dataesr/scientific-paragraphs-categorization huggingface dataset:
17  , consisting of 332 human proteins. The remainder of the procedure used to build the virus-host PPI is the same as before. Figure  2  shows a visualization of the human interactome where proteins targeted by viruses are highlighted. It is worth noting that viruses target a certain number of proteins which have interesting functions in the interactome. In fact, based on our dataset, TP53 (Tumor Protein p53, NCBI Gene ID: 7157) is the most targeted node: it is responsible for inducing changes in metabolism, DNA repair, apoptosis and cell cycle arrest, and its mutations are associated with several human cancers. Other relevant targets (see Fig. 2 ) include GK (Glycerol Kinase, NCBI Gene ID: 2710), an important enzyme contributing to regulate metabolism and glycerol uptake, and its mutations are associated with glycerol kinase deficiency; TBP (TATA-box Binding Protein, NCBI Gene ID: 6908), which composes the transcription factor IID, which coordinates the activities of more than 70 polypeptides to initiate the transcription by RNA polymerase II; TLR4 (Toll Like Receptor 4, NCBI Gene ID: 7099), relevant for recognizing pathogens and activating innate immunity; STAT2 (Signal Transducer and Activator of Transcription 2, NCBI Gene ID: 6773), acting as a transcription activator within the cell nucleus: it is likely that it contributes to block interferon-alpha response by adenovirus; PTGS2 (Prostaglandin-endoperoxide Synthase 2, NCBI Gene ID: 5743), a key enzyme involved in the process of prostaglandin biosynthesis; IFIH1 (Interferon Induced with Helicase C domain 1, NCBI Gene ID: 64135), encoding MDA5, an intracellular sensor of viral RNA responsible for triggering the innate immune response: it is fundamental for activating the process of pro-inflammatory response that includes interferons, for this reason it is targeted by several virus families which are able to hinder the innate immune response by evading its specific interferon response.
Gene ontology, reactome pathway, and clustering analysis. The compar-eCluster function in clusterProfiler R package was used to perform the Reactome pathway enrichment analysis on viral target proteins with a p value cutoff of 0. 005. The parameters "enrichPathway" and "enrichGO" with ontology "BP" were used to retrieve enriched Reactome pathways and biological processes from Gene Ontology, respectively. They are based on hypergeometric distribution to calculate enrichment test for GO terms and Reactome pathways, determining whether some protein sets within the same Reactome pathway or defined by particular GO terms are more represented than expected randomly. Enrichment analysis output results were binarized and clustering was performed using pheatmap R package with binary distance and complete method.
75 km from the monitoring source system (Fig. 2a-c ). Because the cable was folded back in some parts, the total survey line is ~ 700 m. The cable was in trenches at a depth of ~ 15 cm to record the horizontal motion parallel to the cable (Figure  S2 ). We recorded the DAS data from 19 September to 5 November 2019. The spatial sample interval or channel spacing was set to 1 m, while the gauge length was 4 m which acts as moving average filter. This experiment used two types of fiber-optic cables, which yielded identical results. The sampling rate was 1 ms in this onshore monitoring experiment. Deployment at offshore area (seafloor cable). To evaluate whether DAS can be applied to the seafloor cable for offshore monitoring purposes, we used fiber-optic cable deployed on the seafloor off the Kamaishi, northeast Japan (Fig.
2d, e ). Many CO 2 storage projects are suitable for offshore environments, especially in the countries close to the ocean (e. g. , Norway, Japan and Indonesia). We recorded the DAS data from 10 October to 23 November 2020. The spatial sample interval was set to 10 m, while the gauge length was 20 m in this test survey. We recorded the seismic signal for the 60 km cable by considering the attenuation of signal propagation of the DAS system. Therefore, we successfully recorded the monitoring signal at 6,000 channels. The sampling rate was 2 ms in this offshore experiment.
Acknowledgements This work was conducted under the " Sustainable CCS  project" of the  Ministry of the Environment, Government of Japan. T. Kunitomo  and  T. Watanabe  ( Nagoya University ) and  J. Kinoshita  ( Kyushu University ) supported the deployment of the monitoring system and seismometers. We are grateful to the  National Research Institute for Earth Science and Disaster Prevention (NIED)  for providing us with the Hi-net data. We thank  Y. Yamaura  ( West Jec ) for supporting the deployment of seismometers. We also thank to  Kyushu Electric Power CO. , INC. , because the company provided the geothermal data shown in Fig. 8b. This study was partially supported through the  Japan Society for the Promotion of Science KAKENHI  Grants  JP20H01997  and  JP20K04133.
Methods. This paper proposes a new method, coined MCCD (Multi-CCD PSF modelling), that creates, simultaneously, a PSF field model over all of the instrument's focal plane. This allows to capture global as well as local PSF features through the use of two complementary models which enforce different spatial constraints. Most existing non-parametric models build one model per Charge-Coupled Device (CCD), which can lead to difficulties in capturing global ellipticity patterns. Results. We first test our method on a realistic simulated dataset comparing it with two state-of-the-art PSF modelling methods (PSFEx and RCA). We outperform both of them with our proposed method. Then we contrast our approach with PSFEx on real data from CFIS (Canada-France Imaging Survey) that uses the CFHT (Canada-France-Hawaii Telescope).
This has been widely used for the HST (Hubble Space Telescope) (TinyTim software, Krist 1995), though it was later shown that a relatively simple PSF estimation from the data, which does not assumes a model for the instrument, provides better fits to stars for both photometry and astrometry measurements  (Hoffmann & Anderson 2017). Furthermore, such a solution can not readily be applied to ground-based observations, where the atmosphere plays an important role and adds stochasticity to the PSF. Other methods, based on imaging-data only, use unresolved stars in the field as direct measurements of the PSF, and reconstruct an accurate PSF from these observed stars. A very impressive range of methodologies have been proposed in the past to perform this task: Moffat modelling  (Bendinelli et al.
1988) , polynomial models  (Piotrowski et al. 2013; Bertin 2011) , principal component analysis  (Jee et al. 2007; Schrabback et al. 2010; Gentile et al. 2013) , sparsity  (Ngolè et al. 2015) , neural networks  (Herbel et al. 2018; Jia et al. 2020b,a) , and optimal transport  (Ngolè & Starck 2017; Schmitz et al. 2018). The PSFEx software  (Bertin 2011)  is the most widely used. The Resolved Components Analysis (RCA) method  (Ngolè et al. 2016; Schmitz et al. 2020)  was proposed in the framework of the Euclid space mission in order to deal with PSFs that are both undersampled and spatially varying in the field. Cameras are often mosaics of several CCDs, but all mentioned methods can only build one PSF model per CCD, with the exception of the approach used by  Miller et al. (2013) , and the recently proposed approach by  Jarvis et al.
PSFEx and RCA PSFEx  (Bertin 2011 ) is a standard and widely-used software 1. RCA  (Ngolè et al. 2016 ) is a more recent method that was developed with the Euclid Visible Imager's PSF in mind, to deal with the under-sampling of the observed star images. The software is also freely available 2. It is important to remark that these two approaches rely solely on the observed data: they are blind with respect to the optical system involved in the image acquisition process.
PSFEx For a given exposure, this method builds one independent model for each CCD. It was designed as a companion software for SExtractor  (Bertin, E. & Arnouts, S. 1996) , which builds catalogues of objects from astronomical images. Each object contains several measurements that PSFEx then uses to describe the variability of the PSF. Each selected attribute follows a polynomial law up to some user-defined maximum polynomial degree d. The model for CCD k can be written as: ĤPS FEx k = S k A k , (2) where A k has m rows corresponding to the number of polynomials used, and n k star columns corresponding to the number of observed stars used to train the PSF model. The matrix S k is learned during training and has n 2 y rows (the number of pixels in each image), and m columns. For example, if d is set to 2 and the attributes chosen are the pixel coordinates (x, y), each column i of the A k matrix corresponding to the star i at location u k i = (x k i , y k i ) is a k i = [1, x k i , y k i , (x k i ) 2 , (y k i ) 2 , x k i y k i ] T.
1. We repeat the procedure with the global component matrix, Ãk , in order to obtain Ãk,u , another column vector with the interpolated values. At this point we note that we handle the global and the local contributions independently. Once we have calculated the two interpolated vectors, the reconstructed PSF is obtained following the MCCD data model as can be seen in the next equation Ĥ(u) = S Ãk,u + S k A k,u. (26) We found that restricting the N RBF neighbours to a single CCD for the global components gave better results. This might be due to the fact that the global components are able to capture some of the discontinuities from one CCD to another and therefore the interpolation is degraded when using stars from different CCDs. The number of neighbours N RBF should be chosen as a function of the available number of stars per CCD in the training set and as the RBF kernel chosen.
Training set Our simulation pipeline considers a Moffat PSF profile with normalised flux drawn using the Galsim software 7  (Rowe et al. 2015)  for each position in the exposure. To simulate the PSF shape variation, we used two radial analytic functions which define our ground truth shape ellipticities distortions. Shearing stars leads naturally to a size variation. Fig. 3  shows the resulting e 1 , e 2 and size maps. Our pipeline performs the following steps: 1. Simulate Moffat stars with a size fixed to the mean size measured in the real exposure. 2. Shear the simulated stars as a function of their position using the two analytical functions. 3. Apply a random sub-pixel shift following a uniform distribution centred in zero. 4. Apply a binning to get a 51 × 51 pixels image, with a pixel size equivalent to CFIS MegaCam's maps, i.
e. 0. 187 arcsec. 5. Add a constant white Gaussian noise to the images, with standard deviation σ, derived from the desired SNR level SNR = y 2 2 σ 2 p 2 , ( 27 ) where y is the image postage stamp consisting of p 2 pixels. Each experience will consist of a constant SNR value, as we will later see, that will be drawn from the set {10, 30, 50, 70}. As PSFEx was designed as a companion software of SExtractor we need to follow a different procedure to generate the simulated data. We first need to process our simulations with SExtractor, so that the catalogue produced can be used as inputs for PSFEx. To accomplish this we mimic a complete CCD so that SExtractor is able to process it. We create star images as we already described for the MCCD method but without noise 7 https://github. com/GalSim-developers/GalSim as it will be added later.
Model parameters Based on experiments with simulated and real data, we have chosen the following parameters -PSFEx: we use the following configuration: PSF_SAMPLING 1. 0 PSF_SIZE 51,51 PSFVAR_KEYS XWIN_IMAGE,YWIN_IMAGE PSFVAR_GROUPS 1,1 PSFVAR_DEGREES 2 PSFVAR_DEGREES refers to the maximum polynomial degree, and, XWIN_IMAGE and YWIN_IMAGE, to the windowed centroid positions in pixel coordinates. The PSFEx software 8  does not include publicly an interpolation method, so we use an available PSFEx interpolation module 9. -RCA: we set r equal to 8 local components, the denoising parameters K RCA σ to 1, and the other parameters to their default value from its official repository 10. -MCCD: we use the same parameters as RCA for the local component, and a maximum polynomial degree of 8 for the global components.
One can see on the right column of the residual maps in Fig. 5  that PSFEx's ellipticity residuals follow the global pattern from the dataset. This means that is not captured in the model, showing some of the difficulties found when modelling a global ellipticity pattern using independent models for each CCD. The MCCD-RCA algorithm, which builds up a model for the whole focal plane, does a better job in capturing the global ellipticity pattern. MCCD-RCA's residuals are smaller and less correlated with the dataset's pattern. Concerning the third row of Fig. 5 , where the size of the simulated PSFs is practically constant, we observe that MCCD-RCA has slightly larger errors when the training star density is low, as in the bottom-right corner (see Fig. 2 ).
Dataset We analyse a subset of around 50 deg 2 from the whole CFIS survey area, that, in total, will span 5000 deg 2. It corresponds to the subset named W3 described in  Erben et al. (2013) , and includes 217 exposures. Each CCD from each exposure has been processed independently with SExtractor. The stars were selected in a size-magnitude diagram, in the magnitude range between 18 and 22, and a Full-Width Half Maximum (FWHM) range between 0. 3 and 1. 5 arcsec. In order to validate the PSF models, we randomly split the stars into a testing and a training dataset, trying to estimate the first set of stars while constructing our model only with the second. The training dataset is composed of 80% of the detected stars and the test dataset of the remaining 20%. We consider a fixed threshold on the number of training stars per CCD, meaning that if the number of training stars in a given CCD is less than 30, we discard the CCD.
Conclusion We have presented a family of non-parametric PSF (Point Spread Function) modelling methods coined MCCD, including its bestperforming extension MCCD-HYB, which are built upon the existing RCA (Resolved Component Analysis) method and are capable of constructing PSF models that span all the CCDs 11 https://github. com/CosmoStat/mccd (Charge-Coupled Device) from an instrument's focal plane at once. Naturally, the use of more stars for the training allows us to build more complex models that can capture evasive features. Our model is composed of global components, spanning all the CCDs, and local components that are CCD-specific. By using this structure we can better capture global patterns and features that might be lost when using only a local model like in RCA or, the widely used algorithm, PSFEx.
The method was first tested with a set of simulated PSFs following a real star spatial distribution over MegaCam's focal plane, an instrument from the CFHT (Canada-France-Hawaii Telescope). Its use leads to better performance in all the evaluated metrics when compared to PSFEx. We then tested the method on a set of real CFIS images, an imaging survey based on CFHT, in order to confirm that it can handle real data. Our method achieves a smaller pixel RMS (Root Mean Square) residual than PSFEx and the estimated model is considerably less noisy. The performance gain of the MCCD methods over PSFEx is higher when using our simulated dataset than when using the real dataset. This can be explained by the fact that our simulated dataset shows more intricate variations in the PSF than the real data does, and MCCD is better at capturing such strong variations.
Acknowledgements. The authors would like to thank the anonymous referee for the fruitful comments on the paper. This work is based on data obtained as part of the  Canada-France Imaging Survey , a  CFHT large program  of the  National Research Council of Canada  and the  French Centre National de la Recherche Scientifique. Based on observations obtained with  MegaPrime/MegaCam , a joint project of CFHT and CEA Saclay, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers (INSU)  of the  Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii. This research used the facilities of the  Canadian Astronomy Data Centre  operated by the  National Research Council of Canada  with the support of the  Canadian Space Agency.
It must be emphasized that the confidence metric results depend on the similarity measure used to compute cost volume. Moreover, some methods perform better near discontinuities. (Poggi et al. , 2017)  show that machine learning based metrics offer better results compared to classical metrics, and especially deep learning approaches. But these are very time consuming and they required a large dataset with ground truth. Another difficulty is their ability to generalize depending on the training dataset. For remote sensing images, at the present time, there are few datasets  (Bosch et al. , 2016; Bosch et al. , 2019)  and they focuses on urban areas. This can be a limitation for the usage of these methods.
Dataset description We use two sets of input images: • Selection of image pairs originated from Data Fusion Contest (DFC) dataset  (Bosch et al. , 2019 ) (World-View 3 images) • Image pair on Montpellier (Pléiades images). Noisy disparity maps are generated with the Pandora's pipeline shown in figure  14. Ground truth (GT) are generated with the methodology described in  (Cournet et al. , 2020). One difficulty is the temporal inconsistencies between lidar and images, especially with regard to trees. This time gap can impact the quality of the neural network training. Moreover, as noisy disparity maps contain a lot of errors, neural network will favor a smoothing on the whole image in order to correct disparity changes. cause cross validation does not detect all errors. The disadvantage of classical methods is that it uses SGM directions to locate valid points.
5. 2. 3 TrainingFor training, images are divided into patches of size 256×256. The loss function used is L1 loss modified to constraint the network to avoid adding errors:Loss = |denoised -GT | * W W = 1. 25 if |denoised -GT | > |noisy -GT | 1 otherwiseFirst results have shown the advantage of combining both image sets. With the same network architecture, results were different for both datasets. With the DFC dataset the denoising is more aggressive. The disparity maps tend to be very smooth but coarse errors are well fixed. With Montpellier dataset the denoising is lighter. Disparity maps are less smoothed and building edges are preserved. But large errors are not corrected. So, each database has its own advantages and disadvantages. In order to combine advantages, training is performed by merging the datasets.
The addition of ambiguity gives suitable results, better than original U-Net architecture. It allows to preserve building edges, whereas without ambiguity building edges are trimed. Compared to classical methods, disparity maps de- noised with deep learning methods are smoother but details are preserved. On figure 15, we can notice that errors introduced with denoising using deep learning methods are mainly located on trees. It derives from the training database where there exists a time shift between images and lidar reference.
Acknowledgements The authors are very grateful to the referees for their careful reading of the manuscript. We are particularly indebted to the referee who pointed out a gap in the original argument of Section. Bridging this gap lead to substantial improvements in the clarity of the exposition. MH gratefully acknowledges financial support from the  Leverhulme trust  via a  Leadership Award , the  ERC  via the consolidator grant : CRITICAL , and the  Royal Society  via a research professorship. YB and MH are grateful to the  Newton Institute  for financial support and for the fruitful atmosphere fostered during the programme "Scaling limits, rough paths, quantum field theory". We would also like to thank  Xue-Mei Li  for numerous discussions about stochastic analysis on manifolds.
INTRODUCTION This dataset was built with the purpose of clarifying the diagnosis of borderline personality disorder (BPD) in ageing. This clarification is necessary for two reasons. First, dimensional approaches have taken an important standpoint as an alternative model for the diagnosis of personality disorders. This approach is fine grained and more sensitive than the categorical model  (First, 2006). Developed in connexion with the theories of personality, the dimensional approach makes it possible to refine psychiatric practise  (First, 2005; De Fruyt et al. , 2017). In contrast, this model is criticised for its difficulty of application and conceptualisation, as well as for its validity (Chaine and  Guelfi, 1999). The significant variability of the profiles isolated only on the basis of the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5, American Psychiatric Association, 2013), requires a combined use of tools to assess the actual presence of BPD, because it is common to find a potential co-morbidity with other disorders such as depression, anxiety or post-traumatic stress disorder, sometimes extreme  (Frost et al.
, 2018)  as well as in its management  (Gordon et al. , 2019). This dataset focuses on the dimensions of personality theorised by Kernberg  (Kernberg, 1975; Kernberg and Caligor, 2005)  and uses a related test: The Inventory of Personality Organisation (IPO)  (Kernberg and Clarkin, 1995). Kernberg's theoretical model situates the borderline personality on a continuum between normal and pathological where the different organisations (neurotic, borderline and psychotic) correspond to specific symptomatological and aetiological criteria. The nature and severity of the disturbances experienced by the participants are assessed according to the level of integration of the identity, the mobilised defence mechanisms and the nature of the reality event. The main difficulty in terms of assessment lies in defining the cut-off scores on the normal-neurotic-borderline continuum.
This dataset was built with 444 responses to a self-assessment questionnaire that were collected using the validated French form of the IPO  (Biberdzic, 2017). Data collected come from men and women over 65 years old. Items were rated on a 5-point Likert scale ranging from "never true" to "always true. " Raw data were the sum of the items corresponding to IPO's scales. Age, gender, level of education, and co-morbidities were also indicated.
Design This research used a cross-sectional design. The research protocol was developed in collaboration with the Universities of Angers and Lyon 2; and as it was considered as routine by the institutions, it did not have to be submitted to the ethics committee. The protocol has been ethically and methodologically validated and was carried out with the approval of the Gerontology Medical Unit of Lucien Hussel Hospital Center. The research population was evaluated in two French nursing homes, one department of geriatric medicine and one longterm care hospital. The establishments concerned authorised this research. The clinical psychologist in charge of the study collected these data in the geriatric establishments. Data were collected from April 1, 2018, to March 31, 2020. Free and informed consent to participate in the research was requested and signed, in accordance with Code de déontologie des psychologues  (1996, 2012).