dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore the correct way to reorder clauses before passing them to the checker is another question of interest. To do eliminate proof search in antedecent lists, we just adjusted the decide expert so that only the head of the DecideList can be selected, as follows: Testing this code requires an exhaustive enumeration of all permutations for each chain, as well as all combinations of each of these permutations. The number of all such combinations for a Trace with n chains, each with variable length c i is: c 1 ! • c 2 ! • c 3 ! • • • c n !. As a result, we were only able to test our smaller samples, as we were working with a Cloud9 5  instance with limited resources. The problems we successfully used for this experiment were: madeUp, madeup2, simpleTrace, readme, and randKsat2; their details can be found in A7.
Conclusion and Future Work In this paper, we considered the problem of certifying very large unsatisfiability proofs with a high degree of trust. Our approach was to write a certifier based on the sequent calculus, a low level-proof system, in the programming language Prolog, which supplies us with unification and backtracking needed for conducting proof search. The use of the sequent calculus allows any programmer with basic understanding in logic to implement our kernel. The use of a logic programming language allows us to separate the implementation of the above algorithms from the amount of trust we can place in the result. The reason for that is the existence of many implementations of Prolog, such as SWI-Prolog 6  and GNU Prolog 7  which we can use in order to execute the kernel program and, therefore, decrease the chance of a bug in those algorithms.
Furthermore, by developing training that can accommodate the exceptionally heterogeneous data that characterize environmental research  (Jones et al. 2006 )-from genes and "critter cam" videos to airborne and satellite sensors-training approaches will be readily adaptable to other fields. One symptom of the current curriculum's shortcomings is the recent emergence of a variety of extramural options for acquiring critical technological skills, including resources such as Software Carpentry, Data Carpentry, and other informatics and computational training workshops hosted at NEON, at environmental synthesis centers worldwide, or at meetings of professional societies such as the Ecological Society of America. Numerous self-guided online tutorials are also available, although such resources may vary widely in quality or are not tightly linked with topical environmental science domains.
Collaboration between multiple researchers with diverse and complementary expertise is essential throughout all aspects of a project to define and integrate relevant concepts, data, models, and tools  (Cheruvelil et al. 2014). Accordingly, collaborative data-intensive research should benefit greatly from community convergence on data structures, protocols for information exchange, and efficient reuse of code. Community convergence on a common set of bestof-class software tools has become more apparent in recent years and may signal community maturation that facilitates more coordinated approaches to training in data-intensive research skills. For example, projects have grown out of the open-source software community using tools developed in R and Python. These free, open-source, cross-platform programming languages have decades of use and community building, and allow scaling up from desktop computers to powerful processing systems, such as HPC (high-performance computing) and cloud computing.
For example, a plant ecologist may want to aggregate and summarize sensor data collected at various temporal frequencies and merge these data with point or regional values extracted from raster files. Adding observational and experimental data on traits such as chemical composition or growth rates would add another layer of complexity. Although essential, this process is rarely taught in courses or described thoroughly in publications. Popular scripting languages (e. g. , R and Python) provide a large set of dedicated tools for researchers to perform the necessary data wrangling steps in a transparent and reproducible manner.
Version control tools such as Git and the userfriendly GitHub interface support cross-team development of processing algorithms, documentation, and code that integrates and synthesizes heterogeneous data, in addition to issue assignment and tracking. Collaborative writing tools such as Google Docs and Mozilla Etherpad support remote meeting participation and community-developed documentation of methods, protocols, and scientific workflows. Data repositories relevant to environmental sciences are on the rise, supporting data sharing, discovery, and documentation  (Michener and Jones 2012). Furthermore, a diversity of new platforms is emerging to integrate the tools for collaboration and data sharing, such as the Open Science Framework (osf. io) and Jupyter/iPython Notebooks (jupyter. org). Of course, these tools do not create a vibrant exchange of ideas by themselves; they only make it easier for researchers to communicate with each other and a broader audience, and this skill set is not one that can be as easily coded.
" Given that addressing environmental questions at appropriately broad scales will likely require the use of large-scale public data (e. g. , NASA, EPA, and NEON), Langen and colleagues' (2014) findings suggest a need to address students' (and instructors') questions about how data-intensive research fits into the scientific endeavor overall. Changing learning objectives for data-intensive training will require educators to restructure existing courses and develop new teaching materials, but collaborating in course design and sharing materials can ease the burden on individual instructors. A variety of initiatives provide freely available data sets to be slotted into existing courses for specific learning objectives (e. g. , the Portal Project Teaching Database,  Ernest et al. 2015; NEON Teaching Data Subsets, https://dx.
doi. org/10. 6084/m9. figshare. 2009586. v9). It is also becoming more common for instructors to openly share their full course materials. Community sharing of course materials allows educators to teach "field-tested" courses broadly, discuss best practices, share experiences and perspectives, and, ultimately, to improve and refine training to be higher quality and more effective  (Teal et al. 2015). Software Carpentry and Data Carpentry have been leading examples of collaborative course development for the workshop model  (Teal et al. 2015) , but other models exist, ranging from single units (www. dataone. org/education-modules) and lesson sets (http://neondataskills. org/tutorial-series) to full-semester courses (www. programmingforbiologists. org). Unfortunately, the growth of learning management systems at many institutions has acted to limit the transferability of course materials, because access is typically limited to members of the institution.
The need to integrate the skills for data-intensive research into higher education has been highlighted repeatedly. For example, the National Science Foundation's Vision and Change in Undergraduate Biology stated, "students should be competent in communication and collaboration, as well as have a certain level of quantitative competency and a basic ability to understand and interpret data. Furthermore, to be current in biology, students should have experience with modeling, simulation, and computational and systems-level approaches to biological discovery and analysis, and should be familiar with using large databases"  (Smith 2015). Data across the curriculum. The potential for data literacy to transform approaches across disciplines and sectors argues strongly for an initiative similar to that of writing across the curriculum in the 1970s, which persists in various forms throughout higher education  (McLeod 1989).
For CDR, NPI-C and quoting of neuropsychological tests, training sessions were organised to optimise standardisation across centres. All data were recorded via secured online electronic data capture. The percentages of missing data entered in the electronic case report forms ranged from 0. 09% to 2. 12% across centres. Twenty centres had less than 1% of missing data. These numbers illustrate the high compliance rate of the centres with the MEMENTO study, as well as the efficiency of data-monitoring procedures (both on-site and centralised).
Neuroimaging procedures As part of the inclusion criteria, participants had to agree to undergo brain MRI, and 86% of participants had a 3. 0-T MRI scan (1. 5 T otherwise). 18 F-fluorodeoxyglucose positon emission tomography (FDG-PET) was optional and was performed in 60% of participants. All neuroimaging acquisition was coordinated by the Center for Automated Treatment of Images (CATI; catineuroimaging. com), a platform dedicated to multicentre neuroimaging  [28]. For the MEMENTO study, CATI harmonised MRI and PET imaging across a network of facilities associated with the network of French memory centres. Physicists and engineers standardised acquisitions according to a systematic qualification procedure, ensuring parameter uniformity and image quality. They had frequent contact with each acquisition site to track any hardware or software upgrades.
PET procedure PET centres participating in the MEMENTO study were equipped with systems set-up between 2003 and 2014 and consisted of 10 GE Healthcare, 6 Philips and 12 Siemens Healthcare systems, as well as 15 different models of scanners. No brain-dedicated high-resolution system or 2D tomography systems were included. Therefore, we chose to harmonise patient data acquisition using phantom experiments prior to patient studies to ensure goodquality imaging data among centres  [29]. Our objective was to optimise contrast recovery while preserving, if possible, spatial resolution. Two phantom studies were acquired for the qualification process of the 22 centres that agreed to perform FDG-PET: the Jaszczak phantom, composed of cold rods and six hollow spheres, and the Hoffman 3D brain phantom.
Hoffman 3D brain phantom scans were segmented in VOIs to compute right-to-left and anteriorto-posterior cortical ratios, as well as caudate-to-white matter, putamen-to-white matter and grey matter-towhite matter ratios. For each centre, the set of optimal reconstruction parameters was chosen as the one maximising ISR and the RC without a noticeable decrease in signal-to-noise ratio. After the set-up visit, we provided recommendations to each centre for acquisition and reconstruction parameters to be used for the MEMENTO cohort. In addition, centres were qualified after the analysis of the images of a first test patient. They received a technical manual describing in detail the procedures pertaining to patient preparation, injected dose, PET-CT image acquisition and data transfer to CATI. Brain FDG-PET scans were obtained 30 minutes after injection of 2 MBq/kg of 2-deoxy-2-18 F-fluoro-D-glucose.
Validated data were moved to a centralised database at the disposal of several teams in charge of analysis. Each analysis of CATI's portfolio was performed following a systematic procedure providing quality control indices. Below is a summary of the measurements included in MEMENTO database: Whole-brain and grey/white volumetry performed using the method "segment" in SPM8 software Hippocampal volumetry performed with SACHA software  [32, 33]  complemented by visual assessment done centrally at CATI by two trained doctors using the Scheltens scale  [34]  Cortical thickness computed with FreeSurfer software for each region of interest (ROI) of the Desikan-Killiany Atlas  [35, 36]  Sulcal span computed with the Morphologist method of the BrainVISA software package for each sulcus of the BrainVISA Sulci atlas  [37, 38]  White matter hyperintensity volumetry using WHASA software  [39]  complemented by visual assessment done centrally at CATI by two trained doctors using the Fazekas and Schmidt scale  [40]  Fractional anisotropy and mean diffusivity in large white matter bundles computed using Connectomist software  [41, 42]  Integrity of the default mode and of the salience network computed as the mean correlation across a set of Talairach coordinates Mean FDG-PET uptake for the ROIs of the Automated Anatomical Labeling atlas relative to the pons reference region  [43] , including partial volume correction Mean FDG-PET uptake for a set of disease-specific ROIs inferred from the ADNI database  [44]  A selection of these measurements is described in this paper.
Additional file 2: Table  S1  displays statistical significance data for comparisons of baseline characteristics across two-by-two cognitive categories. Additional file 2: Table  S2  shows baseline characteristics by age categories and sex. All cardiovascular risk factors except diabetes were more frequent in men than in women, as well as with increasing age. Anxiety frequency decreased with increasing age. APOE genotype was not related to age or sex. As expected, for all neuropsychological tests, mean performance decreased linearly with increasing age. As for sex-related differences, men had, on average, significantly higher performances than women at praxis, visuo-spatial abilities (Rey-Osterrieth Complex Figure Test immediate and 3-minute copies). Women had, on average, significantly higher scores than men for verbal fluency (letter P only), memory tests (DMS48 immediate recall and Free and Cued Selective Reminding Test sum of immediate total recall and sum of free and cued delayed recalls), and executive function (FAB).
However, if questions on generalisability of the findings occur in the future, we have access to two databases that will allow correction for selection:  (1)  the French Alzheimer Databank (Banque Nationale Alzheimer), which contains demographic, diagnostic and treatment information of persons consulting at the 26 CMRRs participating in MEMENTO; and (2) the French data of the Survey on Health, Ageing and Retirement in Europe (SHARE) (general population-representative households with at least one individual aged ≥ 50 years) that include socio-demographic and health status variables. MEMENTO is built as a platform available to researchers. Indeed, external researchers can request access to data via collaborations with the study group through enquiry to the corresponding author.
An efficiency of approximately 20% is reached by  [4]. In this study, we present the results of numerical simulation of the solar cell ZnO: Al / i-ZnO / Zn (O, S) / CZTS /, obtained using the SCAPS-1D software. The CdS buffer layer is replaced by the Zn layer (O, S) of which the band gap and electron affinity are dependent on the sulfur content x = S / Zn. These results concern the effect of this latter and the thickness of the absorbent layer on the electrical characteristics of the solar cell. The originalty and the novelty of this work lies essecialy on the calculation of the the gap energy Eg (CZTS) and the electron affinity in function of the sulfur. We confirm that, through these results that our work is original compared to the litterature. Although, in the field of photovoltaic systems, the materials chosen are abundant, have an important characteristics, and the study made is relevant when we investigate the effect of sulfur on the layers studied.
CONCLUSION In this manuscript, we studied the electrical properties of the solar cell ZnO: Al / i-ZnO / ZnO1-xOx / CZTS / depending on the sulfur rate x and the thickness of the absorbent layer. The goal was to replace the CdS buffer layer by ZnO1-xOx, a free layer of cadmium, toxic materials. For this, we used the SCAPS-1D software. We treated the example of the cell-based CZTS whose physical and geometrical parameters of the thin films, and were taken from literature. The band gap and electron affinity of ZnO1-xOx are calculated based on the sulfur rate x. The results showed that it was possible for a fixed amount of sulfur in the ZnO1-xSx buffer layer; we can deduce the thickness of the absorbent layer, which allows finding the efficiency of the solar cell CdS buffer layer.
In addition, specific challenges exist for mapping MES  (Jobstvogt et al. , 2014; Townsend et al. , 2014). Marine ecosystems and environments are highly dynamic in nature, especially in the pelagic zone  (Webb et al. , 2010). Currents, daily vertical and seasonal lateral migrations, and seasonal changes in basic physico-chemical conditions, may cause important ecological attributes to vary minute-to-minute, season-to-season, and year-to-year. Due to the lack of complete datasets, averaging data over time and space is often necessary; hence the level of spatial accuracy for MES information, including social data, is low. Information on the distribution of habitat is scarce or patchy for most of the world's marine ecosystems  (Townsend et al. , 2014). The ecological functions and processes required to produce many ES, such as biological control on the spread of vector borne human diseases, are poorly understood or not easily quantified  (Goffredo et al.
Acknowledging, quantifying, and mapping the IPs in the ES provision chain will add another dimension to our understanding of how ES benefits are shared between developing and developed countries. Such knowledge can be also used to build broader constituencies for place-based habitat and ecosystem conservation, to inform regional and international policies and commitments, and identify the kinds of incentive mechanisms that could be employed for more sustainable ecosystems by helping identify who should compensate whom  (Convention on Biological Diversity, 2009; Ludwig, 2012).
Acknowledgements The authors would also like to thank the two anonymous reviewers for their comments that helped significantly improve the article. The authors would also like to thank  John Virdin  ( Duke University ) for his help with the fisheries case study, and  Camino Liquete  ( Joint Research Centre, European Commission ) and  Dimitrios Bormpoudakis  ( University of Kent, UK ) for providing valuable insights to the article. Funding E. G. D. and L. P. are funded by the  The Nature Conservancy's Mapping Ocean Wealth  project with funding from the  Lyda Hill Foundation  and by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ".
Lustre is a synchronous data-flow declarative language widely used for safety-critical applications (avionics, energy, transport. ). In such applications, the testing activity for detecting errors of the system plays a crucial role. During the development and maintenance processes, Lustre programs are often evolving, so regression testing should be performed to detect bugs. In this paper, we present a tool for automatic regression testing of Lustre programs. We have defined an approach to generate test cases in regression testing of Lustre programs. In this approach, a Lustre program is represented by an operator network, then the set of paths is identified and the path activation conditions are symbolically computed for each version. Regression test cases are generated by comparing paths between versions.
It is an environment based on the Lustre language and it allows the hierarchical definition of the system components and the automatic code generation. The Lustre/SCADE environment is widely used for safety-critical applications (avionics, energy, transport. ). In such applications, software testing  [2] ,  [3]  for ensuring the quality plays a vital role. The objective of testing activity is to reveal errors in applications as soon as possible in development and maintenance phases. Software maintenance is an activity, which includes enhancements, error corrections, optimization and deletion of existing features. These modifications may cause the system to work incorrectly. Therefore, regression testing becomes necessary. Regression testing is a type of software testing that seeks to uncover new software bugs, or regressions, in existing functional and non-functional areas of a system after changes, such as enhancements, patches or configuration changes, have been made to them.
The intent of regression testing is to ensure that a change such as those mentioned above has not introduced new faults  [4]. In addition, one of the main objectives for regression testing is to determine whether a change in one part of the  methods of regression testing include rerunning previously-completed tests and checking whether program behavior has changed and whether previouslyfixed faults have reemerged. Regression testing can be used to test a system efficiently by systematically selecting the appropriate minimum set of tests needed to adequately cover a particular change. Extensive studies have been carried out focusing on the automatic test data generation. Several tools have been proposed, mainly concerning the formal specification processing to produce test input sequences. Some tools for programs specified in Lustre are GATeL  [5] , Lurette  [6]  and Lutess  [7] ,  [8].
The paper is organized as follows. Section 2 presents an overview of the Lustre language, the SCADE environment, and testing in Lustre/SCADE. The approach of test case generation in regression testing for Lustre/SCADE programs is presented in Section 3. Section 4 introduces the implementation of the LusRegTes tool and Section 5 presents the experimentation. Finally, the paper finishes by the conclusion and future work.
Lustre/SCADE 2. 2. 1. The Lustre language Lustre is a synchronous data-flow language dedicated to programming reactive systems such as automatic control and monitoring systems in various fields like nuclear plants, civil aircraft and automotive systems. Lustre is well suited for programming the critical parts of real-time systems especially thanks to its well-formalized semantics and to the associated verification tools. A Lustre program is structured into nodes  [1]. A node is a set of equations, which define its outputs as a function of its inputs. Each variable can be defined only once within a node and the order of equations is of no matter. Specifically, when an expression E is assigned to a variable X, X = E, indicates that the respective sequences of values are identical throughout the program execution; at any cycle, X and E have the same value.
e. at t = 0). If E and F are expressions denoting, respectively, the sequences (e 0, e 1 , … e n , …) and (f 0, f 1 , … f n , …), then E  F denotes the sequence (e 0 , f 0, f 1 , … f n , …). Lustre does not support loops (operators such as for and while) nor recursive calls. Consequently, the execution time of a Lustre program can be statically computed and the satisfaction of the hypothesis of synchrony can be checked.
The SCADE environment SCADE is a graphical environment commercialized by Esterel Technologies. It is based on the synchronous language Lustre. So, we are often referred to as Lustre/SCADE. Lustre/SCADE is usually used to build the applications of reactive systems. From the SCADE functional specifications, C code is automatically generated, though this transformation (SCADE to C) is not standardized. This graphical modeling environment is used mainly in the aerospace field (Airbus, DO-178B); however it is also used in the domains of transportation, automotive and energy  [1].
Operator network of Lustre program Lustre programs are usually represented as operator networks. An operator network is a labeled graph connecting operators by means of directed edges. An operator (logical or numerical) specifies dataflow transfers from inputs to outputs. An edge specifies the data flow between two operators. An operator network contains usual logical (and, or, not), arithmetical (+, -, /, *) and relational operators (LT (<), LTE (<=), EQ (==), NEQ (< >), GT (>), GTE (>=)), the conditional operator (ITE) and the temporal operators (pre, fby). It is assumed that two functions are associated with any operator op: in(op) returns the set of the operator input edges and out(op) returns the set of the operator output edges. There are three kinds of edges: input, output and internal edges.