dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the mean values for the CPU cost and the relative number of events in each of the slices found in table 3, the total HS06 CPU cost to produce these 200 M events in the Sherpa 2. 2. 1 and 2. configurations is 140 × 10 3 MHS06 seconds and 75 × 10 3 MHS06 seconds, respectively. This represents nearly a factor of two reduction in the computing resources needed to obtain the results shown in figure  15. While the improvements in generated effective luminosity arise largely from the improved numerical efficiency parameters discussed in section 4. 2. 2, the overall decrease in CPU cost originates from the various approximations that were shown in section 4. 1 to reduce the per-event generation time by over a factor of three. Looking towards the HL-LHC, the integrated luminosity of the data set is expected to be at least 3000 fb -1.
Assuming the same sampling pattern as given by figure  15 , in which the MC effective luminosity matches or exceeds that of the data for p V T and H T greater than approximately 100 GeV, roughly 10 billion events will be required for each of the leptonic decay modes of the Z boson. Accounting for the leptonic W boson decay modes as well, roughly 330 billion events will be needed to match the data set luminosity at the HL-LHC. With the developments in the latest Sherpa 2. 2. 11 setup presented in this paper, the CPU cost for this would be 3. 8 MHS06 years. Assuming an aggressive research and development (R&D) programme over the following decade, the CPU budget allocated to event generation is 3. 2 MHS06 for the year of 2031  [77]. Therefore the latest Sherpa 2. 2. 11 setup would exceed the yearly budget by 16% while the Sherpa 2.
JHEP08(2022)089 5 Conclusions The production of a single vector boson plus jets is an important process for study at the LHC and an integral part of the ATLAS physics programme. State-of-the-art predictions for single boson production are routinely used in comparisons with data measurements, and serve as an important ingredient for the modelling of backgrounds in measurements of other SM processes or searches for physics beyond the SM. As such, an accurate description of V + jets processes, along with good statistical coverage across a wide range of phase spaces, is crucial for the success of the ATLAS physics programme. This paper highlights a series of recent modelling and computational improvements in the Sherpa event generator developed beyond the 2. 2. 1 set-up used by ATLAS for the last several years.
The theoretical changes were shown to improve the description of data for a variety of measurements, and provide additional higher-order electroweak corrections for future measurements to probe. The major CPU bottlenecks in the event generation time were identified, and various approximations were implemented that reduce the perevent time by up to a factor of three. Together with a continuous statistical enhancement technique, the resources required to produce the Sherpa 2. 2. 11 configuration were reduced by up to a factor of two relative to the 2. 2. 1 configuration. In addition, the performance of the first alternative ATLAS multi-jet MadGraph5_aMC@NLO+Pythia configuration using FxFx merging with a similar perturbative accuracy to the Sherpa prediction was shown. In summary, the MC configurations reported in this paper form the base standard of single vector boson plus jets simulation moving into the third run period (Run 3) of the LHC for ATLAS analyses.
Republic; DNRF and DNSRC, Denmark; IN2P3-CNRS and CEA-DRF/IRFU, France; SRNSFG, Georgia; BMBF, HGF and MPG, Germany; GSRI, Greece; RGC and Hong Kong SAR, China; ISF and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; NWO, The Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; JINR; MES of Russia and NRC KI, Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, U. K. ; DOE and NSF, U. S. A. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and GIF, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, U.
Acknowledgments We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias ,  Colombia; MSMT CR ,  MPO CR  and  VSC CR, Czech.
Two difficulties concerning the use of solid-gel, compared to cEEGrid EEG still remain: i) data presented in these studies only portray spectral activity, not revealing the efficiency of solid-gel to measure and analyse temporal (ERP) and time-frequency (ERSP) data  (Di Flumeri et al. , 2019) ; and ii) unlike the cEEGrid, their obtrusiveness leads to decreased transparency still disrupting the engagement of participants in more ecological tasks. In the present study, we test the feasibility to measure the electrophysiological correlates of inattentional deafness in a flight simulator using two different comfortable unobtrusive EEG systems. We performed concurrent EEG recording with a cEEGrid system placed around each ear and solid gel electrodes spread over the scalp. The experimental scenario consists in performing two critical approaches and landings similarly to  Dehais et al.
Oddball task During the experimental session, participants were presented with an auditory oddball task. They had to ignore the frequent non-target sounds and use the trigger of the side-stick to respond to the auditory targets. The frequent and odd sounds were two different auditory stimuli (chirp sounds -F S = 44100 Hz, 0. 1 s duration) presented to the participants: an increasing-frequency sound (up-chirp) and a decreasing one (down-chirp)  (Artieda et al. , 2004). Sounds were generated with MATLAB (The MathWorks, Natik, USA) and displayed using the Psychophysics Toolbox extensions  (Brainard, 1997; Kleiner et al. , 2007). The inter-stimulus interval was set at 1. 5 s to which a random jitter drawn from the standard uniform distribution between 0 and 2 s was added. Standard sounds were presented with a 75% rate and odd sounds with a 25% rate.
Procedure Upon their arrival, participants were asked to read and sign the informed consent form stating that they were willing to take part in the experiment. After a quick overview of the imminent tasks, the two cEEGrid (TMSi, Oldenzaal, Netherlands) for the right and left ear (see Fig. 1(b ) and (c)) were positioned according to the manufacturer and literature recommendations  (cEEGrid -Stefan Debener, 2019; Mirkovic et al. , 2016). After verifying the impedance of the electrodes and data quality on the BrainVision Recorder software (Brain Products GmbH, Gilching, Germany), the 8-dry-electrode Enobio cap was positioned and electrode locations  (Fpz, Fp1, Fp2, Fz, Cz, Pz, C3 and C4)  were verified to fit to the International 10-20 layout  (Jasper, 1958). Next, impedance as well as data quality were checked on the NIC2.
Behavioral data Participants' behavioral responses were continuously streamed from the flight simulator via LSL at a 100 Hz frequency rate. Response type and reaction times were extracted and analyzed a posteriori with MATLAB (The MathWorks, Natik, USA). There were on average (mean ± SEM) 75. 5 ± 0. 65% standard and 24. 5 ± 0. 65% odd sounds for the normal visibility task, and 74. 5 ± 0. 32% standard and 25. 5 ± 0. 32% odd sounds for the low visibility scenario. On average, less than 3 false positive (i. e. , responding to a standard sound) and late responses (i. e. , responding to an odd sound more than 2 s after sound display) were observed per participant. Based on these, hit and miss rates were computed for each participant, in every condition (i. e. , low and normal visibility), respectively as the number of correctly detected targets and the number of targets not responded to over the total number of odd sounds.
In addition, based on the Signal Detection Theory, the d' coefficient was computed as a measure of sensitivity to the sounds for each experimental condition  (Swets et al. , 1961; Anderson, 2015). Finally, reaction times (RT) were computed as the time delay between sound display and participant's response. Late responses (RT > 2 s) were not considered in the analysis. Miss rate, d' and RT were analyzed with MATLAB (The MathWorks, Natik, USA) with a pairwise t-test with visibility (low visibility vs. normal visibility) as within subject factor. In addition, for each condition, the difference of the d' from 0 was tested with a t-test, showing whether odd sound detection was higher than chance or not. All results are reported as mean ± SEM.
Pre-processing cEEGrid data were analyzed using EEGLAB software (v. 2019. 1) (Delorme and Makeig, 2004)  on MATLAB (The MathWorks, Natik, USA)(v. 2019a). Several steps were performed to i) extract spectral bands of interest, ii) denoise and clean data and iii) epoch data. First, raw EEG data were down-sampled to 250 Hz and band-pass filtered (FIR using Hamming window with order 414 and 1 Hz transition bandwidth). Cutoff frequencies were adapted according to the requirements for further analyses: [1 -20] Hz for ERP analyses and [1 -40] Hz for frequency and timefrequency analyses. Due to the ecological context and the nature of the cEEGrid electrodes, an extensive and carefully designed denoising procedure has to be performed. Artifact Subspace Reconstruction (ASR) was used as a primary step as it has been shown to be very effective in cleaning data from large amplitude artifacts  (Miyakoshi and Kothe, 2019; Chang et al.
Finally, data were epoched time-locked to the sound stimuli, between -200 and 1000 ms around standard and odd sounds for ERP analyses, and between -1 and 1. 5 s around standard and odd sounds for spectral analyses. Epoched data for ERPs were baseline-corrected in the -200 to 0 ms time-window before stimulus. Two participants had to be removed from the cEEGrid analysis due to large noisy portions of the signal, therefore unusable, after the pre-processing steps described above. For the remaining 8 participants, epoched data were averaged for each experimental condition separately according to the type of stimulus and response of the participant to target sounds (hits and misses) but also to the visibility level (normal vs. low visibility). Usual event-related potentials (ERPs) and power spectral densities (PSD) were computed.
PSD was obtained with a Welch periodogram using a 250 ms window and no overlap. Finally, time-frequency measures (Event-Related Spectral Perturbations -ERSPs) were computed with wavelets implementation from EEGLAB (v. 2019. 1) (Delorme and Makeig, 2004)  with 40 frequencies in the [3 -40] Hz window, 3 wavelet cycles at the lowest frequency, an increasing factor of. 8 and 250 time-points in the -442 to 938 ms time window. At first, we observed the data according to the stimulus-response pattern to target sounds (hits vs. misses) and visibility (normal vs. low visibility) to gain insights into inattentional deafness related activity. In addition, to avoid differences related to the number of trials for spectral activity data, an equal number of trials was randomly selected in each condition at the participant level for frequency and time-frequency analyses only.
Pre-processing To ensure a fair comparison, Enobio data were analyzed using a similar pipeline as cEEGrid data with the EEGLAB software (v. 2019. 1) (Delorme and Makeig, 2004)  on MATLAB (The MathWorks, Natik, USA). Three main differences were to be observed: first, frontopolar electrodes were removed from the analysis, as they induced very bad results with the ASR correction; then, data were not re-referenced, as they were already referenced to the CMS and DRL electrodes; finally, no participants had to be removed from the analysis because of poor signal quality. Similarly, raw EEG data were down-sampled to 250 Hz and band-pass filtered (Hamming window FIR, order: 826, 1 Hz transition bandwidth) between [1 -20] Hz for ERPs and [1 -40] Hz for frequency and time-frequency analyses. Data were denoised with ASR, interpolated and cleaned with automated ICA (using the same parameters as for the cEEGrid processing).
Finally, data were epoched time-locked to the sound stimuli (-200 to 1000 ms for ERPs, and -1 to 1. 5 s for frequency data). Epoched data for ERPs were baseline corrected in the -200 to 0 ms time window before stimulus. One participant was removed from further processing due to technical failure from the recorder. The nine remaining participants were then averaged for each experimental condition separately according to the type of stimulus and response of the participant to target sounds (hits vs. misses). Usual ERPs and PSD were computed, as well as ERSPs, with the same parameters as for the cEEGrid data. In accordance with the literature on oddball paradigms  (Luck and Kappenman, 2011) , the Enobio ERP data were inspected for the N1 component at Cz around 100 ms post-stimulus (the most negative peak in the [50 -150] ms post-stimulus tme-window), and the P300 component at Pz starting around 300 ms post-stimulus onset (the average amplitude in the [300 -500] ms post-stimulus time window).
Here we used the Schäfer-Strimmer shrinkage estimator  (Schäfer and Strimmer, 2005)  as it provides the best results with little training data. Additionally, data in the majority class were down-sampled using the classical Tomek Links procedure to ensure a balanced training dataset. Tomek Links procedure removes instances of the majority class that are the closest from samples of the opposite classes  Tomek (1976) ). Finally, we applied a classical 5-fold cross-validation performance estimation. To summarize, epochs previously extracted for the ERP analyses (-200 to 1000 ms around odd sounds stimulation; see section 3. 2. 1. 1) were selected for the Enobio and cEEGrid recorded data. Covariance matrices with Schäfer-Strimmer shrinkage estimator were computed for each participant, and data were down-sampled.
, 2019; Sauvageat et al. , 2020). Several measurement techniques exist, ranging from impactors with digital microscopes to airflow cytometers that use fluorescence spectroscopy and scattered light to identify particles  (Huffman et al. , 2020). No matter the method used, the fact that there is a higher degree of automation means that these techniques lend themselves more easily to standardisation, whether it be for calibration, the actual measurement itself, or the development of particle identification algorithms. At the same time, the number of different measurement techniques already available and the fast pace at which new methods are developed, provide a challenge to developing such standards. Nevertheless, as an automatic pollen and fungal spore monitoring network is established across Europe under the umbrella of the EUMETNET AutoPollen programme and the number of new observation sites is constantly growing across the world, it is essential that all aspects of the measurement chain are standardised to ensure comparable and high-quality data and information can be provided to end-users.
Operating time and temporal data recall The operating time refers to the amount of time a particular instrument measures, while the temporal data recall is the fraction of data obtained from what theoretically should be available. Specific acceptance thresholds for the temporal recall are set by data users and it should be a mandatory feature of all instruments that all downtimes and incompletely observed time intervals are reported. As a default, averaging should only be carried out over complete datasets. For instance, if an instrument delivers minute-level particle counts, hourly counts can be computed as an average of minute values only if all 60 values have been measured. This is because intermittent failures might indicate a systematic problem under certain conditions, such as saturation at high particle concentrations.
Metadata Table 1 outlines a minimum list of metadata to report with each concentration data point. These metadata are essential to accurately calculate the final pollen or fungal spore concentrations as well as to be able to reanalyse or reconstruct timeseries at a later date, as well to assess the quality and conditions of the measurement. Considering the de facto standards currently used for operational atmospheric monitoring, the decision was made to use the EBAS-NASA-AMES air quality file format. These text files contain a header with metadata as well as a data section. This allows a certain flexibility in terms of the file content, which is particularly important for bioaerosol observations. Through the Copernicus Atmospheric Monitoring Services (CAMS)_23 project, EUMETNET Auto-Pollen has developed links with the EBAS database and portal (http:// ebas.
nilu. no), which was started by the European Monitoring and Evaluation Programme (EMEP) more than 20 years ago and is operated by the Norwegian Institute for Atmospheric Research (NILU). EBAS has sustainable funding from EMEP and the portal is used by a wide range of stakeholders related to atmospheric monitoring, including the WMO Global Atmospheric Watch programme, Arctic Monitoring and Assessment Programme, as well as by a variety of European research projects. The database contains both real-time and validated data and the portal has established access control procedures that allow selective restrictions on data distribution policies. Recommendation: All raw data should be stored in case future changes in algorithms require a reanalysis of an entire timeseries to ensure homogeneity.
Identification algorithms and their development The main difference between manual pollen and fungal spore identification methods and automatic ones is that computer algorithms are used to identify particles rather than humans with a microscope. These algorithms are, to date, largely based on various machine learning methods, whether they be applied to images or other data signals  (Crouzy et al. , 2016; Sauliene et al. , 2019; Oteros et al. , 2019; Sauvageat et al. , 2020; Daunys, et al. , 2021; Schaefer et al. , 2021). A range of different techniques exist to develop training datasets as well as to evaluate the end results, which are further described below. Vol:. (  1234567890 ).
Datasets for training identification algorithms Supervised algorithms used to identify pollen and fungal spores require datasets upon which they can be trained. Training datasets are produced in two main ways, either by exposing the measurement device to aerosolised pollen or fungal spore particles of a known taxa, or for image-based devices, by manual selection from a timeseries of environmental observations. Pollen and fungal spores can either be collected fresh from the respective plant, fungus, or substrate, or bought from business entities, in which case they are dried and can be covered in various chemical substances for storage, which may in turn affect the physical or optical properties of the particles. Two commercially available devices exist to aerosolise pollen particles, the Swisens Atomizer and the Pollen Dispersal Unit (PDU;  Zuberbier et al.
Likewise, little is known about operation in very dry or hot and humid conditions. This issue is discussed in more detail in Sect. 3. 3. When developing identification algorithms, it is important that a large enough number of events, or data points, are used to train the model for each taxon. A minimum of at least 5′000 events per taxa should be used, but it may be helpful to use as many as 10′000 depending on how difficult the species or genera or family is to identify. This also depends on the other confounding particles present in the sample. It should be noted that these numbers refer to events with a signal of sufficient quality to enable identification. Furthermore, it is important that training datasets are made up from samples from a number of different days or plants. This is to ensure that the algorithm is trained on particles under a variety of different conditions (e.
particles in different environmental conditions and from different individual sources (to reflect biological variability). A similar number of data points for each taxon should be used when training an algorithm. It is important to note that supervised identification algorithms have been developed to identify only what they have been trained to identify. Often there is no category for particles that are identified as pollen or a fungal spore but are of an unknown type. This may be a useful category to include in algorithms to avoid the algorithm being forced to allocate a label to a particle, even if generally the certainty associated with such a label will be low. Likewise, identification algorithms should be trained on datasets that include pollen or fungal spores from a range of different atmospheric conditions since this is known to affect their physical and possibly even chemical properties (see  Sect.
For both manual and automatic methods, one needs to consider flow variability in the uncertainty estimate  (Oteros et al. , 2017). While for the manual method the adhesive medium affects sampling efficiency  (Comtois and Mandrioli 1997; Galan and Dominguez, 1997) , for automatic measurements it can be the efficiency in recording signals (e. g. image clarity or fluorescence) of sufficient quality. Automatisation limits intra-observer variation during identification and counting but the reproducibility, accuracy, sensitivity, and specificity of measurements (as defined in point 7. 3 of the manual standard (EN16868:2019)) remain the major sources of uncertainty for automatic methods. The second major source of uncertainty is related to the identification algorithms, in particular when there are large differences between the datasets used to train the algorithm and the real atmospheric composition that an instrument is exposed to.
In addition, it is important what part of the training dataset is selected for validation and what procedures are used for cross-validation. Each dataset used to train an algorithm is generally split into two parts, with the training section usually approximately 80% of the dataset and the validation part the remaining 20%. Ideally, an algorithm should be trained on datasets that stem from more than just one source and time period. This ensures the algorithm is not specific to particular environmental conditions or a certain biological specimen. The validation portion of the dataset is used to choose the optimal model configuration and that the algorithm is not over-trained (i. e. too specific). However, there is always the possibility of obtaining sub-optimal results due to poor feature engineering, the removal of abnormal values from the training dataset, and imbalances between the dataset sizes of the different pollen or fungal spore taxa that are used for training.
A number of other characteristics (data shuffle, the "initial seed" values used in training, which sets adjustable randomisation to ensure reproducibility of predicted results, etc. ) may also be additional sources of uncertainty. Recommendation: Uncertainty estimates should take into account uncertainty from both the measurements (e. g. signal saturation) and the algorithms (e. g. from imbalances between training datasets used). All sources of uncertainty should be reported together with the final cumulative uncertainty estimate.