dataesr/scientific-paragraphs-categorization huggingface dataset:
Live imaging and SIM super-resolution microscopy Spinning disk movies were acquired using a Plan-APO 40×/1. 25NA objective on a Leica DMI6000B microscope enclosed in a thermostatic chamber (Life Imaging Service) equipped with a CoolSnap HQ2/CCD camera coupled to a Sutter filter wheel (Roper Scientific) and a Yokogawa CSU-X1-M1 spinning disk. Metamorph Software (Universal Imaging) was used to collect data. For SIM super-resolution microscopy of aMTOCs, image acquisition was performed in 3D SIM mode, with a N-SIM Nikon microscope (Nikon Imaging Centre @ Institut Curie-CNRS) before image reconstruction using the NISElements software  [48]. The system is equipped with an APO TIRF 100× 1. 49NA Oil Immersion, a laser illumination (488 nm at 200 mW and 561 nm at 100 mW), and an EMCCD DU-897 Andor camera.
FRAP analysis Images were acquired using a Plan-APO 60×/1. 4NA objective on a Ti Nikon microscope enclosed in a thermostatic chamber (Life Imaging Service) equipped with a Flash4. 0 V2 CMOS camera (Hamamatsu) coupled to a Yokogawa CSU-X1 spinning disk. Metamorph Software (Universal Imaging) was used to collect data. All oocytes expressed SiR-Tubulin (from Spirochrome reference SC002, used at 0. 1 lM). For all oocytes, an identical region of interest (diameter of 5 lm) was bleached at spindle poles. Images were acquired every 5 s for 125 s. One image was taken before bleaching. The SiR-Tubulin fluorescence intensity quantification was performed using the Metamorph software (Universal Imaging). Normalization of the measured fluorescence intensities was performed using the Microsoft Excel software.
Quantifications Metamorph (Universal Imaging), Imaris (Oxford Instruments), and Fiji (NIH) software were used to analyze and process data. A homemade plug-in was developed for ImageJ/Fiji software to analyze aMTOCs position within the spindle. This 3D_Spindle_Analysis plug-in is available at https://github. c om/pmailly/3D_Spindle_Analysis. Oocyte boundaries were first extracted with variance filter and triangle method for thresholding from the bright-field channel and used to crop the image in the other two channels. Spindle (GFP channel) was first filtered using 3D Gaussian filter (radius = 2) to reduce noise and then thresholded using MaxEntropy method. aMTOCs (RFP channel) were first filtered using difference of Gaussians (GDSC libraries from Alex Herbert, University of Sussex) to increase spot-like signals, then thresholded using MaxEntropy method.
For each channel, 3D objects were segmented using the 3D ImageJ suite  [49]. The spindle poles positions were computed as the extremities of the larger diameter of the object (Feret diameter). For each aMTOCs, minimum distances to poles, and border distances to the spindle were computed. (vi) The spindle length, central spindle width, and spindle pole measurements were performed in 3D using Imaris software (see Figs  3  and EV3D ). The spindle poles positions were considered as being the extremities of the larger diameter of the spindle, and spindle length was measured as the distance between poles. (vii) The aMTOCs volume measurements were performed in 3D using Imaris software (see Fig  2D  and E ). The input data consist of SIM super-resolution microscopy acquisitions performed in 3D SIM mode, and the total volume of aMTOCs per oocyte was measured.
Statistical analysis Experiments were repeated at least three times, and a sample of sufficient size was used. The statistical analysis was performed using GraphPad Prism version 7. 00 for MacOS, GraphPad Software, La Jolla, California, USA, www. graphpad. com. For comparisons between two groups, the normality of the variables was checked (D'agostino-Pearson normality test) and parametric Student's t-tests (with Welch correction when indicated) or nonparametric comparison tests were performed with a confidence interval of 95%. For chromosome alignment experiments, repartitions were analyzed for statistical significance using Fisher's test used with a confidence interval of 95%. All error bars are expressed as standard deviation (SD). Values of P < 0. 05 were considered significant. In all figures, * corresponds to a P-value < 0.
Acknowledgements We thank  AstraZeneca (USA)  for the gift of  AZ82 ,  Fanni Gergely  ( Cancer Research UK Cambridge Institute, UK ) for the gift of CW069, and Claire E. Walczak ( Indiana University, USA ) for the gift of plasmids. We thank  Renata Basto  ( Curie Institute, Paris, France ) for sharing the unpublished HSET antibody. We thank  Lucie Sengmanivong  for her precious help using the N-SIM Nikon microscope from the  Nikon Imaging Centre @ Institut Curie-CNRS , member of the France-BioImaging national research infrastructure for confocal microscopy. We thank  Christophe Klein  ( Centre de Recherche des Cordeliers-INSERM, Paris, France ) for his help with the FRAP analysis. We also thank  Hugh J. Clarke  ( McGill University, Montreal, Canada ) for critical reading of the manuscript, the members of the Verlhac/Terret team for helpful discussions, and  Anne Dumas  ( Collège de France, Paris, France ) for her help with MTA writing.
We thank the " Fondation Bettencourt Schueller ". This work was supported by grants from the  Fondation pour la Recherche Médicale  (FRM label  DEQ20150331758  to MHV), from the  Fondation ARC  ( PJA20131200412  to MET), and from  Inca  ( PLBIO 2016-270-TRAN ). This work has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by the  ANR , with the references:  ANR-14-CE11/DIVACEN/MHV ,  ANR-10-LABX-54   MEMO LIFE ,  ANR-11-IDEX-0001-02 PSL* Research University.
There are cases however where binary states are not sufficient. For instance, it is sometimes necessary to represent the level of degradation of a component, the quality of a signal, and so on. This kind of models can be easily represented with AltaRica 3. 0 -a high level modeling language dedicated to safety analyses  (Prosvirnova, Batteux, Brameret, Cherfi, Friedlhuber, Roussel, & Rauzy 2013). AltaRica 3. 0 is at the core of the OpenAltaRica project 1 which aim is to develop a complete set of assessment tools for the language, including among others compilers to Fault Trees  (Prosvirnova & Rauzy 2015)  and Markov Chains  (Brameret, Rauzy, & Roussel 2015) , stochastic and stepwise simulators  (Aupetit, Batteux, Rauzy, & Roussel 2015). In this article we study how the notion of prime implicants can be extended to finite domain calculus and how to encode it efficiently.
MOTIVATING EXAMPLE Consider a parametric block diagram use case (see Figure  1 ) with three parameters: • s the number of blocks in series; • p the number of parallel blocks; • q the level of recursivity (depth). These relatively simple but large safety models can be easily represented in AltaRica 3. 0 and handled simply and efficiently by means of the Fault Tree compilation tool chain. Note that without loosing the efficiency of the assessment, in AltaRica 3. 0, it is possible to represent multi-state blocks, e. g. consider the quality of data with the values ok, lost or erroneous, or the level of degradation with the values ok, degraded or failed. This use case is both representative of a class of industrial models and parametric to show the scalability of the approach. We shall use it throughout the article to illustrate the advances in the simplification of Fault Trees.
APPLICATION One of the possible applications of the finite domain calculus presented above is the simplification of Fault Trees automatically generated from AltaRica 3. 0 models. AltaRica 3. 0 is an event-based high level modeling language dedicated to Safety Analyses  (Prosvirnova, Batteux, Brameret, Cherfi, Friedlhuber, Roussel, & Rauzy 2013). Its semantics is based on Guarded Transition Systems  (Rauzy 2008).
CONCLUSION Boolean models are widely used for probabilistic safety analysis. There are cases however where binary states are not sufficient. For instance, it is sometimes of interest to represent the level of degradation of a component, the quality of signal, and so on. This kind of models can be easily represented with AltaRica 3. 0, a high level modeling language dedicated to safety analyses. AltaRica 3. 0 comes with several efficient assessment tools, amongst them a Fault Tree compiler. In this article we presented how the notion of prime implicants can be extended to finite domain calculus. We discussed how the finite domain calculus can be efficiently encoded using a variant of Binary Deci-sion Diagrams. We shown, using a parametric block diagram use case, how these results can be applied to simplify Fault Trees automatically generated from AltaRica 3.
To validate the effectiveness of our proposed method, we built a large image database by collecting images from various sources such as web crawling, the camera capturing and other standard public databases. On our test image set, we obtained an overall classification accuracy of 98. 4% and the processing speed is over 27 fps on a central processing unit (CPU)  (2. 90 GHz). The rest of this paper is organised as follows. Section 2 briefly reviews related works; Section 3 describes the proposed method; Section 4 introduces the image database; Section 5 presents experimental results; and Section 6 makes a conclusion.
Lee et al. [7] t r i e dt o categorise images into art, photograph and cartoon using a neural network model. Five standard MPEG-7 visual descriptors  [8] i n their work were employed for extracting features such as Colour Layout, Colour Structure, Homogeneous Texture, Region Shape and Edge Histogram, which are not only redundant but also time-consuming. Pourashraf et al. [9]  adopted an ensemble model for classifying images embedded in commercial real estate flyers into one of five genres: aerial photograph, map, inside building, outside the building and schematic drawing. However, the model was only evaluated with a small database and the processing speed was not reported. In recent years, deep neural networks, especially the convolutional neural network (CNN)  [10] [11] [12] [13] [14] [15]  has achieved a great success in image recognition tasks including image categorisation, object detection, scene text detection and recognition  [16].
The superiority of CNN is partly attributed to its ability of automatic feature exaction by learning from the large training dataset. However, the CNN suffers from the heavy computation in both training and testing, and so, is usually implemented using graphics PU (GPU) for parallel computation. This hinders its application in processing huge amount of images on the web. Our proposed method for fast genre classification of images uses both global visual features and local texture features which consume low computation complexity and is of moderate dimensionality. The local texture features, extracted from different types of image patches and represented in the BoW framework  [17, 18]  are shown to be effective in differentiating photographs versus non-photograph and scanned versus CPD. 3 Proposed method.
The number of non-zero pixels in I mask1 and I mask2 are calculated and denoted as N 1 and N 2. Finally, we define f 1 = N 2 /N 1. T o demonstrate the effectiveness of this measure visually, we randomly selected 3000 images from NSI, BDI and CPD categories in our database: 1000 samples per class, and calculated the normalised histogram of three types of images over f 1. From Fig. 2a , we can observe that BDIs which have more coherent and highly saturated regions tend to have higher scores than NSIs.
3a  to visualise the discriminability of this feature. Similarly, 100 images per class are selected randomly and grouped together as a small visualisation dataset. Considering the high dimensionality of this feature, we adopt a dimensionality reduction algorithm, the t-stochastic neighbor embedding (SNE) algorithm  [20] , to map the feature vectors from 180-dimensional (180D) to 2D. From Fig. 3a , we can see that most of the SPDs and CPDs points are clustered and form two very distinguishable curves on the reduced map. However, it is also worth noting that there are large overlaps between the BDIs and NSIs points, which means that the colour histogram feature alone cannot differentiate between BDIs and NSIs. Fortunately, features f 1 and f 2 calculated above complement very well. For the large overlaps, one reasonable explanation is that the BDIs samples contain certain small NSI patches, which we will discuss in Section 4.
Database To validate the effectiveness of the proposed method, we have built a large database of four types of images, i. e. NSI, BDI, CPD and SPD. Depending on the degree of difficulty of labelling images, we divide our database into two sets: the single-label (SL) and the double-label (DL). The first set consists of such images that are easily classified by their appearances and tagged with only one label. Roughly more than 90% images in our database belong to the SL. However, there are a small fraction of 'complex' samples such as photorealistic images produced by cutting edge computer graphics effects, images spliced or embedded by other different types of smaller ones, computer graphics that are displayed on liquid-crystal display monitors and then recaptured by a camera, and so on. With such confusing appearances, they cannot be classified clearly into one type.
Hence, for these images, we carefully selected two proper labels as their ground truth labels in order to describe them as accurately as possible. Given the complexity and variety of images on the web, the DL of 3693 images is a salutary supplement to the SL database and makes it more proper and scalable. Totally, we collected 55,185 images from various sources such as web crawling, manually camera capturing and other public databases including SUN397  [25]  and the multilingual hand written (HW) dataset  [26]. More details regarding the distribution of different types of images are listed in Table  1  and some samples are shown in Figs. 4  and 5. In addition, we also calculate the distributions of image square size for each type of images, as shown in Fig. 6.
Experimental setup In our experiments, we adopt the radial basis function (RBF) kernel SVM as our learning algorithm, and all the classification experiments were implemented with the library for SVM  [27]  package. Note that for the first-stage classification, though using a linear SVM can largely improve the classification speed, its accuracy is evidently lower than an RBF-kernel SVM. So, we use RBF-kernel SVM in both stages. Since the global feature vector in the first stage has low dimensionality, the speed of non-linear SVM is still acceptable. As for CNN models, we implemented all the models using the Pytorch  [28]  platform, which is a popular Python package and widely used by researchers in the field of deep learning in recent years. The maximum image size allowed by the system is 1000 × 1000 for processing speed.
Thus, we only present herein their ranges:  5, 20]  and N c2  [ [50, 200]. As for the two system parameters, we will give a detailed analysis in the following section. There are 5175 NSIs in our database coming from the public database SUN397, and most of the SPDs (6036) used here come from the multilingual HW dataset. T s [ [150, 230], T g [ [0. 2, 2. 5], N lp [ [100, 300], S lp [ [5, 30], N c1 [ [.
Performance of the proposed hierarchical classification method: As we know, deep learning models, especially deep CNNs have achieved a huge success in many computer vision tasks. To further validate the effectiveness of our classification method, we also compared our method with several most popular CNN models such as AlexNet  [11] , VGGNet  [12] , ResNet  [13] , DenseNet  [14]  and SqueezeNet  [15]  on both SL and DL datasets. We briefly introduce the key part of each network model and preserve their original structures provided in Pytorch by following the default configuration. All the models we used are pre-trained using the ImageNet  [29]  database, and we then use a transfer learning strategy to fine-tune the models (number of output nodes changed) on our training dataset. As mentioned above, there are five types of CNN models adopted in our comparison experiments.
The last one we chose is a small and energy-efficient deep neural network (DNN) named SqueezeNet  [15]. With fewer parameters, SqueezeNet can more easily fit into computer memory and can more easily be transmitted over a computer network. Table  3  shows the experimental results of different models on the SL dataset. We give the classification accuracy, speed, number of parameters and GPU memory usage for each classifier. Compared to direct classification (Table  1 ), our hierarchical algorithm with two fusion strategies can achieve a comparable accuracy but at much faster speed (comparable to the speed of global feature only). This is because most 'simple' samples have been confidently classified and filtered by the first-stage classifier using global features. As to the comparison with CNN models, it is shown that our proposed method yields comparable accuracy with some typical CNN models such as AlexNet, VGG-11, VGG-13, ResNet-18, ResNet-34 and two SqueezeNet models.
Performance on DL dataset: In testing the images in the DL dataset, when the classification decision is identical to one of the ground truth labels, it is considered as correct. In training, the label of the DL image is randomly selected from the label set. The results are listed in Table  4. In the setting 'SL + DL', the test performance of the proposed method is inferior to those of CNN models ResNet-152 and DenseNet-201 but is comparable with those of other CNN models. While in the setting 'DL→DL', the proposed method performs comparably well with the CNN models. The proposed   method has little loss of performance when training without DL images. In contrast, the loss of performance for CNN models from 'SL + DL' to 'SL→DL' is considerable. This is because deep neural networks largely rely on the training set to guarantee the generalisation performance.
5. 2. 4 Effects of parameters T c and w on classification performance: Fig. 7  shows the effects of two major hyperparameters in testing on the SL dataset. In particular, the parameter -T c controls the number of images sent into the second-stage classifier. When it increases, more images are selected for sending to the second-stage classifier, thus the classification accuracy increases but the speed slows down. As the weighting coefficient of the local feature classifier in the second fusion strategy w is influential to the accuracy of fused classification. It is seen that when w increases from a small value, the accuracy increases gradually and gets saturated at a larger value of w (from 0. 5 to 0. 95) because the local feature information has played a sufficient role.
B. Learning 9] [50] [51]  Each dataset contains thousands of images and each image has part location annotations. The parameters in the proposed model can be divided into three types. First is the parameter θ, θ in the conditional probability distribution learned by LMR-CNN. Then the weight parameters ω is learned by structured SVM. Finally, the K-means algorithm is used to learn the mean relative locations r. During training, we have access to training images with ground truth joint/body locations {I t , P t , M t } T t=1. T is the number of training images. P t denotes the ground truth positions in the image I t , and M t denotes the mixture number.
VI. EXPERIMENT RESULTS Here the experimental results are presented to evaluate the proposed method in articulated pose estimation. First, we introduce the datasets used for evaluating the proposed method. The Leeds Sport Dataset (LSP)  51  is widely used in pose estimation. This dataset contains 2000 images: 1000 images for training and 1000 images for the test. It consists of various human activities. This dataset is used for the full body pose estimation. The second dataset is Frames Labeled In Cinema (FLIC)  7  dataset that consists of 4000 training and 1000 test images. These images are obtained from popular Hollywood movies and labeled with 10 upper body joints. Moreover, the Inria Person dataset  17  is used as the negative training examples.
Evaluation metrics In order to compare with published results, we use a widely accepted PCP (Percentage of Correct Parts)  7, 8, 49, 53  to evaluate the performance of different state-of-art work on the LSP dataset. Here we adopt a stricter definition declaring that the distance between both of the predicted endpoints and the ground truth endpoints is within 50% of the length of corresponding ground truth limb. 49 n the FLIC dataset, Percentage of Detected Joints (PDJ) is used for comparing the performance of pose estimation with other methods. In the PDJ metrics, a predicted part position is considered detected if the distance between the predicted position and the ground truth position is within a certain fraction of the torso diameter. Then a curve of the PDJ is generated to measure the performance of human pose estimation by varying this fraction.
B. Diagnostic experiments for LMR-CNN Here, we analyze how the multi-resolution features influence the model performance on the LSP dataset. It contains three main factors: the number of resolutions for each local part, the center scale e and the offset o. As illustrated in Fig. 6 , the offset o and the center scale e are defined as the constants, and the model performance reaches a peak at 3 resolutions. It can be concluded that the PCP performance of the proposed model is not monotonously increasing with the growth of the resolution numbers. Then, Fig. 7  shows the PCP results with varying center scales e, FIG. 6. Comparison of the PCP performance for human pose estimation with different numbers of resolutions.
Performance comparisons of the graphical-based models To demonstrate the effectiveness of the proposed graphical-based LMR-CNN model, the experimental results are shown for comparing with other methods on the LSP dataset and the FLIC dataset. First, the comparison is presented in Table  I  by the strict PCP metric on the LSP dataset. The proposed method clearly outperforms all listed methods, especially achieving better estimation for limb parts. Take lower arms as an example, the PCP result of the proposed model is 5. 5% higher than the next best performing method. Comparing with the graphical-based model in Ref. 41, the proposed method outperforms it in five out of six joints, and average accuracy of limbs is 3. 1% higher. For the FLIC dataset, the performance of the proposed method is compared by the PDJ metric.
Due to the millions of parameters in LMR-CNN, the data augmentation is used in each dataset by rotating and horizontally flipping each positive example. The random examples from the positive training example that are used as a validation set for the LMR-CNN. In the LSP dataset, {40 * 40, 48 * 48, 56 * 56} pixels and {52 * 52, 60 * 60, 68 * 68} pixels are multi-scale of joint parts and limb parts. Then the joint parts and limb parts are resized to the center scale 48 * 48 pixels and 60 * 60 respectively. In the FLIC dataset, the multi-scale joint parts and limb parts are changed to {48 * 48, 56 * 56, 64 * 64} pixels and {60 * 60, 68 * 68, 76 * 76} pixels, and centered at 56 * 56 pixels and 68 * 68 pixels respectively. The LMR-CNN has been implemented within the Caffe 54 framework by the GTX1080Ti GPUs.
So far, industrial case study requirements and the baseline methodologies provided by the project partners have been collected and analyzed in the context of Work Package 1 (WP1). In addition, a detailed study of the state-of-the art has been performed and corresponding needs for innovation have been identified as part of Work Packages 2, 3 & 4 (WP2-4). In the upcoming phase of the project, we will perform gap analysis between the industrial needs and the baseline methodologies & tools. Moreover, we will suggest the main features of the MegaM@Rt2 framework as well as a related road map presenting how the different components will be further developed. Section 2 outlines the mission and the objectives of MegaM@Rt2. Section 3 discusses the main concepts of the development approach proposed by the project.