dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of data and materials The code is available at https:// github. com/ U1245/ Penet rance-estim ationfrom-family-cohort-in-AD-for-SORL1-APOE  [36]. All pedigrees that were used for this study are provided in Additional file 1: supplementary results ( § pedigrees information). They are displayed in tree format including available SORL1-APOE genotypes, disease status, and age (onset or censoring [last visit or death] respectively for affected and unaffected individuals). Exome sequencing data are personal, potentially identifiable data, which we are not allowed to share publicly based on patients' consents and IRB approvals.
It is affected by mineral dust when https://doi. org/10. 5194/acp-2022-231 Preprint. Discussion started: 11 May 2022 c Author(s) 2022. CC BY 4. 0 License. AERONET version 3. 0 level 2. 0 (or level 1. 5 depending on data available at the station) dataset  (Sinyuk et al. (2020) ; https://aeronet. gsfc. nasa. gov, last access: 22 March 2022) has been used in this work (Fig. 1 ). More specifically, AERONET level 2. 0 data series at IZO is available from October 2004 to December 2020. This data set is composed of records from a total of 16 reference instruments in the period 2004-2020. However, the homogeneity and quality of this AERONET-Cimel AOD data series at Izaña has been confirmed by  Cuevas et al. (2019b)  using a long-term AOD and AE comparison with the three GAW-PFR reference instruments, WMO AOD reference, running at Izaña at the same time period, and also by  Toledano et al.
(2018) , who assessed the suitability of Izaña as a Langley plot calibration site using 15 years of Langley calibrations. AERONET level 2. 0 measurements at SCO and LLO are available from April 2005 and July 2006, respectively, until June 2020. Level 1. 5 is used from June to December 2020. Both datasets are obtained from field instruments that are replaced every year. Regarding TPO, the data series is composed of discontinuous records from field instruments since July 2012. Level 2. 0 is available until December 2020.
Acknowledgements. We gratefully acknowledge the data provided by AERONET network. We wish to express our appreciation to the staffs from  AEMET  and  ULL  for maintaining the instrumentation, and ensuring the quality of the data. AERONET sun photometers at Izaña have been calibrated through  AEROSPAIN Central Facility  (https://aerospain. aemet. es/). This study is a contribution to the  Barcelona Dust Forecast Centre  (https://dust. aemet. es/). The authors also acknowledge the support from  ACTRIS, Ministerio de Ciencia e Innovación from Spain  through the projects  SYNERA  ( PID2020-118793GA-I00 ) and ePOLAAR ( RTI2018-097864-B-I00 ) and by  Junta de Castilla y León  (grant no. VA227P20 ).
Analysis-ready cloud-optimized data In the field of Earth science, model outputs are often archived and distributed in binary, HDF5, or NetCDF formats. While we have greatly benefitted from these formats, they are not optimized for cloud storage or for parallelized cloud computing. However, as Earth scientists, commonly, we do not possess the training in cloud infrastructure or the data engineering required to efficiently convert large-scale archival datasets into formats which allow us to leverage the full performance potential of the commercial cloud. Data engineers, on the other hand, do not know the scientific needs of the data. In collaboration with Pangeo Forge  (Stern et al. , 2022a , https://pangeo-forge. readthedocs. io/en/latest/, last access: 8 July 2022), we have therefore attempted to fill this niche by streamlining the process of data preparation and submission.
To transform their data into analysis-ready cloud-optimized (ARCO) formats, data providers (ocean modeling institutions in our case) need only specify the source file location (e. g. , as paths on an ftp, http, or OPeN-DAP server) along with output dataset parameters (e. g. , particular ARCO format, chunking) in a Python module known as a recipe. The recipe module, which is typically a few dozen lines of Python code, relies on a data model defined in the open-source pangeo-forge-recipes package. Once complete, the recipe is submitted via a pull request on Github to the Pangeo Forge staged-recipes repository (https://github. com/pangeo-forge/staged-recipes, last access: 8 July 2022). From here, Pangeo Forge automates the process of converting the data into ARCO format and storing the resulting dataset on the cloud, using its own elastically scaled cloud compute cluster.
The term "analysis-ready" here is used broadly to refer to any dataset that has been pre-processed to facilitate the analysis which will be performed on it  (Stern et al. , 2022a). An example of such a recipe for eNATL60 described in Sect. 3 is given in Appendix A. We refer the interested reader to  Abernathey et al. (2021a)  and  Stern et al. (2022a)  for further details on the technical implementation. The crowd-sourcing approach of Pangeo Forge, to which any data provider can contribute, not only benefits the immediate scientific needs of a single research project, but also the entire scientific community in the form of shared, publicly accessible ARCO datasets which remain available for all to access. This saves each scientist the cost of duplicating and storing ghost copies of the data and allows for reproducible science.
The model outputs used for this study are stored on the Open Storage Network (OSN), a cloud storage service provided by the National Science Foundation (NSF) in the US. The surface data were saved hourly and interior data in the upper 1000 m as daily averages (due to cloud storage constraints). To facilitate the access of data from OSN, we have further made them readable via intake, a data access and cataloging system which unifies the application programming interface (API) to read and load the data (https: //intake. readthedocs. io/en/latest/overview. html, last access: 8 July 2022). That is, the API to read and load the data is the same for all of the data used in this project, regardless of its distribution format (e. g. , binary, HDF5, or NetCDF), because each of the datasets has been converted by Pangeo Forge into the cloud-optimized Zarr format (https://zarr.
readthedocs. io/en/stable/, last access: 8 July 2022) and subsequently cataloged with intake prior to analysis. This is particularly beneficial for our case, where we would like to systematically analyze multiple data collections. The entire process of zarrifying the data, fluxing them to OSN, and cataloging scaled well for the four regions shown in Fig. 1 ; the net amount of data stored on OSN as of writing sums up to O(2 Tb). While the amount of O(2 Tb) may seem small, the ARCO framework negates the generation and storage of ghost copies and scales as the data size increases. Jupyter notebooks for the results shown in Sect. 3, including the Yaml file to access data via intake, are given in the Pangeo Data swot_adac_ogcms Github repository  (Stern et al. , 2022b , https://github. com/pangeo-data/swot_ adac_ogcms, last access: 8 July 2022).
Regarding LLC4320, the data were accessed via the Estimating the Circulation and Climate of the Ocean (ECCO) data portal. While there was no particular sub-setting applied to their dataset prior to analyses, the data portal and cloud-based JupyterHub being within geographical proximity (within the US) facilitated the data access. The combination of llcreader of the xmitgcm Python package to access their data in binary format (as opposed to NetCDF) also enhanced the efficiency  (Abernathey, 2019; Abernathey et al. , 2021b). T. Uchida et al. :  Framework for inter-comparing submesoscale-permitting realistic ocean models.
Example analyses The model outputs used for this showcase are from the eNATL60  (Brodeau et al. , 2020) , GIGATL  (Gula et al. , 2021) , HYCOM50  (Chassignet and Xu, 2017, 2021) , FESOM-GS and LLC4320  (Rocha et al. , 2016; Stewart et al. , 2018) , ORCA36 (https://github. com/immerse-project/ ORCA36-demonstrator, last access: 8 July 2022), FIO-COM32  (Xiao et al. , 2022) , and HYCOM25  (Savage et al. , 2017a, b; Arbic et al. , 2018)  simulations. The detailed configuration of each simulation is given in Appendix B. In order to motivate the reader on the necessity of intercomparing realistic submesoscale-permitting simulations, we show in Fig. 2  the surface relative vorticity normalized by the local Coriolis parameter on 1 February at 00:00 GMT from each model. Despite their similar spatial resolutions, the spatial scales represented vary widely across models.
We will further show in Sect. 3. 2 an example of 3D diagnostics of the submesoscale flow, which significantly increases the computational cost and burden of transferring data; the 3D diagnostics will highlight the strength of the data-proximate framework where we can consistently apply the same diagnostic methods across different datasets. In Fig. 3 , we show the time mean and temporal standard deviation of ocean dynamic sea level from the eight models in the GS separation region. We also show the time mean ocean dynamic sea level estimated as ADT from the Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) data for reference. We do not show the standard deviation for AVISO as the spatiotemporal interpolation and smoothing limit its effective resolution to O(100 km) and O(10 d)  (Chelton et al.
, 2017a, b; Arbic et al. , 2018; Torres et al. , 2018; Ajayi et al. , 2021; Yu et al. , 2021). The benefit of having tidally forced simulations is that we can develop and test such methods of removing tides. To complement the temporal standard deviation, in Fig. 4 , we show the frequency spectra of SSH in the GS separation region. The frequency periodograms were computed every ∼ 10 km using the xrft Python package  (Uchida et al. , 2021)  and then spatially averaged to compute the spectra. The temporal linear trend was removed, and a Hann window was applied prior to taking the Fourier transform of SSH as commonly done in studies examining spectra (e. g. ,  Uchida et al. , 2017; Savage et al. , 2017a; Khatri et al. , 2021). At frequencies higher than the Coriolis frequency, LLC4320 shows the highest variability and FESOM-GS the lowest for both winter and summer.
Cloud-based computing differs from the conventional workflow, which involves funding of local computational resources and storage. While the cloud-based framework does not allow for an individual researcher or group to have prioritized access over the data and analytical tools, we believe that open access to the data will allow for reproducible science and facilitate international collaboration. Table  B3. The equation of state (EOS), surface boundary layer (SBL) parametrization used, and tidal forcing in each simulation. * Jackett and McDougall (1995, JMD95)  in HYCOM is implemented with the approximation by  Brydon et al. (1999). The potential densities were computed following each EOS with the reference pressure of 0 dbar  (Fernandes, 2014; Abernathey, 2020; Firing et al. , 2021). The EOS for FIO-COM32 is available on Github  (Stern et al.
HYCOM25 Nearest 5 × 5 box average of the 30 s GEBCO_08 20091120 global dataset to each grid point, then smoothed once w/ a 1-2-1 9 pt. smoother except within two grid points of land. Table  B5. The advection and dissipation schemes used for each simulation. Note that some models have biharmonic viscosities and others do not.
Acknowledgements. We thank the editor  Riccardo Farneti  and  Stephen Griffies ,  Mike Bell ,  Andy Hogg , and  Joel Hirschi  for their reviews. Takaya Uchida  was supported by the  French "Make Our Planet Great Again" (MOPGA)  initiative managed by the  Agence Nationale de la Recherche (ANR)  under the  Programme d'Investissement d'Avenir , with the reference  ANR-18-MPGA-0002. This work is a contribution to the  Consistent OceaN Turbulence for ClimaTe Simulators (CONTaCTS)  project and  SWOT mission Science Team  (https://www. swot-adac. org/,last access:  8 July 2022 ). Julien Le Sommer ,  Laurent Brodeau , and  Aurélie Albert  thank the  PRACE 16th call project ReSuMPTiOn  ( Revealing SubMesoscale Processes and Turbulence in the Ocean, P. I. Laurent Brodeau ) for awarding access to the  MareNostrum supercomputer at the Barcelona Supercomputing Center.
Operational costs for the cloud-based JupyterHub were funded by  CNES  through their participation in the  SWOT Science Team. Ryan P. Abernathey  and  Charles Stern  were supported by  NSF  award  2026932  for the development of  Pangeo Forge  and OSN storage. This study is a contribution to projects  S2: Improved parameterisations and numerics in climate models (Sergey Danilov ),  S1: Diagnosis and Metrics in Climate Models (Nikolay Koldunov ), and M5: Reducing spurious di-apycnal mixing in ocean models ( Sergey Danilov ) of the  Collaborative Research Centre TRR 181 "Energy Transfer in Atmosphere and Ocean " funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -project no. 274762653  -and the  Helmholtz initiative REKLIM (Regional Climate Change ;  Qiang Wang).
Brian K. Arbic  and  Jay F. Shriver  were supported by  NASA  grant  80NSSC20K1135. Eric P. Chassignet  and  Xiaobiao Xu  were supported by  ONR  grants  N00014-19-1-2717  and  N00014-20-1-2769. Baylor Fox-Kemper was supported by  ONR   N00014-17-1-2963  and  NOAA NA19OAR4310366. Fangli Qiao  and  Bin Xiao  were supported by the  National Natural Science Foundation of China  with grant no. 41821004. Clément Bricaud  was supported by  EU H2020 projects IMMERSE  (grant agreement no. 821926 ) and  ESIWACE2  (grant agreement no. 823988 ). Jonathan Gula  is grateful for support from the  ANR  through the  DEEPER  project ( ANR-19-CE01-0002-01 ). Jonathan Gula  and  Guillaume Roullet  thank  PRACE  and  GENCI  for awarding access to HPC resources  Joliot-Curie Rome  and  SKL  from  GENCI-TGCC  (grants  2020-A0090112051  and  2019gch0401  and  PRACE  project  2018194735 ) and  HPC facility DATARMOR  of  "Pôle de Calcul Intensif pour la Mer" at Ifremer, Brest, France.
aviso. altimetry. fr,last access:  8 July 2022 ). The geographic figures were generated using the  Cartopy Python package (Met Office ,  2010-2015 ). Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant nos. ANR-18-MPGA-0002  and  ANR-19-CE01-0002-01 ), the  National Science Foundation  (grant nos. 2026932 ,  OCE-182956 , and  OCE-2023585 ), the  Deutsche Forschungsgemeinschaft  (grant no. 274762653 ), the  National Aeronautics and Space Administration  (grant no. 80NSSC20K1135 ), the  Office of Naval Research  (grant nos. N00014-19-12717 ,  13034596 , and  N00014-17-1-2963 ), the  National Oceanic and Atmospheric Administration  (grant no. NA19OAR4310366 ), the  National Natural Science Foundation of China  (grant no. 41821004 ), the  Partnership for Advanced Computing in Europe AISBL (ReSuMP-TiOn ; grant no.
Code and data availability. The model outputs from eNATL60, GIGATL, HYCOM50, FESOM-GS, ORCA36, FIO-COM32, and HYCOM25 at the SWOT Xover regions are all publicly available on the Open Storage Network (OSN). The Jupyter notebooks and Yaml file used to access and analyze the data are available on Github (Stern et al. , 2022b, https://github. com/pangeo-data/swot_adac_ogcms, last access: 8 July 2022, and https://doi. org/10. 5281/zenodo. 6762536). The LLC4320 data were accessed via the NASA ECCO Data Portal (https:// data. nas. nasa. gov/ecco/data. php?dir=/eccodata/llc_4320, last access: 8 July 2022) using the llcreader of the xmitgcm Python package (Abernathey et al. , 2021d; Abernathey, 2019). The raw model outputs of eNATL60 and GIGATL are available at https://doi. org/10. 5281/zenodo. 4032732 (Brodeau et al.
The unprecedented yields offer a unique opportunity to complement the world-wide program of searches for New Physics with the help of a new intensity-frontier facility that is complementary to the high-energy and the precision frontiers. The SHiP experiment is designed to both search for decay signatures of models with HS particles, such as heavy neutral leptons (HNL)  [1] , dark photons (DP)  [2] , dark scalars (DS), etc, by full reconstruction and particle identification of Standard Model (SM) final states, and to search for LDM scattering signatures by the direct detection of recoil of atomic electrons (or nuclei) in a high-density medium  [3]. The experiment is also optimised to make measurements on tau neutrinos and on neutrino-induced charm production by all three species of neutrinos.
Simulation and reconstruction The SHiP experiment relies on an accurate simulation of the background components, in particular related to combinatorial events from muons, and muon and neutrino deep inelastic scattering (DIS) in the material of the detector. The design of the muon shield also relies on an accurate knowledge of the muon spectrum. The SHiP software framework for simulation, reconstruction, and analysis is based on the Fair-Root package  [47]  and is called FairShip. The FairShip code is largely a specialisation of the generic C++ base classes provided by FairRoot, mainly profiting from the methods to describe the detector geometry, implement detector classes, and performing simulation and reconstruction. The framework incorporates GEANT4  [6]  to trace particles through the target and the experimental setup, PYTHIA8  [48]  for the primary proton fixed-target interaction, PYTHIA6  [49]  for muon deep inelastic scattering, and the GENIE [50] MC generator for interactions of neutrinos.
In addition some specific auxiliary libraries needed by SHiP are included, like GENFIT  [51]  for reconstruction of tracks. The steering of the simulation flow, and the main parts of the reconstruction and analysis are based on Python classes and functions. For background studies and detector optimisation, a total of 6. 5 × 10 10 protons on target have been simulated with an energy cut of 10 GeV for transporting particles after the hadron absorber. This simulation was run with strongly enhanced muon production from QED processes, such as resonance decays and gamma conversion, such that for the studies of muon-induced backgrounds, the sample corresponds to 6. 5 × 10 12 protons on target. In addition, a total of 1. 8 × 10 9 protons on target have been simulated with an energy cut of 1 GeV. Heavy flavour production is both a source of signal and background.
3±3. 6 kHz. After applying the acceptance and selection cuts listed in Table  3 , together with a cut of <25 on the sum of hits in all tracking stations and no hit in the SBT/UBT, about 10 8 pairs of muons are expected for the partially reconstructed topology in the lifetime of the experiment. Under the assumption of a flat time structure for the 1 s proton spills, these are suppressed to a level of 10 -2 muon pairs in 2 × 10 20 protons on target by requiring the muons to coincide in a time of 340 ps, corresponding to > 2. 5 times the time resolution of the HSDS timing detector. In reality, non-uniformity in the spill structure and the probability of this background can be measured by relaxing the timing criterion. A fast high-resolution spill structure monitor is also under study. The information from the monitor will be recorded with the data to have a continuous measure of this background probability.
We developed three models for different magnifications (2. 5x, 5x, 20x), and the histological findings were mapped comparing the original digital slides Using the independent dataset, we performed a subsequent analysis based on the morphological features extracted by MIXTURE. We created random forest and support vector machine models to predict usual interstitial pneumonia (UIP), a histological pattern suggestive of progressive disease, and validated the accuracy in the independent validation dataset. Finally, we investigated the clinical significance of various histopathological parameters for the prognosis of interstitial pneumonia. Results: MIXTURE was highly accurate in mapping the characteristic morphological patterns in different subtypes of interstitial pneumonia. The random forest model based on these extracted features predicted the UIP with high accuracy.
Testing and debugging language Virtual Machines (VMs) is a laborious task without the proper tooling. This complexity is aggravated when the VM targets multiple architectures. Simulation-based VM generator frameworks allow one to write test cases on the simulation, however they do not ensure the correctness of the generated artifact due to the semantic gap between the environments. In this article we propose Test Transmutation. It extends simulationbased VM generator frameworks to also generate simulation test cases and execute them on the generated VMs. It extends such frameworks to translate test cases and applies differential testing and non-semantic-preserving mutations. Test Transmutation detects bugs that are representative of typical VM modifications. Moreover, we apply it to a set of real test cases of the Pharo VM and find several issues.
Experimentation Platform We applied Test Transmutation to the Virtual Machine of the Pharo language implementation. Pharo is an object-oriented dynamicallytyped language from the Smalltalk tradition  [7]. The Pharo VM is an industrial level Virtual Machine written in Pharo itself and generating a C version using a VM-specific generator called the Slang VM generator  [15]. The VM generator is in charge of generating from high-level object-oriented Pharo code, low-level functional/imperative C code and applying VM specific optimizations to it. Most debugging and development happens in the simulation environment and is compiled to a machine executable VM  [20, 25]. The Pharo VM has around 1000 unit test cases  [24]  written in the simulation environment, covering the bytecode interpreter, the language native functions, and the Just-In-Time compiler.
Rutherford et al. [27]  present an automated approach on top of distributed system simulations using mutations. Aichernig et al. [2] [3] [4]  present a mutation-testing approach for UML models. To the best of our knowledge, Test Transmutation is the first approach applying mutation-based testing for Virtual Machines, and particularly, Virtual Machine simulation environments. PharoJS  [8]  validates generation from Pharo to Javascript with application test cases. The Pharo environment execute the test case in both Pharo and Javascript environments. It executes the test case in Javascript by sending messages to the Javascript environment rather than compiling the test cases. Moreover PharoJS does not apply mutations.
Accurate geometry representation of large outdoor point clouds is essential for highquality sensor simulation. In this work, we introduce adaptive splats (AdaSplats) to overcome the limitations of previous splatting and meshing techniques by using local geometric cues and point-wise semantic labels. Splat modeling is more flexible than a mesh reconstruction, and the size of each splat can be adapted by semantic or local geometric information. SS on 3D point clouds is the task of assigning point-wise semantic labels. There exist a plethora of methods for the SS task  [12] [13] [14] [15]. We leverage  KPConv [12]  for its reported accuracy on a variety of datasets  [16] [17] [18]. For example, KPConv achieves an average mean Intersection over Union (mIoU) of 82% on the Paris-Lille-3D dataset  [16].
This is an outdoor dataset collected using a LiDAR mounted on a MMS and close to the Paris-CARLA-3D (PC3D) dataset  [19]  that we use in our experiments. Using KPConv, we perform SS on the outdoor point clouds to obtain point-wise semantic labels that are later used for the generation of AdaSplats. LiDAR point clouds acquired using a MMS are highly anisotropic  [16]. Resampling the point cloud reduces the anisotropy and increases uniformity by redistributing the points. Although previous upsampling methods  [20, 21]  have addressed this problem, they require passing through computationally expensive representations. Deep learning methods for point cloud upsampling  [22, 23]  are used to increase the uniformity of the points distribution, and deep learning for depth completion  [24, 25]  can also help in the case of sparse point clouds.