dataesr/scientific-paragraphs-categorization huggingface dataset:
The disadvantage is that we obtain a cruder estimate of occluded regions, however, results are usually adequate. The process of disparity estimation centres on minimising an energy cost that is composed of a correspondence data term and a smoothness term. The correspondence data term comes from computing a measure of similarity between points in both images of the stereo pair. In the case of PaLPaBEL, the similarity measures are the Zero-mean Normalized Cross-Correlation (ZNCC) score using colour information and the Hamming distance using census transformed data. These similarity measures were chosen as they can tolerate noise and can handle difficult lighting situations. The smoothness term penalises adjacent pixels that have different disparity values. Having a superior smoothness term has a significant bearing on the overall success of the stereo technique.
Synthetic imagery This paper demonstrates the PaLPaBEL technique on synthetic data as it provides a way of imitating the texture and irregular shape of natural marine growth in an underwater setting while retaining complete control of the scene and the stereo cameras. This cannot be achieved for laboratory based experiments, such as those carried out by  Gaurier et al. 2014 , where the shape of the artificial marine growth is known yet it does not appear natural, and errors from external sources, such as camera imperfections, make performance evaluation inexact as external problems may contribute towards incorrect matches. The synthetic data consists of a virtual 3D model of a jacket-type platform that was colonised by marine growth, as shown in Figure  3. The 3D model was set in a physically accurate underwater environment, where the common issues affecting underwater optical imaging systems were present, such as scattering and colour absorption.
This method chooses a root point in a region and then grows that point while the energy function remains equal or less than a certain value. Otherwise, a new root point is selected in another region. This algorithm employs the Sum of Absolute Differences (SAD) as a similarity measure. These techniques are applied to the stereo pair shown in Figure  3  and the resulting disparity maps are shown in Figure  9. The performance is evaluated on the basis of the Normalised Root-Mean-Square-Error (NRMSE) score. Normalising the Root-Mean-Square Error facilitates the comparison between datasets or models with different scales. This is achieved by dividing the Root-Mean-Square Error by the number of disparity levels that is being considered by a given stereo pair. A low NRMSE score is indicative of a strong performing stereo matching algorithm as it corresponds to cases where there is a smaller deviation between the computed disparity map and the ground truth disparity map.
Design tools such as Computer-Assisted Drawing (CAD) software or Building Performance Simulation (BPS) software are also not included in our scope of ecodesign tools. Although these tools are indispensable in building design, and are very popular  (Attia et al. (2011)  count no less than 389 tools for BPS), they do not have a specific environmental orientation and therefore leave responsibility to the user when applying them for environmental purposes. This choice is in line with the ecodesign literature which usually does not include mainstream CAD and simulation tools.
Barriers and opportunities for ecodesign tool implementation Although everyone interviewed claimed to take the environment into account in their projects, the range of practices is very wide. It goes from limited adaptation of usual practices and emphasis on certain criteria (e. g. work on architectural compactness for architects) to the introduction of new tools in the design process (e. g. an environmental management system based on custom-made spreadsheets for decision traceability, or databases on innovative materials). Overall, knowledge of tools and of their application is limited. All interviewees were familiar with the French certification scheme HQE, although one of them declared that he did not apply for the certification for his projects and used it only as a guideline. The others generally applied for the certification for their projects.
Opportunities and barriers for LCA deployment Even if most interviewees knew about LCA, few of them had put it into practice. When we asked interviewees why they did not use LCA more, two types of barriers were identified: implementation and communication. Implementation barriers are the problems faced when trying to conduct LCA in a design process. The first implementation barrier is that LCA comes too late. LCA is suitable for evaluating completely defined products, at the end of the design process, but at this stage modifying the product is too costly  (Millet et al. , 2007). The second implementation barrier is the difficulty of obtaining data. LCA requires a lot of data on materials and this data can be hard to come by  (Reap et al. , 2008a). In France, since 2014, industrialists who wish to communicate on the environmental qualities of their products need to produce an Environmental Product Declaration (FDES in French) and input it into a central database called  INIES (Passer et al.
, 2015). However, according to interviewee #19 smaller companies do not all have the resources or the ambition to take these steps. Therefore, one concern for the interviewees (#1 and #19) is that "innovative" products (bio-sourced products for instance) often do not appear in LCA databases. Third, the fact that results vary from one software and one database to another deters professionals from using LCA. This variation in the results has been evidenced by  Takano et al. (2014). Confidence in LCA is limited due to these variations, which also generates indecision on which LCA software to choose. The final implementation barrier is economic: LCA is perceived as expensive compared to the benefit it brings. Communication barriers are those which prevent the exploitation of LCA results either within or outside the company.
Data treatment -DSM Value Bucket analysis Having presented the results of the data acquisition phase, we now present an analysis of these results using the DSM VB tool. Seven problems have been extracted from the list of issues reported in Section 4. 2. This is because they were the most frequent, shared by at least two interviewees. These issues are problems experienced by practitioners when trying to practice ecodesign and to monitor, manage and reduce the environmental impact of the buildings they design. The seven problems are:  Low level of environmental data: this occurs when not much is known about the components or the usage, or the data is mainly qualitative  Low level of technical data: for example, drawings are not available for the analysis  Difficulty to compare the environmental impact to other criteria: such as investment cost, maintenance cost, ease of maintenance, or functional aspects  Difficulty to position the project: this is when a project is analyzed but it is difficult to say if the project's performance is "good" or "average" or "poor"  Cost of analysis is too high: because existing methods are too expensive  Delay of analysis is too high: when existing methods take too long compared to the lifecycle of the project  Difficulty to compare options: when multiple criteria need to be considered and no priorities are set, or when the analysis carried is not able to differentiate options for instance.
An example of application is the EcoCamps project to design sustainable holiday camps  (LIFE Programme, 2004). The rationale behind this choice of tools is to cover a wide range of ecodesign tools, from LCA to guidelines. These tools were not mentioned in the interviews, except for LCA. We can therefore assume that they have at best a low visibility in the French construction sector. The objective here is to test whether, if those tools were widely known, there would still be gaps in the toolset available to designers. Scales for completing the matrices were 0. No/Never; 1. Very few; 2. A few/rarely; 3. Some/sometimes; and 4. Many/Often. The data was obtained as follows:  For Matrix A (situations * problems), expert data was obtained from two environmental consultants. Each interviewee filled in a matrix, after which mean values were calculated.
For matrix C we looked at whether a tool is helpful at one stage. For instance, when choosing the HVAC system, EcoDesign Pilot is too generic for a very specific and complex system. When choosing programme requirements, LCA is powerless because data is almost nonexistent, therefore only broad projections can be made.  Weighting coefficients for matrix D come from expert opinion. The authors asked three experts to rank problems and usage situations. Problems were ranked with regard to their importance and difficulty to solve. Usage situations were ranked with regard to the perceived impact of decisions made at this stage on the final environmental impact of the building. The experts were one academic carrying research on building design, a construction project management consultant with over twenty years of practice and teaching, and an architectengineer with more than ten years in architecture, technical supervision and environmental coordination, the two latter had been interviewed previously.
Lack of environmental and technical data Lack of data is usually considered a key issue for building ecodesign, e. g. (Khasreen et al. , 2009; Reap et al. , 2008b)  in the case of LCA. Here, this barrier is rated very low after computation with the DSM VB tool. There are two reasons for this: many tools exist which do not require extensive quantitative data (in our analysis, the HQE guide, EcoDesign Pilot, and QFD-E and ESQCV), and looking at matrix A in Figure  5  experts consider this problem to be less pressing than issues such as delay in analysis or multicriteria evaluation. New studies on the impact of taking generic data in LCA  (Silvestre et al. , 2015)  should help to address this issue. "Lack of technical data" also scores very low. This issue is rated high by experts for the program and sketches stages, but here again, tools exist for stages where there is still a high uncertainty on the specific technical choices.
Virtual Machine Monitor "A virtual machine (VM) provides a faithful implementation of a physical processor!s hardware running in a protected and isolated environment. Virtual machines are created by a software layer called the virtual machine monitor (VMM) that runs as a privileged task on a physical processor. " Merkat meets the tenant demands, while also allowing the organization to get the best out of its infrastructure, by maximizing its resource utilization. Figure  1  gives an overview of Merkat. Merkat relies on: (i) a virtual machine market to allocate resources and internal currency to manage the priority of tenants' demands and (ii) a set of virtual platforms that manage and elastically scale applications to meet tenant SLOs. To be able to run applications on the infrastructure, tenants are assigned budgets by an administrator in the form of a virtual currency.
Currently, Merkat does not consider the application's or the cluster's topology. However, it can be further extended to allow tenants to express and pay for running their application processes as close as possible to each other. I/O resources. The current resource allocation algorithms could be extended to consider network and storage resources. Such resources can become a bottleneck when network or data intensive applications are executed. Thus, it is normal to make them available at a price that reflects the total resource demand. Regarding the network resource, software tools can be used to limit the bandwidth available to each virtual machine and ensure a proportional share. We envision that similar mechanisms could be applied for storage too. High Availability. To provide tenants with a production system, Merkat services require self-healing capabilities to make the platform highly available.
Acknowledgements This work was done while the first author was a PhD student at  INRIA  and EDF R&D and was supported by  ANRT  through the  CIFRE sponsorship  No. 0332/2010. Experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the  INRIA   ALADDIN development action  with support from  CNRS ,  RENATER  and several Universities as well as other funding bodies (see https://www. grid5000. fr).
Introduction Alt-Ergo is an SMT solver designed for checking logical formulas generated by deductive program verification frameworks. For instance, Alt-Ergo is used as a back-end in the Why3 plateform  [9]. It is also used to discharge formulas derived from C programs in Frama-C  [11] , from Ada programs in SPARK  [2]  or from B machines in Atelier-B  [4, 7]. The Alt-Ergo input files produced by such tools share the same structure. They start with a prelude that contains a set of definitions (datatypes and logical symbols) and axioms for the encoding of theories specific to program verification (complex data structures, memory models, etc. ). The rest of the file contains a proof obligation (PO) generated by a weakest precondition calculus. Alt-Ergo checks such input files by the use of a combination of decision procedures (SAT, simplex, congruence closure, etc.
), and linear arithmetic (+, -, 1). It is made of two parts: the hypotheses a = b + 1 and s2 = add(b,s1), and the conclusion mem(a -1, add(b,si)) we would like to prove valid. Thanks to the second hypothesis and a ground instance of axiom mem_add (where x is replaced by a -1, y by b, s by s1 and α by int), the conclusion is equivalent to (a-1 = b ∨ mem(a-1, s1)). Moreover, the latter formula always holds because a -1 = b is equivalent to the first hypothesis modulo linear arithmetic. We thus conclude that the goal is valid. Alt-Ergo handles such proof obligations following the architecture given in Figure  3. The solver can be called either via its command-line "alt-ergo" or via its graphical user interface "altgr-ergo". The front end provides some basic operations such as parsing, type-checking, triggers inference 2  and translation of input formulas to richer data structures manipulated by back end modules.
Syntactic Operations A lot of the time when trying to use SMT solvers on real world examples (coming from program verification tools for instance), the size of the logical context and the sometimes heavy axiomatizations (that make liberal use of quantifiers at varying degree of alternation) make the problem hard for purely automated tools. However, only a fraction of the actual context is usually necessary to prove the desired goal. Identifying useful information in very large problems can be challenging. When the information provided by the feedback features described in Section 3 allow to identify a potential issue, AltGr-Ergo offers a number of functionalities to perform syntactic manipulations on the context that is shown in textual format. This allows for an iterative (and slightly interactive) approach to SMT solving, where users can experiment and quantify the effect of different manipulations.
Conclusion and Future Work We've presented AltGr-Ergo, a graphical front end for the SMT solver Alt-Ergo. It provides real time feedback to users and allows to interactively manipulate problems. We believe such a tool is beneficial both from an end-user and developer point of view, especially for the kind of goals that arise from deductive program verification. We have personally (as the developers of Alt-Ergo) found the interface precious to tune the solver and tackle larger verification problems. Future directions to improve this tool include turning AltGr-Ergo into a full featured editor for SMT solvers (at the moment the source buffer window cannot be edited directly). There are still some inefficiencies in the interface when one wants to load very large files, due to some of the more advanced features, but also inherent to GTK itself.
Such gradient-based approaches have also been used for cell segmentation and tracking in zebra-fish embryogenesis  [8] [9] [10] [11] [12] [13]. Certain level set based methods have also been implemented in common image processing packages. For instance, the image analysis platform ImageJ  [2, 14]  has level set plugins, but for the moment only for 2D images, while the use of the level set implementations of the ITK library  [15]  require some experience in both image processing and programming. Here we present a simple level set implementation for the segmentation of plant tissues at both organ-and Fig. 2  LSM for tissue contour. a-c Section of a developing flower at the boundary between the peripheral zone and the forming sepal. a Tissue contour detected with the "Edge Detect" process of the MorphoGraphX software.
Therefore we implemented a threshold function that varies linearly along the z axis. Using ImageJ or an equivalent software, the user may manually determine the initialisation threshold parameter values at the desired distance from the sample at the top and bottom of the image, and may provide these threshold values as input parameters. Notice also that in general, the anticlinal walls are much brighter than periclinal outer walls, producing a brighter zone in the crease between cells. On this initial surface this will produce crests in these zones (see white arrows in Fig. 2a ), showing that a surface detection based just on thresholding cannot be faithful to the shape of cells. Thereafter, a suitable indicator function is defined, that is a specific f in (3) is given. Here we use the most frequently used gradient based indicator function  [19, 23] , which guides the evolving contour towards the edge of the nearest object, where Î is a smoothed version of the image I.
The accelerating term typically forces the con- tour into the crease (Fig. 2c , where we compare α = 0 in green and α = 1 in red and all other parameters are the same and of default values). The smoothing parameter β is of use when, instead of fidelity to all cellular details (Fig. 2d  with β = 0), one wishes to detect global tissular aspects (Fig. 2e  with β = 1 ). This is the case for tissular curvature maps, where accurate surface detection is important without cellular details on the surface  [24]. An interesting observation is that mean curvature maps on 3D surfaces have maximal values in creases. Thus the map can be segmented using watershed segmentation on the surface and can be used to automatically identify the boundaries between different organs. In Fig. 2h  of an Arabidopsis meristem with growing flower primordia we show the mean curvature map computed with the software MorphoGraphX  [7]  on the surface detected with the level set method.
Segmentation of nuclear images While segmenting tissues at cell resolution can be crucial to quantify growth, it can also be valuable to identify and characterise nuclei, in terms of their shapes and/or in terms of expression levels of a given reporter. To this end, the use of the cellular level set code can also be extended to segmenting nuclei in 3D images. We applied our code on high-resolution 3D reconstructed images obtained by the fusion of multiple confocal images taken at different angles, based on the MARS pipeline  [5]. We use two-channel stacks, where a cell membrane marker and a nuclear-localised reporter were simultaneously imaged at different emission wavelengths (Fig. 5a ). (5) f 2 (x) = Î. (6) f 3 (x) = min( 3 (x), 0). To segment the nuclei using the level set method, we first initialise with an eroded cellular segmentation.
Availability The contour-detection as well as the cellular level set code is written in C++ and uses the CImg image processing library  [29]. The cellular level set tool is parallelized using OpenMP. They are put together in a package for Linux and Mac OS named lsm3d. The standalone package lsm3d is distributed under a permissible licence for non commercial use, namely the Free Software licence CeCILL v2. The source code is accessible together with the associated installation and usage advises on the following webpage https://forge. cbp. ens-lyon. fr/redmine/projects/lsm3d. It was tested on Linux as well as on Mac OS systems.
Understanding large multidimensional datasets is one of the most challenging problems in visual data exploration. One key challenge that increases the size of the exploration space is the number of views that one can generate from a single dataset, based on the use of multiple parameter values and exploration paths. Often, no such single view contains all needed insights. The question thus arises of how we can efficiently combine insights from multiple views of a dataset. We propose a set of techniques that considerably reduce the exploration effort for such situations, based on the explicit depiction of the view space, using a small multiple metaphor. We leverage this view space by offering interactive techniques that enable users to explicitly create, visualize, and follow their exploration path.
Introduction Information visualization (infovis) aims to leverage user ability to retrieve insight from data representation thanks to the usage of computer-supported, interactive, and visual representations  [1]. One of the main challenges of infovis is providing insight into high-dimensional datasets. Such datasets consist of many elements (observations), each having multiple dimensions. Many visualization techniques for high-dimensional data can be explained as element-based plots. In such a plot, every element of the dataset is depicted separately (and in the same way as the other elements). Examples of such plots are the classical 2D or 3D scatterplots (every element is a point), parallel coordinate plots (every element is a polyline), multidimensional projections, and graph drawings (every element is a graph edge).
To address this data exploration issue, we propose a set of innovative techniques where controlled animation plays a central role. Our techniques considerably reduce the exploration effort by allowing the user to directly sketch the exploration path over a visual depiction of the view space created by a small multiple metaphor. This way, a potentially infinite set of intermediate views can be created easily and intuitively. Real-time linking of the view-space navigation and the display of the intermediate views allows one to go forward, backtrack, or change the exploration path. Finally, data patterns found in different views can be selected and interactively combined to generate new views on the fly. In summary, our contributions are • a novel representation of the view space based on a small multiple metaphor • a set of interaction techniques to continuously navigate the view space and combine partial insights obtained from different views We demonstrate our proposed techniques with the visual exploration of real-world multidimensional datasets represented by multidimensional projections and bundled graph drawings taken from the domains of air traffic control, software maintenance, and machine learning.
Element-Based Plots Let D = {d i }, 1 ≤ i ≤ N, be a dataset containing n-dimensional elements d i ⊂ R n. Further, let P = {p i } ∈ P be the set of parameters that controls a visualization and V(P, D) ∈ V the resulting view for the given dataset D. In the following, we use the term visualization to denote the function that creates a depiction (image) of a dataset, and the term view to denote such a depiction. That is, a visualization is a function that inputs datasets, is controlled by parameters, and outputs views. Moreover, for notation simplicity, we will shorten V(P, D) to V(P) when the input dataset D is constant and only the visualization parameters P vary, and respectively to V(D) when the visualization parameters P are constant but only the input datasets D change. We define an element-based plot as a 2D drawing V(D) = {V(d i )} ⊂ R 2 consisting of N visual shapes V(d i ), each mapping a dataset element d i.