dataesr/scientific-paragraphs-categorization huggingface dataset:
When the overlapped objects have the same direction, the velocity sign is not enough to reidentify them correctly. In this case, the Euclidean distance between their positions is used. The ID corresponding to the feature with the smallest distance is assigned to the object. Figure  5  shows the number of times that the object's ID change in the PETS2009 dataset. The number of change per object is high in GMPHD tracker. This number is reduced, using our approach. The motion feature performs better than the color histogram because, in this dataset, the color of the cloths is not very different from one human to another. Using 256 bins per channel, in the color histogram, allows more robust reidentification than the case where only 8 bins are used per channel. Even with our approach, the number of the objects' ID change is not zero.
Indeed, a lot of changes are caused by miss detection, when the objects are occluded by the environment, typically the light pole in the middle of the scene. GMPHD tracker has an average of 4. 58 change of ID per object. However, our method achieve an average of 1. 89 with the motion feature and 2 with the color histogram 256 bins. The same experiment is performed on CAVIAR dataset. Using GMPHD tracker, the first object changes its ID 2 times and the second 4 times. With our approach, this change is reduced to zero for both objects independently on the used features. Several examples of images are presented in figure 6. In each example, the states: before occlusion, during occlusion and at the end of the occlusion are showed for our approach and for GMPHD tracker. Concerning the complexity of the algorithm, our tracking system is implemented using C language.
CONCLUSION In this paper, we presented a low complexity multiobject tracking algorithm that can be used in a node of a global tracking system to ensure a low cost system. Our approach is based on GMPHD filter and can handle the occlusion between objects. The obtained results on PETS2009 and CAVIAR datasets show that our approach offers a high improvement in estimating the number of objects and in ensuring a consistent targets labeling over time, compared the original GM-PHD. Furthermore, our approach exceeds 60 fps on a PC. This makes it a good candidate for an embedded implementation. The future work consists in implementing our method on an embedded processor with low computing resources connected to an image sensor in order to form a smart camera. The idea is to extend our approach to a network of smart cameras.
Acknowledgments The authors thank the  French ANRT (Agence Nationale pour la Recherche et la Technologie ) and also the  Region Rhone-Alpes  for financial support and  LEPMI (Laboratoire d'Electrochimie et de Physico Chimie des Materiaux et des Interfaces ,  St Martin  d'heres, 38402 Grenoble, France) for interesting discussions.
In the cavity floor (y = -H ) the boundary conditions for the velocities are of Dirichlet type due to the no-slip condition at the solid wall. The boundary condition for the pressure is of Neumann type, as follows from the momentum equation. The customary parallel flow assumptions V = 0, P = const and ∂ x Q = 0 are applied. The one-dimensional base state of the velocity field U = f (y) was nondimensionalized with the average velocity Ũ and the momentum thickness eff. It reads U (y) = 1 + tanh 1 2 y. ( 3 ) The EVP together with the base-flow assumptions yield the dispersion relation of the system, given by the complex equation (ω r ,ω i ,k r ,k i ) = 0. ( 4 ) Its roots ω(k) provide the eigenfrequencies and growth rates. The dispersion relation is solved numerically using a Matlab code based on a Chebyshef collocation method with N = 100 collocation points in the wall-normal direction y.
ACKNOWLEDGMENTS We wish to acknowledge the support by the  CON-ICET  under Grant No. 3303 ,  UBACYT  under Grant No. 100228 ,  Science-Accueil d'Université Paris-Sud ,  LIA-PMF/FMF , and the  13STIC-08-P-MVP project of the Sti-cAmSud program. F. T. greatly acknowledges support of his PhD thesis by  CONICET, Erich-Becker-Studienstiftung , and the  DAAD.
2015). • Pipeline 5, presented here, relies on previous pipeline steps and is the application of the ICA method to separate the light from old stars and dust. Additionally, at some points in this paper, and especially for the analysis of results (Sect. 7), we will make use of a set of ancillary information. For distances, whenever available, we rely on the mean redshiftindependent distance provided by the NASA Extragalactic Database (NED); otherwise, they are based on the observed radial velocity, also from NED (compiled by  Muñoz-Mateos et al. 2015). We utilize the morphological classification at 3. 6 µm from  Buta et al. (2010)  and  Buta et al. (2015). SFRs are derived from IRAS photometry at 60 µm and 100 µm obtained from NED following  Larsen & Richtler (2000).
Quality flagging To account for the varying quality of solutions, flags are provided along with the final products. Flagging has been independently performed by two of the authors (Sharon Meidt, Miguel Querejeta) and an external classifier (Emer Brady), according to well-defined criteria: 1. Is the physical distribution of the identified dust in accordance with the signatures that appear in the color map? 2. Are there significant oversubtractions in the map of old stellar flux? 3. Are there any artifacts that prevent ICA from obtaining a correct solution? Three quality flags have been established: 1-excellent, 2-acceptable, 3-bad, and the statistical mode is chosen as the final classification (cases in which all three classifiers disagree have been revised and individually discussed). Depending on the specific application of the mass maps, either only those galaxies classified as 1 or both 1 and 2 will be suitable; also, some of the solutions classified as 3-bad correspond to galaxies in which the input data was contaminated by artifacts (e.
The fraction of total is therefore referred to the 2308 galaxies with good P3 data at the moment we ran this Pipeline 5. Fig. 7. -Histograms showing the distances, morphological types and star formation rates spanned by the galaxies in our science sample (blue). Overplotted in red is the distribution of the galaxies to which ICA was not applied (compatible with old stars). The global S 4 G distributions are shown by the empty black histograms. ple shown in Fig. 9  is all S 4 G galaxies with ICA solutions compatible with old stars (within their uncertainties) and with availability of SFRs, which makes a total of 819 galaxies. Galaxies with higher SSFRs have on average higher fractional contamination from dust, which makes the fractional flux due to stars drop (see running median in Fig. 9 ).
A. 3. Findings The difference between the stellar and dust colors imposed on each model and the s1 and s2 colors found by ICA are shown in Fig. 12 , as a function of the dust color of the model. To improve the visibility of the plotted data points, we introduce a small artificial random scatter around each discrete dust color value (a random dithering within ±0. 05). The symbol size of each point is proportional to the dust fraction, while the colors denote the original galaxy on which the models are based (highlighting the effect of 2-dimensional dust distribution). Most striking is the clear trend for increased scatter in the ICA-determined dust colors (and stellar colors, although less so) when the 'intrinsic' dust colors are redder. ICA underestimates the dust color more severely for low contamination fractions, and, conversely, it tends to overestimate the stellar color (but not as much) at the highest contamination fractions.
We are grateful to the anonymous referee for very insightful comments and suggestions, which have helped us improve the quality of the paper. We acknowledge financial support to the DAGAL network from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme FP7 /2007-2013/ under REA grant agreement number  PITN-GA-2011-289313. M. Q. acknowledges the  International Max Planck Research School for Astronomy and Cosmic Physics at the University of Heidelberg (IMPRS-HD). We thank  Emer Brady  for acting as an external classifier for assigning quality flags, and  Simón Díaz-García  for helpful comments. K. S. , J-C. M-M, and T. K acknowledge support from the  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
The camera locations were outside the tank perpendicular and close to the glass surface. Figure  2  shows the experimental installation including the aorta under water fixed to the support with the image acquisition cameras installed outside. The pressure was measured with two pressure sensors EPB-PW-3. 5BS-/L3M installed inside the inlet pipe and the aortic arch for accurate pressure measurements. The measurement frequency was 50 Hz. A trigger was used to synchronize the image acquisition and pressure sensors. An average flow rate of 5 L/min was provided from a hydraulic pump and the aorta expansion was obtained thanks to an adjustable valve at the output. The experimental setup also measured the leaks through a displacement sensor present in the tank. The displacement field in the aortic arch was determined with Vic3D©  [10]  software.
Physiological Loading Conditions The in-vivo loading conditions of stents and the corresponding number of cycles per year are summarized from  41, 50  in Table  2. Table  3  reports the loading conditions adopted in stent simulations and Fig. 4  presents four schematic examples of loading conditions. All the reported loading conditions are cyclic, i. e. , assume a constant amplitude. The pulsatile pressure is generally assumed to range between 60 and 160 mmHg. The values of compression and bending loading are usually defined according to clinical evidences, e. g. , studies on cadavers. 41, 67 Such clinical data generally refer only to specific case-studies and we do not dispose of detailed patient-specific databases to assess these values. The imposed cyclic character of the loading can be extended to variable amplitude loading as it is has already been done on engineering structures,  46  but no discussion in this sense has been found for stents.
ACKNOWLEDGMENTS This work is partially funded by: the  Cariplo Foundation  through the Project No. 2009. 2822 ; ERC Starting Grant  through the Project  ISOBIO: Isogeometric Methods for Biomechanics  (No. 259229 );  Ministero dell'Istruzione, dell'Universita`e della Ricerca  through the Project No. 2010BFXRHS ; the  French National Research Agency  (Project  Fast3D - ANR-11-BS09-012-01 ).
Samples (200 mL) were taken at regular time intervals, vacuum filtered through 0. 45 mm cellulose nitrate membrane filters (Millipore), and washed twice with 10 mM EDTA in phosphate buffer. After drying, the filters were transferred to scintillation vials with 5 mL scintillation fluid (Ultima Gold F, Perkin Elmer) for counting in a Packard Tricarb 2100 scintillation counter. Control values obtained from HYD723 carrying the empty vector pBAD18 were subtracted to correct for non-specific  63  Ni binding. For kinetic parameter determination, the uptake assay was performed with the addition of 50 to 1000 nM  63  Ni. Initial linear uptake rates were used to calculate the K m and V max parameters using a nonlinear regression curve (Sigma Plot 12. 5 software). The effect of organic acids and L-Histidine on Ni uptake was measured in the presence of 150 nM  63  Ni over a 5 min incubation period.
Acknowledgements We thank  J. Cayron  for technical assistance with gel analysis of HupE mutants, and  Anabel Bautista  for reporter enzyme activity determination. This work was supported by grants from  Spain's Ministery of Science and Innovation  ( BIO2010-15301  and  BIO2013-43040  to J. P. ), from  Fundacion Ramon Areces  (to J. I. ) and by a  PICS grant  ( SYMBIONI no 5992 ) from the  Centre National de la Recherche Scientifique  (to A. R. ).
C. Calibration Till now the yield coefficients for the steady state were estimated. This was possible by taking some assumptions like the constancy of the acidogenic and methanogenic ratio v, and the correlation between V SS measurement and the biomass concentration in the reactor and, last but not least, the certainty of measurements at steady state. Therefore, to enable the model to predict as correct as possible the process behavior in both steady and transient states, we propose to calibrate the yield coefficients using online data measurements. Usually, very restrictive information about the process can be obtained and only acetate S 2 , alkalinity Z, and gaseous flow rates are measured for known inputs (often uncertain input concentrations). Therefore, we assume that S 2 , Z Q c and Q M are measured online to perform the nonlinear identification depicted in Fig.
Relation between the Variabilities of  0 and  0. The variability of  0 and  0 between the second and first sessions (Δ 0 2-1 and Δ 0 2-1 ) and between the third and second sessions (Δ 0 3-2 and Δ 0 3-2 ) was calculated according to the following formulas: Δ𝐹 0 2-1 = 100 𝐹 02 𝐹 01 , Δ𝐹 0 3-2 = 100 𝐹 03 𝐹 02 , Δ𝑉 0 2-1 = 100 𝑉 02 𝑉 01 , Δ𝑉 0 3-2 = 100 𝑉 03 𝑉 02. (4) 2. 4. Statistical Analyses. Statistical procedures were carried out using Statistica 7. 1 Software (StatSoft, France). Data of  0 ,  0 , and  max are presented as mean and standard deviation (mean ± SD). Before statistical analysis, each performance variable was tested for normality with the Shapiro-Wilk test. With the assumption of normality confirmed, systematic change in performance from trials 1 to 3 was examined using one-way ANOVA with repeated measures and a Tukey's post hoc test.
V. MODEL VALIDATION A. Comparison between different approaches Comparisons among the present analytical model, transfer matrix approach (TMM), and virtual measurements obtained with a 3D acoustical FEM simulations using COMSOL software have been performed. A three microphones method  12  is used to get the virtual FEM measurements. In the FEM model on COMSOL, parabolic tetrahedral elements were used to mesh the different domains of the tube and an effective fluid of density and bulk modulus given by the JCA model 9,10 fills the pores. In the FEM simulations (Fig. 7 ), several aspects have been considered to optimize the precision and computation time: A sufficient number of elements per domain to be meshed has been chosen in order to ensure a good precision while keeping the computation time minimum, an adaptive mesh has been used with increased number of elements in the vicinity of geometrical discontinuities or in smaller domains (with respect to other domains), sufficiently smooth variations were considered in the mesh element sizes in the vicinity of geometrical discontinuities and domains size variations.
1. Initial values: r0 and f Î illu;l g 0 homogeneous 2. Cycle i-object m estimation steps by approaching the zero by using the gradient of F: 3. Cycle i-grating n estimation steps by approaching the zero by using the gradient of F: @F r;f Î illu;l g ð Þ @ r for f Î illu;l g ¼ f Î illu;l g iÀ1 fixed (m iterations) @F r;f Î illu;l g ð Þ @ Î illu;l for r ¼ ri from previous step and fixed (n iterations) 4. End of cycle i: estimated values of r and f Î illu;l g updated. 5. Go to step 2 and repeat for cycle i + 1 until i = N In practice, we found that m = 5 for the first cycle i = 1, m = 25 henceforth and n = 5 yield good results. The toolbox was implemented in MATLAB (R2012a, Mathworks, Natik, MA, USA) and the gradient-based optimization process uses the minFunc function developed by Mark Schmidt and freely downloadable from  [11].
Poisson noise was introduced in the data so that the expected value of the brightest pixel in the sum of all images corresponds to a count of 4 Á 10 5 photons. The data were then convolved by a 3D PSF, simulating imaging through a microscope objective of numerical aperture (NA) 1. 3 at an emission wavelength of 500nm. The total object was 512 × 512 × 9 voxels, with a square voxel size of 25 × 25 × 200nm 3. Out of this 3D stack, only the focal plane data was selected. One of the 9 images with visible out-of-focus contributions is depicted in Fig 2d. The wide-field (WF) images for comparison were obtained by summing all individual raw SIM images. First, the 2D blind-SIM algorithm was applied to the single-slice data (Fig  2e ). We observe that the out-of-focus blur is still present. We then applied the thick slice blind-SIM algorithm with the reconstruction parameters as presented in Table  1  In addition to this resolution slide simulation, we tested several other simulated objectsincluding hollow spheres-which confirmed the optical sectioning ability of thick slice blind-SIM.
Acknowledgments We thank  Michael Reuter  for the preparation of the actin samples and  Julien Savatier  for the preparation of the paxillin samples. We acknowledge the work of  Roland Ayuk  and  Hugues Giovannini  who acquired the raw data corresponding to Fig 1. Many thanks to our collaborators and colleagues for many useful discussion and constructive remarks, mainly  Ernst Stelzer ,  Bo-Jui Chang ,  Hui-Wen Lu-Walther ,  Martin Kielhorn ,  Ronny Förster  and  Sapna Shukla. The data underlying this manuscript is available as supporting information S1 Raw data.
Introduction In the Northern European Seas offshore wind farms are planned as clusters. The wind farm wake from one wind farm thus has the potential to influence the power production at neighboring wind farms. The expected wake loss due to wind farm cluster effects is investigated in the present study. The wind farm wake cluster effects are strongly dependent upon the atmospheric conditions. These vary spatially and temporally. One remote sensing method for observing ocean surface winds is satellite Synthetic Aperture Radar (SAR). The advantage of SAR is that a large area is observed and several wind farms are covered. The derived wind map from SAR provides a snapshot of the wind field during a few seconds at the time of acquisition. In the satellite SAR data archives covering the North Sea, thousands of wind turbines are visible as white dots in calm conditions.
Satellite SAR SAR data from RADARSAT-2 and Envisat ASAR WSM are used. From RADARSAT-2 only one scene is investigated. It is ScanSAR Wide in VV polarization. The wind field retrieval requires input information about the wind direction. From the RADARSAT-2 image the wake direction has been estimated as 40° and using this input for wind direction, wind speed has been retrieved using CMOD-IFR2  [6]. It is the equivalent neutral wind (ENW) at the height 10 m. The calculated wind speed is presented in Figure  2a. The original ScanSAR Wide product has spatial resolution 100 m. The spatial resolution is reduced to approx. 1 km in connection with the processing of wind fields. This is performed to eliminate effects of random noise and long-period waves. The Envisat ASAR data were processed to wind fields as part of the project NORSEWInD  [5].
Wake Modelling with PARK and WRF A modified version of the PARK wake model  [7] , also implemented in the Wind Atlas Analysis and Application Program (WAsP)  [8] , is here used for wake calculations. The main difference between this modified version and that in WAsP is that the former does not take into account the effects of the "ground reflecting back wakes" and so it only takes into account the shading rotors both directly upstream and sideways. The PARK wake model is based on the wake deficit suggested by  [9] , who derived a mass-conservation-like equation for the velocity immediately before a turbine u2, which is affected by a wake: 1 1 ( 1 ) where u1 is the upstream wind speed, a the induction factor which is a function of the thrust coefficient (Ct), kw the wake decay coefficient, x the downstream distance and rr the turbine's rotor radius.
The square of the total wake deficit is estimated as the sum of the square of all contributing wake deficits. We implemented the model in a Matlab script. This allows us to compute wake deficits at any given point. The wake model can be compared to satellite derived wind maps which contains information over a large area. We use kw = 0. 03 for the wake computations. The Weather Research and Forecasting (WRF) mesoscale model  [10]  is also used for wake modelling. The advantage of WRF is that the dynamic synoptic flow is considered. The computational cost is much higher than that for the PARK model though. Mesoscale models have been developed to simulate the atmosphere flow over areas on the order of hundreds of kilometers. Due to their low horizontal resolution unresolved processes, such as turbulence and turbine induced wakes have to be parametrized.
The wind speeds started to increase at the London Array only shortly before 18:00 UTC. It might be that the increasing model wind speeds are for some hours out of phase, which would explain the longer wakes behind Greater Gabbard and Thanet wind farms in WRF compared to SAR. In summary, the wind farm wakes from several wind farms are visually compared between SAR and WRF simulations of the velocity deficit obtained without wind farm and with wind farms using the EWP scheme. The wind farm wake directions and extension of wake are found to compare well despite that mesoscale features, such as that resulting from unsteady flow conditions, are noted in the wind farm wakes in the WRF simulation. We cannot expect the WRF simulations to match the observed velocity and wind direction in SAR satellite data perfectly.
Wind Farm Wake Climatology Geo-Located Wind Maps The case in Section 3 was selected based on clear visual observation of wind farm wakes at several wind farms within one satellite SAR image. However, we would like to study the behavior of the wind farm wake in a climatological fashion and investigate whether this can be performed using our Envisat ASAR WSM data set. We select to study the Horns Rev 1 and 2 wind farms for which we have 356 SAR scenes in total for the period of dual wind farm operation from September 2009 to the termination of the Envisat mission in March 2012. As the wake behavior is highly dependent on the inflow wind conditions, in particular the wind direction, we perform the study for 12 directional sectors based on the ECMWF model wind direction used to retrieve the SAR winds.
The SAR scenes are first binned according to the wind directions extracted for a single point near the two wind farms. The data set is then filtered such that only scenes with wind speeds in the range 4-14 m•s -1 at the same point are included (total of 241). This is the range where wind farm wakes are expected to be most detectable. At lower wind speeds, the turbines are not operating and at higher wind speeds, wind penetration through the wind farms is expected. For each directional bin, we extract the inflow conditions from a point upstream of the wind farms for every SAR scene in the bin. The reference points are located on two circles circumscribing the wind farms Horns Rev 1 and Horns Rev 2 with radii of 7. 5 km and 10 km, respectively, as shown in Figure  4. To get representative inflow conditions, the satellite winds are extracted within a radius of 10 km for Horns Rev 2 and a radius of 7.
Discussion The very long wind farm wakes observed in the RADARSAT-2 scene have in qualitative terms successfully been modeled both by the PARK and WRF model at several wind farms in the southern North Sea. The comparison is qualitative due the different nature of data. The SAR-based results are near-instantaneous observations of the sea surface while wake model results are time-averaged results withbest representation of the conditions at around hub-height. Thus we focus on the apparent wind farm wake direction and the length of the wakesin this comparison instead of the wake deficit at any given location. The SAR image has the advantage of clear visible wake features. Thus the retrieved wind field can be used to evaluate the wake model results in qualitative terms. We are interested in developing more robust SAR-based wind farm wake data representation for the evaluation of wake models.