dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to increase model generalizability, data augmentation techniques were used during training. In detail, the 3D data from MOCAP1 were randomly rotated at each training epoch, with added pan Dq p 2 [ 45 , +45 ], added tilt Dq t 2 [ 20 , +20 ] and added roll Dq r 2 [ 5 , +5 ]. The proposed DNN is implemented with Keras [52]  on top of TensorFlow  [53]. All hidden layers use Rectified Linear Unit activation  [54] , with Dropout to prevent overfitting  [55]. RMSProp was used as the gradient optimizer  [56]. Six neuron layers were stacked, with sizes  [28, 28, 28, 28, 28, 14]. The proposed Image ! 2D ! 3D pipeline for processing the 3D upper body pose from signers in RGB frames is shown in Figure  6.
This is mostly due to the fact that hands can move fast in SL production, causing motion blur around the hands and forearms. Moreover, the hand shapes used in SL can be very sophisticated, and somehow never used in the daily life of non-signing people. Therefore, in all likelihood, the data that were used to train the OP models did not include such hand configurations, which sometimes makes predictions unreliable. That being said, the OP hand module can still be seen as a good and light proxy for hand representation. Let us note that although Image ! 3D hand pose models have been developed (see for instance  [49] ), we have not found any that was able to provide a reliable estimate on hand pose on real-life 25 fps SL videos. Indeed, these models are even more sensitive to the issues encountered by the Image ! 2D estimators.
Several layers of BLSTMs can then be stacked, as shown on Figure  8  detailing a two-layer BLSTM. Our experiments usually include one to four BLSTM layers. An interesting addition for helping the network build relevant features is to set up the first layer as a one-dimensional temporal convolution. Temporal convolutions can help with noisy high-frequency data like ours, and are good to learn temporal dependencies  [66]. A convolution layer with a kernel width of three frames is included on Figure  8. Using the Keras library  [52]  on top of TensorFlow  [53] , we then built a modular architecture (https: //github. com/vbelissen/cslr_limsi/) with a convolutional layer (with as parameters the number of filters and the kernel size), one or multiple LSTM or BLSTM layers (with as parameters the number of units), a FC layer and a final softmax operation for classification.
Qualitative Analysis on Test Set Although performance metrics provide interesting insights on the results of the proposed model, a more qualitative analysis is needed. In this section, we analyze the prediction results of the proposed model, on two test sequences of Dicta-Sign-LSF-v2 (Video clips are visible at https://github. com/ vbelissen/cslr_limsi/blob/master/Clips. md). The signer representation is decided from the optimization Tables  8  and 9. The chosen setup is signer-independent and task-dependent (SI-TD). The test signers are then unknown both from the training and validation sets. These results complement preliminary analyses focused on DSs and developed in  [67]. In this analysis, we have trained four binary descriptors, corresponding to FLSs, DSs, PTSs and FBuoys. In Figures 9 and 10 we show, from top to bottom: a few key thumbnails, a proposed English translation, expert annotations for FLSs and PLSs-each on three tracks, corresponding to right-handed, two-handed or left-handed units-and model predictions.
Conclusions In this work, we have first focused on improving the input data for CSLR systems, proposing Dicta-Sign-LSF-v2, a LSF corpus previously made by linguists. We then developed a general description of the problem of CSLR with adapted metrics. In order to realize a first series of experiments with this broader definition of CSLR, we have introduced and implemented an original combination of signer representation and learning model, using a mix of publicly available and self-developed models and a convolutional and recurrent neural network. Finally, we have conducted a thorough analysis of the recognition performance of the proposed model for four very different linguistic DSs, PTSs and FBuoys-on Dicta-Sign-LSF-v2. We have shown that promising performance values are met. A qualitative analysis on the test set then illustrates the merits of the proposed approach.
Continuous Sign Language datasets. The top corpora have been developed and used by the SLR community, but they are either artificial or not representative of natural Sign Language. Others have been built by linguists, with natural discourse and detailed annotation, although they are not always consistent. To the best of our knowledge, Dicta-Sign-LSF-v2 and NCSLGR are the only two corpora built by linguists that have been used for beyond gloss-level CSLR experiments.
To better constrain transponder positions, we also consider the baseline lengths and the relative depth-differences between different pairs of them. We illustrate the usefulness of our forward modeling approach and least-square inversion by simulating different experimental protocols (i. e. , platform trajectories, with or without information on the distance and depth between transponders). We find that the overall accuracy of a GNSS-A experiment is significantly improved with additional information about the relative depths of the instruments. Baseline lengths also improve the accuracy, but only when combined with depth differences. The codes in Python3 used in this article are freely available online.
Least-Squares Approaches Least-squares methods are commonly used in geodesy (e. g. ,  [20] ). They allow adjusting parameters of a known model to a dataset and find the parameters that best fit the data (i. e. , for which the sum of the square of the residues is minimum). They have the advantage of being linear, which eases their implementation and helps run a large number of simulations and tests. LSQ approaches are widely documented in the literature and can be described in many different ways. Here, we present a general formulation adapted to our problem and show how to combine observations or observable quantities (input data) of different types and to constrain them to certain values to obtain estimates of unknown parameters (output data). The notation conventions are given in the Supplementary Materials (Section S1).
For such trajectory, the best accuracy level is reached when depth-difference and baseline length measurements are used. Note that the precision of the depth differences has to be of the same order of magnitude as the expected final positioning accuracy. Thus, the embedment in seafloor transponders of high-quality sensors, such as self-calibrating pressure sensors, able to jointly record accurate depth and baseline lengths would be highly beneficial to future experiments. Python 3 source codes developed for this article are freely available at an online GitHub git repository upon request to the corresponding author.
Tracker program and data collection The GPS-trackers were programmed using grafcet language (e. g. David, 1995)  to describe as accurately as possible the bottle trajectory while saving battery as much as possible (up to two month survey). The GPS tracker can be in three states. "State 0" means that the tracker is off. It shifts to "state 1" when the tracker is disconnected from the charger and when the programmed start time is reached. When in "State 1", the tracker records one position every 2 h in order to get several positions for a nearly 12-h tidal cycle and positions are sent to the server by GSM/GPRS once a day. If the tracker does not detect any motion for 14 h, it is considered that the tracked bottle is stranded somewhere for more than one tidal cycle (~12 h). In such a case and to save battery, the GPS tracker moves to "state 2" where no new positions are recorded, while the previous positions are sent only once a week.
The program goes back to "state 1" if the tracker detects a motion again. And it goes back to "state 0" when the tracker is connected to the charger. Data were sent using GSM/GPRS systems first to a commercial platform (Deverylock) and after a few months to a homemade and free data center. GPS positions were processed with the GIS open-source software QGIS (https://www. qgis. org/en/site/). A Python program was developed to assign a kilometric point (pk) to each position, which enabled to calculate travel distances. A pk is equivalent to a "mile marker" found on roads. In summary, the program projected all GPS-positions on a virtual streamline in the middle of the channel called hydro_line. Then, the program measured the distance between each position relative to pk on the hydro_line in order to assign a pk for each position.
Integration of mean-field models in TVB The Virtual Brain  19, 20  is a simulation environment which allows one to integrate the connectivity between different brain regions, estimated for example using diffusion imaging, into a network of many nodes connected according to this connectome. Figure  2  schematizes the integration of the AdEx mean-field models in TVB. We used a human connectome determined from diffusion magnetic resonance imaging collected in the Human Connectome Project, which can be found in https://zenodo. org/record/4263723. X9vvhulKg1J (berlin subjects/DH_20120806). Mean-field models of AdEx networks will be placed at each node of the TVB simulation, and will be connected by excitatory connections according to the connectome. Using such a scheme, is is then possible to simulate large scale networks using AdEx-based mean-field models in TVB, hence the name "TVB-AdEx" model.
We believe that this work opens several perspectives. First, the enhanced propagation of stimuli during asynchronous states could be used as a basis to explain why stimuli are perceived in asynchronous states, and what kind of modulation of the network activity could support phenomena such as perception or attention. Second, mean-field models can be set to also display pathological states, such as hyperactive or hypersynchronized states, and the TVB-AdEx model could be used to investigate seizure activity. Other features could also be included in mean-field models, such as neuronal heterogeneity  14  , and further increase their biological realism. Finally, the anatomical backbone and functional parameters of this model can be substituted to study other animals. For example, the Allen Institute offers a rich database of neuronal connectivity from axonal tracing studies in mouse (https://alleninstitute.
1a ). Before explaining these elements in more detail, we will describe HPP's criteria underlying the establishment of the high-stringency human proteome. Defining the human proteome at high-stringency. The HPP relies upon a coordinated system developed by neXtProt and UniProtKB, which attributes five levels of supporting evidence for protein existence (PE)  6. Evidence at PE1 indicates clear experimental evidence for the existence of at least one proteoform, based on credible identification by MS, Edman sequencing, Xray, nuclear magnetic resonance (NMR) structure of purified natural protein, reliable protein-protein interaction and/or antibody data. PE2 indicates evidence limited to the corresponding transcript (cDNA, reverse-transcriptase PCR, northern blotting). PE3 indicates the existence of orthologs in closely related species.
PE4 refers to entries based on gene models without evidence at protein, transcript or homology levels. PE5 classifications indicate that coding evidence is doubtful and/or probably corresponds to an incorrect in silico translation of a non-coding element. As PE5 entries are largely non-coding, the HPP preferentially tracks only PE1,2,3,4 protein-coding entries. Proteins classified as PE2,3,4 are colloquially referred to as missing proteins  7, 8. Since 2013, neXtProt and the HPP (Fig. 1b ) have issued annual tallies of PE1,2,3,4,5 status (www. nextprot. org/about/protein-existence), which have been reported in annual collaborative metrics manuscripts (ref. 9 and references therein). The latest neXtProt HPP reference release (https://www. nextprot. org/about/statistics, data release: 17-01-2020) designates credible PE1 evidence for 90.
HPA also integrates multi-omics data. It contains extensive transcriptome data and neXtProt PE assignments, and contributes to the open-access catalogue Antibodypedia, containing >4 M Abs (www. antibodypedia. org)  42 against >19,000 targets that assist the community to select application-appropriate Abs. At HPP launch, the HPA had detected >50% of the proteincoding genome  43. Currently, ~87% of the proteome is targeted by >1 HPA Ab, detected though an encyclopaedia of >10 M annotated high-resolution digital images, partitioned into a number of sub-atlases that are interconnected  [44] [45] [46] [47] [48]. These currently comprise the; Tissue Atlas (protein distribution across all major tissues), Cell Atlas (subcellular localization and heterogeneity in single cells), Pathology Atlas (correlations between gene expression and patient survival in major human cancer types), Blood Atlas (protein profiles across major immune cells and blood levels), Brain Atlas (protein distribution in the brain), and the Metabolic Atlas (various tissue metabolic enzymes localizations).
Over the decade, HPA's open-access database  [44] [45] [46] [47] [48]  has become one of the world's most visited biological resources (>3. 6 M visits in toto annually). The HPA also plays an emerging role in establishing guidelines around the appropriate use of Abs and ensuring immunoassay validation  [49] [50] [51]. It recently spent considerable effort validating the selectivity of their Abs, including championing efforts of the International Working Group for Ab Validation proposing many new approaches implemented across >10,000 Abs  42. In addition, HPA's massive collection of images has supported a multitude of publications and become a citizen science resource for developing AI classification learning models  52, 53. The HPA is sustained through community contribution to ELIXIR  54  , that allows scientists from academia and industry to explore spatio-temporal aspects of the human proteome  [55] [56] [57].
PeptideAtlas (http://www. peptideatlas. org) uses sequence search engines Comet 66 , X!Tandem  67  and SpectraST  68  to reprocess publicly uploaded MS/MS data deposited through ProteomeXchange (PX). Data are aggregated using rigorous criteria including peptide spectral matching with FDR ~0. 0009% in the latest PeptideAtlas build to achieve ≤1% FDR at the protein level. MassIVE searches public datasets using the MS-GF+ search engine  69  , also with strict criteria  70  to enforce global <1% FDR at the protein level for single-peptide identifications and stricter <0. 01% FDR for proteins identified by >2 peptides. PeptideAtlas and MassIVE peptide lists are integrated into current neXtProt builds. neXtProt then cross-references all peptides to protein entries and validates PE levels, requiring at least two MSidentified uniquely mapping 9-mer non-nested peptides coming from either PeptideAtlas or MassIVE.
neXtProt builds on UniProtKB/SwissProt PE1 entries that include MS, partial or complete Edman sequencing, X-ray or NMR structure, reliable protein-protein interaction data or Ab detection by considering additional PeptideAtlas/MassIVE data that meets minimum peptide uniqueness, number, length, nestedness and other requirements to upgrade entries to PE1. Noticeably, neXtProt is becoming more reliant on MS than non-MS data (e. g. , 1860 non-MS data PE1s in 2016 down to 950 in 2020). To ensure only high-quality Ab data are used, in 2018 neXtProt/HPP Ab pillar revised criteria to upgrade entries to PE1, including specificity and other rigorous criteria suggested by the International Working Group for Ab Validation  50. As an example, neXtProt recently analysed 41 Ab-based publications and upgraded three PE2,3,4 entries to PE1.
Missing protein analyses. neXtProt protein descriptors and associated data can be used to analyse protein groups/families according to their Chr location or PE classification. Missing protein (PE2,3,4) analysis indicates that some groups/families have been upgraded to PE1 more successfully than others (Fig. 3a ). For example, between 2011 and 2020, 372 zinc (Zn) finger proteins, 171 transmembrane proteins, 93 carbohydrate metabolism proteins, 90 testes-, sperm-, prostate-associated proteins, 78 coiled-coil domain-containing proteins and 58 homeobox proteins have been upgraded from PE2,3,4 to PE1. These represent the six most prominent protein groups upgraded to PE1 over the decade (Fig. 3a , green). In contrast, two G protein-coupled receptor (GPCR) chemosensory families prove particularly resistant to PE1 upgrade: many olfactory receptors (ORs) are still missing in 2020  76  (417 of the 2011 PE2,3,4 entries, including putative and uncharacterized ones) and 17 of 20 taste receptors remain PE2,3,4 s (Fig.
Community impact on the human proteome One barometer of community engagement in the HPP is the magnitude of investigator-submitted raw MS data that have been re-analysed. Many journals require raw data submission and HPP action was a significant factor in journals adopting requirements aligned to HPP data deposition guidelines. Raw data deposition occurs through PX, which registers and standardizes capture/dissemination of public MS data from partner repositories, including founders PRIDE  62  and PeptideAtlas and recent members Mas-sIVE  64  , jPOST  85  , iProX  86  and Panorama Public  87. As of 2020, a total of 4634 human MS datasets have been received. Each PX dataset is branded with a unique PXD identifier with depositors, publications and voluntary metadata noted  88, 89. Illustrating the magnitude of this community data, ~470 TBs of data have come from 5658 human datasets (~47% of 1 petabyte PRIDE volume), with only 358 (6.
3%) of these specifically tagged by depositors as from the HPP. The HPP encourages raw human MS data/metadata submission (including association to HPP) through PX, and that journals request that PXD identifiers be published in accordance with FAIR principles  17  as discussed above. To provide an additional measure of global scientific impact, HUPO commissioned the website construction of the Human Proteome Reference Library (HPRL; https://hupo. org/HPP-HPRL/), where all HPP-associated PubMed searches are hyperlinked and can be accessed and re-run routinely by the community. These hyperlinked searches automatically produce the latest PubMed outputs in a manner where all PubMed filtering, ranking and timeline tools can be applied subsequently by a user as required. As an example of the fascinating data unearthed, a 'human proteome project' search showed that the structural biologists Montelione and Anderson first suggested the possibility of building a HPP in their 1999 Nature Structural Biology publication  90  -well before HUPO or the HPP began.
The HPRL can also be used to measure impact (Fig. 5 ), where PubMed identifiers (PMIDs) from 2010 to 2019 references can be captured using web APIs or software, e. g. , NCBI E-utilities like https://www. ncbi. nlm. nih. gov/books/NBK25501 or the easy-PubMed R interface (www. rdrr. io/cran/easyPubMed) that enable extraction and aggregation of PubMed bibliometric records. Paralleling the low percentage of PX datasets tagged as emanating from the HPP, HPRL data show only ~1000 PubMed title and abstract searches that specifically and cumulatively mention either the terms HPP, C-HPP and/or B/D-HPP (or their unabbreviated counterparts) out of a total of more than 50,000 'human AND proteomics' title/abstract search outcomes over the decade. In contrast to this and more reassuringly, HPRL searches of the community's biology/ or disease studies covered by nine of the B/D-HPP teams generated >5000 publications each since 2010 (Fig.
Of note, melanoma tumour genomic BRAF driver mutations match corresponding protein sequences  97  , illustrating that proteomic landscapes add value to genomic data, when considered with patient histopathology and clinical metadata  98. Proteomics substantially benefits a comprehensive understanding of precision medicine. To illustrate this, NCI's Clinical Proteomic Tumor Analysis Consortium (CPTAC; proteomics. cancer. gov) in collaboration with the B/D-HPP cancer team, established guidelines, data sharing, and standards in analytical and computational workflows to ensure rigour in designing and performing research  [99] [100] [101] [102] [103]. CPTAC applied these standards and workflows to tumours previously genomically characterized by The Cancer Genome Atlas. In doing so, CPTAC pioneered the integration of proteomics with genomics (i.
e. , proteogenomics) to produce a more unified and comprehensive understanding of cancer biology and implemented its transition into cancer clinical research studies  96, 104, 105. Largely due to these efforts, NCI hosts open-access repositories of unified proteogenomics datasets, assays and reagents, including the Proteomic Data Commons (pdc. cancer. gov), a fit-for-purpose targeted assay site (assays. cancer. gov)  102, 106 and an Ab portal (antibodies. cancer. gov)  107. These activities empowered the recent creation of the International Proteogenome Consortium (ICPC; icpc. cancer. gov)  108. Collectively, CPTAC and ICPC collaborators have comprehensively characterized 13 cancer types at the proteogenomics level, with all datasets publicly accessible  [109] [110] [111] [112] [113] [114] [115] [116] [117].
MALDI-time-of-flight (TOF)-MS, once considered revolutionary, is now established as a routine tool in clinical microbiology  133. Classical phenotypic tests identify unknown and potentially pathogenic microorganisms, but may require incubation for several days, with misidentification resulting in adverse treatment consequences. MALDI-TOF-MS provides significantly shortened analyses (now minutes) with improved accuracy on single colony or bacterial pellets for difficult-todetect microorganisms, using automated spectra acquisition and extensive reference spectra databases  134. Minor spectral differences enable typing below species levels  135  , allowing subspecies identification through epidemiological analyses. Bacteria and yeasts (most clinical identifications), mycobacteria  136  and moulds  137  can now be identified accurately and rapidly.
Acknowledgements HUPO acknowledges collaborators, proteomic scientists, independent partners, industry vendors and members of the scientific community who have contributed to the HPP. A full alphabetical listing of the Human Proteome Project members appears in the Supplementary Information. In recognition of the many accomplishments, HUPO has produced a publicly available HPP timeline available through https://hupo. org/Proteomics-Timeline to be released with this HPP Blueprint. Parts of this work were supported by grants to ProteoRed  PRB3-ISCIII, PT17/0019/0001  Comunidad  de Madrid Grant   B2017/BMD-3817  (F. J. C. );  Korean Ministry of Health and Welfare   HI13C2098  and  HI16C0257  (Y. K. P. );  NIH  grants  P30ES017885  and  U24CA210967  (G. S. O. ),  5U01HL-13104204 ,  PADOM-SPO11347  and  PARYB-SPO112285  (M.
P. S. );  NCI CPTAC U24CA210985  and  NCI EDRN U24CA115102  (D. W. C. );  NIH National Institute of General Medical Sciences   R01GM087221  (E. W. D. /R. L. M. ) and  R24GM127667  (E. W. D. );  NIH National Institute on Aging   U19AG023122  (R. L. M. );  NSF   DBI-1933311  (E. W. D. );  CIHR   COVID-19 Rapid Research Funding  ( F20-01013 ),  CIHR Foundation  Grant FDN:14840 and  Canada Research Chair  (C. M. O. );  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé   ANR-10-INBS-08  ( Proteomics French Infrastructure ProFI  (Y. V. );  Wellcome Trust   WT101477MA  and  208391/Z/17/Z  (J. A. V. );  Knut and Alice Wallenberg Foundation  (M. U. , C. L. , J. M. S. , E. L. ); Brazilian  CAPES 88887. 130697 , CNPq 440613/2016-7 ,  FAPERJ E-26/210. 173/2018 (G. B. D. ) and  FAPERJ   E-26/202.