dataesr/scientific-paragraphs-categorization huggingface dataset:
d. Robustness To determine the robustness of the model, the data used for calibration was divided into two subsets: data from odd years and data from even years. In comparing the data, it was observed that essential features such as the seasonal rainfall signal were preserved for both the magnitude and frequency of events. Each dataset was then used to calibrate a set of parameters in Stochastorm and subsequently generate rainfall event simulations. Figure  12  shows a summary of this analysis. We did not find a significant difference in the simulations produced by each subset of data. This leads us to conclude that the model is robust enough to be calibrated on rainfall series of relatively short durations while keeping its simulations skills.
(2017)  and  Benoit et al. (2018a)  would be able to adapt the Sahelian case study presented here. More generally, the question of genericity calls the community of highresolution space-time SRGs to gather around intercomparison experiments based on various datasets from various areas. Acknowledgments. The research leading to these results has received funding from the NERC/DFID Future Climate For Africa program under the AMMA-2050 project, Grant NE/M020428/1. The AMMA-CATCH regional observing system (www. amma-catch. org) was set up thanks to an incentive funding of the French Ministry of Research that allowed pooling together various pre-existing small-scale observing setups. The continuity and long-term longevity of the measurements are made possible by undisrupted IRD funding since 1990 and by continuous CNRS-INSU funding since 2005.
AMMA-CATCH also received support from OSUG, OREME, OMP, OSUG@2020 LabEx, SOERE RBV, and CRITEX EquipEx (Grant ANR-11-EQPX-0011). Data availability statement. The raw precipitation data from the AMMA-CATCH observation network are available via the AMMA-CATCH database portal (http://bd. amma-catch. org). Their use is regulated by the Creative Commons Attribution 4. 0 International License (CC-BY 4. 0). The Stochastorm model presented in the article is available upon request in the form of an R package.
, 2011; Quegan et al. , 2019). In this study, we hypothesize that remote sensing-based assessments of aboveground biomass (AGB) can decrease the dependency on expert knowledge when setting up CWTs and as such contribute to the automation of the land cover class mapping into PFTs for land surface models. The main rationale is that the aboveground biomass content of an ecosystem provides information on the fraction of tree PFTs of that ecosystem. In this context, the objective of this study are to (1) construct a framework of data assimilation in which biomass remote sensing products can be routinely used to update an existing or create a new CWT, (2) constrain a crosswalking table used to convert the ESA-CCI Global Land Cover map into a PFT map and (3) propagate the credible interval from using a CWT in the production of PFT maps to the simulation results of biomass and albedo maps derived from a land surface model.
, 2018. The continental AGB map was derived as follows: (1) stratification into wet and dry season areas in order to account for seasonal effects in the relationship between PAL-SAR backscatter and AGB, (2) the development of a statistical model relating the PALSAR backscatter to observed AGB, (3) Bayesian inversion of the direct model to obtain AGB and its credible interval for pixels where no observations are available and (4) masking out non-vegetated areas using the ESA-CCI Land Cover dataset (but see Sect. 2. 1. 1). The resulting AGB map was visually compared with existing AGB maps  (Saatchi et al. , 2011; Baccini et al. , 2012; Avitabile et al. , 2016)  and cross validated with AGB estimates obtained from field measurements and lidar datasets  (Naidoo et al. , 2015). Cross validation revealed a good accuracy of the dataset, with an RMSD between 8 and 17 t ha -1.
Knowledge gain from using the AGB map In the absence of an AGB map, previous efforts to build cross-walking tables  (Poulter et al. , 2015)  had to rely in part on expert knowledge. That generation of cross-walking tables can be considered as the best-available knowledge in the absence of AGB data or other information on the land surface cover. The method developed and demonstrated in this study mostly relies on data but comes with its own assumptions and statistical complexities. The key assumptions are that (1) previous cross-walking tables  (Poulter et al. , 2015)  are a reliable source to set the prior distribution for PFT cover, (2) the biomass map  (Bouvet et al. , 2018)  is a reliable source to set the prior distribution of the reference biomasses, and (3) the land cover classification contains homogeneous land cover types  (Defourny et al.
The latter uses deep learning techniques with two adaptive deep neural networks based on MobileNet-V2 and ResNet-50V2. The second method analyzes the videos and detects driver's activities in every frame to learn all features automatically. We leverage the advantage of the transfer learning technique to train the proposed networks on our training dataset. This solves the problem of limited training datasets, provides fast training time, and keeps the advantage of the deep neural networks. Experiments were conducted to test the effectiveness of our methods compared with other methods. Empirical results demonstrate that the proposed method using deep learning techniques can achieve a high accuracy of 97%. This study provides meaningful solutions in practice to prevent unfortunate automobile accidents caused by drowsiness.
A drowsiness detection method using CNN-based machine learning for android applications was introduced by Rateb et al. [10]. The method detected facial landmarks by a camera and passed through a CNN model to detect drowsy driving, with an average accuracy of 83. 33%. Zuopeng et al. [11]  introduced a driver fatigue detection method using a proposed EM-CNN to detect the states of the eyes and mouth from the region of interest images. The proposed algorithm, EM-CNN, showed an accuracy of 93. 62%. Biswal et al. [12]  proposed an IoT-based smart alert system for drowsy driver detection using Raspberry Pi3 and Pi camera modules to make a persistent recording of face landmarks for eye detection. This method was based on the idea of determining blinks through an eye aspect ratio (EAR) and Euclidean distance of the eye.
There were only two features of the eyes and mouth trained on these two CNN models for drowsiness detection without considering other physiological factors. Moreover, experiments were implemented on a small dataset of 2423 subjects, including 1192 people with closed eyes, and 1231 people with open eyes. In addition, Madhav et al. [14]  presented a deep learning approach to detect driver drowsiness. This method extracted the characteristics of the driver's eyes on each frame using Dlib's API and passed through a classification model to predict the state of drowsiness. Adam was used as the optimizer and the average accuracy reached 94%. Unfortunately, most of these studies focused on analyzing the mouth and eye regions to detect blinks and yawns without considering other regions of the head and face.
We make the following contributions: (1) the collection of a dataset of drowsiness and nondrowsiness from images and videos monitored through a camera for experiments; (2) the proposal of two methods for drowsiness detection and prediction using facial landmarks and deep learning. In the facial landmark-based method, we improve drowsiness detection by determining the appropriate thresholds of blinks and yawns for each driver. In the deep learning-based method, we propose the use of two adaptive deep neural networks with the transfer learning approach for drowsiness detection. We designed and perfected these networks developed on the advanced networks of MobileNet-V2 and ResNet-50V2, which are more efficient in terms of memory and complexity. The proposed networks are very good feature extractors, since they can capture and learn relevant features of drowsiness automatically; (3) the use of a transfer learning approach to well solve the problem of fast training, a small training dataset and accuracy improvement; (4) the comparison and discussion the performance, accuracy, and advantages of the proposed methods with other methods.
MobileNet-V2 Network MobileNet-V2  [20]  is a significant improvement over MobileNet-V1, enhancing image recognition capabilities on mobile devices including classification, object detection, and semantic segmentation. Figure  4  illustrates the MobileNet-V2 network architecture. Overall, MobileNet-V2 models are faster with the same accuracy across the entire latency spectrum, using 2 times fewer operations, 30% fewer parameters, and being 30-40% faster than MobileNet-V1. MobileNet-V2 has the advantages of being fast, lightweight, and having high accuracy, which makes it suitable for training with limited datasets. MobileNet-V2 is trained on datasets such as ImageNet, COCO, and VOC  [21] , and has been applied in object classification such as Fruit Image Classification  [22]  and Flower Classification  [23] , with accuracy up to 85.
ResNet Network ResNet  [24]  won the ImageNet Large Scale Visual Recognition Competition in 2015 for image classification, object localization, and object detection. The main challenge in training deep learning models is that the accuracy decreases with the depth level of the networks. ResNet converges very quickly and can be trained with hundreds or thousands of layers. At the same time, ResNet is easy to optimize and can achieve accuracy gains from greatly increased depth, producing better results  [24]. ResNet-50V2 was trained on ImageNet and CIFAR-10 datasets  [25]. The ResNet-50V2 network architecture is shown in Figure  5. The results show that ResNet-50V2 performs better and gives better results than ResNet50 and ResNet101 on the same ImageNet dataset. ResNet-50V2 applies the transfer learning technique with ImageNet pre-trained weights, which are used in object classification, such as COVID-19 and pneumonia detection from chest X-ray images and video classification with accuracy up to 94-98%.
Model Evaluation Metrics A confusion matrix is usually represented as a table to describe the performance of classification models. The values of TP (True Positive), FP (False Positive), TN (True Negative), FN (False Negative) are defined as in Table  1. When building a classification model, it is necessary to know the accuracy of that model. In general, it is a ratio of correctly predicted observation to the total observations  [26]. Accuracy helps us evaluate the prediction performance of a model on a dataset. Accuracy is calculated as Equation (3).
Proposed Methods In this work, we propose two efficient methods for doze alert systems. The first method is based on a combination of two blink and yawn features (EAR, LIP) using facial landmarks. However, adaptive thresholds of blinks and yawns were calculated appropriately for each driver without needing to pre-determine for all drivers. The second method uses advanced deep learning techniques with the transfer learning approach. Facial images are detected with the SSD-ResNet-10, and then forwarded to the proposed networks to detect drowsiness. We designed and perfected two adaptive deep neural networks developed on the advanced networks of MobileNet-V2 and ResNet-50V2 by making improvements in some layers to adapt the drowsiness detection. In addition, we applied the transfer learning approach to train the proposed networks in order to achieve faster learning, no requirement for large training datasets, and an improvement in classification accuracy.
Step 2: Detection of Facial Landmarks We detect and identify facial landmarks on faces in the images as presented in Section 2. 2. The facial landmarks considered in this work include the eyebrows, eyes, nose, mouth, and jaw (see Figure  8 ). In this step, we use 32 feature points based on the identification of facial landmarks in step 2 (Figure  9 ). We determine the coordinates of points on the mouth and eyes. We first detect faces in these images using the SSD network (Single Shot MultiBox Detector) with ResNet-10. Then, the facial images are normalized to the size of 224 × 224 to create the training dataset. Step 2: Feature Extraction and Training In order to extract features and train, the training dataset is passed through deep neural networks (DNNs). From the advantages of the neural networks described in Section 2.
3, we develop two adaptive networks for drowsiness detection, inspired by ResNet-50V2 and MobileNet-v2, by making improvements in some layers of these networks to adapt the drowsiness detection. We construct an adaptive network by adding layers such as Flatten, Relu (Densen), Dropout, Dense (Sigmoid) and by suppressing the AveragePooling2D layer of the MobileNet-v2 network, as shown in Figure  11. It aims at solving the problem of overfitting and helps the model to converge faster. Additionally, we propose another adaptive network by adding layers such as Flatten, two Relu (Densen), Dropout, Dense (Sigmoid) to the ResNet-50V2, as shown in Figure  12. Taking advantage of the transfer learning approach, the proposed networks are pretrained on datasets of Bing Search API, Kaggle, and RMFD. We use the pre-trained weights and re-train them on our training dataset to fine-tune the parameters of these networks.
Phase 2: Testing After training, the input data for testing are images extracted from videos tracking from a camera to detect drowsiness by applying the proposed network models from training phase. The results of this phase are detailed in the below section. It is not safe to make field experiments for driver's drowsiness on real roads. Therefore, in order prevent any risk for drivers, we generate an experimental dataset consisting of drowsy and non-drowsy faces extracted from datasets of Bing Search API, Kaggle, RMFD, and iStock by selecting images and videos recorded from cameras that are related to the driver's drowsy states. The main advantages of this work are its efficiency, low cost, safety, and the ease of data collection. In these images and videos, driving environments are relatively similar to actual road experiments.
The dataset contains 16,577 images of faces with 2659 images of drowsy states, 3789 images of non-drowsy states, and 10,129 images of both states. In particular, for method 2, the dataset of 6448 images is divided at the ratio of 80% (5158) for training and 20% (1290) for testing. The dataset directly affects the accuracy of drowsiness detection and prediction. The number and size of the datasets are described in Table  2.
for drowsiness prediction. Experiments were conducted on a large dataset to test the effectiveness of the proposed method in accuracy and reliability. Therefore, our method achieves the high accuracy of 97%; (c) leveraging the advantage of transfer learning, the proposed networks are trained on our training dataset. This solves the problem of small training datasets, training time, and keeps the advantage of the deep neural networks; (d) both of our methods can detect drowsiness accurately in the case of the driver wearing a mask (Table  9 ), while some methods do not consider or provide accuracy in this case. Besides, the proposed methods can recognize drowsiness well for drivers with very small eyes.
This method does not require the definition of input thresholds as in method 1, especially the eye-opening and yawning thresholds. Additionally, method 2 uses a combination of many typical signs of drowsiness to give accurate results, such as eye-opening, head movements, eyebrows, mouth, etc. Moreover, we leverage the advantage of transfer learning to pre-train the proposed networks on datasets of Bing Search API, Kaggle, and RMFD. We then use the pre-trained weights and re-train them on our training dataset to fine-tune the parameters of these networks. This helps to solve the problem of small training datasets and gives a fast training time. Experiments were conducted to test the efficacy of the proposed approaches. The results show that the proposed methods can achieve a high accuracy of 97% using deep.
63 kpc. All magnitudes are measured using AB system. Table  1. Summary of MUSE observations for all three clusters. Columns 1 to 3 indicate respectively the name of the cluster, its average redshift, and the ID of the ESO programme (PI: Kneib for all observations). For each pointing, we give the observation date in column 4, the total exposure time in column 5, the right ascension (R. A. ) and declination (Decl. ) of the center of the field of view in columns 6 and 7, and the FWHM of the seeing during the observations in column 8. 1  gives the details of the observations, including dates, pointing positions, ID, seeing conditions, and total exposure time for each dataset. Observations were taken using MUSE WFM-NOAO-N mode, in good seeing conditions with full width at half maximum (FWHM) of ≈ 0.
Cluster The data were reduced with version 1. 6. 4 of the standard MUSE pipeline  (Weilbacher et al. 2012 (Weilbacher et al. , 2014)). We use a set of standard calibration exposures taken daily to produce bias, arcs and flat field master calibration files. Dark current is neglected due to its very low value with MUSE (≈ 1 electron/h  Bacon et al. 2015). We first subtract the master bias exposures from each dataset, and perform an illumination correction using in combination the master flat field, the twilight sky exposures taken at the beginning of the night, and the illumination calibration taken soon before/after the science observations. We carry out wavelength, geometrical and astrometric calibrations in order to assign the World Coordinate System (WCS) right ascension and declination, and the wavelength to each pixel of the datacube.
The flux calibration is carried out using standard star observations taken at the beginning of the observing night. For each pointing, the three individual exposures are then combined into a full datacube using a single interpolation step. We apply the Z A P ( ;  Soto et al. 2016 ) software version 1. 0, which uses a principal components analysis to analyse objects-free regions in the datacube and subtract systematics due to sky subtraction residuals. To create the objects mask, we use the segmentation map obtained by applying the S software  (Bertin & Arnouts 1996)  on a white-light image collapsing the datacube along its wavelength axis. The wavelength range of the final datacube stretches from 4750 Å to 9350 Å in steps of 1. 25 Å, and the spaxel size is 0. 2.
In order to maximize the number of extracted spectra, we carry out a blind search in the datacube using 6. This software is part of the MUSE P D A F ( ;  Bacon et al. 2016). It builds a new datacube, the narrow-band datacube, within which each wavelength plane is the mean of the 5 closest wavelength planes in the science datacube. then uses S to extract a catalogue of sources at each wavelength in the narrow-band datacube. The latter are finally merged and sorted, providing a continuum and a single line emission catalogues. Finally, all catalogues are merged to provide a master catalogue, which is then displayed on the high resolution image so that the user can determine whether and S detections are matching the same source. This results in a set of spectra that we then analyze to measure the associated redshifts.
SUMMARY AND CONCLUSIONS In this paper, we present new strong-lensing mass models for three galaxy clusters, MS 0451, MACS J2129, and RX J2129, which include new VLT/MUSE observations. We combine the MUSE datacubes with high resolution imaging from HST available for each cluster to maximize the number of extracted spectra. We measure the redshift of each source with a dedicated software, -  (Rexroth et al. 2017) , allowing for a wavelet-based filtration of the spectra. Our conclusions are as follows: • We measure 158, 171, and 189 secured or likely spectroscopic redshifts in the RX J2129, MS 0451, and MACS J2129 MUSE datacubes respectively. For MS 0451, we identify 2 new systems of multiple images located in the North of the cluster core, the least constrained region, confirm the redshift of System A measured by  Borys et al.
Implementation The UCT+DoE framework described in this paper has been implemented in Python3, with a modular design and a relatively few number of required parameters. Our implementation heavily relies on the NetworkX library for all graph-related algorithms, and Numpy whenever applicable. For computing the KL-UCB score, our implementation uses the scipy. optimize. brentq solver and for updating the linear model associated with the internal model Hamiltonian, and estimating the covariance change, we use the Recursive Least Squares algorithm rank-Greville. 47 The code is available on GitHub, along with a rich documentation and examples. The MCTS RS module was designed as a versatile tool, with numerous flags/options for addressing most cases. For example, our implementation can easily be turned into a purely pseudorandom sampler, or even using a pre-exploration step on a pseudorandom sampling, leading to a typical variance-based active learning algorithm.
A table containing the annotation of TFBS in each enhancer is available in Supplementary Table  2. Among all enhancers, 489 (32. 7%) did not harbor any significant TFBS, including 411 (37. 5%) husk-specific enhancers, 32 (18. 3%) V2-IST-specific enhancers and 46 (20. 6%) shared enhancers. Using a resampling approach (see section 4), we tested for TFBS enrichment in enhancers of each tissue. We found that 60. 6% of the V2-IST-specific enhancers, 60. 6% of the husk-specific and 66. 8% of the shared enhancers were significantly enriched for TFBSs compared to randomized sequences. On average, an enhancer contained 11. 4 TFBSs (ranging from 0 to 255), which covered on average a total of 34. 3 bp (ranging from 0 to 442 bp) or 2. 5% of the enhancer sequence length. Because the JASPAR TFBS motif database used for this annotation is mostly based on ChIP-seq and DAP-seq data generated in Arabidopsis thaliana, we wanted to check whether the TFBSs we predicted in maize were supported by ChIP-seq and DAP-seq data in maize.
, 2016). All enhancers (i. e. , these found in husk or in V2-IST and that contained at least one annotated TFBS) were used. Because about 25% of enhancers are indicated to be downstream of their target genes  (Ricci et al. , 2019) , we included all genes independently of their genomic orientation. To build tissue-specific regulatory networks, we combined this prior with gene co-expression data  (Glass et al. , 2013; Kuijjer et al. , 2019). We had access to mRNA-seq data from husk and V2-IST (6 replicates each) from  Oka et al. (2017). To obtain high confidence tissue-specific networks  (Kuijjer et al. , 2019) , we enriched this dataset with mRNA-seq data that we generated from 11 tissues in triplicates (see section 4, Supplementary Figure  1  and Supplementary Table  1 ). In total, our merged dataset included 45 samples and a total of 46,430 genes.
We retained genes expressed in at least 3 samples in at least 1 tissue (see section 4), corresponding to a total of 36,041 genes. Read counts were then normalized and corrected for the singleend/paired-end mRNA-seq data type (see section 4). As shown by correlation analyses (Supplementary Figure  2 ), replicates correlate more between themselves (average biological replicate Pearson correlation coefficient R = 0. 95) than with any other samples (average inter-tissue Pearson correlation coefficient R = 0. 83). Moreover, expression levels from the V2 inner immature tissues from both datasets (V2-IST and V2_L-I) are strongly correlated (average pairwise Pearson correlation coefficient R = 0. 93). The principal component analysis of normalized RNAseq data (PCA, Supplementary Figure  3 ) show that biological replicates cluster together, and that the data from both datasets are highly comparable.