dataesr/scientific-paragraphs-categorization huggingface dataset:
Photo-z Validation The last component of Photo-z Training pipeline is the Photo-z Validation. It is responsible for checking the quality of the photo-zs computed in a validation sample, as an estimate of the quality of the photo-z to be estimated for the large photometric datasets. 16 http://www. numpy. org/ To meet science-driven requirements, sometimes one needs to perform training and validation in samples which are independent of each other. Hence, we created a new pipeline (keeping the first one active) called Photo-z Validation to perform only the validation step, using the result of training from a previous run of the pipeline Photo-z Training, but with the possibility to receive a completely different matched sample as input data. This pipeline uses the same component Photo-z Validation as the Photo-z Training pipeline, therefore the methodology is the same.
Photo-z Compute The actual photo-z calculation in the Portal is done by the Photo-z Compute pipeline. It estimates photo-zs for DES objects present in the photometric catalogs, regardless of the object's nature (e. g. , star or galaxy), using the training file(s) produced by Photo-z Training. Once the photo-zs are calculated,   they can be used in the creation of science-ready catalogs considering, e. g. , different star/galaxy classifiers, color selections, and magnitude limits. It is also possible to download the photoz resulting tables, or to deliver them to the collaboration through the export tool, connected to DESDM database. Since the same object can be tagged as galaxy by one classifier or star by another, it is essential to have photo-zs available for all objects. Therefore, at this stage, the distribution of redshifts, N(z), obtained with the Photo-z Compute pipeline is not representative of the galaxy distribution yet.
Only later, when the final catalogs are produced after pruning, the N(z) can be used for scientific analyses. The Photo-z Compute is composed of five components. The dashed line in Figure  8  highlights the part of the workflow that runs in parallel processing. The first component, Photo-z Separate handles with the training files inherited from the previous process. The second component, Sky Partitioner, defines the data partition by dividing the area in the Sky covered by the input dataset, based on the user choice of partition unit (as detailed below). It distributes the information about the data partitions into the nodes where the photo-z codes run in parallel. The next two components (enclosed by the dashed line in Figure  8 ) run in parallel, each item of the partition defined by the previous one.
The component Partition Retriever loads the contents of data partition in each computing node. To deal with large photometric samples efficiently, the data access uses HDFS, which previously distributed chunks of data in the cluster nodes, minimizing time spent with data transfer. Therefore, the Partition Retriever reads from the cluster nodes, as discussed in Section 2. It is only in the fourth component that the photo-zs are in fact estimated. This step is the most computationally intensive of all the tasks related to photo-z estimation. There is one component available for each algorithm. All of them contain a python wrapper that prepares the input data, runs the code, standardizes the outputs, and delivers it to the consolidator. Thus, the workflow calls only the one that corresponds to code chosen in the configuration screen (see the menu on the left side displayed in Figure  22 ).
The consolidated jobs and their resulting photo-z table are ingested in the database by the last component, called Join Photo-z Compute. The product of Photo-z Compute is one of the leading ingredients to compose a science-ready catalog for extragalactic sciences, as discussed in Fausti  Neto et al. (2018). Since the Portal provides flexibility on the parallelization strategy, we performed a series of tests to illustrate the use of our computer cluster. In the following paragraphs, we compare the computing of photo-zs by varying the size of the data chunk, and consequently, the number of data partitions. For simplicity, we use the original data division from DES, based on tiles (see discussion ahead). The methodology details of the parallelization are discussed in Section 2. We use the most extensive dataset of Y1A1 data release (SPT, details in Appendix A.
e. , 1 round of 843 jobs running four tiles each, or 4 rounds of 912 (actually 3 rounds of 912 plus one of 637) jobs running one tile each. Since the infrastructure time is not zero, and it is cumulative, four tiles per node are better than 1, because it occupies almost 20  the whole cluster with jobs, without leaving any other job waiting on a queue. The small queue of four files to be processed within a job seems to work more efficiently than a large queue of individual files distributed to the cluster. As expected, for N > 4, the larger the N, the longer the processing time. This is true because large N reduces the number of CPU cores used, wasting the capacity of the cluster. In summary, according to our tests, the optimal number of tiles distributed per job depends on the dataset size as the following: If the number of tiles of a dataset is less than 912 (maximum number of jobs running in parallel) than the optimal choice is to distribute one tile per job.
If the total number of tiles is larger than 912, the optimal number of tiles per job is the integer number closest to the result of the total number of tiles divided by 912. In the example shown in Figure  22 , the total number of tiles in the SPT dataset, 3373, divided by 912 is ∼3. 7. Therefore, the fastest run using this particular dataset was the one with four tiles per job, as observed in Figure  23  and Table  2. Interestingly, the data partitioner and the consolidator performances are very stable, independently of the number of partitions they handle. In black circles, the total duration of the runs. In green diamonds, the sum of the durations of all components. In red squares, the time spent with the parallelized part of the process, i. e. , the actual photo-z estimations by DNF (see Figure  8 ).
Photo-z PDF The use of redshift probability density functions (PDFs), instead of single estimates of photometric redshifts, is a necessary approach to incorporate the measurement's uncertainties on scientific analyzes. For large astronomical surveys, the storage of billions of PDFs can be a challenge, furthermore if they are measured several times, as when using different methods. To overcome these issues, we adopt two procedures when dealing with PDFs in the Portal. The first one is to compute PDFs only for objects selected in a science-ready catalog, avoiding wasting time and storage space with PDFs for stars or bad data. That is the reason for the pipeline not to be present in Figure  8. The second procedure is to apply a method for data compression and store just a reduced list of coefficients representing the PDF, instead of the complete PDF for each galaxy.
The details of this method are explained in  Rau et al. (2018, in preparation). In a similar way to the previous pipelines, we show examples of configurations, such as the photo-z code to be used, and the redshift range for the PDF in Figure  24. The resulting redshift distribution strongly depends on the stage it is obtained in the Portal, as evident in Figure  25 , where we show, as an example, for a small sample (dataset COS-MOS D04, defined in Appendix A. 1) obtained using DNF. The pipeline Photo-z Compute provides photo-zs for every object present in the "raw" photometric samples, including stars and poorly sampled objects (left panel). The pipeline responsible for creating science-ready catalogs removes those objects, but its product still contains only point photo-z estimates (middle panel), which can be severely biased.
Summary In this paper, we describe the infrastructure available in the DES Science Portal to create training sets, training files and to compute photo-z using different algorithms. It is an easy-touse framework that concatenates different pipelines involved in the calculation of photo-zs, ensuring consistency between these processes. The database registers all the steps; the Portal framework eases the task of carrying out a large variety of tests and comparing their results. Considering the volume of data, the number of algorithms and the various releases of photometric and spectroscopic data, having a structured framework like the one presented here is critical for vetting of DES algorithmic improvements, and the systematic production of photo-zs for future DES releases. Although the Portal is currently accessible only for the members of DES collaboration, the methodology and lessons learned here can be useful and subject of interest for anyone that uses photo-zs in a wide range of science applications.
The database associated with the Portal ingests spectroscopic data regularly. Although the redshift repository continuously grows, the list of surveys used is reported and registered, so that a process can be reproduced or use only the catalogs of interest in another experiment. After preparing training sets and photo-zs we can compare to spec-zs for quality checks. The pipeline used in the estimation of photo-zs for large datasets is parallelized to improve performance. The tests presented in Section 3. 4 reveal that a good parallelization strategy is to distribute the data to the CPU cores using data partitions that are small enough to occupy the whole computing cluster, but large enough to avoid creating a queue of idle jobs. The optimal number of tiles or HEALPix pixels to be processed per job is then dependent on the size of the dataset in question and its original data partition.
This approach is discussed in a separate paper that focuses on the method of preparing catalogs ready for Portal science workflows  (Fausti Neto et al. , 2018). For the near future, there will also be pipelines available to be executed through Jupyter Notebooks  (Kluyver et al. , 2016; Perez and Granger, 2007) , as an alternative to the regular workflow system. There is already a prototype that has been tested using the multi-user web application JupyterHub 19  , but the current implementations are not related to photo-zs. All the examples shown in the figures and supplemental videos use data from the Y1A1 data release. Nevertheless, the same infrastructure is valid for any other DES data release and also for simulations. Besides already allowing one to handle large datasets and easing a lot of scientific applications, the DES Science Portal has been a useful laboratory of methodologies and a precursor of implementations for the next generation of photometric surveys.
Acknowledgments We thank  P. Egeland  and  F. Ostrovski  for the contribution in the early phases of development of this infrastructure. We also thank  R. Brito ,  J. G. S. Dias ,  V. Machado ,  L. Nunes , and  G. Vila Verde  for the contributions in the Portal's basic infrastructure, essential for the realization of this work. JG is supported by  CAPES. ACR is supported by  CNPq  process  157684/2015-6. ML is partially supported by  CNPq  and  FAPESP. MA is supported by  CNPq  process  165049/2017-0. Part of this research is supported by  INCT do e-Universo  ( CNPq grants   465376/2014-2 ). Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the University of is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation.
The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2015-71825 ,  ESP2015-66861 ,  FPA2015-68048 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478. We acknowledge support from the  Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) , through project number  CE110001020 , and the  Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe  ( CNPq grant   465376/2014-2 ).
Reference FE simulation model for systolic phase To examine the abnormal tissue size effects on global and regional deformation, we need to explain the model which was used for FE systolic phase. We employed a healthy volunteer LV extracted from 4D US  [18]  at end diastole (ED). A 3D mesh with eight-noded linear hexahedral elements was created using Abaqus® mesh generation software. The reference mesh had 155,172 nodes and 141,405 elements. To incorporate the known material anisotropy of the LV during the systolic phase, we defined a local curvilinear coordinate system aligned with the local fibre orientation, to model myocyte contraction  [19, 20]. We assumed a helical fibre distribution, with a helical angle varying from À70 at the epicardial surface to 60 at the endocardial surface (0 is the circumferential direction).
Ellipsoid reference mesh A truncated ellipsoid mesh has been generated using Abaqus ® software. The height of the mesh was 9 cm, and the wall thickness was 1 cm. The fibre directions were assigned as outlined earlier. We defined a small zone at the equatorial level of the septal wall (see Figure  2 ) as acutely ischemic, i. e. non-contractile. The active stress in this region was set to zero, while the rest of the reference FE model, a f = 160 kPa, was attributed. The same condition as explained in Section 2. 1 in terms of boundary conditions, ES pressure, contraction model and MPs was used to perform this model. In order to relate the results to the echo images, we defined a midwall surface mesh that was split into 17 segments as shown in Figure 3  [14]. We will refer to this mesh as the midwall mesh throughout this chapter.
Prediction of abnormal tissue zone for LBBB patients A cohort of patients suffering from LBBB was available for studying our proposed method for abnormal zone detection (with chronic infarcts). Left bundle branch block (LBBB) is a cardiac conduction abnormality which causes the LV to contract later than the right ventricle. We presented, previously, our FE method for a healthy LV contraction. In order to introduce the patient data to the FE software, we developed an in-house code to morph the reference FE mesh to the patient geometries. We demonstrated the steps for estimating homogeneous active contraction value data of acute ischemia in Section 2. 2. Eight LBBB patients were recruited after informed consent at Rikshospitalet, Oslo, Norway. LV triangulated mesh geometries were acquired by a standard GE Healthcare echocardiography examination  [21].
For this, endocardial and epicardial nodes of the deformable mesh were projected onto the patient's triangulated surfaces from a defined LV centerline  [17]  with rigid and non-rigid transformation methods employing an in-house developed Matlab® code. In order to deform the reference bulk model with intermediate nodes, we used FE elastic rigid body deformation employing the displacement vectors obtained from boundaries projection trajectories  [17]. An in-house program coded in Matlab® found the closest nodes to the midwall mesh nodal positions obtained from the EchoPac® system for each patient at ED. Therefore, we can follow the evolution of LV during its systolic phase by calculating the deformations of each segment's mesh in area, radial circumferential and longitudinal strains. For each patient, after application of the mesh morphing method, we employed the pressure values at ES from pressure curves obtained during the medical intervention (Table  1 ).
Our main purpose is to present our experience working on a free spoken-language corpus in Spanish. As part of my doctoral thesis project, concerned with rephrasing used in spoken interaction, I decided to study this phenomenon by analyzing the data available in the PRE-SEEA corpus. In order to annotate a part of this corpus, we created a TextGrid file on PRAAT associated with each audio file. Then, we imported those files onto ELAN to start the annotation process, with categories related to five language levels relevant for our research: prosodic, syntactic, macrosyntactic, interactional and rephrasing phenomena. In addition to sharing our methodological work experience, we would like to emphasize the importance of free spoken corpora for the development of the research in linguistics.
Alineación entre la transcripción y el audio en PRAAT La alineación entre el audio y la transcripción es un procedimiento indispensable, pues permite crear anotaciones y segmentaciones en un mismo software. En esta etapa utilizamos el software PRAAT (Boersma y Weenink, versión 6. 0. 37) para la creación, a partir de la transcripción en texto simple, de un archivo tipo TextGrid alineado con cada audio. La figura 1 muestra la trascripción en su estado bruto, sin ningún vínculo automático con el audio. En la figura 2, tenemos la misma transcripción, esta vez 3 MEXI_M22_060, MEXI_H22_054, MEXI_H11_078, LHAB_H22_049 y LHAB_M22_055. Figura 1. Transcripción texto simple Figura 2. Captura de pantalla de TextGrid en PRAAT alineada en PRAAT. Las dos primeras líneas corresponden a las dos locutoras presentes en la interacción.
Segmentación y anotación en ELAN Una vez creado el TextGrid, procedimos a su importación a ELAN (Max-Planck-Institute for Psycholinguistics, versión 5. 2). Este software permite asociar diferentes categorías a un mismo segmento discursivo, gracias a un sistema de múltiples líneas, que observamos en el costado izquierdo de la Figura 3. Las líneas 'Jacobo' y 'Juan', contienen la transcripción hecha en PRAAT, las otras corresponden a un nivel de anotación y contienen categorías de codificación fijas. Así, por ejemplo, a la línea 'CodAcMicroJac' corresponde etiquetas ligadas a las acciones conversacionales  (Selting, 2000) : pregunta, respuesta, marca de recepción, etc. De igual manera, hemos establecido etiquetas para otros niveles: la sintaxis (unidad completa, no verbal, abandonada, etc. ), la prosodia (contorno conclusivo, suspensivo, etc.
Agradecimientos Agradezco al proyecto PRESEEA y a sus políticas de contenidos, particularmente a los equipos del Colegio de México y de la Universidad de La Habana. Agradezco igualmente el apoyo de mis directoras y a mi asesora de tesis, las profesoras Nathalie Rossi-Gensane, Véronique Traverso e Isabel Colón de Carvajal. Asimismo, quiero agradecer la ayuda de mis colegas Elizaveta Chernyshova, Biagio Ursi y Carole Etienne.
2, financiada por la Escuela doctoral 3LA (http://3la. univ-lyon2. fr/). 2 El objetivo del proyecto SegCor (Segmentation de corpus oraux, ANR-15-FRAL-0004) es la anotación de múltiples niveles (chunk, sintaxis, macrosintaxis e interacción) de un corpus oral francés (corpus Clapi, ICAR UMR 5191, http://clapi. icar. cnrs. fr/ y ESLO, LLL UMR 7270, http://eslo. huma-num. fr/) y alemán (corpus FOLK, Institut für Deutsche Sprache, http://agd. ids-mannheim. de/folk. shtml). Una parte de los objetivos y de la metodología de trabajo del proyecto SegCor ha sido aplicada a mi trabajo de tesis. No obstante, el corpus del presente estudio no hace parte del corpus de trabajo del proyecto SegCor.
Evaluation This section presents a performance study of our SSER + transactional memory described in Section 3. To conduct this evaluation we implemented and integrated our algorithm inside TINYSTM  [14] , a state-of-the-art software transactional memory implementation. Our modifications account for approximatively 500 SLOC. We run Algorithm 1 in disjoint-access parallel mode. As explained in Section 3. 5, in this variation the clocks of the processes are not accessed. A detailed evaluation of the other variations of Algorithm 1 is left for future work. The experiments are conducted on an AMD Opteron48, a 48-cores machine with 256 GB of RAM. This machine has 4 dodeca-core AMD Opteron 6172, and 8 NUMA nodes. To evaluate the performance of our implementation on this multi-core platform, we use the test suite included with TINYSTM.
In Figure  1 (a), we execute a base scenario with 10k bank accounts, and a locality of 0. 8. We measure the number of transfers performed by varying the number of threads in the application. In this figure, we observe that the performance obtained with TINYSTM merely improves as the number of thread increases: 48 threads achieve 2. 8 million transactions per second (MTPS), scaling-up from 2. 2 MTPS with a single thread. Our implementation performs better: with 48 threads Algorithm 1 executes around 68 MTPS, executing ×31 more operations than with one thread. To understand the impact of data locality on performance, we vary this parameter for a fixed number of threads. Figure  1 (b) presents the speedup obtained when varying locality from 0, i. e. , all the accounts are chosen at random, up to 1, where they are all chosen in the local branch.
The sample was composed of GPs that were randomly selected from the GROG physicians. A minimum sample size of 120 GPs, with a gender distribution consistent with the national distribution (70 men and 50 women), was achieved. The GPs were invited to participate in the study by telephone. The GROG coordination team trained all participating GPs on the study protocol and on the data collection method by phone and by a written procedure sent by mail. A specific training on PSIs in primary care was also provided by phone or by web with a video (available on the study website). The aim was to ensure that all GPs had a same understanding of what constituted an incident to be reported in an effort to reduce the selection bias and reporting bias.
Ethical aspects No nominative, sensitive or personal health data concerning patients were systematically collected. The ESPRIT study  [5]  was therefore not, strictly speaking, in the field of biomedical research (fields of application of the French reference methodology MR 001), nor was it within the framework of Chapter X of the French Data Protection Act. For this practice-oriented study, the only nominative data collected were GP profile data that were available in a database declared to the French CNIL (number 1684220). GPs provided their written informed consent to participate by email. The National Council of the French Medical Association was also informed due to the indemnification of the participating GP. All ethical approvals of the study were given by the Ile-de-France Paris III committee and by the CNIL (French Data Protection Authority).
Coding process All of the PSIs related to a patient were extracted from the ESPRIT study database. For each PSI, the investigators were required to independently search for the underlying factors contributing to their occurrence according to a qualitative approach (content analysis). Then, the investigators had to select a main contributing factor (MD), called "dysfunction" in French, and, if relevant, a secondary contributing factor (SD) in CADYA  [17]. The choice between designating the factor as a MD or SD depended on the estimated impact of the contributing factor to the PSI. In the case of a factor related to a human attitude or behaviour, the investigators had to specify a keyword to describe its nature (e. g. , "anger", "stress" or "tiredness") according to an incremental glossary (a keyword that was already identified, or, if necessary, a new keyword).