dataesr/scientific-paragraphs-categorization huggingface dataset:
• The purpose of the research is to make existing scattered heterogeneous data available, organising and finalising it, to allow teaching/learning procedures, providing step by step methodologies not only for shaping a multidimensional vision but also for actively undertaking the regeneration of rural regions. • Within this research framework, RRE architecture's main aim is designing and realizing a novel, integrated and user-friendly digital platform for an evidence-based holistic and dynamic knowledge-building and sharing of CNH within territories through tool integration. • The specific objective is to foster and facilitate actions across a variety of tools. With this aim, RRE enables the exchange and re-use of data and information by re-directing the same data gathered and organised into various datasets by different research partners to specific tasks, thanks to the integration of tools and datasets.
Furthermore, the Data sources Layer also integrates third-party platforms, such as Copernicus Land Monitoring Service (CORINE) Land Cover  [20]  and Natura 20 0 0  [28]. CORINE Land Cover is part of the Pan-European component, while Natura 20 0 0 is part of the local component of Copernicus and both are coordinated by the European Environment Agency. The RRE data is autonomously retrieved by the respective software platforms. The Distributed Core Components Layer provides developers with a set of Services designed using the SOA approach by exploiting REST Web Services  [29]  to develop a distributed software infrastructure that can be deployed on different servers and cloud systems. REST stands for Representational State Transfer, guiding both design and development of distributed Services that exploit the widely used HTTP (Hypertext Transfer Protocol) for data exchange over the World Wide Web (www).
Consequently, the various Services (i. e. : software components in Fig. 2  ) can interoperate by sharing and autonomously exchanging information over the Internet, establishing Machine-to-Machine communications. Each individual Service provides endpoints in the form of URLs (Unique Resource Locators) returning information following JSON and GeoJ-SON, which have been chosen as the main data-formats for data exchange amongst the different Services and applications in the whole RRE. JSON stands for JavaScript Object Notation. It is an open and standard data format for storing and transmitting information as human/machine-readable text. GeoJSON is a variant of JSON which reports non-spatial information with geographical features (e. g. : georeferenced points, line strings, polygons and multipart collections of these types).
This layer aims to (i) allow interoperability across heterogeneous technologies, (ii) store all the data and make it available in the Data Source Layer, (iii) post-process, correlate and analyse the heterogeneous data and (iv) provide remote services to develop distributed applications and tools. We developed different autonomous services to expose the heterogeneous data, forming the Distributed Data Storages shown in Fig. 2. In its core, each data storage Service implements a Mon-goDB database ( https://www. mongodb. com/ ). MongoDB is a nonrelational, document-orientated database management system. It moves away from the traditional table-based structure of relational databases in favour of JSON-style documents with dynamic schema. This makes the integration and management of heterogeneous information more flexible.
For further extensions, new services can be developed thanks to the SOA approach adopted to develop RRE, implementing other database management systems that can better address requirements to store new information needed by future applications and tools without reimplementing the whole RRE from the scratch. This data, stored in MongoDB and made available by REST Web Services, is mainly gathered from static datasets or uploaded by end-users via RRE tools, such as My Cult-Rural Kit and DRHH. The End-users Applications Layer represents the highest layer of the infrastructure proposed. It consists of a set of applications for end-users. At this level, interoperability is enabled between lowlevel heterogeneous technologies. Thanks to the software interoperability amongst the different services (i.
e. : the blocks shown in  It is worth noting that the whole RRE has been developed in consideration of possible future extensions, which could also include the development of new digital tools and interoperability with third-party software. More database management systems can be integrated in RRE without refactoring or redesigning the whole RRE and the system has been conceived for storing different and heterogeneous data, according to the availability of information that can change territory by territory (size depending only on the quantity of data available in the territories according to the structure presented in Fig. 2  ). Examples of possible future extensions are new Apps and/or new software analytics coming from further case analyses and implemented by tool developers. These software extensions coming from new case analyses can provide new requirements and requests from other territories and interested stakeholders.
The way to obtain the weight of a KPI is through Group Decision Making (GDM), collecting, analysing and combining opinions from domain experts through the Analytic Hierarchy Process  [38]  and Ordered Weighted Averaging  [39]  While providing its own web application, the Monitoring platform has been integrated with the Atlas for the visualisation of KPIs for each territory [ Fig. 5  ]. RRE also includes co-monitoring methods by hosting the My Cult-Rural toolkit. Martin et al. [40]  describe how it combines community workshops with ubiquitous mobile phone technologies to extend the reach of the engagement. These two approaches to data gathering comprise (i) Physical tools based on three participatory workshop methodologies: Mini-Landscapes, Object Mapping and Walking Maps, and (ii) Digital tools that use two participatory mapping methods facilitated by mobile phone (tablet) apps: Rate my View and Landscape Connect.
Free mobile phone applications (apps) are available for the two major mobile platforms. The apps are free to download and they allow the collection and georeferencing of text and images, using smartphones or tablets. Both apps allow in-the-field user data collection, combined with a server-based back-end that allows real-time data analysis by researchers and workshop facilitators [ Fig. 6  ]. The images and text collected by Rate my View are uploaded as JSON documents with the picture taken by the end-user to the Rate my View database, exploiting the REST Web Services provided by Rate my View back-end. The REST Web Services are also used by the Atlas to retrieve information from Rate my View. The Landscape Connect Mobile App exploits the REST Web Services provided by Landscape Connect back-end to download and upload questionnaires and responses stored in the Landscape Con-nect database.
The different coordinators of the local hubs and their stakeholders can access the forum with a moderator of discussions, to share and exchange ideas on possible actions to be implemented during the co-development phase of the heritage-led regeneration plans. Moreover, the DRHH also includes educational and capacitybuilding activities through a series of webinars on the topic of each systemic innovation area. The DRHH has been created using the open-source application NodeBB ( https://github. com/NodeBB/NodeBB ). It is a modern discussion platform that also provides features for instant interaction and real-time notification. Starting from the bare minimum installation, we customized Graphical User Interface to satisfy the requirements. The NodeBB functionalities have been extended by developing new REST Web Services to fully integrate the DRHH into the whole RRE.
Consequently, the raw and post-processed information can be easily shared with other Services and tools over the Web. Fig. 7  shows the DRHH software schema. In addition, users can create their posts using a simple text box with features to attach multimedia files. The DRHH back-end shown in Fig. 7  integrates MongoDB as database management system, as the information to be stored does not have a predefined data structure. DRHH back-end allows instant interaction and real-time notification, and provides features for social network integration and streaming discussions. In addition, DRHH back-end provides additional custom-built REST interfaces i) to support other tools in RRE in accessing constantly updated information, even in (near) real-time, if necessary, such as the DSS, and ii) to retrieve content filtered by categories from the DRHH database.
Processing and filtering data and strategies The information collected from the case studies during the first phase of the project enabled a detailed analysis of heritage-led regeneration processes. The role models were systematically studied to extract key factors to offer methods and actions to capitalise on the cultural and natural capital of rural areas. Systemic Innovation Actions and Community Capitals Frameworks were used as harmonisation tools in order to build a multilevel database of best practices. The information was gathered through three campaigns and the knowledge was extracted and codified through an experience mining process to support the replication of the successful strategies. These analyses also allowed the identification of 70 common lessons learnt. This abstraction and conceptualisation of best practices have been included in a multilevel database as specific replicable strategies for replicators.
Consequently, the database has codified information regarding role models at Systemic Innovation Action, process, specific action and lesson learnt level. A detailed description of the process and preliminary results can be found in Egusquiza et al. [15]. Based on these results, replicators are supported by the digital tools available in the RRE, including the DSS, which is a system for supporting the discovery and composition of possible heritage-led regeneration scenarios considering previous initiatives and providing suggestions for finalizing strategic actions. In the methodology considered, the DSS combines the best practices and an Inventory of Lessons Learnt dataset, which includes the data gathered from various role models and processed, allowing the choice of comprehensive programmes to be implemented by other local stakeholders who aim to undertake a regeneration process.
Fig. 8  shows the main Services distributed by the RRE which cooperate to compose the DSS. The DSS Web Portal is a module designed to allow users to interact with the DSS through a web service page. Users are able to explore data from the datasets integrated in the RRE and make use of the multiple functionalities that the DSS provides for filtering and addressing new purposes. The DSS Wizard functionality uses an internal data model combined with statistical, filtering and sorting mechanisms that were designed based on relevant features that were indicated by RU-RITAGE rural regeneration experts. For multiple scenarios, the results have been reviewed and approved by these experts and have been described as relevant with correlated information. This way the Wizard can make smart selections offering the user an initial good collection of information to start exploring.
Furthermore, the RRE goes beyond a platform that includes parallel tools with their individual functionalities. It merges data from different sources by integrating existing data, data gathered and processed, and data developed by several different research developments. Data from institutional datasets and scientific research is merged with data collected using the bottom-up approach. By integrating tools, functionalities and all this data RRE enables a holistic digital and heritage-led knowledge of territories for actions. It fosters a user-centred heritage digital transformation of rural areas. Lastly, the RRE enables a novel dynamic and evolving vision of territories with their ongoing change through time. Some functionalities within the ecosystem continue to feed and new external inputs can also be incorporated as the RRE is an open platform.
The final outcome also stems from a strong integration of historical knowledge of CNH and territories with operational data and solutions for management requirements. The necessary cross-fertilisation and intersectoral collaboration between ICT experts (requiring "complete" information for customizing tools) and humanities fields experts (gathering data and processing information from different data sources and for different purposes with progressive improvements), is challenging. This paper includes humanities and ICT contributions with their different terminology and scientific understandings and references, illustrating the challenge of this collaboration and its scientific outcome across the H2020 research project. Multidisciplinary cross-fertilisation fostered the achievement of a novel holistic user-centred digital ecosystem for heritage by supporting the digital transformation of rural areas.
The notably high-quality diffusion MRI datasets from the ~1000 Human Connectome Project (HCP) healthy young adults  28  , together with well-refined diffusion MRI tractography, were used to virtually reconstruct callosal fibers (Fig. 1 ). For each mCC voxel, two weighted cortical connectional topographic maps were generated at the population level; for each pair of homotopic cortical regions, two weighted mCC connectional topographic maps were generated at the population level. Furthermore, we developed an online tool to 1) interactively visualize such a comprehensive atlas and 2) estimate cortical or mCC topographic maps for customized mCC or cortical regions of interest (ROIs).
Discussion Using a high-quality diffusion MRI dataset from a large group of healthy adults we constructed the first bidirectional population-based connectional atlas between the mCC and cerebral cortex of the human brain. This comprehensive atlas provides connectional topographic maps for both the mCC and cortical surface at the highest resolution ever (i. e. , mCC voxel and small cortical patch). The validity and reproducibility of the estimated topographic maps were proven by comparisons with existing histological data and between population sampling, respectively. A user-friendly interactive online tool, i. e. , CCmapping, was further developed for easily visualizing and accessing these maps. Through these topographic maps, preliminary analyses revealed positional dependence and microstructural correlations between callosal fibers and their connected cortical regions in the human brain, demonstrating the added value of these maps in understanding human callosal connections.
Additionally, the ultimate topographic maps were generated by combining individual tractography data from almost 1000 healthy adults. The combination across such a large group of healthy adults can effectively reduce noises or errors that occur randomly at the individual level, further improving the accuracy of the ultimate topographic maps. The present study offers efficient solutions for two key issues of usability: 1) thresholding and 2) accessing these maps. First, rigorously thresholding a brain map is often required before analyses and interpretation. Numerous thresholding methods have been developed for statistical parametric maps (e. g. , t map, p map)  [40] [41] [42]. However, they are not suitable for our data. Therefore, an in-house thresholding method based on permutation and Moran spectral randomization was developed  43  , which should be usable for other studies with similar data structures.
Future investigation is warranted to determine the functional mechanisms underlying this link. Finally, a few limitations should be addressed. First, the validation for our topographic maps is based on the histological data in the literature. These data are limited and describe relatively coarse anatomical locations, leading to our validation analysis at a very low resolution. Next, our estimated maps are derived from healthy adults. It is unclear whether they can be extrapolated to other populations (e. g. , newborns and patients with brain diseases). Future studies are encouraged to map topographic maps for other populations and even other species (e. g. , macaque). Finally, in addition to the population-based atlas, individualized topographic maps are of great value for personalized analysis and interpretation but are beyond the scope of our current study.
MRI dataset The dataset included all possible HCP participants for whom both diffusion and T1 images were available. It comprised 928 healthy young adults (female/male: 503/425; age range: 22-37 years). Informed consent was obtained from all subjects, and the protocol was approved by the Institutional Review Board of Washington University. MRI scanning was performed using a customized Siemens Connectome Skyra 3T scanner. Diffusion-weighted (DW) images were acquired using a spin-echo echo-planar imaging (EPI) sequence with the following parameters: repetition time (TR) = 5520 ms, echo time (TE) = 89. 5 ms, flip angle = 78°, FOV = 210 × 180 mm 2 , matrix = 168 × 144, slices = 111, and resolution = 1. 25 mm × 1. 25 mm × 1. 25 mm. Diffusion weightings of b = 0, 1000, 2000, and 3000 s/mm 2 were applied in the 18, 90, 90 and 90 directions, respectively.
99. To ensure voxel-wise comparability across individuals, the individual mCC was nonlinearly aligned to a manually outlined template mCC on the MNI152 template image  49. Specifically, the 2D sagittal image of the template mCC was set as the target image (or the fixed image), and the 2D sagittal image of the individual mCC was set as the source image (or the moving image). The two 2D images were first smoothed with a 2D Gaussian smoothing kernel (sigma = 1) by using the 'imGaussfilt' function in MATLAB. Next, we linearly aligned the source image to the target image (i. e. , performed a 2D affine transform of 8 degrees of freedom, including translation, rotation, scaling, and shearing) using the 'imregtform' function in MATLAB. Next, we performed nonlinear registration from the linearly aligned source image to the target image using the demons algorithm that was implemented by the 'imregdemons' function in MATLAB.
e. , fibers traversing them were discarded). Here, subcortical nuclei, including the thalamus, hippocampus, amygdala, caudate, putamen, palladium, and accumbens, were extracted using the FIRST tool of the FMRIB Software Library (FSL)  53. To improve the biological accuracy of the tractograms, the anatomically constrained tractography (ACT) framework was used, which incorporates prior anatomical information into the tractography  38. The anatomical information was obtained by segmenting T1W images into tissue partial volume maps (PVMs) for WM, GM, and cerebrospinal fluid (CSF) using FSL tools. The resultant PVM images were then taken as anatomical priors into the ACT framework. For each HCP participant, the minimal preprocessed T1W image was well aligned with the DW images; therefore, the mCC and PVM images were already in the DW image space.
Reproducibility analysis To test the sampling effect on our currently observed population-based topographic maps, we re-estimated topographic maps with split-half HCP samples (464 HCP subjects for each sample) and assessed the similarities of resultant topographic maps between the two split-half samples. Next, to evaluate the sibling confounding effects on the maps, we re-estimated the topographic maps using the 93 unrelated (nonsibling) subjects from the HCP unrelated set and evaluated their similarities with the main maps. Finally, a larger number of initially generated callosal streamlines from tractography is preferred for estimating stable topographic maps. However, an excessively large number comes with a very large burden of data storage and computation. To assess whether the 10 M initially generated callosal streamlines from tractography for our main analysis was enough, we compared the topographic maps between our main scheme (i.
An online interactive viewer for both mCC and cortical topographic maps We developed a web-based viewer using JavaScript to interactively visualize each populationbased topographic map above. This tool was largely based on the open-source library BrainBrowser 54. BrainBrowser supports real-time visualization of the 3D cortical surface, brain volume, and various kinds of neuroimaging data in any modern web browser by WebGL, HTML5, and other technologies. In addition, Echarts and Node. js technologies were applied.
Acknowledgments This work was supported by the  Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project of China  (grant no. 2021ZD0201701 ,  2021ZD0200502 , G. G. ) and  National Science Foundation of China  ( 82172016 , G. G. ). Data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
PARAMETER SETTINGS AND ALGORITHMS We use two reinforcement learning algorithms: a gradient policy search algorithm (PPO) and a direct policy search algorithm (CMAES). Both algorithms are used to learn the parameters of the focal agent's decision modules. For both algorithms, the performance of a policy (i. e. the return or the fitness, depending on the vocabulary used) during one episode is computed as the sum of rewards during the episode (cf. Section 2. 1), which is either zero, or the value of the unique non-zero reward obtained before the episode ends.
e. Θ ∈ R 34 ). Table  2  summarizes the parameters used for the CMAES algorithm. As CMAES is mostly parameter-free, there were no need to perform extensive preliminary search, and we used the default values. We choose   = 1. 0 for the initial standard deviation and a vector of zeros as initial guess. The population size  is the default population size in the python CMAES implementation  [12] , i. e. = 4 + ⌊3 × ln( )⌋ = 14 with  the number of dimensions in the model. Once the  candidate solutions are evaluated, a new population is generated according to their performance. A new population is generated every 14 episodes, and so forth until the evaluation budget is consumed. A candidate solution for the focal agent is evaluated on one episode only, which length may vary depending on when the focal.
RESULTS The environment, the models and the learning algorithms are implemented with ray 2  , rllib 3  and pytorch  4. We use the cma  5  package in python for the CMAES implementation. Source code is available at https://github. com/PaulEcoffet/RLCoopExp/releases/tag/v1. 1. For a given value of probability of rare significant events , we performed 24 independent runs for each algorithm. A run lasts 200 000 episodes. The maximum duration of an episode is fixed as described in Section 2. 1 so the expected number of significant events remains identical independently from the actual rarity throughout one episode (cf. equation 4). In practical, an episode lasts at most 100 (resp. 200, 500, 1000) iterations for  = 1. 0 (resp. 0. 5, 0. 2, 0. 1). Performance of the current policy is plotted every 4000 iterations, which corresponds to the batch size used by both PPO instances for learning.