dataesr/scientific-paragraphs-categorization huggingface dataset:
GLAC-1a ice reconstruction The GLAC-1a reconstruction is based on a set of glaciological models that are derived from a plausible climate forcing based on PMIP1 and PMIP2 results for LGM and that fit independently derived ice margin chronologies, within explicit uncertainties. The climate forcing involves an interpolation between present-day observed climatologies and the set of highest-resolution LGM fields from PMIP1 and PMIP2 data sets. The interpolation is weighted according to a glaciological inversion of the GRIP record  (Tarasov and Peltier, 2003)  for regional temperatures over the last glacial cycle. The North American and Eurasian reconstructions are derived from separate Bayesian calibrations of the Glacial Systems Model (GSM). The GSM incorporates a 3-D thermomechanically coupled ice-sheet model based on the shallow ice approximation, a permafrost resolving bed thermal model, an asynchronously coupled down-slope surface drainage/lake depth solver, and also includes thermodynamic lake ice, sub-glacial till deformation, buoyancy and temperature-dependent ice calving, and an ice-shelf representation  (Tarasov and Peltier, 2004 , 2005 , 2007; Tarasov et al.
Some interactions occur between the separate ice-sheet solutions, requiring further iterations as each ice-sheet model is modified. The field data from Antarctica are insufficient to use a similar approach to reconstruct ice-volume changes. Volume changes for the Antarctic ESL were obtained as the difference between the global ESL  (Lambeck et al. , 2014)  and the NH ESL, the latter being the sum of the individual icesheet contributions, and including mountain deglaciation in both hemispheres  (Lambeck and Purcell, 2005). The ice in Antarctica was then distributed using the LGM ice margins proposed by  Anderson et al. (2002) , and assuming the ice profiles followed the quasi-parabolic function proposed by  Paterson (1994). The retreat history is determined by the difference between the global ESL function and the combined Northern Hemisphere mountain-glacier contributions.
Conversion to common grid The difference in the surface elevation at the LGM can be computed as Surf(21 ka) = Surf(21 ka) -Surf(0 ka). (2) However, each of the individual ice-sheet reconstructions provides different outputs corresponding to the terms on the right-hand side of this equation (Table  1 ). ANU provides estimates of the change in thickness between the LGM and the present day ( Thick) and relative sea level (RSL), GLAC-1a provides Thick and topo(21 ka), while ICE-6G provides Topo(21 ka) and bathymetry Bath(21 ka) as well as providing explicit masks for 21 and 0 ka. In order to produce the composite CMIP5/PMIP3 data set, it was therefore necessary to transform the original outputs before interpolating these data onto a common grid. The domain of ICE-6G v2. 0 is the same as that used in the composite CMIP5/PMIP3 reconstructions, so no spatial transformation was needed.
The difference in the surface elevation at the LGM compared to the present day was computed from the original variables as Surf(21 ka) = max 0, Topo(21 ka) -max 0, Topo(0 ka). (3) The ice mask, Mask 1 (21 ka), was extracted directly from the original reconstruction. The ice-shelf mask, Mask 2 (21 ka), was computed from Topo and Bath as Mask 2 (21 ka) = 2 if Topo = Bath, Mask 1 otherwise. (4) The ANU reconstruction provides RSL and ( Thick) for four separate regions (Table  1 ). RSL over the British Isles was computed under the assumption that the present day is in equilibrium, with a mantle density of 4500 kg m -3. These terms were first interpolated to the PMIP3 spatial grid, but no attempt was made to attribute values to grid points beyond those covered by the original data set. The LGM topography was computed as Topo(21 ka) = Topo(0 ka) + Thick -RSL , (5) where Topo(0 ka) was derived from the ETOPO1 data set  (Amante and Eakins, 2009).
The topography relative to the LGM sea level is computed as Topo(21 ka) = topo(21 ka) + 116 m. (8) The resulting values were then interpolated to the PMIP3 grid, although no attempt was made to attribute values to grid points beyond those covered by the original data set (i. e. Antarctica). Grid points that were ice-covered but below sea level were corrected using the same floating-ice adjustment as used for the ANU reconstruction. Surf was then computed using Eq. (  3 ). The ice mask was computed from Thick(21 ka) as Mask 1 = 0 if Thick(21 ka) = 0 , 1 otherwise. ( 9 ) The ice-shelf mask was computed using Eq. (7).
Integration of the three reconstructions The CMIP5/PMIP3 composite ice-sheet reconstruction was created from the three transformed individual reconstructions. The LGM ice mask was taken as the maximum pos-sible coverage: Mask 1 , ave =    1 if at least one of the three Mask 1 is 1 , 0 otherwise. (10) The surface elevation for ice-free grid cells was taken from ICE-6G v. 2, while the difference in the surface elevation for ice-covered grid cells was computed by averaging the three reconstructions: Surf =        ( Surf (ANU) + Surf (ICE-6G) + Surf (GLAC-1))/N d where Mask 1 , ave = 1 , Surf(ICE-6G) otherwise , (11) where N d is the number of individual data sets (i. e. between 1 and 3) which provide a value for Surf for a given grid point, and Surf is the surface elevation field extended over undefined grid points such that Surf = Surf if defined , 0 if undefined (no quantity).
Discussion and conclusions There is currently no consensus about the form of the LGM ice sheets. Differences between existing reconstructions reflect the fact that new information is still emerging about the details of ice-sheet margins at the LGM and their retreat history, and the lithologic and geomorphic parameters that are used as constraints for ice-sheet modelling. While it is useful to explore the consequences of differences between reconstructions through sensitivity experiments, the use of a unified data set facilitates model-model intercomparison focusing on the role of structural differences between models. This was the motivation for the construction of a composite set of ice-sheet-related boundary conditions for the CMIP5/PMIP3 LGM experiment. It is heartening that the differences between the individual reconstructions contributing to the composite are relatively small and have only a minor impact on simulated NH radiative forcing and temperature.
, 2014; Zhang et al. , 2014). Thus, it is important that the differences between the reconstructions are examined carefully so that better-constrained reconstructions are available for future PMIP analyses. However, simulations made with the composite CMIP5/PMIP3 ice sheet have more realistic temperatures over Antarctica, falling within or very close to the uncertainty range of estimates of the LGM cooling derived from ice core data than the majority of PMIP2 simulations  (Masson-Delmotte et al. , 2013). While this may reflect model improvements to some extent, it would be unlikely to occur if the ice-sheet configuration of CMIP5/PMIP3 were substantially wrong. There are differences between the actual, observed margin of each ice sheet at the LGM and the margins reconstructed by inverse modelling.
Our knowledge of the LGM ice-sheet/ice-shelf reconstruction is continually improving, as are the models that are used to reconstruct the most likely distribution of ice mass  (Whitehouse et al. , 2012; Anderson et al. , 2014; Bentley et al. , 2014; Mackintosh et al. , 2014; Briggs et al. , 2014; Lambeck et al. , 2014; Peltier et al. , 2015; Hall et al. , 2015). Indeed, there have been updated reconstructions of the LGM ice-sheet configuration since the CMIP5/PMIP3 ice sheet was constructed. For example, the ICE-6G_C (VM5a) reconstruction is an updated version of the ICE-6G (VM5a) v2. 0 model discussed here, and is informed by a much richer database of space geodetic information  (Argus et al. , 2014; Peltier et al. , 2015). It is inevitable that there will be further changes in the future, although less clear when there will be a consensus about their form.
Acknowledgements. We thank  Rosemarie Drummond  and  Rumi Ohgaito  for technical assistance. We thank  Catherine Ritz ,  Peter Clark ,  Anders Carlson ,  Jun'ichi Okuno ,  Heinz Blatter , all of those who contributed to the  PMIP Wiki , and the participants in the PMIP and PALSEA workshops for scientific discussion. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 3 of this paper) for producing and making available their model output. For CMIP the  US Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
On the other hand, Perez and Igawa have developed a sky criterion, respectively and S i , that is function of the diffuse, direct and global irradiance. Thus, when luminance data are not available, sky conditions can still be estimated using the Perez or the Igawa model. According to Ferraro  (Ferraro et al. , 2012) , the Igawa method is more accurate for the prediction of the absolute irradiance while the Perez method is more accurate for the prediction of the relative luminance. More recently, Igawa released an improved version of its previous model  (Igawa, 2014) , but no extensive evaluation of this model has been performed yet. Table  1  summarizes the specifications of the databases used during the models development.
Sky luminance/radiance distribution measurement using a sky imager As said before, the empirical models have been developed using measured data of the sky radiance and luminance across the whole sky. These measurements were performed with a photometer or a spectroradiometer mounted on a tracker. Depending on the studies, about one hundred points, evenly distributed across the sky, were recorded during each scan. The main problem of such a technique is the instrument response time for each complete sky scan (see Table  1 ). For instance, a sky scanner like the MS-321LR Sky Scanner, developed by Eko Instruments, is able to scan the whole sky (145 points) in about 4. 5 min. This scanning time is a problem since atmosphere may change between two consecutive measurements at different viewing angles.
Summary To conclude this section, it must be remembered that the sky radiance and luminance distributions depend on the zenith angle PZA and the angle SPA between the Sun and the considered sky element. Both distributions have similar behaviour and so they can be formulated using the same equation. Empirical models have the advantage, over radiative transfer models, of not requiring complex atmospheric data as well as being fast and easy-to-use. Up to now, the best empirical sky model is the CIE Standard General Sky if luminance data are available, and the Perez or Igawa model otherwise. The sky radiance/luminance distribution is currently measured using a sky scanner. However, this device is costly and slow to operate. A new alternative, which is under development, is based on sky-imaging systems.
2 ). A CMOS sensor has been preferred to a CCD one because, in the CCD structure, charge flows in the direction where pixels are read, whereas in CMOS sensors the readout is performed locally at each pixel. It results that CMOS sensors are inherently more resilient to smear effect and blooming near the Sun area. Moreover, the camera is simple and easy to setup and operate thanks to its on-board video server that offers an Fig. 2. PROMES-CNRS sky imager and a few snapshots. intuitive browser-based interface. It is also easy to integrate thanks to the power supplied via Ethernet and the minimal memory requirements because of the MJPEG data compression format. Images are collected every 20 s at a resolution of 1920 Â 2560 pixels, with 8 bits per channel. Finally, the PROMES-CNRS sky imager is not equipped with a solar occulting device which is frequently used to reduce the light intensity reaching the sensor.
Analysis of the clear-sky image generation performance The PROMES-CNRS sky imager is operational since July 2013 and has collected more than 750,000 images  1. Get SZA, SAA and calculate SPA (Section 3. 3). 2. Upload the image and correct it using the solid angle map X p (Section 4. 1). 3. Pre-process the data and build I H p (Section 5. 1). 4. Fit Eq. (  14 ) or Eq. (  17 ) on I H p (Section 4. 2). 5. Generate the clear-sky image from Eq. (  14 ) or Eq. (  17 ). Table  4  gives the execution time of each step and the total time. The computational cost is a strong limiting factor in our application. Images are collected every 20 s; because we are also calculating cloud cover, cloud motion and other atmospheric properties, each step must be as fast as possible in order to get the full process below 20 s.
With this strategy, previous studies of the hen uterus transcriptome during formation of the eggshell identified a large number of functional genes that participate in eggshell formation and ion transport for its mineralization  [21, 22]. Additionally, a recent uterine transcriptomic study which compared the expression of uterine genes in the presence and absence of calcification further updated our knowledge of genes encoding proteins that supply ions for shell formation  [23]. In the current study, we report a re-evaluation of the dataset utilized for uterine-specific expression, in order to gain insight into white isthmus-specific functions. We test the hypothesis that comparison of gene expression in the white isthmus, where the eggshell membranes are formed, with two adjacent segments (magnum -where the egg white is formed, and uterus -site of eggshell calcification) will reveal specific over-expressed genes encoding proteins involved in the formation and structure of the eggshell membranes.
However, only 135 clones hybridizing to over-expressed transcripts were common to the two comparisons (Fig. 1 ); according to our hypothesis, these over-expressed genes are related to white isthmus-specific functions (Additional file 1). The 135 clones that were common to WI/Ma and WI/Ut comparisons were further assessed. Nine of them could not be further analyzed, since either the clone ID was no longer in the current nucleotide GenBank or in the University of Delaware chick EST database, or Blast searching did not reveal any hit. After removing examples of redundant clones, the over-expressed transcripts corresponding to 107 unique Gene IDs were retained and correspond to over-expressed genes in the white isthmus that possess an NCBI non-redundant Gallus gallus gene ID. Functional annotation clustering analysis using DAVID Bioinformatics Resources revealed 2 significantly enriched Gene Ontology (GO) term clusters involved in protein translation and RNA binding/splicing (Table  1 ).
A GenePix 4000 B scanner (Axon Molecular Devices, California, USA) was used to scan the microarray slides, Alexa Fluor® 555 was scanned at 532 nm and Alexa Fluor® 647 was read at 635 nm. GenePix Pro 6. 0 software was used to analyze the spot intensity of microarray data. GenePix report (GPR) files containing spot intensity raw data were stored in the BioArray Software Environment (BASE) of SIGENAE (Système d'Information du projet d' Analyse des Génomes des Animaux d'Elevages) and the microarray data was uploaded in the NCBI Gene Expression Omnibus (GEO) database under the series accession numbers GSE17267 and GSE52491.
Statistical data analysis Gene expression was compared between the white isthmus and the uterus as well as between the white isthmus and the magnum (2 samples/microarray slide, 1 microarray slide per hen, for a total of eight hens for each comparison). The 'anapuce' package in R was used to identify differentially expressed genes  [72]. Spot intensities were calculated using the median value. These values were transformed to log2 values and normalized using global locally-weighted regression (Lowess) in order to eliminate any bias arising from the efficiency of fluorescent dye incorporation. Subtraction of the median value corrected a block effect and the spot intensities were kept only when present in >50 % of samples. Gene variance was estimated using a mixture model integrated into the VarMixt method  [73]  followed by a unilateral statistical t-test to identify genes over-expressed in the white isthmus compared to either the magnum or uterus.
Gene ontology enrichment analysis Gene Ontology (GO) terms represent the functions of proteins encoded by over-expressed genes that are revealed by microarray analysis. Expression Analysis Systematic Explorer (EASE) software from the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources 6. 7 was used for functional annotation clustering to identify biological themes for 135 over-represented genes in the white isthmus where the formation of the eggshell membranes occurs  [76]. Each GO term corresponded to an EASE score (a modified Fisher Exact P-Value and high enrichment value), using GOTERM_BP_FAT and GOTERM_MF_ FAT. Only GO terms with an EASE score ≤ 0. 05 were considered to be significantly enriched.
Secreted protein identification Two protein informatic softwares, SignalP 4. 1  [77, 78]  and SecretomeP 2. 0 [79, 80], were applied to identify the overexpressed genes in the white isthmus that encoded sequences specifying either endoplasmic reticulummediated export (SignalP) or protein sequences predictive of unconventional secretion (SecretomeP). For SignalP 4. 1 analysis, the proteins were only accepted for secretion if the D-score was above the 0. 450 cutoff threshold. However, for the SecretomeP analysis the NN-score cutoff was 0. 5 for unconventional secretion.
Acknowledgements Funding for this study was partially provided by funds from the  Discovery program of the Natural Sciences and Engineering Research Council of Canada (NSERC)  ( 155449-2011 ) to MTH. The authors gratefully acknowledge the  European Community  for its financial support to  RESCAPE  project ( RESCAPE Food   CT 2006-036018 ), and  SABRE program  (European  Integrating  project  Cutting-Edge Genomics for Sustainable Animal Breeding Project 016250 ). This research was also supported by the  French National Research Agency  ( OVO-mining ,  ANR-09-BLAN-0136 ,  IMPACT ,  ANR-13-BSV-0007-01  and  ANR-13-BSV-0007-06 ). The  Del-Mar 14 K Chicken Integrated Systems microarray was developed under our original functional genomics  project funded by  United States Department of Agriculture Initiative for Future Agricultural and Food Systems (USDA-IFAFS  Grant  00-52100-9614 ) to LAC.
Micromagnetic calculations. STXM images are compared with micromagnetic calculations done using the LLG Micromagnetics SimulatorTM  20  and the OOMMF code  26  (especially for the additional materials). The uniaxial anisotropy (NiFe : 4. 10 3 erg/cm 3 , Co : 3. 10 4 erg/cm 3 ) and magnetization material (NiFe : 800 emu/cm 3 , Co : 1420 emu/cm 3 ) parameters have been extracted from magnetic measurements on full films. The cell size has been optimized to 5 nm, about 50 times lower than the extension of the observed 360u domain wall. The shape and position of the localized inhomogeneity of the reference Co layer has been extracted from the STXM. It has been modeled by a fluctuation of anisotropy with a different value of the anisotropy constant. The angle of 50u and the anisotropy value of 8.
That means that one cannot be certain to obtain a result under this ratio with any polynomial algorithm. However, the latter complexity results are given for any kind of distribution in the mono-representation case. One can ask if good multi-represented coverages can be achieved efficiently on data following Zipf's law, which is usual in the domain of NLP. Within the field of speech processing, the most frequently used strategy is a greedy method based on an agglomeration policy. This iterative algorithm selects the sentence with the highest score at each iteration. The score reflects the contribution of the sentence to the covering under construction. In  Gauvain, Lamel, and Eskénazi (1990) , this methodology has been applied to build a database of read speech from a text corpus for the evaluation of speech recognition systems using hierarchically organized covering attributes.
A greedy algorithm is led by a score function based on the Kullback-Liebler divergence. A similar method is used in  Krul et al. (2007)  to design a reduced database in accordance with a specific domain distribution. Kawai et al. (2000)  propose a pair exchange mechanism that  Rojc and Kačič (2000)  apply after a first reverse greedy algorithm-also called spitting greedy-deleting the useless sentences. In  Cadic, Boidin, and d'Alessandro (2010) , the covering of "sandwich" units (defined to be more adapted to corpus-based speech synthesis) is carried out by generating new sentences in a semi-automatic way. Candidates are generated using finite state transducers. The sentences are ordered according to a greedy criterion (their sandwiches richness) and presented to a human evaluator. This collection of artificial and rich sentences enables an effective reduction of the size of the covering but requires expensive human intervention to obtain semantically correct sentences that will be therefore easier to record.
The phonological annotation of the Gutenberg corpus comes from the Arctic/ Festvox database (see  Kominek and Black 2003) , and the annotation of the Le-Monde corpus is a by-product of the Neologos project, detailed by  Krstulović et al. (2006). For each corpus, we have collected every phoneme, diphoneme, triphoneme, and their occurrences in each sentence so as to define the set U of units to cover and the matrix A. A is built by collecting one sentence after the other following the ordering inside the corpus, and one unit after the other inside the sentences. After this matrix translation, we obtain two description files and two index files. The first file describes the matrix A and the second one the cost vector C. Because of the low matrix density, we have chosen a sparse representation to save space and computation time: For instance, the 2-phoneme Gutenberg matrix is about 2.
Corpora Two corpora are used in this experiment. The first one, Learning corpus, is an annotated acoustic corpus used to provide speech data for the TTS engine. It is an expressive corpus in French, spoken by a male speaker reading Albertine disparue, an excerpt from À la recherche du temps perdu by Marcel Proust. The corpus is composed of 3,138 sentences automatically annotated using a process described in  Boëffard et al. (2012). The overall length of the speech corpus is 9 hr 57 min. When creating a voice for a unit selectionbased TTS system, long sentences are generally removed or split into syntagm groups in order to help the speaker. A second corpus, named Test corpus, is a text corpus that is synthesized and used in the listening experiment. It is composed of 30 short sentences randomly extracted from a phonetically balanced corpus in French, proposed by  Combescure (1981).
B. Wide-band observations based on asymptotic methods results The second observation here presented in Fig. 3  is based on commercial asymptotic tool. SE-WorkBench software suite is co-developed by Oktal-SE and Onera  [14]. The main processing software here used is SE-Ray-EM. SE-Ray-EM performs all basic operations such as the SBR (Shooting and Bouncing Rays) algorithm and the use of asymptotic techniques to model EM interactions with the environment such as PO and GO for multiple reflections or ECM for edge diffraction. This main processing unit is coupled to a more specific module for propagation and channel modelling in mobile context. This wide-band module allows us to post-process the raw EM field. Using information such as DoA (Direction of Arrival) or propagation path length of each ray, the complex CIR can be computed.