dataesr/scientific-paragraphs-categorization huggingface dataset:
Definitions. The following definitions give the formal notations associated with the buffers and matches concepts used in this article. [40;80[ [40;80[ [16;56[ [0;40[ [-8;32  As shown in Figure  6 , it is possible to match a contiguous sub-range of a buffer into non-contiguous sub-ranges. This example also illustrates the possibility of matching sub-ranges of a buffer partially outside the initial range of bytes of another buffer. Although memory scripts offer great liberty for defining custom matching patterns, a set of rules must be respected to ensure the correct behavior of an application. These five rules are checked for each match created during execution of a memory script and errors are immediately reported to the developer. Rule R1 enforces the validity of the matches. Indeed, it is impossible to allocate a contiguous sub-range of n bytes within a memory range of m bytes if m = n.
Rules R2 and R3 prevent scripts from generating matching patterns that would result in a destination merge issue (see Section 5. 1). Rules R4 and R5 regulate the creation of matches with bytes falling outside the initial range of bytes of buffers. A byte is called "virtual" if it does not belong to the initial range of bytes of a buffer, otherwise the byte is called "real". All "virtual" and "real" bytes of a buffer are mapped in memory when a buffer is allocated. As illustrated by the Split actor (Figure  6 ), memory scripts can be used to match a subrange of a buffer partially outside the range of "real" bytes of another buffer. Without further limitations, this feature could be used to merge a buffer completely out of the "real" byte range of another buffer, thus resulting in no memory reuse between the two buffers.
Data Ports Annotations As illustrated by the Split actor, memory scripts allow the creation of overlapping matches. Applying overlapping matches results in merging several sub-ranges of output buffers in the same input buffer. Hence, actors reading data from the merged output buffers are accessing the same memory. To ensure the correct behavior of the application, the memory optimization process must check that actors accessing the merged buffer do not write in this shared memory. If one of the consumer actor does not respect this condition, its corresponding output buffer should not be merged and it should be given a private copy of the data. Indeed, writing in the shared memory would modify the input data read by other actors, which might change their behavior. By default, the most flexible actor behavior is assumed; all actors are supposed to be both writing to and reading from all their buffers during their execution.
All output ports of these special actors are thus automatically marked as writeonly ports, and all input ports, except the first ports of the Roundbuffer actors, are marked as read-only ports. For user-defined actors, it is the developer's responsibility to make sure that the internal behavior of actors is consistent with port annotations. The memory scripts and port annotations presented in this section allow the developer of an application to specify how the input and output buffers can be merged, and how actors access these buffers. In the next section, an optimization process is presented to make use of these inputs to reduce the memory footprints of SDF graphs.
MEMORY MINIMIZATION PROCESS The execution of memory scripts during the compilation of an SDF graph produces a list of matches that represent merging opportunities for the input and output buffers of actors. Some matching pattern specified by memory scripts may result in a corruption of the application behavior (see Sections 4. 1. 3 and 5. 1). The purpose of the memory minimization process is to apply as many matches as possible without corrupting the application behavior. For this purpose, the memory minimization process first builds Directed Acyclic Graph (DAG) of buffers and matches by chaining the lists of matches produced by the memory scripts. The combination of all the buffers and matches of an application results in the creation of a forest (that is the creation of several unconnected match DAGs).
The LTE PRACH application is a description of the preamble detection part of the Long Term Evolution (LTE) wireless communication standard  [Pelcat et al. 2012]. Finally, the Large FFT application is an implementation of an FFT algorithm optimized for processing a very large number of samples (65536 complex values in our SDF implementation)  [Takahashi 2000 ]. In Table  I , the SDF graph column presents the number of actors and FIFOs in the SDF graphs of the applications. The Associated scripts column gives the number of actors of the graphs that are associated with custom and special memory scripts. Custom scripts are the memory scripts written by the application developer to specify the memory reuse opportunities for user-defined actors written specifically for an application whereas special scripts corresponds to memory scripts associated with actors with a pre-defined behavior such as Fork, Broadcast, and Roundbuffer actors.
The Executed scripts column presents the number of executions of both custom and special memory scripts. The numbers presented in this column are much larger than those presented in the Associated scripts column because each script associated with an actor of the original SDF graph may be executed several times after the duplication of actors during the single-rate transformation. Special actors that are automatically inserted in SDF graphs during the single-rate transformation also increase the number of executed memory scripts. Table  II  presents the memory footprints allocated for the mono-core execution of 5 applications with 6 different optimization techniques. Footprints given in the None column are obtained when no memory optimization technique is used and each buffer of the single-rate SDF graph is allocated in an dedicated memory space.
Hence, model II regressions are not frequently used in ecology and rarely so when non-linear models are involved. In this study, we derived a density-dependent survival function for Gracilaria chilensis, a red seaweed with a classical isomorphic biphasic life cycle, and tested for differences among its haploid males, haploid females and diploids stages. We developed a non-linear model II regression with an improved line-fit even with small sample sizes. It is an iterative, two-step, Linear-in-the-parameters, Oblique Least Squares (LOLS) method, that is numerically stable in situations where other methods are usually unstable. To compare the parameters obtained from the several algorithms tested, we developed a user-friendly Matlab-based software package that includes a simple and short tutorial (S1 File).
This work was supported by the Research on Gracilaria chilensis was supported by  Fondo Nacional de Desarrollo Cientı ´fico y Tecnologico  (http://www. conicyt. cl/fondecyt/fondecyt-program/) under grant number  1090360. VV was funded by  European Regional Development Funds  (http://ec. europa. eu/regional_policy/en/funding/erdf/) of the  Competitiveness Factors Operational Programme-COMPETE  and national funds of the  Foundation for Science and Technology (FCT-www. fct. pt)under the project  UID/EEA/50009/2013. AHE was supported by fellowships  SFRH/BPD/63703/2009  and  SFRH/ BPD/107878/2015  of the FCT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
1 to 0. 1. This feature enables to get the values of FacetReference and FacetAttribute without using a query. Furthermore, we added a new EReference named fOpposite to create a reflexive reference mechanism like eOpposite on EReference in the Ecore meta-model. After the modification of the meta-model, we implemented the new behaviour in the Java source code of the Facet engine called FacetManager. To store the new virtual attributes and references without queries, we extended the existing mechanism of Facet persistence, the SerializationManager engine. The extended EMF Facet can now extend and weave EA models. We updated Table  1  according to the Serialization criteria, making the new Facet be the most efficient technique. We successfully submitted it as a contribution to the open-source Eclipse EMF Facet 4.
Henceforth, using the FacetReferences links, stored during the alignment by Facet, one can navigate through the various meta-models e. g. browsing bottom-up or top-down. The TreeEditor is compatible with EMF Facet and enables one to browse hierarchically model layers by the FacetReference, but it is not very convenient. Architects also require facilities related to quality, e. g. to define various kinds of alignment or traceability metrics to drive a dashboard. OCL is the standard language to express statements including model navigation. Since EMF offers OCL expression parsers on Ecore models, we extended the query engine in order to make Facet virtual feature compatible and used as property in OCL statements. This extension was made publically available in a plug-in to EMF Facet project.
SAMM is composed of a Java source code with 334 000 classes and an enterprise repository in the form of a web portal exported from models described with the MEGA Enterprise Architecture software  5. We extract the application model using reverse engineering tools and transformation processes as detailed in  [12]. The resulting application model is very huge and it was a scalability challenge for the heap memory, especially to check if it can be loaded and mapped with our tools. The repository contains only a documentation of the business process diagrams (web pages and images) because the MEGA source files were not found. Consequently, we manually specified only twelve representative diagrams of the BPM model before mapping them to the App instance using the extended Facets. The original MEGA repository had no connection to the applications concepts, our mapping enables to navigate from processes toward application services.
To fill the application model, we applied a reverse-engineering process to get the data objects from an old hierarchical database and the application components were deduced from SQL Stored procedures. After, an architect audited the employees and the business responsible of SAMUT to identify and model the functional blocks. Last we achieved manually the alignment mapping between the blocks and components with Facet references. For this case, conducted from scratch, our tool facilities really helped the architect to create the mapping, because usually he uses a simple spreadsheet program to store the namespaces in a table. Then, OCL queries enabled to identify orphan components (not classified in blocks) to refine the mapping.
At first they challenged the annotation mechanism against meta-model customization and language built-in mechanisms, which do not keep the languages consistent with the modelling framework and tool support. An example of extension (customization in the above category) is given in  [29]  where ArchiMate is extended to telecom specific concepts. The ArchiMate graphical editor is also extended but it is a DSL. These works confirm lessons learned from our comparison in Section 3 and also that UML is not appropriate to elaborate a meta-model extension because UML Profiles add complexity to the extension intention; otherwise it must be confined to standard UML concepts. They propose a multi-level modelling approach based on the Orthogonal Classification Architecture (OCA) for language enhancement (on the same domain) and language augmentation (from other domains).
We have some similarities with the work of Meertens et al. : materialised solutions and techniques (practice), business-application mapping, and insurance case studies. However, that work has some limitations: the mapping considers only two models while we carry the chain from source code to business; the technique is not equipped with tools; the models conform to standards while we use generic meta-models (pivot) and we can define bridges from and to standard meta-models (we did it for MEGA) in order to accept wider EA frameworks. A potential contribution would be to implement their rules with our Facet approach in writing the mapping with a FacetSet definition. Methods and tools for EA model alignment. Clark et al. proposed a light-weight method for EA alignment based on the LEAP executable language  [7] ,  [34].
Compared with ArchiMate, LEAP concepts gather several ArchiMate concepts, it appears to be more general, and looks like Architectural Description Languages (ADL) despite it is mentioned it is not an ADL. LEAP specifications can be refined to physical architecture. The alignment is based on a refinement relation between model elements. However, the evolution is also concerned since they compare the as-is implementation with the to-be logical model. A tool is provided to edit models and analyse the refinement conformance by simulation: the logical and physical EA produce the same results. We share the pragmatic view of the LEAP method. A drawback of that method is that the business part of EA is rather neglected and LEAP focuses on the IT part. Wegmann et al. also proposed a method (SEAM) and a tool (SeamCAD) for Business-IT alignment  [35] ,  [36].
The (multi-level) models are formalised using Alloy 7  to enable a formal definition of alignment and its checking. Similarly to the work of Clark, the alignment is based on compatible behaviours. This means that the EA models of different layers must support comparable (groups of) concepts. Both LEAP and SEAM use specific models and tools, while we use standard modelling facilities. No support is given for mapping models thus the alignment is a manual operation. No reverse-engineering is provided for legacy systems. A mapping tool is proposed by Limyr et al. [37]  but the idea is to prepare patterns for model transformation, not to map heterogeneous models. Different commercial software tools offer solutions to model the information system based on architecture enterprise methodology. Some of such software tools are CaseWise, Aris, Modelio, and Sparx Enterprise.
Each referential provides generic meta-models and concepts for the different IS viewpoints. To navigate between different viewpoints, a connection with a traceability link is possible. It is different from our alignment method because the traceability links are generic and have no ontology (cf. the semantics criteria in Section 3). The interrelationships of Figure  2  are those kind of ontology we need and the improved Facet takes it into account with typed links including the separation of data and processes. The alignment analysis is also limited, but impact analysis facilities exist. For instance, Modelio offers an impact analysis diagram to identify the consequences of a change. MEGA also provides an automatic impact analysis for every object. To compare different models versions, Aris Architect supplies an "as-is" and "to-be" comparison model with an impact query builder to compute the result in a spreadsheet view.
Al Mosawi et al. [20]  proposed an Enterprise Architecture Integration (EAI) architecture based on five types of models that separate business and IT parts: technology specific model, transaction service model, generic application service model, intra-application model, and inter-application model. However, it was a general purpose discussion. Mapping discovery and verification. The Facet approach enables to define and store model mappings but the architect also requires help to discover potential mappings. To go further than manual mapping, we implemented basic strategies based on simple comparisons (concept names and types) that happen when the models are edited using writing rules (name convention for concepts and modules, data and process distinctions and so on). However, more powerful tools are needed, like the EMFCompare a framework to compare instances of models except that we use different DSL for the different layers.
A trail is to use ontologies-based algorithm to compute a similarity factor that combines concept names, types, attributes, and also relations. For instance, in Figure  2 , a <BPM Activity -App Service> link can be a candidate mapping if they use mapped data objects <BPM DataObject -App DataObject>. We use such transitive information to compute consistency alignment metrics, illustrated in the reporting dashboard mentioned in Section 6. 3. Further information on alignment metrics can be found in  [42]. Antunes et al. [43]  experimented an ontology approach in the context of EAI. OWL ontologies are extracted from ArchiMate layered models (several layers including the alignment mappings), these ontologies enable to query models to analyse and compare EA models. Working with ontologies removes the useless information details to focus on the pertinent information.
+ The global meta-model requires no additional processing to be used. -Requires a new global meta-model for each case study, this prevents Domain Specific Languages, DSLs. -Maintaining meta-models is complex since their life cycles are coupled. -At the model level, merging requires specific tools like Eclipse EMF Compare. -Working with big size models increase the risk of finding model elements conflicts, including those that do not belong to alignment. Resolving the conflict can take much time.
General description Currently available commercial digital PCR platforms rely on two distinct approaches. The first to be developed was chamber digital PCR (cdPCR), which relies on 2D arrays of microchambers to partition the sample  [35]. Once filled with the PCR mix, the microchambers are thermocycled on a flat-block thermocycler, then imaged using fluorescence to reveal the amplified positive partitions. The second approach is droplet digital PCR (ddPCR TM ), in which the sample is instead partitioned in a bulk emulsion of microdroplets using platform-specific consumables. The emulsion is transferred to a PCR tube or plate for thermocycling. After PCR amplification, data acquisition is performed in a process analogous to flow cytometry, whereby droplets are fluorescently read one by one as they pass in front of a single laser excitation source  [36].
As more and more droplets are produced, they self-arrange into a compact hexagonal 2D monolayer. At the same time, the flow of PCR mix into the oil-filled chamber forces oil to flow through the waste channel and into the sealed outlet port, compressing the air remaining in the port. The flow stops A. The samples are pipetted into the Sapphire chip, which is then sealed using caps. B. The chips are then placed onto the Naica Geode, and the program that will generate droplets prior to thermal cycling to endpoint is started by the user. C. When thermocycling is completed, the chips are transferred to the Naica Prism3, and images acquired for the Blue, Green and Red detection channels. D. Finally, data is analyzed using the Crystal Miner software, which integrates quality control of the experiment, post-treatment such as fluorescence spillover compensation, as well as the calculation of concentrations.
High resolution images are obtained by stitching 15 sub-images locally acquired with the use of the CCD camera. Exposure times for each channel can be changed by the user (range: 0-1000 ms) depending on assay requirements. Droplet crystals generated using the Naica system are very stable and can be read for up to two weeks after PCR. An image analysis algorithm then identifies and characterizes each droplet in the droplet crystal to extract the fluorescence intensity of the droplet in each channel, its size, position, shape and texture. The main steps in the algorithm are background subtraction, local maxima detection and watershed segmentation around identified maxima. Artefacts such as dust particles and droplets of inappropriate size are filtered out using size, shape, texture and position criteria in order to keep only the fluorescence values of non-coalesced droplets for data analysis.
Crystal digital PCR instrument parameters The Naica Geode was programmed to perform the sample partitioning step, followed by the PCR thermal cycling program: 95 • C for 10 minutes, followed by 45 cycles of 95 • C for 10 seconds and 60 • C for 15 seconds. Image acquisition was performed using the Naica Prism3 reader using the following exposure times: blue channel: 100 ms; green channel: 50 ms; red channel: 50 ms. Total droplet enumeration and droplet quality control, enabled by the detection of the reference dye FITC in the Blue channel, was performed by the Crystal Reader software. Extracted fluorescence values for each droplet were then further analyzed using the Crystal Miner software (Stilla Technologies, Villejuif, France). Thresholds were set using the automated tool available in the Crystal Miner software.
Quality control of the analytic process A key feature of the Naica system is the ability to access raw data, ie the images of the droplet crystals generated by the Prism3 device, which allows the user to perform a preliminary qualitative assessment of the experiment, and check that the acquisition parameters, such as exposure times, are adequate for each channel (Fig. 2A-C ). Moreover, advanced features within the Crystal Miner analysis software also allow users to visualize which objects have been classified as droplets, and have been included into the analysis. This means that the user has access to the entire analytical chain, including steps preceding the representation of extracted fluorescence data on scatterplots.
In order to characterize the performances of the assay, we first evaluated the specificity of the probes detecting the mutations against wild-type DNA. This preliminary characterization allowed us to calculate a limit of blank (LoB), defined as the number of events measured where no mutations are present, i. e. the false positive rate (results available in Table  S1 , please see Section 2. 5. 1 for details on calculations). The assays were then characterized by testing serial dilutions of reference DNA carrying the EGFR L858R, L861Q or the T790 M mutations in a background of control DNA at a final concentration of 500 copies/uL, at ratios varying from 0. 03% to 3% (Fig. 11 ). A regression curve was then fitted to the observed values and showed good linearity of the data (R2 values>0. 9). Moreover, theoretical values and associated uncertainties, representing quantification of ratios in an ideal digital PCR system, were also plotted.
A CFI S Fluor 4x (0. 2 Numerical Aperture NA), a 10x (0. 45 NA) and a 20x (0. 75 NA) objective are used to image the cells. The microscope is controlled by the IQ2 software (Andor). High-resolution images (1040 x 1390 pixels) are acquired in bright field, GFP-positive cells are imaged using a 491 nm laser, TAMRA-labeled collagen fibers and red beads are imaged using 561 nm laser. The laser is typically used at 10-15% power. 40 μm z-stacks (5 μm slice separation) of spheroids are acquired every 1-3 hours. To analyze collagen contractile flow, a technique equivalent to PIV is used. Bright field images of collagen are acquired as described above and the optical inhomogeneities of the sample are used to determine the collagen contraction  [51]. The bright field images are combined with fluorescent images of GFP-positive cells and collected on a spinning disk microscope.
Images are saved in a 8-bit TIFF format. "Maximal Intensity" projection along the z-axis is created using the ImageJ software. In the case of bright field images, only the middle slice of the Z-stack is analyzed. Both bright field and fluorescent images are corrected for drift using the "StackReg" and for photobleaching using "Bleach Correction" functions of ImageJ. To facilitate edge detection of the spheroids, contrast and brightness are corrected (contrast level should be relatively high). The diameter of spheroids is measured using ImageJ.