dataesr/scientific-paragraphs-categorization huggingface dataset:
Historically, the list of object types in SIMBAD was mostly phenomenological, and organized as a linear hierarchy. This list is still the one given to the users on the web site when clicking on 'Object types. " It is also used by the cross-identification program COSIM (section 5). Objects with the highest priority for the main object type are at the bottom, while objects with the lowest priorities are at the top. Object types at the same level in the historical hierarchy are assumed to be incompatible, which lower the cross-identification score of COSIM. This historical list will be replaced in the coming months. It has been reorganized as much as possible on astrophysical concepts, especially the evolutionary stage for the stars, taking advantage of the wide expertise of the astronomers attached to the CDS team.
SQL usage The relations between object types can be structured in tables of the SIMBAD database. In order to do that, SQL is generated to create all the hierarchy. This can be used by the astronomers to query directly SIMBAD and filter objects on a category. For instance, the figure  1  shows the schema inside the database that could be used by anyone who wants to query SIMBAD. Thanks to such a schema, other databases or services will be allowed to query SIMBAD programmatically and exploit at best the hierarchy of object types for solving complex queries.
Graphical visualisation Documentation can be automatically generated from the stored information of this hierarchical organization, and can be displayed as a nice graph (thanks to the graphviz 1 open source tool) as you can see on figure  2. In this kind of graph, the links between objects are more obvious, and on the left part you can see that we show different levels of granularity. The generation is done automatically with a program reading information of links in the database and generating a file in "dot" format, that is used to generate this graph. So that, any modification in the SIMBAD hierarchy will automatically be reported in the graphical documentation.
Cross-matching object types Each SIMBAD object is a composition of all observations on many telescopes and satellites. That is why we could have secondary object types, depending on the points of view. In any case, before the insertion of astronomical objects in SIMBAD, we should compare these object types to check if there is no discrepancy according to the types hierarchy. The "COSIM" software (Comparisons of Objects for SIMbad  [2]  : internal cross-identification tool) is used to compare astronomical objects coming from a table, and compute a score of compatibility on each data, and specially on object type:. Search around ICRS 20:16:31. 91+37:39:09. 1. 2 object(s) found from coo 1/1: PoC/*iC ( -10) 60. 0"D ( 2. 9) +++ 2/2: **/*iC ( 1) 8. 8"B ( 0. 0) COSIM is going up and down in this hierarchy to quantify the relation between object types.
MC simulations The MC simulations were performed using the GATE software (release 8. 0)  [24]. The Geant4 Application for Emission Tomography (GATE) open-source simulation platform, based on the Geant4 toolkit, has been developed since 2001  [25] [26] [27]  by the OpenGATE collaboration (www. opengatecollaboration. org) and is currently widely used by the research community involved in molecular imaging, radiotherapy. A module dedicated to optical transport in tissues is available within GATE since release 6. 2  [25]. Absorption, Mie scattering, refraction and reflection at boundaries are simulated. The simulation of photon transportation in biological tissues relies on a commonly used and validated implementation  [26]. A set of parameters including refractive index (n), anisotropy (g), the absorption length (L a ), and scattering length (L s ) are defined for each type of tissues.
g. VChR1, C1V1, and ReaChR, Chrimson, Jaws)  [38] [39] [40] [41]. What are the limitations of the MC simulations? GATE was used in the present study to obtain fluence and resulting temperature increase maps. Other public MC code are available for the simulation of photon transport Monte Carlo multi layered (MCML), MCextreme or tMCimg  [26, 42, 43] , but do not allow to simulate thermal effects simultaneously. MCML has been used to simulate the transport and detection of photons in the context of brain neurosurgery for the safe and accurate positioning of electrodes  [44]  and is limited to semi-infinite layer geometries. tMCimg and MCextreme allow to simulate more realistic geometries based on segmented MRI or CT images but require data to be formatted into meshed files, which are not straightforward to handle for neurobiologists or neurosurgeons.
Martin et al. (2016a)  presented a spectroscopic study of Dra II using the 2015 dataset, however, the heliocentric velocities of the 2015 stars in this work are slightly different: using the usual method of the  Ibata et al. (2011)  pipeline to derive the velocities, the average difference of the 2015 and 2016 velocities is not 0 km s -1 as expected, but is shifted of a few km s -1. These effects appear when the velocities are derived through a nonflexible, but supposedly more precise method in the pipeline of  Ibata et al. (2011) , that was used in the paper of  Martin et al. (2016a). In this work, using a slightly less precise, but more flexible method of the same pipeline to extract the velocities, we are able to get rid of these systematics and find the expected mean difference in velocities for all stars observed twice of 0 km s -1.
BROADBAND PHOTOMETRY ANALYSIS The CMD of Dra II for sources within two half-light radii (2r h , see below) is presented in Figure  2  (left panel). For comparison, the CMD of a field region of the same coverage but selected in the outskirts of the MegaCam field of view is shown in the middle-left panel. The main sequence observed in the Dra II CMD is consistent with an old and metal-poor stellar population (see below) as originally pointed out by  Laevens et al. (2015) , but the MegaCam data is much deeper and traces the main sequence of the system more than three magnitudes below the turnoff. The exquisite MegaCam CMD is highlighted by the narrowness of this sequence. The 50 per cent completeness of the data in the g band is reached at g = 25. 2 mag and i = 23. 9 mag. We confirm that the main sequence of Dra II contains very few stars brighter than the turnoff and that the satellite is particularly faint.
Structural and CMD analysis We take advantage of the deep MegaCam data and of the better sampling of the system to revisit the structural analysis performed by  Laevens et al. (2015). The analysis is based heavily on the algorithm presented in  Martin, de Jong & Rix (2008)  and  Martin et al. (2016b)  and we separately infer the CMD-properties of Dra II. Altogether, we aim to estimate the structural properties of the system (the coordinate offsets of the centroid from the literature values, X0 and Y0, the half-light radius along the major axis, r h , the ellipticity 1 , , the position angle of the major axis east of north, θ, and the number of stars within the MegaCam data, N * ), along with its distance modulus m -M , Age A, metallicity [Fe/H] CMD , and abundance in α elements [α/Fe]. For any star k in our sample, the pieces of information used at this stage are the coordinates of the star X k and Y k , projected on the sky on the plane tangent to Dra II's centroid, and the MegaCam magnitudes, g k and i k.
Finally, the colourmagnitude space over which the PDF is calculated is implemented with pixel sizes of 0. 01 magnitude on the side, so we further convolve the resulting PDF by a Gaussian of dispersion 0. 01 mag to avoid aliasing issues in our representation of the PDF. Since this PDF is supposed to describe the observed Dra II features of the CMD, the completeness of the data needs to be taken into account, therefore, each track is weighted by the product of the completenesses in g0 and i0. This completeness is computed following the model built by  Martin et al. (2016b)  on similar MegaCam data, simply shifted to the appropriate reference median magnitude (the median magnitude of all stars in our photometry with photometric uncertainties between 0. 09-0. 11). The final step normalizes this PDF to unity so it is properly defined.
For this reason, there is no need to take them into account in our analysis, and the following CMD and structural analyses are performed only with stars with 15 < g0 < 24. 5, and -0. 5 < (g -i)0 < 2. 0. The resulting two-dimensional marginalised PDFs are presented in Figure  4  for spacial and 5 for CMD parameters. Our results are compatible with the ones presented by  Laevens et al. (2015)  in the discovery paper of Dra II. From the deeper MegaCam data, we confirm the half-light radius of the satellite to be r h = 3. 0 +0. 7 -0. 5 arcmin (vs. 2. 7 +1. 0 -0. 8 arcmin before). Overall, the deeper MegaCam data allows for better constraints with smaller uncertainties on all parameters. The use of a Plummer profile instead of an exponential profile yields similar results. The radial profile of the favoured spacial model is presented in Figure  8.
In order to do so, we assume that the distribution of photometric metallicities in the Dra II sample corresponds to a Gaussian-distributed Dra II population and a contamination model L bkg , which is constructed empirically from the field data outside a 5r h radius. The metallicity distribution of this contamination sample is binned, then smoothed with a Gaussian kernel of 0. 1 dex to account for poor number counts in some metallicity bins. Finally, we assume the following metallicity distribution model: L({[Fe/H] CaHK,k , δ [Fe/H],k }| [Fe/H] CaHK DraII , σ [Fe/H] ) = k G([Fe/H] CaHK,k | [Fe/H] CaHK DraII , σ k ) + L bkg ([Fe/H] CaHK,k ), ( 9 ) with G(x|µ, σ) the value of a Gaussian distribution of mean µ and dispersion σ evaluated for x, δ [Fe/H],k the uncertainty on the photometric metallicity of star k, and σ k = σ 2 [Fe/H] + δ 2 [Fe/H],k.
The inference analysis yields the two-dimensional joined PDF of [Fe/H] CaHK DraII and σ [Fe/H] presented in Figure  14. The metallicity of the system is found to be [Fe/H] CaHK DraII = -2. 7 ± 0. 1 dex, with a metallicity dispersion lower than 0. 24 dex at the 95% confidence level. The favoured systemic metallicity confirms that Dra II is significantly metalpoor. The fraction of Dra II stars favoured by the analysis is η ∼ 0. 6, corresponding to a total of 41 stars. The metallicity dispersion of the satellite cannot be resolved with this dataset. Performing the analysis using an asymmetrical gaussian does not change significantly change our results. In order to validate our inference based on the CaHK metallicities, the same analysis is performed on the Pristine data of two metal-poor globular clusters, M15 and M92.
Globular clusters are crucial in this case as their metallicity dispersion is expected to be too small to be resolved using purely photometric metallicities and they are a good test of the quality of our constraints on σ [Fe/H]. Carretta et al. (2009a, C09)  and  Carretta et al. (2009b, C09b)  showed that both clusters have a similar spectroscopic metallicity, with [Fe/H] C09b = -2. 34 ± 0. 06 dex for M15 and [Fe/H] C09 = -2. 35 ± 0. 05 dex for M92, as well as metallicity dispersions around ∼ 0. 05. The application of our inference model to the globular cluster Pristine datasets yields [Fe/H] M15 = -2. 32 ± 0. 04 dex and [Fe/H] M92 = -2. 38 ± 0. 05 dex, compatible with the values of C09. As expected, the inferred metallicity dispersions are unresolved for both clusters, as can be seen with the coloured contours in Figure  14.
SPECTROSCOPIC ANALYSIS We now investigate the dynamical properties of the satellite using our spectroscopic data, for which the processing was detailed in section 2. 2. Examples of spectra can be found in Figure  4   Dra II stars clearly stand out in Figure  16  as they form a peak around -345 km s -1 , as was already pointed out by  Martin et al. (2016a)  in their initial analysis of the 2015 data set. A broader distribution around ∼ -45 km s -1 corresponds to stars from the Milky Way disc while Milky Way halo stars are responsible for the sparsely distributed velocities throughout the range shown here. In order to better constrain the dynamical properties of the system, one has to isolate Dra II members as well as possible. Particular care should be taken when handling the contamination by Milky Way halo stars that are distributed within a broad velocity range that includes the systemic velocity of the satellite.
Conclusion The properties of Draco II tend to indicate that the satellite is a potentially disrupting dwarf galaxy, which could explain its total luminosity, metallicity, size, and relatively low velocity dispersion. The orbit of the satellite, constrained with Gaia PMs, shows that the satellite is very likely to be affected by tidal processes, which is backed up by potential tidal structures observed in the field. However, the impossibility, with our current dataset, to constrain the metallicity dispersion of Dra II casts a doubt of the nature of the satellite, which might well be a globular cluster observed at the very end of its disruption process. Even though challenging, obtaining high signal-to-noise spectra of the faint main-sequence stars of Dra II currently provides the only hope of unravelling the mystery still surrounding Dra II.
ACKNOWLEDGMENTS RI, NL, and  NFM  gratefully acknowledge funding from  CNRS/INSU  through the  Programme National Galaxies et Cosmologie  and through the  CNRS grant   PICS07708. We gratefully thank the  CFHT  staff for performing the observations in queue mode. N. F. Martin  acknowledges the  Kavli Institute for Theoretical Physics in Santa Barbara  and the organizers of the " Cold Dark Matter 2018" program , during which some of this work was performed. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY11-25915. BPML gratefully acknowledges support from  FONDECYT postdoctoral  fellowship No. 3160510. Based on observations obtained at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council of Canada , the  Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France , and the  University of Hawaii.
Some of the data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California  and the  National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the  W. M. Keck Foundation. Furthermore , the authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  under Grant No. AST-1238877 , the  University of Maryland , and  Eotvos Lorand University (ELTE). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e. g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS).
The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type.
Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www. clinica. run) and the paper-specific code is available at: https://gitlab. icm-institute. org/aramislab/AD-ML.
, 2014; Haller et al. , 2011; Rathore et al. , 2017)  for reviews). Some of them make use of a single imaging modality (usually anatomical MRI)  (Cuingnet et al. , 2011; Fan et al. , 2008; Klöppel et al. , 2008; Liu et al. , 2012; Tong et al. , 2014)  and others have proposed to combine multiple modalities (MRI and PET images, fluid biomarkers)  (Gray et al. , 2013; Jie et al. , 2015; Teipel et al. , 2015; Young et al. , 2013; Yun et al. , 2015; Zhang et al. , 2011). Validation and comparison of such approaches require a large number of patients followed over time. A large number of published works uses the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. However, the objective comparison between their results is almost impossible because they differ in terms of: i) subsets of patients (with unclear specification of selection criteria); ii) image preprocessing pipelines (and thus it is not clear if the superior performance comes from the classification or the preprocessing); iii) feature extraction and selection; iv) machine learning algorithms; v) cross-validation procedures and vi) reported evaluation metrics.
Because of these differences, it is arduous to conclude which methods perform the best, and even whether a given modality provides useful additional information. As a result, the practical impact of these works has remained very limited. Moreover, the vast majority of these works use the ADNI dataset (ADNI1 for earlier papers and most often a combination of ADNI1, ADNI-GO and ADNI2 for more recent works). Therefore, assessment of generalization to another dataset is rarely done, even though other publicly available datasets exist such as the Australian Imaging Biomarker and Lifestyle study (AIBL) and the Open Access Series of Imaging Studies (OASIS). Comparison papers  (Cuingnet et al. , 2011; Sabuncu et al. , 2015)  and challenges  (Allen et al. , 2016; Bron et al. , 2015)  have been an important step towards objective evaluation of machine learning methods by allowing the benchmark of different methods on the same dataset and with the same preprocessing.
Nevertheless, such studies provide a "static" assessment of methods. Evaluation datasets are used in their current state at the time of the study, whereas new patients are continuously included in studies such as ADNI. Similarly, they are limited to the classification and preprocessing methods that were used at the time of the study. It is thus difficult to complement them with new approaches. In this paper, we propose a framework for the reproducible evaluation of machine learning algorithms in AD and demonstrate its use on classification of PET and MRI data obtained from three publicly available datasets: ADNI, AIBL and OASIS. Specifically, our contributions are three-fold: i) a framework for the management of publicly available datasets and their continuous update with new subjects, and in particular tools for fully automatic conversion into the Brain Imaging Data Structure 3  (BIDS) format  (Gorgolewski et al.
, 2016) ; ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking of different components; iii) a large-scale evaluation on T1 MRI and PET data from three publicly available neuroimaging datasets  (ADNI, AIBL and OASIS). We demonstrate the use of this framework for automatic classification from T1 MRI and PET data obtained from three datasets (ADNI, AIBL and OASIS). We assess the influence of various components on the classification performance: modality (T1 MRI or PET), feature type (voxel or regional features), preprocessing, diagnostic criteria (standard NINCDS/ADRDA criteria or amyloid-refined criteria), classification algorithm. Experiments were first performed on the ADNI, AIBL and OASIS datasets independently, and the generalization of the results was assessed by applying classifiers trained on ADNI to the AIBL and OASIS data.
Datasets Part of the data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative database (adni. loni. usc. edu). The ADNI was launched in 2003 as a publicprivate partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. Over 1,650 participants were recruited across North America during the three phases of the study (ADNI1, ADNI GO and ADNI2). Around 400 participants were diagnosed with AD, 900 with MCI and 350 were control subjects. Three main criteria were used to classify the subjects  (Petersen et al.
5 years worth of longitudinal data: 211 AD patients, 133 MCI patients and 768 comparable healthy controls. AIBL study methodology has been reported previously  (Ellis et al. , 2010 (Ellis et al. , , 2009)). Briefly, the MCI diagnoses were made according to a protocol based on the criteria of,  (Winblad et al. , 2004)  and the AD diagnoses on the NINCDS-ADRDA criteria  (McKhann et al. , 1984). Note that about half of the subjects diagnosed as healthy controls reported memory complaints  (Ellis et al. , 2010 (Ellis et al. , , 2009)). Finally, we used data from the Open Access Series of Imaging Studies project whose aim is to make MRI datasets of the brain freely available to the scientific community. We focused on the "Cross-sectional MRI Data in Young, Middle Aged, Nondemented and Demented Older Adults" set  (Marcus et al.