dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, since the dataset does not provide the coordinates of manually picked top-view particles, we cannot apply a similar performance comparison for the top-view picking results of gEMpicker. To obtain an independent validation of our results, we uploaded the picks obtained by gEMpicker to the 3D Electron Microscopy Benchmark (http://i2pc. cnb. csic. es/3dembenchmark/) for 50 KLH micrographs. This generated the following statistics: Precision: 78. 8%; Recall: 93. 6%; False Discovery Rate: 21. 2%; F-measure: 85. 6%; Average distance from manual pick: 4. 7 pixels. Regarding timing, the total time to compute the correlation maps for the 82 micrographs in this dataset was 5,972s when using one CPU core on Dirac compared to 223s using one C2075 GPU. This corresponds to a GPU/CPU speed-up factor of ∼27.
However, in this case it is probably fairer to compare one GPU with one quad-core CPU, which reduces the speed-up factor to ∼9. Effectively, the 82 micrographs in this small dataset may be processed in less than 4 minutes using a single GPU or in just over 28 minutes using all 8 cores of a modern workstation. A higher speed-up is expected using a greater number of templates. In contrast, the FindEM program requires 9,430s to compute the 82 correlation maps using one CPU core on Dirac. Thus, the speed-up obtained by using one  C2075 GPU in gEMpicker when compared to FindEM is ∼ 42×. Assuming the almost linear speed-up demonstrated by our cluster calculations (Figure  5 ), we estimated that the entire KLH picking exercise could be completed in about 1 minute on our 4-GPU Mbiserv machine. However, the actual observed time is almost 3 minutes.
This is because for micrographs of size 2048 × 2048, the time required to process four templates in four GPUs is less than the time required to read four templates from the storage device. Hence the consumer threads often have to wait for data to become available. In addition, using multi-threading leads to the additional overhead of combining results at the final step. Similar phenomena are also observed on the Adonis and Griffon clusters. Thus, by exploiting GPUs for the particle picking problem, the rate-limiting factor is no longer raw computing power but the bandwidth of the hard disk drives.
Conclusions We have presented gEMpicker, a highly parallel multithreaded cryo-EM particle picking tool which implements Roseman's NCC matching algorithm on multi-CPU and multi-GPU computer systems. Our results on picking particles in the KLH dataset indicate that gEMpicker performs at least as well as Roseman's FindEM algorithm. Our computational experiments show that gEMpicker's automatic particle picking calculation is approximately 30-40 times faster on a contemporary GPU than on a single CPU core. Compared to a quad-core CPU running four gEMpicker threads in parallel, the speed-up from using one contemporary GPU is a factor of ∼ 9×. We have shown that increasing the number of GPUs speeds up the calculation linearly with almost no additional overhead. We have also demonstrated how the picking task may be distributed over multiple nodes in a computer cluster.
Availability and requirements Project name: gEMpicker Project homepage: http://gem. loria. fr/gEMpicker. html Operating system(s): Linux OS Programming language: C++, CUDA Other requirements: Boost 1. 49 or higher, FFTW 3. 3 or higher, CUDA Toolkit 4. 2 or higher License: Unlimited for academic use Any restrictions to use by non-academics: license needed Endnote a Available at http://ami. scripps. edu/redmine/projects/ ami/wiki/KLH_dataset_I.
Herschel/ Pacs observations We observed HD 113766 A with the Pacs photometer  (Poglitsch et al. 2010)  onboard Herschel  (Pilbratt et al. 2010). The dataset consists of 4 Pacs mini-scan maps (program "OT1_jolofsso_1"), two in the blue filter (70 µm, OBSIDs 1342227026 and 1342227027), and two in the green filter (100 µm, OBSIDs 1342237934 and 1342237935). The observing duration was 220 × 2 sec in the blue filter and 670 × 2 sec in the green filter. When observing with Pacs with the blue camera (blue or green filters, at 70 and 100 µm, respectively), data is also simultaneously acquired with the red camera (160 µm). We therefore combined the 4 mini-scan maps in the red filter to obtain the photometry at 160 µm. The data were processed using the Herschel interactive processing environment (HIPE, version 10.
0. 667), using standard scripts that include bad pixel flagging, detection and correction for glitches and HighPass filtering in order to remove the 1/ f noise (see  Poglitsch et al. 2010  for more details). We discarded the first column of each detector sub-array in order to eliminate electronic crosstalk. Finally, we processed the data with different HighPass filter widths in order to test the robustness of the data reduction. The different values are tabulated in Table  1  (column "HP"). The aperture photometry in all three filters was extracted within HIPE from the combined maps. The aperture radii are set at 10 for the blue and green filters and 20 for the red filter. The final flux values do not depend on the aperture sizes since an aperture correction is performed to compensate for flux loss outside of the aperture.
This correction is based on a calibrated encircled energy fraction parametrisation contained in the calibration data that is included in HIPE. In order to estimate the noise level and thus the quality of the detection in all filters, we randomly placed apertures within a distance of 100, 75, and 50 pixels (for blue, green, and red filters, respectively) to the central pixel. Doing so, we avoided borders of maps that are more noisy due to a smaller detector coverage, and are therefore not representative of the noise close to the source. We extracted the photometry for all of these apertures and then fitted a Gaussian profile to the flux distribution histogram to estimate the σ uncertainty from the width of the Gaussian. Overall, these uncertainties are consistent with predictions from the HerschelSpot 1  software.
(2012). These observations were performed on the night of the 2009-May-11 in low resolution mode as well, for two spectral windows centered at 8. 8 and 11. 4 µm (program 083. C-0775(E)). Both datasets were processed using standard scripts provided within the ESO Gasgano software 2. Right panel of Figure  2  shows the Irs and Visir spectra obtained in  2004, 2009, and 2012  (open black circles, thick red, and thin blue lines, respectively). One should note that both Visir spectra, taken almost three years apart agree surprisingly well. However, the spectrum obtained in our program in the 9. 8 µm spectral window displays an offset of about 10% higher in flux. Such "stiching" problems are not unusual, but the exact causes are not clear. We checked the atmospheric conditions during our observing program: the seeing and atmospheric pressures appear to be constant over the run.
Besides the 9. 8 µm band offset, both Visir spectra show a deficit of flux of about 30% compared to the Irs spectrum. Smith et al. (2012)  already discussed in detail these differences (e. g. , spatially extended emission) and concluded these differences to be within the absolute calibration uncertainites of Visir (see discussion in  Geers et al. 2007 ). The archival Midi data presented in  Smith et al. (2012)  were processed using scripts dedicated to observations of faint targets with Midi (see  Burtscher et al. 2012  for more details) interfaced with the MIA+EWS software 3. Table  2  summarizes the observing log for the interferometric measurements. As in  Smith et al. (2012) , we found that the observations performed on the night of the 2007-May-30 (program 079. C-0259(F)) had a low signal to noise ratio, and we did not include them in our analysis.
The calibrated visibilities and correlated fluxes for HD 113766 A are displayed in the left and middle panels of Fig. 2. The Irs spectrum is also shown for comparison to the correlated fluxes. Be-2 http://www. eso. org/sci/software/gasgano/ version 2. 4. 0 3 http://home. strw. leidenuniv. nl/∼jaffe/ews/ version 2. 0 cause of the challenge of calibrating accurately the Visir spectra, we used the Irs spectrum to compute visibilities from the correlated fluxes. The calibrated dataset compares very well with the data reduction of  Smith et al. (2012) , except for one single run (083. C-0775(B), observed at 05:32:55). When calibrating these observations with their corresponding calibrator (HD 112213, observed at 05:15:45), we found a correlated flux much higher than the Irs spectrum. We constructed the transfer function based on all the calibrators observed during that night.
The best-fit disk model is found via a Monte-Carlo Markov Chain (MCMC) exploration of the parameter space (emcee package, Foreman-Mackey et al. 2012). All the uncertainties quoted in the next sections represent the standard deviations of all the values tested during the fitting process. In the following, we will first present a model of the debris disk assuming that the dust is distributed in a single belt around the central star (referred to as model "1B" for "one belt"). We will test the robustness of this model, comparable to the one presented in  Olofsson et al. (2012) , with respect to the new far-IR photometric observations and mid-IR interferometric measurements. The Herschel data provide new constraints on the cold counterpart of the debris disk, while the mid-IR Midi data are more sensitive to the warmer dust and provide direct information on the geometry of the dust distribution.
As a matter of fact, the interferometric observations suggest the region emitting in the N-band is compact ( 10 mas, or 1. 2 AU in diameter, according to  Smith et al. 2012). On the other side, our previous best-fit model described in  Olofsson et al. 2012  was found to be fairly extended (r out ∼ 12 AU, with a density profile with an exponent of α = -1. 8). We can therefore already anticipate that fitting the SED and the Midi data simultaneously will be challenging. In order to compare our model to the Midi and Visir observations we updated the Debra code to produce synthetic images at different wavelengths. To do so, at a given wavelength, we distribute the thermal surface brightness distribution (F ν (r), between r in and r out ), assuming the disk is axisymmetric and infinitely flat. Possible inclinations effects are taken into account by projecting pixels of the image onto the sky plane.
This is also true for the Visir observations. The modelled surface brightness profiles over-predict the observed 2D Gaussian profiles assumed to be representative of the PSFs, in the N-band and even more in the Q-band (the disk was not resolved in the Visir observations). As a final test, we tried to include the Midi observations in the fitting process (by summing the reduced χ 2 r from the fits to the SED and Midi data), but could not achieve any better results. Both the Midi and the mid-to far-IR part of the SED are underpredicted. This can be explained as a tradeoff between the compactness of the dust belt and its contribution in the far-IR. Under the assumption of a single dust belt, the fits to the SED and interferometric data are diametrically opposed. From a modelling point of view, the main conclusion of this exercise is that SED modelling can be misleading: as shown in the upper left panel of Fig.
Two belts (model "2B") Several recent studies have shown that debris disks may contain several planetesimal belts (e. g. ,  Morales et al. 2011  or the disk around τ Ceti, di  Folco et al. 2007; Greaves et al. 2004 ). We have shown in Sect. 3. 1 that the assumption of one single dust belt around HD 113766 A cannot account for all the available dataset. We can therefore start discussing the possibility of two, spatially separated dust belts around the central star: an inner dust belt, compact, close to the star that can reproduce the Midi data and the near-to mid-IR flux, and a second dust belt, farther away from the star that will contribute to the emission in the far-IR. To account for an additional belt, the modification to be applied to the Debra code is straightforward as we simply duplicated the computations done for one dust belt to the second one.
4. 1). We set the minimum grain size to s min = 4 µm for all dust species in the outer belt, as there are no observational evidences, such as emission features at long wavelengths, that would require including smaller dust grains. Because we assumed in our model that crystalline dust grains have sizes smaller than 1 µm, they are not accounted for in the outer belt (see Sect. 4. 3 for further discussion). This time, for each model of the MCMC run we fitted the SED and the Midi observations simultaneously. For the inner dust belt, we obtained a best fit to the data with dust being located between (0. 6 ± 0. 1) AU and (0. 9 ± 0. 2) AU, a density profile in α = -1. 7 ± 0. 3, and a grain size distribution exponent of -3. 5 ± 0. 1 (consistent with the prediction for a collisional cascade p = -3.
Model Belt # r in r out α p M dust  [AU]  [AU] [M ⊕ ] One belt ("1B") 1 0. 4 ± 0. 2 51. 9 ± 25. 2 -1. 9 ± 0. 3 -3. 0 ± 0. 1 1. 3 × 10 -2 Two belts ("2B") 1 0. 6 ± 0. 1 0. 9 ± 0. 3 -1. 7 ± 0. 3 -3. 5 ± 0. 1 9. 0 × 10 -5 2 9. 0 ± 1. 4 13. 1 ± 3. 4 -1. 2 ± 0. 3 -3. 8 ± 0. 2 2. 4 × 10 -3 (17 AU at 123 pc) for the maximal extent of the disk  (Smith et al. 2012). Disk parameters for the best-fit model are reported in the last two lines of Table  3. The lower four panels of Fig. 3  show the results for the "2B" model, with the same arrangement as described in Sect. 3. 1. For the left and middle left panels (SED and Irs spectrum) the total fit is shown as a solid green line, while the two contributions of the inner and outer dust belts are represented as red and blue solid lines, respectively. Compared to the "1B" model, the best-fit model is much more compact and therefore both the Midi and Visir data are much better reproduced (except the "problematic" Midi run discussed in Sect.
However, we further pushed our modeling effort and tried to fit the dataset with a single dust belt that shows a "break" in the density distribution (i. e. , a single but not continuous belt as opposed to Sect. 3. 1). The motivation being to mimic an inward radial drift of dust grains, followed by the formation of a dust ring at the sublimation radius, as described in  Kobayashi et al. (2009)  for instance. We therefore run a new set of models, for which we force the inner radius of the outer belt to be the same as the outer radius of the inner belt (seven free parameters instead of eight). The best fit results for the SED and the Midi data could not reach the quality of the best-fit "2B" model. We find that the disk extends from 0. 6 AU up to 16. 5 AU, with a break in the density at about 0.
Discussion Results presented in Sect. 3 show that the available dataset for HD 113766 A can be best reproduced under the assumption of two spatially separated dust belts. Even though the SED alone can be reproduced under the assumption of a single dust belt, the Midi observations proved extremely useful to constrain the spatial extent of the region responsible for the emission in the N-band. If the inner dust belt extends farther out than ∼ 1 AU, the modelled Midi visibilities are greatly under-predicted at all baselines. Our model is in good agreement with the results of  Smith et al. (2012)  who modelled the Midi data with either a symmetric Gaussian model with a FWHM of 10 mas (1. 2 AU in diameter) or a ring model with a radius of 6 mas (∼ 0. 7 AU). Our modelling results, supported by the new Pacs observations enabled us to go one step further and demonstrate the presence of an additional planetesimal belt farther out in the system.
Conclusion In this study, we presented a new model for the debris disk around HD 113766 A. This model accounts for new far-IR photometric observations as well as archival mid-IR interferometric measurements. We underlined the limitations of SED modelling and the importance of spatially resolved observations. Our approach of modelling simultaneously the SED from near-IR to far-IR wavelengths and interferometric measurements has enabled us to have a deeper understanding of the morphology of the debris disk around HD 113766 A. We found that the available dataset can be best explained by two spatially separated dust belts. A first inner dust belt is located within 1 AU from the star, a distance well constrained by the Midi observations. This inner belt contains warm sub-µm-sized dust grains. These small grains are responsible for the emission features observed via mid-IR spectroscopy.
able insight into the dynamics involved for extreme catchment response and their dependency on physiographic properties and flood severity. The main objective of this study is to analyze flash-flood-dedicated hydrologic model sensitivity with a new approach in hydrology, allowing model outputs variance decomposition for temporal patterns of parameter sensitivity analysis. Such approaches enable ranking of uncertainty sources for nonlinear and nonmonotonic mappings with a low computational cost. Hydrologic model and sensitivity analysis are used as learning tools on a large flash flood dataset. With Nash performances above 0. 73 on average for this extended set of 10 validation events, the five sensitive parameters of MARINE process-oriented distributed model are analyzed. This contribution shows that soil depth explains more than 80 % of model output variance when most hydrographs are peaking.
Seven elderly persons from a city near Madrid, Spain, participated in in-depth interviews carried out in-situ, in their homes. The results from the qualitative data analysis indicated a great variability in the coping capacity of the participants. Feelings of loneliness and lack of motivation appeared as common burdens in their lives. Robot roles were proposed that could help fulfil the needs of independent elderly people. Self-efficacy and other related constructs were identified to have an influence on older people' s motivations and their predisposition to disability. Finally, a "motivational" robot role was proposed that could enhance the self-efficacy of independent elderly in physical therapy contexts, hence decreasing their risk of losing independence.
The knowledge-driven rule-based activity recognition system used  [18]  has three main advantages over learning based methods  [17, 68]  that 1) it does not require large variety of datasets (which are difficult to obtain from our target group of elderly people), 2) rules defining each activity can easily be adapted to similar environments (i. e. through sensor remapping), and 3) rule definitions for new user activities can be easily created. This system is very similar to other knowledge-based methods  [3, 34, 59 ]. Current work focuses on improving the Care-O-bot' s proxemic behaviour when approaching the user for interaction. Literature has indicated the importance of proxemics in human-human interactions  [1, 32, 58]  as well as in human-robot interactions  [41, 74, [78] [79] [80]. Findings from the human-robot interactions literature has also indicated that users' proxemic preferences vary depending on the robot' s appearance, context of interaction as well as their robot experience and familiarity with the specific robot they are interacting with.
Our goal was to make such facilities available to non-technical personnel such as the elderly persons themselves, carers or relatives. The underlying ideas for the approach are based both on 'behaviour based' approaches and 'deliberative' architectures  [2, 21, 53, 81]. The learning approach is based on previous work described here  [65]. The learning architecture was implemented following an analysis of the various robot components, user needs via scenario conceptualisation  [44]  and an analysis of the robot house ontology. This analysis led to a design for a centralised relational database which formed the central memory hub and overall ontology for both the robot, the house sensory network and the users. Additionally the database has been designed to support the procedural and behavioural components for the robot including behavioural rules, actions, goals and pre-and post-conditions.
Actions on the robot can be physical, sensory (light/speech), virtual (setting new conditions/goals) or user generated via calls to the users tablet computer. The interface also allows direct creation of python programs to control the robot directly. costs  [30, 51]. Global methods are usually slow in computation (order of seconds and more) but very accurate whereas local methods compute fast while producing inaccurate and blocky depth estimates. The proposed method  [23]  is novel in the sense that it uses semi-global optimization for fusing the depth estimates from stereo vision and a depth sensor to yield accurate depth maps with a speed of 10 Hz on a Intel i7-2860QM with 2. 5 GHz. In particular, it first undistorts and rectifies the colour and range images. The projected time-of-flight measurements serve as a first guess for the disparity computation from the rectified stereo images.
We show that the two persons (P1 and P2) in the camera image are also detected by the laser scanner. In our system, the two data source are fused to jointly estimate human locations. erfaces  [7]  that asserts the name of the found person if the person is in the database of known people or tells that the person is new to the robot. To increase the robustness of face recognition, each face is preprocessed by gamma correction  [27]  and discarding the low-frequency Discrete Cosine Transform coefficients  [14]  to decrease the sensitivity against different lighting and shadows. Furthermore, the head orientation is determined by finding the eyes and the nose, and then a virtual frontal perspective is computed for each face. This measure limits errors originating from faces that are poorly aligned with the camera.
Integration workflow Thinking early about integration is the key to lead a robotics project with multiple partners to success. A good start in ACCOMPANY was to formulate project goals and present all project partners with the current status of hardware and software right at the beginning. Consequently, necessary hardware modifications could be identified immediately, as detailed in Section 2. 5. 2. Furthermore, apart from the available software modules a list of required functionalities was established quickly. Dividing functionalities into self-contained software modules allowed to distribute necessary development work suitably among the project partners. To simplify the later integration of software modules developed by numerous people it has proven very valuable to define clear interfaces between modules at an early project stage.
Using the robot operating system ROS as common middleware, which offers a couple of standardised ways for communication with a large set of defined message types, supported the early definition of communication channels between software modules further. Consequently, the experiences from integrating the first scenario showed that many components in ACCOMPANY could work together quickly because of the preparation ahead. As the project proceeds with more sophisticated scenarios for the second and third year many components will have to mature with more elaborate functionalities or algorithms. To implement new functions in an ordered manner without harming the whole system to fail the following integration process, developed from the experiences in a German research project with many partners integrating their components into one common demonstrator  [61] , has been adopted in ACCOMPANY.