dataesr/scientific-paragraphs-categorization huggingface dataset:
The set of solutions is presented to the decision-maker by the designer for the selection of optimal solutions. The methodology we propose is presented in the next sub-section. The requirement and structure model are adapted for the optimization, including objective function definition, variability and constraints. We assume that the system design is done using the SysML language. Also, a component repository is available including parameters for objective functions. All the SysML diagrams of this paper are built up with the Papyrus tool from CEA  [7]. In a preliminary step, the requirements are captured using requirement diagrams. Architecture requirements are taken into account. This allows to express constraints and to add traceability between requirements and architecture elements.
Problem statement Optimization models have been developed to select software or hardware components and redundancy levels. Most of them are formulated using zero-one variables. The system (see Figure  11 ) consists of independent subsystem S i. S i is associated to a given block with instance variability (the VideoSensors aggregation in Figure  11 ). Subsystem S i is composed of components selected in a repository of components C i. C ij represents the component number j in the repository C i. Each selected component has a position k in the final subsystem S i , after the problem resolution. Figure  11  shows there exists two possible positions for a selected component in the final subsystem. Table  1  gives the association with SysML model elements:.
RELATED WORK In recent literature, there are approaches on the integration of SysML with external analysis tools and solvers. One of them  [14]  is Paramagic for integration of SysML and Modelica, Matlab and Mathematica. However these approaches lack support of multi-criteria optimization that help designers to perform design space exploration and trade-off analysis. The approach proposed by P. Van Huong  [3]  and Spyropoulos  [4]  allows the user to perform multiple analyses in the same environment. These approaches are adapted to the component parameters optimization like CPU frequency or memory size, not to the architecture composition and redundancy problem we want to address. In  [8]  an optimization technique is proposed for a microwave module design, with combination of alternatives for part modules, but without redundancy constraint.
IV. SIMULATION RESULTS AND DISCUSSION The simulation has been carried out using Matlab Simulink package, under a wind speed profile whose mean value is (11. 5 m/s). The evaluation tests have been done with the wind speed input defined by Fig. 8 , got from a Matlab Simulation. The system parameters are given in the appendix A. The MPPT controller ensures the tracking of the optimum power points at variable wind speeds, by maintaining the power coefficient to its maximum value as shown in Fig. 9    The control algorithm based on MEPO and TSR achieve the highest average value of to an approximate value of 0. 480. By comparison, the SMC gives an average value of , which is approximately equal to the value obtained from the P&O for (x=0. 1) control. Since the average value of of the classical P&O for (x=5) method is 0.
Quantitative analysis of fusion index The evaluation of myoblast fusion was determined as previously described. 28 At various times after inducing differentiation, cells were fixed, permeabilized, and stained for myosin, as described earlier, and nuclei were stained with Hoechst. The extent of fusion was determined as follows: fusion (%) = (number of nuclei in myotubes)/ (total number of nuclei in myoblasts and myotubes) × 100. At least 10 random fields (100× final magnification) were analyzed per experiment, using the ImageJ software. The number of myosin positive myotubes per field with five or more nuclei was also quantified.
Figure7NP uptake enhances apoptosis required for myoblast fusion. Notes: (A) Representative annexin V-PE/7AAD labeling pattern of myoblast populations in HS medium 24 hours (h) after NP exposure or not (control), in the absence and the presence of calcium. The population positive for annexin V and negative for 7AAD (Q4) was used to quantify PS exposure at cell surface. (B) Exposed myoblast population positive for annexin V analyzed after 6 h in HS, 24 h in HS, and 24 h in FBS, followed by 24 h in HS. (C) Percentage of cells positive for cleaved caspase-3 analyzed after 24 h in HS and 24 h in FBS, followed by 24 h in HS. Data are expressed as mean ± standard error of the mean from three independent experiments (**P,0. 01). Abbreviations: NP, silica nanoparticle; HS, horse serum; FBS, fetal bovine serum; 7AAD, 7-amino-actinomycin D; PE, phycoerythrin; PS, phosphatidylserine.
Acknowledgments This work was supported in part by  TiSiTrans  ( PNR-EST-2010/2/79 ),  ANSES (Agence nationale de sécurité sanitaire de  l'alimentation, de l'environnement et du travai) grants, and  Conseil Régional d'Aquitaine  (20121301007) grants. We acknowledge the  Bordeaux Imaging Center  for use of the confocal spinning disk system and  V Pitard  at the cytometry.
Acknowledgements  N. Sabaté  thanks the  Ramon y Cajal postdoctoral program  of the  Spanish Ministry of Science and Innovation  for financial support. Support by the  GICSERV program , project  NGG-225  and by the  MICAELA  project ( TEC2009-14660-C02-01 , funded by the  Spanish Ministry of Science and Innovation  and the  FEDER program ) is gratefully acknowledged.
Acknowledgements This work was partly supported by the  Agence Nationale de la Recherche  through the  ANR   TOPWAVE  project, the  Labex CEMPI and Equipex FLUX  through the  Programme d'Investissement d'Avenir , by the  French Ministry of Higher Education and Research , the  Nord-Pas de Calais Regional Council  and  Fonds Européen de Développement Régional (FEDER)  through the  "Contrat de Projets Etat Région  (CPER) 2007-2013" and the " Campus Intelligence Ambiante (CIA) ", and by the by the  Italian Ministry of University and Research (MIUR)  under Grant  PRIN 2012BFNWZ2. We are grateful to  Francois Copie  for producing Figure 1.
Number of observations the prediction errors, we randomly select 20% of the entries as a test set, and the remaining entries are split between a training set (80%) and a validation set (20%). For this dataset, ratings range from 1 to 5. To consider the benefit of a binomial model, we have tested each rating against the others (e. g. , ratings 5 are set to 0 and all others are set to 1). These results are summarized in Table  4. For the multinomial case, we find a prediction error of 0. 59 for the logistic model against a 0. 63 for the Gaussian one. 4  Binomial prediction error when performing one versus the others procedure on the MovieLens 100k dataset.
Acknowledgement This contribution has been prepared under project No. 2/0035/15 : Development trajectories of localities and regions -product of sector and spatial policies, territorial capital and decisions, financed by the  VEGA Grant Agency. The authors used the results of the project No. WTSL. 02. 01. 00-14-087/08INFRAREGTUR , co-financed by the  European Union's European Regional Development Fund  and by the state budget within the  Cross-Border Cooperation Programme, Republic of Poland -Slovak Republic ,  2007-2013.
An improved database for fast diffusion paths was obtained. The diffusion parameters in type C kinetic on fast paths in a-Zr for Cr and Co were used. Simulated diffusion profiles were compared with previous experimental results. This comparison and the adequacy of the improved database are discussed. Diffusion parameters for fast diffusion paths in Zr at low temperature were assessed for Co and Cr. Diffusion profiles on grain boundaries in a-Zr for Cr and Co are presented in the temperature range of 380e460 K.
Several of these changes are associated with the change of position of atoms in the lattice and one of the important mechanisms for this is diffusion. In other technological sense, the CANDU (CANada Deuterium Uranium) reactor uses hundreds of pressure tubes (Zr based alloys) to contain the fuel and coolant. These tubes are in contact with alloy steels (with Cr, Co and other possible elements) at 300 C under normal conditions. Finally a diffusion process is suggested between the Zr based alloys and the alloys steel with metallurgical process associated: segregation, hydrogen absorption, etc  [2, 3]. The study of the diffusion of different alloying elements as well as the possible impurities present in a multi-component system is useful in the characterization of the mobility of such atoms in the studied alloy and thus supports the development of kinetic databases.
These databases are useful in the prediction of atom movements caused by chemical potential gradients such as, for instance, phase transformation, generation of concentration profiles at different times and temperatures, growth of oxide layers, etc. Besides bulk diffusion, grain boundary diffusion can also be important. The relative importance of both bulk and boundary diffusion depends, among other aspects, on the temperature of the material  [4]. A grain boundary (GB) is defined as the transition region between two adjacent crystals in a single-phase material, which are in contact and only differ in the crystallographic orientation. Most boundaries are considered as a two-dimensional zone (approx. 5e10 wide) and highly disordered compared with the adjacent crystallographic regions. This classical definition applied to the socalled "high angle" or "incoherent" grain boundary.
Kinetic In the presence of a concentration gradient, a net flux is established for the specie evaluated. The law which relates the flux and the concentration gradient is Fick's first law. Considering a case at constant temperature and pressure, on a given phase of an alloy, with the k specie diffusing on z direction, it is expressed as: J k ¼ ÀD k vc k vz The basic model for only one active diffusive phase was used in this work. In this case, the flux in a multicomponent system is defined as: J k ¼ À X nÀ1 j¼1 D n kj vc j vz (1) where equation (1) is solved using numerical methods  [12]. DICTRA  [13]  software was used for the corresponding numerical simulations based on the databases generated with the diffusion parameters previously established in different kinetic regimes  [2, 14].
Thermodynamic For pure elements, the following thermodynamic model is normally used to describe the Gibbs energy at sufficiently high temperatures  [15]. G m À H SER m ¼ a þ b$T þ c$T$lnðTÞ þ X d i $T i where: G m À H SER m is the Gibbs energy relative to the Standard Element Reference (SER) state. H SER m is the enthalpy of the mechanical mixture of the pure elements in their stable phase at T ¼ 298. 15 K and P ¼ 10 5 Pa (SER), and is normally taken as zero for pure elements. The coefficients a, b, c, and d i are model parameters. In the case of the solution phases and compounds with various elements the following Gibbs free energy expression is used: G ¼ G þ id G mix þ xs G mix þ G mag where: G is the Gibbs energy due to the mechanical mixing of the phase constituents, id G mix is the ideal contribution to the mixing Gibbs energy, xs G mix is the excess mixing Gibbs Free energy, G mag is the magnetic contribution to the Gibbs free energy for ferromagnetic or antiferromagnetic phases Developing a database for the composition and temperature dependence of all the D n kj , including the cross terms can be very complex.
To avoid this complication a database of mobilities is created. The diffusion coefficients used for the simulation are the product of a thermodynamic and a kinetic factor. The thermodynamic factor is basically the second derivative of the Gibbs energy with respect to concentration and the kinetic factor contains the atomic mobilities. For a detailed discussion see Refs. [17, 18]. Thermodynamic data used for GB diffusion are the ones corresponding to the materials volume, then the thermodynamic factor that affects the diffusion coefficient may not be exact for GB. <10 <25 <10 Element N N a N b N i O P P b S i S n T a T i U V W <20 <5 <50 <36 423 <3 <25 <10 <10 <50 <25 <1 <25 <25.
Numerical vs analytical solutions for GB diffusion Diffusion parameters used for GB kinetic databases were those obtained using type C kinetic: the diffusing element moved exclusively along the GB and was confined to it. This type of diffusion presents analytical solutions equivalent to those applied to volume diffusion. In particular, in the case of quick path diffusion, the conditions imposed by analytical solutions are not compatible with technological processes, hence numerical solutions are more adequate. Fig. 7  compares both solutions: at the same temperature, both solutions are equivalent for short periods of time. For larger periods, numerical solution results in a larger concentration on the right end of the curve due to the lack of the semi-infinite condition imposed in the analytical solution.
Results The validity of the above linear program has been tested on a set of small examples (including the one presented in Fig. 2 ) randomly generated. The linear model is solved using CPLEX 12. 4 solver. In Fig. 3  is presented the Gantt diagram of a problem without any energy consideration and the Gantt diagram of the same problem extended with energy consumptions and an energy threshold varying over time. In this figure, it is also plotted the energy consumed by each operation to show the impact of the threshold on the schedule. On Fig. 3  it can be stressed that third operation of J3 is the most consuming one and can only be scheduled during a given time-window, thus all its predecessors on the job must be scheduled before. The other operations start only if enough energy is available.
The area under the curve (AUC) was calculated for all parameters and compared in a Hanley-McNeil test. Sensitivity, specificity, negative and positive predictive values, negative and positive likelihood ratios and the percentage of correct classification were calculated after defining a cutoff value. The threshold for statistical significance was set to p < 0. 05. Statistical analysis was performed with MedCalc software (version 12. 2. 1, MedCalc Software, Mariakerke, Belgium).
Spectral measurements and simulations The transmission (T) and reflection (R) spectra of the HPPS and NPA were measured at normal incidence with the 20/20 PV dual microspectrophotometer from CRAIC Technologies, on sample areas of 100 × 100 µm 2. The normalization of the spectra was performed with respect to the glass substrate. The reflection spectra were also measured at various incidence angles with respect to the sample's normal, using a Lambda 950 Perkin Elmer UV/VIS/NIR spectrometer equipped with the Universal Reflectance Accessory. The absorption (A) spectra were deduced from the relation A = 1 -R -T. The T, R, A spectra of both types of structures were simulated by solving Maxwell's equations using the threedimensional finite-difference time-domain method, implemented in the freely available MEEP software package  [34].
These features correspond nicely with the existence of an LSPP mode at this wavelength. However, the small difference in optical response between simulated and experimentally obtained data is due to the fact, that the simulated optical response are calculated assuming the perfect defect free structure/array. We also performed similar spectral measurements and simulations on the Au NPA, see right panels of figures 2 and 3. The optical properties of metallic nanoparticles are influenced by particle size, shape, environment and, possibly (i. e. if close enough) by their mutual orientations. The minimum transmission exhibited at around 1 µm, that coincides with an absorption maximum, suggests the oc- curence of an LSPP resonance. This absorption peak is rather broad. One might think, at first glance, that the large width could be due to a disordered arrangement of the pillars in the array, or to the pillars varying slightly in size or shape.
Acknowledgement: This research was supported by the  Interuniversity Attraction Poles program  of the  Belgian Science Policy Office , under the grant  IAP P7-35  photonics@be, by the  F. R. S. -FNRS , and by the  Région Aquitaine (France). L. O. acknowledges financial support from the  Fonds David et Alice Van Buuren. B. K. acknowledges financial support from the  Smart  film grant 830039 in the framework of  Convergence Project of the Region Wallonne and action de Recherche Concertee  ( BIOSTRUCT  project) of the  University of Namur (Unamur). BK additionally acknowledges the support from the  Nanoscale Quantum Optics COST -MP1403 action.
Independent component analysis (ICA) Principal component analysis, Factor Analysis, Projection Pursuit are some popular methods based on linear transformation. But ICA is different because it looks for the components in the representation that are both statistically independent and non Gaussian. ICA separates statistically independent components, which are the original source data, from an observed set of data mixtures. All information in the multivariate data sets are not equally important. There is often a need for extraction of the most useful information. ICA extracts and reveals useful hidden factors from the whole data sets. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. Contrarily to PCA, the components are not imposed to be orthogonal.
METHOD The k-Nearest Neighbor (NN) algorithm is very intuitive. It starts from a training set for which we have the class labels. In order to make a prediction about a new observation, one looks at the labels of its k nearest neighbors and uses a majority vote to make the prediction (Fig. 1 ). As the number of neighbors used in making the prediction increases, the decision boundaries become smoother, the bias increases, but the variance decreases. Ball et al. (2007)  explored the k-Nearest Neighbor technique for determining photometric redshifts in petascale databases using 55 746 quasar spectra from the SDSS. The algorithm is trained on a representative sample of the data. The main result is that the comparison between the photometric and the spectroscopic redshifts shows no region of catastrophic failure where the two derived values differ a lot, contrarily to other methods used to derive photometric redshifts.
The program SplitTrees4  (Huson and Bryant, 2006)  permits to construct outer planar networks from a distance matrix. In practice, the perfect order is not known or not feasible. The difference between the perfect order and the order one obtains with a given data set is called the contradiction. The minimum contradiction analysis  (Thuillard, 2007 (Thuillard, , 2008) )  finds the best order one can get. It is a powerful tool for ascertaining whether the parameters can lead to a tree-like arrangement of the objects  (Thuillard and Fraix-Burnet, 2009). Using the parameters that fulfil this property, the method then performs an optimisation of the order and provides groupings with an assessment of their robustness. We believe that outer planar networks will gain importance in applications outside of biology as they furnish a real alternative to the standard classification methods.
Comparison of results is a valuable task since it brings a lot of information on the nature of the data, the objects and their parameters. Also a classification is never definitive, and necessarily evolves with our knowledge and the discovery of new objects. We wish to end this review with the cluster validation question. This is an important issue in clustering and classification. In general, cross-validation and bootstrap techniques are rather easy and provide good estimates of cluster robustness. Some other validation indices are Dunns Validation Index  (Dunn, 1973) ,  Davies-Bouldin Validity Index (Davies and Bouldin, 1979) , C Index  (Hubert and Schultz, 1976)  and Silhoutte Validation Index  (Rousseeuw, 1987). Many more are given in the clusterCrit package of R. In most of the clustering algorithms, the number of clusters are user specified.
The farmers' field network Data were collected in 20 commercial fields located on 19 farms in the Bourgogne region (Burgundy) in France (Table  1 ). Fields were located in a 1500 km 2 area ranging from 46°54 0 to 47°16 0 N and from 5°02 0 to 5°46 0 E in east central France. The field survey was carried out during three growing seasons between 2009 and 2011. Water deficit (estimated as the difference between evapotranspiration and precipitation accumulated over the whole growing period from April to October) reached over the 3 years 241, 143 and 276 mm, respectively. This can be compared with a long-term annual average (estimated over the past 20 years) of 222 mm. All fields were located in the supply area of a cooperative society created in 2009 in Burgundy to commercialize pellets of M. giganteus, switchgrass and wood.
Shoot mass (SH_MASS) was estimated from the fresh mass of 250 shoots. Moisture content of aerial biomass was determined in a subsample of around one kg by oven drying for 72 h at 105 °C. Both types of yield provide complementary information: pYIELD is similar to experimental yields and can be related to yield components whereas cYIELD gives access to productivity data under real farming conditions and has a better ability to take into account field heterogeneity. In addition to yields and yield components, weed cover (WEEDS) was assessed on ten 0. 25 m 2 microplots located at random throughout the field at the end of winter (March): on each microplot, the percentage of weed cover was estimated visually. Establishment year. As mentioned above, crops were crushed at the end of the first growth year.
We therefore regarded maximal rooting depth (and as a consequence SWC MAX ) as constant during the growing season. ETR was defined as follows: ETRðjÞ ¼ K s ðjÞ Â K c ðjÞ Â ETPðjÞ; where, ETP(j) = potential evapotranspiration on day j; K c (j) = cultural crop coefficient on day j, defined as a function of degree-days  [Audoire, 2011) ; data from the French multisite experimental network REGIX) from the sugarcane K c as defined by the FAO  (Allen et al. , 1998)   (Itier, 1996). An indicator assessing the intensity of water deficit was derived from the above equation on a daily basis as follows: ]; K s (j) = stress coeffi- cient on day j; K s (j) = 1 if SWC(j) ≥ 2/3*SWC MAX and K s (j) = SWC(j)/SWC MAX otherwise WATERðjÞ ¼ ETRðjÞ K c ðjÞ Â ETPðjÞ ¼ K s ðjÞ: It was then averaged over the time period defined for each yield component.
Establishment year The same indicators of limiting factors were calculated to characterize the establishment year through the shoot density established at the end of the year (SH_DENS_1). The water balance computation was however, slightly different. First, as the above-ground growth of the crop is postponed and lower than that in the following growth years, we defined cultural crop coefficients suited to the establishment year based on French unpublished phenology data (INRA UR AgroImpact, personal communication). Unlike in growth years 2 and 3, we cannot assume maximal rooting depth to be constant during the first growing season: we assumed that rooting depth increased from 20 cm to the maximal rooting depth, which occurred during the establishment year (80 cm, as defined in section Measurements).