dataesr/scientific-paragraphs-categorization huggingface dataset:
7-10 ms). A data acquisition card (PCI-6221, National Instruments, Austin, TX, USA) completes the platform and collects the data from the crank encoder, allowing for the synchronization of the stimulation with the pedaling cadence, as well as triggering the recording of the force measuring pedals, and setting up the motor cadence and stimulation parameters. Both the motor and the stimulator have an emergency stop button that disconnects them from the power supply. The force-measuring pedals are factory-calibrated by the manufacturer, while the crank angle and pedal inclination encoders are calibrated before starting each new trial. A crank angle of 0 • is achieved when the left crank arm is in the forward position and parallel to the ground. Motor rotational speed control was verified for cycling cadences ranging from 10 rpm to 50 rpm in steps of 10 rpm.
The electrodes were placed on shaved and cleaned skin, between 1. 5 and 2 cm apart, parallel to the muscle fibers, in accordance with the surface electromyography for the non-invasive assessment of muscles (SENIAM) project guidelines  [25]. The recordings were performed on the subject's dominant leg, during constant-cadence motor-assisted cycling. The subject cycled seated in the Carbontrike, legs attached to the force-measuring pedals using leg orthoses, at a cadence of 50 rpm. The EMG recordings were processed and analyzed in Matlab (Mathworks, Natick, MA, USA). The raw signals were filtered with a [10 Hz, 30 Hz] 4th order band-pass Butterworth filter. The processed EMG signals were normalized for each revolution and subsequently averaged. The resulting signals were plotted against the crank angles on a 360 • plot (Figure  4 ).
The stimulation frequency was set to 40 Hz, the pulse width to 350 µs and the pulse amplitude to 70 mA. Stimulation parameters were chosen based on the prior experience with FES cycling of the subject, who usually trains 2 to 3 times a week, one-hour sessions on either RT300 (Restorative Therapies, Nottingham, MD, USA) or Motomed (Reck, Betzenweiler, Germany). After the initial warm-up, the measuring began with obtaining the force profiles. As a compromise between the number of samples acquired and the muscle fatigue produced by the stimulation, the cadence was set to 30 rpm during the first phase. Throughout the other phases of the protocol, the cadence was set to 50 rpm. Following the rest period, the second phase of the protocol was carried out. During the break, recorded data were processed, and the MFP was analyzed using a custom-made Python script.
Results The results of the motor cadence control test are presented in Table  1. The error of the cadence control system increased with the cadence, as shown in Figure  9. by the stimulation, the cadence was set to 30 rpm during the first phase. Throughout the other phases of the protocol, the cadence was set to 50 rpm. Following the rest period, the second phase of the protocol was carried out. During the break, recorded data were processed, and the MFP was analyzed using a custom-made Python script. The angular shift was measured to compensate for the delay, and the MFP pattern was set up. During the third and the fourth phases of the protocol, EMG and MFP patterns were respectively used. The net-forces were calculated from the recorded cycling data. Angular velocity together with the net-forces were used to calculate the mean power produced during one cycle by muscles stimulated with the EMG pattern (PEMG) and with the MFP pattern (PMFP).
Acknowledgments: We are grateful to  Anil Efe Aksöz  for his immense help with the design of the system and to  Denis Letourneau  for building the platform. Also, we are thankful to  Sara Molla Casanova  for helping with the EMG recordings,  Emmanuel Shushku  for visualizing the data and to  Adam Sunturlic  for the graphical representations of the platform. Finally, our warmest thanks goes to H. G. for agreeing to have his EMG recorded and to  J. J. for the intensive personal investment and motivation in participating in all our investigations.
This work is part of an artistic-research residency where composer Aaron Einbond seeks to apply audio descriptor analysis and corpus-based synthesis techniques to the spatial manipulation of instrumental radiation patterns for projection with a compact spherical loudspeaker array. Starting from a database of 3D directivity patterns of orchestral instruments, measured with spherical microphone arrays in anechoic conditions, we wish to derive spatial descriptors in order to classify the corpus. This paper investigates the use of spherical cross-correlation as a similarity measure between radiation patterns. Considering two directivity patterns f and g as bandlimited, square integrable functions on the 2-sphere, their correlation can be computed from their spherical harmonic spectra via a spatial inverse discrete Fourier transform.
Introduction Spherical correlation is a tool that allows the evaluation of similarity between radiation data on the sphere based on its spherical harmonics expansion. This is well discussed in the mathematical literature and widely used for 3D shapematching in the field of image processing. The state of the art is introduced in  [17] , building on the foundation of the spatial Fourier transform  [5]. However, so far, the practical use of spherical correlation in the field of acoustics remains limited and challenging. Guillon  [6, 7]  uses spherical correlation to compute similarities between spatial frequency response surfaces (SFRS), and later to clusterize a dataset of SFRS. This work is probably the most detailed usage of spherical correlation in spatial audio thus far. Moreau  [20, 21]  employs spatial correlation to assess qualitatively a directivity model of spherical microphone arrays against measured data.
However, his work does not operate in the spherical harmonics domain and does not apply rotational matching. Similar to our study, Hohl and Zotter  [10, 30]  use the spherical correlation coefficient as a similarity measure of radiation patterns of musical instruments. They examine whether different partials at the same frequency, but originating from different played pitches, exhibit similar radiation on a given instrument. This is promising work, but their short paper does not provide much detail. Our study is both motivated and facilitated by the availability of a large database of anechoic measurements of acoustic instrumental radiation data produced at the Technische Universität (TU) Berlin  [28, 24]. With radiation patterns for 41 modern and historical orchestral instruments, the database suggests several questions : how do the radiation patterns of partials and registers of a given instrument correlate with each other, how do they correlate with those of different instruments, and how could these correlations be used to classify and navigate the database ? 2 Theoretical background 2.
Numerical simulations 3. 2. 1 Impact of SO(3) sampling As discussed in the previous paragraphs, the choice of the sampling grid for SO(3) might have an influence on the accuracy and efficiency of the maximization problem 7. For the sake of simplicity, we consider only the regular Euler sampling of equation 8. However, we investigate the use of "oversampling" i. e. we use a smaller step size in discretizing the Euler angles (α, β, γ). Instead of using 2B samples for each parameter, we use N ≥ 2B. This seems relevant as our scenario involves spatial functions with relatively low bandwidth (B = 5 if we consider the radiation patterns available in the TU Berlin database) compared to other authors (Kostelec  [17]  typically presents results with B = 128). With such low bandwidth, the angular step size is very large (π/B = 36 • ), and therefore high angular misalignment might occur.
Visualization with multidimensional scaling With an approach to pairwise spherical correlation in place, we can now examine applications to classify and navigate F I G U R E 4 -Example of rotational matching. Left : visualization on the sphere. Right : restriction on the horizontal plane. larger collections of 3D radiation patterns. One approach to apply multidimensional scaling (MDS). MDS is used to translate information about the pairwise distances (or dissimilarities) among a set of n objects or individuals into a configuration of n points mapped into abstract Cartesian space. MDS is therefore a means of visualizing the level of similarity of samples within a dataset. We begin with a proof of concept using the functions Y m n (Ω) as our dataset. First we compute the normalized crosscorrelation between all pairs Y m1 n1 (Ω) and Y m2 n2 (Ω).
MDS applied to TU Berlin data Another motivation for this work is to classify the TU Berlin database for subsequent manipulation with based synthesis techniques : by instrumental samples according to spatial characteristics, the resulting low-dimensional representation can be used, along with other audio descriptors, to navigate the sample corpus. We applied MDS analysis as presented section 4 measured radiation patterns of different instruments for various played pitches. An example is presented in Figure  7. The color code corresponds to the three categories of instruments proposed by Shabtai  [24]. In red : instruments with one expected radiation point ; this mainly includes the brass instruments. In blue : instruments with several expected radiation points, such as woodwind instruments, with sound radiated by the bell, the fingering holes, and the mouthpiece.
Conclusion In this paper, we discussed the use of cross-correlation on the sphere as a similarity measure for the classification of 3D radiation patterns. We showed that this tool can facilitate thorough analysis of directivity pattern similarities across partials of one instrument or between different instruments. However, spherical correlation can only capture rotational resemblance ; it might therefore be necessary also to consider energetic criteria when comparing radiation patterns. Future work will investigate other spatial descriptors for the classification of the 3D database of orchestral instruments. A further direction will be transformations of radiation patterns based on interpolation between correlated and rotated pairs, which we hypothesize will give more consistent results than interpolation between unrotated patterns, to be applied in the context of regression for a machine learning algorithm.
Evidence Gathering and Appraisal Using an SR approach, relevant data are collected to answer each clinically relevant question and then synthesized into different levels of recommendation to consider a candidate diagnostic item for final diagnostic criteria. We propose this step in the framework is completed by theme-focused working groups covering domains such as clinical, imaging, wet biomarker, genetics, or pathology. The data collected in the SR may include the analytical validation (reliability and validity) of the quantification process for a candidate diagnostic measure (likely more applicable to a biomarker test) and the clinical validation using the documentation of the diagnostic accuracy measures listed earlier. The level of evidence and its determination follows a semiquantitative approach in which a reviewer takes as starting point an agreed-upon high-quality study design and weighs in a list of factors that downgrade or upgrade the level of evidence,  17  namely the risk of bias, imprecision, inconsistency, indirectness, and publication bias (Table  2 ).
Exposure to hospital admission The waiting time between exposure and hospital admission was estimated as the sum of the incubation period and the delay between symptom onset and hospitalisation. We assumed that the two waiting times were independent due to the absence of evidence otherwise. A meta-analysis of global, but predominately, Chinese data found that the SARS-CoV-2 incubation period was log-normally distributed with parameters μ = 1. 63 and σ = 0. 50  [31] , corresponding to a mean incubation time of 5. 78 days (standard deviation of 3. 97 days). The distribution of waiting time between symptom onset and hospitalisation was estimated assuming a gamma distribution by Public Health England with a mean of 5. 14 days (standard deviation of 4. 2 days)  [32]. We fitted a gamma distribution to the simulated sum of the two distributions to represent the timing between exposure to infection and hospital admission (shape =4.
Acknowledgments LM wishes to thank  Jean-Claude Zambrini  for numerous and fruitful discussions on the Schrödinger problem, and acknowledges support from the  Portuguese Science Foundation  through  FCT  project  PTDC/MAT-STA/22812/2017 SchröMoka. LT acknowledges financial support from  FSMP Fondation Sciences Mathématiques de Paris. DV was partially supported by the  FCT  projects  UID/MAT/00324/2020  and  PTDC/MAT-PUR/28686/2017.
Ethics approval The protocol was approved by the Committee for Patient Protection Ile-de-France II (n2 0. 12. 21. 34036 MS1 RIPH 2 HPS) and by the French Data Protection Authorities. The study was also approved by the Scientific and Ethical Committee of Assistance Publique-Hopitaux de Paris (AP-HP) clinical data warehouse (IRB00011591) allowing to retrieve the ED flow data. AP-HP clinical data warehouse initiative ensures patients' information and consent regarding the approved studies through a transparency portal in accordance with European Regulation on data protection and authorization (number 1980120) from the National Freedom and Informatics Commission. The trial was registered at ClinicalTrials. gov (NCT04756609).
Randomization The unit of randomization was the ED. The strategy (control/intervention) applied during the first period was assigned using a balanced block randomization process with stratification based on ED flow and, for feasibility concerns, whether the ED had a SARS-CoV-2 screening device (block size of 2 to 6). The alternative strategy was applied during the second period after a minimum 1-day washout interval. The allocation schedule was computer generated in SAS by an independent statistician. The duration of each period was a minimum of 1 month with the possibility of a 15-day extension for the interested EDs, retaining similar durations for the same center in each period. The patients and teams could not be masked to allocation due to the nature of the intervention.
For asymptomatic patients, samples were tested with the Abbott ID NOW COVID-19 assay, an isothermal nucleic acid amplification assay based on a nicking and extension amplification reaction (NEAR) technique. The assay, further referred to as the rapid molecular SARS-CoV-2 test, targets the RNA-dependent RNA polymerase (RdRp) gene segment and provides results within 5 to 13 min  [22]. Nasopharyngeal swabs were tested directly, without dilution in viral transport medium. Patients with a positive SARS-CoV-2 test were cared for according to local procedures. During the intervention period, a clinical research assistant was on site 5 days a week to facilitate staff adherence and monitor data collection.
Measurements Data from the DEPIST-COVID questionnaires were entered into a database (CleanWEB Telemedicine Technologies SAS, Boulogne-Billancourt, France). ED flow data, including patient and center characteristics, and SARS-CoV-2 routine screening data were extracted from electronic ED databases. Community screening data routinely collected for all individuals screened in the Paris metropolitan region  [23] , including symptomatic status, were provided by SantePublique France.
Statistical analysis Primary analysis was performed according to the intention-to-treat principle. The primary outcome was analyzed using a generalized linear mixed model (Poisson distribution) with the center as a random intercept, the center-by-period interaction as a random effect and the strategy (intervention versus control), period, and community incidence rate-by-period interaction as covariates. The logarithm of the number of patients was included as an offset term. The parameters of the model were estimated using a full maximum-likelihood method with the Laplace method (SAS PROC GLIMMIX). The P values reported for covariates were based on t tests using the Kenward-Roger approximation for the denominator degrees of freedom. The period was modeled as a weekly categorical variable  (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15).
Fitted to daily regional hospital admissions, the model allowed to estimate the total number of cases and the detection rate of virological monitoring in the region. The analysis was performed using SAS software (version 9. 4; SAS Institute, Cary, North Carolina, USA) and R 4. 1. 3 (2022-03-10). The code of the transmission model developed in Python 3 is available  [28] [29] [30]. This study is reported as per the cluster randomized trials extension of the Consolidated Standards of Reporting Trials (CONSORT) guidelines (Appendix L in S1 File)  [31].
**** For this variable of the DEPIST-COVID questionnaire, data were collected in 8 centers, and it was not possible to collect them later in the remaining 10 centers. I1. Coefficient estimates from the generalized linear mixed model with a Poisson distribution including the center as a random intercept, the center-by-weekly period interaction as a random effect and the strategy, weekly period and incidence rate-by-weekly period interaction as fixed effects (SAS PROC Glimmix). Table  I2. Significant random effects. Table  I3. Analysis considering a binarized period (0/1). Table  I4. Analysis including variables used for randomization (screening equipment in emergency departments and flow in emergency departments). Table  I5. Analysis including strategy * week_since_roll_out interaction. Table  I6.
Acknowledgments The authors would like to thank all participating patients, emergency department nurses, and emergency department teams, as well as the  Clinical Research Unit Paris-East  and the  Clinical Research Center Paris-East  teams for their support of the study, the  Assistance Publique-Hopitaux de Paris (AP-HP ) clinical data warehouse ( EDS AP-HP ) team,  Sonia Larid  and the " Observatoire Regional des Soins Non Programmes Ile-de-France " ( ORSNP IDF ) team,  Nathalie de Castro ,  Renaud Piarroux ,  Martine Piarroux ,  Fatiha Djennaoui ,  ChloeMcAvoy , and  Mohamed Hamidouche ,  Santepublique France I ˆle-de-France, France. This study has been labeled as a National Research Priority by the  National Orientation Committee for Therapeutic Trials  and other researches on Covid-19 (CAPNET).
This lack of evidence likely stems from both data limitations and remaining uncertainty about the relative importance of precise paths of transmission (e. g. through direct person-to-person contact; transient viral carriage on hands, garments or shared medical devices; stagnant air in poorly ventilated spaces). Particularly early in the pandemic, nosocomial COVID-19 data came primarily from contexts of emergency outbreak management rather than routine data collection through stable surveillance systems. For future waves of SARS-CoV-2 and other infectious diseases, the estimation of epidemiological parameters may be made easier by harnessing large databases that systematically gather patient and HCW tests results, administrative data and healthcare exposures across diverse healthcare facilities over time.
g. after the introduction of a novel SARS-CoV-2 variant into a hospital via newly admitted patients, short-stay visitors, or members of staff infected in the community). Models are also used to enable the in silico assessment of public health interventions through the mechanistic implementation of interventions (e. g. testing, isolation, PPE provisioning, contact restrictions, vaccine deployment). Intervention impact can then be evaluated by simulating counterfactual outbreaks with and without the intervention in place. Finally, models are key to analysing reported data from real outbreaks (e. g. time series data, individual line lists of SARS-CoV-2 cases), accounting not only for unobserved processes (e. g. , virus transmission) but also incomplete infection data, whether due to the presence of asymptomatic infections, imperfect reporting, or limited surveillance capacity.
Funding This study received funding from the  MODCOV  project from the  Fondation de France  (Grant  106059 ) as part of the alliance framework " Tous unis contre le virus ", the  Université Paris-Saclay  ( AAP Covid-19 2020 ) and the  French government  through its  National Research Agency  project  SPHINX-17-CE36-0008-01  and the " Investissement d'Avenir" program ,  Laboratoire d'Excellence " Integrative Biology of Emerging Infectious Diseases"  (Grant  ANR-10-LABX-62-IBEID ). The work was also supported directly by internal resources from the  French National Institute for Health and Medical Research (Inserm) ,  Institut Pasteur ,  le Conservatoire National des Arts et Métiers , and  l'Université Versailles Saint-Quentin-en-Yvelines/Université Paris-Saclay.
Background: Although France was one of the most affected European countries by the COVID-19 pandemic in 2020, the dynamics of SARS-CoV-2 transmissions within France, Europe and worldwide remain only partially characterized during the first year of the pandemic. Methods: Here, we analyzed GISAID deposited sequences from January to December 2020 (n = 638,706 sequences). To tackle the huge number of sequences without the bias of analyzing a single sequence subset, we produced 100 independent and randomly selected sequence datasets and related phylogenetic trees for different geographic scales (worldwide, European countries and French administrative regions) and time periods (first and second half of 2020). We applied a maximum likelihood discrete trait phylogeographic method to date transmission events and to estimate the geographic spread of SARS-CoV-2 to, from and within France, Europe and worldwide.
Description of the datasets and global diversity of SARS-CoV-2 sequences Prior to any phylodynamics investigation, we checked whether the sets of sequences produced for each geographic scale (i. e. , worldwide, Europe and French regions) and time period (i. e. , the first and second European epidemic waves) matched with the number of SARS-CoV-2 infections in 2020. This step was necessary to ensure that our sampling method did not introduce too strong biases from the start. For each geographic scale and time period, we noticed a positive correlation with the estimated SARS-CoV-2-related deaths (Spearman's rank correlation, p < 0. 001; r = 0. 94 for the lowest correlation). We also confirmed that the number of sequences per territory was, on average, properly temporally distributed within each time period (Figure  2 ).
Some countries and French administrative regions were however discarded in the analyses because they were not sufficiently represented in the GISAID database. Overall, a total of 41,566 and 43,368 distinct SARS-CoV-2 sequences were included across the 100 phylogenies for the worldwide dataset, respectively for the first and the second time periods investigated (Table  1 ). At the European scale, 28,235 and 29,369 different SARS-CoV-2 sequences covering eleven countries were analyzed across the 100 replicates (Table  1 ). Focusing on French administrative regions, sequences available on the GISAID database were very sparse. The Provence-Alpes-ô e ' z r ( S eas rance, Marseille area) was the only region that highly sequenced SARS-CoV-2 in 2020. Île-de-France (center of France, Paris area) and Auvergne-Rhône-Alpes (ARA, East of France, Lyon area) have sequenced much less than PACA, but provided sufficient data to investigate SARS-CoV-2 transmission events within France.