dataesr/scientific-paragraphs-categorization huggingface dataset:
JSO received scholarship support from the  University of Queensland  and  QIMR Berghofer Medical Research Institute  Conflict of interest. Dr Vorspan  received over the last 5 years the following support/funding: Congress registration and travel: Lundbeck Pharmaceuticals (2015). European or French Ministry of Research competitive research grants:  Labex BioPsy  ( ANR, Grant/Award Number :  ANR-11-IDEX-0004-02 ;  Investissement d'Avenir programme ): 2013, 2014, 2016; ANR Samenta 2013; ERA-net Neuron 2013, 2017. Fondation pour la Recherche en Alcoologie (FRA) , hébergée par la fondation de France: 2016, 2018. The other authors declare no conflict of interest with regard to this study.
Immunohistochemistry. At the end of the experiments, the left lungs of the mice were removed and fixed in 4% paraformaldehyde overnight. The fixed samples were washed in PBS and maintained in 10% and then 20% sucrose solutions, each time for 24 h, before being snap-frozen in optimal cutting temperature (OCT) embedding medium. Sections (8 μm) were stained with hematoxylin and eosin before histological examination. The areas occupied by edema and by the arterioles were quantified using Image J software on images acquired with a Leica DMLB microscope and the ratio was calculated. The investigator was blinded to the various experimental groups. Flow cytometry. Briefly, after TRALI experiments, lungs were perfused with PBS via the right ventricle and an incisure was made in the vena cava as an outlet.
Statistical analyses. Statistical analyses were performed with GraphPad software (Prism 5. 02). Survival studies were analyzed using the log-rank test (Figs 1D, 2A,B and 3B) or the χ2 test (Fig. 2B ). Other data are reported as the mean ± SEM and were analyzed using 1-or 2-way ANOVA followed by a Dunn's test or a Bonferroni post hoc analysis as appropriate. A P-value of <0. 05 was considered to be statistically significant.
1987; Stemprok, 1987)  and quartz (generally very pure except for only a few trace elements,  Breiter et al. , 2013)  to achieve this goal. Focusing on a single phase (i. e. , mica) rather than on multiphase assemblages (i. e. , whole rock) greatly limits the complexity of the modelling. Compared to this latter approach, the former appears as a first necessary step. It is also more robust given the uncertainties in the mineral/melt Kd database (see below). Last, it has the advantage to eliminate issues about the significance of whole rock assemblages (e. g. ,  McCarthy and Groves, 1979). Implicit in the approach is the assumption that micas preserve magmatic compositions. For muscovite, this assumption can be tested relatively easily from major elements  (Miller et al. , 1981). For biotite, distinguishing between magmatic and hydrothermal compositions requires a case-by-case evaluation.
Evaluation of the Kd database and limitations Journal Pre-proof J o u r n a l P r e -p r o o f During crystallization, trace elements partition between liquid and minerals. The incompatibility (or compatibility) of a trace element is expressed by the partition coefficient between a given mineral and the melt (Kd = concentration of trace element i in mineral / concentration of trace element i in melt). In principle, Kd varies with temperature, the nature and composition of the mineral, the liquid composition and certain compositional and intensive parameters such as fO 2 (e. g. Icenhower and London, 1995; Nash and Crecraft, 1985; McIntire, 1963; Pichavant et al, 2016). experiments, (2) at temperatures between 750 and 620 °C and (3) at fO 2 between NNO-2 and NNO+3  (Icenhower and London, 1995; Pichavant et al.
, 1996). In the following, we use the entire range of Kd (minimum and maximum values selected using the three criteria above, table 1; figure  2 ) instead of a single value to derive compositions of equilibrium liquids. This has the disadvantage of increasing the dispersion of the calculated liquid concentrations. Despite being less sensitive, this approach is considered as more robust than the choice of a single value because it better accounts for the uncertainties in the available Kd database. While the temperature range above is adequate for leucogranite, crystallization of pegmatitic melts commonly extends to lower than 620 °C (e. g. ,  Pichavant et al. , 1987b; Sirbescu and Nabelek, 2003; London, 2015). Owing to their different crystallization temperatures, partition coefficients may thus differ between pegmatite and leucogranite melts.
Indeed, values in the Kd database vary with temperature (table 1; figure  2 ). For example, the Kd biotite/liquid for F and Li both decrease when lowering temperature from 720-750°C to 620°C. The Kd muscovite/liquid for Li drops by a factor > 5 between 650 and 620°C, but the lowest values correspond to charges more oxidized than the highest  (Pichavant et al. , 2016; Icenhower and London, 1995). For Rb, Ba and Cs, the data at 650°C show significant dispersions (figure  2 ). Given the variability in the Kd database, it is difficult to extract the Journal Pre-proof J o u r n a l P r e -p r o o f 10/12/2018 Mica-liquid trace elements partitioning Chem Geol 8 effect of temperature as would be necessary if leucogranite and pegmatite crystallization are to be modelled at different temperatures. Therefore, below, the same Kd range will be assumed to be equally applicable to leucogranite and pegmatite liquids.
Journal Pre-proof J o u r n a l P r e -p r o o f 10/12/2018 Mica-liquid trace elements partitioning Chem Geol 11 The laser beam was focused to yield either a 44 or a 58 μm spot, with energy of 5 J/cm² and 3 Hz repetition rate. Data were calibrated against the NIST 612 (Pearce et al 1997) glass which was analysed every 20 spots. A BCR ® glass was used as a secondary standard. Accuracy is commonly better than 3 % except 7 Li and 181 Ta for NIST 612 (3. 19% and 5. 91 respectively). Precisions are strictly below 2 %. Details for sampling and analytical accuracy and precisions are provided in Electronic supplementary material 1.
, 2015). Zinnwaldite compositions appear from types IV and are present in type V and VI, the latter containing also lepidolite  (Raimbault, 1998; Deveaud et al. , 2015). Mica structural formulae were calculated on a 22 O basis with Li 2 O contents determined from LA-ICP-MS analysis. No chemical zonation was found within the analysed micas. Therefore, the compositional ranges observed for a given sample and mica (see below) reflect inter-crystal variability in the same thin section and not intra-grain zonation. Our major element data for muscovite and biotite in SSLC leucogranites (table  4 ) are similar to the results of  Monier and Robert (1986)   and Monier   (1987). In general, biotite composition in leucogranites ranges from Fe-biotite to siderophyllite, overlapping with biotite in pegmatites (figure  5 ).
Trace element composition of micas In pegmatites, the trace elements contents (table  4 ) globally agree with the evolution proposed by  Deveaud et al. (2013 Deveaud et al. ( , 2015)). Contents in incompatible elements (Li, Cs and Ta) progressively increase with differentiation (i. e. , from type II to VI), while contents in compatible elements (Ba) decrease. More in detail, biotite in type II pegmatite contains homogeneous Li and more variable Cs, Ba and Ta. Type III and IV pegmatites have micas with rather homogeneous trace element contents. Li, Cs, Rb and Ta are higher or equal (Ba) in biotite than in muscovite. Micas from Type V show the same type of behaviour for Li, Cs, Rb and Ta; however, for Ba, some concentrations are higher in muscovite than in biotite. It is worth stressing that the database for type V pegmatites includes one point for biotite and three different (and grouped) points for zinnwaldite (figure  6 ).
We propose to rely on a translation of BIP architectures into open pNets. Parameterised Networks of synchronised automata (pNets) is a formalism for defining behavioural specification of distributed systems based on a parameterised and hierarchical model. It inherited from the work of Arnold on synchronisation vectors  [3]. It has been shown in previous work  [28]  that pNets can represent the behavioural semantics of a system including value-passing and many kinds of synchronisation methods, including various constructs and languages for distributed objects. The VerCors platform uses pNets to design and verify distributed software components  [19, 27]. There is no bound on the number of elements inside a pNets or the valuation of parameters. When restricted to finite instantiations, it becomes possible to us pNets for finite model-checking approaches.
Closed pNets were used to encode fully defined programs or systems, while open pNets have "holes", playing the role of process parameters. Such open systems can represent composition operators or structuring architectures. It is possible to reason, in an SMT engine, on the symbolic automaton that represents the behaviour of a pNets with holes and that communicates values  [36]. The encoding of open pNets into Z3 that is under development is the starting point of this article. We benefit from the possibility to reason on a pNet in an SMT engine in order to prove properties on BIP architectures. The main contributions of this paper are: 1) The addition of data to the theory of BIP architectures, including a theorem about preservation of data dependent properties by compositions. 2) An encoding of architectures with data into open pNets, allowing for analysis of their temporal properties using pNet's software tools.
The paper is illustrated by a running example based on the failure monitor architecture from the CubETH nanosatellite on-board software  [34]. This running example also relies on the maximal progress assumption, whereby larger interactions are preferred to smaller ones. Due to space limitations, we only discuss this informally. However, proofs of the results provided in the appendix formally account for maximal progress. The rest of the paper is structured as follows. In Sect. 2, we present notations and background material on pNets. The theory of architectures with data is presented in Sect. 3. In Sect. 4, we present the encoding into open pNets and discuss verification of the running example. Section 5 discusses related work. Section 6 concludes the paper.
Encoding of architectures into open pNets We define the encoding of BIP architectures into pNets by associating to each architecture A = (C, V A , P A , ε A , Γ ) with C = (Q C , q 0 C , V C , σ 0 C , P C , ε C , - →), for each C ∈ C , and a partition D ⊆ 2 P A of its dangling ports (i. e. D∈D D = P A \ C∈C P C ), the corresponding pNet enc(A, D). For the sake of clarity, we define the encoding without any priority model. Then, we provide a brief sketch of the modifications necessary to encode maximal progress (implicitly assumed). Recall that Γ is an interaction model over the interface (V A , P A , ε A ), i. e. these interface elements are implicitly involved in the definition of Γ. We define enc(A, D) def = (enc(C)) C∈C , D, enc(Γ ) , where enc(C) and enc(Γ ) are the encodings of a coordinator C and the interaction model Γ respectively.
Conclusion BIP architectures are composition tools that enforce safety properties; the composition of architectures entails the composition of the associated properties. We have extended architectures with data-sensitive interactions, and proved that this extension still guarantees the preservation of safety properties by architecture composition, under reasonable assumptions. This extends the original compositional methodology offered by BIP architectures. Then we use pNets as a semantic formalism to encode architectures with data. pNet is a low level coordination model for open systems, in which composition preserves bisimulation equivalences. pNet is equipped with tools computing its behavioural semantics in terms of symbolic automata, allowing model-checking and equivalence checking with algorithms relying on SMT engines.
As a result, we obtain automatic and compositional guarantees of safety properties with data where compositionality is given by the BIP architectures, and pNet tools provide automatic verification of the properties of each architecture. The translational approach allows us to benefit from the methods and tools developed separately in BIP and pNets communities, avoiding the additional effort of designing the corresponding tools in both contexts from scratch. The presented work opens a number of avenues for future work, among which the most immediate ones consist in 1) developing tools that would implement the discussed encoding and verification techniques; 2) studying the preservation of liveness properties by architecture composition under assumptions similar to those discussed in  [5]  and 3) generalisation to priority models other than maximal progress.
A lot of cloud systems are adopted in industry and academia to face the explosion of the data volume and the arrival of the big data era. Meanwhile, energy efficiency and energy saving become major concerns for data centers where massive cloud systems are deployed. However, energy waste is quite common due to resource provisioning. In this paper, using dynamic voltage and frequency scaling (DVFS), a frequency selection approach is introduced to improve the energy efficiency of the cloud systems in terms of resource provisioning. In the approach, two algorithms, genetic algorithm (GA) and Monte Carlo tree search algorithm (MCTS), are proposed. A cloud database system is taken as an example to evaluate the approach. The results of the experiments show that both algorithms have its advantages.
I. INTRODUCTION To cope with challenges of Big Data, an increasing number of data centers are constructed. Cloud systems are developed to meet users' rapidly growing data processing needs. With the explosive growth of the construction of data centers, the problem of energy waste becomes more serious. The energy bills for major cloud service providers are typically the second largest item in their budgets due to the increased number of computational resources. [1]. Cloud database system is one of the typical cloud systems. To cope with the huge data storage and query needs, massive cloud databases are established, such as, HBase  [2] , Hive  [3]  and Cassandra  [4]. To build energy efficiency cloud database systems, a lot of researches have been done. For example, to achieve green computing, the measurement model and approach of the energy efficiency of cloud database systems were defined by Song et al.
[5] , and the EE characteristics of cloud database were investigated. Similarly, Lang et al. [6]  studied the tradeoff between performance and energy efficiency for parallel DBMS to give principles of designing an energy efficiency database system. Typical cloud databases are wasting energy. Since users' activities are dynamic, the workloads of the system vary with time: users' activities are more intense in daytime, whereas they do little at night. In this case, part of the energy is wasted if the system's configuration at night remains the same as the one during daytime. The energy waste comes from resource provisioning. To cope with resource provisioning in cloud systems, some self adjusting systems emerged, i. e. Ursa  [7]  and WattDB  [8]. WattDB is a self adjusting distributed database management system, in which it tries to switch nodes on and off according to current workload.
Ursa is a self adjusting cloud storage system, in which it tries to migrate data from the hotspot node to underutilized nodes with minimizing latency and bandwidth manner, and it tries to turn off the underutilized node to save energy if possible. However, these strategies might not suitable for cloud database systems since switching nodes on and off would cause an unacceptable migration cost and damage system availability. In WattDB,  Schall and  Härder took advantages of SDD to reduce the IO cost during the migration process. In Ursa, You et al. tried to turn off the underutilized nodes which do not contain primary replicas. Since there are different consistence policies in cloud database systems, the approach is not suitable either. Chihoub et al. [9]  introduced another idea to improve the energy efficiency of cloud database systems, in which they analyzed the tradeoff between energy and consistency of the system by means of Dynamic Voltage and Frequency Scaling (DVFS).
DVFS  [10]  is an efficient technology to control power consumption of processors. Power control policies can be made with DVFS technique. In modern Linux operating systems, five different power schemes (governors) are available to dynamically scale CPU frequency according to CPU utilization. The dynamic tuning strategies use CPU's running information to adjust its frequency, which does not reflect the state of cloud databases' workload lifecycle including memory and disk transfers. The current power schemes do not exactly match their design goal and may even become ineffective in improving energy efficiency of cloud database systems  [11]. Therefore, it makes sense to control frequency in a fine-grained way. In  [9] , Chihoub et al. proposed a property approach. They assigned highest frequency to half of the nodes and lowest frequency to another half, and they achieved 23% of energy saving.
However, this approach is a static method for assignment frequencies, and the usage scenario is limited. To extend Houssem-Eddine's work, a frequency selection approach is introduced in this work to cope with the resource provisioning for cloud systems especially for cloud database systems. Instead switching nodes on and off, we try to assign the frequencies which are chosen according to the workload predictions of the system. Meanwhile, if there are underutilized nodes and overloaded nodes, a migration approach is introduced to avoid violation of Service-Level Agreement (SLA). Energy wasted by idle machines and SLA violation due to overloaded machines are reduced, which together improve the energy efficiency of the system. The main challenge of the approach is its scalability. In a small case with 30 nodes and 8 available frequency options, there are 8 30 frequency combinations in total.
In our environment, the optimal solution of above case can be found by the entire searching within 5 hours. However, a common case has hundred nodes within a cluster. For example, the technical group from instagram claimed that their biggest Cassandra system contains 1000+ nodes and biggest cluster contains 100+ nodes in Cassandra summit 2016  [12]. Therefore, searching the optimal solution is unrealistic under this huge search space. Proposing an approach for searching a suboptimal solution with high accuracy and good performance is our goal. Besides that, when SLA violation occurs, the migration approach should be introduced which causes more effort to find the proper solution. This paper is an expanded version of our previous conference paper  [13]. In the previous work, we focus on the resource provisioning problem within cloud system, specially for cloud database system, and we made following contributions: • By means of the DVFS technology and workload prediction, a general frequency selection model is proposed in aspect of energy efficiency for cloud system.
Then, the model is specialized for cloud database system. • Based on the frequency selection model, a genetic based algorithm and a monte carlo tree search based algorithm are proposed. • Corresponding experiments are designed and implemented to verify the model and algorithms. In this expanded version we add following contents: • The model is described with more details about its constraints and objective. • In model simplification section (IV-A), proofs of the upper bounds are given. • Compared with the short version, we give more details and new result of the experiments. • Since the prediction errors are inevitable, the robustness of proposed algorithms is analyzed in this extension (see Section VI). The remainder of the paper is organized as follows: Section II reviews related works. A generic frequency selection model and a specialized model for cloud databases are proposed in Section III.
II. RELATED WORK A variety of the past researches have been done to study and analyze the energy efficiency in cloud systems and cloud database systems. In general parts, Da Costa et al. [14]  gave a lot of possible techniques of reducing energy consumption in large scale distributed systems. Lang et al. [6]  gave guiding principles for building up the energy-efficient cloud DBMS with query properties and scalability taken into account. Subramaniam and Feng  [15]  measured the power consumption and the performance of a Cassandra cluster, and used power and resource provisioning techniques to analyze the energy proportionality of the cloud database system. Tsirogiannis et al. [16]  analyzed the energy efficiency in distributed RDBMS systems, and they concluded that the most energy efficiency configuration is the highest performance one.
In our work, their energy efficiency model is applied for cloud database system and the resources are provided according to the current workload to make sure the system is at its energy efficiency point. In this work, we focus on the energy efficiency for cloud systems especially for cloud database systems. The main idea of the work is to take advantage of DVFS technique to optimize the resource provisioning. To improve the energy efficiency of cloud systems and cloud database systems, a straightforward idea is to switch underutilized nodes off and switch it on when necessary. there is not migration cost at all. Compared with above related works, we do not chose to switch the nodes on and off, but assign a frequency according to the workload amount. Meanwhile, when SLA violation occurs and there is no higher frequency can be assigned, a migration approach is introduced.
In this work, we try to use DVFS technique to improve the energy efficiency of cloud systems, especially cloud database systems. There are some researches have been done where they used Dynamic Voltage and Frequency Scaling (DVFS) or Dynamic Voltage Scaling (DVS) technique. Yu et al. [22]  studied the power efficiency scheduling problem of real-time tasks in an identical multi-core system, and presented Node Scaling model to achieve power-aware scheduling. Liu and Guo  [23]  studied energy efficient scheduling of periodic real-time tasks on multi-core processors with voltage islands, in which cores are partitioned into multiple blocks and each block has its own power source to supply voltage. Above works proposed heuristic algorithms to cope with voltage scaling problem in power saving manner.
Compared our work, we try to assign the frequency to each node of cloud database systems according to the workload. Thus, we proposed a meta-heuristic algorithm (Genetic Based Algorithm) and heuristic search algorithm (Monte Carlo Search Tree Based Algorithm). In term of the use cases, in our approach, we do not switch nodes on and off to avoid the unacceptable migration cost and unavailability of the system, and for each time window the workloads would not be total reassigned but be migrated according to the assigned frequencies and previous workloads. Chihoub et al. [9]  explored the tradeoff between consistency and energy efficiency on the energy consumption in Cassandra. Meanwhile a prototype model, Hot-N-Cold, is introduced to reduce energy consumption in Cassandra by means of setting the frequencies manually.