dataesr/scientific-paragraphs-categorization huggingface dataset:
Event-Driven Control The Oro server supports two paradigms to access its content: RPC-style queries (based on the standard SPARQL language) or events. A module can subscribe to an event by registering an event pattern (in its simplest form, a partial statement like ? type Book ) and a callback. Each time a new instance of a book appears in the knowledge base, the callback is triggered. This allows us to write reactive robot controllers with a high level of expressiveness: for instance, by subscribing to the event HUMAN1 desires ?action, ?action type Give, ?action actsOnObject ?obj, ?obj type Book , we effectively trigger a behaviour when the human expresses (through dialogue, gestures. ) that he wants the robot to give her a book. The robot controller designer does not need to directly care about how this desire is produced (this is delegated to perception modules), he can instead focus on the semantics of the desire.
Spark identifies the CARDBOARD_BOX as being pointed at, as well as looked at, by the human and updates the ontology with this new information. The reasoner applies a rule available in the common-sense ontology pointsAt(?ag, ?obj) ∧ looksAt(?ag, ?obj) → focusesOn(?ag, ?obj). The Dialogs module merges both sources of information, verbal ("this") and deictic, by issuing a query to the knowledge base that eventually lifts the ambiguity: find(?obj type Box; USER_B focusesOn ?obj) ⇒ ?obj = [CARDBOARD_BOX] Finally, Dialogs queries the ontology about the content of the box and the question can be answered: "Wall-E" (the label of the object had been statically asserted in the ontology at start-up). At this point User B wants to know where is the other tape: "And where is the other tape?". Dialogs is able to convert "the other tape" into a new query using the differentFrom OWL predicate: ?obj type VideoTape ?obj differentFrom WHITE_TAPE ⇒ ?obj = [BLACK_TAPE] Since there is only one possible "other" videotape, no specific disambiguation is required.
Acknowledgements Building such a robotic architecture is the work of many hands, and we would like to acknowledge here the worthwhile contributions of  Samir Alili ,  Raquel Ros Espinoza ,  Mamoun Gharbi ,  Julien Guitton ,  Matthieu Herrb ,  Jim Mainprice  and  Amit Kumar Pandey. This work has been partially supported by  EU   FP7  CHRIS (grant  215805 ) and  SAPHARI  (grant  ICT-287513 ) projects, and the  EU H2020 Marie Sklodowska-Curie Actions project DoRoThy  (grant  657227 ).
We refer to our action proposal as Tubelets in this paper, and summarize their generation in Fig. 1. Our second contribution is explicitly incorporating motion information in various stages of the analysis. We introduce independent motion evidence as a feature to characterize how the action motion deviates from the background motion. By analogy to image descriptors such as the Fisher vector  (Sánchez et al. 2013) , we encode the singularity of the motion in a feature vector associated with each super-voxel. We use the motion as an independent cue to produce supervoxels segmenting the video. In addition, motion is used as a merging criterion in the agglomerative grouping of supervoxels leading to better Tubelets. A preliminary conference version of this article appeared as  Jain et al. (2014).
We adopt the use of long term trajectories for temporal refinement and pruning of our action proposals, but we do not restrict ourselves exclusively to improved dense trajectories as representation for action classification. Our post-processing methods are heuristic but intuitive for the problem of action localization and considerably improve the performance while keeping the number of proposals manageable. In addition to technical novelty, the current paper adds: (i) detailed experimental evaluation of motion-based segmentation for better proposals, leading to large gains in both proposal quality and action localization, (ii) apart from UCF Sports and MSR-II we also consider the much larger UCF101 dataset, (iii) revised experiments for all three datasets considering both the quality of the proposal as well as their suitability for action localization using modern video representations  (Sánchez et al.
These sets are again grouped by union into a set of Tubelets. The final stage is post-processing that includes pruning and spatiotemporal-refinement of action proposals with a robust penalty function as θt = arg min θ t p∈Ω ρ(r θ t ( p, t)), ( 1 ) where ρ is the robust function. To solve (1), we use the publicly available Motion2D software by  Odobez and Bouthemy (1995) , where ρ(. ) is defined as the Tukey function. ρ(r θ t ) produces a maximum likelihood type estimate: the so-called M-estimate  (Huber 1981). Indeed, if we write ρ(r θ t ) = log f (r θ t ) for a given function f, ρ(r θ t ) supplies the usual maximum likelihood estimate. Since we are looking for action related moving points in the image, we want to measure the deviation to the global (background) motion. This is in spirit of the Fisher vectors by  Perronnin and Dance (2007) , where the deviation of local descriptors from a background Gaussian mixture model is encoded to produce an image representation.
Datasets UCF Sports This dataset consists of 150 videos of actions extracted from sports broadcasts with realistic actions captured in dynamic and cluttered environments  (Rodriguez et al. 2008 ). This dataset is challenging due to many actions with large displacement and intra-class variability. Ten action categories are represented, for instance 'diving', 'swinging bench', 'horse riding', etc. We use the disjoint train-test split of videos (103 for training and 47 for testing) suggested by  Lan et al. (2011). The ground truth is provided as sequences of bounding boxes enclosing the actors. The area under the ROC curve (AUC) is the standard evaluation measure used, and we follow this convention. MSR-II and KTH MSR-II dataset consists of 54 videos recorded in a crowded environment with many people mov-ing in the background.
Each video contains multiple actions of three types: 'boxing', 'hand clapping' and 'hand waving'. An actor appears, performs one of these actions, and walks away. A single video has multiple actions (5-10) of different types, making the temporal localization challenging. Bounding subvolumes or cuboids are provided as the ground truth. Since the actors do not change their location, it is equivalent to a sequence of bounding boxes. The localization criterion is subvolume-based, so we follow  (Cao et al. 2010 ) and use the tight subvolume or cuboid enveloping Tubelet. Precision-recall curves and average precision (AP) are used for evaluation  (Cao et al. 2010). As standard practice, this dataset is used for cross-dataset experiments with KTH  (Schüldt et al. 2004)  as training set. UCF101 The UCF101 dataset by  Soomro et al.
(   2012 ) is a large action recognition dataset containing 101 action categories of which 24 are provided with localization annotations, corresponding to 3204 videos. Each video contains one or more instances of same action class. It has large variations (camera motion, appearance, scale, etc. ) and exhibits much diversity in terms of actions. Three train/test splits are provided with the dataset, we perform all evaluations on the first split with 2290 videos for training and 914 videos for testing. Mean average precision is used for evaluation. Example frames of some of the action classes are shown in Fig. 7  for each dataset.
Experiments: Quality of Tubelets In this section, we first analyze and evaluate the three stages of Tubelet extraction on the training set of the UCF Sports dataset. The initial step, super-voxel segmentation, is discussed in Sect. 5. 1. Then, we evaluate different grouping functions over the initial set of super-voxels in Sect. 5. 2 and also show that segmenting iMotion maps is complementary to segmenting input video frames. In Sect. 5. 3, we evaluate the impact of spatiotemporal refinement and pruning on all three datasets. In all our evaluations, we do not use any additional constraint to keep proposals that last for the entire length for videos that are trimmed for actions. Finally, in Sect. 5. 4 we compare Tubelets with the state-of-the-art. We evaluate Tubelets with modern representations for action localization in Sect.
Pruning and Spatiotemporal Refinement In this section, we evaluate the impact of pruning and spatiotemporal refinement on the quality of action proposals of UCF Sports, MSR-II and UCF101. The validation for grouping functions and segmentation is already done on the training set of UCF Sports. Now, we report results when considering all the videos of these three datasets, to be com- With overlap pruning the number of proposals goes down further to ∼8% of the original number, with a small loss in MABO and Recall scores. The loss is compensated by spatial refinement of Tubelets parable with the numbers reported by other methods. Before moving to results, we provide the implementation details of pruning and spatiotemporal refinement. Implementation Details For motion pruning we set P = 50, so that at least fifty proposals are retained from each video.
Also, motion pruning is only applied to T vid , since proposals from T iMotion are expected to have enough motion content. Overlap pruning is similar to non-maximum suppression, but applied without classification scores and therefore can affect the recall. To minimize its impact on Recall, we set a high overlap threshold of 0. 8 for overlap based pruning. For spatial refinement, we set N equal to 5% of the frame width. UCF Sports In Table  6 , we evaluate the impact of pruning and spatial refinement on MABO, Recall and the average number of proposals per video for UCF Sports dataset. The results for T vid ∪ T iMotion for all 150 videos of UCF Sports is similar to that on its train set. Now, with motion pruning there is no loss of MABO and Recall while only ∼26% of original proposals are used.
MSR-II The MSR-II dataset has untrimmed videos with multiple instances of different types of actions in the same video. This poses additional challenges for temporal localization, which is experimentally illustrated in This calls for temporal refinement of Tubelets spatially but extends to the frames where there is no action of interest. This is due the tendency of super-voxels to continue to cover the actor even when the action is completed. We overcome this limitation by temporal refinement. In Table  8 , in addition to pruning and spatial refinement, we also report for temporal refinement to improve temporal localization. First, motion pruning maintains the MABO and Recall while reducing the number of proposals to only a quarter of initial number. This pruning needs to precede temporal refinement to limit the number of proposals.
Second, temporal refinement leads to a massive improvement of 30. 1% in Recall and 9. 3% in MABO. Note that temporal refinement also includes overlap pruning to filter-out newly added very similar proposals. Also, to limit the number of proposals temporal refinement is exclusively applied to 'T vid + Motion pruning', which means only overlap pruning is applied to 'T iMotion + Motion pruning'. Finally, with spatial refinement another huge improvement of ∼12% is achieved in Recall along with ∼3% improvement in MABO. Overall, we achieve an improvement of 12% of MABO and 42. 3% of Recall while decreasing the number of proposals by about 72% compared to the initial set, T vid ∪ T iMotion. The gain due to temporal refinement is easy to understand for this dataset of untrimmed videos. However, we also get impressive boost by spatial refinement that is much more than we get for the other two datasets.
We attribute this to the exploitation of information from motion trajectories, which is paramount for MSR-II as noted before in  van Gemert et al. (2015) ;  Chen and Corso (2015). Localizing actions is more challenging when multiple untrimmed actions happen simultaneously in the same frames. We analyze Tubelets for such cases in Fig. 8. Temporally, Tubelets sometimes miss action for a few frames or continue for a few extra frames, but it does find multiple actions in the same frame consistently. In general, temporal localization pose a bigger challenge than localizing multiple actions in the same frame. Overall, Tubelet does well to handle these challenging cases. UCF101 In Table  9 , we report the impact of pruning and spatial refinement on MABO, Recall and the average number of proposals per video for UCF101 dataset.
Experiments: Action Localization In this section we evaluate our approach for action localization on UCF Sports, MSR-II and UCF101. For positive training examples, we use the ground-truth and our Tubelets that have localization score or overlap greater than 0. 7 with the ground-truth. Negative samples are randomly selected by considering Tubelets whose overlap with ground-truth is less than 0. 15. This scheme is followed for UCF Sports and UCF101. In case of MSR-II cross-dataset evaluation is employed, the training samples consist of the clips from KTH dataset while testing is performed on the Tubelets from the videos of MSR-II. We apply power normalization followed by 2 normalization before training with a linear SVM. One round of retraining on "hard-negatives" was enough as additional rounds did not improve performance further.
Here, the memory requirement is limited, and feature computation is the costly operation, motivating the need for a compact set of action proposals. Comparing Representations We now analyze the impact of the above three Tubelet representations on the UCF Sports dataset, following the process described in Sect. 4. 2. Following popular practice, we use area under ROC curve (AUC) as the evaluation measure, as common for this dataset. Figure  9  compares the performance of the various Tubelet representations for a varying overlap threshold. We observe a clear improvement when moving from BoW to FV, to CNN and eventually the combination of FV and CNN, especially for higher thresholds (σ ≥ 0. 4).
Comparison with State-of-the-Art Methods We now compare our approach with state-of-the-art methods on the three datasets. include Tubelets with a FV representation, which does better for most of the thresholds. Figure  11  shows some examples of action localizations from UCF Sports. MSR-II This dataset is designed for cross-dataset evaluation. Following standard practice, we train on KTH dataset and test on MSR-II. While training for one class, the videos from other classes are used as the negative set. We use the FV representation to be more comparable with the competitive work of  (van Gemert et al. 2015) , which also generates action proposals in an unsupervised manner like Tubelets. In Table  12 , we compare with several state-of-the-art methods; mean average precision (mAP) along with the APs for the three classes are reported.
Following the usual practice on this dataset we report results for an overlap threshold of 0. 125. Apart from  Chen and Corso (2015) , our approach outperforms all other methods by 5% of mAP or more. Chen and Corso (2015)  very well utilizes information from motion trajectories and samples action proposals by clustering over a space-time trajectory graph. Motion trajectory based approaches are particularly well-suited for MSR-II dataset, as observed with our spatiotemporal refinement of Tubelets and also in  (van Gemert et al. 2015). Similarly, the approach of Chen and Corso (2015) that is mainly focused on trajectories lead to excellent performance on MSR-II but its performance on UCF Sports is modest (Fig. 10 ). Finally, compared to the Tubelets in  Jain et al. (2014) , we improve mAP by 24.
5%. Again, we claim the importance of using both input video frames and i Motion maps for segmentation and spatiotemporal refinement of Tubelets. Figure  12  shows some examples of localizations for MSR-II. UCF101 UCF101 is much larger than the other two datasets, with 24 action classes, and is currently the most challenging dataset for classification of proposals. Again, we represent Tubelets with FV following  (van Gemert et al. 2015). In Fig. 13 , we report mAPs for different overlap thresholds and compare Tubelets with three other approaches that report results on UCF101 dataset. Despite the use of human detection, the approach by  Yu and Yuan (2015)  is about 10% behind our method for an overlap threshold of 0. 125. Weinzaepfel et al. (2015)  uses bounding-box level action class supervision while generating proposals.
Despite their additional supervision and use of two-stream CNN features, we achieve better mAP for 3 out of 4 overlap thresholds. Using box-level annotations their approach performs better at higher overlap thresholds. For limited number of classes they have an edge, whereas our approach would be more useful for generating proposals when the number of classes increases. The only other approach that uses proposals generated in an unsupervised manner, as we do, is APT by  van Gemert et al. (2015). Tubelets outperform their approach while requiring only about a fifth of proposals (see Table  11 ). Figure  14  displays some examples of action localizations from UCF101. With 24 classes this dataset offers larger variety in types of actions. Poor localization (shown in red) mainly happens in case of multiple actors, when during the action one of the actors gets occluded (see 'Salsa Spin').
Additionally, we introduced spatiotemporal refinement and pruning of Tubelets. Spatiotemporal refinement overcomes the tendency of super-voxels to sometimes follow the actor even after the action is completed. This led to improved MABO and Recall scores, especially on the untrimmed videos of MSR-II (Table  8 ), while pruning kept the number of Tubelets limited. The impact of these and the other components of Tubelet generation are extensively evaluated in our experiments. We evaluate our method for both action proposal quality and action localization. For action proposal quality, Tubelets beat all other existing approaches on the three datasets with much fewer of proposals (Table  11 ). For action localization, our method leads to the best performance on UCF101 and second best on UCF Sports and MSR-II.
Timings In Table 10, we report execution times per video for all stages of Tubelet generation. We focus on MSR-II as it is the only dataset containing all proposed stages (including temporal refinement). On average there are 766. 9 frames per video. The experiments were performed on Intel(R) Xeon(R) CPU, 2. 90GHz. Conclusions In Tables 6, 8 and 9, we show many propos- als are filtered out by motion and overlap pruning; and the boost provided by spatial/temporal refinement. For all the three datasets motion pruning filters out a large fraction of proposals, leading to a fourfold decrease. Temporal refine- ment, only applicable to MSR-II, boosts MABO (+9. 3%) and Recall (+30. 1%), while keeping the number of propos- als limited because of the overlap pruning that is part of temporal refinement.
Mixed stages of L. elongatus were collected from soil samples from various farms in Scotland and identified visually under a binocular microscope before being frozen in liquid nitrogen. Longidorus elongatus were hand-picked from samples that were previously identified under a microscope as L. elongatus. No other Longidorus species were present from the site sampled. RNA was extracted using an RNEasy Plant Mini kit (Qiagen, Crawley, UK) following the manufacturer's instructions with the on-column DNAse digestion. Sequence was generated on an Illumina MiSeq (Illumina, San Diego, CA, USA) to produce 150 basepair (bp) paired-end reads using standard protocols. All raw RNA sequencing (RNAseq) data produced for the research described in the manuscript is available under the Sequence Read Archive (SRA) accession numbers PRJEB8328 and PRJEB22758 for L.
Reads Quality Control and Transcriptome Assembly Read quality, length and composition were checked using FASTQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). For X. index, ribosomal RNAs were detected and removed using SortMeRNA  [29]. PRINSEQ  [30]  was used to remove sequences with a minimum quality score of 30 and less than 50 nucleotides long. Cleaned reads were assembled de novo using Trinity v2. 2. 0  [31]  with 4 different k-mer sizes  (22, 24, 25 and 26). We concatenated the 4 assemblies and eliminated redundancy using MegaBLAST  [32]  and CAP3  [33]. Reads were mapped back to the X. index assembly and normalized expression for each transcript in each library was calculated using the Trinity wrapper scripts for edgeR  [34]  and RSEM (https://deweylab. github. io/RSEM/) (Supplementary Table  S1 ).
For L. elongatus, 150 bp paired-end reads were trimmed of bases with low support (Phred score < 30), and cropped to a minimum length of 50 nucleotides and a maximum length of 100 nucleotides to remove non-random base distributions. Paired and unpaired reads which passed the quality criteria were concatenated and used for de novo assembly using Trinity v2. 2. 0 using a k-mer size of 25 and a k-mer coverage of 3. Transcripts in the resulting assembly were scored based on the left and right read pair mapping characteristics using Transrate  [35] , and low supported transcripts were removed in two iterations. We calculated N50 to measure contiguity and used BUSCOv2  [36]  to evaluate completeness based on the dataset of 978 groups of proteins highly conserved in Metazoa.
Identification of L. elongatus GH12 To identify the L. elongatus GH12 sequence, a draft low coverage assembly was generated using Trinity with a minimum k-mer coverage of 1. The X. index GH12 was used as query in BLAST to identify a GH12 fragment. The 5 and 3 ends of this transcript fragment were computationally extended using an iterative approach: raw reads overlapping with a minimum k-mer of 26 nucleotides were retrieved and assembled to extend the fragment using MITObim_1. 6 (https://github. com/chrishah/MITObim). Four rounds of this extension produced a complete GH12 transcript.
Open Reading Frame Prediction Open Reading Frames (ORFs) were predicted in both transcriptomes using a custom pipeline developed to more faithfully predict 5 start codons. Initially, TransDecoder 2. 0. 1  [31]  was used to predict ORFs encoded by the assembled contigs, with the modification that the hard-coded value within the TransDecoder script for redundancy removal of 80% was changed to 99%. BLASTp results are used by TransDecoder when determining the coding sequence: the SwissProt database (September 2015) including the gene models for Globodera pallida  [5] , G. rostochiensis  [6]  and B. xylophilus  [7]  were used as reference. Pfam A and B domain definitions (2014) were also used to guide TransDecoder coding sequence (CDS) prediction. The final TransDecoder predicted coding sequences were subjected to a 5 CDS refinement tool, available at: https://github.
Pfam and Gene Ontology Annotation We used PfamScan (available at ftp://ftp. ebi. ac. uk/pub/databases/Pfam/Tools/) to predict conserved protein domains and repeats in the X. index and L. elongatus predicted proteins. Each protein was scanned against the Pfamv30 collection of Hidden Markov Models (HMMs)  [42]  using PfamScan default parameters and with a prediction of active sites. We used the pfam2GO association file maintained at http://geneontology. org/external2go/pfam2go to assign gene ontology terms based on the presence of predicted Pfam domains in proteins. We used the hypergeometric test implemented in FUNC  [43]  with a False Discovery Rate (FDR) threshold of 0. 05 to detect gene ontology terms significantly enriched in genes putatively acquired via HGT.