dataesr/scientific-paragraphs-categorization huggingface dataset:
For such approaches, a bias in the performance estimates must be considered (see also  [135] ). This bias occurs because the training data sample size is smaller than for the full dataset, and therefore prediction models built on the training dataset tend to have somewhat worse performance than a final model built on the full data. The latter is typically used for further evaluation. This bias becomes larger the smaller the training dataset is compared to the full dataset. This aspect is less relevant if the sample size of the full dataset and thus of the training dataset is very large. One misleading practice is use of resampling procedures for multiple different prediction modelling methods or for different parameter values, and then reporting results for only the model with best performance.
This practice leads to over-optimism in model performance because it neglects to acknowledge and account for the fact that the reported model was the result of another optimization process  [201]. Such studies aiming to find "best" models occur quite frequently in the context of HDD. While it would be naive to expect that investigators will not try multiple approaches to develop a prediction model, the key is transparency in reporting how many models were actually produced and evaluated, and appropriately accounting for the additional selection step. One should either validate the final selected model using an independent dataset (see  Table 26) , or when such a dataset is not available, embed the selection process in the cross-validation procedure, i. e. , perform a so-called nested cross-validation procedure  [190, 202].
PRED2. 3: Identification of influential points Identification of possible influential observations, defined as those for which inclusion or exclusion in model development might substantially alter characteristics of the final model  [204] , is an important aspect of prediction modelling that is often neglected in HDD settings, and even frequently in low-dimensional settings. Model alterations can be related to variable selection (see, e. g. ,  [205] ), functional forms (e. g. ,  [206] ) or parameter estimation (e. g. ,  [207] ). Influential points can be outliers in some of the variables (observations suspiciously different from the rest, such that they are probably generated by a different mechanism  [208] ), but they do not need to be. 17  Receiver operating characteristic (ROC) curve that illustrates the predictive performance of a gene signature including 227 genes for the prediction of chemotherapy response in serous ovarian cancer, obtained using the TCGA (The Cancer Genome Atlas) data set.
Fan  [220]  released the R package HighDimOut, which contains three high-dimensional outlier detection algorithms. However, none of the approaches seems to have gained popularity in practice. More guidance and comparisons of available approaches are needed. PRED2. 4: Sample size considerations Recent guidelines for calculating sample size when developing a risk prediction model  [16]  are not specifically tailored for Table  26  Methods for validation of prediction models: Subsampling, cross-validation, bootstrapping, use of external datasets Subsampling Subsampling is probably the most straightforward procedure to address the stability issue discussed above. Instead of relying on the result of one single split in training and test sets, the prediction measure is computed for a large number (at least 100) of splits.
In contrast to subsampling, bootstrapping generates training sets of the same size as the original sample, by resampling observations with replacement  [194, 195]. Since some observations are then used multiple times in the bootstrap-generated training set, other observations are not included at all, and these then form the test set on which the model is evaluated Bootstrapping is known to overestimate the prediction error, since the training datasets are smaller than the full dataset, as discussed above. Adjustments to the method have been proposed, for example, the 0. 632 bootstrap and the 0. 632+ bootstrap  [196]. Both modifications balance the overestimated bootstrap-based error estimate with the heavily underestimated corresponding error estimate computed on the training set. An overview of many different bootstrapping approaches for practitioners and researchers was provided by  [197].
Use of external datasets ("external validation") While resampling-based approaches can be useful to evaluate and compare performance of prediction models, they do not meet validation standards typically desired in real-world scenarios. Generally, the goal is to develop a prediction model that generalizes well to independent patient cohorts. This refers both to future patients from the same, say, clinical centers as those from which the data used for the construction of the model were obtained, and to patients from different clinical centers  [198] [199] [200]. Resampling techniques reflect the model performance for independent patient data only if the distribution of the independent data is the same as the original. This assumption can justifiably be questioned when high-dimensional omics or other biomarker data are involved, which may be generated in a new laboratory or according to modified methods, or at very least subject to different batch effects (see section "IDA3.
2: Batch correction"). For all of these reasons, validation on external data (cohorts) is essential to have sufficient confidence in the performance of a predictor for clinical use applications involving variable or shrinkage methods (such as LASSO or Ridge Regression). This is the situation in high-dimensional settings, where variable selection or dimension reduction is needed to identify prognostic variables or components. The available methods for sample size planning  [17, 18]  and references therein are based either on simulations that require assumptions of feature independence or on the availability of a pilot dataset or preliminary data, but these methods are hardly used in practice. Moreover, penalized estimation has often been proposed for situations with potentially large overfitting problems, while recent evidence suggests that it yields unstable results, especially for small sample sizes  [221] , when overfitting is a major concern.
Introduction Recently, the market share of electric vehicles (EVs) has increased. Meanwhile, the European Commission decided to end the sale of new diesel and petrol cars and vans by 2035  [1]. This decision will accelerate the production and sale of EVs. Therefore, research has been conducted on different methods allowing the optimization of the use of such vehicles. Mawonou et al. (2021)   [2]  developed an algorithm for the estimation of the state of health of EV batteries. They randomly selected 186 cars from a big database of more than 180,000 vehicles for learning of a random forest algorithm. Shen et al. (2022)   [3]  worked on the use pattern of EVs and load forecasting. Jirdehi et Tabar (2023)   [4]  optimized EV infrastructures integrated with renewable energies and implemented a demand-side strategy considering uncertainties.
• The parameters of simulations, mainly PV surface, consumption in Wh per km, system efficiency, battery size, shading and use profile. Figure  1  shows the inputs, outputs, parameters and the characteristics of the model simulating the PV energy transferred to the engine and the distance covered by the VIPV. The equations of the model are coded in a software developed by the authors in a Microsoft Excel environment. The Excel workbook contains several sheets: inputs (usage pattern and parameters); solar irradiance hourly distribution; monthly solar energy; equations; and 12 sheets for each month of the year. These last sheets consider the differences among the days for every month. To simulate the model, it is sufficient to modify only the sheet inputs. The values are, thus, automatically modified in the other sheets and the outputs are summarized in the output sheet.
Acknowledgments: Authors acknowledge the contributions of  Stéphane Cattelani  (System efficiency calculation);  Nouha Gazbour  ( Life Cycle Assessment Expert ) and  Stéphane Guillerez  (PV producible) from  CEA ;  Arnaud Richard ,  Jean-Yves Stineau  and  Guy Thivin  from  RENAULT  (electronics);  Jacques Roux-Michollet  and  Albert Tienou  from  SEGULA  (variable shading modelling) and  Ghazi Jad  from  FEV  (measurements on commercial vehicle). Timea Bejat  from  CEA  for reviewing.
Pseudo Maximum Likelihood (PML) An alternative way to obtain ML estimates of the GPD parameters has been introduced by Villaseñor-Alva and González-  Estrada (2009)  and implemented in the R package gPdtest  (Estrada and Alva, 2012). The estimator is constructed differently depending on the sign of ξ; given this composite strategy and the passage through intermediate steps not based on the likelihood function, we will refer to this method as "pseudo maximum likelihood". Here we only provide a sketch and refer the reader to the aforementioned article for full details. For ξ ≥ 0, gPdtest implements a maximization of the asymptotic likelihood, based on the logarithm of the k largest values in the ordered sample x 1:n , x 2:n ,. , x n:n : W j = log x j:n with j = n -k + 1, n -k + 2,. , n. For n → ∞ the log-likelihood can be expressed as a function of W j , and the two ML estimators follow: ξk = -W n-k+1 - 1 k k j=1 W n-j+1 (8) σk = ξk exp W n-k+1 + ξk log k n.
Even though the package authors recommend the use of gPdtest for all values of ξ, Eq. s 8-9 show that an unbiased estimation is not possible for ξ = 0, since if ξ was correctly estimated, it would necessarily result in σ = 0, corresponding to a degenerate distribution with 0 variance, and to an infinite estimated dimension. Since the performance of the estimators is mostly independent of the true value of σ (or d), and convergence with n is reached for n 200, we can fix n and average over d to obtain a synthetic representation on the relationship between ξ and the relative estimation error affecting d. As we pointed out earlier, the ML estimator error shows some dependence on d for ξ 0. 2. However, the range of relative value errors is small, in particular compared to the mean and the PML estimators.
Climate Data Finally, we replicate the previous analysis for the ERA5 dataset. In this case, it is not possible to compare estimates to the true value, as in the Monte Carlo experiment, or to previous knowledge about the attractor dimension, as for the L63 system. The goal of considering real-world data is rather to check the stability of local and attractor dimension estimates when using different estimators. This is particularly relevant because many existing climatological studies have been based on the mean estimator, while the climate system is expected to behave as a non-Axiom A system, characterized by ξ < 0. While these differences are statistically significant, from the point of view of many climatological applications, the estimates are equivalent. As an example of this type of application, we mention a study  (Fery et al.
We will not investigate the issues of this particular estimator, that may be due to the implementation of the R package gPdtest. At this point, it is clear that the other estimators constitute better alternatives, both because they are more accurate, and because they do not require a prior knowledge of the sign of ξ. The correlations among the other estimators are positive and large, ranging between 0. 88 and 0. 97. This shows that, even under the erroneous assumption of ξ = 0, the mean estimator produces time series of dm well synchronized with the ones obtained by its competitors that assume a GPD as the exceedances distribution. To ensure that seasonality and modes of interannual variability are consistently reflected by all the estimators, we also inspect the autocorrelation function (ACF) and the spectral density function (SDF) of the corresponding time series of d, both shown in Fig.
cereus group bacteria have been subject to analysis with a wide variety of phylogenetic typing systems. Here we present, based on analyses of 173 complete genomes from B. cereus group species available in public databases, the identification of a set of 1568 core genes which were used to create a core genome multilocus typing scheme for the group which is implemented in the PubMLST system as an open online database freely available to the community. The new cgMLST system provides unprecedented resolution over existing phylogenetic analysis schemes covering the B. cereus group.
B. thuringiensis is pathogenic to insect larvae, and other invertebrates such as nematodes and mites, and is therefore widely used commercially as a biological insecticide  [11, 12]. The bacterium produces specific insecticidal protein toxins responsible for entomopathogenicity, and which are frequently plasmid-encoded, but in addition carries many of the same chromosomal virulence factors as B. cereus  [13]. More recently, a total of at least 25 new species have been proposed for the B. cereus group  [14] , and are used by DNA sequence repositories such as GenBank and the EMBL databases. Several of these species have been defined on the basis of average nucleotide identity (ANI) values  [15, 16] , in some cases with only one isolate known for each new species suggested, which contradicts formal rules for attribution of the species status to bacteria.
cgMLST scheme creation In a typical cgMLST analysis, a scheme is first developed using complete (or nearly complete) genomes and then applied to all other genomes of a given species (or group of species). The latest assemblies of 2458 B. cereus group genomes available at the time of study (October 2021) were downloaded from the NCBI GenBank FTP site. Among those, 173 were complete (i. e. , ungapped, fully closed chromosome and plasmid sequences; genome list in Supplementary Table  S1 ). The complete genome set included representatives of 12 of the recognized and proposed B. cereus group species, with one to five genomes per species, except for the main species B. anthracis, B. cereus, and B. thuringiensis which had 50 representatives. The cgMLST scheme was defined using the chewBBACA 2. 8.
For the sake of comparison and consistency, Prodigal was trained on the genome sequence of the B. anthracis Ames Ancestor strain, which was used as a reference for creating the B. anthracis cgMLST scheme  [32]. Prodigal 2. 6. 3 was run with the options "-p single" and "-c". It has been shown that the B. cytotoxicus species has a significantly smaller genome, and thus gene content, than other members of the B. cereus group (~1 Mbp smaller; ~1000 genes less;  [34] ). As the scheme was designed for the whole B. cereus group, a set of 76 genes that were missing only in B. cytotoxicus were removed from the scheme (genes listed in Supplementary Table  S2 ). For comparison, we also analyzed the core genome using the most recent pan-genome software, Panaroo  [39]. The 173 closed B. cereus group genomes were first annotated using Prokka 1.
14. 6  [40]  (run with options "-addgenes -usegenus -genus Bacillus -kingdom Bacteria -gcode 11 -evalue 1e-09 -coverage 50 -mincontiglen 200") to provide consistent annotation files (in GFF3 format including gene sequences) for Panaroo (note that Prokka also makes use of Prodigal to predict the CDS). Using these annotations, Panaroo 1. 2. 10 was run in strict mode ("-clean-mode strict") with a core threshold of 99% ("-a core -core_threshold 0. 99"), the MAFFT 7. 407 alignment program ("-aligner mafft")  [41]  and all other default options. Because the locus identifiers generated by chewBBACA and Panaroo are different, the correspondence between the loci common to the two programs were determined by searching for sequence identity between the allele sequences of B. anthracis Ames Ancestor in chewBBACA and Panaroo using BLASTNþ 2.
To make the cgMLST scheme publicly accessible, maintained, and updated over time, the scheme was included in the central PubMLST database (https://pubmlst. org/organisms/bacilluscereus;  [35] ). To build the allelic profiles, the loci sequences for the reference B. anthracis Ames Ancestor strain annotated in NCBI RefSeq were used (assembly accession GCF_000008445. 1). In chewBBACA CDS sequences are predicted using Prodigal and are given custom identifiers. The correspondence between the chewBBACA/Prodigal allele sequences and the RefSeq CDS was found by first aligning the chewBBACA/Prodigal alleles for the B. anthracis Ames Ancestor strain to the B. anthracis Ames Ancestor genome sequence using BLASTNþ 2. 9. 0 (with parameters "-evalue 1. 0e-10 -dust no -word_size 7") to retrieve their genomic coordinates, and then by intersecting those coordinates with the coordinates of the CDS annotated in RefSeq using the "inter-sectBed" utility of the BEDTools 2.
30. 0 package (with options "-f 0. 50 -r -loj";  [43] ). As expected, there was a unique and complete or large overlap between the chewBBACA/Prodigal and GenBank loci for virtually every gene in the cgMLST scheme. Five loci that did not overlap with the GenBank annotation were deleted from the scheme (listed in Supplementary Table  S4 ). Starting from the loci sequences of the reference strain B. anthracis Ames Ancestor, homolog identification and allele calling for all loci in >2500 B. cereus group genomes were performed using the automated computational pipelines of pubMLST which are based on BLAST analysis  [35].
Reconstruction of cgMLST phylogenetic tree A phylogenetic tree was reconstructed using GrapeTree  [44]  and the matrix of allelic profiles for 2458 B. cereus group genomes generated by chewBBACA. The FastME V2 (Fast Minimum Evolution) Neighbor-Joining  [45]  pairwise distance method was employed (option "-method NJ" in GrapeTree). Missing loci were ignored specifically for each pairwise comparison of profiles, which is the default behavior in GrapeTree. The output tree (in Newick format) was then converted to phyloXML format (using the "phy-loxml_converter" utility of the "forester" software libraries, run with option "-f ¼ sn"; https://www. phylosoft. org/forester/) and subsequently imported into the iTOL web server  [46]  to annotate and color-code the tree figure according to the species and the seven major clusters defined by Guinebretiere and co-workers  [2].
A core-genome MLST scheme for the B. cereus group In this study we created a core-genome MLST scheme for the whole B. cereus group. The scheme was built using the MLSTdedicated chewBBACA software  [36]  on the basis of 173 complete and closed B. cereus group genomes, and consists of 1568 genes that are conserved in 99% or more of those genomes (Supplementary Table  S5 ; Supplementary file S1). The chewBBACA pipeline identifies homologous alleles and assigns allele numbers only to loci that satisfy a number of criteria, in particular a defined size range, sequence identity threshold to homologs, and that do not show duplication within loci or cross-homology between loci. Thus the 1568 selected core genes are those for which an unambiguous allele could be assigned in 99% or more of the closed genomes.
The possible effect of nucleotide errors in sequences is rarely considered during cgMLST analyses. However, the vast majority of bacterial genomes to date were sequenced using high coverage and low error rate short-read Illumina methodology. In the case of the B. cereus group, among the 2215 genomes for which information about sequencing technology and genome coverage was readily available (in the NCBI GenBank files) at the time of the present analysis, 91% are Illumina-derived and 95% have a sequencing depth of 20X or higher. Thus, the number of sequencing errors would be assumed to be small, and even if some gene sequences contain errors (leading to a wrong allele assignment) this should have little impact on the phylogenetic reconstructions, and certainly smaller impact compared to conventional MLST, given the large number of loci used in cgMLST.
An interactive version of the figure is provided in Supplementary Data (Supplementary Fig. S1 ). sequences and isolate information, as well as tools to analyze and visualize phylogenetic relationships among isolates, and integration of the B. cereus cgMLST scheme in PubMLST will allow systematic maintenance and update of data, and ensure public availability of the scheme to the international community over time. 3. 2. Gene content of the B. cereus group cgMLST scheme and overlap with the B. anthracis cgMLST scheme From a comparison of the B. cereus group core genome, as defined in our analysis (1568 genes), to the core genome of B. anthracis (3803 genes), as defined from previous cgMLST analysis  [32] , 1225 out of the B. cereus group core genes defined here are also part of the B. anthracis core used for cgMLST, while unexpectedly 343 loci are not (Supplementary Table  S6 ).
Phylogenetic reconstruction of 2458 isolates from the B. cereus group with a complete genome sequence employing the devised cgMLST scheme A phylogenetic tree was reconstructed using the GrapeTree software from pairwise comparisons between the allelic profiles of the 2458 B. cereus group isolates for which a genome sequence was available at the time of study (Fig. 2 ). The branches of the tree were colored according to the seven major clusters defined by Guinebretiere and colleagues (clusters II and III corresponding to clade 1, cluster IV corresponding to clade 2, and clusters I, V, VI, and VII corresponding to clade 3)  [2, 3]. The colors were set for isolates that were unambiguously assigned to a given cluster based on MLST, AFLP, and MLEE analyses, following previously published work  [2, 52].
The cgMLST scheme here at its inception incorporates 2458 isolates, only about half of the number of isolates typed by the     Core-genome B. cereus group phylogenetic tree based on 2458 available genomes. The tree was reconstructed from a distance matrix between 2363 distinct cgMLST profiles using the FastME V2 Neighbor-Joining  [45]  method and the GrapeTree software  [44]. Branches of the tree are color-coded according to the seven major clusters defined by Guinebretiere et al. [2] , indicated by Roman numbers (I-VII). The colors were set for isolates that were previously assigned to a particular cluster based on MLST, AFLP, and MLEE analyses. Species are color-coded on the outer circle, as indicated. The tree was drawn in iTOL  [46]  and can be browsed interactively at https://itol. embl.
de/shared/wYoEDoWlZcu1. The tree scale indicates the number of allelic differences. conventional 6e7 gene MLST schemes (e. g. 5332 in PubMLST Oct 12, 2022; https://pubmlst. org/organisms/bacillus-cereus). With the continuous development in sequencing technologies and associated lower sequencing costs however, and thereby increasing numbers of isolates/strains for which whole genome sequence data is available, this situation will undoubtedly change proceeding from here. The development of a cgMLST scheme for the full B. cereus group is therefore timely, ensuring high-resolution strain typing of B. cereus group isolates in an open and fully public accessible online user interface for the future.
The core of video anomaly detection technology is the identification of abnormal events from a series of continuous videos. However, in the real world, abnormal events cannot be accurately defined and have no boundaries  [5]. Therefore, it is impossible to label all abnormal events to generate datasets for training supervised models. In addition, it is difficult to collect a sufficient number of types and quantities of abnormal data; hence, to the use of supervised learning algorithms for video anomaly detection tasks is impractical. Existing algorithms are mostly unsupervised or semi-supervised  [6]. Many video anomaly detection algorithms use convolutional neural networks (CNN) to learn video features including temporal and spatial features. Inverse coding is then used to reconstruct the video or combine it with optical flow technology to predict the next frame.