dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimizing RADseq experiments with small-scale sequencing Small-scale sequencing (spike-in) proved to be a powerful resource to control library preparation by providing preliminary information on the endogenous DNA content of each sample and the balance among individuals in a given pool. The weighted re-pooling strategy F I G U R E 5 Summary statistics characterizing the performance per library of the datasets before (Stacks output -left) and after enhanced SNP filtering (All filters -right). Individuals pooled in different libraries present different genetic summary statistics, including mean coverage (a), missing rate (b), heterozygosity (c) and genotype error rate (d). considering endogenous DNA content allowed for a guaranteed set of shared loci with similar coverage across multiple individuals with variable DNA quality.
DATA AVA I L A B I L I T Y S TAT E M E N T The raw sequencing data are deposited in the Genbank with the NCBI BioProject accession no. PRJNA951614. The preprocessing Snakemake pipeline is available at https://git. imp. fu-berlin. de/ begen div/radse q-prepr ocess ing-pipeline, the script used for the linear model analysis and the SNP data is available at https://git. imp. fu-berlin. de/begen div/hyena sproject, and the 'Genotype Accuracy based on trios' script is available at https://git. imp. fu-berlin. de/.
F I G U R E 3 The impact of PCR duplicates, number of PCR cycles and coverage on genotype accuracy. (a) Percentage of PCR duplicates in libraries grouped according to the number of PCR cycles used in library preparation for the high coverage dataset. (b) Genotype error rate compared between PCR-duplicates filtered and unfiltered in low-and high-coverage datasets. Individuals were grouped according to the number of PCR cycles that they underwent during library preparation. Points represent the means and error bars represent 95% confidence intervals.
F I G U R E 4 The impact of different SNP filters on summary statistics, genetic diversity and genotype error rate. (a) Fragment length distribution of two individuals of each of the 14 libraries, which are displayed in different colours. The upper limit differs among individuals belonging to different libraries. The two peaks at 326 and 417 bp are likely repetitive paralogous loci. (b) Coverage per locus along the fragment length distribution for the Stacks output. The collapsed paralogues remain in the baseline-filtered dataset. (c) Genotype error rate, (d) number of SNPs, (e) coverage per individual, (f) missing rate per individual, (g) heterozygosity and (h) inbreeding coefficient for the datasets with no posterior SNP filtering (Stacks output), and treated with unique or combined SNP filters.
4. 1 | 1 Optimizing RADseq experiments with small-scale sequencing Small-scale sequencing (spike-in) proved to be a powerful resource to control library preparation by providing preliminary information on the endogenous DNA content of each sample and the balance among individuals in a given pool. The weighted re-pooling strategy F I G U R E 5 Summary statistics characterizing the performance per library of the datasets before (Stacks output -left) and after enhanced SNP filtering (All filters -right). Individuals pooled in different libraries present different genetic summary statistics, including mean coverage (a), missing rate (b), heterozygosity (c) and genotype error rate (d). considering endogenous DNA content allowed for a guaranteed set of shared loci with similar coverage across multiple individuals with variable DNA quality.
2020; Berta et al. 2021). Based on the successful NOEMA Pilot Program, the z-GAL Large Program has extended this work by observing 126 high-redshift Herschelselected galaxies in the 2 and 3 mm NOEMA wavebands  (Cox et al. 2023; hereafter Paper I)  with the goal to measure precise spectroscopic redshifts. In addition to the molecular and atomic emission lines that were detected in all the sources, the continuum emission flux densities were extracted for each source in at least four, and up to ten, sidebands. The combined Pilot Program and z-GAL Large Program constitutes the final z-GAL sample of 137 sources, for which robust redshifts were derived for 135 sources, spanning the range 0. 8 < z < 6. 5. The present study of the dust properties of high-z galaxies is based on this sample, the largest with precise redshifts to date.
Data reduction The NOEMA data were reduced and calibrated with the GILDAS 1 package and uv-tables of each sideband were produced in the standard way. The main calibrators adopted were MWC349 and LkHα101. More details are provided in Paper I. The absolute flux uncertainty is 10% and the positional error is 0. 2 arcsec. For each z-GAL target, stacked maps were produced combining all channels of all available sidebands, including both continuum and spectral lines (hereafter called all-channels maps). Such stacked maps were then used as reference for the detection of sources in the NOEMA fields centered on the Herschel positions. Continuum maps of each sideband were produced by combining all channels of the given sideband, but excluding the spectral ranges including the emission lines. As an example, Fig.
The 850 µm SCUBA-2 flux density is only available for the HerBS sources  (Bakx et al. 2018 (Bakx et al. , 2020) )  with 1. 5 < S /N < 7 (see  Cox et al. 2023 , for a table of the Herschel and SCUBA-2 flux densities). The NOEMA 2 and 3 mm flux densities for all sources have been measured with 1 < S /N < 9. For two sources in the sample, additional data in the 1 mm band are available, namely for HeLMS-17 (see Paper I) and HerBS-89  (Berta et al. 2021 ). In the bottom panel of Fig. 3 , we summarize the z-GAL millimeter sampling, illustrating the uniqueness of the z-GAL sources that have up to ten measured continuum flux densities in the 2 and 3 mm bands. In our further analysis, we use data below 1 mm in the rest-frame (see Sect. 3. 1) so that the effective number of flux density measurements varies between 2 and 8 data points in the millimeter, where most of the sources have two or more detections (S /N > 3).
5 and 7 for the SCUBA-2 flux density, and between 1 and 9 for the NOEMA flux densities. To mimic real observations, we perturb the flux densities on a normal distribution with 1σ, which is equivalent to the error bar of each flux point. The final mock catalog covers the 250, 350, and 500 µm Herschel flux densities, the 850 µm SCUBA-2 flux, and five millimeter data points that vary between 79. 76 and 162. 32 GHz (or 3. 7 and 1. 8 mm). We then fit the SEDs to estimate the output dust parameters of the mock galaxies using the MBB. Throughout this paper, we use the EMCEE package (Foreman-Mackey et al. 2013), which is a Markov chain Monte Carlo sampler, to fit the SEDs using the MBB corrected for CMB effects. We introduce the MCMC sampler with flat priors for each dust parameter: (i) 0 < T dust < 100 K, (ii) 0 < β < 4, and (iii) 10 5 < M dust (M ) < 10 15.
056 dex on average), as shown in the right panel of Fig. 4 , and is determined by the data rather than the fitting method. We performed a final check to measure the effect of the lack of a flux density measurement at 850 µm. Only the HerBS sources in the z-GAL sample were observed at this wavelength with SCUBA-2  (Bakx et al. 2018) , whereas the HeLMS and HerS sources lack an 850 µm measurement (representing a total of 56 sources). To this effect, we fit the SEDs of the mock catalog generated using the same strategy but removing the flux density at 850 µm; however, we do not find any significant change in the output. The standard deviation of the (outputinput) parameters is only slightly higher, with ±0. 2 for β, ±7. 07 K for T dust , ±0. 069 dex for M dust , and ±0. 059 dex for L FIR. Therefore, we do not distinguish between sources with and without this measurement because NOEMA data cover observations along the RJ tail, which allows us to constrain the dust parameters well.
12. For these sources, we constrain the prior additionally by choosing the highest probability values between the dust temperature and dust masses from the first run, then rerun the MCMC sampler with an additional Gaussian prior on dust temperatures. The Gaussian is centered on the maximum probability from the previous chains with a width of 7K. We find β to lie on the identity relation when using either method with a slight systematic shift when β ∼ 3 and a median β = 2. 17 (β MBB = 2. 16), which underlines once more the robustness of the z-GAL sampling in constraining the value of β. As expected, the dust temperatures are higher when using the GMBB with ∆T dust = 5-15 K where the median T GMBB dust = 37. 7 K (7. 7 K higher than T MBB dust ). With the data in hand, we cannot confirm that the derived temperatures using GMBB are correct until the source size is known.
Consequently, the difference in the derived dust temperatures affects the estimated dust masses as shown in the third panel of Fig. 14 , where the MBBderived M dust are lower than the GMBB-derived ones. Finally, we find that the inferred apparent dust luminosities are in very good agreement between MBB and GMBB-Sizes. Similar to the mock output results, our sources show a flat distribution when setting the size as a free parameter with R(output) varying between 3 and 4. 5 kpc for most sources. In Fig. 15 , we plot the derived λ thick values using the output parameters we fit for our SEDs, and we find that it varies between 25 and 75 µm for most sources, but with very low precision resulting from a flat distribution in the derived source sizes. As we find similar trends between the mock and the z-GAL sources, we cannot distinguish whether our sources are optically thick or thin until we have new high-angular resolution data that will constrain their sizes.
g. , HeLMS-19), but such cases do not dominate our sample. Fitting this relation to the well-constrained sample (a total of 27 sources), where the relative uncertainties are within 10%, yields a result of α = 0. 57 ± 0. 09, which falls within the confidence range. The underlying nature of the β -T dust relation, whether it is physical or is a result of parameter degeneracy, remains an open question. Previous studies used a hierarchical Bayesian fitting method (e. g. ,  Kelly et al. 2012; Juvela et al. 2013; Lamperti et al. 2019) , which resulted in a significantly reduced degeneracy. However, these studies focused on datasets with limited millimeter data that cover the RJ tail. In the case of z-GAL, the rich sampling along the RJ tail provides better constraints and larger weights on the derived β values, which reduces the necessity for more complex statistical approaches.
Acknowledgements. This work is based on observations carried out under project numbers M18AB and, subsequently D20AB, with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). The authors are grateful to  IRAM  for making this work possible and for the continuous support that we received over the past four years to make this large program a success. The authors are also grateful to the  IRAM director  for approving the DDT proposal that enabled to complete the survey. This work benefited from the support of the project  Z-GAL ANR-AAPG2019  of the  French National Research Agency (ANR). The anonymous referee is thanked for providing useful comments that helped to improve the contents of this paper. The authors would like to thank  I. Cortzen  and  Cynthia Herrera  for their contributions in the early stages of this project and recognise the essential work of the z-GAL Cat-Team and Tiger-Team, who performed the reduction, calibration and delivery of the z-GAL data.
D. I. extends her heartfelt appreciation to  Alfred Gulum  for being a pillar of support. A. J. B. and A. J. Y acknowledge support from the  National Science Foundation  grant  AST-1716585. A. N. acknowledges support from the  Narodowe Centrum Nauki  ( UMO-2020/38/E/ST9/00077 ). D. A. R. acknowledges support from the  National Science Foundation  under grant numbers  AST-1614213  and  AST-1910107  and from the  Alexander von Humboldt Foundation  through a  Humboldt Research Fellowship  for Experienced Researchers. SS was partly supported by the  ESCAPE  project. ESCAPE -The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under Grant Agreement no. 824064. C.
Y. acknowledges support from  ERC Advanced  Grant  789410. H. D. acknowledges financial support from the  Agencia Estatal de Investigación del Ministerio de Ciencia e Innovación (AEI-MCINN)  under grant ( La evolución de los cíumulos de   galaxias desde el amanecer hasta el mediodía cósmico ) with reference ( PID2019-105776GB-I00/DOI:10. 13039/501100011033 ) and acknowledge support from the  ACIISI, Consejería de Economía, Conocimiento y Empleo del Gobierno de Canarias  and the  European Regional Development Fund (ERDF)  under grant with reference  PROID2020010107. T. B. acknowledges support from  NAOJ   ALMA Scientific Research  Grant Nos. 2018-09B  and  JSPS KAKENHI  No. 17H06130 ,  22H04939 , and  22J21948. A. N. acknowledges support from  INAF -Osservatorio astronomico d ' Abruzzo Via  maggini  SNC 64100   Teramo.
Successive layers were manufactured at ± 45°to the x -axis of the board. The test specimens were manufactured using the Markforged X7 industrial series 3D printer, which can print industrial parts in a 330 mm × 270 mm × 200 mm volume with industrial part quality. The X7 printer uses a wide range of materials (onyx, onyx FR: flame retardant, onyx FR-A: flame retardant-aeronautic-, or white nylon) that can be reinforced with continuous carbon, glass, or Kevlar fibers. It has several print variables (accessible via its Eiger slicing software), such as layer thickness, orientation, geometric positioning of parts on the platen, number of fiber layers, fiber orientation (0°, 45°, 90°), fiber placement type (concentric or isotropic), and type of printable pattern (triangular, rectangular, hexagonal, gyroid, or solid)  [ 13 , 24 ].
The studied specimen was modeled using Inventor software and subsequently exported to the Standard Tessellation Language (STL) format before being imported into Markforged's slicing software (Eiger). The tensile tests were performed according to ASTM D638. The geometry of the specimen ( Fig. 2  ) was derived from ASTM D3039, which is dedicated to the tensile testing of fiber-reinforced parts. This shape was chosen to avoid the breakage of the jaws during the tensile test.
This printer parameter induces a difference in the internal structure of the printed parts and, consequently, a difference    in the mechanical behavior. For example, a specimen oriented at 0°has all its layers at ± 45°to the tensile axis, whereas a specimen oriented at 45°has layers oriented at 0°/90°relative to the tensile axis. A single-axial tensile test was performed in this study. An Instron 5569 tensile machine with a force cell of 50 kN maximum force was used. The strains were measured using an extensometer with an initial length of 12. 5 mm. Several tests were performed to determine the test speed according to the ASTM D638 recommendations. The displacement speed selected for the tests was 10 mm/min. Data acquisition was performed using Instron's Bluehill Universal software. Data were processed according to the recommendations of the standard used.
Events of interest are selected using a two-tiered trigger system. The first level, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of about 4 μs  [36]. The second level, known as the high-level trigger, consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage  [37].
Data and Monte Carlo simulation We split the data collected with the CMS detector in 2016-2018 into four distinct periods: 2016a, 2016b, 2017, and 2018. The integrated luminosities of the corresponding samples are 20. 0, 16. 6, 42. 0, and 61. 3 fb -1  [38] [39] [40]. Data from the 2016a period were affected by strip tracker dynamic hit inefficiency. The problem was resolved in August 2016 and the rest of the 2016 data-taking period is referred to as 2016b. Before the 2017 data-taking period, the pixel detector was upgraded, improving the acceptance, redundancy, and resolution. During data taking in 2017, the pixel detector had synchronization issues at the beginning of the run, followed by DC-DC converter failures  [33]  leading to about 11% of the detector being unresponsive. Most of these issues were resolved before data taking began in 2018.
We use multiple samples of MC simulated events to evaluate the signal efficiency, the detector response, and the background yields. The simulated event samples are generated with pythia 8. 212  [41]  using the CP5 underlying event tune  [42]  and propagated through the CMS detector model using the Geant4  [43]  package. The decays of B hadrons are simulated using the evtgen 1. 3. 0 [44]  program and final-state photon radiation is described using photos 3. 56  [45]. Multiple interactions within the same or nearby bunch crossings (pileup) are simulated for all samples by overlapping the hard-scattering event with several minimum bias events, with the multiplicity similar to the one observed in data (averaging to 23 for 2016 and 32 for 2017-2018).
The last class of observables is designed to detect nearby decay products present in semileptonic decays of b and c hadrons. We compute the number of tracks compatible with the μ + μ -SV. In addition, isolation variables are calculated for the B candidate as well as both muon candidates. The isolation observables have been optimized in the previous studies  [7]  to maximize the separation power between the B 0 s → μ + μ -signal and the background, while maintaining a reasonable agreement between the MC simulation and the data for the B + → J/ ψK + normalization channel. The isolation is defined as I = p T p T + trk p T (trk) , where p T is the momentum of either the B or muon candidate and the sum includes all charged-particle tracks with p T > 0. 9 GeV in a cone of radius R = √ ( η) 2 + ( ϕ) 2 around the candidate momentum direction, with ϕ representing the azimuthal angle in radians.
The two methods give results compatible with each other. Based on the difference between the two methods, we assign a 2 (3)% systematic uncertainty in the corrections for the B 0 s → μ + μ -and B 0 → μ + μ -signal efficiencies for the d MVA > 0. 90 (0. 99) selection ("d MVA correction"). The hadron tracking efficiency uncertainty is obtained by comparing the ratio of the measured branching fractions of the twobody D 0 → K -π + to the four-body D 0 → K -π + π + π -decays to the world average value  [29]. This gives a "Tracking efficiency" uncertainty of 2. 3%  [50]  for each kaon. As a result of the different kinematics and triggers for the signal and normalization channels, the trigger efficiency effects do not fully cancel and are corrected using MC simulation. The simulation of the trigger efficiency is checked by comparing with the efficiency measured using data obtained from other triggers.
Compared with the latest LHCb measurement  [9, 10]  B(B 0 s → μ + μ -) = (3. 09 +0. 46   -0. 43 (stat) +0. 15 -0. 11 (syst)) × 10 -9 , our result with the combined systematic uncertainty including the external uncertainties, is about 1. 2 standard deviations higher. These two measurements will shift the world average from its current value of B(B 0 s → μ + μ -) = (2. 69 +0. 37 -0. 35 ) × 10 -9  [11]  to a larger value, more consistent with the SM prediction, thus reducing the overall tension. The new measurement of the B 0 s → μ + μ -branching fraction is an important input to the global fits to the flavor data (e. g. , Ref. [56] ) in light of the reported b → s + -anomalies (where lepton = e or μ). The uncertainties in the branching fraction and effective lifetime measurements are dominated by the statistical component, which means that significant improvements can be expected in the precision of future measurements with the LHC Run 3 data.
Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MOST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MES  and  NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI  and  PCTI (Spain) ;  MoSTR  ( Sri Lanka );  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI  and  NSTDA (Thailand) ;  TUBITAK  and  TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
In this spirit, Guo et al. [9]  propose CounterNet, a neural network based architecture for the prediction and counterfactual generation along with a novel variant of back propagation to ensure the stabilization of the training process. Compared to a post-hoc approach, they are able to produce counterfactuals with higher validity. A counterfactual is said to be valid if it succeeds in reaching a different prediction. A limitation of the CounterNet approach is that counterfactuals it generates may lack realism w. r. t. the data points of the class where they are positioned. The proposed approach, VCNet, tackles this issue: similarly to CounterNet, it combines a predictor and a counterfactual generator that are jointly trained. The difference lies in the counterfactual generator based on conditional variational autoencoder (cVAE) whose latent space can be controlled and tweaked to generate realistic counterfactuals.
Our approach is inspired from John et al. work about learning disentangled latent spaces in the context of text style transfer  [10]. Our main contribution is the proposal of a cVAE for counterfactual generation in order to generate realistic counterfactuals. Our second contribution is a self-explainable architecture of a classifier that embeds a cVAE, used as a counterfactual generator. In this architecture, both the model and the cVAE, are jointly trained with an efficient single back-propagation procedure. After recalling the properties of the variational autoencoder models (Section 3) that interest us in the context of this paper, we describe our proposal (Section 4). Then we present extensive experimental studies on synthetic and real data (Section 5). We compare the quality of the counterfactuals produced with those of CounterNet on different datasets through state-of-the-art metrics.