dataesr/scientific-paragraphs-categorization huggingface dataset:
Application: According to the announcement in Lemma 3. 3, the classification error of FDTW during the learning phase (training error) is less than or equal to that of DTW for all the considered datasets. We used Wilcoxon signed rank test with continuity correction to test the significance of FDTW against IDDTW. The Wilcoxon signed rank test gives a p-values, p < 0. 01, which demonstrates that FDTW achieves a significant reduction of the classification error of IDDTW. This also demonstrates that FDTW allows to find segment numbers for PAA that are of better quality than those found by IDDTW during the learning phase. Comparison with IDDTW : To evaluate the quality of FDTW, we compared its classification errors with that of IDDTW and the minimal one. The minimal classification error was find by applying Brute-force search (BF) on both training set and testing set.
FDTW and IDDTW used the training set to find the segment number N with minimal training error using threefold cross validation, and then used this number of segments on the testing set to compute the classification error. The value of the segment number N found on the training set may in some cases not be appropriate for the testing set. We speak of a generalization error which is due to the representativeness of the training set (Tab. 2). If two numbers of segments N 1 and N 2 are associated with the same training error, we retain the largest. IDDTW tested all the values of N that were equal to a power of two during the learning phase and kept the one that had a minimum classification error. The experiments showed that FDTW is more performant than IDDTW. Actually, FDTW resulted in a lower generalization error than IDDTW on 22 datasets and the same generalization error than IDDTW on eight datasets.
The Wilcoxon signed rank test gives a p-values, 0. 01 < p ≤ 0. 05, which demonstrates that FDTW achieved a significant reduction of the generalization error of IDDTW. Results also show that FDTW managed to find the minimum error for nine datasets (Coffee, ECGFiveDays, Gun-point, ItalyPowerDemand, OliveOil, Plane, Synthetic control, Trace, Two patterns) and outperforms the smallest classification error reported in the literature on dataset CBF (No. 5).
Comparison with other classification algorithms: To evaluate the quality of FDTW, we compared its classification errors (generalization error) with that of 35 other classification algorithms  [2]  of the literature on 84 datasets of UCR archive. The performances of the algorithms are compared using the Nemenyi test that compares all the algorithms pairwise and provides an intuitive way to visualize the results (Fig. 6 ). The Nemenyi test allows ranking classification algorithms according to their average accuracy on 84 datasets. FDTW obtained good results on the simulated datasets in terms of average accuracy (3rd/37 algorithms, Fig. 6 ) because the data of the training set and the testing set are generated by the same models. However, to evaluate the significance of the difference between the classification algorithms on 84 datasets, we used the Wilcoxon signed rank test with continuity correction, which has more statistical power.
The results of these experiments show that despite data compression, • FDTW have better performance than Naive Bayes (NB), C45, logistic regression (Logistic), BN;     These results demonstrate the competitiveness of FDTW. Moreover, this algorithm outperforms the best result reported in the literature on UWaveGestureLibraryAll dataset (Fig. 7 ). The challenge with this dataset is to recognize the gesture made by a user from measurements made by accelerometers. As reported in  [1]  the best accuracy obtained on this dataset is 83. 44% with TSBF algorithm; FDTW outperforms this result and allows to obtain 91. 87% of accuracy. Additional experiments are available here  [24].
Conclusion and perspective This paper deals with the problem of choosing an appropriate number of segments to compress time series with PAA in order to improve the alignment with DTW. In this aim, we proposed a parameter Free heuristic named FDTW, which approximates the optimal number of segments to use. The experiments showed that FDTW increased the quality of alignment of time series especially on synthetic datasets where DTW associated with PAA performed better than any other variant of DTW on a classification task and was rank 3rd/37 behind two ensemble classification algorithms COTE and EE. This algorithm allows reducing the storage space and the processing time of time series while increasing the quality of the alignment of DTW. As a perspective, the problem we have dealt with in this paper could be modeled as a multi-objective optimization problem where one objective function would be compression and the other the classification of time series.
Background: Data on survival and prognosis factors in incident cohorts are scarce in systemic sclerosis (SStc). To describe survival, standardized mortality ratio (SMR), and prognosis factors in systemic sclerosis (SSc), we analyzed a multicenter French cohort of incident patients and performed a systematic review of the literature and meta-analysis. Methods: A multicenter, French cohort study was conducted between January 1, 2000, and December 31, 2013. Patients were followed-up until July 1, 2016. A systematic review of the literature was carried out in MEDLINE and EMBASE up to July 2017. Meta-analysis was performed using all available data on SMR and hazard ratios of prognosis factors. Results: A total of 625 patients (493 females, 446 lcSSc) were included. During the study period, 104 deaths (16.
Previous studies  [7, [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]  reported risk factors for poor survival in SSc such as male sex, diffuse cutaneous subtype, and specific organ involvement. Recently, Elhai et al. developed a prognostic score from the large EUSTAR database, which accurately predicts 3-year mortality  [2]. To our knowledge, two meta-analyses combined the results of the available literature to assess prognosis factors  [5, 27]. However, these meta-analyses did not assess prognosis factors such as auto-antibody profile, genetic background, and cancer and did not assess the influence of prevalent versus incident cases. The aim of the present study was to fill these gaps by assessing survival and prognosis factors in a multicenter French cohort of incident SSc patients and by performing a systematic review of the literature and meta-analysis including all available prognosis factors and SMR.
Population The French National Scleroderma Cohort includes 42 centers. The present analysis was restricted to five university hospitals, Lille, Paris (two centers), Nantes, and Lyon to ensure better quality of data, especially on survival data. These five centers participated in recruiting about two thirds of the National Cohort. Data were retrospectively collected before 2010 and then prospectively collected. Patients were included between January 1, 2000, and December 31, 2013, if they met the following inclusion criteria: (i) be aged over 18, (ii) fulfill the ACR 1980 preliminary classification criteria  [28]  or ACR/EULAR 2013 classification criteria  [29]  for SSc, (iii) have at least one additional visit after the inclusion visit, and (iv) be incident cases, defined as patients having disease duration from time of diagnosis to enrolment in the study of less than 3 years.
Collected data and variable definition Data collected at the inclusion visit were patient demographics, history of Raynaud phenomenon (RP) and first non-RP symptom, SSc subtype and modified Rodnan skin score (mRSS), auto-antibody profile, and organ involvement. Disease onset was defined as the time of onset of first non-RP symptom. Interstitial lung disease was diagnosed on HRCT or chest x-ray. Pulmonary function tests including forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) were collected. Six-minute walking distance (6MWD) was collected. Pulmonary hypertension (PH) was suspected on a Doppler echocardiogram when systolic pulmonary arterial pressure (PAP) was estimated to be > 35 mmHg or maximum tricuspid regurgitant jet velocity > 2. 8 m/s. Pulmonary arterial hypertension (PAH) was confirmed by right heart catheterization (RHC) when mean PAP was found to be ≥ 25 mmHg at rest, with mean pulmonary arterial wedge pressure ≤ 15 mmHg.
Systematic review and meta-analysis The meta-analysis was conducted according to MOOSE guidelines  [31]. MEDLINE and EMBASE databases were queried by two of the authors (MRP and DL) using the following search terms: ((systemic sclerosis [Title]) OR (scleroderma, systemic[Title])) AND ((death) OR (mortality) OR (prognosis) OR (survival)). Cochrane did not retrieve additional abstracts. All records published before July 1, 2017, were included in the search. Language was restricted to English or French. Reference list of selected studies was hand-searched for additional relevant studies to be included in the meta-analysis. Two of the authors (MRP and DL) independently screened the titles and abstracts of the retrieved records to identify eligible articles. The full text of eligible articles was read for inclusion in the meta-analysis.
Selected articles were compared, and in case of disagreement, decisions were made by consensus. Cohort studies of unselected adult SSc patients assessing routine clinical and laboratory prognosis factors and SMR were included. Studies which included patients diagnosed with SSc overlap with other connective tissue diseases were excluded. Studies from the same centers were included if their respective study periods were different. If, for the same center, two studies covered an overlapping study period, data from the largest cohort were kept in the analysis. A study was recorded as an incident according to the authors' definition. Quality of the studies was assessed using the Newcastle-Ottawa scale  [32]. Data were extracted and entered into a predefined spreadsheet table which included the following items: study design, length of follow-up, definition of disease onset, disease duration, SMR, and adjusted or, if unavailable, unadjusted hazard ratios (HR) for each studied prognosis factor.
Data analysis Characteristics of the population were described using mean ± standard deviation or median (interquartile range (IQR)) in case of non-normality, for quantitative variables, and number (percentage) for qualitative variables. Comparisons between limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc) patients were conducted using the Student t test or Wilcoxon test in case of non-normality for quantitative variables and Fisher's exact test for qualitative variables. Survival was estimated from diagnosis using the Kaplan-Meier method. Prognosis factors were assessed by Cox regression analysis in the non-adjusted analysis and subsequently adjusted for age, sex, and SSc subtype. The assumption that hazard ratios were constant over time was verified. SMR was calculated as the ratio of observed death in the cohort to the number of death of the French age/sex-matched population in 2014.
The impact of diagnosis of PH by RHC on the association of PH with mortality was evaluated. Separate analyses were performed for (i) SMR according to whether a given study included incident cases only and (ii) HR of PH diagnosed by either echocardiography and/or RHC and PH diagnosed by RHC. All analyses were performed using R software with the survival and metafor packages. p values less than 0. 05 were considered significant.
Acknowledgements French National Scleroderma Cohort coauthors: Zahir AMOURA Z,  Olivier AUMAITRE ,  Eric AUXENFANTS ,  Marie-Hélène BALQUET ,  Cristina BELIZNA ,  Alice BEREZNE A, Bernard BONNOTTE, Pascal CATHEBRAS ,  Emmanuel CHATELUS ,  Joël CONSTANS ,  Vincent COTTIN V ,  Gonzalo DE LUNA G, Robin DHOTE ,  Elisabeth DIOT ,  Olivier FAIN ,  Anne-Laure FAUCHAIS ,  Yves FRANCES ,  Jean-Gabriel FUZIBET ,  Jean-Baptiste GAULTIER ,  Tiphaine GOULENOK ,  Brigitte GRANEL ,  Jean-Robert HARLE ,  Pierre-Yves HATRON ,  Arnaud HOT ,  Bernard IMBERT ,  Jean-Emmanuel KAHN ,  Gilles KAPLANSKI ,  Pierre KIEFFER ,  Noémie LE GOUELLEC ,  Alain LE QUELLEC ,  Olivier LIDOVE ,  Nadine MAGY-BERTRAND ,  François MAURIER ,  Sylvain PALAT ,  Thomas PAPO ,  Jean-Louis PENNAFORTE ,  Jacques POUCHOT ,  Grégory PUGNET ,  Thomas QUEMENEUR ,  Viviane QUEYREL ,  Laurent SAILLER ,  Thierry SCHAEVERBEKE ,  Marie-Elise TRUCHETET ,  Denis WAHL.
DR15 contains 4824 reconstructed 3D data cubes, of which 4688 are target galaxies (the remainder are ancillary targets which include galaxies, parts of galaxies, and some deep sky fields). This dataset includes 67 repeat observations, so that the total number of unique galaxies in this data release is 4621. Most of these galaxies are part of the MaNGA main sample, but ancillary target galaxies are also included in this count (see Table  4  for a summary of these). 2. In addition to the MaNGA data cubes, DR15 also releases for the first time data products generated by the Data Analysis Pipeline (see section 4. 1. 2). These products are available for all data cubes in DR15, with the exception of the cubes generated by some ancillary programs (i. e. , Coma, IC342 and M31) if they do not have redshifts (e.
DATA DISTRIBUTION The DR15 data can be accessed through a variety of mechanisms, depending on the type of data file and the needs of the user. All data access methods are described on the SDSS Web site (https://www. sdss. org/dr15/ data_access), and we also provide tutorials and examples for accessing and working with SDSS data products at (https://www. sdss. org/dr15/tutorials. We describe our four main data access mechanisms below. All raw and processed imaging and spectroscopic data can be accessed through the Science Archive Server (SAS, (https://data. sdss. org/sas/dr15). This site includes intermediate data products and VACs. The SAS is a file-based system, from which data can be directly downloaded by browsing, or in bulk mode using rsync, wget or Globus Online. Bulk downloading methods are outlined at https://www.
sdss. org/ dr15/data_access/bulk. All data files available on the SAS have a data model (https://data. sdss. org/ datamodel), which provides a detailed overview of the content of each data file. Processed optical and infrared spectra, as well as processed imaging, can also be accessed on the SAS through the Science Archive Webapp (SAW), an interactive web application (webapp; http://dr15. sdss. org links to the DR15 version). In DR15, the SAW is serving MaStar spectra for the first time. Through this webapp, users can display individual spectra and overlay model fits included on the SAS. There is a search option available to select spectra based on e. g. , plate number, coordinates,  redshift or ancillary observing program. Searches can be saved for future references as permalinks. Spectra can be directly downloaded from the SAS through the webapp, and links are included to the SkyServer Explore page for each object.
Westfall et al. , in prep; F. Belfiore et al. , in prep) , see §4. 1. 2. We provide the sky footprint of MaNGA galaxies released in DR15 in Figure  1 ; while the projected final survey footprint is shown overlaid on the footprint of other relevant surveys and for two different expectations for weather at the telescope in Figure  2. The MaNGA Data Reduction Pipeline (DRP) is the IDL-based software suite that produces final fluxcalibrated data cubes from the raw dispersed fiber spectra obtained at APO. The DRP is described in detail by  Law et al. (2016)  and consists of two stages. The '2d' DRP processes individual exposures, applying bias and overscan corrections, extracting the one-dimensional fiber spectra, sky-subtracting and flux-calibrating the spectra, and combining information from the four individual cameras in the BOSS spectrographs into a single set of row-stacked spectra (mgCFrame files) on a common wavelength grid.
Where those plates are not filled in they have HI follow-up from the HI-MaNGA program  (Masters et al. 2018 ; some, but not all of these data are released as a VAC in DR15 -see §4. 5. 3). For the most up-to-date version of this plot see https://www. sdss. org/surveys/manga/forecast/ reference line list to improve LSF estimation and wavelength calibration in the far blue by rejecting poor-quality lines. The DRP data products contain additional extensions to describe this new information, including a 3D cube describing the effective LSF at each spaxel within the MaNGA data cube as a function of wavelength; this combines the information known about the LSF in each individual fiber spectrum to describe the net effect of stacking spectra with slightly different resolution. The LSF changes and assessment against various observational calibrators will be described in greater detail by D.
2003) , as given by the CALSPEC database (black) and by MaNGA in DR14 (red) and DR15 (blue) averaging over 9 exposures taken on plate 7444. The small difference at the blue wavelengths can be seen more obviously in the bottom panel where we divide these three spectra by a T=100,000 K blackbody spectrum normalized at 6000-6100A. Ignoring the absorption lines, this provides a test to our flux calibration. Using the BOSZ templates in DR15, the resulting continuum of this white dwarf agrees much better with the blackbody spectrum below 5000A, significantly improved compared to DR14, which uses the version of the Kurucz models  (Kurucz 1979; Kurucz & Avrett 1981)  which were produced in 2003, and to CALSPEC. (One can also compare this with Figure  9  of  Yan et al. (2016b) ). columns include an estimate of the targeting redshift z that is used as the starting guess by the DAP when analyzing the MaNGA data cubes.
The Data Analysis Pipeline The MaNGA data-analysis pipeline (DAP) is the SDSS-IV software package that analyzes the data produced by the MaNGA data-reduction pipeline (DRP). The DAP currently focuses on "model-independent" properties; i. e. , those relatively basic spectral properties that require minimal assumptions to derive. For DR15, these products include stellar and ionized-gas kinematics, nebular emission-line fluxes and equivalent widths, and spectral indices for numerous absorption features, such as the Lick indices  (Worthey & Ottaviani 1997; Trager et al. 1998 ) and D4000  (Bruzual A. 1983). Examples of the DAP provided measurements and model fits are shown in Figure  4 , discussed through the rest of this Section. An overview of the DAP is provided by K. Westfall et al. , (in prep).
This allows the user to select all the unique measurements by finding the locations of all unique bin ID values, ignoring anything with a bin ID of -1. Tutorials for selecting the unique measurements in the DAP output maps are provided via the datarelease website at https://www. sdss. org/dr15/ manga/manga-tutorials/dap-tutorial/. 3. Corrections are provided for a few quantities in the MAPS file that have not been applied to the data in the output files. The stellar velocity dispersion and ionized-gas velocity dispersions are provided as measured from the core pPXF software (Cappellari & Emsellem 2004; Cappellari 2017) used by the DAP. This means that any instrumental effects present during the fitting process are also present in the output data. For both the stellar and ionizedgas dispersions, we have estimated the instrumental corrections for each measurement and provided the result in extensions in the MAPS file.
Marvin Access to MaNGA Marvin  (Cherinka et al. 2018)  4 is a new tool designed for streamlined access to the MaNGA data, optimized for overcoming the challenges of searching, accessing, and visualizing the complexity of the MaNGA dataset. Whereas previous generations of SDSS took spectra only of the centers of galaxies, MaNGA takes many spectra of each galaxy, in a hexagonal grid across the face of each (IFU bundle), which are combined into a final data cube. This means that for each object there is not a single spectrum, but in fact a suite of complex results in one or more data cubes. The motivation of Marvin arises from the additional complexity of MaNGA data, namely, the spatial interconnectivity of its spectra. Marvin allows the user to: • access reduced MaNGA datacubes local, remotely, or via a web interface.
• access and visualize data analysis products. 4 https://www. sdss. org/dr15/manga/marvin/ • perform powerful queries on metadata. • abstract the MaNGA datamodel and write code which is agnostic to where the data actually lives. • make better visualization and scientific decisions by mitigating common mistakes when accessing these type of data. Marvin has two main components: a webapp and a Python package of tools, both using an underlying Marvin API (or Application Programming Interface). The webapp, Marvin Web 5  , provides an easily accessible interface for searching the MaNGA dataset and visual exploration of individual MaNGA galaxies. The Marvin suite of Python tools, Marvin Tools, provides seamless programmatic access to the MaNGA data for more indepth scientific analysis and inclusion in your science workflow.
Marvin Web The Marvin Web provides quick visual access to the set of MaNGA galaxies. It provides a dynamic, interactive, point-and-click view of individual galaxies to explore the output from the MaNGA DRP and DAP ( §4. 1. 1 §4. 1. 2 respectively), along with galaxy information from the NSA catalog (Blanton et al. 2011) 7. We show a screen-shot of the View-Spectra page of Marvin Web in Figure  5. By clicking anywhere within the galaxy IFU bundle on the SDSS three-color image, or any Data Analysis 2D Map, the user can explore the spectrum at that location for quick inspection. The visualized spectrum is interactive as well, allowing panning and zooming. Additional pages Marvin Web provides are: • a Query page, for searching the MaNGA dataset through an SQL-like interface. • a Plate page, containing all MaNGA galaxies observed on a given SDSS plate.
• and an Image Roulette page, for randomly sampling images of MaNGA galaxies. Tutorials for navigating Marvin Web can be found at https://www. sdss. org/dr15/manga/ manga-tutorials/marvin-tutorial/marvin-web/. Marvin Web is designed as a gateway to entry into real MaNGA data, providing commonly-desired functionality all in one location, as well as code snippets to help transition users into a more programmatic environment using the Marvin Tools.
The Marvin Tools Marvin Tools provides a programmatic interaction with the MaNGA data, enabling rigorous and repeatable science-grade analyses. Marvin Tools come in the form of a Python package that provides convenience classes and functions that simplify the processes of searching, accessing, downloading, and interacting with MaNGA data, selecting a sample, running user-defined analysis code, and producing publication quality figures. The Marvin Tools are a pip-installable product, packaged under sdss-marvin, with full installation instructions at the Marvin documentation 8 , and source code on Github 9. Overall, Marvin Tools allow for easier access to the data without knowing much about the data model, by seamlessly connecting all the MaNGA data products, eliminating the need to micromanage a multitude of files.
Queries in Marvin Both Marvin Web and Tools provide interfaces for searching the MaNGA dataset through a Structured Query Language (SQL)-like interface, either via a webform or a Python class. The Marvin Query system uses a simplified SQL syntax that focuses only on a filter condition using boolean logic operators, and a list of parameters to return. This eliminates the need to learn the full SQL language and the detailed MaNGA database layout. With this query system, users can make queries across the entire MaNGA sample using traditional global galaxy properties (functionality to perform intra-galaxy queries using individual spaxel measurements is planned for a future release). Tutorials for querying with Marvin can be found for the web 10  and for the tools 11. Stellar spectral libraries are an essential tool for many fields in astronomy.