dataesr/scientific-paragraphs-categorization huggingface dataset:
DBy : y = y 1 ,. , y n = f (p 1 ),. , f (p n ) ∈ n. (4) The database DBp is initialized by a Latin hyper cube sampling method  [24]. To build the estimator ŷ of the Gaussian process F x , m and σ 2 are estimated by a mean least square computation using the following equations: m = 1t C -1 y 1 t C -1 1 , (5) σ = y -1m t C -1 y -1m n. ( 6 ) 1 is the unit vector of size n, 1t the transpose unit vector, and C the covariance matrix defined in Eq. (  7 ). C = corr p i , p j i, j ∈[1,n]. (7) The covariance matrix is built using the correlation function: corr x, x , θ, τ = exp - d i=1 x i -x i θ i 2 τ. (8) The function given in Eq. (  8 ) evaluates the correlation between 2 points x and x , which are 2 points of the space X. Gaussian functions are often used in the analysis of computer design experiments as correlation functions.
Sequential optimization strategies To describe the optimization procedure, we assume that a master points database and the associated objective function values are given. The goal of the meta-model is to define new points that need to be computed in order to enrich the meta-model and, in the end, to find the global minimum of the optimization problem. Using a Kriging meta-model gives two intermediary results: a mean prediction ŷ(x) of the Gaussian process F x (Eq. (  9 )) and a variance prediction σ2 (Eq. (  10 )). These two results can be used to solve the global optimization problem. The mean and variance predictors are used for exploitation and exploration stages: • Exploitation is the improvement of a local minimum that has already been detected. The new master point to be computed is obtained by minimizing the mean predictor function: p n+1 = min x ∈X ŷ(x).
The q-EI criterion is approximate thanks to an iterative method. The main idea of the iterative q-EI approximation is to set the value of the interesting extracted points to a virtual value. Then, a new meta-model is built using the master point database enriched by the virtual point, which is considered as known. Finally, a new point of interest is extracted according to the virtually enriched master point database. This procedure is performed iteratively while the maximum number imax of "black box" function calls is not reached. This procedure is described in Algorithm 3 and in Fig. 1. Moreover, the method is applied on a simple example in Fig. 1. In Algorithm 3, the q points of interest are extracted and a virtual enrichment is used. However, if one of these virtual enrichments is wrong, the following extracted point can be inaccurate.
2b ). The maximum of the EI is identified at x = 0. 4 (green point). • Virtual enrichment step: a new master point is defined at x = 0. 4 (where EI is found to be maximum at the previous step), and the value of the new master point is set to a virtual value according to the meta-model prediction (the KB approach is used here). With this new database of master points (which includes real master points and one virtual point, in green on the graph Fig. 2c ), the Kriging meta-model is updated and a new maximum of the EI is found at x = -0. 75 (green point in Fig. 2d ). This point becomes a new master point. • Final step: at this stage, two new master points have been proposed by the method, two calls of the black-box function can run in parallel to evaluate the cost function exactly for these two new points (green squares in Fig.
However, the cost dedicated to calibration is slightly higher for anisotropic Kriging. This additional cost was evaluated by measuring the CPU time for the Hartman 6D function. For a database of 50 points, isotropic Kriging takes 0. 031 s, while anisotropic Kriging takes 0. 37 s. Therefore, there is a factor of 10 in terms of additional costs. However, the absolute duration of anisotropic Kriging remains very low compared to the time spent for the "black box" function calculation. This is particularly true when using finite element computations. The use of an anisotropic correlation for Kriging thus induces a significant reduction of the CPU time for a given optimization problem.
As an example, if a given problem is solved with the sequential version of the algorithm in 30 iterations (T s = 30), the same problem is solved with parallel version in 15 iterations (T p = 15). Therefore, the obtained speed-up is S p = 2. The number of parallel tasks p is set to 6 values p ∈  [1, 2, 3, 4, 8, 16]. Two different types of virtual enrichments are tested: KB Eq. (  16 ), and CLmin Eq. (  17 ). An anisotropic correlation is used because of the results of the previous section. The results obtained with the CLmax criterion are not presented. This criterion leads to bad speed-ups at best, or at worst no results are obtained. This can be explained by the introduction of a pessimistic point in the master points database during the virtual enrichment phase. Two close points can have very different values (minimum and maximum of the database in the case of the CLmax criterion).
Influence of the initial size of the data base In previous tests, the databases of master points were always initialized with a three level full factorial design plan (i. e. , a regular 3-by-3 grid of points) for the modified Branin-Hoo function. In this section, the influence of the size of the initial plan on the speed-up is studied. Two additional full factorial plans are tested  [35] : a two-levels full plan (i. e. , a regular 2-by-2 grid of points), and a four-levels full plan (i. e. , a regular 4-by-4 grid of points). For these tests the KB virtual enrichment is used. The speed-up is calculated thanks to (Eq. (  21 )). The Ts values are presented in Table  2. It appears that the use of different initial data base sizes leads to different convergence rates for the method. The larger the size of the initial database, the smaller the number of iterations required for convergence.
The speed-up results are shown in Fig. 6. Some remarks can be made: • For all three database sizes, the parallel algorithm is always efficient. In fact, the speed-up is always greater than 1. • The three-levels plan and two-levels plan shows good and equivalent speedup. Therefore, using a larger initial database may not be the best option. In fact, it seems that the four-levels plan has a lower speed-up than the three-levels plan. It can be seen that the choice of the initial data base has an impact on the speedup. The tests on the modified Branin-Hoo function show that a full three-levels plan is a good choice for this function. The decrease in efficiency with the increase in the initial database size can be explained. Indeed, the points added during the minimization procedure are placed by maximizing the expected improvement, whereas the full plans are not optimal regarding the expected improvement criterion.
In addition, these results are always validated when other correlation functions or initial database sizes are considered. In fact, the test on the modified Branin-Hoo function shows that the initial database size does not have a significant impact on the speed-up. A third test is performed on a realistic identification case. The identified parameters give a numerical load-displacement curve in accordance with the experimental curve. The optimization algorithm shows its efficiency on a realistic application. Moreover, the Kriging surfaces are very interesting in order to evaluate the sensitivity of each identified parameter on the objective function. These surfaces can help defining more appropriate objective functions or new observable to improve the efficiency and accuracy of the identification procedure.
DBp is the database of parameter set, DBp ∈ X 3: DBy = f (DBp) // "black box" function call. DBy is the database objective function value associated to parameter set 4: While (i < i max) do 5: Meta =MetaModeling(DBp,DBy) // meta-model generation and calibration 6: newDBp =argmin(Meta) // exploration and exploitation 7: DBp = DBp ∪ newDBp // data enrichment 8: DBy = DBy ∪ f (newDBp) // "black box" function call 9: i = i + 1 10: end.
Typically, big data requires to collect a large amount of measurements to create an intelligent manufacturing system  [4]. For instance, predictive maintenance helps to replace hardware before a failure/breakdown targeting to decrease the shutdown time  [5]. We here focus on the end-devices of the Industrial Internet of Things, that use wireless transmissions to send/receive their packets. To reduce the deployment cost, some of the devices may forward the packets from others in order to reach a gateway with a wired connection. The devices are battery-operated and need to turn off regularly their radio chipset to save energy. Thus, a transmitter must know when the receiver will schedule its next wake-up. In the rest of this article, we will use the term node to designate all these devices, that generate and forward data packets.
A. Simulation Setup We use here the 6TiSCH Simulator  [23] , a discrete-event simulator written in Python. We consider the following scenarios: 1) Constant Bit Rate (CBR) flows 2) Topologies with a variable number of nodes (by default 39 nodes and one border router) to assess the scalability of our Scheduling Function We generate random topologies, where each node is randomly located in an area of 2000X2000 m 2. Thus, each node has at least 3 neighbors. The propagation model of 6TiSCH Simulator is based on the Pister-Hack model  [24]. Table II regroups all the values of the different parameters. We extended the 6TiSCH Simulator with three additional scheduling functions (LDSF, LLSF  [18]  and Stratum  [15] ). For the LDSF and Stratum algorithms, each node has to know its hop distance from the border router.
The same cell is used every two blocks for transmissions and retransmissions. Since each block comprises 5 timeslots in LDSF, the transmitter has to wait on average 10ms * 5 timeslots * 2blocks = 100ms. Our implementation (simulation code, scripts, and raw data) is freely available     (https://github. com/vkotsiou/Scheduling for the implementation, and https://doi. org/10. 5281/zenodo. 3748712 for our dataset).
Implementation and evaluation We have implemented the abstract domains and transformers presented above in the constraint solver AbSolute  (Pelleau et al. , 2013) , which is programmed in OCaml and available online 6. Our experiments are replicable and all the results are also publicly available. One of our design goals was to keep the solver as close as possible to its underlying theory. To achieve this goal, we relied on OCaml functors, such that each domain transformers is a functor parametrized by its sub-domains. See Appendix A for an example of the OCaml code modeling the abstract domain FJS 1. The experiments are all performed on an Intel(R) Xeon(TM) E5-2630 V4 running at 2. 20GHz on GNU Linux. We evaluate three solvers: AbSolute v0. 10, GeCode v6. 1  (Schulte et al. , 2019)  which is a state of the art propagation-based constraint solver, and Chuffed v0.
Each solver is run once on each instance for a maximum of 10 minutes. The results are exposed in Table  1. For each solver, we read in column ∆ LB the percentage of how far is the obtained solution from the best known lower bound. For example, the value 66 in bold in Table  1  indicates that Chuffed found 66 strictly better bounds than GeCode. Firstly, although AbSolute is only a prototype, we observe that on edata it finds 36 bounds that are better than the ones found by GeCode, and 23 bounds better than Chuffed. This demonstrates that communication between domains brings a computational advantage. For data sets with more machines, the efficiency of AbSolute drops behind the other solvers. This is because we do not treat machines in a special way in contrast to GeCode or Chuffed that use a cumulative global constraint.
* This work was partially supported by  ANR-15-CE25-0002  Coverif from the  French Agence Nationale de la Recherche. The  Centre de calcul intensif des Pays de la Loire (CCIPL)  provided the infrastructure to perform the benchmarks. The authors thank the anonymous reviewers for their constructive comments to improve the clarity of the paper. We thank  Yinghan Ling  for the English proofreading.
The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11. 28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.
Two prominent databases are available which supply weather forecasts to the public for general use. The European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS)  [7]  and the Global Forecast System (GFS)  [8]  produced by the National Centers for Environmental Prediction. These two global databases provide a wide range of meteorological forecasts. Although these forecast providers offer reliable and readily available data, a need arose for intermediary (secondary) services that take data from these sources and apply some techniques to improve the accuracy and reliability. Many of these intermediary weather services exist  [9]  and are used by energy managers for solar installations, wind installations, meteorologists, sailors, farmers, weather enthusiasts, and many more.
Materials and Methods Ground station measured data has been taken from strategic locations along the SSC route. The chosen locations were Pretoria (University of Pretoria), Bloemfontein (Central University of Technology), Port Elizabeth (Nelson Mandela University), and the Cape Town area (Stellenbosch University). The ground station data was obtained from a public data source provided by the Southern African Universities Radiometric Network (SAURAN)  [20] , in Stellenbosch South Africa, where data for 2018 to the end of 2019 was requested from this public source. This data source stores hourly averaged measured values for a variety of meteorological variables; however, only the GHI and TCC components were of concern for this work. SAURAN has a total of 23 ground measurement stations across South Africa.
The limitation of a maximum allowed daily data point requests (as imposed by the forecast provider Meteomatics AG) limited the Application Program Interface (API) requests to four locations in South Africa only. All four cities are major stopover locations for the SSC event and span the entire route from start to finish. Forecast data was made available by Meteomatics AG. Their forecast data originates from the ECMWF database; however, Meteomatics AG improves the forecast before making it available to its users. An API script was developed and used over two years (2018-2019) to request daily the GHI and TCC forecast for each of the four locations spanning a nine-day horizon. This data was then stored in a multi-dimensional matrix format (Matlab by MathWorks in Natick, Massachusetts, USA) with an appropriate date and location information stamp for identification.
At the end of the API script, an email is sent automatically to the researcher daily to verify that the data was received as expected. After all the forecast data had been captured for the two years, the SAURAN ground station-based data were imported into the Matlab environment with some initial pre-processing of the data. The data before 6:00 and after 18:00 was removed each day. This reduced the risk of having bias results as nighttime GHI data is not statistically meaningful (near zero values). The forecast, as well as measured data for each of the four locations, were then analyzed and compared. To increase the local GHI forecast performance (compared to the raw ECMWF and the improved Meteomatics AG forecast), a MOS correction was applied to the data in the form of a simple multiplication scaling function.
Energy Forecasting for a Solar Car in South Africa The solar car team from Tshwane University of Technology (TUT) participated in the SSC 2018 with a solar car named Sun Chaser III (SCIII). The team had developed an energy simulation platform to assist the energy manager in making informed decisions in regards to the speed and distance they should travel each day based on the dynamics of the car, the route to be driven, the weather conditions, and other characteristics such as remaining stored battery energy. The authors  [30]  showed simulated SoC results compared to real-life recorded data during the SSC 2018 for SCIII for five of the eight days. The remaining three days' data was not recorded due to technical difficulties. Figure  4  shows the SCIII solar car followed by its telemetry support vehicle responsible for recording and analyzing data during the SSC 2018  [30].
Double strand sequencing of PCR products was performed by Eurofins (Germany). Sequences were analysed with the CLC Main Workbench 20 software. The 3D7 strain of P. falciparum (PF3D7_1343700) was used as the reference sequence, to identify polymorphism. All electropherograms were visualized to detect isolates with mixed alleles that were considered to be mutated for the purpose of mutationfrequency estimation. The quality control was assessed by including three blinded quality-control samples in each 96-well sequencing plate.
Frequency of mutations in the Pfkelch13 gene A total of 192 amplicons were sequenced to screen for mutations. Among the 187 interpretable sequences, four mutations (2. 14%) were detected, including one non-synonymous mutation (Y653N, 0. 54%) and three synonymous mutations (C469C, D464D, A627A, 1. 6%) (GenBank Accession Numbers: MT434117-MT434120). The Y653N mutation was observed in a sample collected in 2019 at the CPB (Table  2 ). The Y653N mutation is located on blade 5 of the Pfkelch13 propeller domain (Fig. 2 ). The model predicts that the substitution from aromatic (Y) to polar, non-charged (N) residue at 653 position.
Multiobjective optimization based Backtracking Search Algorithm In this paper, BSA, which was initially developed for singleobjective optimization problems only, is extended to handle MOPs. This development maintain the BSA's spirit and allows a good efficiency with great benefit from new capacities of exploration of the search space and of an adapted search direction matrix due to the adaptation of BSA's main strategies and algorithm simplicity. The main development should concern the fitness values assigned to individuals in the population. Algorithm 1 gives the proposed pseudocode of BSAMO (Backtracking Search Algorithm for Multiobjective Optimization). It integrates BSA's mutation and crossover operators presented in  [24]  and the fast non-dominated sorting and the crowding distance of Deb et al.
FLUENT and MOP coupling process The flowchart in Figure  6  shows the multiobjective optimization process of CFD problems. The optimization work was entirely automated via a main script written in MATLAB. First of all, starting the Multiobjective optimization process begins with the script generation of an initial population using a uniform random distribution function. Then multiple sets of decision variables (i. e. , multiple geometries) are submitted and wait for each individual objective functions values before proceeding further. For changing the input parameters to compute the aerodynamic responses taken as objective values of the new shape, MATLAB calls FLUENT software and then it returns back those values to the optimization process for executing the multiobjective algorithm and displaying the optimal results.
Conclusion In this paper a workbench project, including FLUENT, has been used to compute the transonic, compressible flow over a NACA0012 airfoil. The implicit density based solver with solution steering was employed and the computed results have been compared to published experimental data and good agreement was achieved. A case comparison has been carried out within CFD Post to compare the pressure fields at Mach 0. 5 and Mach 0. 7. Then the drag minimization and lift maximization study was carried out for the airfoil NACA0012 by using BSAMO and NSGA-II coupled with FLUENT. The more challenging Navier-Stokes computation was carried out to demonstrate the reliability and robustness of multiobjective optimization algorithms based on a main script written in MATLAB and its successful coupling with the CFD software.
IPIN 2019   Competition, sixth in a series of IPIN competitions, was held at the CNR Research Area of Pisa (IT), integrated into the program of the IPIN 2019 Conference. It included two on-site real-time Tracks and three off-site Tracks. The four Tracks presented in this paper were set in the same environment, made of two buildings close together for a total usable area of 1000 m 2 outdoors and and 6000 m 2 indoors over three floors, with a total path length exceeding 500 m. IPIN competitions, based on the EvAAL framework, have aimed at comparing the accuracy performance of personal positioning systems in fair and realistic conditions: past editions of the competition were carried in big conference settings, university campuses and a shopping mall. Positioning accuracy is computed while the person carrying the system under test walks at normal walking speed, uses lifts and goes up and down stairs or briefly stops at given points.
The Octave application Evaalscore is used to produce all the statistics, graphical output and a movie representing the path and estimation errors, useful for organisers and competitors. The source code of Evaalscore and its output computed over the competitors' results is available at http://evaal. aaloa. org/2019/detailed-results. StepLogger runs in the foreground, while the competing app which estimates the device position acts as a background service. The integration between the two apps exploits the AIDL formalism (Android Interface Definition Language). The AIDL file defines the interface IStepLoggerService which implements the logPosition(timestamp, x, y, z) method. The competing app can discover and bind to a provider of IStepLoggerService by following two simple steps: • create an Android Intent with BOUNDSERVICEPAC KAGE set to an BOUNDSERVICECLASS set to StepLog-gerService; • invoke the bindService(.
) method in order to bind to the Android service implementing the IStepLoggerService interface. The StepLoggerClient app is a development aid mimicking the expected behaviour of a competing app. Last, StepLoggerV2 is a modified version of StepLogger which runs in the background, thus allowing the competing app to run in the foreground. Figure  2  shows a screenshot of StepLogger, StepLoggerClient and StepLog-gerV2, respectively. The software package is available at http://evaal. aaloa. org.
IV. TRACK 1 -SMARTPHONE-BASED (ON-SITE) A. TRACK DESCRIPTION The purpose of the first on-site Track, namely ''smartphonebased'', is to assess and measure the ability of competing systems to accurately identify their position inside a large, public indoor area using a hand-held smartphone only. To this purpose, competitors are requested to develop a smartphone application able to estimate indoor positions in real-time. Only one commercially available smartphone per competitor can be used. The competing app must run entirely on the smartphone and can use any built-in sensor available (i. e. no access to remote services like external databases or remote servers is allowed). The app is integrated with the measurement app (see section III-C. Competitors are provided in advance with a detailed map of the area, while the chosen path for the competition is disclosed only when the competitors start the trial.