dataesr/scientific-paragraphs-categorization huggingface dataset:
• The Clustering Degree is used to specify how nodes are interconnected to each other. These criteria could provide an initial insight on some performance metrics. For example, a small diameter and a low average distance allows fast communication between farthest nodes. A high degree allows many close neighbours perform fast communications. A higher clustering degree indicates that nodes close to each other are strongly connected. Thus, if an OCI architecture has a small diameter, a high degree, and a high clustering degree, then it could support various data traffic patterns. In order to show the efficiency of the inserting links between the strategic nodes, a theoretical evaluation has been first conducted using MatLab. Indeed, many topologies (Ring, 2D Mesh, Torus, WK, X-Mesh and Spidergon) have been     [6].
State-of-the-art background This section presents limitations of current full-reference (FR), reduced-reference (RR), and no-reference (NR) metrics for standardized models. Most of the models in ITU-T recommendations were validated on video databases that used one of the following hypotheses: frame freezes lasting up to 2 seconds; no degradation at the beginning or at the end of the video sequence; no skipped frames; clean video reference (no spatial or temporal distortions); minimum delay supported between video reference and video (sometimes with constant delay); and up or down-scaling operations not always taken into account  [17]. As mentioned earlier, most quality models are based on measuring common artefacts, such as blur, blocking, and jerkiness, for producing a prediction of the MOS.
Slicing The slicing artefact appears when a limited number of video lines (stripes) is severely damaged. A sample video frame containing slicing is shown in Fig. 1f. It is caused by a loss of video data packets. A decoder can reconstruct (predict) lost slices in a image based on adjacent slices. Detection of the slicing artefact is performed by analysing image stripe similarity, both in vertical and horizontal directions. The threshold refers to the minimum value of the similarity value above which the distortion is noticeable.
Experimental set-ups for concept verification This section introduces four experimental set-ups for concept verification: CONTENT 2009 experimental set-up (described in Section 5. 1), VQEG HDTV 2010 experimental set-up (described in Section 5. 2), INDECT 2011 experimental set-up (described in Section 5. 3), and VARIUM 2013 experimental set-up (described in Section 5. 4). Table  3  shows a mapping between KPI and experimental set-ups used for KPI concept verification. The CONTENT 2009, VQEG HDTV 2010, and INDECT 2011 subjective experiments were based on MOS. During the data analysis the scores given to the processed versions were subtracted from the scores given to the corresponding reference to obtain degradation/differential MOS (DMOS). Then, the artefact perceptibility threshold was set to DMOS = 4, based on degradation category rating (DCR) procedure test instructions (see ITU-T P.
VQEG HDTV 2010 experimental setup In 2010, VQEG presented the results from the HDTV validation test of a set of objective video quality models. Six subjective experiments provided data against which model validation was performed. The experiments were divided into the four 1080p and 1080i video formats. The 720p content was inserted into the experiments as test conditions, for example by converting 1080i 59. 94 fields-per-second video to 720p 59. 94 frames-per-second, compressing the video, and then converting back to 1080i. The original videos represented a diverse range of content, as shown in Fig. 3. The subjective experiments included processed video sequences with a wide range of quality. The impairments included in the experiments were limited to MPEG-2 and H. 264, both 'coding only' and 'coding plus transmission' errors  [22].
VARIUM 2013 experimental setup We considered data from an experiment of the VARIUM project, developed jointly by the University of Brasília (Brazil) and the Delft University (The Netherlands). The data and the experiment methodology are described by Farias et al. in  [2]. In this experiment, subjects judged the quality of videos with combinations of blocking and blur artefacts. Seven high definition (1280 × 720, 50 fps) original videos were used. The videos were all eight seconds long and represented a diverse range of content, as shown in Fig. 4. To generate sequences with blocking and blur, the authors used a previously developed system for generating synthetic artefacts  [3] , which is based on the algorithms described in the Recommendation P. 930  [6]. The method makes it possible to combine as many or as few artefacts as required at several strengths.
PDM systems were initially designed to manage Computer Aided Design (CAD) files, thus PLM systems were developed mostly around this type of data in the context of manufacturing industry. However, more and more works address other domains, such as Computer Aided Engineering (CAE), Mechatronics, Architecture Engineering and Construction (AEC), Services and Bio-Medical Imaging (BMI). These domains are multidisciplinary and handle heterogeneous data linked by complex relationships inside which it is not easy to browse. The specific needs coming from new application domains of PLM have been overlooked so far, but the whole PLM community could benefit from them. Because the complexity of data relationships is growing in manufacturing industry and new application domains of PLM, it is important to study relationships management in PLM systems so as to improve their understanding and analysis.
However, web-based clients are little by little becoming a unavoidable evolution of PLM systems in manufacturing industry in order to access data everywhere and on new mediums such as tablets. The main purpose of web-based clients is to enable easy browsing and retrieval of data, but their interface is not suitable enough. Some interesting PLM web clients have been developed recently -for instance web ARAS system or new Teamcenter 10 active workspace client -, but they still propose an overloaded interface. Therefore the study of two case studies is proposed to decide whether: 1. The interfaces of PLM systems both comply with the requirements of manufacturing industry and of a new application domain of PLM, such as Bio-Medical Imaging, 2. The PLM research community should address more work on PLM interfaces.
Context and data to migrate In 2011, ACME decided to migrate its Windchill PLM system to Teamcenter PLM system. The CAD software Pro/E remained the same after the migration, so there was no conversion operation. A PLM system handles two distinct types of data: metadata which are stored in a relational database, and data files which are stored in the vault, a securised file system accessible only through PLM. Relationships between data files and metadata are described in the PLM system. In the case of ACME migration, data files are drawings and 3D CAD assemblies. An Extract Transform Load (ETL) type of implementation was set up for the migration. Indeed, Windchill and Teamcenter do not have the same concepts and data models, so an intermediate migration platform is needed to transform the data.
The migration platform is composed of a temporary data files storage and a Graph DataBase (GDB), Neo4J. PLMXQuery tool  [16]  is used to populate the migration platform, which constitutes the Extract phase of ETL. Through XQuery language, the metadata are converted into PLMXML language  [14]  and the data files are represented with their dependencies in a ASCII instruction file. Finally, during the load phase of ETL, the target Teamcenter platform is populated thanks to PLMXML Import Tool for the metadata and IPEM tool for the data files. A GDB was chosen to store temporarily the metadata, because a PLM system can be seen as a set of unique objects having a set of attributes (key or value) and being linked together by typed relationships. A graph corresponds as well to this definition, as it is composed of nodes and edges having attributes.
When a user open a CAD file locally, the target CAD file and all downloaded from the vault. The relationships dependency during the migration, as data files with dependencies the dependent files. Thus, the imports have to be occurred during the migration, due to wrong referencing obsolete instances, missing references between CAD files and drawings, dependency cycles. These issues were a real migration process which lasted for one year instead of the because the initial data had to be "cleaned". An algorithm was designed to analyse referencing errors on the GDB. the errors occurred in time, and to solve automatically algorithm was applied on the Neo4J database. Once passed through the whole database, there were still some inconsistency of complex relationships, and they had to be lesson from the migration is the unexpected number relationships between the levels of granularity in PLM company was not aware that these issues were existing it is difficult to analyse dependencies and identify ween objects in a PLM system.
A PLM database in neuroimaging domain Neuroimaging domain is multidisciplinary "by its very nature"  [20] : the study of brain require an active interaction between many specialities -physics, medicine, mathematics and engineering among others. Magnetic Resonance Imaging (MRI) is one of the most promising imaging technique to study brain complexity. Structural MRI examines brain anatomy, whereas functional MRI analyses what happens while a subject is performing a given task. A Bio-Medical Imaging (BMI) research study can be represented by four stages that constitute a cycle  [1] : study specifications (stage 1), raw data (stage 2), derived data (stage 3) and published results (stage 4). Between stages, it is crucial to keep all the information to be able to understand the context of computing and the history of data, which is a requirement to reproduce derived data result or to reuse them.
What a piece of data is, when, where and how it was produced, why and for whom it was performed is called provenance  [15]. Due to costs, trends to huge cohorts of subjects and growing complexity of analyses, neuroimaging researchers must collaborate and share data between disciplines and laboratories  [21] , which are similar issues than the one of manufacturing industry. PLM was proposed and shown to be relevant to manage efficiently neuroimaging heterogeneous data and to enable the conditions of sharing and reuse of data  [2]. Since 2010, the GIN 2  has been using the GIN first Brain Imaging Laterality (BIL&GIN1) dataset, which is composed of 300 subjects -balanced by gender and handedness -and which was acquired between 2009 and 2011  [13]. In 2013 a PLM system -Teamcenter 9 -was installed at the GIN, populated with the BIL&GIN1 dataset.
Limits of PLM systems browsing The figure  2  shows the two main ways of browsing information in Teamcenter -the concepts are similar in other PLM systems -: hierarchy view (descending) and impact analysis view (ascending). It is easy to see where an object has been Fig. 2. Screenshots shows that acquisition processing sequence top objects PCR_ROIgraph hierarchy. used on close levels, of relationships. However, be able to navigate requires data exploration allows no overall view The feedback of the capacities of the ever, they were very rich client display): vironment. Besides, What they are looking with few choices to ment (browsing, con they express the will end, the GIN researchers 11th International Conference -PLM14 Japan, 7th-9th July 2014 7 Screenshots of Teamcenter rich client browsing interfaces.
Information access: query In neuroimaging there is a trend for cross-domain analyses: imaging results are correlated with demographical, clinical, psychological and genetics data. Therefore, numerous joins between concepts are required for one single query, which make the search process complex. Indeed, to design customized queries, users have to know the exact data model organisation. From user's point of view, querying is textual browsing: the search fields that are filled in make the query engine browsing among data relationships. Without efficient retrieval capacities, a database is just a storage room. Apart from the limitation of users' daily data search, the lack of effective querying in PLM systems has an impact on the understanding of data provenance and therefore data reuse. PLM data models should be transparent to the users and a proposal is to design a graphical query builder, with which users would be query-autonomous.
The IFIP WG5. 1 11th International Conference -PLM14 Yokohama, Japan, 7th-9th July 2014 9 Some future work should address new ways of information display in data management systems, particularly focusing on multi viewpoints display. On figure  1 , different levels of information are displayed in the Neo4J graph. The positive aspect is that it is easy to access to all the relationships between data and concepts, but the positive aspect is that all the edges look the same, and nothing distinguishes one level of information from the other, for instance CAD files view and product view. So multi viewpoints developments should take this remark into account.
Acknowledgments The authors wish to thank in particular the  Association Nationale de la Recherche et de la Technologie (ANRT)  for its financial support to their work ( CIFRE 2012/0420 ). The work presented in the paper is conducted within an  ANR  ( Agence Nationale de la Recherche)  founded project (n o  ANR-13-CORD-0007 ) for thematic axis n o 2 of the Contint 2013 Call for Proposal: from content to knowledge and big data.
Implementation A prototype of the proposed model has been implemented using the simulation platform AnyLogic  (AnyLogic, 2010). AnyLogic can simulate models based on multiple modelling approaches: discrete event simulation as well as systems dynamics and multi-agent systems. It is based on the object-oriented language Java. Regarding the multi-agent approach, AnyLogic offers two types of entities: Agent and Ac-tiveObject. ActiveObject can be understood as objects in object-oriented programming. The behaviour of objects (i. e. any entity of the model) can be implemented using state charts, simple functions, data flow diagrams, activity diagrams, etc.
X-Ray Crystallography Crystals were grown by hanging drop vapour diffusion at 18uC by mixing 1 ml of protein solution (25 mg/ml) with 1 ml of well solution composed of 22% PEG3350, 0. 1 M sodium citrate buffer (pH 5. 3), 0. 2 M potassium sodium tartrate, 5 mM 1,4-dithiothreitol, 0. 02% (w/v) sodium azide. Crystals appeared after one day with typical dimensions 0. 3-0. 5 mm. Soaking experiments were performed for fragments 1, 2 and 3 by adding 0. 2 ml of a 100 mM solution in DMSO before flash-cooling at 100 K in liquid nitrogen. Fragment 4 was co-crystallized with the protein and directly flash-cooled in liquid nitrogen. The data were collected on beam-lines ID23-1 and ID14-4 at the ESRF (Grenoble, France). All measurements were indexed and integrated using iMOSFLM program  [20]  and merged with the SCALA program.
Statistics for data collection and processing are given in Table  S1. The solution of the crystal structures were obtained by the molecular replacement method using the program MOLREP of the CCP4  [21]  suite and the structure 1HD2 as a model  [22]. The structures were refined using the program REFMAC5 of the CCP4 suite. The structures have been deposited at the RCSB Protein Databank (PDB codes 4K7I, 4K7N, 4K7O and 4MMM).
Docking All docking computations were performed with AutoDock4. 2. 3  [23]. 200 independent runs were conducted for each fragment using the Genetic Algorithm with standard settings. Structure PDB entry 3MNG containing PRDX5 in interaction with DTT was used as the 3D template for the docking, as no apo-structure is available. The DTT coordinates were removed from the crystal structure. Protein and fragment structures were prepared with AutoDock Tools. The standard AutoDock-Potential scoring function was used.
The binding mode of the ligand is taken as that which exhibits the best agreement between calculated and experimental protein 1 H (usually the amide protons) CSPs. PRDX5-fragment complex structures were generated using AutoDock software  [23] , and the CSPs were calculated for each ligand position, as described in the experimental section. The fragment orientations exhibiting the best agreement between the experimental and calculated CSPs are selected using the P score value. Starting from 200 ligand orientations for each fragment (Figure  3A  and Table  S2 ), the CSP filter selected ligand positions exhibiting roughly the same orientation of the catechol group for fragments 2-4 (Figure  3B ), indicating that the CSPs support a conserved binding mode for these compounds. Regarding fragment 5, the catechol moiety does not superimpose well on those of fragments 2-4 for any of the selected binding modes, suggesting that the addition of a phenyl group at position A induces a binding mode change that translates into a different orientation of the catechol ring (Figure  3B ).
Local methods give less accurate results than global methods, but they are much faster. In this paper, only local methods are considered for real time considerations. The Middlebury Stereo Vision Website  [3]  is a reference for the comparison of Stereo Matching algorithms. The best ranked algorithms of this database and the related papers has been studied. As the highest ranked algorithm is not published, the algorithm proposed by Mei  [4]  has been targeted for a DSP Implementation. First, the original algorithm  [4]  will be introduced, and all algorithm blocks will be detailed. Then, real time limitations of this algorithm will be discussed and solutions to bypass those limits will be exposed. To finish this section, some implementation details will be given.
A. Quality Assessment The quality of algorithm output has been evaluated with Middlebury evaluation tools  [3]. The Middlebury evaluation gives the number of bad pixels in different parts of the image. In this paper two values are considered for quality assessment: • The global number of bad pixels. It gives a good representation of the overall image quality. • The number of bad pixels in non-occulted areas of the image. This algorithm does not deal with occulted areas, and the disparity map on those areas is very noisy, thus results on occulted areas are partially random. Comparing random results together does not give clear results. That is why, when comparing two versions of this algorithm, it is better to look at non-occulted parts of images. 1) Data Set: Seven images from the Middlebury database  [3]  are used to evaluate quality for the proposed algorithm (Aloe, Art, Laundry, Map, Sawtooth, Tsukuba, Venus).
The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to detect microcalcifications (MC) in human breast tissue seen in mammograms and to characterize the fractal geometry of benign and malignant MC clusters. This was done in the context of a preliminary analysis of a small dataset, via a novel way to partition the wavelet-transform space-scale skeleton. For the first time, the estimated 3D fractal structure of a breast lesion was inferred by pairing the information from two separate 2D projected mammographic views of the same breast, i. e. the cranial-caudal (CC) and mediolateral-oblique (MLO) views. As a novelty, we define the ''CC-MLO fractal dimension plot'', where a ''fractal zone'' and ''Euclidean zones'' (non-fractal) are defined. 118 images (59 cases, 25 malignant and 34 benign) obtained from a digital databank of mammograms with known radiologist diagnostics were analyzed to determine which cases would be plotted in the fractal zone and which cases would fall in the Euclidean zones.
The method was originally developed as a multifractal formalism to analyze highly complex 1D signals  [53, 54] , 2D surfaces  [40, 55, 56]  and 3D surfaces  [57]. In particular, the 1D WTMM method was successfully applied to demonstrate the multifractality in physiologic dynamics and its breakdown with disease  [58] [59] [60] [61] [62]. A preliminary approach demonstrating the potential of the 2D WTMM method to detect microcalcifications and to quantitatively assess and discriminate between fatty vs. dense tissue was first presented in 2001  [63]. The results presented in this paper were obtained by using the WTMM method on screening mammograms that were taken from an online databank. They demonstrate how this tool could be used as a possible Computer-Aided Detection method as well as a Computer-Aided Diagnostic method.
Additional statistical analysis The Bayesian analysis presented above takes into account the limited size of the dataset and the robust statistical conclusions that are drawn from this analysis are indeed significant. Nevertheless, an additional statistical analysis was performed in order to further validate the significance of our results. Instead of constructing a statistical estimator and evaluating its frequency properties, a statistical model was developed by constructing a joint probability distribution and by checking it against the observational data, as suggested in  [68]. A ''coin toss'' experiment was performed 100,000 times (see Methods section) to arrive at a discrete probability distribution, which showed that the model was an adequate representation of the data, as demonstrated by the red bars being at neither end of the distributions in Figures  7A  and 7B.
There has been research done on the shape of cells and also the vascular structure feeding tumors  [17, 73]. Studies such as these have shown the fractal structure of cancer at these levels, implying the global shape of the breast tumors may be driven by the interactions of smaller scale agents and their environment. The nature of this study is exploratory. Future analyses on larger sets will allow the investigation of the automation of the selection process for the thresholds on h and on log(K). The delineation of the boundary between the fractal zone and the Euclidean zones in the CC-MLO fractal dimension plot, which was defined somewhat qualitatively, should also be explored. However, even though the size of the dataset investigated in this study is indeed limited, the robust statistical conclusions that are drawn from the analysis did take into account this limited sample size.
Data The images that were analyzed were obtained from the Digital Database for Screening Mammography (DDSM) at the University of South Florida  [74, 75]. The databank contains over 2,600 studies made up of normal, benign, benign without call back and malignant mammograms all categorized by an expert radiologist. Each study has two images of each breast, consisting of a mediolateral-oblique (MLO) view and cranio-caudal (CC) view with any suspicious region circled by a radiologist. The suspicious region could contain a mass and/or microcalcifications (MC), but only the cases that were classified as having exactly one tumor composed of only microcalcifications in the benign and malignant categories were looked at in this particular study. In addition to only considering tumors consisting of MC, any mammographic images that contained artifacts inside the radiologist's encircled region were discarded due to the impact it has on the analysis.
, 2000). This new trend also introduces some difficulties both for students (that need to comprehend how to follow it), and for teachers (that must find a just way to assess individually each student). These troubles can, once again, be overcame resorting to the support of intelligent software tools, like Automatic Grading Systems (AGS), as will be discussed along the following sections (this topic is the second motivation for the work here described). After this motivation and before conclusion, the paper has 3 sections. Section 2 discusses in general the support that computers can bring to education. In Section 3 we introduce our proposal -the main characteristics that an effective AGS should have. Then, in Section 4 we show the architecture of iQuimera, an AGS that we are developing to realize our proposal.