dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, our method operates as a standalone Python application and can do complete analyses with only one command line. In addition, we compared SLALOM to BEACON and bedtools. As one can see from our short summary on functionality (see Table  6 ), BioConductor is the most comprehensive package for annotation comparisons. However, it is quite tricky to actually apply it for some problems without writing a substantial amount of R code, although it contains the library IRanges for generic types of data. Among others, evaluation of protein motif predictors and time series correlation analysis, which are illustrated by our case studies are not readily doable with BioConductor. We have developed a BioConductor-dependent R script (available as Additional file 3) for answering the three following questions: (1) What share of residues in a motif-containing sequence does belong to motifs of a specific type? (2) What share of motif instances described in the older version of the ELM database has exact matches in a newer version? (3) What share of motif instances described in the older version of the ELM database are at least to 50% covered by a distinct motif instance of the same ELM class in the newer version? One single command choosing specific options (D1, Additional file 1) addresses questions 1 and 2 using SLALOM: on average, 1.
Only classification on the basis of residue counts can be specified in the built-in functions (e. g. , countOverlaps), by using the keywords 'maxgap' and 'minoverlap' with integer numbers. The intersect function cannot be used for this task, as it does not distinguish between matches for repeated, perhaps even overlapping, motifs in the same sequence. The pintersect function also cannot be used, as an equal number of motifs in the old and new version of the ELM database is not guaranteed. As a result, the user would have to loop over all the motif instances individually to answer question 3, which goes beyond the applicability of IRanges and GRanges objects. Therefore, we consider this question unanswerable in a simple way with BioConductor. Second, large databases require additional handling.
The sequence length database is quite long (more than 90 million records), although only a few thousand records are relevant for the comparison discussed here. There is no straightforward way to do the required filtering while reading in the sequence length file in R. Here, we first read the file into a data frame and then filter it for better performance of consecutive operations. If the file was even bigger, additional memory limitations could arise depending on the computational setup available to users. In this case, the BioConductor user needs to additionally code a line-by-line reading of the file. This is already implemented in SLALOM, so that the user can work with original databases that are larger-thanmemory. bedtools addresses the core of all three questions with the sub-tool coverage.
SLALOM has even more advantages, if one needs to compare a predictor against a 'golden-standard' database rather than comparing two datasets to each other. In this case, one needs not only to handle the overlaps, but also to calculate benchmark statistics, such as accuracy or F1 values. Neither of these calculations in the complexity we describe them here are easily doable with Bio-Conductor or available for bedtools. In general, one can observe that BioConductor and bedtools represent powerful solutions for a broad scope of genomic comparisons and analyses. In this area, their functionality goes far beyond that of our method. However, they are not designed to answer a broad range of statistical questions about the underlying data. For instance, these tools are not designed to perform quality assessment and benchmarking, while comparing a pair of annotations.
Discussion In this study, we present the tool SLALOM for conducting in-depth comparative and statistical analyses of annotations of continuous sequence elements in a given grouped collection of sequences, which has so far not been available for this type of analysis on this level. Our main goals for developing the method and conducting the associated case studies were to provide a software tool for quick performance and/or correlation estimates of multiple sequence annotations, while implementing the most suitable application logic of handling ambiguities. We wanted to increase the awareness of the user for possible implications of the overlap, grouping, and averaging choices on the performance values. Finally, one of our goals was to provide the framework for in-depth analyses of differences in performance values upon changes in the chosen application logic.
Instead, it operates exclusively with sequence and group identifiers as well as integer values representing sequence lengths and border positions of the annotated elements. This level of abstraction grants two distinct advantages. First, it provides versatility. The method can be applied to any type of sequence data. In biology, this includes protein, RNA, and DNA sequences. Second, its runtime is generally not dependent on database size, total sequence length or length of annotated elements. Thus, SLALOM operates with high speed and low memory usage even on very large datasets, such as collections of genomes. While we have shown the applicability of our tool to compare positional features in DNA or protein sequences, as well as time-series data, SLALOM itself is very versatile and can be adapted to any comparison of positional data and thus can address a multitude of questions.
Conclusions In this study, we present SLALOM, a method and its associated software package, for in-depth analysis of positional data annotating continuous sequence elements in a grouped collection of sequences. With our examples, we show that the choice of rules and principles to treat duplications and overlaps can affect the results to a significant extent. Furthermore, we demonstrate that some statistical measures become unreliable if applied to data with some form of unbalance in it. With this study, we not only increase awareness about these statistical pitfalls, but also provide the method to detect and deal with them. With the implemented functionality, SLALOM effectively fills the gaps in existing software solutions for comparative, statistical analysis of positional data.
Methods SLALOM produces a table with relevant statistics describing the input data on CSE, sequence, group or dataset level and other optional output files that are derived from the input pair (e. g. , as union or intersection). The method is initiated with four files: the sequence length file (sequence identifiers -SIDswith the associated sequence lengths), two annotation files to be compared, as well as the optional group-mapping file (group identifiers -GIDsmapped to member SIDs). The annotation files are lists of records, each of which contain: SID, GID, begin and end positions in the sequence, and optionally the CSE names. If the group-mapping file is not provided, GIDs are not read in and the whole input sequence collection is considered as a single group. If only one sequence is analysed or all the sequences have the same length, the sequence length file can be omitted too.
However, if the former contains many more SIDs than the latter (for instance in case the database lists all Uniprot records and the mapping consists of only few protein complexes in a specific organism), it may be practical for performance reasons to pre-parse the group-mapping file. This option (invoked by the option '-preparse/-preparse_mapfile') does not influence the output, but speeds up the calculations and reduces RAM usage considerably in case of large sequence length files. Each of the annotation files is read into two distinct internal objects: a list of sites (sequence identifier, begin, end) and a map of residues (each residue from the database is mapped to an integer, which represents the number of sites from the given annotation traversing this residue). The annotations are also checked for validity.
The following criteria must be satisfied: a) A start position is positive and an end position does not exceed the sequence length, unless the Fig. 5  The pipeline of SLALOM. The pipeline of SLALOM is shown, which is divided into nine separate modules (M1-M9). For further information, see Main Text sequences are circular; alternatively, the overflowing sites can be trimmed to fit the sequence b) An end position is no less than the corresponding start position If an annotation record's SID is not present in the sequence length file or the GID is not present in the group-mapping file, the record is ignored and a warning is issued. All input files may contain duplicate records. In case of the sequence length file and the group-mapping file, duplicate entries are ignored; however, in the annotation files, all duplicates are considered, which influences the results.
To do this, the difference d between numbers of occurrences in the second and first annotations is calculated for each residue. Those with d > =n are classified as RE2 (relatively enriched in the second), while those with d < =n are classified as RE1. The rest is classified as NRE (not relatively enriched). Similarly to Module 2, the averaged measures E1, E2, EE, NE, RE1, RE2, NRE are calculated for the groups and the whole input dataset. It is true that E1 + E2-EE + NE = 1, RE1 + RE2 + NRE = 1, E1 > =EE, E2 > =EE, E1 > =RE1, and E2 > =RE2. Module 4. Calculating residue-wise performance measures. This module is called in all modes; for an enrichment mode, PP = EE, AA = NE, PA = E1-EE, AP = E2-EE. These measures show, how good the annotations correspond to each other and are calculated solely on the basis of the residue-wise basic measures.
If division by zero is encountered, a nan value is assigned. The measures are averaged across all the groups, applying simple averaging if either sequence-or group-wide averaging is chosen and micro averaging, if dataset-wide averaging is chosen. Nan values are by default ignored during the averaging; however, the user can also enforce counting them as zeros (for details, see Additional file 1: Table  S1 ). Note that there are a few small differences from the metrics used to originally evaluate the HH-MOTiF webserver in our earlier publication. First, the calculation of residue-wise PC did not consider the overlaps in the benchmark (ELM) annotation, which was implemented in the current version of SLALOM. Second, the balanced accuracy was phased out and replaced with informedness.
For a schematic example, see Fig. 4. Four counts are calculated: M1 (matched for the first, number of CSEs in the first annotation that have matches in the second annotation), NM1 (not matched for the first), M2, NM2. In the benchmarking mode, NM1 becomes FN (false negatives) and NM2 becomes FP (false positives), while both M1 and M2depending on the metric being calculatedcan be considered as TP (true positives). If no CSEs are annotated for the corresponding sequence in the annotation x, then Mx = NMx = 0. These counts are never averaged at the sequence or group level, but always simple-averaged to produce dataset-wide averages. Thus, they are not influenced by the averaging approach. Module 6. Calculating site-wise performance measures. This module is called only when the tool operates in a non-enrichment mode.
The calculated measures follow the similar logic, as the residue-wise ones. Note, however, that there is no analogue for AA (true negatives). Therefore, some measures cannot be calculated (for details see Table  2 ). To compensate for considerably lower number of measures, a new measure (positive correlation value) is introduced. This results in the following list of site-wise measures (the counts M1, M2, NM1, NM2 are introduced in Module 5): Recall for the second annotation: SiteTPR ¼ M1 M1þNM1 Precision for the second annotation: SitePPV ¼ M2 M2þNM2 Performance coefficient for the second annotation: SitePC ¼ M2 M2þNM1þNM2 F1 score: SiteF1 ¼ 2ÃM1ÃM2 2ÃM1ÃM2þNM1ÃM2þNM2ÃM1 Positive correlation value (an author-defined measure): SitePCV ¼ M1þM2 M1þM2þNM1þNM2 Zero division resolving and dataset-wide averaging are conducted as for the residue-wise measures (see Module 4).
Module 7. Calculating residue-wise enrichment coefficients This module is called only in the enrichment mode. The only measure calculated is based on the residue-wise basic enrichment measures: Enrichment asymmetry coefficient, or share of absolutely enriched residues that are also enriched in one of the annotations over the other (the measures E1, E2, EE, RE1, RE2 are introduced in Module 3): EAC ¼ RE1þRE2 E1þE2-EE Zero division resolving and database-wide averaging are conducted as for the residue-wise performance measures (see Module 4).
Geometrical and appearance quality requirements set the limits of the current industrial performance in injection molding. To guarantee the product's quality, it is necessary to adjust the process settings in a closed loop. Those adjustments cannot rely on the final quality because a part takes days to be geometrically stable. Thus, the final part geometry must be predicted from measurements on hot parts. In this paper, we use recent success of Generative Adversarial Networks (GAN) with the pix2pix network architecture to predict the final part geometry, using only hot parts thermographic images, measured right after production. Our dataset is really small, and the GAN learns to translate thermography to geometry. We firstly study prediction performances using different image similarity comparison algorithms.
A. Experimental dataset creation Under industrial setting, it is usually difficult to create a big dataset. This paper aims to use a GAN network architecture to solve this limit. Generative networks are known to require a small dataset. We actually have a dataset with ten times fewer elements than classical dataset used for GAN. Our study is exploratory and aims to study the performance of GAN in industrial constraints. The dataset creation was done using a Taguchi orthogonal L12 Design of Experiments  [8] , with twelve different trials. Furthermore, we choose to put two parts of each trial in the training dataset. An exception was done for on trial which parts are not at all in the training dataset but only in the validation dataset. These special not learned parts will be used for further validation of the network performance.
We use a robotic arm to grab the part right after molding and to show them in front of a thermographic camera. A still frame was acquired for each part 10 seconds after the ejection of the part Then, we have been waiting a month before geometrical measurements to be sure that parts were geometrically stable. Surfaces were scanned on a confocal optical profilometer (Altimet AltiSurf 520). The scanning takes one hour; this duration is the actual limit to the size of our dataset. From the nearly two hundred parts we have produced and measured on-line, we have actually only scanned 37 parts: 23 parts are used as a training dataset and 14 parts are used for validation.
B. Dataset preprocessing The robotic arm and the vacuum prehension device were not enough repeatable to guarantee pixel to pixel position similarity between measurements. Thus, thermographic images were stabilized using the Lucas-Kanade optical flow tracking algorithm  [9]. Images were normalized on the minimum and the maximum values of all images. Images were then cropped and scaled to 71 by 71 pixels in grayscale, without multiple color channels. Finally, images are upsampled (bicubic interpolation) to 128x128 to be used as networks input. Upsampling the small definition acts as a 1. 8x1. 8 pixels Gaussian filter. We voluntary choose small size images to simulate industrial constraint where multiple parts must be imaged with a unique camera, leading to a small image definition peer part.
For our problem, the patch size will certainly change the prediction performance of various geometrical scale. The generator and the discriminator have the same layer architecture: each layer has a convolution layer and LeakyReLu  [14]  as an activation function. The augmentation part of the generator use Rectified Linear Unit  [13]. In our study, we do not use any batch training nor normalization because we have a small dataset. Finally, pix2pix introduces a L1 distance loss between the generated images and the real ground truth images. Thus, the generator must fool the discriminator and minimize the L1 Manhattan distance with the real image. The complete network architecture is show in Fig. 2. We have only trained two hundred epochs for now. We use Xavier layers initialization  [15]  based on success in the literature.
A. Statistical and Haralick features comparisons Generated images visually show good similarities with real images. In order to validate the similarity, we compute different statistical features on each couple of real and generated images. We compute the mean, median, kurtosis, skewness and multiple quantile of the images. We also compute the Haralick features  [22]  as they are good textural descriptors. We study the difference between those values. We compute the p-value to validate the result on this really small population (14 elements). The standard deviation on all the tested results is high and the p-value was superior to 5% on most of the metrics. Thus, we cannot reject the null hypothesis  [23]. We then must increase the test dataset size to be able to get significant results with these textural parameters.
B. Real vs generated image similarity We avoided pixel to pixel comparison because our generated images can be slightly translated due to the dataset augmentation methods used. Histogram comparisons are robust to scaling and movements. We used various distance metrics on histograms (Fig. 8 ): Bhattacharya  [24] , Khisquared, correlation  [25] , cosine (similar to Euclidian L2), Hellinger, Kullback-Leibler  [26] , Manhattan L1 and Minkowski distances. We used the p-value test to test for the null hypothesis and only the cosine and correlation distances were kept. We also compute the Structural Similarity  [27]  and the Peak Signal to Noise Ratio (PSNR), two widespread measures for image comparison. Results on the test dataset are shown in Table  I. test06 and test06-bis are images obtained for the same settings in the design of experiment: no part with similar settings were present in the train dataset.
Through this work, we hope to show what types of shapes and deformations the network is able to generate the best. As explained in III. E. , DMD is not the only method to geometrically characterize surfaces. It could be also interesting to apply other type of decompositions and compare with the DMD analysis. In this study, we have shown the possibility of using Generative Adversarial Networks pix2pix on a very small dataset, to comply with industrial constraints. Based on the measured similarity between final image and generate image, we show that the prediction is acceptable in the context of our very limited learning study. Quantities of information contained in the thermal image seem sufficient to predict our geometry in our specific application case. We introduce Discrete Modal Decomposition (DMD) to analyze the prediction performance from a geometrical parameterization side.
The DMD presents a clear meaning to analyze geometrical properties but it could also further be used to analyze any generative network performance. In thermoplastic injection molding, we don't need to predict the rugosity (high modes in DMD) but only geometric form defects (low modes in DMD). Our problematic is to anticipate the global cold part geometry, so, the first modal coefficients are appropriate. Performance evaluation and hyperparameters tuning must be done on a larger dataset. Further studies can also be designed to analyze more precisely the GAN performances to retrieve independent modes to show what shapes can be best predicted. Finally, further research needs to be done on the complete 3D model generation based on thermographic prior, as literature shows encouraging result in 2D images to 3D shapes transformation  [28].
Modelling Approach and Results The Afar triple junction forms in a context where a plume interacts with a continental lithosphere subjected to bi-directional far-field forcing that combines northward pull from the Neo-Tethys slab subduction and EW extension. We use this configuration to design a model geometry that we implement into high-resolution 3D thermo-mechanical numerical calculations using the viscous-plastic I3ELVIS code  41  , which is based on a combination of a finite difference method with a marker-in-cell technique (see Methods). We use a 1500 × 1500 × 635 km rectangular model domain with 297 × 297 × 133 nodes that offers a spatial resolution of ca. 5 × 5 × 5 km per grid cell. The laterally homogenous lithosphere consists of a bi-layer, 36-km-thick crust and 114-km-thick lithospheric mantle.
Acknowledgements This study is co-funded by the  Advanced ERC  Grant  290864  RHEOLITH ( E. Burov-A. Koptev ) and  ERC   Consolidator Grant   615703  EXTREME (T. Ehlers-A. Koptev), as well as a  U. S. National Science Foundation  grant  EAR-0538119  and  French INSU-CNRS program Tellus-Rift  ( E. Calais ). Numerical simulations were performed on the ERC-funded SGI Ulysse cluster of ISTEP. Figure 1b is modified from  Bellahsen ,  N. , Faccenna , C. , Funiciello, F. , Daniel,  J. M. & Jolivet ,  L. Why did Arabia  separate from Africa?Insights from 3-D laboratory experiments. Earth and Planetary Science Letters  216 ,  365-381 (2003 ), with permission from Elsevier.
Concerning inland waters, the classic Charnock  (Charnock, 1955)  formulation is used. Physiographic data are initialized with the ECOCLIMAP database  (Masson et al. , 2003)  at 1 km resolution. The orography is computed from the GMTED2010 database at 250 m resolution  (Carabajal et al. , 2011). The FAO HWSD database at 1 km resolution is used for the fraction of clay and sand in the soil. The HIRLAM parameterization of orography-radiation interactions  (Senkova et al. , 2007)  has been adapted and implemented in the SUR-FEX version. Orographic shadowing and slope parameterizations are used operationally to modify solar direct radiative fluxes. One main effect of including shadowing and slope effects is that the clear-sky sunshine duration is drastically modified in mountainous areas, with values changed from almost constant to highly varying (sunshine duration can for instance locally reach about zero on grid points with all-day shadow conditions in the French Alps).
Somewhat less accurate gaseous transmissions are counterweighted by the full interaction of clouds and gasses with radiation, ensuring realistic model feedbacks. The second version, called ACRANEB2  (Mašek et al. , 2016; Geleyn et al. , 2017) , was developed with the goal to increase the accuracy of gaseous transmissions, cloud optical properties and the NER technique, while still keeping the full cloud-radiation interaction. Several spectrally unresolved effects had to be parameterized. Cloud optical properties were refitted against modern datasets and the shortwave cloud optical saturation was revised. The computational ef- Davies scheme  Davies (1976) ,  Radnóti (1995) ,  Termonia et al. (2012)  ficiency of the scheme is ensured by selective intermittency, where rapidly varying cloud optical properties are updated at every model time step, while slowly varying gaseous transmissions only once per hour.