dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of hidden neurons should be less than twice the size of the input layer  [14]. According to these rules, we will tune our grid search as reported in Table  3. Training a deep neural network remains always a challenging problem to make it generalize well to new data. A model composed with a weak capacity cannot learn (underfitting) whereas a model with too much capacity will fit correctly to the training dataset but fails to fit the validation dataset (overfitting). There are several ways to adapt a model to prevent overfitting with playing on some parameters. For example, dropout layers can remain a convenient way to prevent overfitting. Indeed, a dropout layer will randomly drop some of the connections between layers. Using dropout improve the computational efficiency of networks with large amounts of parameters  [16].
The genetic algorithm package for Keras was developed by using the R6 method, which allows doing object programming. The package is available in the git repository or directly in R with devtools. Since we are in a classification task, we will use classification accuracy as our fitness function (objective function). To guarantee the existence of at least one hidden layer, we apply the constraint: Hidden layer = N i=1 H i ≥ 1 (8) Where H i is a binary variable, which equals the value 1 if the i hidden layer is used otherwise zero. The second constraint is if the models do not use a hidden layer, consequently their neurons are not used. neuron ij is a binary variable that equals the value 1 if the i neuron of the i hidden layer is used; otherwise, it equals zero. We note n i the number of neurons in the i th hidden layer.
The purpose of diagnosing, prognosis, and treatment selection. Deep learning is not to replace humans, but to provide decision support tools that assist cancer researchers to study the disease and health professionals in the clinical management of people with cancer  [26]. The two GA and PSO packages that we have developed in R language for Keras can be re-adapted according to the problem to be resolved. A new training data phase has to be performed to obtain a deep neural network architecture according to your problem to March 1, 2022 24/27 be resolved.
All data are stored in our local MySQL database (Figure 1). Biologists manually validated variants as pathogenic if the following statements were true: Variant allele frequency ≥ 5% • Variant reads ≥ 300 reads • Amino acid change is different from synonymous (̸ =p. (=)). A synonymous variant will probably have a low influence on the gene because the amino acid does not change • Polyphen score is in the range of [0. 85;1] (case of substitution variant) • Grantham score is in a range of [5;215] (in the case of substitution variant). [0,50] = conservative, [51;100] moderately conservative, [101;150] moderately radical, over 150 radical • Manual inspection on different databases and prediction tools: Arup database, VarSeak, Varsome, UMD Predictor, Cancer Genome Interpreter • We use the tool Integrative Genomics Viewer (IGV) to check if alignment sequences are clear and show no strand bias in the region where the variant is located.
Another way to prevent overfitting is to stop the training process earlier. Instead of training for a specified number of epochs, we stop as soon as validation loss rises. When we use early stopping, the performance of the model must be monitored during training. We split the training dataset and use a subset as a validation dataset (30% of the dataset) used to monitor the performance of the model during the training. This validation set is not used to train the model and we used the loss on the validation dataset as the metric to monitor accuracy as we are in the case of classification problems. The performance of the model is evaluated on the validation set at the end of each epoch, which computes an additional computational cost during training. The training is stopped as soon as the performance on the validation dataset decreases as compared to the performance on the validation dataset at the prior training epoch (in our case, an increase in the loss value).
Results The experiments we conducted to evaluate the performance of the predictive model are detailed in this section. We evaluated the predictive models on validation sets and we compared the results of validation to obtain the best deep neural architecture for identifying pathogenicity in BRCA1 / BRCA2 genes. Finally, we tested the best model based on a test dataset on other genes to obtain general predictions on oncosomatic mutation variants. To assess the performance of each model, accuracy, recall (sensitivity), precision and sensibility were calculated to measure the performance of classification models obtained with the particle swarm optimization and the genetic algorithm. These parameters are defined as follows: True positive (TP) and true negative (TN) are defined as the number of mutations that are classified correctly as pathogenic and benign.
As mentioned above, the used Eurostat data set version is from 2016, because it was the most complete and recent set of data when this task was undertaken. In the pilot methodology, data for cultivated crops were collected from the Eurostat database 2016 (https://ec. europa. eu/eurostat/fr/data/database). Eighty-six crops were identified, and scientific names assigned at species level when possible. When no data were communicated by Member States, they were considered as null. In the final methodology, other sources (e. g. FAOSTAT) were considered to collect data for minor crops not included in the Eurostat database. Those additional data were also stored to be available afterwards for the scoring of other pests with the same host plants. In the pilot methodology, data on forests were retrieved from the EFISCEN database (https:// www.
efi. int/knowledge/models/efiscen/inventory). This is a forest inventory database for European countries based on inputs from national inventory experts, the only source where data at species level are available for most European countries. However, data on forests could be improved because they present two weak points: • Data are not available for Cyprus, Greece, Malta and Spain, which represent sizeable part of EU forest areas. • Data input dates back to 2006, so data are not up to date. In the light of these findings, these forest data need consolidation from other resources or from national forest inventories (e. g. the European Atlas of Forest Tree Species from the JRC).
C2-C5 are related to pest entry, C8-C11 to pest establishment, C12 and C13 to pest spread, C14-C16 to pest impact. A rapid screening of data availability regarding the pests to be ranked showed that data could be missing for some pests, such as those lacking any pest risk analysis or CPC datasheet. This alerted to the need for a 'missing data' management strategy and to the need to point out uncertainties when evaluating any pest. Whenever data are missing when answering a question (or scoring criterion), the user takes two actions: • Fill-in the missing data value in order to reduce the number of missing values in the score matrix following the worst-case scenario, the midpoint scenario or no score for no information. In this last option, the score is replaced by a question mark. • Highlight uncertainty in order to allow the construction of an informative uncertainty matrix.
2. 3. Step 3: analyse the multi-criteria matrix Once the multi-criteria matrix has been obtained, it is submitted to a multi-criteria analysis. The multi-criteria analysis method PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) has been chosen. This method was developed in 1986 by Brans et al. and is cited in 1142 publications according to a request made in the Scopus database (October 2018), including 42 in agricultural and biological sciences and 185 in environmental sciences. The implementation of the PROMETHEE method in a system to rank pests threatening plants in a geographic area is the subject of a more recent publication  (Tayeh et al. , 2021). This method is easily applicable because it benefits from a graphical application via the Visual PROMETHEE (VP) software, which can be used to import a matrix, to define the weight of criteria and offers many tools to study the ranking results.
Groups of criteria In the long term, the dynamic potential of the system to meet the context of 'new or emerging pests' is reliant on data updating. This is an important action point that needs to be addressed. Indeed, data on the distribution of pests and land use should be regularly updated. These updates could become burdensome when a large data set is concerned, especially since this kind of operation cannot be automated for this model under its current spreadsheet format. The inconvenience of using spreadsheet-based models is that this format can generate a high error risk when handling large files. On the other hand, its accessibility to non-experts in the field of computer programming is a great advantage, because developing databases as well as web or graphical interfaces requires advanced skills in computer programming.
• Exclusion of the criterion C3 'Total volume of host plant commodity traded into the PRA area' This criterion was initially included to score pests in relation to the existence of a trade pathway through the volume of host plant commodities imported in the EU. Only the total imported volume was used because it was considered not feasible, in a quick exercise, to take into account the origin of the imported host commodities and to distinguish whether they come from a country where the pest is already present. In the first scoring exercise, the difficulty in interpreting the CN codes and consequently a reliable and reproducible estimation of the imported volumes from the EUROSTAT database was noted. The removal of these uncertainties, in a quick exercise, was considered impossible. Furthermore, comparing different kinds of commodities (e.
Construction of the reference data set The reference data set is composed by pests selected from two different projects: i) EFSA pest categorisations: The pests were selected among those assessed between 2017 and 2020 (find the published outputs at the dedicated virtual issue  3  ). Those recommended by EFSA as candidates to the EU quarantine regulation were used as 'positive pests', while those not recommended by EFSA as quarantine pests were used as 'negative pests'. ii) EU candidate priority pests: The candidate priority pests identified by the MSs and submitted to EFSA assessment (EFSA, 2019) are all pests expected to have the most severe potential economic, environmental or social impact as defined by the Regulation (EU) 2016/2031. For this reason, pests selected from this list were all allocated to the 'positive pests' category.
First step: screening based on exclusion criteria The exercise was carried out into two steps. In the first step, a preliminary screening of the pests identified during the horizon scanning exercise was performed based on exclusion criteria. This allowed to focus the scoring efforts only on pests for which such an exercise is deemed necessary for risk managers' decision. During the horizon scanning activities between February 2017 and December 2019, EFSA found 302 non-regulated pests, 81 reported in media newsletters and 221 in scientific newsletters (Table  25 ). As indicated in Section 2. 6, pests already object of an EFSA or EPPO assessment were excluded from the exercise. The preliminary screening also excluded from the scoring pests (i) already established in the EU, (ii) for which there was not enough data available for scoring, (iii) which are non-pathogenic, (iv) which were non-regulated when reported in the newsletters but which were subsequently regulated or (v) which were reported in the newsletters for subjects other than findings, outbreaks or spread.
Second step: scoring pests As a result of the analysis described above, a total of 13 criteria were retained after their individual evaluation on the reference data set. These are listed in Table  26  and described in Appendix G. The PROMETHEE methodology (Appendix B) was used to rank the reference pests with these 13 remaining criteria and to calculate the overall Phi-score for each pest, which is the basis for the final ranking. Table  27  shows these overall scores for the reference pests including their classification as health threats for plants in the EU. The overall score ranges from '-1' for the lowest suitability for further action to '1' for the highest suitability for further action (e. g. pest categorisation). The scores of the reference dataset were used to estimate the distribution within the 'negative' and 'positive' populations of reference pests, which are shown in Figure  19   The two distributions in Figure  20  present a partial overlap.
Glossary Control pests definition applying to the Pilot phase (chapter 2). Pests whose scoring, either positive or negative, was well known before conducting the exercise: the two tested 'positive control pests' are known as high-risk pests to the EU and are therefore regulated, while the two tested 'negative control pests' are known as no-or low-risk pests. Core pests definition applying to the Pilot phase (chapter 2). Pests which always top-ranked, independently from the applied weighting scenario. Reference pests definition applying to the Development of the PeMoScoring methodology (chapter 3). The reference pests belong to a set of 43 pests (Appendix F) selected among EFSA pest categorisations and the EU candidate priority pests, with the scope of composing the reference data set for the definition of the threshold.
threshold, 2. Regulatory status of pathways 0: all pathways prohibited by current legislation, 2: no efficient control measures are described, or pest's Entry 1: at least one pathway is allowed by current legislation. populations have been never eradicated or reduced to a 3. Possibility of entry into the PRA 0: pest not having the ability to enter in the EU by natural level below the economic threshold. Entry area by natural means means, 1: pest having the ability to enter in the EU by natural means. 4. Need for a vector and its 28: not need of a vector, 28-x (where x = number of MS General occurrence in the PRA area where the vector is not present). 5. Cultivated host plant surface Hectares of cultivated host plant in the PRA area Establishment area in the PRA area (EUROSTAT and EFISCEN database for forestry).
6. Cultivated host plant Number of Member States where host plants are present Establishment distribution in the PRA area (EUROSTAT and EFISCEN database for forestry). 7. Occurrence of host plants that 0: no reported presence of major non-crop host plants in Establishment are not crops in the PRA area the EU, 1: at least one major non-crop host plant is present in at least one Member State. 8. Climate suitability 0: Neither climate nor productions conditions are suitable General to pest establishment, 1: the EU does not provide one climate for establishment, but production conditions of host plants do (e. g. greenhouses), 2: the EU provides at least one climate suitable for establishment. 9. Possibility of spread by natural 0: no natural means of spread or vector is the only mean Spread means of natural spread and vector is absent in the EU, 1: pest can spread on local scale via flight at very short distance, rhizome growth or terrestrial movements by itself (jumping, walking), 2: pest can spread over long distance via wind or flight or water (including irrigation) by itself or by a vector present in the EU.
Statement of need SimSGamE was created in order to study the scheduling of game engines. Given the lack of studies on this problem, our efforts have been dedicated to finding and adapting algorithms and heuristics proposed in other contexts. We find that there is value in bringing to light new applications and knowledge about existing algorithms. The software simulates the behavior of game engines using two files: • a dependency file describing links between tasks and the structure of the game engine to be simulated, and • a tasks file describing key values for tasks  (steps, mean, minimum, maximum, std_dev)  for both moderated and intensive use of the engine. Using these two files, SimSGamE generates randomized tasks composing a frame respecting the statistical repartition and their dependencies (generation may be seeded in order to enable direct comparisons).
With a d-spacing of 3. 135 A ˚, Si 111 is not very effective for large Bragg angles (3 keV corresponds to 41. 2 and the minimum energy attained is 1. 977 keV at normal incidence). Therefore, other crystals with large cell parameters must be investigated for applications using tender X-rays. Many natural crystals have been proposed. An exhaustive list of crystals with larger d-spacing is given by  Underwood (2001). Databases such as DABAX (Sanchez del Rio, 2011a) and Stepanov's X-ray server  (Stepanov, 2004 ) contain long lists of crystal structures that can be used in X-ray monochromators. We have compiled in Table  1  a list of crystal reflections with large d-spacing, including useful energy range, Darwin width ( d ), relative energy resolution (ÁE/E) and peak reflectivity (R). Despite such a long list, it is difficult to acquire a suitable crystal for the tender X-ray regime.
, 1995). About 14 years ago, at the XAFCA beamline at Singapore Synchrotron Crystals with large 2d-spacings with the usable photon energy range, Darwin width ( d ), energy resolution (ÁE/E) and peak reflectivity (R). The lowest energy corresponds to normal incidence. d and R and ÁE/E = d cot( B ), where B is the Bragg angle, are calculated at 1. 1 times the lowest energy. Calculations are performed with code developed in this work scanning the crystal structures available in DABAX (source code 0) and Stepanov's X-ray server (source code 1). Light Source  (Yu & Moser, 2008)  working in a photon energy interval of 0. 85-12. 8 keV, we adopted a sagittalfocusing DCM with Si (111) crystals to cover the energy range 2. 15-12. 8 keV. However, for energies below 3 keV in the original optical design, we looked at three crystals, InSn (111), beryl (10 " 1 10) and YB 66 , to cover the lower-energy range.
However, grating monochromators can easily tune the resolution by playing with the slit aperture and customizing ruling values. To prepare for possible upgrades of synchrotron beamlines and the use of YB 66 and other complex crystals, we have upgraded the ray-tracing code SHADOW3 (Sanchez del Rio, 2011b) and other tools in OASYS to include, in a seamless way, any crystal structure. The code algorithms and modifications are presented in this work. It is now possible with the ShadowOUI (Rebuffi & Sanchez del Rı ´o, 2016) add-on of OASYS (that interfaces SHADOW) to ray-trace YB 66 crystals and any other crystal of interest for an X-ray monochromator or analyser.
Moreover, other information could be added to compute the temperature factor, as described below. Note that the summation goes over the n atoms in the unit cell. In crystallography, the list of all atoms is created by applying the symmetry operations from the space symmetry group to a reduced number of atoms (the asymmetric unit). (2) The atomic scattering factors. The elastic scattering f 0 depends to a good approximation only on q = (sin )/, with the grazing incidence angle and the photon wavelength. The so-called 'anomalous' scattering factors f 0 and f 00 depend on the nature of the atom and on the photon wavelength. Although the structure factor can be efficiently calculated by a single piece of code, it requires quick access to data usually stored in libraries or databases. Four sources of information are needed: unit-cell information, f 0 values, and the anomalous scattering values f 0 and f 00.
The ab initio calculation of the scattering factors can only be performed using complex quantum mechanics calculations, and it is out of the scope of most crystallography codes. Tabulated data from some references may be used, usually linking the code to available data files or databases. This linkage makes the software structure complicated and reduces portability. This is why in the ray-tracing code SHADOW (and also in OASYS crystal tools) the calculation is performed in two steps: (i) a preprocessor code that accesses necessary data from databases and creates a 'crystal material file' with the basic ingredients needed to build the structure factor, and (ii) the calculation of the structure factor in the SHADOW kernel using only the information in the preprocessor file, without any further link to databases.
The first version of SHADOW used internal tabulations to retrieve the f 0 and f 00 values, but did not provide any f 0 data. The old SHADOW interface in XOP (Sanchez del Rio, 2011a) took the data from DABAX, an ad hoc compiled collection of material data, available from http://ftp. esrf. eu/pub/scisoft/ DabaxFiles, where several tabulations for the same kind of data (e. g. f 0 ) coexist. The OASYS package uses xraylib  (Schoonjans et al. , 2011; Schoonjans, 2021)  which is a compiled library that allows fast access of a large collection of X-ray data. OASYS can also use DABAX where we included the new data needed for this work.
However, data for ions with integer or fractional charge is sometimes needed. For example, for YB 66 the calculation of form factors f 0 requires the ionic states 'Y 3+ ' and 'B À. ' (the dot indicates a tiny negative charge in the B atoms. We follow the formula of  Higashi et al. (1997) , so the charges removed from the neutral Y atoms are equally allocated among all boron atoms. Some f 0 coefficients for ionic states are available in International Tables for X-ray Crystallography  (Ibers & Hamilton, 1974)  (hereafter called ITC). For some particular ionic states, like in the case of Y 3+ (needed in the YB 66 crystal), the f 0 data are found in the ITC tabulation. However, in most cases, such as for the tiny charge of B in YB 66 , there are no tabulated data for f 0. Values of some ionic states of B, such as B 3+ , are found in a table in ITC (p.
202). We fitted B 3+ data to obtain the nine parameters that were added to the DABAX file f0 InterTables:dat that contains the ITC tabulation. DABAX is incorporated into our tools for retrieving the nine parameters of the f 0 coefficients for both neutral and ionic atoms. In cases of ions with fractional charge, like B À0. 0455 required in the YB 66 crystal, the nine coefficients for parametrizing f 0 are calculated by interpolating the data from two entries in the table with different charges. Using the form factors data f 0 (B), f 0 (B 3+ ), f 0 (Y 3+ ) from this reference, and assuming 'B À. ' carries a negative charge of = 0. 0455, the form factors for 'B À. ' can be calculated as a linear interpolation, f 0 B À : ð Þ ¼ f 0 B ð Þ þ =3 ð Þ f 0 B ð Þ À f 0 B 3þ À Á Â Ã : ð3Þ A comparison plot of f 0 versus q for neutral atoms of Y and B is shown in Fig.
1. It shows good agreement between xraylib and DABAX for the neutral species, but the new ionic states are only available from DABAX. The intense elastic scattering f 0 can be reduced if the incident X-ray radiation has a frequency close to the natural oscillation frequency of the electrons of a given atom. This effect is called anomalous dispersion (although there is nothing 'anomalous') and is represented by f 0 and f 00 , the real and imaginary components, respectively, of the anomalous fraction of the atomic scattering factor. These are functions of the photon energy. Anomalous scattering factors used in most software packages come from old calculations using quantum electrodynamics data (EPDL97, https://www-nds. iaea. org/ epdl97/), mixed experimentally evaluated data  (Henke et al. , 1993) , or a combination of them.