dataesr/scientific-paragraphs-categorization huggingface dataset:
The energy of the output image after application of a bench of Gabor filters on an original document is always regarded as a characteristic being able to identify the script even in the presence of small base of documents. The gray-level co-occurrence matrices define the relations between the different gray levels of an image. The highest rate of correct identification is obtained with gray-level co-occurrence matrices. In previous work  [23] , we tested all of the above script identification techniques to discriminate between Arabic/Latin and machineprinted/handwritten word. We present the obtained results for each technique on a database of 1720 words. A comparative study was performed to compare between the techniques and an attempted combination of features has been proposed to improve results.
we cite now the most recent work that addresses the same issue, which are three works:  [15] ,  [30]  and  [28]. In  [15]  present a performance comparison of curvelet, dual-tree complex wavelet and discrete wavelet transform in handwritten words classification (Arabic and Latin). A database of 1086 handwritten Latin (HL) and Arabic (HA) word images are used, 543 for training and 543 for testing. This database is constructed from two publics database : IFN/ENIT-database  [25]  for Arabic handwritten words and IAM-database  [26]  for Latin handwritten words. The feature vector was built using the energy and entropy calculated at different decomposition sub-bands. Support vector machines (SVM) and K-NN are used for classification. The experiments showed that the best identification results are reached using curvelet transform.
In our former work  [30] , we used pyramid histogram of oriented gradients (PHOG) to discriminate Arabic/Latin and handwritten/printed words. PHOG features, counts occurrences of gradient orientation in localized portion of an image. It has been showed as an effective tool for supplying spatial distribution of pixels. The feature vector of an image is composed of 129 values after selection with genetic algorithm. Experiments have been conducted using a database of 2000 words. An average identification rate of 98. 3% was achieved using Bayes based classifier. Different features are considered by  [28]  for the classification of a given word image into one of four classes: Arabic-Handwritten, Arabic-Printed, French-Handwritten and French-Printed. The word image is normalized to a size of 30 pixels height.
Database We consider here word images from two public databases: IAM database for Latin handwritten  [26]  and IFN-ENIT  [25]  for Arabic handwritten words. For Latin and Arabic machine-printed scripts, we build our own database by extracting words from various magazines and newspapers which contain variable font styles and sizes. A scanning resolution of 300 dpi is employed for digitization of all the words. The training and test words have 4000 samples, consisting of equal number of Printed Arabic (PA), Handwritten Arabic (HA), Printed Latin (PL) and handwritten Latin (HL) words. In the training phase, the features and the correct classification are used.
Identification with different classifiers We compared the performance of 4 typical classifiers: AODEsr, k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes (see Table  9 ) using two features selection methods: GA and PCA. Note that GA is efficient method for function minimization. In descriptor selection context, the prediction error of the model built upon a set of features is optimized. The GA mimics the natural evolution by modeling a dynamic population of solutions. The members of the population, referred to as chromosomes, encode the selected features  [24]. The goal of PCA is to derive a smaller set of features which accurately represent the original dataset. In particular, PCA finds the linear subspace of lower dimensionality that maximizes the variance of the original set, which is called principal subspace.
07%. Table  10  shows identification rate by class when using AODEsr. When observing the confusion matrix (see Table  11 ), we noted that it is about confusion cases between handwritten Latin and Arabic scripts which mainly come from their cursive nature. In Fig. 19 (a), a handwritten Latin word has been identified as machine-printed Latin because letter stems and legs are straight. In Fig. 19(b) , a handwritten Latin word has been classified as handwritten Arabic because some scribes write letters tiled to the left as generally done in handwritten Arabic script. In Fig. 19(c ), a machine-printed Arabic word has been confused with handwritten Latin word due to legs which are oriented to the right. Notice that the origin of confusion in Fig. 19 (a) is caused by the lack of connections between letters.
When we tested with straight words but with linked letters (see Fig. 19(d) ), their script have been correctly identified. If the handwritten Latin word in Fig. 19(b ) was confused with a handwritten Arabic word, it is not only and mainly caused by the right to left orientation, specific to the Arabic script, but also because the word contains few letters and these letters are disconnected. Consequently, there was not enough information on the word overall orientation to distinguish it from a handwritten Arabic word. Indeed, we tested with other handwritten Latin words whose stems are tilted to the left and these words have been properly classified (see Fig. 19(e) ). This is explained by the fact that there are enough connected letters helping to correct the overall word orientation. Using CP-HOG features and after features selection (268 among 936 features) and AODEsr classifier, the proposed script identifier system captures significant amount of the differences between machine-printed and handwritten Arabic and Latin words providing a good solution for this task.
Comparing with existing identification system We compared CP-HOG (our proposed system) with systems proposed by  [7]  and  [28]  which deal with the same problem. To compare CP-HOG with the steerable pyramid transform, proposed in  [7]  work, we used a common database (our database composed of 4000 word samples). Table  12  summarizes the obtained results. Notice that when testing with steerable pyramid transform  [14] , as done by  [7]  but on a larger database (4000 words instead of 800 words), the identification rate was reduced from 97. 5% to 93. 32%. As it can be seen, the use of CP-HOG features with AODEsr classifier achieves an identification rate of 99. 07% which is clearly better than 93. 32% (the identification rate obtained using features from the steerable pyramid transform with k-NN as classifier and tested on the same database).
Background and Summary Detailed knowledge of genome-level diversity is an important prerequisite for understanding the inheritance of complex traits related to crop performance, and for implementation of genome-based performance prediction to accelerate breeding progress. Ultra-high throughput, array-based genotyping platforms enable cheap and efficient whole-genome profiling in any species for which suitable DNA sequence polymorphism datasets are available. The recent publications of reference sequences for the allopolyploid genome of oilseed rape  1  and its diploid progenitor species, Brassica rapa 2 and Brassica oleracea  3  , open the possibility for large-scale genomic resequencing to discover and utilise DNA sequence diversity in the primary and secondary gene pools of these important crops.
Whole-genome resequencing data was generated for the 50 accessions from the diversity panel, and for the two recurrent parents, using the Illumina HiSeq 2000 next-generation sequencing platform. Paired-end, 100 bp sequence reads were generated to an average depth of between 12x and 15x genome coverage. A total of 3. 4 billion quality-trimmed paired reads (685 Gb) was utilized for the alignment to the B. napus Darmor-bzh v4. 2 reference genome. Using different, stringent variant calling strategies, a total of 4. 3 million high-confidence, genome-wide single-nucleotide polymorphisms (SNPs) were detected across the 52 genomes. This paper reports these DNA sequence and SNP variant datasets in their entirety. These data are expected to be of considerable interest to breeders and geneticists working with Brassica crops, and to evolutionary biologists investigating polyploid evolution and the genome-scale consequences of natural or artificial selection.
Sample preparation and whole-genome sequencing The 52 B. napus accessions, including 30 adapted, 20 resynthesized and 2 elite lines, were resequenced using an Illumina HiSeq 2000 platform. For each sample 1 ug of gDNA has been fragmented into small fragments of 400 bp using the Covaris standard technology without any genomic reduction. The NEBNext DNA library kit was used to prepare the library, following the standard protocol. Cleaning and size selection were done using Ampure magnetic beads. Each amplified fragment has been paired end sequenced, generating short-reads of 100 bp to an average coverage of 12-15x (corresponding to 20 Gb per accession). The full sequence data for all 52 accessions is archived at the European Nucleotide Archive (www. ebi. ac. uk/ena) under the project numbers PRJEB5974 (Data Citation 1) and PRJEB6069 (Data Citation 2).
Raw data processing The quality of the raw sequence data was validated using the FastQC software (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Adapters were removed using cutadapt (https:// cutadapt. readthedocs. org/) and the reads were trimmed for low quality bases with the fastx quality trimmer algorithm (http://hannonlab. cshl. edu/fastx_toolkit/), using the settingst 30l 90 for trimming bases with a Phred quality score below 30 and removing reads shorter than 90 nt. Paired reads were then synchronized using an in-house Perl script (Data Citation 3). General quality statistics of raw and trimmed reads were generated using fastx_quality_stats (http://hannonlab. cshl. edu/fastx_toolkit/). In total over 3. 4 billion quality-trimmed, paired-end reads were used, providing a total pre-processed data volume of 685 Gbp.
Whole genome alignment against the Brassica napus Darmor-bzh reference genome The 1,130 Mb genome of the allopolyploid species B. napus 1 is characterized by two highly similar sub-genomes. The A subgenome, derived from B. rapa, consists of 10 chromosomes, while the C subgenome contributed by B. oleracea has 9 chromosomes. The preprocessed sequence reads were mapped to the B. napus Darmor-bzh v4. 2 reference genome using the SOAP2 software  11  with the options m 0,x 500,v 2. To achieve high confidence when aligning reads to the allopolyploid genome, we used an un-gapped alignment. This approach considers the close similarity of the two subgenomes, increasing the ratio of reads that were aligned to the correct subgenome. The SOAP2 output files with the positions of the aligned reads were converted to the Sequence Alignment/Map (SAM) format applying the 'soap2sam.
pl' script (http://soap. genomics. org. cn/down/soap2sam. tar. gz), and then further converted into the binary version of the SAM format (BAM) using SAMtools view command  12. For the 52 different accessions an average of ~90 million reads per accession were aligned as proper read pairs, ranging from ~66. 0 million reads at the first quartile (Q1) to ~102. 6 million reads at the third quartile (Q3). A high average mapping quality was observed, with an average MAPQ value of ~69. 1. The average insert sizes between corresponding read pairs ranged from 158 to 319 bp for the individual genotypes (Table  2  (available online only)). Regarding the proportion of the reference genome with sufficient read coverage, we observed large differences between genotypes particularly between natural versus synthetic accessions.
Variant filtering A first posterior filtering was applied independently to all variant data sets (VCF files) before merging of the results from the multiple calling methods for construction of the final variant set. The independent application of multiple variant calling methods allowed us to use the variant caller count (VCC) as an additional confidence value for the prediction of variant positions. The VCC indicates how many variant calling methods predict a particular VP. All successful VP calls fulfil the following criteria in at least one of the genotypes: bi-allelic, SNP quality score > = 100, homozygous, read depth > = 4 and a VCC > = 2. The final diversity set is published as a SNP matrix (Data Citation 4). To construct the SNP matrix the BAM files were converted into the PILEUP format using SAMtools 'mpileup' and together with the discovered variant positions these information is integrated by a custom script (Data Citation 3).
Acknowledgements The resources described here were generated within the  German Plant 2030 research consortium ''Pre-BreedYield : Precision breeding for yield gain in oilseed rape'. Funding was provided by grant  FKZ0315964  from the  German Federal Ministry of Education and Research (BMBF)  with co-funding from the breeding companies  German Seed Alliance GmbH ,  Norddeutsche Pflanzenzucht H. G. Lembke KG ,  Deutsche Saatveredelung AG ,  KWS Saat AG ,  Limagrain GmbH ,  Bayer Crop Science AG , and  Syngenta Seeds GmbH. We thank all participants of the ' Pre-BreedYield ' consortium for fruitful discussions.
Code availability All custom scripts applied for data processing are deposited as DOI (Data Citation 3). The developed custom scripts are available without restrictions. Applied software including version details are BLASTN (version 2. 2. 30), SOAP2 (version 2. 21), Bowtie2 (version 2. 2. 5), cutadapt (version 1. 8), FastQC (version 0. 11. 2), FaSD (version downloaded January 2015), Freebayes (version 0. 9. 21), SAMtools (version 0. 1. 19), BCFtools (version 1. 2) and e!DAL (version 2. 3. 9). Applied parameter settings are described in the corresponding sections.
The concept underlying the functioning of the MIMU is the combination of these three types of information (accelerometers, gyroscopes, magnetometers) through sensor-fusion algorithms  [20, 22]  to estimate a drift-free 3D orientation of the MIMU sensor. Geometrically, MIMU sensor 3D orientation is represented by a technical coordinate system (CS) expressed with respect to an earth-based global CS. This global CS is defined using the gravity (measured by the accelerometers) and the magnetic north (measured by the magnetometers) reference vectors  [16]  and thus it is common to all MIMUs. The relative motion between two consecutive segments can be calculated by attaching a MIMU sensor on each body segment of interest. However, this motion measurement has no anatomical signification and so cannot be converted into interpretable data such as joint angles (i.
This raises the question of repeatability/reproducibility. To the authors' knowledge, only De Vries et al. [9]  and Parel et al. [28]  have investigated such a criterion for MIMU-based upper limb kinematics in adults, focusing on the repeatability of segment axes and on the reproducibility of the scapulohumeral rhythm, respectively. As a general observation, it is difficult for the researcher/the practitioner (1) to know which sensor-to-segment calibration should be chosen from the literature as well as  (2)  to know what the global accuracy of MIMU-based upper limb kinematic data is. The present study aims to answer these two points by proposing a comparative assessment of the sensor-to-segment calibrations for the MIMU-based estimation of wrist, elbow, and shoulder (humero-thoracic) joint angles.
Technologies Used A total of four wireless MIMU sensors (MTw, version firmware 2. 0. 8, Xsens, Enschede, Netherlands) were used for this experiment. The MTw is a small and light device (34. 5 × 57. 8 × 14. 5 mm, 27 g) that consists of a 3D linear accelerometer, a 3D rate gyroscope, and a 3D magnetometer. Each MIMU communicates its data to a station (Awinda, Xsens) connected to a laptop, via a Bluetooth protocol communication. The MIMU technical CS with respect to an earth-based global CS is provided as an output, using Xsens fusion algorithm. Since MIMU data quality can be affected by magnetic disturbance, a homogeneous magnetic environment was maintained during the experiment. An optoelectronic system (Eagle 4, Motion Analysis Corporation, CA, USA) with 10 cameras was used as the reference system for measuring the kinematics (REF).
Assessment of Precision The precision of upper limb kinematics obtained using the MIMUs with the different sensor-to-segment calibrations and with the REF method was assessed using two approaches: the Coefficient of Multiple Correlation within-protocol (CMCwp)  [38]  and the approach proposed by Schwartz et al. [41] , which is based on standard statistical means. Prior to both approaches, the experimental dataset of each session was divided into uniform cycles of movement. According to Schwartz et al. [41] , the overall reproducibility of a system can be divided into intrinsic and extrinsic errors. The intrinsic error corresponds to the error that occurs naturally in any experiment, i. e. , intra/inter-subject variability, and is unavoidable. Conversely, the extrinsic error corresponds to experimental errors, for instance caused by the operator, and which can be subject to improvements.
Goal and General Results The aim of this study was to compare sensor-to-segment calibrations for the MIMU-based estimation of wrist, elbow, and shoulder (humero-thoracic) joint angles. Calibrations were selected from the literature, mainly from the work of De Vries et al. [9] , and included TECH, STATIC and FUNCT classes of calibration. For each joint, the comparison was made by examining both trueness and precision validity criteria. This study demonstrated that only very small discrepancies occurred between calibrations, with the exception of a few cases, and that MIMU-based upper limb joint angle estimation can be performed with good trueness and very good precision. It should be mentioned that the error values presented in this study are specific to this experiment. The whole assessment of MIMU-based upper limb kinematic data relies on the Xsens data fusion algorithm.
0°-26. 2°] and comparable to previous results of 20° and 10°-20° reported in the literature  [9, 11]. Referring MIMU-based data to optoelectronic system-based ones (REF) conforming to ISB standards is certainly of interest; however, it is important to keep in mind that this reference can be called into question for several reasons. First, all non-invasive techniques suffer from STA effects, so reference data is not systematically representative of the underlying bone movements  [27]. Second, the kinematics computations associated with the REF necessarily rely on arbitrary choices. Indeed, several scientific points are still open to discussion regarding ISB standards, such as the estimation of the gleno-humeral joint center and the definition of the hand segment CS. Third, other measurement protocols associated with the optoelectronic system have been proposed in the literature, such as the CAST method  [6]  and functional approaches  [25].
Conclusions and Outlook This study compared sensor-to-segment calibrations for the MIMU-based estimation of wrist, elbow, and shoulder (humero-thoracic) joint angles. No particular discrepancy between calibration classes (TECH, STATIC, FUNCT) was highlighted, except poorer results for TECH for the shoulder. Moreover, this study characterized the global accuracy of MIMU-based upper limb joint angles (trueness and precision) under extended human variability conditions (subject and operator), demonstrating a good trueness ("close to the reference") and a very good precision (reproducibility) in the range of 5°-10°. The overall accuracy of MIMU-based joint angle data seems to be more dependent on the level of rigor of the experimental procedure (operator training, STA awareness, etc. ) than on the choice of calibration itself.
tone followed by an electric shock) should be easy to design. On the other hand, these tools are also intended to serve computer scientists as a way to design artificial autonomous systems, driven by brain models. In this case, it is important for the supposed properties of the models (e. g. , capacity to learn, robustness to noise or changing rules) to be assessed by rigorous evaluation procedures, as it is defined for example in the domain of machine learning. In this case also an easy access must be proposed both to the inner circuitry of the models and to the specification of the external world. With in mind this double goal of offering convenient tools to both scientific communities, we report in this paper the specifications that we have elaborated and present the corresponding software platform that we call VirtualEnaction.
Fig. 2  Left: The software interface is trivial: each implementation of a brainy-bot provides an initialization routine initBot() and a stop function stopCondition(), while at each time-step the brainDo() method is called. Right: All status, input and output functionalities are available via a simple API. For instance, hand or vital input, body and head displacement, gestures of resource injection and attack against predator are shown. An adaptation of the minecraft open game software yields the proper answer to this wish-list and the so called virtualenaction is an open-source freelicense implementation of these specifications. Each user buys a end-user low-cost mojang license (< 20$) for minecraft, while virtualenaction is free of use under a CeCILL-C license. Fully-documented scripts facilitate the installation of the software bundle under Linux OS.
Neuroscience application Fig. 4  The software's graphic user interface. In order to observe and control the experiment, a view of the bot vision, status, input and output is available via interactive panels. It is also possible to "cheat" by controlling these variables values, in order to debug an experiment and understand in details what happens in such a complex system interaction. Let us now discuss how this setup constitutes a step towards integrative neuroscience digital experimentation. First of all, let us compare this project with complementary connected projects. The AnimatLab is a software platform allowing to simulate embodiment (biomechanical simulation of a body) allowing to investigate the relation between brain and body  [Cofer et al. , 2010]. Furthermore, it proposes a neural network architecture for the implementation of cognitive functions.
On the contrary, the present framework has a rather limited description of the embodiment, but a much larger set of possible interactions with the environment. A step further, not only artificial neural network models are usable in VirtualEnaction, whereas the interface with any existing neuroscience simulation tool (e. g. , python based neural simulators, see  [Brette et al. , 2007 , Davison et al. , 2008] ) is straightforward. This feature is essential, since we must simulate the system at different modeling scales, as developed now. The Morse is a generic simulator for academic robotics, with realistic 3D simulation of small to large environments, allowing complete integration with any simulation tools. It outperforms concurrent systems like Webot. The interest of VirtualEnaction with respect to Morse is twofold: Since we target integrative cognitive tasks of survival which is exactly what happens with the Minecraft environment, using this specialized product is far simpler and somehow more demonstrative.