dataesr/scientific-paragraphs-categorization huggingface dataset:
In standard LC classification tasks, global methods are known to provide significantly more accurate results  (Schindler, 2012)  since contextual knowledge is integrated. This is all the more true for VHR remote sensing data, especially in case of a large number of classes (e. g. , 10,  (Albert et al. , 2016) ), but presents two disadvantages. For large datasets, their learning and inference steps are expensive to compute. Furthermore, parameters should often be carefully chosen for optimal performance, and authors that managed to alleviate the latter problem still report a significant computation cost  (Lucchi et al. , 2011).
General strategy The proposed method assumes that a label map is provided for the areas of interest, and is accompanied with a class membership probability map, which provides, for each pixel of the image, the posterior class membership for all classes of interest. These are the necessary inputs for all methods described below. In practice, the strategy proposed in  (Dechesne et al. , 2017)  is as followed: a supervised classification is performed on a selection of features extracted both from 3D lidar point clouds and aerial multispectral images. The training pixels are selected according to an existing forest LC geodatabase. The used classifier is the Random Forest (RF) classifier. This is an efficient classifier, that directly handles multiple classes, and provides posterior probabilities for each class.
Data The different smoothing methods have been conducted on 4 mountainous test areas. Each area has a surface of 1 km 2. The IRC ortho-images of the test areas are presented in Figure  1. The proposed areas exhibit a large range of species (>4). The airborne multispectral images were captured by the IGN digital cameras  (Souchon et al. , 2012). They have 4 bands: 430-550 nm (blue), 490-610 nm (green), 600-720 nm (red) and 750-950 nm (near infra-red) at 0. 5 m ground sample distance (spatial resolution). The airborne lidar data were collected using an Optech 3100EA device. The footprint was 0. 8 m in order to increase the probability to reach the ground. The point density for all echoes ranges from 2 to 4 points/m 2. Our multispectral and lidar data fit with the standards used in many countries for large-scale operational forest mapping purposes  (White et al.
1 ), which reached a final depth of 3405 m in December 2011. The ice fabric in this core suggests that its stratigraphic layering has remained undisturbed, providing a robust and complete climate record 2. The WDC06A ice core has already provided the basis for important insights into changes in the carbon cycle during postglacial warming, as well as phasing of abrupt climate change events between the northern and southern polar regions  [3] [4] [5]. Sulfur dioxide emitted during volcanic eruptions, and introduced into the stratosphere, converts to sulfate aerosol, which can produce short-lived global cooling 6. One of the exciting new datasets provided by the WDC06A ice core is the sulfur-based record of volcanism, supplemented by electrical conductivity and particle counter measurements, which, when integrated with a Greenland ice record, provide insight into volcanic forcing of climate fluctuations 7.
%. Studies suggest that this technique is effective at minimizing the unavoidable Na loss associated with analyzing small particles, and does not adversely affect the analytical accuracy, although the precision for a set of points may be slightly decreased  19. Duplicate samples were prepared for microbeam geochemical analysis, and these were analyzed separately during different analytical sessions. A set of secondary standards, including rhyolitic and comenditic glasses VG568 and KN18 were analyzed during both analytical sessions to monitor accuracy and precision. In addition, four known samples of tephra from the Oruanui eruption sampled from well-documented terrestrial and marine sites were analyzed by electron microprobe using the same instrument and methods as the ice core samples. All data are presented in Supplementary Information S1.
P. ) is statistically indistinguishable. Most known Antarctic tephra from this age range are trachytic  17, 24  , and no rhyolitic Antarctic tephra of an appropriate age are known. The striking similarity of age and geochemistry and indistinguishable age between tephra produced by the Oruanui eruption and that found in the WDC06A ice core at 2660. 3 m depth strongly support this correlation. Individual glass shard compositions for tephra derived from the Oruanui eruption, compared to the chemical composition of tephra analyzed from the WDC06A ice core. Cross represents analytical precision (see Supplementary Information S1). Data from Allan et al. 21  and Vandergoes et al. 13  are glass shard analyses from terrestrial samples of tephra from the Oruanui eruption, sampled at known localities.
Note location of the WDC06A ice core in West Antarctica. Initially, the umbrella cloud expands radially, covering all of New Zealand and is ultimately swept into the polar jet, enveloping the Southern Hemisphere and depositing trace amounts of ash over Antarctica. Maps created using the atmospheric transport model Ash3d  25. was initialized from the 2. 5 degree NCEP/NCAR Reanalysis2 dataset (https://rda. ucar. edu/datasets/ds091. 0/). We identified 80 time periods within this dataset that fit within the plausible range of LGM conditions during the time of the Oruanui eruption, with northwesterly flow over New Zealand (Kidson type 'Blocking HE') and strong circumpolar westerlies around Antarctica  [29] [30] [31] [32]  (see Supplementary Information S2). The model grid covered the entire globe from 88 o S to 88 o N with a nodal spacing of 1 degree in the horizontal and 2 km in the vertical (see Supplementary Information S2 Table  1 ).
Acknowledgements This work was supported by  US National Science Foundation (NSF)  grants  NSF-PLR-1142069  ( Dunbar ),  NSF-PLR-1142007  ( Kurbatov ),  NSF-PLR-1142115  (Dunbar), and  NSF -PLR-0839093  (McConnell). The authors appreciate the support of the  WAIS Divide Science Coordination Office at the Desert Research Institute, Reno, NE, USA  and  University of New Hampshire, USA , for the collection and distribution of the WAIS Divide ice core and related tasks ( NSF  Grants  0230396 ,  0440817 ,  0944348 ; and  0944266 ). Van Eaton  acknowledges a U. S.
Event Generation Because of the presence of beamstrahlung photons in the colliding electron and positron beams, it is necessary to generate MC event samples for e + e -, e + γ , γ e -, and γ γ interactions. The main physics backgrounds, with up to six particles in the final state, are generated using the WHIZARD 1. 95  [20]  program. In all cases the expected energy spectra for the CLIC beams, including the effects from beamstrahlung and the intrinsic machine energy spread, are used for the initial-state electrons, positrons and beamstrahlung photons. In addition, low-Q 2 processes with quasi-real photons are described using the Weizsäcker-Williams approximation as implemented in WHIZARD. The process of fragmentation and hadronisation is simulated using PYTHIA 6. 4  [28]  with a parameter set tuned to OPAL e + e -data recorded at LEP  [29]  (see  [11]  for details).
Simulation and Reconstruction The GEANT4 detector simulation toolkits MOKKA  [32]  and SLIC  [33]  are used to simulate the detector response to the generated events in the CLIC_ILD and CLIC_SiD concepts, respectively. The QGSP_BERT physics list is used to model ii A Higgs boson of 125 GeV is used in the process e + e -→ tt H. the hadronic interactions of particles in the detectors. The digitisation, namely the translation of the raw simulated energy deposits into detector signals, and the event reconstruction are performed using the MARLIN  [34]  and org. lcsim  [35]  software packages. Particle flow reconstruction is performed using PANDORAPFA  [15, 16, 36]. Vertex reconstruction and heavy flavour tagging are performed using the LCFIPLUS program  [37]. This consists of a topological vertex finder that reconstructs secondary interactions, and a multivariate classifier that combines several jet-related variables such as track impact parameter significance, decay length, number of tracks in vertices, and vertex masses, to tag bottom, charm, and light-quark jets.
A fit to the recoil mass distribution of the selected events (in both the Z → e + e -and Z → µ + µ -channels) is used to ex-tract measurements of the ZH production cross section and the Higgs boson mass. The shape of the background contribution is parameterised using a fourth order polynomial and the shape of the signal distribution is modelled using Simplified Kernel Estimation  [48] [49] [50]  that provides a description of the ZH recoil mass distribution in which the Higgs mass can subsequently be varied. The accuracy with which the Higgs mass and the number of signal events (and hence the ZH production cross section) can be measured, is determined using 1000 simulated test data samples. Each test sample was created by adding the high statistics selected signal sample (scaled to the correct normalisation) to the smooth fourthorder polynomial background, then applying Poisson fluctuations to individual bins to create a representative 500 fb -1 data sample.
The selection is chosen to maximise the signal significance. The expected numbers of selected events for 1. 5 ab -1 of √ s = 1. 4 TeV data are listed in Table  27. The contributions from other investigated background processes were found to be negligible. The tt H cross section can be measured with an accuracy of 12 % in the semi-leptonic channel and 11 % in the hadronic channel. The combined precision of the two channels is 8 %. To translate the measurement of the tt H cross section into a measurement of the top Yukawa coupling, a correction is applied to take into account the contribution from the Higgsstrahlung diagram, where the Higgs boson is radiated off the intermediate Z boson in e + e -→ tt  [62, 63]. To evaluate the small degradation in sensitivity, the WHIZARD program is used to calculate the cross section for the inclusive process e + e -→ tt H as a function of the value of the top Yukawa coupling.
To provide a first indication of the overall impact of the CLIC physics programme, simple fits considering only the statistical uncertainties of the measurements are performed. Two types of fits are used: A modelindependent fit making minimal theoretical assumptions, and a model-dependent fit following the strategies used for the interpretation of LHC Higgs results. Both fits are based on a χ 2 minimisation using the MINUIT package  [70]. The measurements which serve as input to the fit, presented in detail in the preceding sections, are either a total cross section σ in the case of the measurement of e + e -→ ZH via the recoil mass technique, or a cross section × branching ratio σ × BR for specific Higgs production modes and decays. To obtain the expected sensitivity for CLIC it is assumed that for all measurements the value expected in the SM has been measured, so only the statistical uncertainties of each measurement are used in the χ 2 calculation.
Acknowledgements This work benefited from services provided by the  ILC Virtual Organisation , supported by the national resource providers of the  EGI Federation. This research was done using resources provided by the  Open Science Grid , which is supported by the  National Science Foundation  and the  U. S. Department of Energy's Office of Science. The authors would like to acknowledge the use of the  Oxford Particle Physics Computing Cluster. This work was supported by the  Comisión Nacional de Investigación Científica y Tecnológica  ( CON-ICYT ), Chile; the  Ministry of Education, Youth and Sports, Czech Republic , under Grant  INGO II-LG 14033 ; the  DFG cluster of excellence "Origin and Structure of the Universe", Germany ; the  EC HIG - GSTOOLS  project, under contract  PITN-GA-2012-316704 ; the  European Union 's  Horizon 2020 Research and Innovation programme  under Grant Agreement no.
Introduction The ITER plasma control system is designed to allow reliable and stable operation throughout all discharge phases. It will react to any deviation from the predicted behavior which includes application of active disruption avoidance schemes. Despite these effort s, disruptions caused by plasma instabilities, loss of control events or plant failures cannot be excluded. Should a disruption remain unmitigated, melting and vaporization of the ITER metallic plasma-facing components (PFC) cannot be excluded  [1]. A disruption mitigation system (DMS) is being designed for ITER to mitigate the thermal loads deposited during unavoidable disruptions. The ITER DMS is being designed based on a comprehensive experimental database collected from existing tokamaks. The mitigation strategy relies on the injection of high Z noble gas (NG), namely neon or argon, possibly combined with deuterium for prevention of runaway electrons generation.
Local and regional climate change assessments based on downscaling methods crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling via regional climate models (RCMs) observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. Focusing on the evaluation of RCMs, we here analyze the influence of uncertainties in observational reference data on evaluation results in a well-defined performance assessment framework and on a European scale. For this purpose we employ three different gridded observational reference grids, namely (1) the well-established EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available.
In terms of climate models five reanalysis-driven experiments carried out by five different RCMs within the EURO-CORDEX framework are used. Two variables (temperature and precipitation) and a range of evaluation metrics that reflect different aspects of RCM performance are considered. We furthermore include an illustrative model ranking exercise and relate observational spread to RCM spread. The results obtained indicate a varying influence of observational uncertainty on model evaluation depending on the variable, the season, the region and the specific performance metric considered. Over most parts of the continent, the influence of the choice of the reference dataset for temperature is rather small for seasonal mean values and inter-annual variability. Here, model uncertainty (as measured by the spread between the five RCM simulations considered) is typically much larger than reference data uncertainty.
For parameters of the daily temperature distribution and for the spatial pattern correlation, however, important dependencies on the reference dataset can arise. The related evaluation uncertainties can be as large or even larger than model uncertainty. For precipitation the influence of observational uncertainty is, in general, larger than for temperature. It often dominates model uncertainty especially for the evaluation of the wet day frequency, the spatial correlation and the shape and location of the distribution of daily values. But even the evaluation of large-scale seasonal mean values can be considerably affected by the choice of the reference. When employing a simple and illustrative model ranking scheme on these results it is found that RCM ranking in many cases depends on the reference dataset employed.
52 ± 0. 34) × 10 -5 (18) is known to 10% accuracy. An update including the entire Run-1 dataset of 3/fb is in progress, and further updates from Run-2 data will follow up. Hence the statistical component of the error on this measurement -about half of the error quoted in eq. (  18 ) -will decrease steadily. 9 As a consequence one can expect to extract the amplitudes at the 5% level, which compares favourably to a theory error which is above 10%. There is a complication though, in that B( B0 s → φγ) ∼ | Â B0 s →φγ ⊥ | 2 + | Â B0 s →φγ | 2 and does not provide enough information for two complex amplitudes. This situation can be improved through theoretical knowledge and related observables in this channel. First in the SM the amplitude is dominated by the form-factor component  [43, 46, [48] [49] [50] 62].
Acknowledgements We are grateful to  Gerhard Buchalla ,  Francesco Dettori  and  Dmitri Melikhov  for important insights. We also acknowledge useful feedback from  Olivier Deschamps ,  Mikolaj Misiak ,  Francesco Polci  and  Peter Stangl. DG  and RZ also thank  Gino Isidori  for kind hospitality at the  University of Zurich , where part of this work was completed. The work of DG is partially supported by the  CNRS  grant  PICS07229.
Concerning H3N2, the J02090 sequence named Aichi2 from Japan  [20]  was chosen as primary reference sequence. Indeed it is proposed as the first H3N2 flu virus to infect human  [21]. The 3HMG quaternary structure  [22]  from the protein databank (PDB) was chosen as tridimensional reference sequence for H3N2 and corresponds to the best resolution of the tri-dimensional structure of the Aichi2 strain. Analysis of the H1N1 HA 3D structures downloaded from the PDB indicates that 1RU7  [23]  is the one that has the nearest primary sequence to our sequence reference. Biological tests (antibody recognition) have proven that the protein used to determine the quaternary structure is indeed a HA  [23]. Figure  2  shows the amino acid sequence of HA H1N1 1RU7. The numbering of the positions used here is in italics and corresponds to that used in the protein databank (PDB) file of the three-dimensional reference sequence (1RU7.
Construction of Sequence Dataset From the website "Influenza Research Database" (fludb. org), 13,793 nucleotide sequences of genes encoding HA of patients infected with influenza A H1N1, were downloaded on 15 March 2016. The following parameters were used: pH1N1 (p for pandemic) excluding laboratory strains. From the same website, 13,290 nucleotide sequences of genes encoding HA from H3N2 strains were downloaded on 20 December 2016. Sequences are aligned at the 5'-end: they all have variable lengths due to sequencing deletion errors of some of them. Rather than using multiple alignment methods that are extremely time-consuming due to the necessity to align over ten thousand sequences, we defined two filters to clean up our dataset of its background noise. As stated above, the primary sequence of the H1N1 Weiss43 HA was adopted as reference sequence and Aichi2 for H3N2.
These sequences are 1701 nucleotides long. The first filter excludes sequences of another length than that of the reference sequence. This filter sets the new sequence dataset at 11,769 HA sequences from H1N1 strains and 13,267 for HA sequences from H3N2 strains. The second filter applied eliminates sequences that have a higher number of mutations than three standard deviations of the average number of mutations per sequence. Once the second filter has been applied, the alignment of HAs of 10,781 H1N1 and 12,225 H3N2 remain. The new set of sequences is then modified to correspond to the residue numbering of the quaternary structure of the reference sequence. Concerning the H1N1 HA alignment, first the signal peptide was not part of the mature protein, and then nucleotides corresponding to its sequence were removed.
Identification of the Background Linkage Disequilibrium (BLD) Using sets of DNA sequences, it was possible to determine mutated codons causing synonymous mutations and non-synonymous mutations. Sequences were recoded as follows: if compared to the reference sequence, non-synonymous residues are noted A, synonymous residues are noted S, identical residues are noted 1, non-identified residues are noted N; identical or synonymous residues are noted W. Preferential association of two alleles at two different loci (designated pos i and pos j ) is known as linkage disequilibrium. A linkage disequilibrium coefficient D' is computed with the encoded sequence dataset as explained by the group of Lewontin  [29, 30]. Using this coefficient, we determined the background linkage disequilibrium  [31, 32]  correlated to the number of couples sharing a common ancestor, and whose covariation was not the consequence of functional interdependencies.
Determination of Binding Sites Generating Little Resistance to a Small Drug Molecule Networks of spatially close (less than 10 Å) SLs and invariant positions are defined as targets. The Fpocket software  [33]  is used to determine pockets in the quaternary structure reference. To find small pockets we reduced minimum and maximum radii or alpha spheres to 2. 5 Å and 4 Å respectively. Pockets with a volume between 60 Å 3 and 500 Å 3 and at least five residues of one of the targets (groups of "SL + Invariants")  [28]  were retained.
Subsequently, only spatially close SL couples (whose residues are separated by less than 10 Å) and invariant positions (those having less than 0. 3% variability in the alignment) adjacent to these couples and at the surface of the protein were retained. Finally, employing the Fpocket software  [33]  and the HA 3D structure, we determined the "druggability" of the retained sets of residues, so as to list the most relevant binding sites on this protein.
265 ). An SL group  (188-192-198 ) and the invariant position 216, compose the four residues of target E. Finally, the sixth target F combines 13 residues: two couples of SLs (98-101 and 572-575) and nine invariant positions (567, 569, 571, 573, 578, 579, 582, 583, 585). Figure  4  depicts the 3D localization of these six potential targets defined by analyzing the PDB 1RU7 structure of the HA by running the PyMOL 1. 8. 2. 0 software. Targets B, C, D, and E are located on the globular head of HA. This head is at the distal end of the protein which is at the surface of the virus and hence accessible to a potential small drug molecule. Target A is located at the bottom of the stem of HA. This area is directly in contact with the virus envelope and is certainly difficult to access. This target is mainly composed of invariant positions that can be explained by its localization.
In fact, being buried implies that this part of the protein is less subject to environmental selection pressures. Target F is at the junction between HA1 and HA2. This target could be docked to block the formation of the protein quaternary structure prior to forming virions. Figure  4  depicts the 3D localization of these six potential targets defined by analyzing the PDB 1RU7 structure of the HA by running the PyMOL 1. 8. 2. 0 software. Targets B, C, D, and E are located on the globular head of HA. This head is at the distal end of the protein which is at the surface of the virus and hence accessible to a potential small drug molecule. Target A is located at the bottom of the stem of HA. This area is directly in contact with the virus envelope and is certainly difficult to access. This target is mainly composed of invariant positions that can be explained by its localization.
Can These Pockets Tie Up Small Drug Molecules? The six potential targets contain "SL + Invariant" positions predicted to be good spots to avoid resistance. To be of therapeutic interest, these targets should constitute binding sites, meaning pocketshaped locations and composed of atoms that a drug-like molecule (small molecule) can bind. From the PDB three-dimensional HA structure 1RU7, Fpocket software determines protein regions that can form binding pockets. To select less escape-prone targets, pockets composed of at least five residues positioned in one of the targets previously described, were retained. As our aim was to determine pockets binding small molecules, we also restricted pocket volumes to a range between 60 Å 3 and 500 Å 3 and set the Fpocket parameters to a minimum radius of alpha spheres at 2.