dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead, they focused on whether the reflected signal could occupy a three-dimensional region. For this reason, they defined a singularity index as the maximum of the two eigenvalue ratios. This singularity index gives an indication of the degree to which the matrix compresses incoming illuminant signals into either a two-dimensional or a onedimensional subspace of three-dimensional cone-excitation space. The higher this singularity index, the less three-dimensional is the space occupied by the illuminant signal in cone-excitation space. Our function ''si_calculator'' determines the singularity index based on the eigenvalues of the transformation matrices (see the MatLab functions section in the supplementary materials). The algorithm of this function is equivalent to the one of  Philipona and O'Regan (2006).
Panel a illustrates examples of daylight illuminants, with red, green, and blue curves corresponding to five examples from the Grenada database  (Romero et al. , 1997) , the Maryland forest illuminants  (Chiao et al. , 2000) , and the simulated daylight spectra  (Judd et al. , 1964) , respectively. Panel b shows the 12 illuminants F, where the red curves correspond to the semibroadbent illuminants F1 through F6, the green curves correspond to the broadbent illuminants F7 through F9, and the blue curves correspond to the narrow triband illuminants F10 through F12. Panels c and d illustrate 12 examples of random-spline and monochromatic spectra, respectively. The three curves in the graphs of the second row (panels e through h) show the first three principal components of the illuminants (red ¼ first component, blue ¼ third component).
Molecular analysis All samples (mosquito pools and human sera) were screened for the presence of WNV genome by one-step real-time RT-PCR using a commercial kit (West Nile Virus Real-TM, Sacace Biotechnologies). Positive samples were further tested by RT-PCR using primers VD8, FU2, cFD3 and WNV9368f, targeting the NS5 genomic region  [5, 28, 29]. When possible, a fragment spanning the envelope glycoprotein region (E) was amplified using primers WNVII 870f and WNVII 1630r  [5]. All amplicons were sequenced (3130 Genetic Analyzer, Applied Biosystems) and the resulting sequences were aligned with ClustalW, BioEdit version 7. 0. 5. 3  [30]. Maximum-likelihood phylogenetic analysis was conducted with Mega 6 software  [31] , which was also used for choosing the fittest nucleotide substitution model.
unguiculata mosquito collected in 2012 and 2013 (Figure  4 ). Similar strains have already been reported by Russian authors from this mosquito species, known to feed on amphibians, and from frogs collected in Volga Delta, as well as from Dermacentor marginatus ticks collected in the Caucasus  [32]. NS5 and E partial sequences described in this study are available in GenBank under the following accession numbers: HE984574, HE984575, HG328830, HG328831, HG514461-HG514468,HG918026-HG918037, LK022077-LK022085, LN808678 and LN808682.
Although there is a high degree of similarity (96%) between our sequences and those obtained from other isolates circulating in central and southern Europe in recent years, our sequences clearly clustered with the Volgograd 2007 isolate. This distribution of a distinct strain of lineage 2 WNV in eastern Europe may be related to the Mediterranean/Black Sea flyway of northward migrating birds, as it is documented that The analysis was conducted on a 460 nt sequence (positions 934-1,393 in isolate Reb_VLG_07_H, GenBank acc. no. FJ425721) using maximum-likelihood method, Tamura-Nei model, 1,000 bootstrap replicates. A sequence obtained from strain Q3574-5 (Cyprus, 1968; GenBank acc. no. GQ903680) was used as an outgroup. birds play a crucial role in the spread of the virus  [44]. The Volga and Danube deltas are also connected by autumn migration, and there is evidence that WNV may be introduced as birds travel to their owerwintering sites in Africa  [45].
Conclusion The neurovirulent strain Volgograd 2007-like of lineage 2 WNV has been circulating in Romania in mosquito populations and causing disease in humans since at least 2010, as shown by previous  [14]  and present findings. The distribution of this strain may be linked to the flyways connecting Africa to eastern Europe and the Danube and Volga deltas, followed by virus maintenance and endemic circulation in the region. Analysis was conducted on a 365 nt sequence (positions 9,479-9,843, in isolate LEIV-Krnd88-190, GenBank acc. no. AY277251) using maximum-likelihood method, Kimura 2-parameter model, 1,000 bootstrap replicates. A sequence obtained from a Japanese encephalitis virus isolate (GenBank acc. no. L48961) was used as an outgroup. Lineages were defined as previously proposed  [48].
A. Preliminary design of MaGIM As it can be seen, the MaGIM design is a complex task. There are many electromagnetic interactions between the different armatures which makes it difficult to have a simple sizing procedure like in conventional electrical machines. For a preliminary design, the machine and the gear sides are considered separately. First, the machine has been sized using the electrical and magnetic loading concept together with past experience from machine manufacturers  [5] -  [6]. A large number of physical and geometrical design parameters arises (~20). Constrained multiobjective Genetic Algorithms (GA) optimization has been used to deal with the preliminary design. The computations are carried out thanks to the NSGA2 Matlab implementation of GA  [7]. The objective functions are to maximize the efficiency and to minimize the machine active volume.
Many simulations are needed to achieve a good compromise between the optimal performances of the machine and the gear sides. In the final stage, finite element analyses are carried out to consider the overall system in which we need to accommodate the boost winding. The FE refinement is done manually but it doesn't take a long time since the GA preliminary design already gives a good starting configuration. We have used "MAGNET" software from INFOLYTICA to perform the FE computations. Fig. 2  presents the optimal Pareto fronts obtained for the designed MaGIM. The optimization time is very short for the machine side (~ 2 min) but much longer for the gear side (~ 2 hours). The optimal parameters for the final design are those given in Table I obtained with 2D finite element computations. The ferromagnetic parts are made from Silicon iron laminations (grade EN 10106, 0.
B. Introduction of the boost winding In order to study MaGIM when the boost winding is connected, a circuit-based model has been developed under Matlab/Simulink -Simscape SimPowerSystems. A dq model is used for the machine. The wound rotor terminals are connected to the ac side of the 3-phase diode rectifier whose dc side is connected to the boost winding (represented by its R, L parameters). Notice that a forward voltage of 0. 5 V and an internal resistance of 1 mΩ are assumed for the diodes. The boost winding resistance and inductance are those given in Table  I  (Rb, Lb) for 1 turn per slot. For Ndc turns per slot, the boost winding resistance and inductance become Rdc=Ndc 2 Rb and Ldc=Ndc 2 Lb, respectively. In the simulation studies, the torque developed by the machine is fixed to 970 Nm (rated power at 120 rpm).
Prðy i ! jÞ ¼ Fðs j À x i Þ; j ¼ 0; ::: ; J À 1 Prðy i ¼ JÞ ¼ 1 À Fðs jÀ1 À x i Þ x i ¼ x i b þ w k w k ; Nð0; r 2 Þ Inference was conducted in a Bayesian framework, which requires the specification of a set of prior distributions. We used diffuse priors for all parameters: b ; N p ð0; 10 8 I p Þ s 0 ; Nð0; 10 2 Þ Iðs 0 , s 1 Þ s j ; Nð0; 10 2 Þ Iðs jÀ1 , s j , s jþ1 Þ; j ¼ 1; :::; 2 s 3 ; Nð0; 10 2 Þ Iðs 2 , s 3 Þ r ; Uð0; 5Þ; where N p (l, R), is the multivariate normal distribution with mean vector l and covariance matrix R, I p is the p-dimensional identity matrix, and U (a,b) is the uniform distribution with lower limit a and upper limit b. The indicator function IðÁÞis notational shorthand to indicate that the cutoff points must follow the constraint: s 0 , s 1 , s 2 , s 3. All models were implemented in the R environment using the rjags package to access the JAGS sampling engine  (Plummer 2014).
We ran two parallel MCMC chains of 100,000 samples each. The first 50,000 iterations of each chain were discarded (burn-in) and the following 50,000 iterations were thinned (1 in 50) to yield 2000 samples from the posterior distribution. The Brooks-Gelman-Rubin criterion implemented in package CODA was used to check for convergence of the chains. Software code and data are.
ACKNOWLEDGMENTS We thank  Alexandra M. Schmidt  for discussion on an earlier draft of this work. A. Bertolo  acknowledges support by the  ''Fonds d'Animation de la Recherche '' of  UQTR  ( FAR Project  ' 'Ecologie et evolution des reseaux d'interaction en milieux aquatiques peu profonds ''). G. Lacroix was supported by the  French National Research Agency (ANR BLANC ,  PHYT-BACK  project  10-BLAN-1709-01 ). The preparation of the  International Programs For Scientific Cooperation  (project  PICS06763 ), supported by the  French National Center of Scientific Research , stimulated the publication of the paper. M. A. Rodrı  ´guez acknowledges the support of the  Natural Sciences and Engineering Research Council of Canada. A. Bertolo  and  G. Lacroix  conceived and conducted the experiments. M.
Positive control consisted of three cases of PGNT confirmed by Bridge and colleagues as described in the original article  [12]. Specimens were considered SLC44A1-PRKCA positive if signal fusion(s) were detected in more than 20 % of nuclei analyzed. Results were recorded using a DM600 imaging fluorescence microscope (Leica Biosystems, Richmond, IL) fitted with appropriate filters, a CCD camera, and digital imaging software from Leica (Cytovision, v7. 4).
KIAA1549-BRAF qRT-PCR analysis RNA was purified from tissue section with RNeasy Micro Kit (Qiagen) according to the manufacturer' s recommendations. Hydrolysis probe assays to detect K-B exon junctions were designed according to Tian and colleagues and purchased from Life Technologies (Darmstradt, Germany)  [15]. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA was used as internal control. PCR amplification was carried out on cDNA from 10 ng of RNA using AmpliTaq Gold DNA polymerase (Life Technologies). Reactions were in duplicate with incubation at 50 °C for 2 min, then 95 °C for 10 min, and then for 50 cycles of 95 °C for 15 s and 60 °C for 1 min. Fluorescence was recorded and cycles to threshold (CT) are calculated using ViiA7 RUO Software (Life Technologies). A reference of total RNA obtained from nonneoplastic cerebellum tissue sample (Stratagene, LA Jolla, CA) was used as negative control.
Introduction Data used to estimate the likelihood of adverse ecological effects typically include responses of survival, growth, or reproduction of individuals measured after a specific exposure duration under constant laboratory conditions and in absence of ecological stress (e. g. predation and competition)  [1]. These organism-level endpoints are far from the ecological features that the process aims to protect. Indeed, ecological risk assessment should protect the long-term persistence of populations of species in space and time under naturally varying field conditions and in the presence of other stressors (e. g. food limitation). However, except the ecotoxicological data provided by mesocosm experiments and a few field studies  [2] [3] [4] [5] , data on impacts of chemical substances on populations or higher biological levels are very sparse.
The fish were measured at 49, 56, 63, 70, 77, 91, 104, and 107 dpf. The measurements of the standard length (mm) were measured by placing the fish onto a Petri dish containing water (less than 5 mm deep). The Petri dish was placed on graph paper and photographed with a digital camera. The digital file was then used to measure the length of the fish to the nearest 0. 1 mm using Image J software  [25]. Another experiment (following the same protocol) was held with fish groups composed of 30 individuals per group and only two feeding treatments (three replicates per feeding treatment, six fish groups). These individuals were fed following two feeding treatments (6% of their median mass): (i) each day (Food 1), (ii) every two days (Food 0. 5). At the end of the experiment, these individuals were sexed by observing their gonad under a binocular microscope.
[23]  the zebrafish growth curve could be a sigmoid. The authors used several dataset from Eaton and Farley  [28] , Bagatoo et al. [29] , Schilling  [30] , Lawrence et al. [31] , Best et al. [32] , and Gòmez-Requeni et al. [33]  to assess their growth curve hypothesis and to estimate parameters of their DEB model. Augustine et al. [23]  put forth the hypothesis that the metabolism of larvae accelerates after birth until juvenile stage, i. e. fish of about 10 mm total body length and with adult fins and pigments  [34]. The hypothesis of metabolic acceleration comes with concomitant anisomorphic growth  [21]. The metabolic acceleration concept is assumed to impact both the growth curve and the incubation time. Here we propose alternative hypotheses based on feeding limitation and environmental factors.
Individual-based model of zebrafish population dynamics The individual based-model (IBM) description follows the ODD (Overview, Design concepts, Details) protocol  [37]. Our IBM was developed from the DEB model presented in the previous section. Mating behaviour and survival sub-model was adapted from the model proposed by Hazlerigg et al. [17]. The detailed description of the Design concepts, initialization, Input data and submodel are provided in supporting information (S3 Text). The model was implemented under Netlogo 5. 0. 4  [38]  and the code of the model is provided in supporting information (S4 Text, S1 File and S2 File). Purpose. The zebrafish IBM was developed to extrapolate from individual data measured on organisms under laboratory conditions to biological endpoints convenient to support ecological risk assessment of toxic compounds.
Sensitivity analysis of the IBM was performed using Morris method  [43]. This method identifies the few important factors at a limited cost of simulations. It also gives information on the global contribution of each parameter and the linearity of the effects or the interaction with other parameters. Details of these analyses are provided in (S5 Text). Model parameterisation. DEB model parameters calibrations were performed using GNU MCSim software  [44] , which is a program for the statistical analysis of Bayesian hierarchical models by Markov Chain Monte Carlo (MCMC). Details are provided in the supporting information (S2 Text). The parameter values of the IBM were picked-up from the literature (Tables  2  and 3 ) and three parameters were fitted (no a priori information): π a , π c, and F in (survival parameters and food input during the monsoon; Table  3 ).
Two different datasets were used to calibrate and assess predictability of the DEB-IBM model: data collected by Hazlerigg et al. [17]  and data collected by Spence et al. [24] , respectively. These authors have measured length on a random sample of fish captured from a sub-part of the monitored populations (field sampling methods were ineffective in catching smaller individuals). These data were compared with the fish length distribution predicted by the model for the sub-part of the population considered (after the simulation of three years to stabilise the population; 1000 simulations). Hence, we assume that the probability to be sampled for a given fish was related to the frequency of the fish length in the sub-population. Calibration was done using a genetic algorithm provided by the open-source software BehaviorSearch  [45]  with a generational population-model and a mutation-rate, crossover-rate, population-size, and tournament-size equal to 0.
DEB and IBM model calibration The DEB calibration was performed using three independent MCMC chains. The Gelman and Rubin convergence statistics were inferior to 1. 2 for all parameters  [48, 49]. This indicates that the three chains converged to the same solution. Our DEB model provided a relevant fit of both the growth and the reproduction data for several datasets at several temperatures from both new experiment and the literature (Figs  4  and 5 ). The f parameter, linked to environmental feeding conditions was fitted for each experiment. Prior and posterior distributions of the parameters are presented in Table A in S2 Text except for the f parameter which is presented with the relevant figures. To fit the three IBM parameters (survival parameters and food input during the monsoon), three independent fitting to the data collected by Hazlerigg et al.
The relative feeding level is determined assuming that fish feeding has a paralleling mechanism, i. e. the food resource is shared equally to all the fish (proportionally to their need). Nevertheless, the food resources are probably different between juveniles and adults, and dominant individuals attempt to monopolise a food source  [56]. In addition, as no mortality would occur in adult female zebrafish after 21 days of starvation  [60]  and as any other data are available, no explicit modelling of the starvation effect on mortality was integrated. This assumption is realistic, as many fish species evolved the capability to endure prolonged food shortages due to seasonal change in food availability  [19]. However, further experiments are needed to understand the mechanism of the food partition and starvation, and to improve this part of our model.
Finally, the annual cycle of zebrafish growth rates should be investigated. Currently, the energy fluxes modelled by the DEB model are identical between the non-breeding period (winter) and the period with active reproduction (spring and summer) and energy use for reproduction is supposed dissipated in winter. No observation on this process was found for the zebrafish. In other fish species, it was observed that non-reproducing females grew more than the reproductively active females  [19]. New data on growth obtained during photoperiodic offset of the reproduction should allow us to propose more precise modelling of the non-breeding period.
g. ,  Vasilakos and Lisetti, 2010; Tsai et al. , 2012; de Melo et al. , 2014; Gaffary et al. , 2014). Affective computing research aims to develop software that can better recognize and display emotions. This type of technology can now be used in combination with the objective neurophysiological markers of human empathy to pinpoint the most relevant and help study online changes in these markers during social interactions. The systematic study of empathy is limited by the fact that this process may change based on context and on the individuals present in the interaction. Indeed, the literature suggests that empathic responses are different depending on the nature of the relationship between the observer and the target  (Monin et al. , 2010a,b). Thus, studying empathy systematically requires flexibility both in the choice of markers and in the level of experimental control over the context.
All of these data are gathered by a system for physiological measurement (MP150, Biopac Systems Inc. ) and the FaceReader™ software encodes FACS information. We implemented a methodology for scanning the head of a real person using a high-resolution 3D scanner (Creaform Gemini™ with the Geomagic Wrap® software). Once scanned, these highresolution 3D models are transferred by wrap iterations on a single basic low polygon model. This model is able to express a variety of emotions by the use of motion captures (to simulate natural movements of intermediate idle phases between expressions), and blend shapes. Blend shapes (morph target animations) are convex combinations of n base vectors and mesh formed in the same topology. The movements of vertex points in xyz space generate animations of shapes.
MEASURES OF NEUROPHYSIOLOGICAL AND MOTOR RESPONSES This component of EEVEE consists of a series of apparatus that will allow real time measurement of behavioral and physiological responses of the participants. A number of inputs have already been integrated in EEVEE, and the number and types of devices can be changed and optimized over time. The current version of EEVEE uses an emotional face recognition tool (Noldus FaceReader™), measures of heart electrical activity, respiration rate, and skin conductance (MP150, Biopac Systems Inc. ), as well as eye-tracking and pupillometry (Smart Eye Pro, Smart Eye®). The instant values for 19 of the FACS AUs and aggregated values for 6 mood expressions detected on the observed face are acquired using Noldus FaceReader™ with the action units and external data modules.
MULTIMODAL INTERFACE AND ONLINE COMPUTATIONAL INTEGRATION This component of EEVEE translates the different responses (behavioral, physiological, neurophysiological) into vectors that can modulate the motor responses of the avatars (facial expression, eye/head movements) thereby producing a truly interactive task. The signal processing, feature extraction and scenario for the avatars' responses are configurable through a scenario designer implemented in a standalone EEVEE application. All of these parameters are being built into a modifiable, simple, and intuitive drag-and-drop interface. It is also possible to program more complex scenarios, thus enabling a great variety of contexts. The system is currently designed to project one avatar at a time, but the projection of two avatars or more will be part of the next  development phase.
Depending on the interaction mode selected, the avatar's animation (facial features and posture) can be predetermined or triggered in real time to respond to the observer's behavior, posture or physiology. It is also possible to activate a mirror mode for the avatar: recognizing and imitating the facial expressions of the user (see description of the modes below; see Video 1 in the Supplementary Material for a demo of the mirror mode). A dedicated EEVEE server is used to collect, over the network, all the data acquired through different application program interfaces (APIs) and software development kits (SDKs), making it possible to display the avatar and collect the physiological measures at different sites. The signals are then processed with mathematical routines to extract their salient features and reduce their bulk.
SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: http://www. frontiersin. org/journal/10. 3389/fnhum. 2015. 00112/abstract Demo | Video 1 demonstrates the interactions between an avatar in the EEVEE platform and the FaceReader™ software. The avatar mirrors the pain action units  (AU 6, 7, 9, 10, and 43)  of the participant as detected by FaceReader™. As described in Section Pain Facial Actions Units, the blendshape's value increase linearly depending of the FACS' intensity and the blendshape's value is interpolated over time.
ACKNOWLEDGMENTS This work was made possible thanks to funding from the  Canadian Foundation for Innovation and Natural Sciences and Engineering Research Council of Canada  to PLJ. PLJ was also supported by salary-grants from the  Canadian Institutes of Health Research  and the  Fonds de recherche du Québec -Santé. We also extend our gratitude to all the people from the lab who participated in many challenging discussions throughout the development of EEVEE and in particular to  Marie-Pier B. Tremblay.