dataesr/scientific-paragraphs-categorization huggingface dataset:
The Discrete Image Space Reconstruction Algorithm (DISRA) The DISRA code was derived from a method initially developed for Positron Emission Tomography (PET). It is based on successive projections/backprojections of simulated data. To summarize, the starting tomogram (simulated 3D object) at zero order is guessed from the direct FBP of experimental projections (i. e. energy loss for STIMT, number of events for PIXET). This initial tomogram is then weighted, using an a priori global scaling factor. This factor is applied so that the content of every voxel of the initial tomogram can be of the same order of magnitude as the real physical parameter to be reconstructed (i. e. a mass density for STIMT, or a mass fraction for PIXET). Additional corrections (normalization, zeroing, etc. ) are also brought, that we will not discuss here.
From this first "numerically guessed" tomogram, the iterations start: the physical processes of X-ray emission and attenuation are simulated, taking into account the detection solid angle specified by the user. A local correction factor is applied at every step, by comparing, voxel by voxel, the FBP of simulated data to the FBP of experimental projections, taken as a "reference reconstruction". To avoid noise amplification inherent to FBP, the originality of DISRA is that every voxel value is limited by a bandwidth AD(¡) at the ¡th iteration. This limitation is performed in a smart way, as the bandwidth is reduced at each iteration, since the voxel values are getting closer to convergence. More precisely, it follows the formula: AD (i) = d x 2-(2+i) In this formula, d is an arbitrary numerical factor different for STIMT and PIXET data.
Current limitations and prospects DISRA has been proven to generate accurate mass density and mass fractions results, both on simulated "phantom" objects and on experimental data  [14] , at least for inorganic samples presenting a good density contrast and high X-ray yields. However, for less contrasted objects, such as biological samples, limitations of DISRA may appear. From an experimental point of view, the duration of PIXET data acquisition may be of a few hours per slice, depending on the number of pixels, number of projections and element concentration. To give an example, for isolated cells or for small organisms such as the nematode presented here, it typically takes about 1-2 h beam time to map the mineral content (typical concentration about a few per thousand in dry mass). To give a comparison, a full 3D (128 slices) STIMT experiment may take about the same time.
In these conditions, both experiment duration and sample damage would be prohibitive to perform 3D PIXET. Instead, it appears advisable to probe isolated PIXET slices, in regions of interest selected from 3D STIMT reconstruction. A modification of the DIS-RA code was introduced to handle this configuration  [13]. A second difficulty in DISRA is the arbitrary numerical factor used for discretization. The default values imposed in the code can turn out to be inappropriate in certain conditions, such as for low mass fractions. In these conditions the iterations are prohibitively slowed down and the source code has to be modified "manually", according to the considered sample  [15]. We could see a last obstacle to the application of DISRA for biological studies, which is inherent to the FBP process used for reconstruction.
Noise amplification in the image outside the sample was eliminated by image processing. Qualitative PIXET reconstruction was performed the same way. Satisfactory images were obtained. However, because FBP is intrinsically an analytical method, we believe that other algorithms should be considered as more promising options for limited angle tomography. For all these reasons, we would like here to propose an alternative to FBP for the reconstruction of low-contrast objects such as biological samples. To tackle the full problem of the reconstruction of PIXET data as precisely as DISRA is able to do it (when all conditions are fulfilled for the convergence to be reached) will definitely take a long effort. We propose here a new development of the TomoRebuild data reduction software package  [18] , able to perform quantitative STIMT and qualitative PIXET reconstruction.
All experimental parameters are defined through parameter files written in simple text format -to avoid any re-compilation of the code. Although every step may be executed as a simple command line, a multi-platform user-friendly graphic interface is proposed to facilitate parameter input and code execution. This interface was developed as a portable Java plugin in ImageJ, which is a public domain, open-source Java-based image processing program developed at the National Institutes of Health (http://rsbweb. nih. gov/ij/). It uses the same text parameter files as the command line version. A detailed user guide in English is provided to use the code, with or without graphic interface. The reconstruction is performed without requiring the installation of any graphic library or numerical tool. Sinograms and final reconstructed images are generated in usual binary formats that can be read either using the Amira® graphic library (http:// www.
amira. com) or public domain softwares, such as ImageJ. Optional graphic applications using the ROOT interface may be used to display and fit energy spectra for any projection(s). However these optional programs were intentionally kept separated from the TomoRebuild code, to avoid any dependence to non-standard external libraries. The FBP code was optimized so that the duration was reduced by a factor of about 10 compared to the previous version. For instance, the FBP reconstruction of the example hereafter (255 slices, 128 x 128 pixels, 100 projections) takes less than 2 min on a conventional x86 double core Pentium D PC, 1024 Mb random access memory, using the Window XP operating system.
5 pixel. In this way, a reconstruction in the conditions described above requires about 12 min for MLEM and 5 min for OSEM. To our knowledge, this is the first time  Reconstructed mass density values were coded on a linear gray scale ranging from 0. 01 (black) to 0. 9 g/cm 3 (white), except for slice 217, which presents denser regions, ranging from 0. 25 to 1. 1 g/cm 3. that the OSEM code has been implemented for ion beam microtomography and, from a broader perspective, one of the rare OSEM freeware versions available. Our version was based upon the calculation of probability matrix described by Loudos  [23]  for SPECT. Quantitative calculation of mass density, based on the determination of the sample stopping power, is achieved by assuming that samples have a uniform composition of major elements, as it is usually the case for biological samples.
more visible in MLEM and OSEM compared to FBP. Beyond image quality, iterative statistical methods present the capability to generate reconstructed images using less projection data than FBP. This advantage was put forward several years ago for ion beam tomography by Ng et al. [24] , showing the reconstruction of a 60 x 60 pixels simple shaped object from only eight projections. However, to our knowledge, this code has never been implemented elsewhere for STIMT or PIXET. Fig. 3  shows the results on the same 128 x 128 pixels slice as Fig. 2 , obtained from only 20 projections over 180°, using FBP and MLEM. The FBP reconstruction is completely blurred by the too sparse sampling, whereas MLEM appears as more robust. Another intrinsic limitation of FBP, to require projection angles over at least 180°, is not necessary for iterative statistical methods.
Conclusion and outlook This new version of TomoRebuild has been optimized for a rapid and easy data processing of STIMT and PIXET experiments. Although it could be used for any type of sample, it appears as particularly suited to biological samples for two main reasons: (i) the noise correction procedures proposed for sinograms improve image quality, which is especially important for low contrast images and (ii) the MLEM and OSEM methods open a wider possibility to reconstruct data from limited angular range or from a small number of projections. The development of fully quantitative PIXET reconstruction, according to a method as complete as the DISRA algorithm, including especially the modeling of X-ray absorption in a large solid angle, would constitute a long-term evolution of this software package.
Acknowledgements The authors are grateful to  Laurent Daudin ,  Stephanie Sorieul ,  Philippe Alfaurt ,  Franck Delalée  and  Laurent Sérani ,  Centre d'Etudes Nucléaires de Bordeaux Gradignan, France , for their support in the technical developments on the beamline. We would also like to thank  George Loudos ,  Athens School of Technological Applications, Greece , and  BenoTt Recur ,  Laboratoire Bordelais de Recherche en Informatique, Talence, France , for fruitful help in implementing the OSEM code. We are grateful to  Denis Dupuy ,  Instituí Europeen de Chimie et Biologie, Pessac, France , for providing the C. elegans strain to our laboratory. This work has been partly supported by the  European Community  as an  Integrating Activity "Support of Public and Industrial Research Using Ion Beam Technology (SPIRIT) " under EC contract no.
The Digital Access to a Sky Century@Harvard (DASCH) project is digitizing the ∼500,000 glass plate images obtained (full sky) by the Harvard College Observatory from 1885 -1992. Astrometry and photometry for each resolved object are derived with photometric rms values of ∼0. 15mag for the initial photometry analysis pipeline. Here we describe new developments for DASCH photometry, applied to the Kepler field, that has yielded further improvements, including better identification of image blends and plate defects by measuring image profiles and astrometric deviations. A local calibration procedure using nearby stars in a similar magnitude range as the program star (similar to what has been done for visual photometry from the plates) yields additional improvement for a net photometric rms ∼0.
Introduction The Digital Access to a Sky Century@Harvard (DASCH) is an ongoing project to digitize and analyze the scientific data contained in half a million astrophotographic plates that cover both the northern and southern skies from the 1880s to the 1980s  (Grindlay et al. 2009 (Grindlay et al. , 2012)). During the development phase for the high speed DASCH scanner  (Simcoe et al. 2006 ) and analysis software, we have scanned over 20, 000 plates in five selected fields covering M44, 3C 273, Baade's Window, the Kepler field, and the Large Magellanic Cloud (LMC). The initial photometry and astrometry developments are described in  Laycock et al. (2010; hereafter L10). The pipeline and database is described in  Los et al. (2011). Further astrometry development is described in  Servillat et al.
g. Fig. 2  in L10). Most Harvard plates are blue sensitive without filters, with a color response close to Johnson B. However, a small fraction of the plates used red and yellow filters. In order to generate consistent and precise magnitudes, it is essential to understand the color response in each plate and correct it. We derive the color term C, by minimizing rms in the calibration curve, as shown in Fig. 10   A particularly interesting field for long-term variability studies with DASCH is the ∼ 10 • × 10 • field observed by the Kepler mission  (Borucki et al. 2010 ) which provides very high sensitivity measures of variability on short timescales. We have scanned 3735 plates taken from 1885 to 1990, in or covering part of the Kepler field. These plates are from 17 different series, each typically represents a single telescope (except the mb series which consists of data from multiple telescopes).
The Kepler field was, unfortunately, not observed as much by the more sensitive Harvard plates (e. g. MC or A series) which cover many other fields, so these magnitude limits do not reflect the entire DASCH database. For photometric calibration, we used the Kepler Input Catalog (KIC) which contains Sloan-like griz photometry  (Brown et al. 2011). KIC covers ∼ 177 square degrees centered at RA 19:22:40 and Dec +44:30, which is smaller than our scanned region. In other fields, we used the GSC2. 3 catalog  (Lasker et al. 1990) , and the newly released AAVSO APASS catalog  (Henden et al. 2012 ; http://www. aavso. org/apass) for photometric calibration (the APASS catalog will be the default calibration for production DASCH data releases). In this paper, we focus on the region covered by KIC. DASCH light curves of Kepler planet host stars are presented in a companion paper,  Tang et al.
Statistical Measurements for Defining Variable Search We have developed a set of statistical measurements for the purpose of selecting variable stars. We first define 'good' points (AF LAGS == 0 in the DASCH database; see the DASCH website 1  ) for the definitions of flags, excluding the following cases: 1. Blended images either flagged by SExtractor or our pipeline (see section 3); 2. Images flagged as defects or with a large as-  used for selecting variables are listed in the "summary table" shown in Table  2 , and are described in the following paragraphs. Description of other parameters in the summary table can be found at DASCH website 2. Two amplitude measurements are calculated. The first one is called range local, which is the difference between the brightest and the faintest points, minus the sum of their errors.
The second one is called range local2, which is similar to range local, but after removing the brightest and the faintest points. Light curve rms is calculated after 5σ clipping, and is called lightcurverms1 in our database. We also de-trend the light curves by smoothing the light curves using four different combinations of spans and methods, and then calculate the rms of the residuals, to look for variables with trends, which have residual rms significantly smaller than their light curve rms. Methods used for de-trending are listed in Table  2. The first two (lightcurverms2 and lightcurverms3) are sensitive to variables with slow variations over years to decades, while the other two (lightcurverms4 and lightcurverms5) are sensitive to variables with more than 10 -15 adjacent points in the trend.
Summary In this paper, we describe our photometry and variable identification software that extend the original pipeline  (Laycock et al. 2010) , in order to improve our science goal of finding real variables on the 100 years of Harvard plates being digitized by DASCH. These developments signif-icantly improved the efficiency of removing dubious measurements, as well as reduced the overall photometric uncertainty to 0. 1 -0. 12 mag level. DASCH variable identifications can now start with the summary table  (Table 2 ), by selecting candidates with excess variability measurements compared with neighbor stars with similar magnitudes. Details of an actual implementation of a variable search using the methods described here are presented in our Kepler field variable search, reported in  Tang et al (in prep.
We thank the referee,  George Djorgovski , for many helpful comments and suggestions that improved the paper. There are many colleagues who have helped out. In particular we thank our team members  Alison Doane ,  Bob Simcoe ,  Jaime Pepper ,  David Sliski , and  Silas Laycock  for their work on DASCH. We would also like to thank many volunteers who have helped digitize logbooks, clean and scan plates (http://heawww. harvard. edu/DASCH/team. php). This work was supported in part by  NSF  grants  AST0407380  and  AST0909073  and now also the Cornel and  Cynthia K. Sarosdy Fund  for DASCH. This research has made use of the APASS database, located at the AAVSO web site. Funding for APASS has been provided by the  Robert Martin Ayers Sciences Fund.
Simulations We used a 40 * 40 cm wide simulated robotic platform (see Figure  6A ) equipped with 2 wheels, proximity sensors for obstacle avoidance and a pan-tilt camera used to extract points of interest in the visual panorama and a fixed camera to perceive vanishing points (a copy of our robulab platform from Robosoft). Our simulation software (Webots from Cyberbotics) provides physically realistic model for the robot and obstacles but neither noise nor 3D objects near walls are taken into account (2D realistic snapshots of our lab are simply stuck on simulated walls). Setup 1: The place/action strategy is put ON while the road following strategy stays OFF. The purpose of the experiment is to test the self-assessment mechanism on the place/action strategy. The environment is a simulated room of 15 * 15 m (see Figure  6B  with a uniform floor and salient landmarks on walls.
ACKNOWLEDGMENTS The authors would like to thank  M. Belkaid  and  C. Grand  for their assistance in part of the modeling and experiments. Many thanks also to  A. Pitti  for the interesting discussions and guidance,  A. Blanchard  for the work he did on the Neural Network simulator used in these experiments and  F. Demelo  for the robots hardware support. Thanks to the  AUTO-EVAL  project, the  NEUROBOT French ANR  project and the  Centre National de la Recherche Scientifique (CNRS)  for the financial support.
Plant sampling We previously showed that invasive olive populations from South Australia and NSW have probably derived from introductions of Mediterranean and southern African trees  (Besnard et al. 2007a,b). To better document the geographic origins of invasive populations, we used both plastid and nuclear DNA markers to characterize native and invasive trees of both subspecies cuspidata and europaea. For each marker, we used a different sample of trees. First, the plastid DNA (cpDNA) variation was investigated on a large sample of trees (2126 individuals). A recent study reported cpDNA haplotype profiles for 1797 trees of subsp. europaea (including 534 Mediterranean cultivars and 1263 oleasters;  Besnard et al. 2013). Here, we characterized 81 accessions from 30 locations covering the whole native range of the African olive (Table  S1 ) and representative of lineages A, C1 and C2  (Besnard et al.
2007b ). In addition, 244 individuals from 11 locations were sampled in the invasive olive range (Table  S1 ): ten locations in Australia (NSW locations: Bringelly, Luddenham, Mount Annan, Camden Park, Harpers Hill, Ravensworth, Maitland Park; South Australia locations: Shepherds Hill, Lonsdale, Brownhill Creek; Fig. 1 ) and one location in Hawaii (Maui). Lastly, to test for putative multiple origins of populations of African olive in its whole invasive range (GISD 2010), four additional herbarium cuspidata samples from Raoul Island, Auckland Bay, Norfolk Island and Saint Helena Island were also characterized (Table  S1 ). Second, to investigate the colonization history of invasive populations, we analysed a subsample of 332 trees with nuclear microsatellites (see below). The 11 invasive populations from Australia (218 individuals) and Hawaii (26 individuals) were characterized and compared with putative source gene pools from the native range, namely the Cape Town location for the African olive (20 individuals), and a set of Mediterranean cultivars for subsp.
Pairwise F ST was also computed using FSTAT between the ten invasive populations. Signifi-cance of pairwise differentiation was assessed using Bonferroni corrections. Relative contribution of gene flow by pollen versus seeds (r) was estimated according to  Ennos (1994) , but only in South Australia where substantial plastid DNA diversity was observed (H T = 0. 50; see results) allowing estimating F ST values for cytoplasmic markers. STRUCTURE version 2. 3. 4  (Pritchard et al. 2000)  was used to estimate the number of genetic clusters in our data set. This model-based clustering method uses multilocus genotype data to infer population structure and assign individuals to populations. Using the ad hoc DK statistic based on the rate of change in the log probability of data between successive K values, STRUCTURE can be used to identify the number of genetic clusters  (Evanno et al.
Demographic models and introduction scenarios An ABC approach was used to infer the recent colonization history of invasive African olive in Australia and Hawaii. In this study, we show that invasive trees of subsp. cuspidata found in Australia and Hawaii were most probably introduced from South Africa or at least belong to the same genetic cluster (see below). We thus focused on this taxon to determine which among several scenarios of sequential colonization was the most probable. Only nuclear SSR data were used for these inferences because plastid DNA loci provide low variation among populations, especially in the invasive range (see below). We used two different implementations but, due to space limits, only the implementation based on the DIYABC version 2. 0 program  (Cornuet et al. submitted)  is presented in detail here.
The other implementation uses the ms program  (Hudson 2002) , which allows users to simulate extremely complex demographic histories, together with several in-house scripts and published R packages (see Data S1 for details). Three demographic scenarios or models were considered differing in the order of introductions all from a South African source (Fig. S1 ). In scenario 1, we assume a first introduction to Australia followed by a second introduction from Australia to Hawaii. In scenario 2, we assume a first introduction to Hawaii that is followed by second introduction from Hawaii to Australia. Finally, in scenario 3, we assume two independent introductions to Australia and Hawaii, which may have been at different times. For each scenario, several demographic parameters were defined: the current effective population size (in units of diploid individuals, not genes) for the three sampled populations (N e1 , N e2 and N e3 for South Africa, Australia and Hawaii, respectively), the number of founders in the introduced populations (N 1 and N 2 , respectively, for the first and second colonization events) and the duration of the initial bottleneck (db i ) which may be seen as a latency phase assumed to have taken place just after the introduction events for the two invasive populations (db 1 and db 2 , respectively).
2008). To validate the results of our ABC modelling approach, we first simulated data under each of the three models and applied the ABC algorithm to these data sets to determine whether we would correctly identify the scenario under which they were simulated. To do this, we used the 'confidence in model choice' function, implemented in DIYABC, which allowed us to estimate the proportion of type-1 and type-2 errors. We also evaluated the sensitivity of our inferences by testing different priors. Finally, we evaluated a Bayesian equivalent of goodness of fit of the selected scenario, using the model checking option of DIYABC version 2. 0  (Cornuet et al. submitted). This option allows us to evaluate to what extent the selected scenario and associated posterior distributions are corroborated by the observed data.
e. hybrids between subspecies europaea and cuspidata). Two individuals from SA (Lonsdale no 16 and 22) also appear to be admixed with a percentage of assignment to cluster C of approximately 10-20%. The Mediterranean and African chloroplast lineages match with the two clusters as all invasive individuals assigned to cluster E and C with P > 0. 8 show, respectively, a chlorotype of europaea and cuspidata (Fig. S3 ). In contrast, all individuals exhibiting Mediterranean chlorotypes in NSW (Camden Park, Bringelly no 21 and Mt Annan no 17) are admixed. At K = 5, cultivars and invasive olives from South Australia are relatively well distinguished into two clusters (namely E N and E I ), while most individuals from South Africa, NSW and Maui are, respectively, assigned to a specific cluster (namely C SA , C NSW and C H ).