dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 3. 2 The Semantic Matching. The semantic matching is used to complement the terminological methods matching since they were insufficient, they can compare only textual entities while neglecting their semantic and designations. Thereby, we use the WordNet as an electronic lexical database written in English. This resource is integrated in our system to find all the synonyms, hypernyms and hyponyms for each concept. For this attempt, we identify two types of semantic matching: 1. Semantic Matching between concepts noted "MSC" 2. Semantic Matching between properties noted "MSP" For each case, we establish our assumptions, for the first one: If two concepts defined by WordNet have synonyms in common then these two concepts are similar. We calculate the degree of similarity as follows, e S and e T are two terms with σ: S × T → [0 1] The set of synonyms for every concept, the similarity is calculated as follows: For other cases, they are not similar The next table shows the result of this method.
In that context, an IOSI is a collection of information technology resources, which includes communication network, hardware, IT applications, standards for data transmission, and human IT skills and experiences. IOSI helps organisations to establish and maintain interfirm relationships such as alliances, partnerships, and buyersupplier relationship  [5]. Fours stages of evolution of IOSI may be distinguished  [1] : -stage 1: IOSI using paper supports (order forms, delivery orders. ). We suggest to extend this stage to the use of Excel or Word files. -stage 2: IOSI using EDIs. -stage 3: IOSI using an ERP. -stage 4: IOSI using internet portals or platforms. We shall see that these four stages coexist in the IOSI used by Electra and his suppliers. Finally, it is possible to characterise IOSI according to the inter-enterprise coordination mechanisms  [6] , by distinguishing those based on bilateral relationship, e.
In terms of training, tools or training modules should be systematically suggested to the new users, the supported process being relatively complex. An extensive training of all the users on all the functions of the portal could be of immediate benefit. A better communication with the suppliers on the resources and competences that could support them should be done. The animation of a real "users' community" using social tools (Facebook or dedicated tools  [17] ) could be considered. The reasons for the use of Excel files should be investigated more thoroughly, and solutions inside the web portal should be found. The suppliers should be helped for finding practical solutions in order to integrate the data provided by the web portal in their information system.
s. lim M →+∞ card 1 ≤ m ≤ M : |X m -x i | < min j =i |X m -x j M , i = 1,. , M, where (X m ) m≥1 is a sequence of i. i. d. random vectors with distribution µ. In case of a not too large dataset (a situation commonly met in signal transmission) one computes the weights w i by assigning its Voronoi class to each data (when the dataset is too large a statistical approach is adopted).
What else is quantization for? 1. 3. 1. Data mining, clustering, automatic classification Let (ξ k ) 1≤k≤n be an R d -valued dataset and let µ be the uniform distribution over this dataset -the empirical measure of the dataset -defined by µ = 1 n n k=1 δ ξ k (1. 9) where δ a denotes the Dirac mass at a ∈ R d. In such a framework, n is usually large, say 10 6 or more, and optimal quantization can be viewed as a model for clustering i. e. the design of a set of N prototypes of the dataset, with N n, obtained as a solution to the mean quadratic (or more generally L p -) optimal quantization at level N ≥ 1 of the distribution µ (p ∈ (0, +∞) being fixed). This reads as the L p -minimization problem min (x1,. ,x N )∈(R d ) N 1 n n k=1 min 1≤i≤N |ξ k -x i | p. 2 The sender A only needs a codebook to discriminate the elementary quantizers x i i.
e. a codebook where all x i are known with a fixed length 1 (dyadic) bits in its dyadic representation. The receiver B may need arbitrary accurate values for the elementary quantizers x i in his/her codebook in order to reconstruct a posteriori the signal. The existence of such an optimal N -quantization grid Γ (N,p) of prototypes follows from the above Proposition 1. 1. Such a distribution does assign mass to hyperplanes and in particular to the boundaries of polyhedral Voronoi cells. However, owing to Theorems 4. 1 and 4. 2 in  [33]  (p. 38), we know that the boundaries of the Voronoi cells induced by an optimal grid Γ (N,p) are always µ-negligible. Once an optimized grid of N prototypes has been computed (see Section 3 devoted to the algorithmic aspects), it can be used to produce an automatic classification of the dataset by making up "clusters" of points of the dataset following the nearest neighbour rule among the prototypes.
Formulated equivalently, one defines the N clusters as the "trace" of the dataset on the N Voronoi cells C i (Γ (N,p) ), i = 1,. , N. From a mathematical point of view, investigations on this topic are carried out be replacing the deterministic dataset (ξ k ) 1≤k≤n by a sequence of i. i. d. random vectors (X k ) k≥0 defined on a probability space (Ω, A, P) with distribution µ. The quantities of interest become, in short, the sequence of optimization problems induced by the random empirical measures µ n (ω, dξ) = 1 n n k=1 δ X k (ω) (dξ), ω ∈ Ω. This has given rise to a huge literature in Statistics and has known a kind of renewal with the emergence of clustering methods in the "Big Data" world, see  [10]. We consider, for every ω ∈ Ω, the optimization problem min (R d ) N 1 n n k=1 min 1≤i≤N |X k (ω) -x i | p = R d min 1≤i≤N |ξ -x i | p µ n (ω, dξ).
3. 2. 3. The special case of an empirical measure (Data mining). Note however that, when µ is the empirical measure of a dataset (ξ k ) 1≤k≤n given by (1. 9), the deterministic gradient descent (3. 41) at a given level N n can always be implemented since each computation of ∇G 2,N will require n computations of the known function appearing inside the expectation operator in  (3. 40). In this framework, the procedure is also known as Forgy's algorithm or batch k-means procedure by practitioners in Data mining. However, even in that case, it can be too much time consuming in practice when the size n of the dataset is too large. So, practitioners often come back to the above stochastic gradient by sampling at random uniformly in the dataset the input datum at each iteration which can be seen as bootstrapping.
When implemented at level N ≥ 1 for optimal classification purposes, the resulting procedure is designed to -hopefully -converge toward a(n at least local) minimum of G 2,N. If so, it produces N prototypes of the dataset (the resulting N -quantizer). The classification is finally obtained by assigning to each datum of the dataset the label of its Voronoi cell among the prototypes. Still starting from a starting N -tuple x(0) = x 1 (0),. , x N (0) in Hull supp(µ).
3. 4. Nearest neighbor search: how to speed it up. In both above described procedures -CLV Q and randomized Lloyd's procedures -the most time consuming phase is by far the nearest neighbour search which determines the Voronoi cell in which the new input X k+1 falls at step k + 1. This nearest neighbour search is well-known to have an exploding complexity when d and N are large. Its reduction or, at least, its control is known as a highly challenging problem in computer science. There are several methods to speed it up, at least in medium dimension. We consider in what follows, either an M -sample (X m (ω)) m=1,. ,M obtained by simulation of the distribution µ or a true dataset denoted in both cases by (ξ m ) m=1,. ,M to alleviate notations. Partial Distance Search principle (P DS, Chen). This idea is quite simple and very efficient: a nearest neighbour search amounts to check, in a canonical Euclidean framework, whether a squared norm 2 is lower than a record value, say δ 2 rec.
To test the inequality |ξ| 2 < δ 2 rec , one proceeds by induction on the coordinates as described in Algorithm 1 below. |ξ| 2 = |(ξ 1 ,. , ξ d )| 2 = (ξ 1 ) 2 + • • • + (ξ d ) The testing procedure is stopped as soon as the partial computation of the squared norm is higher than the record value. This procedure is known as Chen's Partial Distance Search protocol. Adaptation to other common norms is straightforward. It can still be improved in medium dimensions, say up to d = 10, by storing the data in a tree structure as described below. Space partitioning tree (see  [29] ). As a first step, one divides the dataset into two subsets of size M 2 according to the median of the first coordinate. Then, one divides again each sub-sample following the second coordinate and so on. At each step, the procedure is applied either to a new coordinate or, when the dimension is exhausted, to the one with the highest index.
Each leaf of the resulting tree is labelled. The data are coded in the tree by their leaf label and a local index inside its assigned leaf. After this pre-processing phase, whose complexity is O(M log M ), the search cost of a single datum in the tree is O(log M ). It is commonly shared by users that the depth of the tree should be fixed to 7 to get the best compromise. In many situations of interest, this choice seems more or less independent of the size of the dataset and its structure. Principal Axis Tree (McNames, see  [51] ). The additional improvement brought by this approach lies in a preliminary Principal Component Analysis (P CA) inducing a change of coordinates which makes the search more efficient, especially in view of applying the P DS principle: in the new basis induced by the P CA the absolute value of coordinates tends to decrease when the coordinate index increases implying that Chen's P DS procedure stops sooner in average.
Materials and Method Material We used T1-weighted MR images of the human brain obtained from a variety of sources. Scanner manufacturers, sequence characteristics, acquisition strategies, postprocessing, and field strengths varied between the test sets, as well as the visual appearance of the images (Fig  1 ). We refer to the combination of an image with a spatially corresponding, manually or automatically generated label volume by the term atlas. Hammers brain atlases. The Hammers atlases are a publicly available database of manually labelled cranial images of 30 adult subjects (age range 20 to 54, mean 31 years; www. braindevelopment. org). The images originate from a Signa Echospeed scanner (field strength 1. 5 Tesla; GE Medical Systems, Milwaukee, USA) at the National Society for Epilepsy in Chalfont, UK.
Further acquisition details are available in Hammers et al. [10]. Image postprocessing consisted in bias field correction (N3  [11] ), reorientation along the anterior and posterior commissures, and isotropic reconstruction (0. 9375 × 0. 9375 × 0. 9375 mm 3 ) using a single windowed sinc interpolation. The Hammers atlas database includes spatially corresponding label volumes that identify 83 cortical and subcortical regions. To obtain reference brain masks, we merged these multiregion label sets into a single binary mask. We applied additional processing to fill intracranial regions that are not covered by the resulting label: blurring with a Gaussian kernel (8 mm width), thresholding at 50%, erosion with a 3 × 3 × 3 voxel kernel in two iterations, and merging with the original combined label.
We use H as a shorthand reference to the Hammers atlases. OASIS brain atlases. This data set consists of images from 30 healthy subjects (age range 18 to 90, mean 34. 3 years), originating from the Open Access Series of Imaging Studies (OASIS) (www. oasis-brains. org/), with labels provided by Neuromorphometrics, Inc. (neuromorphometrics. com/) under academic subscription. The images had been acquired at 1. 5 Tesla. The atlases had been published for the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling  [12]. From the original set of 35 atlases, we excluded those that were based on repeat scans of the same subjects. We applied N4ITK bias field correction without masking to the images  [13]. The label volumes map 138 regions. As in the case of the Hammers atlases, the labels do not cover the entire intracranial space.
We generated brain masks using the filling procedure described in Section Hammers brain atlases. The resulting data set is denoted by O. IXI brain images. To obtain images acquired at 3 Tesla, we accessed the publicly available IXI repository of MR images (brain-development. org). The database consists of 575 images of healthy volunteers, 185 of which had been acquired at 3 Tesla at Hammersmith Hospital, London, UK (Intera scanner; Philips Medical Systems, Best, The Netherlands). No segmentation labels were available for this database. We randomly selected 30/185 images and labelled this set as X (age range 24. 3 to 74. 6, mean 49. 4 years). From the remainder of 155, we selected another random set of 30 and combined the resulting set with X to yield a set of 60, denoted by X+ (age range 20.
2 to 74. 6, mean 45. 3 years). All images underwent bias field correction with N4ITK without masking. LPBA40 atlases. The LPBA40 repository holds images of 40 adults (age range 16 to 40, mean 29. 2 years) imaged at 1. 5 Tesla (shattuck. bmap. ucla. edu/resources/ lpba40/  [14] ). We obtained the full MR images in native space as well as their skull-stripped versions. We thresholded the skull-stripped images at zero to obtain binary brain masks. We use L to refer to this data set.
3. A margin mask M l was generated, consisting of all voxels within a distance of 4 voxels from the boundary of C l. This serves to reduce the data to be considered in the subsequent ranking evaluation. 4. The n l individual atlas subjects were ranked in descending order of the similarity (normalized mutual information) of the transformed atlas image with the target image within the margin mask. The n l+1 top-ranking atlases were identified for use in the subsequent level l +1, with n lþ1 ¼ max fn l ffiffiffiffiffiffiffiffiffi ffi 8=n 0 3 p ; 7g. This approach ensures that selection only takes place if the number of atlas sets is larger or equal to 10, and that at least 7 atlas sets are used at every level. It also entails more aggressive selection when atlas set sizes are larger. 5. The selected transformed labels were fused by summation to create a fuzzy label F l.
Image registration Image registration consisted in optimizing a transformation based on maximizing normalized mutual information between a given atlas and target  [15]. Initially, the degrees of freedom were set to 6 (rigid translation and rotation). For affine registration, scaling and shearing were additionally allowed (12 degrees of freedom). Nonrigid registration consisted of applying displacements to the atlas image via a lattice of control points (spacing 6 mm), blended using Bspline basis functions. The stopping condition for the optimization was either no further improvement in similarity or reaching 40 iterations. All registration steps were carried out using the Image Registration Toolkit (IRTK, https:// github. com/BioMedIA/IRTK).
An overview of the experiments is shown in Table  1. Quality measures. To assess the quality of a generated brain label, we assessed its agreement with the reference mask using four complementary measures: the overlap ratio or Jaccard coefficient (JC; intersection divided by union), the volume error, the sensitivity [intersection divided by (intersection + false negatives)], and the 95th percentile of the symmetric surface distance. We did not determine specificity (true negative rate), because it depends on the size of the background and is therefore meaningless for comparisons between data sets with differing fields of view. Overlap measures do not yield information about volume differences between the label pair. We therefore determined volume error as a percentage, calculated as DV ¼ 200 Á V r À V g V r þ V g ð1Þ where V r is the volume of the reference label and V g the volume of the generated label.
Since averaging of positive and negative volume errors can hide poor performance, we also calculated the mean of jΔVj. We determined the JC between the final fused brain mask C 0 2 (generated at Step 6, fused from n 3 transformed atlas labels) and mask C 2 (generated at Step 2, fused from n 2 transformed atlas labels). We investigated the value of this "success index" as a predictor of segmentation accuracy that is independent of reference segmentations. Within-group cross-comparisons. To determine within-group performance of pincram, we carried out three leave-two-out cross-comparisons; one within each of the Hammers (HH: H), OASIS (OO:O), and LPBA40 (LL:L) set. Each image was labelled in turn, using n-2 data sets as atlases, excluding the target itself as well as the mirrored version of the target.
5 T image data. The Hammers-based synthetic atlas set (HX, 30 unflipped and 30 flipped atlases) was used to segment the OASIS images, and the OASIS-based synthetic atlas set (OX, 30 unflipped and 30 flipped atlases) was used to segment the Hammers images. Both result sets were then compared to the original manual segmentations (HXO:O, OXH:H). The accuracy of the output segmentations is thus affected by two traversals of the field-strength boundary between the respective data sets. The accuracy of the direct segmentations (Hammers with OASIS, OH:H and vice versa, HO:O) served as a reference for estimating the size of the accuracy-reducing effect. Secondary synthetic atlases. We set out to assess the effect on accuracy of customizing an atlas set to an ensemble of target images. The reasoning was that if pincram yields inaccurate results in a part of a given ensemble due to acquisition differences between the atlas and target images, accuracy is potentially recoverable by exploiting the similarity of the images within the ensemble.
We set up three experiments of this type, one using a subset of the target ensemble and two using the full target set to generate synthetic atlases. To assess the effect of forward selection on accuracy, we used the Hammers atlases to segment the LPBA40 images and evaluated the resulting segmentations against the reference masks provided with the latter (HL:L). From the thirty most successful segmentations on the JC criterion, we built an atlas set HL 30 , applied this back to all 40 LPBA40 sets, and compared with the reference masks (HL 30 L:L). Since a reference for evaluating segmentation success is not normally available, we examined the effectiveness of non-selectively building a synthetic atlas set from all images in the target ensemble. We used the HXO and OXH data described in the previous section to this end, segmenting again the data in O and H, respectively.
The shorthand notation is thus HXOO:O and OXHH:H. Effect of mirroring on atlas accuracy. Adding left/right-flipped versions of each atlas and label is a frequently employed procedure that doubles the size of an atlas resource  [7, 8] , exploiting the slight shape asymmetry that occurs naturally in the head. Whether an atlas database that has been enlarged in this fashion is equivalent to an equally large database consisting only of original, native images is open to question. To find the answer in the context of using pincram, we compared the OX and HX sets described above (Section Cross-scanner accuracy), which had been doubled by mirroring, with sets consisting of 60 native images each (OX+ and HX+). Again, we used the OASIS-derived atlases to segment Hammers images and the Hammers-derived atlases to segment OASIS images.
We compared the results of the experiment OXH:H with those of OX+H:H. Likewise, we compared HXO:O with HX+O:O. We assumed that significantly lower quality measures for the doubled sets would indicate that the doubling manoeuvre incurs a cost in terms of accuracy. Effect of varying atlas number. For all experiments described above, we set the number of atlases n 0 to the maximum possible, given the size of the database and the experimental constraints. To examine the impact on accuracy of using fewer than the available atlases, we set up the following experiment. We chose X+ as the atlas set, with labels generated from H. This enabled a maximum n 0 of 60. As the target set, we chose H (n = 30). We then obtained random subsets of varying size (HX+ n H:H with n 2 7, 9, 12, 17, 25, 38, 60) and used these to segment H, yielding runs of the pattern HX+ n H:H.
Independent evaluation We followed the procedure for independent evaluation of brain extraction methods provided by the Segmentation Validation Engine (SVE, sve. bmap. ucla. edu [19] ). The program we used for the submission was an early development version of pincram. The current version could not be evaluated because the SVE was closed to new submissions at the time of manuscript preparation. As expected, the overlaps were largest (JC > 0. 96) for the within-group comparisons, with LL:L leading the ranking. This may be due to the strong similarity between the images in L. Also, the reference masks appeared visually smoother and more generous for L than for H and O. The mean volume of the reference masks was larger in L than in the other two, albeit not significantly so (1. 36 L (L); 1. 34 L (H), 1.
For the H cohort, the average JC was 0. 933 ± 0. 010 (compare with "Overlap JC" in Table  2 , rows 1 to 5); for O, it was 0. 810 ± 0. 074 (compare with "Overlap JC" in Table  2 , rows 6 to 10). Deviations from the reference masks occurred predominantly as overinclusions in the pharynx region. Evaluation results on the SVE placed pincram on rank 3 of the archive (submission number #287). For the early development version of pincram that we used for the SVE evaluation, the Jaccard coefficient was 0. 9631± 0. 0067, corresponding to a Dice coefficient of 0. 9812± 0. 0035. The sensitivity was 0. 9883 ± 0. 0038 and the specificity 0. 9953± 0. 0019. At the time of manuscript preparation, the SVE site was closed for new submissions; we could therefore not test the current version. One of the higher-ranking entries is "groundTruth", an apparent test   submission that does not pertain to a competing method (#229).
Discussion This study describes a new atlas-based brain masking method, pincram, and provides strong evidence of its accuracy and robustness in a series of experiments mimicking real-world brain extraction tasks. The method combines a set of tried and tested approaches in a new fashion (label propagation  [20]  using nonrigid registration  [15] , atlas selection  [21, 22] , and marginmask based data reduction  [8] ). It includes a novel self-monitoring feature that produces a success index as a metadatum. The method is implemented as open-source software, available for download from http://soundray. org/pincram under the MIT ("Expat") licence. While other library-based methods are similarly  [7, 8, 23]  or more accurate  [9] , they tend to have considerable restrictions in practice. Pincram is unique among library-based methods in that it processes any given T1-weighted target head image on the basis of data sets of labelled T1-weighted images acquired on other scanners.
Users of pincram will thus have a broad choice of atlas sets, as several are freely available with moderate licence restrictions. Other will only work on images acquired with the same sequence on the same type of scanner as the atlas data set. The image registration programs that pincram employs (rreg2, areg2, and nreg2  [15, 24, 25] ) are comparatively robust towards differences in image intensity distributions-even those resulting from differences in field strength-thanks to the use of normalized mutual information as the optimization target. The resulting advantage is that pincram does not require laborious generation of a target-specific atlas set to achieve segmentations of reasonable accuracy. Nevertheless, a custom atlas set can yield benefits of accuracy, even if automatically generated, as exemplified in our experiments HXOO:O and OXHH:H.