dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper is divided into two parts. First, the 2S2P1D linear viscoelastic model is presented. The physical meaning of this model is described. The differences with the classical fractionalderivative models are shown. Second, the model is applied to three different materials. First, a polymeric damping material  [22, 38]  is considered. Even if this first damping material is not an acoustical material, it has been chosen here to validate the 2S2P1D model as done by Ref. [22]  for the five-parameter fractional-derivative model. The same data have been used. The data are covering about ten decades. Second, the model is applied to an open-cell polyurethane foam  [39]  used as acoustic insulator. For this material, the complex shear modulus data were obtained by using a quasi-static method. In this case, the TTSP may be used.
Case of a Polymeric Damping Material. A polymeric damping material  [22, 38]  is considered. The experimental values of the cyclic shear properties of the commercial available damping material are taken from Ref. [38]  and are plotted in Fig. 5. The cyclic properties were measured by the composite beam method at several temperatures. As underlined in Ref. [22] , "data covering about ten decades of frequency were determined by means of the frequency-temperature equivalence principle. " The low scatter and the smoothness of the reduced frequency curves testify the reliability of the experimental data. The 2S2P1D model is applied to this material. The initial parameters can be easily determined directly from the experimental data (for example by taking the limit values) and optimized then by the procedure described in Sec.
For H&E, a Tissue-Tek VIP autoprocessor (Sakura, Torrance, CA) was used to process tumors which were then embedded in Paraplast paraffin (melting temperature 56-60°C), sectioned to 5 μm and mounted on glass slides. Tumor sections were then stained using Mayer's H&E to facilitate histology and morphology evaluation. Tumor samples were also immunostained for activated caspase 3 using a rabbit anti-mouse activated caspase 3 polyclonal antibody (1:1000, Promega, Madison, WI). A goat anti-rabbit secondary antibody was used for signal amplification (Vectastain ABC Kit, Vector Labs, Burlingame, CA). The antibody complexes were detected using the DAB Peroxidase Substrate Kit (Vector Labs). Both H&E and caspase stained slides were scanned using an Aperio XT Scan-Scope (Aperio, Vista, CA) and digital images were accessed from the Spectrum database associated with the ScanScope and processed.
MR Imaging A Bruker Biospec 70/30 (7T) small animal scanner (Bruker BioSpin MRI, Ettlingen, Germany) equipped with a 154 mm internal diameter, circularly polarized coil operated in transmit-only mode; signal reception was achieved by a four channel rat brain phased array surface coil in a cross coil configuration. T2 and DW scans were performed prior to gadolinium contrast administration at all time points. Data were acquired and images were reconstructed using Para-Vision 5. 1 (Bruker BioSpin MRI). Parametric images were generated with either ParaVision 5. 1 or MATLAB (Mathworks). A segmented echo planar sequence (echo time (TE)/repetition time (TR) = 38. 2/3000 ms, field of view (FOV) = 3. 2 × 3. 2 cm 2 ; matrix size (MTX) = 192 × 192; slice thickness (ST)/slice interval (SI) = 1 mm/1 mm, 7 slices, 5 segments, receiver bandwidth (BW) = 238 kHz) was used to acquire a series of DWIs with varying diffusion weighting (b = 0, 100, 200, 400, 600, 800, 1000, 1250, 1500, 2000 s/mm 2 ), which were reconstructed to ADC maps by fitting to a monoexponential curve within ParaVision.
Acknowledgments People having worked in AGAT collaboration over the years are indebted for their valuable help in measurements. This work has been partially supported by the project  ENE2011-28200  of the  Secretaria de Estado de Investigacion, Desarrollo e Innovacion (Spain). We acknowledge computational facilities provided by the  Mésocentre de Calcul Intensif Aquitain at University of Bordeaux  [52] and the  Centro de Computacion Cientifica at Universidad Autonoma de Madrid. Part of this work (GL and AD) was supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02.
46 µm for the initial microstructure. However, as the average grain size in the annealed samples became larger, the step size was accordingly increased to 1. 44 µm, which appeared as a good compromise for measuring a sufficient number of grains, but still completing each map within a reasonable acquisition time. The mapped area of the initial microstructure contains more than 5000 grains. The EBSD maps recorded after grain growth include 150 to 800 grains (twins being excluded in grain counting). The OIM™ software (EDAX, Mahwah, NJ, USA) was used to analyze the EBSD data.
3 m 3 ¨J´1 and D 0 « 2 µm, where the parameter values were identified based on experimental data (Figure  4 ) by an inverse method. A MATLAB function "fminsearch" was used to identify the parameters that minimize the cost function, which is defined as the sum of the squared residuals. A residual is defined here as the difference between a value in the experimental data and the corresponding value predicted by Pande's model. Even though Pande's model can indeed describe the correlation between the annealing twin density and the average grain size, the underlying modeling assumptions are not consistent with the experimental observation of annealing twin evolution. Accordingly, a mean field model will be attempted in the next section to address this gap.
Furthermore, a 3D extension of the present mean field model could be considered, but will require 3D data as an input. The two-dimensional grain size distribution can be reasonably converted to an approached three-dimensional distribution  [29]. However, the two-dimensional annealing twin boundary content cannot easily be converted to three-dimensional data. The application of the mean field model on the 3D dataset in  [20]  will be considered as a perspective of the present work.
ACKNOWLEDGMENTS We acknowledge the financial support provided by the  Comunidad de Madrid  trough the program MADRID-PV-CM ( S2013/MAE2780 ) and by the  European Commission  through the project  SOPHIA  ( EU Ref N: 262533 ). Rubén Nunez  is thankful to the  Spanish Ministerio de Economía y Competitividad  for his  FPI grant. This work made use of the Scipy stack [13], an open-source Python-based scientific computing environment.
49 emu g À1 , respectively. By assuming that Ms of the magnetite nanoparticles is 60 emu g À1 ,  [44]  then the amount of magnetic material is 60 wt%, with 40 wt% organic material (oleic acid and surfactant from the seed emulsion and polymer from the polymerization step). These results were very close to that obtained by TGA analysis. [33] milarly, Nguyen and co-authors reported the synthesis of poly(methyl methacrylate)-coated magnetite nanoparticles. For characterizing the samples, TGA data were used for estimating both polymer contents and polymer-shell thickness. Then, the results were correlated with magnetization. [45] Another interesting work was reported by Chen and co-workers. [46] Zwitter ionic polymer-coated core-shell magnetic nanoparticles (Fe3O4@ SiO2@PMSA) were obtained for applying in specific capture of N-linked glycopeptides.
Acknowledgements This work was supported by the  COST Action   CM1202 PERSPECT-H 2  O, the  French National Research Agency  ( Labex program ,  ARCANE ,  ANR-11-LABX-0003-01 ), the  FCH Joint Undertaking (ArtipHyction Project , Grant Agreement n. 303435), the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement n. 306398 and the  Life Science Division of CEA  ( 2011 DSV-Energy program ).
An activity area is a polygonal or circular area, which can be fixed or linked and updated with an entity's (object, human or robot) position. For now, we studied and experimented two different activity areas: a) Information Screen Area, linked to information screens present in the environment; b) Touristic Point Area, linked to interesting attractions in the environment. Using these areas, the system can detect human activities (e. g. human is looking at an information screen, human is looking at an attraction). Detecting and tracking persons is complex. In this paper, human tracking is done using motion capture. In order to simulate realistic behaviors, we filter data provided by the motion capture in order to account for occlusions from the environment. The system has also been linked in the european project SPENCER 1  to a laser based human tracking component.
Experiments and Analysis We performed a first set of experiments with a person following a robot on a predefined path, in order to test the behaviors of the robot. Data from these experiments are shown in Table  1   2. We start by showing speed adaptation tests: adapting slow and fast: in these two tests (Figure  2 ) we used our system to guide respectively a user that would like to move at a slow pace, and a user that would like to move at a fast speed. no adaptation: in this experiments the robot won't adapt to the speed of the user, setting its own pace and stopping if it is too far. Looking at the data we can see that our system shows lower values for the variance of speed and distance, which means that after a certain time it's able to find a condition of equilibrium with the human follower.
Acknowledgement: We thank  A. Gadallah  for initial help with the bibliography. We thank  A. de Rossi  for his careful reading of the manuscript and useful comments. C. Couteau would like to acknowledge support from the  CNRS   PEPS ICQ "InteQ" project and the "Labex AC-TION" program  (contract  ANR-11-LABX-01-01 ). This work was conducted within the european  COST programme 'Nanoscale Quantum Optics'. A. Larrue  thanks the  French National Research Agency (ANR)  for funding under Grant  ANR-2011-NANO-028-01. C. Soci  acknowledges support from  Nanyang Technological University  (project reference  M4080538  and  M4080511 ) and the  Singapore Agency for Science, Technology and Research (A*STAR ,  SERC  Project No. 1223600007 ). C. Couteau  and  C. Soci  acknowledge support from the  Singapore Ministry of Education Academic Research Fund  (project reference  MOE2011-T3-1-005  and  MOE2013-T2-044 ).
Acknowledgments This work has been performed in partnership with the laboratories LAMIH (Valenciennes, France),  LESTE (Monastir, Tunisia) ,  PC2A  ( Lille, France)  and  CCM (Dunkerque, France). Financial support has been provided by the  Région Nord-Pas-de-Calais , by the  French National Association of Research and Technology ANRT  (doctoral scholarship) and by the company Enerbiom in the framework of the regional project  Sylwatt , by the  European Commission  within the  International Research Staff Exchange Scheme (IRSES)  in the 7th Framework Programme FP7/2014-2017/ under REA grant agreement  n°612230 , and by the  Tunisian Ministry of Higher Education and Scientific Research  (doctoral scholarship). These supports are gratefully acknowledged.
In Section III we develop an information geometry framework for active contours from the exponential family and propose a Riemannian steepest descent method to compute the contour's evolution. More precisely, we derive a smooth natural gradient descent algorithm  [15]  for nonparametric active contour, with application to image segmentation. Following on from this in Section IV we present some results illustrating the power of the approach and contrast the performance on alternative existing approaches. Finally we draw some conclusions and discuss potential extensions of the approach. A MATLAB implementation of the proposed method is available at http://www. stats. bris. ac. uk/~mp12320/code/SmoothNaturalGradient. zip.
Finally, all experiments have been computed on an Intel i7 quad-core workstation running MATLAB 2013a and by using the values of the algorithm parameters that yield the best segmentation results. Comparison with the Sobolev gradient  [21]  and Generalized Newton  [1]  methods using the synthetic shape image from  [1]  (216 × 187 pixels, additive Gaussian noise, SNR 5. 36 dB). (a) Proposed (  23 ). (b) Sobolev  [21]. (c) Newton  [1].
A. Comparison With State-of-the-Art Algorithms for the Chan-Vese AC 1) Comparison With Other Steepest Descent Algorithms: In the first part of this experiment we compare our method with two other fast steepest descent algorithms: the generalised Newton method  [1] , and the Sobolev gradient  [21]. These methods have been specifically designed for the Chan-Vese AC, which represents the image foreground and background using Gaussian distribution. For this experiment we also implemented our method using the Chan-Vese AC and the corresponding Bregman divergence, which is provided in the second row of Table  I. To guarantee that the comparisons are fair we have used a synthetic shape image from  [1]  and the accompanying MATLAB codes. This image contains 216 × 187 pixels and is contaminated with white additive Gaussian noise of 5.
ACKNOWLEDGMENT The authors would like to acknowledge the support of the  Hospital of Toulouse, Pierre Fabre Laboratories and Magellium  for the ultrasound image acquisition and interpretation. The authors would particularly like to thank  Dr. Nicolas Meyer ,  Dr. Siham Lourari , and  Mr. Jérôme Georges. The authors are also grateful to  Dr. Frédéric Courbon  and his team at the  Claudius Regaud Cancer Institute of Toulouse  for the PET phantom image used in Section IV-C. Finally, the authors would like to thank the anonymous reviewers who commented on an earlier version of this paper which have significantly improved it.
This work was supported in part by the  Engineering and Physical Sciences Research Council (EPSRC)  through the  SuSTaIN Program  under Grant  EP/D063485/1 , in part by the  Department of Mathematics, University of Bristol, Bristol, U. K. , in part by a  Post-Doctoral Fellowship  through the  French Ministry of Defence , and in part by the  EPSRC  under Grant  EP/J015180/1. The associate editor coordinating the review of this manuscript.
K. ACQUISITION The original microchannel-plate detector of the Bruker instrument is used to detect the ions after the Tof. It is connected to a preamplifier and independently to two Agilent data acquisition boards: the original one from the Q-Tof, and an additional one (U1084A high-speed digitizer, Agilent Technologies, Santa Clara, USA). This latter board was added to handle IMS-MS acquisitions, which are more demanding than simple MS acquisitions (see below). The main characteristics of this board are: Memory 512 Mb, Dual channel, 2-4 GS/s, on-board signal averaging and processing. Different acquisition modes are available. They rely on different sequences for the application of the pulsed voltages. Synchronization between the different pulses including the Tof pusher is software controlled.
This yields 2-dimentional datasets. One dimension corresponds to AT and is related to the drift time in DT2. The number of bins is n TOF and the bin width is  TOF /n TOF (inverse of the Tof repetition rate). The other dimension is the time of flight of the ions in the Tof (which gives the m/z of the ions). The bin width in this dimension depends on the sampling rate chosen for the acquisition board. The size of the resulting dataset is limited by the size of the memory buffers, data processing times and transfer times. Note that time-of-flight were transposed to m/z values using ESI-L low concentration tuning mix (G1969-85000, Agilent Technologies, Santa Clara, CA, USA) for mass calibration. For tandem IMS measurements, the acquisition sequence is given in Figure  5. The acquisition cycle begins with the injection of the ions from FunA2 to DT1.
SUMMARY AND OUTLOOK IV. We described a new experimental set-up to perform tandem ion mobility spectrometry measurements coupled to time of flight high resolution mass spectrometry. This instrument allows recording IMS-MS maps. Alternatively one can record IMS data for a selected ion using an on-axis detector, the ion being mass selected with the quadrupole. The geometry of the instrument allows to select ions according to their ion mobility, activate them and examine the ion mobility of the product ions. IMS-IMS experiments on Ubiquitin are reported. We selected a given isomer of charge state +7 and explored its structural rearrangement following collisional activation between the two drift tubes. The set up was built to permit a laser irradiation of the ions in the transfer region between the two drift tubes.
ACKNOWLEDGMENTS The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013 Grant agreement N°320659 ). We acknowledge  Oliver Räther ,  Jean-Michel Billmann  and  Claude Marfisi  from  Bruker Daltonik  for technical support and  Serge Vialet  ( SMGOP ) for fruitful discussions during the design of the instrument.
Moreover, tissue-equivalent substitutes are often used to calibrate CT scanners just by matching their density to the density of real tissues. As their elemental composition diverges from that of real tissues, calibration curves of CT number to density can differ from reality. In their work, Vanderstraeten et al. (5)  intended to deal with these two pitfalls using a stoichiometric CT calibration method, previously described by Schneider et al. (6)  This resulted in ten different bone subsets that were selected by dosimetric properties. From this work, a general inverse proportionality between hydrogen content and bone density of the obtained tissue composition subsets can be deduced. Furthermore, using the database of the National Institute of Standards and Technology (NIST) XCOM  (7)  and ESTAR,  (8)  one can easily notice that hydrogen differs from other human body elements in both mass attenuation coefficient and total mass stopping power.
B. Monte Carlo dose calculations An in-house developed Monte Carlo system based on MCDE  (13)  , called MODERATO, is currently used in our center as a dose verification tool for quality assurance for all CyberKnife treatment plans performed in our radiotherapy department. This system is based on the EGSnrc  (14)  code and its simulation engines BEAMnrc  (15)  for the accelerator phase space, and DOSXYZnrc  (11)  for the particle transport in the patient geometry. This required a professional reprogramming of MCDE to obtain a more robust user-friendly system using a JavaScript GUI that allows launching the calculations on different servers, and evaluating the obtained results. Different Monte Carlo engines (EGS, Geant4  (16)  ) can be selected for different parts of the simulation (e. g. , Geant4 can be used to determine the phase space, and EGS to perform the dose calculation in the patient geometry, or vice versa).
The CyberKnife accelerator has been modeled using BEAMnrc (and the egs++ geometry package to model the iris collimator), generating phase space files at the exit plane of the secondary collimator. (12) The system allows selecting a treatment plan established on the MultiPlan (Accuray, Inc. ) treatment planning system (TPS), and to compare the dose distribution and dose-volume histograms (DVH) between MultiPlan and the Monte Carlo dose engine. The different dosimetric subset schemes were retrospectively applied to patient treatment plans using the Monte Carlo system. The simulations were realized on 25 CyberKnife treatments divided in anatomical locations, according to Table  1. Different anatomical areas were used to cover a large panel of tumor tissues and tissues near the tumor as, for example, the bone metastasis, which allow testing the modelization of the bone subsets for the HO scheme.
OUR APPROACH To deal with occlusion between objects we extend the original GMPHD tracker, presented in section-3, by occlusion detection module and objects reidentification module, as shown in the figure  1. The occlusion detection module consists in computing a distance between the predicted Gaussian components representing active targets. We consider a target as active if the weight of its Gaussian component is above the estimation threshold th estimation , for more than N successive frames. If the distance between the predictions of active targets is lower than a defined threshold, the active targets are considered overlapping. In this case, the weights, means and covariance matrices of the Gaussian components are merged using the merging method described in section-3 to form a composed target.
Indeed, our improvement will affect the functionalities (2) and (3) when objects overlapping occurs. Our approach does not change the way that the position of objects is estimated, the estimation is the same as the one of GM-PHD tracker. Because of that, the functionality (1) is not discussed. Our approach is evaluated using the sequence S2L1-view1 of PETS2009 dataset  (pets2009, 2009)  where several humans (the maximum at the same time is 9) walk in the scene and several occlusions happen between them. The second sequence is Meet WalkTogether1 of CAVIAR dataset  (caviar, 2004)  where two humans meet and walk together. The tracking is performed from frame 201 to 700 in PETS2009 dataset. The 200 first frames were used to initialize the background model. For CAVIAR dataset, the tracking is performed on the frames 1250 to 1450.
The frame 1000 to 1250 are used to initialize the background model. Firstly, the accuracy of our method in estimating the number of objects in the field of view (FoV) of the camera is evaluated and compared to the ground truth. The ground truth is manually evaluated by counting the number of humans in each frame. As the number of estimated objects is directly related to the detector quality, both number of detected objects and estimated objects are given in figures 3 and 4. The number of objects in each frame is estimated on both datasets with both methods. Figures  3  and 4  show that our method estimates well the number of objects in FoV of the camera and outperforms GMPHD tracker. Indeed, even if the detector does not return the correct number of objects in the frame because of the occlusion, our method can handle that and gives the right number of objects.
This is due to the occlusion detection module. The error in estimating the number of objects in the frames 506 to 540 of the PETS2009 dataset is because two objects entered the FoV already occluded. When the occlusion ends, the corrected number of object is estimated. The number of object estimated by both methods is not correct in the frames 690 to 790 of PETS2009 dataset. This is because the human opening the trunk of the car present in the scene is very small to be detected. The performance of finding the correct ID of each object, after an occlusion, is evaluated by counting the number of times that the object's ID changes for each real object over the tracking. When an object leaves the scene and enters again, it is considered as a new object. As each object is represented by a unique ID, the less the object's ID changes, the most efficient the tracking algorithm is.
This is evaluated on both datasets and two different features are tested: color histogram and motion feature. The results obtained are compared to GMPHD tracker. There are seventeen objects, in PETS2009 sequence and two objects in CAVIAR sequence. The color histogram feature is used as explained in the example of section-5. The use of motion feature involves to save in the composed target the state vectors of occluded objects. At each time iteration, these features undergo the prediction step of GM-PHD. When the composed target splits, the motion direction is used to reidentify the objects. The direction can be deduced from the sign of velocity coordinates of the state vector. Indeed, if two objects cross each other, their velocity coordinates will have different sign. In this case, the split object will get the ID corresponding to the feature having the same velocity sign.