dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper, we have investigated the performance of a coupled Lattice-Boltzmann and Immersed Boundary method that simulates the contribution of solid behavior within an incompressible fluid. While LBM has been widely studied on heterogeneous platforms, the coupling of LBM with Immersed-Boundary methods has received less attention. We have reviewed different strategies to enhance the performance on three state-of-the-art parallel architectures: an Intel Xeon server, a NVIDIA KeplerGPU and an Intel Xeon Phi accelerator. Our baseline LBM solver uses most of the state-of-the-art code transformations that have been described in previous work. Notably, performance results (MFLUPS) on the GPU and the multicore processor are close to ideal MFLUPS estimations. On the Xeon Phi, the gap with these ideal estimations is higher, but it also achieves competitive performance.
Overall, the best results are achieved on the GPU, which peaks at 550 MFLUPs, whereas the Intel Xeon Phi peaks at 450 MFLUPs. Our main contribution is the design and analysis of a hybrid implementation that takes advantage of both the accelerator (GPU/Xeon Phi) and multi-core in a co-operative way to solve the coupled LBM-IB problem. For interesting physical scenarios with realistic solid volume fractions, these hybrid solvers are able to hide the overheads caused by the IB correction and match the performance (in terms of MFLUPS) of state-of-the-art pure LBM solvers. This has been possible thanks to the effective cooperation between the accelerator and the multi-core processor and the overlapping of data transfers between their memory spaces with useful computations. Based on the presented LBM-IB framework, we envision two main research lines as future work.
Acknowledgments This research is supported by the  Spanish Government Research  Contracts  TIN2012-32180 ,  Ingenio 2010  Consolider  ESP00C-07-20811 ,  MTM2013-40824  and  SEV-2013-0323 , by the  EU-FET  grant  EUNISON 308874  and by the Basque government BERC 2014-2017 contract. We also thank the support of the computing facilities of  Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT ) and  NVIDIA GPU Research Center program  for the provided resources.
In their analysis, the intense (up to 50 nA m 2 ) eastward current was only evident on the dayside, which is in contradiction to previous studies. Jorgensen et al. (2004)  analyzed the magnetic field data from the CRRES satellite by spatial location and produced magnetic field maps to calculate then the local current systems by taking the curl of the magnetic field. They found an eastward-directed component to the ring current, as well as a westward-directed component, and these two currents were consistent with the plasma pressure peak located at approximately 3. 5 R E. Several studies have been performed to explicitly include the eastward ring current into the magnetospheric magnetic field modeling. The first attempt was made by  Lui et al. (1994). This current system was missing from the existing global magnetospheric magnetic field models, including first versions of the Tsyganenko models like T87  (Tsyganenko, 1987)  and T89  (Tsyganenko, 1989).
They found that seven of these features were consistent with the solar wind dynamo mechanism. Following these results,  Reistad et al. (2014)  explored whether the difference in solar wind dynamo efficiency can have a statistically significant impact on the aurora. In their study, the entire IMAGE WIC data set was used. Careful selection criteria were implemented to avoid the effect of other possible mechanisms. See  Reistad et al. (2014)  for details. The results are shown in Fig. 11c-h. In the Northern Hemisphere the superposed images (Fig. 11c  and d ) are comprised of more than 150 observations in the MLT sector from 17 to 24, while for the Southern Hemisphere images (Fig. 11f  and 11G ) there are more than 80 observations in the same MLT sector. As can be seen in Fig. 11e  and 11h , there are distinct intensity differences between the negative and positive IMF B x cases.
Antonova et al. (2009)  analyzed the radial profiles of plasma pressure gradients obtained from the THEMIS-B satellite data for the period 2 June-29 October 2007 in the equatorial plane near noon at geocentric distances from 7 to 12 R E. The dayside configuration of the geomagnetic field has been obtained using the T02 Tsyganenko (  2002 ) magnetic field model. The estimated value of integral current was 5. 8 ⇥10 5 A in both hemispheres. This value was in agreement with estimations obtained by  Lui and Hamilton (1992 ) and De Michelis et al. (1997 , 1999).
Acknowledgements. The authors thank the  International Space Science Institute in Bern, Switzerland , for their support of an international team on " Resolving Current Systems in Geospace ". The work of  N. Ganushkina  and  S. Dubyagin  was partly supported by the  Academy of Finland. The work of  N. Ganushkina  was also partly supported by  NASA  award  NNX14AF34G. The part of the research done by  N. Ganushkina  and  S. Dubyagin  leading to these results received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  606716  SPACESTORM and from the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement  637302  PROGRESS. The work at  Birkeland Centre for Space Science, University of Bergen , Norway, was supported by the  Research Council of Norway/CoE  under contract  223252/F50.
e. request by a component C j a value taken from array X and check that this value satisfies equations {Eq}, which is denoted by Spotcheck i,j (X, {Eq}) with j Table  3. Privacy Architecture Language. A ::= {R} R ::= Hasi (X) | Receivei,j ({S}, {X}) | Compute i (X = T ) | Checki ({Eq}) | Verif Proof i (Pro) | Verif Attest i (Att) | Spotcheck i,j (X, {Eq}) | Trusti,j S ::= Pro | Att Att ::= Attesti ({Eq}) Pro ::= Proof ({P}) P ::= Att | Eq a component index). Last but not least, trust assumptions are expressed using Trust i,j (meaning that component C i trusts attestations from component C j )  6. The semantics S (A) of an architecture A is defined as the set of states of the components C i of A resulting from compatible traces as described in  [1]. A compatible trace contains only events that are instantiations of relations (such as Has i (X), Receive i,j ({S}, {X}), etc.
CAPRIV tool The CAPRIV computer aided privacy engineering tool has been developed to help non-expert designers to build architectures by following the strategies presented in this paper and to verify that the privacy requirements are met according to the formal model detailed in  [1]. The interface and the back-end have been themselves developed with privacy by design in mind. For example, two components are not linked by any direct communication channel unless explicitly declared by the designer. The design of the tool makes it possible to hide the formal aspects of the model to the designer who does not want to be exposed to mathematical notations. This is mainly achieved through the use of a graphical user interface (GUI) and natural language statements. CAPRIV implements an iterative design procedure allowing the designer to come back to previous steps at any time.
The first verification concerns the consistency of the architecture (for example, the fact that the arguments of a computation must themselves be produced in one way or and be available to the component performing the computation). The designer can then formally verify the satisfaction of the confidentiality and integrity requirements. Implementation. Some of the verification features rely on external theorem provers and CAPRIV acts as a frontend interface for the Why3 framework  [4]. Why3 is a platform in which theories can be expressed in an ML-flavored language. Standard libraries are provided to easily model structures such as rings and arrays for instance. Why3 relies itself on external provers such as Alt-Ergo  [5]  or Z3  [23]  to prove theorems. CAPRIV automatically generates theories corresponding to the architecture (as axioms and conjectures to be proven), then calls Why3, and finally handles its answer.
Architecture Design. 1. Constraints. The first task of the designer is to identify the unavoidable constraints that must be taken into account in the design of the system. For this case study, the locations are measured by the on-board units: Has OBU (loc). The designer has also to make explicit other predefined choices (either imposed by the customer or resulting from his own knowledge or experience). We assume here that he chooses to locate the computations on the on-board units to minimise personal data disclosure as follows: Compute OBU (p = F (loc)) and Compute OBU (fee = + (p)). Another obvious constraint is for the provider to get the fee: Receive SP,OBU (∅, {fee}).
Assessment. The last tasks for the designer are to check the consistency of the architecture and, if necessary, to add the missing elements to get a consistent architecture. In our example, it is necessary to add Compute OBU (cp = H (p)) to ensure that a component is in charge of computing the cp variables. Figure  3  illustrates the design view of CAPRIV when the choices made previously have been input. Figure  2  pictures the architecture (which is a simplified version of  [2] ) obtained at the end of the design process. This latter is expressed in the formal language detailed in  [1]. Architecture Verification. The designer can then formally verify that the architecture meets the requirements. This verification can be made (either automatically or interactively) using the CAPRIV tool sketched in Section 3 which implements the axiomatics presented in  [1].
The BAN consists in a set of sensors deployed on the body of the patient for monitoring physiological parameters. The base station can be a tablet, a mobile phone or a portable device which collects and aggregates the health data produced by the BAN and sends them to the remote server over the Internet. The remote server provides assistance to patients and helps doctors on the management of their patients, and at the same time saves the patient's data in the hospital's database for electronic health records. There is a wide range of applications for PHSs, some provide passive safety like requesting help to caregivers in case of a fall event  [1] , while others are focused on the prevention of diseases like depression  [31]. In all cases, the development of such systems have some technological challenges in common that must be taken into account.
The design of this PHS takes into consideration the previously explained issues. The main component of this system is the MAGPIE agent platform  [8] , which has been designed to run in Android handheld devices. The use of agents in PHSs can simplify the modeling of medical knowledge as they are autonomous software entities, that pursue a set of goals  [44]  in an intelligent way, by applying Artificial Intelligent reasoning techniques such as deduction, and act proactively, without necessarily receiving a stimulus from the user. This set of properties can benefit the current definition of PHSs, by having monitoring tools that are capable of reasoning in a complex and proactive way on the current patients' physiological parameters. Moreover, the deployment of the agents in the Tier 2, that is in the mobile device from each patient, improves the scalability of the PHS in comparison with the current state-of-the-art approach where the computations for patient monitoring are done in Tier 3.
System overview The PHS for DM monitoring reported in this paper has two different actors: patients and doctors (see Figure  1 ). The medical side of the system corresponds to the Tier 3 of a PHS. In this case, healthcare professionals can interact with the system by means of a web application that allows them to visualize and analyze data from patients, and to manage and define specific monitoring rules for each of the patients. Regarding the patient side it maps to the Tier 1 and Tier 2, where patients with DM are monitored by means of using an Android smartphone (Tier 2) and a set of sensors that conforms the BAN (Tier 1). The smartphone has installed a mobile application with the purpose of managing the DM. This application is developed with the MAGPIE framework, so its based on a multiagent system that is able to perceive the physiological values measured by the sensors.
Web interface for monitoring rules The agents of MAGPIE must provide alerts according to monitoring rules, and medical doctors are the users of the system who have the knowledge to define such monitoring rules. However, they may not have knowledge on logic programming to define specific monitoring rules that are understandable by agents. To help medical doctors to play their role in an independent way, one of the components of the PHS is a web interface where doctors can program monitoring rules for the agents in a graphical way. Figure  2  shows how this application looks like. In the context of the reported PHS a monitoring rule is defined as a combination of events that trigger an alert to be notified to a medical doctor; where an event is considered as the measurement of a physiological parameter categorized as high, normal or low.
Recognition of event patterns A retrospective analysis on COMMODITY 12 data has been done for evaluating the ability of the system in detecting patterns in real data. Table  2  summarizes the statistics of the dataset. The dataset consists on 21 diabetic patients that used the sensors described in Section 2 during a period of six weeks approximately. The rows in samples per day indicate how well the patients follow the treatment. Ideally these values should be 1. 71±1. 89, 1±0, 1±0 for the glucose, blood pressure and weight respectively. Table  3  shows the results concerning the triggering of the rules given the three previously presented selected patterns. The interesting aspect of these results is that given the patients data, the system is able to find the patterns defined by the doctors.
Related work The work of this paper applies agent technology in the domain of healthcare to monitor chronic patients suffering from DM. Other heathcare systems also use agents to perform different tasks like coordinating data exchange between healthcare institutions  [29, 39] , or to support people in Ambient Assisted Living  [6, 27]. Other systems and uses of agents in healthcare are reviewed in  [21]. In the context of PHSs, MADIP  [36]  is a relevant work that is build on top of the JADE agent platform. In this system, different kind of agents are used with the main purpose of monitoring vital signs and to inform healthcare professionals when abnormal situations are detected. However, the use of software for desktop computers can limit the mobility of the patient. A similar monitoring system is presented in  [3]  where data from sensors is analyzed locally in a PDA but in the context of arrhythmia detection.
Fluidify The basic version of Fluidiy (termed Fluidify (basic)) directly implements the ideas discussed in Sec. 2. 2 (Fig. 4 ): each node n first chooses a random logical neighbor (noted p, line 2), and then searches for the physical neighbor of p (noted q) that offers the best reduction in cost (argmin operator at line 3) 1. The code shown slightly generalises the principles presented in Sec. 2, in that the nodes p and q are chosen beyond the 1-hop neighborhood of n and p (lines 2 and 3), and consider nodes that are k data and k net hops away, respectively. G data the logical graph (N, E data ) Γ k net (n) k closest nodes to n in Gnet, in hop distance Γ k data (n)k closest nodes to n in G data , in hop distance p ← random node from Γ k data data (n) 3: q ← argmin u∈Γ k net net (p) ∆(n, u) 4: conditional swap(n, q, 0) The potential cost reduction is computed by the procedure ∆(n, u) (lines 5-8), which returns the cost variation if n and u were to exchange their roles in the overlay.
Experimental Setting and Metrics Unless otherwise indicated, we use rings for both infrastructure graph G net and overlay graph G data. We assume that the system has converged when the system remains stable for 10 rounds. The default simulation scenario is one in which the system consists of 3200 nodes, and use 16-NN logical and physical neighborhoods (k net = k data = 16) when selecting p and q. The initial threshold value for simulated annealing (K 0 ) is taken as |N |. r max is taken as |N | 0. 6 where 0. 6 was chosen based on the analysis of the number of rounds Fluidify (basic) takes to converge. We assess the protocols using two metrics: -Proximity -captures the quality of the overlay constructed by the topology construction algorithm. Lower value denotes a better quality. -Convergence time -measures the number of rounds taken by the system to converge.
ACKNOWLEDGEMENTS We thank an anonyous referee for a careful reading and suggestions which help improve this paper. We thank  Joachim Puls  for computing a FASTWIND model for us and for discussion about wind sensitive optical lines. J. -C. Bouret  is indebted to  S. R. Heap  for her invitation to work at  NASA/GSFC  when this work was initiated (work was supported by  NASA  grant  NNX08AC146  to the  University of Colorado at Boulder ). DJH acknowledges support from  STScI GO  grant  HST-GO-12867. 001-Aand STScI theory grant  HST-AR-12640. 01. We acknowledge financial support from " Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU, France. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France.
Scope: The Phenol-Explorer web database details 383 polyphenol metabolites identified in human and animal biofluids from 221 publications. Here, we exploit these data to characterize and visualize the polyphenol metabolome, the set of all metabolites derived from phenolic food components. Methods and results: Qualitative and quantitative data on 383 polyphenol metabolites as described in 424 human and animal intervention studies were systematically analyzed. Of these metabolites, 301 were identified without prior enzymatic hydrolysis of biofluids, and included glucuronide and sulfate esters, glycosides, aglycones, and O-methyl ethers. Around one-third of these compounds are also known as food constituents and corresponded to polyphenols absorbed without further metabolism. Many ring-cleavage metabolites formed by gut microbiota were noted, mostly derived from hydroxycinnamates, flavanols, and flavonols.
Circulating metabolites are unlikely to be the same compounds as ingested polyphenols, but derivatives formed either by endogenous metabolism or by the microbiota, which allow effective transport and elimination from the body. Many of these metabolites have been identified in human and animal biofluids from controlled intervention studies with polyphenol-rich foods or pure polyphenols. All metabolites described in the literature have been extracted and stored in Phenol-Explorer, a web database originally conceived as a repository of polyphenol food composition data  [6, 7]. The database enables the retrieval of all known polyphenol metabolites formed upon uptake of a particular food or polyphenol with accompanying pharmacokinetic data, if available. Until the release of this database, the identities of polyphenol metabolites were scattered throughout a large volume of literature and frequently difficult to obtain.
Previous studies have reviewed polyphenol absorption and metabolism from controlled intervention studies, taking into account the dose size and mode of administration  [8, 9]. Instead, the aim of this study is to exploit all Phenol-Explorer data to systematically analyze the polyphenol metabolome and, more specifically, the nature of the metabolites reported in intervention studies and their pharmacokinetic properties. Polyphenol metabolites are distributed to tissues and may act, e. g. , by modulating signaling cascades, which govern biological processes, such as endothelial function  [10]  and cell-cycle control  [11]. Thus a notable use of the database might be to find out which metabolites circulate after administration of a given polyphenol-rich source that is suspected of imparting beneficial effects upon health  [12].
Sources of data on polyphenol metabolism and pharmacokinetics All polyphenol intervention studies in the Phenol-Explorer database were included in the analysis  [7]. Publications were required to meet the following base criteria: (i) be intervention studies using a single or repeated orally administered dose of a normal food source, an experimental food (e. g. , food extract, dried, and powdered foods) or an oral supplement; (ii) be conducted in vivo on disease-free humans or animals; (iii) use an appropriate analytical technique capable of reliably identifying metabolites; (iv) detect or quantify at least one polyphenol metabolite in urine or plasma. In brief, separate "interventions" were identified from each publication, where one intervention was defined as the administration of a given dose of polyphenol source (or control) to a single species, with subsequent collection of biofluids.
Data analysis Phenol-Explorer tables on metabolites detected, metabolite concentrations, plasma pharmacokinetics, and urine pharmacokinetics were exported and data analyzed using Microsoft Excel and R statistical software (http://www. R-project. org/). Chemical similarities, based on Tanimoto scores, were calculated for each pair of Phenol-Explorer metabolites using the PubChem structure clustering tool. The MetaMapp tool (http://metamapp. fiehnlab. ucdavis. edu) was used to format and filter the PubChem similarity output matrix, associating only pairs of metabolites with similarity scores >0. 7  [14]. The matrix was mapped to a network graph using Cytoscape open-source software  [15]. The metabolic transformations of pure compounds administered to humans and animals were visualized using Circos open-source software  [16].
Median T max was much shorter in rat than in human (median time = 0. 71 versus 2. 18 h). In rat, peak concentrations usually occurred within 1 h of polyphenol administration and few values >5 h were observed (Fig. 3B ). In humans, T max of flavanone and isoflavone metabolites was usually in excess of 5 h (Fig. 3C ). For most other subclasses, T max was usually less than 2 h. Seventy-one plasma half-life values were compiled for the Phenol-Explorer database. Median half-life for all polyphenol metabolites in humans was 2. 8 h (interquartile range = 4. 05 h). The proportion of dose excreted in urine was measured for 80 compounds until 24 h or more after administration. For extracts incubated with either ␤-glucuronidase or sulfatase to group all conjugates with a common parent polyphenol, proportion of dose recovered could be used as a measure of overall bioavailability.
Discussion Biological databases are essential for the efficient retrieval of relevant information from large numbers of scientific publications  [17]. In the present study, all polyphenol metabolites reliably identified in human and animal biofluids and described in literature were collated and regarded as the experimentally determined polyphenol metabolome. This is only a small subset of all metabolites theoretically possible, since coverage is governed by research design and analytical capabilities. For example, less glucuronides and sulfates and more aglycones and esters were described in intervention studies included in Phenol-Explorer than might be expected. Conjugates are indeed difficult to identify and the metabolites whose position of conjugation were unknown were not included in Phenol-Explorer.