dataesr/scientific-paragraphs-categorization huggingface dataset:
Rscript on the resulting data. SNPs were annotated using SNPeff (Cingolani et al 2012)  and the GCF_000699145. 1 genome annotation. Annotated vcf files are available upon request. Mean coverage depth of filtered SNPs per allele was 6. 93X, standard deviation 1. 07X. 1. 4 SNPs polarisation in ancestral and derived alleles Annotated vcf files were parsed using a custom python script (vcf2missenseFreq. 2d. py; https://github. com/emitruc/ExpressionLoad). For each SNP position we summed up the joint derived allele counts for King and Emperor penguin samples using Adelie and Gentoo penguins samples as outgroups. The resulting summary table (daf. joint; Supp. Tab. 1) included the following information: -counts of derived alleles and total alleles in Emperor penguin (der1; tot1), King penguin (der2; tot2), and Adélie and Gentoo penguins (der_out; tot_out) samples; -average allele coverage (avgCov); -ancestral (ref) and derived (alt) allele; scaffold position flagPol flagQual der1 tot1 der2 tot2 der_out tot_out avgCov ref alt vartype effect NW_008793941.
coverage depth of 50 (--max-meanDP 50 which is ca. three times the average individual coverage depth; whole locus discarded if above threshold), and retained only if biallelic across all samples. A total of 44 sex-linked scaffolds were identified and removed from the dataset by running samtools idxstats on all bam files and the SATC (Nursyifa et   al 2022) -genomic site type (vartype) based on the first annotation by SNPeff as missense if containing the word 'missense', synonymous if containing the word 'synonymous', intergenic if containing the word 'intergenic', intronic if containing the word 'intron', else otherwise; -genomic site predicted effect (effect) based on the the first annotation description as HIGH, MODERATE, LOW, MODIFIER; -a flag whether the polymorphic site was originally called as MNP or SNP by freebayes (fyellow bellied toadlagQual: haplo, snp); -a flag to track how the derived allele was called (flagPol) on the basis of the options shown in Supplementary Figure 1.
Limitations of the study We focused on GSH1 because there were limitations to progress with the other prospective GSHs 2-4. The CHOPCHOP software predicted only a single CRISPR-Cas9 target in these three GSHs, and moreover, the site predicted in GSH4 was not specific and showed potential off-target hits elsewhere in the genome. For GSH3, CHOPCHOP predicted only low, <35%, editing efficiency. Overall, locating a Cas9 PAM  51  is constrained by the AT-rich nature of the schistosome genome. 52 Since this investigation deployed multiple overlapping gRNAs  18  to facilitate homology-directed insertion of transgene, we ranked GSH1 as the most qualified for our purposes because multiple PAMs were present, along with the absence of off-target activity. Although a distance >2 kb from known genes was one of our criteria for GSHs in S.
ACKNOWLEDGMENTS Schistosome-infected mice were provided by the Schistosomiasis Resource Center of Biomedical Research Institute, Rockville, MD, through NIH-NIAID contract HHSN272201700014I. This research was funded in part by the Wellcome Trust grant 107475/Z/15/Z (Flatworm Functional Genomics Initiative, PI, Karl F. Hoffmann). For open access, the author applied a CC by public copyright license to any author-accepted manuscript version arising from this submission. This study is set within the framework of the Laboratoire d'Excellence (LabEx) TULIP (ANR-10-LABX-41) with the support of LabEx CeMEB, an ANR Investments d'avenir program (ANR-10-LABX-04-01) and the Environmental Epigenomics Core Service at IHPE (Interactions Hôtes Pathogenes Environnement). P. J. B. gratefully thanks LabEx CeMEB for support as a short-term visiting scientist at IHPE, Universitede Perpignan via Domitia.
Weather conditions prewild re Stations in the meteorological network of the Czech Hydrometeorological Institute (CHMI) were used to analyze local climatic and meteorological conditions. These stations are routinely maintained, and all data undergo quality control. The stations used for the evaluation of long-term uctuations were thus homogenized and had complete measurements in the period from 1961 to 2021. The Tokan weather station is in the BNSP (50. 8764 N, 14. 4172 E, 402 m a. s. l. ). Although it is operated by the national park authority, it is part of the CHMI measurement network. Weather observations have been collected at Tokan since 1 October 2008. The Tokan station may not be representative of the climate within the entire park, mainly due to the complex relief and terrain. Especially in the case of wind measurements, measured wind values may not always correspond to all parts of the studied area.
Taller trees around the station may shelter the station, thus in uencing measured wind speeds (reducing them). Another problem is the calibration of the wind speed instrument (WAA 151). It was last calibrated in August 2020, so at the time of the re, it was just before the end of the required maintenance and calibration period (calibration every 2 years). In most cases, wind measurements are underestimated  (Zahradníček et al. (2019) ;  Crosby and Chandler (2004) ). Data from the ECMWF numerical forecast model, which is not affected by the measurement conditions, were also used as reference values for comparing wind values. The changes in basic meteorological elements affecting re risk were evaluated based on the climatological period (1961 to 2021). A statistically signi cant increasing trend of the average air temperature is observed in the study area.
Model description and input data format For the modeling of wild re extent under different conditions (see below), we used the FARSITE model  (Finney 1998) , which is part of the FlamMap application. FARSITE allows the simulation of re spread and behavior of a re under spatially variable fuels, topography and fuel moisture with timevarying weather conditions. We obtained spatial information about land use and vegetation cover within the BSNP area. This information was used to classify fuel types considering land use patterns and vegetation types across the landscape (e. g. , meadow, pine forest with low undergrowth, spruce forest, water area, built-up area, etc. ) and consideration of potential re behavior. The required re behavior fuel types were derived for Czech Republic conditions based on the methodology described by  (Scott and Burgan 2005).
The selected re behavior fuel models describe individual fuel loadings by dead size class (1, 10, and 100-hour dead size classes) and fuel loadings for live herbaceous and live woody. FlamMap/FARSITE requires eight geospatial data themes describing fuels ( re behavior fuel model, canopy cover (CC), stand height (StH), crown base height (CBH), and canopy bulk density (CBD)) and topography across an area and is commonly referred to as a landscape or landscape le  (Finney 1998 ) (Fig. 7 ). Other nonspatial input data requirements are information for dead and live fuel moisture, hourly meteorological data (temperature, precipitation, relative humidity, cloudiness), and wind speed and direction. The landscape data used in the BSNP wild re modeling were at a resolution of 5 × 5 m. The DMR 4G database managed by the State Administration of Land Surveying and Cadastre was used as a source of the digital terrain model (Czech O ce for Surveying 2020).
Slope (degrees) and aspect were derived from the digital terrain model using standard GIS tools. Data for CC, StH, CBH, and CBD were obtained from an aerial LiDAR survey of the NP area in 2021 (Czech University of Life Sciences 2021) and 2022 (Global Change Research Institute of the Czech Academy of Sciences 2022). The data from 2022 were obtained post re, and therefore, they were lled in with data from 2021 in the burned perimeter. CC (in %) was de ned as the horizontal percentage of the ground surface that is covered by the vertical projection of the tree crowns and is a standlevel descriptor (Finney (1998);  Jennings et al. (1999) ). CC was calculated as the percent of surface in a pixel that was completely covered by the tree canopy. Based on expert knowledge, CC values were capped at 80% (Scott and Reinhardt 2005),  (Stratton 2006).
StH was calculated as the 98th quantile in the point cloud above each 5 m pixel, representing the difference between the ground surface and the top of the canopy while also eliminating high outliers. CBH in re behavior models should represent the effective vertical distance between the surface and crown fuels  (Scott and Reinhardt 2001)  In our study, it was calculated by counting empty layers in the point cloud in a block above each pixel. A layer, 0. 5 m high, is marked as empty when it contains less than 2% of all points in the corresponding block. The length of the rst empty sequence of layers above ground is recorded as a CBH estimate. CBD is the dry mass of available canopy fuel per unit volume and is the bulk property of the stand and not of individual trees (Scott and Reinhardt 2001) and was calculated based on the point cloud data with the following series of steps: 1/total aboveground biomass (AGB) was calculated with the area-based approach when we applied a general model trained for the mountainous forests in the Czech Republic  (Brovkina et al.
During this comparison, we observed some unreliable values of wind speeds measured at the Tokan station. The station average hourly wind speed for the period from July 23 to July 31, 2022 was 0. 9 m/s, while it was 2. 7 m/s as predicted by the model. The average station maximum hourly wind speed gusts were 3. 7 m/s and 7. 3 m/s according to the model, i. e. , twice as high. Analyzing the data from the Tokan station also shows that 13% of the average hourly wind speeds were 0 m/s, i. e. , no wind. Based on this analysis, we assume that the measuring device at the Tokan station had a problem with ball bearing seizing and therefore underestimated the real wind speed values. On the other hand, the model may not capture the correct timing of the onset of a windier episode.
The Kamenice River was marked as a barrier in the modeling because it represented a natural barrier to re spread. The Scott/Reinhardt crown re method  (Scott and Reinhardt 2001)  was used to simulate crown re spread and intensity. Weather data from the Tokan meteorological station, which is in the eastern part of the NP and is approximately 11 km from the re ignition, were used for the modeling. Based on the data analysis, this station is the most suitable compared to the surrounding available stations from the CHMU network (distance, altitude, terrain). To capture the correct extent and spread direction of the re, it is often necessary to adjust the measured wind speed and directions. This process is routinely used in FlamMap simulations because a remote weather station cannot capture the actual wind information at the re location spatially and temporally at the scale of in uence  (Stratton (2006) ;  Cochrane et al.
Scenario 7: Native mixed forest In the last simulated scenario, we replaced the spruce stands (both dead and live) with a fuel type corresponding to a native mixed forest that would have most likely occurred in the area without human interference. The original mixed forest was a predominance of beech (Fagus sylvatica), and r (Abies alba) prevailed here  (Härtel et al. n. d. ). The age of the trees was set to be the same as in Scenario 1. There was a change in surface fuel types, canopy cover and crown layers. CC and crown layers were determined based on the age of the hypothetical stand. The determination of CC, StH, CBH and CBD values was carried out based on the data of individual layers from Scenario 1 for speci c fuel types and age categories.
An important factor is the measurement device used and its calibration. Given all these circumstances, it is likely that measurements at the stations underestimate the real condition in the atmosphere, even if all WMO conditions are met. Therefore, it is advisable to supplement the wind measurement by calculating the numerical prediction model and subsequent reanalysis with the WindNinja software that is used to simulate the wind movement and speed across a landscape accounting for terrain features. Furthermore, we worked with different wind speeds, which reveal how the speed affects the spread of the re (Scenario 1, 2). The following text summarizes the modeled scenarios that evaluate the spread of res in different meteorological and vegetation conditions. It is important to mention that no model can ever capture/explain 100% of reality, and there is still much information that is not currently available and is not accounted for in our analysis.
The possibility of scenario analyses of the de ned spectrum of input conditions and the possibility of analyzing the importance of individual factors are especially valuable. The simulations clearly show that the wind speed and fuel types had the highest effect on the spread of re. The studied wild re was a unique re event in the Czech Republic. This fact undoubtedly leads to a wide variety of potential research, and many valuable data are still processed. Many studies in this area will continue to be conducted, with an emphasis on widening the territory beyond the Czech national border. Further research will greatly bene t from seamless landscape coverage of the Czech-German border, which has already started as a part of further cooperation with the German side. The re brings a new possibility for the renewal of the territory.
Impact Statement Detection of defects and identification of symptoms in monitoring industrial systems is a widely studied problem with applications in a large range of domains. In this article, we propose a novel method, dCAM, based on a convolutional neural network, dCNN, to detect precursors of anomalies in multivariate data series. Experiments with several synthetic datasets demonstrate that dCAM is more accurate than previous classification approaches and a viable solution for discriminant feature discovery and classification explanation in multivariate time series. We then experimentally evaluate our approach on a real and challenging use case dedicated to identifying vibration precursors on pumps in nuclear power plants.
Supervised detection of anomaly precursors In the previous section, anomalies were considered unknown. However, one can assume that experts know precisely which event they want to detect and have a data series collection corresponding to these anomalies. In that case, we have a database of anomalies at our disposal. As a consequence, one can decide to adopt supervised methods. A question that naturally arises is the following: is it possible to detect subsequences that happened before the known anomaly that might lead to an explanation of the anomaly e30-2 Paul Boniol et al. (and potentially predict it)? Such subsequences can be called precursors or symptoms of anomalies. Usually used in medical domains, we can infer the medical definition and adapt it to the task of anomaly detection in large data series.
As Problem 1 is defined as a supervised task, one can decide to use time series classification approaches. The latter can be performed using either (1) pre-extracted features or (2) raw values of the time series. For the first category (1), the main idea is to use the dataset of time series (or subsequences of time series) to create a dataset whose samples are described by features common to all-time series. For the specific case of time series, the feature extraction step can be performed using TSFresh Python library  (Christ et al. , 2018)  (Time Series Feature extraction based on scalable hypothesis tests). The latter is used for automated time series feature extraction and selection based on the FRESH algorithm  (Christ et al. , 2016). More specifically, it automatically selects relevant features for a specific task.
This is achieved using statistical tests, time series heuristics, and machine learning algorithms. Then, using the featurebased dataset, standard machine learning classifiers can be employed to classify each time series. Examples of traditional machine learning classifiers are Support Vector Classifier (SVC)  (Boser et al. , 1992)  (i. e. , classifiers that map instances in space in order to maximize the width of the gap between the classes), naive Bayes classifier  (Zhang, 2004 ) (i. e. , classifiers based on Bayes' theorem to predict the class of a new instance based on prior probabilities and class-conditional probabilities), Multi-Layer Perceptron (MLP)  (Hinton, 1989)  (i. e. , fully connected neural networks), AdaBoost  (Freund and Schapire, 1995)  (i. e. , boosting ensemble machine learning algorithms), or Random Forest Classifier  (Ho, 1995)  (ensemble machine learning algorithm that combines multiple decision trees, where each tree is built using a random subset of the features and a random sample of the data).
On top of the aforementioned classifiers, explainability frameworks, such as LIME  (Ribeiro et al. , 2016)  and SHAP  (Lundberg and Lee, 2017)  can be used in order to identify which features have the most important effect on the classification prediction. However, feature-based classification and explanation methods can be limited when applied to time series. Whereas features are efficient for summarizing time series datasets (e. g. , setting a constant number of features for variable length time series), it might miss important information in the shape of consecutive values, which may be crucial for anomaly detection and precursor identification. Moreover, the choice of features heavily impacts the classification accuracy and is not desirable in tasks related to precursors identification with unknown properties.
Contributions In this article, we will focus on solving Problem 1. First, we explore the possibility of using a supervised time series classification algorithm to solve precursor discovery tasks. We then propose a novel method, dCNN/dCAM, that overcomes the limitations of previous supervised algorithms. Moreover, dCNN/ dCAM permits the identification of specific patterns (without any prior knowledge) and alerts the expert on the imminent occurrence of the anomaly. Finally, we demonstrate this latter claim in a real industrial use case. The article is structured as follows: • Section 2: We first describe the notations and the concepts related to time series and neural networks. We then describe the usual Class Activation Map and explain its limitations regarding multivariate data series in detail.
Class activation map Once the model is trained, we need to find the discriminative features that led the model to decide which class to attribute to each instance. Class Activation Map  (Zhou et al. , 2016)  (CAM) has been proposed to highlight the parts of an image that contributes the most to a given class identification. The latter has been experimented on data series  (Wang et al. , 2017; Fawaz et al. , 2019)  (univariate and multivariate). This method explains the classification of a certain deep learning model by emphasizing the subsequences that contribute the most to a certain classification. Note that the CAM method can only be used if (1) a Global Average Pooling layer has been used before the softmax classifier, and (2) the model accuracy is high enough. Thus, only the standard architectures CNN and ResNet proposed in the literature can benefit from CAM.
Thus, for an input data series T, A m T ð Þ would become a multivariate data series for the variable m ∈ 1, n f Â Ã , and A d ð Þ m T ð Þ ∈ A m T ð Þ would be a univariate time series that would correspond to the dimension d of the initial data series. We call this solution cCNN, and we use cCAM to refer to the corresponding Class Activation Map. Figure  1b  illustrates cCNN architecture and cCAM. Note that if a GAP layer is used, then architectures other than CNN can be used, such as ResNet and InceptionTime. We denote these baselines as cResNet and cInceptionTime. Nevertheless, new limitations arise from this solution. First, the dimensions are not compared together: Each kernel of the input layer will take as input only one of the dimensions at a time. Thus, features depending on more than one dimension will not be detected.
A first architecture: dCNN In order to have the best of both cases, we propose the dCNN architecture, where we transform the input into a cube, in which each row contains a given combination of all dimensions. One kernel (of size D, ℓ,1 ð Þ) slides on all dimensions D times. This allows the architecture to learn features on multiple dimensions simultaneously. Moreover, the resulting CAM is a multivariate data series. In this case, one row of the CAM corresponds to a given combination of the dimensions. However, we still need to be able to retrieve information for each dimension separately, as well. To do that, we ensure that each row contains a different permutation of the dimensions. As the weights of the kernels are at fixed positions (for specific dimensions), a permutation of the dimensions will result in a different CAM.
Formally, for a given data series T, we note C T ð Þ ∈ ℝ D,D,n ð Þ the input data structure of dCNN, defined as follows: C T ð Þ ¼ T DÀ1 ð Þ T 0 ð Þ … T DÀ3 ð Þ T DÀ2 ð Þ : : … : : T 1 ð Þ T 2 ð Þ … T DÀ1 ð Þ T 0 ð Þ T 0 ð Þ T 1 ð Þ … T DÀ2 ð Þ T DÀ1 ð Þ 0 B B B @ 1 C C C A : (6) Note that each row and column of C T ð Þ contains all dimensions. Thus, a given dimension T i ð Þ is never at the same position in C T ð Þ rows. The latter is a crucial property for the computation of dCAM. In practice, we guarantee the latter property by shifting the order of the dimensions by one position. For instance, in equation (  6 ), the dimension order of the first row is T 0 ð Þ , T 1 ð Þ ,…,T DÀ2 ð Þ , T DÀ1 ð Þ Â Ã (i. e. , the first dimension of T is at the first position in the row and the last dimension of T is at the last position in the row), and the dimension order of the second row is T 1 ð Þ ,T 2 ð Þ , …, T DÀ1 ð Þ ,T 0 ð Þ Â Ã (i.
Random permutation computations Given those different combinations of dimensions (i. e. , one row of C T ð Þ) produce different outputs (i. e. , the same row in CAM C j C T ð Þ ð Þ) , the positions of the dimensions within the C T ð Þ rows have an impact on the Class Activation Map. Consequently, for a given combination of dimensions, we can assume that at least one dimension at a given position is responsible for the high value in the Class Activation Map row. For the remainder of this article, we use Σ T as the set of all possible permutations of T dimensions, and S i T ∈ Σ T for a single permutation of T. For example, for a given data series  3  depicts an example of Class Activation Maps for different permutations. In this figure, for three given permutations of T (i. e. , S 0 T , S 1 T , and S 2 T ), we notice that when T ¼ T 0 ð Þ , T 1 ð Þ , T 2 ð Þ È É , one possible permutation is S i T ¼ T 1 ð Þ , T 0 ð Þ , T 2 ð Þ È É.
g. , see dotted red rectangle in Figure  5b : this pattern corresponds to a nondiscriminant subsequence of the dataset). On the contrary, for discriminant dimensions, we observe a strong variance for the activation per position: either high values or low values (e. g. , see solid red rectangles in Figure  5b : these patterns correspond to the (injected) discriminant subsequences highlighted in red in Figure  5e ). We thus can extract the significant subsequences per dimension by computing the variance of all positions of a given dimension. We can filter out the irrelevant temporal windows using the averaged μ M C j T ð Þ À Á for all dimensions, and use the variance to identify the important dimensions in the relevant temporal windows. Formally, we define dCAM C j T ð Þ as follows. Definition 3.