dataesr/scientific-paragraphs-categorization huggingface dataset:
The experimentation itself can follow varied protocols according to varying hypotheses, and thus the same initial knowledge leads either to alternative ways to reach alternative outcomes, or to the same outcomes reached by alternative ways. Comparing ways to outcomes is one source of research assessment and one strategic way to exploit search history in GRAPHYP. ) among which science has to choose and to optimize its access routes to results. In this field, research impact assessment is challenging in that it must establish a relevant demarcation between the directions traced by scientific outcomes and their routes, and identify the associated semantic changes in the scientific vocabulary, as well as the related practices in data stewardship. These tasks are included in the purpose of the SKG GRAPHYP.
Subnetwork Analysis and Comparisons of Cliques in Cognitive Community (ii) Representing cliques in cognitive communities at various scales Cooperative or connecting routes can be identified by the GRAPHYP data structure, GRAPHYP shows an undirected weighted graph structure (each edge is bi-directional and receives a distinct weight from the nodes linked inside a network), which is connected (one can reach any node from all other nodes inside identified paths) and builds a minimum spanning tree as a subgraph containing all nodes, connected here with the minimum possible number of edges. (ii) Representing cliques in cognitive communities at various scales Cooperative or connecting routes can be identified by the GRAPHYP data structure, which involves adjusting the size of the selected community to an optimized scale.
(ii) Representing cliques in cognitive communities at various scales Cooperative or connecting routes can be identified by the GRAPHYP data structure, which involves adjusting the size of the selected community to an optimized scale. This in turn raises questions of the optimal calibration of subgraphs in the perspective recently called by Michael Bronstein, that of "latent graph learning" and the associated methodology for counting isomorphisms of subgraphs  [40]. As illustrated by Figure  5  below, GRAPHYP with nodes A, B, C, etc. , is built by the addition of graphs of the same shape, and open thus to a self-similar substitution (https://tilings. math. uni-bielefeld. de/glossary/selfsimilar-substitution/ 2 August 2022); this self-similarity characteristic of GRAPHYP  [46]  allows knowledge extraction at any scale and allows operating scalability from perimeters of information processed from operations of addition, subtraction, multiplication and division, which, to the best of our knowledge, has not yet been outlined in graph architectures.
Sparsity based methods, such as wavelets, have been state-of-the-art for more than 20 years for inverse problems before being overtaken by neural networks. In particular, U-nets have proven to be extremely effective. Their main ingredients are a highly non-linear processing, a massive learning made possible by the flourishing of optimization algorithms with the power of computers (GPU) and the use of large available datasets for training. It is far from obvious to say which of these three ingredients has the biggest impact on the performance. While the many stages of non-linearity are intrinsic to deep learning, the usage of learning with training data could also be exploited by sparsity based approaches. The aim of our study is to push the limits of sparsity to use, similarly to U-nets, massive learning and large datasets, and then to compare the results with U-nets.
In this paper, we investigate whether using massive learning and large available datasets in a sparsity framework could allow us to achieve U-nets performance or if the chained non-linearities are equally important. We propose a new network, called Learnlets, which makes use of one of the strongest advantages of neural networks, learning via gradient descent to enhance the expressive power of wavelets, while keeping some interesting wavelet properties such as exact reconstruction. We choose to test this network on a denoising problem, a task where wavelets have historically well-performed but are now overtaken by deep learning approaches. In parallel, we also propose a new U-net denoising scheme that guarantees an exact reconstruction when the noise tends to zero. The full implementation of our method is open source in Python 1.
Related Work Different studies have attempted to work at the intersection of wavelets and neural networks. In  [11] , the authors cast the wavelet transform as an auto-encoder where the latent representation has to be sparse and learn the filters. In this architecture only a simple highpass and low-pass filter pair is learned. Similarly, but pushing further the idea,  [12]  developed a learning strategy to design new wavelet filters with certain properties 1 https://github. com/zaccharieramzi/understanding-unets imposed such as what they call perfect reconstruction (which we termed exact reconstruction) or vanishing moments. Their work was inspired by  [13]  where the authors chose to use data patches to learn their transform rather than noise like in  [12]. Observing U-nets, two parts are very similar to synthesis and analysis concepts in wavelet decompositions,  [6]  proposed to use the wavelet transform to perform a better pooling/unpooling strategy than simply maxpooling/bilinear upsampling.
Data The data used was the BSD500 dataset  [16]. This data consists of natural images of sizes 481 × 321 and 321 × 481. The train and tests subsets of BSD500 were used as the training dataset. The validation subset of BSD500, containing the BSD68  [17]  images was left out. We used BSD68 as the test dataset. This choice is motivated by the fact that many other denoising studies  [18] ,  [19]  use this dataset for comparison.
Testing In addition, the networks were compared to wavelets denoising  [21] , which was implemented by using the code of PySAP  [22]. The wavelets family was the Biorthogonal 7. 9, 5 scales were used, a hard-thresholding was used with a thresholding level of 3 (except for the first scale where it was 4). Table  1  Runtimes of the different models for the denoising of one image. Parameters used are the same as Figure  3.
Conclusions Pushing the limits of sparsity using massive learning and training data, we have proposed a novel neural net- work architecture -named Learnlets -with the following properties: -Although their performances are inferior to U-nets, learnlets generalize better on noise levels that were not present in the training data and in the exact reconstruction domain. They also do better on the astronomical image which is different than the images present in the training dataset. In this case, the U-net's result is very poor. -Learnlets can be forced to guarantee exact reconstruction when no thresholding is applied. This allows an embedding of the learnlets in applications where there is a need for guarantees of retrieval like in medical imaging. By contrast, U-nets suffer from a loss of performance at high noise levels.
Learnlets therefore bridge the gap between parsimony and neural networks, by combining massive learning and the computing power of GPUs as in neural networks, but keeping a perfect understanding of how results are obtained, with all the theoretical guarantees existing in the area of parsimony. Learnlets do clearly not outperform U-nets in denoising images compatible with the training dataset, which would indicate that massive learning and large datasets are not enough to explain the difference between sparse techniques and neural networks. The highly non-linear processing in U-nets brings certainly a critical aspect in achieving high quality results. Our main message is that we have clearly identified in this study a trade-off to be made in any application between performance and generalization.
Generally, these tools were first developed for seismic risk and later added risk analysis for other natural hazards such as tsunamis, landslides, floods, etc. Concerning first stand-alone applications for risk assessment, we can mention some of the software's best known and most used (e. g. HAZUS  (FEMA 2004) ; RiskScape  (King and Bell 2005; Schmidt et al. 2011) ; EQRM  (Robinson et al. 2006 ); MAEviz-HAZTURK  (Elnashai, et al. 2008) ; CAPRA-Central America Probabilistic Risk Analysis  (Gill et al. 2009 ); SELENA-SEismic Loss Estima-tioN using a logic tree Approach  (Molina et al. 2010) ; ELER-Earthquake Loss Estimation Routine  (Hancilar et al. 2010) ; Armagedom  (Sedan et al. 2013) ). These tools perform either deterministic or probabilistic analysis and are based on the convolution between the hazard and the vulnerability of the exposed elements.
The hazard is described either by PGA (peak ground acceleration) and spectral values of acceleration or by intensity value. The vulnerability is described by a taxonomy at which we attached either the parameter for the description of the capacity curve and of the fragility curve (most of software's) or a vulnerability index (e. g. Armagedom software). These tools have been developed by different teams around the world and have been applied, firstly, specifically in the respective territory (e. g. Capra mainly used in South America, HAZUS in the US, and RiskScape in New Zealand, Armagedom in France) before being extrapolated for use worldwide if the required input data are available. Most of these stand-alone tools have evolved, in general, into what we call now a "platform", as they are meant to be deployed to a large community of users, to offer a wide variety of methodological approaches, with the ability to be used with a user-friendly front-end web application.
For example, initial Capra multirisk assessment tools become a platform  (ecapra. org). SELENA open source tool  (Molina et al. 2010 ) is currently in implementation in the platform developed in TURNKey project (https:// earth quake-turnk ey. eu/). The standalone Armagedom software  (Sedan et al. 2013) , which calculated EMS98 damage degrees of building affected by earthquake based on the methodology development on RISK-UE project  (Mouroux and Le Brun 2006) , is currently implemented in VIGIRISKS platform. Moreover, in parallel the above-mentioned software which have adapted their IT infrastructure to become a platform, a new generation of platforms (e. g. Openquake, Rasor, risk. EFEHR) in field of natural's hazards was developed more recently and there are based on open-source codes and on WebGIS technologies.
These new platforms are not necessary based on previous standalone tools, but, as the previous ones, on the most update research methodologies in the field of natural hazard engineering. Basically, we will consider here that a platform is a combination of computational scripts consisting of commands integrated with some visualization and documentation, and in our case, all of these functions being oriented towards multirisks mapping. Table  1  (Supplementary material) attempts to define some of the functions of these web-tools that might be interesting to know from the user's point of view, i. e. how they can be used, whether they provide known databases and/ or case study datasets, whether we can import our own files into any study area to make calculations and whether we can download the results.
Discussion related to the manner of using the web-tools (Column 2 -Table 1 in supplementary material) • For the manner of using the web-tools, some of them can be download and used like any other desktop software (e. g. Capra, Prevention Web), other need registration for accessing to a Web portal (e. g. PREVIEW, INFORM RISK) and some others are compatible with Qgis/ARCGIS plugin (e. g. HAZUS-MH) or some of them allow several of these uses together (e. g. Openquake, VIGIRISKS, Rasor, …). The Column 2 in Table  1  in supplementary material tries to describe the manner of use of each tool/platform (e. g. software desktop and standalone application/plugin Qgis/Web portal). Beside of these use modes, the automatic mode for a rapid response can be considered independently or in relation with some other institutions and teams that can provide hazard ShakeMap.
In relation with ShakeMap (USGS4. 0), VIGIRISKS developed automatic damage calculation that are presented in SEISAid reports, for Mayotte Islands, and, for French-Spanish-Andorra borders, in the ongoing European project Pocrisc, (https:// pocri sc. eu). Along the same lines,  Silva and Horspool (2019)  combined USGS shakemaps and the openquake-engine for damage and loss assessment. Considering this specific issue, the software and tools that treat about the Rapid earthquake response the authors suggest to the readers to look to the detailed paper of  Guérin-Marthe et al. (2021). Discussion related to hazards/phenomenon's treated (Column 5: Table  1  in supplementary material) • Considering the hazards treated, some of them consider one hazard (e. g. for earthquake Openquake, risk. EFEHR, IRMA, for landslide and flood  Aye et al.
Discussion related to what the platform does (Column 6: Table  1  in supplementary material) • From the user's perspective, it is important to mention what the platform does. We have noted that the different tools perform several tasks and that each of them has its importance and usefulness in different risk management contexts. Although it is difficult to establish exact categories, knowing that all of them are interconnected, we can mention some of them that seem to us to be the most specific: displaying or visualizing, crossing maps, providing indicators in tabular form, presenting pre-calculated scenarios and direct calculation by the user. This last issue has a strong link with the possibility of upload and use of your own dataset and with the possibility to download your own results or to download the provided information (Column 3-Table  1  in supplementary material).
An analysis from the point of view of using InaSAFE is the process whereby a hazard layer, an exposure layer and an optional aggregation layer are used to determine the potential impact of the hazard data on the exposure. The analysis results are grouped by region (as defined in the aggregation layer). It considers several hazards and the vulnerability is mainly related to the people and demographic description. RASOR platform  (Rudari et al. 2015; Arrighi et al. 2018 ) developed during Rapid Analysis and Spatialization and Of Risk (RASOR) EU project performs multi-hazard risk analysis to support the full cycle of disaster management. RASOR adapts the 12 m resolution TanDEM-X Digital Elevation Model (DEM) to risk management applications, using it as a base layer to interrogate datasets and develop specific disaster scenarios.
RASOR platform performs calculation and provide datasets for the case studies development in the project. Among platforms based on previously performed calculations, we can cite the PRE-VIEW Global Risk Data Platform  (Giuliani and Peduzzi 2011) , which is a multiple agencies effort to share spatial data information on global risk from natural hazards. This is very useful for the users that want to visualize, download or extract data on past hazardous events, human & economical exposure and risk from natural hazards. It covers almost all natural hazards (e. g. tropical cyclones and related storm surges, drought, earthquakes, biomass fires, floods, landslides, tsunamis and volcanic eruptions). The main force is that the collection of data is made via a wide range of partners (see about for data sources on the website https:// previ ew.
grid. unep. ch). This was developed as a support to the Global Assessment Report on Disaster Risk Reduction (GAR) and replace the previous PREVIEW platform already available since 2000. The CEDIM Risk Explorer Germany  (Müller et al. 2006; Köhler et al. 2006 ) is a web-based map viewer that interactively presents the results of the CEDIM Research Focus Risk Map Germany. In 2021, the platform moved and was integrated into the CEDIM/Risklayer Explorer (risklayer-explorer. com). The CEDIM software was developed by the Center for Disaster Management and Risk Reduction Technology in Potsdam, Germany, but it was used for damage and risk analysis on other earthquake prone areas of the world. In the fields of floods and landslides, we can mention  Aye et al. (2015)  that develop a prototype web-GIS (Geographic Information System) decision support platform.
Discussion related to database and datasets (Column 4: Table 1 in supplementary material) • Another important attribute of a platform is the possibility to share database and datasets. Significant efforts have been made in the last 25 years towards the development of regional, national, continental, or even global databases that can be used for risk models, such as the National Risk Assessment (NRA) for Italy  (Dolce et al. 2019 (Dolce et al. , 2020)) , the Global Earthquake Risk Model developed by the Global Earthquake Model (GEM) Foundation  (Crowley et al. 2018; Silva et al. 2020 ) and, more recently, the European Seismic Risk Model ERSSRM20, which is an output of the European Horizon 2020 SERA project  (Crowley et al. 2020b). Some of the platform provide database (e. g. risk. EFEHR, Openquake, PREVIEW, Hazus, Capra, …), some others provide case studies datasets (VIGIRISKS, Rasor, …) and are using many open data sources (usually the one provided by the other platforms) to compose its own datasets.
risk. EFEHR, Openquake, Hazus are the most representative for the standardized database provided. Hazus provide building stock and vulnerability characterization for the US territory. As a product of GEM foundation, Openquake engine  (Silva et al. 2014; Pagani et   Fayjaloun et al. (2021)  present earthquakes damage calculation for Luchon area using 3 datasets with different resolution level: Europe resolution using the European soil map and the European database for the buildings (issued from SERA project), France national resolution using the National soil map and the national statistics database for the buildings  (Tinard et al. 2020) , at the municipal level and Luchon specific area resolution using the soil maps and building data issued at the district level (SISPyr and POCRISC projects).
The VIGIRISKS platform includes a workflow for the extraction of the building census information from the national resolution database of France, adapted during the project  Tinard et al. (2020)  based on National statistics Institute information (INSEE). Column 7 in Table  1  in supplementary material shortly resumes the approaches used in the platforms. Some are using statistical approaches based on inventories (e. g. RISK-UE; Thiery 2007), some other are using complexes description of the phenomenon  (Vandromme et al. 2020) , some are performing probabilistic Risk Analysis (e. g. SELENA, CAPRA, Prevention Web, …) some others deterministic scenarios and some other both of them (e. g. Openquake). For more complete and details information about each platforms the main references are presented in Column 8 in Table  1  in supplementary material.
The main complexity for developing a multirisks assessment platform is that two communities that, generally, do not work together should consider the subject conjointly (e. g. software developers and phenomenological researchers). Indeed, software developers are concerned by subjects that are quite obscure for the phenomenological researchers, such as automated and reproducible builds, the need and process of thorough testing, or the importance a flexible design. On the other way, software developers need to have reliable methodologies and dense input databases that describe a physical phenomenon like earthquake, landslides and the exposed elements that can be affected. In the following, we present the basis of the VIGIRISKS multirisks web platform  (Tellez-Arenas et al. 2019; Negulescu et al.
2019). This web platform has the ambition to propose computations allowing multi-risks mapping in a realistic manner, e. g. including cascading effects, vulnerability assessment and damages evaluation. We particularly show the computing infrastructure, the different digital functionalities, the different ways to use the platform as digital lab to develop methodological risks assessment algorithms and the way to test, manage, and disseminate cases studies where the VIGIRISKS platform is used. Although we have tried to be as comprehensive as possible, given the rapid emergence of new technologies and platforms, the introduction to this article does not present an exhaustive view of web-based risk platforms and related risk datasets used in risk assessment. The main objective of our paper is to present the current advances of the VIGIRISKS platform in terms of web-based infrastructures and risk assessment capabilities for different natural hazards.
General considerations VIGIRISKS is a web platform designed by the BRGM for developing, managing and disseminating multirisks mapping products for decision-making purposes. VIGIRISKS is composed by different components that communicate with each other in order to improve the relation between researcher develop code to user and the interoperability between other platforms. From a simplified point of view and with the concern of being straightforward, we can summarize these components as follows: VIGIRISKS User Web Portal,  VIGIRISKS Web Services and VIGIRISKS Scientific codes (Fig. 1 ). Note that in this paper we use these three syntaxes in Italic and Bold in all the paper in order to indicate in a homogeneous way the three parts of VIGIRISKS. The development of the VIGIRISKS platform is based on GITLAB, which is a server that allows code to be versioned.
For each workflow (Fig. 2 ), the list of entries to be used is displayed in the form of dialog boxes. Depending on the type of entry required, it becomes possible, for example, to enter text, specify a file from the uploaded data, or select a field from a drop-down list. Then, a button is used to launch the execution of the workflow during which the logs produced by the code are displayed in real time. If an error occurs, execution stops and alerts the user. Otherwise, when all the outputs have been calculated and produced, a list of output files are displayed with the possibility of downloading them. These different parts of VIGIRISKS platform are detailed in next two chapters Sects. 2. 2 and 3. 1.
Conceptual framework of the platform The architecture includes a repository of available datasets, documents, links and reproducible versioned workflows in order to build an informational environment useful to risk engineer for elaborating the most reliable risks assessment solution. The executions and records of the results are described by metadata allowing discovery, evaluation and use. This is possible thanks to the use of "Cromwell", which is a Workflow Management System geared towards scientific workflows most of all related to human genetics research  (Tanjo et al. 2021). The use of "Cromwell" for VIGIRISKS platform is quite innovative comparing to existing natural risks platforms. The heart of the architecture (Fig. 3 ) is the workflows description and execution mechanism, based on JSON and python descriptions defining acyclic graphs of processes, executed through interoperable web services.
This is particularly important to test algorithmic solutions and disseminate them to the community. Figure  3  details from software point of view Fig. 1 , hence we try to correspond the software components that make up the three parts of the VIGIRISKS platform mentioned in Fig. 1 : VIGIRISKS User Web Portal in blue, VIGIRISKS Web Services in grey and VIGIRISKS Scientific codes in orange. All the words in "quotation marks" in this Chapter can be find in Fig. 3  that shows their importance and position in the software architecture. It is important to note that the VIGIRISKS Web Services (Fig. 3  in grey), that makes the web integration, has two parts one lighter (scientific workflow engine "Cromwell") and one darker (preparation form for web integration). The darker part named "web-workflows", together with the "wkf_xxx.