dataesr/scientific-paragraphs-categorization huggingface dataset:
The convolution is done by filtering in frequencies space as y = F † Λ H F x +n, where x stands for the true image. The tested noise inverse variance γ n are 100, 1 and 0. 1. The horizontal and vertical gradients are computed via the frequency space x h = F † Λ h F x and x v = F † Λ v F x. The MCMC algorithm produces 1000 samples [x, b, γ x , γ b ], with a burn-in period of 500 samples. These numbers are chosen to have visually a sufficiently good exploration after the burn-in period. The algorithm, implemented with Python and Scipy has been run on a 1. 9 GHz processor. The total time to produce 1000 samples is around 80 seconds, with 0. 08 seconds per iteration. The majority of time (60%) is spent inside the icdf simulation and special functions evaluation. Fig. 1  shows the true image x and the data y with γ n = 1.
Network-like Similarity Graphs A dataset of chemical compounds can be represented through NSGs by showing all molecules and their similarity relationships. NSGs are graphs in which molecules are represented by nodes. Edges connecting individual nodes represent pair-wise similarity relationships. Only pairs of molecules exceeding a predefined threshold of similarity are connected by an edge. To visualize the distribution of potencies, nodes are coded by applying a continuous spectrum from green (lowest) to red (highest) potency. The compound discontinuity score codifies SAR characteristics of individual compounds and it is represented by node scaling representing the deviation in potency of a molecule from its structural neighbors. Large nodes reflect molecules inducing a high discontinuity and vice versa  [47, 48].
5 and consequently, an overall Dual Binding Desirability (D Dual ) value ≥ 0. 5. The SARANEA input file is a zip folder containing a subfolder including the above mentioned information, and an additional subfolder including the files with the settings used for each applicable similarity threshold. Specifically, the D Dual values corresponding to the 63 compounds analyzed with the NSG approach were provided to SARANEA as [(1-D Dual )x1000] to fit the visualization requirements of the software. This zip file is provided as Supporting Information.
Molecular Docking The SAR information extracted from the NSG-based SAR analysis of the benzothiazinones and caffeine derivatives reported in  [30, 38, 39]  guided the design of two chromone analogs. These analogs were based on the best dual MAO-B/A 2A AR benzothiazinone and caffeine derivative, respectively. The molecular structure of the chromone analogs and the corresponding dual MAO-B/A 2A AR benzothiazinone and caffeine derivatives are provided in Fig. (1). A three dimensional conformer for each chromone analogue was generated using the ChemAxon's 3D coordinate/ conformer generation and analysis module (Generate3D) available in the MarvinSketch GUI. The geometry optimization performed by the Generate3D module is based on the Dreiding force-field  [53]  and started from a standardized 2D structure with explicit hydrogens, as described below in the Data Curation and Standardization section.
Afterwards, charges were incorporated to each conformer by resorting to the ChemAxon's calculator (cxcalc) available also form the tools panel of the MarvinSketch GUI, considering the resonant structures and major microspecies at pH = 7. 4. The crystallographic structures of the A 2A AR in complex with the antagonist ZM241385 (PDB code 3PWH) and of the complex between MAO-B and a coumarin inhibitor (PDB code 2V61), were obtained from the Protein Data Bank (www. wwpdb. org). Receptor preparation was carried out with the UCSF Chimera software  [54]. During receptor preparation all water molecules and ligands were removed and hydrogen atoms and charges were added. For both receptors the ligand binding pocket was defined as any residue lying at a distance below 5Å from the crystallographic ligand structure.
Molecular docking was performed with the DOCK v6. 6 software  [55]  considering the ligand as flexible  [56]. A maximum of 5000 conformations per ligand was explored allowing a maximum of two bumps between the receptor and the ligand. Bumps were defined as any pair of atoms closer than the 75% of the sum of their Van der Waals radii. The energy grid-based scoring function was chosen for the evaluation of poses quality. The top 20 lowest scored poses for each ligand were saved. To prevent redundancy on the predicted binding modes of each compound the resulting conformations were clustered using a threshold of 2 Å for the RMSD. For interaction energies calculation, a grid was precomputed for the receptor binding pocket region. The grid spacing was set to 0. 3 Å and the attractive and repulsive Van der Waals coefficients were set to 6 and 12 respectively.
Dual MAO-B/A2AAR Ligands and Decoys Data Collection A set of sixteen dual MAO-B/A 2A AR ligands (caffeine derivatives and benzothiazinones) were compiled from  [30, 38, 39]. Only compounds exhibiting values < 1000 nM of MAO-B IC 50 and A 2A AR K i were considered dual MAO-B/A 2A AR ligands. So, the remaining 48 caffeine derivatives and benzothiazinones were considered as dual MAO-B/A 2A AR decoys. An additional set of 32 chromones reporting human MAO-B IC 50 and A 2A AR K i values ≥ 1000 nM was compiled from  [57] [58] [59] [60]  and used as dual MAO-B/A 2A AR decoys. Additionally, dual MAO-B/A 2A AR decoys were extracted from individual MAO-B IC 50 and A 2A AR K i data, respectively. As dual MAO-B/A 2A AR decoys were considered only those compounds with MAO-B IC 50 or A 2A AR K i values ≥ 1000 nM.
The human A 2A AR antagonists dataset was retrieved from the literature  [57, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77]. It encompasses 327 compounds and contains nitrogen and oxygen heterocyles such as pyrazolo  [3, 4-c] quinolines, 1,2,4-triazolo  [4,3-a] quinoxalin-1-ones, pyrazolotriazolo-pyrimidines, quinoxalines, mercaptopyrimidines, adenine derivatives, chromones and benzylketoindoles. The binding data was collected from different sources, but the protocols for competitive radioligand binding were similar. Only measurements using human A 2A AR subtype cloned in Chinese hamster ovary cells and [ 3 H]NECA as the radiolabeled ligand were considered. The spectrum of binding affinities (A 2A AR K i values) ranged from 1. 00 nM to >10000 nM. Only 191 compounds with A 2A AR K i values ≥ 1000 nM were considered as dual MAO-B/A 2A AR decoys since their weak A 2A AR antagonist profile automatically exclude them as dual MAO-B/A 2A AR binders.
Data Curation and Standardization The chemical structures of the 574 compounds collected from the literature were subject to standard curation procedures  [94]. First, each SD file was generated using the JChem for Excel software  [95]. The molecular structure representation was then standardized using ChemAxon´s Standardizer  [96]. The parameters of the standardization process were set to obtain clean 2D molecular structures in SD format with benzenes in the aromatic form, explicit hydrogens, and a common representation (normalization) of specific chemotypes such as nitro or sulfoxide groups. The "Find duplicate structures" option of the EdiSDF program  [97]  included on the ISIDA project  [98, 99]  was used to detect and eliminate duplicate molecules.
Chemical Structure Codification and Chemical Similarity The chemical structure of the 574 compounds was codified by the hashed chemical fingerprints (ChFP) implemented in the JChem for Excel program  [95]. The chemical similarity relationships was established by using the Tanimoto coefficient as chemical similarity metric and ChFP as chemical reference space by resorting to the JCDissimilarityCFTanimoto function implemented in the JChem for Excel program  [95].
Group Fusion Similarity Search In the group fusion approach for similarity search (GFSS)  [100] , a user-defined set of structurally diverse reference structures (with the desired property/activity profile) is searched against a database using a common similarity measure. Here, the set of similarity values between each pair of reference and database compounds is combined into a new fused similarity score used to decreasingly rank the database. In this way, it is possible to "capture" the structural patterns determining the biological property under study. In this work, the set of reference structures consists of five structurally diverse dual MAO-B/A 2A AR ligands. That is, among the sixteen dual MAO-B/A 2A AR ligands above described, the five compounds sharing the lowest structural similarity (using Tc based on ChFP) were selected.
The molecular structure, identification code, MAO-B IC 50 value, A 2A AR K i value and dual binding desirability (D Dual ) value of each reference compound are depicted in Fig. (2). The set of Tc similarity values between each reference dual MAO-B/A 2A AR ligand and each database compound is used to rank the compounds library (11 dual MAO-B/A 2A AR ligands and 558 dual MAO-B/A 2A AR decoys). Finally, several data fusion schemes are applied to obtain several fused similarity scores or fused ranks, which will be used as the definitive ranking criterion. The applied data fusion schemes were based on both, Tc similarity values and rank positions. Specifically, the maximum similarity (MAX-SIM), minimum similarity (MIN-SIM), mean similarity (MEAN-SIM), maximum rank (MAX-RANK), minimum rank (MIN-RANK) and mean rank (MEAN-RANK) data fusion schemes were applied  [101].
Virtual Screening Tool for Dual MAO-B/A 2A AR Binders Based on a Group Fusion Similarity Search Approach As previously described, six different fused scores coming from six data fusion schemes (MAX-SIM, MIN-SIM, MEAN-SIM, MAX-RANK, MIN-RANK and MEAN-RANK) were used as ranking criteria in order to find a convenient VS strategy for the automatic prioritization of dual MAO-B inhibitors/A 2A AR antagonists dispersed in a dataset of decoys. In doing so, we decided to simulate an experiment to assess the ability of the GFSS approach to retrieve just 11 dual MAO-B/A 2A AR ligands dispersed in 558 dual MAO-B/A 2A AR decoys collected from the literature. This VS simulation set successfully fulfil the minimum requirements for a proper and unbiased estimation of the enrichment ability of a VS tool. First, it is composed by decoys which are in fact confirmed inactive compounds.
On the other hand, the ratio of actives (dual ligands) R a = 0. 02 (52 decoys per ligand) of this dataset fulfil the minimum of 36 decoys proposed in  [107]. Yet, we considered the relative error associated to the overall enrichment or early recognition metrics. In Table  1  are provided the relative errors associated to the enrichment metrics derived from this dataset as well as details on its size and composition. As can be noted from Table  1 , the relative error is not higher than 1% for most of the enrichment metrics, reaching a maximum of 7%. In fact, the enrichment metrics with associated relative errors around 3% are just those corresponding to the top 1% of the dataset, where the saturation effect becomes more critical. So, this data provides enough evidence to state that the effect of using this dataset does not significantly affects the inferences on the enrichment deduced from the enrichment metrics computed from it.
Only the MEAN-SIM and the MIN-RANK fusion schemes exhibited attractive EF values at this fraction. Both provided a top 1% fraction about 8 times more enriched with dual MAO-B/A 2A AR ligands than a random 1% fraction of the dataset. The rest of the schemes produced rankings not better than a random selection at this early fraction of screened data. At later fractions (top 5%, 10% and 20%) most of the fusion schemes exhibited favorable values of the EF metric, except for the MAX-SIM and MAX-RANK, which showed no enrichment at all. These results suggest an attractive overall (deduced from ROC and AUAC) and local (deduced from EF) enrichment ability of the GFSS approach. This is particularly true for the MEAN-SIM and the MIN-RANK fusion schemes. This assertion can be confirmed by visually inspecting the ROC curves of the six data fusion schemes employed under the GFSS approach, which are provided in Fig.
Therefore, the analysis of metrics such as BEDROC and RIE are crucial to efficiently estimate this critical feature on a VS protocol, particularly when very large datasets are intended to be screened. The analysis of RIE at the respective top 1%, 5%, 10% and 20% fractions allows to deduce that the early recognition ability of all the data fusion schemes follows a behavior similar to that observed for the EF metric. The MEAN-SIM and the MIN-RANK fusion schemes stand up among the rest of the schemes as those with best early recognition ability. Although the MIN-RANK schemes looks like the most promising one, the early recognition ability of the MEAN-SIM scheme at very early fractions (top 1%) suggests a better performance of this one for the VS of large chemical libraries. On the contrary, the significantly better early recognition ability of the MIN-RANK scheme makes it   better suited for the VS of smaller chemical libraries where larger fractions (top 5% -20%) can be assayed.
INTRODUCTION The Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS: PI. S. Faber and H. Ferguson; see  Grogin et al. 2011 and Koekemoer et al. 2011)  is the largest Multi-Cycle Treasury (MCT) program ever approved on the HST, with more than 900 orbits, and it was designed to use deep observations by the Wide Field Camera 3 (WFC3) and Advanced Camera for Surveys (ACS) instruments to study galaxy formation and evolution throughout cosmic time in five fields in many different bands. The observations were done by the HST/WFC3 as the main mode with ACS observations in parallel. The CANDELS images are publicly available, and multi wavelength photometric catalogs are made available by the CANDELS team following the release of the images. All CANDELS photometric catalogs were selected based on the WFC3 F160W band and this is the reference image for all the other HST and non-HST data.
We summarize our results in Section 7. Throughout this paper we assume a cosmological model with H 0 = 70 kms -1 Mpc -1 , Ω m = 0. 3 and Ω Λ = 0. 7. All magnitudes are in the AB system where m AB = 23. 9 -2. 5 × log(f ν /1µJy)  (Oke & Gunn 1983). The CANDELS COSMOS photometry catalog will be publicly available through the CANDELS website 2 along with the physical properties estimates and all the documentations. These will also be available on the Mikulski Archive for Space Telescopes (MAST) 3  , via the online version of the catalog and through Centre de Donnees astronomiques de Strasbourg (CDS). We will also make these data available through the Rainbow Database 4  (Pérez-  González et al. 2008; Barro et al. 2011 ).
202 arcsec pixel -1. The data were processed using the IMCAT package 10. The individual frames were combined, flat-fielded and photometry and astrometry calibrated. We refer the reader to  Capak et al. (2007)  and  Taniguchi et al. (2007 Taniguchi et al. ( , 2015) )  for further description of the observations and data processing. There is also further observations of the COS-MOS field by Subaru/Suprime-Cam in twelve intermediate bands along with narrow-band data across two filters  (Capak et al. 2007; Taniguchi et al. 2007 Taniguchi et al. , 2015) )  covering the wavelength range of ∼ 4000 -8500 Å. These observations were processed similarly to the broad-band optical data discussed above  (Taniguchi et al. 2015). Although slightly shallower than the optical broad-band observations, these filters have higher resolving power than the former (with R = λ/∆λ ∼ 23;  Taniguchi et al.
2015) , equivalent to low resolution spectroscopy in the optical. The resolving power is even higher for the two narrow-band filters (R ∼ 50 -100;  Taniguchi et al. 2015)  although with smaller wavelength coverage. This provides a unique dataset for studying emission line galaxies and high-redshift systems such as Lyman-α emitters  (Shimasaku et al. 2006; Iwata et al. 2009; Koyama et al. 2014). Table  2  summarizes the Subaru intermediate and narrow-band observations. We used the Subaru V2 mosaics for broad-band and NB816 observations and V1 mosaics for intermediate and NB711 data available from the IRSA 11  in this work. The number of galaxies in the CANDELS COSMOS catalog in bins of F160W magnitude (black filled circles). The number counts of the cold-mode selected galaxies (bright sample) and hot-mode only selected galaxies (faint sample) are shown in blue and red respectively.
HST Photometry We performed photometry on the high resolution (ACS + WFC3) data using SExtractor software version 2. 8. 6  (Bertin & Arnouts 1996)  in dual mode with the WFC3 F160W as the detection band consistently with the multi-wavelength catalogs in the other four CAN-DELS fields  (Galametz et al. 2013; Guo et al. 2013). SExtractor software was modified in several ways to enhance the sky measurement, add a new cleaning procedure and fix isophotal-corrected magnitude calculations λ eff = ( S(λ)λdλ)/( S(λ)λ -1 dλ) with S(λ) the filter response function  (Tokunaga & Vacca 2005). ‡ : The 5σ limiting magnitude calculated within a circular aperture with a radius rap = FWHM of the PSF in each filter. 1  and 2 ) as a function of the wavelength. The symbol sizes are proportional to the filter response function widths.
Validation Checks with Public Photometry We also checked the TFIT photometry by comparing it with the public photometry available from the 3D-HST survey 18  (Skelton et al. 2014). The 3D-HST photometry was measured by SExtractor on PSFmatched combined HST/WFC3 images in three bands (F125W, F140W and F160W) as the detection  (Skelton et al. 2014). The HST images were reduced similarly to CANDELS using the same pixel scale and tangent point  (Skelton et al. 2014). Photometry on the low resolution bands was performed using the MOPHONGO code  (Labbé et al. 2005 (Labbé et al. , 2006;; Wuyts et al. 2007; Labbé et al. 2013 ) which takes into account the variations in the PSF size across different filters and in particular the source confusion problem in the low resolution images by using a combined PSF-matched WFC3 images as a high resolution image prior for photometry estimation  (Labbé et al.
2013; Williams et al. 2014; Barro et al. 2014 ). In the past few years there have been several studies that predict the presence of these systems at z ∼ 3  (Nayyeri et al. 2014; Wang et al. 2016 ) and some predictions as high as z ∼ 5 -6  (Mobasher et al. 2005; Wiklind et al. 2008 ). Identification of these ob-jects requires deep observations in infrared wavelengths where they have the peak of their emission due to colder and redder stars  (Wuyts et al. 2007; Nayyeri et al. 2014 ). The CANDELS COSMOS deep HST/WFC3 near infrared observations and the Spitzer/IRAC TFIT photometry measurements are crucial in separating red galaxies from blends of fainter sources and lower redshift dusty star-forming galaxies. The multi-band data in the CAN-DELS COSMOS, especially the wealth of data available on both sides of the Balmer/4000 Å break help constrain the SEDs of old systems and separate them from the red dusty star-forming galaxies.
This also becomes the reference catalog to measure the photometry in the low resolution (ground-based and Spitzer/IRAC) bands. SExtractor was run using two sets of parameters (the so-called hot and cold modes) that were adjusted to detect both faint/small and bright/blended objects with the combined catalog getting contributions from both. • We used TFIT to measure the photometry in the low resolution bands. This involves using prior information from the high resolution SExtractor runs (the position and light distribution profiles) to construct templates that were fit to objects in the low resolution image and from which the photometry was measured. • Our combined final catalog contains photometry in all four HST bands observed by CANDELS (two ACS and two WFC3), the optical broadband and narrow-band observations by Subaru, optical data by CFHT, near infrared medium-band and broad-band observations by NEWFIRM and UVISTA respectively and infrared observations by Spitzer/IRAC in all four channels.
, under NASA contract NAS5-26555. This work is based in part on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Financial support for this work was provided by NSF through AST-1313319 for HN and AC. HN further acknowledges support from NASA (grant No. NNX16AF39G). This work is based in part on data products from observations made with ESO Telescopes at the La Silla Paranal Observatories under ESO programme ID 179. A-2005 and on data products produced by TER-APIX and the Cambridge Astronomy survey Unit on be-half of the UltraVISTA consortium. This study was based in part on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Science de l'Univers of the Centre National de la Recherche Scienti que (CNRS) of France, and the University of Hawaii.
This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. APPENDIX A: THE HOT/COLD MODE SEXTRACTOR PARAMETERS The cold mode SExtractor parameters were optimized to detect the bright galaxies while avoiding deblended sources. For this reason we use a Tophat filter with a small deblending threshold and minimum count. The hot mode was adjusted to detect low surface brightness and small galaxies. Therefore we chose a Gaussian filter which is more suitable for smaller targets and larger deblending parameters. The main SExtractor parameters are listed below with the differences in the cold/hot mode marked in bold. 47,2. 08,2. 94,4. 17,5. 88,8. 34, 11.
Lifelong Learning dimensions Researchers discuss important dimensions in the Lifelong Learning  (Narciss et al. , 2007)   (Sloep et al. 2011). These dimensions are: capitalization of learning experiences including work and long term learning, learning recognition, learning goals management, personal learning management, and social learning. In a long term perspective, the capitalization of learning experiences should be provided. It has to be able to manipulate different data: learning traces, learning evidence, learning confidence, professional outcomes, and recommendations. Learners will need to organize them, and evaluate achievements. This self-managed database should be organized to support Self-Regulated Learning Process (SRLP), according to relevant Learner Models. Out of this database, we pay a special attention to learning recognition: diplomas, certificates, recommendations as they constitute external support of SRLP.
5. "My Future Course" is a combination of recommendations by the community and a wish list. Recommendations could be for example for a software engineer trainings of new technologies (MongoDB, ElasticSearch…) identified as relevant in his community. The wish list is a translation of continuing development. This services is based on the open learner features that must be shared, exchanged, compared, etc. By granting access of his open learner model and to the community learner models, a learner can manage its future course. 6. "Alumni feedback" is an example of external service that could provide fruitful collaboration. By granting access of his open learner model to an alumni community, a learner can get back relevant information, such as possible occupations and corresponding learning paths.