dataesr/scientific-paragraphs-categorization huggingface dataset:
Errors associated with the first principles-derived k-factors provided by the instrument manufacturer have been reported at approximately 8% using the Co-K and Pt-L lines  (42). However, calculated Fe and Ni k-factors for K lines are known to have particularly low errors (1% to 2%)  (43)  and are systematic and of similar small magnitudes for Fe and Ni (and will therefore be substantially reduced in relative composition). As such, we report 2% errors for EDS quantification in line with the X-ray counting statistics. APT. APT experiments were carried out on a LEAP 3000X-HR instrument (University of Oxford), running in laser mode with a 532 nm beam operating at 0. 4 nJ. A small subset of samples were also run in voltage mode, to confirm the accuracy of composition data, although the data yield was lower from these.
The specimen stage temperature was kept at 55 K for all experiments. Data were reconstructed using IVAS software (3. 6. 12). The composition of the islands and matrix was obtained by placing numerous cylindrical regions of interest at least 1 nm away from the nearest interface, in order avoid sampling neighboring phases (SI Appendix, Fig. S3 ). The atoms inside multiple cylinders across multiple datasets were summed to produce the average compositions quoted. The compositional variations of either phase between different APT datasets were not statistically significant. SPED. SPED allows for detailed crystallographic mapping of a sample by precessing the electron beam about a small angle. This results in diffraction patterns where the dynamical effects of scattering are minimized and we are able to record a larger region of the Ewald sphere  (44).
For Fig. 6 , a custom distance metric was used, based on the identification of peaks and their cumulative distance from those in other patterns. The distances themselves form clusters; these were used to derive the localization maps. The representative patterns were derived as the weighted means of the patterns in those areas. Micromagnetic Modeling. The Finite Element Method/Boundary Element Method (FEM-BEM) micromagnetics package MERRILL (Micromagnetic Earth Related Rapid Interpreted Language Laboratory) was used to solve for the magnetic scalar potential inside each particle and thereby calculate the demagnetizing energy of the system  (47, 48). This approach avoids the need to discretize the nonmagnetic volume outside the particle. Tetrahedral meshes of average 1. 6 nm spacing were used for modeling magnetic behavior of the particles to ensure that exchange interactions were being accounted for appropriately.
ACKNOWLEDGMENTS. J. F. E. , P. A. M. and R. J. H. acknowledge funding under  ERC  Advanced Grant 320750-Nanopaleomagnetism. S. M. C. and P. A. M. acknowledge funding under  ERC  Advanced Grant  291522-3DIMAGE. S. M. C. acknowledges the  Henslow Research Fellowship and Girton College, Cambridge. A. S. E. and B. H. M. acknowledge financial support from the  Royal Society.
Radiation force field simulation The shear wave propagation field was based on a Green's function formalism  (Aki and Richards 1980, 2001). The simulated medium was assumed to be semi-infinite, isotropic or transverse isotropic, elastic and homogeneous. For all the details in Green's function formalism, interested reader may refer to the appendix. The radiation force field was calculated using the Field II ultrasound software  (Jensen and Svendsen 1992, Jensen 1996). The matrix-array transducer used to simulate fived push-beams was designed numerically with the parameters: 32 × 32 transducer elements, 9. 6 mm 2 size, 0. 3 mm pitch, 5 MHz central-frequency. Push-beams were simulated at five different lateral and elevational locations (i. e. at the x-and y-directions parallel to the surface of the matrix-array probe): at (0, 0) mm, i.
Post-processing analysis A 3D delay-and-sum beamforming using spatial coherent compounding was performed, as described by  Provost et al (2014) , to reconstruct the ultrasound volumes (i. e. beamformed IQ demodulated data). In in vitro and in vivo results, volumes of (9. 6 × 9. 6 × 25) mm 3 and (19. 2 × 19. 2 × 25) mm 3 were reconstructed, respectively. Tissue velocity estimates were obtained through the Kasai autocorrelation method  (Kasai et al 1985). Prior to phase estimation and the 3D directional filtering, the data was filtered using a spatiotemporal clutter filter  (Demene et al 2015). The final tissue velocity movie was then composed of 38 imaging volumes of (9. 6 × 9. 6) mm 2 and 25 mm depth. Tensor estimation was then performed using the method described in section 2. 2. Finally, the 3D volume rendering of the shear wave propagation and a tissue fiber tractography representation (i.
e. the 3D elastic tensor representation) were performed using the volren and illuminated streamlines functions of the Amira software (Visualization Sciences Group, USA), respectively. All beamforming and elastic tensor assessment processing were performed offline using a graphical processing unit (Tesla K40, NVidia, USA) within a Matlab (2014b, The MathWorks Inc. , USA) interface. The post-processing time was approximately 10 min. This duration included all 4D ultrafast data transfers and calcul ations, i. e. the beamforming, directional filtering, shear wave velocity vector components estimation, fiber tractography.
An estimate of -1 means that all ties are the same as the respondent on smoking status (ie homophilous) and 1 reflects that none of the ties are like the respondent on smoking status (heterophilous). Finally, percentage of network ties who smoke, percentage of network ties the same age or older than the respondent, and percentage of network ties with the same or more education were calculated. All analyses were performed using Statistical Analysis Software v9. 3 (SAS; http://support. sas. com/software/93). Differential distributions of sociodemographic characteristics by smoking status were analyzed using χ and Kruskal-Wallis tests. To determine the association between selected individual, interpersonal (including social network), workplace and neighborhood/community-related characteristics and smoking status (categorized as never, former and current smoker), generalized linear models (using the generalized logit link) were fit using smoking status as the dependent variable and multilevel characteristics as reported by each woman as independent variables.
Non-response bias is of concern because it limits one's ability to generalize findings to the population of interest. In addition, the sample was primarily white; although representative of the local study region, this presents challenges for generalizability. In addition to the limitations caused by representativeness, participants reported the smoking status of their network members, increasing potential for misclassification on key factors including smoking status. Finally, due to the cross-sectional nature of the data and the modeling methodology , relationships between multilevel characteristics and smoking status cannot be interpreted as causal. Disparities in tobacco use between privileged and vulnerable populations have increased due to differential rates in access and treatment response to existent tobacco cessation intervention.
Acknowledgements This study was supported by a grant from the National Cancer Institute (P50CA105632) and the Behavioral Measurement Shared Resource at the Ohio State University Comprehensive Cancer Center (P30CA016058). This project was also supported by a grant from the National Center For Advancing Translational Sciences (USA) (UL1TR001070). Grant sponsors had no role in study design; collection, analysis and interpretation of data; article writing; or decision to submit this manuscript for publication. REFERENCES:.
, 2015). A website was developed with a 31-item questionnaire to collect data on sociodemographic and anthropometric characteristics, quality of life (QoL), obesity-related life experiences, expectations about weight loss and management, and other obesity-related psychosocial issues. The only condition for participating in the survey was being 18 years of age or older. Every attempt was made to avoid stigmatization, as described in a previous paper by  Pétré et al. (2015).
Statistical analysis Quantitative data were summarized by median and interquartile range (IQR: P25-P75). For qualitative variables, the number and percentage of subjects in each category were given. Distributions were also displayed graphically by Boxplots. Logistic regression analysis was used to determine the relationship between weight-loss goal (< 10% vs. ≥10%) and each determinant separately and in combination. The strength of association was measured by the odds ratio (OR) and its 95% confidence interval (95%CI). Calculations were always carried out on the maximum number of data available. No specific treatment of missing values was performed. Statistical tests were two-sided and results were considered significant at the 5% level (P < 0. 05). Statistical calculations were done using SAS (version 9.
Methods The lower second molar of specimens was used whenever available (Fig. 2 ). Whenever this tooth was unavailable or altered, other lower or upper molars were selected for study. This information is provided for every specimen in the supplementary data (Table  S1 ). Following standard procedures  (Ramdarshan et al. , 2016) , each tooth was carefully cleaned with acetone. The disto-lingual facet of the protoconid was molded with a high-resolution polyvinylsiloxane elastomer (Regular Body President, ref 6015-ISO 4823, medium consistency, polyvinylsiloxane addition type; Coltene Whaledent). The silicon mold was scanned with a Leica DCM8 white light confocal surface profilometer with a 100 × objective (numerical aperture = 0. 90, working distance = 0. 9 mm) at the IPHEP lab (CNRS and University of Poitiers, France) in order to avoid the resin-based cast step.
For each specimen, a 251 × 333 m surface (2584 × 1945 points) was scanned at the center of the dental facet. A 200 × 200 m sub-surface was generated from this surface, leveled and mirrored in Z. A smooth shape replaced the few missing points using an algorithm calculated from the neighboring points. A macro  (Merceron et al. , 2016)  was run in order to automatically erase abnormal peaks. Remaining bigger artifacts and exogenous particles were removed manually and replaced by a smooth shape before proceeding to a final leveling of the surface with Mountains Map Premium 7. 3 software. Surfaces were then analyzed using a Scale Sensitive Fractal Analysis (SSFA) with ToothFrax and Sfrax (Surfract, Browsing habits result in an intermediate to high complexity coupled with low to intermediate anisotropy.
Acknowledgements We thank  S. Ramdarshan  (https://srtranslations. wordpress. com/)for corrections of the manuscript and  S. Riffaut  for help with Fig. 2. For the access to fossil material, we thank  E. Robert  ( Geological Collections, UMR CNRS 5276 Laboratoire de Géologie de Lyon -CERESE, University Lyon 1, France ),  D. Berthet  ( "Collection du musée des Confluences", Lyon, France ),  C. Jacquier ,  M. Fouché  ( "Musée paléontologique de Chilhac", France ) and  N. Spassov  ( National History Museum of Sofia, Bulgaria ). For the access to the reference material, we thank  Rafał Kowalczyk  ( Mammal Research Institute, Polish Academy of Sciences, Białowie ża ) and  Concepción Azorit  ( University of Jaén, Spain ). We also would like to thank the editor  Lorenzo Rook ,  Daniel DeMiguel  and two other anonymous reviewers for making constructive comments and suggestions leading to an improvement of the manuscript.
Metagenomic insights into the Chr. okenii population in Lake Cadagno To assess the genomic potential for light-independent, aerobic sulfide oxidation by Chr. okenii in Lake Cadagno, we sequenced two metagenomes, one from the chemocline and one from a phototrophic, sulfide-oxidizing enrichment culture obtained from the lake (Supporting Information Table  S1 ). From a combined metagenomics assembly, we reconstructed a high quality (90% complete, <1% contaminated) metagenome-assembled genome (MAG) of a PSB highly abundant in the sulfur-oxidizing enrichment culture (Supporting Information Fig. S12 ). The recovered MAG had a low average nucleotide identity ANI (<70%) to any sequenced Chromatiaceae genomes (data not shown). However, it encoded an rRNA operon, including a complete 16S rRNA gene with 99% sequence identity to the 16S rRNA gene of Chr.
Sampling The meromictic Lake Cadagno is situated in the Piora Valley in the Swiss Alps at an altitude of 1921 m. Data presented here were collected during field campaigns in September 2013, August 2014, June 2015, August 2015 and August 2018. In 2013 and 2014 in situ measurements were performed with a profiling ion analyser (PIA; see  Kirf et al. , 2014  for description) lowered from a platform anchored at the deepest part of the lake (20. 7 m). Conductivity, turbidity, depth (pressure), temperature and pH were measured with a multiparameter probe (XRX 620, RBR). Dissolved oxygen was recorded online with a type PSt1 normal (detection limit 125 nM) micro-optode and a type TOS7 trace (reliable detection limit 50-100 nM) microoptode (PreSens). The oxygen sensors were calibrated by parallel Winkler titrations.
Chemical analyses Sulfide was measured using the colorimetric method of  Cline (1969). Sulfate was measured on a 761 Compact ion chromatograph (Metrohm) equipped with a Metrosep A SUPP 5 column. Intracellular sulfur on filters was extracted by sonication in methanol for 15 min in an ice bath. Samples were analysed on an Acquity H-Class UPLC system (Waters Corporation) with an Acquity UPLC BEH C18 column coupled to a photodiode array (PDA) detector using UPLC-grade methanol as eluent. Data were acquired and processed using the Empower III software. Intracellular glycogen was analysed following the procedures of the assay kit (MAK016, Sigma Aldrich). Briefly, cells were extracted by scraping them from GF/F filters and homogenizing in 200 μl extraction buffer and centrifuged two times to clear the supernatant.
The DNA was gel-purified using SYBR Green I Nucleic Acid Gel Stain (Invitrogen) and the QIAquick Gel Extraction Kit (Qiagen) according to the accompanying protocols. DNA concentration was determined fluorometrically at 260 nm, using the Qubit 2. 0 Fluorometer and the Qubit dsDNA HS Assay KIT (Invitrogen) and sent to the Max Planck-Genome Centre (Cologne, Germany) for sequencing. The metagenome was sequenced (100 bp paired end reads) by Illumina HiSeq (Illumina Inc. , USA) sequencing following a TruSeq library preparation. Metagenomic reads were adapter-and quality-trimmed (phred score 15, bbduk function of the BBMap package, https:// sourceforge. net/projects/bbmap/) and paired-end reads were de novo assembled with the uneven depth assembler IDBA-UD  (Peng et al. , 2012). The metagenome assembly was binned based on tetranucleotide frequencies, differential coverage, taxonomic classification and conserved single-copy gene profiles with the Metawatt binning software (version 3.
5. 2; Strous et al. , 2012). The completeness and contamination of the binned MAGs was evaluated with CheckM  (Parks et al. 2014). The bulk metagenome and the MAG identified as Chr. okenii were automatically annotated in IMG  (Markowitz et al. 2011) , and the Chr. okenii MAG was manually screened for the presence of genes of interest to this study. Assembled data are available in IMG, under the IMG genome IDs 3300010965 (bulk assembly) and 2700988602 (Chr. okenii MAG). Cytochrome c and quinol oxidase subunit I and the cytochrome bd oxidase subunit I proteins were extracted from the metagenome via Pfam motif search (PF00115 and PF01654 respectively), and manually inspected to exclude NO reductase subunit I (NorB) proteins and proteins shorter than 100 aa prior to downstream analysis. A total of 130 out of 178 identified cytochrome c and quinol oxidase subunit I proteins and 76 out of 82 recovered cytochrome bd oxidase subunit I proteins passed the manual curation step.
Fig. S13. Phylogenetic tree showing the fraction of diversity of (A) subunit 1 of the cytochrome cbb3-type hemecopper oxidases and (B) subunit 1 of the cytochrome bd-type oxidases recovered in the Lake Cadagno metagenome, with NorB as the outgroup. Only those sequences affiliated with bacteria known to participate in oxidative sulfur cycling are shown. Chr. okenii sequences from the bulk metagenome and MAG have been de-replicated and both gene IDs are shown. Table  S1. Sequencing and assembly statistics for the metagenomics datasets recovered from the Lake Cadagno chemocline and a phototrophic, sulfide-oxidizing enrichment culture. Movie S1. Video of Chr. okenii swimming in the dark.
Acknowledgements We are grateful to the 2014 and 2015  Cadagno Field Expedition Teams  from  EAWAG  and  MPI Bremen  for assistance in the field, and to the  Alpine Biology Center Foundation (Switzerland)  for use of its research facilities. We would especially like to thank  Abiel Kidane  and  Sten Littman  for help with nanoSIMS measurements as well as  Nadine Rujanski ,  Gabriele Klockgether  and  Gaute Lavik  for mass spectrometry analyses. Funding was provided by the  International Max Planck Research School of Marine Microbiology , the  Max Planck Society  and the  Deutsche Forschungsgemeinschaft  (through the  MARUM Center for Marine Environmental Sciences). A. S. was supported by the  Human Frontier Science Program  ( Cross Disciplinary Fellowship ,  LT000993/2014-C ).
Individual Electrical Consumption Data: A State-of-the-Art Individual consumption data analysis is, according to the development of smart meters, a popular and growing field of research. Composing an exhaustive survey of recent realizations is then a difficult challenge not addressed here. As detailed in  [3] , individual consumption data analytics covers various fields of statistics and machine learning: time series, clustering, outlier detection, deep learning, matrix completion, online learning among others. Given a data set of individual consumptions, a first natural step is exploratory: clustering, which is the most popular unsupervised learning approach. The purpose of clustering is to partition a dataset into homogeneous subsets called clusters (see  [8] ). Homogeneity is measured according to various criteria such as intra and inter class variances, or distance/dissimilarity measures.
In  [23] , a k-means procedure is applied on features consisting of mean consumption for 5 well chosen periods of day, mean consumption per day of a week and peak position into the year. In each cluster a deep learning algorithm is used for forecasting and then the bottom up forecast is the simple sum of clusters forecasts. Results showing a minimum gain of 11% in forecast accuracy are provided on the Irish data set and smart meter data from New-York. On the Irish data again, Ref. [3]  propose to build an ensemble of forecasts from a hierarchical clustering on individual average weekly profiles, coupled with a deep learning model for forecasting in each cluster. Different forecasts corresponding to different sizes of the partition are at the end aggregated using linear regression. We propose here a new approach, following the previous work of  [24] , to build clusters and forecasting models that are performant for the bottom-up forecasting problem as well as from the computational point of view.
The paper is organized as follows. After this first section introducing the industrial context and a state-of-the-art review of individual electrical data analysis, Section 2 provides the big picture of our proposal for bottom-up forecasting from smart meter data, without technical details. The next three sections focus on the main tools: wavelets (Section 3) to represent functions and to define similarities between curves, the nonparametric forecasting method KWF (Section 4) and the wavelet-based clustering tools to deal with electrical load curves (Section 5). Section 6 is specifically devoted to the upscaling issue and strategy. Section 7 describes an application for forecasting a French electricity dataset. Finally, Section 8 collects some elements of discussion. It should be noted that we tried to write the paper in such a way that each section could be read independently of each other.
, N} for the locations b and {a j , j = 1,. J} for the scales a. The boundary scales (smallest and greatest) are generally taken as a power of two which depend respectively on the minimum detail resolution and the size of the time grid. The other values correspond usually to a linear interpolation over a base 2 logarithmic scale. While the measure is justified by the power of the wavelet analysis, in practice this distance implies heavy computations involving complex numbers and so requires of a larger memory space. This is one of the two reason that renders its use on the original dataset intractable. The second reason is related to the size of the dissimilarity matrix that results from its applications and that grows with the square of the number of time series. Indeed, such a matrix obtained from the SC is largely tractable for a moderate number of super customers of about some hundreds, but it is not if applied on the whole dataset of some tens of millions of individual customers.
Algorithm Description The massive dataset clustering algorithm is as follows: 1. Data serialization. Time series are given in a verbose by-column format. We re-code all of them in a binary file (if suitable), or a database. 2. Dimensionality reduction. Each series of length N is replaced by the log 2 (N) energetic coefficients defined using a wavelet basis. Eventually a feature selection step can be performed to further reduction on the number of features.
• an ASCII file, one sample per line; very fast, but data retrieval will depend on line number; • a binary format (3 or 4 octets per value); compression is unadvised since it would increase both preprocessing time and (by a large amount) reading times; • a database (this is the slowest option), so that retrieval can be very quick. Since we plan to deal with millions of series of thousands time steps, binary files seemed like a good compromise because they can easily fit on disk-and often also in memory. Our R package uses this format internally, although it allows to input data in any of these three shapes. If we were speaking of billions of series of a million time steps or more, then distributed databases would be required. In this case one would only has to fill the database and tell the R package how to access time-series.
Data Presentation We work on the data provided by EDF also used in  [24]  which is composed of big customers equipped with smart meters. Unfortunately, this dataset is confidential and cannot be shared. Nevertheless we suggest to the reader interested in bottom up electricity consumption forecasting problems to refer to the open data sets listed in  [3]. The dataset consists in approximately 25000 half-hourly load consumption series over two years  (2009) (2010). The first year is used for partitioning and the calibration of our forecasting algorithm, then the second year is used as a test set to simulate a real forecasting use-case. The initial dataset contains over 25,000 individual load curves. To test the up-scaling ability of our implementation, we create three datasets of sizes 250,000; 2,500,000 and 25,000,000.