dataesr/scientific-paragraphs-categorization huggingface dataset:
(e) Human blood, feces, and urine were selected using ReDU metadata filters and re-analyzed together using molecular networking. A portion of the molecular network associated with clindamycin is displayed. Nodes are colored by the UBERON ontology in which it was detected: node size reflects the number of MassIVE datasets in which it was detected, node shape indicates whether annotated via library search (annotated, diamond or unannotated, circle). Putatively annotated clindamycin metabolites are indicated using dashed arrows and numbers, blue, corresponding to the proposed structures (1-9). (f) ReDU sample locations (includes public data from samples of environmental, natural products and other cohorts for which this information is provided) colored by number of annotations per file (latitude and longitude), log10-scale, using the ReDU database.
(g) ReDU database (filtered to include human data) analyzed to visualize the distribution of chemical annotations tagged as drug or drug metabolite using 'ili. 12 scladinose azithromycin was detected in blood plasma, breast milk, saliva, urine, and fecal samples. The number of annotations are divided by the number of files per sample type (i. e. UBERON). A distribution map of all drugs in ReDU is provided.
) molecular family matched multiple datasets and sample types (Fig 1e). Using propagation through molecular networking (e. g. delta mass and MS/MS spectral interpretation), we annotated clindamycin metabolites (2-9) Fig S1-S2, Table S2-S3. Ndesmethylclindamycin sulfoxide (6) was observed in multiple sample types across six different datasets. At the repository scale, we can map sample geographical location and identify the number of chemical annotations for each sample (Fig 1f), or locate specific molecules of interest (e. g. drugs) by mapping on the human body offline (Fig 1g, Video S1). These are representative analyses uniquely enabled by the ReDU infrastructure.
Acknowledgments The authors would like to thank the individuals involved in the funding, administration, sample collection, and data acquisition of the public data used in ReDU. The authors recognize the financial support of the  U. S. National Institutes of Health  ( P41 GM103484 ,  R03 CA211211 , and  R01 GM107550 ),  Sloan Foundation (RK) ,  Gordon and Betty Moore Foundation (PD, NB, KLM) ,  American Society for Mass Spectrometry (AKJ) ,  NSF  grant  IOS-1656481  (PCD and AMCR),  Netherlands eScience Center No. ASDI. 2017. 030(JJJvdH),  Krupp Endowed Fund (RC) the  Department of the Navy, Office of Naval Research Multidisciplinary University Research Initiative (MURI) Award , Award number  N00014-15-1-2809  and the  University of California, San Diego Center for Microbiome Innovation SEED  grants.
The approach we propose in this paper is to use an ANN-based deep learning method as a classifier. We train the ANN to learn the relevant areas of an image (i. e. the pupil edges) and irrelevant areas of an image (rest of the image). We then use the output of ANN classification followed by image data compression and its transmission in compressed form for further processing. In the previous publication  (Morozkin et al. , 2018, March) , we presented the classical eye image approach and the NN-based eye image compression approach, neural network construction and training as well as testing framework. It should be noted that based on the neural network  (Demuth, Beale, De Jess, & Hagan, 2014)  the ROI image blocks are classified into 2 classes (blocks contain/does not contain pupil edges). To this purpose, Torch7 (Torch framework ) software (neural network 'nn' and optimization 'optim' packages) is used with 'convnet' (convolutions+2-layer mlp), where 2-layer mlp is multilayer perceptron, and '2-layer mlp' (pure 2-layer mlp) models.
Experimental results During testing we used two NN models: '2-layer mlp' model (Table  2 ) and 'convnet' model (Table  3 ). Both models were trained on training dataset and tested on test dataset (to assess the performance, i. e. generalization property of the trained NN). Since, in this paper, we did not focus on the tuning of the hyperparameters of the NN, we did not use validation dataset. As for the training dataset it is possible to use two modes: (1) train NN on the eye images generated by the simulator and (2) train NN on the real eye images from the According to the application results (Tables  2 3 ), both 'convnet' and '2-layer mlp' models show relatively promising results. In particular, increasing the number of training iterations (from 50 to 400) results in higher classification efficiency and lower classification purity.
We configured the JPEG encoder to keep JPEG quality at 75 (max. value is 100), which allows to keep high PSNR (>36-38 dB), because the decompressed ROI blocks will be further used in the image processing-based eye tracking algorithm. As it can be seen in Figure  10  with the proposed approach it is possible to reach up to 99. 70% of gain in terms of data size reduction even with standard baseline JPEG codec. To these results we should add the same note as to previous results: for the compression algorithms for the eye tracking system, the term 'frame' has more priority over the term 'pixel', because the final performance is measured in the FPS (Frames per Second). This is the reason for calculating the 'size'size of compressed frame and 'Mbps'bit rate at 100 FPS (Figure  10 ). Generally professional eye tracking systems are commercial products.
Average results of ROI image block classification quality (expressed as efficiency/purity) and average bit budget (expressed as bits per pixel) needed to transmit the compressed ROI images with extra blocks removed. Use of '2-layer mlp' model. video file (these eye images are obtained from the camera sensor installed in the Weetsy™ Frame). As for the test dataset it is possible to use two modes: (1) test NN on the eye images generated by the simulator and (2) test NN on the real eye images from the same video file or from the different video file (or set of video files stored in the special database). It should be noted that since training and testing on the simulator-based eye images does not produce interesting results (in this case NN classifies ROI blocks almost perfectly), we omit showing these results.
Secondly, there is also not much interest to train NN on the video and test on the simulator. Lastly, since, in this paper, we do not focus on the improving of the generalization property of the trained NN, i. e. a case, when the NN was trained on the simulator-generated eye images and tested on the set of video files (real images) we do not yet provide the results and consider them as a future work. As a result, for both used NN models we present results of two modes: (1) for training dataset: train NN on the one video file and then test on the same video file (testing quality of the training) and (2) for test dataset: train NN on the simulator and test on the video file (quick preliminary testing of the generalization property).
Secondly, there is also not much interest to train NN on the video and test on the simulator. Lastly, since, in this paper, we do not focus on the improving of the generalization property of the trained NN, i. e. a case, when the NN was trained on the simulator-generated eye images and tested on the set of video files (real images) we do not yet provide the results and consider them as a future work. As a result, for both used NN models we present results of two modes: (1) for training dataset: train NN on the one video file and then test on the same video file (testing quality of the training) and (2) for test dataset: train NN on the simulator and test on the video file (quick preliminary testing of the generalization property). Bits per pixel (bpp) Block FOI Mode Size Epochs 1 % p % ROI FOI (reordered) Training Trained on video, 20 50 97.
85 1. 24 2. 15 1. 33 (38. 14%) 0. 88 (59. 07%) dataset tested on video 100 98. 89 0. 46 2. 15 1. 33 (38. 14%) 0. 89 (58. 60%) 200 99. 19 0. 48 2. 15 1. 33 (38. 14%) 0. 89 (58. 60%) 400 99. 58 0. 49 2. 15 1. 33 (38. 14%) 0. 89 (58. 60%) 10 50 70. 64 20. 12 2. 15 1. 06 (50. 70%) 0. 50 (76. 74%) 100 70. 14 19. 65 2. 15 1. 04 (51. 63%) 0. 50 (76. 74%) 200 70. 87 17. 54 2. 15 1. 04 (51. 63%) 0. 49 (77. 21%) 400 76. 69 14. 06 2. 15 1. 07 (50. 23%) 0. 52 (75. 81%) Validation Trained on simulator, 20 50 79. 08 20. 33 2. 15 1. 35 (37. 20%) 0. 90 (58. 17%) dataset tested on video 100 81. 09 17. 74 2. 15 1. 35 (36. 91%) 0. 89 (58. 49%) 200 82. 05 16. 87 2. 15 1. 35 (36. 95%) 0. 89 (58. 49%) 400 85. 50 20. 79 2. 15 1. 38 (35. 55%) 0. 94 (56. 03%) 10 50 61. 45 28. 12 2. 15 1. 13 (47. 41%) 0. 51 (76. 05%) 100 63.
90%) 0. 86 (59. 99%) Validation Trained on simulator, 20 50 89. 90 11. 68 2. 15 1. 37 (36. 26%) 0. 90 (58. 05%) dataset tested on video 100 92. 83 16. 09 2. 15 1. 42 (33. 67%) 0. 96 (55. 07%) 200 93. 07 16. 51 2. 15 1. 43 (33. 49%) 0. 97 (54. 81%) 400 93. 15 16. 77 2. 15 1. 43 (33. 37%) 0. 97 (54. 65%) 10 50 62. 06 38. 45 2. 15 1. 18 (44. 92%) 0. 55 (74. 51%) 100 62. 71 44. 13 2. 15 1. 25 (41. 94%) 0. 59 (72. 64%) 200 62. 58 43. 91 2. 15 1. 26 (41. 25%) 0. 59 (72. 57%) 400 67. 72 48. 18 2. 15 1. 32 (38. 43%) 0. 65 (69. 65%).
1 ). A reconstruction algorithm is then used to build the 3D internal structure of the sample (i. e. the spatial distribution of µ(x, y, z)) from the radiographs series. Those algorithms may be based on two different approaches: the software may either solve a set of linear equations to compute µ(x, y, z) (algebraic approach) or use a backprojection of the detected intensity by a Fourier transformation (analytical approach). Although the analytical approach is faster to compute, it requires a full dataset with no missing views from the radiographs series. This method is referred to as filtered backprojection reconstruction. For more details about these techniques and about their theoretical backgrounds, see  [10, 19]. The results presented in this paper were obtained by Figure  1 : X-ray tomography principle a filtered backprojection approach.
Postprocessing tool A time series of radiographs sets is delivered by the experiments detailed above. The reconstructed volume of the central part of the sample for the initial state of load is showed in the Fig. 7a. Those raw grayscale images, display 3 visible phases (see Fig. 6b  to Fig. 6d ): the adhesive, the air porosities and the glass beads introduced by the manufacturer. The raw data is initially encoded in 16 bits. In order to reduce the volume of data and thus facilitate post-processing steps, the raw 16-bits data were converted to 8-bits grayscale levels. This step results in a lighter dataset and in faster processing steps without damaging excessively the quality of the data to observe. The data obtained with X-ray tomography were then post-processed in order to extract the following information: the spatial distribution and rate of each phase as well as their evolution during the different load applied.
Preprocessing As it may be seen in Figs. 6b to 6d, the radiograms include a large part of the substrates and some blank space around the samples. Hence, the reconstructed volume also contains these elements, resulting in some useless volume slices (with only Aluminum showing). The adhesive being absent from these pictures, they are removed from the dataset. By doing so, it is possible to reduce the number of slices to be analyzed from several hundreds to 65.
Filtering and thresholding The filtering and the thresholding steps are designed to smooth the signal (i. e. remove as much noise as possible from the flattened signal) and to identify precisely the transition between the adhesive and the porosities in terms of grayscale levels (Fig. 11 ). To correctly tune the corresponding parameters (t f and n s , see Fig. 9 ), an optimization loop is performed using Matlab TM. The filter used in the process is a classical 2D median filter  [23] , characterized by its size t f. The main advantage of this formulation is that it is edge-preserving  [24]  (i. e. it will preserve the edges between the adhesive and the porosities), which is paramount to accurately detect the porosities included in the adhesive. The threshold n s is trivially defined as the minimal grayscale level shift to consider to detect a microstructural phase switchover.
The obtained results are summarized in Tables  3  and 4 , where i and σ i are resctively the mean value and the standard deviation of the quantity i. Even though fairly similar results are obtained in terms of porosity ratio p obt with both methods (Table  3 ), it is clear from Table  4  that the M1 method is not able to accurately detect the correct number of porosities in the artificial datasets. Quantitatively, the error on the dectected number of porosities is above 170% using M1, whereas it is only of 6% using M2. This is explained by the fact that M1 tends to detect erroneous porosities for small diameters (Fig. 13 ). p th [%] p obt [%] σ p obt [%].
The visualization of these simultaneous phenomena may be performed on the reconstructed volume using FiJi software  [26] , as presented in Fig. 20. The global increase in volume of the porosities in the adhesive is easy to visualize in Fig. 20. This suggests that it is a phenomena which occur in the entierety of the joint, and not only in specific, predisposed locations. Moreover, it is possible to see in Fig. 20  that there are a few locations where the porosities seem to be more concentrated. The adhesive bond is therefore weaker in these spots, and one may hypothesize that such concentrations of porosities could trigger mechanical failure mechanisms (microcracking, stress concentrations, localized plasticization, etc. ) when stressed, and lead to the premature ruin of the assembly. Finally, the porosities in Fig.
Conclusion Porosities are commonplace defects in adhesive joints, formed during the mixing of the adhesive and during the bonding step. As such, adhesively bonded samples were made in order to investigate the porosity state within the bulk of their joints. The influence of tensile loadings on this porosity state was characterized using in-situ X-ray microtomography, until the mechanical failure of the samples. In order to process the tomographic datasets, a processing tool was designed and calibrated on artificial datasets. This calibration was performed using optimization algorithms to tune the parameters of this processing tool: the size of kernel of the median filter, and the grayscale threshold used to identify the porosities in the reconstructed volumes. Two methods were proposed for this parameters optimization, and their performances were discussed.
Introduction Time Domain Astronomy is currently a scientific priority of the International Virtual Observatory Alliance (IVOA). The IVOA is the international initiative that coordinates the development of astronomical interoperability standards. The most common data challenges related to Time Domain Multi-Messenger Astronomy are the following:  Multi-wavelength/messenger approach  Transmission of alerts and follow-up observations  Visualisation & navigation thought the data  Coordination & transmission of information The Virtual Observatory, and tools provided to astronomers, should match the common needs of the different scientific use cases. In the following sections, we will give some highlights of the ASTERICS DADI work on enabling Time Domain Astronomy in the VO, and on how we are coordinating the international effort in IVOA, and information about other relevant developments, in particular at the Strasbourg astronomical Data Centre CDS.
Visualisation & navigation through the data Hierarchical Progressive Survey (HiPS)  [6]  and Multi-Order Coverage map (MOC)  [7] , both IVOA specifications widely used by the community, allow the users to quickly navigate through datasets in space (collection of images or catalogues). HiPS is a hierarchical scheme for the description, storage and access of sky survey data, which allows the possibility of creating HiPS-cubes where the third dimension typically used is the wavelength, however nothing prevents users to create HiPS-cubes where the third dimension is a temporal one to create a time-view of the sky. Based on a hierarchical approach of the sky a MOC gives the approximate area covered by different missions, enabling the capacity of comparing coverage maps of large datasets in a seamless way.
PoS(Asterics2019)056 Exploring Time Domain Multi-Messenger through the VO A quick look at the spectral energy distribution of a source can bring light to its nature. With this idea in mind, photometric viewers are widely used. Based on the location on the sky and a search radius around that position, fluxes are displayed against frequency, for data in well defined filters 4. Information on filter characterization has been collected in a standardized way 5. A quick look at the light-curve of an object can also help to learn about its nature. Ideally photometric viewers would have the capability to include the time as a possible extra dimension, to become a light-curve viewer. In a first step a user would perform a cone search around a position and within a certain interval of time. The key point for being able to create such a tool or service is to have a standard annotation for time 6 and to transform the time values into a pivot format in which the data can be compared.
Coordination & transmission of information Planning observations involves knowledge on target visibility, telescope time availability, and prioritization of the scheduled time. Different visibility and schedule tools exist but they have inputs and outputs which are very heterogeneous. Some level of standardization would facilitate the planning and organization of observations. With that aim two protocols are being developed within the IVOA framework: an Object Visibility Simple Access Protocol 7 and an Observation Locator Table Access Protocol 8. A facility database is also being built with the aim of documenting observatories, locations, telescopes, instruments, filters, etc. that are (or have been) available to the community  [8]. In this context, one of the ideas that is also being discussed and prototyped is the possibility of having an exchange platform dedicated to such type of follow-up.
Conclusion Time Domain Astronomy is a scientific priority of the IVOA. Data challenges related to Time Domain Multi-Messenger Astronomy involve multi-wavelength/messenger approach, fast transmission of alerts and follow-up observations, providing tools for rapid and easy visualisation and navigation thought the data and finally soothing coordination & transmission of information in international collaborations. IVOA standards have been and continue to be developed to facilitate the astronomers' and facility managers' experience in all these aspects. In this paper we have summarised some of the standards relevant for Time Domain Astronomy, a comprehensive list of all the IVOA standards can be accessed through the IVOA Documents repository under http://ivoa. net/documents/.
This paper shows the valuable interest of the data measured by AMULSE instrument during the APOGEE measurement campaign, specifically for the vertical profiles of CO 2 and CH 4 , which remain very sparse. We have carried out several experiments showing that the measured profiles have several applications: for the validation of simulations of infrared satellite observations, for evaluating the quality of chemical profiles from Chemistry Transport Models (CTM) and for evaluating the quality of retrieved chemical profiles from the assimilation of infrared satellite observations. The results show that the simulations of infrared satellite observations from IASI and CrIS instruments performed in operational mode for NWP by the Radiative Transfer Model (RTM) RTTOV are of good quality. We also show that the MOCAGE and CAMS CTMs modeled ozone profiles fairly accurately and that the CAMS CTM represents the methane in the troposphere well compared to 1.
Then in Section 3, we will describe the different characteristics of the AMULSE instrument as well as comparisons with another instrument. Finally, we will use the data from the APOGEE measurement campaign on one study case to evaluate the sensitivity of infrared observation simulations on CO 2 , CH 4 and O 3 information and we will use the in situ profiles to evaluate a priori profiles from the CTMs and retrieval profiles by 1D assimilation experiments.
Tethered balloon application Tethered balloons offer great opportunities in order to characterize the temporal atmospheric evolution of the three measured species up to 800 m altitude. In such application we can control the motion speed of the balloon, we can have a bigger payload (which depends on the type of the balloon used), we can even acquire data from a fixed stationary point. Tethered balloon is a carrier that allows measurements to be made between a fixed tower and under-aircraft measurements. The costs of a captive balloon are much lower than the installations mentioned above. A campaign was conducted to monitor the evolution of the atmospheric boundary layer early in the morning at sunrise. We carried out 21 ascents/descents (up to 50 metres above sea level). The time of an ascent is about 6 minutes, which results in a spatial resolution of between 20 and 50 cm.
Objectives One of the goals is to carry out weather balloon measurements in co-location with the IASI satellite, in orbit around the earth, developed jointly by CNES (Centre National d'Etude Spatiale) and EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). The measurement made by AMULSE instrument will provide comparative data that can be used to validate CNRM/Météo France meteorological models. In addition, the combination of all these measures offers an opportunity to analyse physico-chemical processes that have not yet been studied in the stratosphere and the interface between the troposphere and the stratosphere, commonly known as UTLS (Upper Troposphere -Lower Stratosphere). To achieve these objectives, we carried out measurements from the GSMA site (49 • 14'29.
The trajectories of the balloon are updated in real time by the rawinsonde RS92  the flight is done using both satellite and radio communications. A Vaisala radiosonde RS92 is connected in order to send instantaneous P, T, relative humidity and GPS data to a ground mobile station to inject those data in the trajectory model and hence ensure the tracking/recovery of the instrument. Note that the trigger release system can be controlled by either home-made smartphone application or web application using Iridium satellite communication system. Theses applications can also track AMULSE position as the latter transmits its GPS position, pressure and temperature data every 5 min ensuring redundant retrieval system.
Use of data from the APOGEE measurement campaign The data produced during the APOGEE measurement campaign have different applications. Initially, they were used as satellite validation data for the IASI and CrIS infrared sounders. They were then used to evaluate the quality of vertical ozone and methane profiles extracted from the MOCAGE and CAMS Chemistry Transport Models. Finally, they were used as verification data to compare the ozone profiles returned in one-dimensional variational assimilation experiments (1D-Var) with the a priori ozone profiles from MOCAGE and CAMS. the CrIS spectrum divided into three parts: between 650 and 1095 cm -1 with a spectral resolution of 0. 625 cm -1 , between 1210 and 1750 -1 (1. 25 cm -1 spectral resolution) and between 2155 and 2550 cm -1 (2. 5 cm -1 spectral resolution).