dataesr/scientific-paragraphs-categorization huggingface dataset:
Our results for the PLN-ARIS dataset highlight the method's robust performances for eels 4-9 m from the camera. Eels closer to the camera were not detected well since their echoes were sometimes hidden or corrupted by arcing effects. These errors are a known issue in DIDSON videos due to fish or highly reflective targets moving near the camera  [13, 46] , but they were not observed in BlueView videos. Mitigating the signal intensity on the recorded videos may decrease the influence of those arcing effects but may also affect the efficiency of automatic fish detections. The specific shape of these artifacts (i. e. the arc of a circle) could be addressed by specific pre-processing of each frame. The ability to identify all eels that passed in the camera FOV peaked 4-10 m from the camera, with a recall of ca.
Theoretically, we assume that fish identification will improve at a higher resolution. The difference in resolution between MZC and PLN may also have influenced performances at PLN, making it more difficult to distinguish eels from other objects, especially those further from the camera (63% of FP in the PLN dataset). An overly low resolution may also increase the fragmentation of eel echoes that cause discontinuities and the need to rebuild the eel skeleton. In addition to camera recording parameters, future studies should focus on environmental conditions during recording and their influence on identification. Among other factors, water flow can influence the migration activity of silver eels  [48]  as well as influencing image quality via the number and speed of objects that pass in the camera FOV.
Conclusion Our study demonstrates the feasibility of using computer vision and morphological analysis to automatically identify large anguilliform fish in situ, using two acoustic camera models with different characteristics, up to a range of 10 m, even when images of fish become fragmented. Unless fish length emerges as a limitation of this method-and future studies should be performed to define its operational boundaries more clearly. Our innovative method can provide relevant ecological data from acoustic cameras during long-term monitoring, in a faster way compared to reading of the entire dataset by a human operator, pushing further the advantage of using acoustic cameras to monitor migratory fish populations without any disturbance on their behavior and health conditions face to more intrusive methods as stow nets.
Acknowledgments This study was supported by  Electricitede France  and the  French Association Nationale de la Recherche et de la Technologie. We sincerely thank the  CEFREM team (University of Perpignan)  for the PLN dataset and its visual analysis. We are grateful to the EDF and MIGADO teams who visually analyzed the Mauzac datasets. Finally, we sincerely thank  Guy d'Urso  for his help and wise advices.
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  [25]. 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  [26]. A detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [23].
III. DATA AND SIMULATED SAMPLES The data sample analyzed has been collected by the CMS experiment with trigger algorithms that require large hadronic activity in the event. In particular, two sets of trigger algorithms are used; single-jet triggers that require a jet with p T > 550 GeV to be present in the event and triggers requiring the scalar p T sum for all jets with p T > 30 GeV and jηj < 3. 0 in the event, to exceed 1050 GeV. The q q → Z 0 → b b signal Monte Carlo (MC) simulated events are produced at leading-order (LO) accuracy in quantum chromodynamics (QCD) with the MADGRAPH 5_aMC@NLO v2. 4. 2 matrix element generator  [27]. Various Z 0 mass hypotheses in the range of 1. 6 TeV to 8 TeV are considered for these narrow resonance signals, which have a natural width smaller than the experimental dijet mass resolution (2-3%).
L. ) if additional parameters are needed to model the individual background distributions. Depending on the dataset considered for the fit, a three-parameter (p 0 ð1 -xÞ p 1 =x p 2 ), four-parameter (p 0 ð1 -xÞ p 1 =x p 2 þp 3 logðxÞ ) or five-parameter (p 0 ð1 -xÞ p 1 = x p 2 þp 3 logðxÞþp 4 log 2 ðxÞ ) functional form is necessary to describe the data. The observed dijet mass spectra are well modeled by the background functional forms, as shown in Fig. 3 , where the widths of the dijet mass bins correspond to the dijet mass resolution. The expected shape of the reconstructed signal mass distribution is extracted from the simulated signal samples. The b Ã signal shapes are dijet mass distributions obtained directly from the simulation, as shown in Fig. 1 , while the Z 0 signal shapes are parametrizations that are fit separately to the simulation samples for each category, using a double-sided Crystal Ball function  [44].
VI. SYSTEMATIC UNCERTAINTIES The background shape is estimated from the fit to the data in the considered categories, and no assumption or constraint is applied to function parameters. These parameters are considered as uncorrelated among categories and data-taking periods. As described in Sec. V, different parametrizations of the fit function were studied and introduce no additional source of uncertainty. The dominant uncertainties in the signal arise from the b tagging and the jet-reconstruction uncertainties. The uncertainty in the b-tagging scale factors  [45]  yields an uncertainty in the signal normalization of 2% and 4% in the 1b and ≥ 1b categories, respectively, and 15% in the 2b and muon categories. Uncertainties in the reconstruction of the hadronic jets affect mainly the shape of the reconstructed resonance mass.
The four-momenta of the reconstructed jets are scaled and smeared according to the uncertainties in the jet p T and momentum resolution. These effects result in a 2% uncertainty in the mean, and 10% in the width of the signal. The signal normalization is also affected by the uncertainty in the selection of the muons inside the jets. The efficiency of the identification of the muons inside jets is measured in a statistically independent and almost pure sample of high-p T b jets, originating from the decay of pairproduced top quarks. This uncertainty is deduced from the statistical uncertainty in the efficiency measurement, ranging up to 43% for muons with p T < 100 GeV. An uncertainty of 100% is assumed for jets with the p T of an associated muon beyond this 100 GeV threshold, because no data was available in this region.
VIII. SUMMARY A search for heavy resonances decaying into b quarks has been presented and no excess has been found over the standard model (SM) expectations. The data were collected by the CMS experiment at ffiffi ffi s p ¼ 13 TeV during 2016-2018 and correspond to an integrated luminosity of 138 fb -1. Model-independent upper limits are set on the product of the cross section of the resonance and its branching fraction to b quarks. Signals of Z 0 bosons decaying to pairs of b quarks are considered, for both the previously explored sequential standard model (SSM), and also for a new heavy vector triplet (HVT) model. The decays of Z 0 bosons in both the SSM and the HVT Model A are excluded at 95% confidence level for masses from 1. 8 TeV to 2. 4 TeV, and limits are set on the coupling strengths of the HVT boson to SM bosons and fermions.
ACKNOWLEDGMENTS 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).
Systematic differences in photometry used An additional possibility is that the difference sources of host galaxy photometry between DES and ZTF is causing some systematic offset between the two samples. It may be that the inclusion of u -band data in the SED fits for the ZTF hosts is causing a systematic difference compared with the griz -only SED fits for the DES hosts (Section 2. 4 ). We remo v e the u -band data from the ZTF host photometry and repeat the SED fits using only griz , but find that the ZTF rest-frame colours from griz fits consistent with those from ugriz fits and thus that the difference between DES and ZTF host colours remains. Alternatively, there may be differences between DES and SDSS photometry that cause an offset when considering the same bands. To investigate this we match DES supernovae the SDSS host catalogue using a 5 arcsec search radius, finding 47 objects with SDSS host galaxies.
Summary In summary, difference host galaxy rest-frame colour between the ZTF and DES samples is not obviously caused by selection biases in the two samples or systematic differences in the SED fitting for DES and ZTF, and metallicity evolution with redshift can only partially explain this offset. We further note that the difference in host colour is much more pronounced in the SN II host sample: if there were some o v erall systematic bias, we would expect to see the same effect in the SN Ibc sample as well. It remains unclear what may be dri ving the dif ference in host colour, and more data are required to study this in further detail.
Were these effects real, we also cannot rule out causes such as differing host extinction without measurements of this. (iii) There are differences in the host galaxy properties of the LOSS CCSNe compared to the DES and ZTF CCSNe, but these are e xpected giv en that LOSS is a galaxy-targeted surv e y while DES and ZTF are untargeted. (iv) There are also differences in the host galaxy properties of DES CCSNe compared with those in ZTF. The host galaxy stellar masses of both samples are consistent across both SNe II and SNe Ibc. Ho we ver for SNe II, DES host galaxies are significantly bluer than the ZTF hosts with a significance levels of 3. 4 σ and 3. 8 σ from two-sample KS and AD tests, respectively. (v) We explore correcting the host galaxy colours to account for redshift evolution, and study the possibility that this difference is caused by selection biases in the DES or ZTF samples or systematic differences in the data used, but find that none of the are able to adequately explain the differences.
work was supported by the Science and Technology Facilities Council (grant number ST/P006760/1) through the DISCnet Centre for Doctoral Training. MS acknowledges support from EU/FP7-ERC grant 615929. PW acknowledges support from the Science and Technology Facilities Council (STFC) grant ST/R000506/1. Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Te xas A&M Univ ersity, Financiadora de Estudos e Projetos, Funda c˜ ao Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cient ífico e Tecnol ógico and the Minist ério da Ci encia, Tecnologia e Inova c˜ ao, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Surv e y.
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 programme of the Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Se venth Frame work Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
Large blocks of heterochromatic interstitial telomeric repeats gain H3K27me3 in 2h1 plants To explore how H1 selectively contributes to repress H3K27me3 marking on TE cluster 1, we first envisaged the H3K27me1 heterochromatic mark as a potential substrate for H3-Lys27 trimethylation. Analysis of public datasets  (Ma et al. , 2018)  showed however that, in contrast to other TEs, H3K27me1 is not abundant on TE cluster 1 nor on ATREP18 elements (Figure  S7D ). We further examined the epigenomic profile of loss-of-function plants for the three major histone H3K27me3 demethylases EARLY FLOWERING 6, RELATIVE OF ELF 6, and JUMANJI 13  (Yan et al. , 2018). This revealed no evidence for active H3K27me3 removal on TE cluster 1 elements in WT plants (Figure  S8 ), altogether ruling out a specific role for H1 in promoting transitions conversion of H3K27me1 into H3K27me3 or in H3K27me3 erasure at these loci.
Raw ATAC-seq data were treated using the custom-designed ASAP (ATAC-Seq data Analysis Pipeline; https://github. com/akramdi/ASAP) pipeline. Mapping was performed using Bowtie2 v. 2. 3. 2  (Langmead and Salzberg, 2012)  with parameters --very-sensitive -X 2000. Mapped reads with MAPQ<10, duplicate pairs, and reads mapping to the mitochondrial genome as well as repetitive regions giving aberrant signals  (Quadrana et al. , 2016)  were filtered out. Concordant read pairs were selected and shifted as previously described by 4 bp  (Schep et al. , 2015). Peak calling was performed using MACS2  (Zhang et al. , 2008)  using broad mode and the following parameters: --nomodel --shift -50 --extsize 100. Heatmaps and metaplots were produced from depth-normalized read coverage (read per million) using the Deeptools suite  (Ramírez et al.
RNA-seq Seedlings grown in long days were fixed in 100% cold acetone under vacuum for 10 min. Cotyledons from 100 plants were dissected and grinded in 2 ml tubes using a Tissue Lyser (Qiagen) for 1 min 30 sec at 30 Hz before RNA extraction using the RNeasy micro kit (Qiagen). RNA was sequenced using the DNBseq platform at the Beijing Genomics Institute (BGI Group) in a 100 bp paired-end mode. For raw data processing, sequencing adaptors were removed from raw reads with trim_galore! v2. 10 (https://github. com/FelixKrueger/TrimGalore). Reads were mapped onto combined TAIR10 genome using STAR version 2. 7. 3a  (Dobin et al. , 2012)  with the following parameters "--alignIntronMin 20 --alignIntronMax 100000 --outFilterMultimapNmax 20 --outMultimapperOrder Random --outFilterMismatchNmax 8 --outSAMtype BAM SortedByCoordinate --outSAMmultNmax 1 --alignMatesGapMax 100000".
Peaks of H3K27me3 read density were called using MACS2  (Zhang et al. , 2008)  with the command "macs2 callpeak -f BAM --nomodel -q 0. 01 -g 120e6 --bw 300 --verbose 3 --broad". Only peaks found in both biological replicates and overlapping for at least 10 % were retained for further analyses. We scored the number of H3K27me3 reads overlapping with marked genes using bedtools v2. 29. 2 multicov and analyzed them with the DESeq2 package  (Love et al. , 2014)  in the R statistical environment v3. 6. 2 to identify the genes enriched or depleted in H3K27me3 in mutant mutant plants (p-value < 0. 01). To account for differences in sequencing depth we used the function sizeFactors in DESeq2 applying a scaling factor calculated as in  (Nassrallah et al. , 2018). For the H1. 2-GFP and H3 ChIP-seq datasets, raw reads were processed as for H3K27me3.
Tracks were visualized using Integrative Genomics Viewer (IGV) version 2. 8. 0  (Thorvaldsdóttir et al. , 2012). Metagene plots and heatmaps were generated from depth-normalized read densities using Deeptools computeMatrix, plotHeatmap, and plotProfile. Violin-plots, histograms and box-plots were drawn using the package ggplot2 v3. 2. 1 (https://cran. rproject. org/web/packages/ggplot2/) in R statistical environment. All scripts used will be made publicly available. In Shuffled controls in ChIP analyses, where present, were produced with random permutations of genomic position of the regions if interest. The permutations were generated with bedtools v2. 29. 2 and the command "bedtools shuffle -chromFirst -seed 28776 -chrom". For MNase-seq analyses, MNase read density  (Choi et al, 2017)  was obtained from NCBI GEO under the accession GSE96994.
Acknowledgements The authors are very grateful to  Erwann Cailleux  ( IBENS, Paris, France ) and  David Latrasse  ( IPS2, Orsay, France ) for technical guidance with ATAC-seq; to  Magali Charvin  ( IBENS, Paris, France ) for technical assistance with the IBENS plant growth facility, to  Frédérique Perronet  ( IBPS, France ) for providing Drosophila samples;  Kinga Rutowicz  ( University of Zurich, Switzerland ) and  Angélique Déléris  ( IBENS, Paris and I2BC, Gif-Sur-Yvette, France ) for sharing unpublished work.
Funding FB benefitted from grants of the  Agence Nationale de la Recherche (France)  (projects  ANR-10-LABX-54 ,  ANR-18-CE13-0004-01 ,  ANR-17-CE12-0026-02 ). Collaborative work between FB and CeB was supported by a research grant from the  Velux Foundation (Switzerland). Collaborative work between FB and AP was supported by  CNRS EPIPLANT Action (France). GT benefitted from a short-term fellowship of the  COST Action CA16212 INDEPTH (EU)  for training in Hi-C by  SG in UG's laboratory , which is supported by the  University of Zurich (Switzerland)  and the  Swiss National Science Foundation  (project  31003A_179553 ). SA benefitted from a  CAP20-25  Emergence research grant from  Région Auvergne-Rhône-Alpes (France). Work in JF team was supported by the  Czech Science Foundation  (project  20-01331X ) and  Ministry of Education, Youth and Sports of the Czech Republic  -project  INTER-COST  ( LTC20003 ).
A recent study showed that inadequate magnesium levels have an adverse impact on telomere attrition rate in older people with sleep disturbances  [9]. Moreover, magnesium may also enhance melatonin secretion which promotes sleep onset  [10]. Magnesium is important for the synthesis of N-acetyltransferase, which converts 5-hydroxytryptamine into N-acetyl-5-hydroxytryptamine, which can then be converted to melatonin  [9]. Magnesium also contributes to the maintenance of a normal circadian rhythm and sleep quality and thus may reduce insomnia. Findings from analyses of The National Health and Nutrition Examination's database have found that aging is a superposed risk factor for deficient magnesium consumption and that magnesium consumption has a progressive decrease with age  [11]. Indeed, the literature has shown that intestinal magnesium absorption and bio-variability vary with age  [12].
Those who were detected to have hypermagnesemia (≥2. 6 mg/dL) according to serum magnesium values were also excluded  [24]. [14]  Patients were assessed by a geriatrician whereas study data were collected by a gerontologist. The following variables were reported: participant age in years, sex, education status, marital and living status, number of medications, and chronic comorbid diseases (hypertension, diabetes mellitus, chronic obstructive pulmonary disease, coronary artery disease, congestive heart disease, dementia, periferic artery disease, cerebrovascular event, Parkinson's disease, and osteoarthritis).
Compherensive Geriatric Assessment [14] Patients were assessed by a geriatrician whereas study data were collected by a gerontologist. The following variables were reported: participant age in years, sex, education status, marital and living status, number of medications, and chronic comorbid diseases (hypertension, diabetes mellitus, chronic obstructive pulmonary disease, coronary artery disease, congestive heart disease, dementia, periferic artery disease, cerebrovascular event, Parkinson's disease, and osteoarthritis). The Barthel Index (BADL) was used to evaluate the patients' basic activities of daily living, and the Lawton index was used to evaluate instrumental activities of daily living (IADL). BADL includes functional status and the level of independence in basic daily living activities for feeding, bathing, dressing, bowel or bladder control, using the toilet, transfers, mobility, and stair climbing.
These variables were hypertension, diabetes mellitus, coronary artery disease, Parkinson's disease, hemoglobin, HDL cholesterol, HbA1c, triglyceride, and number of drugs used; 95% confidence intervals are given for odds ratio values. The Backward LR (likelihood ratio) method was used as a variable selection method in the model. Model explanatory power was examined with the Nagelkerke R square value, and the fit of the model was examined with the Hosmer and Lemeshow test. The statistical significance level was taken as 0. 05 in the calculations and IBM SPSS Statistics for Windows, Version 26 (IBM Corp, Armonk, NY, USA) was used.
The optimization and advanced study of a laser-plasma electron injector are presented based on a truncated ionization injection scheme for high quality beam production. The SMILEI code is used with laser envelope approximation and a low number of particles per cell to reach computation time performances enabling the production of a large number of accelerator configurations. The developed and tested workflow is a possible approach for the production of a large dataset for laser-plasma accelerator optimization. A selection of functions of merit used to grade generated electron beams is discussed. Among the significant number of configurations, two specific working points are presented in detail. All data generated are left open to the scientific community for further study and optimization.