dataesr/scientific-paragraphs-categorization huggingface dataset:
Tracking precision and accuracy of the HTC Vive System is adequate for behavioral research purposes  ( Ahrens et al. , 2019 ). The HMD was equipped with an SMI eye-tracking system (250 Hz). At all testing sites, stimuli were controlled and delivered using a LDLC ZALMAN PC (OS: Windows 10 (10. 0. 0) 64bit; Graphic card: NVIDIA GeForce GTX 1060 6GB; Processor: Intel Core i7-7700 K, Quad-Core 4. 2 GHz/4. 5 GHz Turbo -Cache 8 Mo -TDP 95 W) using Steam VR software and the development platform Unity3D (Unity Technologies, San Francisco, CA). Participants were seated on a rotating armless chair with no chin rest, in the center of the room. The rooms had the following dimensions: Trento: 4 × 3 m; Lyon: 3 × 6 m; Toulouse: 3 × 5 m. All rooms were quiet, but none was specifically treated for being anechoic and sound-proof.
Real free field auditory stimuli were delivered by an unseen loudspeaker (JBL GO Portable, 68. 3 × 82. 7 × 30. 8 mm, Output Power 3. 0 W, Frequency response 180 Hz -20 kHz, Signal-to-noise ration > 80 dB), whose position was continuously tracked in space. They were 3 s white noise bursts, amplitude-modulated at 4 Hz, and delivered at about 60 dB SPL, as measured from the participant's head (using Decibel Meter TES1350A placed at ears level). This solution allowed us to track the position of the speaker, the hand of participant and the Head Mounted Display, via a dead reckoning process using gyroscope and accelerometer, plus a correction signal from the lighthouse system every 8. 333 ms. Both tracking method allowed us to track this position with a frequency sample of 250 Hz. The software is designed to guide the experimenter to align the real loudspeaker (the sound source) with each of the 8 pre-determined position in the virtual environment in each trial.
Hand pointing responses: the effects of adding a visual frame Having established performance in the three dimensions in the gray visual condition, we turned to examine to what extent adding a visual frame affected sound localization performance. To this aim, we entered absolute and signed errors into ANOVAs with visual condition (gray, grid) and antero-posterior sector as within-participant variables, separately for each spatial dimension. We focused on the anteroposterior position of sounds because we predicted that the effect of the 1 We interpreted these differences observed for near sounds in right space as the consequence of bio-mechanical constraints related to the fact that all participants were asked to point to the sounds using their right hand. To assess this hypothesis empirically, we analyzed the data of a pilot experiment in which 12 participants performed the same task reported in the present manuscript but using their preferred hand on a trial-by-trial basis.
An ANOVA on absolute errors in distance on this dataset, using again side , antero-posterior sector and distance as factors, yielded no significant main effect or interaction (all Fs < 3. 20, all ps > 0. 10). On signed errors in distance, we found only a significative interaction between antero-posterior sector and distance , F (1,11) = 7. 22, p = 0. 02,  2 = 0. 03. This provides initial evidence that at least part of the specificities observed for right-sided sounds could depend upon the imposed use of the right hand for the response. visual manipulation could be maximal for sounds were delivered in the front space rather than back space. The analyses on absolute and signed errors in azimuth, elevation and distance, did not reveal any main effect or interaction involving visual condition ( all Fs < 1.
Acknowledgements We thank all the participants and the colleagues who helped during research. We thank  Giordana Torresani  for graphic solutions ( Fig. 1 A). Funding : C. V. was supported by a grant of the  Università Italo-Francese (UIF) / Université Franco-Italienne (UFI)  and the  Ermenegildo Zegna Founder 's Scholarship. F. P. , C. V. and AF were supported by a grant of the  Agence Nationale de la Recherche  ( ANR-16-CE17-0016 ,  VIRTUAL-HEARING3D , France) and by a prize of the  Fondation Medisite (France). The study was supported by the  IHU  CeSaMe  ANR-10-IBHU-0003  and it was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon. We thank  JL Borach ,  S Alouche ,  S Terrones  for administrative support, and  Eric Koun  for informatics support.
For further improvement, Lee et al. [136]  proposed the addition of a recurrent network to the existing CNN network to account for the spatiotemporal information in the image or video sequence for better depth estimation. In  [137] , Kuznietsov et al. proposed using a semisupervised deep learning method that can take the advantages of both supervised and unsupervised methods. The proposed technique uses the sparse ground truth data for learning and utilizes CNN for depth prediction. Another significant module that contributes much to the accuracy of the SLAM system is loop closure. This module checks the previously visited and mapped places and uses the results to reduce the error of the built map. Previously, classical approaches were used to perform detection and classification, such as bag-of-words (BoW), scale-invariant feature transform (SIFT), and speeded-up robust features (SURF) approaches.
Despite this, deep learning has improved the overall accuracy, and in some cases it has solved critical issues, such as operating in highly dynamic environments. With these continuous improvements, it is conceivable that in the near future there will be an end-to-end deep learning SLAM algorithm with superior accuracy and computational efficiency. Another point that can be observed from Table  7  is the diversity of the testing datasets in the previous studies. Unlike the traditional algorithms presented in Table  6 , these algorithms are tested on different datasets; hence, a conclusive comparison of their performance based on the published results is not easy. Visual odometry (VO) is defined as the process of obtaining the pose of a vehicle by tracking the change of its position from consecutive images over time.
The proposed method was tested for outdoor driving and the results were comparable to those produced through classical algorithms. However, the performance of the algorithm degraded when under certain conditions, including fast driving or driving in open areas, with fewer features leading to more outliers. One solution is to increase the size of the training dataset for the network to learn to reject the outliers. This requirement triggers a question-is it always possible to increase our testing dataset? The challenge rests in the process of labeling these data. This leads to exploring the field of self-supervised and unsupervised learning. Unlike supervised learning, an unsupervised learning network does not rely on labeled data or ground truth data for training. Instead, it trains the model by minimizing the photometric error.
Issues to Solve: Cybersecurity, Reliability, and Repeatability While deep learning approaches have dramatically improved different AV perception and localization modules, it is important to stress that these approaches require large datasets that are generated over an extended period of time. The outcomes of these approaches depend on the quality and comprehensiveness of the training datasets, and hence the results could vary in terms of relevance and reliability. Merging classical model-based methods and deep learning approaches can improve the robustness and reliability of the existing methods. One important concern regarding the certification and homologation of the perception and localization of deep learning-based approaches is to guarantee the maintenance of their high level of efficiency.
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506 eyes who underwent pars plana vitrectomy for rhegmatogenous retinal detachment (RRD) by a single surgeon at a tertiary-care hospital between 2012 and 2019. Two ophthalmologists without coding experience used an interactive application in MATLAB to build and evaluate ML algorithms for the prediction of postoperative PVR using clinical data from the electronic health records. The clinical features associated with postoperative PVR were determined by univariate feature selection. The area under the curve (AUC) for predicting postoperative PVR was better for models that included pre-existing PVR as an input.
As of recently, the development of machine learning (ML) predictive models in healthcare had been reserved for AI experts with knowledge of coding and computer science. The feasibility of deep-learning design (a subset of AI) by physicians without coding experience was demonstrated in a first-of-its-kind report by Faes et al. in 2019  17. This major advance in the democratization of AI was made possible by the release of automated ML (AutoML) programs by major companies allowing any individual to develop high-quality AI models  18. In this study, two ophthalmologists without coding experience built ML predictive classification models using an interactive application in MATLAB (MathWorks, Natick, MA) and explored the discriminative performance of those models for the prediction of postoperative PVR.
Methods The study included consecutive patients who underwent RRD repair using pars plana vitrectomy by a single surgeon (MS) from 2012 to 2019, at the Centre Hospitalier de l'Université de Montréal (CHUM), a tertiary teaching hospital in Montreal, Quebec, Canada. The institutional review board approved the study (CÉR CHUM: 19. 227) and granted a waiver for informed consent given the retrospective and anonymous nature of the study. The study adhered to the tenets of the Declaration of Helsinki and was compliant with our hospital guidelines regarding the clinical data collection and extraction from EHRs. The primary outcome of this study was the discriminative performance of ML models, developed using AutoML by ophthalmologists without coding experience, in correctly predicting PVR. Prediction of PVR was examined by area under the receiver operatic characteristic curve (AUC).
Data set preparation and feature collection from the EHR. We collected 15 features from the EHR as described in Table  1. All categorical features were evaluated based on the presence or absence of the clinical examination finding at the preoperative clinical encounter, with the exception of "postoperative lens status" that was evaluated on postoperative day 1. For this feature, pseudophakia and aphakia were counted together. Pre-existing PVR was defined as the presence of PVR Grade C involving more than 1 clock hour of the retina, according to the updated version of the Retina Society Classification  19. Although we collected intraoperative parameters such as type of endotamponade and use of endolaser and cryotherapy, we elected to exclude those surgeon factors from the predictive models in order to limit confounding.
The labeling was performed by an ophthalmologist-in-training (FA) and an experienced vitreoretinal surgeon (MS) based on the Data set preparation before model development. A scientist with knowledge in data mining (GK) prepared the dataset. Figure  1  summarizes the dataflow during the study. Within the total clinical cohort of 506 eyes, 460 were No PVR (majority class) and 46 were PVR cases (minority class of interest), representing a 9. 1% incidence of PVR. Complete data were available for all categorical variables. For continuous variables, 77 data points (15. 2%) were missing for the "duration of symptoms" variable and 90 data points (17. 8%) were missing for the "intraocular pressure" variable. We conducted a missing value analysis and found the data to be Missing Completely at Random (MCAR) using Little's MCAR test (p = 0.
933). Thus, imputation using the median of the non-missing values in the variable column was performed. Before the dataset could be used for training by the two ophthalmologists (FA and RGC), we employed datalevel solutions to tackle the imbalanced dataset problem. This was important to perform prior to any data training in order to not undermine the predictive performance of the models trained by the two ophthalmologists. We performed random undersampling (RUS) of the majority class using Waikato Environment for Knowledge Analysis (WEKA), an open-source data-mining software made available by the University of Waikato in New Zealand  20. We set the case:control ratio to 2:1, leading to a new dataset containing 92 controls and 46 PVR cases and a disease prevalence of 33. 33%. While higher matching ratios could have been used, we used this ratio to avoid perpetuating the class imbalance problem  21.
The clinical characteristics of the original and new datasets are presented in Table  2  and Supplementary Table  S1 , respectively. The significantly different features remained unchanged in the new dataset, highlighting the absence of significant change in the distribution of the data. This diagram demonstrates the workflow for predictive modeling of proliferative vitreoretinopathy (PVR) using automated machine learning (AutoML). After data preparation and class balancing using random undersampling (RUS), the two ophthalmologists used the Classification Learner App to design support vector machine (SVM) and Naïve Bayes (NB) algorithms. Details about the feature sets and models are described in the methodology. In parallel to the design by the two ophthalmologists, manually coded algorithms were also prepared as a benchmarking measure.
The codes are available on GitHub (https ://githu b. com/ghofo k/PVR_Predi ction ) and their predictive performance is shown in Supplementary Table  S2. Statistical analysis and performance metrics. Continuous variables are presented as mean, median, range, and standard deviation. Categorical variables are presented as counts and percentages. Since all the continuous variables were not normally distributed (p < 0. 001, Shapiro-Wilk test), we used nonparametric tests (Mann-Whitney U). We used the two-sided Fisher's exact test and Chi-Square test for categorical variables. We defined statistical significance as p-value < 0. 05. All statistical tests were performed using SPSS v. 25. 0 (SPSS, Chicago, IL). For consistency, we adhered to the standard test accuracy terminology-sensitivity (recall), specificity, PPV (or precision), and NPV-that were calculated from the confusion matrix presented in the Classification Learner App.
Case 3 shows a common case of simple macula-off RRD with no obvious risk factors for PVR, correctly classified as "No PVR". In case 2, the algorithm misclassified the case as "PVR" probably due to the long duration of symptoms and the presence of vitreous hemorrhage. In case 4, the algorithm failed to predict PVR despite the presence of a giant retinal tear. The normal intraocular pressure, absence of pre-existing PVR, and macula-on status might have influenced the classifier's decision. From a methodological standpoint, our study has several strengths. Our original clinical cohort was large and included 506 eyes with a PVR rate of 9. 1%. The sample size was sufficiently large to find statistically significant risk factors for PVR; however, the low incidence of PVR led to an imbalanced dataset problem, a common challenge in ML classification using real-world data  16, 29.
To address this issue, data preparation was required before the two ophthalmologists could use the data for training and testing  [30] [31] [32]. After exploring different resampling techniques, we found that the most suitable option was undersampling to create a case-control study design  [33] [34] [35]. We used cross-validation, which is a rigorous internal validation measure that determines the robustness and generalizability of the predictive models  22. All efforts were made to adhere to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines  36. We were able to build optimized ML models using AutoML, a feature of the software that automates and streamlines complex processes in the ML workflow such as hyperparameter tuning. This AutoML feature can significantly improve prediction accuracy  37.
However, it remains to be seen if clinicians with no coding experience can independently build ML models without any input from data scientists and engineers. For balanced datasets and image classification tasks, independent model building appears to be feasible. However, in the case of low-incidence diseases, clinicians will be faced with the class imbalance problem and contributions from data scientists might be indispensable. In parallel, as the technology of AutoML further matures, benchmarking with manually coded algorithms will no longer be needed. In future studies, we aim to explore the external validity of our models. In particular, we wish to study the benefits of closer postoperative follow-up for patients predicted to develop PVR and aim to look at retinal redetachment as a separate outcome.
The most common method to measure time and distance between points is to use road networks in the GIS package with information on speed limit criteria  [7, 9, 23]. The limitations of this approach include that the census was conducted with intervals and therefore hysteresis, that there were errors by cutting the map, and that using road network maps may lead to rough estimates. Therefore, this study aims to measure spatial accessibility to county hospitals and its disparity for county residents in 73 administrative counties in Shaanxi Province of western China. We rstly used navigation data of online digital maps to assess the time and distance from residents to county hospitals in China. The navigation data was collected from AutoNavi, a Chinese web mapping, navigation, and location-based services provider, via our request to open an application programming interface (API) using web crawler technology.
Data Collection Scheme In order to measure spatial accessibility of health resources, three aspects of data were basically needed: geographical distribution of population, the geographical location of hospitals, and time and distance between residents and hospitals. Therefore, we collected data in three steps. Firstly, considering the uneven distribution of population, we used the geographical location of the villages and neighborhoods to identify the population distribution. Two strategies were adopted in our study: i) for villages and neighborhoods with village clinic: we selected the coordinates of the village clinics to represent the population distribution since the village clinics should be in a relatively concentrated area of the village population to cover the population of the village to the greatest extent when they were set.
ii) for the villages and neighborhoods whose village clinic cannot be acquired or have multiple village clinics, we selected the default coordinates provided by web map navigation service This coordinate usually defaults to the location of the villages and neighborhoods' o ce that is usually located in a populated area. Secondly, we collected the name of county hospitals from the Health Commission of Shaanxi Province, and then we directly used the name of hospitals to get the geographical location from the web map. Thirdly, the time and distance between each village and neighborhood to the county hospitals were collected from the results of navigation of the web map navigation service. Chose the fastest but not the highway route (because China's highway import and export are usually set around the county) to get the time and distance from villages and neighborhoods to the local county hospital by using the real-time navigation data of AutoNavi map under the driving mode.
Data Collection Method Firstly, we obtained the names of village clinics and county hospitals in overall Shaanxi Province from the Shaanxi Provincial Health Statistics Annual Report in 2017 that was provided by the Health Commission of Shaanxi Province. In addition, we also obtained the name of village and neighborhood committees in overall Shaanxi Province from the website of the National Bureau of Statistics  [25]. Secondly, we used the geocoding interface of AutoNavi map to collect the coordinates of villages and neighborhoods and county hospitals. The requests for the API of geocoding of AutoNavi map were conducted by using a web crawler in the Python 3. 6 program  [26]. The URL of this geocoding interface can be found here. AutoNavi map knows as Gaode in Chinese is founded in 2011 and is one of the largest web mapping, navigation, and location-based services providers in China.
It offers map services at Amap. com and a mobile app. Thirdly, navigation data, including driving time and distance, were collected by using the path planning interface by setting the coordinates of the villages and neighborhoods as the starting point and the coordinates of a county hospital in the district as the endpoint. The URL of path planning interface can be found here. Considering the in uence of tra c conditions at different times, this study was performed four times randomly: the morning (10:00 to 11:00) and afternoon (14:00 to 15:00) on November 23, 2018 (Friday) and November 27, 2018 (Tuesday). During the time period, 4 times crawling requests by Python were made to the AutoNavi map, and took the average value of 4 times. Finally, data on 10,350 villages and neighborhoods (total 13,074 villages and neighborhoods) from 73 counties of Shaanxi Province were obtained in our study (Fig.
We calculated Getis-Ord Gi* statistics for the spatial association of each county to explore the disparity of spatial accessibility  [27, 28]. The Gi* statistic returned for each county is a z-score  [29]. A high positive z-score and small p-value for a county represent a spatial clustering of high values (hot spot). A low negative z-score and small pvalue represent a spatial clustering of low values (cold spot). The higher or lower the z-score, the more intense the clustering. A z-score close to zero means no signi cant spatial clustering. Getis-Ord Gi* statistics were calculated by 'spdep' package of R language  [30]. The spatial relationships of counties were de ned as Queen's Case. The distance and time of counties were the averages of distance and time of villages and neighborhoods.
In summary, combining the navigation data from web map and two-step oating catchment area modi ed methods to measure spatial accessibility of health resources has high feasibility and application prospects. We still can't avoid some limitations in this study. First, since it is very di cult to collect speci c population in villages and neighborhoods level, only the relatively simple nearest distance method was used to evaluate the spatial accessibility, which ignored the supply and demand of health resources. Second, distance and time data were only extracted under the driving mode, the gure within public transportation or other modes was not analyzed, which may not be consistent with the actual mode of travel of rural Chinese residents. Lastly, not all villages and neighborhoods in Shaanxi Province were involved because of the small amount of data were unobtainable.
Availability of data and materials The data were collected from AutoNavi map, a Chinese web map and navigation service provider. This web map provides an open application programming interface for freely using data after registering as a developer. The application programming interface can be found here: (https://lbs. amap. com/api/webservice/guide/api/georegeo#geo) & (https://lbs. amap. com/api/webservice/guide/api/direction#driving).