dataesr/scientific-paragraphs-categorization huggingface dataset:
The dataset describes each job with a well identifier (UWI_API), the time limits of periods of injection/operation, spatial positions of the injection, and total volumes per month. There is very low precision on time and volumes. The reliability and completeness of the HF and SWD data sets are unknown. Skoumal et al. (2018)  found that ~8% of HF wells between 2014 and 2016 in the Oklahoma Corporation Commission well completion database were not reported to FracFocus and note apparent errors in reporting.
For each associated event-well activity pair, the event is relocated with its hypocenter constrained at the heel-toe center point for a HF job or well location for SWD. Resulting phase residuals for all constrained locations are accumulated through a weighted mean using each event-well activity association probability as a weight to form PPGT station/phase corrections. See supporting information S1 for details. We used the PB02 25-km event subset of the PB3D reference locations and all jobs in the HF dataset or all SWD wells within 12 km of PB02 for  where Δd is the difference in horizontal distance between an event and the center point between the toe and heel coordinates for a HF job or well location for SWD. L (Δt) and L (Δd) are shown for the optimal, rounded mean association parameters in Table  1.
Selection of Association Parameters To select association parameters Δt crit , σ t , Δd crit , and σ d in equations (  1 )-(  3 ), we used the HF dataset and performed a grid search over a range of each of the parameters and maximized the number of causal relative to acausal HF-event associations. We expected coincidental associations, because of the high spatial and temporal rates of HF activity and seismicity in the study area. Acausal application of the probabilistic association procedure, in which the time relation between HF start and stop dates and seismic-event origin time is reversed, can produce only coincidental associations. In contrast, normal, causal association will produce a combination of coincidental and true, causal associations. For the time parameters, given that most durations in the HF dataset are >5 days, we searched for minimum Δt crit over 4, 5, 6, 7, and 8 days and for σ t over 2, 3, 4, 5, and 6 days.
Journal of Geophysical Research: Solid Earth There are three SWD wells in our dataset in this area. One, in cluster X, associates with a subset of the events associated to HF well activity forming cluster F in Apr 2018 (Figures  8b  and 9c ), another SWD, in cluster Y, associated to events north of cluster F in Jul 2018 (Figures  8a  and 9d ). The third SWD well (in cluster Z in Figures  8b  and 9d ) associates with a subset of the events associated in HF cluster G in Dec 2017 and May 2018 (Figures  8a  and 9d ). Only SWD cluster Y shows evidence for possible causality between SWD activity and seismicity (at end July 2018) in the absence of preceding HF activity (Figure  9c ) though a small date error in the HF dataset could mask association of this seismicity with apparently later HF activity (in early August 2018).
We expect that these data sets will provide high-quality ground-truth sources in the area and help validate our PPGT procedures. These efforts should greatly improve epicentral accuracy and may help reduce the absolute depth uncertainty. We conclude that our probabilistic, PPGT association and correction procedures can correctly account for absolute errors in seismic-velocity models and produce real improvement in absolute locations. We expect that further development of this procedure, perhaps combined with high-precision, relative-location information, and application throughout the west Texas Delaware Basin study area and other TexNet priority areas, will help improve the accuracy of TexNet location of seismic sources and reliable association of seismic events with geographic locations of human activities.
We used SeismicityViewer (http://www. alomax. net/software), Folium  (http://python-visualization. github. io/folium), Leaflet (https://leafletjs. com)and Generic Mapping Tools (GMT; http://gmt. soest. hawaii. edu)for seismicity maps and charts, and OpenOffice. org(http://www. openoffice. org)for spreadsheet calculations and charts.
Data and Resources The TexNet catalog is available (http://www. beg. utexas. edu/texnet-cisr/texnet/earthquake-catalog). TexNet phase data will be available through TexNet FDSN web-services, as soon as TexNet becomes an authoritative network from USGS/ANSS. Prior to this phase data can be available by request via the TexNet website. Tables with hypocenters plotted in Figures  5  and 15  are available through the Texas Data Repository (https://doi. org/10. 18738/T8/BT9FMG). The NonLinLoc software package is available (http://alomax. net/ nlloc). Hydraulic stimulation information is available through the IHS Markit Well Database (https://ihsmarkit. com/products/us-well-data. html), Frac Focus Chemical Disclosure Registry (http://fracfocus. org) and Texas Railroad Commission. SWD data are available through the Texas Railroad Commission (https:// www.
12 The digital plant phenotyping platform (D3P) includes two components: a 3D canopy structure model and simulators of the phenotyping observations from LiDARs and multispectral or RGB cameras (Fig. 5 ). Three-dimensional canopy structures were simulated using the FSPM modeling platform OpenAlea  (Pradal et al. , 2008). OpenAlea is used in D3P to create 3D meshes of virtual canopies. LiDAR data are simulated using the 3D crop modelling library Plantgl  (Pradal et al. , 2009). Multispectral and RGB images are simulated using the Persistence of Vision Raytracer (POV-Ray, Version 3. 7), which renders complex 3D scenes for a range of camera specifications. Optical properties of plant organs are simulated with the PROSPECT model  (Jacquemoud and Baret, 1990 ) using the Python library PyProSAIL.
RGB simulator is a special case of multispectral camera for red, green, and blue channels. By defining the sensor properties and the observational configuration (Supporting Information Supplemental Table  S1 ), we can mimic with a very high realism any phenotyping measurement (Supplemental Video S1). The accuracy of the LiDAR simulator has been previously evaluated through comparison with LiDAR measurements  (Liu et al. , 2017). The performance of POV-Ray based radiative transfer simulation was evaluated through radiation transfer model inter-comparison using an on-line model checker, ROMC  (Widlowski et al. , 2008). The evaluation of POV-Ray (in Supplemental Figure  S1 ) shows satisfactory results. D3P is programmed in Python. All D3P dependencies are open-source and their code is accessible from the code repositories and websites given in Table  S2.
Simulation of synthetic datasets We simulated RGB images of wheat canopies using D3P with our simplified version of ADEL-Wheat. We rendered 2 × 2 m scenes containing 11 rows with and inter-row spacing of 17. 5 cm and a sowing density of 250 seeds•m -2. Note that plant density was not considered as an unknown parameter since high resolution RGB imagery techniques have been developed to accurately measure it and document the associated sowing pattern  (Jin et al. , 2017; Liu et al. , 2017). A total of 2,500 combinations of the five influential parameters of ADEL-Wheat before tillering (Table  1 ) were randomly drawn using a Latin Hypercube sampling scheme. The parameters were assumed to follow a uniform distribution within their range of variation (Abichou, 2016; Table  1 ). During tillering, a similar sampling strategy was used for the five influential parameters during that period (Table  1 ).
15 values will be called 'pseudo-observations' in the following (illustrated in Supplemental Figure  S2 ). Each of the 2,500 input parameter combination were also associated to two additional traits: the GAI at each of the 10 dates and the number of axes with more than three leaves ( 3 ) at the end of the tillering period. Training and validation processes were conducted with 85% and 15% of the synthetic dataset, respectively.
Parameter adjustment was completed using a neural network (NN) machine learning approach, which is well adapted to our case where the simulations are time consuming, preventing from using iterative optimization approaches  (Kimes et al. , 2000; Baret and Buis, 2008). We used a one-layer feed-forward network with tangent sigmoid transfer functions in the first layer and a linear transfer function in the output layer. The number of neurons in the hidden layer is based on the geometric pyramid rule proposed by Masters  (1993). The optimal number of neurons in the hidden layer should be close to √ with  and  being the number of inputs and outputs, respectively. Then the synaptic weights and biases are tuned using the Levenberg-Marquardt optimization algorithm  (Marquardt and Mathematics, 1963)  to best match the output values over the training database.
16 directions, the ones providing the best retrieval performances for the ADEL-Wheat parameters and GAI. Pseudo measurements of GF were assimilated into D3P using the trained NN for each of the 961 configurations considered the same way as described above for the five dates and five directions. A 5% Gaussian noise was applied on GF values simulated by D3P. Retrieval performances were quantified as the average rRMSE computed on the targeted traits and GAI for validation dataset (375 among the 2,500 combinations of the parameters presented in Table  1 ). For GAI, the rRMSE was computed for the five dates before tillering. Comment citer ce document : Liu, S. , Martre, P. , Buis, S. , Abichou, M. , Andrieu, B. ,  Baret, F. (2019)   measurements with the reference values for the first growth period (between crop emergence and ligulation of leaf 3).
Synthetic GF data were obtained from five view directions with 5% noise. The five parameters are:  sen , number of green leaves at the start of leaf senescence on the mainstem;  min , minimum number of green leaves on the mainstem;  til , final number of tillers per plant;  til , inclination of the base of tillers relative to mainstem inclination; α til , change of tiller inclination angle with the number of emerged leaves.
/openalea. gforge. inria. fr/doc/alinea/adel/doc/_build/html/user/manual. html). Therefore, a reduced number of parameters was required in order to estimate them from HTP observations. We reparametrized the leaf dimension representation in ADEL-Wheat using a large dataset covering 28 winter wheat experiments conducted over several years in Grignon, France, with a range of sowing dates, cultivars, and nitrogen levels(Abichou, 2016). The modifications proposed are detailed in Supplemental Methods S1. A total of 10 influential parameters controlling the canopy development from emergence to the beginning of stem elongation was finally necessary to drive the simplified version of ADEL-Wheat model. Before tillering starts, i. e. before ligulation of the third leaf on the main stem.
/openalea. gforge. inria. fr/doc/alinea/adel/doc/_build/html/user/manual. html). Therefore, a reduced number of parameters was required in order to estimate them from HTP observations. We reparametrized the leaf dimension representation in ADEL-Wheat using a large dataset covering 28 winter wheat experiments conducted over several years in Grignon, France, with a range of sowing dates, cultivars, and nitrogen levels(Abichou, 2016). The modifications proposed are detailed in Supplemental Methods S1. A total of 10 influential parameters controlling the canopy development from emergence to the beginning of stem elongation was finally necessary to drive the simplified version of ADEL-Wheat model. Before tillering starts, i. e. before ligulation of the third leaf on the main stem detailed presentation of ADEL-Wheat see http:/ 13 www.
Mask Grand Canal Figure  4 : Input bracketed exposure sequences "Mask" and "Grand Canal", each composed of three images: under-exposed, correctly exposed, and over-exposed. The input sequence "House" is displayed in Figure  2. The EF method has clear out-ofrange artifacts, whereas EEF has not. The fusion results are displayed as is, without affine rescaling. To fit the displayable range, the EF result needs compression, and thus loses contrast. On the contrary, the final dynamic range obtained with EEF is generally smaller than [0, 1], thus its contrast is increased. rescaling would be much more serious. We nevertheless suggest a correction to avoid this issue, described in Section 3. 2. Additionally, we applied EF and EEF on the bigger bracketed sequence dataset of Ma et al. [7]  and measured the scaling factors applied by the robust normalization on the fused images.
Acknowledgment We thank  Jean-Michel Morel  for fruitful comments and discussions. Work partly financed by Office of Naval research grant  N00014-17-1-2552 ,  DGA Astrid project "filmer la Terre " n •  ANR-17-ASTR-0013-01 , and  ANRT CIFRE Ph. D. scholarship n •  2014/1323  of the  French Ministry for Higher Studies, Research and Innovation.
Background: There are over 25 tools dedicated for the detection of Copy Number Variants (CNVs) using Whole Exome Sequencing (WES) data based on read depth analysis. The tools reported consist of several steps, including: (i) calculation of read depth for each sequencing target, (ii) normalization, (iii) segmentation and (iv) actual CNV calling. The essential aspect of the entire process is the normalization stage, in which systematic errors and biases are removed and the reference sample set is used to increase the signal-to-noise ratio. Although some CNV calling tools use dedicated algorithms to obtain the optimal reference sample set, most of the advanced CNV callers do not include this feature. To our knowledge, this work is the first attempt to assess the impact of reference sample set selection on CNV detection performance.
Methods: We used WES data from the 1000 Genomes project to evaluate the impact of various methods of reference sample set selection on CNV calling performance of three chosen state-of-the-art tools: CODEX, CNVkit and exomeCopy. Two naive solutions (all samples as reference set and random selection) as well as two clustering methods (k-means and k nearest neighbours (kNN) with a variable number of clusters or group sizes) have been evaluated to discover the best performing sample selection method.
In CANOES, the maximum number of samples in reference set is fixed and can be modified by the user (default 30), whereas in ExomeDepth this number is determined by the algorithm which maximizes the posterior probability in favour of a single-exon heterozygous deletion call by ExomeDepth's model. In CLAMMS, the number of selected samples in the reference set is also set by the user. CLAMMS uses kNN algorithm as well, but, in contrary to CANOES and ExomeDepth, the distance metric between samples is counted basing on Binary Alignment Map features (extracted by Picard (http://broadinstitute. github. io/ picard)). In this work, we investigated four different approaches to the selection of reference sample set labelled "all samples", "random", "kNN" and "k-means". They were applied to the subset of 1000 Genomes data to study the influence of a selection method on the performance of CNV callers.
Benchmark dataset To evaluate the influence of the reference sample set selection on the CNV calling performance of selected algorithms, we used 1000 Genomes project phase  3   The investigated samples were sequenced by the seven research centres  [9]. The correlation analysis of coverage Fig. 2  Workflow of the research method presented in the study. The input dataset is a set of numbers which depict the depth of coverage in samples on specified exons. Each sample from this dataset is processed by a reference sample set selector module, which is responsible for designating a set of samples that will be the reference collection. As a consequence, every element from the input dataset has its own, independent reference set. The normalization step uses the determined reference sets to perform normalization.
This step is performed only once for "all" method, once per sample in "kNN" and "random" approach and once per cluster in "k-means" strategy. Then, for each generated sample, we apply CNV calling performed by three callers: exomeCopy, CODEX and CNVkit. The input for the CNV detecting tool is a set of samples consisting of the investigated sample and its reference panel. After calling CNVs, the events in the investigated sample are filtered and, this set of events is added to the final set of CNVs. Having processed all samples from the input dataset, the union of all partial per-sample results stored as the output call set for each approach combining selection method and variant caller. The evaluation of the results is performed against CNVs call set gold standard, delivered by 1000 Genomes Project.
Reference set selection To assess the impact of reference set selection on the performance of a CNV caller, we have examined four approaches. The first method ("all samples"), considered as a baseline solution, encompasses all samples from the input dataset, with no selection performed. The second one ("random") is a naive approach to selection, which is an arbitrary subset choice, implemented as a draw with no repetitions. We iterated this experiment 10 times with various sizes of reference collection (from 50 to 500). The third method ("kNN") is the approach used in previous works (i. e. , Canoes, ExomeDepth, CLAMMS), where only k most similar samples are included in the reference set (k nearest neighbors algorithm  [11] ). The distance metric between the elements is based on Pearson correlation between read depth of samples.
Normalization and CNV calling In our experiments, normalization of read depth data and CNV calling was performed by three state-of-the-art tools (CODEX v. 1. 8. 0, exomeCopy v. 1. 22. 0 and CNVkit v. 0. 9. 3) that do not perform the reference set selection on their own. CODEX  [13]  algorithm is based on a multi-sample normalization model, which is fitted to remove various biases including noise introduced by different GC content in the analyzed targets. CNVs are called by the Poisson likelihood-based segmentation algorithm. Exome-Copy  [14] , on the other hand, implements a hidden Markov model which uses positional covariates, including background read depth and GC content, to simultaneously normalize and segment the samples into the regions of constant copy count. CNVkit  [15]  uses both the targeted reads and the non-specifically captured off-target reads to distribute the copy number evenly across the genome.
k-means vs. kNN based approach The experiments proved that performance metrics for reference sets chosen by "kNN" and "k-means" methods are similar. Although the existing tools (Canoes, ExomeDepth, CLAMMS) use kNN-based methods as the reference set selection algorithm (see comparison to CLAMMS method in Additional file 1) in this study, we have shown that k-means has much less time complexity. As Fig. 6  clearly states, the "kNN" approach is significantly (approximately 200x) slower. The reason for this is the need to invoke a long-lasting normalization process as many times as the number of input elements is, while "k-means" requires normalization only once per cluster. Therefore, for large data sets the "k-means" method is recommended approach. The reference set selection with the k-means algorithm could be performed on any data set by splitting samples on a given number of groups with simultaneous monitoring of Dunn index, Silhouette width and Davies-Bouldin index values.
Further research The solution that would automatically determine, based on the input data set, the best parameters for CNV calling would facilitate the entire process. To achieve this, we are designing a module for the injection of artificially generated CNVs into the user data and the comparison of the detected CNVs with the events injected. This technique will enable the choice of an appropriate method for the reference sample set selection for any WES data set as well as automatic selection of parameters of a given method for the selection of reference sample set including the number of clusters in the k-means algorithm. This process could be included in complex tools, e. g. applications for optimization CNV callers like Ximmer  [5].
Conclusions We have shown that proper reference sample set selection leads to improved sensitivity and precision for all considered CNV callers. Our results revealed that kmeans and kNN based approaches guarantee a very similar performance of CNV calling while the former one is significantly faster and requires less computational resources. Finally, we have shown that the optimal number of groups for k-means algorithm (corresponding to the highest accuracy of CNV calling) can be estimated using internal clustering metrics (including Dunn index, Silhouette width and Davies-Bouldin index). To summarize, these conclusions can be used as a guideline that helps in appropriate implementation and fine-tuning of CNV calling pipelines from WES data in the clinical and research environment.
Method In this section we describe the mathematical formulation of DIVE (section 2. 1), then we show how to fit the model using Expectation Maximisation (section 2. 2) and we describe further implementation details of the algorithm (section 2. 3). Afterwards, we outline the synthetic data-generation process (section 2. 4) for testing the model in the presence of ground truth, as well as the pipeline for pre-processing the ADNI and DRC datasets (section 2. 5). Figure  1. Diagram of the proposed DIVE model. DIVE assumes that biomarkers of pathology (e. g. cortical thinning) can be measured at many vertices (i. e. locations) on the cortical surface (A), where each vertex has a distinct trajectory of change during disease progression (B). The model thus assigns to every cortical vertex one of a small set of temporal trajectories describing the change in some image-based measurement (e.
Fitting the Model using Generalised Expectation-Maximisation We choose to fit our model using Expectation-Maximisation (EM), because it offers a fast convergence given the large number of parameters that need to be estimated and the huge dimensionality of relevant datasets (e. g. 1973 subjects x 163,842 vertices in ADNI). In the next two sections we outline the E-step and M-step. While both of these steps have no closed-form solution, we will solve them using numerical optimisation, which only results in an increase in the objective function at each iteration. However, the EM algorithm is still guaranteed to converge, and this approach is called Generalised EM  (Bishop, 2006). Algorithm 1 shows the model fitting procedure using the EM algorithm. The procedure first initialises (line 1) some parameters required to start the EM algorithm: the subject time shifts α and β and the latent parameters z lk which represent the assignment of vertices to clusters.
Finally, we achieved a significant speedup in the evaluation of objective functions by computing a z lk -weighted average of vertex measurements within each cluster (see Supplementary section 4). This resulted in a convergence time of around 6 hours for the larger datasets (ADNI). For optimising λ, we again try to optimise in the M-step the expected full data likelihood under the Z estimates from the previous iteration: We simplify the above equation by expanding the likelihood model and approximating the joint over Z with the product of the marginals z lk over all vertices l. This results in the update equation from Alg. 1 line 17see supplementary material for full derivation. In this final equation we also replaced z lk with a function ζ lk (λ) over λ, which updates z lk based on the current value of λ being evaluated.