dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to thank  Dr. Brugaletta  from  Hospital Clínic (Barcelona, Spain ) for his support and additional information about LAAO-based therapies. We would like to give an special mention to the undergraduate students from Biomedical Engineering degree involved in the development of the first CFD models ( E. Gonzalez ,  T. Orejon ,  J. Aviles ,  I. Sainz ,  A. Iribar ,  J. Pose  and  A. Serra ). Also thank  G. García  for providing the code to obtain semi-automatically the numerical indices for risk of thrombus formation, and  M. P. García  for her help in the development of VIDAA platform.
Interstation correlation is the basic operation in seismic noise and coda wave interferometry for signal extraction in imaging and monitoring applications. Conventional cross-correlations evaluate the similarity between two signals along lag time and they are efficiently computed by the fast Fourier transform (FFT), valuable to manage the large data volumes that ambient noise applications demand. The phase cross-correlation (PCC) method contributes to increase convergence, a key issue in seismic ambient noise imaging and monitoring; however, it is much more computationally demanding. PCC evaluates similarity by subtracting the modulus of the sum and difference of the instantaneous phase of two signals. We introduce solutions to dramatically reduce the high computational cost of PCC. We show that PCC can be rewritten as a complex cross-correlation and computed by the FFT when the moduli are raised to the power of 2, and we demonstrate PCC can improve waveform coherence and increase convergence compared to the default processing flow of 1-bit amplitude normalization and standard cross-correlation.
Introduction Cross-correlation is a ubiquitous method in seismic data processing important for picking, empirical Green's function extraction, ambient noise monitoring, waveform comparison, and signal, event and pattern detection or identification among others. Phase cross-correlation (PCC) was conceived to better detect weak P-to-S conversions at the upper mantle discontinuities  (Schimmel, 1999). In this work, PCC is introduced to estimate travel-time differences by searching for the lag time that maximize an amplitude-unbiased misfit function between the pilot and converted waveforms, their phase coherency. Phase coherency is useful for detecting weak signals in the coda of stronger waves because it effectively considers a larger portion of the waveforms than conventional correlation-based methods that consider mainly the most energetic components.
Later on, PCC was found useful to improve waveform coherence and convergence of interstation correlations thanks to its amplitude-unbiased property  (Schimmel et al. , 2011). Essentially, PCC can be useful for applications demanding more information on the waveform. Interstation correlations of seismic ambient noise and scattered coda waves complement direct arrivals from earthquakes in surface-wave tomography, monitoring, and Earth's deep interior studies in regions otherwise poorly illuminated. Convergence to the empirical Green's function or a robust seismic signal extraction increases compared to the direct correlation of raw seismic records through customized processing flows that improve the balance of noise sources and reduce the influence of anomalous signals. These processing flows compute many correlations on relatively short data sequences.
This is mainly, because pre-processing is done per station and correlation and denoising operations are done per station pair. Ob-sPy  (Krischer et al. , 2015)  and MSNoise  (Lecocq et al. , 2014)  are open-source packages for Python that are excellent for proof-of-concept studies and often sufficient for pre-processing. Compiled programming language solutions using the central processing unit (CPU) or the graphics processing unit (GPU) are however necessary in order to apply the most demanding operations to large data volumes in a reasonable amount of time. Several tools have been introduced recently for improving the computational efficiency of key operations along interstation correlation processing flows. Zeng and Thurber (2016)  present a GPU implementation of the time-frequency phase-weighted stack (tf-PWS;  Schimmel et al.
, 2011)  which is up to 20 times faster than equivalent CPU implementations using the FFT. Ventosa et al. Conventional cross-correlations, and in particular GNCC, are computed directly only if a very small number of lag times are required; otherwise, they are computed much faster using the FFT. The FFT is very efficiently implemented on the CPU by the fastest Fourier transform in the west (FFTW) library  (Frigo and Johnson, 2005)  and can be further accelerated on the GPU using, e. g. , the compute unified device architecture (CUDA) platform. Despite the advantages of PCC compared to the 1-bit cross-correlation, the application of this method to the large data volumes that current seismic applications demand is limited by its high computational cost. In this article, we introduce two solutions to speed up the computation of PCC by a factor of 100 in conventional settings, so that PCC becomes only 2 to 4 times slower than conventional cross-correlation.
Implementation To our knowledge, the PCC equation with a power factor v different from 2 does not simplify and the operation complexity cannot be reduced by algorithms such as the FFT; however, it can be accelerated on the GPU. GPUs are optimized for computing the same operations on large datasets in parallel and CPUs for long sequential series of operations. Thus, GPUs are optimal to solve simple but computationally intensive operations on relatively small datasets such as PCC, where the wall-clock time reduction of the computations largely compensates for the time penalty of copying data to and from the GPU memory. Conversely, 1-bit GNCC and PCC2 are conventionally more efficiently implemented in the CPU using a few FFT operations than in the GPU, where the acceleration of the FFT computation is not worth the extra time required to move data between the CPU and GPU memories.
We develop CPU only and GPU enhanced codes of 1-bit GNCC and PCC (see Data and Resources) that compute all the correlations of a station pair at once. We use OpenMP to compute these correlations in parallel on the CPU, instead of parallelizing each correlation individually, to reduce scheduling overhead and improve scaling with the number of cores. To accelerate the PCC1 calculation when a GPU is available, we compute the analytic representation of the data sequences on the CPU using OpenMP and, in parallel, the subsequent operations on the GPU. To speed up computations of the actual correlations, eq. (  3 ), we distribute the computations of one correlation across many GPU-thread blocks. Each thread in a block computes the correlation between 256 samples of the data sequences at one lag time using copies of the portions of the sequences needed on the small shared memory (much faster and with lower latency than the global memory) available to each block.
Example We illustrate the computational performance of 1-bit GNCC and PCC using continuous data on the vertical component. The pre-processing flow consists in removing the mean and trends, correcting the instrument response to produce ground velocity, band-pass filtering from 20 to 33 mHz (30 to 50 s period), dividing data into 1 day-long non-overlapping sequences, and rejecting sequences with high seismic activity either containing strong earthquakes or having a mean energy much higher than the average. We specifically reject rawdata sequences with absolute maxima greater than the median plus 100 times the median absolute deviation (MAD). Then, we reject pre-processed sequences with a standard deviation greater than the median of the standard deviation of the sequences recorded by a given station plus 500 times its MAD.
The test environment is a workstation equipped with a 4-core 64-bit Intel Haswell CPU at 3. 50 GHz (i5-4690K) with 16 GB of DDR3 at 1866 MHz, an Nvidia GTX 1060 graphics card, and an SSD disk to store the data. samples per day, which is 4. 17 times slower than 1-bit GNCC and 1. 79 times slower than PCC2, and 1. 9 s on a single NVIDIA Tesla K20 GPU from the S-CAPAD IPGP cluster. In the next example, we have selected data from the years 1990 to 2017 recorded by 299 broadband seismic stations distributed worldwide as shown in Fig. 2. In particular, we have selected all stations from the global and regional networks of GEOFON (GE), GEOSCOPE (G), GSN (IU,II) and New China Digital Seismograph Network (IC). In addition, we have included some stations from Geoscience Australia (AU), the Canadian National Seismograph Network (CN), the Danish Seismological Network (DK), the Pacific21 (PS) network, and the Canfranc Underground Laboratory (LC) to improve global Earth coverage.
We have computed 1-bit GNCC, PCC2, and PCC1 of day-long sequences downsampled to sampling periods of 4 seconds per sample. Then, we have rejected correlations with an anomalous standard deviation and stack the rest linearly. Under these settings, the faster algorithms would have spent most of the time on reading and writing operations. To accelerate these operations, we have gathered all data from a given station in one file and we have used a ramdisk for temporal results such as the daily correlations. Using the above workstation, the correlation and stack operations require 939 minutes for 1-bit GNCC, 1,291 minutes for PCC2, and 2,380 for PCC1 on the GPU.
Conclusions We have introduced approaches that enable the application of PCC on large data volumes using conventional workstations. The stand-alone code implementing PCC and 1-bit GNCC is designed to be easily integrated with current processing flows, whether it is executed directly or the main functions implementing each method are called from other codes. We have shown that PCC simplifies to a conventional cross-correlation of unitary phasors when using the power factor 2. This cross-correlation equation is fully analogous to 1-bit GNCC with the single difference that 1-bit GNCC considers the sign of the real signals and PCC2 the phase factor of analytic signals. Since phase carries more information than sign, most applications using 1-bit GNCC should see a gain in waveform coherence and increased convergence when applying PCC2.
By writing PCC2 as a complex cross-correlation, we have achieved a lower operation complexity and, in conventional settings, about 100 times faster code than direct implementations. This reduces the computational cost of PCC2 to about two times that of the 1-bit GNCC method using only the CPU. When using other powers, GPU acceleration has allowed us to apply PCC1 to the large data volume needed to extract body wave signals from interstation correlations using a conventional workstation and a low-cost GPU. In general, these computational cost reductions are important to efficiently find and tune the best adapted methods to a given problem and, especially, to process the large data volumes that modern seismic ambient noise imaging and monitoring applications demand with phase cross-correlation.
Data and Resources The    by the fast Fourier transform (FFT), valuable to manage the large data volumes that ambient noise applications demand. The phase cross-correlation (PCC) method contributes to increase convergence, a key issue in seismic ambient noise imaging and monitoring; however, it is much more computationally demanding. PCC evaluates similarity by subtracting the modulus of the sum and difference of the instantaneous phase of two signals. We introduce solutions to dramatically reduce the high computational cost of PCC. We show that PCC can be rewritten as a complex cross-correlation and computed by the FFT when the moduli are raised to the power of 2, and we demonstrate PCC can improve waveform coherence and increase convergence compared to the default processing flow of 1-bit amplitude normalization and standard cross-correlation.
The computational cost of the signal processing methods conventionally used in the pre-processing of the raw time series is small compared to the correlation, weighted stacking, and denoising operations subsequently applied. This is mainly, because pre-processing is done per station and correlation and denoising operations are done per station pair. Ob-sPy  (Krischer et al. , 2015)  and MSNoise  (Lecocq et al. , 2014)  are open-source packages for Python that are excellent for proof-of-concept studies and often sufficient for pre-processing. Compiled programming language solutions using the central processing unit (CPU) or the graphics processing unit (GPU) are however necessary in order to apply the most demanding operations to large data volumes in a reasonable amount of time. Several tools have been introduced recently for improving the computational efficiency of key operations along interstation correlation processing flows.
Zeng and Thurber (2016)  present a GPU implementation of the time-frequency phase-weighted stack (tf-PWS;  Schimmel et al. , 2011)  which is up to 20 times faster than equivalent CPU implementations using the FFT. Ventosa et al. Conventional cross-correlations, and in particular GNCC, are computed directly only if a very small number of lag times are required; otherwise, they are computed much faster using the FFT. The FFT is very efficiently implemented on the CPU by the fastest Fourier transform in the west (FFTW) library  (Frigo and Johnson, 2005)  and can be further accelerated on the GPU using, e. g. , the compute unified device architecture (CUDA) platform. Despite the advantages of PCC compared to the 1-bit cross-correlation, the application of this method to the large data volumes that current seismic applications demand is limited by its high computational cost.
In this article, we introduce two solutions to speed up the computation of PCC by a factor of 100 in conventional settings, so that PCC becomes only 2 to 4 times slower than conventional cross-correlation. We reduce operation complexity of the PCC when using modulus raised to the power of 2, and we present optimized CPU and GPU codes (see Data and Resources) to accelerate computations when using other powers, in particular, using the default power of 1 (PCC1) as recommended in  Schimmel (1999)  and  Schimmel et al. (2011). These codes focus exclusively on the cross-correlation operation and allow for an easy integration with current processing flows.
We have computed 1-bit GNCC, PCC2, and PCC1 of day-long sequences downsampled to sampling periods of 4 seconds per sample. Then, we have rejected correlations with an anomalous standard deviation and stack the rest linearly. Under these settings, the faster algorithms would have spent most of the time on reading and writing operations. To accelerate these operations, we have gathered all data from a given station in one file and we have used a ramdisk for temporal results such as the daily correlations. Using the above workstation, the correlation and stack operations require 939 minutes for 1-bit GNCC, 1,291 minutes for PCC2, and 2,380 for PCC1 on the GPU. Fig. 3 (a) shows 110 million day-long interstation correlations using PCC2 from 39,656 station pairs band-pass filtered from 20 to 33 mHz as a function of distance stacked on bins of 0.
data used in this study were obtained from the Incorporated Research Institutions of Seismology (IRIS) and the European Integrated Data Archive (EIDA). The CPU and GPU accelerated versions of code implementing 1-bit GNCC and PCC are open source under the LGPL v3 license and available at https://github. com/sergiventosa/FastPCC. The fastest Fourier transform in the west (FFTW) is available from http://www. fftw3. organd the CUDA documentation and installation guides from https://docs. nvidia. com/cuda/index. html. Takano, T. , T. Nishimura, and H. Nakahara (2017). Seismic velocity changes concentrated at the shallow structure as inferred from correlation analyses of ambient noise during volcano deformation at Izu-Oshima, Japan, J. Geophys. Res. , 122, no. 8, 6721-6736, doi:     10. 1002/2017JB014340.
Acknowledgments We thank to the editor ✿✿✿ the ✿✿✿✿✿✿✿✿ editors and the anonymous reviewers for improving the manuscript. This work was supported by the  MISTERIOS  project  CGL2013-48601-C2-1-R  and the  ANR   MIMOSA  project  ANR-14-CE01-0012. We also acknowledge support from discussions within  TIDES COST  Actions  ES1401. Numerical computations were partly performed on the S-CAPAD platform, IPGP, France.
Acknowledgments We thank the editors and the anonymous reviewers for improving the manuscript. This work was supported by the  MISTERIOS  project  CGL2013-48601-C2-1-R  and the  ANR   MIMOSA  project  ANR-14-CE01-0012. We also acknowledge support from discussions within  TIDES COST  Actions  ES1401. Numerical computations were partly performed on the S-CAPAD platform, IPGP, France.
The enormous amount of freely accessible functional genomics data is an invaluable resource for interrogating the biological function of multiple DNA-interacting players and chromatin modifications by large-scale comparative analyses. However, in practice, interrogating large collections of public data requires major efforts for (i) reprocessing available raw reads, (ii) incorporating quality assessments to exclude artefactual and low-quality data, and (iii) processing data by using high-performance computation. Here, we present qcGenomics, a user-friendly online resource for ultrafast retrieval, visualization, and comparative analysis of tens of thousands of genomics datasets to gain new functional insight from global or focused multidimensional data integration.
Indeed, for optimal reuse, it is essential to reprocess public data under standardized conditions and to evaluate their quality to exclude lowquality or potential artefactual data, which could generate bias and lead to improper or wrong data interpretation. To address data quality, we have previously developed a quality control system for functional genomics data  (Mendoza-Parra et al, 2013b) , which has been used for qualifying at present more than 82,000 publicly available enrichmentrelated datasets; this quality assessment database comprises ~70% of all publicly available ChIP-seq assays generated worldwide. Starting from this quality assessment, we have developed a user-friendly suite of big data analysis tools-qcGenomics (http:// ngs-qc. org/qcgenomics/)-a publicly available resource to retrieve datasets of user-defined quality according to a multitude of query options and visualize them through a dedicated genome browser.
More importantly, we have implemented solutions for both global and local comparative analyses to study from two up to several hundreds of datasets to reveal, among others, common features/signatures. Thus, without the need of collecting and reprocessing the data, non-specialist users will be able to interrogate large amounts of functional genomics data, visualize enrichment patterns or identify, for instance, co-occurring binding patterns from a multi-profile comparison. Importantly, users can upload their own data-without having to install additional software-to visually compare it with those available in the public domain.
Results qcGenomics: a web-access solution for an intuitive interaction with functional genomics data released on the public domain We previously established an automated pipeline to download and realign raw datasets from the sequence read archive (SRA) to provide global and local quality assessments of large amounts of functional genomics data (Fig  1 ). This generated a public database (http://ngs-qc. org/database. php) in which quality indicators provided by the "next generation sequencing quality control (NGS-QC) Generator" (Mendoza-Parra et al, 2013b) are currently associated with >82,000 ChIP-seq and similar enrichment-related datasets, as well as to long-range chromatin interaction data (Hi-C and related; http://ngs-qc. org/logiqa) (Mendoza-Parra et al, 2016). We have now implemented a dedicated data portal (termed "NAVi" for Nucleic Acid Viewer) that allows to query publicly available data by combining intuitive keywords such as cell/tissue type, model organism, target molecule, accession numbers, associated quality score, author names, and keywords in the title or abstract of a corresponding article.
As a consequence, NAVi displays the user-selected query in a table format where further information, including the source of the public data (GSM ID) and the number of total mapped reads. In addition, users can select datasets of interest and visualize their enrichment patterns with the dedicated NAVi genome browser. Notably, NAVi provides flexibility by displaying on-demand HiC contact maps and ChIP-seq enrichment coverage in a single view  (Fig 2) , thus providing optimal conditions for comparative studies and intuitive searches. In the illustrated example, Hi-C long-range interaction maps in the surrounding of the SOX2 locus are displayed together with the enrichment patterns for the histone modifications H3K4me3, H3K4me1, and H3K27ac. Note that two (or more) analyses can be displayed; in the example, HiC and ChIP-seq patterns for a given region in embryonic stem cell (ESC) datasets are presented together with those of neuroectodermal cells.
To facilitate retrieval of public data, queries in NAVi can contain multiple terms surrounded by double quotes to search for an exact group of words and can be combined with a plus sign to combine the results of multiple independent queries (e. g. , "MCF-7 H3K4me3 ChIP-seq + MCF-7 ATAC-seq"). As an outcome, NAVi provides a list of all datasets matching the query, accompanied by several descriptors, including the associated model organism, type of biological source (cell/tissue type), target molecule, number of mapped reads, quality score, and a direct link to the original accession ID  (Figs 3A and S1). Data retrieved by NAVi can be subsequently analyzed and visualized by a number of additional tools. First, a fraction (or all) of the retrieved data can be selected and displayed with the NAVi genome browser  (Fig 3B).
Exploring public datasets for ultrafast comparative analysis and discovery of novel features NAVi can be used for a variety of issues, ranging from data variability to comparative data analysis which can provide novel testable hypotheses. This is illustrated for the ERG transcription factor (TF) binding to the WNT7B gene in the normal prostate epithelial cell line RWPE-1 (Fig  S2 ). The four datasets show vastly different signal-to-noise ratios, with two (GSM2195110 and GSM2195106) revealing no apparent binding in this region, whereas small peaks can be seen in GSM2195103 with the same antibody. In contrast, GSM927071 reveals strong peaks in the first and third exon of WNT7B with another antibody. Interestingly, the top three ChIP-seq profiles were constructed from 23 to 29 million mapped reads, whereas GSM927071 originates from about 15 million mapped reads.
Using the latter as a guide, small peaks can be discerned also in the other four experiments for the peak within the first WNT7B in intron. Thus, within a few seconds the user can select and compare different datasets to search, find evidence, or gain confidence for a speculated binding event of a TF and validate this finding with gene-specific approaches. Comparing RWPE-1 data with those of the TMPRSS2-ERG fusionpositive VCaP prostate cancer cells provides even information about those genes which have become under the control of the misexpressed ERG in the cancer cells. Indeed, although no significant ERG binding is apparent at the WNT2 upstream region in RWP1 cells, ERG binding close to the transcriptional start site is readily seen in VCaP cells  (Fig S3). This suggests an acquisition of ERG binding and (de)regulation of WNT2 expression in the cancer cells which acquire the TMPRSS2-ERG fusion.