dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Behavioral data: RT coefficient of variation (SD/M) was calculated for every block of 10 trials. EEG data were filtered using. 5 Hz high pass filter and 60 Hz notch filter for electrical noise. Data were re-referenced to channels TP9 and TP10 and corrected for ocular artifacts using ICA. Matlab with EEGLAB toolbox  76  were used for analyses. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted October 12, 2021. ; https://doi. org/10. 1101/2021. 10. 01. 462777 doi: bioRxiv preprint Preprocessing: First, bad channels were rejected and then interpolated using EEGLAB's rejchan and eeg_interp functions. Stimulus-brain coupling was assessed by first applying Morlet wavelet filtering at every single Hz from 1 to 50 Hz.
Then, the Hilbert transform was done on each Hertz to get phase angle of stimulus and of EEG data, and the coupling between stimulus and EEG was assessed as the phase-locking value where plv(ch) = abs(sum(exp(1i*(phase_filtered_EEG(ch,:) -phase_filtered_music)))). This was applied in parallel to EEG data and to sound stimuli. The Pink Noise stimulus was generated in real time using Max/MSP (i. e. , was not a stored file) and so the stimulus-brain coupling analysis was not applied to the Pink Noise data.
Experiment 4 : 4 Behavioral study Participants Recruitment and experimental procedure (including screening) was identical to Experiment 1. Experiment 4 involved 221 participants (120 males; 99 females; 2 other/chose not to respond; mean age = 36, SD = 11. 10). The final dataset for Experiment 4 comprised 175 of the initial 221 participants (79% passed screenings).
, 2018). There is another class of earthquakes that offers a reduced set of multiple raypaths: Earthquakes that occur on the plate interface  (Uchida et al. , 2010) , whose location precludes conversions or reflections from the interface itself. For that reason, interface earthquakes will prove valuable in this study. In this study, we explore how the source region of SAM earthquakes can be determined from seismograms recorded at short distances from the source. We present two methods, one to calculate synthetic seismograms (spectral element method (SEM)), and the other to compute multiphase arrival times (fast marching method) and describe the respective models. Synthetic seismograms are used to explore how secondary arrivals can be identified based on specific criteria. We then use observed seismograms from a data set of well-located earthquakes in the WHSZ, applying a processing scheme to enhance the signal of secondary arrivals.
Different shades of gray represent seismic velocities, which are specified for each region in (a). The inset in (d) shows an example of the spectral element mesh within the corner of the mantle wedge. A movie of the four simulation runs is available at doi. org/10. 5281/zenodo. 4334041 (see also caption for Movie S1). we consider multiple interactions with discontinuities (i. e. , conversions, reflections, or combined reflection plus conversion of P-and S-waves), the number of all potential secondary arrivals quickly increases beyond what can be realistically observed on the records (e. g. , 80 potential arrivals within 10 s for wave interactions with two out of the three discontinuities). Synthetic waveforms can tell us which secondary arrivals we can expect to observe, based on the prominence of each phase.
Waveform Modeling Based on Spectral-Element Method In this first step, we employed the SEM as implemented in the Specfem2D software, which accurately simulates the complete wavefield in 2-D heterogeneous media and allows synthetic seismograms to be extracted at any given point  (Komatitsch, 2005). The SEM is widely used to simulate the wave propagation at global (e. g. ,  Bozdağ et al. , 2016) , regional (e. g. ,  Zhu et al. , 2015) , and local scales (e. g. ,  Tape et al. , 2009; Tong et al. , 2014). At its core, SEM solves the weak form of the seismic wave equation on a finite element mesh in the time domain. It allows complexities such as topography at the free surface, internal discontinuities, anisotropy, (an-/poro-) elasticity, and any lateral variations of elastic parameters and density.
Journal of Geophysical Research: Solid Earth all points in a region, even in the presence of LVLs or ray shadow zones  (de Kool et al. , 2006; Rawlinson & Sambridge, 2004). For multiphase traveltime calculations in an accurate model of the WHSZ, we constructed a grid comprising four seismic discontinuities embedded in a 3-D background P-and S-wave velocity model (see  Figure 1b). The four discontinuities are the surface topography/bathymetry, the overriding Moho, subduction plate interface, and the slab Moho. We defined these discontinuities from the following data sources: topobathymetric data are from the Shuttle Radar Topography Mission  (Farr et al. , 2007)  and the Smith & Sandwell bathymetry database  (Smith & Sandwell, 1997) , the overriding Moho model is from CRUST1. 0  (Laske et al.
The sources lie straight below the central station, and are vertically separated by 5 km. To highlight coherent phases, we aligned seismograms on the direct P-wave and show the vertical (Z) and horizontal (X) components independently, sorted by event depth (shallowest at the top). We show both explosion (upper panel) and earthquake sources (lower panel), because it facilitates the identification of observed phases: As the explosion source radiates only P-wave energy, only P-to-P and P-to-S interactions appear on the plot (P-S-P conversions are usually negligible). We identified the phases by tracking the wavefronts on snapshots of the wavefields, taken every 0. 3 s, that we combined into three movies (available at doi. org/10. 5281/zenodo. 4334041 and doi. org/10. 5281/zenodo. 4944977; see captions for Movies S1-S3).
Mismatches Between Modeled Arrivals and Observations Our systematic search for secondary arrivals largely confirms the expected earthquake locations and the assumed velocity-discontinuity model for the WHSZ, but it also points out some model-data mismatches. It is generally difficult to verify phase identifications across a network as observed arrivals vary between stations. This variability arises from the station geometry of the Medusa network, which follows a SW-NE line with stations spreading out in a triangular shape toward the back arc. Designed for receiver function imaging, this spread-out configuration was chosen because the dip direction of the slab was not well known prior to the deployment. While the average station spacing along the profile is 8. 5 km in the forearc, the profile-normal spacing is up to 40 km.
Acknowledgments We thank the Associate Editor and two anonymous reviewers for thoughtful and constructive reviews, which helped improve the initial version of the manuscript. This study was funded through the  Research Council of Norway  under project  Subduction Water and Metamorphism: a Modeling and Imaging Study  (number  231354 ) and an  RCN personal overseas research grant  to  F. Halpaap. Q. Liu  received funding through a discovery grant (number  487237 ) from the  Natural Sciences and Engineering Research Council of Canada.
Data Availability Statement Seismic waveform data from the MEDUSA network are archived at the Incorporated Research Institutions for Seismology Data Management Center (http://ds. iris. edu/ds/nodes/dmc) under temporary network code XS (2006XS ( -2009)). Waveform data of the EGELADOS stations can be downloaded from the Observatories & Research Facilities for European Seismology European Integrated Data Archive (http://www. orfeus-eu. org/data/eida/) under temporary network code Z3 (2005-2007). The high-resolution catalog of SAM earthquakes in Greece is available from the PANGAEA database (www. pangaea. de) under the doi: 10. 1594/ PANGAEA. 894351, and the plate interface is archived under the doi: 10. 1594/PANGAEA. 894350. Shuttle Radar Topography Mission topography data for the discontinuity model in Figure  1b  were downloaded from the United States Geological Survey at https://dds.
cr. usgs. gov/srtm/. Bathymetry data for that model are from the Smith and Sandwell bathymetry database at the Scripps Institution of Oceanography, University of California at San Diego, at http://topex. ucsd. edu/marine_topo/. Supplementary movies S1-S3 show wave propagation simulations and are available at doi. org/10. 5281/zenodo. 4334041 and doi. org/10. 5281/ zenodo. 4944977. We performed wave propagation simulations with the SpecFem2D code that is, available from http://github. com/geodynamics/specfem2d. We computed multiphase arrival times with the FM3D code that is, available from http://www. iearth. org. au/codes/3Dfastmarching/. Maps were created with the m_map Matlab toolbox (http://www. eoas. ubc. ca/∼rich/map. html). The GISMO code for seismic data processing in Matlab is available from Zenodo under http://doi.
We also train a 3D U-Net model to obtain a semantic segmentation map of the cells. We then perform a marker controlled watershed on the probability map of Stardist using the U-Net segmentation as a mask image to prevent the overflow of segmentation regions. To avoid the error of sub-optimal seeding we developed a seed pooling approach taking advantage of strength of both the Stardist and U-Net networks. We benchmark our segmentation result on a challenging dataset comprised of epithelial cells of mouse embryonic mammary glands with membrane labelling. These cells are highly irregular in shape and have a low signal-to-noise ratio to obtain an accurate segmentation only based on the boundary information. Using this dataset, we obtain different metrics showing that our approach is able to obtain shape approximation for the overlapping cells that go beyond the star convex shape.
Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain the final cell segmentation result (F). All images are displayed in Napari viewer with 3D display view. centroid information may lead to wrong cell assigmements, hence we need to include other cell attributes such as the shape and intensity information while making the links between the cells in successive time frames. Trackmate is a popular tracking software that uses customizable cost matrix for solving the linear assignment problem and uses Jaqman linker as a second step to link segments of dividing and merging cells [TPS + 17]. In this paper, we introduce BTrackmate, a Fiji/ImageJ plugin to track the previously segmented cells. The major advantage of BTrackmate is the ability to track the cells inside a tissue.
It allows the input of the cell and tissue segmentation image files and/or a csv file of the cell attributes. Furthermore, we also add some biological context in the tracking process where after segment linking is done a track inspector removes segments that are shorter than a user defined time length. Such short segments are unlikely to be true division events if they are too short and manually removing them can be tedious when many tracks are present. The users can choose this parameter in time units and can set it to 0 if removing such short segments is not required. Finally, the tracking results obtained with BTrackmate are saved as an xml file that can be re-opened in an Euler angle based viewer in python called Napari, allowing volumetric viewing of the tracked cells using the track layer feature  [UVCL20].
Preparation of the dataset We used fluorescent microscopy images of mouse embryonic mammary glands stabilized in an ex vivo culture previously collected in the laboratory of Dr. S. Fre at Institut Curie. All images were acquired with an inverted confocal laser scanning microscope (e. g. Zeiss LSM780/880) equipped with long-working distance objectives to acquire high-resolution 3D image stacks. We acquired images of pixel size (22, 512, 512) with calibration of (3, 0. 52, 0. 52) micrometer. The quality at which these images are acquired is determined by the spatial resolution of the used optical device, desired temporal resolution, duration of the experiment and depth of the acquired Z-stacks. We perform unsupervised image denoising  [KBJ19]  on our dataset, an algorithm we chose based on its performance compared to other methods  [Ric72] ,  [Luc74].
Post-restoration of the 3D images, we developed a method to perform the segmentation of the cells using deep learning techniques. We created a training dataset with hand drawn segmentation of 14 Z-stacks. We performed data augmentation on the microscopy images by denoising, adding Poisson and Gaussian noise, random rotations and flips to create 700 Z-stacks. We chose a patch size of (16, 128, 128) and created 11,264 patches for training Stardist and U-Net network. For the Stardist network we chose 192 rays to have a better shape resolution for the irregular shaped cells.
Parameter Setting Stardist predicts object instances based on probability threshold and non maximal suppression threshold to merge overlapping predictions. These parameters can be automatically determined using the optimize threshold program that we provide with the segmentation package. Higher values of the probability threshold yield fewer object instances, but avoids false positives. Higher values of the overlap threshold would lead to oversegmentation. We used 32 Z-stacks to determine the optimal parameters of probability threshold of 0. 76 and non maximal suppression threshold of 0. 3.
We use the probability map of Stardist to start the watershed process to obtain a better shape approximation for the irregular shaped cells that goes beyond the star convex shape. The code for the merging of U-Net and Stardist seeds is the following: The code for doing watershed in 3D using the complete set of seeds on the probability map of Stardist is the following: Accuracy of segmentation results is assesed by comparing the obtained labels to the ground truth (GT) labels. The most commonly used metric is to compute intersection over union (IOU) score between the predicted and the GT label image. We define GT, labels and IOU score as: GT = {gt}, SEG = {seg} are two sets of segmented objects. IOU(a, b) is the value of the IOU operation between two segmented objects a and b. A threshold score value τ ∈ [0, 1] is used to determine the true positive (TP), false positives (FP) and false negatives (FN) defined as: T P = {seg ∈ SEG, ∃ gt ∈ GT, IOU(gt, seg) > τ} FP = {seg ∈ SEG, ∀ gt ∈ GT, IOU(gt, set) < τ} FN = {gt ∈ GT, ∀ seg ∈ SEG, IOU(gt, seg) < τ} We use the Stardist implementation to compute accuracy scores which uses the hungarian method (scipy implementation)  [Kuh55]  to compute an optimal matching to do a one to one assingement of predicted label to GT labels.
Detailed Procedure The software package we provide comes with training and prediction notebooks for training the base U-Net and Stardist networks on your own dataset. We provide jupyter notebooks to do so on local GPU servers and also on Google Colab. Network Training: In the first Jupyter notebook we create the dataset for U-Net and Stardist training. In the first cell of the notebook the required parameters are the path to your data that contains the folder of Raw and Segmentation images to create training pairs. Also to be specified is the name of the generated npz file along with the model directory to store the h5 files of the trained model and the model name. The model parameters are specified in the next notebook cell. These parameters are described as follows: 1) NetworkDepth = Depth of the network, with each increasing depth the image is downsampled by 2 hence the XYZ dimension of the data / 2^depth has to be greater than 1.
7) n_patches_per_image is the number of patches sampled for each image to create the npz file, choose an optimal value so that the file fits in the RAM memory. 8) Rays stand for the number of rays used to learn the distance map, low rays decreases the spatial resolution and high rays are able to resolve the shape better. 9) use_gpu_opencl is a boolean parameter that is set true if you want to do some opencl computations on the GPU, this requires GPU tools python package. After the network has been trained it will save the configuration files of the training for both the networks along with the weight vector file as h5 files that will be used by the prediction notebook. For running the network prediction on XYZ shape images use the prediction notebook either locally or on Colab. In this notebook you only have to specify the path to the image and the model directory.
The only two parameters to be set here are the number of tiles (for creating image patches to fit in the GPU memory) and min_size in pixel units to discard segmented objects below that size. We perform the watershed operation on the probability map as a default. However, this operation can also be changed to use the distance map coming out of Stardist prediction instead by setting 'UseProbability' variable to false. The code below operates on a directory of XYZ shape images:.
Tracking After we obtain the segmentation using VollSeg, we create a csv file of the cell attributes that include their location, size and volume inside a region of interest. For large datasets memory usage could be of concern while loading the images into memory, hence inputs via csv could prove helpful. Tracking is performed in ImageJ/Fiji, an image processing package. We developed our code over the existing tracking solution called Trackmate [TPS + 17]. Trackmate uses linear assignment problem (LAP) algorithm to do linking of the cells and uses Jaqman linker for linking the segments for dividing and merging trajectories. It also provides other trackers such as the Kalman filter to do tracking of non-dividing cells. Trackmate comes with a fully interactive track editing interface with graph listener to show the selected cell in the trackscheme and vice versa, to click on the graph and have the selected cell being highlighted in the image, making the process of track editing interactive.
Post-editing the tracks are saved as an xml file which can then be loaded back into the program to do more track editing if needed. When a cell divides, the track is splitted up in two tracklets. In order to aid in track editing, we introduced a new parameter of minimum tracklet length to remove tracklets in a track that are short in the time dimension. This introduces a biological context of not having very short trajectories, reducing the track editing effort to correct for the linking mistakes made by the program. For testing our tracking program we used a freely available dataset from the cell tracking challenge of a developing C. elegans embryo  [Cel]  [MBB + 08]. Using our software we can remove cells from tracking which do not fit certain criteria such as being too small (hence most likely a segmentation mistake) or being low in intensity or outside the region of interest such as when we want to track cells only inside a tissue.
Track Analysis After obtaining the tracks from BTrackmate, we save them as Trackmate xml file, which contains the information about all the cells in a track. Since the cells can be highly erratic in their volumetric motions, we use Napari, an Euler angle based viewer, to visualize such tracks from different reference positions. We made a python package to export the xml files previously saved in ImageJ/Fiji and convert them into the tracks layer of Napari. We made a customised widget based graphic user interface (GUI) to view selected tracks, display the track information and save the cell track along user selected view, as shown in Figure  6  A. On the top left panel, the image and tracks layer properties are displayed and can be changed (1). In the bottom left, there is a dropdown menu enlisting all the tracks (2).
Conclusions We have presented a workflow to do segmentation, tracking and track analysis of cells in 3D with irregular shape and intensity distribution. For performing segmentation we developed VollSeg, a jupyter notebook based python package that combines the strengths of semantic and instance deep learning segmentation methods. Post-segmentation we create a csv file containing the information about the cells inside a region of interest which serves as an input to Btrackmate, the ImageJ/Fiji plugin we created for doing the tracking. The tracking software uses existing track editing interface of Trackmate and saves the track information as an xml file. To view and analyze such volumetric tracks we created napatrackmater, a python package to export such trajectories as  track layer of Napari and we provide jupyter notebook based enviornment for track analysis with two example notebooks.
Acknowledgements We acknowledge the  Cell and Tissue Imaging Platform (PICT-IBiSA) of the Genetics and Developmental Biology Department  ( UMR3215/U934 ) at  Institut Curie , member of the  French National Reserch infrastructure France-Bioimaging  ( ANR-10-INBS-04 ). We thank specially  Olivier Renaud  for supporting the software development. We are grateful to  Dr Silvia Fre  for support and constructive discussions. We thank  Leo Guginard  for insightful comments about the manuscript. V. K is supported by  Labex DEEP at Institut Curie  ( ANR-11-LBX0044  grant). C. C is supported by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  666003.
Target selection The low-redshift galaxy population with stellar mass ∼ 10 8 M ⊙ should have r ∼ 22 at z spec ≃ 0. 15, thus we select the galaxies with r < 22. The four CLAUDS fields are covered by SDSS, thus the galaxies with r < 17. 7 have complete spec-z. So we limit our targets to 18 < r < 22. When the observations were initiated, the CLAUDS survey was essentially complete, with full coverage in XMM-LSS and ELAIS-N1, while HSC data were not yet fully released. Since the r band 22 magnitude is still above the limit magnitude of the previous survey projects, we selected our targets from the CFHTLS catalogue  (Gwyn 2012)  for the CLAUDS-XMMLSS field, ELAIS-N1 catalogue  (González-Solares et al. 2011)  for the CLAUDS-ELAIS-N1 field and the Stripe-82 catalogue  (Jiang et al. 2014)  for the CLAUDS-DEEP23 field.
The observations are run in queue mode and the user provides a previously calculated configuration file containing for each object the R. A. and Dec. positions, magnitudes and rank, using the xfitfibs 3 code developed by the Hectospec Instrument team  (Roll et al. 1998). For each run, we assigned 3 https://www. cfa. harvard. edu/mmti/hectospec/xfitfibs/ at least 40 fibres for the sky, 10 fibres for F-star flux calibrators and 3 fibres for the Guide stars. xfitfibs will assign the fibre by the rank and class of each target. To avoid fibre entanglement, some of the fibres may not be assigned to an object. So we also add some 8µm detected sources from the SWIRE survey with low rank to make full use of the available fibres. xfitfibs calculates several configurations in a single field simultaneously in order to sample as many objects as possible in a given rank (i.
ACKNOWLEDGMENTS We thank the referee for careful reading and constructive suggestions. C. C. would like to thank  Daniel Fabricant  for approving the usage of  MMT/Hectospec. We thank  Zheng Cai ,  Song Huang  and  Cheng Li  for helpful discussion. This work is supported by the  National Key R&D Program of China  grant  2017YFA0402704  and by the  National Natural Science Foundation of China , No. 11803044 ,  11933003. This work is sponsored (in part) by the  Chinese Academy of Sciences  (CAS), through a grant to the  CAS South America Center for Astronomy (CASSACA). We acknowledge the science research grants from the  China Manned Space  Project with NO. CMS-CSST-2021-A05. This research uses data obtained through the  Telescope Access Program (TAP). Observations reported here were obtained at the  MMT Observatory , a joint facility of the  University of Arizona  and the  Smithsonian Institution.