dataesr/scientific-paragraphs-categorization huggingface dataset:
9 command. The running times are computed as the sum of the User and System times and the memory usage as the Maximum resident set size. The error rates of each method was measured using the SER on the 500 trio children. Specifically, we enumerated all heterozygous genotypes (i. e. , hets) in the 500 trio children that can be phased using Mendel inheritance logic (i. e. , no triple hets and no Mendel inconsistencies). Then, we computed the SER as the fraction of successive pairs of hets that are incorrectly phased over all possible pairs. Confidence intervals for the SER are defined as binomial 95% confidence intervals and were computed using the R/binconf of the R/Hmisc package (https://cran. r-project. org/web/packages/Hmisc/index. html). For the analysis in which we introduced genotyping errors, we measured switch error rates only between genotypes without simulated errors.
The GIAB data sets. To assess performance of SHAPEIT4 on sequencing data, we use the high-quality phased genotype data generated for the NA12878 individual by the GIAB consortium  23. In order to get population scale data, we merged the high-coverage genotype data of the GIAB with 502 unrelated European individuals sequenced as part of the phase 3 of the 1000 Genomes project (KGP3)  25. To do so, we proceeded as follows. First, we only used variants on chromosome 20. Second, we assume NA12878 to be homozygous reference allele at all KGP3 variants not typed in GIAB and removed all GIAB variants not typed in KGP3. Third, since NA12878 has also been sequenced in KGP3, we only retained variants for which GIAB and KGP3 genotypes are concordant. Finally, we only kept variants that were phased by GIAB owing to some family data (i.
e. , multiple sequenced individuals from the NA12878 family). Of note, NA12878 was not included in the final set of 502 KGP samples we used for merging. In total, this procedure gave us a data set on chromosome 20 comprising genotype data for 503 European individuals across 507,181 genetic variants amongst which 478,581 are SNPs and the rest is a mixture of short indels and large SVs. Of note, all data management was done using bcftools v1. 4  31. The GIAB consortium has generated sequence data using multiple sequencing technologies for NA12878. In the context of this work, we used: (a) SOLiD5500W, (b) Complete Genomics, (c) Illumina HiSeq, (d) PacBio, and (e) 10x Genomics. Summary statistics for all this sequence data is given Table  1. We used the five sets of sequencing data to phase as much as possible of the NA12878 genotype data.
For the first scaffold, we used genotype data derived from Illumina OMNI2. 5M for a larger set of individuals containing multiple trios and duos. Out of the 503 individuals with sequence data, 190 of them (37. 8%) are present in the OMNI data together with parents or children so that we could fix the phase at 6. 59% of the heterozygous genotypes in the sequence data using Mendel inheritance logic. For the second scaffold, we phased the 16,805 variants in the overlap with UKB data on chromosome 20 using 795,678 UKB haplotypes as reference panel. This allowed fixing the phase at 6. 25% of the heterozygous genotypes in the sequence data. Phasing runs on the GIAB data sets. Using SHAPEIT4, we phased the 503 individuals with sequence data across all possible combinations of six types of sequencing reads (i.
Acknowledgements This work was conducted under UK  Biobank  project  35520. O. D. is funded by a  Swiss National Science Foundation (SNSF)  project grant ( PP00P3_176977 ). M. R. R. is funded by a  Swiss National Science Foundation (SNSF)  project grant ( 31003A-179380 ) and by core funding from the  University of Lausanne. J. L. M. acknowledges funding for this work from the  European Research Council  (ERC; grant  617306 ). E. T. D. is funded by the  Louis-Jeantet Foundation ,  European Research Council and Swiss National Science Foundation.
Data availability We used the full release of the UKB (http://www. ukbiobank. ac. uk/). This work was conducted under UKB project 35520. We used the following release of the GIAB (ftp://ftptrace. ncbi. nlm. nih. gov/giab/) and this release of the phase 3 of the 1000 Genomes project (ftp://ftp. 1000genomes. ebi. ac. uk). We downloaded this version of the larger set of 1000 Genomes samples genotyped on Illumina OMNI2. 5M (ftp://ftp. 1000genomes. ebi. ac. uk). The sequencing data we used for GIAB are available from this location (ftp://ftp-trace. ncbi. nlm. nih. gov/giab/ftp/data/NA12878/) and 10x Genomics VCF populated with PS field (ftp://ftp-trace. ncbi. nlm. nih. gov/giab/ftp/data/NA12878/10Xgenomics_ChromiumGeno me_LongRanger2. 1_09302016/NA12878_hg19/NA12878_hg19_phased_variants. vcf. gz).
2015). Finally, there is a problem with storing large data collections, as often very high image overlapping ratio is needed for accurate mosaicking  (Torres-Sánchez et al. 2018). Advanced photogrammetric software allows quick transformation of data from the use of drones. The cloud storage accessible in the field allows the generation of various products: an orthomosaic DTM or the state of health assessment of vegetation using moisture content. The accuracy of end products depends on the quality of the Global Navigation Satellite Systems (GNSS) receiver which is the most important positioning tool with the inertial measurements unit (IMU). Depending on the platform, inexpensive UAV solutions can achieve accuracy of positioning at about 2 meters with a single frequency GNSS receiver  (Clark 2017).
iForest -Biogeosciences and Forestry Accuracy of different software for processing low-cost UAV data was 3 m s -1. The images obtained had good photographic sharpness, favourable colour tonality of the orthomosaic without shadow. For accuracy assessment, reference points with clearly distinguishable objects/ details have been collected. The reference points were distributed evenly across the whole area. A Topcon HiPer SR surveyingclass navigational receiver was used. Altogether, 53 reference points were recorded in RTK mode with horizontal measurement precision at ±0. 03 m. They were measured at 10 epochs averaging and real time differential correction was delivered by ASG-EU-POS network of reference stations. Processing of the acquired images was conducted using six different photogrammetric applications: AgiSoft PhotoScan™ (3499 US$ node-locked license), APS™ (4500 US$ one year license), Pix4Dmapper™ (3000 US$ one year license), and the cloud services DroneDeploy™ (1000 US$ one year license), Maps Made Easy™ (around 150 US$ per 2000 hectares), and PrecisionMapper™ (3500 US$ one year license).
Fig. 1  shows part of the orthomosaic results generated from all the tested methods, which do not visually show any significant differences. The first three methods were applied on a desktop computer. The speed of mosaicking can vary depending on the capacity of a given computer. For each tested type of photogrammetric software, it took about 7 h for creating a final orthomosaic (the PC was equipped with a fifth-generation Intel Core i5 2. 7 GHz processor and 8GB RAM DDR3 1867 MHz). All software programs offer the functions of creating an orthomosaic, DSM/DTM model, 3D point cloud, texture models, capacity measurements, 2D measurements, maps of NDVI indicators, video animations from the 3D model, exporting of vector and raster data, and sharing of products through cloud storage. The solutions offered by the DroneDeploy, Maps Made Easy, and Preci-sionMapper providers are based on a cloud service, which means that the user must send all acquired images and metafiles to the servers.
The original GSD was at 5 cm (Fig. 2 ). GCPs were not used for the generation of the mosaic. All measurements and finished orthomosaics were transformed from the WGS 84 ellipsoid system to the 2000 national reference (EPSG code 2178) grid system. Flight missions were realized by MapPilot ® for iOS (Drones Made Easy, San Diego, CA, USA) which guarantees optimal trajectory.
The time of data processing depends on the number of photographs. A finished orthomosaic can be downloaded to a computer or shared directly through the software producer's server. In the case of the Maps Made Easy and PrecisionMapper software, the user has no possibility of configuring any parameters. The main feature of desktop applications such as Agisoft PhotoScanPro software is that they allow flexibility in configuring numerous elements like the selection of a coordinate system, camera parameters, and the level of filtration of a point cloud, as well as the possibility of marking GCPs oneself. The user also can choose among a wide range of formats for data exporting, along with a detailed report documenting the stereomatching process. Default configuration is always used to obtain data unless fine-tuning is necessary.
For each tested software, it took about 7 hours for obtaining the final orthomosaic. The averaged RMSE error for all types of software was about 1. 24 m. It should be noted that it is nearly 4 times higher for the X axis than the Y axis (Fig. 3 ). As reported by  Willmott & Matsuura (2005) , the average error had slightly smaller values and was on average 1. 18 m for the X axis and 0. 43 m for the Y axis. The minimum values of RMSE error for all the tested software models were about 1 m and the maximum values about 1. 55 m. Only the PrecisionMapper computational cloud diverges from the rest, with a minimum error of 0. 17 m and a maximum exceeding 3 m. Elimination of the systematic error (Bias) allowed higher precision of the finished orthomosaics, significantly lowering the error of positioning of the GNSS receiver.
iForest -Biogeosciences and Forestry Brach  ror calculated from eqn. 6-9, at the level of 0. 13 m for both X axis and Y axis. The results from PrecisionMapper computational cloud, however, deviate significantly from the average performance. This is related to large errors in the process of stereo-matching in areas where the trees are close to reference points. For the most popular processing tools tested in this study, no significant differences were found. The time of processing of an orthomosaic was similar in all the studied cases and amounts to a few hours. For the software programs installed directly (AgiSoft, Pix4D, APS) on the personal computer, they allow more configuration options and thus better adjustment of the end product is realized. DroneDeploy, Maps Made Easy, and PrecisionMapper are cloud-based and have presented practical and efficient solutions.
Cloudbased computation gives the possibility to process several separate areas simultaneously. It does not overload the computer and provides significantly faster processing of an orthomosaic than a program that process images on PC. The good results of DroneDeploy in terms of accuracy and precision, correct trees shape representation (Fig. 1A ) and very easy user-interface, led to use it for further analysis in forested areas. The advantage of such a solution is the complete independence from computational capacity of the user's computer. In comparison to desktop solutions, the user has only a limited range of configuration options and control over the final product. For the Pokrzywnica forest, it is clear that image texture has a significant influence on orthomosaic precision. The Structure for Motion algorithm does not work so well without clear edges and similar pixel colors because with rather homogenous forest texture, fewer tie points are available to aid image matching  (Kedzierski & Wierzbicki 2015).
Acknowledgements This research was funded by  REMBIOFOR  project titled " Remote sensing estimation of tree biomass and coal resource in forests " and co-financed by resources provided by the  National Centre for Research and Development , as part of the program titled "Natural environment, agriculture and forestry" BIOSTRATEG, as stated in agreement no. BIOSTRATEG1/267755/4/NC BR/2015. The authors would like to thank  Prof. Janusz Walo  for his advice in the preparation of this study. Also, special thanks to  Mr. Lech Stanczak  for his help in field work and conducting calculations.
Categorical perception of phonemes and visual attention span are cognitive processes that contribute independently to poor reading skills in developmental dyslexia. We here explored whether training programs specifically targeting one or the other process do improve reading performance in dyslexic children. The dyslexic participants were trained using either the RapDys© program designed to improve phonemic perception or the MAEVA© program targeting visual attention span. Each participant was provided the two programs successively for intensive training. Results show specific effects of RapDys© on phonemic discrimination and pseudo-word reading. MAEVA© specifically improved visual attention span and irregular word reading. Phonemic awareness and regular word reading improved after application of both training programs, suggesting similar positive effects of both methods although effects of concomitant phonic training cannot be ruled out (as there was no control group).
Some used the Play-On R software  (Danon-Boileau and Barbier, 2002)  to train phoneme discrimination through audiovisual exercises in which a CV syllable was orally displayed (/pa/) followed by the presentation of two printed syllables ('pa' or 'ba'), one of which corresponded to the oral target. The intensive use of the Play-On R software resulted in positive effect in reading  (Magnan et al. , 2004; Magnan and Ecalle, 2006). In another study  (Veuillet et al. , 2007) , the same software was used to train children with dyslexia who were selected to have an allophonic discrimination. Following audiovisual training, categorical perception and reading skills improved, and a change in lateralization of the medial olivocochlear system (MOC) of the ascending auditory pathways involved in the mechanisms of speech perception in noise was reported.
Although the theoretical background of these studies focused on the phonemic component of the Play-On R program, we cannot ignore the printed material involved. Results were less conclusive when phoneme discrimination was trained without reference to printed material. A positive effect of pure discrimination training on PA and reading skills has been reported for typical readers  (Moore et al. , 2005) , but the effect was only found for PA in dyslexic children, without transfer to reading  (Hurford, 1990 ; for similar results on poor readers:  Thomson et al. , 2013). We will here explore the effect of RapDys©, a pure phoneme categorical perception training program (without printed material), on PA and reading skills in children with dyslexia. RapDys© was found useful to improve PA when proposed to children with specific language impairment  (Collet et al.
The Present Study In the present study, we will compare the effects of a new VA span training program, MAEVA©, with those of the RapDys© program (adapted from  Collet et al. , 2012)  on the reading performance of children with dyslexia. 1  RapDys© does not include any orthographic material and MAEVA© mainly uses non-orthographic stimuli. If VA span and phoneme categorical perception deficits causally and independently relate to developmental dyslexia, we would expect a positive effect of the two training programs on reading skills. RapDys© should improve not only categorical perception but further PA and reading performance without any significant effect on VA span. Reversely, MAEVA© should improve VA span and reading performance without affecting PA skills. The dyslexic participants were randomly assigned to one of two training groups (the RapDys-MAEVA vs.
Procedure and Timeline Pretest measures included VA span, PA, phoneme categorical perception and reading measures. The tasks were proposed in a random order, except for the reading tasks that were always administered at the end of the assessment. Two software programs, MAEVA© and RapDys©, were then proposed to the participants for intensive training (6 weeks, 5 days a week, 15 min a day). Twenty-two children practiced first with RapDys©, 23 with MAEVA©. Post-test measures were collected after the first training session (Post-test1) and children were then engaged in a second training session using the other software, MAEVA© for the group previously trained with RapDys© and RapDys© for the group previously trained with MAEVA©. The participants' skills were further assessed after the second training session (Post-test2).
Phonemic discrimination score The discrimination data were used to calculate five discrimination scores, each corresponding to the mean of the responses to same and different stimulus pairs centered on -60, -30, 0, +30, and +60 ms (e. g. , for the 0 ms VOT pair: mean of the response's to the -15/-15, +15/+15, -15/+15, +15/-15 pairs). For each stimulus pair, the discrimination scores were converted into d-prime (d') scores by taking the difference between the standard normal deviates (Z-values) of the same and different pairs  (Mcmillan et al. , 1977). A phonemic discrimination score was then calculated by taking the difference between the 0 ms VOT discrimination d' score and the mean of the four other d' scores (-60, -30, +30, and +60 ms VOT).
Reading Tasks To assess the evolution of reading skills while avoiding potential test-retest effects, reading tasks of regular words, irregular words and pseudowords and a text reading task were created in three versions (A, B, and C), for each of the three assessment sessions (Pre-test, Post-test1, and Post-test2). We administered these four tasks in a random order, but always at the end of the cognitive assessment. The children had to read aloud 27 regular words, 27 irregular words, and 27 pseudowords matched in length. Specifically, the pseudowords were created by substituting a few letters of the regular words, while keeping their graphemic and syllabic structure similar (e. g. , 'capsule' becomes 'copsale' or 'tente' becomes 'taude'). The words used in each of the three A, B, and C versions were matched in lexical frequency, from the MANULEX database  (Lété et al.
Training Groups The children were trained with one of the two programs during the first session and then switched to the other program for the second session. Half of them (N = 22) practiced first with MAEVA© (MAEVA©-RapDys© order, hereafter M-R order), the other half (N = 23) started with RapDys© (RapDys©-MAEVA© order, hereafter R-M order). As shown on.
MAEVA© The MAEVA© software was developed to improve VA span, i. e. , to increase the number of visual items to be processed simultaneously at a glance. Training was based on visual categorization tasks involving five categories (or families) of characters: lowercase letters, pseudo-letters, numbers, Japanese Hiragana characters, unknown geometric shapes. Using the algorithm proposed by  Pelli et al. (2006) , we ensured that visual identification efficiency was similar between categories. The size of each character was fixed at 1 • when perceived 60 cm from the screen. The characters were presented in black on a white screen. Between-characters spacing was enlarged (1. 3 times normal spacing) to minimize crowding effects. Each training session began by a 5-min familiarization to the different character families.
RapDys© The RapDys© software (running under MATLAB Compiler Runtime V. 8. 1. ) used in this study is an adaptation of the software used by  Collet et al. (2012). The method is based on the perceptual fading task  (Jamieson and Morosan, 1989). Basically, the idea was to gradually decrease the acoustic distance between two phonemes presented in a task discrimination. The pairs were synthesized to create a /d@/-/t@/ continuum varying in VOT. The gradual reduction of the acoustic distance was set up within five levels of increasing difficulty tailored to the child's performance. Each training session consisted of five blocks of 20 trials. all five blocks. The pairs were presented in a random order that differed for each child and each session. Each phoneme was associated with the same cartoon as previously used for assessment.
However, effects of concomitant phonic training cannot be ruled out (as there was no control group). In contrast, there is previous report of positive effects of RapDys© on PA in SLI children. Collet et al. (2012)  showed greater PA improvement in a group of SLI children who benefited from intensive RapDys© training than in a control group who benefited from no specific training. The current results suggest that the RapDys© effect is not limited to children with specific language impairment but further extends to children with dyslexia (but no SLI), thus suggesting that RapDys© has the potential to improve PA when impaired, whatever the child neurodevelopmental disorder. The existence of a link between categorical perception and PA  (Manis et al. , 1997; Zoubrinetzky et al. , 2016)  and evidence that PA improves following phoneme discrimination training in mainstream school children  (Moore et al.
, 2014) , the direct impact of each program to one of these cognitive skills would more efficiently improve reading performance in double-deficit children when combined. Future studies are needed to explore how to adjust the duration of trainings to each child specific needs. It is well admitted that improvement in reading outcomes partly depends on training duration and we expect more severely impaired children with dyslexia to require more training to more substantially improve their reading skills. Another remaining issue is whether the observed positive effects at the end of trainings would lead to long-term progress in reading after the end of any special remediation. Last, we should expect each of the RapDys© and MAEVA© training programs to more efficiently improve reading performance if proposed to subgroups of children with dyslexia with specific phonological or VA span deficits, respectively.
The average location estimation error is 30 cm. In  [16] , the locations of the mobile nodes are captured by the underground nodes using Wi-Fi networks. The result of the simulation shows the method reach location accuracy up to 1. 6 m. The work presented in  [17]  studies the scheme known as Owl Positioning Systems (OwlPS) for underground mining tunnels. OwlPS is usable as a personal positioning system to track mine workers in the event of an incident. But the system has problems in terms of the propagation of the radio signal, similar to Wi-Fi networks. In  [18]  a positioning system is proposed based on Zigbee technology. The global positioning system is composed by the control center, monitoring base stations, gateways, reference nodes, and mobile nodes. The software and hardware design is detailed in the manuscript, but results of the proposed system are not indicated.
A monitoring software is deployed on the surface. The average location estimation error is quantified as approximately 2. 5 m. In  [21] , an underground personal positioning system is proposed. The system is composed of an underground personal positioning station based on Zigbee, which includes a wireless RF receiver, communications, and electronic device for data processing. The system uses the maximum probability estimation algorithm to determine the precise location of the device. Whereas in  [22] , a new location system for miners is presented. It is a wireless sensor network based on Zigbee. The system is capable of obtaining in real-time the accurate location of miners within the tunnels of the underground mine. Through the RSSI, the location node takes advantage of its built-in location engine to calculate its coordinates and location.