dataesr/scientific-paragraphs-categorization huggingface dataset:
Procedure Step 1: Create an account First of all, create an account on Recogito. 8 It's completely free, the user interface is available in several languages, including English, Spanish, German and Italian, and it works with most supported browsers (Firefox, Chrome, Safari). Once you have your account, you can start annotating straight away. This global version of Recogito doesn't require any installation. However, since Recogito is open-access software, you can also download a local version of it to customise, for example, by adding an additional gazetteer specific to your needs. You can also annotate a document that another user has shared with you. We will discuss this possibility when talking about sharing options (see Step 8). Step 2: Upload a document With Recogito you can annotate an array of digital documents (including image formats), but in this tutorial we just focus on text documents.
To upload a text document to Recogito, we recommend using the. txt format. If your document is in another text format (e. g. doc), you first need to convert it into the Unicode format UTF-8. You can do this in any of the most popular text editors, such as Word, Writer or Google Docs, simply by using the option Save as. When you export the text file in. txt, please check that the text you are uploading is the final version: as Recogito is not a text editor, you won't be able to make changes on the text once it is uploaded. Although the optimum format for working on text documents in Recogito is currently. txt, Recogito now also has the capacity to enable the annotation of TEI-XML. We talk about this option later in the tutorial (see Step 7). If you upload more than one text document at the same time, Recogito will collate the files to create a metadocument that brings them together.
Recogito also offers the option of creating semi-automatic annotations using a Named Entity Recognition (NER) algorithm. NER algorithms are language specific, so you should select the most appropriate from those available in Recogito. At the moment, it offers NER in English, French, Spanish and German. More experimental NER algorithms are also available in Hebrew and Latin. If none of the available algorithms match the language of your text, you may try one that is linguistically close (for example, the Spanish algorithm for an Italian text). The algorithm parses the text and tries to identify all words that can be place names or person names. When the NER recognises a word as a possible place name, it will also try to match it automatically with an entry in one of Recogito's global gazetteers.
A dotted line will appear connecting the two annotations, along with a text box: you can fill this in to describe (or tag) the relationship. The line also has an arrow, which indicates the direction of the relationship. This is crucial for relationships that are hierarchical, as in, for example, isPartOf or isDaughterOf. The relations created in Recogito can be exported in two formats: a basic CSV, and two separate tables for nodes and edges. 14 Both can be visualised in network analysis software such as Gephi. 15 If you are using the simplest option, just remember to change the denomination of the columns from from_quote to source, and to_quote to target, and simply upload the spreadsheet as an edge table (selecting the option create missing nodes). If you want to use the nodes and edges format instead, to have more control over your network visualisation, please bear in mind that, when downloaded in this format, each relation receives a different ID -and so the data will need consolidation before being processed in Gephi.
16 n the example shown in Figure  12 , we are marking the relationship between different South American ethnic groups: the Guayanás and the Guaraníes, also known (by Ruy Diaz) as Arachanes, and their enemies, the Charrúas: When you have finished annotating entities (such as places or people) in your text document, and you have also marked the relations between them, your Recogito annotation screen will probably look something like this:  14  In Graph Theory, an edge is a visual representation of a relation. It is a line that connects two nodes. 15 Gephi: https://gephi. org/. 16 For more information about annotating relations and the workflow with Gephi, please see https://github. com/ pelagios/pelagios. github. io/wiki/Recogito-Tutorial:-Download-Options-for-Text. As well as UTF-8 plain text, Recogito now allows you to annotate TEI-XML documents.
2019c): 19 Step 8: Share the annotations (and credit) Recogito allows you to work on your own or in a team. You can modify your sharing options, add collaborators and share your annotations in the tool section (upper menu). You can also follow up, backup your work or delete it. 17 The TEI guidelines for encoding a digital edition are available at: https://tei-c. org/release/doc/tei-p5-doc/en/ html/SG. html. 18 About Minimal Editions with Ed/Jekyll, see: https://github. com/minicomp/ed. 19 Video accessible at: https://youtu. be/AgE3KcrxTfw. Remember that if you choose to leave your document open on the Web, it will be searchable by any engine and Google will index it. If you are doing teamwork, remember to use a proper name and email that give credit to all your collaborators. You will also be able to see your collaborators in the map view:.
Our main motivation for choosing the specific definition in the following is therefore driven not by physical elegance but mostly by practical concerns for the implementation. In return, we obtain a formalism that allows us to enforce our constraints to high accuracy and, vice versa, to obtain the derivatives with respect to the atomic spins, i. e. , the equivalent to atomic forces in spin configurational space from the Lagrangian multipliers. Our DFT code SPHInX  [38]  adopts the PAW formalism  [30]. We therefore choose a definition of atomic spin linked to the partial-wave projection of each atom, namely, M a = i j D a,↑ i j -D a,↓ i j d 3 rφ i (r)φ j (r) (r cut -|r -r a |), (8) with the usual one-center density matrix  [30]  for atom a D aσ i j = n f nσ ψ nσ |p i p j |ψ nσ. ( 9 ) The φ i in Eq.
G. Simulation protocol The DFT calculations are performed for each vacancy configuration in the given SQS using the electronic-structure code SPHInX  [38]. This allows us to benefit from the method for spin constraints outlined in Sec. II B using the algorithm given in Sec. II C. After completing the electronic minimization, the Hellmann-Feynman forces are spin-space averaged following the SSA scheme  [6]  originally developed for paramagnetic phonons. The SSA forces are defined as  [6 ] F i SSA = K=1,N p K F i K , ( 41 ) where F i K is the Hellmann-Feynman force on the ith atom in the Kth magnetic configuration, N is the total number of magnetic configurations, and F i SSA is the spin-space averaged force on the ith atom. p K denotes the Boltzmann weight for the Kth magnetic configuration, given as p K = exp[-E BO K /k B T ]/Z , where E BO K is the magnetic Born-Oppenheimer energy for the Kth magnetic configuration, k B is the Boltzmann constant, and Z is the magnetic partition sum.
1 ). To develop and disseminate such a complex simulation protocol, we have used the PYTHON-based framework pyiron  [53]. Like an integrated development environment for programming languages, pyiron allows one to interactively implement and test simulation protocols and to upscale them for high-throughput simulations on large computer clusters. One of the advantages of this framework is that the different software tools can run interactively; that is, the communication between them occurs via named pipes on the fly, without terminating one of the codes before starting the next atomic relaxation step. This yields a substantial improvement of computational efficiency.
III. COMPUTATIONAL DETAILS The DFT calculations with the SPHInX package  [38]  are performed within the framework of the PAW  [30, 54]  formalism, using the Perdew-Burke-Ernzerhof flavor of the generalized gradient approximation  [55, 56]. A supercell of 54 atoms (3×3×3 cell) is used for the ferromagnetic (FM) and for the paramagnetic (PM) SQS bulk calculations. An energy cutoff of 600 eV and a Monkhorst-Pack k-point mesh of 6×6×6 are used. All calculations are performed with Fermi-Dirac smearing, with a width of 0. 1 eV (electronic temperature close to the Curie temperature of 1041 K). We verified that halving the smearing width changed the vacancy formation energy by less than 2 meV. The convergence criterion for the total energy in the electronic relaxation loop is 10 -5 eV. The convergence criterion for forces in the ionic relaxation loop is 0.
To this end, we have used the interactive development environment pyiron. Within this environment, we specifically employed the concept of interactively linking simultaneously running atomistic software tools. This allowed us to run, interrupt, and subsequently continue the electronic self-consistency cycle in simultaneously performed DFT calculations. Each DFT calculation treats an individual spin-state configuration. The SSA and the update of positions are performed externally by the pyiron framework. To benchmark and analyze the SSA relaxation scheme we applied it to compute formation and migration energies of a vacancy in PM bcc Fe. We then compared the results of the SSA approach in the adiabatic spin limit with different approximative relaxation schemes that are commonly employed in the literature.
Innovative technologies and naturalistic driving data sources provide a great potential to develop reliable autonomous driving systems. Understanding the behaviors of surrounding vehicles is essential for improving safety and mobility of autonomous vehicles. Onboard sensors like Radar, Lidar and Camera are able to track surrounding vehicles motion and to get different features like position, velocity and yaw. This paper proposes a hybrid approach to integrate maneuver classification using neural networks and trajectory prediction using Long Short-term Memory (LSTM) networks to get the future positions of adjacent vehicles. In this study we use the Next Generation Simulation (NGSIM) public dataset that provides a real driving data. The proposed approach is validated experimentally using VEDECOM demonstrator data.
Lane change prediction problems are mainly treated as classification problems  [3] ,  [4] ,  [6] -  [9]. This maneuver can be classified into three main classes: Left Lane Change (LLC), Right Lane Change (RLC) and Lane Keeping (LK). For the detection of lane change performed by other drivers, perception sensors data should be used as features. The properly selected features determine the detection performance  [8] -  [11]. This problem is addressed in the literature from different points of view, most of them are based on probabilistic methods. Bayesian Networks Approach proposed to recognize driving maneuvers on highways and the experiments are performed on real world vision data  [6]. The authors of  [3]  used Hidden Markov Models (HMM) for situation modeling and recognition. A maneuver is defined as a distribution over sequences of states, the HMM try to identify each one of these states.
• It was a bright idea to evaluate NGSIM trained model with on-board sensors data. This could provide a valid reference while selecting proper dataset for trajectory prediction. The remainder of this paper is divided into the following sections. A system overview is introduced in Section II. Section III describes the driving behavior representation and the features selection. Section IV details the specific implementation of the proposed approaches and presents the experimental results to prove the effectiveness of the proposed approach. Finally, Section V concludes the paper with an outlook and a discussion of future work directions.
A. ARTIFICIAL NEURAL NETWORK Artificial Neural Network (ANN) is a system inspired by the functioning of biological neurons configured to perform specific tasks such as: pattern recognition, signal processing, learning by example, memorization and generalization. An ANN is organized in layers (Fig. 3 ), each of these layers comporting several neurons, each of these neurons, that represents an autonomous processing unit, is connected to the totality or certain neurons of the preceding layer. It is a supervised learning algorithm that learns a function by training on a dataset. Given a set of features and a target, an ANN is able to learn a non-linear approximator for either classification or regression. It is a complicated, non-linear, dynamic system in which the neurons are connected in some topological structure.
III. FEATURES SELECTION AND DRIVING BEHAVIOR REPRESENTATION A. DATASET All of the aforementioned research areas require interactive vehicle driving data from real-world scenarios, which is the most fundamental and indispensable asset. Additionally in this work, the public dataset Next Generation Simulation (NGSIM) is used for training and testing. NGSIM dataset is one of the most popular dataset used in vehicle motion comprehension and prediction studies  [32]. NGSIM is a program funded by the U. S Federal Highway Administration. It contains detailed vehicle trajectory information. Data is collected from 8 synchronized digital cameras mounted on top of buildings adjacent to US-101 and I-80 highways, including 600 meters of study area, with a total of 45 minutes of data are available in the full dataset captured at 10 Hz.
B. LANE CHANGE REPRESENTATION Our work is set to predict three types of maneuvers: Lane Change to the Right (LCR), Lane Change to the Left (LCL) and Lane Keeping (LK). Lane changes were extracted and labeled automatically following several criteria. ''Lane ID'' feature is used to extract all available lane changes in NGSIM dataset. The increasing of this feature can refer to a left lane change. Contrariwise, the decreasing refers to right lane change. Therefore, we can extract lane changes automatically and we can also label the direction of changes (right, left). For each event we take 50 time-steps (5 s) before, and 50 time-steps (5 s) after lane crossing. So, if the instant of intersection between trajectory and lane making is considered as a reference time t 0 , the interval [t 0 -5 s, t 0 +5 s] is the lane change trajectory segment.
Based on the chosen criteria, 380 lane changes to the right, 420 lane changes to the left and more than 1000 lane keeping are extracted from NGSIM. In order to create a homogeneous dataset, we take the same number of trajectories for each class. So we take 300 trajectories of each maneuver for training and 80 trajectories for testing. Many variables must be involved in the classification. In this study, a set of 4 features are extracted from the NGSIM data. The extracted features are: • Yaw angle w. r. t the road: yaw is the vehicle orientation heading angle related to the road. Changes in yaw can be a very important sign to detect a lane change. • Yaw rate: yaw rate is the first derivative of the yaw. This feature used to flow the rate of changes in vehicle orientation w. r. t the road. • Lateral velocity and acceleration: lateral dynamic is a very significant feature that gives a lot of information before the lane change event happens.
Therefore, we have used local coordinates related to the road to calculate lateral acceleration and velocity. NGSIM data have some disturbances due to measurement noise and estimation error, which may cause problems in the training step. All input data are filtered using first order Savitzky-Golay filter  [37]  to make them smoother (Fig. 5 ), and to accelerate the convergence of the loss function during training phase. It should be noted that the data were normalized between (-2) and (+2) to be used as an input for the model. After filtering and normalizing, the data are organized into time-series of different sizes. To develop and evaluate the model, the dataset was divided into training and testing datasets. To investigate the influence of different vehicle features on lane change over time, we propose a graphical boxplot representations of all extracted features (Fig.
1) MANEUVER CLASSIFICATION RESULTS The goal here is to check if the correct maneuver is detected before the vehicle reaches the intended lane. Using neural network-based modeling, we train with time-dependent real traffic data to classify and predict the lane change. The ground truth is thus determined by analyzing vehicle Lane ID changing. As shown in Table  1 , the accuracy of classification is 86. 2%. It can be seen that long sequences give the best classification. For 6 s sequences, there are 97. 49% correctly predicted maneuvers. The goal of classification is not only to understand the driving situation, but also to provide a supplement input for the prediction model. The validation results demonstrate the effectiveness of ANN.
C. TWO CASES ANALYSIS In order to evaluate our approach, we randomly selected two vehicle trajectories, including lane-changing and lane-keeping maneuvers from the NGSIM dataset. The first contains two lane changes to the right, and the second is a succession of three left lane changes. As shown in Fig. 8  and Fig. 9 , the real and the predicted positions are displayed, as well as the classification results. In this case, two parts exist, namely: the lane-changing and lane-keeping parts. The average velocity is 7. 35 m/s for first vehicle and 8. 03 m/s for the second vehicle. The used sequences are of 6s length and the predicting horizon is of 5 s. The result of vehicle trajectory prediction based on LSTM and maneuver classification in the right lane-change scenario are shown in Fig. 8. Left lane change scenario is presented in Fig.
D. EXPERIMENTAL RESULTS This part presents some results of evaluating trajectory prediction approach using lane changes real data. In our experiment, we used VEDECOM demonstrator as a test vehicle. This demonstrator is an automated vehicle based on Renault ZOE platform equipped with a long-range radar (Continental ARS 408), Lidar (Velodyne VLP-16), GPS RTK and a camera from Mobileye (Fig. 10 ). We collected the test data from driving on Satory test tracks in Versailles, France (Fig. 11 ). During driving, the test vehicle collects the sensor measurements for lane changes scenarios with an average of 22m/s speed. We focused on the behaviors of a single driver with the hope of expanding the test to more drivers in the future. To evaluate the trained model and proceed with prediction steps, the trajectory of the test scenario shown in Fig.
V. CONCLUSION AND PERSPECTIVES In this paper a new hybrid approach (Artificial Neural Network based maneuver classification and Long-Short term Memory Network based trajectory prediction) is proposed. The study demonstrated the ability of the proposed approach to effectively predict the vehicle position with an interesting accuracy. The role of classification model is to understand the driving scenario and provide this information to the prediction model that generates the future trajectory according to the performed maneuver. The model has been proven to achieve a low prediction error level on a large dataset of real freeway vehicle trajectories. Using the large amount of the trajectory data extracted from NGSIM dataset, we trained the ANN to classify the three maneuvers (LCR, LCL and LK), and LSTM to learn complex dynamics of the vehicle's motion and predict its location in the future.
Firstly, the classification model is trained with a maneuvers dataset that contains 6s length features sequences. Secondly, trajectory prediction model is trained with the detected maneuver with past positions sequences. The global model is able to predict vehicle trajectory using past vehicle dynamic sequences. The approach is validated with a real data collected from VEDECOM demonstrator. We show that on a data with a high variability, our system is able to accurately anticipate the intended maneuver 2. 2 seconds in advance, and predict the future trajectory with 0. 30 m and 3. 1 m of lateral and longitudinal position errors respectively. The results show that the proposed method provides an accurate prediction on the vehicles trajectory. This proves that our method can be a promising solution to predict the behavior of traffic participants in real road.
In addition, by interrogating multiple cis-SNPs at the same time, no single SNP is required to be significant, which can uncover combinatorial effects not identified otherwise  [10]. code GSE60690). Gene expression data from CF nasal mucosal epithelial RNAseq samples are uploaded to dbGaP for controlled access for researchers who meet the criteria for access to confidential data (https://view. ncbi. nlm. nih. gov/ dbgap-controlled). Data dictionaries and variable summaries are available on the dbGaP FTP site (https://ftp. ncbi. nlm. nih. gov/dbgap/studies/ phs002254/phs002254. v1. p1/). The public summary-level phenotype data may be browsed at the dbGaP study report page (http://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id= phs002254. v1. p1). The summary GWAS data from CF Gene Modifier Consortium studies and summary results of phenotype trait association testing are publicly available at GitHub (https:// github.
com/danghunccf/CF-GWAS-dataMiningPaper). Here we report the use of PrediXcan and TWAS methods to mine the CF GWAS data for genetic regulation of gene expression associated with CF lung disease severity. We use a combination of our own CF training data sets  [11, 12]  and reference GTEx data sets of multiple human tissues  [4, 5]  to generate a list of genes with evidence of association with CF lung disease severity. Leveraging the strengths of diverse approaches  [9, 10] , and querying multiple tissues produced 379 potential modifier candidates. From this list, 52 consensus genes met the statistical cutoff from both approaches, and 28 of these were within 1 mega-base (Mb) of significant GWAS loci. We sought indirect validation of some of these candidate CF lung disease modifier genes by examining their known functions in literature and annotation databases, and we highlight potential relevance of some of the findings to CF biology.
These gene expression data provided training data to build predictive models using the PredictDB_Pipeline (used by PrediXcan from Im lab) for GTEx v7 release. Models for LCL gene expression available from PredictDB repository (http://predictdb. org/ from Im lab), were compared to our CF LCL models to assess the quality of our predictive models. Full details of genetic and transcriptomic datasets utilized in the modeling, and the modeling procedures are described in S1 Methods in S4 File. Additionally, GTEx models from 48 human tissues and a large data set from Depression Genes and Networks (DGN) whole blood  [13]  were downloaded from the PredictDB (PrediXcan) data repository  [9] , and TWAS  [10]. Imputed SNP genotypes from the CF GWAS cohorts  [14]  were used as input for PrediXcan model training  [9].
The analyses were done using the Bioconductor LIMMA package and the robust regression utilized iterated re-weighted least squares by the rlm function from the R package, MASS. For disease phenotype association testing using predictive models trained on CF nasal epithelial biopsy and LCL data sets, the samples used in predictive model training (122 nasal and 753 LCL samples were part of GWAS) were excluded from the association testing, resulting in 5,634 and 5,003 final sample size for nasal epithelial biopsies and LCLs, respectively. Alternatively, summary GWAS statistics were used to test imputed gene expression association from 48 GTEx tissues to KNoRMA using Functional Summary-based Imputation, or FUSION software from TWAS  [10]. Briefly, summary GWAS statistics for SNP associations to CF lung disease phenotype (n = 6,365) and reference linkage-disequilibrium (LD) data from 1000 genome projects were used as input for FUSION, with TWAS predictive models from 48 GTEx v7 human tissues downloaded from FUSION website (http://gusevlab.