dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, removing features does not improve predictions. The data retrieved to build our dataset are all experimentally proven by tandem mass spectrometry, which is a conventional method currently used to identify O-GlcNAcylated sites, and/or by site-mutagenesis. Although experimentally proven, we cannot exclude that false positives and negatives may exist, which adds noise to the training. The FP and FN may be related to technical issues or experimental conditions, in particular considering that O-GlcNAcylation in an invivo environment can be more or less efficient than in an invitro environment. The lack of positive data which leads to the limitation of the unbalanced data (which add bias in prediction) can explain the low PPV obtained by our method but also by the previously published tools.
This problem is linked to the oversampling of trained data but training on undersampled data is not possible in this case because of the lack of positive data (we tried to train RF models on undersampled data, which gave on average a sensitivity = 22. 12% and a PPV= 0. 54%). It highlights the fact that the amount of available experimental data is crucial for any machine learning algorithm. Thus, it will be worthwhile to apply our methods again when significantly more positive experimental data are available. Here, we showed that the PPV of any tool is currently so low (<9%) that there is a necessity to develop a new and efficient tool based on a bigger dataset of positive data. The RF, GBT and SVM classifier methods we used showed in the past to be very efficient for other predictions,  [36] [37] [38]  and we are convinced now that the problem resides in the data and associated features used, rather than in the algorithms themselves.
Another intriguing point that could help to improve the predictions if taken into account is that O-GlcNAcylation is a reaction that can occur co-translationally,  41  meaning that protein folding and O-GlcNAcylation can occur at the same time. These sites should be analysed separately from the posttranslational ones to be able to improve predictions but such annotation is currently lacking in databases. Therefore, predicting an O-GlcNAcylation as cotranslational is an even more difficult task. OGT presents several isoforms exhibiting different numbers of TPRs in the N-terminal domain. This variation in the number of repetitions could play a role in the enzyme capacity to recognize and modify sites which are on structured or unstructured parts of proteins, with unstructured sections that would be able to enter the lumen of the TPR superhelix.
For instance, the structure of human O-GlcNAc Transferase (PDB ID: 4N3B)  42  harbors a modified peptide from HCF-1 inside the superhelix of the first TPR repeat, which indicates that OGT is able to accept unfolded structures inside its TPR domain. The ncOGT may thereby be able to accept longer unfolded structures inside its longer TPR repeats domain compared to sOGT. Furthermore, these O-GlcNAcylation sites could be presented to OGT with the help of its partners. These interactors could help the enzyme to discriminate between sites to O-GlcNAcylate or not. The key for an efficient prediction may thereupon require a detailed study of OGT's partners.
Acknowledgments TM is a recipient of a fellow from the  "Ministère de l'Enseignement Supérieur et de la Recherche". We thank the "Centre de Formation par Alternance" of the University of Rouen , the  CNRS  ( Centre National de la Recherche Scientifique ) and the  University of Lille  for their support. We acknowledge support from the  High Performance Computing Mesocenter of the University of Lille  and the bioinformatics platform bilille for providing access to cluster and cloud computing resources. We thank  Thierry Lecroq  ( University of Rouen, Normandie, France ) and the "O-GlcNAcylation, signalisation cellulaire et cycle cellulaire" team of the  UGSF lab  for their advice and discussion in this project.
The binary asteroid 65803 Didymos-Dimorphos is the target of the first asteroid deflection test (NASA's Double Asteroid Redirection Test, DART) and the first binary asteroid system that will be characterized by a rendezvous mission (ESA's Hera). The cohesive strength of the fast-spin-primary Didymos is a key factor that could affect the impact outcome and stability of this system. To support the preparation and data interpretation of these missions and gain a better understanding of the formation and evolution of this system, we investigate the structural stability and cohesive strength of Didymos based on current observational information. We use the Didymos radar shape model to construct rubble-pile models consisting of ~40,000 to ~100,000 particles with different arrangements and size distributions.
, 2018) , impact cratering  (Arakawa et al. , 2020; Raducan et al. , 2020) , and tidal disruption  (Holsapple and Michel, 2008; Zhang and Lin, 2020). Moreover, with the wide diversity of rubble-pile asteroids' properties (e. g. , bulk density, packing configuration, spin rate, shape), the effect of cohesion is very complex and remains largely unknown  (Walsh, 2018; Hestroffer et al. , 2019) , calling for further investigation. Following a similar approach developed in paper I, we construct some granular aggregates with Didymos's radar shape model and use the SSDEM version of the N-body code pkdgrav to model the gravitational and contact forces between the components of the aggregates. Thanks to a recent implementation of a cohesive force model in pkdgrav  (Zhang et al. , 2018) , the contacts between the components can undergo a certain amount of tension.
Acknowledgements Y. Z. acknowledges funding from the  Université Côte d'Azur  " Individual grants for young researchers" program  of IDEX JEDI. Y. Z. and P. M. acknowledge support from  CNES  and from the  Acad. 2 and 3 of  Univ. Côte d'Azur IDEX JEDI. D. C. R. , O. S. B. , and H. F. A. acknowledge support from the  NASA   Planetary Defense Program  and the DART mission, under  NASA  Contract # NNN06AA01C  to  Johns Hopkins University/Applied Physics Laboratory. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ). Simulations were performed on Mésocentre SIGAMM hosted at the  Observatoire de la Côte d'Azur  and on the Deepthought2 HPC cluster administered by the  Division of Informational Technology at the University of Maryland.
As stated by Arroyo  Ohori et al. (2018) , BIM-GIS integration methods can be developed at the data, application or process level or through a unified model encompassing -or defining relations between -the two standards. Nevertheless, a completely lossless conversion between BIM and GIS open standard formats is not yet feasible in practice, due to differences between the concerned formats, to variability in the possible ways of creating and managing information in these standards (especially IFC) and to professional practices in the architecture, engineering and construction (AEC) domain  (Stouffs et al. , 2018 , Noardo et al. , 2020a , Salheb et al. , 2019 , Tauscher 2020). In current professional practices, joint use of BIM and GIS data remains a challenge and specific methods have to be developed for most use cases.
Despite these technical difficulties, several uses of GeoBIM, defined as the integration of 3D GIS city models with BIM data  (Noardo et al. , 2020a)  are developed in practice, relying on various data extraction or data conversion processes and use of these data either in a GIS, BIM or third-party data environment (i. e. , tools designed for specific tasks: simulation, asset management or digital twin management for instance). The building permit processing through GeoBIM is today both a relevant subject for many practitioners and the subject of many scientific works, which rely on the joint use of GIS and BIM data  (Noardo et al. , 2020b). Facility and asset management is another investigated topic, where data from GIS, BIM, and, from time to time, both types of datasets, are mobilized  (Garramone et al.
, 2020). Other uses include design support system, sustainability analysis through simulation and visualization of one or more scenarios  (Gil, 2020). Finally, there is a growing field of uses at a "CIM-like" scale (from the scale of a public space to the district scale) relying mostly, if not only, on BIM data, whether representing a singular (but sometimes large) construction project or a project and its surroundings, in a BIM format  (Correa, 2015 , Chen et al. , 2018). From a CIM perspective, this can be considered as a BIM+ approach, as proposed by  Gil (2020). In this context, technical challenges include georeferencing BIM, extracting or converting information from BIM, realizing district-scale analysis in BIM tools and organizing data in BIM so as to be able to use them for various purposes at the district scale.
Most of the BIM+ use cases focus on engineering analyses and simulations, taking advantage of BIM precise urban morphologic data (precise geometry and information on materials and construction composition in general). Sirakova (2018)  studies the microclimate around a metro station,  Delval et al. (2018)  perform acoustic, solar comfort and aeraulic simulations, as well as life-cycle assessment (LCA) analyses. However, larger scale urban analysis is rarely performed with BIM-like models extended at the district or city scale. Delval et al. (2018)  do propose a biotope area factor (BAF) based on a typology of surfaces in a district of La Défense (sealed, partially sealed, semi-open and with vegetation), but do not go beyond the calculation of surfaces areas, which are then considered in the BAF with a specific weight.
Indeed, the data will be available when the district is inhabited. Moreover, real-time data informing on the exploitation of private and public spaces are currently missing. This would be especially important in the case of urban analysis performed on operational districts to assess and envision possible and /or desirable evolutions. In this case, data could be later provided by sensors connected to the CIM. However, to perform urban analyses during the design phase, data could be simulated in order to carry out various prospective scenarios. Social parameters could thus be evaluated, provided simulated or projected data are produced.
Proposed evolutions for the CIM Our intermediate results show that some of the data usually contained in a BIM (impermeable soils, building shapes and materials) and in a BIM+ CIM (permeable surfaces, road surfaces, trees, street furniture, etc. ) can be very useful for urban analyses at the district scale. Nevertheless, some data are missing or not easily exploitable, especially if we want to be able to automate our calculations, which is needed for a usable indicator, i. e. , an indicator that can be easily used by practitioners. We have developed three main solutions to enrich our experimental CIM: -for missing data that can be attached to existing objects in our BIM+ (i. e. , to an existing IFC class), properties or property sets can be added. -for missing data that are at a larger scale than what is modelled in our BIM+ (i.
Therefore, we opted for solutions as light as possible, i. e. , the creation of entities is limited to IfcSpace containing some entities (for instance building and parking lots access) and a few linked properties in a property set (for instance width of access or number of lanes). From our use-centred perspective, the ability to perform urban analysis with a BIM+ CIM is key so that it can be done during the design process. As said, in order to achieve more sustainable and comfortable private and public spaces, many analyses need to be performed on envisioned designs, so as to select the most suitable ones. In this perspective, in many situations, project managers might lack GIS data (new developments or significant district renewals) and will only be able to perform analyses and simulations on BIM + models.
, 2020). This need for entities in various LODs in the design phase raises issues for CIM modelling and calls for a precise description of needs for specific uses. As far as interoperability is concerned, our proposed solutions limit the addition of specific entities in IFC, so that we can easily automate the translation to other formats. Without any BIM+ open data model and/or ontology designed at the urban scale, an operational choice was made together by researchers and practitioners. We do not convert IFC to CityGML but to simpler GIS compatible formats, when needed. Nevertheless, we do add specific IfcSpace that would not be taken into account in already developed conversion tools between BIM and GIS standards. In addition, we are faced a common dilemma for IFC BIM + modellers for some objects that do not yet have specific classes in IFC (for example trees, street furniture, etc.
). For these objects, generic IFC entities such as BuildingElementProxy are used and objects are named according to the CIM nomenclatures so as to be able to filter them for various purposes. This leads to reconsider the sometimes-linear vision of BIM level of details, which are often considered as progressing with a project's development phases, thus preventing many analyses to be performed during the design phase. Our exploratory study shows that with a few key data, urban analyses can be performed early in the process, without requiring many additions to a BIM+ CIM, but that, even for some analysis useful at the design stage, some detailed data can be needed for simulations or calculations.
Similarly, Bertsimas et al. applied novel ML techniques to design an interactive calculator for emergency surgery  [9]. By using data of the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, the authors designed the POTTER application (Predictive OpTimal Trees in Emergency Surgery Risk), to predict postoperative mortality and morbidity  [9]. Differently from the standard predictive models, POTTER accounted for nonlinear interactions among variables; thus, the system reboots after each answer, interactively dictating the subsequent question. A similar tool, predicting eight specific postoperative complications and death within one year after major surgery, was developed and validated by Bihorac et al. [10]. MySurgeryRisk can accurately predict acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 h, mechanical ventilation >48 h, and wound, neurologic, and cardiovascular complications with AUC values ranging between 0.
Surgical performance has also been measured to allow surgeons to learn from their experience and refine their skills. Either by using surgical video clips or by applying virtual reality scenarios, deep learning models were trained to estimate performance level and specific surgical skills  [14, 15]. In the near future, we will be able to personalize training through ML tools such as these. Other studies have used ML for prediction of staging  [16] , for treatment decision making  [17] , and to improve case duration estimations  [18] , further expanding the applicability of ML. Despite these advantages, ML presents several challenges, such as the need to process a large amount of data before it can be analyzed, the necessity of repetitively training the model, and of refining it according to the various clinical scenarios.
Anastomotic leak  [26] , deep venous thrombosis, myocardial infarction and pulmonary embolism were also frequently investigated and results from a recent metaanalysis  [27]  demonstrated that performance of NLP methods in detection of postoperative complications is similar, if not superior, to non-NLP methods, with a sensitivity of 0. 92 versus 0. 58 (p < 0. 001), and comparable specificity. Moreover, NLP models seem to be better than non-NPL models for ruling out specific surgical outcomes, owing to an optimal true-negative identification power. Interestingly, the ability of algorithms to self-correct can increase the utility of their predictions as datasets grow to become more representative of a patient population. These NLP applications are surely beneficial for patient management, providing better understanding of peri-operative data, but it can be a useful tool for surgeons as well, particularly when applied to surgical education.
For example, decoding intra-operative dialogues between residents and faculty, combining NLP and CV, can create and implement a dataset of technique and surgical strategy, thus, creating a real-life textbook of surgery. In addition, NLP has been efficiently used  [28]  to assess Entrustable Professional Activities (EPAs). EPAs describe specific behaviors associated with different training levels of residents and it can potentially enhance the understanding of their training and autonomy in surgical practice. NLP can be used to validate datasets that are the basis of surgical risk predictive models, but the main limit of their widespread use is the non-homogeneity of NLP models and EHRs data entry forms across institutions and countries. A future improvement would entail expansion of registries from local to national and international levels to set algorithms that can be externally validated on various populations.
These newly developed methods for localizing and recognizing objects of interest from images have resulted in significant and unprecedented progress in the field of medical image analysis and understanding  [55]. For example, CNN-based methods continue to play a key role in advancing classification tasks in the medical domain and providing tools for supporting decision making in clinical settings. Pomponiu et al. [56]  used a pretrained CNN to learn the underlying representation of images and extract representative features that help detect skin cancer. Then, they fed the extracted features into a k nearest neighbor classifier (kNN) and reported an average accuracy of 93% with similar sensitivity and specificity. Similarly, Esteva et al. [57]  trained a CNN model using a dataset of 129,450 clinical images representing various skin diseases.
MIL is based on dividing the image into a set of patches (patch-based convolutional neural network), and using the prior knowledge of positive and negative images while the proposed method learns which patches in the positive images are negative  [33]. Three different public datasets were used, and competitive results were reported. Similarly, Schlemper et al. [63]  presented a method incorporating Attention Gate (AG), a mechanism used to help identify salient image regions, into CNN architecture to learn and highlight arbitrary shapes and structures in medical images, and used it for classification and object localization tasks, where promising results were reported in terms of overall prediction performance and segmentation of 3D CT abdominal images. This progress and advancement in solving such complex vision tasks can be attributed to three main factors.
First, the rapid development of the underlying algorithms based on deep neural networks (DNNs) and deep CNNs  [39]. Second, the computing power, and the availability of Graphics Processing Unit (GPU)-based machines and cloud services have made it possible to train very deep models using large volumes of images and unstructured documents. The third reason for this progress in CV is the availability of public medical image datasets, helping to accelerate research and development in this field. Examples include datasets related to musculoskeletal radiographs such as MURA  [64] , which contains 40,561 images from 14,863 studies representing 11,184 different patients. Another example is the colon cancer screening dataset  [49] , containing 40 colonoscopy videos and containing almost 40,000 image frames, lung image datasets  [65] , and others.
The authors used a dataset of 80 videos of cholecystectomies performed by 13 surgeons to train the CNN, and promising results were reported in terms of the model's ability to handle complex scenarios and outperform other traditional tools. Similarly, Yu et al. [67]  used five different algorithms including CNN for handling the of videos of cataract surgery and Recurrent Neural Networks (RNN) for handling time series data with labels. The results clearly showed that deep learning techniques (CNN and RNN) provide better options for learning from time series data and video images, and can provide accurate and automated detection of phases in cataract surgery. Khalid et al. [15]  used deep convolutional neural networks with embedding representation for phase recognition. The authors used 103 video clips of table-top surgical procedures, performed by eight surgeons, including four to five trials of three surgical actions.
Promising results with respect to precision, recall and accuracy were reported in terms of the model's ability to classify performance level and surgical actions. Object detection and tracking is another important aspect of AI in surgery that has progressed due to the latest developments in deep learning and deep convolutional neural networks. Sarikaya et al. [69]  used a dataset of videos from 10 surgeons and applied a deep convolutional neural network to speed up detection and localization of instruments in robot-assisted surgery (RAS). The authors used multimodal CNNs to capture objects of interest and the temporal aspect of the data (motion cues). Results with 91% precision were reported, along with relatively good performance in terms of computational time (0. 1 s per frame). Tracking of objects of interest across different frames is another key aspect of AIS that has also been advanced due to the latest developments in computer vision.
Initial work was performed using a simulation and then transferred to real life. They also used MDPs to accomplish these tasks. Ultimately, these researchers were able to program the robot to identify fake blood and shrapnel and to remove this from real life box trainers. These researchers ultimately made this technology open source and called it dVRL, or da Vinci Reinforcement Learning  [83]. Other groups also used RL with the da Vinci robot simulator, but with reference to model-free RL algorithms, or non-MDP algorithms  [84]. A simple way to understand model-free RL is to think of it as learning based on "trial and error", as opposed to other approaches that are based on "rewards" or, as mentioned above, "models". A group from Johns Hopkins termed their non-model algorithm Hybrid Batch Learning (HBL) and found that they could improve the speed of their robot using this approach.
Data Availability Although various datasets are available in the public domain (as discussed earlier), a very small number of these datasets are designed for autonomous or semi-autonomous surgery. Such datasets are very important for advancing research in AIS and semiautonomous surgeries  [90]. An example of an attempt to prepare a dedicated dataset for AIS is the work presented in  [91] , which presented an experimental framework to evaluate the performance of CV methods in analyzing operative videos to identify steps of a laparoscopic sleeve gastrectomy (LSG). The authors collected videos capturing operations from start to finish for 18+ patients, and using DNNs, they reported an overall accuracy of 85. 6% in identifying operative steps in laparoscopic sleeve gastrectomy, such as port placement, retraction of the liver, liver biopsy, etc.