood variables are jointly signi…cant at the 1% level. We …rst include childhood conditions without sanitation, as missing observations on this variable reduce the sample by more than a half.