dataesr/scientific-paragraphs-categorization huggingface dataset:
We use more than 2 years of magnetic data from the Swarm mission, and monthly means from 160 ground observatories as available in March 2016, to update the CHAOS time-dependent geomagnetic field model. The new model, CHAOS-6, provides information on time variations of the core-generated part of the Earth's magnetic field between 1999. 0 and 2016. 5. We present details of the secular variation (SV) and secular acceleration (SA) from CHAOS-6 at Earth's surface and downward continued to the core surface. At Earth's surface, we find evidence for positive acceleration of the field intensity in 2015 over a broad area around longitude 90°E that is also seen at ground observatories such as Novosibirsk. At the core surface, we are able to map the SV up to at least degree 16. The radial field SA at the core surface in 2015 is found to be largest at low latitudes under the India-South-East Asia region, under the region of northern South America, and at high northern latitudes under Alaska and Siberia.
Moreover, ground observatory SV estimates are fit to a Huber-weighted rms level of 3. 1 nT/year for the eastward components and 3. 8 and 3. 7 nT/year for the vertical and southward components. We also present an update of the CHAOS high-degree lithospheric field, making use of along-track differences of CHAMP scalar and vector field data to produce a new static field model that agrees well with the MF7 field model out to degree 110.
This requires a much larger number of free parameters than is the case in the CHAOS models. A series of models of similar complexity to the CHAOS model, but derived only from CHAMP and ground observatory data, is the GRIMM series of models  (Lesur et al. 2008 (Lesur et al. , 2010 (Lesur et al. , 2015)). The latter study is particularly interesting because it proposed field models with time dependence controlled by co-estimated core flows. The main purpose of this article is to present CHAOS-6, providing a reference for users regarding its construction. In the "Data" section, we detail the input data, while the model parameterization and estimation scheme are described in the "Field modelling" section. Model results and related discussion are presented in the "Results and discussion" section, including the fit to Swarm and CHAMP satellite data as well as ground observatory SV in the sections "Fit to satellite data" and "Fit to secular variation estimates from ground observatories" , respectively.
Data The database of magnetic observations used to construct CHAOS-6 is essentially an extension of that used by  Finlay et al. (2015)  to construct the CHAOS-5 model in September 2014. The ground observatory vector field data have been updated as available in March 2016 (see the section "Ground observatory data"), while vector and scalar field data from the Swarm constellation up to 30 March 2016 have been included. The data selection criteria for satellite data at high latitudes have also been slightly altered compared with previous versions of the CHAOS model; further details are given in the "Satellite data" section. A major improvement compared with CHAOS-5 is the inclusion of field spatial differences (i. e. approximate gradients) as data, along-track for both CHAMP and Swarm Alpha, Bravo and Charlie, and also east-west between Swarm's lower satellite pair Alpha and Charlie.
Along-track field differences approximate north-south gradients in non-polar regions  (Kotsiaros et al. 2014; Sabaka et al. 2015) , while the east-west differences provide information on the longitudinal gradient of the field  (Olsen et al. 2015). We constructed along-track field differences from data points from the same satellite track, separated by 15 s. East-west difference data were found by searching Swarm L1b 1 Hz magnetic data for a datum from Swarm Charlie, with latitude closest to a selected Swarm Alpha datum, within a maximum time difference of 50 s. This procedure typically resulted in time shifts of about 10 s between the contributing Alpha and Charlie data.
Satellite data The basic Ørsted, CHAMP and SAC-C dataset, sampled at a rate of 1 datum per 60 s, is the same as that employed in the earlier CHAOS-4  (Olsen et al. 2014)  and Fig. 1  Locations of ground magnetic observatories whose data are used in the derivation of CHAOS-6. IAGA codes for the observatories are AAA, AAE, ABG, ABK, AIA, ALE, AMS, AMT, API, AQU, ARS, ASC, ASP, BDV, BEL, BFE, BFO, BGY, BJN, BLC, BMT, BNG, BOU, BOX, BRW, BSL, CBB, CBI, CDP, CKI, CLF, CMO, CNB, CSY, CTA, CZT, DLR, DLT, DMC, DOB, DRV, DVS, EBR, ELT, ESA, ESK, EYR, FCC, FRD, FRN, FUR, GAN, GDH, GLM, GNA, GNG, GUA, GUI, GZH, HAD, HBK, HER, HLP, HON, HRB, HRN, HTY, HUA, HYB, IPM, IQA, IRT, IZN, JCO, KAK, KDU, KEP, KHB, KIR, KIV, KMH, KNY, KNZ, KOU, KSH, KSH, LER, LIV, LMM, LNP, LOV, LRM, LRV, LVV, LYC, LZH, MAB, MAW, MBO, MCQ, MEA, MIZ, MMB, MOS, MZL, NAQ, NCK, NEW, NGK, NMP, NUR, NVS, OTT, PAF, PAG, PBQ, PET, PHU, PND, PST, QGZ, QIX, QSB, QZH, RES, SBA, SBL, SFS, SHU, SIL, SIT, SJG, SOD, SPT, SSH, STJ, SUA, TAM, TAN, TDC, TEO, TFS, THJ, THL, THY, TIR, TND, TRO, TRW, TSU, TUC, UPS, VAL, VIC, VSK, VSS, WHN, WIK, WNG, YAK, YKC CHAOS-5  (Finlay et al.
Acknowledgements We wish to thank  ESA  for providing access to the  Swarm L1b data. The staff of the geomagnetic observatories and INTERMAGNET are thanked for supplying high-quality observatory data, and  BGS  are thanked for providing us with checked and corrected observatory hourly mean values. The support of the CHAMP mission by the  German Aerospace Center (DLR)  and the  Federal Ministry of Education and Research  is gratefully acknowledged. The Ørsted Project was made possible by extensive support from the  Danish Government ,  NASA ,  ESA ,  CNES, DARA  and the  Thomas B. Thriges Foundation. CCF acknowledges.
The projected sensitivities and precise dates of this run are now being actively planned and remain fluid. 2024/2025 -2026 (O5): O5 will begin with a four-detector network incorporating the A+ upgrade for the aLIGO instruments and the AdV+ Phase 2 upgrade for Virgo. The target range for aLIGO is 330 Mpc and for AdV it is 150 -260 Mpc. KAGRA will operate at or above its O4 sensitivity of 130 Mpc. Fig. 2  The planned sensitivity evolution and observing runs of the aLIGO, AdV and KAGRA detectors over the coming years. The colored bars show the observing runs, with achieved sensitivities in O1, O2 and O3, and the expected sensitivities given by the data in Fig. 1  for future runs. There is significant uncertainty in the start and end times of the planned observing runs, especially for those further in the future, and these could move forward or backwards relative to what is shown above.
Localization for compact binary coalescences Providing prompt localizations for GW signals helps to maximise the chance that electromagnetic observatories can find a counterpart. Localizations are produced at several different latencies, with updates coming from more computationally expensive algorithms that refine our understanding of the source. For CBC signals, rapid localization is performed using BAYESTAR (Singer and Price 2016), a Bayesian parameter-estimation code that computes source location using output from the detection pipeline. BAYESTAR produces sky localizations (as in Fig. 5 , top plot) with latencies of only a few seconds. It also provides distance estimates  (Singer et al. 2016a ). These are communicated as an additional component of the sky localization (3D sky map): for each line of sight, the distance posterior probability is approximated as a Gaussian multiplied by the distance squared  (Singer et al.
Localization for unmodeled signals Sky localizations are also produced for unmodeled triggers and distributed for follow up. The lowest latency sky localizations are produced as part of the COHERENT WAVE BURST (CWB) detection pipeline  (Klimenko et al. 2008 (Klimenko et al. , 2016)). Sky localizations are produced using a constrained likelihood algorithm that coherently combines data from all the detectors. The cWB sky localizations are calculated with a latency of a few minutes. Following detection, an unmodeled burst signal is analyzed by parameter-estimation codes: LALINFERENCEBURST (LIB), a stochastic sampling algorithm similar to the LALINFERENCE code used to reconstruct CBC signals  (Veitch et al. 2015) , and BAYESWAVE, a reversible jump Markov-chain Monte Carlo algorithm that models both signals and glitches  (Cornish and Littenberg 2015).
Alert Contents The alert contains information to support the search for counterparts including: -A candidate identifier, which can be used to examine the event properties in the Gravitational Wave Candidate Event Database. 18 -The FAR of the candidate in Hz. -The localization given as a posterior probability distribution of the sources sky position. For CBC events, we send a 3-D sky map, which also contains the directiondependent luminosity distance. The localization is encoded as a HEALPIX projection in FITS file format. -For Burst candidates the central frequency in Hz, the duration in seconds and the GW fluence in erg/cm 2. -For CBC candidates the probability p astro , that the signal is astrophyiscal (see  Sect. 3. 1). This probability comes from evaluating whether the source belongs to 1 M 3 M 5 M m 1 3 M 5 M m 2 BBH NSBH MassGap m 1 m 2.
Acknowledgements The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for.
The authors gratefully acknowledge the support of the  NSF ,  STFC ,  INFN  and  CNRS  for provision of computational resources. This work was supported by  MEXT ,  JSPS Leading-edge Research Infrastructure Program ,  JSPS   Grantin-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2905 :  JP17H06358 ,  JP17H06361  and  JP17H06364 ,  JSPS Core-to-Core Program A. Advanced Research Networks, JSPS   Grant-in-Aid for Scientific Research (S)   17H06133 , the joint research program of the  Institute for Cosmic Ray Research, University of Tokyo in Japan ,  National Research Foundation (NRF)  and  Computing Infrastructure Project  of  KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06.
Data Heterogeneity in Companies' Information Systems In order to perform a broad and efficient a reverse engineering workflow, we need to gather, clean, sort, process and make sense of a large amount of data. As these data are highly related to some specific engineering expertise, they could be labelled engineering data. These data could be found inside a vast variety of systems, such as PLM systems, Product Data Management (PDM) systems, Enterprise Resource Planning (ERP) systems, Knowledge Based Engineering (KBE) systems, Databases, etc. They are, by nature, utterly heterogeneous. We mean that the data type and format vastly vary between the data. Here are some examples to illustrate our point. In addition to the heterogeneous aspect, companies are facing a rapidly growing amount of generated and stored data.
Most manufacturing machines are now connected to the local network and operated by a computer. They produce manufacturing logs which can be stored in raw files, in databases or in systems such as ERPs (which basically run over databases). Emerging technologies such as NFC/RFID tend to produce even more data allowing advanced and complex tracking of every part during the whole product lifecycle. It is in this way that car manufacturers can gather a very large amount of data, during maintenance phase, about the product. Moreover, generalization of extended companies, extreme personalization and the recent emergence of the Internet of Things tend to reinforce this trend. All of these facts have led to an explosion in data amounts stored in Information Systems.
Available Solutions on the Market Over the last decades, improvements on data acquisition techniques have permitted to elaborate complex methods to perform geometrical recognition and canonical form detection  [7]. Mathematical and geometrical analysis has been previously performed in order to clean and fill holes in a 3D mesh obtained by laserscan for instance  [8]. Such advances could, of course, be the ground basis of RE oriented software. If we have a look to the available solutions on the market to answer the RE needs, we could find the well-known market leading solution GeoMagic Design X (formerly RapidForm XOR) developed by 3D systems. This software allows the user to generate CAD Models from 3D Scan Data (mesh or point clouds). Metris 3D developed similar software: Focus Reverse Engineering which basically offers the same features and possibilities.
Knowledge-Based Approaches in Reverse Engineering Based on that observation, many research projects were launched aiming knowledge integration. In this sense, Knowledge-Based Engineering (KBE) appears to be the answer for capturing and reusing previous knowledge. These approaches gain meaning especially through a reverse engineering process. The main aspects of KBE could be summarized as follows: knowledge, engineering and automation  [9]. One of the most-known methodologies is MOKA (Methodology and software tools Oriented to Knowledge-based engineering Applications)  [10] , an eight steps KBE lifecycle. We could also highlight the methodology KOMPRESSA (Knowledge-Oriented Methodology for the Planning and Rapid Engineering of Small-Scale Applications)  [11]  which focus on Small to Medium Enterprises.
What do we mean by data science? Data Science is the field of expertize which focus on making sense of the data in a very broad view. Analysing and understanding the data are two pillars of the methods. It aims to elaborate a model able to explain the data and make accurate predictions on new data. Prediction accuracy is measured by a set of statistical tools that we won't develop here. On the other hand, predictions mainly refer to classification tasks. It is fundamental to point out that Data Science and more specifically Machine Learning (ML) techniques are not a solution for every type of problem. However, ML techniques can greatly help to solve problems that would be inconvenient to treat in a conventional way. • The rules are hidden, or not very well known: Many human tasks require complex decision processes, the rules may vastly vary between each of us and we might not even be conscious of this process and why we made such a choice.
statistics Results are represented as the mean ± SEM of the indicated number of replicates/experiments. We performed computations and created charts using the GraphPad Prism 5. 03 software. For calculation of statistical differences among the groups, we used ANOVA test with Bonferroni post hoc correction, where applicable. Adjusted p-values <0. 05 were considered to be statistically significant. aUThOr cOnTriBUTiOns MB, MP, and JK planned and performed most of the in vitro and in vivo experiments, conducted data analysis, and wrote the manuscript. CR, DK, DW, CM, JH, and RoBi performed in vivo and in vitro experiments and conducted data analyses. AB, RaBo, and SS performed micro-Raman spectroscopy, X-ray photoelectron spectroscopy, and transmission electron microscopy analyses. GS and MaHo provided scientific input and wrote the manuscript.
This work was partially supported by the German Research Foundation (DFG) to MaHe (CRC1181-C03, KFO257, SPP 1468 Osteoimmunology IMMUNOBONE) and MaHo (CRC1181-C03, SPP1468 Osteoimmunology IMMUNOBONE), by the EU H2020-MSCE-RISE-2015 project Nr. 690836 PANG to RoBi, LEM, and SS, by the Ministry of Healthcare of Ukraine project 0115U000055 to RoBi, by the National Science Foundation of China (81273286) to YZ, by the doctoral training program GK1660 of the DFG to JK and by Ardea Biosciences, Inc. to MaHe and LEM. YZ and MaHe were also supported by the international collaborative project in Science and Technology of the Sichuan province (2015HH0050). The authors acknowledge support by Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) within the funding program Open Access Publishing.
acKnOWleDgMenTs This work was partially supported by the German Research Foundation (DFG) to MaHe (CRC1181-C03, KFO257, SPP 1468 Osteoimmunology IMMUNOBONE) and MaHo (CRC1181-C03, SPP1468 Osteoimmunology IMMUNOBONE), by the EU H2020-MSCE-RISE-2015 project Nr. 690836 PANG to RoBi, LEM, and SS, by the Ministry of Healthcare of Ukraine project 0115U000055 to RoBi, by the National Science Foundation of China (81273286) to YZ, by the doctoral training program GK1660 of the DFG to JK and by Ardea Biosciences, Inc. to MaHe and LEM. YZ and MaHe were also supported by the international collaborative project in Science and Technology of the Sichuan province (2015HH0050). The authors acknowledge support by Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) within the funding program Open Access Publishing.
In particular the design for powers of large sparse matrices makes it possible to work on large images (many megapixels). The use of a NMF algorithm has several advantages over other clustering methods. First, it utilizes and approximates the diffusion framework that past works have shown to work extraneously well 140:6 • Y. Lockerman et al. [  Lu et al. 2009b; Lu et al. 2009a; Lockerman et al. 2013 ]. Second, a NMF algorithm requires a similarity map as input, allowing it to work with datasets where the creation of feature vectors is not possible. Third, we obtain information as to how well each superpixel fits into each cluster. While the last two properties are only indirectly used in this paper, they will allow for future works to use our algorithm for other graphical purposes. Thus, we hope that the introduction of NMF based methods will be useful beyond our particular application.
Acknowledgements We would like to thank  Sylvain Thery  for data processing, as well as  Steven Zucker ,  Dana Angluin ,  Stanley Eisenstat ,  Noam Tanner , and anonymous reviewers for their insightful comments. We also thank  Frédéric Larue ,  Samuel Brenner  and  Joseph Lanzone  who helped proofread this paper. Images courtesy:  Eli Lockerman  (Tower), Mayang's Textures (Bubbling rusty metal, Daisylike flowers, Rust), www. public-domain-image. com (Ground), www. textures. com(Plaster damaged), Rosenberger et al. [2009] (images of figure 7 1 ), Flowered wall: " A True Wall Flower " by  Alan Levine , resized, licensed under  CC BY 2. 0 , other images taken by authors. This research has been partially supported by  NSF  grants  IIS-1064412  and  IIS-1218515.
Software. We have implemented the algorithm in MATLAB using home-made routines. The code for analysis and a set of simulated and real example data are included in the Supplementary Software S1. Simulation maps. Photon model. The relative intensity of each of the fluorophores in each pixel was computed by numerically integrating a normal cumulative distribution function with a pre-defined Full-Width at Half Maximum (FWHM) over the area of the pixels. The simulated emitters can switch between a bright and a dark fluorescent state, which is described by a two-state model where k on = 1/τ off and k off = 1/τ on are the rate constants from the "on" to "off " and the "off " to "on" states, respectively. The activity of the fluorophores was modeled by a time-continuous Markov process of which the characteristic times were sampled from exponential distributions with the corresponding decay times.
Optical imaging. Imaging experiments were performed on an Olympus cellTIRF microscope described elsewhere  23  equipped with an additional EMCCD camera (ImagEMX2, Hamamatsu). Image sequences of 1,000 frames were recorded with constant 488 nm laser excitation and a camera exposure time of 30 ms. The direct EM gain was varied between 50 and 900 to accommodate for the differences in fluorescence output. Data files were exported as tiff files without any further software processing.
Acknowledgements The authors thank  Johan Hofkens  for discussion and helpful comments. We would also like to thank the anonymous reviewers, who contributed to improve the manuscript. C. R. and M. S acknowledge the financial support of the  Agence National de la Recherche  ( ANR-14-CE08-0015-01 Ultrafast Nanoscopy ). S. D. thanks the  Agency for Innovation by Science and Technology (IWT)  for a doctoral fellowship. P. D. thanks the  Research Foundation-Flanders (FWO)  for a postdoctoral fellowship and the  KU Leuven  for a research professorship. This work was supported by the  FWO  (grant number  1521915  N to P. D. ).
In images annotated by a pathologist, each patch can be considered positive or negative according to its central pixel: if a pixel is in an annotated lobular region, then its patch is positive, otherwise it is negative. Our CNN is a function that can predict the class of a patch (positive or negative) based on its featureless description. This function has a fixed form defined in the "CIFAR10 Quick" example provided with the software Caffe  [31]. The associated deep network architecture is designed to perform multiclass classification of small RGB images  [32] , which is conveniently similar to the task of patch classification. We can use it as a black box although its parameters (weights) need to be learned during supervised training. In order to estimate the prediction error of the method on unseen data for a given staining, we perform a 3-fold cross-validation using nine annotated images arbitrarily divided in three groups: A={NB02, NB05, NB11}, B={NB12, NB16, NB20}, and C={NB25, NB28, NB34} (see Table  1  for general information on ground truth contents).
In fold 0, the method is trained on B∪C and evaluated on A; in fold 1, the  method is trained on A∪C and evaluated on B; and in fold 2, the method is trained on A∪B and evaluated on C. For any fold, training consists of 20 iterations using error backpropagation with adaptive learning rate (ADAGRAD  [33] ) on a dataset composed of 600,000 patches (50,000 for each class and each image). Given as input an image at 4 µm/pixel, this method will output a binary image where positive and negative pixels have different values. An additional post-processing step is used to denoise the result by removing small elements below a size threshold. In each fold, this threshold is learned by maximizing the macro-F1 score (average of F1 scores for each class) on the 6 training images.