dataesr/scientific-paragraphs-categorization huggingface dataset:
INFERNO's architecture exploits these ideas in spiking recurrent neural networks, having two coupled systems, one inferring the state of the other and trying to 'correct' its data based on feedback error. In this formulation, the supervising system (AM) attempts to learn a model of the afferent network (RNN) in accordance to the evidence that a particular policy is being pursued in order to control it for generating long sequences. INFERNO exploits noise (or FE) in spiking neurons for exploring different policies in which the iterative minimizing of prediction errors (or FEM) for one policy augments gradually its expectation. This may correspond to different places in the brain for decision-making and perceptual inference  [30]. Here, we propose to link INFERNO to the cortico-basal system for habit formation and sequence retrieving  [31] , see Fig  2.
Neural units and STDP-like algorithm We used in the recurrent neural network a variant of the Hebbian equations, the Rank-Order Coding (ROC) algorithm, which grasps well the structure of the Spike Timing-Dependent Plasticity algorithm and of the classical Delta rule in the spatio-temporal domain  [55]. STDP has been discovered to modulate the neural activity of temporally related neurons in many brain regions by reinforcing their links. The Rank-Order Coding algorithm has been proposed by Thorpe and colleagues as a discrete and faster model of the derivative integrateand-fire neuron and of the standard STDP reinforcement learning algorithm  [56]. The rationale is that ROC neurons are sensitive to the sequential order of the incoming signals; that is, their rank code. The distance similarity to this code, say rank(x) -, which corresponds to the argsort function in Matlab,-is transformed into an amplitude value by the function f ðxÞ ¼ 1 rankðxÞ.
Neuromorphic computation and symbolic AI systems In comparison to computer memories, the human Working Memory has developed the ability to deal with uncertain data sets and to initiate flexible and robust decision making. Next generation of neuromorphic architectures of spiking neurons based on the functional organization of the brain will be able to mimic the attributes of the Human Working Memory to learn, predict and generate sequences, this will have major impact in the design of next generation computers and autonomous devices such as robots  [34, 94]. Different concepts of AI have been applied to the understanding and the modeling of brain functions. Nonetheless, their use in large scale recurrent spiking neural networks is not trivial. INFERNO is an attempt to design a cognitive architecture based on predictive coding and free-energy minimization for categorizing external inputs prone to uncertainties, for generating and stabilizing long-range sequences.
Acknowledgments We thank  Souheil Hanoune  and  Karl Friston  as well as the reviewers for interesting comments and fruitful feedback on anterior versions of the paper. This work was partially funded by  EQUIPEX-ROBOTEX (CNRS) , chaire d'excellence  CNRS-UCP  and project  Labex MME-DII  ( ANR11-LBX-0023-01 ).
Introduction The SAMINT methodology allows for the coupling of differential and integral data evaluation in a continuousenergy framework  [1]. More specifically, SAMINT was developed to allow the nuclear data evaluator to update the parameters of a resolved resonance region evaluation based on information from integral experiments, just as is traditionally done for differential cross section measurements. The computer code SAMINT works together with the mathematical machinery of the evaluation code SAMMY  [2]  to update resolved resonance parameters based on integral data. Traditionally, SAMMY has used differential experimental data-i. e. , σ (E i ) vs. E i -to adjust nuclear data parameters such as resonance energies, resonance widths, the number of prompt neutrons emitted per fission, etc.
Background A new resolved resonance region (RRR) evaluation for the two naturally occurring isotopes of copper-63 Cu and 65 Cu-was performed at Oak Ridge National Laboratory. Copper is commonly used as a minor structural material in many fission power facilities. In fact, a search through the ICSBEP handbook reveals that copper is significant in 32 of the 586 evaluated criticality safety benchmarks and is found in 1,084 cases documented in ICSBEP. Copper is also an important structural component in Scandinavian spent fuel final disposal canisters. Copper is an important heat sink material for fusion power reactors, and it is also used for diagnostics, microwave waveguides, and mirrors in the International Thermonuclear Experimental Reactor (ITER). The available nuclear data evaluations for stable 63 Cu and 65 Cu are not as consistent as required for accurate activation and particle transport calculations in fusion systems.
Consistent, correct data evaluations are of high priority for applications to the International Fusion Materials Irradiation Facility (IFMIF), where the equipment must resist high-energy particle irradiation. As a prerequisite, the general-purpose neutron data files must include all information required for a wide spectrum of nuclear analyses, including simulations of neutrons and the assessment of the nuclear heating, damage, and gas production. The more accurate nuclear data are an important element of quality analyses for the design and optimization of all fusion related facilities including ITER and its planned successor reactor, the DEMOnstration Power Plant (DEMO), as well as the International Fusion Materials Irradiation Facility (IFMIF) neutron source. The neutron cross section of the copper isotopes is also important to the astrophysics community.
They extended the resolved resonance region of the two copper isotopes from 99. 5 keV to as far as the resolution of the experimental data allowed: 300 keV. This put resonance data into a larger portion of the intermediate and fast neutron spectrum criticality safety benchmarks. Furthermore, experimental capture cross section measurements were analyzed for the first time in the resonance evaluation of copper. Analyzing both the capture and the transmission experimental data sets allows for the capture-to-scattering ratio to be set correctly for the entire resolved resonance region. There are at least twenty-four criticality safety benchmarks from the ICSBEP  [3]  available which demonstrable sensitivity of the benchmark quantity with respect to changes in the parameters of the resolved resonance region for the two isotopes of copper.
Methods Both the continuous-energy TSUNAMI-3D  [8]  module of the SCALE 6. 2 Code System  [9]  and MCNP6  [10]  can be used to produce energy-binned sensitivity coefficients of the integral system eigenvalue to different reaction cross sections based on continuous-energy physics. We define the relative sensitivity coefficient S i jn of the eigenvalue of system i, k i , with respect to partial cross section j in energy bin n as In the SAMMY Bayesian updating process, the derivative of the continuous-energy reaction cross section j with respect to resonance parameter t, P t , is needed and is therefore calculated by SAMMY. The symbol P t is a general symbol that stands for all resonance parameters E λ , γ , n , f etc. We define this derivative as G jt (E, where S i jn = σ j (E) n k i ∂k i ∂ σ j (E) n.
Further, the SAMINT code was used to combine the computed sensitivity coefficients of the integral benchmarks with the calculated derivatives of the cross sections with respect to the resonance parameters (see Eq. (  5 )) to produce input files into the evaluation code SAMMY. The defining characteristic of the SAMINT approach is that in the following step, suggested resonance parameter adjustments from the integral experiments are combined using the GLLS mathematical framework with the resonance parameter adjustments based on the differential experimental data, thereby ensuring that the adjustments to the resonance parameter will not disrupt the fit of the experimental cross section measurements outside of statistical limitations. Figure  2  presents the ratio of the calculated value for the benchmark quantity for each of the integral experiments to the respective experimentally measured value.
In this article, we present a joint effort of the wheat research community, along with data and ontology experts, to develop wheat data interoperability guidelines. Interoperability is the ability of two or more systems and devices to cooperate and exchange data, and interpret that shared information. Interoperability is a growing concern to the wheat scientific community, and agriculture in general, as the need to interpret the deluge of data obtained through high-throughput technologies grows. Agreeing on common data formats, metadata, and vocabulary standards is an important step to obtain the required data interoperability level in order to add value by encouraging data sharing, and subsequently facilitate the extraction of new information from existing and new datasets. During a period of more than 18 months, the RDA Wheat Data Interoperability.
For the following data types of interest to the WDI-WG, the surveys confirmed the existence of adequate consensus regarding data exchange formats: DNA sequence and any associated variants, genome/transcriptome annotations, gene expression data, and physical maps. As such, the WDI-WG recommended the formats that were the most used and/or compliant with the most popular tools and/or already interoperable with other data formats. For example, the GFF3 file format (http://gmod. org/wiki/GFF3) is found to be widely used by the community to represent genome annotations. Moreover, a Genbank-to-GFF3 script converter is available (http://www. hpa-bioinformatics. org. uk/biosnippets/ snippets/115), in addition to a GFF3 validator tool (http:// genometools. org/cgi-bin/gff3validator. cgi). Thus, the WDI-WG recommended GFF3 for the representation of genome annotations.
Grant information The WDI-WG was partially funded by WI funding received by the WheatIS Expert Working Group. L. C. and M. -A. L were supported by the National Science Foundation award IOS #1340112 to the Planteome Project for the development and maintenance of the Plant Ontology, Plant Trait Ontology and Plant Experimental Conditions Ontology. CJ was supported by the French National Research Agency (grant ANR-12-JS02-01001) and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 701771. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Can you also mention the most recent resource for wheat genome build, the most authoritative (or most widely used) gene naming of wheat genome (as of writing)? This is very important info this type of data. Another example could be a snippet of a phenotyping experiment result (a field book table, for example), wherein the MIAPPE terms, and the ontology terms tagging the phenotyping data observed/measured appropriate for the experiment can be seen? This gives the reader/researcher ideas on how the data standards/guidelines are used in real-world applications. I went to the http://datastandards. wheatis. org/ externally referred site and I did not easily see a sample dataset that could be used as template. In summary, the paper is already in a very mature and good state, just having some important summaries of the survey directly mentioned and providing examples of applications of the standards in easily accessible sample documents would be a welcome addition.
The  WDI-WG  was partially funded by WI funding received by the  WheatIS Expert Working Group. L. C. and M. -A. L were Grant information: supported by the  National Science Foundation  award IOS # 1340112  to the  Planteome  Project for the development and maintenance of the Plant Ontology, Plant Trait Ontology and Plant Experimental Conditions Ontology. CJ was supported by the  French National Research Agency  (grant  ANR-12-JS02-01001 ) and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  701771. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Following Part 1 (Permadi et al. , 2017a) which focuses on the preparation of emission input data and evaluation of WRF/CHIMERE performance in 2007, this paper presents Part 2 of our research detailing the quantification of co-benefits resulted in the future (2030) from black carbon (BC) emission reduction measures for Southeast Asia (SEA) countries. The business as usual (BAU2030) projected emissions from the base year of 2007 (BY2007) assuming "no intervention" with the linear projection of the emissions based on the past decadal 15 activity data (Indonesia and Thailand) and the sectoral GDP growth for other countries. The RED2030 featured measures to cut down emission in major four source sectors in Indonesia and Thailand (on-road transport, residential cooking, industry, and biomass open burning) while for other countries the representative concentration pathway 8.
5 (RCP8. 5) emissions were assumed. WRF/CHIMERE simulated levels of aerosol species under BAU2030 and RED2030 for the SEA domain using the base year meteorology and 2030 boundary conditions 20 from LMDZ/INCA. The extended aerosol optical depth module (AODEM) calculated the total columnar AOD and BC AOD assuming the internal mixing state for the two future scenarios. Health benefits were analyzed in term of the avoided number of premature deaths associated with ambient PM2. 5 reduction while the climate benefits were quantified using the reduction in the BC radiative forcing under RED2030. Under BAU2030, the average number of the premature deaths per 100,000 population in the domain would increase by 30 from BY2007 25 while under RED2030 the premature deaths would be cut-down (avoided) by 59 from the RED2030.
Premature death The number of avoidable premature deaths due to the applied emission reduction measures was quantified based on the changes in ambient PM2. 5 levels and was calculated using eq. 1  (Wang and Mauzeral, 2006 and Saikawa et al. 2009 ). We considered only the premature deaths because of the less comprehensive information available on the association between the morbidity and air pollution exposure as compared to that of the mortality  (Saikawa et al. , 10 2009 ). The PM2. 5 annual concentration was used rather than BC alone following the recommendations of the WHO Task Force (WHO, 2012) that PM2. 5 should continue to be used as the primary metric in quantifying the human exposure to PM and resulting health effects. Concentration response (CR) data was obtained from Smith et al.
(2009) who indicated that every increase in PM2. 5 by 1 μg m -3 is approximately associated with an 1. 006% increase in the risk of all cause adult mortality for individual of 20 years and older. The Iref data representing the adult 15 mortality rate for all causes and both sexes (per 1,000 of population) for every nation included in the study domain was obtained from the United Nations database (http://data. un. org/Data. aspx?d=WHO&f=MEASURE_CODE%3AWHOSIS_000004) for the base year of 2007. The gridded population (POP) data of 0. 5° resolution was obtained from the Potsdam Institute for Climate Impact Research (PICIR) (http://clima-dods. ictp. it/d10/fcolon_g/) for both 2007 and 2030, and the data were further 20 gridded to a finer resolution of 0. 25° of the domain. The proportions of adult population (age ≥20 years) for the countries were taken from the World Bank database (http://data.
Acknowledgements This research was financially supported by the  French government  under the  Asian Institute of Technology 30 (AIT)/Sustainable Development in the Context of Climate Change (SDCC) -France Network cooperation  and by the  United States Agency for International Development (USAID)  under the  PEER-SEA (Partnerships for Enhanced Engagement in Research for Southeast Asia ) research project. Atmos. Chem. Phys. Discuss. , doi:10. 5194/acp-2017-316,2017 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 2 May 2017 c Author(s) 2017. CC-BY 3. 0 License.
In situ Observations The in situ data set used for the SynSenPFT satellite validation and improvement of OC-PFT approach was built with HPLC pigment data compiled from several databases and individual cruises (Table  1 ). The spatial distribution of the in situ data set is depicted in Figure  S5  (Supplementary Section 3). Chla of PFTs from in situ phytoplankton pigment data was derived using the Diagnostic Pigment Analysis of  Vidussi et al. (2001)  and  Uitz et al. (2006)  modified as in  Hirata et al. (2011)  and  Brewin et al. (2015). The weights were revised in accordance to this enlarged in situ pigment data set (Table  1 ). We refer the reader to the Supplementary Material (Section 1) for more details. The final version of the global data set is available at https://doi. pangaea. de/10.
Match-Ups with In situ Observations Daily SynSenPFT products at ∼4 km spatial resolution were matched with samples from the global in situ validation dataset. The in situ dataset was matched with satellite data from OC-CCI Chla product (but restricted to the life time of SCIAMACHY) at daily temporal resolution and satellite values were retained when located at the same date within a distance of 4 km from the in situ measurement. Note: for OC-PFT model development  the in situ pigment data not matching the OC-CCI TChla were used, such that the validation data are independent of the data used to train the model. In the case of more than one in situ sample within one satellite pixel, respective match-ups were treated independently. To finally derive high quality match-ups, those were considered valid where the coefficient of variation of the 3 x 3 pixels of satellite TChla (OC-CCI) around each in situ was lower than 0.
ACKNOWLEDGMENTS We are thankful to  Steven Delwart  ( ESA-ESRIN ) for valuable and inspiring discussion throughout the  SynSenPFT  project. We thank the reviewers and Editor for their nice comments and constructive suggestions leading to the improved manuscript. We thank  ESA  for OC-CCI data, MERIS and SCIAMACHY data;  NASA Global Modeling and Assimilation Office (GMAO)  for  NOBM PFT output, NASA Goddard Space Flight Center 's Ocean Data Processing System (ODPS) for the MODIS and SeaWiFs data. We thank  Dr. Rajdeep Roy  ( CSIR National Institute of Oceanography (NIO), Goa, India ) and all the scientists and crews involved in HPLC data collection and analyses for providing their pigment data. PFT Chla data sets used and produced within our SynSenPFT study are available at https://doi. org/10.
Using the mouse embryo, we have developed a novel procedure to generate qualitative and quantitative datasets of shape changes in 3D of the looping heart, captured by high-resolution episcopic microscopy (HREM). We provide a novel dynamic 3D computer simulation of heart looping which is based on these data. Our combined modelling and experimental approach shows that asymmetries at the fixed heart poles, generating opposite deformations, associated with the progressive release of the heart tube dorsally, are sufficient to generate looping of a tube growing between fixed poles. Our predictive model is validated in four experimental conditions, using cell labelling, time-lapse imaging and molecular deficiencies. The novel model of heart looping that we propose functions as a generator of asymmetric organ morphogenesis, in the sense of  Brown and Wolpert(1990) , able to amplify initial left-right differences between heart precursors.
Although we did not use the atrioventricular sulcus as a marker of staging, it can be monitored from our 3D reconstructions (Figure  1 , Figure  1 -source data 1), showing that LS-II is equivalent to E8. 5g/h. LS-III, which corresponds to ventricular looping, is equivalent to E8. 5h-j. We now provide 3D reconstructions and quantitative spatio-temporal measures to assist staging of a continuous process. Several geometrical parameters can be used to assess the progression of heart looping, such as the length of the heart tube, the right ventricle/left ventricle orientation, the rotation of the arterial pole, the position of the venous pole, the extent and localisation of the dorsal mesocardium breakdown. These tools will be instrumental to analyse defects of heart looping beyond the simple description of looping direction, as reported previously in mutant embryos.
HREM imaging HREM acquires images of the surface of the resin block, in which the embryo is embedded, to produce perfectly registered digital image stacks capturing the 3D tissue architecture at high resolution. Resulting datasets comprise 1000-2000 images of 1 Â 1 mm resolution produced by repeated removal of 1-2 mm sections. HREM was performed on E8. 5 or E9. 5 embryos as described previously  (Mohun and Weninger, 2012) , using the optical high-resolution episcopic microscope (Indigo Scientific).
3D reconstruction and quantification Hearts were segmented from HREM images using the Imaris software (Bitplane). The contour of the myocardium was manually outlined at regular Z intervals, and the Create Surface function was used to reconstruct the 3D surface. The notochord was similarly segmented to serve as a reference longitudinal axis. 3D visualisation was produced with ICY (Institut Pasteur, Paris) for raw images, and with Blender (Blender Foundation, Netherlands) or DAZ Studio (Daz Productions Inc. ) for segmented images. 3D PDF files were built with Acrobat Pro (Adobe Systems Inc. ) after exporting the Imaris file in WRL format, and conversion into a U3D format with Meshlab (Visual Computing Lab). The axis of the cardiac tube was reconstructed from the Imaris surface, using the Oblique Slicer function to intersect the tube perpendicularly, proceeding along its length.
On each slice the polygon outlining the myocardium was drawn. The centroids of the successive polygons were computed with Matlab (geom3d library), and the 3D line exported in X3D format (figure2xhtml function). The X3D file was then imported in Blender and superimposed on the segmented myocardium. To quantify the average perimeter of the heart tube, 10 polygons, evenly distributed along the length, were selected and the perimeters of theoretical circular discs of the same area were taken as values. The tranverse sector, in which the tube axis is inscribed, was obtained after rotating the 3D line to superimpose the two extremities of the axis. The sector angle was measured from this view. The orientation of the right ventricle-left ventricle axis was similarly obtained by intersection of the Imaris surface at the level of the interventricular sulcus.
The polygon outlining the myocardium was drawn and its centroid computed. The axis was defined as the line perpendicular to this polygon. It was projected on the frontal plane, taken as perpendicular to the dorsal-ventral axis of the embryo. The orientation was measured as the angle between this projection and the notochord axis. The thickness and the dorso-ventral elongation of the dorsal mesocardium, the positions of its ventral and dorsal aspects, as well as the angle between the heart tube and the dorsal pericardial wall, were measured in transverse sections, after aligning the HREM cubic images on the notochord axis with the ICY software (StackRotationByAngle plugin). Mouse embryo culture E8. 5 embryos from wild-type [Swiss] mice, or from the T4-nlacZ [Swiss] transgenic line  (Biben et al.