dataesr/scientific-paragraphs-categorization huggingface dataset:
In their seminal work, Abadi and Rogaway  [2002]  show that it is sometimes possible to prove computational soundness of Dolev-Yao models. Such soundness results guarantee that a proof in a Dolev-Yao model can be translated into a proof in the computational model, yielding strong guarantees while benefiting from tool support in the more abstract model. We refer the interested reader to a survey paper  [Cortier et al. , 2010]  on this topic for an extensive description and bibliography. There have also been some attempts to formalize and (partially) automate proofs directly in computational models. The main successes in that directions are the CryptoVerif prover  [Blanchet, 2006]  and the easycrypt tool  [Barthe et al. , 2011] , a dedicated, interactive theorem prover. Another challenge is to directly analyse the source code rather than working with abstract models, be it symbolic, Dolev-Yao models or computational ones.
The CSure tool  [Goubault-Larrecq and Parrennes, 2005]  relies on dedicated abstract interpretation techniques to translate security protocol written in C to a set of Horn clauses that can then be analyzed. The ASPIER tool  [Chaki and Datta, 2009 ] also allows to analyze C programs, combining software model checking techniques (in particular counter-example guided abstraction refinement) with symbolic protocol verification. Some works also build on general purpose program analysis tools to analyze protocols written in C  [Dupressoir et al. , 2011]  and JAVA  [Küsters et al. , 2012]. Type systems, such as F7  [Bengtson et al. , 2011]  have also been used to analyze protocols written in functional languages. Yet another approach is to generate the protocol code from verified specifications, rather than try to directly analyze existing code, see  [Pironti and Sisto, 2010, Cadé and Blanchet, 2013].
Acknowledgements We would like to thank the reviewer for the careful reading and the helpful suggestions of improvement. We also thank the editors for their suggestion to write this tutorial and their patience and support until it came to a final version. This work has received support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n  • 258865  and the  ANR  project  ProSe  (decision  ANR   2010-VERS-004 ).
Human610 SNP chip data for individual K8101-49685s (paired with SRP019719 Illumina exome sample SRX265476) was reported in the Illumina "TOP" format, so the reverse complement of the allele was used when the IlmnStrand and RefStrand did not match (as defined in the human610-quadv1_h. csv manifest file). Allele sequences were provided without respect to a reference sequence, so SeattleSeq Variant Annotation (http://snp. gs. washington. edu/SeattleSeqAnnotation138/) was used to determine the reference sequence to focus on variants that differ from the reference sequence (to make results comparable to the Illumina sequencing variant calls). This is the latest version of the manifest file, but it was designed using dbSNP 131 and some discordant SNPs were due to annotations where the forward strand may vary from the hg19 reference sequence.
Concordance Definitions Concordance was defined as recovery of a set of lower-throughput variants (validated, SNP chip, targeted exon, amplicon), except for the targeted exon technical replicates where concordance was defined as (2 * Number of Overlapping Variants) / (Sum of Variants from both samples). For clearity, we therefore refer to recovery of lower-throughput results as the "recovery rate" in the results and reserve the term "concordance rate" for only the technical replicate analysis. When defining variant concordance between the SNP chip data and exome data, recovery of the known variant is counted as a concordant variant (even if multiple variants are called at a given position). Unlike most comparisons in this study, the SNP chip comparisons do not specifically focus on the coding variants.
To examine processes over different timescales, we establish the core hydrologic fluxes using the Penn State Integrated Hydrologic Model (PIHM)and then augment PIHM with simulation modules. For example, most land-atmosphere models currently do not incorporate an accurate representation of the geologic subsurface. We are exploring what aspects of subsurface structure must be accurately modelled to simulate water, carbon, energy, and sediment fluxes accurately. Only with a suite of modeling tools will we learn to forecast -earthcast --the future CZ.
To understand evolution of the CZ and how we are perturbing it requires numerical models that can explore the tight coupling of physical, chemical, and biological -including human --processes. Such models must target the important processes in response to tectonic, climatic, and anthropogenic forcings to "earthcast" the changing CZ. No single model can cross all the relevant timescales of CZ evolution. Since the evolution of the physical CZ is largely driven by the biota-catalyzed response of rocks to circulation of surficial fluids, the team of scientists at the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) in Pennsylvania (U. S. A. ) is developing a suite of models that is based on the Penn State Integrated Hydrologic Model (PIHM). By developing simulation modules using PIHM as the core, we are promoting greater understanding of the evolution of the CZ at all timescales (Table  1 , Figure  1 ).
PIHM: Modelling hydrologic fluxes The Penn State Integrated Hydrologic Model (PIHM) is a multi-scale, open-source hydrologic model where the major hydrological processes are fully coupled using the semi-discrete finite volume method  [2]. PIHM represents our strategy for simulating distributed hydrologic processes at the catchment scale. Our interest focusses on devising a concise representation of watershed and/or river basin hydrodynamics that can simultaneously simulate the interactions among major physical processes. At the same time, our goal is to maintain the flexibility needed to add or eliminate states/processes/constitutive relations depending on the objective of the numerical experiment or Figure  1. To quantify the evolution of the CZ requires multiple models and datasets to simulate processes at different timescales.
The unsaturated flow is incorporated using the 1-D vertically integrated form of Richards' equation. A simplified strategy for matrix-macropore flow is implemented based on a published method  [6]. Recently, a new module for estimating the age of environmental tracers was tested at Shale Hills  [4]. PIHM simulations have been conducted for time scales ranging from minutes to decades and spatial scales that range from the plot scale (1-10 m) to hillslopes (10-100 m) to river basins (>10 4 m).
The newest version of Flux-PIHM adds an optional topographic solar radiation module, which enables the quantitative investigation of the effects of aspect on land surface and hydrologic processes in small watersheds. This addition to Flux-PIHM was developed because aspect has an important effect on some fluxes. Flux-PIHM thus provides high resolution prediction and reanalysis (i. e. , model retrospective analysis driven by the meteorological forcing produced from atmospheric data assimilation systems) of watershed hydrologic and land surface states on time scales from minutes to decades. Because PIHM is capable of simulating lateral water flow as well as the saturated zone when it is within meters of the land surface, Flux-PIHM is able to represent some of the land surface heterogeneity caused by topography, including the spatial structure of the coupling between groundwater and SEB.
Flux-PIHM has been calibrated at the Shale Hills watershed with both hydrologic predictions and SEB predictions. An evaluation of the model shows good agreement with observations. Flux-PIHM has also been used for parameter sensitivity analysis  [8]  and data assimilation experiments  [10]. Unlike PIHM which only requires net radiation as the radiation forcing, Flux-PIHM requires downward solar radiation (separated direct and diffuse radiation if topographic solar radiation is turned on) and downward longwave radiation forcing for full SEB simulation. Because of the added land surface component, Flux-PIHM also requires more land surface parameters, e. g. , the land surface emissivity, the water vapor exchange coefficient, and the Zilitinkevich parameter  [7, 8].
Flux-PIHM BGC: Modelling carbon and nitrogen cycling The terrestrial carbon and nitrogen cycles are now commonly incorporated into the LSMs used in earth systems models. These earth systems models facilitate study of the interactions between changing climate, atmospheric composition and terrestrial ecosystems. As noted above, however, most LSMs do not carefully describe subsurface waters. Thus the interactions of watershed topography with the structure of ecosystems and their biogeochemical cycles cannot readily be simulated for all systems. An ecosystem biogeochemical module such as the core of Biome-BGC will be incorporated into Flux-PIHM to explore this topic at time scales ranging from hours to decades. CZ data for Shale Hills and the larger Shavers creek watershed will be used to explore the capability of the coupled modelling system to simulate ecosystem carbon and nitrogen fluxes and stocks across the CZ landscape, focusing particularly on the interactions of ecosystem biogeochemistry with subsurface hydrology.
Bed evolution is predicted by the Exner Equation. All hydrological and sediment transport processes are defined on discretized unit elements as a fully coupled system of ordinary differential equations (ODEs) using a semi-discrete FVM. In addition to the datasets required for PIHM, PIHM-SED requires spatial fields of sediment (soil) grain sizes and densities, critical shear stresses for sediment entrainment, sediment porosities, and Manning's roughness coefficient.
RT-Flux-PIHM: Modelling solute fluxes and mineral-water reactions To simulate water-mineral-gas interactions, we are developing reactive transport modules (RTM)  [11]  coupled with PIHM. Using these modules allows integration of land surface hydrologic processes with subsurface solute transport and biogeochemical reactions. The module currently includes mineral dissolution and precipitation, ion exchange, sorption and desorption, and will have the capability to simulate abiotic and biotic redox reactions. Two modules will be developed focusing on two different temporal scales. The first module is RT-Flux-PIHM, which couples an RTM directly with Flux-PIHM  [12] , and targets processes over time scales of months to years. The second RT model is described in the next section, and is targeted at longer simulations.
Such a cascade of models will be useful in modelling ridgetop (1-D) profiles. With incorporation of an erosion module based on SED-PIHM, such a model cascade could also enable understanding of weathering along hillslopes. To constrain these models, data describing the current soil profile (mineralogical composition) and physical properties of rock and regolith (e. g. , permeability) in the vertical direction, as well as parameters describing erosion such as shown in Figure  2  and discussed in the next section will be required.
LE-PIHM: Modelling landscape evolution Over geologic time, processes not simulated by PIHM-SED become important, such as downslope creep of regolith due to freeze-thaw, tree-throw, landslides, and mudflows, and bedrock incision. Current landscape evolution models simulate these processes but do not include interactions among groundwater and overland flows, bedrock weathering, and the evolving landscape. To incorporate these processes, we developed LE-PIHM, a 3-D hydrologic-morphodynamic model for regolith formation and landscape evolution. Given that this module has Figure  2. Governing equations for PIHM-SED. Here, z is the elevation of the ground surface (m), α is an empirical factor in the soil production equation (1/m), H is regolith thickness (m), qx is the volumetric flux of regolith in the positive x direction (m 2 /y), U is uplift rate (m/y), E is the rate of regolith transported via overland flow (m 2 /y), e is the elevation of the bedrock-regolith interface (m), h is regolith thickness (m), Kw is the hydraulic conductivity (m/y), K1 is the viscous creep transport efficiency(m 2 /y), K2 is the transport efficiency attributed to tree root growth and decay(m 2 /y), K3 is the transport efficiency associated with tree-throw (m 2 /y), g is the acceleration due to gravity (m/s 2 ), Qw is volumetric water discharge (m 3 /s), and A is the contributing drainage area (m 2 ).
Bedrock channels incise at a rate proportional to the local flux of bedload and inversely proportional to the proportion of bed covered by alluvium. The bedrock and ground surfaces evolve to produce streams and hillslopes as an emergent property of the interacting hydrologic and sedimentary processes. Three non-dimensional parameters govern the behavior of the system as defined by the following equations: W*=P o /U (1) K*=K/UL (2) S*=D/UL (3) Here, P o = the weathering rate of bare bedrock (m s -1 ), U = the bedrock uplift rate (m s -1 ), K = the hillslope diffusivity (m 2 s -1 ), L = a system length scale (m), and D = the overland sediment transport diffusivity (m 2 s -1 ). The latter parameter is a strong function of the local unit water discharge. In addition to the datasets required for PIHM, LE-PIHM requires spatial fields of sediment (soil) grain sizes and densities, critical shear stresses for sediment entrainment, sediment porosities, Manning's roughness coefficient, and estimates for P o , U, and K.
Conclusion: Earthcasting With a suite of models such as those described here, scientists will explore the tight coupling and feedbacks inherent to the complex CZ system. As models improve, it should also become possible to project --earthcast --the CZ into the future for different scenarios of human activity. An array of modules will be needed to project different processes. Here we have described one method of achieving this goal by using PIHM as the hydrologic backbone of many advanced and developing modules. This approach allows the CZ scientist a flexible and evolving modelling strategy for including new processes and for exploring other model codes and inter-comparisons. One example of how an earthcast might be completed is related to the study of rates of soil formation on loess along the Mississippi valley that has already been mentioned  [19].
Somewhat counterintuitively, changes in soil atmospheric CO 2 were unimportant because, although higher temperature caused higher subsurface CO 2 production rates, higher diffusive loss rates due to the elevated temperatures kept soil CO 2 relatively constant into the future. The cascade of models led to identification of a new concept, the terrestrial lysocline -the layer of the soil where carbonate mineral saturation changes rapidly with depth and which, like the oceanic lysocline, moves up or down in response to environmental change. We will similarly explore cascades of models including ARPEGE -CARAIB → RT-Flux -PIHM or ARPEGE -CARAIB → LE -PIHM to simulate soil formation or landscape evolution respectively at the SSHCZO and then to make earthcasts into the future. By utilizing modules to quantify important processes within a flexible and reusable framework we will unravel the important feedbacks and couplings that make it difficult to understand evolution of the CZ.
The lack of efficient software design techniques increases both software development costs and implementation risk in terms of costs and delays. Parallelism, heterogeneity, complex memory structures, efficient power monitors and controllers, are among the list of new functionalities provided by recent multicore systems that require to be adequately tackled by new design ⇑ Corresponding author. tools, as proposed by the PHARAON (Parallel and Heterogeneous Architecture for Real-time ApplicatiONs) project.
The PHARAON project The PHARAON project is a European collaborative initiative between universities, research labs and companies that is aimed at proposing solutions to overcome these limitations. It is sponsored by the European Commission that covers part of the costs and assists partners in the project management. The objective of PHARAON is to achieve a breakthrough towards broader adoption of multicore architectures and to enable the development of complex systems with high processing needs and low-power requirements. For such purpose, the project focuses on solving two major problems appearing in these types of systems. First, the development of parallel software, capable of exploiting multiple processor cores, is much more complex and, therefore, more expensive than traditional sequential software, which increases the product cost.
Second, the increased complexity of services provided by the systems requires more energy and, hence, is associated with a reduction of autonomy. To overcome these problems, the PHARAON project targets the development of two different sets of techniques and tools, aiming at best exploiting the low-power capabilities of modern multi-core processors, both at design time and at runtime (Fig. 1 ). These techniques and tools tackle both the programming and power management challenges mentioned previously. The first set directly affects the design flow, from UML/MARTE specifications to implementation on multicore platforms. The objective is to assist the designer in finding the most adequate software architecture while taking into account hardware constraints at design time. To do so, tools developed in PHARAON can evaluate the parallel structure of an application and propose improvements, in terms of parallelization constructs.
At the same time, the toolset will be able of automatically generating the multi-processor embedded code required to deploy the communicating SW components on the processing cores of the system, including DSPs and GP-GPUs. The second set of techniques and tools affect the runtime behavior of the application. The objective is to adapt the performance of the platform (frequency and voltage, for example) in order to consume only the required energy. For this purpose, project partners are developing monitoring and control techniques that are integrated in the code generated at design time to map the SW to the processors of the platform. This middleware automatically adapts platform services to application requirements during execution, and therefore reduces power consumption. A reconfiguration system and a low power scheduler are integrated with other run-time components on top of the platform to do so.
As a result, the PHARAON project has the goal of reducing the software development cost by 25% and to increase the battery life of embedded systems by nearly 20%. The project is coordinated by Thales Communications & Security which is a large French company. Tedesys (Spain) and Vector Fabrics (Netherlands) are two SMEs completing the industrial partners. Academic partners include Politecnico di Torino (Italy), Ecole Normale Supérieure (France) and University of Cantabria (Spain). Finally, the Interuniversitary Micro-Electronics Centrum research institute (Belgium) completes the consortium. As a result, the next sections present the improvements achieved during the first two years of the project, in which design tools have been developed and their application to the project use cases have started.
Evolution beyond the state of the art Code parallelization is one of the most widely studied topics in compilers for parallel machines since the 1970s. However, the level of parallelism that can be identified using automated techniques is very limited, since they require specific coding styles (e. g. perfectly nested loops, no conditionals and affine indexing) and hence have limited applicability. Recent approaches like the Compaan project at the University of Leiden  [2] , or the Pico Express high-level synthesis software from Fig. 1. PHARAON global approach and tools interactions. Synopsys  [3]  are interesting examples in this area. Other efforts like MORPHEUS  [5] , CRISP  [6]  and MEGHA  [7] , produce parallel code for execution on embedded or other special type platforms but usually are tailored to the target architecture.
Several compilation and debugging tools, often based on dedicated extensions of the C language like OpenCL and CUDA, have also been proposed for GPU architectures. The ARTEMIS ASAM project  [8]  is aimed at performing efficient design space exploration in order to optimize Application Specific Instruction-set Processors (ASIPs) to specific applications. The goal of the ALMA project  [4]  is to tailor the application to the available hardware. The FP7 TERAFLUX project  [9]  proposes new programming models and solutions for harnessing large-scale multicore platforms in the area of thousand cores in an efficient way by exploiting data-flow parallelism. On the other hand, the goal of the PHARAON project is not limited to parallelism discovery and exploitation, but integrates it with solutions oriented at optimizing power consumption, while using heterogeneous cores of the target platform.
The PHARAON flow starts from UML/MARTE models and combines code synthesis, parallelism analysis and stream-oriented OpenMP extensions with power and performance evaluation tools and run-time managers. That combination of design-time and run-time tools enables solving the trade-off between performance improvements and power consumption minimization. UML is a very common solution for high-level system design. [20]  pays special attention to the importance of UML models for industrial applications and the effort that they require. UML is a very broad and flexible language. Hence different profiles have been derived for specific application area. Currently MARTE  [21]  is the OMG standard profile for Modeling and Analysis of Real-Time and Embedded systems. Several UML-based methodologies also focus on HW/SW communication synthesis.
In  [22] , a semi-automatic solution using Remote Method Invocation (RMI) semantics for generating HW/SW infrastructure from UML models is presented. Similarly,  [23]  describes a flow to generate code from high-level MARTE for implementation on dynamically reconfigurable SoCs. Regarding code parallelism analysis, the ParTools toolset has been developed during the PHARAON project. It goes beyond previous techniques, such as the one proposed in  [41]  by providing techniques based on data compaction and advanced visualization. They are meant to effectively display huge trace data sets, and thus improve the developer analysis time while searching for the best parallelization opportunities. In the same vein, the tools from Crit-icalBlue can predict application performance under different thread decompositions, and display the corresponding inter-thread dependencies.
Like in our case, the assessment of parallelization opportunities is bound by the quality of the test bench used. However, the visualization capabilities are closer to those of a traditional profiler. After analyzing parallelization opportunities, solutions to implement them are required. OpenMP has been selected as a background technology, since, it is the de facto standard to program shared memory parallel computers. An extension of OpenMP for data-flow and stream computing, called OpenStream, has been developed in the project, leveraging previous results in the area  [48, 49]. Finally, in the context of run-time management for low-power design, different task-scheduling techniques have been also developed before the project. These techniques cover solutions such as the proportional, integral and derivative (PID) controllers  [24] , or fuzzy logic controllers  [25].