dataesr/scientific-paragraphs-categorization huggingface dataset:
Objectives Lung cancer (LC) is the leading cause of cancer death in 2020, responsible for almost one in five (18. 0%) deaths. This paper provides an overview of the descriptive epidemiology of LC based on national mortality estimates for 2020 from the International Agency for Research on Cancer (IARC), and in the context of recent tobacco control policies. Design and setting For this descriptive study, agestandardised mortality rates per 100 000 person-years of LC for 185 countries by sex were obtained from the GLOBOCAN 2020 database and stratified by Human Development Index (HDI). LC deaths were projected to 2040 based on demographic changes alongside scenarios of annually increasing, stable or decreasing rates from the baseline year of 2020. Results LC mortality rates exhibited marked variations by geography and sex.
DATA SOURCES AND METHODS The number of deaths from, cancers of the lung (International Classification of Diseases, Tenth Revision C33-34, including trachea and bronchus) were extracted from IARC's GLOBOCAN 2020 database for 185 countries or territories, by sex and 18 age groups (0-4, 5-9, …, 80-84, 85 and over). 2 10 11  Corresponding population data for 2020 were extracted from the United Nations (UN) website. 12 The data sources and hierarchy of methods used in compiling the cancer estimates have been described in detail elsewhere. 10 In brief, the GLOBOCAN estimates are assembled at the national level using the best available sources of cancer incidence and mortality data within a given country. The methods used to derive the 2020 estimates corresponding to those used to derived for previous years  [13] [14] [15]  where applicable, priority is given to short-term predictions and modelled mortality to incidence ratios, while validity is dependent on the degree of representativeness and quality of the source information.
The results are presented by country and aggregated across 20 UN-defined world regions 12 and according to the UN's four-tier HDI in 2020,  19  as a means to assess the cancer burden across four levels of development (low, medium, high and very high HDI). Throughout, we use the terms transitioning, emerging and lower HDI countries/ economies as synonyms for nations classified as low or medium HDI, and transitioned or higher HDI countries/ economies for those classified as high or very high HDI. The Global Cancer Observatory (https://gco. iarc. fr) includes facilities for the tabulation and graphical visualisation of the GLOBOCAN database, including explorations of the current 2 and future 20 burden for 36 cancer types.
Acknowledgements The project was implemented with the support from the  National Research, Development and Innovation Fund of the Ministry of Culture and Innovation  under the  National Laboratories Program  ( National Tumor Biology Laboratory  ( 2022-2. 1. 1-NL-2022-00010 ))and the  Hungarian Thematic Excellence Program  (under project  TKP2021-EGA-44 ) Grant Agreements with the  National Research, Development and Innovation Office.
This is particularly advantageous for boosted particle decays that may exhibit merging at multiple scales. Third, by training on minimally processed data rather than heavily filtered or clustered data, the ML algorithm may learn to adapt to more varied, higher-dimensional changes in the data, potentially developing a greater robustness to evolving data-taking conditions. Finally, there is the computational simplicity of using a single ML algorithm versus a hierarchy of algorithms each with its own intermediate optimizations. Admittedly, transitioning to a fully end-toend ML-based particle reconstruction framework would involve an extensive overhaul of the workflows employed by the LHC experiments such as CMS. Moreover, to what extent the entire particle reconstruction workflow can or should be reduced to a single ML algorithm remains an open question.
The calibrated ECAL detector data exist at various levels of processing: from minimally processed to the filtered and clustered version more easily accessible at the analysis level. Since the clustered detector data are historically optimized for the needs of PF, it is worth revisiting this choice for the end-to-end ML technique. As discussed in Appendix A 1, training on minimally processed instead of clustered data significantly improves our results. We thus emphasize our earlier statement that using minimally processed detector data is critical in realizing the full potential of ML. We make exclusive use of minimally processed detector data in this paper. One caveat is that accessing such data is becoming logistically challenging because of the trend toward more compact CMS file formats, necessitated by the growing volume of LHC data.
V. CANDIDATE SELECTION AND DATASETS To address the most challenging diphoton particle decays, we require that the diphoton system be misreconstructed by the PF algorithm as a single photon candidate, labeled Γ. As a consequence of the PF ambiguities in categorizing low-boost A → γγ decays ðγ L ≲ 50Þ as either one-or two-photon objects, this requirement does not necessarily imply that the diphotons are merged. For diphotons that are barely resolved, if one of the photons is too low in energy (p T < 10 GeV), it is not reconstructed by PF as a separate photon even if it is sufficiently separated from the other photon. Acceptance of this information results in a range of boost regimes. Reconstructed photons are also required to pass shower shape and isolation criteria that would accept single photons with >90% efficiency.
B. Data To validate the findings described above from simulated events, we perform a cross-check using γ þ jet events from CMS data, where the jet is reconstructed as a photon candidate. The CMS dataset was acquired in 2017 at the LHC with proton-proton collisions at ffiffi ffi s p ¼ 13 TeV. In Sec. IX B, we also compare the results from this dataset with a similar one acquired in the 2018 LHC running. These two datasets correspond to integrated luminosities of 41. 5 and 56. 9 fb -1 , respectively. If the jet contains an energetic, collimated neutral-meson decay (p T;Γ ≳ 20 GeV), typically a π 0 → γγ or η → γγ, it will be misreconstructed as a single photon Γ and the event will pass a diphoton trigger. Since the energy of the jet will, in general, be shared among several constituent particles, the π 0 is more likely to be reconstructed as the lower-energy photon in the event.
Thus, a data sample enriched in merged photons is obtained by selecting events passing a diphoton trigger and selecting the lower-energy reconstructed photon, which we additionally require to pass our photon selection criteria. The selected sample is then given to the m Γ regressor, whose output we study below. Further details about the trigger and photon selection criteria can be found in the H → γγ analysis  [13]. We emphasize that the mass regressor is being used to regress the m Γ of individual reconstructed photon candidates, which we assume to be merged photons, not the invariant mass of the reconstructed diphoton event itself. As before, only reconstructed candidates in the range jη Γ j < 1. 44 are used. An important caveat in regressing the m Γ of energetic photons (p T;Γ ≳ 20 GeV) within jets is the presence of other hadrons within the jet.
B. Mass dependence on detector conditions A critical question surrounding the end-to-end ML technique is how well it accommodates mismodeled detector conditions, or even conditions unseen in the training set; in particular, whether exposure to the full granularity of the detector data results in an algorithm that is overly dependent on detector conditions or the amount of pileup. To address these questions using the same π 0 → γγ data, we study the regressed mass versus the mean number of interactions per bunch crossing. Importantly, because of challenges with the LHC during the data-taking period used in this study, the proton bunch scheme was significantly altered in the latter half of the year. This resulted in the pileup distribution being significantly skewed toward a higher number of interactions per bunch crossing.
During these time segments, the electronic noise in the ECAL barrel increased by 25%. Using the same fitting procedure described earlier, the positions and widths of the peak in the latter two time segments are within 1% (middle) and 4% (end). These differences are similar to those measured in these datasets using established calibration techniques. As an additional check, in Fig. 8  (lower right), we show the invariant mass spectrum separately for the entire 2017 and 2018 data-taking periods. Between the ends of these two data-taking periods, the electronic noise in the ECAL barrel increased by about 10%. The positions and widths of the π 0 peak are consistent within uncertainties using a similar fitting procedure. Robustness to changes in detector conditions is thus a principal strength of the end-to-end ML technique when using minimally processed detector data.
It can potentially be used instead of particleflow techniques to reconstruct the four-momenta of resolved decays. Sensitivity gains in these cases, however, are likely to be more modest, and must be balanced against the challenges of accessing the wider event content of the CMS datasets. The technique of training via domain continuation can be exploited independently of the end-to-end method. Indeed, it is applied to the training of the photon neural network used as a benchmark. The application of this technique is not specific to high energy physics. It should be applicable in any machine learning regression task that seeks to regress a quantity near a boundary, physical or otherwise, that is close in scale to its resolution. When end-to-end particle reconstruction is combined with domain continuation, diphoton showers that are completely unresolved can now be reconstructed.
ACKNOWLEDGMENTS We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and Data / MC FIG. 9. The agreement in the regressed m Γ spectrum between electrons in data versus simulation. Left: contours of 68% (solid line) and 95% (dotted line) confidence level (CL) in the χ 2 test statistic as a function of the (s scale , s smear ) hypothesis. The best fit point (s scale ¼ 1. 040, s smear ¼ 0 MeV) is indicated by the red diamond. Right: the regressed mass distributions in data (points) and the best fit Monte Carlo (MC) simulation (blue region) for electrons from Z → e þ e -events. The difference between the simulated distribution under the null scale and smearing hypothesis versus the best fit hypothesis (Syst) is plotted as a green band. Each of the distributions are normalized to unity, including samples outside m A -ROI.
Collision centrality is further discussed in section 4. This analysis uses pp, p+Pb, Pb+Pb, and Xe+Xe data recorded by the ATLAS detector to measure charged-hadron spectra and nuclear modification factors. The pp, p+Pb, and Pb+Pb collisions have nucleon-nucleon centre-of-mass energy √ s = √ s NN = 5. 02 TeV while the Xe+Xe collisions have √ s NN = 5. 44 TeV. The pp reference for Xe+Xe collisions is obtained by an extrapolation of the existing pp data at lower √ s. The pp data were recorded in 2015 with an integrated luminosity of 25 pb -1 ; the p+Pb collisions were recorded in 2013 and they have an integrated luminosity of 28 nb -1 ; the Pb+Pb collisions recorded in 2015 have 0. 50 nb -1 of data; and the Xe+Xe collisions were recorded in 2017 with an integrated luminosity of 3 µb -1. The measurements use tracks with p T > 0.
3 GeV for all samples except for the Pb+Pb sample, where the p T threshold is increased to 0. 5 GeV. The measurements cover |η| < 2. 5 for all samples, apart from p+Pb, which uses -2. 5 < y * < 2. 0. Unlike the others, the p+Pb collisions are asymmetric; protons with E = 4 TeV collide with lead nuclei of energy E = 82 × 4 TeV. The resulting p+Pb centre-of-mass reference frame is boosted relative to the laboratory frame with a rapidity shift of ∆y = ±0. 465. This paper is organized in nine sections: section 2 reviews the ATLAS detector; section 3 introduces the data and simulation samples; section 4 provides a brief overview of the centrality definition; section 5 outlines the event and track selection; section 6 describes all the corrections applied to the data; section 7 summarizes the systematic uncertainties of this measurement; section 8 presents the results; and finally section 9 contains the conclusions.
The zero-degree calorimeter (ZDC) consists of two arms, positioned at z = ±140 m from the centre of the ATLAS detector, and detects neutrons and photons with |η| > 8. 3. Signals from the ZDC are used by the trigger systems. The ZDC thresholds were set to select events with even a single neutron reaching the ZDC. The ZDC was installed only during the Pb+Pb data-taking. An extensive software suite  [26]  is used in data simulation, in the reconstruction and analysis of real and simulated data, in detector operations, and in the trigger and data acquisition systems of the experiment. A two-level trigger system was used to select events  [25] , with a first-level (L1) trigger implemented in hardware followed by a softwarebased high-level trigger (HLT) that reconstructs the event in a manner similar to the final offline reconstruction, accessing information from all ATLAS subdetectors.
7•10 6 minimum-bias events in total. For the overlay samples, the Pythia hard-scattering events, using the same parameter tune and PDFs as for pp simulation, were embedded in real p+Pb data events specifically recorded for this purpose with dedicated triggers. Thus, data overlay events contain an underlying-event contribution identical to that in data. Only one hard-scattering event is embedded in each p+Pb event. The events are split into five subsamples according to the generated jet p T : 0-20 GeV, 20-80 GeV, 80-200 GeV, 200-500 GeV, and 500-1000 GeV. The data overlay samples consist of 33. 9 • 10 6 events in total. The samples for Pb+Pb data also use Hijing and 'data overlay'. The Hijing sample has 1. 5 • 10 6 events. The Pythia events, split into 12 subsamples analogous to those of JHEP07(2023)074 pp, were embedded in real Pb+Pb data events, recorded for this purpose with dedicated triggers.
The data overlay sample has 16 • 10 6 events in total. For Xe+Xe data, 'Hijing overlay' and 'data overlay' MC samples are used. The Pythia events, split into 12 subsamples analogous to those of pp, were embedded either in Hijing events or in real Xe+Xe data events, recorded for this purpose with dedicated triggers. There are 2. 9 • 10 6 Hijing overlay events and 16 • 10 6 data overlay events in total. Once the MC events were generated, Geant4  [38]  simulated the detector response. It was configured with geometry and digitization parameters matching those of the data  [37]. The MC events were reconstructed using the same configuration as the corresponding data  [39]. To achieve precise correspondence between data and MC samples, a reweighting of the MC samples is necessary. This is done on an event-by-event basis as well as on a particle-by-particle basis.
Momentum bias. A residual detector misalignment present in Pb+Pb data causes a momentum bias that is absent in the MC simulations. An additional correction is introduced to compensate for this effect. Its uncertainty is propagated to the measured distributions. No such misalignment is present in other data samples. The uncertainty is higher at high p T and in more peripheral collisions. Track-to-particle matching quality. The efficiency corrections used in the analysis rely on matching the reconstructed tracks to generated particles. To account for ambiguities in the matching procedure, the matching probability, defined in ref. [50] , is varied to assess the systematic uncertainty. This uncertainty is most pronounced at low p T in the most central collisions. Particle composition. The systematic uncertainty of the measurements of particle compositions  [40, 41] , as well as the full difference between the model and the data  [43, 52] , are taken into account when estimating particle weights for the analysis as described in section 3.
The model correctly describes the trends measured in the data, but has a somewhat shorter variational range, it overestimates the R AA in the most central collisions in both the Pb+Pb and Xe+Xe systems, and underestimates it in the most peripheral Xe+Xe collisions. The DREENA-B framework  [58, 59]  is based on Bjorken 1+1D medium evolution  [60]  that introduces different evolution scenarios before thermalization of the QGP and the same evolution after thermalization. It is also capable of describing charged-hadron R AA and elliptic flow v 2  [61]  at the same time. The model is compared with data in figure  23. The model correctly describes Pb+Pb data for p T 8 GeV, but predicts stronger suppression in peripheral Xe+Xe collisions than is measured in the data.
JHEP07(2023)074 Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; 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 MINERVA, 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, United Kingdom.
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;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias , Colombia; MEYS CR, Czech 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, Netherlands; RCN, Norway; MEiN, Poland; FCT, Portugal; MNE/IFA, Romania; 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 ,.
C. Cell Culture, Exposure, and Immunofluorescence The human malignant A375 melanoma cell line was purchased from American Type Culture Collection (ATCC, Molsheim, France). Cells were cultured as previously described  [9]  in a standard humidified incubator at 37 °C and 5% CO 2. Experiments were performed at earlier passages between 4 and 10, to avoid a drift of the cellular population. For exposure, cells were seeded in 3 wells of a 12-well tissue culture plate at a density > 3•10 4 cells per well. One well was exposed to MMW and the other two served as a control (negative control and positive control with chemical treatment to induce heat shock response or apoptosis [data not shown])  [9]. Cells were exposed to MMW for 90 min as previously described  [9]. Sham exposures were performed under identical experimental conditions, but with the generator switched off.
1(b) ) around 42 °C. Average temperature was calculated using a moving average filter with 75 s span through the smooth function of MATLAB. The average temperature around 42 °C was chosen as it is within the typical temperature range used in the clinical practice of thermotherapy  [28]. Note that in order to obtain the same temperature dynamics for both pulse durations, the average power for 6 s pulses (0. 334 W) was higher by about 20% compared to the 1. 5 s pulses (0. 275 W). This allows to compensate for the effect of thermal convection which tends to decrease the peak amplitude and increase cooling of longer thermal pulses  [20]. For 90 min of exposure, the pulse train was composed by 270 and 194 pulses, for the 1. 5 s and 6 s pulses, respectively. The steady state was reached after about 40-50 minutes from the beginning of exposure.
Quantification of covering tissue thickness and surface area. The occluder and covering tissue binary images were split in the XY plane to separate left and right disks into independent image volumes. A homemade Matlab routine was developed to quantify the tissue covering the outer occluder surfaces as follows: by iterating through each voxel in the XY plane, the Z axis was assessed in a direction starting from the outside of the device (in the atrial cavity). The number of voxels containing tissue prior to reaching the occluder surface were counted, converted to thickness using the image spatial resolution factor, and assigned to the corresponding xy pixel coordinates in a map of dimensions XY. A second map of equal dimensions for tissue coverage plotted values of 1 in locations where the occluder was observed prior to detecting tissue, and a value of 2 if tissue was observed followed by the occluder material.
Search Strategy The articles in this review were selected after a search of the following databases: IEEE Xplore, SpringerLink, Google Scholar, and Web-of-Science. Some Boolean operators with specific keywords including "EEG" OR "Electroencephalography" AND "physical work" OR "physical task" OR "physical exercise" OR "physical activity" OR "physical movement" OR "movement-related cortical activity". Conducted searches were not restricted to publication dates. A brief explanation of the strategy we followed for the literature search, including the used data bases, the combined searches with the Boolean operator, and keywords/terms are presented in Supplementary Material B. We further included an example of how we selected some articles from "IEEE Xplore database" according to our criteria of inclusions and exclusions.
Screening Process and Study Selection Based on the updated PRISMA as shown in (Fig. 1 ), 830 articles were retrieved from databases. Duplicate studies (n = 130) were removed, and (n = 427) studies were removed as an initial screening based on study title with some exclusions including studies on brain diseases, studies on children or infants, and studies on animals. Therefore, (n = 273) records originally screened for eligibility. After reviewing all abstracts of the remaining articles, (n = 188) were further excluded. To collect all relevant articles during the literature search, the reference lists of the candidate articles (n = 75) were reviewed. Three researchers independently reviewed the 190 full text articles specifically (n = 115 from database and n = 75 from citations search) for inclusion and exclusion criteria.
Artifact Removal Methods The raw EEG signals in physical tasks can be contaminated with artifacts due to body movements, including the neck and muscle activity. Therefore, several artifact removal methods have been developed and applied including (1) visual checking (n = 48); (2) filters such as bandpass filters, low pass filter, high pass filter, notch filter, spatial filters, Butterworth filter, moving average filters, infinite impulse response filter (FIR), and infinite impulse response (IIR) (n = 44); (3) bad channel rejection (n = 38); (4) power line removal (n = 12); (5) regression methods (n = 3); (  6 ) blind source separation including independent component analysis (ICA) and principal component analysis (PCA) (n = 35); (  6 ) artifact subspace reconstruction method (ASR) (n = 4); and (7) automatic artifacts rejections software including EEGLAB  [51] , NeuroScan's software  [52] , and Fieldtrip  [53].