dataesr/scientific-paragraphs-categorization huggingface dataset:
The FSEs with the proposed model and the SFM are shown in Figure  11  on the 4 scenarios. Again, the two models produce very similar FSEs. In all scenarios no significant differences between the two models were found (p-values > 0. 05). The FSE is higher for the unilateral and bilateral scenarios than in back and front scenarios. This is expected because in lateral interactions, pedestrians need to adapt their velocity, and depending on whether the pedestrian is accelerating or slowing down, the speed error can be large. Moreover, an analysis of the dataset videos show that in bilateral scenarios some pedestrians have more unusual behaviors; running to pass when the car is close or stopping early when they would have had time to pass. More data are needed to study these behaviors since other factors can affect pedestrians' reactions (e.
In some cases, two pedestrians in the exact same situation with the vehicle may take very different decisions. For example, we observed in the dataset that in a lateral interaction, one pedestrian decides to run in front of the car while another pedestrian decides to stop to let the car pass. In such a case, two very different trajectories may be realistic for a given pedestrian. However, if the real pedestrian stops, the error will be smaller for a simulated trajectory where the agent collides with the car than for a simulated trajectory where the agent runs in front of the car and avoids collision. We therefore completed the quantitative evaluation with additional measures. In each simulation and for each pedestrian, we computed the error in the distance of closest approach with the vehicle (DCAE).
Unfortunately, real world data about pedestrian-vehicle collisions is very difficult to find, especially for the low traffic speed considered. At this speed, a collision is very unlikely to be fatal or serious and is thus not reported in the available collision statistics. If collision data were available, they could be used to further evaluate the simulation results. How accurately does the model predict pedestrian trajectories? Overall, the proposed model performs very similarly to the SFM for predicting pedestrians' displacement, speed, orientation, while better predicting pedestrian behavior in close-to-collision situations. The proposed model better predicts the approach distance around the AV in fundamental interactions and produces more realistic collision rates than the SFM. The proposed model produces less accurate predictions on the Nantes dataset than on the CITR dataset.
This is expected because part of the CITR dataset was used to calibrate the model. However, the prediction errors remain in acceptable ranges: the mean ADE is 0. 89m on CITR and 1. 21m on Nantes; the mean ASE is 0. 43m/s on CITR and 0. 48m/s on Nantes; the mean AOE is 12°on CITR and 31°on Nantes; the mean DCAE is 0. 71m on CITR and 1. 93m on Nantes. Mann-Whitney U tests confirm that there is a significant difference in the ADE, FOE, AOE and DCAE (p-values < 0. 01) produced on the two datasets. However, for the FDE, FSE and ASE, the tests found no significant differences between the two datasets. In addition, for all of the Nantes' scenarios considered, the proposed model produces collisions between pedestrians and the vehicle in 0. 35% of cases, thus twice more than on the CITR dataset but still very few.
The proposed model remains reliable for predicting pedestrian speeds and movements in a new context and without prior recalibration. The proposed model produces trajectories that are realistic in terms of collisions with the car. However, the model is more limited in predicting pedestrian orientations and approach distances around the vehicle in a new context. For approach distances, this may be due to cultural differences. Indeed, the social distances pedestrians keep vary with culture  (Hall, 1966). We have calibrated the model on the CITR dataset recorded in the USA, and it is possible that in the Nantes dataset recorded in France, the distances kept around a car are different. Moreover, the AV's size impacts a pedestrian's feeling of safety  (de Clercq et al. , 2019) , as well as the novelty of the AV design  (Dey et al.
, 2019). The CITR dataset uses a golf cart, which can be representative of small AVs, like the Waymo Firefly car, but pedestrian approach distances can differ with bigger AVs. The application of the model on data from the car's on-board sensors helps to anticipate problems that will be encountered in a real-life application. Detection and tracking problems, e. g. undetected pedestrians, pedestrians detected twice, pedestrians perceived too late, have an influence on the predictions' quality. In the Nantes dataset, not all pedestrians are perceived and detected correctly by the sensors; some pedestrians appear in the dataset videos but are not present in the dataset trajectories because they are not perceived by the sensors. Therefore, some pedestrians are not reproduced in the simulation and are not included in the predictions.
However, the real trajectories are influenced by these undetected pedestrians. Contrary to the CITR dataset, where we have access to all pedestrians' information at any time during the interaction, in the Nantes dataset, accurate predictions are more difficult, because they are based on incomplete data. Similarly, in the Nantes dataset videos, some static obstacles, i. e. cars parked on the side of the parking lot, constrained pedestrian movements. These obstacles are not recorded in the dataset and are therefore not reproduced in the simulation or incorporated in the predictions. Finally, with the proposed model the simulation runs in real time even with a large number of pedestrians. At 25Hz, a simulation with an AV in a crowd of 100 pedestrians at a density of 0. 5 pedestrian/m 2 runs in real time on a standard PC hardware (Intel Core i7-7920HQ, 4.
10GHz). The simulator can be accelerated to run faster than real time and thus be used for online predictions. How well does the model generalize and apply to real-world scenarios? Overall, the proposed model is reliable for predicting pedestrian trajectories in a new context, even if the model is less accurate on a new dataset than on the dataset used for calibration. The model could be used by a real AV to predict the trajectories of surrounding pedestrians, coupled with advanced detection and tracking systems.
Moreover, the proposed model performs better than the SFM for predicting the pedestrians approach distances around an AV in fundamental interactions. In addition, the proposed model produces fewer collisions than the SFM, while maintaining a similar accuracy in predicting pedestrians' displacement, speed and orientation. The proposed model generalized well to a new dataset, without prior recalibration. The evaluation of the model's accuracy on a dataset recorded from on-board sensors shows that the model is applicable for in-vehicle prediction, in real-time. Moreover, the approach can anticipate problems that will be encountered when applying the model to real-world cases.
What Was Learned? From this work, lessons have been learned that can be applied to other works. • If the predictions are to be used for decision making by an AV, it is essential that the model is transparent and that the predicted trajectories are explainable for ethical or legal issues. In addition, our work shows that providing explainable trajectories is crucial to assess the realism of the predictions with respect to human behavior, as error measures alone are insufficient to evaluate a model's realism. Sometimes two predicted trajectories produce similar prediction errors, while one produces a collision and is less realistic than the other. It is therefore important to carry out both a qualitative and quantitative evaluation, and to have explainable models and results. • Whereas in simulation a scenario can be repeated many times in order to generate several possible trajectories, real pedestrians trajectories are rarely repeated in the datasets.
What Is Next? The limitations of this work can lead to future work. Our model could be used for both conventional vehicles and AVs. In the absence of data with AVs, we used datasets with manually driven cars. We based our model on the observation from previous studies that pedestrians will act with an AV as they do with a conventional vehicle, once accustomed  (Rothenbucher et al. , 2016; Palmeiro et al. , 2018; Clamann et al. , 2017). However, some studies expect some differences, like the emergence of overtrust with AVs  (Millard-Ball, 2016)  or the lack of pedestrian-driver communication  (Lundgren et al. , 2017). Once pedestrians have become used to AVs, we need experimental studies to assess their interactions with AVs. Changes in pedestrian reactions are easy to implement in the proposed model simply by tuning the model parameter values, e.
Large groups of tourists, following a guide for example, may also have a different dynamic from the four types of groups considered. Datasets with annotated pedestrian groups interacting with a car could confirm these hypotheses. Moreover, we did not consider the detection of groups by the AV. Research on automatic detection and recognition of social relationships within pedestrian groups as proposed by  Yucel et al. (2019) , could complement our model in order to improve AV's predictions. Finally, the datasets used to evaluate the model have some limitations. The CITR and Nantes datasets are derived from controlled experiments, which are more easy to reproduce in the simulations because pedestrian numbers, starting points and destinations are controlled. However, pedestrian behavior is controlled and the pedestrians are aware of the experiment.
Thus, their behaviors may diverge from their natural behavior. Moreover, in all three datasets, the participants are students or academics on a campus, which can create a bias. This is a persistent issue in experiments on pedestrian-car interaction. The participants will not behave as they would in a leisure area, nor as parents with their children, or as elderly people on a walk. In addition, these are often participants who are particularly aware of technologies. More extensive studies are needed to collect more representative behaviors. Finally, the datasets used for the evaluation are from three different countries; China, USA and France. Previous studies have shown that pedestrian behavior can differ according to culture  (Hall, 1966; Pelé et al. , 2017). Even in the same country, for two cities of different sizes, pedestrians can have significantly different interaction behaviors.
Generalizability to New Scenarios and Applicability to Embedded Prediction (Research Question 3)In order to evaluate the generalizability and applicability of the proposed model, we computed the same measures on the Nantes dataset without prior recalibration. The prediction errors obtained with the proposed model on all 19 interactions are shown in Figure14; FDE, FSE and FOE after 5s of prediction; ADE, ASE and AOE on the first 5s of prediction; and DCAE between the pedestrians and the vehicle.
Acknowledgments This work has been conducted as part of the  HIANIC  project  (Human Inspired Autonomous Navigation In Crowds ), funded by the  French Ministry of Education and Research  and the  French National Research Agency  ( ANR-17-CE22-0010 ). We acknowledge the LS2N ARMEN team and the  Inria Chroma team , partners of the  HIANIC  project for sharing the Nantes dataset. This paper extends a paper presented at AAMAS'21, where it was awarded the  Pragnesh Jay Modi Best Student Paper Award. Prédhumeau, M. , Mancheva, L. , Dugdale,  J. ,  & Spalanzani , A. ( 2021). An Agent-Based Model  to  Predict Pedestrians Trajectories  with an Autonomous Vehicle in Shared Spaces. In Proceedings of the 20th International Conference on Autonomous Agents and  MultiAgent Systems  ( AAMAS21 ). We thank the anonymous reviewers of the current version of this paper, as well as the conference version, for their helpful comments.
6. 3 3 Generalizability to New Scenarios and Applicability to Embedded Prediction (Research Question 3)In order to evaluate the generalizability and applicability of the proposed model, we computed the same measures on the Nantes dataset without prior recalibration. The prediction errors obtained with the proposed model on all 19 interactions are shown in Figure14; FDE, FSE and FOE after 5s of prediction; ADE, ASE and AOE on the first 5s of prediction; and DCAE between the pedestrians and the vehicle.
Reconstruction procedure This subsection describes the procedure to retrieve 2D and 3D phase reconstructions using in-line holography at NanoMAX. First, we performed flat-field corrections to the acquired data. For such purpose, we recorded: (i) flat frames, i. e. images without the sample, and (ii) dark frames, i. e. images without X-rays that estimate the electronic noise of the acquisition system. Flat and dark frames were collected every time we performed an acquisition at a different defocusing distance. Averaged flat and dark images were used together with the data to obtain flat-field corrected images to reduce the fixed-pattern noise. Second, we performed image registrations for the different flat-fieldcorrected images recorded at different defocusing distances in order to prepare them for phase reconstruction using these multiple defocusing distances.
This process comprised two steps. The first one was demagnification, i. e. we used the physical size of the effective pixel to bring all the images to the same scale. In our case, we brought all the images to the largest magnification. The second step was a similarity registration that includes rotation, translation, and scaling operations. The scaling was introduced to refine the estimated demagnifications. Given that the propagation artifacts are different for each defocusing distance, performing a simple intensity registration approach was not possible. To address this issue, we used mutual information methods  (Weber et al. , 2018). The described process is implemented in PyPhase  (Langer et al. , 2021) , a modular open-source phase-reconstruction package implemented in Python. Specifically for the registration, we used Elastix  (Klein et al.
, 2010; Shamonin et al. , 2014)  via its Python wrapper PyElastix  (Klein, 2019). Third, we performed phase reconstructions for each individual projection by combining the registered images at different defocusing distances. The phase reconstructions were performed using contrast-transfer function approaches (CTFs)  (Guigay, 1977)  implemented in PyPhase  (Langer et al. , 2021). Specifically for the samples here presented, we used a simplified version of CTF assuming a pure phase object, which was a valid approach for both samples as the total transmission is above 95%. The chalk sample required extra steps for the 3D reconstruction. To accelerate the 3D reconstruction workflow, we rebinned the frames by a factor of three in both directions. This operation does not limit our resolution, given the expected resolution limit compared with the effective pixel size.
For the 3D sample, we performed two independent reconstructions by dividing the projection dataset into two, i. e. 650 projections evenly distributed for each 3D reconstruction. The results of the FSC, using the aforementioned apodization function and masking the pixels outside the reconstructed volume, are depicted in Fig. 6(d). From the FSC and a 45 nm voxel size, the estimated resolution was 155 nm using the 1/2 bit criterion.
Discussion The here presented experiments demonstrate the unique capabilities of NanoMAX to explore length scales from the mesoscale down to the nanoscale by exploiting the unique focusing possibilities and high-coherent flux provided by MAX IV. The 2D test pattern was reconstructed with diffractionlimited resolution. This was validated by using FRC as depicted in Fig. 5(b ). The data were acquired with 5 s exposure for each frame. The chalk sample was exposed for 1 s per projection. This resulted in a 22 minute acquisition time per tomogram with 1300 projections and per distance. As we recorded tomograms at four distances, the total acquisition time was 88 minutes for this sample. The results presented here demonstrate the possibility to perform fast acquisitions and reconstructions at the mesoscale, exploiting the unique capabilities of NanoMAX.
Nonetheless, the current setup has certain limitations. First, the alignment of the sample to the center of rotation was done using manual stages together with an optical microscope. This is a time-consuming process, which can be improved by adding motorized linear stages on top of the rotation stage. Second, although the acquisition times were 1 s for the 2D and 3D samples, the total exposure time was much longer due to overheads. In fact, the tomographic scan took a total of 2 h. This unnecessary exposure of the sample to the X-rays while not acquiring data could be reduced by using a fast shutter. Third, to increase the sensitivity of the method while minimizing dose, one would like to exploit higher photon energies, which would also enable higher resolutions  (Fourme et al. , 2012; Villanueva-Perez et al.
| Study design We assessed different types of SMSs including one hypothesisdriven score (mCSMS), one mixed hypothesis-and data-driven score (mixed score), and several data-driven SMSs generated with MASKair ® data following different approaches. The data-driven SMSs were developed de novo in this study, with different methodological approaches being used given the absence of a single gold standard (i) measure of the daily control of AR and (ii) method of analysis. For each SMS, we assessed concurrent validity, test-retest reliability and responsiveness. We used (i) quality-of-life (EQ-5D visual analogue scale [VAS] and the ninth question of the Work Productivity and Activity Impairment: Allergic Specific [WPAI:AS]-'degree allergy.
18 en responding to the MASK-air ® daily monitoring questionnaire, it is not possible to skip any of the questions. The data are saved in the dataset only after the final answer, which precludes missing data. In addition to symptom daily monitoring, MASK-air ® users may respond (albeit in a non-mandatory way) to the other questionnaires that were used in this study as SMS comparators: • EQ-5D-5L assesses the respondents' health status through five dimensions/questions (each with five levels) followed by a VAS assessing the general health status on that day. 19 The EQ -5D-5L has been widely assessed (in different populations) on its validity, reliability, discriminatory power and feasibility, displaying a better performance than previous health status questionnaires, particularly regarding mild health problems.
Added value of this study: In this study, we used real-world data (obtained with an app) from patients with allergic rhinitis to generate data-driven CSMSs as well as to assess their validity, test-retest reliability and responsiveness. Such properties were also assessed for a hypothesis-driven CSMS and for a mixed hypothesis-and data-driven CSMS (mixed CSMS). We assessed data corresponding to 317,176 days of MASK-air ® use from 17,780 users in 25 countries. The mixed CSMS and most of the data-driven CSMSs were found to have moderateto-strong validity, high test-retest reliability and moderate-tolarge responsiveness. The best-performing data-driven CSMS was also found to have high accuracy (AUC-ROC = 0. 904), with values<15. 8 (on a 0-100 scale) indicating good AR control, and values >35. 3 indicating worse AR control.
| Data analysis For the assessment of concurrent validity, Spearman correlation coefficients were computed to measure the correlation between each SMS and EQ-5D VAS, CARAT, VAS Work, 'WPAI:AS work' and 'WPAI:AS activities'. Test-retest reliability was assessed for each SMS in users with two measurements of a validated comparator (ie EQ-5D VAS, CARAT, VAS Work, 'WPAI:AS work' and 'WPAI:AS activities'), indicating clinical stability. Clinical stability was assumed whenever a user had two consecutive SMSs of less than three or five weeks apart, with results for validated comparators having a difference smaller than the minimal clinically important difference (MCID) value. We used previously described MCID values (namely in the case of CARAT, for which differences ≤3 were considered to be lower than the MCID)  23  or values determined according to distribution-based methods.
In fact, in comparison with the EQ-5D VAS, the mCSMS displayed a correlation coefficient of -0. 46, compared to between -0. 53 and -0. 56 for the remaining scores. On the other hand, compared to CARAT, the mCSMS displayed a correlation coefficient of -0. 50, compared to between -0. 55 and -0. 59 for the remaining scores. Weaker correlations were also observed when comparing the mCSMS to WPAI:AS than when comparing it to the remaining scores. The same patterns of correlation were found when assessing individual countries with over 10,000 days of reporting in the dataset, with a similar ranking order of SMSs (Figure  1 ; Table  S6 ). Correlations between VAS Work and the different SMSs (except mCSMS) were very similar in all countries, except for Brazil. Wider across-country differences were observed for other comparators.
| DISCUSS ION A validated CSMS is needed in AR, from clinical practice to clinical trials. The current study using real-world data obtained from 17,780 patients in 25 countries showed that (i) both a mixed CSMS and several  and the Rhinitis Control Assessment Test (RCAT). 33 However, these questionnaires assess rhinitis symptoms for a period longer than one day. In addition, RCAT does not have a specific question on AR medication, while for CARAT, there is one single question that does not discriminate between asthma and AR medication or between medication classes. Differently from these existing questionnaires, the developed SMSs assess AR symptoms and medication use simultaneously for the period of a single day. Further rhinitis control computer-based calculators, or even print versions, can be specifically developed and used for that purpose (particularly as the CSMS formulae are available in Table  1 ).
Text in italics indicates the comparators used in the generation of the respective data-driven scores. The colour code reflects the effect sizes-the highest effect size measures (corresponding to high responsiveness) are marked in green, while the lowest are marked in red. Abbreviations: 'WPAI:AS: activities', Work Productivity and Allergy Impairment: Activity Specific-Percent activity impairment due to allergy; 'WPAI:AS: work', Work Productivity and Allergy Impairment: Percent overall work impairment due to allergy; CARAT, Control of Allergic Rhinitis and Asthma Test; ESM I, Cohen's effect size; ESM II, standardized response mean; VAS, Visual analogue scale. a Insufficient (<30) number of participants. mandatory within the daily monitoring of MASK-air ®. Limitations in sample size (along with the absence of datasets similar to those analysed) precluded the derivation and validation of data-driven scores in totally different data sets.
This study was funded by ARIA. MASK-air ® has been supported by EU grants (EU Structural and Development grant, POLLAR: EIT Health and Structural and Development Funds) and educational grants from Mylan-Viatris, ALK, GSK, Novartis and Uriach. CO N FLI C T O F I NTE R E S T RA reports personal fees from operation POCI-01-0145-36 FEDER-029130 ('mINSPIRE-mHealth to measure and improve adherence to medication in chronic respiratory diseases-generalisation and evaluation of gamification, peer support and advanced image processing technologies') cofunded by ERDF (European Regional Development Fund), COMPETE2020 (Programa Operacional Competitividade e Internacionalização), Portugal 2020 and by Portuguese Funds through FCT (Fundação para a Ciência e a Tecnologia). JB reports personal fees from Chiesi, Cipla, Hikma, Menarini, Mundipharma, Mylan, Novartis, Sanofi-Aventis, Takeda, Teva, Uriach, other from KYomed Innov, personal fees from Purina.