dataesr/scientific-paragraphs-categorization huggingface dataset:
Confirmation This project was supported by a Gorilla grant that provided free online task hosting (to L. F. ). This research was also supported by the  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d'Avenir  ( ANR-16-IDEX-0005 ; to D. N. ); the  ATIP-Avenir Program  (to R. Q. ),  National Brain Research Program  (Project  2017-1. 2. 1-NKP-2017-00002 ). Project no. 128016  has been implemented with the support provided by the  Ministry of Innovation and Technology of Hungary  from the  National Research, Development and Innovation Fund , financed under the  Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI)/OTKA K Funding Scheme  (to D. N. ). The preparation of this article was supported by the  Richter Gedeon Talentum Foundation , established by  Richter Gedeon Plc.
Scanning strategy The heterodyne receivers currently available at the IRAM 30 meter telescope can only record the emission toward a single direction of the sky at any time. They are thus called single-beam receivers. To make an image with such a detector, we need to scan the sky at a constant angular velocity along lines of constant right ascension or declination. The signal is continuously recorded and dumped at regular time intervals. This observing mode is called on-the-fly observations. The data consist of a set of spectra that cover the target field of view in a set of parallel lines. The angular distance (∆θ) between the lines is set to satisfy the Nyquist sampling criterion ∆θ = λ 2 D , ( 13 ) where λ is the smallest observed wavelength, and D is the singledish telescope diameter (30 m here).
Neural networks Artificial neural networks are a class of statistical machine learning methods that were originally designed to simulate the behavior of the brain. Today, they are widely used in data science because they allow any nonlinear functions in high dimensional spaces to be modeled easily. More precisely, we use architectures derived from the multilayer perceptron (Shalev-Shwartz & Ben-David 2014). Multilayer perceptrons are composed of a succession of matrix products and nonlinear functions called activation functions. They are interesting because they are universal approximators of any continuous function when they have at least one hidden layer and this layer contains enough neurons  (Hornik et al. 1989). Appendix E gives more details. The modeling of a nonlinear function by a neural network can be considered as a global optimization problem that is solved through stochastic gradient descent.
Estimating the intrinsic dimension of a dataset When denoising by reduction dimension, the amount of denoising is related to the redundancy in the input data, which allows one to reduce the dimension without loosing relevant information. If the dimension of the input data is called the extrinsic dimension and the dimension of the bottleneck the intrinsic dimension, we thus wish to measure the intrinsic dimension of the data. The extrinsic dimension is necessarily greater than or equal to the intrinsic dimension. An autoencoder neural network is interesting here because it is a practical algorithm that encompasses the whole category of methods that assumes a reduction of dimension to denoise the data (see Sect. 1). We use the autoencoder to analyze the intrinsic dimension of the signal with respect to the extrinsic dimension of the data, and thus emphasize the amount of redundancy that could be used to increase the S/N.
Comparison of the intrinsic dimension between the ORION-B cubes and a typical hyperspectral cube Figure  10  compares the evolution of the mean absolute deviation as a function of the dimension of the bottleneck for two datasets: The ORION-B 13 CO (1 -0) line cube on the left panel, and a Earth remote sensing hyperspectral cube, named Indian Pines 4 , that is used to benchmark denoising algorithms on the right panel. This comparison is useful because  Licciardi & Chanussot (2018)  showed that dimension reduction with a neural autoencoder is particularly efficient to denoise the latter dataset. The intrinsic dimension of Indian Pines can be estimated at around 4. In constrast, the curve for 13 CO (1 -0) only has a clear elbow at about 27. This implies that the intrinsic dimension of the signal is close to its extrinsic dimension.
This confirms our previous finding that the measured mutual information scale is small for the ORION-B line data (see  Sect. 4. 3). Two main properties explain the different behaviors of the 13 CO (1 -0) and Indian Pine cubes. The astronomy line cube contains many signal-less channels that are irrelevant for scientific purpose but can be used to characterize the noise properties. Moreover, the achieved spectral resolution still limits the amount of redundancy inside the sampled line profile. In contrast, almost all the channels of Indian Pine cube are scientifically relevant and (anti-)correlated. In this respect, denoising by dimension reduction would be easier for astronomy hyperspectral cubes observed with direct detection imaging spectrometers used to study the spectral energy distribution of the sources, in-4 The latter dataset, named Indian Pines, has been acquired with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor over an agricultural area located at northwestern Indiana, USA.
To handle varying S/N values the distance is usually weighted by the standard deviation of the noise. In our case, the baseline part of the spectrum enables us to easily estimate the 5 Unlike a dense layer used in a perceptron which is composed of a matrix product, a convolutional layer is composed of a linear filter. noise standard deviation σ k for the spectrum d k at pixel k = i x + n x (i y -1). We thus will modify the loss function as L(A, d) = 1 K K k=1 A(d k ) -d k σ k 2. ( 21 ) This normalization avoids the variation of the data "energy" just caused by noise, which would overweight the noisiest pixels. We recognize here the reduced χ-squared merit function that is regularly used in astronomy. In contrast, the machine learning community mostly uses the MSE. To address the problem of sparsity of the signal inside the cube, we balance the loss function by giving prior information about the channels that have a large probability to be just noise.
Detailed setups of the autoencoder and ROHSA We use the Python framework PyTorch to implement our numerical neural network experiments  8. The segmentation of the line cubes is implemented in a new IRAM software named CUBE and distributed inside GILDAS  9. The associated Python and CUBE scripts are available in a GitHub repository 10. We use the approximately 800 000 spectra of 240 channels as input to the autoencoder. We tagged as mostly signal the voxels that belong to the first 200 segments obtained with a S/N threshold of 2, and the reminders as mostly noise. The hyperparameters of the autoencoder were optimized as follows. The width of the sliding window is set at 7 channels according to the mutual information scale (see  Sect. 4. 3). Most of the other hyperparameters were set with a typical cross validation procedure  (Refaeilzadeh et al.
2009). In short, we first defined a set of possible values to explore. For each set of hyperparameters, we then optimized the network on a training dataset and we compute its performance on a different validation dataset. In order to reduce the variability of the results depending on the choice of the training and validation sets, this procedure is performed several times, varying the test and validation sets so that each sample has been selected once in the validation set during the procedure. This gives for the local encoder: A bottleneck size of 75% the number of input channels (here 180), and 3 hidden layers of size  [35, 14, 7]  per perceptron. During this cross validation procedure, the hyperparameters that are assumed noncritical are fixed to usual values: The Adam stochastic optimizer  (Kingma & Ba 2014)  was used with a batch size of 100, 50 epochs, and a learning rate that decreases exponentially from 10 -3 to 10 -6.
Conclusion In this paper, we have proposed a promising approach to denoise radio-astronomy line data cubes, inspired by a method developed to denoise hyperspectral cubes in Earth remote sensing. To do this, we first characterized in-depth the properties of the noise and signal for two radio-astronomy position-positionvelocity cubes that are part of the ORION-B IRAM 30m large program, namely the 13 CO (1 -0) and C 17 O (1 -0) cubes. -The additive noise is well represented by a Gaussian random variable. Its RMS value varies spatially and spectrally. It can be modeled as the product of a spatial and a spectral contribution. -The spatial variations come from a combination of the source scanning strategy, variations of the atmospheric conditions between winter and summer runs, for example, and the source elevation during each observing session.
We adapted the typical architecture to our line data as follows. 1. The proposed architecture took the fast decorrelation of the signal into account as a function of frequency. 2. We took the sparsity of the signal into account inside the spectrum by adapting the loss function of the autoencoder depending on whether the voxels contain mostly signal or mostly noise. This implies an a priori position-positionfrequency classification algorithm. 3. For signal voxels, we weighed the distance between the data and the autoencoded data by the inverse of the noise variance. For noise voxels, we used the L 1 norm between the autoencoded data and zero to ensure that the autoencoder would not create or destroy flux for low S/N voxels. We finally compared the denoising performance to that achieved by the ROHSA algorithm that represents the spectra as a set of Gaussian fits.
Acknowledgements. This work is based on observations carried out under project numbers  019 -13 ,  022-14 ,  145-14 ,  122-15 ,  018-16 , and finally the large program number  124-16  with the IRAM 30m telescope. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This work was supported by the  French Agence Nationale de la Recherche  through the  DAOISM  grant  ANR-21-CE31-0010 , and by the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP , co-funded by  CEA  and  CNES. This project also received financial support from the  CNRS  through the  MITI interdisciplinary programs. JRG and MGSM thank the  Spanish MCINN  for funding support under grant  PID2019-106110G-100. Part of the research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ).
Confounding factors Information on socioeconomic status (SES) was collected, based on nationally available data sources, in the following countries using the information, for individuals from the following countries, representing 32. 3% of the EPI-CT cohort: • Belgium: SES derived from the healthcare reimbursement classification based on the annual income of the household (two categories: lower or normal); • France: SES based on Townsend deprivation scores, obtained from linkage of residential postal code (five quintiles) with census data; • the Netherlands: SES derived from average household income and house value for six-digit postal codes (average population, 40 persons) of cohort members' residential addresses from Statistics Netherlands; • Spain: SES based on the Synthetic index of urban vulnerability generated according to the socio-economic characteristics of the census tract that included the area of residence (five quintiles).
Statistical analysis Descriptive analyses included the distribution of cases and cohort members by sex, country, age-at-exposure, attained age and time since exposure. Dose-response analyses were conducted for all outcomes listed above by modeling the RR as 1 + βΖ, where Z is the cumulative dose and β is the ERR per unit dose. The model was fit with proportional hazards regression using the custom-developed R module rERR: Excess Relative Risk Models R package version 0. 1 (ref. 43). Exact age of the individuals was used as the underlying time variable, and all models were stratified by sex, country and birth cohort (1960-1979, 1980-1984, 1985-1989, 1990-1994,  1995-1999, 2000-2004 and 2005-2012). We also fit an EAR model using the PEANUTS module of the EPICURE software (version 2. 00. 02) to estimate the absolute excess number of hematological malignancies per 10,000 PYs and per dose Z.
We also terminated follow-up 2 years above the age limit for inclusion of scans in each country (as doses received later in life were not collected within the project) and excluded the subcohorts lacking mortality follow-up. We repeated analyses using the number of CT examinations instead of ABM dose. To allow comparison of our estimates with those of the atomic bomb survivor study, we conducted analyses of leukemia and HL risk in that study using publicly available grouped incidence data  19  , restricted to the population and follow-up most relevant for EPI-CT: less than 20 years old at time of bombing, with attained age less than 35 years. These analyses, adjusted on attained age, sex, birth cohort and city, were conducted using the AMFIT module of EPICURE.
Acknowledgements The authors are grateful to the radiologists, clinicians, physicists and administrators of participating hospitals and the national/regional cancer, mortality and cause of death registries that provided exposure and radiological data for the study. The authors gratefully acknowledge scientific and technical assistance provided by:  Belgium :  J. Geens  and  H. Bosmans  from the participating Belgian hospitals and to the  Belgian Cancer Registry  for providing cancer data. Denmark: the radiological departments participating in organizing data collection. IARC :  C. Chassin  for administrative assistance in the overall study coordination. France: the radiologists, physicists and administrators working in the participating hospitals who took so much of their time to provide us with the necessary radiology and clinical data:  D.
The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , the  Department of Health or Public Health England. This report makes use of data obtained from the  Radiation Effects Research Foundation (RERF) , Hiroshima and  Nagasaki, Japan  and cites risk estimates calculated by  RERF. RERF is a private, nonprofit foundation funded by the  Japanese Ministry of Health, Labour and Welfare  and the  US Department of Energy , the latter through the  National Academy of Sciences. The conclusions in this report are those of the authors and do not necessarily reflect the scientific judgment of RERF or its funding agencies. This work was partly supported by the  European Community's  Seventh Framework Programme (FP7/20011-2017) (grant number 269912 -EPI-CT: Epidemiological study to quantify risks for paediatric computerised tomography and to optimise doses) (A.
K. , E. C. , M. H. , M. -O. B. , A. J. , H. O. , H. E. , C. J. , M. B. , M. K. and K. K. ). In Spain, this study was partially supported by grants (E. C. and M. B. B. ) from the Instituto de Salud Carlos III-ISCIII from the Spanish Government (reference: PI16/00120) cofunded by FEDER funds/European Regional Development Fund (ERDF)-a way to build Europe. Additionally, complementary Spanish funding was received from the Consejo de Seguridad Nuclear (E. C. ) and M. B. B. was the recipient of a fellowship of the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP) for a short stay abroad at Newcastle University. ISGlobal also acknowledges support from the grant CEX2018-000806-S funded by MCIN/AEI/10. 13039/501100011033, from the Generalitat de Catalunya through the CERCA Program and from the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat of Catalonia through AGAUR (the Catalan Agency for Management of University and Research Grants) (Project 2017 SGR 1487) to EC.
The International Agency for Research in Cancer (IARC) received complementary funding from The Ministry of Health, Labour and Welfare of Japan (grant agreement number 2012-02-21-01) (A. K. and J. S. ). In France, complementary funding was obtained from the association 'La Ligue contre le Cancer' (grant number PRE09/MOB) and from the French Institute of cancer (INCa, grant number 2011-1-PL-SHS-01-IRSN-1, grant number SHS-ESP-2019-025) (M. -O. B. ). In Germany, complementary funding was provided by the German Federal Ministry of Education and Research (grant numbers 02NUK016A, 02NUK016B and 02NUK016CX) (M. B. ) for the German KICT study. In the Netherlands, Worldwide Cancer Research, formerly known as the Association for International Cancer Research, provided partial funding (Grant 12-1155) (M.
H. ). C. R. was supported by a personal grant for Junior group leaders from the Dutch Cancer Society (Grant UVA2021-5517). The original UK cohort study was funded by the UK Department of Health and the US National Cancer Institute, and further funding for the study has been obtained from Cancer Research UK. In Norway, it was funded by the Norwegian Research Council through the EURATOM program, project no. 209096/ E40 (K. K. ). Denmark received complementary funding from the Danish Cancer Society (C. J. ).
Code availability The software used to fit ERR models (the rERR R package) is freely available at https://rdrr. io/cran/rERR/. All EAR models were performed using the PEANUTS module of the EPICURE software (version 2. 00. 02) commercially available at https://risksciences. com/epicure/. The EAR code applied is available at https://github. com/Mbb2022-23/ EPI_CT_EAR'. The ERR code applied is available at https://github. com/ radiationISGlobal/EPI_CT_Scripts.
The distribution of runs of homozygosity (ROH) may be shaped by a number of interacting processes such as selection, recombination and population history, but little is known about the importance of these mechanisms in shaping ROH in wild populations. We combined an empirical dataset of >3000 red deer genotyped at >35,000 genome-wide autosomal SNPs and evolutionary simulations to investigate the influence of each of these factors on ROH. We assessed ROH in a focal and comparison population to investigate the effect of population history. We investigated the role of recombination using both a physical map and a genetic linkage map to search for ROH. We found differences in ROH distribution between both populations and map types indicating that population history and local recombination rate have an effect on ROH.
This resulted in a final data set of 39,587 autosomal SNPs genotyped in 3046 individuals  (Johnston et al. 2017). This study also used equivalent genotype data for 157 individuals from a mainland population of red deer from Argyll, Scotland. Details of sample collection and DNA extraction can be found in  McFarlane et al. (2020). Samples were genotyped as above and quality control was carried out by  McFarlane et al. (2020)  as above with the exception of SNP genotyping success >0. 9. These individuals were originally genotyped as part of a study of red × sika deer (Cervus nippon) hybridisation and were pure red deer according to that study  (McFarlane et al. 2020).
2021). For consistency between searches, we assumed 1 Mb ≈ 1 cM (Actual value: 1 Mb = 1. 04 cM;  (Johnston et al. 2017) ). The following parameters were used to identify ROH in both cases: -autosome-num 33 (a flag to specify the 33 autosomes in red deer), -homozyg-snp 40 (minimum number of SNPs in a ROH), -homozyg-kb 2500 (minimum length of a ROH in kb), -homozyg-density 70 (minimum density, 1 SNP per 70 kb), -homozyg-window-snp 35 (sliding window size), -homozygwindow-missing 4 (number of missing SNPs allowed in a window), homozyghet 0 (number of heterozygote SNPs allowed in a ROH), and -maf 0. 01 (minor allele frequency threshold). Following the use of these parameters in PLINK 35,132 autosomal SNPs and 3046 individuals were analysed in the Rum dataset and 34,673 autosomal SNPs and 157 individuals in the Argyll dataset.
Following assessment of both populations using the bp and cM datasets, we chose a minimum density of 23 SNPs per 1500 kb to minimise the correlation, but maximise consistency between methods and the number of SNPs remaining in the analysis (Supplementary Fig. 2 , Supplementary Table  1 ). All SNPs within windows that fell below this density were discarded from the analysis of hotspots. Additional post-ROH-search quality control removed the first and last 40 SNPs from each chromosome to account for the fact that fewer ROH will have been called in these regions as a ROH cannot span past the ends of a chromosome. (See Supplementary Table  1  for the number of remaining SNPs post-ROH search).
Haplotype diversity As an additional analysis to detect possible signatures of selection through a drop in genetic diversity, we estimated haplotype diversity for the Rum dataset and in our simulated data. Phased autosomal haplotypes for each individual in the Rum dataset were obtained using AlphaPeel v0. 0. 1  (Whalen et al. 2018). Phasing was conducted using the Rum red deer pedigree and genomic dataset, using multi-locus peeling, which takes into account that nearby loci are more likely to be inherited jointly from a parental haplotype. Five peeling cycles were run, and only phased genotypes with a probability of >0. 95 were retained. Phasing success was very high overall, with an individual mean phasing rate (i. e. , the proportion of an individual's genotypes that were phased) of ~99% in individuals born after 1980.
05). Following simulation runs 100 (and 7500 for the no bottleneck scenario) individual genomes were output from SLiM and ROH were called with parameters as described above without the minor allele frequency threshold. In addition, we carried out haplotype diversity calculations on these outputs as described above, further details are given in Supplementary Figs. 7  and 8. All SLiM scripts are available at: https://github. com/ annamayh/ROH_distribution_red_deer.
9 ). Model 4, including strong selection, does not overlap with the empirical data for the ROH threshold, with the empirical value for this falling outside the 95% confidence intervals (Fig. 4A  middle panel, Supplementary Figure  11 ). This indicates that most ROH hotspots in the Rum dataset falling over this threshold are not caused by strong selection. However, the maximum ROH density in the Rum dataset is within the 95% confidence intervals from Model 4 and coincides with the mean across iterations (Fig. 4B  middle panel, Supplementary Fig. 10 ), suggesting that the maximum ROH hotspot peak(s) seen in Fig. 2  could be areas of strong selection. However, this maximum value also overlaps with the confidence intervals of other models (Supplementary Fig. 10 ).
Recombination rate To investigate the effect of recombination rate on ROH in our dataset we used two maps, physical and genetic, which affected the number of ROHs detected and their location (Tables  1  and 2 ; Fig. 2 ). In the Rum population three ROH hotspots found using the physical map were no longer classed as hotspots when using the genetic map. In addition, we found that some hotspots reached further over the hotspot threshold in the genetic map than in the physical map (Fig. 2 ). The differing results are due to the negative correlation between recombination rate and ROH density, which is known from previous studies  (Bosse et al. 2012; Pemberton et al. 2012). In contrast to expectation and past literature  (Bosse et al. 2012; Kardos et al. 2017 ), the addition of varied recombination rate in our simulation did not yield results that differed significantly from a neutral model (Fig.