dataesr/scientific-paragraphs-categorization huggingface dataset:
The box turns by a step of two degrees. In every position, the box checks if it contains a sufficient amount of points. This sufficient amount of points is defined in the script as nbOfPointsNeeded (number of points needed). The command used to do this is IsPointInSurface. At each position Rhino knows the coordinates of the corners of the box and verifies for every point in the cloud if it is in this domain or not. When there are enough points found, the box moves on by a step of one degree in order to find the most points. If the box finds less points, it returns to its previous position and stops rotating. It can happen that the box does not contain all points of the wall. The box will translate along the wall in search of all points. After this, the box can repeat the moving algorithms until the last surface is found.
External outlines of the roof With the first part of the script, the outlines of the walls were found. With this second part the outline for the roof will be found. At each wall a copy of the box was kept. In order to find the points of the roof, the boxes need to rotate around the upper edge of the wall. Because not all boxes necessarily need to rotate, the script interacts with the user by asking if the indicated box needs to be rotated. After running through all copies, the intersection of the boxes can be calculated. Again, by drawing cylinders at the edges, the points needed for the contour are found. Once the external outline of the building has been found using the first script, the internal outlines have to be found. These internal outlines are openings in the wall like windows, doors, carports, etc.
As explained earlier, because of the simplified models there are some restrictions. A point cloud acquired with a 3D laser scan will have points on the place of the framework of the window. Moreover, when the doors and carports are not entirely made of glass, there will be a lot of points on these so called openings. We emphasize once more the fact that this is a simplification and the script will not suffice for complex, in situ obtained, point clouds. Anyway, these simplified models and script can give a good understanding of how to treat a point cloud. The proposed algorithms are based on the comparison of vectors. Due to the first script we have stored the points of each facade in separate lists. If we call these lists, we can access the points of a wall with openings in it. Thereafter, we have a list with points that define all the openings in one facade.
The script will interact with the user, asking to differentiate the groups by selecting them by hand. Because there is no command in Rhinoceros to draw a rectangle through a set of points, the script uses vector characteristics. First the points for the horizontal lines are separated from those for the vertical lines. The principle used to do this is to search for parallel vectors. Parallel vectors have the characteristic that their cross product is equal to zero. For the collection of vertical vectors (and there corresponding points) we know the vector is of the type of vector V in the equation below. Because of the sorting command in Rhinoceros, ran after selecting the group of points, the value Vz from each side differs by positive or negative sign. This gives us a list of points for right side and a list of points for the left side.
CONCLUSION AND FUTURE WORK We presented an approach to automate the modeling from point clouds. The designed scripts enable the automation of the 3D modeling in Rhinoceros 5. 0 of the shape of a building represented by a simplified model. The scripts are based on simple mathematics. The first script is able to eliminate the biggest part of the noise and to draw the exterior shape of the building with a minimum of human interaction. The second script adds the outlines of the openings in the wall to the model. Simplified point clouds were used during this project. These point clouds had less detail and less points then an in situ point cloud. This simplification limits the input that the scripts can process, but still can give a good understanding of how to treat a point cloud. With more improvements, refinements or modifications we are confident that the scripts will be capable of treating more complex point clouds and generate more complex models.
Jenke 2006) script is more suitable for artifacts than buildings. They use a probability distribution and Bayes' theorem to make a reconstruction with the biggest probability possible. Bayes' law is a result that is of importance in the mathematical manipulation of conditional probabilities. In their paper Vosselman and Pu describe their approach to extract automatically building features from terrestrial laser scanned data. First the point cloud is segmented into several planar parts according to the point's position and direction. Each segment is considered a potential building feature. Several important properties (size, direction, topology, position etc. ) are.
Single Point of Access Major difficulties of interorganisational collaboration consist in heterogeneous data models (structuring and naming of entities) and incompatible interfaces based on different technologies. In ComVantage we decided to use semantic data harmonization based on RDF, Linked Data and ontologies. RDF  [6]  is used as uniform data format based on the Linked Data design principles  [7]. Within each domain of the collaboration network, a single point of access is provided by the Domain Access Server (Figure  1 ). The Web Layer of the server exposes a uniform interface for applications based on SPARQL  [8]  and provides an interface for enforcing access control policies. The Data Integration Layer is responsible for distributing requests to the connected data sources of the domain and merges all results to a combined result set.
Integration of Legacy Systems at Run-Time without Modifications Integration of heterogeneous data sources is crucial to data harmonization. The ComVantage approach is based on Linked Data adapters that will perform a mapping of syntactic data to RDF. The adapters are provided as generic components which will be configured with the Domain Data Model of the actual domain in order to connect to a specific data source. Using adapters offers the advantage of integrating legacy systems without modifications. Hence, the ComVantage approach can be used on top of an existing IT infrastructure and in parallel to already existing business applications. The adapters are provided for several technologies which are most common in micro-company environments. The Linked Data adapter for databases is based on the open source project D2RQ  [9].
The adapter translates SPARQL queries to SQL and returns results based on a domain-specific mapping. While the mapping is defined at design-time, the content is lifted to RDF on demand at run-time which avoids the problem of keeping redundant data in sync. Since the database doesn't contain the semantic information that is required for this transformation, it is provided in a mapping file. Furthermore, adapters for Excel spreadsheets  [10]  and machine middleware systems are provided  [4].
Access Control on Individual Enterprise Data Assets Interorganisational collaboration relies on trust between partners and the fact that information from heterogeneous nature is accessible to authorized members only. An access control model is required that supports decentralized decision-making and that enables policy negotiation, establishment, management, monitoring and enforcement for a multi-domain access to Linked Data sources. Additionally, the mechanism should be simple enough to be applicable for micro companies. We propose an authentication process based on SAML  [18]. Identity federation and security credentials interchange is performed in the first place and, afterwards, a multi-tiered authorization process takes place to provide multi-domain access control for Linked Data at two levels: (1) Rewriting of SPARQL queries by adding control checks related to the requesters user role.
ComVantage responds to this by offering a modelling workbench for business processes and a model driven development approach for mobile apps taking into account the agility of workflows supported by the App Orchestration Concept. Furthermore, small and micro companies have a need for nonexpert support for specific design-time tasks. We offer a couple of templates, guidelines and tools for facilitating tasks like ontology engineering and app development. Finally, the lightweight infrastructure of ComVantage simplifies the introduction of software tools to technology unaware environments as usually being found in small and micro companies and enables a complete end-to-end transparency among the whole production and supply chain of a product. Tracking and monitoring of used resources and related costs of a specific product will be much easier across the whole production and supply chain.
Leica Geosystems HDS 6" planar circular targets -both blue&white and black&white -were used. The target registration errors were less than 5 mm. After registering all the scans, the whole point could of the abbey was georeferenced using the GPS targets. The georeferencing accuracy depends on that of the GPS one (planimetric and height): about 5 cm RMS. As a prerequisite, the point cloud is cleaned up before working on it. This is semiautomatically performed thanks to Leica Cyclone v. 7. 3 software from Leica Geosystems. Thus, points relative to some targets, tripods, some trees and other surrounding points are deleted (Figure  6 ).
Related works Virtual reality (VR) implies full immersion in the digital environment. In our case, a virtual visit is proposed on the desktop. Two main approaches exist. In the first one, the model is downloaded by the client in a specific format and it is visualized in standalone mode using an adapted viewer. In the Figure  8. Textured mesh over the rosette of the abbey-church using the refined mesh and the texture image from Figure  7. second approach, the model is not downloaded on the local device; navigation is possible using a Web browser and a plugin. Typically, VRML (Virtual Reality Mark Up Language), and more recently X3D, are standard file format for 3-D model display, associated with viewers such as Cosmo Player, OpenVRML or FreeWRL  (Meyer et al. , 2006; Guarnieri et al. , 2010).
GoogleEarth is also used in some works as a Web player  (Wells et al. , 2010; Appolonio et al. 2011). The second strategy is preferred for two main reasons: easiness for the remote user and preservation of the data property. The technical solution is commonly leveraged by video games based on graphic engines, relying on Direct3D, OpenGL or more recently WebGL. Thus, software exists for 3-D data viewing and manipulation, but not all are appropriate for interactive immersion (that is to say, the internet user can move in the virtual environment using a mouse, the keyboard or a touchscreen). Possible software is commercial, such as Unity (UNITY, 2013), or free, such as Blender (BLENDER, 2013). Our work is based on Unity. Moreover, this software was preferred in  (Pauwels et al. , 2011) , where a recent comparison of game engine software in the context of architectural, engineering and construction domains is proposed.
Unity is developed in C/C++ languages and relies on Direct3D, OpenGL, OpenGL ES and proprietary API, depending on the programming platform. The regular version is a free download. It can be used to develop a virtual visit for a desktop in standalone mode or on the Web, using a specific plugin for browser rendering. It also supports the deployment on mobile devices and consoles. The implementation is straightforward with Unity. This software has already been used in the archaeological domain, for instance  (Agugiaro et al. , 2011). Otherwise, augmented reality offers another way of discovering the abbey. According to (Thomas, 2012), AR is defined as "the registration of computer-generated graphical information over a user's view of the physical world". For instance, AR has already been used to enrich a museum visit  (Bationo-Tillon et al.
In the future, the rendering will be improved by adding the real DEM (Digital Elevation Model) with the real locations of the trees. Indeed, a land survey and the surveying of each tree around the abbey have already been carried out and processed. Next, the abbey 3-D model will be embedded into the DEM while preserving smooth connecting borders  (Agugiaro and Kolbe, 2009). Then, a user camera is defined, which allows the displacement in the mockup. We limit the movement so that the user cannot go outside the defined box, too close to or through the walls. The last step is Web posting. For this purpose, the software proposes different exports. We export our visit to Web player. In this way, the virtual visit can be launched from any computer using an internet navigator and the related Unity plugin (Figure  9 ).
In this framework, one solution could be that the user would be able to interactively "remove" a roof, revealing the inner room. Finally, an important task concerns the interactivity between the user and the virtual visit or the AR application. This interactivity will aim at adding educational content. The principle is that one could select a flag that appears in the model and visualize corresponding historical or architectural information, in the form of text, audio or video. This work will include the creation of the database and the flag embedding interface, the development of the connection between the database and the virtual model and the creation of an adapted Web interface.
In response to the rapid development of DNA Microarray Technologies, many differentially expressed genes selection algorithms have been developed, and different comparison studies of these algorithms have been done. However, it is not clear how these methods compare with each other, especially when we used different developments tools. Here, we considered three commonly used differentially expressed genes selection approaches, namely: Fold Change, T-test and SAM, using Bioinformatics Matlab Toolbox and R/BioConductor. We used two datasets, issued from the affymetrix technology, to present results of used methods and software's in gene selection process. The results, in terms of sensitivity and specificity, indicate that the behavior of SAM is better compared to Fold Change and T-test using R/BioConductor.
In this context, a comparative study could guide the practitioners to a suitable tool. Here, we comparatively review, three DEG selection methods: Fold change, T-test and SAM using two software's tools R/Bioconductor and Bioinformatics Matlab Toolbox. To conduct our study we used two datasets issued from the Affymetrix technology [11] since we showed in our last study that there is no significant difference, in term of differential analysis, between Affymetrix and other technology like Agilent  [12]. The aim is to evaluate by means of sensitivity and specificity the impact of statistical tools on the DEGs selected. In the next section, we will justify the choice of statistical tools and we will give a concise description of the DEGs selection workflow followed for all proposed algorithms and tools.
Results & Discussions: The aim of this paper is to understand how gene selection algorithms differ from one another and how it is influenced by using different software's. In this study, we compare results for three gene selection methods, SAM, FC, and T-test in both R/BioConductor and Bioinformatics Matlab Toolbox using two datasets. We evaluate first the influence of statistical tools on the size of selected genes using the Venn diagram then we evaluate the sensitivity and the specificity of each methods and tools using the ROC curves. The first result is represented in the three gene selection methods and the genes identified as differentially expressed in each dataset (denoted by "identified genes" or "id" in Figure  1 ). The lists of genes generated as differentially expressed differ between methods, software's and datasets used.
Viewpoint methods, SAM identified 80% to 89% of all genes in each dataset under R/Bioconductor relative to t-test and FC methods. While, meeting lists of each gene selection method in Bioinformatics Matlab Toolbox show a close between the three gene selection methods used with a reduced number of genes. In term of software's; the number of DEGs selected was still large in R/BioConductor , but was considered reduced in Bioinformatics Matlab toolbox (for example in spike-in data set ,1224 gene was selected by R/BioConductor face to 101 genes selected by Matlab toolbox for the same data). After this first study we can conclude that Bioinformatics Matlab Toolbox allows a selection of a reduced number of genes compared to R/BioConductor. But are remains unclear whether the selected genes are relevant.
For this reason we used the ROC curves that compare the performance of the three methods and the software's in each dataset used in terms of sensitivity in specificity (Figure  2 ). Figure  2A  represents the ROC curve using R/BioConductor for the breast cancer dataset. This figure shows that SAM and T-test report differentially expressed genes in a stricter manner than FC. This is not surprising given that the former two methods take into account the variance of an observation between different conditions, which is not the case of FC. As the figure shows, the SAM is more stringent than the T-test, the explanation coming from the "fudge factor" that reduces the bias of genes with large variances without necessarily biological significance. FC has a significant false positive rate, almost half of its results.
Conclusion: A fundamental step of microarray studies is the identification of a small subset of DEGs from among tens of thousands of genes probed on the microarray. DEGs lists must be concordant to satisfy the scientific requirement of reproducibility, and must also be specific and sensitive for scientific relevance. A Bioinformatics Matlab Toolbox allows a selection of a reduce list of DEGs used different methods proposed relative to the R/ BioConductor. Concerning the gene selection methods, the R/BioConductor using SAM is favored for microarray selection compared to the other methods notably the FC which is uncertain. While, the ROC curve when using Bioinformatics Matlab Toolbox shows that there is a not significant difference between the three genes selection methods with reproducible results.
We conclude that the SAM approach is over conservative in R/ BioConductor but the FC has an extremely low power in the two datasets used. On the other hand, the three genes selection methods display a high power in Bioinformatics Matlab toolbox. Therefore, this latter would be selected if we prefer to select a small list of DEGs. In conclusion, a DEGs list should be chosen in a manner that concurrently satisfies scientific objectives in terms of inherent tradeoffs between reproducibility, specificity, and sensitivity.
[16]uses a statistic called "relative difference" for gene. This is very similar to a t-statistic with equal variance, except that the gene "gene-specific scatter" in the denominator is offset by a fudge factor. This latter is an exchangeability factor chosen to minimize the coefficient of variation and is computed with a sliding window approach across the data. These DEGs selection methods have been already integrated in the R/BioConductor project [14]. In Bioinformatics Matlab Toolbox the two first DEGs selection methods have been also integrated. But for the SAM algorithm under this software, it was recently implemented by [15]. We applied feature selection methods to two data sets issued from the affymetrix technology. The First one is the Spike-in datasets[16]. These data sets are designed to address questions about the correctness of microarray data and have been used extensively to compare among different analysis methods.
Data sets It provides accurate estimation of inter-array variability through a robust background correction and quantile normalization computed over the whole datasets. To simplify analysis, we follow in our comparative study the workflow presented below: Raw Affymetrix Data (. cel)→ Normalization (RMA)→ Different DEGs Selection Methods and Tools→ Results of The Comparative Study [1]. Typically, an arbitrary threshold is chosen and the difference is considered as significant if it is larger than the threshold. The FC method is often used because it is simple and intuitive. T-test, which is [22]en from the second class, is extensively used in gene expression analysis. It demonstrates whether the difference between two groups or samples is significant[2]. In our study P_values calculated by T-test have been used without any multiple testing corrections.
The DEGs were obtained by setting two criteria of P_values <0. 05 and FC >=2. The last approach provided from the third class is the SAM method[5]. Currently there are four major spike-in datasets available for the Affymetrix microarray platform: the Affymetrix spike-in dataset for cross platform comparisons [17], the Affymetrix Latin square dataset [18], the Gene Logic spike-in dataset[19]and the Golden Spike dataset[20]. The new wholly defined Affymetrix spike-in dataset consisting of 18 microarrays. Over 5700 RNAs are spiked in at relative concentrations ranging from 1-to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive versus negative fold change. The second data set is recently represented in[21]. These cancer data sets consist of 18 breast cancer patients' usual-risk controls undergoing reduction mammoplasty (RM), and histologically normal (HN) patients.
In this work, we analyze the total of 86 genes presented as differentially expressed between RM and HN samples. All Chips from the two datasets present variations due to the experiences background noise and systematic errors. For this, the standardization is applied to all chips to be sure that the distributions of intensities across chips are homogeneous. Several studies have focused on the performance of different normalization methods[22]. In this study we use the Robust Multichip Analysis algorithm (RMA).
TLM and ADM codes can therefore be created from the forward code by hand or through the use of automatic code generators. A variety of these are available, such as TAMC (http:// www. autodiff. com/tamc) and TAPENADE (www-sop. inria. fr/tropics/tapenade. html), which create a TLM or ADM from a supplied forward model without the need for the timeconsuming hand-coding process. Whether coded by hand or automatically, the creation process may produce errors or inconsistencies in the adjoint code, due to human error or through bugs introduced in the automatic coding process  (Nehrkorn et al. , 2006) , and it can often be more efficient to code by hand in the first place. A major problem with automatic procedures is that they may also reduce the potential for optimisation of the adjoint code, such as parallelisation.
For these simulations, the 3-D SF 6 field was initialised on 1 January 1988, with initial values provided by TransCom, and ran up until 31 December 2010, with full 3-D output every 3. 75 days. The model timestep was 60 min. We linearly interpolated the TransCom initial concentration field from a 2. 8 • × 2. 8 • horizontal grid with 60 vertical levels to the coarser 5. 6 • × 5. 6 • , 31 level TOMCAT grid. Due to the long spin-up time for this simulation, the initial field should not significantly influence the results in the more recent years. Emissions were also supplied by TransCom, and were originally taken from the Emission Database for Global Atmospheric Research (EDGAR), Version 4. 0  (Olivier and Berdowski, 2001) , and scaled as in  Reddmann et al. (2001). Figure  1  shows annual mean SF 6 emissions for the year 2008 on the 5.