dataesr/scientific-paragraphs-categorization huggingface dataset:
[68]  in Powheg-Box-Res in principle also allows us to use OpenLoops  [139] [140] [141]  for the evaluation of the relevant amplitudes, but we have not made use of that feature. Note that, as discussed before, our V H MiNNLO PS generators include the R I and R II real contributions, first calculated in ref. [45] , while we neglect the V I and V II virtual amplitudes, which contribute below one percent. The calculation of D in eq. (2. 4), which is the central ingredient to include the NNLO QCD corrections through MiNNLO PS , involves the evaluation of several convolutions with the parton distribution functions (PDFs), which are performed through hoppet  [142]. Moreover, the collinear coefficient functions require the computation of polylogarithms, for which we employ the hplog package  [143].
Acknowledgments We are grateful to  Daniele Lombardi  for various fruitful discussions and to  Carlo Oleari  for some clarifications. We thank  Wojcek Bizon  for his support with the original results of ref. [126] and for providing the relevant event samples as well as  Francesco Tramontano  for assistance in the use of the VHNNLO code. GZ thanks  Gavin Salam  for discussions on the comparison of different jet-algorithms. We have used the Max Planck Computing and Data Facility (MPCDF) in Garching to carry out part of the simulations related to this work, as well as the  CNRS/IN2P3 Computing Center in Lyon. Silvia Zanoli  is supported by.
Additional information (e. g. location, time, etc. ) to strengthen the classification of activities by including it as additional streams. The proposed architecture is capable of classifying in a robust way different activities of a group as well to deal with the one-class problems. Moreover, the use of a simple descriptor that transforms a sequence of color images into a sequence of two-image streams can reduce the curse of dimensionality using a deep learning approach. The generic deep learning architecture has been evaluated with different datasets outperforming the state-of-the-art approaches providing an efficient architecture for single and multi-class classification problems.
In  [14]  the authors propose a vision-based solution to identify Activities of Daily Living (ADL), through skeletal data captured with an RGB-D camera. After the decomposition of a skeletal sequence into short time segments, the activities are classified through a two-layer network called Long-Short Term Memory Network (LSTM), which allows analyzing the sequence at different levels of temporal granularity. The proposal is evaluated in the dataset Watch-n-Patch  [56] , in which there are examples 11 different daily activities of people such as: bringing things from the fridge, turning things to the fridge, spilling liquid, drinking a liquid, leaving the kitchen, bringing things from the oven, putting things into the microwave, preparing food, filling a kettle, connecting a kettle to the electrical outlet, moving the kettle.
The proposed model involves contextual learning using a bottom-up approach, learning from individual human actions to group-level activities, and learning from scene information. Taking into account that it can identify group and individual activities would be considered a progress in this field of research, since it is one of the first works that can identify behaviors according to the number of people in the scene, clarifying that it is not yet reached the level of identification of behaviors in crowds with the same application  [51] ,  [52]. Finally, it is important to analyse the one-class classification approaches. They become particularly relevant when an adequate dataset is not available for all the classes that the classification models have to learn. There exists many situations in which it is necessary to identify one class among others but only having examples of this class.
In particular, it is important to highlight the work of Chalapathy et al. [9]  that proposed a model of a one-class neural network (OC-NN) to detect anomalies in complex datasets. Among the work specifically addressing activity recognition in groups or crowds, different neural network models have been used to solve the problem. For example, the use of Convolutional Neural Networks (CNN) is shown in the work of Li et al. [27]  where a new colorization of images including other information as optical flow is used as input of CNNs to detect objects and their anomalies. Also, in  [48] , Su et al. integrate the one-class Support Vector Machine into a CNN proposing the Deep One-Class (DOC) model. One widely used model has been autoencoders (AE) where they attempt to extract features from images to form a new space in which to decide the existence of normal activities.
III. PROPOSED GENERIC ARCHITECTURE Currently, the methods of Deep Learning are achieving great results that are revolutionizing the way to address the problems of Artificial Vision. These techniques can solve problems that previously could not be solved, especially in image recognition. End-to-end (E2E) architectures have various advantages, such as the simplicity of coding and the training process. Also, E2E networks internally learn features that describe the data in a way to produce an expected result. Nevertheless, they are highly dependent on the dataset diversity to be generally applicable. Also, a large number of examples is required in most cases to train a large number of parameters in the networks  [5] ,  [47]. Some human behaviour datasets are quite small, or not fully labelled.
That hardens the training of E2E deep learning architectures. Some proposals have overcome this problem using transfer learning, such as in  [70]  where pre-trained weights of a VGG16 are used. However, this approach still depends on the previously trained parameters. In order to cope with these problems, a different dual-stage solution is presented in this paper. It uses pre-defined features that convert the data to a different known space and train a network with those features. Using descriptors reduce the space of solutions, helping to learn tasks with fewer data. In this way, we detach the motion description (D-ADV in this case) from the behaviour classification and allow the system to train ones regardless of the specific dataset since we train the network from the features. Since those features are known (i.
e. they always have the same shape and range) and not learnt from a specific dataset, and the network learns from them, the generality is implicit regardless of the original data. This idea has been used before with generic descriptors  [20]  or simple optical flow  [49] , but not with specific descriptors for behaviour classification. Another advantage of this solution is that a smaller amount of data is required to train the architecture, which is important for both small and large datasets. In the case of small for the reasons explained before, and for large to avoid the need of fully labelling the entire dataset, which can be a tedious and time-consuming task. Furthermore, the learning part is based on the paradigm of ''ensemble learning'' with, in the general case, any number of classifiers, and in the specific instances of this paper, two specific classifiers for the vertical and horizontal motion, and three for the one-class classification that includes the context.
Finally, we use a fully connected layer with sigmoid activation function to connect the fusion layer and predict multiple classes. To overcome the challenge of training the architecture with small datasets such as BEHAVE and CAVIAR  [7] ,  [18] , we perform transfer learning of the trained models with the ImageNet dataset  [25]. As a result, we refined the CNN-based network three times. First, we replaced the fully connected layer of the ImageNet architecture with a new one that matches our classes. Second, we trained a subset of the lower layers because the LRF and UDF inputs are different from the RGB input images expected by ImageNet. Finally, we retrained a subset of the upper layers for fine-tuning. For the training step, we use the binary cross-entropy as an objective function to consider each output class as an independent Bernoulli distribution.
[44]  providing a deep model for training a neural network by minimizing the volume of a hypersphere enclosing the data network representations. This proposal differs from the work of Chalapathy et al. [10]  by combining the ability of CNN-based networks to progressively learn from a subset of images that are the representation of the input data along with the one-class target. Unlike the latter work, which uses auto-encoders to establish the representation of the input data by defining the center of the hypersphere, in this paper some layers of the CNN-based network are trainable, allowing the architecture to continue training   both the center and adjusting the radius of the hypersphere. To avoid the problems of large datasets to train our model and with the goal that it can be used for small datasets, transfer learning of the models trained with ImageNet is used.
V. EXPERIMENTAL RESULTS In this section, the experimental results for the two proposed instances are presented. First, the Deep-ADV Multi-Class Classification (D-ADV-MC) instance has been validated with the INRIA, CAVIAR and BEHAVE datasets  [7] ,  [18]. On the other hand, the Deep-ADV One-Class Classification (D-ADV-OC) has been tested with the Ped 1, Ped 2  [34]  and Avenue  [29]  datasets. The general experimentation configuration for both instances are as follows: • For the calculation of the optical flow, Gunnar Farneback's algorithm  [17]  has been used, which allows a dense calculation. • Size of the cells that conform the images LRF and UDF is 224 x 224. • The CNN based image classifier is the ResNet50. • The images LRF and UDF provided by the D-ADV representation stage have been normalized as input of the ResNet50 to the range between 0 and 1 by dividing each cell (pixel) by the maximum value of each component (L,R,U,D and F).
A. MULTI-CLASS CLASSIFICATION RESULTS A 10-fold cross validation has been used to calculate the performance of the architecture for the different datasets. Moreover, 25% of the training data in each fold has been used for the validation set. Specifically, the performance of the D-ADV has been evaluated using the sensitivity, specificity (see Table  1 ) and the AUC and ROC curves (see Figure  5 , Figure  6  and Figure  7 ). These values have been calculated for frames and for sequences. That is, the performance per frame is calculated according to the prediction made on each individual frame independently of the sequence. The performance per sequence is calculated according to the prediction made for all the frames in the sequence. For this, the prediction of the sequence is the one corresponding to the one corresponding to at least 80% of the frames of the sequence.
Finally, in order to evaluate the ability of the representation to synthesize the information extracted from the scene, two different values for the windowsize (ws) parameter have been tested: 10 and 40 (i. e. 0. 5 sec. and 2 sec. ). The per-frame performance results with a window size (ws) of 10 reach according to the sensitivity and specificity, for the INRIA dataset, 71. 70% and 84. 85% on average, 91. 47% and 94. 51% for BEHAVE dataset, while 78. 18% and 87. 12% respectively for the CAVIAR (corridor) dataset. Using a larger window size (i. e. ws of 40), the results are improved in the three datasets, obtaining 89. 93% and 95. 65% (senditivity and specificity) for INRIA, 92. 55% and 94. 79% for BEHAVE dataset, and 79. 00% and 88. 88% for the CAVIAR dataset (see 1). In terms of performance per sequence, D-ADV achieves very high results.
Considering a window size of 10, for the INRIA dataset, a total of 91. 67% of sensitivity and 95. 83% of specificity is achieved, while 95. 07% and 95. 52% for the BEHAVE dataset, 80. 00% and 93,06 respectively for CAVIAR dataset. Again, the results considering a larger value for the window size (ws = 40) are improved achieving the best ones. On average, 95. 83% of sensitivity and 97. 92% of specificity for the INRIA dataset, 95. 52% and 95. 70% respectively for the BEHAVE dataset and, finally, 80. 58% and 94,27 for CAVIAR dataset. Finally, the D-ADV architecture is compared with the methods proposed in  [1] ,  [12] ,  [36] ,  [67] ,  [64]  considering the seven classes of the BEHAVE dataset. Only  [12]  and our previous work (GADV) consider the seven classes as well. The rest of the works use a subset of four classes.
B. ONE-CLASS The previous instance of the architecture to solve the multi-class problem has been tested with small datasets to validate the architecture is able to be trained with a small amount of data. Now, larger datasets have been used to validate the use of context in improving the performance of the model. Here, the D-ADV-OC architecture has been evaluated using different instances. The simplest one is the D-ADV instance that uses the original ADV descriptor  [4]  with 15 x 15 cells followed by a fully connected layer of 15 x 15 x 5 neurons corresponding to the space of the ADV. The D-ADV+CNN uses two Resnet50 neural networks to learn the LRF and UDF images without the top and the flatten layer connected to two concatenated 2D global average pooling layer (one per image stream) followed by a fully connected layer of 4096 neurons.
Moreover, D-ADV+Context and D-ADV+CNN+Context uses the previous configurations with a third stream: the context recognition. In this case, a YOLO neural network trained with VOC has been used. The weights used for training are w a = 0. 9 (activity) and w c = 0. 1 (context). For all instances, two loss functions has been used: the OC-SVDD  [44]  and the OC-NN  [10]. The window size (ws) as the number of consecutive frames considered in the accumulative process (see Figure  3 ) is 5 for the Ped1 and Avenue dataset and 10 for Ped2. Finally, the instances have been tested using the splits of training and tests predefined in the datasets. The experimental results considering the sensitivity and the specificity as performance measure can be seen in Table  3. As can be seen, high performances are obtained for all combinations in the different datasets.
Having an average value in all cases higher than 70% in all cases for both sensitivity and specificity, and reaching values on average close to 80% for the D-ADV-Context instance with OC-SVDD. This configuration is the one that reaches the highest values for the Ped2 and Avenue datasets. Reaching close to 90% in both parameters for Ped2. Although the configuration with OC-NN loss function has a very similar performance, it is in the Ped1 dataset where it achieves the best results. As for the use of the third stream with the Yolo object detection based context, it is shown that in all cases it improves the accuracy rates, avoiding a higher number of false alarms.
1) ABLATION STUDY In this section, an ablation study on the architecture to investigate the effect of each component of has been carried out. First of all, the average performance results for the PED1, PED2 and Avenue datasets have been calculated. As can be seen in 4, the architecture trained with a OC-SVDD loss, without the CNN network, and using the context information has the best results achieving an AUC of 86. 12%. It will be the reference architecture for comparison.
The number of frames of the training set for PED2 is the smallest (2550 frames) having a difference in AUC parameter of 8. 66% (91. 85 % w/o CNN → 83. 18 % with it). Considering the Avenue dataset, the number of samples is 6 times higher (15329) what reduces drastically the difference in performance to an AUC of 2. 87% (82,11 % w/o CNN → 79,23% with it). Therefore, although the architecture allows training with small datasets, the larger the input, the higher the performance achieved.
2) COMPARSION WITH OTHER METHODS Finally, the experimental results are showed and compared with other state-of-the-art methods in Table  7  at frame-level. Results for the UCSD Ped 1 dataset show that the lowest value of EER is 23. 50% provided in the work by Vu et al. [53]  and the highest AUC value is 86. 26% in the work by Lu et al. [33]. According to EER for the UCSD Ped2 dataset, the best results (4. 68%) are obtained again in the work by Vu et al. However in AUC, the maximum value is achieved by Ionescu et al. [22]  (97. 80%). For the Avenue, the best AUC is again for the work by Ionescu et al. (90. 40%) and the best EER for other work proposed by Li et al. [28]  (21. 50%). Our proposed architecture with the different instances is not the best for any dataset but the performance is in accordance with those obtained in the other works.
The proposed architecture has been urged to address multi-class classification (D-ADV-MC) and one-class classification (D-ADV-OC) in a robust way. The starting hypothesis has been validated with the D-ADV-MC experimentation using small datasets in which the number of people in the scene varies improving the results of the state of the art. Regarding the experiments for the D-ADV-OC classification, large datasets have been used to validate the use of context in improving the performance of the model, outperforming the state-of-the-art approaches. In terms of future lines, it is proposed to include new streams in order to determine which ones can have a greater impact on performance. In addition, in the short term, it is proposed to replace the calculation of the displacement of the D-ADV with a deep learning-based optical flow module to speed up the calculations.
Similarly, in the medium term, it is proposed to study the inclusion of the calculation of the D-ADV directly in the network architecture. This would imply the design of a deep network that would allow the calculation of the displacements (based on optical flow), the background and the accumulations over time. In addition, in the long term, we plan to study higher levels of semantics for groups or crowds. In this case, given the difficulty of having a larger time window, the generation of new datasets is proposed. Finally, we plan to transfer the study to other areas such as the study of groups of vehicles, animals, etc.
This discretization must be able to properly handle the spatial singularity of the source and, furthermore, to remove the ray effect. Secondly, the first-collided source is calculated by applying the collision kernel to the uncollided flux. A second calculation is then used for evaluating the collided component of the flux. In general, this is done using a standard discrete-ordinates method. We can find examples of FCS methods in several general-purpose deterministic transport codes. These techniques generally differ from each other by the transport method used for the calculation of the uncollided flux. Historically, one of the first FCS methods is the semi-analytic technique developed by Alcouffe et al. within the 2D transport code TWODANT  [7]. The method uses a ray-tracing technique for evaluating the flux attenuation at the vertices of the spatial mesh of each region.
By means of semi-analytical relations, it evaluates the currents at the interfaces of each spatial region and finally, it calculates the region average fluxes by solving an angle-integrated balance equation. The same code presents also a second First-Collision Source method based on a stochastic approach  [5]. This second algorithm relies on a Monte Carlo estimator for integrating the region average uncollided fluxes. The method operates the ray tracing along randomly sampled trajectories crossing the entire domain. Each of them is generated by sampling both the source point and the propagation direction. A different FCS strategy has been implemented in the transport code ATTILA  [8] : it makes use of a 3D quadrature formula for the computation of the angular moments of the uncollided flux in each spatial region.
Many existing indexes are not robust since the performance depends on the individual selectivities of its path and value predicates. If either the path or value selectivity is high, these indexes produce large intermediate results even if the combined selectivity is low. This happens because existing solutions either build separate indexes for, respectively, content and structure  [25]  or prioritize one dimension over the other (i. e. , content over structure or vice versa)  [6, 11, 41]. Scalability means that even for large datasets an index can be efficiently created and updated, and is not constrained by the size of the available memory. Existing indexes are often not scalable since they rely on in-memory data structures that do not scale to large datasets. For instance, with the memory-based CAS index  [42]  it is impossible to index datasets larger than 100 GB on a machine with 400 GB main memory.
We call such a byte a discriminative byte and propose a dynamic interleaving that interleaves the discriminative bytes of paths and values alternatingly. This leads to a well-balanced partitioning of the data with a robust query performance. We use the dynamic interleaving to define the RSCAS index for semi-structured hierarchical data. The RSCAS index is trie-based and efficiently supports the basic search methods for CAS queries: range searches and prefix searches. Range searches enable value predicates that are expressed as a value range and prefix searches support path predicates that contain wildcards and descendant axes. Crucially, tries in combination with dynamically interleaved keys allow us to efficiently evaluate path and value predicates simultaneously. To scale the RSCAS index to large datasets and support efficient insertions, we use LSM trees  [32]  that combine an in-memory RSCAS trie with a series of disk-resident RSCAS tries whose size is doubling in each step.
Insertions are performed on the in-memory trie using lazy restructuring. Bulk-loading creates large disk-optimized tries in the background. Merging is applied when the in-memory trie overflows to combine it with a series of disk-resident tries (Section 7). -We conduct an experimental evaluation with three realworld and one synthetic dataset. One of the real-world datasets is Software Heritage (SWH)  [2] , the world's largest archive of publicly-available source code. Our experiments show that RSCAS delivers robust query performance with up to two orders of magnitude improvements over existing approaches, while offering comparable bulk-loading and insertion performance (Section 8).
Second, the archive grows constantly by continuously crawling public data sources such as collaborative development platforms (e. g. , GitHub, GitLab), Linux distributions (e. g. , Debian, NixOS), and package manager repositories (e. g. , PyPI, NPM). The archive growth ratio is also very significant: the amount of archived source code artifacts grows exponentially over time, doubling every 2 to 3 years  [37] , which calls for an incremental indexing approach to avoid indexing lag. For instance, during 2020 alone the archive has ingested about 600 million new revisions and 3 billion new file blobs (i. e. , file contents never seen before). Last but not least, short of the CAS queries proposed in this paper, the current querying capabilities for the archive are quite limited. Entire software repositories can be looked up by full-text search on their URLs, providing entry points into the archive.