dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational efficiency evaluation On a single GeForce GTX 1080Ti GPU with 11GB memory, the runtime of OpenPose  [12]  is less than 0. 1s per frame on a image size of 800 × 450 pixels. On the Human3. 6M dataset  [17] , the 3D pose estimation stage takes around 15ms to complete a pass (forward + backward) through each stream with a mini-batches of size 128. Each epoch was done within 3 minutes. For the action recognition stage, our implementation of ENAS algorithm takes about 2 hours to find the final architecture (∼2. 3M parameters) on each subset of MSR Ac-tion3D dataset  [18] , whilst it takes around 3 hours on the SBU Kinect Interaction dataset  [19]  to discover the best architecture (∼3M parameters). With small architecture sizes, the dis-covered networks require low computing time for the inference stage, making our approach more practical for large-scale problems and real-time applications.
Our experiments on public and challenging action recognition datasets indicated that the proposed framework is able to reach state-of-the-art performance, whilst requiring less computation budget for training and inference. Despite that, our method naturally depends on the quality of the output of the 2D detectors. Hence, a limitation is that it cannot recover 3D poses from 2D failed output. To tackle this problem, we are currently expanding this study by adding more visual evidence to the network in order to further gains in performance. The preliminary results are encouraging.
With their high recording rate of hundreds of thousands of points acquired per second, speed of execution and a remote acquisition mode, SLAM based mobile mapping systems (MMS) are a very powerful solution for capturing 3D point clouds in real time, simply by walking in the area of interest. Regarding indoor surveys, these MMS have been integrated in handheld or backpack solutions and become fast scanning sensors. Despite their advantages, the geometric accuracy of 3D point clouds guaranteed with these sensors is lower than the one reachable with static TLS. In this paper the effectiveness of two recent mobile mapping systems namely the GeoSLAM ZEB-REVO RT and the more recent GreenValley LiBackPack C50 is investigated for indoor surveys. In order to perform a reliable assessment study, several datasets produced with each sensor are compared to the high-cost georeferenced point cloud obtained with static laser scanning target-based technique.
Various datasets The study presents a variety of datasets that have been acquired, processed and evaluated to define the survey accuracies that may be achieved using both laser scanners. Moreover, the diversity of the data in terms of geometry, access complexity and cluttering will make it possible to define the performances of MMS for a given type of survey. Figure  1  illustrates the three datasets chosen for our study. Given its higher accuracy compared to dynamic systems, static scanner FARO Focus3D X330 has been used for producing reference datasets for every study area.
The CloudCompare software (EDF R&D) is used for alignments and comparisons. This is a wellknown software designed for the comparisons between point clouds. A two steps alignment is proposed: A rough alignment is first achieved by clicking manually common points between the two clouds. As soon as at least 3 points are selected in both point clouds, the resulting RMS is displayed. This step is illustrated Figure  2  for the tower dataset. The alignment is secondly refined thanks to the Iterative Closest Point (ICP) algorithm.
Deviation analysis In order to evaluate the accuracy of the mobile systems, distances between the MMS clouds and the cloud obtained by the static scanner are analysed for several surveys. The point cloud acquired by the static scanner is the reference point cloud since this system has the highest measurement accuracy. In addition, distances between the point clouds obtained with the two MMS are also analysed for one of the datasets. For the tower, the point cloud captured on the whole edifice is considered in the deviation analysis, therefore the impact of the edifice's geometry will also be studied. For the other test sites, only samples of common parts are mentioned in this paper. To analyse deviations between point clouds, the M3C2 plug-in available in CloudCompare was used. The M3C2 plug-in computes signed and robust distances between two aligned point clouds  (Lague et al.
, 2013). It is less sensitive to noise than the cloud-to-cloud function included in CloudCompare and has the advantage of calculating positive and negative distances. At the end of distances calculation, a point cloud with the calculated deviations as a scalar field is created. Thanks to a colour map, deviations can be easily visualized in the software. One can also straightforward select and display points for which the deviations belonging to a specific range. In our study, the range -5 cm to +5 cm was considered for the analyses of the results. Additionally, a statistical analysis based on deviations is proposed by CloudCompare. It provides notably the accuracy associated to the point cloud compared to the reference.
Discussion about completeness and density of points of MMS data One advantage of the MMS compared to the static scanner is that the environment is acquired more easily. One can move around objects of the scene. In order to acquire the whole scene with a static scanner, it is necessary to increase the number of scanning stations. This is generally not done because it's time consuming and there are therefore some missing parts in the point clouds because of occluding objects. On the other hand, the MMS data has a lower density of points than the static scanner. This made the comparisons more difficult for the laboratory dataset which is quite large. Regarding the number of points per second and the resolution provided by Table  1 , the LiBackPack C50 data will provide a higher point density than the ZEB-REVO RT.
Measurement noise assessment based on a plane In order to quantify the measurement noise, the dispersion of points around a plane is analysed. A portion of a wall face smooth of about one per two meters was segmented in the three point clouds. Using the Fitting tool provided by RealWorks software (Trimble), a plane is fitted to each point cloud. As expected, the lowest standard deviation of points around the fitted plane concerns the TLS cloud. The mobile devices deliver noisier data, however the noise observed in measurements performed with the ZEB-REVO RT is lower than the noise measured in the LiBackPack cloud.
CONCLUSION AND FUTURE WORKS In this paper the effectiveness of two recent mobile mapping systems namely the GeoSLAM ZEB-REVO RT and the GreenValley LiBackPack C50 was investigated for indoor surveys. In order to perform a reliable assessment study, several datasets produced with each sensor were compared to the high-cost georeferenced point cloud obtained with static laser scanning target-based technique. Quantitative analyses of the results implied the analysis of differences between the point cloud produced with these SLAM based sensors and 3D reference point clouds. An analysis of the distances between the registered point clouds produced automatically with the mobile devices and after processing with the static scanner shows that more than 90% of the deviations between the two clouds are less than 5 cm for the first and second dataset.
The key element of the CHARMS method is the refinement equation that links the coarse basis functions to the refined basis functions. This equation depends on the refinement pattern. It can be generalized and written as follows for a basis function ϕ i related to a reference element K: ∀i ∈ [[0, N -1]], ϕ (0) i = b N (1) -1 k=0 β ik ϕ (1) k with β ik = ϕ (0) i ( a (1) k ), (1) where superscripts (0) and (1) denote the reference element and the refinement pattern respectively, N is the number of nodes, ϕ • are the basis functions and a (l) k is the node linked to the basis function ϕ (l) k.
Validation Strategy A diagram that gives an overview for this section is shown in Fig. 3. We employ a validation scheme similar to that of RAVEL 10 that assesses if a calibration increases reproducibility of already shown associations between features and biological labels. For this purpose, we use two signatures that were often shown to be predictive for their use case: the Aerts signature  20  for patient survival prediction in oncology and the low-attenuation area (LAA) signature consististing of LAA910 and LAA950 for emphysema assessment  21. For the Bayesian optimisation needed to construct the technome, we integrated an established Python implementation  22  in our pipeline. The acquisition function 'upper confidence bound' , 8 initial seeds, 500 iterations and a Kappa of 5 as tradeoff between 'exploration' and 'exploitation' were chosen.
Both train-test split scenarios are summarised in Table  2. phantom dataset -feature stabilisation. To validate the technome stabilisation mode, 103 CT images of a liver-lesion phantom  26  acquired with a Siemens Somatom Zoom Scanner are analysed (Fig. 4 , right). The images were acquired with a variety of scan and reconstruction parameters. Varied parameters are e. g. kvp, mAs, slice thickness, voxel size. The 3 texture features of the Aerts signature are extracted within a 'tumour-like' ROI and the 844 PyRadiomics 27 features are used as surrogates S for the CRs air and 'adipose tissue'. The ROIs are segmented by a semi-automated algorithm  28. The training loss is defined as variance R 2 of feature j that is explained by g GLM for the current qualified surrogate selection averaged over all analysed features = -L Rg avg (( ( )) j j train 2 GLM.
For the calibration loss, only the qualification criterion q jl inVivo is used for phantom scans, as no biological induced variation needs to be excluded. copD dataset -centrilobular emphysema prediction. To validate the technome predictive mode ability to enhance centrilobular emphysema prediction in the presence of only non-reducible technical variation, 676 partly contrast-enhanced CT scans of 676 patients (age: 84. 1 ± 14. 8 y) each reconstructed with a soft (B36) and hard (B71) kernel, i. e. 1352 clinical CT images were acquired with a Siemens Somatom Force in Lille, France  29. The cohort consists of patients with different symptoms of chronic obstructive lung disease (COPD). The study was approved by the local ethics commitee with waiver of the informed consent because CT examinations were part of routine clinical practice.
While a hard kernel is clinically considered inadequate for emphysema assessment due to larger inter-patient noise variation, we expect a predictive performance similar to the results for a soft kernel after calibration. mcRc dataset -one-year-survival prediction. To validate the technome predictive mode ability to enhance one-year-survival prediction in the presence of non-reducible and also reducible technical variation, 118 contrast-enhanced CT scans of 75 patients (age: 61. 9 ± 11. 4 y) with 686 analysed liver tumours acquired with a variety of scanners from different vendours (GE, Philips, Siemens, Toshiba) in Munich, Germany, are analysed  33. The cohort consists of metastatic colorectal cancer (mCRC) patients with liver metastases. The study was approved by the local ethics commitee with waiver of the informed consent because CT examinations were part of routine clinical practice.
4 , right) with a variety of scan and reconstruction parameters were used. Training and test loss are defined in analogy to the COPD dataset. For each fold, the tumours of a patient were either in the training or the test data and a split was not allowed. Again, the predictive performance is assessed as the AUC on the test data. For comparison purposes, all PyRadiomics features are extracted in the tumours and entered in a random forest classifier to predict one-year-survival. This random forest is optimised by hyperparameter tuning on the training data. With this step, we can assess, whether our calibration outperforms a hyperparameter tuned classifier using an abundance of features within the ROI. Finally, we compare the predictive performance of our technome classifer with an advanced deep learning method.
Results phantom dataset -feature stabilisation. The results are shown in Fig. 7. The technome stabilisation mode gives a stabilisation performance [% variance reduction on test set] in a 10-fold CV (SSD) of 90. 4% (91. 8%). The RAVEL-like calibration yields 79. 3% (76. 7%) and the naive approach via GLM 74. 7% (79. 3%) or via random forest 76. 7% (42. 7%). Apparently, 21 training datasets of the SSD setting were not sufficient for a complex calibration as observed by the stabilisation performance of the random forest regression with all surrogates. copD dataset -centrilobular emphysema prediction. The results are shown in Fig. 8. The predictive performance [% AUC on test set] in 10-fold CV (SSD) improved to 81. 5% (74. 4%) with technome predictive mode in comparison to GLM with 60. 7% (60.
0%) or random forest with 58. 1% (59. 1%). The technome was also superior to a RAVEL-like calibration of 70. 4% (64. 6%) and the naive approach via GLM with AUC of 70. 8% (65. 4%) or via random forest with 74. 8% (67. 0%). As a comparison, the predictive performance of LAA signature on the soft kernel B36, which is the clinical standard for emphysema classification, was 76. 6% (74. 3%), which is numerically inferior to the technome calibration for the hard kernel B71. mcRc dataset -one-year-survival prediction. The results are shown in Fig. 9. The predictive performance [% AUC on test set] in 10-fold CV (SSD) improved to 66. 5% (53. 4%) with technome predictive mode in comparison to GLM with 49. 2% (49. 9%) or random forest with 57. 6% (49. 3%). The technome was also superior to a RAVEL-like calibration of 58.
Acknowledgements We thank  Grzegorz Soza ,  Felix Durlak ,  Andreas Wimmer  ( Siemens Healthineers, Forchheim, Germany ),  Andreas Friedberger ,  Oleg Museyko ,  Klaus Engelke ,  Oliver Chaudry  ( Institute of Medical Physics, FAU Erlangen-Nürnberg, Germany ),  Jan Moltz  ( Fraunhofer MEVIS, Bremen, Germany ), and  Wolfgang Goldmann  ( Biophysics Group, FAU Erlangen-Nürnberg, Germany ) for valuable discussions. This project has received funding from the  German Federal Ministry of Education and Research  as part of the  PANTHER  project und grant agreement no. 13GW0163A.
MetamORF enables new analyses at loci, gene, transcript and ORF levels, that should offer the possibility to address new questions regarding sORF functions in the future. The repository is available through an user-friendly web interface, allowing easy browsing, visualization, filtering over multiple criteria and export possibilities. sORFs could be searched starting from a gene, a transcript, an ORF ID, or looking in a genome area. The database content has also been made available through track hubs at UCSC Genome Browser.
Indeed, the latter should reveal important sORFs features, thus enabling the characterization and the identification of their functions. However, the fact that (i) the publicly available data are scattered across different databases and (ii) datasets are aligned on different genome builds, differently annotated and formatted, calls for an uniformized resource where each sORF is individually described. With this in mind, we have built a resource database of publicly available sORFs identified in the human and mouse genomes, by gathering information from computational predictions, Ribo-seq and proteomic experiments. The curation of data, their homogenization in order to merge the redundant information into unique entries, the completion and computation of missing information (e. g. , sequences, Kozak contexts) and the reannotation of sORF classes represent the added value of this database.
Inclusion criteria for publicly available sORF-related data A total of 18 data sources, either H. sapiens and M. musculus original datasets or re-processed publicly available sORFs repositories, have been considered for inclusion in our database (Supplementary Table  S1 )  (5, 7, 11, 12, 14, 15, (17) (18) (19) (20) (21) (22) (32) (33) (34) (35) (36) (37). These data sources provide results from computational predictions, Ribo-seq experiment analyses and mass spectrometry (proteomics / proteogenomics) analyses. The data sources not providing the absolute genomic coordinates of the ORF start and stop codons  (5, 17, 20, (32) (33) (34)  or fully included in another data source considered here  (21) , have been discarded. Databases that did not allow export of their content in a single file or to automate the download of all the files from their website, have also been discarded  (19, 35).
Despite their short size, it has been noticed that sORFs can be spliced. Theoretical lengths of the ORFs have been computed as the distance between the start and stop codons, eventually removing the intron length(s) when information about the ORF splicing was provided. Due to splicing, the theoretical length and the one reported by the data source may be different. Data sources harboring this difference for more than 95% of their entries were discarded as this indicates the splicing information was missing  (10). Finally, data sources for which we were not able to perform this assessment as they were not providing information regarding the splicing of the ORF and did not provide any ORF length  (15, 36)  have not been included as well. Hence, the database has been made by collecting data from six distinct sources (Figure  1 ), including either original datasets (Table  1  and Supplementary Table  S2 )  (11, 12, 14, 18, 22)  or reprocessed data  (37) , and discarding 12 of them (Supplementary Table  S1 ).
Notably, we have included data from sORFs. org  (37) , considered as the main and most comprehensive repository of sORFs identified by genome-wide translation profiling (Ribo-seq), that currently integrates re-processed data from 73 original publications. For each of these sources, a set of features essential to properly characterize the sORFs, related to their location, length, sequences, environmental signatures and cell types (i. e. cell lines, tissues or organs) in which they are expressed, have been collected (see Table  2  for a full list of features considered for inclusion). When it was not provided by the source, the symbol of the gene related to the sORF was recovered using the transcript identifier (ID, if provided) or searching for the gene(s) or ncRNA(s) overlapping with the sORF coordinates in the original annotation version by querying Ensembl databases in their appropriate versions (v74, 75, 76, 80, 90) with pyensembl (v1.
8. 5, https://github. com/openvax/pyensembl). In addition to these features, information regarding the transcript(s) harboring the ORFs have been collected from the data sources when available. This is of particular interest as some ORF features, such as the ORF class, may depend on the transcript they are located on (e. g. an ORF may be located in the 5'UTR of a transcript and be overlapping with the CDS of another transcript). Finally, 3,379,219 and 2,066,627 entries from these six data sources have been collected and inserted in MetamORF for H. sapiens and M. musculus, respectively (Table  3 ).
Homogenization of genomic coordinates As the data sources were providing genomic coordinates from different genome annotation versions (e. g. GRCh38 and GRCh37), all the genomic coordinates registered in our database have been lifted over the latest annotation version (GRCh38 for H. sapiens, GRCm38 for M. musculus) using pyliftover (v0. 4, https://pypi. org/project/pyliftover). The lift-over has been considered as failed for an entry if (i) at least one of the coordinates (i. e. start, stop or one of the start or end exon coordinates) was located on a strand different from all the others, or (ii) the chromosome of the position changed during the lift-over, or (iii) the distance (in nucleotides) between the sORF start and stop codons has changed after the liftover. All the entries for which the lift-over failed were removed from the database.
Merge of redundant information As our database aims to provide a repository of unique identified sORFs of the human and mouse genomes, all the redundant entries describing the same sORFs have been merged. In a first step, we identified all the sORF entries for which all the identification features were provided (chromosome, strand, start position, stop position, splicing status and splicing coordinates). sORFs sharing the same feature values were merged. In a second step, we identified all the remaining entries with only partial identification features provided: the chromosome as well as either (i) both the strand and the start position, or (ii) both the strand and the stop position, or (iii) both the start and the stop position. Those entries were merged to the best matching fully described entries identified in the first step.
If no matching fully described entry was found, then the entries were removed. In order to keep track of the number of times a same sORF has been described in the original data sources, the initial number of entries merged together was registered for each sORF. During this merging, information regarding the transcripts that harbor the sORFs have been registered too. Hence, when several sORFs were merged into one single entry in MetamORF, the resulting new sORF entry was registered as harbored by all the distinct transcripts related with the original entries. After this removal of redundant information, we were finally able to identify 664,771 and 497,904 unique sORFs for H. sapiens and M. musculus, respectively (Table  3 ). It should be noticed that all unique sORF entries generated at this stage have been kept, including the ones describing ORFs longer than 100 amino acids.
Completion of missing information and computation of relative coordinates In the original data sources, the only information provided (when provided), on the transcripts was the transcript ID. Detailed information was retrieved from Ensembl databases (v90) through their REST API and inserted in our database: (i) the biotype, (ii) the transcript start and end genomic coordinates, (iii) the codon of the canonical coding sequence (CDS, for protein-coding transcripts only) start and stop genomic coordinates and (iv) the full nucleic sequence. In addition, the sequence flanking the start codon  (20)  has been recovered. As the sORF nucleic and amino acid sequences were not systematically provided by the data sources, these were downloaded from the Ensembl databases using their genomic coordinates.
Cell types Original data sources do not use a common thesaurus or ontology to name the cell types (e. g. , 'HFF' and 'Human Foreskin Fibroblast') or use non-biological meaning names (e. g. , sORFs. org (37)  provides the name of the original publication as a cell type). In order to provide an uniform informative naming, we manually recovered the name of the cell line, tissue or organ used in these datasets and defined an unique name to be used in our database for each cell line, tissue or organ (Supplementary Table  S3 ).
ORF classification Despite the use of a common nomenclature by the wide majority of the scientific community to annotate the open reading frames, based on their relative position on their transcript (e. g. , short, upstream, downstream, overlapping), no clear consensus about the definitions of these categories nor their names has been defined so far  (25). In order to homogenize this information in MetamORF, we created a new annotation of the ORFs using both the ORF length, transcript biotype, relative positions and reading frame information when available. In this annotation, a threshold of 100 amino acids has been used to define the "short ORFs", as this value is the most commonly used for historical reasons  (2, 4, 6, 8, 24).