dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the English dictionary provided by sklearn library  [37]  to remove the stop-words. Furthermore, we build a vocabulary that merely considers the highest max-features (determined by TfidfVectorizer) ordered by term frequency across the dataset. As we know the number of PR-Issue links regardless of the cardinality among them, then the number of clusters will be the number of PR-Issue links. For example, if an Issue is linked to many PRs, these links are considered a cluster consisting of that issue and their corresponding PRs (and vice versa). Moreover, we fixed the randomness of the initial cluster centers to obtain consistent results for comparisons. Finally, the threshold used by the BIRCH algorithm denotes the maximum distance between the cluster's members from the cluster's center.
1) RQ1: HOW FAR ARE THE RECOVERED PR-ISSUE LINKS CORRECT? Figure  8  illustrates the homogeneity scores for KMeans and BIRCH using the lexical weighting (TF-IDF) and based on the selected features on different sizes of the dataset. We can observe that the homogeneity scores of KMeans are strongly incremental with the size of the dataset, while BIRCH is almost consistent (roughly flat). Moreover, the homogeneity scores of trained titles and bodies are good by the two algorithms (between 73% and 90%) rather than the homogeneity scores of trained comments (31% -50%). Nevertheless, the highest homogeneity score is 90. 4%, obtained by the BIRCH algorithm. Figure  9  shows the homogeneity scores when KMeans and BIRCH are applied to the dataset using the semantic weighting (BERT) based on the selected features.
2) RQ2: HOW FAR ARE THE RECOVERED PR-ISSUE LINKS COMPLETE? Figure  10  illustrates the completeness scores for KMeans and BIRCH for all selected features on different sizes of the dataset using the semantic weighting. We can see that all selected features have high completeness scores (83. 3% -92. 7%). The highest completeness score is 92. 7% obtained by the BIRCH algorithm. Figure  11  displays the completeness results in case of using the semantic weighting in both KMeans and BIRCH based on the selected features across all dataset sizes. In this figure, there is a minor incremental growth of KMeans and BIRCH curves against the dataset size. Also, KMeans and BIRCH have high completeness scores in all features as these scores take a high range in (84% -92%). Moreover, BIRCH outperforms KMeans with a maximum completeness score 92% while the maximum completeness score in KMeans equals to 91%.
3) RQ3: HOW FAR ARE THE RECOVERED PR-ISSUE LINKS ACCURATE? Figure  12  illustrates the V-measure scores for KMeans and BIRCH for all selected features on different sizes of the dataset. We can observe that both Title and Body features give high scores (76. 8% -91. 5%), but the Comment feature gives a low one. The highest v-score obtained by the BIRCH algorithm is 91. 5%. In Figure  13 , V-measure scores are presented for both KMeans and BIRCH using semantic weighting across all selected features and dataset sizes. As shown in this figure, BIRCH curves across all features and dataset sizes are flatter than KMeans curves. For selected features, we note that Title and Body features give high V-measure results, (81% -90%) respectively. Also, this figure confirms that BIRCH, as usual, outperforms KMeans in terms of V-measure with a maximum value of 90% while KMeans has a V-measure maximum value of 88%.
Regardless of the weighting measure used (semantic or lexical), the Title and the Body features achieve promising and good homogeneity, completeness, and V-measure scores over different sizes of datasets in KMeans and BIRCH. Firstly, the homogeneity scores, respectively obtained by KMeans and BIRCH, are (72. 9%-89. 5%) and (88%-90. 4%) for the Title, and 69%-85% and (81. 1%-82. 6%) for the Body. Secondly, the completeness scores, respectively obtained by KMeans and BIRCH, are (88. 3%-91. 3%) and (90%-92. 3%) for the Title, and (86. 4%-89. 6%) and (89%-87. 8%) for the Body. Finally, The V-measure scores, respectively obtained by KMeans and BIRCH, are in a range of (79. 9%-90. 4%) and (89%-91. 5%) for the Title, and (76. 8%-85%) and (84. 9%-85. 5%) for the Body. The Comment feature performs poorly in homogeneity either using the semantic weighting or the lexical weighting over different sizes of datasets in KMeans and BIRCH as shown in Figure  8.
As a summary, we can observe that BIRCH outperforms KMeans in terms of homogeneity, completeness, and V-measures in both the semantic and lexical weighting. Also, BIRCH outperforms KMeans by using Title and Body features but Comment feature does not. However, these scores of both algorithms using the Comment features are not promising. Moreover, BIRCH has demonstrated its effectiveness on different sizes of the dataset using Title feature where maximum homogeneity, completeness, and V-measure scores are (90. 4%, 92. 3%, and 91. 5%), whereas KMeans has varied scores based on the size of the dataset for using the same feature (89. 5%, 91. 3%, and 90. 4%). Consequently, the BIRCH algorithm is a better fit in our case.
1) COMMON TOPICS We found some instances of Issues that have common descriptions (similar text). Where the dataset is extracted from Android projects, most of these instances are related to the Android platform. For example, some of these Issues arise when a new level of Android API is released. The new API might provide new features that should be implemented by Android developers. Accordingly, these Issues are raised due to the implementation of new features by Android developers in their projects. While these Issues represent the same topic, then the probability of having similar titles will increase. Consequently, this will group these similar Issues in the same cluster and leads the model to misprediction.
F. THREATS TO VALIDITY We determinated a set of issues as potential threats to the validity of this research work. These threats are as follows. As an internal threat, the used clustering algorithms are sensitive to the vocabulary used in Titles, Bodies, and Comments of linked PRs and Issues. In the case of using different vocabularies, this may degrade the prediction model's accuracy. However, this threat is shared among all research works employing textual matching to find the similarity. As an external threat, the effectiveness of KMeans and BIRCH algorithms is evaluated using Android-based groundtruth dataset. This gives a first-glance impression that these clustering algorithms can not be applied to other types of applications. In fact, these algorithms work with any type of application hosted on GitHub.
V. RELATED WORK To the best of our knowledge, there is no research work in the literature on this subject for recovering the links between PRs and Issues in GitHub and other social coding platforms. The closest works in the literature aim to recover Issue-Commit links but not PR-Issue links. These works are evaluated using different and non-benchmark datasets. In this section, we present the most relevant and recent works that are related to our work. These research works are classified into two categories depending on the type of linked software artefacts: recovering links between Issue and Commit, and recovering links between other software artefacts.
[43]  introduced a simi-automation tool called LINKSTER to facilitate recovering Issue-Commit links. Also, Wu et al. [44]  proposed a tool called ReLink to recover such links taking into account three heuristics: Issue and commit reports should be similar, their resolving times should be the same or close to each other, and their contributors should be the same. ReLink recovered Issue-Comit links with better accuracy than traditional heuristics approaches, with 89% precision and 78% recall compared with 91% precision and 64% recall for the traditional heuristics ones. In  [45] , Bissyande et al. proposed an improvement for ReLink. In  [46] , Nguyen et al. proposed another heuristic-based approach called MLink. This approach is based on code heuristics to recover Issue-Commit links based on the code snippets' similarity in Issue report and Commits.
VI. CONCLUSION AND FUTURE WORK In this paper, we proposed an approach to recover links between PRs and Issues in collaborative software development repositories on GitHub, based on the textual representation made in issues and PRs. The proposed approach investigates the results of two clustering algorithms (KMeans and BIRCH) to recover these links based on semantic and lexical measures. To do so, we trained KMeans and Birch using different features on the ground-truth dataset in the subject. The results show that PRs-Issues links can be recovered with an accuracy of 91. 5% using Birch clustering algorithm. We plan to employ deep-learning techniques to recognize the rules and patterns between similar Issues and similar PRs that reside in different GitHub repositories. Therefore, when a developer has a new Issue, the PR associated with this Issue could be found in another repository.
Background Oriented Schlieren (BOS) is a non-intrusive optical method used to visualize density variations in fluids. A camera is used to measure the distortion of a background image displaying a random pattern due to the refraction of the light induced by the local gradients in the refractive index of the under study. In contrast to conventional schlieren, for which direct observation is possible, the images obtained with the BOS technique have to be processed. In this contribution, a computationally affordable approach based on linear least-squares minimization and adapted from optical flow techniques is presented in detail and tested in the context of BOS with the Matlab GUI application comBOS. A series of synthetic images are first analyzed to devise design rules for the definition of an optimal random Gaussian dot background pattern.
Tests are performed for comBOS, the commercial BOS module included in DaVis 10, and the free PIV tool PIVlab. It is found that the diameter of the dot images on the camera sensor should be at least 3 pixels, but can be larger without affecting the accuracy of the measurement with comBOS and DaVis 10, while PIVlab severely degrades the quality of the results for dots larger than 5 pixels. The contrast and the density of dots must also be sufficient such that a minimum of 75 % of the surface of the background is shaded. If these conditions are met, the maximum systematic error in the measured distortions is 0. 006 pixel for comBOS and 0. 001 pixel with DaVis 10 when local gradients are small. Similar errors are found with PIVlab for dots smaller than 5 pixels in diameter, but they increase rapidly for larger dots.
These two concerns are in fact not answered by the few former studies  [7, [17] [18] [19] 27, 28]  addressing the capabilities of BOS measurements. Only Hartberger  [29]  focused on the properties of the pattern, but with a limited number of adjustable parameters. A brief description of the BOS principle is presented first, followed by a description of the post-processing method implemented in comBOS. Design rules are then devised from a series of benchmarks to design an optimal pattern with comBOS and the commercial BOS package included in DaVis 10 by LaVision. It should be noted that although there is no public description of the algorithm used by LaVision for the BOS module, it is thought to be an algorithm specifically designed for BOS and not a cross-correlation based algorithm. Based on these rules, the errors induced by both software are evaluated for realistic constrains including large displacements and gradients.
Sensitivity of comBOS and DaVis 10 algorithms to the speckle properties A series of benchmarks are proposed in this section to evaluate the performance of comBOS and compare it to the BOS module of the commercial software DaVis 10. Comparisons with the free PIV tool PIVlab are also included as it is a commonly used tool for PIV and BOS. The result of this benchmarking is a list of design rules that can be used to generate optimal speckles.
Results are presented in Fig. 3(d ) and (e) for a variety of configurations that involve comBOS, PIVlab, and additionally for this first benchmark, the PIV module of the commercial software Davis 8. 4, as the laboratory does not own the PIV license for DaVis 10. First, it appears that all tested configurations using a PIV algorithm produce results with a larger average error than the least-squares method implemented in comBOS when applied with two passes according to the procedure described at the end of Section 3. 2. This error is also found to be highly dependent on the dot size, although increasing the number of passes seems to mitigate this problem with PIVlab. The noise in the PIVlab's results follow the same trend as the average error, reaching values comparable to comBOS for the optimal dot diameter and if the ''robust correlation'' option is set to ''high''.
This algorithm is included in the Matlab application comBOS that is found to perform with a similar level of accuracy as the module dedicated to BOS implemented in DaVis 10, and is more accurate and less prone to produce noise than the free PIV tool PIVlab in most applications. The discrepancies between the results of the two BOS algorithms are indeed limited to a maximum of 1. 2% for displacements close to 0. 5 pixel. PIVlab, however, is affected a deviation of 2% from the reference for  displacements of 0. 25 pixel, together with a significantly higher level of noise than with comBOS and DaVis 10 when the speckles deviate from the optimal conditions. Determining these optimal conditions is also one of the objectives of this article, the analysis of which allowed to establish a set of general rules regarding the speckle and the quality of the images that are valid for both comBOS and DaVis 10.
Precision errors for TBS using Hologic or GE DXA systems and different TBS software versions, are comparable to BMD precision  [84]  and have ranged from 0. 8 to 2. 1%CV with equivalent LSC ranging from 2. 2 to 5. 8%. The average precision error and LSC from studies to date are 1. 4%CV and 3. 8% respectively  [9, 36, 40, 63, 68, [86] [87] [88] [89] [90] [91]. The corresponding LSC unit change in TBS has been reported to be 0. 05 based on a precision of 1. 4%CV  [85].
g. sclerotic lesions, osteophytes, syndesmophytes, laminectomy and fracture; • Spinal degenerative changes minimally impact TBS, unlike BMD. As such, it is not unusual to observe discordance between spine TBS (lower) and BMD (higher); • If two or more vertebrae are excluded, neither BMD nor TBS can be used for diagnosis; • When a combination of vertebrae other than L1-L4 is used, combination-specific threshold adjustments are automatically applied in the current TBS iNsight version (3. 1 and higher). • The currently available TBS software is valid for use in individuals within the BMI range of 15 to 37 kg. m -2. Interpretation of TBS results outside of this range should be made with caution.
Acknowledgements We gratefully acknowledge information on technical considerations from Medimaps Group. The ESCEO Working Group was funded by the  ESCEO. The ESCEO receives unrestricted educational grants to support its educational and scientific activities from non-governmental organisations, not-for-profit organisations, non-commercial or corporate partners. The choice of topics, participants, content and agenda of the Working Groups as well as the writing, editing, submission and reviewing of the manuscript are the sole responsibility of the ESCEO, without any influence from third parties.
RCaNmodel is an R package dedicated to the development and exploration of time-dynamic food-web models. The associated user-friendly JavaFX graphical user interface (GUI) RCaNconstructor is specifically developed to facilitate the co-construction of such models with non-modelers. Together, RCaNmodel and RCaNconstructor ensure the traceability and replicability of food-web model developments, and provide a fast and efficient solution to handle the complex mathematical steps that arise from dynamic linear inverse modelling and polytope sampling in high dimensions. The software includes standard graphical outputs for the exploration of food-webs dynamics and the detection of trophic controls, which are key questions in trophic ecology.
RCaN files can be constructed using the RCaNconstructor GUI, which ensures internal consistency between the data tables. Alternatively, data tables can be provided in R dataframe format to facilitate the use of external databases or version control through the use of plain text files. • Based on the input data provided in the tables, the function buildCaN constructs the matrices that define the polytope (see  Planque & Mullon (2020)  for the underlying mathematics). The use of symengine  (Ma et al. , 2020) , a library for symbolic manipulation, facilitates the automatic translation of user-specified literal constraints (see supplementary material for details on the syntax) into matrices. • The function checkPolytopeStatus checks if the polytope is bounded and not empty, two conditions that are necessary for the food-web model to be valid.
If the polytope is empty, the function findingIncompatibleConstr can be used to identify the minimum set of constraints that need to be relaxed to make the polytope feasible (inspired from http://web. mit. edu/lpsolve/doc/Infeasible. htm). Functions getBoundParam and getAllBoundsParam return the possible minimum and maximum values of all dimensions of the polytope, i. e. , of all flows at all time-steps and of all biomasses at time-step 1. RCaNmodel uses linear programming tools to perform these tasks, using the package lpSolveAPI  (Konis & Schwendinger, 2020 ). • The function sampleCaN samples valid food-web trajectories using a Gibbs sampling algorithm that has proved to be especially efficient to achieve a uniform sampling in high dimensions polytopes  (Andersen & Diaconis, 2007; Laddha & Vempala, 2020).
The function returns an object of class mcmc. list that can be manipulated/explored using the package coda  (Plummer et al. , 2010) , e. g. to check the convergence of the MCMC algorithm. • Finally, several graphical functions are available to explore the results per trophic groups and time periods, including diagrams describing the dynamics of flows and biomass, relationships among species and trophic controls (Figure  3 ). All these steps can be done either directly through R scripts or using the RCaNconstructor GUI. An example using R commands is presented in the supplementary material. It should be noted that most of the functions to check the status or to sample the polytope can be used with other packages (e. g. LIM modelling package). Finally, the chance and necessity modelling approach is applicable to a wide range of problems beyond food-web models, and RCaNmodel is currently being generalised to other kind of models that rely on similar principles.
We present two open-source Python packages: ''electron spectro-microscopy'' (espm) and ''electron microscopy tables'' (emtables). The espm software enables the simulation of scanning transmission electron microscopy energy-dispersive X-ray spectroscopy datacubes, based on user-defined chemical compositions and spatial abundance maps of constituent phases. The simulation process uses X-ray emission cross-sections generated via state-of-the-art calculations made with emtables. These tables are designed to be easily modifiable, either manually or using espm. The simulation framework is designed to test the application of decomposition algorithms for the analysis of STEM-EDX spectrum images with access to a known ground truth. We validate our approach using the case of a complex geology-related sample, comparing raw simulated and experimental datasets and the outputs of their non-negative matrix factorization.
Introduction Modern scanning transmission electron microscopes (STEM) equipped with an energy dispersive X-ray (EDX) spectroscopy detector allow one to acquire a three-dimensional STEM-EDX datacube, also called spectrum image  [1] , thereby enabling chemical mapping down to the nanometric or even atomic scale. From such a datacube, experimentalists often seek to determine which phases are present in the sample, as well identify their respective chemical compositions and spatial distributions. In other words, they are interested in segmenting the dataset into pure phase spectra and their spatial distributions. However, in samples with a complex microstructure, such an analysis can be challenging for multiple reasons. First, the sample can contain several phases with different chemical compositions that overlap along the beam direction, complicating the determination of the chemical nature and spatial distribution of each constituent phase.
Secondly, the total electron dose is distributed among the pixels; thus, when the dose has to be reduced to limit beam damage, the spectra from individual spatial pixels can be very noisy. Finally, the datasets are usually large with, for an average datacube, 512 × 512 to 1024 × 1024 pixels 2 in the spatial dimensions and 2048 channels in the spectral dimension. The large dimensions of the spectrum images make their manual inspection and analysis time consuming and challenging. In order to tackle such challenges, many researchers have turned to multiple machine learning approaches that can decompose noisy high dimensional datasets into a small set of components. This decomposition takes advantage of the entire data set, resulting in low-noise components. The application of classical decomposition algorithms such as principal component analysis (PCA)  [2, 3] , non-negative matrix factorization (NMF)  [4] [5] [6] , vertex component analysis  [7, 8] , multivariate curve resolution  [9, 10]  or independent component analysis (ICA)  [6, 11, 12]  is widespread in the electron microscopy community.
These allow one to compare the outcome of the algorithm with a ground truth, leading to a better understanding of whether the algorithm succeeded or failed in reconstructing the data. Since the result of a decomposition depends strongly on the characteristics of the data, the closer the simulated data are to the experimental data, the easier it is to correlate this understanding with the output of the machine learning analysis of real experimental data. Moreover, in our case, the creation of a synthetic STEM-EDX datacube is key to exploring quantitatively the behavior of a decomposition algorithm; for example, to observe how its output changes when the data noise increases, when the features become spatially smaller, or when some phase elements are diluted. Such systematic investigations are often impossible on experimentally-acquired datasets.
In terms of existing simulation approaches, EDX spectra can be modeled using several methods such as Monte-Carlo, e. g. with the DTSA II software  [15] , with cross-section tables  [14] , or with inelastic scattering simulations  [16]. Both the latter work by Allen et al. [16]  and the DTSA II approach from Ritchie  [15]  simulate the path of electrons in the materials, and thus require a deep knowledge (e. g. atomic structure or 3-dimensional distribution of phases) of the simulated sample. Regarding spectrum images, Titchmarsh  [14] , Potapov et al. [2]  and Parish et al. [3]  developed codes to calculate either 1D or 2D EDX maps. In a later work, Parish applied an updated approach in which the full STEM-EDX datacube was simulated  [17]. From the information available, the underlying conceptual approach to these simulations is similar to that which we use here.