dataesr/scientific-paragraphs-categorization huggingface dataset:
8 , regions SEMC and TI in Fig. 9 ). However, for much of the remaining areas, the European interpolation dataset corresponds better with APGD (see Germany in Fig. 8 ; regions BAV, and AARE in Fig. 9 ). The two downscaling datasets provide clearly improved 22-km precipitation fields compared to the original regional reanalysis (HIRLAM, see Fig. 8 ). This is evident in areas with dense station coverage (e. g. domains SEMC and BAV in Fig. 9 ), where RMSE and SEEPS scores are smaller by a factor of 2 to 5. In the other regions improvements are smaller but still measureable (Fig. 9 ). An exception to this is the Apennine region, where HIRLAM has slightly smaller RMSE and SEEPS (Fig. 8 ). Comparison of the downscaling datasets against E-Obs reveals that MESAN and MES-CAN rarely improve over the interpolation of station data alone when the observational data input is comparable (i.
Intra-seasonal to inter-annual variations In the Alpine region, the variations of precipitation within and between years can be considerable. Departures from the long-term monthly mean typically exceed the range of +/-25 % in more than half of the months, even when considering averages over mesoscale subregions. Several studies have also reported on longterm variations and trends in parts of the region (e. g. Schmidli et al. , 2002; Brunetti et al. , 2006; Efthymiadis et al. , 2006; Zolina et al. , 2008). In this context, the evaluated datasets examined in this study could provide a basis for monitoring the precipitation climate in the Alps and for studying its dynamical relationships (e. g. Klein  Tank et al. , 2002; Quadrelli et al. , 2001). In view of such applications we briefly test the various datasets for their representation of intra-seasonal to inter-annual variations over the recent past.
For this purpose we have derived monthly time series of precipitation anomalies (ratios between actual monthly totals and long-term means of the calendar month) in several subdomains and separately for each dataset. The common 22-km grid was used to ensure consistent calculation of subdomain means, with higher-and lower-resolution datasets re-scaled (see Section 4). APGD is again used as reference. The evaluation is restricted to datasets covering the full 15 years  (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) , i. e. UKMO and MESCAN are omitted. The evaluation of the temporal anomaly series is presented in the form of Taylor diagrams in Figure  10 , comparing the correlation (angular direction) and the temporal standard deviation (radial direction) of the datasets with respect to APGD (circle on x-axis,  Taylor, 2001).
For temporal anomalies over the entire Alpine region (Fig. 10a ) all the datasets provide very consistent time series. Correlations with APGD all exceed 0. 95. Also the magnitude of the variations is accurately captured, except that ERA-INT underestimates the standard deviation significantly, likely due to the limited resolution of mesoscale patterns. Larger differences between the datasets, however, emerge for meso-beta scale subdomains, where resolution and density of input data are more critical (Fig. 10b-d ). Best correspondence with APGD is found for GPCC and MESAN (all subdomains) as well as for E-Obs in areas with dense station coverage (Bavaria). The correlations for CRU, HIRLAM and ERA-INT are clearly smaller and these datasets also underestimate variability. In the mountainous regions Ticino and SE-Massif Central, monthly precipitation anomalies are often strongly influenced by a few events of intense precipitation.
Errors in their spatial extent and amplitude, due to limited station coverage and smoothing, affect the accuracy of the time series. This is reflected, for example, in the substantial improvement of MESAN over E-Obs in SE-Massif Central. It is interesting that MESAN also improves on E-Obs in Ticino, despite similar data density and limited correlation of its background dataset (HIRLAM). It is unclear whether methodological differences in the spatial interpolation/downscaling are responsible for this. In summary, the present evaluation indicates that most of the evaluated datasets provide quite consistent information regarding the temporal evolution of monthly anomalies. Even the coarse resolution global dataset GPCC can provide accurate regional monthly anomaly series. However, there is a tendency to underestimate variability, notably over the smaller domains.
Conclusions We have evaluated and compared several existing and newly developed climate datasets that extend over the entire European continent (at least) and offer themselves for application in environmental research, climate monitoring and the study of climate change impacts. The suite of evaluated datasets encompasses rain-gauge-based interpolation datasets (E-Obs, CRU, GPCC), model-based regional (and one global) reanalyses (UKMO, HIRLAM and ERA-INT) and downscaling datasets (MESAN and MESCAN). The comparison is conducted for daily precipitation in the region of the European Alps. Their topographic complexity and climatic diversity provide an ideal test bed for datasets that promise to resolve mesobeta scale climatic features and heavy precipitation better than existing global and monthly datasets.
The reference is a gridded interpolation from highresolution rain-gauge networks  (1971 ( -2008 ( , Isotta et al. , 2014)). The APGD dataset stands out by uniquely dense (5500 stations on average) and spatially more homogenous data coverage than any of the evaluated datasets. Inaccuracies in the reference dataset are to be expected particularly at small scales, which is why the smallest of the considered scales (5 km) is used for visual comparison only. At the intermediate (22 km) and coarse (55 km) resolution, the reference dataset is expected to have smaller errors than the major discrepancies of the evaluated datasets that we describe here. The new model-based regional reanalyses (UKMO and HIRLAM) show much richer and more realistic spatial variations in the climatology of mean Alpine precipitation and several statistics of daily precipitation than the driving global reanalysis (ERA-INT).
They also correct for several unrealistic spatial features seen in global and continental interpolation datasets, deficiencies related to the limited station coverage available for these latter datasets. In absolute values, however, the regional reanalyses exhibit biases and misplacements of regional anomalies that can result, locally, in discrepancies of similar or larger amplitude like those of the driving reanalysis or from interpolation datasets. The regional reanalyses overestimate mean precipitation in the Alps and they misrepresent mountain-valley and mountainflatland contrasts (overemphasized in HIRLAM and underemphasized in UKMO). They also overestimate the frequency of wet days (light/moderate events) and underestimate that of intense precipitation. The 4DVar UKMO reanalysis underestimates intense events less and exhibits a better temporal correspondence (RMSE, SEEPS) with the reference dataset than the 3DVar HIRLAM reanalysis.
The accuracy of datasets that explicitly use in-situ rain-gauge observations (for interpolation or downscaling) is found to strongly depend on the station density available in their construction. As a result, their performance is very heterogeneous over the Alpine region. In areas with poor data coverage important mesoscale features are missing and there are biases in the frequency distribution (too frequent wet days, too small high quantiles) that relate to reduced effective resolution (larger search neighbourhood, see also  Hofstra et al. , 2009). In areas with dense station coverage, however, the continental interpolation dataset E-Obs is less biased and has better correspondence (RMSE and SEEPS) with the reference than the regional reanalyses. Likewise, when fed with dense station data, the two downscaling datasets MESAN and MESCAN largely correct for biases in the underlying regional reanalysis and reduce RMSE and SEEPS errors substantially (a factor of 2 to 5).
In areas where E-Obs and the downscaling datasets have similar input of station data the skill of the two methodologies is comparable and it is unclear whether downscaling has profited from the usage of a regional reanalysis on top of the station data. Our visual comparisons and reported error patterns (frequency distribution) suggest that all datasets do not fully resolve the spatial variations that one might expect to be resolved on the basis of their respective grid spacing. In model-based regional reanalyses this is due to a smooth topography, processes not resolved at the grid-spacing scale, and, in the case of UKMO, a data assimilation coarser than the model grid. In the interpolation and downscaling datasets it is the limited station network that compromises effective resolution (e.
g. Ensor and Robeson, 2008; Hofstra et al. , 2010; Turco et al. , 2013). As a consequence, there is considerable uncertainty about and spatial heterogeneity in the scales effectively resolved in the datasets. On the one hand this causes difficulties in defining a strictly comparable reference dataset for the evaluation (e. g. Frei et al. , 2003). On the other hand, several applications of such datasets -notably in hydrology and with extremes -are resolution dependent (e. g. Tetzlaff and Uhlenbrook, 2005; Lobligeois et al. , 2013). Here, we recommend to presume that the datasets are of coarser resolution than the grid spacing, which would reduce impacts from the smoothing-related biases seen in our evaluation. Despite biases and inaccuracies in the climatological patterns, the evaluated datasets provide very consistent results on intraseasonal to interannual variations of monthly mean precipitation.
Correlations with the reference dataset are mostly larger than 0. 9 when considering subdomains of 100 × 100 square kilometres or larger. Even the coarse resolution global dataset GPCC is found to provide accurate regional monthly anomaly series in Alpine subdomains. This is a promising result for the utility of the evaluated datasets as a basis for trans-European climate monitoring. What remains to be assessed is, however, how accurately the datasets can reproduce longer-term variations and trends, and how the underestimate of temporal variability, observed with most of the datasets, affects the statistical significance of the trends. Our evaluations illustrate that regional reanalyses provide a valuable new data resource also in regions with a complex climatology. Yet, there are still considerable biases involved and applications will continue to involve bias corrections and downscaling procedures that rely on direct observations.
Acknowledgements The research leading to these results has received funding from the  European Union ,  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n  °242093. We are grateful to the following persons and institutions for providing the evaluated datasets and for commenting on early results:  Tomas Landelius  and  Per Undén  ( Swedish Meteorological and Hydrological Institute, HIRLAM and MESAN );  Eric Bazile  and  Cornel Soci  ( Météo-France , MESCAN);  Peter Jeremy ,  Richard Renshaw  and  Dale Barker  ( Met Office United Kingdom, UKMO );  Anna Ghelli  and  Hans Hersbach  ( ECMWF, ERA-INT ). We also acknowledge the EOBS dataset from the  EU-FP6  project  ENSEMBLES  (http://ensembles-eu. metoffice. com) and the data providers in the  ECA&D  project (http://eca. knmi.
Automated cluster generation and paired-end sequencing with dual index reads was performed in a single 39-h run in a 2 × 250 bp. On the instrument, the global cluster density and the global passed filter per flowcell were generated. The MiSeq Reporter software (Illumina) determined the percentage of indexing and cluster passed filter for each amplicon or library. The raw data were configured in fastaq files for R1 and R2 reads.
Data processing: filtering the reads, dereplication and clustering From the raw fastq files supplied by Illumina Miseq, the Paired End sequences were assembled using pandaseq. 17 Sequences were then extracted from the fasta file (produced by pandaseq) only if the sequences contained the primers that were used in PCR amplification. In the next step, all the sequences containing N and the sequences shorter than 200 nts were removed. The primers were also removed from the sequences. After these filtering steps, the high-quality sequences were strictly dereplicated (clustering of duplicate sequences) and were sorted by decreasing number of abundance. 18 These sequences in sorted order were then clustered at k = 10 (⩾97% identity) number of differences as described previously. 18, 19 Next, the extraction of operational taxonomic units (OTUs; representative sequences of each cluster) was performed, where the representative OTUs are the unique sequences from each cluster that has the maximum number of occurrences during the PCR amplification.
Building the reference database We built our local reference database. First, the release 115 of the SILVA SSU and LSU database  20  was downloaded and from this a local database of predicted amplicon sequences was built. During the construction of our local reference database, we considered only those SILVA SSU reference sequences containing the two primers (which were used in the PCR amplifications), by allowing three differences between each primer and the SILVA reference sequences. 20 Finally, our local reference database contained a total of 479 927 sequences.
Taxonomic assignments The OTUs (representative sequence from each OTU) extracted in the previous step were searched against our local reference database by using a Needleman-Wunsch global alignment algorithm. The best matches 480% similarity with each of the unique sequences were extracted from the reference database. Sorting of these extracted reference sequences was then performed according to the decreasing percentage of similarity, and the fractional values were rounded to an integer. The reference sequences with the highest percentage of similarity with the OTUs were used for taxonomic assignments, and taxonomy to each rank was obtained by the consensus of these taxonomies when there were more results with same percentage of similarity. For example, a tag with 98% similarity to the class Gammaproteobacteria and Alphaproteobacteria was only assigned to the phylum Proteobacteria.
Statistical analysis We calculated the richness and biodiversity index of the OTUs by using the mothur software package  21  with the implementation of the Chao1 and non-parametric Shannon formula. 22 We estimated richness using the Chao1 index and diversity, which depends on how uniformly sequences are spread in different OTUs using the non-parametric Shannon formula. One-way analysis of variance and Tukey's HSD (Honestly Significant Difference) tests were used to statistically analyze BMI, age of the participants, OUTs and the Chao1 and Shannon index from sequence reads of all individuals. Non-parametric Kruskal-Wallis along with Mann-Whitney analyses were performed to identify significantly different bacterial taxa in the study participants. Statistical analyses were performed using SPSS version 20 (IBM's Corporate Privacy Office, New York, NY, USA).
Introduction Word-embeddings are a representation of words with fixed-sized vectors. It is a distributed representation  (Hinton, 1984)  in the sense that there is not necessarily a one-to-one correspondence between vector dimensions and linguistic properties. The linguistic properties are distributed along the dimensions of the space. A popular method to compute wordembeddings is the Skip-gram model  (Mikolov et al. , 2013a). This algorithm learns high-quality word vectors with a computation cost much lower than previous methods. This allows the processing of very important amounts of data. For instance, a 1. 6 billion words dataset can be processed in less than one day. Several authors came up with different methods to align word-embeddings across two languages  (Klementiev et al. , 2012; Mikolov et al.
Trans-gram In this section we introduce Trans-gram, a new method to compute aligned word-embeddings for a variety of languages. Our method will minimize the summation of mono-lingual losses and cross-lingual losses. Like in BilBOWA  (Gouws et al. , 2015) , we use Skipgram as a mono-lingual loss. Assuming we are trying to learn aligned word vectors for languages e (e. g. English) and f (e. g. French), we note J e and J f the two mono-lingual losses. In BilBOWA, the cross-lingual loss function is a distance between bag-of-words representations of two aligned sentences. But as  (Levy and Goldberg, 2014)  showed that the Skipgram loss function extracts interesting linguistic features, we wanted to use a loss function for the cross-lingual objective that will be closer to Skipgram than BilBOWA.
Therefore, we introduce a new task, Transgram, similar to Skip-gram. Each English sentence s e in our aligned corpus A e,f is aligned with a French sentence s f. In Skip-gram, the context picked for a target word w e in a sentence s e is the set of words c e appearing in the window centered around w e : s e [w el : w e + l]. In Trans-gram, the context picked for a target word w e in a sentence s e will be all the words c f appearing in s f. The loss can thus be written as: Ω e,f = (se,s f )∈A e,f we∈se c f ∈s f -log σ( w e • c f ) (2) This loss isn't symmetric with respect to the languages. We, therefore, use two cross-lingual objectives: Ω e,f aligning e's target vectors and f 's context vectors and Ω f,e aligning f 's target vectors and e's context vectors. By comparison BilBOWA only aligns e's target vectors and f 's target vectors.
Implementation In our experiments, we used the Europarl  (Koehn, 2005)  aligned corpora. Europarl-v7 has two peculiarities: firstly, the corpora are aligned at sentence-level; secondly each pair of languages contains English as one of its members: for instance, there is no French/Italian pair. In other words, English is used as a pivot language. No bi-lingual lexicons nor other bi-lingual datasets aligned at the word level were used. Using only the Europarl-v7 texts as both monolingual and bilingual data, it took 10 minutes to align 2 languages, and two and a half hours to align the 21 languages of the corpus, in a 40 dimensional space on a 6 core computer. We also computed 300 dimensions vectors using the Wikipedia extracts provided by  (Al-Rfou et al. , 2013)  as monolingual data for each language.
Reuters Cross-lingual Document Classification We used a subset of the English and German sections of the Reuters RCV1/RCV2 corpora (Lewis and Li, 2004) (10000 documents each), as in  (Klementiev et al. , 2012) , and we replicated the experimental setting. In the English dataset, there are four topics: CCAT (Corporate/Industrial), ECAT (Economics), GCAT (Government/Social), and MCAT (Markets). We used these topics as our labels and we only selected documents labeled with a single topic. We trained our classifier on the articles of one language, where each document was represented using an IDF weighted sum of the vectors of its words, we then tested it on the articles of the other language. The classifier used was an averaged perceptron, and we used the implementation from (Klementiev et al.
) coupled with a Synapt G2 HDMS mass spectrometer (Waters Corp. ) and nano-electrospray ionization, as previously described by Foster et al. (64). Raw MS data were processed either using a Mascot Distiller (version 2. 4. 3. 1) for peptide and protein identification and isobaric quantification or using a Progenesis QI (Nonlinear Dynamics, United Kingdom) for label-free quantification. The MS/MS spectra were searched against the UniProt Clostridium acetobutylicum database using the Mascot search engine (version 2. 4. 1) with the following search parameters: full tryptic specificity, up to two missed cleavage sites, carbamidomethylation of cysteine residues as a fixed modification, and N-terminal methionine oxidation as a variable modification. Determination of DNA, mRNA, and protein contents.
(82) , except that the concentration of H 2 SO 4 was changed to 0. 5 mM, as required for mobile phase optimization. The concentrations of formate and fumarate were measured using a formate assay kit (Sigma-Aldrich) and a fumarate assay kit (Sigma-Aldrich), according to the manufacturer's instructions. Metabolic enzyme expression and purification. Information on metabolic enzyme expression and purification is provided as Text S1 in the supplemental material. Microarray data accession number. The microarray data can be accessed at GEO through accession number GSE69973.
Note that the time difference between dropsonde releases at the sonde locations during the outward and return legs decreases from west to east ranging between 173 (B1) and 31 (B7) min in the morning and between 206 (B1) and 28 (F6) min in the afternoon (see Table  1  for release times). Between the morning return and afternoon outgoing flights, the time difference between repeat sampling ranges between 4 h 12 min (B1) and more than 7 h (B8). Quality control processing of the dropsonde data was performed using the Atmospheric Sounding Processing Environment (ASPEN) software v3. 1-7562 provided by NCAR (http://www. eol. ucar. edu/data/software/aspen). The dropsondes measure pressure, temperature, and relative humidity every 0. 5 s. The instrumental measurement errors are expressed as accuracy repeatability which is the standard deviation of differences between two successive repeated calibrations (k = 2 confidence level) and were obtained from the RD94 technical data sheet (http://cn.
0, and 4. 0 km above MSL, respectively. Dropsondes have a descent speed of about 11 m s À1 near the surface which means that the last measured pressure level over water is at maximum about 5. 5 m above the water surface. Over land, more accurate surface pressure measurements can be achieved because the sondes continue to broadcast after they reach the surface. Therefore, the raw data of each dropsonde were manually inspected to identify the correct surface pressure and the time when the dropsonde reached the surface. The last measurement time was then used to override the ASPEN reported end-of-drop time in cases where it differed. Upward integration from the surface was performed by the ASPEN software whereby the surface altitude was   [Tachikawa et al. , 2011]  at the geographic location where the dropsonde reached the ground.
Acknowledgments The  Fennec  project is the result of the joined efforts of many people from many organizations including  NERC ,  NCAS ,  FAAM, DirectFlight and Avalon Aero  from the UK, five British universities, European partners in France,  Germany , and  Serbia , and the national meteorological offices of Algeria and Mauritania. The authors would like to thank  Malcolm Brooks  ( UK Met Office ) for preparing Africa-LAM forecasts and  Philip Rosenberg  ( University of Leeds ) and  Axel Wellpott  ( FAAM ) for help with the dropsonde measurement errors. Fennec was funded by a  NERC  consortium grant ( NE/G017166/1 ). In addition, Fennec received support from the  NERC National Centre for Atmospheric Science (NCAS) , the  Agence Nationale de la Recherche  ( ANR 2010 BLAN 606 01 ), the  Institut National des Sciences de l'Univers (INSU/CNRS)  through the  LEFE program , the  Centre National d'Etudes Spatiales (CNES)  through the  TOSCA program , and  Météo-France.
Airborne data from the BAe146 were obtained using the BAe-146-301 Atmospheric Research Aircraft operated by  Directflight Ltd  and managed by  FAAM , which is a joint entity of  NERC  and the  UK Met Office. Airborne data from the F-20 were obtained using the Falcon 20 Environment Research Aircraft operated and managed by  SAFIRE , which is a joint entity of  CNRS ,  Météo-France , and  CNES. BAe146 data sets are available through the  British Atmospheric Data Centre. Falcon 20 data sets are available through the AMMA data portal at http:// database. amma-international. org. Journal of Geophysical Research: Atmospheres 10. 1002/2015JD023123.