dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of centroids for K-means algorithm which provides the best trade-off on all experiments is 60 for BOG. As we do not see any improvement by using multiple windows in the BOG descriptor, we use only 0. 4 second windows for gesture extraction. One result of this experiment is that the introduced descriptors provide complementary results depending on the databases. Short Term Position has the best performances among other Short Term descriptors on the CUHA dataset which deals with several static actions (standing still, relaxing on a couch) whereas Short Term Motion performs better on the TUM dataset, which contains dynamic actions (opening a door, closing a door). Bag-of-gestures (BOG), which combines pose and movement on a middleterm duration, has better results than any individual short term descriptor on CUHA and TUM datasets.
It can also be noted that both short-term descriptors and bag-of-gestures lead to results similar or better than those of the literature on the two databases. The size of short-term descriptor and bag-ofgesture descriptor are really different since the first vector size is 25 × 13 = 325 while the second one is 60 × 13 = 780. Moreover, as the best short-term descriptor depends on the database, we propose to combine these short-term descriptors into a single one with size 325 × 4 = 1300. As shown in Figure  1 , this new descriptor called short-term combination leads to better results on the TUM database and is less effective on the CUHA database. As its size is much larger, a good compromise is to use the bag-of-gesture descriptor which leads to better results than the state-ofthe-art methods, regardless of the test database.
As it has been noticed by previous work, skeleton based approaches provided high performances. In this work, we show that on datasets where skeleton is provided (CUHA, TUM), our approach leads to high results and outperforms the state-of-the-art. However if skeleton based approaches seem to be robust, one may wonder what happens on real sequences where the skeleton is obtained only intermittently. Indeed, some algorithms such as DTW combined with the nearest neighbour classification framework which relies directly on comparison between skeleton articulations positions can not deal with missing observation. In the last section, we describe the behaviour of our algorithm on intermittent data. More precisely, we show that the BOG descriptor is flexible enough to adapt to missing data and provides results comparable to state-of-the-art ones.
BEHAVIOUR OF THE METHOD ON INTERMITTENT DATA In order to evaluate the performance of our system in presence of missing skeleton data, incomplete sequences could be generated from complete ones by removing data. However real-life failure can be correlated with specific part of the action and in the worst cases, with the most discriminative parts of the action. Hence, we decide to study the performance of our system on real intermittent data instead of artificial ones. In that purpose, we use the NITE software suite to extract skeleton from RGBD-HuDaAct dataset. As this database is not designed for skeleton extraction, some problems occur with side or back views of people, or when the subject has very specific postures that does not allow skeleton extraction. This provides incomplete sequences, and, we evaluate our algorithm according to various percentages of missing data.
Dataset The RGBD-HuDaAct dataset is presented in  (Ni et al. , 2011). It deals with 12 classes (+ one random class) of daily-life activities performed by 30 people. It is composed by 1189 RGBD-video sequences, around 1 minute each. Contrary to other datasets like  (Tenorth et al. , 2009; Sung et al. , 2011) , RGBD-HuDaAct is not designed for skeleton extraction. In TUM dataset  (Tenorth et al. , 2009) , skeleton stream is provided by a body-part tracker which is manually helped when tracking failures occur. In CUHA dataset  (Sung et al. , 2011) , skeleton stream is provided by the general public NITE software suite, but there is no skeleton extraction failures, maybe be- cause actions are fronto-parallel to the camera and relatively constrained. In RGBD-HuDaAct, sequences seem more natural (actions are not fronto-parallel to the camera.
) and skeleton extraction based on NITE software suite suffers from many failures. NITE software provides for each skeleton articulation, a boolean describing if the position is considered as reliable by the system. Hence, we consider that the skeleton is correctly estimated in a frame, if and only if, one skeleton only is extracted from the frame and for all body-parts of interest (feet, knees, hips, shoulders, head, elbows and hands) the corresponding booleans are true. In order to measure the intensity of failures in skeleton extraction, the ratio between the number of frames where skeleton is correctly estimated and the total number of frames is computed for each sequence of the dataset. This ratio expressed in percentage is called, in this work, SER for Skeleton Extraction Ratio, and is used as a measure of the sequence validity.
The histogram corresponding to the number of sequences versus SER is presented in figure  5. We can already notice that for more than ten percents of the sequences, the skeleton is never extracted during the entire sequence. Figure  6  shows the average SER for each action of the database. It varies between 51. 3% for stand up (T) and 82. 3% for put on jacket (D). It can be noticed that the sequences where the skeleton is not well extracted do not rely on the performed actions. The large range of SER presented in figure  5  allows us to evaluate our action classification system with different levels of skeleton extraction failures. For this purpose, we extract all sequences with a SER greater than λ% (where λ is an integer varying from 0  to 100) and evaluate our algorithm on this subset using the leave-one-subject-out scheme as in  (Ni et al.
Results on Intermittent Data The evolution of the correct-classification rate versus λ (minimal SER allowed) is presented in figure  7. We do not evaluate the system for λ greater than 88 as only 10% of sequences from RGBD-HuDaAct have a SER greater than 88%. The correct-classification rate is low both when there are too few data (λ close to 88) or when data contains some heavily corrupted samples (λ close to 0). However, for λ = 69 (corresponding to 442 sequences) a good compromise is found between the number of sequences and their quality: the algorithm has 82. 41% of correct-classification rate. In order to link these results to the state-of-the-art, let us remind that the best known results  (Ni et al. , 2011)  on this dataset is a correct-classification rate of 81. 5% on 59% of all sequences (655 sequences ran-domly sampled).
Two multi-modal strategies, combining color and depth information, have been developed: spatio-temporal interest points (STIPs) and motion history images (MHIs). These two methods do not use skeleton and thus are not sensitive to the failure problems during skeleton extraction. It is not easy to predict their results on the complete dataset, or, on the 442 sequences leading to our 82. 41% of correct-classification rate. Moreover, as the 655 used sequences were randomly selected, it is not possible to build their testing database in order to compare both results. However, our algorithm is competitive with their method as the recognition rates are similar. Hence, we can conclude that skeleton based approaches like our algorithm provide state-of-the-art results even on intermittent data and thus can be used for action recognition in real-life.
CONCLUSIONS In this paper, we evaluate 7 trajectory descriptors in context of human action recognition based on skeleton trajectory. We first presented 6 short term trajectories descriptors based on position, speed or acceleration. The last descriptor is more original since it extends the well-known bag-of-words approach to the bag-of-gestures ones, defined only on 3D position of articulations. To our knowledge, this is the first time that bag-of-gestures are defined using 3D points. Performances of each descriptor and combination of them associated with a same MKL classifier have been evaluated on the public CUHA and TUM datasets, for which skeleton stream is provided. The main result is that the descriptor based on bag-ofgestures outperforms three very recent methods of the state-of-the-art on the two databases : 88.
9% of F 0. 5 measure on CUHA database and 84. 5% of correct classification on TUM dataset. We also study the proposed algorithm on a more difficult database not designed to extract skeleton: the public RGBD-HuDaAct database. During the skeleton estimation performed with NITE software, some problems occurred with side or back views of people, or when the person has very specific postures that do not allow skeleton extraction. Even if the RGBD-HuDaAct database is really challenging to perform action recognition based on skeletons, it has been considered here to represent some conditions of video surveillance system at home. The main result of these tests if that even in these conditions, and with a significant amount of missing data, our descriptor achieves the state-of-the-art performance of 82% of recognition rate.
The goal of pE-DB (http://pedb. vib. be) is to serve as an openly accessible database for the deposition of structural ensembles of intrinsically disordered proteins (IDPs) and of denatured proteins based on nuclear magnetic resonance spectroscopy, smallangle X-ray scattering and other data measured in solution. Owing to the inherent flexibility of IDPs, solution techniques are particularly appropriate for characterizing their biophysical properties, and structural ensembles in agreement with these data provide a convenient tool for describing the underlying conformational sampling. Database entries consist of (i) primary experimental data with descriptions of the acquisition methods and algorithms used for the ensemble calculations, and (ii) the structural ensembles consistent with these data, provided as a set of models in a Protein Data Bank format.
It is difficult to argue against this critique because the structural ensembles themselves often are not deposited on publication, and only conclusions based on their analysis are described. Further, the variety of computational approaches proposed for the calculation of the structural ensembles have never been critically assessed and compared. We propose to remedy to this situation by launching pE-DB, which provides access to the primary experimental data, the algorithms used in their calculation and the coordinates of the structural ensembles themselves. We encourage the community to deposit structural ensembles of novel proteins and even to recalculate ensembles based on the primary experimental data. pE-DB is complementary to other disorder-related databases, such as DisProt  (16) , the database of binary disorder classification based on biophysical data, and two sequencebased disorder databases, D 2 P 2  (17) , which holds disorder predictions, and IDEAL  (18) , which contains manually curated annotations of IDP location, structure and functional sites.
pE-DB is most closely related to Biological Magnetic Resonance Bank (BMRB)  (19) , which hosts primary NMR data linked to pE-DB, but no other type of experimental data or structural ensembles. pE-DB also has an interesting relationship with Protein Data Bank (PDB)  (20) , the major structural database that hosts Xray-and NMR-derived structures of folded (ordered) proteins, resting on the principle that a protein has a single 'real' structure. Last but not least, pE-DB has a resemblance to the Ensemble Protein Database (http://www. epdb. pitt. edu/), which, however, holds sets of structures of folded proteins generated by computer simulation. In the context of these related databases, pE-DB provides a forum for the deposition of models of structural ensembles of IDPs, which predictably will provide a platform for critical evaluation of ensemble calculation methods and eventually lead to the development of experimental and computational standards and protocols that will become accepted in the IDP field and beyond.
We believe creating and publishing the database will stimulate the community to submit their data, and we hope to see a rapid increase in the entries/ensembles/structures deposited. We are committed to stimulating the field to grow and to eventually reach a state of deposition being the condition of acceptance of IDP structural work. We are convinced that this initiative offers the rich reward of bringing the IDP field to maturity through understanding the structural underpinning of IDP function in physiology and disease, with the ultimate prospect of developing novel drugs targeting IDPs involved in disease  (21, 22). Tau K18 FM, ASTEROIDS RDCs, PREs This table is not intended to be exhaustive, but only presents ensembles that contributed to the development of the concept and method development.
After generating the starting pool, experimental data are back-calculated from the conformers to enable a direct comparison with actual observations. For SAXS data, programs are available, e. g. CRYSOL  (38) , to calculate scattering curves for each individual conformer. For NMR data, FM can estimate CSs [using ShiftX, SPARTA  (39) ] or related CS prediction approaches, RDCs using local alignment combined with long-range effects modulating RDC baselines, or global alignment, PREs accounting for local and long-range correlation times, SAXS (using CRYSOL) and J-coupling values for the generated conformer pools, or ENSEMBLE (40) can be used. ENSEMBLE uses CRYSOL for SAXS data, HYDROPRO  (41)  for NMR-derived Rh data, ShiftX  (42)  for CS data, a local-alignment approach  (43)  for RDCs and internal scripts for solvent accessibility, PREs, J-couplings, R 2 relaxation rates and nuclear overhauser effect (NOE) values.
The aim of the ensemble calculation is to select a subset of conformers whose back-calculated values fit the actual experimental data coming from SAXS and NMR measurements. The software Gajoe, part of EOM, deals with the selection of the pool of conformers that fit the theoretical and experimental SAXS curves best. The program ASTEROIDS  (25, 44)  starts from the statistical coil model derived from FM, and selects ensembles, iteratively repopulating underlying potential energy landscapes and recalculating all experimental data from each newly calculated ensembles. The approach uses a genetic algorithm to converge to ensembles whose elements are different in each ensemble, but that are in equal agreement with the experimental data, within the level of the experimental noise. The approach makes extensive use of cross-validation of data that are not used in the selection procedure to generally test the predictive nature of the approach and to guard against over-fitting.
First, the quality of the final ensemble depends strongly on the quality of the experimental data. Aggregation, degradation or sample purity issues can severely affect the reliability of measurements and hence of the corresponding ensembles. In case of techniques such as SAXS, experiments always yield interpretable results, i. e. data has to be carefully examined and controlled. Although the predictive nature of the different ensemble approaches can in principle be tested against data that are not included in the selection, this is rarely done in practice. Furthermore, if insufficient data are used (as is invariably the case), there could be multiple structural ensembles that are equally consistent with them, hence preventing an unambiguous answer to the problem of determining the correct structural ensemble.
In addition, given the large number of degrees of freedom and the astronomical number of potential structures and IDP visits, multiple different ensembles can be always computed describing the experimental data with the same level of agreement, which will happen in all circumstances, as it is inherent to ensembles of disordered proteins. Despite these ambiguities, due to constraints coming from SAXS and NMR, the ensembles have to show similarities in hydrodynamic behavior and also in local structural preferences. The level of similarity, however, has to be established, and the purpose of pE-DB is to help resolve these issues and drive the development of robust methodologies and concepts for deriving physically realistic structural ensembles.
DATABASE STRUCTURE AND CONTENT pE-DB is implemented as a relational MySQL database that consists of a core set of generic tables storing metainformation and dedicated modules for NMR and SAXS experimental parameters (Figure  1 ). The core tables record information on the proteins used in the experiments (e. g. sequence, molecular weight, mutations, posttranslational modifications, etc. ), cross-links to relevant databases, such as UniProt  (45) , Ensembl  (46) , BMRB  (19)  or DisProt  (16) , the organisms and expression systems used and meta-information regarding the authors and-if applicable-related publications. The SAXS and NMR modules consist of multiple tables recording the complete description of the experiments. Database entries have unique four-letter identifiers that are the primary keys used to link related tables to the core table.
These identifiers connect the meta-information recorded in the database and the actual data files stored on the pE-DB file server. Three types of data files are stored locally: NMR-related values, i. e. lists of CSs, RDCs, PREs or J-couplings, scattering curves from SAXS measurements and sets of structural ensemble files in PDB format. Ensembles consist of a few dozen to hundreds (and possibly even more) of conformers and each entry may have more than one ensemble associated to it, since multiple ensembles may fit the experimental data equally well. The database is open to submissions from the community and researchers are encouraged to submit their data to pE-DB using the online submission interface. Data submission is initiated by filling out a pre-submission form describing briefly the experiments and providing related publications, if applicable.
Data can also be submitted before publication; in such cases, the entry will be released only after the date specified by the authors. Presubmission forms are processed and if found suitable, the pE-DB crew contacts the submitters requesting additional data and information. Submitters are required to provide meta-information by filling out an online submission form, followed by uploading their experimental data and Figure  1. Structure of the pE-DB database. The relational data model of pE-DB consists of a set of tables organizing modules, all connected to the main table recording the four-letter unique pE-DB identifier. Supported data types have dedicated table sets, storing relevant information to provide full description of the structural ensembles, the calculation procedures and the underlying experimental data.
Searching and browsing The online user interface of pE-DB provides support for accessing data in multiple ways from browsing and quick searches to bulk downloads, complex queries and SQL commands. pE-DB can be browsed according to different criteria, such as accession identifier, protein name and data type. Selecting any of these options leads to a list of pE-DB entries with relevant information depending on the selected browsing option. The number of entries per page can be specified using the scroll window next to the 'Browse by' label and pressing the 'Go' button. Searching the database can be done by typing the query string at the 'Search' section at the top of the window. By default, this will search entries with any type of data and in every string category. Optionally, the type of the string can be specified with the scroll-down button next to the text field.
The type of experimental data type can also be specified using the bottom menu of the section under the text field. Using the advanced search interface, an arbitrary number of query strings can be used. Again, the type of the string and the experimental data type can be specified, and users need to specify the Boolean operator (AND/OR). Both search methods return a list of matching entries with brief descriptions and direct links to download data (Figure  2 ). Advanced users may perform complex searches by using an online SQL terminal. The data scheme of the database required for formulating selection queries can be found under the 'Documentation' section of the Web site. Users may only carry out 'SELECT' type commands.
Data retrieval The key for data retrieval from pE-DB is the unique identifier of each accession. In case of single entry downloads, users may navigate to the accession screen using any of the methods detailed above and select from various download options, i. e. downloading the complete data archive, only specific data types, sequences or structural ensembles. Bulk downloading can be done by navigating to the 'Download pE-DB' section on the Web site. Here, the complete pE-DB can be downloaded as flat SQL file or tab-separated. sv file. NMR, SAXS and structural data along with nonredundant sequences (in FASTA format) may also be retrieved. By providing a list of pE-DB identifiers, users may download sets of sequences, experimental data, structural archives as well as complete entries.
Clicking on any of these figures leads to a new window where users may find each ensemble and each conformer with its corresponding radius of gyration (R g ) and D max values. Each conformer can be visualized using a built-in customizable Jmol applet (Figure  4 )  (47, 48). The SAXS and NMR sections display experimental parameters and settings, as well as links to download the data archives, and in the case of SAXS data to visualize the scattering data with normalized Kratky plots, P(r) distance distribution plots, Guinier-plots and the scattering curve itself. In the case of NMR data, since CSs are the primary requirement of any NMR investigation, and thus always available, these are used to produce secondary structural propensity plots that indicate the propensity of different parts of the polypeptide chain to adopt secondary structural conformations.
AVAILABILITY The database is freely available at http://pedb. vib. be. We encourage users to register free accounts, to be able to Figure  3. Jmol applet and list of conformers. Entries in pE-DB may have multiple ensembles, which may fit equally well the underlying experimental data. By navigating to the Jmol applet screen, the user can view the R g distribution of each ensemble, the number of conformers and the average values for the R g and the maximal distance (D max ). By clicking on the 'Show/Hide' button, a list of the conformers appears, featuring R g and D max values and a Jmol button. Clicking on the Jmol button, every single conformer can be selected to be visualized by a fully customizable Jmol applet. Figure  4. pE-DB entry screen. pE-DB entries display all the available meta-information for each accession, direct download links to various data types, sequences and structural ensembles, and a selection of figures and plot to visualize the data.
CONCLUSIONS AND OUTLOOK We believe that the establishment of pE-DB represents a cornerstone in the evolution of the IDP field, opening the way to assessing and perfecting methodologies for the structural descriptions of the disordered state, a goal which is critical for developing quantitative structurefunction models of IDPs  (4, 27). In this new era of structural biology, description of biomolecules as single static structures is increasingly recognized as being inadequate for understanding function. Rather, proteins must be described as ensembles of thermally accessible conformers. Since the pE-DB database represents a radical break with our traditional ways of looking at protein structures, it may also provoke novel modes of structure visualization addressing the multiplicity and dynamics of structures, such as by videos or continuum spatial functions.