dataesr/scientific-paragraphs-categorization huggingface dataset:
D. GloVe CONTENT-BASED FILTERING RECOMMENDER SYSTEM The second sub-recommender in EnsGG orients the hybrid to learn the personal preferences of each user concerning resources' traits, including but not limited to their type, genre, and content. First, the recommender extracts ''tags'' from items' descriptions or reviews and uses them to calculate each item's embedding vector with the help of Stanford's pre-trained GloVe vectors  [25]. Afterward, for each user, the recommender employs the stacking ensemble learning technique to learn his profile  [56]. The goal of ensemble learning is to gain a predictive accuracy edge by combining the power of the constituent models. Let L = {L 1 , L 2 ,. , L l } be different regression models, and x train be the training dataset. U and emb are independent variables, R is the dependent variable.
Our base regression models' hyperparameters are ∀l ∈ L θ l. The number and the type of regression algorithms is tunable. Each l ∈ L is trained separately with the same training dataset. Each model provides predictions for the outcomes (R) which are then cast into a meta-learner (blender). In other words, the L predictions of each regressor become features for the blender. The latter can be any model such as linear regression, SVR, Decision Tree, etc. , as expressed in  (5). f features (x) = f STK (L 1 (x), L 2 (x),. , L l (x)) (5) A blender model can then be defined and tuned with its hyperparameters θ blender. It is then trained on the outputs of the stack L. It learns the mapping between the outcome of the stacked predictors and the final ground-truth ratings. The final prediction is expressed as follows: R BL = φ(f blender (x), f STK (L 1 (x), L 2 (x),.
IV. EXPERIMENTAL SETTINGS A. DATASETS Two datasets from real-world applications are used to evaluate EnsGG: MovieLens and Amazon Review. MovieLens are user-movie ratings collected from a movie recommendation service  [58]. It is the most popular Movie-Lens Dataset version that is widely used for benchmarking recommender systems. It contains 1 million ratings from 6000 users over 4000 movies. Amazon Review is a set of interactions and reviews collected from millions of items provided by Amazon. It contains real information about interactions between users and a colossal number of products of different types and categories. Initially mined by Jianmo Ni et al. in 2014, it is updated constantly  [59]. The last version was released in 2018. The whole Amazon review dataset includes around 233. 1 million ratings/reviews.
B. VALIDATION PROTOCOL First, EnsGG is composed of two sub-recommenders: A a GRU-Based Matrix Factorization recommender system, which is a deep neural network, and a GloVe-content based filtering recommender system (GloVe-CBF), that is essentially based on a stacking ensemble of traditional machine learning models (namely a linear regression, an RBF-kernel SVM, a poly-kernel SVM, and an ARD regression as meta learner). The (aggregated) output of these two recommenders is passed to a variational autoencoder, which is also a deep neural network. The dataset is subdivided into two subsets: 75% for training, and 25% for testing in a stratified manner (i. e. the same proportion of appearance of each user is kept the same in training and test sets as they are in the whole dataset). Let us note the training set and the test set as Tr, Ts respectively.
D. BASELINES To ensure the integrity of comparisons, baselines are executed in the same evaluation environment as EnsGG. Furthermore, the NeuRec library, which implements 33 neural recommender systems using TensorFlow is used. It is created by a research team led by Bin Wu and released on Github as open-source software under MIT licensing  [62]. EnsGG is compared with the following recommendation methods: • GRU-based Matrix Factorization: It is the first component of EnsGG hybrid (section. III-C). It is a novel, state of the art collaborative filtering recommender system, based on the neural matrix factorization technique. It takes its power from the GRU units that adjust the embeddings while learning user and item biases thanks to their Update and Reset gates. • GloVe-content-based filtering Recommender: It is the second component of EnsGG hybrid (section.
III-D). This content-based recommender system uses the pre-trained GloVe vectors and item descriptions to represent the latter in the form of embeddings, which are used afterward in a stacking ensemble model to create a profile model for each user. • Neural Collaborative Filtering (NCF):  [15]  This recommender system applies the multi-layer perceptron to learn the user-item interaction function. The number of parameters for NCF grows linearly with the number of users and items, and thus training it on a large dataset would be problematic. • Neural Attentive Item Similarity Model (NAIS)  [63] : Developed by the same research team that created NCF, this item-based CF method implements an attention network to distinguish which historical items in a user profile are more important for a prediction.
Therefore, tweaking the meta-learner's hyperparameters would be sufficient  [57]. The analysis is performed on 3-1 stacks. It focuses on the learning algorithms used by each learner alongside the meta-learner's hyperparameters. The first step is to find the combination of regression models that compose the best stack. A grid search is performed over five algorithms (polynomial-kernel support vector machines (SVM poly ), RBF-kernel support vector machines (SVM rbf ), decision trees (DT), automatic relevance detection regression (ARD) and linear regression (LR)). Table  3  reports the results of the top 5 stacks for k ∈ 10, 30, 50 averaged over MovieLens and Amazon datasets. The structure of each stack is as follows The second step is to fine-tune the stack's learners and the meta-learner's hyperparameters.
The value of one hyperparameter is modified while fixing others to their best values. Then, the resulting neural network is trained and evaluated. The process is repeated for each hyperparameter. The resulting neural networks are trained and tested over both MovieLens and Amazon Review Data. Evaluation is performed using the average MAP obtained from both datasets. As shown in Fig. 7 , the embedding size α, GRU size θ, and the number of hidden layers after concatenation τ all play their part in tuning the performance of the recommender system. Also, the relu activation function dramatically decreases the performance. Therefore, it is not recorded in any on of the top-4 hyperparameter settings (see Fig. 7. d). The influence of the embedding sizes and the hidden unit size in GRU is shown in Fig.
B. PERFORMANCE COMPARISON WITH THE BASELINES (RQ2) In this section, results of extensive comparisons are reported between various baselines and the EnsGG, GRU-MF, and Glove-CBF methods. Table  5  and Table  6  present the recommendation performance of all methods on the MovieLens and Amazon Electronics datasets (RQ2). EnsGG outperforms all baselines on both datasets according to the mean average precision. The most likely reason is that the ensemblist model is based on learning biased and unbiased rating patterns of each user. A list of recommended items is obtained for k = 10, 30 and 50 on both datasets. Fig. 9. a) and Fig. 10. a) present the MAP@k performance versus k-top items. It appears that EnsGG can recall the relevant items for the user better than the other models VOLUME 8, 2020 TABLE 5.
Recommendation accuracy scores (%) of compared methods conducted on MovieLens 1M dataset. We generate Top-10, 30 and 50 items for each user. The best performance of MAP@k and NDCG@k are highlighted as bold font. TABLE 6. Recommendation accuracy scores (%) of compared methods conducted on Amazon Electronics dataset. Top-10, 30 and 50 items are generated for each user. The best performance of MAP@k and NDCG@k are highlighted using a bold font. with a significant margin. Furthermore, both Glove-CBF and GRU-MF produce competitive empirical results on both datasets. For example, on the MovieLens dataset, GloVe-CBF and GRU-MF achieve respectively MAP@10=0. 80 and MAP@10=0. 78 which is higher than the baselines. The Glove-CBF recommender acquires the item similarities and creates a user personalized task recommendation justifying its high recorded scores.
The GRU-MF recommender as a variant of neural matrix factorization discovers the user-item interactions with a non-linear function and the gated recurrent units produce a noticeable improvement. Based on the results shown in Fig. 9. b) and Fig. 10. b), one can observe that the GloVe-CBF recommender obtains a high NDCG score on Amazon Electronics dataset and GRU-MF records a high NDCG score on the MovieLens dataset. It means that the order in which an item appears in the top-k recommendation list is close to its order in the ground-truth list. Also, the EnsGG recommender and NAIS method still exhibit good NDCG scores. Similar to the GloVe-CBF recommender, NAIS method characterizes a user with his historically interacted items and recommends items similar to the user's profile. The sparsity rate difference between the two datasets produces slight differences with the score of GRU-MF and the score of GloVe-CBF.
C. IS ENSEMBLIST GRU/GLOVE FRAMEWORK HELPFUL (RQ3)? We further evaluate the performance of Top-K recommended lists where the ranking position K ranges from 10 to 50, for the EnsGG and its two sub-recommenders to see whether using a hybrid architecture is beneficial to the recommendation task (RQ3). Towards this end, Fig. 9  and Fig. 10  report detailed evaluation results for each recommender system using the MAP and NDCG metric, respectively, over both datasets. EnsGG records the best performance according to the mean average precision metric (MAP). As discussed in section B, this is because EnsGG tears its recommendations from combining the GRU-MF and GloVe-CBF. The GRU-MF recommender discovers and exploits the similarity patterns across users and items. The GloVe-CBF recommender learns each user profile as a model.
Furthermore, despite using a simple aggregation function, EnsGG manages to produce accurate predictions without major penalties to the recommender's overall performance. Empirical evaluations show that both EnsGG and GRU-MF/ GloVe-CBF provide competitive performance with EnsGG significantly outperforming the state-of-the-art baselines on influential real-world datasets. Future work aims to explore more deeply the aggregation function on sub-recommenders comparing various functions, such as the Bayesian approach, to gain further improvements on the recommendation accuracy. Technically speaking, another interesting direction worth exploring is to integrate context-awareness which generates items relevant to the users according to a specific context. Indeed, the proposed framework is a highly flexible approach allowing to incorporate high dimensional context information.
However, despite the importance of stellar spectroscopy for Galactic dynamics and Galactic archaeology, the data situation in the early 2000s was far from satisfactory. RVs were listed for some 50,000 stars in the databases of the Centre de Données astronomiques de Strasbourg (CDS), an astonishingly small number compared to the approximately one million spectra available for galaxy redshifts listed at that time. Furthermore, these RVs and their underlying spectra comprised a very heterogeneous sample in terms of selection, resolution, epoch or signal-to-noise ratio (SNR). The situation changed somewhat with the advent of the Geneva Copenhagen survey (CGS,  Nordström et al. 2004 ), which provided radial velocities, effective temperatures, and metalicities for a homogeneous sample of 14,139 stars.
However, this sample covered only a sphere of about 100 pc radius around the Sun (the so-called Hipparcos sphere). The RAdial Velocity Experiment (Rave) was originally set up as a pilot survey using the existing 6dF multi-object spectrograph at AAO's UK Schmidt telescope (UKST) to observe about 100,000 stars in ∼ 180 nights of unscheduled bright time during the years  [2003] [2004] [2005]   (Steinmetz 2003). Spectra were to be taken covering the IR Ca triplet region also employed by the Gaia RVS system (see  Recio-Blanco et al. 2016). Motivated also by the astrometric satellite mission concepts DIVA (Deutsches Interferometer für Vielkanalphotometrie und Astrometrie,  Bastian et al. 2001)  and FAME  (Full-sky Astrometric Mapping Explorer, Johnston 2003) , this pilot survey was intended as a path finder for a considerably larger campaign targeting up to 40 million targets using a new Echidna-based multi-object spectrograph for the UKST, thus providing a vast kinematic database three orders of magnitude larger than any other survey planned in this period.
DR6-1 will focus on the spectra taken and is accompanied by a data base of wavelength calibrated and flux normalized spectra for 518 387 observations of 451 783 unique stars. DR6-2 provides a new set of stellar parameters employing parallax information from Gaia DR2  (Gaia Collaboration et al. 2018) , a robust [α/Fe] ratio, and individual [Fe/H], [Al/H] and [Ni/H] ratios. DR6-1 is structured as follows: in Section 2 we give an overview of the survey facility and performance. We outline the data reduction and provide direct references to sections of previous papers where the interested reader can find further details. Section 3 presents the spectra in the Rave spectral catalog, which we are releasing here for the first time, and the reduction procedure of the Rave raw data. Section 4 presents the automated classification of Rave spectra.
The field centers of the first input catalog are 6 • apart. The early input catalog of the first 2 years of operation thus contained about 300,000 stars of which about half the sample originated from Tycho 2, the other half from SSS. The first three data releases of Rave (DR1, DR2, and DR3) are entirely based on this input catalog. An a posteriori comparison with DENIS DR3 (DENIS Consortium 2005) revealed that, owing to saturation effects for I < 13, a ≈ 1 mag offset between DENIS and SSS at I ≈ 11 emerged. As a consequence, while Rave comprises a kinematically unbiased sample, the early input catalog exhibits some color biases (see discussion in DR1 Section 2. 3). A new and more refined input catalog was brought into use in March 2006. The main sample has |b| > 25, and uses DENIS DR3 cross-matched with 2MASS to define targets to I = 12 with a default of four pointings on each field center -two bright and two faint.
Observing procedure Observations for Rave followed a sequence of target field exposures, arc and flat. Ne, Rb, and Hg-Cd calibration exposures were obtained for each field, together with a quartz flat field for spectrum extraction in the data reduction. Typically, one 10s RbNe arc exposure and five 15s fiber flats (quartz halogen) were taken before and after each field exposure series. The field exposure series themselves consisted of five consecutive exposures (see below), allowing adequate SNR to be obtained in the summed spectra, while minimizing the risk of saturation from particularly bright stars. In the case of poor conditions or low sky transparency, additional exposures were made. Several target fibers were reserved in order to monitor the sky for background subtraction. Each of the Rave target frames contained spectra of at least 10 sky samples, obtained using dedicated sky fibers.
Major changes in the performance of the Rave survey Rave observations span a period of ten years. Based on the lessons learned with early data releases, in particular DR1 and DR2, a number of procedural optimizations were introduced. Furthermore, maintenance and refurbishments of the telescope and the facility resulted in a few modifications. We summarize the most relevant ones in the following: • The red color of early selected targets (DR1,  Steinmetz et al. 2006 ) and a predicted low efficiency of the spectrograph in its 2nd order did not call initially for a blue-light blocking filter. A Schott OG530 blue-light blocking filter was however inserted in the collimated beam of the spectrograph on 2 April 2004, to fully suppress the contamination visible expecially on warmer targets. This allows for an unambiguous placement of the continuum level and so permits the derivation of values of stellar parameters, in addition to the radial velocity (DR2).
determination of atmospheric parameters combining Rave spectroscopic information with additional photometry and Gaia DR2 parallax priors using BDASP (Bayesian Distances Ages and Stellar Parameters, DR6-2 Section 3. 3, see also  McMillan et al. 2018). 8. recalibration of the stellar parameters for giant stars based on K2 asteroseismic information (DR6-2, Section 3. 4, see also  Valentini et al. 2017). 9. determination of the abundance of iron group elements and an overall [α/Fe] ratio with the pipeline GAUGUIN (DR6-2, Section 4). The output of these pipelines is accumulated in a PostgreSQL data base and accessible via the Rave website http://www. rave-survey. org (Section 7 and DR6-2, Section 7).
Survey Selection Function In order to draw robust conclusions from the data gathered via large spectroscopic surveys such as Rave, it is crucial to understand the relationship between the observed targets and their underlying population, known as the selection function. A comprehensive overview of the selection function of Rave is given in  Wojno et al. (2017) , which we summarize here. Rave targets were selected from a number of input catalogs. These targets were selected uniformly over the entire Southern hemisphere, with the exception of regions where a mild color-cut of (J -K > 0. 5 mag) was enforced (Section 2. 2). Figure  1  shows the completeness fraction (number of Rave stars divided by number of 2MASS stars per area on the sky) for the observed I magnitude bins. The I magnitude is in principle available from catalogs as DENIS, however DENIS I suffers from saturation effects for I < 10.
Repeats Rave was designed as a survey with its main focus on studies of Galactic dynamics and Galactic evolution. The primary design goals were therefore to have an unbiased input catalog and observing procedure, a wide coverage of the accessible sky, and a magnitude limited layout aiming at high completeness from the brighter to the fainter magnitudes. The technical boundaries (large multiplex and long configuration times combined with weather patterns and block-out periods around the full moon) made it difficult to account systematically for repeat observations following a fixed cadence, in particular without compromising the aims of coverage and completeness. On the other hand, repeat observations are critical to check for coherency and repeatability of Rave data products, and to allow modellers to account for the effects of unresolved binaries.
Typically, fringing jumps that reach up to 20% of the flux are damped to ∼ 1% with this approach, but note that techniques of Gaussian filtering would be required to reach a better continuum normalization on scales of ∼ 20 Å, a typical fringe width. A small fraction of Rave spectra suffer from internal reflection of light within the spectrograph, which causes an emission ghost located blue-ward of the Ca II 8498 Å line. This is an additive feature that can be removed through careful spectral normalization, though its width of ∼ 23 Å makes this a challenging task. Such spectra have a problematic continuum, so they are flagged with 'c' (Table  4 ) in the final database. Extracted one-dimensional spectra of object, flat-field, and arc-lamp exposures need to be corrected for fiber cross-talk and for scattered light contributions from all fibers with flux levels above a set threshold.
Validation with repeat observations A further way to validate the quality of the Rave data products is to compare the parameters derived for multiple observations of the same object (see Section 2. 7). In the following analysis we calculate for each star k that has N k repeat > 1 observations, that fulfills the quality threshold for the RV00 sample, and that has a match in the Gaia DR2 catalog. This corresponds to a total of 95068 spectra, or about 18% of the total Rave database. We determine the uncertainty ∆RV in the radial velocity by three methods: • From internal errors: ∆RV k for star k is randomly sampled assuming a normal distribution with a width corresponding to the internal error estimate k HRV. • From repeat observations: The difference ∆RV k i between the radial velocity of star k determined from observation i (1 ≤ i ≤ N k repeat ), RV k i , and the mean RV k for the respective repeat sequence.
Trial design This was a randomised controlled pilot trial that compared two parallel groups for superiority of intervention versus placebo. Ethical approval for this study (Ethics Committee No. P2016/404/2016-003918-27) was provided by the Ethics Committee Erasme Hospital, 808 route de Lennik, B-1070 Brussels, Belgium (Chairperson Prof J. -M. Boeynaems) on 24 October 2016. The study was registered with the European Clinical Trials Database (EudraCT: reference no. 2016-003918-27). All included patients signed a written informed consent before participation. This study adheres to CONSORT guidelines for reporting clinical trials (see additional le: « CONSORT Checklist »).
Data collection Endothelial function and the EG were investigated using three techniques: concentration of syndecan-1, measurement of the tissue oxygen saturation (StO 2 ) during the vascular occlusion test (VOT), and contemporary measurement of ow-mediated dilation (FMD). Data and blood samples were collected before surgery (T0), at 1-3 h post-surgery in the recovery room (T1) and 24 h post-surgery in the surgical ward (T2). The concentration of syndecan-1, a marker of EG shedding, was measured by classic sandwich enzymelinked immunosorbent assay (ELISA), based on the manufacturer's instructions (Syndecan-1 ELISA kit; Tebu-bio, Boechout, Belgium). Blood samples were collected in dry tubes, centrifuged to obtain the serum and stored at -80°C for a maximum of 6 months before nal analysis. The StO 2 was measured continuously (every 2 seconds) and noninvasively using near-infrared spectroscopy (NIRS) (ForeSight ® ; CASMED ® ; CAS Medical Systems, Inc.
The FMD was assessed by automated edge detection software (FMD Studio™ (CardioVascular Suite™); Quipu srl, Pisa, Italy),  19  based on the experts' guidelines. 20 e following FMD-derived variables were analysed: brachial artery baseline diameter, FMD-maximum (i. e. the maximal diameter during reperfusion) and the area under the curve of estimated shear rate of hyperaemic ow until FMD-max (Image 3 in « Additional File 3 » shows the evolution of the brachial artery diameter and shear rate during a test in one participant). Data concerning uid requirements were prospectively collected during surgery and during recovery in the postanesthesia care unit (PACU). Hemodynamic variables were collected only during surgery by the EV1000 clinical platform.
Acknowledgments The authors would like to thank the following individuals:  Veronique Corbière ,  Françoise Mascart  and  Audrey Godefroid of the Laboratory of Vaccinology and Mucosal Immunity, Université Libre de Bruxelles (Brussels, Belgium)  for their substantial help in laboratory analysis of syndecan-1;  Anita Verleije ,  Kimberly Gonsette ,  Shalini Raj-Lawrence  and all post-graduate students of the  Department of Anaesthesiology of Erasme Hospital  for providing care to the study patients and help in data collection;  Luc Barvais  and  Alexandre Joosten  for their critical discussion of this study's methods and results. Presentation.