dataesr/scientific-paragraphs-categorization huggingface dataset:
To determine the best localization accuracy and reduce the training time, we worked with four simulation scenarios: 10 APs and a grid of size 5 m × 5 m, 5 APs and a grid of size 2 m × 2 m, 5 APs and a grid of size 5 m × 5 m, and 10 APs and a grid of size 2 m × 2 m. We used a two convolutional layer architecture and one FC layer. Figure  7  illustrates the accuracy of localization related to the parameter T. We notice that the localization accuracy became almost stable from T = 20. We notice that the training time increased considerably from T = 20 (Tables  5  and 6 ). The trade-off between localization accuracy and complexity led us to use a parameter T equal to 20. We mention that our computing and memory resources did not allow us to construct RSSI databases with T > 20 for 10 APs and a grid of size 2 m × 2 m.
Lagrangian relaxation and linear search techniques are proposed to solve the problem. Grasset-Bourdel et al. [14]  worked on the automatic planning of activities of a constellation of AEOSs. The scheduling of observation missions and data downloads are simultaneously considered in their model. These studies focused on some specific characteristics of the AEOS scheduling, which cannot be compared with our work. In addition to the time-dependent transition time, the time-dependent profit is another crucial feature in our problem. In practice, the commercial value of a satellite image is significantly influenced by its image quality which depends on the observation start time. However, very few works on AEOS scheduling have considered the image quality and its time-dependency. Wolfe and Sorensen  [15]  define the "window constrained packing problem" to model the AEOS scheduling problem, with a particular quality function associated with each VTW.
This mathematical model cannot be directly solved by CPLEX due to the non-linear transition time constraints (  9 ) and the non-linear objective function  (4). The transition time between each pair of tasks is calculated based on the data of look angles per second during their VTWs which are given in a table look-up fashion and cannot be directly passed to the solver. In order to solve this problem, we build a CPLEX model where each moment during a VTW is regarded as a vertex with each a specific profit. Each task is now divided into multiple vertices. For example, given a time step of 5 seconds for a VTW of 5 minutes results in 60 vertices. For each pair of tasks, directed arcs are constructed among all their vertices for which the transition time constraint is satisfied. So, between two tasks with a VTW of 5 minutes, up to (60*60=)3600 directed arcs can be constructed.
We employ a dichotomy algorithm to accelerate these calculations. For simplicity, we only give the pseudo code of procedure EarliestStartT ime ij (h i ) in Algorithm 6. This procedure calculates the earliest start time es j of the next task j and its minimal transition time mintrans ij (h i ) after the previous task i is observed at moment h i. First of all, the start time st Despite a considerable amount of memory usage, this pre-calculation method only costs a few seconds. After the minimal transition times for each pair of tasks at each moment are pre-calculated, the computational time needed to calculate the four auxiliary features (see Section 4. 1) for each insertion is significantly reduced.
The pseudo code of the insert procedure is presented in Algorithm 7. As mentioned in Section 4. 1, the earliest start time and the latest start time are recorded for each task in the solution. These two auxiliary features are inspired from an evaluation metric (called "maxshift") in recent work on TOPTW  [1]  and TDOP  [2]. The difference is that in our algorithm, the observation start time of each task in the current solution is only determined during the evaluation of an insertion. As shown in Figure  6 , the earliest start time can be iteratively calculated from the first task to the last task in the solution by using the auxiliary function EarliestStartT ime(). The earliest start time of a task is calculated based on that of its previous task, and the earliest start time of the first task is equal to the start time of its visible time window.
The development of the Brain Imaging Data Structure (BIDS;  Gorgolewski et al. , 2016)  gave the neuroscientific community a standard to organize and share data. BIDS prescribes file naming conventions and a folder structure to store data in a set of already existing file formats. Next to rules about organization of the data itself, BIDS provides standardized templates to store associated metadata in the form of Javascript Object Notation (JSON) and tab separated value (TSV) files. It thus facilitates data sharing, eases metadata querying, and enables automatic data analysis pipelines. BIDS is a rich system to curate, aggregate, and annotate neuroimaging databases. While BIDS was originally intended for magnetic resonance imaging (MRI) data, it has extensions for other data modalities including: magnetoencephalography (MEG;  Niso et al.
, 2018) , electroencephalography (EEG;  Pernet et al. , 2019) , and intracranial encephalography (iEEG;  Holdgraf et al. , 2019). Software packages analyzing MEG, EEG, and iEEG are now starting to support data organized using the BIDS standard, thereby becoming "BIDS compatible". Within the Python ecosystem, MNE-Python  (Gramfort et al. , 2013 ) is a major software package for electrophysiology data analysis, and extending its functionality to support BIDS would be a great benefit for its growing user base. For this reason, we developed a dedicated Python software package MNE-BIDS with the goal of providing a programmable interface for BIDS datasets in electrophysiology with MNE-Python. MNE-BIDS allows users to re-organize data into BIDS formats, store associated metadata after anonymization, extract information necessary for preprocessing, and read the data into MNE-Python objects, ready for source localization.
Starting with a single directory full of data files with arbitrary names, MNE-BIDS can be used to extract existing metadata, reorganize the files into the BIDS format, and write additional metadata. All the conversion routines are thoroughly tested by running the output through the BIDS validator. Moreover, MNE-BIDS supports converting data formats that are not BIDS compatible into permissible formats. These utilities allow users to easily convert their datasets to BIDS in a matter of minutes, rather than hours of manual labour. In addition to this core functionality, MNE-BIDS is continuously being extended to facilitate the analysis of BIDS formatted data. Some features include: reading a BIDS dataset as a set of Python objects for analysis with MNE-Python, defacing T1-weighted anatomical MRI images to anonymize data and facilitate sharing, and saving anatomical landmark coordinates to enable coregistration between the MEG/EEG and MRI data, which is necessary for computation of forward and inverse solutions.
Users can easily install MNE-BIDS on all major platforms via pip and conda, and its functionality is continuously tested on Windows, macOS, and Linux. Other than the core dependencies for scientific computing (numpy, scipy) and handling of MEG/EEG data (mne), MNE-BIDS has minimal dependencies, all of which are optional. The Application Programming Interface (API) of the package is stable and extensively documented and explained in examples (https://mne. tools/mne-bids/). In addition, a command-line interface is provided that allows non-Python users to benefit from the core functionality. As of writing, MNE-BIDS has received code contributions from 15 contributors and its user base is steadily growing. Code development is active and the developer team is committed to provide timely support for issues opened on the GitHub issue tracker.
MNE-BIDS is used as a dependency in several other software packages such as the MNE-studytemplate, an automated pipeline for group analysis with MNE  (Jas et al. , 2018) , and Biscuit, a graphical user interface to format BIDS data. Lastly, several large institutions have adopted MNE-BIDS for their workflows such as the Martinos Center for Biomedical Imaging. The developer team is excited to improve the state of the art in data handling and looking forward to welcoming new contributors and users.
Acknowledgements MNE-BIDS development is partly supported by the  Academy of Finland  (grant  310988 ),  NIH NINDS   R01-NS104585 ,  ERC Starting  Grant  SLAB ERC-YStG-676943 ,  ANR  meegBIDS. fr, BRAIN Initiative and National Institute of Mental Health  ( 1R24MH114705 ), the  Bezos Family Foundation , the  Simms Mann Foundation , and the  Google Summer of Code 2019.
Results Curation of a high-confidence set of pathogenic non-coding variants associated with monogenic Mendelian disease genes Pathogenic non-coding variants from the Human Gene Mutation Database  [37]  (HGMD-DM), ClinVar  [38] , and Smedley'2016  [20]  were manually curated to obtain a high-confidence set of pathogenic variants associated to monogenic Mendelian diseases. The number of high-confidence pathogenic non-coding variants obtained from each resource is represented in Fig. 1  ("Methods" section). The majority of causal variants were assigned to the closest protein-coding gene in the reference source (94%, 98%, and 89%, respectively). Thus, the available set is mostly constituted of proximal cis-regulatory variants (Fig. 1a ), with distal cis-regulatory and trans-acting variants scarcely represented.
A B C D E Fig. 1  Curation of high-confidence pathogenic non-coding SNVs associated with monogenic Mendelian disease genes. a Number of high-confidence pathogenic non-coding SNVs obtained from the Human Gene Mutation Database  [37]  (HGMD-DM), ClinVar  [38] , and Smedley'2016  [20] , after filtering out SNVs overlapping exonic and splice sites of protein-coding genes and SNVs associated with non-coding RNAs ("Methods" section). Only highconfidence pathogenic non-coding variants associated with the same protein-coding gene by both the original resource and the annotation process done in this work (depicted in orange) were retained for downstream analysis. b Retained variants in a were further classified according the OMIM category of the associated gene, i. e. , non-Mendelian disease gene, Mendelian disease gene associated with a disease phenotype differing from the one reported in the original resource (i.
e. , presenting a conflicting disease description), complex Mendelian disease genes, and monogenic Mendelian disease genes. Only high-confidence pathogenic non-coding SNVs associated with monogenic Mendelian diseases with no homozygous individuals in GnomAD database  [35]  (depicted in green) were finally retained for downstream analyses. c Distribution of the high-confidence pathogenic noncoding SNVs associated with monogenic Mendelian disease genes according to the type of gene region they overlap: intronic, 5′UTR, 3′UTR, upstream, downstream, and intergenic regions. d Distribution of the high-confidence pathogenic non-coding SNVs associated with monogenic Mendelian disease genes according to the original annotation source, i. e. , HGMD-DM, ClinVar, and Smedley'2016. e Corresponding number of monogenic Mendelian disease genes collectively involved by SNVs in d.
Validation on a set of recently discovered non-coding pathogenic variants We evaluated NCBoost's performance in a fully independent validation set of non-coding pathogenic variants. For this, we retrieved 70 SNVs in non-coding regions of protein-coding genes newly reported in recent updates of the HGMD and ClinVar databases. The new pathogenic SNVs collectively associated with 47 genes and involve a total of 45 human diseases (details provided in Additional file 5: Table  S4 ; "Methods" section). For the purpose of this analysis, each pathogenic SNV was associated with a unique set of ten randomly sampled negative common human variants, matched to the positive set to maintain the same fraction of variants per type of region (Additional file 5: Table  S4 ). Figure  5  shows the predictive ability of NCBoost in such validation test in terms of AUROC and AUPRC values.
As in previous settings, NCBoost outperformed reference methods, including GWAVA and ncER. Importantly, NCBoost values on the validation set (AUROC = 0. 90 and AUPRC = 0. 53; Fig. 5 ) were similar to those observed in the independent test performed by mimicking a tenfold cross-validation (AUROC = 0. 84 and AUPR = 0. 47; Fig. 4 ). The consistency of values observed between the test and validation datasets supports the lack of overfitting in the training process.
Mutations were detected by whole exome sequencing, confirmed by Sanger sequencing, and segregated with the disease in the family. In the patient's fibroblasts, the NC_00001 1. 9:g. 134086816T>C mutation leads to skipping exon 3 which, in combination with the frameshift variant in trans, resulted in markedly reduced NCAPD3 protein expression. Low levels of normally spliced transcript and wild-type protein were nevertheless detected. Thus, the mutation caused a decrease in NCAPD3 function but was not considered functionally null  [1, 45]. This variant was poorly scored by all reference methods evaluated, with a median of within-individual rank percentiles ranging between 90. 69% (ReMM) and 35. 11% (FunSeq2) (Additional file 2: Figure  S12 ). In contrast, NCBoost scored the g. 134086816T>C variant as highly pathogenic, i.
g. , in the case of burden tests and variance component tests implemented in popular software such as PLINK/SEQ, SEQSpark, and SKAT. In these approaches, a continuous weight function can be used in the aggregation of rare variants in order to up-weight those predicted to have more damaging consequences. A similar weighting strategy can be proposed for rare variant extensions of the transmission disequilibrium test in the analysis of parent-child trio data  [57]. In both previous families of statistical tests for rare variant analysis of WGS from Mendelian disease studies, the pathogenic scores led by the supervised learning approach implemented in this work, NCBoost, may be used to weight the aggregation of candidate pathogenic SNVs across heterogeneous cis-regulatory elements in a consistent way overcoming position and region biases.
High-confidence pathogenic variants Three sets of high-confidence pathogenic variants in non-coding regions were obtained: (1) regulatory disease-causing mutations ("DM" set) from the Human Gene Mutation Database (HGMD, professional version, accessed on 2018/01/03  [37] ), manually annotated as involved in conferring the associated clinical phenotype; (2) pathogenic SNVs from ClinVar  [38]  manually annotated as "pathogenic" with no conflicting assertions (GrCh37 release from  [58] ); and (3) a manually curated set compiled from the medical literature of non-coding SNVs associated with Mendelian diseases and validated by experimentation or co-segregation studies, or for which other convincing evidence of pathogenicity was available.
Variant mapping and annotation of non-coding SNVs Only single-nucleotide variants (SNVs) where considered through the study. Variants were annotated using ANNOVAR software  [59]  (downloaded from  [60] ) using the gene-based annotation option based on RefSeq (assembly version hg19) in order to obtain (i) the gene region affected by intragenic variants or (ii) the nearest flanking gene in the case of intergenic variants. Exonic variants and variants within 10 base pairs (bp) of a splicing junction of protein-coding genes were removed (ANNOVAR splicing_threshold = 10). At this stage, variants from HGMD-DM, ClinVar, and Smedley'2016 overlapping non-coding RNAs within an exon (n = 143, 2, and 68, respectively), intron (n = 24, 3, and 13, respectively), or 10 bp from a splicing junction (n = 1, 0, and 0, respectively) were filtered out.
High-confidence pathogenic non-coding SNVs associated with monogenic Mendelian disease genes were further inspected manually to check consistency between the disease phenotype reported in the original source (i. e. , HGMD-DM, ClinVar, and Smedley'2016) and the ones described in OMIM database for the same gene. A total number of 138 variants for which the agreement was unclear or non-existent were filtered out for downstream analyses. In the remaining set of pathogenic non-coding SNVs, we then inspected whether variants were detected as heterozygous or homozygous among the individuals included in the GnomAD database  [35]  (version r2. 0. 2), using both WES and WGS data. Variants present as homozygous in at least one carrier were filtered out for downstream analysis. Thus, only high-confidence pathogenic non-coding SNVs associated with monogenic Mendelian diseases, with no homozygous individuals in GnomAD and overlapping intronic, 5′UTR, 3′UTR, upstream, downstream, and intergenic regions, were finally retained for downstream analysis (Additional file 1: Table  S1 ).
Negative logarithmic percentiles associated with each of these scores were used with values ranging from 0 (indicating positive selection) to 6 (indicating purifying selection). The background selection score (B statistic  [68] ), assessing the expected fraction of neutral diversity that is present at a site, was obtained from CADD annotations (version v1. 3). B statistic values close to 0 represent nearly complete removal of diversity as a result of selection whereas values near 1 indicate no conservation. The B statistic is based on human single-nucleotide polymorphisms from Perlegen Sciences, HapMap phase II, the SeattleSNPs NHLBI Program for Genomic Applications, and the NIEHS Environmental Genome Project. Context-dependent tolerance score (CDTS) for 10-bp bins of the human genome computed on 11,257 unrelated individuals was obtained from  [36].
Both in the case of ncRVIS and ncGERP, the non-coding sequence was defined in the original publication as the collection of 5′UTR, 3′UTR, and an additional non-exonic 250 bp upstream of the transcription start site (TSS). Gene age, estimating the gene time of origin based on the presence/absence of orthologs in the vertebrate phylogeny, was taken from  [74]. It varies from 0 (oldest) to 12 (youngest, corresponding to human-specific genes). The number of human paralogs for each gene was collected from the OGEE database  [75]. For all scores, gene names were mapped to approved gene symbols from HUGO Gene Nomenclature Committee (HGNC  [76] ). Missing values were imputed through the median value computed over all protein-coding genes.
Epigenetic features Epigenetic features such as histone modification marks, nucleosome position, open chromatin profiles, and transcription factor binding site (TFBS) profiles generated by the ENCODE project  [17]  were extracted from CADD v1. 3 annotations. DNA accessibility was assessed using the following sets of features: Potential transcription factor activity was assessed using (i) the number of different overlapping TFBS (TFBS), (ii) the number of overlapping TFBS peaks summed over cell types (TFBSPeaks), and (iii) the highest value of the overlapping TFBS peaks across cell types from ChIP-seq (TFBSPeaksMax). In addition, the following histone modification marks were used: the maximum methylation peak at H3K4 (EncH3K4Me1, enhancers-associated), maximum trimethylation peak at H3K4 (EncH3K4Me3, promoter-associated), and maximum acetylation peak at H3K27 (EncH3K27Ac, associated with active enhancers).
Independent set of newly discovered non-coding pathogenic variants Recently reported pathogenic variants in non-coding regions were obtained from (1) Regulatory disease-causing mutations ("DM" set) from the Human Gene Mutation Database (HGMD, professional version, accessed on 2018/10/08 and not previously reported on 2018/01/03  [37] ) and (2) pathogenic SNVs from ClinVar  [38]  manually annotated as "pathogenic" with no conflicting assertions (GrCh37 release from 2018/09/30 not previously reported in  [58] ). Variant mapping and annotation of non-coding SNVs was performed as described above. No further curation was performed on this set.
Availability of data and materials NCBoost pre-computed scores, scripts to annotate SNVs with all features used in this study and software to score pathogenicity with NCBoost v1. 0. 0 (ABCD model) are available online at GitHub: https://github. com/RausellLab/NCBoost [78]  and Zenodo: https://doi. org/10. 5281/zenodo. 2537087 [79] , licensed under the Apache License 2. 0. The curated set of DM-HGMD public non-coding variants, ClinVar non-coding pathogenic variants and Smedley'2016 non-coding disease variants reported in Methods is provided in Additional file 1: Table  S1. The corresponding set of region-matched random common variants from dbSNP used for learning, as reported in Methods, is provided in Additional file 4: Table  S3. The fully independent validation set is provided in Additional file 5: Table  S4.
Concerning the numerical modelling, this work has extensively developed recent one dimensional KPFM modelling work  [7]  to enable the evaluation of the influence of surface defects. This development has generalised the 1D approach to two dimensions in order to be able to take location of electrodes on the sample into account, and also perform AFM scans over the surface of semiconductor samples. This allows the evaluation of semiconductor doping and compositional junctions. It has also developed a complex description of defects of acceptor and donor types, with Gaussian and tail distributions of variable peak, width, and characteristic energy. This has been further developed as a separate software tool KELSCAN acting as an interface to SILVACO which enables parameter scans over a wide range of parameters.
Modelling The simulations were performed using in-house software KELSCAN developed by the IPVF institute, and which delivers a targeted KPFM modelling tool using SILVACO software  [6]. The model uses numerical solutions of semiconductor transport equations coupled with Poisson's equation and includes standard radiative and nonradiative (Auger and Shockley-Read-Hall) recombination mechanisms. These aspects have been described in detail in previous work of one dimensional Silvaco KPFM modelling by Huang et al. [7]. The KELSCAN solution extends previous work in two directions. The first is a two dimensional definition of samples which allows a realistic representation of charge distribution within the volume of the device, particularly from the perspective of placement of electrodes on the sides of the samples (orthogonal to the KPFM tip) or at the lower surface of the sample (below the KPFM tip).
In 2017 the Global HFR Network was recognized by the Joint Technical WMO-IOC Commission for Oceanography and Marine Meteorology (JCOMM) as an observing network of the Global Ocean Observing System (GOOS). In this paper we will discuss the development of the network as well as establishing goals for the future. The U. S. High Frequency Radar Network (HFRNet) has been in operation for over 13 years, with radar data being ingested from 31 organizations including measurements from Canada and Mexico. HFRNet currently holds a collection from over 150 radar installations totaling millions of records of surface ocean velocity measurements. During the past 10 years in Europe,.
, 2016). This series of meetings introduced the radar network to global organizations like the Global Ocean Observing System (GOOS) and the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM). JCOMM coordinates oceanographic measurements, data and services between the Intergovernmental Oceanographic Commission (IOC) and the World Meteorological Organization (WMO). The GOOS is executed within the IOC. In 2017 the Global HFR Network along with ocean gliders were recognized by JCOMM as an observing network of GOOS.