dataesr/scientific-paragraphs-categorization huggingface dataset:
For all of these tests, for DES we use the synthetic data vectors (for the baseline case and the systematic shifts described below), but for the external data sets -CMB, BAO, RSD, and SN Ia -we use the actual, observed data vector. The changes to modeling assumptions that we consider are: 1. Baryonic effects: we synthesize a noiseless data vector including a contribution to the nonlinear power spectrum caused by AGN feedback using the OWLS AGN hydrodynamical simulation  [128]  and following the methodology of  [40]. 2. Intrinsic alignments, simple case: we synthesize a noiseless data vector with the IA amplitude A IA = 0. 5 and redshift scaling η IA = 0. 5 using the baseline nonlinear alignment model used in Y1KP. While we explicitly marginalize over these IA parameters in our analysis, this systematic check is still useful to monitor any potential biases due to degeneracy between the cosmological parameters and (A IA , η IA ) and the presence of non-Gaussian posteriors.
B. Validation of assumptions using DES data In addition to the tests in the previous section that constrain potential biases due to our modeling assumptions and approximations on synthesized noiseless data, we implement several validation tests that modify how we analyze the actual DES data vector. In particular, we test the following assumptions: 7. Intrinsic alignments, free redshift evolution: while the fiducial analysis assumes IA to scale as a power-law in redshift (see Sec. III B), we relax that here by assuming four uncorrelated constant amplitudes per source redshift bin. 8. Conservative scales: to gauge how our results depend on the range of angular scales used, we adopt the conservative set of (basically linear) scales used in the modified gravity extension, and apply it to the other three extensions (curvature, N eff , dynamical dark energy).
2  and 7 , apart from those that have a known, statistical explanation (see Sec. IV A). We have made two modifications to the analysis after the results were unblinded. First, we identified that the incorrect Planck data file (PLIK LITE V18 TTTEEE. CLIK) was used for our (w 0 , w a ) results and reran these chains with the correct file (PLIK LITE V18 TT. CLIK). We verified that this modification does not lead to appreciable differences in the final constraints, though it does lead to a difference in the reported Bayesian evidence ratios for this case. Second, we adopted the GetDist code to evaluate the marginalized posteriors, as it is more suitable to handle boundary effects in the posteriors  [136]. This leads to small differences in cases where the constraints are strongly informed by the prior boundaries, such as N eff.
DES Y1 = 0. 0020 +0. 0037 -0. 0032 DES Y1 + External  (13)  while for the number of relativistic species, the lower limit hits against our hard prior of N eff > 3. 0 so we quote only the 68% (95%) upper limits N eff < 5. 28 (-) DES Y1 < 3. 28 (3. 55) DES Y1 + External. ( 14 ) where the dashes indicate that we do not get a meaningful upper limit from DES alone at the 95% since the constraint hits against the upper limit of our prior. Figure  3  indicates that DES alone constrains curvature weakly, showing mild (∼ 1-σ) preference for positive values of Ω k ; note also that this constraint is informed by the upper prior boundary. The DES-only constraint on N eff is also relatively weak, and is fully consistent with the theoretically favored value N eff = 3. 046. Moreover, the DES Y1 data do not appreciably change the existing external-data constraints on these two parameters.
be understood as follows. The DES data prefer a lower Ω m than the external data, leading to a slight increase in h such that the posterior distribution in Ω m h 3 is downweighted at the high values of this parameter combination. Because Ω m h 3 is highly correlated with N eff -they both generate out-of-phase changes in the CMB temperature power spectrum -adding DES to external data also has the consequence of slightly suppressing N eff. We also compare the cases where the number of relativistic species is fixed at N eff = 3. 046 (the standard model) and N eff = 4. 046 (standard model, plus a single fully thermalized sterile neutrino). Preference for one model over the other is assessed using the evidence ratio, R N eff = P (d|N eff = 4. 046) P (d|N eff = 3. 046). ( 15 ) where P (d|N eff ) is the Bayesian evidence, given by the integral over the parameter space of the likelihood times the prior; see Eq.
) We now turn to dynamical dark energy. The constraints are shown in the left panel of Fig. 4. We find w 0 = -0. 69 +0. 30  -0. 29 , w a = -0. 57 +0. 93 The DES Y1 data alone are therefore consistent with the cosmological-constant values of (w 0 , w a ) = (-1, 0); they do not appreciably change the constraint from external data alone. The pivot equation-of-state (see definition in Eq. (  5 )) is obtained to be w p = -0. 91 +0. 19   -0. 23.
DES Y1 = -1. 01 +0. 04 -0. 04 DES Y1 + External. (16)  For the DES-only and DES + External cases, the pivot redshift is found to be z p = 0. 27 and z p = 0. 20, respectively. Figure  5  shows the constraints in the (w p , w a ) plane. Do the DES data favor the introduction of two new parameters, w 0 and w a , to the ΛCDM model? Again, we calculate the Bayesian evidence ratio R (w0,wa) = P (d|w 0 , w a ) P (d|w 0 = -1, w a = 0). For DES data alone, we find R (w0,wa) = 0. 11, while the DES+external data give R (w0,wa) = 0. 006. Therefore, Bayesian evidence ratios strongly support ΛCDM, and do not favor introduction of the additional parameters w 0 and w a. Finally, we turn our attention to modified gravity, the extension for which DES carries the most weight. Recall from Sec. IV A that we have decided to quote only the constraint on the parameter Σ 0 in the DES-only case.
We now compare our extended-model cosmological con-straints to those obtained using KiDS-450  [28]  shear measurements, and to the Planck 2018 (P18) CMB measurements  [32]. KiDS analysis is similar to ours in that they use their own shear measurements combined with external data; one difference is that we use the full 3 × 2 data vector which, in addition to shear, also includes galaxy clustering and galaxygalaxy lensing  14. Planck, on the other hand, uses the DES Y1 shear measurements as an external weak lensing data set, combining it with their CMB information. It is important to note that both KiDS and Planck fix the neutrino mass to m ν = 0. 06eV in their baseline ΛCDM model, while we vary the neutrino mass as part of the fiducial model. Therefore, our cosmological constraints are expected to be weaker, but more robust with respect to the neutrino mass, than they would be with the same assumptions as KiDS and P18.
For comparison with Planck we only consider the modifiedgravity case, as this is the ΛCDM extension where DES Y1 information appreciably improves the constraints obtained from Planck and other external data. P18 constraints on modified gravity  [32]  employ the base parameters µ and η, with µ P18 = 1 + µ DES being defined to have the redshift variation same as ours in Eq. (10) ; they also quote constraints on Σ P18 = 1 + Σ DES , whose redshift dependence however does not coincide with ours. Planck considers a similar set of other data as we do: their SN and RSD datasets are identical to ours; they use a more extensive selection of BAO data, but their DES information includes only the weak lensing (shear) information and not the full 3 × 2 data vector as in the present paper. Therefore, a somewhat direct although not exact comparison of the combined constraints between DES Y1 and P18 is possible.
We refer to Table  7  of  [32]  where P18 report constraints from the combination of Planck and external data, the latter of which includes DES Y1 shear. The central values of Σ 0 and µ in our DES+external analysis are very close to the corresponding values in P18. Our DES+external errors on Σ 0 (µ 0 ) are about 30% (80%) weaker that those in P18, which is probably chiefly due to our marginalization over neutrino mass, and possibly also to the aforementioned differences in the selected data sets. On the whole, the DES and P18 constraints that combine all data are consistent both mutually and with predictions of general relativity. The new information that the DES Y1 data contribute to the overall constraints on modified gravity that we presented in this paper illustrates that near-future DES data should provide sharp tests of the modified-gravity paradigm.
VI. CONCLUSIONS The results in this paper extend the work done in the Y1KP  [35]  by analyzing the models beyond flat ΛCDM and wCDM. In Y1KP, we found good agreement with the standard cosmological-constant dominated universe, and produced constraints on the matter density and amplitude of mass fluctuations comparable to those from the Planck satellite. We now extend that work into four new directions, allowing for: 1) nonzero curvature Ω k ; 2) number of relativistic species N eff different from the standard value of 3. 046; 3) time-varying equation-of-state of dark energy described by the parameters w 0 and w a (alternatively, the values at the pivot redshift w p and w a ); and 4) modified gravity described by the parameters Σ 0 , µ 0 that modify the metric potentials. For the first three of these four extensions, we find that the DES Y1 data alone are consistent with values of zero curvature, three relativistic species, and dark energy parameters corresponding to the cosmological constant model.
We also find that DES Y1 data do not significantly improve the existing constraints which combine the Planck 2015 temperature and polarization measurements, BAO measurements from SDSS and BOSS, RSD measurements from BOSS, and type Ia supernova measurements from the Pantheon compilation. When DES Y1 information is combined with that from the external data, the constraints on curvature are Ω k = 0. 0020 +0. 0037 -0. 0032 , while that on the dark-energy equation of state pivot value and its variation are w p = -1. 01 +0. 04 -0. 04 and w a = -0. 28 +0. 37 -0. 48 , respectively. The upper bound on the number of relativistic species is N eff < 3. 28(3. 55) at the 68% (95%) confidence level from the combination of DES and external data. DES Y1 alone provides a stronger constraint on the fourth extension of ΛCDM that we consider -modified gravity -giving Σ 0 = 0.
43 +0. 28  -0. 29. The apparent DES-alone preference for positive Σ 0 is consistent with parameter volume effects discussed in Sec. IV A. When combining DES with external data, the Σ 0 constraint is shifted downwards with respect to the external-only constraint, which can be explained by the fact that DES data prefer a lower lensing amplitude than that predicted by external data in ΛCDM. Combining DES Y1 with the external data gives Σ 0 = 0. 06 +0. 08 -0. 07 and µ 0 = -0. 11 +0. 42 -0. 46 , both of which are fully consistent with the ΛCDM values (Σ 0 , µ 0 ) = (0, 0). We applied a suite of validation and null tests both to our analysis and to our theory modeling; the results of these tests are shown in Figs. 2  and 7. In nontrivial model spaces such as modified gravity, we compared the results obtained by two independently developed parameter inference pipelines, CosmoLike and CosmoSIS, and also compared the constraints used obtained using two different samplers, emcee and multinest.
ACKNOWLEDGMENTS We are grateful to the anonymous referee for many useful questions, comments, and clarifications. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovacão , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
3D imaging approaches based on X-ray microcomputed tomography (microCT) have become increasingly accessible with advancements in methods, instruments and expertise. the synergy of material and life sciences has impacted biomedical research by proposing new tools for investigation. However, data sharing remains challenging as microCT files are usually in the range of gigabytes and require specific and expensive software for rendering and interpretation. Here, we provide an advanced method for visualisation and interpretation of microCT data with small file formats, readable on all operating systems, using freely available Portable Document Format (PDF) software. Our method is based on the conversion of volumetric data into interactive 3D PDF, allowing rotation, movement, magnification and setting modifications of objects, thus providing an intuitive approach to analyse structures in a 3D context.
While the analysis of histological sections remains a mainstay in developmental biology, the reconstruction of 3D volumes from 2D slices has provided important information to understand morphogenesis in mouse and human embryos  [3] [4] [5] [6]. Non-destructive technologies such as 3D imaging by confocal microscopy and light sheet methods  [7] [8] [9]  , optical projection tomography (OPT)  [10] [11] [12]  and micro-computed tomography (microCT)  [13] [14] [15] [16]  constitute emerging powerful methods to analyse the topography of developing structures in 3D context, to gain insight into the pathogenesis of congenital disorders. However, one major challenge of 3D approaches is sharing of complex datasets effectively and intuitively between colleagues from different fields for discussion, and ultimately presenting them in a publication format.
Generally, 3D datasets need special software for visualisation or are reduced to 2D pictures in which important information might be lost. The study of the developing head constitutes a good example of the need for 3D approaches given its complex anatomy. The craniofacial region is built of diverse embryonic cell types giving rise to hard and soft tissues including skeletal, muscular and nervous components  17. The final shape of the face strongly depends on the geometry of the skeletal elements and their interaction with adjacent soft tissues such as muscles and the nervous system  15, 16. Numerous congenital craniofacial abnormalities affect the form and function of the face and explanations of these malformations still await the fundamental understanding of the underlying failure of morphogenesis  18.
The microCT approach followed by 3D reconstruction has enabled high-quality information of the complex morphological aspects of head and face development  15, 16. To facilitate assimilation and visualisation of microCT datasets, interactive 3D Portable Document Format (PDF) has been used in different field including in developmental biology  6, 19, 20  , in human physiology and anatomy  4, [21] [22] [23]  , in entomology  24  and marine biology  25, 26. However, a major limitation of previously published methods is the requirement of advanced programming skills and/or installation of further prepaid software packages  21. Most of these approaches depend on the use of Adobe Acrobat Pro Extended software  4, 6, 19, [23] [24] [25] [26] [27] [28]  or JavaScript programming language  20, 27. Here, we provide an alternative, user-friendly way to create interactive 3D PDF files from microCT datasets taking the complexity of mouse craniofacial anatomy as a model example.
Simple shapes such as the eyeball do not show notable errors besides the slight deformation of the sphere. However, further simplification of complex shapes, such as the chondrocranium and extraocular muscles (EOM), reduces the model quality and some details are lost (Fig. 4 , red arrows). Thus, depending on the structures analysed and the resolution needed, different face reductions should be tested and validated before further analysis. Therefore, the compression of the data should be customised according to the complexity of the model and the resolution of tomographic slices. Among the 3D printing software, some free software enable colour modifications or mesh simplification. Here, we made use of the Meshlab 47 and Blender 48 packages for this purpose. Notably, the latter allows unifying different sub-models into one mesh preserving the individuality of each object (see Supplementary Material 1 for more details).
Creation of an Interactive 3D PDF Once the models are colour-labelled and simplified, they are then converted into an interactive file. We exploited 3D PDF Maker Standalone  49  taking advantage of the pre-prepared model that provides an interactive window in the PDF file by clicking on Add 3D button. Before embedding the model into an interactive file, the user can prepare an ordinary PDF file using standard software (e. g. Microsoft Office, Apache OpenOffice etc. ). This file is used as a template showing, for example, individual structures or predefined views. These areas (i. e. images or signs) are taken into life in 3D PDF Maker by assigning whatever area in the page to specific view/structure (see for more details Supplementary Materials 1 and 2). The model document presented here (Supplementary Material 3) consists of four predefined views (ventral, dorsal, lateral and medial) and a possibility of showing individual craniofacial structures (brain, cartilage, bone and teeth).
The exposure time was 600 ms in each of the 2200 projections acquired over a total angle of 360°. Three projections were acquired and averaged in every position to reduce the noise in the tomographic data. Thus, the scanning time was 73 minutes. The acceleration voltage and current of the X-ray tube were 60 kV and 200 μA, respectively. The beam was filtered by a 0. 2 mm-thick aluminium filter. The linear voxel resolution of the measurement was set to 4. 2 μm in all dimensions. The tomographic reconstruction was conducted using the software GE phoenix datos|x 2. 0 (GE Sensing & Inspection Technologies GmbH, Germany). Data processing and analysis. Segmentation of craniofacial structures was performed semi-manually in Avizo (Thermo Fisher Scientific, USA). We used some automatic tools using a region growing method and thresholding on 2D slices.
Segmentations were done on one of three to five slices, and the mask was interpolated. Subsequently, the segmented models were smoothed in VG Studio MAX 3. 2 (Volume Graphics GmbH, Germany) according to  45. The segmented mask was then exported into a mesh (*. OBJ format) and was colour-labelled and adjusted in Meshlab  47  and Blender software  48. The colour-coded models were transferred into a pre-prepared PDF file in 3D PDF Maker Standalone  49. The detailed manual can be found in Supplementary Material 1. The use of the interactive 3D PDF is available in Supplementary Material 2.
Acknowledgements This research was carried out under the project  CEITEC 2020  ( LQ1601 ) with financial support from the  Ministry of Education, Youth and Sports of the Czech Republic  under the  National Sustainability Programme II, CEITEC Nano Research Infrastructure (MEYS CR ,  2016-2019 ) and) and  Ceitec Nano+  project,  CZ. 02. 01/0. 0. /. 0. 0. /16 _013/0001728  under the program OP RDE. We acknowledge support by grant  AFM Telethon 21853. J. K. and M. T. acknowledge the support of the  Brno University of Technology  through grant  FSI-S-17-4506. M. T. was financially supported by grant  CEITEC VUT-J-19-5764  and by the  Brno City Municipality  as a  Brno Ph. D. Talent Scholarship Holder.
The increase in the scale of LHC computing expected for Run 3 and even more so for Run 4 (HL-LHC) over the next ten years will certainly require radical changes to the computing models and the data processing of the LHC experiments. Translating the requirements of the physics programmes into computing resource needs is a complicated process and subject to significant uncertainties. For this reason, WLCG has established a working group to develop methodologies and tools intended to characterise the LHC workloads, better understand their interaction with the computing infrastructure, calculate their cost in terms of resources and expenditure and assist experiments, sites and the WLCG project in the evaluation of their future choices. This working group started in November 2017 and has about 30 active participants representing experiments and sites.
First results Some of the first elements to address are the diversity of expenses across sites and their definition. A simple and quick exercise was made by four sites, aiming to get a first estimate of the financial cost to run a given workflow and to store a given amount of data, in terms of IT resources and power consumption. The answers to this exercise differed up to a factor of two from site to site. The reasons were found to originate from the intrinsic variety of costs, the measurement method, and the understanding of what a given metric means. Those results clearly showed the need of a consolidated and common model and method to measure costs across WLCG data centres. For example, one may consider the cost of a server providing a given capacity, including (or not) the equipment that is shipped with it (rack, switch, adaptor etc.
Compiler and software improvements WLCG sites provide a variety of CPU models and HEP code is usually built to run on the oldest available architectures. This, and advancements in compilers motivated further studies. Studies on GEANT simulation, reconstruction and NLO generator code show gains by compiler and link-based optimisations to be around 20-25%, including compilation for individual target CPUs, the use of Intel's commercial compiler and the use of feedback-directed optimisation (AutoFDO from Google and Intel). Compiler-based vectorisation of current production code showed little or no improvement. Reducing the overhead of shared libraries by building large libraries resulted in large gains on older architectures (e. g. , a 10% on Ivy Bridge for ATLAS simulation). It has to be noted that the use of feedback-directed optimisation requires that the objects are built statically, which is not always trivial.
Storage The LHC community has recently agreed to consider a scenario where managed storage is consolidated at a few, very large data centres (the "data lake") as the most promising to achieve significant cost savings  [10]. For what concerns operational effort, the 2015 WLCG site survey  [11]  showed that on average Tier-1 sites require 2. 5 full time equivalent (FTE) for operating storage and Tier-2 sites 0. 75 FTE, with a weak dependence on the amount of storage. By concentrating managed storage at a few sites and using only disk caches at most sites, one can estimate a decrease of the overall number of FTE for storage operations from around 100 to around 60. Nearly 80% of used disk in WLCG consists of data formats used for analysis. Data popularity studies show that on average datasets exist at ∼ 2 sites and are accessed less than 10 times over a period of six months, with most accesses in the first month.