dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, the bootstrapping procedure allows improving results' accuracy through the application of a bias-correction to a percentile CI. Biased-corrected and percentile confidence intervals are non-symmetric and better reflect the sampling distribution of the conditional indirect effects. Specifically, we compute the conditional indirect effects for three different values of the moderating variable: low moderator (mean À 1sd), medium moderator (mean), high moderator (mean þ 1sd). To run our models, we use an additional macro creating the command MODMED in SPSS  (Preacher et al. , 2007). To assure the robustness of our results, we replicate the same analysis while considering ROA as the main dependent variable and the average of ROE recorded in the five years before the questionnaire administration as moderating variable; and measuring family-TMT as a dummy variable with value 1 if at least one family member sits in the TMT, and 0 if no family member is in TMT (family-TMT (presence)).
Thin-walled stiffened shell structures are very common as industrial products and their analyses by the finite element method can be difficult, in particular when the part is geometrically complex as found in the field of packaging. When the initial CAD definition is not available, reverse engineering is necessary, often based on the use of 3D scanners, in order to obtain a performing 3D geometrical model before considering finite element computations. The process of data capture to FEM can be time consuming and difficult for parts with several 3D stiffeners, depending on the strategies to reconstruct the part. This work focuses on the evaluation of three strategies applied to quite simple thin-walled parts using Geomagic and Abaqus software for the reconstruction and the simulation, respectively.
Data Capture In this work, the OptoTOP-HE, based on the Structured Light technology, developed by BREUCKMANN [10] has been chosen to scan the part. The working principle of this scanner is to project different patterns made up bands of light on an object. These patterns deformed by the surface is captured by a CDD camera forming a non-zero observation angle with the projector direction. Thereafter, the deformations, intensity and contrast of the bands are analyzed by OPTOCAT 2013 software (supplied with the BREUKMANN device) to generate a cloud of points computed by triangulation. 20 shots with a HE-175 lens (160mm (width) x 100mm (height) x 100mm (depth of field)) have been performed to obtain a complete geometry of the part within 2h30. The full mesh of the part has then been saved as a STL format and transferred to another software for pre-processing.
Pre-processing For our application, the commercial software oriented RE Geomagic Design X has been used. It offers tools to correct the point cloud and the 3D mesh. It can repair meshes containing topological errors (non-multiple edges, auto-intersections) and remove nonexisting structures (small holes, small components). 6 minutes have been needed to obtain a clean and closed STL mesh of the simple part of Figure  1. This step may be essential depending on the chosen reconstruction strategies, in particular for Strategy 1 a clean 3D mesh is required for use by the FEM software. However sometimes the proposed mesh must be transformed because some triangles are too distorted and can stop the computation process. One way to avoid this problem is to re-mesh the part in Geomagic Design X [11] according to geometric parameters on the triangular elements.
For example, in Figures 2, an average size of 2. 8 mm or 1. 5 mm on the edges of triangles and an angle of 60° on each vertex have been imposed for all triangles. This remeshing has been performed in 1 min. Strategy 2 ("Freeform strategy") requires also a clean full mesh to generate the external surface of the digitized object to automatically reconstruct a closed surface. Regarding Strategy 3 ("Functional Reconstruction Strategy"), if the functional surfaces necessary for the reconstruction of the part are relatively clean, a pre-processing is not needed as it will be shown later. 1. 5mm, in Geomagic Design X software.
Segmentation and region classification The simple part of Figure  1  has many canonical surfaces that can be correctly reconstructed by Strategy 3 ("Functional Reconstruction Strategy"). Geomagic Design X software can recognize these canonical surfaces by sub-dividing the point cloud or 3D mesh into cells consistent with the geometric features of the scanned model and according to geometrical criteria such as the curvatures. Thereafter, the geometry of these cells are identified, if possible, with canonical forms (plane, cylinder,. ) or as a free form. Note that this operation is entirely automated and has been performed on the plate in 1min. This step is not necessary for Strategies 1 and 2 because they do not need shape recognition to generate geometries. It is also interesting to use the segmentation and region classification for the alignment of the digitized part on the axis system of the software.
FEM FREQUENCY ANALYSES An FEM frequency analysis on the plate has been carried out using the commercial software Abaqus 6. 14. The Young's modulus, Poisson's ratio and density of the plate are 210 GPa, 0. 3 and 7800 kg. m-3 respectively. The first free eigenfrequencies have been first calculated with the original CAD model of the plate to obtain reference values of the frequencies and modes (see Figure  5 ). These results have been obtained with a mesh consisting of 57,452 hexahedral quadratic solid elements C3D20 and prismatic triangular solid elements C3D15 with a size of 2 mm, (3 elements in thickness and 819,255 degrees of freedom). The geometries of Strategy 1 have been generated in Abaqus from imported STL meshes using a plug-in called "Create a geometry from a mesh" to be added to Abaqus.
These regular meshes are generated by carrying out a prior adapted partitioning of the piece for 35min30s. For quadratic elements, results converge rapidly from the mesh with an element size of 30 mm, whereas for their linear counterpart, equivalent results are only achieved considering an element size of 6 mm. The CPU time for each computation is less than two seconds which is very reasonable. C3D8 and C3D20 are linear and quadratic hexahedral elements, respectively. C3D6 and C3D4 are linear and quadratic triangular prismatic elements, respectively. SC6R and SC8R are prismatic and hexahedral solid-shell elements. S4 is shell elements. Other types of elements are available in Abaqus Software  [16]. As the part is thin, it is possible to use shell elements  [13]  to quickly obtain accurate results.
According to the author, the app raises a privacy paradox  [30]  where immediate benefits (eg, the reduction of contacts with infected people) are preferred to the value of privacy. Since the app does not seem to be effective given its limited use, it is not worth risking the breach of one's privacy. A second study  [2]  analyzed the political and scientific discourse around the promotion of the StopCovid app. Digital solutions like contact tracing apps might represent a form of alienation including government distrust. By collecting and analyzing media, scientific, and policy articles mentioning StopCovid, the study reported the contradictions of the government in handling the COVID-19 crisis based on partial and imprecise knowledge about the virus. In this context, government officials did not explain in plain language the security, privacy, data collection, processing, storage, and reuse of the StopCovid app.
The Field Survey: Recruitment This study was conducted within the framework of the larger ongoing i-Share (Internet-Based Students Health Research Enterprise  [31] ) cohort study, a French, nationwide web-based survey on the health and well-being of university students, whose principal investigators and operational staff are based at the University of Bordeaux  [32]. The field survey consisted of a paper questionnaire administered face-to-face by five undergraduate students (interviewers) who had been trained to take notes, fill in the questionnaire, and describe the app to respondents. Interviewers approached their peers in the halls, canteen, courtyards, library, and study rooms at the health sciences campus of the University of Bordeaux. The collection of the data started on September 25, 2020, and ended on October 16, 2020.
Theoretical Framework The questionnaire was based on the KABP scheme, which stands for the assessment of knowledge, attitudes, beliefs, and practices of populations about a specific health-related topic. This scheme is extensively used as a quantitative method (predefined questions formatted in a standardized questionnaire) that provides access to quantitative and qualitative information. Thus, items of the questionnaire were developed to capture students' KABP about the StopCovid app. The results are discussed following the four components of this scheme. Collected data were interpreted a posteriori through the prism of the technology acceptance model (TAM)  [34]  and the protection motivation theory (PTM)  [35]. The TAM posits that an individual's intent to use (ie, accept) a technology and use behavior (ie, actual use) is influenced by perceived ease of use and usefulness, which are mediated by external variables such as individual differences, system characteristics and complexity, and social influences.
Data Analysis All data from the paper questionnaires were entered by the student interviewers in a digital database through the EpiData software version 3. 1  [36]. A descriptive analysis was performed, presenting all variables and measures in the form of numbers and percentages for qualitative variables and means and SDs for quantitative variables. Chi-square or exact Fisher frequency comparison tests were used to identify statistically significant differences by age, gender, field, and year of study, modified to binary variables if necessary. Data were normally distributed. Statistical significance was defined with a P value <. 05. Statistical powers were calculated for each frequency comparison test, chi-square or Fisher exact test, with the condition of a minimum sample size for the Fisher exact test.
Principal Findings and Interpretation As far as knowledge is concerned, 1 out of 5 students had never heard about the StopCovid app; this rate is surprisingly high considering that students in the health domain should be informed of existing tools to limit the spread of COVID-19. Those who knew the app had heard about it mostly through the media (216/246, 87. 8%). The majority of students (179/246, 72. 8%) correctly knew that the app was promoted by the government. However, concerning the functioning of the app, some students did not know how the contact tracing system worked and how data was managed: percentages of errors in describing the app ranged from 10. 4% (33/318) to 49. 4% (157/318). For them, the app was not straightforward; in the light of the TAM, reduced ease of app use determines lower acceptance.
A study exploring the real uptake of an existing app in Singapore, the TraceTogether app, had an uptake of 20%  [25]. This higher percentage, compared to our study, might be justified by the fact that Asian countries are often referred to for their decisive and authoritative responses to pandemics. More convincing communication around the app might have increased its uptake. Furthermore, TraceTogether has been a real pioneer in COVID-19-related apps given its high performance, which might have further supported its use. However, the TraceTogether app received criticism for draining mobile phone batteries, which was one of the faults reported in this study about the StopCovid app. In fact, excessive use of battery and data storage were mentioned by some of our students as reasons for not downloading the app (27/210, 12.
Distrust in data security and fear of being geolocated were mentioned by our sample as the fourth reason for not downloading the app (33/210, 15. 7%). Researchers worldwide, from Europe to Asia, have emphasized the privacy controversies of contact tracing apps, presenting them as the main fault of this type of technology  [9, 17, 22, 25, 38]. Fears of greater surveillance and that the app might be hacked are mentioned in these studies as barriers to app use. In France, the question of data privacy related to the StopCovid app has been particularly explored; the app does not come without short-term and long-term risks of privacy and surveillance. French people face a moral dilemma: the app can prevent the spread of the pandemic, especially protecting older adults, but limit freedom to move, data security, and privacy, which are usually sensitive issues in the French culture and politics  [9].
Strengths and Limitations This was one of the first studies reporting data on students' KABP about a contact tracing app in a pandemic context. Previous studies have explored the intention of downloading this type of app as a general idea but were not based on a developed and currently diffused app  [17] [18] [19] 23]. Reasons for downloading and using the app were presented to inform future steps to increase its diffusion. The specific focus on students was another strong point of this study: young people were especially concerned by the transmission of the virus in subsequent COVID-19 waves. Mobilizing this population to adopt the app is pivotal in this particular epidemiological context. Limitations of this study include the relatively small sample. More than 300 students in the health domain were interviewed among a total population of 18,000 students.
Acknowledgments The project is led by the  University of Bordeaux  in close collaboration with the  University Hospital of Bordeaux , the  Regional Health Authority , and the  National Institute for Research in Digital Science and Technology (Inria). The authors wish to thank the i-Share team [31] for managing and conducting the study and for revising the questionnaire:  Julie Arsandaux ,  Fadi El Khoury ,  Shérazade Kinouani ,  Mélissa Macalli ,  Elena Milesi ,  Marie Mougin ,  Garance Perret , and  Clothilde Pollet. Within the i-Share team , the authors wish to thank especially  Edwige Pereira  and  Aude Pouymayou  for their support in statistical analysis. The authors are also indebted to the student ambassadors that carried out the field survey:  Idriss Boukili ,  Justine Char ,  Jade Giacosa ,  Oleg Hounkpatin ,  Jeanne Langlois , and  Marie Lanneluc.
All data generated or analyzed during this study are included in this manuscript. The full data set is available on reasonable request. The i-Share team is currently supported by an unrestricted grant of the  Nouvelle-Aquitaine Regional Council  ( Conseil Régional Nouvelle-Aquitaine ; grant no. 4370420 ) and by the  Bordeaux Initiatives d'excellence program of the University of Bordeaux  ( ANR-10-IDEX-03-02 ). The team has also received grants from  Public Health France (Santé Publique France , contract no. 19DPPP023-0 ) and the  Nouvelle-Aquitaine Regional Health Agency (Agence Régionale de Santé Nouvelle-Aquitaine). The funding bodies were not involved in the study design or in the collection, analysis, or interpretation of the data.
PyCBC Live, first introduced by  Nitz et al. (2018) , is one of several search codes analysing the data in realtime to observe compact binary mergers, other analyses being GstLAL  (Messick et al. 2017; Sachdev et al. 2019) ,  MBTA (Adams et al. 2016; Aubin et al. 2021) , SPIIR  (Hooper et al. 2012; Chu 2017; Chu et al. 2020 ) and the generic-transient search cWB  (Klimenko et al. 2016). Having multiple analyses provides for redundancy, both in terms of the possibility that one of the analysis fails for any reason, and in terms of the independent methodology that each of these analyses applies to identify compact binary mergers. PyCBC Live is based on the more general PyCBC software package  (Nitz et al. 2020a ) and uses a precalculated bank of compact binary merger waveform models combined with matched filtering in the frequency domain  (Allen et al.
Search space and template bank Although it is an improvement to the configuration of PyCBC Live rather than the code itself, we begin by describing the search space and template bank adopted during O3, as a reference for future work. The bank covers the same mass, spin and waveform duration space as that proposed by Dal Canton & Harry (2017) and previously adopted during O2. The same waveform models are also employed: TaylorF2  (Buonanno et al. 2009; Bohé et al. 2013)  for BNS and lowmass NSBH templates, and a reduced-order frequencydomain version of SEOBNRv4  (Bohé et al. 2017; Pürrer 2014)  for BBH and heavy NSBH templates. However, the O3 bank utilises a template placement method based on an optimized hybrid geometric-random approach  (Roy et al. 2017 (Roy et al. , 2019;; Roy et al. 2018)  which is more efficient than the "manual" combination of geometric and stochastic methods used for the O2 bank  (Dal Canton & Harry 2017)  in the sense that the obtained bank is ∼ 25% smaller and can be produced much faster.
Improved rejection of instrumental transients Loud instrumental transients in GW data (glitches) which last much less than a second can corrupt the results of transient searches on a time scale much longer than the glitch itself  (Dal Canton et al. 2014). In particular, due to the relatively long-lasting impulse response of the various filters involved in the SNR calculation, loud glitches can cause the SNR time series for a given template to cross the trigger-generation threshold many times over several seconds. The resulting high-SNR triggers then dominate over quieter triggers from the underlying stationary noise and possible astrophysical signals, effectively blinding the search for the entire duration of the impulse response of the filters. In early O3, this issue appeared prominently in the results of PyCBC Live as occasional gaps in the production of triggers from a given detector, lasting from a few seconds to several tens of seconds, depending on the glitch.
A simple and widely-used solution to this problem, called gating, consists of windowing out the GW strain data for a short window centered on the glitch prior to matched filtering  (Abbott et al. 2016; Usman et al. 2016; Sachdev et al. 2019). PyCBC offline searches already implement this method by detecting glitches as loud excursions in the whitened strain, and then multiplying the data with the complement of a Tukey window centered on the glitch time. We adopted the same algorithm in PyCBC Live during O3. We used a threshold of 50σ on the absolute value of the whitened strain time series as a glitch detector. Each detected glitch was gated with a symmetric complemented Tukey window, configured to have 0. 5 s of central zeroes and 0. 25 s of smooth taper on both sides. This approach significantly reduced the impact of high-SNR non-Gaussian transient noise with no visible impact on the latency of the analysis.
Hence, in order to start exploiting the benefits of a larger and more symmetric network, the PyCBC Live analysis has been modified for the O3 run. In its O3 configuration, PyCBC Live correlates the full bank of template waveforms with data from all operating detectors. Then for each detector pair, we independently perform the same double-coincident analysis used for LIGO-Hanford and LIGO-Livingston during O2. 4 When a pair of detectors labelled A and B observe a coincident candidate, a false-alarm rate F AB is computed using time-shifted analysis of these two detectors's data, as done in O2. The F AB estimate is local, using only the last 5 hr of data, and therefore tracks possible slow changes in the properties of the detector noise. At times when A and B are the only operating detectors, any candidate events are then submitted directly to GraceDB with false-alarm rate F AB , the analysis being effectively identical to O2.
To calculate the expected rate of louder triggers, as well as the trigger distribution of Eq. 9, we must estimate the overall rate of triggers. This is done by simply counting the triggers which pass the specified thresholds in the daily fits, and for the mean fit, this is simply an addition of the daily trigger counts. For the conservative fit choice, we choose the 95 th percentile daily trigger count and multiply by the number of days over which the fit smoothing is performed. Using the originally recorded trigger from LIGO Hanford, and the conservative fit values calculated from the O2 data from July 2017, we estimate the single-detector trigger false alarm rate in LIGO Hanford of GW170817 to be 1 per O(10 9 ) yr. If we choose the mean fit combination, then the estimated false alarm rate would be 1 per O(10 11 ) yr.
The sparseness of the template bank will generally also drive the reported template parameters away from the maximum SNR. This could in principle introduce biases in the rapid spatial localization, and more generally affect any low-latency result which uses the mass and/or spin parameters of the search template, such as the source classification we described above in Section 2. 5. See  Biscoveanu et al. (2019) ;  Chatterjee et al. (2020)  for investigations of such possible biases. In order to remove some of the sources of these biases, we developed a followup process that starts after PyCBC Live reports a candidate to GraceDB. The process reanalyzes a short amount of strain data around the candidate, and uses differential evolution  (Storn & Price 1997 ) to find the template parameters that maximize the network SNR.
Sensitivity in simulated data We first characterize the search sensitivity by simulating a population of astrophysical signals, adding the signals to a portion of simulated Gaussian noise, analyzing the data with PyCBC Live and counting how many signals are recovered at a given false-alarm rate. The noise models correspond to final design sensitivities of Advanced LIGO and Advanced Virgo  (Abbott et al. 2018). We focus on evaluating the significance calculations described in Sections 2. 3 and 2. 4. To this end, we compare the sensitivity of the search under different network configurations: HL, HLV, H, L and V, where H, L and V indicate respectively LIGO-Hanford, LIGO-Livingston and Virgo. In the HL and HLV configurations, all observatories are assumed to be observing at the same time. We construct a population of BNS signals with component masses distributed uniformly between 1.
Sensitivity in real data Here we repeat a similar test as presented in Subsection 3. 1, with the difference that we consider broader ranges of masses and spins (therefore effectively including BBH and NSBH systems) and we add their signals to real data from the third observing run of Advanced LIGO and Advanced Virgo, as opposed to simulated stationary Gaussian detector noise. We use ∼ 8 days of O3 data starting from 2019-05-04 13:15:32 UTC. In the simulated binaries, neutron stars have masses distributed uniformly between 1 M and 3 M , and spin magnitudes distributed uniformly between 0 and 0. 05. Black holes have masses distributed uniformly between 3 M and 100 M , and spin magnitudes distributed uni- formly between 0 and 0. 985. Spins are aligned with the orbital angular momenta in all cases.
BNS signals are simulated using a post-Newtonian waveform model, while NSBH and BBH signals use the SEOBNRv4 opt inspiral-merger-ringdown model  (Devine et al. 2016; Bohé et al. 2017). The resulting sensitivity for BNS mergers is shown in Figure  4. We can see that the improvement in detection rate is around 10% at false-alarm rate thresholds relevant for public alerts. This estimate is consistent with the earlier estimate from simulated data at design sensitivities. NSBH and BBH mergers, albeit arguably less interesting for rapid alerts, show similar relative improvements at the same false-alarm rate threshold, and are not shown here.