dataesr/scientific-paragraphs-categorization huggingface dataset:
The altimeter and colour and temperature products for the Kerguelen area were produced by  Ssalto/Duacs  and  CLS  with support from  CNES  and kindly prepared by  Emmanuel Laurenceau-Cornec  ( UTAS ) and  Francesco d'Ovidio  ( LO-CEAN -IPSL, Université Pierre et Marie Curie ). We thank  Isabella Rosso  ( Australian National University ) for useful discussion. This KEOPS-2 project was supported by the  French Research program  of  INSU-CNRS LEFE-CYBER  ("Les enveloppes fluides et l'environnement" -"Cycles biogéochimiques, environnement et ressources"), the  French ANR ("Agence Nationale de la Recherche ",  SIMI-6 program ,  ANR-2010-BLAN-614 KEOPS2  and,  ANR-10-JCJC-606 ICOP ), the  French CNES program  (" Centre National d'Etudes Spatiales")  and the  French Polar Institute IPEV (Institut Polaire Paul-Émile Victor ).
III. THE FINITE ELEMENT MODEL The objective of the FEM simulation is to obtain the impact load and the radius of the residual imprint after 10 impacts at a given energy to develop databases that will be used in the inverse method. Repeated impacts w e r em o d e l e dwi t hafinite element model developed in ABAQUS-Explicit. 14 This model was already described in a previous work. 13 Figure  1  shows the FEM model, the substrate is considered as semi-infinite solid with a behavior law related to k and n Hollomon parameters, and the ball is a 1 mm radius deformable elastic solid. To validate this model, the simulated load signal was compared with the experimental load signal and the calculated force value  15  under pure elastic conditions. A typical error of 9% has been observed on M2 treated samples.
V. THE INVERSE METHOD The inverse method has been developed by Kermouche et al. 9  to identify the local stress-strain curve using dynamic impact tests. This method was first based on the comparison between experimental results of radius and depth of residual imprints, and their corresponding values obtained from FEM simulations with several k and n parameters of the Hollomon'slaw(r 5 ke n ). The method and associated databases have been developed for several impact energies. The inverse method identifies the values of k and n, which allow to reach the best approach between the experimental results and those of the FEM simulation. Figure  4  represents the general principle of the inverse method. Referring to Kermouche et al. ,  9  the evolution of the load and radius values as a function of the number of impacts (N 5 1-10) can be described by the following equation: xN ðÞ ¼ A x ln N ðÞ 2 þ B x ln N ðÞ þ C x ; ð8Þ where N is the number of impacts, x is the characteristic parameter of the impact, i.
e. , radius r and/or load f. As the simulations have been done for an already known impact energy, A x , B x , and C x are functions of (k and n) couple and describe the global evolution of x, they are stored in 2 databases, one for the radius (A r , B r ,a n dC r ) and one for the load (A l , B l , C l ). The experimental values of the radius of residual imprint and impact load after N impacts for a given energy can be noted r e (N)andf e (N), whereas r(N)andf(N) are the radius and load values resulting from the FEM simulation. Considering the experimental curves r e (N) and f e (N) and the curves of FEM simulation r(N) and f(N), an identification method was developed to seek the minimal gap between the experimental and numerical curves and propose the best couple of k and n parameters of the constitutive law.
ACKNOWLEDGMENT The authors gratefully acknowledge financial support from  UTBM  and the international relations that allocated funds for  Mr. RR Ambriz  as a visiting professor. They also wish to thank  Mr. D. Schlegel  and  Mr. Th. Couturier  for their help in the design and implementation of various indenter-holders and  Mr. D. Tumbajoy  for his help in the sample preparation and compression tests on pure iron and pure copper.
thaliana to B. rapa can be obtained from the current paleogenomics data and associated AK system, as illustrated by the example of the FLC locus (Fig. 4b ). FLC is a MADSbox gene that drives the vernalization response in lateflowering ecotypes of A. thaliana. Our paleogenomics data provide the evolutionary scenario of the FLC locus since the ABK. It is clear from this that the FLC is an ancestral gene located on ABK6 that has been conserved in the extant species (C. rubella chromosome 6, A. thaliana chromosome 5, A. lyrata chromosome 6, T. parvula chromosome 6) and retained in three copies in B. rapa following the hexaploidization event  [32]. This example illustrates the immediate access to robust orthologous relationships between species and the identification of putative functional orthologs in B.
The most recent study aimed at investigating the ancestral crucifer karyotype using a catalog of A. thaliana genes (alternatively blocks) putatively spanning the eight ancestral chromosomes  [8]. The inferred ABK reconstruction we present here includes 30,968 protogenes based on conservation of gene adjacencies observed between five extant Brassicaceae genomes so that only 93 % (25,359 genes in ABK out of 27,206 annotated genes) of the modern A. thaliana gene repertoire can be considered as ancestral. The current data therefore represent a significant advance in understanding Brassicaceae evolution compared with published studies by considering plant genome evolution in general, and Brassicaceae evolution in particular, from the standpoint of ancestral genomes as a paradigm shift compared with current practice that has so far relied on pairwise comparisons of extant genomes or used a single pivotal or model genome such as A.
Genome sequences A. thaliana (Arabidopsis Genome Initiative, 2000), A. lyrata  [9] , C. rubella  [10] , T. parvula  [11] , and B. rapa  [12]  draft genome sequences were downloaded from Phytozome  [35]  (http://www. phytozome. net/). For these extant species, we downloaded all the protein-coding sequences, together with their gene location and gene family name from the EnsemblGenome Compara database  [36, 37].
We thus obtained families containing orthologous and paralogous plant genes which were used as input for the AGORA (Algorithm for Gene Order Reconstruction in Ancestor) program to infer the gene content of each ancestor as presented here. The order of the ancestral genes was computed independently for each ancestor. In a first step, for a given ancestor, pairwise comparisons of gene order were performed between all pairs of extant genomes connected by branches that cross the ancestral genome of interest (i. e. , two direct descendants or a descendant and an outgroup). For example, to compute the gene order in ABK, 129 pairwise comparisons of extant species were computed and analyzed to infer the ancestral gene organization. Secondly, the pairwise comparisons were used to identify conserved gene adjacencies, which occur when two genes, a1 and b1 in species 1, are adjacent, and their respective orthologs, a2 and b2 in species 2, are also adjacent.
Acknowledgments This work was supported by a grant from the  Agence Nationale de la Recherche  ( ANR Blanc-PAGE ,  ANR-2011-BSV6-00801 , and  ANR   Investissement d'Avenir Ancestrome ,  ANR-10-BINF-01-03 ). We thank  Hélène Berges ,  Arnaud Bellec ,  Emmanuelle Permal  and  Thomas Faraut  for fruitful discussions in the frame of the  ANR Blanc-PAGE program.
The rest of this paper is organized as follows: we briefly review, in Section 2, several state-of-the-art sentence alignment tools and evaluate their performance, first on two small reference datasets of gold alignments, then on a much larger set of approximately correct alignments. Section 3 presents our two-pass alignment algorithm; one of its distinguishing feature is its use of external resources to improve its decisions. Experiments on two language pairs (English-French and English-Spanish) are presented and discussed in Section 4, before we recap our main findings and discuss further prospects in Section 5. 2 Aligning literary texts: solved or unsolved? Commenting on the unsatisfactory results achieved by all sentence alignment systems during the Arcade evaluation campaign  (Véronis and Langlais, 2000)  on the single test book, Jules Verne's De la terre à la lune,  Langlais et al.
Baseline tools Baseline alignments are computed using several open-source sentence alignment packages: GMA  (Melamed, 1999) , 5 BMA (Moore, 2002), 6 Hunalign (Varga et al. , 2005), 7 Gargantua (shortened to Garg in the tables)  (Braune and Fraser, 2010) , 8 and Yasa  (Lamraoui and Langlais, 2013). 9 These tools constitute, we believe, a representative sample of the current state-of-the-art in sentence alignment. Note that we leave aside here approaches inspired by Information Retrieval techniques such as  (Bisson and Fluhr, 2000) , which models sentence alignment as a cross-language information retrieval task, or OCTOBER 2015  Sennrich and Volk's (2010)  approach, also based on some automatic translation of the "source" text, followed by a monolingual matching step. GMA, introduced by  Melamed (1999) , is the oldest approach included in this sample, and yet one of the most effective: assuming "sure" lexical anchor points in the bitext map, obtained e.
We then computed the difference in performance observed when replacing the gold alignments with semi-automatic ones. For these three books, the average difference between the evaluation on pseudoreferences and on actual references is less than 2 points in F-measure; furthermore, these differences are consistent across algorithms. A second comforting observation is that the same ranking of baseline tools is observed across the board: Gargantua, GMA and Yasa tend to produce comparable alignments, outperforming Hunalign and BMA by approximately 2 to 3 F-measure points. It is also worth pointing out that on the two large datasets less than 3% of the sentence links computed by BMA actually cross the reference paragraph boundaries; this warrants our assumption that BMA actually computes sure 1:1 links.
We give in Section 4. 2 the details of the features used in our model. A second major difference with the existing approaches is our use of a very large set of high-confidence alignment links to train our model. Indeed, most sentence alignment systems are endogenous: they only rely on information extracted from the bitext under consideration. While this design choice was probably legitimate in the early 1990s, it is much more difficult to justify now, given the wide availability of sentence-aligned parallel data, such as the Europarl corpus  (Koehn, 2005) , which can help improve alignment systems. This is in departure from our own past work, where the training data for MaxEnt was identified during the first pass, resulting in small and noisy datasets:  Yu et al. (2012a)  observe that for an extreme case of poor first-pass alignment, MaxEnt is trained using only four positive examples.
Because the new features are all based on IBM Model 1, the size of the training corpus also has an important effect. We have thus set up three datasets of increasing sizes. The first set contains around 110,000 tokens, which is the typical amount of data that Moore's algorithm would return upon aligning a single book; the second one contains 1,000,000 tokens; the third one includes all the parallel literary data collected for this study and totals more than 5,000,000 tokens. Each data set is split into a training set, a development set and a test set, using 80% for training, 10% for tuning, and 10% for testing. For these experiments, the model is trained with 30 iterations of the L-BFGS algorithm with a Gaussian prior, which is tuned on the development set. Table  4  gives the performance of the MaxEnt model on the test set as more features are included.
The final system The final system is constructed as follows (the procedure is identical in both languages pairs). Each test document is processed independently from the others, in a leave-one-out fashion. We use 80% of the sure parallel sentence pairs from the other 23 books for the auto en-fr corpus as positive examples, and 16 books for auto en-es; we also include the 1:1 links computed by BMA in the left-out corpus. For every pair of parallel sentences f i and e j , we randomly sample three target (respectively, source) sentences other than e j (respectively, f i ) to construct a negative pair with f i (respectively, e j ). Each positive example thus gives rise to six negative ones. We train a distinct MaxEnt model for each test book, using 80% of these book-specific data, again using 20% of examples as a held-out data set.
Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0. 3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can.
5 K  [21]. Similar cases can be observed in other satellite sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instruments. Therefore, it is crucial to perform angular normalization of the LST data, i. e. , to translate LST data obtained in different directions to a specified direction such as the nadir direction  [3, 25]. Multi-angular observation of the same target is considered to be the most promising method of solving this problem, and such observations can be achieved using ground goniometers: the Portable Apparatus for Rapid Acquisitions of Bi-directional Observations of Land and Atmosphere (PARABOLA)  [26] , the field goniometer system (FIGOS)  [27] , the portable Multi-Angle Observation System (MAOS)  [23, 28] , and others  [29, 30].
However, only a small number of studies have been published on LST angular normalization or on the simultaneous retrieval of directional emissivity and temperature from multi-angular TIR images mainly due to a lack of multi-angular observations at satellite level. To date, only ATSR (Advanced Along Track Scanning Radiometer) series satellites  [31, 32]  have provided multi-angular observations of thermal infrared (TIR) data because of the difficulty and the complexity of manufacturing highly controlled multi-angular sensors and also because no studies that discuss requirements in terms of the number of direction specifications of angular observations have been published. To address this situation, we investigate the minimum requirements of viewing observations and directions for achieving angular normalization of LSTs using multi-angular TIR data to provide technique support for the future design of such sensors.
Airborne WiDAS System As stated above, to date, no satellite sensors providing multi-angular observation in the TIR domain have been deployed. Therefore, we used an airborne multi-angular image dataset. This dataset was produced using the WiDAS system, which was one of the major airborne sensors used in the WATER synthetic field campaign conducted in the spring to summer of 2008 on the Heihe River watershed in west China  [34]. The WiDAS system acquired images using four Charge Coupled Device (CCD) cameras in visible/near-infrared channels (VNIR, 550, 650, 700 and 750 nm) and two broad-band thermal cameras in the mid-infrared (MIR, mainly 3~5 μm, AGEMA 550 from the FLIR company and TIR (mainly 8~12 μm, S60 from the FLIR company) channels. The MIR and TIR cameras were radiometrically calibrated using a man-made blackbody instrument in the temperature range from 273.
16 to 358. 16 K with an interval of 5 K. The thermal sensitivity of the MIR and TIR cameras were <0. 1 K at about 300 K. The spatial resolutions of the MIR/TIR and VNIR images were respectively approximately 7. 9 m and 1. 25 m at flight height of approximately 1. 5 km above the surface. The cameras in the WiDAS system acquired sequential surface photos at a high frequency during the flight  [33] , and the overlap between two sequential images was more than 80% in the VNIR channels and more than 85% in the MIR and TIR channels, which meant that the same ground point can almost be simultaneously observed in several sequential images. After performing geometric corrections, a multi-angular dataset was obtained from the collections of the same ground point in the sequential WiDAS images. The WiDAS system was designed to observe the surface in the MIR and TIR channels at a total of seven zenith angles: nadir and backward and forward angles of 10°, 20° and 40°.
However, because the time interval between two sequential images was very short (<4 s), the number of observation angles was larger than designed, and the variations in two adjacent VZAs and even VAAs were consequently very small, thereby causing two sequential observations to contain no more angular information about the surface than would only one observation. Moreover, the error in the additional observations might influence the final results of the retrieval, but this dataset provided us an opportunity to analyze the influence of observation angles on the angular normalization of LST using the TIR-BRDF model.
Study Area The WiDAS image used in this paper was located in the ZY-YK-HZZ flight zone, which is a typical oasis agricultural area  [22, 33]. The main land covers included maize, wheat and vegetables. The WiDAS system acquired data over this area on 1 June, 29 June and 7 July 2008. Only the data from 7 July 2008 were used because of its clear-sky conditions and high quality. Figure  15a, b  show the VNIR false color image and one TIR image for this area, and these images were acquired at approximately UTC 3:58 (Beijing Time: 11:58) on 7 July 2008. The solar zenith and azimuth angles at that time were approximately 125. 7° and 24. 4°, respectively. The aircraft flew from north-east to south-west. Because the camera produced a central projection image at every exposure, the VAA and VZA of the image consequently varied alongside the spatial variation in the pixels, as shown in Figure  15c, d.
Angular Normalization of Temperature Based on the dataset from the WiDAS system after atmospheric correction using synchronously measured atmospheric profiles (including air temperature, air pressure, and humidity) to drive the MODTRAN code, Ren et al. [22]  proposed a daytime temperature-independent spectral indices (TISI) method  [12]  to measure directional emissivities ε(θv, φv) and directional effective temperatures (Te(θv, θs, φ), see Equation (  1 )) from the observations of both MIR and TIR channels in at least four directions by combining the kernel-driven BRDF model and the TISI method. Their retrieval process generated an effective directional temperature Te for each direction, whereby the number of directions is equal to the number of observations and is larger than three. Therefore, the three kernel coefficients in the TIR-BRDF model in Equation (  4 ) can be fitted in theory and subsequently used to calculate the temperature at the specified nadir direction.
Discussions Based on the simulated directional thermal radiation from a newly developed model, this paper investigated the minimum requirements for viewing angles for the angular normalization of LST using the TIR-BRDF model and subsequently used the airborne WiDAS multi-angular dataset to validate the TIR-BRDF model and the influence of the number and angle intervals of the viewing directions. Although this paper calculated the component fractions using the parameterization model of the SAILH model always used for homogenous canopy, the new TIR-BRDF model can be also applied to perform angular normalization of LST for heterogeneous surface because the TIR-BRDF is similar to its original kernel-driven BRDF model, and the original model has been successfully used to fit directional reflectance for different land covers to generate surface albedo product (e.
This effect reduces the angular variation in the DBT that is observed by the edge pixels in different arrays and consequently causes the TIR-BRDF model to be more sensitive to the noise in the observed DBT data. Finally, based on the analysis of the temperature RMSE and the temperature difference in the nadir DBT, we determined that the VZA in the nadir array cannot be larger than 45° to ensure temperature RMSEs within [0. 0, 1. 0] K and the temperature difference in the nadir DBT within [-1. 0, 1. 0] K for most cases (85%). Furthermore, we verified the TIR-BRDF model using the multi-angular dataset collected by the airborne WiDAS system and analyzed the influence of the number of viewing directions and angle intervals on the results of the TIR-BRDF model. The results showed that a small number of viewing directions but with large angle intervals can improve the accuracy of the TIR-BRDF model and even produce a better result compared to the use of all viewing angles.
This result was most likely due to the large angle interval generally corresponding to large temperature differences between the viewing directions and consequently reducing the residual error in the regression of the TIR-BRDF model. In contrast, additional viewing directions cannot guarantee a better result if their angle intervals are not significant and the errors in the additional data might in turn reduce the accuracy of the final results of the TIR-BRDF model. Based on the angular normalization of the temperature, we found that the nadir temperature was generally larger than that of the slant direction by approximately 0. 5~2. 0 K for vegetated pixels and up to several K for non-vegetated pixels such as rural settlement. From this viewpoint, it is more important to perform the angular normalization for the non-vegetated surface.
Acknowledgments The authors would like to thank the two anonymous reviewers for their constructive and thoughtful comments and suggestions, and thank the colleagues who collected the multi-angular images of the WiDAS system and the atmospheric data that was used for the atmospheric correction in this paper. This work was supported by the program of the  National Natural Science Foundation of China  (Grant No. 41401375 ,  41331171 ,  41230747 , and  41231170 ), the  China Post-doctoral Science Fund  (Grant No. 2014M550551 ), the  National Basic Research Program of China  (Grant No. 2013CB733402 ), and  International S&T Cooperation Program of China  (Grant No. 2012DFG21710 ).
MR Imaging Data acquisition was performed on a 3T MR imaging system (Achieva; Philips Healthcare, Best, the Netherlands), which is the first-line imaging technique available 24/7 in our center. It was therefore possible to initiate optimal management without delay. 12 The imaging protocol consisted of parenchymal brain imaging sequences (axial DWI, GRE T2, and FLAIR) and angiographic sequences (3D-TOF and TR-CE MRA). The MRA acquisition parameters are provided in Table  1. The total average acquisition time for all MR imaging sequences was 12 minutes. The TR-CE MRA sequence was based on a k-t Broad-use Linear Acquisition Speed-up Technique (BLAST)-accelerated 3D sequence, performed without moving the patient, with a wholebody birdcage coil used as a receiver. 29, 30 This technique provided wide coverage, from the aortic arch to the M3-M4 segments of the MCAs, with high temporal and spatial resolution.