dataesr/scientific-paragraphs-categorization huggingface dataset:
Dissemination The Dissemination component provides all software that is needed to access data, software and documentation. The data access system enables the user to browse the CTA archive and to retrieve some data. Selection of data sets of different categories will be allowed with various criteria (e. g. , pointing position, observation epoch, data quality, etc. ). The possibility to adapt an existing Browse System (such as HEASARC's Xamin) is under study, but also a custom development that could be embedded in a VO environment is under investigation. Figure  4  shows a screenshot for a prototype of such a Browse System that is developed using the Django, jQuery and Bootstrap frameworks. Such a system, embedded in the CTA Science Gateway, would provide access to predefined CTA event data, but would also allow to process these data with some analysis tools and visualise the data using VO plotting tools.
In addition to the Browse System, the development of an Event Pruner is being considered. This Event Pruner would give the user the possibility to retrieve customised event lists built according to user defined criteria (sky position, time range, energy range, cuts, hadron/gamma/electron-ess). In addition to event lists, high-level products such as images, light curves, spectra, and catalogues will be distributed through a dedicated VO Data Server. This will provide a standard access to the CTA archive and provide an easy mechanism for discovering data, data mining and cross correlation of multi spectral informations. It is also envisaged to generate high-level VO compliant data products on the fly according to user specified wishes and source models.
PoS(ICRC2015)781 PoS(ICRC2015)781 sortium for science simulations, as illustrated in Fig. 5. The software is also currently tested using data from the existing Cherenkov telescopes H. E. S. S. and VERITAS. The performance of the Science Tools will be validated using Science Challenges. In each Science Challenge, simulated high-level science data will be made available covering all major science topics and possible observations of CTA. The CTA Consortium (and eventually even a larger user community) will then use the current version of the Science Tools to assess the performance of the system with respect to the expected science goals. This includes aspects such as the easiness of performing science analysis, the flexibility of the tools, the performance of the tools concerning accuracy of science results, achieved sensitivity, and computing time.
Conclusions The Cherenkov Telescope Array is a unique observatory for very high-energy gamma-ray astronomy that will benefit science in the fields of astronomy and astrophysics, particle physics and astroparticle physics, cosmology, plasma physics and fundamental physics, by providing observers worldwide with unprecedented data on astrophysical objects over an extensive range of gammaray energies. Community access to CTA data is a backbone of the observatory, and the Observer Access work package will provide the services and tools that are necessary to grant a seamless and easy access to the facility and its data. Following the successful model of other observatory-type infrastructures, the Observer Access work package will implement web-based proposal handling and data dissemination systems, provide easy-to-use open source tools for data analysis, and support users throughout the full cycle from applying for observing time to analysing the data.
Another paper reviews moduli stabilization in type IIB string theory compactification with fluxes. Another paper in this special issue describes supermultiplets wherein a continuously variable "tuning parameter" modifies the supersymmetry transformations. Another paper studies the constraints imposed on the Minimal Supersymmetric Standard Model (MSSM) parameter space by the Large Hadron Collider (LHC) Higgs mass measurements and gluino mass lower bound. Another paper studies helical phase inflation which realizes "monodromy inflation" in supergravity theory. Another paper considers scalar Wilson operators of  = 4 Supersymmetric Yang-Mills (SYM) theories at high spin and generic twist operators in the multicolor limit. Another paper author gives an overview about the features that the Mathematica package SARAH provides to study new supersymmetric models.
The green envelope model embedding into TRNSYS is undertaken by creating a new component type (see Fig. 3 ). When a new user component is created  in the software environment, this one can be used in the same way as any other standard TRNSYS component. Physically, green wall is adhered to an outer wall of the building. Numerically, it goes through a coupling to ensure the continuity of heat flux and temperature values at the interface for each time step of the simulation. See references  [23, 25]  for more details on the numerical development method.
Parallel to the modelling approach, a green wall was set up recently at the University of La Rochelle in order to be studied in real weather conditions (see Fig. 6 ). Actually, the green wall is monitored on a reduced scale mockup of building. Data collected on this mockup will make possible analyzing the thermohydric behavior of the green wall in connection with weather data collected in situ. In addition the same experimental mockup was used to study another kind of green walls which use sphagnum moss as growing medium  [4, 26]. Hence, this experimental  approach will make possible to compare the two green wall technologies on the same building test.
Results and discussion In this part, the green wall model embedded into TRNSYS is used to evaluate the thermal impact of this kind of green walls on buildings energy performance. The case study is a three-story building whose walls are made of 20 cm cinder block and the roof and ceilings made of 12 cm concrete. The building envelope is has inner insulation of 5 cm thick. The east and the west façade have glazing surfaces that represents 20% of the floor area. No internal gains are considered. The infiltration rate is set at 0. 6 ACH. The albedo of reference facades is set to 0. 5. To evaluate the energy effects of Vertiss TM green walls on this building, numerical studies have been conducted for this building with and without the use of the green wall module. So, for vegetated building case, both east and west facades are vegetated.
The green wall substrate depth is 10 cm and its saturation ratio is fixed at 75%. The vegetation coverage ratio is set at 1 and the leaf area index is set at 4. Standard weather data of two different climates are used. The first is the La Rochelle climate and the second is Casablanca weather data. Annual dynamic thermal simulations were performed for the studied building for both climates. The cooling system operates from May to September and the heating system is turned on from October to April. The cooling temperature is set at 26. C and the heating temperature is set at 19. C during day and 15. C during night. The calculated cooling and heating loads are compared for the reference building and for the same building covered with the green walls (see Figs. 7  and 8 ). In summer, the green walls tested on the east and west facades of the building reduce the cooling load in both studied climates (see Fig.
Conclusion In this paper we have adapted the green wall model integrated previously into TRNSYS to simulate the thermal behavior of an innovative green wall. To assess the impact on building energy performance of this kind of green walls, two case studies with and without vegetated east and west facade have been studied for two different climates. It has been found that the green wall impact is more significant for hot climates since it reduces substantially the cooling loads but less the heating loads. Future work is to refine thermal modeling of this kind of green walls and to compare the numerical results with experimental data being collected. Also, sensitivity studies will be conducted to determine the importance of different parameters in order to propose a reduced thermal model. ).
Introduction Manufactured mechanical parts often have geometrical differences compared to their nominal CAD models and are often inspected for these differences during quality control. This inspection is typically performed in two steps: First, preliminary geometric data of the part in its state-of-use position (usually in the form of scanned point clouds or stereolithography (STL) files) are gathered. Next, the gathered data are processed using computer-aided inspection tools (CAI) designed to identify location and magnitude of a number of manufacturing defects (profile tolerance). Though this twofold inspection routine has gained considerable popularity, it is currently limited to parts that are reasonably rigid. Some parts such as skins, parts with thin walls, which are referred to as flexible (or nonrigid or compliant), have a considerably different shape in a free state compared to their nominal CAD models due to the effect of gravity and/or residual stress.
In fact, the geometric deviation of flexible parts is mostly due to such elastic deformations rather than manufacturing defects. As a result, to correctly identify all or the majority of defects, traditionally, one is required to first set up standard or specialized conformation fixtures that would hold the part in the position defined in its nominal CAD model. It is only then that it becomes possible to gather the preliminary geometric data of the part for subsequent analysis in CAI software. A number of downsides exists in using fixtures such as: their time consuming set-up process, considerable acquisition and operation expenses, limitations of standard fixtures in some scenarios, etc. Disadvantages of this sort have led researchers to try to circumvent use of fixtures by digitally deforming (or better called registering) the gathered point cloud data of a flexible part in Euclidean space until it matches the part's corresponding nominal CAD model, thereby elastically deforming the data to reach an optimal assembly shape whilst avoiding neutralization of any existing manufacturing defect.
Similarly, comparing the corresponding stretch differences matrices (eq. 3) shows which algorithm has better respected the intrinsic material properties of the part during registration. Furthermore, though the main focus of this paper is on flexible registration rather than identification of manufacturing defects in scanned parts, nine instances of potential contour (profile 2D) defects were added to the virtual case study and their magnitudes were later manually measured by comparing the aforementioned OPTREG output meshes and the nominal CAD mesh of the case study in PolyWorks CAI software. An illustration of these defects which can be categorized under two types (holes' center offset, and internal cut-outs' offset) are depicted in Fig. 2-a. Naturally, a negligible difference between the measured defect value and the known actual value of that artificial defect in the virtual case study means a satisfactory estimation of the defect magnitude has been achieved.
Conclusion In this paper, a new methodology was proposed to deal with the problematic of fixtureless inspection of flexible parts, which is a real concern in the industry due to the costs it imposes. An algorithm (BOFR1) was developed and tested on a virtual industrial case study. Satisfactory results reflected on the algorithm's capability and precision. Adequate estimation of defect magnitudes demonstrated the potential of the approach for quality control purposes, and in turn forecasting the mountability of parts in an assembly. Future work to be undertaken include: benchmarking the methodology on more case studies (BOFR1 has only been tested on quasi-planar parts), and development of fully automated defect identification tools to calculate defect magnitudes as opposed to the manual measurements conducted in this study via CAI software.
, 2004). The analysis also showed a similar surface density of pits (Figure  4D ) and the presence of a small number of persistent AP-2 spots (likely coated plaques) on the bottom surface of the cells (Figure  4E ). The density of persistent objects increased with the duration of plating (unpublished data), as often observed with other adherent cells studied in vitro  (Ehrlich et al. , 2004; Cocucci et al. , 2012 Cocucci et al. , , 2014)). Rare persistent AP-2 objects (∼0. 1% of events) were also detected on the top surface (Figure  4E ); the coat geometry of such objects remains to be determined. Similar results were obtained using SUM AP-2. 2 and SUM AP-2. 3 cells gene edited in both alleles to express σ2-eGFP and μ2-eGFP, respectively (Supplemental Figures  S1  and S2 ). We took advantage of the high spatial precision of the LLSM data to determine the distance from the plasma membrane of all coated vesicles immediately before uncoating.
As expected, the data showed that the surface area decreased as cells entered mitosis and recovered rapidly as they exited. The recovery was complete before abscission. From the same 3D LLSM data sets used for the surface area calculations, we determined the volume of the SUM-AP-2. 1 cells (analyzed using the AP-2 signal) and BSC1 cells (analyzed using the surface marker) as they transitioned from late stages of mitosis through cytokinesis. The volume of individual metaphase cells was slightly larger than that of prometaphase cells and decreased slightly or remained relatively constant afterward, as determined by the combined volume of the two daughter cells (Figures  5C  and 6B ).
The rapid increase in surface area was accompanied by plasma mem- time point within the tissue volume of the eye is shown in Figure  7F. Supplemental Movie SM 5 illustrates two cells undergoing division located visually and whose segmented cell boundaries were verified manually. We used a custom MATLAB routine to track cells as they divided and then used their cell boundaries, determined by ACME, to calculate the variation of surface area and volume with time. Figure  8  shows a dividing cell within a representative volumetric view of the eye and three planes for five different time points. As in the aforementioned measurements of cultured cells, the area of mitotic cells in the eye and spine increased at the onset of cytokinesis and then was relatively constant during the remaining phases of cytokinesis (Figure  9 ).
DISCUSSION The experiments we report here illustrate the quantitative application of three new and powerful features of lattice light-sheet microscopy. The first is time resolution of ∼2 s across an entire cell for repeated periods of several minutes with low photodamage, high signal-to-noise ratio, and excellent 3D resolution. We used this property to determine the assembly dynamics of most endocytic coated pits and vesicles that form at the plasma membrane of cells during interphase and cell division. The second is determination of changes in the area and software, which reconstructs the cellular boundary and segments out cells from 3D membrane images in dense tissues  (Mosaliganti et al. , 2012 ; see Materials and Methods). An example for a single two. In addition, the speed and high axial resolution of LLSM permit many more optical sections to be acquired over the thickness of the cell, permitting cells to be studied over their entire dorsal surface.
Most 3D renderings were generated using FEI's Amira versions 5. 6 and 6. 0. Both LLSMs were operated using custom LabVIEW (National Instruments) software on a 3. 47-GHz Intel Xeon X5690 workstation with 96 GB of memory running a Windows 7 operating system. Postprocessing of raw data volumes. Data volumes were acquired as multipage TIFF files, with each slice of a volume corresponding to a cross-section of the sample. The images in the raw data appeared as a shear-transformed representation of the sample because the samples visualized with scan mode (sample moved along the s-axis) were scanned at a fixed angle to the glass coverslip of 32. 8° (LLSM. 1) or 31. 5° (LLSM. 2). To enable data processing in real-world coordinates, these volumes were deskewed using an inverse shear transform (imwarp function in MATLAB).
, , 2014)) , except that the spherical aberration correction system was replaced by a 2× magnification lens placed in front of the charge-coupled device (CCD) camera, such that each pixel corresponded to 80 nm in object space. The incidence angle of the excitation light was adjusted to generate an evanescent field with a penetration depth of ∼100-200 nm. The illumination power at the back aperture of the objective was 3. 3 mW. Time series were acquired with an exposure of 60 ms/frame imaged every 150 ms. The dynamics of cytosolic AP-2 incorporation during the first 5 s of pit formation was determined from TIRF data with single-molecule sensitivity acquired using gene-edited SUM-AP-2. 1 cells as described  (Cocucci et al. , 2012 (Cocucci et al. , , 2014)). The centroid of diffractionlimited spots was obtained using the u-track software  (Jaqaman et al.
, 2013). We verified the validity of this approximation for LLSM data by testing areas of homogeneous background and residual signals for normality using the Anderson-Darling test. In the proposed framework, the 3D image of a coated pit was thus modeled as x x x h Ac Ag c n [ ; , , ] [ ; [ ] µ µ µ µ, , σ σ = ] + + where x y z [ , , ] x x = are discrete voxel coordinates, A is the fluorescence amplitude, c is a constant representing the local background intensity for the coated pit, and µ µ µ, , σ σ] = -- + g x [ ; exp( (( ) x 2 x y ( ) )/ (2 ) y x y 2 2 -µ σ --µ σ z ( ) / (2 )) z z 2 2 defines the Gaussian approximation of the microscope PSF. The SDs σ xy and σ z , denoted vectorially as σ, are fixed parameters and will henceforth be omitted from the notation for simplicity; their estimation is described at the end of this section.
Using the foregoing model, we obtain a pixel-level mask of significant signal positions by minimizing v h A c f ( [ ; , , ] [ ]) x x s 2 x k x 0 ∑ = - - ∈ at each voxel k in frame f, i. e. , by minimizing ∑ = + - - ∈ k k v A g c f [ ] ( [ ] [ ]) x x s 2 x k x where g[x] denotes g[x; 0]. Specifically, estimates of the amplitude A and local background c for the Gaussian centered at each voxel k are obtained by solving the system Implementation. All image and data analyses were carried out in MATLAB, using custom-written software available from https:// github. com/francois-a/llsmtools.
2. The algorithm was implemented as a C/MEX function for MATLAB (MathWorks, Natick, MA) using nonlinear optimization routines from the GNU Scientific Library (https:// www. gnu. org/software/gsl/). These routines provide a robust and efficient implementation of the Levenberg-Marquardt algorithm. The SDs σ xy and σ z of the 3D Gaussian PSF were precalculated by fitting the model to an average of bright coated-pit signals or to PSF measurements obtained with fluorescent beads with the SDs as free parameters. Automated tracking of clathrin-coated structure dynamics. Coated pit trajectories were calculated from the detections obtained in individual frames with the u-track software package  (Jaqaman et al. , 2008) , using the parameters described in  Aguet et al. (2013). The u-track software implements tracking in a generic n-dimensional framework, and thus few adaptations were required.
Surface area and volume measurements. The signal-to-noise ratio of cytosolic signal relative to background in SUM-AP-2. 1 cell data was sufficiently high to apply threshold-based segmentation to generate a mask of the cell outline. Specifically, cell masks were obtained by 1) smoothing the deskewed data volumes with a Gaussian filter (SD = 2) and interpolating to isotropic voxels, 2) applying the Otsu thresholding method, and 3) closing potential holes resulting from lower fluorescence at chromatid locations (using the imfill function in MATLAB). Surface area and volume traces extracted from BSC1 cells and zebrafish eye and spinal tissue were smoothed using the smooth function with the default parameters in MATLAB. in 1× Danieau buffer at 22 ± 2ºC starting at 17-22 h postfertilization. A light sheet was generated using a square lattice configuration in dithered mode.
5° and stored as 32-bit images to avoid rounding errors from the interpolated image slices; the image stacks were then cropped to 768 × 768 × 251 pixels and used for surface area and volume determinations. The LLSM. 2 was under control of custom LabVIEW (National Instruments) software operating on a 3. 47-GHz Intel Xeon X5690 workstation with 96 GB of memory under Windows 7 operating system. As indicated, the zebrafish figures and movies were deconvolved for 15 iterations using the Lucy-Richardson algorithm in MATLAB with an experimentally measured PSF. This postacquisition processing was done immediately after acquisition using a 3-GHz eight-core Intel Xeon E5 Apple Mac Pro with 128 GB of memory. Detection and analysis of surface area and volume in isolated BSC1 cells and cells in zebrafish embryos.
The surface area and volumes were calculated in MATLAB using the generated (and manually corrected, when necessary) watershed masks. The original ACME segmentation source code was modified and compiled to take advantage of the dynamic range of the 16-bit images acquired from the BSC1 cells expressing the eGFP-CAAX fluorescence plasma membrane marker, and the subsequent processing of segmentation corrections and surface area/ volume calculations were performed as described for the zebrafish images.
ACKNOWLEDGMENTS We thank  Ian Swinburne  from  Sean Megason's laboratory  for helpful advice and discussions associated with the zebrafish work and  Margaret Robinson  for the CD chimera constructs. S. U. is a Fellow at the Image and Data Analysis Core at  Harvard Medical School  and thanks  Hunter Elliott  and  Tiao Xie  for helpful discussions. T. K. gratefully acknowledges support from the  Janelia Visitor Program. The construction of the lattice light-sheet microscope LLSM. 2 in the  Kirchhausen laboratory  was generously supported by grants from  Biogen  and.
FLEA back-end A) The workflow outlining the FLEA back-end is shown in Supplementary Figure  S1. It can be divided into five sections: B) Quality and contaminant filtering. C) Generating High Quality Consensus Sequences (HQCSs). D) Aligning HQCSs. E) Preparing HQCSs data for visualization. F) Mapping CCS reads into HQCS alignment. A) Using the QV scores provided in the. FASTQ files, the expected number of errors per sequence is calculated using USEARCH  (Edgar 2010) , and CCS reads are filtered on a userselectable threshold (we recommend !99 per cent expected accuracy). CCS reads are then aligned to a potential contaminant reference database containing NL4-3 and HXB2 env sequences, and any reads with !98 per cent identity to these are removed. CCS reads are then mapped to the nearest sequence in the Los Alamos National Laboratory subtype reference alignment, retaining reads with 80 per cent identity to an env reference sequence.
Accessing data Data for several samples presented here have been deposited into the NCBI Sequence Read Archive under BioProject PRJNA320111, including the NL4-3 clone, the NL4-3/CH040 mixture to evaluate PCR recombination, and six time points from donor P018. FLEA pages for donors P018, K453, and H497 can be accessed at http://test. datamonkey. org/flea-demo/P018_ kinetics/, http://test. datamonkey. org/flea-demo/K453_kinetics/, and http://test. datamonkey. org/flea-demo/H497_kinetics/.