dataesr/scientific-paragraphs-categorization huggingface dataset:
A. Testbed setup and dataset collection Fig. 5  shows the experimental testbed, built in the 5TONIC laboratory  3  that is composed of: a 6-axis Niryo One robotic manipulator, a 2. 4 GHz IEEE 802. 11 access point (AP), an L2 switch, a 2. 4 GHz Silvercrest Wireless Transmitter that is used as WiFi jammer, and a joystick that is connected to a laptop with 8GB RAM and 4CPU@2. 4GHz via USB. The communication between the robot and the joystick is comprised of a wireless link from the robot to the AP and an Ethernet link from the AP to the laptop using the L2 switch. The Niryo One robotic arm is equipped with a Raspberry Pi 3 with a 1. 2 GHz 64-bit CPU, 1GB RAM, and an IEEE 802. 11n interface. The robot maximum speed is 0. 4 m/s for the steeper axes and 90°/s for the servo axis with a configured Robot Operating System (ROS) control command frequency of 50Hz (i.
e. , Ω = 20 ms) and command moving offset of 0. 04 rad. The remote control and robot system is ROS version 1. The Niryo One ROS stack expects to receive control commands each Ω ms and considers that a packet did not arrive on time otherwise, i. e. , the tolerance is τ = 0. Niryo One ROS stack uses the prior command ĉi+1 = c i in case ∆(c i+1 ) > Ω. Every received command is passed to the Niryo motion planning layer (MoveIt), which uses Proportional-Integral-Derivative (PID) control. Fig. 6  shows part of the dataset created by performing pick and place actions. The pick and place actions were manually repeated 100 times by two different human operators, an experienced and inexperienced human operator resulting in the creation of two separate datasets. To do so, they used the joystick as a remote controller, issuing a new control command every 20 ms.
The inexperienced/experienced operators' datasets' contain H = 187109 commands. Both datasets store the joint states c i of the robot manipulator under ideal network conditions, i. e. , low latencies and absence of packet collision. To achieve such conditions, the datasets were obtained using Ethernet to send the remote controller commands. The experienced dataset was used to train the ML models while the inexperienced data was used for remote control and testing. In this way, we ensure that the trained ML model operates on data that is tightly related but not the same as the training data.
B. Forecasting accuracy We now evaluate which of the selected forecasting algorithms achieves the highest forecasting accuracy in the collected datasets. The VAR algorithm implemented using statsmodel v0. 12. 1, and seq2seq using Tensorflow 2. 1. 0. Fig. 7  shows the RMSE accuracy of each algorithm as we increase the forecasting window, i. e. , how many consecutive commands are forecasted (a command is sent each Ω = 20 ms). For every algorithm, we considered a record of the last R = 1,. , 20 commands, and Fig. 7  plots the bestperforming R parameter for each algorithm. For the training stage (see §IV-C), we used the α = 80% of the experienced human operator data for training, and a β = 20% of the inexperienced operator data for testing. It is worth mentioning that we performed the 80%-20% split of the datasets only for testing purposes, mainly because of the repetitive nature of the movements, whereby using 20% of inexperienced operator data is sufficient to give us insides of the most accurate model.
The RMSE is computed over the entire robot trajectory induced by the inexperienced human operator, and it considers commands arriving on time ∆(c i ) ≤ τ and out of time ∆c i > τ, without using control command forecasting (top in Fig. 8 ), and with FoReCo using MA and VAR (middle, and bottom rows in Fig. 8 ; respectively). The RMSE error in Fig. 8  is represented in logarithmic scale, and we can appreciate that FoReCo com-4 https://gitlab. it. uc3m. es/5g-team/FoReCo mand recovery constrained the robot trajectory error below 19. 95 mm, 26. 32 mm and 31. 81 mm using MA (middle row) and 9. 27 mm, 14. 90 mm and 19. 83 mm using VAR (bottom row) for 5, 15 and 25 robots on the factory floor, respectively. Fig. 8  shows that the VAR solution outperforms the MA solution in every simulation scenario for approximately 10 mm.
D. Experimental evaluation Motivated by the simulation results, we implemented and integrated a prototype of FoReCo that is based on VAR within the real remote control system presented in Fig. 5. The prototype is following the principles defined in §IV, and uses ROS to interact with the remotely controlled Niryo One robotic arm. All the details about the prototype implementation of FoReCo can be found in our publicly available git repository 4. In our first experimental analysis, we manually introduced loss of control commands in order to evaluate the improvements that the prototype offers under a controlled environment. The complete dataset from the inexperienced user was used to develop a remote controller that was randomly dropping consecutive control commands. Every time consecutive control commands were lost, FoReCo injected predictions from the VAR model.
2), since VAR builds its forecasts ĉi+1 using prior forecasted commands ĉi , ĉi-1 ,. , ĉi-R Thus, the prediction error propagates -see  (5). 2) Jammed experimental evaluation: In our second experimental analysis, we emulate a realistic interference scenario where the Silvercrest Wireless device is used to transmit synchronized radio waves in the same frequency as the robot, introducing unpredictable network delays ∆(c i ) and packet losses -same as the analytical model  [7]  used in prior simulations in §VI-C. The dataset from the inexperienced user was used in order to develop a remote controller that sent the control commands over the jammed wireless network. Every time control command(s) were lost or delayed due to the interference ∆(c i ) > τ, FoReCo injected predictions from the VAR model.
4, 13. 4 ] during more than a second, resulting in the highlighted error in Fig. 10. 3) Training and inference times: Since Niryo One is equipped with a Raspberry Pi3, which is limited in terms of computing power, we executed a set of experiments to measure both training and inference times of VAR whenever executed in the robot itself. While training takes around 5. 99±0. 06 min to train using the experienced human operator dataset, the inference (i. e. , prediction of the commands) only takes 1. 60±0. 16 ms. These results show that the current hardware of the robot is sufficient to not only accommodate FoReCo within the constraints of our prototype but also to support the implementation of more stringent applications control loops. Note that training is only required when we need to build or update the model, which time profiling is presented in Table  I.
C. Real-time Path Tracking Predictions Concerning the ML algorithm for repetitive reference trajectories, given the performance of VAR, our future work will consider other forecasting algorithms as exponential smoothing methods. We will also investigate other ML approaches, such as classification algorithms, to infer the type of command that comes next. It is worth noting that, although VAR performed well on the pick-and-place dataset, a deviation from the defined trajectory is witnessed when the number of consecutive commands losses increases. Future versions of FoReCo may mitigate large periods of consecutive losses by incorporating delayed commands that did not arrive on time, i. e. , commands c i with ∆(c i ) > τ could be used instead of the predicted one ĉi , to infer commands after nΩ ms.
05. Indeed, the test yielded p-values p ≤ 0. 0144 when it checked Grangercausality between every pair of axis; iv) the robot-axis time series are co-integrated, i. e. : ∀l, ∃{a l i } T l i : ∑ l T l ∑ i c l i a l i ∼ I(m), m ≤ min i {d : c l i ∼ I(d)} (14) with c l i ∼ I(d) meaning that time series of axis l has differentiation order d. In other words, there is a linear combination of the robot axis time series with order of integration below the order of integration of each robot axis individually. In our experiments we used the co-integration Johanson's test  [45] , and proved that (  14 ) holds because we sequentially rejected the null hypotheses of having k = 0, k ≤ 1,. , k ≤ 5 cointegrating vectors with significance level α = 0. 05. 2) Parameter estimation: after identifying VARMA as a suitable model, we have to estimate its parameters for our robot dataset.
leveraging the power of comparative analysis, while recognizing the differences between datasets. Besides effective genomic tools like BLAST and the JBrowse genome browser, additional interfaces enable advanced gene search and gene family analyses including multiple alignments and phylogenies. A synteny viewer enables the comparison of genome structures between chromosome-scale assemblies. Interfaces for differential expression analyses, metabolic pathways and GO enrichment were also added. A catalogue of variants spanning the banana diversity is made available for exploration, filtering, and export to a wide variety of software. Furthermore, we implemented new ways to graphically explore gene presence-absence in pangenomes as well as genome ancestry mosaics for cultivated bananas. Besides, to guide the community in future sequencing efforts, we provide recommendations for nomenclature of locus.
All these topics need access to various types of datasets and related query or visualisation interfaces. Here, we present an overhauled and enriched version of the Banana Genome Hub (BGH), a community database that serves as a central online platform for whole genome sequences and related omics data on Musaceae. We detail the implemented interfaces, and the way data were collected and curated. Finally, we list and discuss the status of sequencing projects and propose a locus name nomenclature for future projects about the genomics of Musaceae.
Gene(s) query including orthogroups and omics-related datasets Users have multiple ways to search for genes in the system, either using a gene locus (or a list of them), keywords, genomic coordinates powered by MegaSearch  [38]  or using the BLAST graphical interface searches from Sequenceserver  [39]  (Figure  2a ). Results are connected to genome browsers  [37]  specific to each genome. Comparisons between genomes are facilitated by tracks showing gene annotations projected on other genomes using the lift-over tool. It allows at a glance to see missing genes and investigate possible errors in the prediction of structural gene annotation  [40]  (Figure  2b ). Any gene search result lists several information including gene membership to orthogroups or gene families in Musaceae. The three versions corresponding to the M.
acuminata reference genome ('DH Pahang' v1, v2 and v4) were conserved in the system for traceability. To enable orthogroup visualization, we developed extension modules that support visualisation of multiple genome alignment and phylogenetic tree with all functionalities provided by MSAviewer  [41]  and PhyloTree  [42]  respectively (Figure  2c ) For users interested in gene expression patterns for specific gene(s), we built interactive interfaces based on the shiny apps technology (R package) to enable manipulation of data results from published studies  [29, 43, 44]. For instance, it is possible to search for genes annotated as RGA2, a putative nucleotide-binding and leucine-rich repeat (NB-LRR)-type resistance (R) gene known to be involved in the resistance to Fusarium wilt when overexpressed  [45] , and to check their level of expression in a study linked to Fusarium wilt  [29]  (Figure  3a ).
O R I G I N A L U N E D I T E D M A N U S C R I P T Also, additional datasets can be uploaded in the Diane suite  [46]  to perform differential gene expression analyses, expression-based clustering and gene regulatory network analyses in which Musa references genomes were added. Besides, when a list of genes is identified, users can quickly test in a few clicks for Gene Ontology enrichment for several genomes and without the need to extract functional annotations and use external software (Figure  3b ). With regards to other OMICS, there have been increasing numbers of proteomics and metabolomics experiments in banana  [30, [47] [48] [49] [50]. To complement these resources and enable various options like experimental data overlay on metabolic pathways, we set up the latest version of PathwayTools v25  [51] , named MusaCyc, that comprises a comprehensive set of interfaces to cover user needs.
Genetic variant search and usage This section, powered by the GIGWA tool  [55, 56] , gives access to a range of studies related to genetic diversity  [57] , GWAS  [31, 33] , Genomic selection or chromosome structure exploration  [58, 59]. Notably, available studies include SNPs of the diploid banana panel that was designed specifically for GWAS analyses  [31]  while corresponding plant material for this panel can be ordered for phenotyping at the International Transit Center (ITC) via the Musa Germplasm Information System (MGIS) website  [60, 61]. After filtering with advanced functionality, the datasets can be exported in multiple formats for subsequent analyses such as genetic diversity studies or directly visualized in JBrowse, IGV, Flapjack (and flapjack-bytes) (Figure  4 ). In addition, this catalogue of variants is compliant with BrAPI v1 & v2  [62]  and can be accessed programmatically and used in third party client or databases.
Genome ancestry mosaics viewer Cultivated bananas result from a relatively limited number of sexual events with inter(sub)specific hybridizations and recombination  [67]. The different ancestral contributions can be represented as genomic segments of distinct origin along the chromosomes. To provide access to recent studies that reported recombination between A and B genomes  [59]  and genome ancestry mosaics for a panel of diploid and triploid bananas  [66] , we embedded a new tool, called GeMo  [67]. By selecting an samples like 'Grande Naine' (AAA), an autotriploid cultivar belonging to the Cavendish subgroup, users can immediately spot the ancestral contributors of the M. acuminata subspecies, predominantly 'banksii', 'zebrina', 'malaccensis' (Figure  5b ). This viewer is intended to become a registry for any future studies performing in silico chromosome painting on Musaceae individuals but also enable user to manipulate their own data in a non-persistent way.
Transcriptomics and pathway related datasets Transcriptomics data supplied by the community were included  [12, 43, 44, 79, 81]. RNAseq data were mapped using STAR  [82]  and added in JBrowse as mapped tracks and in the download section. Whenever possible, derived reads count from published transcriptomics studies were collected and connected to the transcriptomics interface  [29, 43, 44]. For pathway related information, enzymes and metabolic pathways were predicted from the protein-coding genes of Musa acuminata 'DH Pahang' v4. Enzyme Classification (EC) numbers were predicted combining both tools PRIAM  [83]  and BlastKOALA  [84]. As a result, data were inferred for 774 O R I G I N A L U N E D I T E D M A N U S C.
Genome and transcriptome sequencing status O R I G I N A L U N E D I T E D M A N U S C R I P T The curated list of SRA genomic resources was searched on NCBI SRA  [88]  by filtering on Taxonomic ids for Musa and Ensete and metadata was extracted from BioSample metadata descriptions. Information on ongoing projects was obtained by personal communications and interactions within the scientific community.
Discussion and perspectives The Banana Genome Hub is a comprehensive platform dedicated to the genomics of a specific plant familythe Musaceae -as it has been developed for other families such as the Rosaceae  [89]  or the Juglandaceae  [90]. The core functionalities are similar by providing access to genome datasets via JBrowse  [91] , BLAST, synteny and gene families viewers. However, the BGH has some specificities taking into account the nature of the plant and the existing ecosystems of tools and databases in the community. An innovative pangenomics-related interface, Panache  [65] , has been implemented to support exploration of presence-absence variation (PAV). Both provides possible valuable resources for the design and exploration of precision genetics studies being conducted in the genus Musa  [52, 92].
Besides, as a vegetatively propagated plant with low fertility, unravelling the genome ancestry mosaics of cultivated bananas has been initiated to decipher it complex domestication history  [66]  and we provide a unique way to store and visualize, through GeMo, future work in that direction. For functional oriented studies, users have now access to handy interface to check gene expression and functional enrichment. Furthermore, the BGH intends to complement other databases on bananas and contribute to a better conservation and use of Ensete and Musa genetic resources. Contrary to the other portal  [89, 90] , the BGH does not intend to develop its own breeding module but rather proposes to implement BrAPI standards  [62]  to increase interoperability with the Banana instance of Breedbase  [93] ; which has been specifically designed for this purpose and that is actively supported by some banana breeding programs.
Like GDR  [89] , a catalogue of variants is curated by a different system, is shared with the Musa Germplasm Information System (MGIS)  [60]  to connect with the existing diversity of genetic resources conserved and documented in genebanks. While the Musaceae family contains 80 species classified in three genera, the Banana Genome Hub includes all publicly available whole genomes for eight species from two genera. Therefore, the BGH is designed to hold more whole genomes, and still has high potential to grow and to propose new tools to efficiently exploit new datasets considering specificities of the crop (e. g. , polyploidy, structural variations). We will continue to curate and add new genome assemblies and related OMICS data as they become publicly available. Given the level of structural variation including chromosome rearrangements that are now well documented between the six species, high quality (N50 nearing average chromosome length) genome sequences (currently supported by Hi-C and/or long-molecule sequencing and genetic mapping data) are required as references.
To guide sampling for future sequencing projects and in an attempt to manage redundancy in data generation, we compile information from public sources or gleaned in conferences or from personal communications that will be regularly updated online (https://banana-genomehub. southgreen. fr/content/sequencing-status). The first observation is that if no genome assembly of known Musa cultivars, mostly triploids, has been released at chromosome-scale, some are underway as well as for additional wild species. Increasing accuracy of long-molecule sequencing is important to assembling haplotypes in triploid hybrids that are so important regionally and in trade. High quality whole-genome assemblies underpin exploitation of survey sequence data for allele mining or GWAS (Genome Wide Association Studies) to identify functional variants.
Acknowledgements This work was partially supported of the  CGIAR Research Program on Roots, Tubers and Bananas (RTB) , the  Agropolis Foundation  ( ID 1504-006 ) ' GenomeHarvest ' project through the  French Investissements d'avenir programme (Labex Agro :  ANR-10-LABX-0001-01 ). XJ. G. acknowledges support of the  National Natural Science Foundation of China  (No. 32070237 ,  31261140366 ). This work is technically supported by the  South Green Bioinformatics platform  and the  CIRAD -UMR AGAP HPC Data Center. We warmly thank all data providers who proactively enrich the BGH with datasets and feedback including  Sebastien Carpentier ,  Julie Sardos ,  Sijun Zheng ,  Nicolas Roux  ( Alliance Bioversity International -CIAT ),  David Studholme  ( University of Exeter ),  Boas Pucker  (CeBiTec),  Chunyan Xu ,  Xiaodong Fang  (BGI),  Ana Almeida  ( California State University East Bay ),  Wei Hu  (CATAS),  Mark Davey  ( KU Leuven ),  Dave Edwards ,  Philipp Bayer  ( University of Western Australia ),  Jose de Vega  ( Earlham Institute ).
For data download, the BGH is structured by organism with regards to individual genome assemblies and also by studies that provide directory listing of the related datasets. A global download section, supported by Drupal Filebrowser module, provides FTP-like browsing capabilities for datasets (e. g. , FASTA, GFF, BAM/CRAM, VCF). The catalogue of variants can also be accessed using Breeding API (BrAPI)  [62]. The BGH is proposed as a FAIR (Findable, Accessible, Interoperable and Re-usable) compliant resource  [94]  (https://bio. tools/Banana_Genome_Hub), and according to FAIR checker (https://fairchecker. france-bioinformatique. fr/check), it scored a high level in terms of accessibility and findability (Figure  S1 ).
Study design and patients The present study was a non-interventional, prospective, single-cohort, post-marketing surveillance study conducted in 83 centers in Japan from 2015 to 2019. The study protocol was approved by the institutional review board of each participating center and was compliant with the Japanese Good Postmarketing Study Practice guidelines  [22]. All participants provided written informed consent prior to study enrollment. The study was registered at ClinicalTrials. gov under the identi er NCT02475356. The patient population of the current analysis was drawn from the J-MIRAI study population. The study included women in Japan diagnosed with HMB and/or dysmenorrhea who underwent insertion of the LNG-IUS 52 mg for the treatment of HMB/dysmenorrhea. Women who had already started treatment with the LNG-IUS at the time of study initiation, as well as those using the LNG-IUS as a method of contraception, were excluded from the study.
Data collection Information such as patient demographic and clinical characteristics, including age, body mass index (BMI), pregnancy and childbirth history, treatment within 1-year of LNG-IUS insertion, and baseline EQ-5D-5L score was collected at 1 month prior to the initiation of LNG-IUS treatment to understand the general health status at baseline for each disease. MDQ and MMAS data were collected at 1 month prior to the LNG-IUS insertion and 3 and 12 months after LNG-IUS insertion. The MDQ and MMAS scores assessed at 1 month prior to the LNG-IUS insertion were considered as the baseline values. MDQ data were obtained in the premenstrual and menstrual periods, and MMAS data were obtained only during the menstrual period. The premenstrual and menstrual periods were de ned as "from 7 days before menstruation to the beginning of menstruation" and "from the beginning to the end of menstruation", respectively.
In the subgroup analysis, repeated two-way analysis of variance (ANOVA) was performed to ascertain whether patient background factors could synergistically in uence the effect of LNG-IUS on the time course trend of MDQ and MMAS scores. Log-normal regression analysis was performed to investigate the statistical association between MDQ and menstrual pain and between MMAS and PBAC scores. Missing values were imputed using the last observation carried forward method. Each analysis was adjusted using Bonferroni's correction method. A p-value < 0. 05 was set as the cut-off value for statistical signi cance. All the statistical analyses were performed using SAS version 9. 4 or later (SAS Institute Inc. , Cary, NC, USA).
In particular, improvements were observed in the domains of 1) pain and 3) behavioral effect. Among all of the domain scores of the MDQ, the median scores for 7) arousal and 8) control were the lowest at baseline, yet the results showed a statistically signi cant score change at the 1-year follow-up. MDQ data were analyzed comparing groups with and without missing value completion. There was no difference in the improvement trend in MDQ scores with or without missing value completion (data not shown).