dataesr/scientific-paragraphs-categorization huggingface dataset:
Fraud is difficult to label To learn a model (be it a set of rules, or decision trees or a black box model) from examples, a dataset of examples of both fraudulent and nonfraudulent operations is required. However, because of the difficulty to assert the fraudulent status of operations, the label of the examples used in the associated machine learning task must be used with caution. Some of the operations might be labeled as "not fraudulent" although they are: the fraud will be revealed much later (see unanticipated fraud in Figure  2 ), or simply no one will ever know because the fraud has gone undetected. In machine learning, this issue of having examples that are wrongly labeled is known under the term of "noise. " From a practical point of view, it means that assessing the error of classification of FDM will require some attention.
Dealing with imbalanced data As seen in Section 2. 3. 1, one of the specificities of fraud datasets is the strong class imbalance, the "fraud" class representing sometimes less than 1% of the whole dataset. Moreover, there is an overlap of both classes (see Section 2. 3. 3). These two phenomena therefore make classical supervised machine learning algorithms ineffective. The clue is to give more weight to the minority class either by modifying the data itself or the algorithm used through three possibilities: under-sampling negative observations, over-sampling positive observations, or hybrid methods allowing to combine under-and over-sampling (see Figure  3  and Table  A1  in the Appendix for more details). Under-sampling consists in removing part of the observations from the majority class.
Dealing with the lack of fully trusted labels Beyond the limits of supervised models on imbalance data, they prove to be even less efficient on poorly labeled phenomena. Indeed, a characteristic of successful fraud is that it has gone unnoticed in history. This means that real fraud datasets often have frauds among the "nonfraudulent" observations. It is therefore recommended, in the case of not fully trusted labeled data, to use anomaly detection methods that are semi-supervised or unsupervised. Figure  4  presents the main algorithms (see full description in Table  A2  in the Appendix). Semi-supervised algorithms are trained on nonfraudulent observations and then detect abnormal, and therefore potentially fraudulent observations, during the test phase. Among them, the best known is Oneclass SVM  (Schölkopf et al.
Experimentation context The experimentation takes place in the framework of a European project financed by the ERDF, called Cerberus, where two private AI actors had to cooperate to propose innovative solutions for fraud management. The project resulted in the deployment of two solutions, closely related to their application characteristics. First, an anti-fraud software, carried by the publisher Bleckwen, is developed for instant cash transfer fraud, characterized by high operation frequencies and limited human involvement. This software is based on the improvement of a black box scoring model (XGBoost), resulting in a fraud probability score, completed with a local interpretative overlay: all operations over a given optimal threshold are suspended and must be investigated. The second solution is an adaptation of the intrinsic interpretable analysis methods based on the Q-Finder algorithm, with proven results in medicine  (Amrane et al.
Data understanding The data collected for the experimentation included the customer characteristics, the cheque deposit characteristics and their dynamic, the other main transactions dynamics, the historical frauds and several other confidential data sources. The collected data represented 85 tables from 5 different sources and covered the period from October 2018 à September 2019. This represented over 16,000,000 historical cheques. All the collected variables, that is more than 200, were clarified in terms of meaning and structure, and qualified following the documentation framework Databook  (Nesvijevskaia, 2021). After this qualification work, the scope of the data was filtered to only those cheques that were usable and suitable for the analyses.
While the question of which approach will give the best answer is still open, we showed on a practical example that it is possible in the meantime to hybridize both approaches to develop improved solutions for the complete fraud management process. Acknowledgments. The authors are grateful for the support provided by Quinten, for colleagues' reviews and for the careful rereading of the final English version, as well as for the long collaboration with Bleckwen, ERDF, and our partner Banque Populaire Rives de Paris who shared their data and business expertise. We would also like to thank the Data for Policy organization team for their kind advices for the conference preparation (submission #35, DOI:10. 5281/zenodo. 3967821). Funding Statement. This research was supported by ERDF under research grants, exclusively covering the mobilized human resources, in the framework of a European project to propose innovative solutions for fraud management.
Continued Advantages • Clusters the data in addition with detecting anomalous observations Drawbacks • Makes harder the separation of classes: more difficult for the model to perform • Illusion that the problem is easier than it is • Insufficient in the case of extremely imbalanced data imbalanced data • Insufficient in the case of extremely • Not easily interpretable • Hard to choose an arbitrary weight • Needs a large dataset as input easy or hard to learn or lapped classes • Does not distinguish observations • May not work in case of over-neously over and under sampling • May not be sufficiently local • Difficult to parameterize simulta-lapped classes neously over and under sampling • May not work in case of over-• Difficult to parameterize simulta-may not work on large data • High computational complexity: • Difficult to parameterize Drawbacks • May not work in case of over-lapped classes Anna Nesvijevskaia et al.
1. Popular current methods for quantifying variation in biological shape are wellsuited to analyses of isolated parts (e. g. the same bone from the skeletons of many individuals). An analytical challenge exists for quantifying variation between the shapes of multiple-part objects where each part has a different position, rotation or scale (e. g. partial or whole articulated skeletons). We investigated regularised consensus principal component analysis (RCPCA) as a multiblock method for quantifying variation in the shape of multiple-part objects. Multiblock methods are routinely used in other big data research fields such as bioinformatics/medicine, marketing and food research, but have not been widely embraced for evolutionary biology research. 2. We have created the new package morphoBlocks for the r programming language to make RCPCA more accessible for shape evolution research.
morph- oBlocks provides a complete workflow for formatting, analysing and visualising the variation between multiple-part objects by integrating functions from a diverse range of other packages. In particular, global components produced by RCPCA provide a consensus space that we present here as a morphospace for multiple-part objects. 3. morphoBlocks is demonstrated with a case study of manually placed landmarks and automatically placed pseudolandmarks from the partial wing skeletons of 15 extant penguin species and five fossil penguin species. Our case study provides quantitative support for a historical hypothesis about the magnitude and mode of morphological change across the evolutionary history of penguins. 4. RCPCA can be used to analyse two-or three-dimensional datasets with 10s of landmarks, or 100s to 1,000s of semilandmarks or pseudolandmarks, from 10s to 100s of specimens comprised of two or more parts.
Shape evolution research produces big n × kp datasets: k = 2 or 3 dimensions for p = 10s of landmarks or 100s to 1,000s of semi-or pseudolandmarks across n = 10s to 100s of objects  (Goswami et al. , 2019). Shape differences are therefore commonly studied by reducing kp to fewer dimensions that explain major sources of variation in the dataset  (Kendall, 1984; Mitteroecker & Huttegger, 2009). Geometric morphometrics (i. e. shape analyses) typically focus on the landmark configuration from one part per individual (e. g. the left femora of 256 people;  Cavaignac et al. , 2016) , where Procrustes transformation is applied to a single n × kp block to give all objects the same Cartesian origin  (Mitteroecker & Huttegger, 2009). Consider that the shape of femora from n individuals is easily compared as a single block of data, but the shape of the lower limb skeleton must account for the shape of each leg bone and also the rotations of bones around the knee and other joints.
, 2020; Profico et al. , 2019). A scaling method that normalises landmark configurations using centroid size is described by  Adams (1999)  and  Collyer et al. (2020) , and has recently been introduced into the geomorph package for the r programming language (see geomorph::combine. subsets;  Adams & Otárola-Castillo, 2013). The scaled and transformed landmark configurations from the fixed parts are then combined and analysed as a single set of landmarks per individual (i. e. Adams, 1999; Collyer et al. , 2020). Downstream analyses of landmark configurations presented as a single block often use principal component analysis (PCA), where components of variation describe distances between samples with respect to the greatest sources of variation in the dataset (although see Bookstein, 2017 for discussion).
e. when performing PCA on a concatenated superblock). Instead, block weighting can only be determined a priori, for example, by using normalised centroid size weighting as a data pre-processing step (discussed in  Collyer et al. , 2020). In contrast, consensus principal component analysis weights the contribution of each block to global components. Before RCPCA, users might still adjust the weight of each block a priori as a data pre-processing step. Normalised centroid size weighting is applied by default as a pre-processing step in morphoBlocks::combineBlocks, meaning that blocks are given similar weighting before RCPCA. One consequence of this a priori weighting is that PCA of the superblock and RCPCA of the individual blocks can give similar results for many datasets. However, there may be analytical reasons why normalised centroid size weighting or other scaling steps are not appropriate for particular analyses (discussed in  Collyer et al.
| Multiple element workflow morphoBlocks  (Harmer & Thomas, 2021 ; https://aharm er. github. io/ morph oBloc ks/) facilitates the processing, analysis and visualisation of variation between shape configurations from objects comprised of multiple parts (see Figure  1  for workflow). Most functions in morphoBlocks are wrappers to conveniently collate functions from a diverse range of other packages (e. g. ape, adephylo, geomorph, Morpho, phytools and rgcca;  Adams & Otárola-Castillo, 2013; Jombart et al. , 2010; Paradis & Schliep, 2019; Revell, 2012; Schlager, 2017; Tenenhaus & Guillemot, 2017). Although morphoBlocks provides a complete workflow within the r environment, some data acquisition steps may require additional software or other resources (Figure  1 ). The workflow begins with acquisition of shape configurations from a dataset of samples that each contain two or more parts.
morphoBlocks::decMesh decimates and evenly distributes vertices and may be used as a preprocessing step before GPSA for meshes with very large vertex counts, or for datasets with highly disparate vertex counts between meshes. Note that the centroid sizes used for configuration scaling are calculated from the pseudolandmark configurations and not from the original meshes before transformation with GPSA. Acquisition mode iii-shape configurations are generated as landmarks placed on two-dimensional images (e. g. Profico et al. , 2019) , spatial coordinates acquired using a Microscribe.
| Combine data blocks Each data block represents shape configurations for a different part in the multiple-part dataset. Two or more data blocks of shape configurations (i. e. block-class objects) are assembled into a list using morphoBlocks::combineBlocks. The shape configurations within each data block are scaled using the normalised cen- Future revision of morphoBlocks::combineBlocks might consider introducing additional scaling methods (e. g. unweighted centroid scaling;  Collyer et al. , 2020). Finally, a column-wise concatenation of each block is added as a final 'superblock' to the list produced by morphoBlocks::combineBlocks.
| Case study A humerus, radius and ulna from 15 extant species of penguin and also from five fossil species of penguin contributed to a dataset of 60 wing bones (Table  1 ). A 3D digital replica of each bone was produced using structured light 3D surface scanning (HP SLS-3 scanner; HP Inc. ). Data collection and 3D model production used HP 3D Scan 5 software (version 5. 6. 0. 2037; HP Inc. ). The minimum resolvable surface feature of the 3D digital replicas was approximately 0. 2 mm. Right wing bones were selected when available. Left wing elements were 3D scanned and digitally mirrored in Blender 2. 8. 1 when right elements were not available (Blender Online Community, 2019). Two analyses are described to show how morphoBlocks can be used for shape configurations acquired by different methods.
| Manually placed landmarks The dataset of 60 digital bone replicas was replicated to produce three identical datasets. A copy of the digital bone dataset was provided to three authors for landmark placement. Landmark schemes were chosen to emphasise muscle attachment sites, articulation and general proportions (see Supporting Information including Figure  S1 ). The humerus scheme featured three single point landmarks, four curves (each divided into 10 semilandmarks) and three patches (each divided into 25 semilandmarks). The radius scheme featured one single point landmark, four curves and one patch, and the ulna scheme featured two single point landmarks, four curves and one patch. Landmarks were placed using Landmark Editor 3. 0 (Institute of Data Analysis and Visualization IDAV, University California Davis, USA;  Wiley et al.
, 2005). Landmark configurations were separated by author and block into nine directories (i. e. 20 humerus. pts files from author DBT in one directory, 20 humerus configurations from author EJH in a separate directory, etc. ) before processing with functions from morphoBlocks. morphoBlocks::readPts-. pts files in each directory were separately read into r version 4. 0. 2 (R Core Team, 2019) with gpa = FALSE (i. e. generalised Procrustes transformation not performed). Landmark configurations from each block replicate were extracted from the @raw term of their respective block-class objects and averaged to control for variation in landmark placement. morphoBlocks::formatBlock-the averaged landmarks were formatted into a data block using the @curves term from one of the replicates and with gpa = TRUE.
| Automatically placed pseudolandmarks The dataset of 60 digital bone replicas was separated into three blocks of 20 bones according to bone type (i. e. humerus, radius and ulna) and the following process was applied to each block independently. Pseudolandmark positions and correspondences were determined for bones in each block using GPSA version 20160308a  (Pomidor et al. , 2016). Here each humerus, radius and ulna was represented by  404,513, 198,947 and 306,841  pseudolandmarks respectively. For full GPSA method details see  Pomidor et al. (2016). Briefly, one 3D digital.
| Consensus space Regularised consensus principal component analysis applied to datasets of manually placed landmarks and automatically placed pseudolandmarks produced broadly similar results (Figures  2  and 3 ). Approximately a quarter or more of the variation in the dataset was explained along component one (C1) of the individual blocks, and along global component one (GC1) of the consensus (Table  2 ). The radius accounted for the most variation of the three individual blocks (0. 37 and 0. 56 average variance explained along C1 for landmarks and pseudolandmarks respectively). The relative positions of scores along C1 and C2 in each individual block and GC1 and GC2 in the consensus were similar where specimens tended to cluster by age (Figures  2  and 3 ). The oldest specimens in the analyses (Palaeocene) were separated from all other specimens along C1 for all three individual blocks and GC1 of the consensus space, and this separation was greatest for the radius block and for the consensus space (Figures  2  and 3 ).
Specimen ages from or compiled by  Slack et al. (2006) ,  Ksepka and Ando (2011)  and  Ksepka et al. (2012)   grafted onto the tree one node down from the most recent common ancestor to crown penguins. We acknowledge that this case study is only demonstrative and that a more robust analysis would include more specimens and additional bones. The proportion of variation explained by GC1 of the automatically placed pseudolandmarks was 0. 33 for the 20 specimens of upper wing skeletons we analysed (Table  2 ). Here we demonstrate a substantial difference in shape for the oldest stem-lineage penguins and comparatively less shape variation between the younger fossil penguins and extant penguins (Figure  4a ). Our small dataset echoes an hypothesis by  Simpson (1946)  in which a theoretical phylomorphospace for penguins shows more rapid morphological change early in the history of Sphenisciformes, and then a reduced rate of morphological change in more recent penguins (Figure  4b ).
| Data source The dataset of manually placed landmarks generated in this study emphasised osteological correlates to functional regions of the bones However, care must be taken when interpreting RCPCA results from a dataset of automatically placed pseudolandmarks that includes damaged or otherwise incomplete bones, as non-homologous shape artefacts will be introduced as a source of variation into the dataset. Our case study includes some damaged fossils (e. g. CM 2016-6-1 Sequiwaimanu rosieae; see  Mayr et al. , 2017)  but we note that the damaged regions of these fossils are not highly loaded along C1 in the individual blocks or GC1 in the consensus. TA B L E 2 Average explained variance expressed as a proportion of variation in the dataset explained by components from regularised consensus principal component analyses of manually placed landmarks and automatically placed pseudolandmarks.
g. Collyer et al. , 2020), the number of dimensions for each point in the shape configurations and a logical statement to determine if generalised Procrustes analysis should be performed. The shape configurations and ancillary information are used by morphoBlocks::formatBlock to generate a block-class data block object (see Section 2. 1. 2). Acquisition mode iv-shape data are simulated. morphoBlocks::-dodecBlock generates a data block of two or more dodecahedra where the vertices of each dodecahedron represent a landmark configuration with 20 points (p) in three dimensions (k) (n is user specified). morphoBlocks::dodecBlock allows the user to change the size, position and rotation of individual configurations within the data block, and to alter the position of one vertex, and therefore introduce a known amount of variation into a dataset.
(e. g. muscle origins), and the dataset of automatically placed pseudolandmarks more-broadly described the shape of the bones. The distribution of scores in the consensus spaces from both datasets was generally informed by similar loadings and thus had a similar structure. The choice of using manually placed landmarks or automatically placed pseudolandmarks is study dependent (e. g. Pomidor et al. , 2016; Watanabe, 2018 ) and here we show the utility of RCPCA for both options. Studies seeking to include more taxa or bones to expand the case study we present here are recommended to use automatically placed pseudolandmarks, at least initially, because of the dramatically reduced processing time compared with manually placing landmarks.
Thus, one aim is to determine the external dimensions as well as the shape of the particles on a few hundred objects. The same sample in liquid suspension form will be then measured by SAXS to get comprehensive information which is independent of the NP orientation. In order to evaluate and interpret the SAXS signal, a specific model needs to be considered. This model uses as input data the shape of the particles as determined by microscopy in order to make the SAXS measurements more reliable. In this way, a new tiered approach could be suggested to laboratories involved in the size and shape measurements of complex nanoparticle. If the nano-object has a simple shape with three equal dimensions (for instance perfect sphere or cube), the determination of a single parameter is needed to characterize the size of the nano-object which can be easily deduced either from measurements of height by AFM or lateral dimensions (i.
The pre-tilted sample holder together with the SEM stage tilt of 40 • resulted in a total sample tilt of -20 • , which is typical for off-axis TKD measurements. The SEM was operated at an accelerating voltage of 30 kV and beam currents of about 16 nA. The TKD maps were collected at Speed 2 (156 × 128 pixels) mode and Gain 2 of the detector, resulting in 372 patterns per second at a step size of 5 nm. Raw patterns were corrected by auto and static correction. Static background was collected in grid area densely covered with NPs. The refined accuracy  [14]  indexing routine was used in the available Aztec software (Oxford Instruments NanoAnalysis, High Wycombe, UK) for indexing patterns with increased angular resolution. For the presentation of the available quality of Kikuchi diffraction from TiO 2 NPs, full camera resolution pattern is presented in the Figure  S2a  and indexed in Figure  S2b.
Small Angle X-ray Scattering (SAXS) The TiO 2 NPs have been analyzed by SAXS in liquid suspension. SAXS is a nondestructive ensemble method which can characterize NP suspensions and is based on the forward scattering of X-rays under small angles from the incident beam direction. Among other factors, the measured SAXS signal depends on the particles' shape, size distribution, electron density contrast between particles and suspension medium, and the number concentration of the particles. The size distribution and number concentration can be extracted from the measured scattering pattern expressed in absolute units by fitting the data under the assumption of an adequate particle shape, which is mathematically described by the corresponding form factor. The SAXS experiments were conducted at the four-crystal monochromator beamline of the PTB laboratory at the synchrotron radiation facility BESSY II at a photon energy of 8 keV  [17].
Evaluation of the Size of the TiO 2 NPs Truncations The dimensions of the truncations were evaluated from the TiO 2 NPs imaged by SEM (Figure  12 ). With this challenging parameter, it is possible to obtain further information on the true TiO 2 NPs geometry. This geometry could then be used to be included a-priori for fine particle size analysis by SAXS. To evaluate the dimensions of the bipyramid truncations, a Matlab routine was developed. This routine was used to fit the theoretical shape of the perfect particle on the bipyramid contours. By calculating, for each particle, the difference between the theoretical length of the perfectly shaped bipyramid and the imaged one, the dimension of the truncations can be evaluated on a large number of TiO 2 NPs. Results are presented in Table  6  for NPs according to their two main orientations 1 and 2.
Funding:  This  project  17NRM04  nPSize has received funding from the  EMPIR  programme cofinanced by the Participating States and from the  European Union 's  Horizon 2020 research and innovation programme  (grant number  EMPIR 17NRM04  nPSize). G. C. acknowledges financial support from  Polish National Agency for Academic Exchange (NAWA)  under grant no. PPN/BEK/2020/1/00375.