dataesr/scientific-paragraphs-categorization huggingface dataset:
The operations in Eq. (  8 )-Eq. ( 14 ) can be parallelized using the GPU based CUDA technique. Under the Compute Unified Device Architecture (CUDA) framework, we set the total number of blocks in grid to the row size of the image, and the total number of threads in each block to the column size of the image  [49] [50]. In implementing the SAFE algorithm, all the threads in the block-grid structure were executed in parallel to complete the involved pixel-wise operations. All the images were processed in a PC workstation (Intel Core™ 2 Quad CPU and 8192 Mb RAM, GPU (NVIDIA GTX475)) with Visual C++ as the developing environment (Visual Studio 2008 software; Microsoft). The restoration performance was quantitatively measured by the peak signal-to-noise ratio (PSNR) and the mean structural similarity index (MSSIM) proposed in  [51] :     2 2 =1 10 PSNR 10 255 , log -            M j j j M I I (15)   2 2 2 2 2 ˆˆ, 1 2 ˆˆ( 2 2 MSSIM )( ) ( )( ) ˆ, I I I I I I m m c m m c c I M                              (16) where,   and I  denote the 8 8  windows in the same position in the restored image  and the original true image I.
are clustered into three datasets based on their mg values. mg j C contains centroid mean values (  ) for respectively the three clusters ( We should note that, due to the inherent intensity variations and the random values of noise points, some classification errors are inevitable with this K-means based clustering. In other words, some mg values from both corrupted and uncorrupted points might exist in all the three clusters. Also, by comparing the plots before and after corruption in Fig. 3, we can understand that the sharp increments in the right parts of the plots were caused by the increased mg values due to noise corruption. So, in calculating the map N j R storing the reliability values for the points in mp j N ,we use the centroid values.
N are clustered into three datasets based on their mg values. mg j C contains centroid mean values (  ) for respectively the three clusters ( We should note that, due to the inherent intensity variations and the random values of noise points, some classification errors are inevitable with this K-means based clustering. In other words, some mg values from both corrupted and uncorrupted points might exist in all the three clusters. Also, by comparing the plots before and after corruption in Fig. 3, we can understand that the sharp increments in the right parts of the plots were caused by the increased mg values due to noise corruption. So, in calculating the map N j R storing the reliability values for the points in mp j N ,we use the centroid values.
Ski-Mounted IMU Device For the two sections of the experimental protocol, subjects were instrumented with an IMU (Pomoca SA, Denges, Switzerland) composed of a 3D accelerometer, 3D gyroscope, barometric pressure sensor, and thermometer (Figure  1 ). Dimensions of the device are: 68. 1 × 33. 6 × 17. 7 mm 3 at 24 g. Accelerometer and gyroscope data were sampled at 128 Hz, and the barometer was sampled at 64 Hz. The system is waterproof for data collection in wet and snowy mountain conditions. Each IMU was composed of the same firmware for all experiments, with raw data stored on the device before being transferred to a computer. The IMU was securely placed on the ski in front of the binding with a magnet (Figure  1b ). For all tests, the IMU was manually started from a button press on the device just before the start of the activity.
Algorithm and Reference Parameters Estimation SkiMo parameters calculated by the algorithm are measured in real time, on a stride-to-stride basis. The data collection follows several definitions presented in this section. The n-th cycle is first marked by the algorithm using forward acceleration peaks (Pn, Pn+1), with a dual threshold-crossing method for robust detection of SkiMo pattern features and avoidance of false-positive detection of other movements (Figure  2 ). A cycle is defined as start of motion at cycle n (Tn) to start of motion of cycle n + 1 (Tn+1). Consequently, the motion period is precisely detected by the algorithm (Tn to Zn). The algorithm also detects the motionless period of each stride (Zn, Tn+1) by comparing the acceleration norm to 0 g. Similar to walking locomotion, a stride consists of two steps, namely, from the sum of right and left legs.
Algorithm and Reference Parameters Estimation SkiMo parameters calculated by the algorithm are measured in real time, on a stride-to-stride basis. The data collection follows several definitions presented in this section. The n-th cycle is first marked by the algorithm using forward acceleration peaks (P n, P n+1 ), with a dual threshold-crossing method for robust detection of SkiMo pattern features and avoidance of false-positive detection of other movements (Figure  2 ). A cycle is defined as start of motion at cycle n (T n ) to start of motion of cycle n + 1 (T n+1 ). Consequently, the motion period is precisely detected by the algorithm (T n to Z n ). The algorithm also detects the motionless period of each stride (Z n , T n+1 ) by comparing the acceleration norm to 0 g. Similar to walking locomotion, a stride consists of two steps, namely, from the sum of right and left legs.
Statistical Analysis Results obtained from the algorithm and the reference were analysed using the Bland-Altman method  [19]  and their association was quantified with Pearson's r correlation (Figures  5  and 6 ). In addition, results are presented with mean absolute difference and mean relative difference with standard deviation (Table  2 ). Results for transition classification were analysed using the Bland-Altman method and results are presented as mean ± standard deviation. In addition, the specificity-sensitivity (SE-SP) values for kickturns detection were analysed for different thresholds values (Table  3 ). Statistical analysis was performed using XLstat software (Addinsoft, France). Skins On (SOn) transitions are defined by a vertical position of the ski between a period of downhill and uphill or at the beginning of the activity.
Statistical Analysis Results obtained from the algorithm and the reference were analysed using the Bland-Altman method  [19]  and their association was quantified with Pearson's r correlation (Figures  5  and 6 ). In addition, results are presented with mean absolute difference and mean relative difference with standard deviation (Table  2 ). for transition classification were analysed using the Bland-Altman method and results are presented as mean ± standard deviation. In addition, the specificity-sensitivity (SE-SP) values for kickturns detection were analysed for different thresholds values (Table  3 ). Statistical analysis was performed using XLstat software (Addinsoft, France).
Monte Carlo simulated events were produced using a dedicated program developed by the collaboration based on the GEANT-4. 10. 1 package  [29]. The program simulates electromagnetic and hadronic interactions of particles in the material of the AMS and generates detector responses. The Monte Carlo event samples have sufficient statistics such that they do not contribute to the errors. Event selection. -The collection time used in this analysis includes only those seconds during which the detector was in normal operating conditions, the AMS was pointing within 40°of the local zenith, and the International Space Station (ISS) was outside of the South Atlantic Anomaly. In addition, those seconds when the AMS detects solar energetic particles accelerated by the Sun are excluded. Because of the geomagnetic field, the collection time increases with rigidity; it is 1.
92 GV. Data analysis. -The isotropic flux Φ i during a Bartels rotation in the ith rigidity bin (R i ; R i þ ΔR i ) is given by Φ i ¼ N i A i ϵ i T i ΔR i ; ð1Þ where, for that Bartels rotation, N i is the number of events corrected for bin-to-bin migration, A i is the effective acceptance, ϵ i is the trigger efficiency, and T i is the collection time. In this Letter, the proton flux was measured in 45 bins from 1 to 60 GV and the helium flux in 40 bins from 1. 9 to 60 GV. Above 1. 9 GV, proton and helium have 40 common rigidity bins with identical bin widths. Bin-tobin migration of events was corrected using the unfolding procedures described in Refs. [22, 23]  independently for each Bartels rotation for the proton samples and for the helium samples. Extensive studies were made of the systematic errors for each Bartels rotation as described in Refs.
We are grateful for the support of  Edward Semones  and his staff of the  NASA Johnson Space Center including resources from Wyle Laboratories  Grant No. 2014/T72497. Research supported by  São Paulo Research Foundation (FAPESP)  Grants No. 2014/19149-7 , No. 2015/50378-5 , and No. 2016/ 10222-9 , Brazil; CAS,  NSFC ,  MOST , the provincial governments of  Shandong, Jiangsu, Guangdong , and the  China Scholarship Council , China; Action  H2020 MSCA-IF-2015  under Grant No. 707543 -MAtISSE ,  European Union ; the  Finnish Funding Agency for Innovation (Tekes)  Grants No. 40361/01  and No. 40518/03  and the  Academy of Finland  Grant No. 258963 , Finland;  CNRS/ IN2P3 ,  CNES, Enigmass , and the  ANR , France;  Pascale Ehrenfreund ,  DLR  under Grant No. 50OO1403  and  JARA-HPC  under Project No.
JARA0052 ,  Germany ;  INFN  and  ASI  under ASI-INFN Agreements No. 2013-002-R. 0  and No. 2014-037-R. 0 ,Italy; CHEP and  NRF  under Grants No. NRF-2009-0080142  and No. NRF-2012-010226  at  Kyungpook National University  and No. NRF-2013-004883  at  Ewha Womans University ,  Korea ; the  Consejo Nacional de Ciencia y Tecnología  and  UNAM, Mexico; FCT  under Grant No. PTDC/FIS/122567/2010 , Portugal; CIEMAT,  IAC ,  CDTI , and  SEIDI-MINECO  under Grants No. ESP2015-71662-C2-(1-P/2-P ), No. SEV-2015-0548 , No. MDM-2015-0509 , and No. RyC-2013-14660, Spain; the  Swiss National Science Foundation (SNSF) , federal and cantonal authorities, Switzerland;  Academia Sinica  and the  Ministry of Science and Technology (MOST)  under Grants No. 103-2112-M-006-018-MY3 , No. 105-2112-M-001-003 , and No.
CDA-105-M06 , former Presidents of  Academia Sinica Yuan-Tseh Lee and Chi-Huey Wong and former Ministers of MOST Maw-Kuen Wu  and  Luo-Chuan Lee , Taiwan; the  Turkish Atomic Energy Authority  under Grant No. 2017TEAK ( CERN ) A5. H6. F2-15 , Turkey; and  NSF  Grants No. 14255202  and No. 1551980 , and  NASA NESSF  Grant No. HELIO15F-0005 ,  USA. We gratefully acknowledge the strong support from CERN including  Rolf-Dieter Heuer  and  Fabiola Gianotti , from the  CERN IT department and Bernd Panzer-Steindel , and from the  European Space Agency including Johann-Dietrich Wörner and Simonetta Di Pippo. We are grateful for.
Material and Methods Data from 731 patients treated in 12 institutions were analysed. All patients received initial EBRT -84% to the pelvis alone and 16% to the pelvis and paraaortic nodes (PAN). 77% patients received concomitant chemotherapy. MRI-based IGBT was used in at least one application in 81% of patients, and CT alone in 19%. In 23% of patients, combined intracavitary/interstitial BT was used in at least one fraction. Treatment failures were classified as local (cervix, upper vagina and/or parametria), regional (pelvic nodes), pelvic (local and/or regional), systemic (excluding PAN relapse) or distant (including PAN relapse). The pattern of failure at time of first relapse was analysed and compared with two large published series treated with RT + conventional BT without chemotherapy (Washington, 1499 patients; French cooperative study, 1875 patients) and the Vale metaanalysis of 13 randomised trials of chemo-RT + conventional BT (3128 patients).
From Figure  2  it can be noted that the annual production is 996 kWh in terms of electricity and 2190 kWh in terms of thermal energy. With respect to the outputs mentioned above, the annual electricity production has been calculated by means of PVsyst software (Source: PVsyst). Moreover, the production of thermal energy has been estimated based on a thermal model which includes energy balance and considers mean daily irradiances. Figure  2. The annual output (in kWh, in terms of thermal and electrical production) of the PVT system. Climatic conditions: Ajaccio, France.
LCI (life cycle inventory), equations and data sources SimaPro 8 (a software that is robust and reliable) (Source: SimaPro) and the database ecoinvent 3 (a database that is comprehensive and consistent with relevant data and high-quality datasets for LCI) (Source: ecoinvent) have been adopted. In Table  2 , details in terms of the components/materials of the PVT system are presented. Regarding the support structure (mass: 295. 27 kg aluminium), it includes multiple aluminium components (holding bars, blades, boxes, fittings, screw bars, etc. ). Concerning the batteries, their LCI is according to the study of  McManus (2011). For EPBT evaluation, the following equation  (Lamnatou et al. , 2014 (Lamnatou et al. , , 2016) )  has been used: ) (. &. &. years E E E E E E E E E EPBT a M O a out disp transp inst mat a M O a out in        (1) where, E in represents the total inputs (in terms of primary energy) for the manufacturing of the materials, the collectors, the batteries and the additional components of the system as well as the total inputs (in terms of primary energy) for the installation of the system, the transportation and the disposal.
For aluminium (primary) the main part of the GWP is due to primary liquid aluminium and electricity inputs (Sources: SimaPro; ecoinvent). It is known that primary aluminium has high environmental impact and this is mainly associated with the high energy consumption and waste generation in comparison to secondary aluminium  (Soo et al. , 2018). Regarding PV-cell impact,  Hsu et al. (2012)  presented a study about life-cycle GHG (greenhouse gas) emissions of crystalline-silicon PV electricity generation. It was highlighted that significant GHG emissions are attributed to silicon and wafer manufacturing utilised in the PV modules. Additional inputs (during manufacturing phase) such as silicon type and grid electricity GHG emission intensity may also contribute considerably, depending on the case  (Hsu et al.
Comparisons in terms of the batteries For the lead-acid batteries (LCI of Table  2 ), CED and GWP 100a have been calculated to be 12. 25 MJ prim and 0. 56 kg CO 2. eq (per kg of battery), respectively. For the above mentioned impacts, the main contributors are the production of lead, polyethylene and polypropylene. More specifically, lead treatment of scrap acid battery and sodium hydroxide present a remarkable impact during lead production. Furthermore, for the production of polyethylene and polypropylene, a considerable contribution to the total impacts is related to incineration processes and hazardous waste (Sources: SimaPro 8; ecoinvent 3). With respect to the literature, the database ICE (  2011 ) presented values of 10 MJ/kg (embodied energy) and 0. 58 kg CO 2. eq /kg (embodied carbon) for recycled lead and it was highlighted that scrap batteries are a main feedstock in the case of recycled lead.
Materials and Methods The Ovine SNP50K BeadChip and Whole-Genome Sequence Data We used publically available Illumina Ovine 50 K (54,241 SNPs) BeadChip SNP and whole-genome sequence data of domestic sheep (O. aries) and their wild relatives (e. g. ,  O. orientalis, O. ammon, O. musimon, O. vignei, O. dalli, and O. canadensis). The ovine SNP BeadChip data consisted of genotypes of 943 females representing 68 worldwide domestic populations/breeds in previous investigations  (Kijas et al. 2012; Zhao et al. 2017) , in addition to 6 males and 2 females of O. orientalis obtained from the NextGen Project (http:// projects. ensembl. org/nextgen/) (fig. 1  and supplementary table S1, Supplementary Material online). Details on quality control for the Ovine SNP50K BeadChip genotypes were provided in Supplementary Material.
05; 2) the proportion of missing genotypes > 50%; and 3) P value of the chi-squared test for Hardy-Weinberg equilibrium (HWE) test > 0. 00001 (P value > 0. 00001). SNPs meet any of the above criteria were filtered. These high-quality SNPs were referred as wholegenome sequence (WGS) SNPs. Further, we examine the accuracy of our high-quality SNPs using the method as described in  Yang et al. (2016). In summary, we compared the SNPs with those from Build 143 of the sheep dbSNP database in the National Center for Biotechnology Information (NCBI; http://www. ncbi. nlm. nih. gov/SNP). We annotated all the high-quality SNPs using the ANNOVAR software  (Wang et al. 2010 ) and categorized them into three groups according to their genomic locations such as intronic, exonic, and intergenic.
Estimates of Nucleotide Diversity and Its Pattern along X Chromosome We calculated within-population genomic diversity metrics and estimated the linkage disequilibrium (LD) pattern between pairwise SNPs (Supplementary Material online). In addition, genetic differentiation and population structure were examined based on the Ovine BeadChip and WGS data sets (Supplementary Material online). The nucleotide diversity (p) on per-site basis was calculated using the formula by  Nei and Li (1979)  in VCFtools  (Danecek et al. 2011). For SNPs in intronic, exonic, and intergenic locations, we calculated p separately. To correct for the effect of potential difference in mutation rates between X-chromosome and autosomes, we calculated mean interspecies divergence (D)  (Jukes and Cantor 1969)  for X-chromosome and autosomes between sheep and cattle (Bos taurus) using the software MEGAv.
Simulations of Genetic Drift We simulate the genetic drift on X-chromosome and autosomes in the WGS data using an updated version of the POWSIM software  (Ryman and Palm 2006)  as detailed in  Lamichhaney et al. (2012)  and  Lamichhaney et al. (2017). Simulations of the expected distribution of F ST among domestic populations were conducted under a selective neutrality model. We then compare the genetic drift on X and autosomes using the distributions of simulated and observed F ST values. To obtain the selectively neutral SNP loci, the following criteria were applied, and SNPs met any of the criteria were removed: 1) SNPs are within genic regions; 2) SNPs had less than five reads from each sample; 3) SNPs had >20% missing rates in a population; 4) SNPs had negative observed F ST values. After the filtering, we obtained 27,167 autosomal and 1,795 X-linked neutral SNPs.
2011 ). In the iHS and XP-EHH analyses, we first inferred ancestral alleles by comparing the BeadChip and the WGS SNPs with sheep. oarv3_1. ancestral_alleles (BeadChip SNPs: http://www. sheephapmap. org/download/results/ AncestralAlleles; WGS SNPs: ftp://ftp. ebi. ac. uk/pub/databases/nextgen/ovis/variants/supplemtary_info/; Jiang et al. 2014). Only SNPs which possess ancestral alleles (BeadChip: 1,132 X-chromosomal and 44,031 autosomal SNPs; WGS: 52,325 X-chromosomal and 6,073,533 autosomal SNPs) were kept for the following analyses. We then inferred haplotypes for each sample using the SHAPEIT v2 program  (Delaneau et al. 2013). Finally, we computed the fraction of extreme iHS (jiHSj > 2) or XP-EHH (XP-EHH > 2) and its empirical P values for each pair of comparison using 1 Mb and 200-kb nonoverlapping windows for the BeadChip and the WGS SNPs, respectively.
Both the analyses were implemented in Selscan  (Szpiech and Hernandez 2014)  assuming 1 Mb ¼ 1 cM across the sheep genome. In addition, we quantified and compared the selective intensities between X-chromosome and autosomes by estimating the ratio of selective SNPs (i. e. , jiHSj > 2 or XP-EHH > 2) to all the X-chromosomal or autosomal SNPs. Further, we conducted functional annotation for genes in selective regions using Oar_v4. 0 (https://www. ncbi. nlm. nih. gov/genome/?term=sheep). For those candidate genes, we performed the phenotype enrichment analysis in the MGI database (Mouse Genome Informatics; http://www. informatics. jax. org/) using the Enrichr program  (Chen et al. 2013; Kuleshov et al. 2016). In addition, we implemented Gene Ontology (GO) enrichment and pathway analyses using the DAVID Bioinformatics Resources 6.
In addition, the number of false calls, despite the statistical treatments and quality control (QC) filtering, might be relatively higher. Thus, the allele callings might influence the final estimates. Nevertheless, we ran a site frequency spectrum (SFS) using the program ANGSD v. 0. 902 (Analysis of Next Generation Sequencing Data;  Korneliussen et al. 2014)  and did not find significant (Student's t-test p ¼ 0. 503) difference in allele frequencies between the ones observed and those expected under the mutation-drift equilibrium (supplementary fig. S11 , Supplementary Material online). In this sense, the influence of missed low or rare variants and false calls could be very slight, if there were.
Scan for Selective Signatures In the selection tests between sheep and Asian mouflon using the iHS (integrated haplotype score), F ST , and XP-EHH (crosspopulation extended haplotype homozogysity) statistics  (Weir and Cockerham 1984; Voight et al. 2006; Sabeti et al. 2007)  we identified a total of 54 and 45 extreme windows in the Xlinked region and autosomes in the WGS data, respectively (supplementary fig. S12 , Supplementary Material online). In these selective windows, we annotated 60 genes on the Xchromosome and 93 genes in the autosomes (supplementary table  S10 , Supplementary Material online). In the phenotype enrichment analysis using the MGI database, we found a set of functional genes associated with behavioral response to stress, nervous system development, lethality, pigmentation, and infertility (fig.
high-quality SNPs in sheep and28,926,374 (autosome: 28,358,469; X: 567,905)highquality SNPs in their wild relatives (supplementary table S4, Supplementary Material online), with 7,605,294 SNPs shared between domestic and wild species. As expected, most of the high-quality SNPs were located in intergenic regions, followed by intronic and exonic regions (supplementary table S5, Supplementary Material online). Overall, 94. 43% of the SNPs identified in sheep and 79. 76% in wild ovines were validated using the sheep dbSNP database, indicating a high reliability of the called SNP variants (SNP validation rate in sheep: 89. 46-96. 21%; Yang et al. 2016; compared with, for examples, cross-species SNP validation for domestic horse Equus caballus in the Przewalski's horse Equus ferus przewalskii: 50.
Acknowledgments This study was financially supported by the grants from the  National Natural Science Foundation of China  (Nos. 31272413  and  91731309 ) and the  Taishan Scholars Program of Shandong Province  (No. ts201511085 ). We acknowledge  Rasmus Nielsen  for helpful discussions and comments on an early version of this work. This study makes use of data generated by the  NextGen Consortium. The European Union's  Seventh Framework Programme  ( FP7/2010-2014 ) provided funding for the project under grant agreement no  244356-NextGen.
Enriched Terms Input Genes Enlarged floor plate Premature death Postnatal lethality, incomplete penetrance Belly spot Thymus atrophy Spina bifida Abnormal neural tube morphology No abnormal phenotype detected Abnormal kidney morphology Pale liver B FIG. 8. -Significantly associated phenotypes for the selective genes in the phenotype enrichment analysis through the MGI database (Mouse Genome Informatics; http://www. informatics. jax. org/) using the Enrichr program. (A) The X-linked candidate genes. (B) The autosomal candidate genes.
Soil batch studies The ability of the soil to adsorb P (measured as dissolved reactive phosphorus, DRP) was investigated in batch experiments by adding 90 mL of varying concentrations (2 -175 mg P L -1 ) of synthetic wastewater to flasks containing 5 g soil (n=3). All samples were shaken on a reciprocating shaker for 24 h at 250 excursions per minute (epm) and on removal, were allowed to settle for 1 h, filtered through a 0. 45 µm filter, and tested colorimetrically using a nutrient analyser (Konelab 20, Thermo Clinical Labsystems, Finland). The data were then modelled using a Langmuir isotherm to establish the maximum soil P-adsorption capacity.