dataesr/scientific-paragraphs-categorization huggingface dataset:
The scheduler was based on bash scripts (Linux flock locking system) to ensure each file was only treated once by a job. It maintained a list of all the jobs that were completed, running or pending: a pre-processing job and an inference job for each image in the test dataset. It then checked availability of resources on the worker nodes and scheduled jobs on these resources when idle (through SSH connections to the worker nodes). The pipeline was developed using a single IBM Power AC922 server offering 22 IBM POWER9 cores, 512 GB of system memory,4 Volta V100 NVIDIA GPU with 32 GB of graphical memory. This server was purposely designed for machine learning and deep learning tasks, providing good performance and robustness. We used additional machine for the final round of the data challenge.
Due to time constraint a parallelized processing by deploying the solution on a cluster of three IBM AC922 for a total for 60 POWER9 cores and 14 Volta 100 GPUs. The interconnection between the jobs was achieved using a fast networking interconnection technology (InfiniBand EDR switch and adapters). The spectrum scale file system had three advantages: first, it removed the need to perform file transfers that was time consuming for larger 3D CT datasets. Second, it ensured consistency of data. Third it provided sustained high performances required during the challenge and better than network file system.
CT intensities (in Hounsfield unit [HU] ) were clipped between -1200 and 600 to keep air, lungs up to bones densities. Lung segmentation was obtained with a 3D U-NET and CT intensities were normalized to HU. Pre-processing is a powerful way to improve the accuracy of the nodule detection model, it helps remove unnecessary data information, by allowing the next pipeline stages to focus on relevant features for their task. Thus, we implemented a complex preprocessing stage for nodule region proposal network, mostly inspired from state of the art techniques. Lung mask was computed using a 3D semantic segmentation CNN model. A last scan intensity normalization step was performed to end the pre-processing phase.
Lung segmentation using a 3D CNN model The aim of the lung segmentation was to reduce the volume to explore for the next 'nodule detection' pipeline stage and to improve the overall response time and avoid wrong detections of nodules outside lungs. It was implemented using a 3D CNN U-Net model and standards Python Computer Vision Techniques, provided by packages such as Pydicom, SimpleITK, nibabel and OpenCV. We have also used the IBM AC922 technology to train this network, which allowed increasing image resolution of the training dataset by 256³ instead of 128³, which was initial the limit related to 32 gigabits graphical processing unit memory of NVIDIA V100. Once the model has inferred the lungs segmentation map from the resampled scan, a morphological closing was applied to the model's output in order to prevent eventual holes in the lungs (Figures  2, 3 ).
CT data transformation The goal of this phase was to clean and normalize the initial 3D image and also remove unnecessary information for the next pipeline stage (Figure  4 ). Every processed image was then written on spectrum scale shared file system for robustness in case of failure. A completion signal was sent to the scheduler, which marked the inference phase for the image as ready. We resized the 878 CT data sets from Lung Image Database Consortium (LIDC) data to a pixel size of 1. 4 × 0. 7 × 0. 7 mm 3. The LIDC dataset were split in 80/20, giving 700 patients for training, and 178 for validation. For each batch generated, patients were randomly picked and kept according to the presence or absence of one or more nodules in the corresponding scan. Then, for each chosen patient, a random crop of size 128 × 128 × 64 was performed.
Nodule detection A deep learning pipeline was setup to identify nodules candidates for each DICOM dataset. Each candidates' regions have been classified to determine whether it contains a nodule with a volume greater than 100 mm³. We used a Retina-UNet model that showed good performance for object detection  [16]. This network takes advantage of the U-Net architecture and has been transferred successfully into 3D  [17, 18]  and cancer detection (Figure  5 ). The network architecture is described in Figure  2. Most important meta-parameters are the patch size taken for tiling: 128 × 128 × 64. The U-shape of the algorithm has 5 steps (ResBlock) for down sampling then up-sampling. Models are trained using Adam optimizer with a 10^-4 learning rate. For each batch generated, patients were randomly picked and kept according to the presence or absence of one or more nodules in the corresponding scan.
Results During the Data Challenge, we had two hours to process 344 new patients, including extracting the ZIP archive containing the dataset. The workload itself was mostly hold by CPUs units. As each AC922 server had 36 physical cores, hence allowing to pre-process more than 100 images in parallel on three servers. Pre-processing required 3. 5 min per DICOM, mostly used for lung segmentation. Data heterogeneity and data format were handled using dedicated DICOM and annotation loader. The training of the deep learning step lasted 4 days. The training process was stopped earlier in order to respect time constraint. For object detection problem, both classification and localization of bounding boxes outputs needed to be evaluated. The best model used for the challenge was selected by maximizing the average precision up to 0.
Discussion In four weeks, we have built a fully functional solution leveraging open source frameworks, algorithms to win the Data Challenge 2019. This study shows good performances to detect pulmonary nodules greater than 100 mm 3 in a very limited time. Based on observations in high-risk patients from lung cancer screening trials a cut diameter below 6 mm (100 mm 3 ) is proposed by most recent guidelines as an indicator of acceptably low cancer risk (< 1%). AI can help us to tackle this challenge by creating an "augmented radiologist". Machine-learning classifiers are used to predict the malignance and after deep learning, especially the convolutional neural network, who might be suitable for pulmonary nodule classification when characterized by small medical datasets and small targets  [15, [24] [25] [26] [27].
This study gives good performances in terms of all metrics measured including AUROC (0. 9058), sensitivity (89%) and specificity (86%). The developed system presents excellent performance using various databases coming from different hospitals, and with different data acquisition protocols. An improved approach might have been to provide more annotated data, or using GPU with larger memory. With larger memory size, tile size can be larger, and more spatial information can be captured by deep learning algorithms. We did not manage to train correctly the network with tile size of 256 × 256 × 64. With that tile size, we could not load more than 4 tiles on the GPU in the network. With such small batch size, the training procedure would have converged too slowly, but this will probably be possible with GPUs next generation.
In this study we have used tile size of 128 × 128 × 64 tiles with a batch size of 26. A larger batch size would allow reducing the iteration noise and would converge faster. Detection can be improved by segmenting more precisely the lung because some errors rise from true nodules localized on the pleura leading the preprocessing algorithm to consider the nodule outside of the lung. Another way to improve the system is the classification of the nodule candidates. In this study we used machine learning technique because the number of patients was low. With a larger dataset we would be able to use 3D deep-learning system classifiers that show good performance in 3D medical imaging  [28, 29]. Finally, some improvements can be obtained by focusing on some situation leading to errors like pulmonary condensations or non-solid and part-solid pulmonary nodules.
Those situations should be considered as additional classes and separated for the rest of nodules such as their confusion is decreased  [30]. To meet the schedule of the JFR data Challenge, we could manually segment the images in the JFR datasets. Therefore, we decided to use the LUNA16-LIDC DataSet for deep learning model training to detect nodules  [31]. LIDC dataset is similar to JFR one but with high quality segmentation nodules. However, we used the JFR dataset to learn the final status of the patients. Researchers have developed an AI scheme to predict the invasive degree of groundglass nodules by using a residual learning architecture  [31]. They showed that deep learning scheme yielded equivalent or even higher performance when compared with the radiologists, and residual learning-based CNN model could improve the performance in classifying between invasive adenocarcinoma and non-invasive adenocarcinoma nodules  [31].
Radiomics is also useful to detect lung nodules, and allows examining large volumes of complex data to identify precise characteristics or outcomes. In the setting of lung nodules and lung cancer, radiomics is aimed at deriving automated quantitative imaging features that can predict nodule and tumor behavior non-invasively  [32]. Our study has some limitations. First, we were only interested in nodules greater than 100 mm3. The quality of the deep learning detection is highly dependent on the diversity learned in the training dataset, and then nodules will be correctly detected only if closed examples are found in the training dataset. In conclusion, our results are very encouraging, in particular for a precise and fast detection of pulmonary nodules using CT examination. This will need further cooperation between engineers specializing in deep learning, data scientists and radiologists to build solutions suitable to clinical practice.
dataset used to train the model was provided by the Data Challenge 2019 group. It was composed by 1056 * 3D-DICOM CT images, with various resolutions and size (from 512 × 512 × 130 to 512 × 512 × 1300). This dataset access was provided in three batches: 343 DICOM patients with their related nodules annotation for the first month of development, 344 DICOM patients with annotation 3 days before the final and 344 DICOM patients without annotation for the 2 hours final round. The whole dataset size was about 243 Gigabytes in total.
This study proposes a new way to manufacture large lightweight aspherics for space telescopes using Stress Mirror Polishing (SMP). This technique is well known to allow reaching high quality optical surfaces in a minimum time period, thanks to a spherical full-size polishing tool. To obtain the correct surface' aspheric shape, it is necessary to define precisely the thickness distribution of the mirror to be deformed, according to the manufacturing parameters. We first introduce active optics and Stress Mirror Polishing techniques, and then, we describe the process to obtain the appropriate thickness mirror distribution allowing to generate the required aspheric shape during polishing phase. Shape optimization procedure using PYTHON programing and NASTRAN optimization solver using Finite Element Model (FEM) is developed and discussed in order to assist this process.
From an objective function, shape optimization refers to the relocation of points belonging to a given shape while topology optimization determines if a model element has to be removed or not based on the level of its involvement in the model rigidity. The shape optimization is based on the variation of design variables depending on an objective function, as mentioned before, which is usually a scalar subject to design constraints. The objective function can be a combination of design responses to provide a single final response. The main issue of using shape optimization is to generate the correct shape basis vectors. Indeed, each selected model gridpoints, also called nodes, will move along these basis vectors directions. NASTRAN software integrates this functionality as described by Kodiyalam  [23]  but can also be generated by a self-programmed code.
Initial design This first study is limited to a two-dimensional axisymmetric problem using Finite Element Analysis (FEA) software. In this approach, CTRIAX6 elements are required to make the calculation possible. However, CTRIAX6 element defines isoparametric and axisymmetric triangular elements with mid-side gridpoints which do not enable a large deformation due to the elements distorsion. Therefore, remeshing the model at each step is critical to pursue the optimization process. The initial design of the "tulip-form" mirror based on the T S A3 variable thickness distribution is shown in Fig. 6. The preliminary parameters introduced for the initial design are listed in Table  1. We use a Zerodur vitro-ceramic substrate from Schott company as primary mirror material due to its low coefficient of thermal expansion perfectly adapted to space mirror stability.
Results and Discussions The goal of the optimization is to deform the mirror by producing 14. 4 µm RMS of 3 r d -order spherical aberration during polishing process to obtain the targeted aspheric, which is an ellipsoid with a conic constant of -0. 96682, for a full aperture diameter of 1080mm. Optimization parameters, c 1 , c 2 and c 3 coefficients have to be updated for each re-meshing step depending on design responses. DVGRIDs data entries are provided in function of design model but the  displacement of each gridpoints is decreasing as the final shape approaches. Because of the axisymmetrical model we are working on, we produce only axisymmetrical modes such as Focus and spherical aberrations. Fig. 8  shows the deformation resulting from the initial calculation before the first optimization.
Conclusions Thanks to this approach, combining SMP and shape optimization, we show a new manner to produce a conicoid, rotationaly symmetric aspheric, from a 2D finite element model by imposing pre-defined loads configuration and boundary conditions. The shape optimization method will assist the SMP process to reach high quality and very precise surface shapes in a minimum of iterations. In this study, DVGRIDs data entry have been defined in a basic way but they can be described with more degrees of freedom or even as a combination of orthogonal basis such as Zernike polynomials. It is also possible to define other theoretical symmetrical or non-symmetrical shapes to produce more complex optics with a combination of high-order modes. The main following step consists of polishing the thin mirror using the shape optimization method developed in this paper.
The spectra obtained from this technique are similar to absorption ones, and equivalent to a double-pass transmission experiment, therefore no data treatment are required. In the case of a gilt leather, the silver leaf can act as a mirror, so infrared reflection-absorption spectroscopy measurements can be carried out, and absorption-like spectra of the organic layer applied on top can be collected. For simplicity in the text, the measurements will be referred as reflectance FTIR. Comparison of the spectra collected on Fougeroux's varnish in ATR and reflection modes gave similar profiles, with only a slight shift of the carbonyl band and the band around 1200 cm -1 in the ATR spectra, likely induced by the ATR mode. The influence of the varnish thickness on the spectra was evaluated by comparing measurements made by reflectance FTIR on silvered areas with varnish thickness around 1 μm and gilt areas with varnish of various thicknesses (Fig.
Acknowledgments The authors are really grateful to  Prof. Dr Andreas Schulze  from the  Hochschule für Bildende Künste in Dresden  for providing sample of the model varnishes prepared during his PhD research,  Jean Pierre Fournet  for sharing his knowledge around gilt leathers,  Thierry Crépin-Leblond ,  Muriel Barbier , and  Chantal Bor  from the  musée national de la Renaissance  for providing access to their gilt leather collections. We also wish to thank  Claire Pacheco  from the  C2RMF  and  Marie Heran  from the  2CRC  for their help, as well as  Céline Daher  for her advice on this paper. Finally, part of this work was initiated during the  CORDOBA  project funded in France by the  Fondation des Sciences du Patrimoine -Labex   PATRIMA  ( ANR-10-LABX-0094-01 ).
Material and methods A workflow of the strategy to develop the computational systems toxicology model is shown in Figure  1. First, existing biological knowledge was extracted from the ToxCast and Comp-Tox databases  (Richard et al. , 2016; Williams et al. , 2017). Specific information on POP-protein interactions was compiled in order to create the network-based model POP-PPAN. Only pro-al. , 2014)  and facilitate decision-making that supports regulation, innovation and competitiveness  (Naderi et al. , 2014). Further, the development of relevant, reliable and cost-effective methods can help to identify new substances with similar activities. New approach methodologies (NAMs) are animal-free approaches that include computational models, in vitro high throughput screening (HTS), omics studies  (Hartung et al.
, 2017) , and the newly suggested 3S approach (systematic, systemic, and systems biology and toxicology)  (Smirnova et al. , 2018). NAMs promise to capture a better understanding of the MoA of chemicals in humans and may help to support chemical safety assessment  (Andersen et al. , 2019). While they may not be able to completely substitute experimental studies, they can help to prioritize further experiments and thus support the 3Rs principle  (Replacement, Reduction, Refinement). During recent years, toxicological and chemical databases have expanded substantially, and the relevance of integrative systems toxicological methods has been recognized  (Corton et al. , 2019; Audouze and Grandjean, 2011; Audouze et al. , 2013). The development of computational approaches for future toxicity assessment has been recommended by several committees including the U.
S. EPA  (Knudsen, 2013) , the National Research Council  (NRC, 2007; Krewski et al. , 2010) , the European Chemicals Agency (ECHA), the REACH legislation (Registration, Evaluation and Authorisation of CHemicals), and the Organization for Economic Co-operation and Development (OECD). Many small molecules, including drugs and environmental chemicals, are known to affect multiple molecular targets and may deregulate proteins and, subsequently, functional pathways  (Yildirim et al. , 2007; Audouze et al. , 2010). Here, we propose a computational model specific to the "biological space" of POPs Fig. 1 : Workflow of the computational systems toxicology approach to predict adverse effects of suspected persistent organic pollutants (POPs) 1-Model generation: Links between POPs from the Stockholm Convention list and proteins were extracted from the CompTox and ToxCast databases (in blue).
The POP-PPAN model was created, based on a protein-protein association network procedure. 2-Prediction: Known proteins linked to suspected POPs (in red) were compiled. The developed model was then screened for these proteins; binary scores were calculated between known (red) and uncharacterized (blue) proteins for the suspected POP, using a neighbor protein procedure. Biological interpretation of the results was done after integration and over representation analysis (ORA) of various datasets (adverse outcome pathways (AOPs), pathways and diseases). teins for which POPs are defined as "active" and with activity below their cytotoxicity threshold were included. A neighbor protein procedure was developed and applied, based on the binary interaction method used in high-throughput experiments to develop a scoring system that allows the prediction of uncharacterized links between 28 POP candidates (compounds listed or under review in the Stockholm Convention or identified from literature studies) and proteins.
Tab. 1: Chemicals currently listed as POPs under the Stockholm convention For the compound classes (designated with an *), specific chemicals that are good representatives of these chemical families were selected, resulting in a total of 35 compounds. for the input proteins in order to determine the first-order interacting proteins and the surrounding networks. This established a network of interacting proteins that contains the input proteins and proteins from the model. The last step was to calculate a binary score (binS) between each pair of proteins to rank and select putative proteins for the sPOPs. The developed binS is based on quality scoring of protein-protein interactions (PPIs) used in high-throughput experiments (such as yeast two-hybrid and protein-fragment complementation assay) to determine the reliability of the PPIs in large-scale datasets  (Yu et al.
Chemical-protein associations Chemical-protein associations were extracted from the publicly available CompTox and ToxCast databases (last accessed November 2019). The CompTox database  (Williams et al. , 2017) , an online database maintained by the U. S. Environmental Protection Agency (EPA), contains information regarding multiple data types (physico-chemical properties, exposure, biological activities, etc. ) for over 87,500 chemicals. The CompTox dashboard was used to identify the biological assays in which the POPs and sPOPs are active to determine the cytotoxicity information and respective links to AOPs. The ToxCast database is a U. S. EPA program that builds large collections of endpoint data of more than 700 high-throughput assays on chemicals to which humans are potentially exposed  (Richard et al.
, 2016; Kavlock et al. , 2012). The ToxCast dashboard was used to obtain information about the assays extracted from the CompTox database, for example the protein identifiers. For the current investigation, only proteins passing the filtering process using the "cytotoxic-associated burst" region were retained  (Judson et al. , 2016)  (Tab. S1 4  ), as it has been demonstrated that compounds activate assays at concentration levels also observed for cytotoxicity or cell stress  (Judson et al. , 2016). Therefore, only assay activities (AC50) that occurred at a concentration below the "cytotoxic-associated burst" region were extracted.
Generating a high-confidence protein-protein associations network model The relevant POP-protein links compiled and filtered from the CompTox and ToxCast databases were used to create the model. The POP-PPAN model was generated based on the previously published protein-protein association network (PPAN) approach  (Audouze et al. , 2010). Each protein (that linked to at least one POP) was represented as a node, and any protein-protein pairs for which at least one overlapping chemical was identified were linked by an edge. These protein-protein associations were converted into a non-redundant list of associations, i. e. , if proteins A and B are linked, the network may have two associations A-B and B-A. In our approach, only one of these protein pairs was retained to create the model. To reduce noise and retain only the most relevant associations, each protein pair was scored (oS) based on the chemical overlapping information, i.
Neighbor protein procedure To predict novel links between sPOPs and proteins, a neighbor protein procedure was developed, which is a three-step procedure: First, input proteins known to interact with the studied chemical were identified from the CompTox and ToxCast databases. Then, the developed POP-PPAN model was screened The binS(P1, P2) is a low-confidence score, as it has six unshared interaction partners (all the other blue ones). The binS(P2, P3) has a higher confidence score as it has only one unshared interaction partner, i. e. , P1. Therefore, proteins P2 and P3 are more likely to be associated in the POP space, and protein P3 is predicted as a potential target for the chemical of interest. cific biomolecular function and toxicity due to general cell stress. These 20 chemicals were linked to 95 unique proteins via 247 associations; chlordecone was associated with 56 proteins (via 97 assays), and the human estrogen receptor 1 (ESR1) was associated with 10 compounds (via 29 assays) (Tab.