dataesr/scientific-paragraphs-categorization huggingface dataset:
The cosmological principle implies that the angular distribution of a field on the sky is statistically isotropic, so the angular power spectrum, C , can contain all the 2point statistical information. The angular power spectrum of the physical shear E-mode shear signal is defined as C ,E = 1 2 + 1 + m=- |a m,E | 2 = C ,κ , (24) where a m are the spherical harmonic coefficients and the brackets average over realisations of the signal. The second equality assumes the flat sky approximation for high 2. We generate a theoretical power spectrum using the Limber approximation with the Cosmosis package  (Zuntz et al. 2015)  with our prior fiducial cosmological parameters: Ω m = 0. 286, Ω Λ = 0. 714, Ω b = 0. 047, h = 0. 7, σ 8 = 0. 82, n s = 0. 962 and w = -1. We use a background galaxy distribution defined from equation 31, and shown in figure  1.
Dark Energy Survey Science Verification Data The shear data are from the 139 deg 2 SPT-E field of the public DES SV data. This initial test data set was taken during an observing run before the official start of the full science survey. The galaxy catalogue comes from the SVA1 (Science Verification) Data Release 3. Due to changes to the catalogues before final release (more galaxy shear measurements are now available to us), the catalogue used in this work is not identical to that used by  Chang et al. (2015) , even when the same data selections are made. All maps are therefore new, and slightly different to the previously published SV map. The photometric redshifts from five optical filters (grizY ) were estimated using the Bayesian Photometric Redshifts (BPZ) code  (Benítez 2000 , Coe et al.
The simulated catalogues contain the lensing ma-trix components, A i j , for each galaxy, calculated with the ray-tracing code CALCLENS  (Becker 2013). This provides the true κ and γ per galaxy, from which we derive the reduced shear. The shape noise due to the intrinsic ellipticities of the source galaxies, s , is simulated by adding an ellipticity component to the reduced shear. Each noise realisation is generated from the data by randomly exchanging the ellipticity values between galaxies in the catalogue to remove the weak lensing signal and leave the shape noise. We attempt to match the redshift distribution of the simulated galaxies to the observed redshift distribution, n(z). We use the stacked posterior probability density functions of individual galaxy redshifts from the selected data catalogue (figure  1 ), giving an estimate of the true underlying distribution.
The final result presented in Sec. 4. 6 compares the reconstruction from the DES SV shear data with foreground galaxy clusters from the redMaPPer catalogue (which are expected to trace non-linearities in the underlying density field). In this work we do not use correlation functions as a test of the map reconstruction. None of the mass mapping methods here are expected to reproduce the correct correlation functions or power spectra. It is simple to show this analytically with the Wiener filter, where despite the filter giving the MAP pixel values, the pixel variance, and therefore the power spectrum, is suppressed.
For the purposes of this comparison the simpler maximum likelihood estimate, ĈMLE , will therefore do. We calculate the maximum likelihood values of µ and C by taking the spherical harmonic transform of our HEALPix map to recover the a m coefficients up to max = 1024, calculating the phase residual as defined by equation 37 between the truth and the reconstruction for each coefficient, and then maximising the likelihood (equation 39). The maximisation is performed using the scipy package BFGS algorithm  (Byrd et al. 1995 , Zhu et al. 1997 , Morales & Nocedal 2011) , using 3 random initialisation values to test for robustness. Figure  7  show the results for the phase reconstruction from 10 simulations using Kaiser-Squires and Glimpse with varying tuning parameters. For Kaiser-Squires the mean phase reconstruction value, ĈMLE , is maximised at σ smooth = 5.
This demonstrates with independent, cosmological data the ability of the methods to reconstruct non-linear structures. The metrics we have used for comparing the three reconstruction methods are generic, and they have been inspired by recent applications of weak lensing mass maps to cosmological studies (e. g. Chang et al. 2016 , Kacprzak et al. 2016 ). These metrics may not be optimal for evaluating every application of mass maps. Future studies can compare the efficiency of the three and other methods in end-to-end analyses; for example, with the estimation of cosmological parameters or identification of galaxy clusters. Applying the Wiener filter and Glimpse methods to the DES Year 1 (Y1) shear catalogue would require extensions of the methods to account for the curved sky at large angular scales.
The Y1 data covers ≈ 1500 deg 2 and contains ≈ 34, 800, 000 galaxies, so is a large increase in data volume from DES SV. This modification has already been done with an extension of Kaiser-Squires to the sphere by  Chang et al. (2017)  for the Y1 DES data. These extensions would also be useful for the upcoming ≈ 5000 deg 2 DES Y3 shear catalogue. Of future interest would be to use the Wiener filter or Glimpse convergence maps for scientific results, as we have shown that they reconstruct the convergence better than Kaiser-Squires according to many different metrics. We have made our map reconstructions (as shown in figure  3  This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U. S. Department of Energy, Office of Science, Office of High Energy Physics.
Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478.
ACKNOWLEDGEMENTS NJ, FBA and  J-LS  acknowledge support from the  European Community  through the  DEDALE grant  (contract no. 665044 ) within the  H2020 Framework Program  of the  European Commission. OL acknowledges support from a  European Research Council Advanced  Grant  FP7/291329  and support from the  UK Science and Technology Research Council (STFC)  Grant No. ST/M001334/1. FBA also acknowledges the support of the  Royal Society  for a  University Research Fellowship. DJJ acknowledges the support of the  National Science Foundation , award  AST-1440254. We are grateful for the extraordinary contributions of our CTIO colleagues and the  DECam Construction, Commissioning and Science Verification  teams in achieving the excellent instrument and telescope conditions that have made this work possible.
Experimental Setup For the purposes of comparison we have created a simple classification task where notes are classified according to one of four work types. The goodness of each of the learned representations are assessed by their performance on this classification task. We extracted two months of Order notes written by telecommunication engineers between March and April 2018. We filtered out any note which contained less than 50 characters, as we judged them not to be adequately meaningful. This resulted in a dataset of 1,610 notes split into four classes -Cabling (CAB -227 notes), Jointing (JRT -789 notes), Overhead (OVH -503 notes) and Power Testing (PTO -91 notes). These classes represent the primary required competence which is associated with each note. The dataset was split into train and test and evaluated using 5-fold cross evaluation.
Results The results are presented in Table  1  2. It is unsurprising that deep metric learners perform better than either tf-idf or Doc2Vec on the classification task, as they are designed to produce embeddings which are optimal for similarity-based return. We can observe that the SNN with the DYNEE training strategy achieved the highest accuracy, though this was only marginally greater than its vanilla counterpart. This is likely due to the small size of the dataset. Tf-idf performed the worst. This was as expected, because as a statistical measure tf-idf does not consider the context of terms. Figure  3  presents a principle component analysis of the representation distribution gained from the Doc2Vec and SNN on a multi-dimension scaling scatter plot. We can observe that the representation distribution gained from SNN demonstrates significantly better similarity clusters.
Self-Management of Cystic Fibrosis One way to foster patient self-management is through personalized apps. However, to deliver high-quality apps, one should base the development on thorough knowledge about user needs, which stems from a user participation methodology. To our knowledge, little research work has been conducted in the area of mobile digital self-management of CF. A search in the Scopus database for articles containing the key words "cystic fibrosis" and either the keyword "app" or "mobile application" returned only two relevant results in May 2015  [19, 20]. Cummings and colleagues  [19]  describe a trial to evaluate the use of an app for CF patients to self-report symptoms and communication with mentors. Their research indicates that digital self-management for CF is promising.
• Design and creation is applied for codesigning, developing, and testing ICT artefacts  [22]. The artefacts are not solely software prototypes but also include paper prototypes and mock-ups. This step maps to the "design" activity in the framework of Gemert-Pijnen et al  [23]. Finally, a pilot trial will be conducted to evaluate the final software prototypes and provide evidence of the usefulness of the solutions. The trial is conducted by the CF care experts involved in MyCyFAPP as an experiment  [22] , investigating changes in physiological parameters and QoL following the introduction of the solutions. In the ICT research study, we do, however, not plan to compare the situations "before" and "after" the trial. The case study, as well as design and creation, follow an interpretive research approach.
We seek to shed light on the tasks needed for the CF care, the challenges encountered in the care, and to derive the implications for ICT support. As a starting point for the study, we use different sources that were earlier summarized in the paper. First, the vision of MyCyFAPP was developed by experts in the CF care, working in six different CF competence centers in five European countries. In addition, a study of the state-of-the-art for digital self-management in health and the testing of available CF-related apps provided us with an initial understanding of the context. During the case study, data were collected through netnography and semistructured interviews.
Semistructured Interviews: Data Collection and Analysis Qualitative semistructured interviews with persons with CF, caregivers, and HCPs with CF care expertise were performed face-to-face, as well as over the phone. Although persons with CF and caregivers describe personal concerns and needs, HCPs share general experience and experience-based advice. These viewpoints are complementary. All interviews were conducted individually, except for practical reasons that of three doctors and one nurse at the Norwegian CF center and of three doctors and dietitians in a Portuguese CF center. The study includes all relevant user groups: children and teenagers with CF in the age range of 4 to 16 years, young adults with CF older than 16 years, parents, and HCPs. HCPs include doctors, nutritionists, and nurses.
"What Digital Support Has the Potential to Usefully Support Persons With Cystic Fibrosis and Their Caregivers in the Cystic Fibrosis Care?" On the basis of the data gathered as described above, we derived a list of main properties and support functions our CF app (or apps) should have to support patients in an optimal way. These properties and functions will be explained in more detail in the following section. Figure  2  gives a summary of this list and shows how app characteristics correspond to the identified needs of the users.
Acknowledgments Authors of this paper, on behalf of MyCyFAPP consortium, acknowledge the  European Union  and the  Horizon 2020 Research and Innovation Framework Programme  for funding the project (ref. 643806). The authors would like to thank all project partners for the collaboration during participant recruitment and project management. Without the dedication of participants in terms of time, effort, and valuable input, this publication would not have been possible. The authors would like to thank all participants who contributed to this work.
Laboratory precautions and controls Throughout the experiment, we strictly applied the laboratory protocols to prevent contamination by alien DNA and PCR products. All pre-PCR laboratory manipulations were conducted with filter tips under a sterile hood in a DNA-free room. The putative presence of contamination was checked at this stage and along the whole laboratory procedure using different negative and positive controls. A large number of research has highlighted several biases occurring at different steps of amplicon HTS (for a detailed list, see  Galan et al. , 2016). These biases can be estimated directly from data by including several controls together with samples in the experiment (for details see Appendix S1): negative controls for DNA extraction (NCext), negative controls for PCR (NCPCR), negative controls for indexing (NCindex: unused dual-index combinations), positive controls for PCR (PCPCR) including mock communities (MC) and positive controls for indexing (PCalien: DNA from beluga whale -Delphinapterus leucasused to estimate the read misassignment frequency).
COI minibarcode, PCR and library construction We used the 133 bp minibarcode of COI described in  Gillet et al. (2015). Its efficiency to identify a wide taxonomic range of arthropods from France has been proven recently: 20 arthropod orders were detected in the diet of Galemys pyrenaicus and 24 in the diet of Neomys fodiens  (Biffi, Gillet, et al. , 2017; Biffi, Laffaille, et al. , 2017). We have verified the discriminatory power of this minibarcode for resolving bat species identification using an in silico analysis based on 444 BOLD (Barcode of Life DataBase) reference sequences corresponding to the 33 bat species found in France (see the phylogenetic tree provided in Figure  S1  and the Table  S2 ). We performed a two-step PCR strategy (see Illumina Application Note Part 15044223) combined with the dual-index paired-end sequencing approach described in  Kozich et al.
Sequence analyses and data filtering We used MOTHUR program v1. 34  (Schloss et al. , 2009)  to create an abundance table for each variant and each PCR product (Table  S3 ). Briefly, MOTHUR enabled to i) contig the paired-end read 1 and read 2; ii) remove the reads with low quality of assembling (> 200 bp); iii) dereplicate the redundant reads; iv) align the variants on a COI minibarcode reference alignment; v) remove PCR primers; vi) remove misaligned variants; vii) correct a part of the PCR and sequencing errors by clustering the variants that are within one mutation of each other for each PCR replicate independently; viii) remove the singleton and rare variants (cut-off = 8 reads) and ix) remove chimeric variants using UCHIME  (Edgar, Haas, Clemente, Quince, & Knight, 2011). Note that step i) enabled to remove an important number of sequencing errors: the pairs of sequences were aligned and any positions with discongruence between the two reads were identified and corrected using the quality score of each read position (see https:// www.
Taxonomic assignment We used BOLD Identification System  (Ratnasingham & Heber, 2007)  and species level barcode records (2,695,529 sequences/175,014 species in January 2017) to provide a taxonomic identification of each variant passing our filtering processes. We provided a 5-class criteria describing the confidence level of the sequence assignments, modified from  Razgour et al. (2011). They were applied to hits with similarity higher than 97% (see details in Appendix  S3 ). For multi-taxonomic affiliation, we kept the common parent of all possible taxa. For sequences exhibiting similarity results lower than 97% in BOLD, we performed a BLAST in GENBANK to improve the taxonomic identification. Finally, results with similarity lower than 97% in BOLD and GENBANK were assigned to the phylum level using the closest taxa, or were considered as unclassified taxa when no match was found in the databases.
Sequencing results & data filtering The MiSeq sequencing of 1162 PCR products including bat samples, positive and negative controls analysed in three PCR replicates generated a total paired-end read output of 6,918,534 reads of the COI minibarcode. MOTHUR program removed seven negative controls because they produced less than 8 reads, 633,788 (9. 2%) of pairedend reads because they were misassembled, 312,336 (4. 5%) of reads because they were misaligned, 125,606 (1. 8%) of reads because they corresponded to rare variants (< 9 reads) and 11,445 (0. 2%) of reads because they were chimeric (Table  S3 ). The remaining reads represented a total of 5751 variants and 5,835,359 reads. The abundance table produced was next filtered. Filtering cross-contaminations using threshold TCC: We observed between 0 and 6,230 reads (total: 34,586; mean: 629 reads; SD: 1289) in the negative controls (NC; n = 58).
In these NC, 90% of the reads represented 11 variants, and 38% belonging to a human haplotype that was detected with a maximum of 3775 reads in the most contaminated negative control (NCPCR). TCC thresholds ranged between 0 and 3775 reads, depending on the variant considered. After filtering the dataset using these TCC thresholds, we kept 5,704,150 reads representing 5697 variants (Table  2 ). Filtering false-assignments of reads using threshold TFA: The 'alien' positive control (PCalien) produced a total of 30,179 reads among which 30,111 were assigned to the nine independent PCRs performed on this DNA. The other 68 reads, i. e. 0. 23%, were misassigned to 52 other samples, with a maximum of 13 reads observed for a given bat faecal sample. The maximum false assignment rate RFA was therefore equal to 0.
043%, and TFA varied between 0 and 244 reads depending on the variant considered. After filtering, the result table included 5697 variants and 5,684,166 reads. Note that TFA excluded reads but not taxa (Table  2 ). Filtering inconsistent results using the three PCR replicates: 74. 1% of occurrence (i. e. cells showing at least one read in the abundance table) were not replicated and were removed. Among these inconsistent results, 74. 6% were positive for only one of the three PCR replicates. The remaining reads represented 2636 variants and 5,172,708 reads (Table  2 ). Finally, for each sample and variant, the reads of the replicated PCRs were summed in the abundance table. The 21 bat samples that did not include any read after data filtering were discarded from the dataset.
Protaetia morio was not detected after data filtering. Insights into the raw dataset showed that some reads were obtained in all three PCRs but with numbers below TCC threshold (TCC = 5 reads for this variant). Sequences of the parasitoid fly Triarthria setipennis were detected at low frequency (0. 2%). The seven insect species included in MC2 were detected, with frequencies ranging from 1. 5% to 27. 3% (expected frequencies 14. 3%). For both mock communities, chimeric reads were detected visually despite the filtering processes using UCHIME program, with frequency levels reaching 3. 4% and 5. 1%, for MC1 and MC2 respectively (see the data for the mock communities in the abundance table after filtering deposited in Dryad : https://datadryad. org/resource/doi:10. 5061/dryad. kv02g/8). The analysis of the 336 bat faecal samples in three PCR replicates led to the detection of 1080 abundant variants and 1232 rare ones.
The abundant variants included 47 variants assigned to bat species (1,974,394 reads) and 925 variants belonging to the phylum Arthropoda (1,619,773 reads). Among these latter, 654 variants were assigned with similarity level higher than 97% in BOLD (1,305,633 reads). Finally, 35 variants could not be assigned to any taxa either in BOLD or GENBANK (80,977 reads) (Fig. 2  and Fig. S2 ). Within samples, the proportion of reads between bats and arthropods was quite balanced, except for Myotis nattereri, Myotis mystacinus and Myotis alcathoe for which lower frequencies of reads from bats were observed (Fig. S2 ).
Diet composition In further diet analyses, we considered the 268 bat faecal samples for which we had a congruent taxonomic identification between morphological and molecular analyses based on the results of one (n = 15 samples), two (n = 15) or three (n = 238) PCR replicates (Fig. S3 ). We removed samples for which no prey data could be analysed, including 20 samples for which only bat sequences were recovered, six samples with only unclassified sequences, eight samples with only sequences of nematodes, plants, fungi and/or algae, and 18 samples for which arthropod sequences were recovered, but with levels of similarity that were too low (<97%) to provide a reliable assignation to a precise taxon. Altogether, diet compositions were described on 216 bat faecal samples, corresponding to 16 bat species.
The reasons might be methodological (e. g. PCR chimera, sequencing errors or PCR drop-out) or biological (presence of NUMTs at low frequencies, low biomass preys, traces of ancient meals). Despite our filtering pipeline, an important proportion of variants that were validated by the three replicates remained at low frequencies. They can be attributed to chimera, pseudogenes or PCR and non-random sequencing errors (see the data for the mock communities in the abundance table after filtering deposited in Dryad: https://datadryad. org/resource/doi:10. 5061/dryad. kv02g/8). We therefore had to eliminate 50% of the less frequent variants (1318 variants over 2636) in order to make taxonomic assignment in BOLD Systems easier. This important number of potentially artefactual variants could be decreased by applying the filtering procedure recently proposed by  Corse et al.
Recent studies have also proposed the use of several minibarcoding primer sets  (Alberdi et al. , 2017; Corse et al. , 2017)  to maximize the taxonomic coverage of metabarcoding approaches and minimize false negative results. Indeed, it is noteworthy that designing COI universal primers generating no PCR amplification biases might not be achievable  (Deagle, Jarman, Coissac, Pompanon, & Taberlet, 2014). Therefore the use of ribosomal rRNA, either mitochondrial 16S or 12S, with conserved flanking regions among taxonomical distant species, might be encouraged. Although this proposal should reduce amplification biases among taxa, it is yet not suitable for arthropod metabarcoding approaches due to the absence of public databases similar to BOLD (i. e. curated database with reference sequences linked to taxonomically verified voucher specimens).