dataesr/scientific-paragraphs-categorization huggingface dataset:
Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.
INTRODUCTION Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. In a smart cities data hub  [6]  information is gathered in several different ways, spanning from the loading of static files to very dynamic data like the ones K-CAP 2015 October 07 -10, 2015, Palisades, NY, USA ACM ISBN  [POSTPRINT]. DOI:  [POSTPRINT]  pushed by sensors. Store retailers load the frequency of customers entering and exiting shops, places in car parks are monitored by sensors that register whether a space is busy or not, etc. Consequently, information is owned by different users, that fix the constraints on the usage of the data they provide under specific terms or licences.
With Datanode, processes can be represented as networks of data objects connected by semantic relations. With these descriptions, propagation rules can be established in order to capture how policies, represented in ODRL, propagate in rich data flows. These rules would then tell us what are, in given circumstances, the set of policies to apply to the output of a complex data processing task. However, having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of possible actions). We aim to provide an abstraction that allows to reduce the number of rules significantly. In this paper we show 1. a methodology to abstract policy propagation rules based on an ontology, 2. how effective this methodology is when using the Datanode ontology, 3.
The RDF Licenses Database  [12]  is a notable effort towards the establishment of a common database of licence descriptions based on RDF and the ontology provided by ODRL (among others). Rule based representation and reasoning on policies  [7, 9]  is required in order to enable secure data access and usage in distributed environments, particularly the Semantic Web  [3]. Processes can be described in the Semantic Web using the Provenance Ontology (PROV-O)  [10]. PROV-O describes workflow executions in terms of agents, actions and assets involved. The Datanode ontology  [5]  has been designed to describe Semantic Web applications by the means of the relations between the data involved in their processes. The ontology is a taxonomy of possible relations that may occur between data object, which might be part of a process execution, such as one represented using PROV-O.
It can therefore be used to further qualify the implications of the actions performed in such a process. Datanode can describe process implications in a data-oriented way: as network of data objects. The RDF Licenses Database and Datanode are the primary resources for the present work. Policies Propagation Rules are Horn rules. Reasoning on Horn rules is one way of dealing with policies, particularly because they allow a treatable defeasable reasoning  [1]. Formal Concept Analysis (FCA) has the capability of classifying collections of objects depending on their features. FCA has been used for ontology alignments (for instance in  [14] ). In our case we use it to detect common behavior of 3 https://www. w3. org/community/odrl/ relations in terms of policy propagation, and then to test and refine the Datanode ontology.
Listing 2: Examples of policies. p e r m i s s i o n o d r l : d e r i v e p r o h i b i t i o n l d r : e x t r a c t i o n duty o d r l : a t t a c h P o l i c y We could have generated the policies by combining any ODRL action with any deontic component. However, this would have led to a large number of meaningless policies (eg: duty odrl:use). The adoption of the RDF License Database permitted to obtain a list of meaningful policies only. We used the Datanode Ontology  [5]  to extract a list of 115 possible relations between data objects. This phase required a manual supervision of all associations between policies and relations in order to enstablish the initial set of propagation rules. The two collections have been used together in the Contento tool  [4] , that generated the FCA formal context, i.
A branch with similar scores is dn:sameCapabilityAs. Listing 8 shows that the only missing relation is the top one. In Datanode, dn:sameCapabilityAs is defined as the relation between two objects having the same vocabulary and the same population (containing actually the same data). However, it is possible that two objects have the same "data" without having the same policies. For example, datasets like lists of cities or postcodes might be imported from different sources, and having different policies while containing the same data! In this case we opted for adding a new relation to Datanode that can abstract all the branches with a more focused semantic: dn:sameIdentityAs. dn:sameIdentityAs tries to capture exactly the fact that two data objects share the same origin, the same population and the same vocabulary.
CONCLUSIONS In this paper we presented a method to abstract Policy Propagation Rules. The approach relies on the Datanode ontology  [5] , a hierarchical organization of the possible relations between data objects. We demonstrated that applying Datanode to this task allows us to reduce the number of rules to a factor of 0. 5. Moreover we have been able to analyse the ontology in relation to this task and enhance it having as result not only a further reduction of rules, but also a better ontology. In addition, applying the ontology and the method we were able to find and correct errors in the rules. A similar approach could be applied to the set of policies, by relying on the ODRL ontology, that contains subsumption relations between actions. However, the hierarchy of actions in ODRL is quite small compared to Datanode.
Companies today have several major issues while managing information. Many subsidiaries and departments have developed their own Data Management which has led to a multitude of Operational Databases and sometimes a multitude of Data Marts, policies and processes. Thus, these systems lack sustainability because they are not dynamic and not self-organizing, and so they do not adapt to the continuous needs arising from evolution that the companies experience. The Dynamic Data Mart architecture is built around 6 main functions, namely the 3Ms (Data Mining, Data Marshalling and Data Meshing) and the 3Rs (Recommendation, Reconciliation and Representation), which will address the aforementioned problems. Once the totality of the data have been loaded into a single Data Warehouse, the Dynamics Data Marts address these problems by mining the user's behavior and the user's decision making processes and continuously and automatically adapting the Data Mart to the needs of the users.
Existing Data Warehouses cannot be dynamic or self-organize This lack of sustainability of the data mart leads to the necessity of managing them constantly, in order to have a Data mart which is continuously consistent with the Business Processes. This process is particularly time-consuming and needs continuous care of the system: the quantity of data is indeed always growing whereas the processes are always evolving inside of the company. Therefore, the Data Warehouse is a hindrance to the development of the company, because it is not as dynamic and self-organized as they should be in order to answer the constantly changing needs of its users.
Also supports search, graph and other advanced capabilities for discovering new information models. There are no specific performance requirements and this option is favoured by advanced users such as data scientists or data miners, resulting in freeform queries across multiple data types. Conventional data warehouses designs, which are primarily centered on relational databases have their own limitations. The two well-known models/schemas, star and snow flake due to their basis in relational models fail to adequately represent the semantics and operations of multi-dimensional data. There is always the problem of running complex (aggregate) queries on complex data. Also efficient execution of SQL queries is limited when drilling down in a data warehouse based on these models. The relatively new model/schema, star flake, which is the merger between the star and snowflake schema, manages to address some of these issues but fails to address all of them.
Later researchers, who adopted Object-Oriented techniques and Multidimensional data modelling for complex data, proposed many variations to the warehouse design. Many of the designs arise from the data mining On-Line Analytical Processing (OLAP) areas. Most of them concentrate on efficiency in query processing and data access rather than data semantics. Most of them were commercially unsuccessful as warehouse designs due to the "un-popularity" of Object-Oriented Database Management Systems (OODBMS) and limitations imposed by relational design constraints  [1, 2]. A data warehouse, primarily contains historical, consolidated data and should not lose its semantics at any point in time. But all methods suggested above are either nonsemantically oriented or process oriented or a little bit of both.
Almost all data warehouse models discussed above fail to capture the business side of the data warehouse. As a starting point, primary users of data warehouses are non-technical, middle and top end managers who have little or no knowledge of databases. No business rules can be captured inside the data warehouse. Most of the data warehouse designs are incapable of interpreting the business cost of data stored in them. As the business changes rapidly, the warehouses cannot or need complex query manipulation or re-design to accommodate the business changes. Though the data stored in them is historical, the information derived from them should accommodate the changes in the core business. At present the models above provide very little or no support for dynamic business information retrieval. We are developing a methodology which utilizes current and new data warehouse design techniques to capture data semantics, business rules and business cost associated with each piece of information stored in or retrieved from the data warehouse.
4) Real-Time Data Warehouse: The traditional data warehouse was designed to store and analyse historical information on the assumption that data would be captured now and analysed later. System architectures focused on scaling relational data up with larger hardware and processing to an operations schedule based on clean data. Yet the velocity of how data is captured, processed, and used is increasing. Companies are using real-time data to change, build, or optimize their businesses as well as to sell, transact, and engage in dynamic, event-driven processes like market trading  [6]. To enable real-time data acquisition, Oracle GoldenGate  [19]  uses log-based, real-time CDC (Change Data Capture and Delivery) capabilities to provide continuous capture and delivery of the most recently changed data between OLTP systems and the data warehouse.
STAGE 2 : Data Progression The data stored in the database dumps in CDW should be extracted, cleansed to remove inconsistencies and fill gaps, and integrated to merge heterogeneous sources into one common schema. In order to achieve this, the first step is different databases integration by defining global schema and storing this in the Meta-Data Repository. In a multiple database system, a global schema created by integrating schemas of the individual databases that provides a uniform interface and high level location transparency for the users to retrieve data. Once the global schema is established, the next step is to use Extraction, Transformation, and Loading tools (ETL) to merge heterogeneous schemata, extract, transform, cleanse, validate, filter, and load source data into a data warehouse.
STAGE 4: User's Dashboard Once the integrated data is efficiently and flexibly accessed to the user friendly Dynamic Data Mart and GUIs (Graphical User Interfaces), managers will be able to analyse items listed according to priorities, they will be able to dynamically analyse individual item/platform details, and will be able to simulate business scenarios. The Dynamic DM will be equipped with descriptive, predictive and prescriptive analytics models that will help managers to better visualize the pool size, history, forecast lead times and asset life etc.
ELT: Extract, Load, and Transform for Dynamic DM In order to create the Dynamic Data Mart, we start with ELT: 1. Extract the data from the operational databases and load them into a corporate database (dump database). 2. Transform the data from the dump database into data marts (SQL scripts or other type of scripting) 3. Define the fact measure and connection to dimensions in order to create the fact tables and views. 4. Apply BI service on the Views.
The Data Model to Represent Fact and Dimension The data model is used to develop dynamic association between fact tables and dimensions and its sub-dimensions. This allows self-organized Business Intelligence by providing automated easy drill-down operation for the end-user with automated data association. Data Association is a technology that is widely used in modern BI tools, that builds the relationship between the concepts, and between the entities or tables, or between data-sets. We create the Dynamic Data Mart with Dynamic Dimensions, Dynamic Tables and Dynamic Views.
Dynamic create and drop views and data Once we have the data on which dimensions are not used, we link this data to a new fact table named unused views and/or directly load the data to a dashboard in order to rank and visualize the views the least used. Therefore we can choose to drop the views automatically if they are not used for more than 2 months (for example) or we let an IT manager decide if the views should be dropped. In the same way we can automatically create views knowing which data users accessed the most. The automatic part would be created using an external application linked to our BI software or using a script inside our BI software if it is powerful enough. Using the data about usage ("name"; "number of times used"), a simple formula targeting views can be used such as: with usage less than A=? and more than B=?.
Conclusion The paper examines the state of the art of Data Warehouses and how they are aligned with the company's processes. There is a strong need for Data Integration from multiple Data Sources and its effective use to answer questions posed by people in the business at different levels plus effective presentation in a form to which these business users can relate. A major weakness of current Data Warehouse and Data Mart approaches is the lack of adaptability so that evolution of the company's processes and Data requirements as well as new Business Questions can be addressed. This paper present an approach based on Dynamic Data Mart to overcome these weaknesses.
The equations that govern geophysical fluids (namely atmosphere, ocean and rivers) are well known but their use for prediction requires the knowledge of the initial condition. In many practical cases, this initial condition is poorly known and the use of an imprecise initial guess is not sufficient to perform accurate forecasts because of the high sensitivity of these systems to small perturbations. As every situation is unique, the only additional information that can help to retrieve the initial condition are observations and statistics. The set of methods that combine these sources of heterogeneous information to construct such an initial condition are referred to as data assimilation. More and more images and sequences of images, of increasing resolution, are produced for scientific or technical studies.
Mathematical formulation By direct assimilation of images, we mean using image observations directly in the cost function of variational data assimilation. In this case, image observations are jointly used with conventional observations to compute the optimal control variable of numerical models. The right panel of Fig. 5  shows a schematic representation of the direct assimilation of images. Images are used directly in the optimality system jointly with conventional observations. This direct use of images in the optimality system requires the definition of a mathematical space for the images with adequate topology and the associated images observation operator. An images observation operator is a mapping from the space of the numerical solution of the model toward the space of images. No prior step to extract pseudo-observations of state variables is needed.
Observation operators based on finite-time Lyapunov exponents and vectors computation Ocean tracer images (SST and Ocean Color for instance) show patterns, like fronts and filaments, that characterize the flow dynamics. They are closely related to the underlying flow dynamics and are referred to as Lagrangian coherent structures (LCSs). They are material curves which exhibit locally the strongest attraction, repulsion or shearing in the flow over a finite-time interval  (Haller and Yuan, 2000; Haller, 2011). Their location and shape are the signature of integrated dynamic information that should be exploited in a data assimilation scheme. LCSs are usually identified in a practical manner as maximizing ridges in finitetime Lyapunov exponent (FTLE) fields  (Haller, 2001). FTLE is a scalar local notion that represents the rate of separation of initially neighboring particles over a finite-time window [t 0 , t 0 + T ], T = 0.
Acknowledgements. The authors thank  A. Vidard  for helpful discussions that led to this paper. The authors are also grateful to  O. Talagrand , three reviewers and the Editor for providing helpful comments that resulted in substantial improvements of the paper. This paper was partly written while  F. -X. Le Dimet  and  I. Souopgui  were at  Florida State University  as visitor and postdoctoral fellow respectively. This work was partly supported by  Région Rhône-Alpes  and  Agence Nationale de la Recherche, France. V. Shutyaev  acknowledges the support by the  Russian Science Foundation  (project  14-11-00609 ). Edited by: W. Hsieh Reviewed by:  J. Ma  and two anonymous referees.
Acknowledgments. The first author was partially supported by the  ANR  project  OPTIFORM. The third author was partially supported by Grant  MTM2011-29306-C02-00 ,  MICINN ,  Spain ,  ERC Advanced  Grant  FP7-246775   NUMERIWAVES, ESF Research Networking Programme OPTPDE  and Grant  PI2010-04  of the  Basque Government. This work was finished while the third author was visiting the  Laboratoire Jacques Louis Lions  with the support of the  Paris City Hall  through the program " Research in Paris ".
The vector w o is the zeroforcing solution, H is the channel matrix, and e is the Once convergence is acquired, the equalizer is run for further 5000 iterations for the computation of steady-state value of EMSE. So 10 Â 5000 ¼ 5 Â 10 4 samples were used in the computation of each EMSE point. Note that, for 16-QAM signaling on channel-1, MMA2-2 ðμ ¼ 3 Â 10 À 5 Þ and βMMA ðμ ¼ 4 Â 10 À 4 Þ required nearly 7000 and 1500 iterations (on the average), respectively, to acquire same (À 20 dB) EMSE floor. overall idealistic (single-spike) channel-equalizer impulse response as defined in Section 2. In Section 4. 2, we have observed that the EMSE, ζ, is proportional to filter-length for the given step-size. The parameter χ on the other hand decreases with filterlength. 6 In our simulation, the value of optimal weight vector w o is obtained as w o ¼ pinvðHÞe where pinvðÁÞ is the MATLAB function for the evaluation of pseudo-inverse.
INTRODUCTION Weak gravitational lensing, the small but coherent deflections of light from distant objects due to the gravitational field of more nearby matter (for a review, see  Bartelmann & Schneider 2001; Refregier 2003; Schneider 2006; Hoekstra & Jain 2008; Massey, Kitching & Richard 2010) , has emerged in the past two decades as a promising way to constrain cosmological models, to study the relationship between visible and dark matter, and even to constrain the theory of gravity on cosmological scales (e. g. ,  Hu 2002; Huterer 2002; Abazajian & Dodelson 2003; Zhang et al. 2007 ). Because of this promise, gravitational lensing has already been measured in many datasets, and there are several large surveys planned for the next few decades to measure weak lensing even more precisely, including Euclid 1  (Laureijs et al.
In each image, the non-trivially complex PSF was provided for the participants as a set of nine images with different centroid offsets. (2) Real galaxy: Differed from the control experiment only in the use of the actual images from the HST COSMOS dataset instead of the best-fitting parametric models. (3) Multiepoch: Differed from the control experiment only in that each field contained six images (representing observations that must be combined) instead of one. For the space branches, the six images were not Nyquist sampled. (4) Variable PSF: Differed from the control experiment only in that the PSF varied across the image in a realistic way, and had to be estimated from star images. (5) Full: Included the complications of the real galaxy, multiepoch, and variable PSF experiments all together.
It also links to download the GREAT3 simulations and truth tables. The GitHub site 7  contains software to reproduce the simulated data and to analyze it using simple methods, and a wiki with information for the participants. Finally, GalSim 8  is the simulation software that was used to make the GREAT3 simulations, and its algorithms, design, and functionality are described in  Rowe et al. (2014). Some physical effects that are not tested in the challenge include object detection, selection, and deblending, because the galaxies are located on grids; wavelength-dependent effects; instrumental and detector defects or non-linearities; star/galaxy separation; background estimation; complex pixel noise models; cosmic rays and other image artifacts; redshift-dependent shear calibration; shear estimation for galaxies with sizes comparable to the PSF; non-weak shear signals (e.
Variable shear For variable-shear simulations, the key test is the reconstruction of the shear correlation function. Submission of results for these branches begins with calculation of correlation functions by the participant 10. The submission consists of estimates of the aperture mass dispersion (e. g. ,  Schneider 2006; Schneider et al. 1998) , which are constructed from 10 Software for this purpose was distributed publicly at https://github. com/barnabytprowe/great3-public. two-point correlation function estimates, and allows a separation into contributions from E and B modes 11. We label these E and B mode aperture mass dispersions ME and MB. The submissions were estimates of ME,j for each of ten fields labelled by index j; this estimate is constructed using twenty subfields in a given field.