dataesr/scientific-paragraphs-categorization huggingface dataset:
The user can load various meshes, setup the material properties, add forces, and eventually run the npFEM solver. Figure  9  presents a closer look to some critical Grasshopper components. The generation of RBC/PLT meshes can be done either in Rhino-Grasshopper (and any other CAD software), or through scripts using formulas to generate the investigated surfaces  [4]. This environment offers an easy way to test and familiarise the user with the npFEM library. The users can experiment with different materials and solver parameters, and observe their impact on the deformed shape of a RBC. Additionally, one can modify the npFEM solver, and graphically observe if the modifications work as expected or not. Setting up new materials: multi-platform Setting up new materials, in a platform-agnostic way, is more tedious than the previous solution.
Dependencies A complete list of prerequisites can be found in Palabos GitLab repository. Linux: • A modern C++ compiler The GPU branch of the npFEM solver supports NVIDIA GPUs with compute capability ≥ 6. 0 (extensively tested and validated). However, the code could support GPUs with compute capability < 6 (tested but not fully validated), by replacing the atomic operations (e. g. atomicAdd) with ones based on atomicCAS (Compare and Swap). For more information, one should consult the CUDA toolkit documentation. In this case, the user should modify as well the CMake file to target the correct GPU architecture (currently set to -arch=sm 60). The periodic boundary conditions introduce a dependency on parallelisation. In more details, the directions with periodicity should be subdivided by at least two subdomains (each sub-domain belongs to a different MPI task).
This is due to how the area per surface vertex is computed, i. e. if there is no domain subdivision, then the crossing bodies (from the outlet to inlet) are considered as stretched (erroneous deformation, leading to code crash). On the contrary, when the outlet and inlet belong to different sub-domains (MPI-wise), then the crossing bodies are duplicated in memory and thus they are not considered as stretched/deformed. This means that Palabos-npFEM depends strictly on MPI, which is not a hard constraint given the computational intensity of blood flow simulations. Of course, this dependency could be eliminated by modifying/extending the library (area computation part). any supported platform. Thus, the users can deploy Palabos-npFEM in cross-platform environments (from personal computers/workstations to supercomputers) and speedup their development & research.
Study design This observational study was conducted at the University Hospital of Besançon. This study is a part of a clinical trial designed to evaluate the impact of fampridine on the functionality of PwMS. It was approved by the French national ethics committee (no. 13/405) and by the French Health Products Safety Agency (no. 2013-A002305-56) and was registered at ClinicalTrials. gov (NCT02849782).
Verbal fluency Cognitive impairment was assessed with the VFT. The tests were performed in a quiet room with a voice recorder. The participants were asked to provide as many words as possible in 2 min without making mistakes and without repeating themselves (cf. posttreatment below). For methodologic considerations (e. g. , good performance in healthy population), we used the letter "P" for phonologic fluency and the category "animals" for semantic fluency  [18]. Afterward, SPPAS, a free software (http://www. sppas. org/), was used to segment the audio track in accordance with the words produced by the participants. SPPAS allows for knowing the beginning and end of the production of each word with accuracy of 1 msec  [34, 35]. After this phase, we listened to all fragments to confirm, identify, and transcribe the words into a spreadsheet (Microsoft Excel).
, 2013)  and is an element of a broader AgMIP effort to explore cropping system responses to climate conditions and climate changes to facilitate applications including toward integrated assessment  (Ruane et al. , 2017). Six other GGCMs also participated in GGCMI but were not considered here as necessary output variables (timing and duration of the growing season for EPIC-BOKU, PEPIC, EPIC-TAMU; aboveground biomass for ORCHIDEE-crop) or simulations (for PRYSBI2) were not provided on the data archive of GGCMI. In GCCMI, all GGCMs followed a common protocol and were forced by the same weather datasets. We focused here on the simulations forced by one of them, the AgMERRA dataset  (Ruane et al. , 2015). We used simulations forced by the AgMERRA dataset as all GGCMs performed these simulations.
Three levels of harmonization have been used in GGCM simulations: default, fullharm, harmnon. In fullharm simulations, all GGCMs have been forced by the same prescribed beginning and end of the growing season, which were derived from a combination of two global datasets  (MIRCA, Portmann et al. , 2010; and SAGE, Sacks et al. , 2010; Elliott et al. , 2015). In harmnon simulations, in addition to forced timing and duration of the growing season, all GGCMs experienced no nutrient limitation through prescribed fertilizer inputs. Besides this harmonization level, two water regimes have been considered: irrigated and nonirrigated. For our analysis focusing on the simulation of potential yield, we decided to select the configuration (harmnon and irrigated). This is true for all GGCMs considered except CGMS-WOFOST.
In fact, the harmnon simulation was not provided for CGMS-WOFOST but because (i) this model does not consider nutrient limitation and (ii) the growing season was prescribed in the default simulation, we assumed that the potential yield could be approached by the (default and irrigated) simulation. For EPIC family models (here, GEPIC and EPIC-IIASA), we used a corrected biom GGCM computed as below, as it has been noticed that some issues related to the variable biom appeared in the outputs available on the GGCMI data archive that are likely related to the output time step of specific variables  (Folberth, personal communication, 2019) : biom GGCM = grain GGCM /HI max , (16) where HI max is the maximum harvest index (no unit), varying in space as a function of cultivars. In EPIC, the actual HI at harvest only differs to HI max if a drought stress occurs in the reproductive phase.
Input variables for SMM We focused our analysis on the growing season starting in calendar year 2000 (and potentially finishing in calendar year 2001). SMM was forced by the short-wave downwelling radiation (rsds in MJ m -2 d -1 ) and the daily mean temperature (tas, in • C) from the AgMERRA weather dataset  (Ruane et al. , 2015). SMM also needs the beginning and end of the maize growing season, and we used the planting day (t p ) and the timing of maturity (t m ), respectively, both being provided in the output of each GGCM. Despite the fact that all GGCMs are forced by the same growing season in harmnon, some GGCMs allow flexibility in regards to t p and t m prescribed as input  (Müller et al. , 2017) , as suggested by the GGCMI protocol: "crop variety parameters (e. g. , required https://doi.
SMM-simulated LAI would be compared to GGCM output and this comparison would further reduce the risk of compensation between processes in SMM. However, LAI was neither available from GGCMI data archive nor upon request to GGCM modellers. We stress the need to incorporate this output variable in the next intercomparison exercise. It is also important to note that the average over the growing season of LAI or LAI at set fractions of the growing season (including anthesis) would be more interesting than LAI at harvest as, under potential conditions, LAI at harvest is very likely close to maximum LAI allowed by the different GGCMs. Therefore, the reasonable match between SMM-simulated aboveground biomass and GGCM-simulated biom is made possible only because of the large range of variation allowed for RUE and GDD 1leaf during the calibration, i.
For GGCMs with n thresh equal to 0, a parametrization based on a better distinction between the emergence of all leaves and the period from flowering to maturity could be interesting. We also showed that n thresh and frac determines harvest index (HI), and we showed that HI varies a lot between GGCMs. Thus, we advise that more effort needs to be directed toward assembling a global dataset of HI for either parametrization or evaluation of GGCMs. The maximum HI that a plant can reach is a cultivar characteristic, and one possibility to build such dataset could partly rely on information from seed companies. Finally, we suggested that the next intercomparison exercise encompass LAI and the beginning and end of the different growing season periods (vegetative period, flowering, etc. ) for GGCMs that distinguish between such stages.
The same applies for SMM. The parameters of the emulator, here fitted to reproduce GGCM output, could also be fitted to global datasets based on census data and observations in a manner similar to that of PEGASUS  (Deryng et al. , 2011)  but related to potential yield (against real yield for PEGASUS). For instance, SMM could be calibrated against the global dataset of potential yield based on statistical approach  (Mueller et al. , 2012) , and the spatial variation of calibrated parameters could be compared to existing knowledge about the spatial distribution of cultivars. Finally, because it allows temporal dynamic simulation, our model could be coupled with simulation of limiting factors (water, nutrients) to investigate the limitation of potential yield at the global scale in a simple but mechanistic manner.
Code and data availability. The scripts that were the basis of this study have been made available at https://doi. org/10. 15454/9EIJWU (Ringeval, 2020). They encompass three Python scripts and shell scripts and the directories to use these Python scripts. The first Python script (called SIM. Py) encompasses SMM equations and performs SMM simulations for different combinations of parameters and for each GGCM growing season. The second Python script (ReadMulti-param_WriteOPTIM. py) performs the SMM calibration against each GGCM output. The third Python script (ReadPlotOPTIM. py) performs the main plots. GGCM inputs and outputs required to force or calibrate SMM are given in  Müller et al. (2019).
Acknowledgements. This research was supported by  INRAE  ( Institut national de recherche pour l'agriculture , l'alimentation et l'environnement) and the AgroEnv division through a  Pari Scientifique 2020. We thank  David Vidal  and  Lionel Jordan-Meille  for discussions that formed the basis of the simple mechanistic model used in this paper,  Marko Kvakić  for helpful discussions at the beginning of this study, and  Mark Irvine  for his help with the computing aspects. Modeling and analysis were performed in using Python ( Python Software Foundation. Python Language Reference , version 2. 7. , available at http://www. python. org,last access: January 2020). Review statement. This paper was edited by  Tomomichi Kato  and reviewed by two anonymous referees.
Acknowledgements. We would like to thank  Sylwester Arabas  and the two anonymous referees for their fruitful comments, which have contributed to improving the paper. The UML class diagram has been generated from UMLlet (Auer et al. , 2003). Financial support. This research has been supported by the  French national program LEFE/INSU (Étude du filtre de KAlman PAramétrique, KAPA). Review statement. This paper was edited by  Sylwester Arabas  and reviewed by two anonymous referees.
Prospective applications of these data sources, including their use in the design and implementation of epidemiological studies, are limited. Here, we examine seroprevalence estimates obtained via venue-based sampling  [4, 9] , with direct applications to other forms of convenience sampling  [5, 12, 13]. We performed a venue-based serosurveillance study in Somerville, Massachusetts and used these data to analyze bias and uncertainty arising from inherent geographic variation in sampling intensity. This analysis showed that substantial loss of precision can occur if the distribution of sampling intensity poorly matches the geographic distribution of true seropositive individuals in the study area. As GPS-estimated foot traffic offers a proxy measurement for the geographic distribution of visitors to different locations, we evaluated the extent to which informed selection of locations for venue sampling reduces uncertainty and bias introduced by geographic heterogeneity in underlying seropositivity.
SARS-CoV-2 seroprevalence study design and participant information We obtained serological and participant demographic data for 398 asymptomatic adults tested at a temporary study site near an essential business location in Somerville, Massachusetts. The study was conducted over 4 days (June 4 th , 5 th , 8 th , and 9 th , 2020), approximately 6 weeks after the first wave of the COVID-19 epidemic peaked in Massachusetts  [14]. The study was designated minimal risk human subjects research and approved by institutional review boards at Massachusetts General Hospital and the Harvard T. H. Chan School of Public Health (Protocol number: 2020P001081). The study recorded participant demographic information including age, gender, and self-reported home locations (by ZIP code and electoral ward). We also collected information on how participants learned about the study in order to distinguish participants directly recruited on site at the study location from those who learned about the study from friends, family, or social media.
GPS-estimated business foot traffic We used GPS-estimated foot traffic (SafeGraph, safegraph. com) to approximate the distribution of home locations for daytime visitors for different locations of interest, which we refer to as the "GPSestimated catchment area. " We use these data to estimate home and work locations at the level of census block group (CBG) for visitors to designated points-of-interest, such as businesses, and specific CBGs. We used CBG-level visitor data for June 2020 as the primary data source for visitors in our analysis. These data were filtered for CBGs with low visitor counts and re-aggregated from CBGs to electoral wards prior to analysis (Supplementary Material, Figures  S1  and S2 ). We used the filtered, reaggregated data to obtain GPS-estimated visitor catchment distributions for two locations: (1) the actual study venue, located in Somerville electoral Ward 2 (denoted  j site ) and (2) a hypothetical alternative study venue located in Somerville electoral Ward 1 (denoted  j alt ).
Likewise, collecting information on non-respondents in venue-based sampling-for example, brief demographic surveys collected before recruitment for serological testing-can help measure and account for potential sources of participation bias. The GPS-estimated foot traffic data used in our study have several important limitations. Identification of visitors and their home locations in this data may be biased by differences in mobile device usage between demographic groups, potentially under-sampling visitors from important populations or oversampling others. In addition, this data can be sparse and thus more subject to stochastic variation when only small numbers of users are captured in either the point of interest or home location. Different forms of participation bias (described above) are expected to skew the geographic distribution of study participants away from GPS-estimated catchment distributions, potentially making it difficult to use this data source in real-world public health practice.
Challenge organization The MISAW challenge was a one-time event organized as part of EndoVis for MICCAI2020 online. It was organized by five people from three different institutions: Arnaud Huaulmé, Kévin Le Mut, and Pierre Jannin from the University of Rennes (France), Duygu Sarikaya from Gazi University (Turkey), and Kanako Harada from the University of Tokyo (Japan). The challenge was partially funded by the ImPACT Program of the Council for Science, Technology and Innovation, Cabinet Office, Government of Japan. All challenge information was made available to the participants through the Synapse platform: www. synapse. org/MISAW. Participation in the challenge was subject to the following policies: Participants had to submit a fully automatic method using kinematic and/or video data. The data that could be used for the training were restricted to the data provided by the organizers and publicly available data sets, including pre-trained networks.
The publicly available data sets only covered data that were available to everyone when the MISAW data set was released. The results of all participating teams were announced publicly on the challenge day. Challenge organizers and people from the organizing institutions could participate but were not eligible for the competition. The participating teams had to provide the following elements: the method's outputs, a write-up, and a Docker image allowing the organizers to verify the outputs provided. Due to the COVID-19 crisis, a pre-recorded talk was also mandatory to limit technical issues during the challenge day (online event). All technical information (how to create a Docker image, the output format, etc. ) was provided to the participants during the challenge on the challenge platform.
The challenge schedule was as follows: The training and the test data sets were released on June 1st and August 24th 2020 respectively. Submissions were accepted until September 23rd (23:59 PST). The results were announced October 4th during the online MICCAI2020. The complete data set was released with this paper at: www. synapse. org/MISAW The organizers' evaluation scripts were publicly available on the challenge platform. Participating teams were encouraged (but not required) to provide their code as open access.
Data acquisition The video and kinematic data were synchronously acquired at 30 Hz by a high-definition stereo-microscope (960x540 pixels) and a master-slave robotic platform  [23] , respectively, by the Department of Mechanical Engineering of the University of Tokyo. The kinematic data were recorded by encoders mounted on the two robotic arms. The kinematic data consisted of x, y, z, α, β, γ. The homogeneous transformation matrices for each robotic instrument were calculated as in equations 1 and 2. The kinematic files also contained information about the grip and the output grip voltage. H right = T x (x)T y (y)T z (z)R x ( 1 18 π)R y (α)R x (β - 5 9 π)R y (γ)) (1) H lef t = T x (x)T y (y)T z (z)R x (- 1 18 π)R y (α)R x (β + 1 18 π)R y (γ)) (2) The workflow annotation was acquired manually by two non-medical observers from the MediCis team of the LTSI Laboratory from the University of Rennes.
The observers used the software "Surgery Workflow Toolbox [annotate]" provided by the IRT b<>com  [24]  to annotate the phases, steps, and activities (action verb, target, and instrument) of each robotic arm according to an annotation protocol. The vocabulary contained 2 phases, 6 steps, 10 action verbs, 9 targets, and 1 surgical instrument (Table  2 ). The protocol described how to recognize each phase, step, and activity of each robotic arm by giving a definition, start and end point, and graphical illustration. For example, the step "suture making" was defined by "insert and pull the needle into artificial vessels. " The start point was the "beginning of the needle insertion on one vessel," the stop point was "the needle completely pass through both vessels. " This is illustrated in Figure  1   Each case was annotated by both observers independently and harmonized by the following protocol (Figure  2 ).
Data pre-processing We pre-processed the videos and initial workflow annotations to have consistent and synchronized data for each case. In the videos, the boundary between the left image and the right image was not consistent (Figure  3 , i. e. , the position of the centerline was a little different within and between the trials. We removed 40 pixels from the center of the stereoscopic image to have two images of 460x540 pixels. The final video resolution was 920x540 pixels. Figure  3 : Comparison between the initial video (3a) and pre-processed video (3b). The software "Surgery Workflow Toolbox [annotated]" produced a description of sequences where each element was characterized by the beginning and the end in milliseconds. We modified it to provide a discrete sequence synchronized at 30Hz with the kinematic data.
Metrics To assess the methods proposed by participants, we used a balanced version of the application-dependent scores  [25]  of the classic metric used in the workflow recognition: accuracy, precision, recall, and F1. Our data sets had a high class unbalance, for example, the phase "Idle" represented around 2% of the frames in both data sets (Figure  4 ). To give the same importance to each class, we decided to use balanced scores. Generally, frame-by-frame scores were used. This type of score assumes that the ground truth is frame perfect. However, this is not possible with manual annotation. Moreover, a clinical application does not need to be 100% accurate at a frame resolution. Application-dependent scores re-estimate classic scores using acceptable delay thresholds for a transitional window (Figure  5 ).
NUSControl Lab The NUSControl Lab team was composed of Chin Boon Chng 1 , WenJun Lin 1,2 , Jiaqi Zhang 1 , Yaxin Hu 1 , Yan Hu 1 , Liu Jiang Jimmy 2 , and Chee Kong Chui 1. The participants noted with "1" were from the National University of Singapore (NUS), Singapore, Singapore; the participants noted with "2" were from the Southern University of Science and Technology, Shenzhen, China. This team participated in the multi-granularity task. As described in the subsection "Ranking method" (2. 4. 2), the model was also ranked in each uni-granularity task. The NUSControl Lab team used both the video and kinematic data. They first extracted the features of the video frames using EfficientNet  [33]. Then, they employed an LSTM module to model the sequential dependencies of the video data. The authors hypothesized that the kinematic data were specifically related to the verbs and steps.
SK The SK team was composed of Satoshi Kondo from Konica Minolta, Inc. This team participated in the multi-granularity task. As described in the subsection "Ranking method" (2. 4. 2), the model was also ranked in each uni-granularity task. The SK team used the video data, kinematic data, and time information as the input for the model. The video frame features were extracted using a 50-layer ResNet  [28]  pre-trained with the ImageNet data set, which led to a 2,048dimension feature vector. While the team used only the left stereo video frame, the kinematic data features: x, y, z, α, β, γ, and grip collected from the left and right arms were used, leaving the output voltage for the grip feature out. The kinematic data were normalized with the mean and standard deviation values for each dimension and then fed to two fully connected layers.
IMPACT The IMPACT team was the non-competing team due to the presence of challenge organizers on it. This team was composed of Arnaud Huaulmé and Fabien Despinoy both from Rennes University, INSERM, LTSI -UMR 1099 and Duygu Sarikaya from Gazi University, Faculty of Engineering, Department of Computer Engineering. The team participated in all proposed tasks. The IMPACT team used both the right video frames and kinematic data and proposed a multi-modal architecture. The authors applied a pre-processing step to the input data. While the right video images were rescaled from 460x540 to 224x224 and the pixel values were normalized by subtracting the mean of each channel over the training set and scale between [0,1], the authors applied a z-normalization to center the kinematic data to 0 with a standard deviation equal to 1.