dataesr/scientific-paragraphs-categorization huggingface dataset:
• Visual information processing, coding, denoising, enhancement, super-resolution • Learning-based processing, coding, denoising, enhancement, super-resolution • 360 °videos, light fields, point clouds, volumetric imaging, holography • High Dynamic Range, Wide Color Gamut • Augmented Reality, Virtual Reality, metaverse Datasets for new emerging modalities or specific application scenarios communication systems and related user studies, with the goal of defining basic guidelines and testing methodologies for user studies. More specifically, the authors first introduce a new taxonomy to describe immersive communication systems. Next, a thorough analysis of QoE assessment methods is presented in the context of the proposed taxonomy. The authors find that most QoE methods follow a common structure, and then observe its effect on QoE features.
Online Job-Seeking Among the uses of the internet that can bring tangible benefits to people's lives is platform-mediated job search. Job seekers have a better chance of finding employment via internet and of that job being better paid  (Lindsay, 2005). Using the internet may reduce the time involved in finding a new job by 25% compared to traditional, offline, channels  (Kuhn & Mansour, 2014). Furthermore, recruiters and prospective employers have access to large databases of potential candidates for their selection processes, which is important at a time when online job-seeking has penetrated most sectors and is especially popular among young people who are more confident in using the internet  (Kroft & Pope, 2014; Piercy & Kyong Lee, 2019). However, these benefits also have significant downsides.
This search strategy provides an additional constraint on the decay process to the existing ATLAS and CMS measurements, in terms of production mode and H p T. The search is performed using a dataset of protonproton collisions at ffiffi ffi s p ¼ 13 TeV, collected with the CMS detector at the LHC, and corresponding to an integrated luminosity of 138 fb -1. Candidate events are selected by requiring a high-p T , large-radius jet with substructure observables compatible with those expected from an H → cc decay. Deep neural network (DNN) discriminators are employed to separate the H signal events from the dominant background, specifically quantum chromodynamics (QCD)-induced multijet events. The discriminators are designed to be independent of jet mass, which allows for both an estimation of the QCD background from control samples in data and for the validation of the analysis procedure through a search for Z → cc decays.
The diboson processes are modeled at LO accuracy with PYTHIA8. 226 [39]. For 2016 data-taking conditions, QCD samples are modeled with MADGRAPH5_aMC@NLO v2. 2. 2, and diboson samples are modeled with PYTHIA8. 205. The differential cross sections for the Z þ jets and W þ jets (V þ jets) samples are corrected with boson p T -dependent functions for higher-order QCD and electroweak (EW) effects. The QCD NLO corrections are derived using MADGRAPH5_aMC@NLO, simulating W and Z production with up to two additional partons and the FxFx matching to the parton shower  [40]. The EW NLO corrections are taken from theoretical calculations of Refs. [41] [42] [43] [44]. Additionally, the total cross sections for the diboson samples are corrected to next-to-NLO (NNLO) accuracy with the MCFM 7. 0 program  [45].
The H → b b contribution is taken from the simulation and is fixed to the SM expectation. While its expected SR yield is greater by approximately a factor of 5 than that of the H → cc signal, its impact is negligible with respect to the overall background uncertainty. The dominant systematic uncertainties for this search are related to the flavor tagging efficiency and jet mass shape. Corrections of the jet mass, jet mass resolution, and N 1;DDT 2 and DDCVB efficiencies are derived from data using W boson jets from semileptonic t t events. These corrections are measured independently of jet flavor, and as such are correlated among all considered resonant (H, Z, W) production and decay processes. The DDCVL misidentification efficiency of the W process is measured here as well. The corrections and their associated uncertainties are given in Table  I.
The efficiency of the DDCVL selection for the signal processes is estimated using data and simulation samples enriched in cc pairs from gluon splitting  [62]. Signal-like events are selected by requiring each of the two SD subjets of an AK8 jet to contain a muon, targeting semileptonic decays of b=c hadrons. The efficiency is extracted from a template fit to the combined mass of all matched secondary vertices; the measured correction factors are given in Table  I. The relative uncertainty of the misidentification efficiency of b b decays is assigned to be 30%. Varying this value from 10% to 50% has a negligible effect on reported results. Other systematic uncertainties are assigned to cover potential mismodeling of the H signal, in particular for the ggF and VBF production modes  [48] , and higherorder corrections to the W and Z processes  [44].
The lower panel shows the residual difference between the data and the overall background (excluding Z → cc), divided by the statistical uncertainty in the data. The near perfect model agreement with data in the failing region (right) is by construction. TABLE II. Sources of uncertainty in the measurement of the signal strength μ H ¼ 9. 4 þ20. 3 -19. 9 , and their observed impact (Δμ H ) in the fit to the full dataset. The impact of each uncertainty is evaluated by computing the uncertainty excluding that source and subtracting it in quadrature from the total uncertainty. The total uncertainty does not match the sum in quadrature of each source because of correlations among the components.
The Dark Energy Spectroscopic Instrument (DESI) is carrying out a five-year survey that aims to measure the redshifts of tens of millions of galaxies and quasars, including 8 million luminous red galaxies (LRGs) in the redshift range 0. 4 < z  1. 0. Here we present the selection of the DESI LRG sample and assess its spectroscopic performance using data from Survey Validation (SV) and the first two months of the Main Survey. The DESI LRG sample, selected using g, r, z, and W1 photometry from the DESI Legacy Imaging Surveys, is highly robust against imaging systematics. The sample has a target density of 605 deg -2 and a comoving number density of 5 × 10 -4 h 3 Mpc -3 in 0. 4 < z < 0. 8; this is a significantly higher density than previous LRG surveys (such as SDSS, BOSS, and eBOSS) while also extending to z ∼ 1.
2001 (Eisenstein et al. , 2005)) , BOSS  (Reid et al. 2016; Alam et al. 2017) , and eBOSS  (Prakash et al. 2016; eBOSS Collaboration et al. 2021) , the DESI LRG sample has a significantly higher target density and extends to higher redshifts (see Figure  1 ). This is made possible by DESI's higher fiber multiplexing, larger telescope aperture, and better spectroscopic performance, and the availability of deeper (and highly uniform) imaging data necessary for target selection. In this paper, we describe the selection of the DESI LRG targets and assess the selection uniformity and spectroscopic performance. Significant efforts were made to minimize the impact of imaging systematics (i. e. , the modulation of the target density caused by variations in image quality across the sky). These include improvements to the image reduction pipeline, which were motivated by the need for uniform target selection and are discussed in D.
Most of the observation is done by the DECam Legacy Survey (DECaLS,  Dey et al. 2019) , and data from other observations, most importantly the Dark Energy Survey (DES, The Dark Energy Survey Collaboration 2005), are also used. The northern (δ  32°) part of the imaging footprint (hereafter "the North"), which is inaccessible from CTIO, is observed by two telescopes at the Kitt Peak National Observatory in two surveys: the Beijing-Arizona Sky Survey (BASS,  Zou et al. 2017 ) observed in the g and r bands using the 90Prime Camera on the 2. 3 m Bok Telescope, and the Mayall zband Legacy Survey (MzLS) observed in the z band using the Mosaic-3 Camera on the 4 m Mayall Telescope. The Mayall Telescope has since been repurposed for DESI. For the same photometric band, small differences in the filter sets, detectors, observing conditions, and image processing between the North and the South cause their photometry to differ slightly.
The relation between radius and W1 magnitude is optimized The upper right panel shows the cut that removes lower-redshift and bluer galaxies. The lower left panel shows the sliding color-magnitude cut that serves as the luminosity cut and also shapes the redshift distribution; the "knee" at W1 ; 19 introduces more galaxies at higher redshift. The lower right panel shows the magnitude limit in z-band fiber magnitude that ensures enough S/N for DESI observations. for the LRGs, so that the excess or deficit of LRG targets at the edge of the mask is less than ∼10%. 3. Gaia/Tycho-2 circular masks: similarly, we obtain the relation between radius and magnitude for Gaia stars. We use stars in Gaia Early Data Release 3 (EDR3, Gaia Collaboration et al. 2021) supplemented at the bright end (where Gaia photometry is unreliable) by Tycho-2 and Two Micron All Sky Survey (2MASS) photometry.
The LRG target catalogs are publicly available 44  (see  Myers et al. 2022 for details). The data points in all figures are publicly available at Zenodo: 10. 5281/zenodo. 6987401. In the simulations, the redshift failure probability of each object is determined by its z fiber and t eff via Equation (4). The fact that the measured distribution matches the simulations (for all except a handful of fibers) suggests that the spectroscopic efficiency is very uniform across the fibers. contamination by bright stars in WISE that can be removed by the veto mask). The major differences between the SV1 IR selection and the Main Survey selection are in the magnitude limit and the sliding color-magnitude cut (r -W1 versus W1). The SV1 magnitude limit is 0. 4 magnitude fainter than the Main sample, which increases the density at z  0.
Data Extraction and Analysis Three different authors (A. M, S. C, and F. N) performed the data extraction. Any disagreements were resolved through discussions among the research team members. The data collected included the patient characteristics, authors' information, year, sample size, study design, statistical analysis, findings, and conclusions. If the data were incomplete, the authors of the included studies were contacted. Electronic database searches were performed using the PubMed, Scopus, and Web of Science databases for studies ranging from 1 December 2001, to 1 June 2021; this was conducted by three different authors using MeSH, entry terms, and related keywords. The search terms that were used were "tracheoesophageal voice", "voice prosthesis", "tracheoesophageal puncture", "esophageal voice", "laryngectomized voice rehabilitation," and "voice-related quality of life".
There is growing interest in using passive microwave observations and Vegetation Optical Depth (VOD) to study the above ground biomass (AGB) and carbon stocks evolution. L-band observations in particular have been shown to be very sensitive to AGB. Here, and thanks to the multi-angle capabilities of the SMOS mission, a new approach to estimate AGB directly from multi-angular L-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables like VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for years 2010, 2017 and 2018 are used as AGB reference. AGB estimates from Artificial Neural Networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; Even so, a decrease in retrieval's performance was observed when models are applied to data from years different than the year used for their training.
The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using brightness temperatures is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed higher correlation with CCI AGB maps (R 2 ∼0. 87 instead of ∼0. 84) and presented a stronger agreement with their spatial structure and less differences in residual maps. Index Terms-AGB, forest biomass, Neural networks, Machine learning, SMOS, L-band, Passive microwaves.
B. Above Ground Biomass reference maps Three AGB maps from ESA's CCI biomass project with global coverage were used as vegetation biomass references for the years 2010, 2017 and 2018 (datasets version 3. 0)  [19] , from now on referred to as CCI2010, CCI2017 and CCI2018, respectively. Reference maps are provided with a spatial resolution of 100 m, and representing biomass density in Mg/ha. The retrieval algorithm used for the production of these data-sets is based on a weighted linear combination of biomass estimates from C-and L-band SAR observations. In densely vegetated areas (such as tropical forests) more weight is given to L-band observations, in other regions to C-band observations. The CCI2010 is produced from ALOS-1 (PALSAR-1) and Envisat ASAR data-sets, the and Sentinel-1 observations of SAR backscatter (additional data-sets were used during the calibration of models)  [34].
B. AGB estimation from parametric functions As mentioned in Section I, currently, the common approach to estimate AGB from passive microwaves is based on determining a relationship between VOD and AGB using a functional fit. Previous studies have explored this methodology and have proposed different parametric functions to estimate AGB from VOD (  [22] ,  [23] ). Here, the one proposed by Rodríguez-Fernández et al  [23]  was used. To do so, VOD datasets are sliced in bins of 0. 05 units of width. For the corresponding AGB values of each VOD bin, the mean and the 5th and 95th quantiles are found. The resulting AGB-VOD curves are then fitted using Eq. (  2 ), in which a, b, c and d, are the set of adjustable parameters used to fit the function to the AGB dataset. AGB = a 1+e -b(V OD-c) + d (2).
However, the spatial structure of the estimates and the residuals maps showed that the hybrid approach does not provide significantly more precise estimates than those produced from the exclusive use of TBs and emissivities. On the other hand, the use of horizontally polarized emissivities (E h ) proved to be useful for a more accurate reproduction of the AGB. This, probably explained by the fact that emissivities depend directly pressume that a spatial relationship found at a given time between VOD and AGB can be used to extrapolate to other periods. This is a strong assumption that can be questioned  [42]. However, a thorough study of the influence that reference maps may have on the temporal extrapolation of models is necessary. In any case, the robustness of training the ANNs with multi-year datasets is a significant step in that direction.
In relation to the influence of data-sets on AGB inversions, when observing the R 2 values in Fig. 3 , it can be seen how estimates produced from ANNs trained on continental learning sets (Africa or South America) tend to have higher correlations with the reference maps than the global ones. Probably, this difference in performance is due to the seasonality of higher latitude pixels and their influence on retrievals done on global observations, as remarked by  [43]. It is true that when computing annual averages at a global scale (as is done here), the seasonality of pixels in the northernmost latitudes (where ground temperatures can pass the freezing point) could have an effect on TBs, and therefore, on estimates. However, an additional filter to the TBs before the calculation of annual means was made (pixels with temperatures lower than 275 K or with a snow depth greater than 10 -4 mm were not taken into account), the estimated AGB values over boreal forest from datasets filtered this way (not shown here) did not diverge significantly from the results obtained with the filters used here (See Section III-A ).
In the case of a sensor with multiincidence angle and full-polarization capabilities, such as SMOS, this implies moving from a high dimensional space (two polarizations times the number of incidence angle bins) to a one-dimensional quantity. Therefore, there is a potential information loss when the final goal is not VOD but AGB. This is probably the origin of the observed degeneration of the VOD to AGB relationship (for a given VOD several AGB values may correspond), which can be partially compensated by using multi-dimensional data as input. An example is the large dispersion found on AGB estimations from VOD, in particular for large AGB values (Fig. 8 ). In other words, a VOD value of 1 can correspond to an AGB from 300 to 450 Mg/ha. When using just the mean value, there are large differences with respect to some of the reference AGB values.
Identification of relevant literature Our work aimed at screening digital interactive technology for the autistic population. The following keywords Relevant papers were identified based on the following fields: Title, abstract, and meta-data (language, publication year, location, number of pages). As technology rapidly becomes outdated, the search focused on recent literature. The date range for the records search was set between 2010 and 2018. The motivation for choosing 2010 as the start year was that it coincided with the launch of the iPad, a platform massively used in healthcare and educational contexts due to its accessibility and portability and the popularity of its manufacturer's products  [31, 50]. The database search was conducted in March 2018. The total number of records returned through the process was 5,496.
Exclusion criteria: • Technology is only used for clinical evaluation, e. g. , screening, assessment, diagnosis, monitoring tools, and fundamental research using technology; • Literature reviews; • Conference papers less than two pages long. Two independent reviewers screened the titles, abstracts, and meta-data (language, year of publication, number of pages) of the 2716 records yielded by the literature search. They applied the above-mentioned inclusion and exclusion criteria to filter out irrelevant papers. The lists of papers they each selected were then compared. In case of discrepancy between them, the decision to include or exclude of a paper was made through consensus by consulting a third reviewer. The screening yielded 792 records that met our inclusion criteria. Our main goal was to evaluate the rating scale in a representative sample of individual research reports (i.
Reliability and validity testing procedure Two independent raters scored the sample of 211 papers (see the supplementary material: S3 All UIDS scored papers). The two raters were the persons who had screened the initial records yielded by the literature search to apply inclusion/ exclusion criteria. To assess the scale's reliability, a random sub-sample (n = 78), amounting to a third of the included papers (after sampling, before full reading), was scored by both raters and served as a test dataset to determine the level of consensus. We used the intra-class correlation coefficient (ICC), an index used in inter-rater reliability analyses  [88, 89]. We also assessed the validity of our UIDS scale. As there are no scales for measuring the quality of information about users guiding the design process of technology-based supports, we could not compare this new scale with an equivalent rating instrument.
Implications The present study designed and tested the UIDS scale, which assesses information about users guiding the technological design process of digital supports for autistic users. This scale demonstrated high reliability and validity. As with other scales, the UIDS scale should be used according to its stated procedures, scoring systems, and concepts considered within the framework of the present article. The scaling instrument presented here should help address the critical challenge posed by the current proliferation of studies on technology-based supports for autistic users. The scale could prove useful to national and international organizations responsible for disseminating knowledge about autism, especially those that manage databases of technology-based supports and need a standardized way to display information about the quality of the design of those supports.
Funding This work was supported by a grant from a consortium composed of the  International Foundation of Applied Disability Research, Orange Foundation , and  UEFA (Union of European Football Associations) foundation  for children (Grant Number:  APa2016_026 ). This publication has also received financial support from the project  Indigo!  ( Spanish Ministry of Science and Innovation and Universidad Autónoma de Madrid , with reference  PID2019-105951RB-I00/ AEI/10. 13039/501100011033 ). The funding sources were not involved in conducting the research study.
Montagne Pelée, on the French island of Martinique, eastern Caribbean Sea, has been one of the deadliest volcanoes in the world, with 30,000 victims following the 1902 eruption. Thousands of people still live nearby, and this volcano is a strategic "water tank" for Martinique Island, providing 40% of the island's water supply. This research aimed to better understand its hydrogeological functioning and the relationship with its complex volcanological evolution, taking advantage of a high-resolution helicopter-borne geophysical survey correlated with hydrogeological data from the boreholes and springs databases. Electromagnetic data, correlated with hydrogeological data, allowed for the identification of unsaturated zones, aquifers, and seawater intrusions, as well as the main geological units.
In addition, data synthesised from pumping tests revealed that the older the unconsolidated pyroclastic deposits, the lower their hydraulic conductivity. The structural asymmetry between the northeastern and southwestern volcano flanks impacts its hydrogeological functioning. Consequently, the Montagne Pelée hydrogeological conceptual model is marked by several distinguishable aquifers. The upper perched aquifer within recent lava domes is directly involved in, and impacted by, phreatic eruptions, and it supports low flowrate springs. The remaining effective rainfall infiltrates to depth and recharges the hydrothermal system through vertical fractures. The other aquifers are categorized into three groups: northeastern, southeastern and southwestern flank aquifers. This research is a new step toward a better understanding of the Lesser Antilles volcanoes and more broadly of the central and proximal parts of the andesitic active volcanoes.
For Montagne Pelée, being an active volcano, the relationship with the hydrothermal system is also of interest and a better description of groundwater circulations should assist towards a better understanding of the eruptive processes. This research takes advantage of a unique high-resolution helicopter-borne geophysical survey  (Deparis et al. 2014)  conducted over the Montagne Pelée (Fig. 2 ) correlated with geological and hydrogeological data from a database of boreholes and springs (Figs. 2  and 3 ) with the aim of deepening the comprehension of the structure and hydrogeological functioning of this active volcano and to define its hydrogeological conceptual model.