dataesr/scientific-paragraphs-categorization huggingface dataset:
Communication and dissemination of project results, first within, and then outside the project consortium, will also be actively pursued. Finally, the 'Exploitation committee', established as one of the project management structures, will also contribute to integration by addressing issues related to intellectual property, data management as well as commercial and non-commercial exploitation of project results. In a couple of places in the paper, 'a low level of awareness of decision-makers, donors and national veterinary authorities' is mentioned. Although there is no doubt that a lack of political will is a factor limiting better control, this may not always stem from a lack of awareness. These factors are discussed in more detail elsewhere in the paper, but this phrasing could be nuanced to reflect that.
Data collection, analysis and storage for evidence-based decision making underpin all COMBAT activities. The project consortium comprises 21 participants, including a geographically balanced representation from 13 African countries, and it will engage a larger number of AT-affected countries through regional initiatives. This ambitious project is just impressive. It smartly covers a wide range of multidisciplinary activities dwelling with almost all the components ruling AT epidemiology. COMBAT will undoubtedly lead to major improvements in veterinary health and the generated knowledge and partner framework will be key for controlling AT in the future.
Acknowledgements The authors would like to thank members of the participating institutions who are contributing to the ongoing implementation of the project. We acknowledge  CIRAD Impact  and  Marketing of Science Directorate [Direction de l'Impact et du Marketing de la Science (DIMS)]  and  CIRAD "EUROPE" delegation  for their financial and human support in the preparation of the  COMBAT  proposal. We are grateful to  Dr Alexandra Shaw  for helpful discussions and critical reading of the manuscript. The  Food and Agriculture Organization of the United Nations (FAO)  contributed to the paper and participates in the  COMBAT  project in the framework of the  Programme Against African Trypanosomosis (PAAT ).
In smart mobility, the semantic segmentation of images is an important task for a good understanding of the environment. In recent years, many studies have been made on this subject, in the field of Autonomous Vehicles on roads. Some image datasets are available for learning semantic segmentation models, leading to very good performance. However, for other types of autonomous mobile systems like Electric Wheelchairs (EW) on sidewalks, there is no specific dataset. Our contribution presented in this article is twofold: (1) the proposal of a new dataset of short sequences of exterior images of street scenes taken from viewpoints located on sidewalks, in a 3D virtual environment (CARLA); (2) a convolutional neural network (CNN) adapted for temporal processing and including additional techniques to improve its accuracy.
Introduction Robust scene perception is a principal requirement within many autonomous mobile applications. Accurate and efficient object detection techniques, depth estimation and semantic segmentation offer highly functional information about the operational environment. The present work focuses on semantic segmentation, which is the process of classifying image pixels into specific categories related to the environment. For training and testing data-driven models that perform this task, the availability of datasets of images with ground-truth annotations is crucial. Whatever the image-processing task involved, getting annotations is generally partly manual, and this operation becomes very tedious for semantic segmentation as well as for depth estimation, as they must be at the pixel level.
There are a few datasets available for semantic segmentation, but most of them are dedicated to experiments on autonomous vehicles with a viewpoint located on the roads. In our case, we are interested in the autonomous mobility of an Electric Wheelchair for outdoor applications. Thus, the existing datasets are not well fitted to our problem, in which the viewpoint of images is located on sidewalks. Even if sidewalks are often visible in existing datasets, this difference of viewpoint may be a too important bias in the development of models designed specifically for sidewalks. Regarding semantic segmentation models, most of them use single images. However, temporal information which exists in image sequences can be very useful, as successive images of sequences are generally highly similar.
Those considerations led us to develop our own dataset of image sequences, taken in an outdoor virtual environment from viewpoints located on sidewalks. Our contributions include a dataset of synthetic scenes generated using CARLA simulator  [1]  and a temporally distributed network based on DeepLabV3+ architecture  [2]. The remainder of this paper is organized as follows. Section 2 presents the state of the art of existing datasets of images for semantic segmentation and models, with a focus on those dealing with image sequences. Section 3 describes our contributions and experiments. Section 4 presents results obtained with our dataset and model, and Section 5 concludes this work.
Datasets Standard benchmarks for semantic video segmentation include Cityscapes  [3]  and Camvid  [4]  datasets. Cityscapes is available in 2 versions: isolated images and sequences. The image version includes 5000 of size 2048 × 1024 annotated for semantic segmentation, for 19 classes. Images are taken from urban environments, under varying weather conditions and in 50 different cities. Another part of the dataset includes 20,000 images coarsely annotated. The sequence version of the dataset includes not only the annotated images but also intermediate ones. Video snippets are made of 30 frames, taken at 17 FPS (Frames Per Second), from which the 20th one is annotated for semantic segmentation. This version of the dataset can be used by models dealing with the temporal aspect of the sequences.
Camvid consists of 5 video sequences in which every frame is labeled, with a total number of annotated images of 701. NYUDv2 is another dataset that includes video sequences of various indoor scenes recorded with an RGB-Depth camera  [5]. DriveSeg  [6]  is another recent dataset dedicated to Dynamic Driving Scene Segmentation. Other real-world datasets for semantic or instance segmentation include  [7] , dedicated to small overlooked indoor objects. Another way to get annotated sequences of images is to use 3D virtual images generated by a software, like in GTA-5  [8]  (24,966 images, 19 classes), SYNTHIA  [9]  (9400 images, 16 classes), Virtual Kitty  [10] , etc. However, when using virtual images during learning and real images during inference, a loss of accuracy called "domain shift" is stated.
So there is a need to transpose the result from the original domain to the destination domain, by finding solutions to reduce this phenomenon. Standard benchmark adaptation tasks are for example SYNTHIA-to-Cityscapes and GTA-to-Cityscapes. The problem can be tackled in various ways like Generative Adversarial Networks (GAN)  [11]  or by learning the correlation between semantic and depth  [12]. The main advantage of virtual datasets is that they can be easily generated, with various conditions and without technical constraints. Their main drawback is that their images are more or less realistic and then we need domain adaptation techniques to try to fill in the gap between the virtual and real domains. In this work, we propose a dataset of virtual images generated through CARLA simulator  [1] , an open-source simulator for experiments on autonomous vehicles in which various maps of urban environments can be loaded.
In DeepLabV3+  [2] , a decoder module is added after the ASPP step to refine the segmentation results, especially along object boundaries. Many other techniques have been proposed to increase the accuracy of these models. In Bayesian-SegNet [23], uncertainty is introduced in the result of the segmentation, allowing accuracy to be improved by 2 to 3%. In DenseA-SPP  [24] , the principle of ASPP is extended but with a more dense connection, leading to better accuracy. In  [25] , Gated Fully Fusion (GFF) selectively fuses features from multiple levels using gates in a fully connected way. Dual Attention Network (DANet)  [26]  captures feature dependencies in the spatial and channel dimensions. Object-Context Network (OCNet)  [27]  uses, for each pixel of the semantic map, object context to incorporate the information of objects belonging to the same category.
Description of Our Dataset and Test with a Semantic Segmentation Model This section describes our contributions. The first one is the generation of a dataset of small sequences of virtual images representing outdoor scenes taken from viewpoints located on sidewalks. The second one consists of adapting an existing model to improve its accuracy and integrate the temporal dimension of the image sequences. A complementary test is a cross-validation between the two parts of our additional dataset, with the objective of quantifying the influence of image viewpoint in the quality of semantic segmentation.
Overview To generate our dataset, we have used CARLA simulator  [1]  (release 0. 9. 11), an open-source 3D simulator for experiments on autonomous vehicles, based on the Unreal Engine game engine. It comes with pre-made city environment maps for use. In the version that we have used, CARLA is distributed with 8 integrated maps as well as the following parameters: • Addition of a varying density of pedestrians along the roads; • Addition of vehicles on the roads (cars, bikes, motorcycles, vans, trucks, etc. ); • Variation of the weather conditions (amount of clouds, rain, puddles and wind); • Variation of the time of day by modifying the position of the sun in the sky. We have made use of these parameters to improve the diversity of situations in the images of the dataset, which is known to increase the generalization ability of a CNN model.
Additional Data Additionally, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames of each part of this set (viewpoints on road/sidewalk) is 7288 (1822 sequences), partitioned in train/validation/test subsets. The numbers of frames of those subsets are, respectively, 5344 (1336 sequences), 500 (125 sequences) and 500. This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road, and testing on images with a viewpoint located on the sidewalk, and vice versa. Figure  2  shows some sequences taken at the same position in the dataset, but from two different viewpoints (road/sidewalk).
Data Augmentation and Normalization During the learning of the model, we have applied data augmentation, consisting of horizontal flipping with a probability of occurrence equal to 50%, and color jittering with 50% variation in brightness, contrast and saturation, in the same way for all images of a sequence. We have also added randomized affine transform (rotation on ±15 • , translation on ±15% of width and ±10% of height). We have also applied pixel normalization (or "standardization"), consisting in applying computation to each RGB channel of the images to get zero mean and standard deviation equal to unity, over the whole dataset.
Results As indicated above, we have tested three variants of our model: (1) without temporal layer; (2) with a temporal layer between encoder and decoder; (3) with temporal layers between each spatial layer of the encoder part and between encoder and decoder. We have also tested two different encoders: ResNet and ResNest. The best result was obtained for the third variant and ResNest encoder, with a mIoU (mean Intersection over Union) of 84. 06% on the test set. Figure  3  shows some examples of inference of the model on images taken from the validation set. Another test that we have made is cross-validation between the two parts of our additional dataset. The objective was to quantify the influence of point of view on the quality of semantic segmentation. For this test, we have used the non-temporal version of our model, as the objective is not to get the best accuracy as possible.
Conclusions and Future Work In this paper, we have presented a new dataset of small sequences of images dedicated to semantic segmentation tasks, generated with the virtual 3D environment CARLA. Images are taken from viewpoints located on sidewalks, to be useful for experiments on autonomous devices like EW. We also have presented experiments made with a CNN model adapted for temporal processing. Future work includes improving efficiency of the model that we tested, in terms of FPS, without sacrificing too much accuracy. Another important extension of this work is to develop a solution to domain adaptation from the virtual world to the real one, in view of the implementation of the model on the embedded system of a real wheelchair. Those two extensions would allow us to make real experiments.
C. Overview on the workflow Here we summarize our new approach for the production of time series DSM from archived aerial images: -I-Download the scanned archived aerial images from online catalogs or manually scanned achieved films, of each campaigns of the studies times series -II-Pre-process all the images from all the campaign with FiducilLib (algorithm 1) in order to transform the scanned images into images taken by digital cameras. -III-Designation of the campaign of reference and selection of the GCPs for the campaign of reference. In our strategy the archived images the closest in time to the dataset (e. g. maps, GPS measurements) used to select the GCPs, is the campaign of reference. Selection of high quality GCPs on the images of the campaign of reference from basemaps of national surveys/institutions (e.
C. Results The landscape dynamics of these two areas is out of the scope of this study where we focused on the methodology. We implemented the two algorithms in a mixture of python codes and MicMac/Apero commands but they can be applied to a various kind of software at the reader's discretion. Example results over the two areas, obtained from our new workflow are shown in Figure  7. Each cloud corresponds to a region of about 80-100 km 2 with a median geodetic distance of 0. 8 meter between the nearest point. As a matter of fact, spatial sampling of the resulting 3D cloud is directly a function of both the image sampling and the image redundancy. Thus, the 3D cloud has a spatial sampling of a factor of 1 to 4 compared to the image. In our regions of interest, some areas are covered with up to 28 images.
VII. ACKNOWLEDGMENTS We thank our undergrad students that contribute on the hard work of collecting the data, identifying GCPs and contributing to the initial work,  David Talec ,  Adrien Sy ,  Marie Lirzin ,  Meropi Kostopoulou , and  Coline Hopquin. We also thank  Luc Girot  and  Laurent Michon  for their constructive inputs. Authors acknowledge financial support by  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ).
Background and rationale {6a} Background According WHO Global Health Observatory Data Repository, pneumonia remains the main cause of death in children in the post-neonatal period, with nearly 800,000 premature deaths globally, each year. According to UNAIDS, the global number of deaths is 110,000 to 120,000 per year including 40,000 deaths due to tuberculosis (TB), despite prompt antiretroviral treatment (ART) initiation. In Africa, 10% of infants die during the first year of life even if on early ART  [1, 2]. Pneumonia is the primary cause of death in these children  [3, 4]. Currently, the World Health Organization (WHO) guidelines recommend treating severe pneumonia in HIV-infected infants with empirical treatment against Pneumocystis jirovecii, S. pneumoniae, and Haemophilus influenzae b  [5].
Additional consent provisions for collection and use of participant data and biological specimens {26b} Other sub-studies and ancillary studies will be performed in the frame of the study in some of the sites upon condition that the specific consent has been provided by the LAR for any of the sub-studies. Participants will be asked to consent to the given options concerning their participation in the study including participating in the main trial but not be involved in any of the sub-/ancillary studies or participate in one or a few of them. Caregivers of dead participants eligible for participation in the Cause of Death minimally invasive autopsy sub-study will be provided the specific informed consent only in case of death. Information about the need for sending samples to Microbiological and Pathology research laboratories associated with ISGlobal in Barcelona (Spain) will be included.
Participant timeline {13} The IMP should be started within 24 h after randomization and should be administered as outlined in the following sections of the protocol. The study staff should follow dose modification rules as established in the corresponding SmPC for the management of related or unrelated AEs. All deviations from protocol-specified dose modifications must be documented. Scheduled visits for data and specimen collection will occur either in the hospital if still in-patient, or at the Outpatient Clinic (for discharged subjects). Following discharge, children will be referred to the pediatric HIV clinic for follow-up, in which a study clinician will be based. Children will be followed up for a total of 12 months after the day of enrollment. Patients will have follow-up visits at day + 3 after enrollment, at discharge from the enrolling hospital, + 15 days, + 30 days, + 60 days (maintenance phase initiation in TB-T), + 90 days, + 180 days (end of TB-T visit), and + 360 days ("last day, last visit"), unscheduled visits, close-out visit form, and log questionnaires as Treatment log, Adverse event log.
Sample size {14} The study aims to enroll 624 HIV-infected infants altogether. The sample size was estimated using WebPower R package (Ref: https://CRAN. R-project. org/package= WebPower). Sample size calculation was performed according to an 80% statistical power and a significance level of 0. 05. Several scenarios were considered according to survival (short-term and long-term) published data in HIV-infected children with pneumonia and TB. Baseline mortality was considered when only SoC had been assigned to the patients.
Long-term mortality CMV treatment effect A benefit of valganciclovir on long-term mortality reduction is not expected. Long-term mortality of 31% is estimated according to short-term mortality (23%) plus the 10% mortality among the 77% survivors (8%). According to this 1-year mortality reduction effect and an estimated 5% of lost-to-follow-up, it is calculated that at least n = 624 patients will be enough to address this arm of valganciclovir treatment effect in long-term mortality. Mortality and the prevalence of CMV and TB will be monitored during enrollment. Should new data arise relating to assumptions in this calculation, the sample size might be re-estimated at mid-enrollment interim analysis, after consultations with DSMB and Ethical Committees  [41].
Sequence generation {16a} Each participant will be randomized in a 1:1:1:1 allocation ratio. Randomization will be stratified by center and clinical severity (presence of danger signs) to avoid that local practice at high-recruitment centers has a disproportionate effect on the results of the trial. An automatic randomization system will be designed in REDCap (Research Electronic Data Capture) using permuted blocks with variable block sizes and will be monitored by the CTU. The allocation sequence will be generated through computer-generated random numbers and a list of any factors for stratification. To reduce the predictability of a random sequence, details of blocking will be provided in a separate document that is unavailable to those who enroll participants or assign interventions.
Concealment mechanism {16b} Interventions will be assigned through a passwordprotected randomization service online. A telephone in the central team will be 24/7 available in case online assignation was not possible. The generated randomization lists will be securely incorporated within the web trial database, and allocation concealed until the point of the next randomization. The designated members of the research staff at each site will be responsible for carrying out the randomization process using a secure electronic system within the trial database. Randomization will not take place until after informed consent has been given and the participant is ready to receive therapy. A reliable manual back-up system will also be available. If the center's internet connection is unavailable at the time of randomization, the screening details can be provided to staff at the FI+12-CTU by phone.
Plans for assessment and collection of outcomes {18a} A data management team (DMT) will be settled with one member of each site and members of the CTU, and they will create a Data Management Plan which will follow for data collection. Source documents will include but are not limited to: Laboratory results will be reported using paper or electronic reports following the habitual practice of each site. Those reported in paper will be included in the child's medical records. Laboratory results that will be processed grouped in a second time will not be reported save those with relevant clinic implications. The incidental findings will be reported following the Tri-Council Policy (see next section "Incidental findings"). Site investigators will maintain and store securely, all source documents throughout the study.
These documents will be retained for at least 5 years after the last child exits the study or more following the local Ethics Committee's requests. Screening Case report forms All study data will be collected by the clinical study staff using designated source documents. Electronic case report forms (eCRFs) will be developed for the collection of the trial's data. Those centers without the capacity of using eCRF will use paper-based CRFs. Study data can be entered directly into the eCRFs during a study visit and uploaded into the server. In sites where paper-based CRFs will be used, data will be then entered into the electronic database as promptly as is feasible. After the introduction, data will be double-checked by a senior researcher when possible. CRFs and laboratory reports will be reviewed primarily by the site clinical team who are responsible for ensuring that they are accurate and complete.
Data management {19} A centralized computerized database, managed by the CTU in REDCap, will be the central data repository for all sites participating in the trial. Each site participating in the trial will be responsible for data entry and first cleaning. Data collected electronically will be transferred from the sites to the CTU using an own secure server, allocated in Hospital 12 Octubre (Madrid) and maintained by the Clinical Trials and CTU in the Hospital 12 Octubre. Sites will be able to self-edit, make online quality check reports, and be informed of the data collected during the clinical trial from their own site. Local investigators will have access during data collection time upon request and under justification to the complete pseudonymized database exportation. Userspecific usernames and passwords are required to log onto the database.