dataesr/scientific-paragraphs-categorization huggingface dataset:
For any diagnostic test, changes in prevalence are directly correlated with changes in PPV and inversely to changes in NPV, so when prevalence is low, the PPV will also be low,  10  even if both sensitivity and specificity are high. 13 It is important to be cognizant of the prevalence of the outcome to be determined. The low prevalence identified in this database for clinical outcomes emphasizes the importance of the NPV of nonclinical testing to ensure patient safety. 10 nother observation from the database related to the impact of false positives. Animal study designs are biased toward these type 1 errors (false positives) since high doses in animal studies aim to achieve a maximum tolerate dose, an exposure saturation, a maximal feasible dose, or a mean exposure margin of 50 times the clinical exposure (ICHM3(R2)  14  ).
It was also noted that animal models do not predict more subjective constitutional signs such as headache, fatigue, or anxiety. Animal models would not be expected to predict such clinical signs,  10, 15  but this still leads to false-negative results. Finally, it was concluded from the database results that the value of testing in two species goes beyond just expanding the breadth of target organ identification. In summary, while nonclinical studies can demonstrate value in the PPV for certain species and organ categories, the NPV was the stronger predictive performance measure across test species and target organs indicating that an absence of toxicity in animal studies strongly predicts a similar outcome in the clinic. 10 These results support the current regulatory paradigm of animal testing in ensuring safe entry to clinical trials and provide context for emerging alternative models.
The EFPIA Preclinical Development Expert Group collected case studies from both its membership and the literature to explore the value of the dog in drug development decision-making and clinical monitoring practices to protect the safety of trial subjects. The project was not intended to be fully comprehensive, but instead sought to collect representative cases illustrating the value of the dog in drug development decisionmaking, rather than an overall industry perspective for when the dog did or did not provide value. Preclinical Development Expert Group members submitted 19 cases for consideration. Of these, 18 were based on toxicology data and 1 on safety pharmacology data. These cases encompassed 9 therapeutic areas, and all cases were for small molecules. Study durations ranged from 2 to 52 weeks, but the majority of the examples were based on studies of 4 weeks' duration or less.
Data were collected within a survey devised by an international working group (consisting of representatives from pharmaceutical and biotechnology companies, CROs, consultancies, academia, and regulatory bodies). Questions focused on current practices with regard to species use within regulatory toxicology studies, including details of the studies conducted within the package and the toxicities observed (target organ level only) in each study. Data were received for 172 molecules (from 18 different organizations) consisting of 92 small molecules, 46 mAbs, 15 recombinant proteins, 13 synthetic peptides, and 6 antibody-drug conjugates (ADCs). For molecules following ICHM3 and/or ICHS9 guidelines (all 92 small molecules and 3 synthetic peptides), toxicology studies were generally performed in two species, as expected.
A key decision for progression of longer term chronic dosing studies in one or two species revolves around the "similarity" of toxicities between the species. A review of target organ toxicities identified in the IND-enabling toxicity study package was performed to determine how often toxicities were similar or different in the two species. For the 115 molecules within the database that used two species for INDenabling toxicity studies (75 small molecules, 13 mAbs, 11 recombinant proteins, 12 synthetic peptides, and 4 ADCs), toxicities were similar in both species for 32%, 85%, 36%, 42%, and 25% per molecule type, respectively. Therefore, there may be opportunities for more or different molecules currently following ICHS6 guidelines, as well as molecules following other guidelines, to reduce to one species for longer term chronic dosing studies in the future.
Human action Recognition has been extensively addressed by deep learning. However, the problem is still open and many deep learning architectures show some limits, such as extracting redundant spatio-temporal informations, using hand-crafted features, and instability of proposed networks on different datasets. In this paper, we present a general method of deep learning for the human action recognition. This model fits on any type of database and we apply it on CAD-120 which is a complex dataset. Our model thus clearly improves in two aspects. The first aspect is on the redundant informations and the second one is the generality and the multi-functionality application of our deep architecture. Our model uses only raw data for human action recognition and the approach achieves state-of-the-art action classification performance.
To recognize actions, there are some methods called traditional methods  (Laptev, 2005)  that consist in three parts: feature extraction, feature coding to generate video-level feature descriptor and the descriptor classification. The main features for action recognition using hand-crafted features are histogram of oriented gradients (HOG), histograms of optical flow (HOF), motion boundary histogram (MBH). Hand-crafted features have achieved great success in the task of action recognition. However, over the last few years, deep convolutional neural networks (CNN) has become the most popular method and achieved the state-of-the-art performance on several datasets. The CNNs offer us the possibility to extract the features instead of the classical method commonly used in computer vision problems.
In contrast to image classification, there are more aspects to be consider when using video data. Firstly, the video-based CNN weights must be trained on video dataset from scratch while to image-based CNN can profit from transfer learning. Secondly the semantic understanding becomes more complex for example when the camera moves or when the background changes in time. Thirdly, the network takes more time to train depending on the architecture. The work in  (Wang et al. , 2014)  shows good results on the CAD-120 dataset but the model is not extensible on another dataset where they get relatively low score. Still in the work  (Zolfaghari et al. , 2017) , the authors use many inputs for their models but these inputs are sometimes using hand-crafted features. Theses methods can have temporal informations but have an impact on the overall performance.
In practice, due to the difficulty in data collection and annotation, publicly available action recognition datasets (e. g. UCF101  (Soomro et al. , 2012) , HMDB51  (Kuehne et al. , 2011) ) remain limited, in both size and diversity. To tackle the above problems, we study three problems: • How to design an effective and efficient videolevel framework for learning video representation that is able to capture long-range temporal structure without using, in addition, hand-crafted features; • How to learn a model given limited training samples; • How to generalize a model to any databases. Our proposition consists in a new deep architecture for human actions recognition by video analysis, precisely with RGB images. To achieve this task, it is important to have both spatial and temporal informations.
Thirdly, features from each clique are concatenate to form a final video-level feature descriptor. Finally, we use full connection layers for action classification. The main contributions in this study are summarized as follows: (i) The uniform temporal sampling method preserves enough video information for action recognition. Furthermore, the network does not need handcrafted features dataset during the training process just raw data for each clique. Then we obtain significant improvement on CAD-120 database, which only contains 120 video clips. (ii) The proposed method allows an arbitrary number of cliques so any number of features to be aggregated into video-level feature descriptor. (iii) Our approach can be adapt to a large dataset and can be generalized to apply on multi-channel video frames.
More recently, impressive results have been obtained using convolution networks. Specifically, CNN 3D  (Rahmani et al. , 2018; Zhou et al. , 2014)  was used to extract spatiotemporal features from raw sequence data for action recognition. Through their hierarchical representation of entities, deep networks learn to capture localized features as well as temporal information for 3D CNN as well as context index and can exploit high-level information from large-scale video datasets. Other work also focused on hand-crafted features and combine with 3D CNNs  (Hou et al. , 2018)  to expect for increasing classification accuracy. With these impressive results of deep learning, our work is naturally in this direction. Recent research showed that most of these approaches are not only computationally expensive, but they also fail on capturing context, highlevel information, spatial, temporal, and interactions information.
Our approach We linearly segment each video  (Wang et al. , 2016)  to avoid repeated information so as to improve classification during the learning phase. The number of cliques is chosen according to the dataset which is important for the recognition of human action to maintain data consistently. The choice of number can also be used to increase the size of data, in place of using overlapping windows. We will demonstrate an example of cliques in section 4 on the CAD-120 dataset. The last fully connected layer is important because it is also chosen according to the extracted characteristics. More we want to have relevant information, more the number of cliques is needed, and more the fully connected layer is raised. Our work shows that with a small dataset it is empirically sufficient to choose a number of clique between 2 to 4.
EXPERIMENTAL RESULTS Following  (Wang et al. , 2014) , we have conducted three types of experiments using the CAD-120 activity dataset  (Koppula et al. , 2013). The CAD-120 dataset contains 120 RGB-D activity sequences of ten categories. Some samples can be saw in Figure  6. This dataset is frequently used in 3D human activity recognition. These activities were done by four different subjects, and each activity was repeated three times by the same actor. The insufficiency of RGB-D data in human activities and the large variance in object appearance, human pose, and viewpoint are common challenges in human action recognition. Most papers on the human actions recognition using CAD-120 work with depth images  (Sung et al. , 2012a; Wang et al. , 2014) , but in our case we will just use the RGB images to face with this challenge.
Architecture implementation details For the sake of reproducibility, we give here all the details of our deep architecture. We will not use the depth images so we do not use the pose estimation for the recognition of human action. Increasing the data is a solution we used to fill the information gap that the dataset offers us and to remedy the non-use of the depth images. We do a data augmentation with well-known methods  (Simonyan and Zisserman, 2014; Wang et al. , 2016)  of image processing like rotation, translation etc. as shown in Figure  7  In the first experiment, the number of decomposed video segments (i. e. actions) is M = 4, and the length of the maximum number of input frames of each segment is m = 9. We scale the size of the input frame to w = 80 and h = 60 in our experiments and for each clique the number of the first 3D convolutional kernels is 7 with a kernel size of 9 × 7 × 3, where each number represents the width, height and temporal length.
Several samples frames and depth maps are presented from the same class. network because the length of our video is quite short. If we continue on this logic we choose a temporal length a little above the limit necessary for a recognition of human action by deep learning. This choice m = 9 (Simonyan and Zisserman, 2014) is due to our dataset which contains different objects in the same label. We have a kernel 1 × 6 × 4 in the last layer of 3D CNN and it behaves like a 2D CNN because we need at this level more spatial information than temporal ones.
Fine-tuning the CNN on the target dataset The architecture is based on the AlexNet deep model  (Wang et al. , 2014; Krizhevsky A, 2012). Generally, a CNN configuration contains many parameters. If the model is trained from scratch on video dataset, it is easy to have an over-fitting phenomenon and it needs several weeks to train depending on the architecture  (Yu et al. , 2017). We introduce a pretraining scheme to optimize our model. It means that we train our model in a first step with the augmented data, and we save the weights. We call these weights the latent variables  (Niebles et al. , 2010). Then, we load the latent variables to initialize our deep architecture by changing the objective function and the hyperparameters and train with the small set of data.
Results Regarding experimentation, in a first test we trained our network and in a second test we make a fine tuning and transfer learning on our network. We apply these methods because the weights and the bias are initialized randomly and we apply these techniques for a rather fast convergence. We studied the evolution of our network by learning firstly on three labels and then four labels. We can notice how our model scores well on three labels and even on four labels,   we can see it in Table  1 , Table  2 , and Table  3. Even if mixing the order of the labels we always obtain excellent results. If we train the network on all the labels of our database we notice in the Table  4  that the overall precision decreases but remains always better than the best state-of-the-art methods. In the Table  4  we also see that we have a higher result by using the methods of fine tuning and transfer learning.
Comparison with state of art On this dataset, we adopt four state-of-the-art methods for comparison  (Sung et al. , 2012b; Koppula et al. , 2013; Xia and Aggarwal, 2013; Ji et al. , 2013). As show in Table  5  our approach obtains the average accuracy of 86. 4%, distinctly superior than results generated by the competing methods. With fine tuning, our method is even higher. In  Method Accuracy  (Scovanner et al. , 2007)  59. 8%  (Koppula et al. , 2013)  80. 2%  (Wu et al. , 2013)  62. 1%  (Ji et al. , 2013)  64. 3% Ours 86. 4% Ours + Fine-tuning 93. 6% engineering  (Xia and Aggarwal, 2013) , and the deep architecture of convolutional neural networks  (Wang et al. , 2014) : in almost all cases we obtain the best results even without fine tuning. Table  6  summarize the average accuracy on CAD 120 dataset against the considered best four existing methods.
It is important to highlight that these methods use also information taken from depth images. It appears that our model outperforms previous works although we do not use depth images. This is a great advantage because in practical applications, depth images are almost never available. Remark that for all methods we train the models using the same data annotation and we use the same evaluation protocol. The main contribution of our model is the fact it works directly on the raw data, i. e on the RGB images. Our experiments are executed on a server with two multi-core processors, 32GB RAM and two Nvidia GTX 1080 Ti. For model learning, we set the learning rate at 0. 001 value, for applying the stochastic gradient descent algorithm. The training times on CAD-120 dataset takes 5 hours for all videos including data augmentation.
CONCLUSIONS We presented an extensible deep architecture ables to model long-term temporal structure and practice it on a complex dataset for human action recognition task. The proposed architecture is able to outperform existing methods, even with less data, in particular not using depth images and it is able to extract information of the sub-actions related to a main action. Our model can still be improved and for that, we anticipate as future work to combine the descriptors extracted with our approach in a more complex architecture to extract the semantic of an action.
Uppaal is the second best, but does not solve as many benchmarks as our algorithm. Our enumerative algorithm quickly fails on these benchmarks, often running out of memory. On asynchronous computation benchmarks, our enumerative algorithm performs remarkably well, beating all other algorithms. We ran our tools on the CSMA/CD benchmarks (with 3 to 12 processes); Uppaal performs the best but our enumerative algorithm is slightly behind. The symbolic algorithm does not scale, while PAT fails to terminate in all cases. The tool used for the symbolic algorithm is open source and can be found at https://github. com/osankur/symrob along with all the benchmarks.
g. median, percentiles and credible intervals) can also be easily performed. However, this same full joint posterior distribution can rarely be computed analytically and sampling algorithms -e. g. Markov Chain Monte Carlo (MCMC) methodsare often needed. Although computationally heavy, MCMC algorithms are nowadays hardly prohibitive. A wide literature exists about the application of Bayesian techniques to various domains. In mechanics it has been used as a way of regularizing complex source identification problems both acoustical  [25]  and vibratory  [26, 27]  and it is now a widely accepted approach to perform model updating  [28, 29]  and structural health monitoring  [30]. In this paper, it is instead used to "retro-propagate" the dispersion of the processed FRF database to the terms of the decomposition model.
Consequently, any FRF sample issued from the identified decomposition model can be considered as a possible outcome of the process whose samples are in the input database. This also means that the type of uncertainties covered by the proposed approach is completely dependent on the database. For instance, if the FRFs in the database are measured on nominally identical vehicles, only the production uncertainty and measurement random error can be covered by the model. On the other hand, if the FRFs are measured on vehicles sharing the same model but different options, then the model will also cover the manufacturing variability. Therefore, resorting to the Bayesian context to deal with the FRFs uncertainty yields two main results: 1. The identified independent components not only help understanding the dynamic behaviour of the system, but through their uncertainty, they also provide some information on how the production protocol influences this behaviour.
A. and N. G. have been partially supported by SPP 2026 (Geometry at infinity), funded by the DFG. B. A. has also been partially supported by the DFG SFB 1085 (Higher Invariants). V. N. has been partially supported by ANR-14-CE25-0012-01. AMS Subject classification (2010): 58J32 (primary), 35J57, 58J32, 35B65. (ii) Si, en plus des conditions de (i), P satisfait une inégalité de Hardy-Poincaré, alors le problème au bord associé à P est également bien posé. En principe, la classe des domaines à laquelle nos résultats s'appliquent est assez large, mais pour des raisons d'espace et afin de réduire au minimum les détails techniques, nous considérons dans cette note principalement les exemples de domaines cuspidaux et wedge. L'ensemble des points singuliers de ces domaines est une sous-variété lisse compacte.
Online social networks play a crucial role in spreading information at a very large scale. Modeling information propagation on social networks has been attracting a lot of attention from researchers. However, none of the data sets used in past works are made available to the research community, while they would be very useful for comparative studies. In this paper, we detail a collection of tweets composed of five data sets for a total of 18 million tweets that we release, and which is designed to evaluate methods on modeling the information spread, in the case of general information and brands marketing information. In addition to tweet IDs and a script to retrieve the whole tweet in JSON from the Twitter API, we release the values of the 29 extracted features for these data sets. These features consist of user based, content based and temporal based features.
Introduction Predicting information diffusion is an important challenge for many tasks from marketing  [6]  to information check-worthiness  [11]. It has attracted a lot of attention in social networks research community  [1, 5, 12, 18]  and which is not yet solved. However, in past studies, the data sets used in the evaluation part of the studies are not made available while it is well acknowledge that benchmark collections are very useful for comparative studies. In this paper, we introduce the TwitCID collection which consist of a set of five data sets of tweets. In total, there are more than 18 million of tweets in about "general" information (two data sets) and "brand marketing" information (three data sets), of which million tweets are forwarded hundreds to dozen thousand times. In addition, we include the description of features we extracted from these tweets and release their values for the five data sets.
These features cover 29 characteristics related to the users who created the tweets, the content of the tweets and the time the tweets were created. Finally, we provide effectiveness results of two information diffusion prediction models defined respectively by Suh et al. [19]  and Hoang et al. [7]. These models reach up to 80% accuracy and could serve as a strong baselines although there is still room for improving these results. Other researchers can use our released material (data sets, extracted features, results of strong predictive models) to evaluate their method of predicting information diffusion. The TwitCID collection and the extracted feature values are provided at http://doi. org/10. 5281/zenodo. 3246705. The rest of the paper is organized as follows: Sect. 2 presents the related work.
Related Work While Twitter-based data sets are available for information extraction  [2, 14] , name entity recognition evaluation  [15, 17] , for sentiment analysis evaluation  [13, 16, 20]  and information cascade modeling  [3, 22] , there is no reference data sets to evaluate predicting information diffusion despite the growing interest in this domain  [4, 5, 7, 9, 19, 21]. In the area of predicting the information diffusion on social networks, various studies have been carried on and widely referenced. Guille et al. [5]  developed a model to predict the temporal dynamics of information spreading on social networks based on machine learning algorithms and the inference of time dependent diffusion probabilities from a multidimensional individual behaviors analysis. They evaluated their method on a large data set which includes 467 million Twitter posts.
Also using machine learning approach evaluated on a tweet data set, Varshney et al. [21]  proposed a model to predict the information diffusion probabilities on social networks. They used a Bayesian network considering user interests and content similarity models. Dong et al. [4]  used two data sets of around one million of tweets posted by Sina-Weibo to evaluate their approach on predicting the propagation of natural disaster information while Kafeza et al. [9] used 13,000 tweets related to the Malaysia airline flight 370 disappearance (#MH370) to evaluate their model on predicting the information spread patterns. However, none of these data sets have been made available to the research community. Suh et al. [19]  introduced several features that may affect the diffusion of a given message on social networks.