dataesr/scientific-paragraphs-categorization huggingface dataset:
The PubAnnotation ecosystem has extended that research in a number of novel directions. First, the previous work on Agile text mining has taken place in a highly restricted domain: with the exception of prior work on the topic was applied to only a single textual genre, electronic health records  (Cormack et al. , 2015; Shivade et al. , 2014) ; in contrast, PubAnnotation has been applied to scientific publications as described here, as well as to database contents  (Wang et al. , 2018). Second, previous work has made a point of separating iterative text mining system development from lexical resource development  (Cormack et al. , 2015). Although there is certainly an advantage to being able to modularize a system such that functionality is separated from resources, the work reported here maintains that modularity while still facilitating iterative development of lexical resources, entirely separately from-and in addition to-iterative development of text mining functionality.
biohackathon. org). For the use Case 1, the customization loop was conducted toward reducing noisy annotation. As the result, the noisy annotation was reduced by 21% (from 6635 to 5241). For use Case 2, the customization loop was conducted toward improving the coverage of annotation. As the result, the coverage was improved by 34% (from 51 123 to 68 708). For both use cases, evaluation of the quality of annotation was determined subjectively by the users, which reflects the goal of the work: to enable users to get annotation for their own purpose as quickly as possible. PubAnnotation also provides a powerful search interface to examine the annotation, even across multiple datasets. For reproducibility, the ecosystem also supports sharing all the dictionaries and annotations. Curators of the GlycoEpitope database evaluated the annotation, which was produced in half a day, potentially to be useful for substantially reducing the curation process, which takes 3-5 h on average to curate one antibody.
, 2004 (Mu ¨ller et al. , , 2018 ) is a highly adaptable platform for the curation of model organism databases. This is important because model organism databases have been major enablers of progress in genomics research, the scientific literature is the major source for the curation of those databases, and use of text mining in that curation work is not practical without user-driven development of interfaces  (Wang et al. , 2016) -a classic application of Agile methodology. Its strength in this area is illustrated by the many organism-centric systems that have been based on it  (Van Auken et al. , 2012). Textpresso is highly customizable at the organizational level, allowing for the incorporation of multiple biomedical ontologies. From an Agile perspective, the ability to easily incorporate new knowledge sources can be seen as exemplary of the capacity to be flexible and responsive to customer-requested changes.
Funding J. D. K. , Y. W. , T. F. and S. O. 's work was partly supported by the Database Integration Coordination Program funded by  National Bioscience Database Center (NBDC) of Japan Science and Technology Agency (JST). T. J. C. and K. B. C. were supported by the  National Institutes of Health  [grant  LM009254  to  Lawrence E. Hunter ].
3  [2]  calculation package. This package is being developed by the CEA with the support of its French partners (EDF, Orano and Framatome); it is the French reference for fuel cycle studies. The package has been extensively validated on a large number of experimental programs based on spent fuel chemical analyses that have been carried out in France since 1993. It has also been validated for decay heat calculations on a more limited number of experimental programs based on elementary fission burst experiments and two integral calorimetric experiments, the MERCI-1 and CLAB experiments. The objective of this paper is to present the methodological orientations to determine accurately the uncertainty associated with the DARWIN2. 3 decay heat calculation. This uncertainty can be determined either by nuclear data covariance matrix propagation or by exploiting the experimental validation through the transposition of the Calculation/Experiment (C/E) discrepancies; these two points will be explained in the following chapters, after a brief description of the DARWIN2.
At the end of the APOLLO2 calculation, a library, called SAPHYB, is generated; it contains, both in the 281-group energy structure, the fuel microscopic self-shielded cross sections, spatially homogenized and the 2D scalar neutron flux, tabulated versus burnup. The APOLLO2 2D transport code is the reference code for neutron transport calculations for the DARWIN2. 3 (for the PWR calculation route) package. The PWR DARWIN/PEPIN2 calculation step solves the Bateman equations, under irradiation or under cooling (i. e. with no neutron flux) of homogeneous mixtures whose compositions are imposed by the user or read from the SAPHYB file, with a depletion chain containing up to 3800 isotopes. Given the fact that PEPIN2 uses homogeneous mixture, no spatial information is given to PEPIN2, and the choice of the 2D APOLLO2 transport code instead of a 3D (e.
g. APOLLO3) does not really matter at this point. The equation solved under neutron flux (with the 4th order Runge Kutta method) uses the self-shielded cross sections and the 281-energy group neutron flux transmitted by the APOLLO2 code thanks to the SAPHYB. The irradiation history in DARWIN/PEPIN2 can be detailed with shutdown periods, bore concentration, moderator and fuel temperature tracking. It makes it possible to calculate precisely the depleted fuel inventory. The convolution of the fuel inventory with nuclear data leads to physical quantities such as decay heat, activity and radiotoxicity. The decay heat formula used in the DARWIN/PEPIN2 module (neglecting here the contribution of the fissions induced by delayed neutrons) is recalled in (1): DHðtÞ ¼ X i Q i ln 2 T 1=2 i N i ðtÞ; ð1Þ where: -Q i is the average decay energy released for a decay of the nuclide i; - T 1=2 i is the half life of the nuclide i; -N i is the isotopic concentration of the nuclide i.
VVUQ process applied to DARWIN2. 3 for the bias and uncertainty control The DARWIN2. 3 simulations are used to predict physical fuel cycle parameters with a quantifiable confidence and across the PWR application domain. The rigorous VVUQ process, conventionally used in many disciplines of science and engineering  [10] [11] [12] , is implemented for the DAR-WIN2. 3 calculations, to assess the biases and uncertainties associated with the determination of the physical parameters; it gives strength to the results for R&D and industrial applications. This process requires the following four steps  [13] : -Verification: it shows that the calculation scheme does not present programming errors and gives the expected numerical results; numerical Validation: in this step, the DARWIN2. 3 results are compared to a "reference calculation", integrating more accurate models than the calculations that have to be validated and using the same nuclear data library.
This numerical validation relies for DARWIN2. 3 on several elements. It relies on the comparisons with TRIPOLI4 ®  [14]  Monte Carlo calculations at step 0 (stationary conditions), before depletion; it has the capacity to validate the APOLLO2 multigroup flux calculation and reaction rates, such as 235 U fission, 238 U radiative capture rates at step 0. The recent coupling of the TRIPOLI4 ® stochastic transport code with the MENDEL  [15]  deterministic depletion solver allows depletion calculations to be performed. Even though this process is not considered as a reference procedure for depletion calculation, the benchmarking between TRIPOLI4 ® and DARWIN2. 3 has been investigated to provide first elements of modeling biases quantification  [16]  for fuel cycle calculations (material balance); this work tends to show that the modeling biases are limited in comparison with the biases coming from nuclear data; experimental Validation: it consists in comparing the calculation results of the set "nuclear data library + calculation scheme + codes" to the values measured with experiments, for the decay heat and the material balance of the main actinides and the fission products involved in fuel cycle calculations and burnup credit criticality calculations; -Uncertainty and bias Quantification: it consists in associating with each parameter calculated by the DARWIN2.
3 package, a controlled uncertainty over a range of applications. Generally speaking, there are two ways to achieve this goal: the first one relies on the analysis of the experimental validation results when there is enough experimental data to cover the DARWIN2. 3 application domain; in this case, the transposition method is then applied in order to transpose the Calculation-over-Experiment (C/E) discrepancies. If the experimental validation cannot be exploited for DARWIN2. 3, the second way relies on nuclear data covariance propagation studies. The next chapters below are dedicated to the presentation of these two ways of uncertainty quantification applied to the DARWIN2. 3 package. 3 Nuclear data covariance matrix propagation for the uncertainty determination of DARWIN2. 3 decay heat calculation.
The DARWIN2. 3 package has recently been enriched with this covariance propagation method  [17]. The DARWIN/PEPIN2 module which manages the sensitivity profile calculations and the nuclear data covariance propagation is called DARWIN/IncerD  [18]. Input data are the covariances taken from the European evaluation JEFF-3. 1. 1 for the decay data (decay periods, branching ratios and mean decay energies) and fission yields, and the CEA/Cadarache covariance matrix database COMAC-V2  [19]  for cross sections. The prior calculation uncertainty resulting from the propagation of uncertainties of nuclear data covariances is as follows: ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi S T A D S A q ; ð3Þ where: -S A is the sensitivity vector due to nuclear data; -S T A is its transpose; -D is the nuclear data covariance matrix.
The choice of a deterministic approach for the uncertainty propagation with DARWIN2. 3 is based on the underlying possibility to have access to sensitivity and variance analyses, enabling the identification of the nuclear data of influence for the decay heat and the main contributors to the decay heat variance. When the hypothesis of linearity of the decay heat parameter to the nuclear data is not straightforward, it must be checked to ensure the legitimacy of this method  [17] ; this is done by comparing the results obtained with this quadratic summation method to those produced with a different uncertainty propagation method that does not need a linearity hypothesis  [18]. This is the case of the sampling approach, implemented in the URANIE/MEN-DEL code system developed at the CEA. URANIE  [20]  is an uncertainty platform and MENDEL is the CEA new generation depletion code.
MENDEL can use the same input data as the DARWIN/PEPIN2 code (same SAPHYB input files, nuclear data libraries and filiation chains). The sampling approach consists in selecting distribution laws for each random input (more often, Gaussian laws) and sampling them with a Latin Hypercube Sampling technique in order to have n realizations of each variable. The MENDEL code is then called n times with n different sets of input data according to the results of the sampling step. Eventually, the distribution of the decay heat is built and the moments are extracted. Thus, the linearity hypothesis has been verified by comparison with URANIE/MENDEL  [21]  and allows us to calculate the sensitivity profiles by direct perturbation and the propagation of nuclear data covariances through the DARWIN/PEPIN2 calculation.
For the calculation of the sensitivity profiles of the decay heat to the cross-sections with DARWIN/IncerD, there is no accurate Boltzmann/ Bateman coupling. The flux and reaction rates are performed in a previous calculation with the neutron code APOLLO2 and then stored in a file which is an input data for DARWIN/PEPIN2. When calculating the decay heat value resulting from the perturbation of a cross section with DARWIN/IncerD, it is the nominal value stored in the SAPHYB file that is used instead of recalculating the neutron spectrum with the APOLLO2 code. However, studies have shown that the impact of the coupling can be neglected on the uncertainty propagation calculation for the decay heat, for cooling times between 0. 1 second and 300 years  [22]. An illustration of the decay heat uncertainty estimated by the deterministic approach for standard PWR fuels is given in Figure  2 ; the considered fuels are a UOX fuel with 3.
1. 1. This nuclear data mainly consists of branching ratios and mean energies (alpha, beta, and gamma). However, we need to put something down to the parameters with no uncertainties, a value which is conservative but realistic. Other nuclear data libraries are studied and the experts' advice is used for this task. There are few covariance matrices for cross sections in the JEFF-3. 1. 1 library. This data is taken in the COMAC-V2 database, which receives the benefit of a constant work for improvement. Even if the major part of the covariance matrices may be associated with the JEFF-3. 1. 1 library, some of the covariance matrices come from a different evaluation than the one giving the centered values. Such is the case of 241 Am which was re-evaluated at the CEA recently for JEFF-3. 2 and its capture cross section which was increased by about 15%.
Today, the Uncertainty Quantification associated with the decay heat calculated by DARWIN2. 3 relies mainly on deterministic nuclear data covariance propagation. The input data for this propagation involves covariances taken from the European evaluation JEFF-3. 1. 1 for the decay data and fission yields and the  COMAC-V2 database for cross-sections. However, these covariances are often incomplete, considering the amount of nuclear data involved in decay heat calculations (more than 40 000 entries). Besides, their quality and accuracy is sometimes a subject of debate for experts in nuclear data (particularly concerning the fission yield correlation matrices). That is why the completion and exploitation of the DARWIN2. 3 experimental validation is needed. The uncertainty level due to nuclear data uncertainties and associated with the decay heat calculation must be confirmed.
This paper uses the software TRNSYS to simulate and analyze the energy output and performance of PVT panels in a small sized prosumer household, comparing two different climate conditions: Bucharest, Romania, and Strasbourg, France. The continental temperate climates are not the ideal locations for PVT systems, as it can be noticed from the literature, and more arid and hot areas are preferred. However, there can be a potential growing market even in colder locations. An interesting comparison can be carried out between France, where the market is developed and there are several manufacturers and distributers of PVT products available, and Romania, where the market is under-developed. Another novelty of this study is related to the explicit TRNSYS model which is analyzed by two performance indicators: First law efficiency and primary energy saving efficiency.
An experimental study  [7]  also carried out for India, showed that PVT system performed better than the simple PV and solar panels, which reinforces the interest for further developing the PVT technology. This paper uses the software TRNSYS to simulate and analyze the energy output and performance of PVT panels in a small sized prosumer household, comparing two different climate conditions: Bucharest, Romania, and Strasbourg, France. The continental temperate climates are not the ideal locations for PVT systems, as it can be noticed from the literature, and more arid and hot areas are preferred. However, there can be a potential growing market even in colder locations. An interesting comparison can be carried out between France, where the market is developed and there are several manufacturers and distributers of PVT products available, and Romania, where the market is under-developed.
Meteorological Conditions The output of a PVT system is dependent upon three meteorological parameters: Ambient temperature, irradiation, and wind speed. The solar irradiation has the most significant impact on the performance of a PVT system, as it determines the electrical output, but also influences the temperature of the materials through radiative heat transfer. The temperatures in the PVT layers are also dependent on the ambient temperature, through convective and conductive heat transfer processes in the solid and liquid layers of the system, but also on the speed of the wind which determines the convective heat losses at the surface and at the back of the panel. The yearly profile of each parameter is obtained from Meteonorm Database. The total radiation comprises of three components: Direct beam radiation, diffuse radiation, and reflective radiation.
DHW and Electricity Demand In order to carry out an accurate simulation, a realistic daily time dependent profile of the household DHW demand is required. The daily profile of DHW is dependent upon various parameters: The number of inhabitants, appliances, ambient conditions, seasonal variations, the average daily hot water volume, and the yearly demand  [21]. The profile used for this simulation was created using the DHW-calc software (v1, University of Kassel, Kassel, Germany) developed for the IEA-SHC Task 26, which can generate realistic DHW profiles for European countries for any required period of time and timestep  [22, 23]. This software has been successfully used and validated in multiple research papers  [21, 24, 25]. The user can define a number of parameters, such as the total mean daily water volume, probability distribution, flow rates, holidays, etc.
TRNSYS Model TRNSYS (transient system simulation tool) is a flexible software that can simulate the behavior of dynamic transient systems. A TRNSYS model was developed to replicate the system in Figure  13 , and it includes: Weather Data (Type 15), PVT collector (Type 50), Stratified Storage Tank with a capacity of 100l (Type 4), Single Speed Pump (Type 114), Inverter/Charge controller (Type 48), a Battery Bank (Type 47) and Controllers (Type 2). The Type 15 component is a tool that reads weather data either from the built-in database or from a user provided spreadsheet, and processes it to provide the input weather data for the simulation (temperature, radiation, wind speed). The storage tank (Type 4) models a tank that can be subjected to thermal stratification, by establishing the number of thermal nodes N.
The first law efficiency is the simplest and widely used method, where there is no distinction made between the electrical and thermal power. The Primary Energy Saving efficiency takes into account the regional differences in energy production, and can be more useful to compare the efficiency of the system in two different locations, as is the case of this study. Some of the limitations of this simulation and its results are: The simplicity of the PVT model compared to a full finite element analysis, the fact that all the components are pre-defined in the TRNSYS software and thus a full customization is difficult, the consumer profiles were obtained by.
The first law efficiency is the simplest and widely used method, where there is no distinction made between the electrical and thermal power. The Primary Energy Saving efficiency takes into account the regional differences in energy production, and can be more useful to compare the efficiency of the system in two different locations, as is the case of this study. Some of the limitations of this simulation and its results are: The simplicity of the PVT model compared to a full finite element analysis, the fact that all the components are pre-defined in the TRNSYS software and thus a full customization is difficult, the consumer profiles were obtained by means of forcing functions and statistical methods and can vary significantly according to the individual end user and the location of the system.
Conclusions and Perspectives This paper has performed a dynamic simulation of a PVT system for a small size domestic household using the software TRNSYS. The system includes both thermal and electrical storage, and is coupled to the simulated electrical and DHW demand of the household. The results show that, with a battery storage system, the household demand can be 58% covered in Bucharest and 48% in Strasbourg. The hot water heating system, provided that an auxiliary heater is utilized, can also be covered 51% and 41%, respectively. Overall, it appears that small scale PVT is a promising solution for maximal harvesting of the solar energy. Residential prosumers are the target market of this technology, which is quickly developing in the Western European countries, but is lagging behind in the East.
Future Work We plan to perform a formalization and a formal soundness proof for our transformation technique, thereby allowing MetAcsl to be reliably used for critical code verification. There is a plan to tackle existing industrial case studies to demonstrate the ability of meta-properties to specify programs that are not necessarily verification-friendly. Finally, we wish to refine the transformation technique in order to allow the proof of the generated specification to scale better when the number of meta-properties or the size of the code increases.
Acknowledgment This work was partially supported by the project  VESSEDIA , which has received funding from the  EU   Horizon 2020 research and innovation programme  under grant agreement No  731453. This work was also partially supported by  ANR  (grant  ANR-18-CE25-0015-01 ). The work of the first author was partially funded by a  Ph. D. grant of the  French Ministry of Defense. Many thanks to the anonymous referees for their helpful comments.
This is due to the fact that some causality constraints need to be relaxed in order to take into account the program order relaxations of TSO, that allow reads to overtake writes. The definitions of the new criteria CCM and wCCM we use in our approach are quite subtile. Ensuring that these criteria are "as strong as possible" by including all possible order constraints on pairs of writes that can be computed (in polynomial time) using a least fixpoint calculation, while still ensuring that they are weaker than SC/TSO, and proving this fact, is not trivial. As a proof of concept, we implemented our approach for checking SC/TSO and applied it to executions extracted from realistic cache coherence protocols within the Gem5 simulator  [5]  in system emulation mode. This evaluation shows that our approach scales better than a direct encoding of the axioms defining SC and TSO  [3]  into boolean satisfiability.
Background: Comparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells). Results: RNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs.