dataesr/scientific-paragraphs-categorization huggingface dataset:
While the sample density is rather homogenous in most parts of the retrieved field of view, some fluctuations are visible. The pipe-like variations (see black arrows in Fig. 3c  and 3d ) are well known features, which can be understood when considering the phase map. Here, phase vortices are co-localised with the pipes of missing intensity, a characteristic feature of dislocations in noise-limited BCDI data  27, [42] [43] [44]. Note that the observations of their sharp structure confirms the good quality of the dataset including possible beamto-sample position uncertainties. Similarly, additional density oscillations appear in regions, where the phase wraps rapidly, e. g. at the top right corner  34. A few density voids are observed in the vicinity of the grain boundary, corresponding to cavities in the crystal.
Finally, at the edges of the raster scan, where both the counting statistics and overlap constraint are reduced the reconstruction quality is degraded. The probe function retrieved simultaneously from the BP dataset (cross-section profile shown in Fig. 3e ) exhibits the expected characteristic features (e. g. the central lobe with size of 450 × 190 nm 2 , intensity FWHM), in excellent agreement with the profile obtained separately and previously shown in Fig. 2b. The missing triangular region at the bottom left of the probe reconstruction is due to the limited extent of the crystal set by the grain boundary. Note the extent of the field of views, of about 0. 6 × 6 × 6 μm 3 (z × y × x) for the 3D sample and 5. 1 × 3. 9 μm 2 (p 1 × p 2 ) for the probe cross-section. The probe field of view is therefore large enough to cover most of the probe extent, including the central lobe and the tails (as shown in Fig.
2b ). If the intensity information were not limited by the grain boundary (non-scattering crystal), we estimate the field of view along x could be as large as 7. 8 μm. Finally, the spatial resolution of this 3D reconstruction is estimated as 37 × 40 × 39 nm 3 along z, y and x respectively (see Supplementary Fig. 4 ). As a matter of comparison, the same dataset was reconstructed using the formerly employed approach  29, 33  , i. e. no angular upsampling and using a fixed probe characterised by transmission ptychography. The 3D reconstruction numerical volume was The intensity information is recorded as a function of (q 1 , q 2 , and q 3 ), the components of the wave-vector transfer q. Using a recently developed formalism  40  , this sampling depicts a direct space frame (r 1 , r 2 , r 3 ). It implies sampling rates along the three directions, that ensure that the illuminated volume (delimited by the green line) is contained within the numerically retrieved direct space (shown as an orange rectangle).
The probe frame is also defined as (p 1 , p 2 , p 3 ). b A typical hard x-ray probe profile presented at the focal plane and (c) numerically propagated along the beam axis (see Methods). The dashed rectangle corresponds to the accessible region based on the parameters used during the acquisition of the BP dataset. The full extent of the probe, visible outside this rectangle, is only retrieved through the implementation of the angular up-sampling approach. Along the propagation direction, note the probe invariance over distances as large as a few hundreds of micrometres. The hue rendering colour scale is indicated in (b). d Sufficiently small sampling steps of Δθ are needed to fulfil the numerical sampling relation. This relation is depicted in the plot, as a function of the probe size W and the sample thickness T.
The 3D strain map (Fig. 4a  and b ) clearly shows the He-implantation induced strain of about 3 × 10 -4 , evident to a depth of approximately 2. 8 μm (Fig. 5b, c ). In the vicinity of the two FIB-processed surfaces, large positive strains, corresponding to a lattice expansion, can be seen. These strains extend to similar depths (about 150 nm for the top surface and 100 nm for the bottom surface) and have similar magnitude (~1. 5 × 10 -3 and ~1. 1 × 10 -3 for the top and bottom surfaces, respectively). Several dislocations are also observed in both implanted and non-implanted layers (highlighted in Fig. 4   and analysed in more details in Fig. 5a ). Besides these, ε zz presents a rather homogeneous behaviour in the whole implanted layer (Fig. 5c-f ). We encourage the reader to view the Supplementary Videos as these provide an interactive way of visualising the multidimensional dataset.
e. the algorithm is left to retrieve the diffracted field for those virtual angles. In our dataset, the angular up-sampling implemented via Eq. (  13 ) was used, with an angular up-sampling ratio set to 3, which corresponds to the insertion of two virtual angles between every two angular measurements within the reconstructed dataset. This gives an effective step of 0. 0017°and an effective window size of 5 μm for the probe reconstruction along p 1. From a computational viewpoint, we note that the updates (Eqs. (  5 ), (  6 ) and (  11 )) were implemented via a modified 3D Fourier transform recently developed  40, 69. This transform allows the unknown quantities to be retrieved in an orthogonal frame determined by the detector plane and the exit beam direction k f while still preserving the statistics of the measured signal, i.
Acknowledgements The BP-CDI experiment was performed on beamline ID01 at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. This work received funding from the  European Research Council (European Union 's  Horizon H2020 research and innovation program  grant agreements No 724881 and No  714697 , and under the  Marie Skłodowska-Curie Actions  grant agreement No. 884104 PSI-FELLOW-III-3i ). The authors acknowledge use of characterisation facilities within the  David Cockayne Centre for Electron Microscopy, Department of Materials, University of Oxford. Micro-beam Laue Diffraction experiments used the  Advanced Photon Source , a  US Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No.
3D) Gaussian filtering kernels are taken into account video analysis as a preprocessing to obtain corresponding gradient-filtered images      (resp. volumes      ). Magnitudes ‖∇  ‖ (resp. ‖∇  ‖) and gradient directions   (resp. ) are then referred from these Gaussian-gradient filterings. After that, -order oriented magnitudes IOM   (resp. VOM   ) are pointed out by decomposing the magnitudes ‖∇  ‖ (resp. ‖∇  ‖) with respect to different ranges of gradient directions   and. Finally, a simple local operator (e. g. , CLBP  [37] ) is utilized for extracting spatio-temporal features from the IOM/VOM-based outcomes. As a result, robust descriptors are then structured with high performances in reasonable dimensions. Experiments for classifying DTs on benchmark datasets have verified the considerable ability of our proposed descriptors in comparison with the state of the art.
Datasets and protocols Hereafter, benchmark datasets and protocols for evaluating our proposal are detailed. A brief of their properties is then shown at a glance in Table  1. UCLA dataset: It has 200 DT videos fixed in 110 × 160 × 75 dimension  [15]. Those mainly characterize disorder motions of waterfall, plant, flower, fountain, fire, boiling water, etc. (see Fig. 6  for several instances of them). For DT classification task, UCLA is often organized in the challenging scenarios as follows: • In order to evaluate DT classification in these two schemes, following to the experimental protocol in  [17, 40, 45] , a half of DT sequences in each class is randomly addressed for testing and the remain for training. The average of 20 trials for each scheme is reported as a final rate. DynTex dataset: It is a challenging dataset for DT classification  [16].
9 )'', and ''fountains(37)'', where the numbers in parentheses denote quantities of sequences in the corresponding categories. DynTex++ dataset: It is composed as follows. 345 DynTex's videos are split and filtered so that only major textural motions are captured  [17]. The obtained sub-videos are then grouped into 36 classes with 100 sub-videos for each of them, i. e. , 3600 sub-videos in total. Be similar to the experimental protocol in  [17, 35, 58] , a half of samples in each class is randomly taken out for training, and the remain for testing. The average of 20 trials is reported as a final result.
𝜎 respectively. 50-LOO and 50-4fold denote results on 50-class breakdown using leave-one-out and four cross-fold validation. MSVOMF , 4  descriptors is mostly achieved. Absolutely, it can be verified in Fig. 9  that with an increase of  from 0. 5 to 2, their performances on DynTex++ dataset are decreased about from 1% to 3% in general. This is due to the lack of appearance features caused by the Gaussian-gradient filterings with large levels of. • Decomposing the Gaussian-gradient filtered outcomes in the same ranges of direction, the obtained MSVOM features are more discriminative than the MSIOM ones (see Fig. 10  for a graphical view of those in settings of  4 and  = 1. 3, see Table  6  for other circumstances in general). This is because there are complements from the intensification of pairs of the gradients in the MSVOM decomposition.
• It can be found out that for the challenging datasets (i. e. , Dyn-Tex35, Beta, Gamma), the proposed descriptors with the odd derivatives often give better effectiveness of DT classification (see Tables  6  and 7 ). Therefore, they should be nominated for applications in practice. • As expected in Section 3. 2, the multi-analysis has significantly improved the discrimination power. Indeed, it can be seen from Tables 6 and 7 that using 2-scale of the Gaussian filterings with different standard deviations, the abilities of MSIOMF , 4 {} and MSVOMF , 4  {} are enhanced and more ''stable'' than those of the single-scale. Also, the 2-order descriptors are better than the single-order ones (see Tables  6  and 8 ). Furthermore, an incorporation of the 2-scale and 2-order features points out the best (see Table  9 ).
Those are due to confusions of similar DT motions in categories shown in Fig. 15  for Beta and Fig. 16  for Gamma. Classification on DynTex++: Just using the simple framework, performances of our proposed descriptors are nearly the same as those of the deep-learning methods. Indeed, it can be verified from Table  11  that results of deep model DT-CNN  [30]  are 98. 18% with the AlexNet framework and 98. 58% with GoogleNet. These are just 0. 3∼0. 6% better than our MSVOMF {1st,4th} {0. 7,1. 0} with 97. 87%. It should be noted that AlexNet and GoogleNet used the complicated algorithms and ∼61M and ∼6. 8M learned parameters respectively for learning DT features on different datasets. For further improvement, the challenging categories expressed in red rates in Fig. 17  should be addressed in future works.
67* 90. 74* 89. 39* - D3 [32] - 100* 100* 98. 11* - DT-CNN-AlexNet [30] - 100* 99. 38* 99. 62* 98. 18* DT-CNN-GoogleNet [30] - 100* 100* 99. 62* 98. 58* Fig. 14. Confusions of MSVOMF {1st,4th} {0. 7,1. 0} on Alpha. Note: ''-'' is ''not available''. Superscript ''*'' are results using deep learning algorithms. ''N'' is rate with 1-NN classifier. Dyn35 and Dyn++ stand for DynTex35 and DynTex++ sub-datasets. Group A denotes optical-flow-based methods, C: geometry-based, D: filter-based, E: local-feature-based, F: learning-based.
Volcanic ash and pyroclasts can weld when deposited hot by pyroclastic density currents, in near-vent fall deposits, or in fractures in volcano interiors. Welding progressively decreases the permeability of the particle packs, influencing a range of magmatic and volcanic processes, including magma outgassing, which is an important control on eruption dynamics. Consequently, there is a need for a quantitative model for permeability evolution during welding of ash and pyroclasts under the range of conditions encountered in nature. Here we present in situ experiments in which hydrous, crystal-free, glassy pyroclasts are imaged via x-ray tomography during welding at high temperature. For each 3D dataset acquired, we determine the porosity, Darcian gas permeability, specific surface area, and pore connectivity.
e. ± 2. 5 K). Four runs were performed, in which the packed free-standing cylinders were heated at 0. 067 K. s -1 to temperatures T = 1053, 1144, 1217 and 1248 K. The experiments were performed in situ at the TOMCAT beamline of the Swiss Light Source and imaged simultaneously via x-ray computed tomography, resulting in a time-series of 3D reconstructions of the evolving geometry of the particle pack throughout the welding process. Full 3D scans were collected at intervals between 1 and 107 s with a spatial resolution of 1. 7 -2. 0 μm voxel edge lengths. The initial H 2 O concentration was 0. 14 wt%  (Wadsworth et al. , 2019). A sub-set of the 3D datasets in each run were segmented to reconstruct the non-pore volume (i. e. the volume of the welding particle phase) and the pore volume between the particles, using Avizo™ using an auto-thresholding algorithm.
e. creeping flow) regime (for more details, see  Llewellin, 2010b). Visualizations of computed fluid flow lines through representative segmented volumes are shown in Fig. 3. The permeability is calculated using Darcy's law for each 3D dataset: k ¼ - μ f ∇P f 〈u〉 Eq:6 The specific surface area s for each the 3D dataset is determined using a 'marching cubes' algorithm  (Lorensen and Cline, 1987; Lewiner et al. , 2003). This algorithm extracts an isosurface for the pore-particle walls in the 3D datasets. Normalizing the area of the surface by the volume of the sample or domain gives s (in units of m -1 ). Using 3D datasets segmented using different segmentation threshold choices, we estimate the uncertainty on the permeability to be 10 -17 m 2 based on the convergence criterion used in LBflow.
The specific surface area during welding s(ϕ) The specific surface area s measured during welding in the evolving in situ datasets varies non-linearly with ϕ. In Fig. 4  we show the measured s values compared with the model (Eq. 5; Section 2. 2). When solving the model we use the moments of a polydisperse pore size distribution calculated from a polydisperse overlapping sphere model used for sintering problems  (Wadsworth et al. , 2017c) , which uses the particle size distribution (Fig. 2 ) and the initial porosity ϕ i as input values. In the case of the data presented here, the pore size distribution moments are 〈a〉 = 8. 01 × 10 -6 m, 〈a 2 〉 = 9. 40 × 10 -11 m 2 , and 〈a 3 〉 = 1. 35 × 10 -15 m 3. With no fitting parameters, the data and the model agree with a coefficient of determination of r 2 = 0.
Time-evolution of permeability k(t) With a validated constitutive law for k(ϕ) (Eqs. 4-5), we can combine our model for k(ϕ) with the vented bubble model for ϕ(t) (Eqs. 1-3), giving a method for calculating the time-dependence of permeability during welding k(t). Fluid flow simulations through the segmented 3D datasets from our in situ experiments yield k(t) data, which we plot in Fig. 6. The data show that permeability drops during welding, and drops more rapidly at higher temperature. In Fig. 6  we also show our model results in which the model for s(ϕ) (Eq. 5; Fig. 3 ) is used to solve the model for k(ϕ) (Eq. 4) using ϕ c = 0. 03 and b = 4. 4 (see Section 4. 2; Fig. 5 ). This solution for k(ϕ) is then mapped to each t by first solving the dimensionless universal form of the vented bubble model (Eq.
Across all datasets, we find reasonable agreement with our model (Fig. 7a ) with a global goodness of fit of r 2 = 0. 80. ¼ -3 1-ϕ ð Þln 1-ϕ ð Þ, which is shown in Fig. We non-dimensionalise the permeability k by a reference perme-  7b , along with data from this study, and literature data from studies using samples which were (or are inferred to have been) initially particulate and which have undergone welding: sintered soda-lime-silica glass beads from  Wadsworth et al. (2017b) , which were also welded using the same in situ technique; glass beads welded ex situ  (Blair et al. , 1993; Wadsworth et al. , 2020b; Eichheimer et al. , 2020) ; obsidian welded ex situ  (Okumura and Sasaki, 2014) ; welded crystal-bearing andesite particles  (Kendrick et al. , 2016) ; and natural welded systems for which s is constrained  (Heap et al.
4 becomes k=k i ¼ ϕ-ϕ c ð Þ = ϕ i -ϕ c ð Þ ½ b and, following the same principles as is shown dimensionally in Fig. 6 , the time dependence for ϕ t À Á is used together with k(ϕ) to predict k t À Á. For the sintering of soda-lime-silica glass beads, we use the monodisperse form of this model, because the beads were sieved to a single size fraction, while for the data presented herein, we use the polydisperse model presented here. This difference results in two different model curves. The agreement between the models and these two datasets leads us to conclude that, while the particle angularity played a minor but measurable role in controlling s(ϕ), it does not strongly influence the dynamics of permeability reduction. Similarly, this suggests     4 5 6 ) rendered dimensionless and compared with available published data.
In all panels the data presented in colour are the experimental data from this study (refer to panel (c) for the temperatures for each colour). The normalization values for each published dataset are given in Table  1. (a) The dimensionless specific surface area as a function of porosity s ϕ ð Þ. Additional data shown are for glass bead packs sintered in situ  (Wadsworth et al. , 2017b)  and ex situ  (Eichheimer et al. , 2020) , and for sphere pack simulations  (Vasseur et al. , 2020). (b) The dimensionless permeability as a function of the scaled porosity k ϕ-ϕ c ð Þ. Additional experimental data shown are for glass bead packs sintered in situ  (Wadsworth et al. , 2017b)  and ex situ  (Blair et al. , 1993; Wadsworth et al. , 2020b; Eichheimer et al. , 2020) , for experimentally welded obsidian particles  (Okumura and Sasaki, 2014) , and for commercially available AmesPore© sintered metal filters (data from: https://amespore.
Using this model: A practical guide We note that the full model presented here is general, such that any pressure-temperature pathway could be incorporated, and the equations solved for ϕ(t) and k(t). In this section, we present a short guide for navigating the solutions to our model framework that may be useful in various scenarios of practical interest. To use this model as presented, we provide a VHub resource 'VolcWeld' (available at https://vhub. org/resources/4568) and a simplified version implemented in Excel™ is provided as Supplementary Material. The full model provided in the 'VolcWeld' program solves the full system of equations presented herein and therefore solves for the diffusive movement of H 2 O. In turn, this diffusion solution is used to solve the 'vented bubble model' for welding and output ϕ(t) and k(t).
The Excel™ implementation (Supplementary Information), neglects the diffusion of H 2 O, and therefore is suited to conditions where H 2 O movement can be neglected on the timescale of welding (Pc ≪ 1 and Pe ≫ 1; see Section 6), but still solves for the 'vented bubble model' using the Euler method for solving ordinary differential equations. The following steps can be followed to determine which solution tool is required: Initial conditions: (1) Constrain the particle size distribution f(R) and find the mean particle size 〈R〉. Both the VolcWeld program and the Excel™ implementation (Supplementary Information) use  Wadsworth et al. (2017c)  to convert f(R) into a pore size distribution f(a i ) and find the mean pore size 〈a i 〉. If no information about the full distribution of particle sizes is known, then take an estimate for the typical particle size and define this as 〈R〉.
g. Hess and Dingwell, 1996)  under initial conditions. Using Γ = 0. 3 N. m -1 , calculate the capillary Peclet number Pc = ΓR 2 /(D i μ i 〈a i 〉) for these initial conditions. If Pc ≤ 0. 1 or Pc ≥ 10, use the initial or equilibrium H 2 O concentration to compute the melt viscosity, respectively. In either case, our Excel™ implementation can be used (Supplementary Information). If instead 0. 1 > Pc < 10, then the diffusive movement of H 2 O renders the problem more complex and the VolcWeld program can be used (available at Vhub). (  5 ) For non-isothermal cooling conditions: As with isothermal conditions, the Excel™ implementation can be used for Pc ≤ 0. 1 or Pc ≥ 10, where the first sheet in the implementation permits input of a temperature rate q. If, however, 0. 1 > Pc < 10, then the VolcWeld program must be used.
Acknowledgments We thank  Holly Unwin  for useful input in the field in the summer of 2019, and  Mathieu Colombier ,  Sebastian Wiesmaier , and  Julie Fife  for beamtime support. We acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for provision of synchrotron radiation beamtime at the TOMCAT beamline of the SLS. Funding was provided by the  European Research Council  (Advanced Grant  EAVESDROP 834225 ;  Starting Grant SLiM   306488 ), the  Leverhulme Trust  ( ECF-2016-325  and  RF-2019-526 \4 ), the  Natural Environment Research Council  grants ( NE/N002954/1 ,  NE/M018687/1 , and  NE/M018687/2 ), and the  National Science Foundation  ( NSF EAR-1852449 ). All raw data associated with the experiments presented herein (c. f. Figs. 456) and required to reproduce our analysis is available in the Supplementary Information.
Cocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of CUD patients and matched healthy controls that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1-weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. The repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform.
Alterations aforementioned areas have been shown to undergo functional connectivity changes that compromise processes such as reward signal, executive function, and impulsivity  10. Taken together, studies using MRI techniques could help to understand CUD pathology and contribute to the improvement of therapeutic approaches. Our dataset comes from a cross-sectional case-control study with the goal of understanding the clinical, cognitive, neuroanatomical and functional pathology that crack cocaine induces. It includes a sample of 75 (9 female) CUD patients and 62 (11 female) healthy controls (HC). The acquired MRI sequences comprise whole-brain: 1) structural (T1-weighted), 2) novel multishell high angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences.
Participants also underwent comprehensive clinical and cognitive evaluations. A participant checklist with the specific MRI sequence acquired can be found in Supplementary 2. Four features from this dataset can be highlighted: 1) the use of the same scanner, protocol and operating procedure, 2) the CUD group shows greater movement, commonly found in psychiatric samples, 3) the novel multi-shell DWI protocol which allows for. further study of brain structure and 4) the extensive clinical and cognitive evaluations the participants underwent. Previous publications have used this dataset using diffusion kurtosis imaging analysis  11  , neurite orientation dispersion and density imaging analysis  12  , and a machine learning approach for the identification of cognitive markers in CUD  13  , among others ,  [14] [15] [16] [17].