dataesr/scientific-paragraphs-categorization huggingface dataset:
It https://www. answers all medical techtimes. com/ queries based on its articles/285845/ large healthcare 20230104/google- datasets. This is research-and- besides Google's deepmind-launch-an- investment in the ai-based-healthcare- NLP platform language-model. htm Bidirectional Encoder Representations from Transformers (BERT). Meta An undisclosed Blenderbot is a https://ai. facebook. amount of chatbot which was com/blog/blender- investment by developed by Meta bot-2-an-open-source- Meta in before ChatGPT was chatbot-that-builds- BlenderBot 3. 0 launched. It is long-term-memory- improving its and-searches-the- chatbot based on internet/ consumer feedback, https://www. wired. with its latest com/story/ version being blenderbot3-ai- BlenderBot 3. 0 chatbot-meta- interview/.
Agents make inductive and dynamic choices that lead them to achieve well-being objectives. The MUST-B model is implemented in the VLE -Virtual Laboratory Environment  (Quesnel et al. , 2009). VLE is a platform for multimodeling and simulation of dynamic systems based on discrete event system simulation (DEVS)  (Zeigler et al. , 2000). VLE enables us to specify complex systems in terms of reactive objects and agents, simulate the system dynamics, and analyze the results of the simulation. The indexes provided also facilitate the development of customized programs. MUST-B was developed using the concept of object-oriented programming, in particular the C++ language.
Acknowledgments The authors would like to thank all of the researchers involved in the MUST-B project for their remarks and observations throughout the discussions that took place:  Anne Bretagnolle  from  Géographie-cités  ( Paris 1 University/Paris 7 University/CNRS ),  Sonia Guelton  from  Lab'Urba (Paris-Est University ),  Moez Kilani  from  LEM (University of Lille/CNRS ),  Nicolas Coulombel  from  LVMT (Paris-Est University ),  Laurent Guimas  from Explain Consultancy and  Ouassim Manout  from  ForCity. We also thank  Région Nouvelle Aquitaine  for their co-financing of the  STRATEGIE  project and the  MSHA  for the financial management of the project and use of their premises.
The authors focus on a 2D Euler SALT equation, showing that the driving stochastic parameter can be calibrated in an optimal way to match a set of given data. Moreover, they show that this model is robust with respect to the stochastic parameters. The work by Long Li, Etienne Mémin, and Gilles Tissot, entitled "Stochastic Parameterization with Dynamic Mode Decomposition", considers a physical stochastic parameterization to account for the effects of the unresolved small scale on the large-scale flow dynamics. This random model is based on a stochastic transport principle, which ensures a strong energy conservation. The dynamic mode decomposition (DMD) is performed on high-resolution data to learn a basis of the unresolved velocity field, on which the stochastic transport velocity is expressed.
2ms -2 is the wind strength. The vertical discretisation consists of 23 layers, with layer thicknesses increasing with depth. For the horizontal discretisation we take a fine grid of spacing 1/4 • and a coarse grid of spacing 1/2 •. At the latitudes we are considering, 1/4 • corresponds to an eddy-permitting model, while 1/2 • may be considered non-eddy resolving [see Hal13]. We run the deterministic model on the fine grid and the coarse grid, and carry out the SALT and SFLT runs on the coarse grid. All coarse-grid runs are begun from the same initial condition, being the final time snapshot after the three-year spin-up period; the fine-grid run is begun from the end of the three-year spin-up on the fine grid. We save data in each case at intervals of 15 days, over a time period of 10 years, for a total of 240 snapshots.
Additionally, a data assimilation procedure is used to take into account the time integrated information obtained from the data being observed along the solution path during the forecast interval as "in flight corrections". In the geoscience community, data assimilation (DA) refers to a set of methodologies designed to efficiently combine past knowledge of a geophysical system (in the form of a numerical model) with new information about that system (in the form of observations). DA is a central component of Numerical Weather Prediction where it is used to improve forecasting by adjusting the model parameters and reducing the uncertainties. To achieve this, a stochastic feedback loop between the model and the observation may be introduced: the assimilation of more data during the prediction interval will then decrease the uncertainty of the forecasts based on the initial data, by selecting the more likely paths as more observational data is collected.
In this work, we wish to investigate the feasibility and viability of probabilistic pathwise approach for calibration. Our general aim is to explore such ideas for a wide class of nonlinear stochastic transport models. This will be very useful in data assimilation problems, as in real world applications the signal is usually observed through discrete observations, but no results of this type for SALT or LU models have been obtained before. Currently, Lagrangian particle trajectories are simulated starting from each point on both the physical grid and its refined version, then the differences between the particle positions are used to calibrate the noise. This is computationally expensive and not fully justified from a theoretical perspective. In the same spirit as  [3]  but with a more complicated noise term and without any smoothing effects of a Laplacian, we propose an approach which uses high-frequency in time and low-frequency in space observations of a single path of the solution, to rigorously infer properties of the stochastic parameters.
Shallow Water Equation (SWE) Case-Study Dataset Description We consider the SWE without wind stress and bottom friction. The momentum equations are taken to be linear, and the continuity equation is solved in its nonlinear form. The direct numerical simulation is carried using a finite difference method. The size of the domain is set to 1000 km × 1000 km with a corresponding regular discretization of 80 × 80. The temporal step size was set to satisfy the Courant-Friedrichs-Lewy condition (h = 40. 41 s). The data were subsampled to h = 40. 41 × 10 and 500 time-steps were used as training data. The models were validated on a series of length 100. As observations, we randomly sample 1% of the pixels with a temporal coverage given in Fig. 2.
P (g z (x) > ρ) ∝ exp(-ρ d(z)) , (A. 1) where d(z) is the local-dimension that we estimate here. The geometric interpretation of this dimension is that in a space of dimension d, the typical number of points inside a sphere of radius r scales as r d. Although such an interpretation of dimension has been connected to the distances to analogues for a long time (see for instance  [18]  and the famous Grassberger-Proccacia algorithm  [19] ), only recent works have used extreme-value theory to provide instantaneous, local estimators of dimension  [20]. These recent tools are particularly suited for the study of local behaviours, while previous works focused on average, global indicators. Recently, distances between analogues x and their target z have been shown to follow distributions whose parameters are given by the length of the available dataset, the analogue rank, and the local dimension as estimated in this paper  [21].
Inverse Persistence θ However, Eq. A. 1 is not valid when the system passes close to a fixed point, as this causes trajectories to slow down. In this case, another parameter called the extremal index, or inverse persistence, comes into play: P (g z (x) > ρ) ∝ exp(-ρ θ (z)d(z)) , (A. 2) with 0 < θ(z) ≤ 1. Low values of θ correspond to highly persistent areas of state-space. It can be interpreted as the inverse mean residence time within a sphere centered on z (if divided by the time-increment between two consecutive points in the dataset, which is 3 h in our case). We estimate this parameter with the Süveges likelihood estimator  [22]. It is based on counting consecutive points inside a ball centered on z (i. e. , analogues of the same point z that are also consecutive points in the time-ordered dataset).
Acknowledgments We are grateful to our friends and colleagues who have generously offered their time, thoughts, and encouragement in the course of this work during the time of COVID-19. Thanks to  A. Arnold ,  B. Chapron ,  D. Crisan ,  E. Luesink ,  A. Mashayek , and  J. C. McWilliams  for their thoughtful comments and discussions. Particular thanks to  B. Chapron , for extensive discussions of satellite oceanography and for providing the satellite data in Figs. 1 and 2. We also thank  B. Fox-Kemper  for constructive discussions of modelling approaches in physical oceanography. These discussions helped us clarify the distinction between the present C•M modelling approach and the classical balance equation approach. The authors are grateful for partial support, as follows. DH for  European Research Council (ERC) Synergy  grant  STUOD -DLV-856408 ; RH for the  EPSRC scholarship  (Grant No.
A. Lobbe A long-term goal in the development of neural network based numerical methods must of course be the rigorous error analysis, which remains a challenging task. Acknowledgments We thank  Pierre Ailliot  for fruitful discussions on Gaussian Mixture Models. This work was financially supported by the  ERC  project  856408-STUOD. Support for the Twentieth Century Reanalysis Project version 3 dataset is provided by the  U. S. Department of Energy, Office of Science Biological and Environmental Research (BER) , by the  National Oceanic and Atmospheric Administration Climate Program Office , and by the  NOAA Physical Sciences Laboratory. We thank the anonymous reviewer for helpful comments and suggestions. Acknowledgments SR has been partially funded by  Deutsche Forschungsgemeinschaft (DFG) -Project-ID  318763901-SFB1294  and Project-ID  235221301-SFB1114.
Signal intensity quantification and post-processing of images Confocal microscopy images were analyzed and edited using the open-source OMERO software (version 5. 4. 10, https://www. openmicroscopy. org/omero/) as an image-processing program. Signal intensities (SI) in distinct organs were quantified using the open-source Fiji image analysis software (version 2. 1. 0/1. 53c, https://imagej. net/software/fiji/) as described  [8]. Controls and corresponding treatment groups were analyzed during the same experiment and using the same laser and microscopy settings. A self-written FiJi script allowed for the automatic quantification of SI based on maximum intensity projections using a selected threshold. SI are presented as fold-change normalized to the mean of the PBS-treated control. Tubular dilatation was scored semi-quantitatively by a blinded observer who evaluated PAS-stained sections of nine microscopy images of three control and three treated mice.
For tomographic imaging, 4,000 projections were recorded over 180i n flyscan mode. Each height step covered a cylindrical volume with 1. 3 mm diameter and 1. 3 mm height and required a scan time of approximately 3. 5 minutes. Depending on alignment, one or two height steps were needed to image the whole zebrafish larva. Tomographic reconstruction was performed using the software pipeline and computational resources available at the ANATOMIX beamline, which uses the PyHST2 software (ESRF, Grenoble, France) for the filtered backprojection step. Prior to reconstruction, projections were phase-retrieved using Paganin's filter  [32]  with a kernel length of 15 μm  [33]. A double flat-field correction was applied to suppress ring artefacts  [33].
Three-dimensional rendering and segmentation The lumen of proximal tubules of ZFL and glomeruli of mouse biopsies were manually segmented using Amira software (Version 6. 2. 0, ThermoFisher, Massachusetts, US). The segmented volume was subsequently analyzed in Matlab (release R2020a, The MathWorks Inc. , Natick, US). Tubules of treated ZFL were compared by extracting their centerlines via medial axis transformation. The medial axis consists of all points which have more than one closest point on the object's boundary. It was extracted with the Matlab function "bwskel". The tubules were aligned based on the manually identified turning point (TP) and the geodesic distance along the centerline. The geodesic distance was determined by reducing the three-dimensional centerline coordinates to one dimension via the Isomap mapping software provided by van der Maaten et al.
Statistical analysis Statistical analysis was performed with GraphPad Prism Version 8. 0. 2 (GraphPad Software, San Diego, CA) using one-way analysis of variance (ANOVA) followed by Dunnett's multicomparison tests or unpaired two-tailed t-test analysis for direct comparisons. Where appropriate, individual data points are presented as dot plots next to the group's average and standard deviation (SD). The number of individual ZFL and mice is indicated as (n). The statistical significance of the difference against the control population was evaluated at the 0. 05 and 0. 001 probability level and marked by asterisks. Data are shown as a box-and-whisker plot indicating the datasets' minimum, maximum, median, first quartile, and third quartile.
Acknowledgments The authors thank  Dr Kai Schleicher  from the  Imaging Core facility of the Biocenter (University of Basel ) for support with confocal microscopy and image analysis. We thank  Prof. Dr. Mario Schiffer  ( University of Erlangen, Germany ) for providing the Tg(wt1b:eGFP) zebrafish line. Prof. Dr Markus Affolter  and  Dr Heinz Georg Belting  ( University of Basel ) are acknowledged for providing support for zebrafish experiments. Further, we thank  Prof. Dr Alex Odermatt  ( University of Basel ) for the scientific exchange and discussions. Dr. Christian M. Schlepu  ¨tz is acknowledged for support with preliminary μCT experiments at the TOMCAT beamline of the Swiss Light Source at the  Paul Scherrer Institute in Switzerland. Dr Diego Calabrese of the Histology Core Facility of the Department of Biomedicine  is acknowledged for support with histology preparations.
This project was supported by a grant ( SCAHT-GL 21-09 ) from the  Swiss Centre for Applied Human Toxicology -SCAHT  -for R. P. https://www. scaht. org/en/). Equipment used for this study (ANATOMIX) is an  Equipment of Excellence (EQUIPEX)  funded by the  Investments for the Future program  of the  French National Research Agency (ANR) , project  NanoimagesX , grant no. ANR-11-EQPX-0031. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Regional scale (100-1000 km across): Highly integrated water column and seafloor mapping operations should combine data collection across multiple technologies and platforms. Regional predictive habitat mapping models show clear improvements in species predictions when physical oceanographic variables are included  30. Data from beyond the scientific community, notably from industry, needs to be integrated. Automated GIS techniques to discriminate geomorphological features, including cold-water coral reefs, can rapidly increase data processing rates  31  and are now being applied for the first time to datasets from the South Atlantic. Local-scale ecosystem structure through ultra-high-resolution mapping (<1-10 km across): It is now possible to conduct centimetre to metre-scale integrated mapping activities in waters >200 m deep.
In the Atlantic, biological time series range from datasets on bacteria and primary producers to VME indicator taxa, whales, tunas and sharks. They include fisheries records, plankton tows, repeat camera surveys and data from observatories including EMSO-Azores, Porcupine Abyssal Plain Sustained Observatory (PAP-SO), Bermuda Atlantic Time Series Study (BATS) and Cabo Verde Ocean Observatory (CVOO). There are fewer deep and open-ocean time series in the South Atlantic than in the North. As funding (see Supplementary Table  1 ) and human capacity to change this is limited, we argue for novel approaches to reconstruct time series in the data-poor deep and open ocean. The iAtlantic programme adopted five approaches to construct time series in places where classical ecological surveys did not exist.
O'Brien et al. 57  used benthic foraminifera from mid-latitude and subpolar North Atlantic sediment cores to show strong shifts in benthic ecosystems over the last ~150 years, with peak responses occurring in areas that experienced large changes in surface circulation, temperature, and/or productivity. (5) Ancient environmental DNA (aDNA) uses eDNA methods and metabarcoding on sediment cores to reproduce temporal changes in pelagic plankton. Using cores from five sites in the North Atlantic, Selway et al. 58  presented clear evidence of aDNA from the ubiquitous coccolithophore Emiliania huxleyi, demonstrating the potential of this technique but also highlighting the importance of minimising contamination during sample collection and storage. As more deep and open ocean ecological time series datasets are examined, one driver of change dominates: temperature.
92  developed a data-driven approach to provide management options to protect VMEs. SCP requires management and conservation goals and objectives to be identified alongside environmental and human activity data. Advanced web-based Geographic Information System (GIS) tools, (e. g. GeoNode) can support this process, allowing data from regions of interest, including conservation zones and areas that may in the future be commercially exploited, to be stored and visualised. The iAtlantic project developed an Atlantic GeoNode (www. geonode. iatlantic. eu) and launched an All-Atlantic Ocean Data Community site on the GEOSS Portal (www. geoportal. org). GeoNode offers all stakeholders an interface to explore and view geospatial data, without requiring technical expertise or providing full access to the original data, facilitating data sharing and communication of management options whilst respecting commercial sensitivities or academic embargos.
The ultimate goal is to produce transparent ocean basin scale management scenarios for the whole Atlantic based on FAIR (Findable, Accessible, Interoperable, Reusable) and Open Data practices. Robust data sharing and dissemination channels, such as the GEOSS portal, connect local, regional and global data infrastructures and services e. g. the South African Environmental Observation Network (SAEON  93  ), PANGAEA, and the European Marine Observation and Data network (EMODnet 94 ), aligning Northern and Southern data providers and services along mutually agreed standards. Priority 6-Capacity building, policy, stakeholder engagement and outreach. There are many international initiatives, directives and agreements to tackle issues threatening the health of ocean ecosystems (see Supplementary Note 1).
Acknowledgements This paper has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 818123  (iAtlantic) and the  United Kingdom Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) One Ocean Hub  (Grant Ref:  NE/S008950/1 ). We thank  Jason Cleland  for assistance in preparing the manuscript and two anonymous reviewers for their helpful comments on the manuscript. This output reflects only the author's view and the  European Union  cannot be held responsible for any use that may be made of the information contained therein.
Numerical simulation of propagating Lamb waves To estimate the Lamb modes that propagate at the occurrence of the Schoch effect, theoretical dispersion curves of the corresponding structure are calculated using Disperse™, a guided waves simulation software  [20]. The software employs a global matrix method representing the layered system as a single matrix. The matrix contains the relation of stresses and displacements in each layer through Hooke's law, Euler's, and Navier's equations of motion. The dispersion curves are assembled from points determined by finding the root of characteristic equations, i. e. , the points that create zero value of the matrix determinants. To simulate mode shape and energy flow, the program calculates amplitude, stresses, and displacement with finite element procedures  [21].
Accordingly, we conclude, based on the present local acousto-optic method, that the order of adhesion strength level for our samples, from high to low, is Sample 3, Sample 2, and Sample 1, which also agrees with the expectation we had when we purchased the samples. In addition, we have also found that the modes sensitive to interlayer adhesion strength in our specimens are modes A6, A5, and S3. Upon inspection of the Schlieren photographs in Figures  3 4 5 6 and correlating to the order of adhesion level of each sample as discussed in the preceding paragraph, we observe two significant phenomena related to the beams' appearance. One phenomenon is that the width and intensity of the second lobe tend to decrease with the level of adhesion. To quantify the beam width and intensity from the image, we employ a software named ImageJ  [22, 23].
Accounting for the environmental performance of farming, but also an increasing disconnect between people and their food, warrants exploring the interfaces between food systems and citizen science  [11]. Legacy data and photos can also improve underlying identification models provided proper tools facilitating integration and annotation are available. The massive digitization of herbarium specimens and annotation of specimen images is one such example  [15]. Pl@ntNet provides the possibility to integrate specific flora previously collected by (amateur) botanists using dedicated semi-automatic tools. In this letter, we explore the use of legacy in-situ expert photos taken and labelled during past EU-wide LUCAS surveys. Taking advantage of the existing Pl@ntNet species identification algorithms and its mobile and web functionality, we present a new application in Pl@ntNet focusing on 'Crops'.
LUCAS cover photos The  Comparing typical Pl@ntNet and LUCAS Cover photos. Pl@ntNet photos distinguish leaves, flowers, and fruit. LUCAS Cover: L1 photos are included in the app (probability > 0. 9), L2 is <0. 9 but >0. 4, while L3 is <0. 4. Reproduced from  [19]. CC BY 4. 0. four cardinal directions from the point. A detailed methodological description, as well as the harmonised data and photos are published in  [18]. Besides the photos mentioned above, previously unpublished 'cover' photos were also taken. The protocol specified that these cover photos 'should be taken at a close distance, so that the structure of leaves can be clearly seen, as well as flowers or fruits'. During the five campaigns, 874 646 LUCAS cover photos were collected, out of which 242 476 were of crops. After a two-step anonymization process relying on computer vision and visual inspection (detailed in  [19] ), these photos will be published in 2022.
Pl@ntNet crop recognition models Pl@ntNet is an automated visual identification system that integrates various novel approaches in handling and classifying imagery  [9]. Pl@ntNet is built on the combination of a deep learning image classification model and a generalist content-based image retrieval method (for details see  [9, 16] ). Besides computer vision based identification, the Pl@ntNet framework is itself synchronized with observations that are validated by a network of expert botanists. This allows the recognition performances to improve with an ever increasing amount of training data. Pl@ntNet datasets and algorithms are also benchmarked in community-steered identification challenges such as LifeCLEF  [1]. This type of long-term evaluation makes it possible to quickly detect major advances in terms of performance and to accelerate their integration into operational production systems such as Pl@ntNet.