dataesr/scientific-paragraphs-categorization huggingface dataset:
9(a) , since the statistical model alone cannot describe those fluctuations. With this procedure the internal consistency of the cross sections was preserved. Additionally, unique AMS 235 U(n,γ) measurements have been published by Wallner et al. [101] , using welldefined neutron spectra relative to well-known neutron capture cross section of gold and 238 U targets, with average incident neutron energies equal to 25 and 426 keV, respectively. Additional reduction of capture was needed to agree with Wallner's data; the relatively large reduction around 25-35 keV is confirmed by Kononov et al. [80] , Vertebniy et al. [102]  and Corvi  [103]  datasets, which were recalculated from measured α ratio using the 235 U(n,f) standards cross sections  [12, 13]. There is some discrepancy in data near 35 keV; the lower data were preferred in the IAEA CIELO evaluation similar to what was done in the ENDF/B-VII.
1 evaluation. Finally, some reduction of the capture cross section from 300 to 500 keV following Wallner measurement  [101]  was made as shown in Fig. 9 (b) compared to the ENDF/B-VII. 1 evaluation. In that energy region, the measured spectrum-averaged cross section by Wallner is in agreement within uncertainties with the Jandel  [32] , Weston  [104] , De Saussure  [105] , and Hopkins  [106]     were recalculated from measured α ratio using 235 U(n,f) standards cross sections  [12, 13]. The agreement of several experimental datasets with Wallner data  [101]  at both 25 keV and 426 keV supports the choices made in the current evaluation. It should be noted that changes made in capture and fission cross sections in the evaluated file are well within uncertainties of the measured total cross sections.
[171] ). The coupled-channel OPTMAN code  [172, 173]  incorporated into the EMPIRE system  [15, 16]  was used for the optical model calculations. The direct reaction contribution to the inelastic scattering was calculated by using the Dispersive Coupled-Channel Optical Model (DCCOM) potential of Quesada et al. [56, 174]. The hard-sphere potential scattering radius R' = 9. 483 fm at zero energy, which was adjusted on the results of the GMA average total cross section, is fully consistent with the effective scattering radius used for the analysis in the RRR. The cross sections calculated from the average parameters are compared with the results of the GMA analysis in Figs. 14  and 15. The average total and capture cross section resulting from the GMA analysis were adopted in File 3 with the LSSF=1 option as infinitely dilute total and capture cross sections, respectively.
[53, 55]  and references therein). A new rotational-vibrational dispersive optical model potential has been derived that couples the low-lying collective bands of vibrational character observed in 238 U nucleus while preserving the good quality of description of other observables  [56, 174]. Special attention is paid to the coupling of almost all excited levels below 1 MeV of the excitation energy, because neutron scattering on these low-lying levels plays a significant role in the energy region from 0. 5 to 1 MeV of maximum interest for fast reactor systems. The coupled-channel OPTMAN code  [172, 173]  was used for the optical model calculations. Additionally, we followed the empirical approach proposed by Young et al. [65] , who postulated the existence of a series of collective 2 + and 3 -states (see Table VI in Ref.
17(bottom). This effect goes in the opposite direction to the width fluctuation correction. A comparison of evaluated total inelastic cross section with experimental data is shown in Fig. 18. The effect of deformation on calculated CN inelastic scattering results in the observed cross section increase compared with the JEFF-3. 2 [5]  and JENDL-4  [6]  evaluations below 500 keV. However, these calculations are still lower than ENDF/B-VII. 1 evaluation  [4]  in the same energy region. Current calculations produce the highest inelastic cross sections from 700 keV up to approximately 1 MeV, as a direct result of the strong coupling between all low-lying collective levels extending up to an excitation energy of 1 MeV (see Fig. 18 ). The agreement of calculated (n,n') cross sections with experimental data is very reasonable considering inconsistencies between measured datasets.
The contribution from the fission reaction was evaluated by I. Stetcu, P. Talou and M. B. Chadwick for the ENDF/B-VIII. 0 library, based on experimental data and prompt fission emission modelling  [139]. A general description of the evaluation procedure is available in the accompanying paper  [139]. An example of the total γ-ray emission spectrum induced by 2 MeV neutrons is shown in Fig. 23. The bump observed in the new evaluation from 1 MeV to 2 MeV of emitted photon energy is due to the contribution from neutron inelastic scattering, which is dominant in that energy region as discussed in Sec. IV C. ENDF/B-VII. 1 evaluation lumped all gammas, while a new ENDF/B-VIII. 0 evaluation successfully combined fission gammas with gammas produced by other reactions.
VI. RPI QUASI-DIFFERENTIAL BENCHMARK FOR n+ 238 U The RPI quasi-differential experiment was described in Ref. [213]  and used in the evaluation process to verify the calculated double differential scattering cross sections. In this experiment the neutron beam that, incident on the 238 U sample, scattered or caused fission; the resulting neutrons were detected by an array of neutron detectors surrounding the sample. The effective energies covered the range from 0. 5 to 20 MeV. The measured data were compared with detailed simulations of the experiment performed with different evaluations of 238 U. An important aspect of this experiment is that all emitted neutrons are detected, including those from elastic and inelastic scattering, fission neutrons and multiple scattering contribution. Comprehensive neutron detection avoids typical uncertainties introduced by splitting the neutron yield into elastic, inelastic and fission neutron contribu-   tions.
The disadvantage is that the cross sections cannot be inferred from the measurements directly, but this is compensated by the high sensitivity of measured data to the neutron production cross sections. In many experiments neutron production is swamped by the elastic component. In the present case the elastic contribution becomes very low at certain angles, raising the importance of other minor channels, including the inelastic scattering. This experiment could be seen as a quasi-differential benchmark for validating nuclear data, where experimental set-up is simple and very well documented. This allows for an accurate Monte Carlo simulation directly in the time domain. As was previously shown  [213] , one of the issues observed with the ENDF/B-VII. 1 library was the low scattering cross section at back angles in the energy range of 0.
Godiva Δk eff is in excellent agreement with the calculated one for the equivalent Russian spherical benchmark HEU-MET-FAST-018 from VNIIEF, Sarov, Russia. It should be noted that such level of Δk eff agreement is achieved in the libraries by final tuning of the prompt 235 U νp in the 1-2 MeV region by about 0. 05%. Such tuning is not reflected in the evaluated uncertainties and is well within the uncertainties of measured differential data (see Fig. 10 ). Unfortunately, the situation is highly unsatisfactory for the remaining benchmarks. They are all extremely sensitive to the 235 U nuclear data. The sensitivity profiles available in the NEA Data Bank's DICE package for the cross sections are very similar. The sensitivities to angular distributions are currently not available for all benchmarks, so a test was performed by a direct perturbation of the P 1 elastic scattering coefficient.
The effect on the predicted reactivity was strong, but practically equal for all benchmarks. Sensitivities to higher threshold reaction cross sections are small, so the differences in the emission spectra are not expected to affect criticality selectively for specific benchmarks. In short, any change in the 235 U data affects all benchmarks about equally. Therefore, it is not possible to explain large discrepancies (well beyond quoted uncertainties and especially a large spread in the calculated Δk eff ) by nuclear data deficiencies. An action would be appreciated by the ICSBEP curators requesting the benchmark evaluators to identify possible reasons for such large discrepancies and to revise benchmark specifications, quoted benchmark uncertainties, or the corresponding MCNP computational models, if needed.
Particularly: • The Russian bare cylinders and spheres HEU-MET-FAST-008, HEU-MET-FAST-015, HEU-MET-FAST-065, and HEU-MET-FAST-018 show a spread of more than 500 pcm with a bias to a lower reactivity using exactly the same nuclear data as the benchmarks mentioned above that showed good agreement. Therefore, HEU-MET-FAST-008, HEU-MET-FAST-015, HEU-MET-FAST-065 benchmark specifications or computational models require attention if we assume that HEU-MET-FAST-001 and HEU-MET-FAST-018 are acceptable. • The Oak Ridge cylinders HEU-MET-FAST-051 have a relatively small spread, but they are calculated lower compared to Godiva (HEU-MET-FAST-001) by about 250 pcm. The HEU-MET-FAST-051 suite actually includes 17 cases. Since they all show similar trends, only six are included in the analysis. Some of them show unreasonably small uncertainties.
B. Other Selected Fast Assemblies Containing 235 U and 238 U A short list of fast-neutron benchmarks was selected, which are traditionally used to test the performance of uranium nuclear data including both 235 U and 238 U isotopes. The IAEA CIELO 235 U evaluated data file was complemented by the IAEA CIELO evaluated 238 U file  [69, 175, 176]  for this exercise. All other relevant isotopes (mainly 234 U) were kept fixed to the ENDF/B-VIII. 0 evaluations  [139]. The benchmark list is given in Table XIII and includes different types of fast assemblies with regard to fuel material, reflectors, enrichment and spectral characteristics. From our experience, these benchmarks are considered to be fairly reliable, since they have been analyzed by many authors and reported in a variety of publications.
The overall conclusion is that the new IAEA CIELO file (β5) for 238 U performs well, and certainly better than ENDF/B-VII. 1. The importance of replacing the PFNS above 8 MeV with the data from JENDL is manifested by the improved agreement with measured data in the leaked neutron energy range from 5 MeV to 10 MeV. Note that the new IAEA CIELO evaluation still underestimates the experimental data from 10-12 MeV probably due to the missing strength of inelastic scattering to the continuum.
VIII. CONCLUSIONS New evaluations of neutron-induced reactions on 235 U and 238 U up to 30 MeV incident energy range have been produced under the IAEA coordination within the CIELO project  [1] [2] [3]. Theory based calculations -performed with the code EMPIRE-3. 2 Malta -combined with the latest experimental data for the capture channel, with newly measured and evaluated PFNS, and with nonmodel evaluation of experimental data from the Neutron Standards, accurately reproduce both differential and integral measured data. This was possible by improving the reaction modelling, by using the latest nuclear structure information provided by experiments and microscopic studies, by using the newest and more accurate experimental differential data, and by constraining model parameters using the feedback from criticality calculations of carefully selected benchmark experiments.
ACKNOWLEDGEMENTS We thank the  Nuclear Energy Agency  for administrative support for the WPEC Subgroup 40 collaboration and for starting the  CIELO  project, in particular we thank  NEA  colleagues  E. Dupont ,  O. Cabellos  and  F. Michel-Sendis  for their encouragement and assistance. Authors acknowledge an important contribution made by the  IAEA Neutron Standards committee , and by all contributors to the  IAEA Prompt Fission Neutron Spectra  project. The work of M. S. was partially supported by the  European Commission  under the contract CHANDA ( EU-RATOM No. FP7-605203 ). Work at  Brookhaven National Laboratory  was sponsored by the  Office of Nuclear Physics, Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-98CH10886  with  Brookhaven Science Associates, LLC.
Influence maximization has attracted a lot of attention due to its numerous applications, including diffusion of social movements, the spread of news, viral marketing and outbreak of diseases. The objective is to discover a group of users that are able to maximize the spread of influence across a network. The greedy algorithm gives a solution to the Influence Maximization problem while having a good approximation ratio. Nevertheless it does not scale well for large scale datasets. In this paper, we propose Matrix Influence, MATI, an efficient algorithm that can be used under both the Linear Threshold and Independent Cascade diffusion models. MATI is based on the precalculation of the influence by taking advantage of the simple paths in the node's neighborhood. An extensive empirical analysis has been performed on multiple real-world datasets showing that MATI has competitive performance when compared to other well-known algorithms with regards to running time and expected influence spread.
They introduced influence oracles which can answer influence queries in an efficient way. This algorithm is designed based on the IC diffusion model. Goyal et al. [40]  proposed a new probability model, the credit distribution model, which directly estimates influence spread by exploiting historical data. This makes the need for knowing the influence probabilities and making Monte Carlo simulations to compute the respective influence redundant-thus avoiding costly computations. In this paper, we propose MATI, an efficient influence maximization algorithm under both the LT and IC diffusion models. By taking advantage of the possible paths that are created in each node's neighborhood, we have designed an algorithm that succeeds in locating the users that can maximize the influence in a social network while also being scalable for large datasets.
Datasets We have used four publicly available graph datasets  [41]  whose statistics are summarized in Table  2. To generate influence weights on all edges, we adopt the classical uniform method by  [29, 42]. More precisely, we set the weight of every incoming edge of a node v to be equal to 1 d v , where d v is the in-degree of node v. It has to be noted that the datasets were transformed from an undirected format to a directed one by simply assuming that if an edge between two nodes exists the one can influence the other. Thus, the number of the edges used in the experiments is twice the one that is reported. • NETHEPT: This is a collaboration network taken from the "High Energy Physics (Theory)" section of http://arxiv. org, with nodes representing authors and edges capturing co-authorship relationships.
Experimental results We compare the performance of the aforementioned algorithms, with respect to the quality of seed sets and efficiency aspects. Under the LT model, the seed sets obtained via MATI are quite competitive in quality compared to those of the GREEDY, LDAG, SIMPATH and SKIM algorithms. For all four datasets, the influence loss for up to 50 seeds is less than 2% with respect to the GREEDY algorithm. Under the IC model, our algorithm is still effective, despite the heuristics involved in the influence estimation. We observe that for both LT and IC models the RANDOM heuristic fails to identify good candidates. On the other hand, the DEGREE and HARMONIC heuristics reveal seed nodes that are actually good candidates for small networks such as WIKIVOTE and NETHEPT but fail to reveal effective seed nodes for larger networks such as EPINIONS and EMAIL-EUALL.
This is due to the fact that the aforementioned heuristics do not take into account that some of the chosen nodes are clustered together and the overlapping influence some nodes occur reduces their final total influence. We have also performed experiments for different values of the parameter θ of our algorithm for the NETHEPT dataset, in order to observe the running time of our algorithm with respect to the influence that is achieved. The results are depicted in Table  3. As θ decreases, the running time is always increasing. This is justified by the fact that a smaller θ allows computation of influence in a greater neighborhood around each node. In most of the cases, the influence achieved also increases. This can be explained by the fact that the formation of paths of greater length provide a more accurate computation of a node's influence.
Efficiency of MATI. We have also examined the running time of the proposed algorithm. Fig  6  reports the execution time required by various algorithms for the LT and IC models respectively. The figures have a logarithmic scale on the y-axis. In all cases, MATI is faster than the GREEDY and LDAG algorithms. In all datasets except WikiVote, MATI also performs better than SIMPATH. It takes GREEDY more than one week to select 50 seed nodes for datasets such as Epinions. The IMM and SKIM algorithms seem to be slightly faster than our algorithm and this is due to the initial computation of the large data structures described in Section MATrix Influence (MATI) algorithm. The latter computations happen once and despite their "expensiveness" they can facilitate the re-computation of the node's influence in case there is a node deletion in the network.
Nevertheless MATI remains competitive in terms of running time and it will be also competitive in the case of a possible change in the network as mentioned whereas this is not guaranteed for the other baselines. Additionally, MATI's running time can always be reduced by increasing parameter θ which will not result in a significant decrease in the influence spread (see Table  3 ). It is also worth noticing that even IMM is faster, the influence spread given by the seed nodes proposed is lower than our algorithm's in some datasets. Specifically we can observe the difference between the influence spread of IMM and MATI in the case of the WIKIVOTE and EMAIL-EUALL datasets for the LT model and that of EPI-NIONS and EMAIL-EUALL datasets for the IC model (see Figs 4 and 5 respectively). SKIM is also competitive in terms of running time but is only designed for the Independent Cascade model while MATI is designed for both aforementioned diffusion models.
Conclusion In this paper we introduced an efficient algorithm for influence maximization in social networks, under both the LT and IC diffusion models. Even though the greedy algorithm has been proven to produce the best results so far in terms of quality of seeds, it does not scale to large networks. MATI takes into consideration the possible paths that are created in each node's neighborhood and pre-calculates the nodes' influences. Our algorithm depends on the creation of large data structures which increases memory consumption, but at the same time facilitates the recomputation of the nodes' influence in the case of a node or edge deletion in the network. After performing extensive experiments in four datasets, we have shown that MATI is competitive regarding both the quality of seeds and the running time when compared to state-of-theart algorithms.
However, randomisation can be computationally expensive (especially for spot randomisation), and the robust interpretation of co-localisation indexes in terms of objects' (spots' or localisations') couples, i. e. , significantly close to each other given the density of objects and the geometry of the cell, remains an open question. We report the development of a method and software named Statistical Object Distance Analysis (SODA) to characterise the relative spatial positioning of several distributions of molecules, in a quantitative and automatic manner. Contrary to previous co-localisation methods, SODA allows to map statistically coupled objects within the cell, i. e. , to compute a coupling probability for each single pair of objects. As SODA uses a marked-point process framework, it does not depend on the PSF characteristics and is robust to noise.
SODA. SODA can be used either on conventional microscopy images that contain clusters of molecules (wide-field, confocal, SIM, or STED) or with single-molecule-based microscopy (e. g. , STORM, PALM or DNA-PAINT) where localisations of single molecules give direct access to their coordinates. To detect the fluorescent spots of aggregated molecules that are significantly brighter than the cell background and extract their coordinates on conventional microscopy images (Fig. 1a-d ), we use a robust and automatic algorithm based on a wavelet transformation of the image and statistical thresholding of wavelet coefficients  9  (implemented as a plugin Spot Detector in the open-source image analysis software Icy (http://icy. bioimageanalysis. org/)  17  ). These spots correspond to clusters of molecules where centre of mass (or intensity) is used as position.
Analysing coupling between single synaptic localisations. SODA uses the framework of point processes to analyse spatial relations between objects. It is thus particularly well adapted to localisation-based microscopy where spatial coordinates of molecules are directly computed by the imaging software. To supplement our study, we image two presynaptic molecules with the 3D-STORM Vutara system (Fig. 5a  and Supplementary Movie 1) (Material and Methods): the vesicular glutamate transporter (VGLUT) that is responsible for the uptake of the excitatory amino acid, L-glutamate, into synaptic vesicles  48  , and Synapsin that is involved in the binding of synaptic vesicles to the cytoskeleton  49. We then delineate automatically putative synaptic boutons and statistically map single and coupled individual localisations inside boutons.
The intersection between VGLUT and Synapsin ROIs (block 9) is, with single-molecule localisations, an input of the SODA STORM 3D block (10)  that statistically computes all the coupling probabilities between individual VGLUT and Synapsin localisations. Finally, a last series of blocks export SODA results (coupling parameters) in files (block 11) and map single and coupled localisations in 3D with VTK (http://www. vtk. org) in Icy (blocks 12-13-14 and 15). Generation of genome-edited cells and fluorescence staining. Hep2, a Human cervical adenocarcinoma cell line (Clone 2B, misidentified BioSample: SAMN03151705) was a gift of A Dautry and was the parental cell line used to obtain the clone Hep2β, stably expressing IL-2Rβ gene as described in (Grassart et al. EMBO R, 2008). This cell line did not have any mycoplasma contamination as verified by the kit MycoAlert from Lonza.
Total internal reflection fluorescence microscopy. Total internal reflection fluorescence (TIRF) microscopy images were captured using Cell MTM software on an Olympus IX-81 microscope using a 100x/NA1. 45 objective and an EMCCD camera IxonEM+ (Andor). A 488 nm solid-state laser (Olympus) and a 561 nm solid-state laser (Olympus) were used to excite GFP and Cy3 fluorophores, respectively. Images were obtained without gain and an exposure time of 800 to 1000 ms. Simultaneous two colour TIRF images were obtained using a DV2 image splitter (Optical Insights) to separate GFP and Cy3 emission signals. Confocal microscopy. Confocal images for synaptic triple labelling were obtained using a confocal microscope Leica TCS SP5 (Leica Microsystems CMS GmbH, Mannheim, Germany) and a ×63 objective (NA 1.