dataesr/scientific-paragraphs-categorization huggingface dataset:
Real data results Finally, we validated MIGP on a very large real dataset, utilising a powerful compute cluster having 1. 25 TB of RAM to estimate the group-level PCA both with MIGP and with the "gold-standard" approach of full temporal concatenation. We used rfMRI datasets from the first 131 subjects publicly released by the HCP. Each subject's dataset comprised 4 15-minute runs, totalling 4800 timepoints. The data had been preprocessed and transformed into the compact "grayordinate" standard space  (Glasser et al. , 2013)  of approximately 90,000 spatial locations; therefore the entire raw dataset comprises 225 GB (if using a 4-bit float for each datapoint)nearly half a Terabyte when using double precision storage. For this analysis, we set m = 4700, i. e. , close to the number of timepoints obtained by combining the 4 runs from each subject.
At the completion of MIGP, we saved out the top 4500 components. MIGP took approximately the same amount of time to run than the gold-standard approach, but it required less than 16 GB of memory. The primary final comparison was of the 90,000 × 90,000 full "dense connectome" estimated on the basis of the two approaches. This showed an accuracy of 99. 99% for MIGP against the gold standard. If the gold-standard analysis was reduced to its top 4500 PCA components before re-estimating the ground-truth dense connectome, MIGP had an accuracy (against this) of 100. 00%, telling us that the 0. 01% error in the initial comparison was in fact due to the PCA approximation of the group dataset to the top 4500 components, and not due to MIGP's approximation to the PCA; this is thus a powerful validation of the overall algorithm.
Conclusions We have shown that two simple approaches for estimating grouplevel PCA can achieve virtually the same accuracy as full temporal concatenation of all subjects' datasets, for any number of subjects, or size of data, without needing large amounts of RAM. Indeed, the RAM requirements do not increase at all with increasing numbers of subjects. Additionally, in the majority of simulation scenarios, the methods (along with the temporal concatenation approach that they approximate) provide more accurate results than the other methods tested. We have not commented much on the computational time required for the different methods. This is partly because this work relates to group-level (study-level) analyses, that by definition would not be carried out very frequently. It is also because most of these methods are parallelisable across multiple CPUs with high efficiency.
In the case of MIGP, subsets of a dataset can be processed in parallel (with randomisation of subject membership across subsets), and then the multiple outputs combined with exactly the same approach, treating each subset's output as if it were an individual subject's dataset (which is straightforward given that MIGP retains the scaling/amplitude information). Such an approach would therefore be almost identical to the fully serial MIGP analysis. MIGP and SMIG have similar memory requirements and similar accuracy. They differ in overall computation time in a way that will be data and computer/network dependent. With respect to computation (CPU) time, SMIG is generally faster than MIGP (to an extent that depends primarily on how many SMIG iterations are applied). However, whereas MIGP only requires each dataset to be read from file once, SMIG requires that every dataset be read from file a + 1 times (i.
e. one more time than the number of iterations, because the approach needs to start by forming the average data), if the entire set of raw datasets cannot be held in memory; hence the additional file read time may in some circumstances outweigh the gain in compute time. Because MIGP uses a one-pass incremental approach to estimate the group-average spatial eigenvectors, it is trivial to add new subjects into a study-level PCA estimation, as they become available (e. g. , in the case of HCP, as more subjects' datasets are acquired and publicly released), without needing to restart the processing from scratch. Also, MIGP is able to utilise datasets from different subjects having different numbers of timepoints. MIGP is implemented in FSL's MELODIC tool, and simple MATLAB code for MIGP and SMIG is given in the Appendices A and B.
We use body-waveform modelling to constrain the source parameters of earthquakes occurring globally in oceanic lithosphere beneath the subduction zone outer rise and outer trench slope. These data are then used to map the stress state in the lithosphere of the downgoing plate as it bends into the subduction zone. Our results provide new constraints on the faulting of oceanic lithosphere at the outer rise, which is important for understanding the transmission of platedriving forces through the subduction system. In all cases, shallow normal-faulting earthquakes are observed at the top of the plate, and are separated in depth from any deeper thrust-faulting earthquakes. No temporal variation associated with large thrust-faulting earthquakes on the subduction interface is seen in the depth extent of each type of faulting at the outer rise.
We will examine the temporal evolution of outer-rise seismicity as part of this study. A further aim of this work is to use our earthquake data set to investigate the material properties of the faults within oceanic lithosphere at the outer rise of subduction zones. The factors controlling the distribution and style of faulting in the downgoing plate at the outer rise, and how these are reflected in the observed seismicity, are also discussed. This study first presents new observations of earthquake source mechanisms and depths within the oceanic plate seaward of subduction zones. Each subduction zone in which significant seismic activity in the outer rise has been observed is considered in terms of the spatial and temporal distribution of seismicity. The relationship between seismicity in the downgoing plate and seismic activity on the main subduction interface is then assessed, and the implications of these intraplate earthquake observations for our understanding of the rheology of oceanic lithosphere, the influence of plate driving forces on intraplate deformation, and the relationship between bending-related seismicity and the thermal structure of oceanic lithosphere, are discussed.
Only by the careful analysis of body-waveform data can accurate measurements of earthquake depths within the plate be made (e. g. Forsyth 1982). We therefore use the modelling of body-waves in this study to determine focal mechanisms and depths for earthquakes at the outer rise, and map out the stress state in downgoing lithosphere close to the subduction trench. In an elastic-plastic model, as has been previously used to explain outer-rise bathymetry and seismicity (e. g. M c  Adoo et al. 1978; Chapple & Forsyth 1979; Forsyth 1980) , plastic deformation occurs when the stress within the lithosphere exceeds a pre-defined yield stress. The stress limit which marks the onset of plastic deformation as a function of depth defines a yield stress envelope. The depth integration of this yield stress envelope, and any deeper ductile support of stresses, determines the total strength of the plate.
The modelling strategy used is similar to that employed extensively in the literature (e. g. Taymaz et al. 1991; Tilmann et al. 2010) , using the algorithm of  Zwick et al. (1994)  to invert P-and SH-wave data at teleseismic distances, approximating the earthquake source as a point source with a finite-duration source-time function. A simple velocity structure is used, consisting of a crustal layer 7 km thick (V p = 6. 5 m s -1 , V s = 3. 8 m s -1 , ρ = 2800 kg m -3 ) overlying a man- tle halfspace (V p = 8. 1 m s -1 , V s = 4. 6 m s -1 , ρ = 3300 kg m -3 ). Given that the sediment thickness and velocity structure are poorly constrained for the majority of subduction regions around the world, rather than trying to incorporate a separate sediment layer, this is approximated by having a slightly slower crustal velocity structure than is typical for oceanic lithosphere.
(2010). A number of recent major earthquakes have been modelled using finite-fault inversion algorithms to estimate the slip distribution on the fault plane (e. g. Ammon et al. 2008; Lay et al. 2010). Where appropriate, these are incorporated into the database compiled here. In a number of places, most notably offshore central America and east of Honshu, accurate micro-earthquake studies utilizing oceanbottom seismometers have been conducted  (Lefeldt et al. 2009; Hino et al. 2009; Obana et al. 2012) , and the results of these are again incorporated into our study.
South of the Louisville ridge, outer-rise earthquake activity is concentrated in the region of the 2011 Kermadec doublet  (Todd & Lay 2013 ). Here, a shallow normal faulting earthquake (rupture from 0 to 25 km depth, Todd & Lay 2013) broke the upper part of the plate, accompanied by extensional aftershocks over the same depth range, and was followed 5 months later by a deeper thrust event (centroid at 39 km, rupture from ∼30 to 52 km depth). A number of major (M W ≥ 7) outer-rise earthquakes have occurred along the Tonga-Kermadec subduction zone that are either too old for digital data to be available, or too large, and the waveforms too complex, to yield robust solutions using the techniques employed in this study (pale mechanisms, Fig. 9 ). Consideration of NEIC centroid depth shows these to be broadly consistent with the overall depths of normal-faulting and thrust-faulting from well-constrained events in the outer rise along the subduction zone.
T H E R M A L C O N T RO L S O N O C E A N I C S E I S M I C I T Y The depth extent of seismicity in the oceans depends on the thermal structure of the lithosphere, and hence on its age (Wiens & Stein  1983; M c Kenzie et al. 2005). Oceanic seismicity away from active mid-ocean ridges and from subduction zones is relatively sparse. As a result, outer-rise earthquakes represent an important part of the data set of oceanic seismicity which can be used to investigate the thermal control on seismogenic behaviour. Comparisons of seismicity to early models for plate thermal structure (e. g. Parsons & Sclater 1977)  indicated that the deepest extent of oceanic seismicity approximately followed the depth of the 750 • C isotherm. However, more recent thermal models, taking into account the temperature dependence of thermal parameters within the mantle, lowered this threshold temperature to ∼600 • C (Denlinger 1992; M c Kenzie et al.
C O N T RO L S O N T H E D E P T H E X T E N T O F E X T E N S I O N A N D C O M P R E S S I O N AT T H E O U T E R R I S E Fig. 14  shows a first-order separation of shallow extensional (white) and deeper compressional (black) outer-rise seismicity. Fig. 15  splits the data set into individual regions, showing the inclination of P-and T-axes for each area, to examine the controls on the depth of the transition from extension to compression. The depth of the transition from normal faulting to thrust faulting (if this occurs) is expected to be controlled by four main factors: the magnitude of the bending moment, the magnitude of any in-plane stresses within the plate (arising from both forces transmitted along the plate from the descending slab, and forces exerted from seaward of the trench), the rheology of the plate, and the subduction interface stress.
C O N C L U S I O N S The expanded database of outer-rise seismicity presented here illuminates a number of key points concerning the seismicity in the downgoing oceanic plate seawards of subduction zones. (i) Oceanic intraplate seismicity is uniformly confined to regions expected to be colder than ∼600 • C, in agreement with the previous findings of M c Kenzie et al. (2005). Nowhere do earthquake centroids indicate seismicity occurring at hotter temperatures within the oceanic mantle. (ii) In all cases, a vertical separation is seen between shallow normal-faulting earthquakes, and, where present, deeper thrustfaulting earthquakes, with a narrow aseismic region separating the two. (iii) Where only normal faulting has been observed, earthquakes appear to be restricted to shallower depths. Beneath some is a region colder than 600 • C, which is expected to be capable of brittle failure, but in which no earthquakes have been observed.
A C K N O W L E D G E M E N T S Seismogram data were retrieved from the IRIS Data Management Centre. A number of figures were made using GMT  (Wessel & Smith 1998). TJC was funded by a Girdler scholarship from the University of Cambridge and a NERC studentship. AC and JJ acknowledge support through the NERC-and ESRC-funded 'Earthquakes without Frontiers' project. We thank Thorne Lay and two anonymous reviewers, along with the editor, Ingo Grevemeyer, for their comments, which helped improve the manuscript.
6 km s -1 , which is consistent with the best-fit velocity for rock 1. This event may have been caused by ground vibrations or bank erosion during the passage of the sedimentladen flow pulse, and resulted in an immediate and sustained increase of 5 % dB in the [9-12] Hz seismic energy recorded at station IGB01. We attribute this increase to a sudden addition of sediment to the flow. Thus, our seismic data suggest that an effective, two-way link exists between the Illgraben channel and the surrounding hillslopes, whereby mass wasting during rainstorms can cause the constitution of a flow capable of transporting significant amounts of sediment, and this flow in turn can induce further mass wasting during pas- for the event locations that correspond to the upper 75% of the dynamic range from t probability density maps (Fig.
Flow propagation velocity Having considered mass wasting in the steep flanks of the Illgraben, we now turn to activity in the central channel of the catchment. The link between pulses of seismic energy recorded at stream-side stations and the flow propagation in the Illgraben channel can be used to infer some characteristics of the channel dynamics. To do this, we tracked the downstream progress and evolution of the three flow pulses with seismic data from stations IGB01, IGB02 and IGB09, located closest to the channel (Fig. 1 ). The propagation velocity of flow pulses in the Illgraben channel was estimated from the envelopes of seismic energy recorded along the main stream. Prior to computing the seismic envelopes, we bandpass filtered the seismic signals of each component (vertical, north and east) between 5 and 20 Hz.
Moreover, the duration of a flow pulse can influence the seismic energy delivered in the streambed. A sharp, strong seismic peak can have a total energy equivalent to that of a long and smooth seismic pulse. Along the 4. 25 km channel reach between IGB01 and IGB09, the three flow pulses had a similar seismic duration of 11 min (±104 s), with limited fluctuations of 5, 16 and 14 % (standard deviation) for pulses 1, 2 and 3, respectively (Fig. S4 ). Therefore, possible effects of stretching in time of the flow pulses can be discarded. The video camera located at CD29 recorded the passage of the debris-flow sequence. With a sampling rate of one picture per second, we noticed an elevation of flow level, as indicated by the flow height data set (Fig. 9c ), but we could not extract additional information that could help to decipher the bedload fluctuation as shown by the impact rate data set (Fig.
For pulse 3, the interpretation of available data is less straightforward. This pulse  had the second highest seismic amplitude of the main flow sequence on this day with a substantial signal below 15 Hz, reflecting a substantial bedload transport rate. However, the pulse had only a limited flow depth and moderate bedload impact rates. Lower than expected impact rates may have resulted from a debris flow with different internal organization of the bedload material and/or from the way in which sediment particle impacts are recorded. This is done with force plates, which register an impact only when its force or acoustic amplitude (geophone) exceeds a pre-defined threshold. The absolute amplitude of such impacts is not recorded. Since the flow depth of pulse 3 was small, sediment particles may have had less energy at impact due to low drop heights and particle velocities, which could explain both reduced impact rates at CD29 and moderate seismic energy at IGB09.
Acknowledgements. This study was supported by the  AXA Research Fund  and the  Isaac Newton Trust of the University of Cambridge. We thank the  SEIS-UK equipment pool (NERC)  and the  École et Observatoire des Sciences de la Terre of Strasbourg  for providing the seismic instruments, the  WSL  for logistic support and  M. Raymond Pralong ,  K. Steiner ,  N. Federspiel ,  T. Glassey  and  F. Dufour  for help in the field. Thanks are also due to  A. Helmstetter ,  M. Attal ,  L. Hsu  and the associate editor  J. Braun  for their comments and suggestions, which have improved the clarity of the paper. The service charges for this open access publication have been covered by a  Research Centre of the Helmholtz Association. Edited by:  J. Braun.
We present an analysis of the clustering of galaxies from z ≈ 2 to the present day using the WIRCam Deep Survey (WIRDS). WIRDS combines deep optical data from the CFHTLS Deep fields with its' own deep near-infrared data, providing a photometric data-set over an effective area of 2. 4 deg 2 , from which accurate photometric redshifts and stellar masses can be estimated. We use the data to calculate the angular correlation function for galaxy samples split by star-formation activity, stellar mass and redshift. Using WIRDS with its' large total area and multiple fields gives a low cosmic variance contribution to the error, which we estimate to be less than ∼ 2. 8%. Based on power-law fits, we estimate the real-space clustering for each sample, determining clustering lengths and power-law slopes.
For star-forming galaxies with stellar masses of M⋆ 10 11 h -1 M⊙, we find a clustering strength of ∼ 8 h -1 Mpc across all redshifts, comparable to the measurements for the passive population. Additionally, for star-forming galaxies we see that clustering strength increases for higher stellar mass systems, however little sign of a mass dependence in passive galaxies is observed over the range in stellar mass that is probed. Comparing our results to the model galaxy population produced by galform, we find good agreement between the model predictions and the observed clustering. Finally, we investigate the connection between galaxy stellar mass and dark matter halo mass, showing a clear correlation between the two in both the WIRDS data and the galform predictions.
In this paper, we present a study of the mass, type and redshift dependence of galaxy clustering in the WIRCam Deep Survey (WIRDS). The paper is organized as follows: Section 2 briefly describes the WIRDS data used here. Following this, in Section 3 we present the clustering analysis if galaxies in the WIRDS fields as a function of type, mass and redshift over the range 0 < z < 2. Section 4 provides a summary and our conclusions. Throughout this paper, all magnitudes are given in the AB system unless stated otherwise. Where relevant, we assume a ΛCDM cosmology given by Ω m = 0. 25 and Ω Λ = 0. 75.
Data and Simulations The work presented here is based on data from the deep fields of the Canada-France-Hawai'i Telescope Legacy Survey (CFHTLS). The CFHTLS Deep incorporates four 1 × 1 deg 2  survey fields (designated D1 to D4) spread across a broad range in R. A. and Declination, the four field centre co-ordinates being: D1 02:25:59, -04:29:40; D2 10:00:28,  +02:12:30; D3 14:19:27, +52:40:56; and D4 22:15:31, -17:43:56. The CFHTLS produced deep optical imaging data within these fields, whilst the WIRDS survey has added deep NIR imaging data and it is the combination of these two datasets that we use here and that are described in more detail in the two sections below. In this section, we also give an overview of the galform semianalytical galaxy formation model, the predictions of which we will confront with our results.
Optical data In this work, we use the CFHTLS T0006 optical data to provide 5-band optical photometry of the galaxy population. This incorporates imaging taken with the MegaCAM imager in the Canada-France-Hawai'i Telescope (CFHT) using the ugriz MegaCAM filters. The CFHTLS provides stacked images based on either a 25% or 85% cut in terms of image quality (i. e. seeing). In this case, we use catalogues based on the 85%-cut stacks. These catalogues have pointlike (i. e. star/bulge) 80% completeness depths of u ≈ 26. 5, g ≈ 26. 0, r ≈ 25. 5, i ≈ 25. 1 and z ≈ 24. 9, whist the image quality is consistently ≈ 0. 7 -0. 8 ′′ across all four fields. This consistency is one of the key benefits of using the CFHTLS data, in that all the data is homogeneous in terms of seeing, depth and filter/telescope properties.
Infrared data WIRDS is a deep infrared imaging survey of the CFHTLS Deep fields, providing infrared data to complement the CFHTLS optical data obtained with MegaCAM. The WIRDS imaging was taken with the WIRCam detector  (Puget et al. 2004 ) on the CFHT and a detailed discussion of the observations and data reduction of the WIRDS data is provided by  Bielby et al. (2012)  and  McCracken et al. (2010). The data used in this paper were taken in a series of observing run from 2005-2007 and were made in co-ordination with the COSMOS consortium. Observations were conducted using three filters: J, H and K s. Transmission plots of the WIRCam J, H and K s filters are available from CFHT 1. The integration times for all J, H and K s band exposures was 45s, 15s and 20s respectively. The observations were carried out in queue scheduled mode at the CFHT.
Photometric galaxy properties, mass constraints and colour selection With the wavelength coverage afforded by the combination of the CFHTLS optical data and the WIRDS NIR data, it is possible to estimate photometric redshifts and stellar masses reliably over a broad redshift range. In particular, the wavelength range afforded by this collection of filters presents the possibility of the 4000 Å break being detectable up to z ∼ 4. We used the Le Phare 2 code  (Arnouts et al. 2002; Ilbert et al. 2006)  to determine photometric redshifts and galaxy properties with a χ 2 template-fitting method. The photo-z were estimated using the median of the probability distribution function (PDFz) rather than the minimum of the χ 2 distribution. The results of the photometric redshift estimation are presented in  Bielby et al.
(2012) , with a full comparison to spectroscopic datasets. Below we provide an overview and derived accuracies of the photometric redshifts. We use a number of spectroscopic redshift data-sets to calibrate the photo-z data in our four fields. In the D1 field, we use spectroscopic redshifts from the VVDS Deep  (Le Fèvre et al. 2005b ) and Ultra-Deep  (Cucciati et al. 2012; Le Fevre et al. 2013 ) spectroscopic samples. The VVDS Deep sample is available publicly and consists of 8,981 spectroscopically observed objects over an area of 0. 5 deg 2 in the CFHTLS D1 field. It consists of a magnitude limited sample with a limit of I ≤ 24 and samples a redshift range of 0 ≤ z ≤ 5. The Ultra-Deep sample consists of ∼ 1500 spectra over an area of ≈ 0. 14 deg 2 and covers a magnitude range of 22. 5 ≤ i ≤ 24.
1,  Kim et al. 2009 ) and high redshifts (z ∼ 1, Gonzalez-Perez et al. 2011). We note that GALFORM uses a Kennicutt IMF, whilst the photometric masses derived from the WIRDS data assume a Chabrier IMF. Based on  Ilbert et al. (2010)  and  Gilbank et al. (2011) , we multiply the GALFORM masses by a factor of 1. 32 to match the Chabrier based stellar masses of the WIRDS photometric catalogues. The Bower et al. model successfully reproduces the stellar mass function up to z = 4. 5 and the number counts of red galaxies at z < 2  (Almeida et al. 2008; Gonzalez-Perez et al. 2009 ). In addition, we have shown in  Bielby et al. (2012) , that the mass function for all galaxies is well matched between our observations and the galform model up to a redshift of at least z ≈ 2.