dataesr/scientific-paragraphs-categorization huggingface dataset:
; lWM • • • • • • • • 2-stream NN; MEVIS • • • • • • • • • Multi-brain • • • Groupwise registration; Bayesian modelling; NiftyReg • • • Bending and Jacobian regularisation; PDD-Net • • • • • • • Dense Corr. ; PIMed • • • • • • • • • UNet; SSTVD similarity; Dense corr. ; Thorley • • Winter • • • • • • • Voxelmorph; Bailiang :  [28]  addressed OASIS and is based on the DeepRegNet framework from Project-MONAI. The input of the encoder is the concatenation of fixed and moving images. A dense vector field (DVF) is predicted from summing over different level decoders and integrated using scaling and squaring. The loss function is composed of LNCC, MIND-SCC, Dice, and a diffusion regulariser. https://github. com/ BailiangJ/learn2reg2021 task3.
ConvexAdam :  [29]  propose a decoupling of deep learning for semantic feature extraction, using an nnUNet, and a very fast and accurate conventional optimisation. They combine a single-level dense discretised displacement correlation with large capture range and convex global optimisation with a local gradient-based instance refinement using the Adam optimiser. The method is applied to all six tasks and uses diffusion regularisation, an inverse-consistency constraint, and MIND similarity, which is applicable for multi-modal and samemodality intra-patient alignment. The method extends the input features to learned label-supervised representations for interpatient tasks: Abdomen CT-CT, Hippocampus, and OASIS brain. https://github. com/multimodallearning/convexAdam corrField : A fast implementation (from  [30] ) of the corrField method  [31]  is introduced as a non-learning based unsupervised baseline.
Driver :  [32]  use a dual-encoder U-net backbone with separated multi-scale feature extractors that comprises Deformation Field Integration (DFI) and non-rigid feature fusion (NFF) module. It produces multi-scale sub-fields that progressively align fixed and moving features. The DFI module integrates sub-fields through up-sampling, re-weighing, and warping operations. The NFF dynamically fuses features of three pathways based on attention mechanisms. The overall framework comprises a rigid transform network and MI or LNCC similarity, weak label-supervision and regularisation. Epicure :  [33]  addresses the lung CT task using a conventional iterative-based registration approach based on Elastix toolbox optimizing the object function that is composed of the NCC similarity and a bending energy penalty term.
Estienne :  [34] ,  [35]  addressed Abdomen CT-CT and Hippocampus with label-supervision. The method combines a diffeomorphic symmetric spatial transformer network with a embedding merging step, that eases the learning by subtracting the embeddings of separately encoded fixed and moving scans and thereby leveraging the prior knowledge that swapped inputs should yield negated velocity fields. They extend the label-based pre-training by including additional public datasets with at least partial overlap in segmentation classes, using segmentation masks produced by a CNN. https://github. com/ TheoEst/abdominal registration Gunnarsson :  [36]  propose an end-to-end learningbased 3D registration method inspired by the PWC-Net  [37]. The method estimates and refines a displacement field from a cost volume at each level of a CNN downsampling pyramid and is supervised by a similarity (NCC) and/or segmentation (DICE) loss, as well as a smoothness penalty.
The network is trained and evaluated on scan pairs from three tasks of the 2020 challenge (Lung CT, Abdomen CT-CT and Hippocampus MR) using the same weights for all tasks. https://github. com/ ngunnar/learning-a-deformable-registration-pyramid Imperial : [Imperial] uses Image-and-Spatial Transformer Networks (ISTN) as the backbone of their method. In the ISTN, the fixed and moving images are first separately processed by the ITN to generate a segmentation mask and a feature map of the input image. Subsequently, both feature maps are used by the STN to predict the displacement field. The loss function consists of a structural-guided and image similarity and a regularisation loss. https://github. com/ biomedia-mira/istn Joutard : Joutard addresses the Abdomen CT-CT task with a weakly supervised deep learning approach.
This fast method won the on-site challenge in both years with robust results across all tasks. https://github. com/cwmok/Conditional LapIRN LaTIM :  [40]  is an iterative technique exploiting vector-valued directional image representations: smooth edgebased fields oriented towards the main image edges (closely related to vector field convolution for active contour segmentation). The method is implemented within the Elastix framework and shows improvements compared to directly using intensities. Lifshitz :  [41]  propose a novel solution of learningbased lung CT registration that comprises a 3D extension of ARFlow with multi-resolution warping, dense displacement correlation, and flow estimation. To address edge-preservation of sliding motion an unrolling of the total variation (L1) regularisation loss computation using variable substitution is proposed and shown to stabilise gradients during training.
This proceeds to the finest level with quasi-Newton L-BFGS optimization at each level. For the Hippocampus task, a deep learning approach with a weakly supervised trained U-Net was applied using the same cost function as in the iterative approach. Multi-brain :  [43]  use groupwise, fully unsupervised registration techniques based on Bayesian modelling and Gauss-Newton optimisation, which learns priors over image intensities and spatial tissue classes. The method requires no pre-processing of the imaging data and does not utilise label information. The method is applied to Abdomen CT-MR, OA-SIS, and Lung CT. https://github. com/WTCN-computationalanatomy-group/mb. NiftyReg :  [12]  is applied as conventional baseline for all tasks without label supervision using NCC for CuRIOUS and otherwise MIND as similarity metric.
Both bending and Jacobian regularisation penalties are applied and the number of pyramid levels is restricted to yield competitive run times (on multi-core CPU). https://github. com/KCL-BMEIS/niftyreg PDD-Net : The PDD-Net (probabilistic dense displacement network)  [44] ,  [45]  uses a compact deformable convolutional network to extract image features and compute a six-dimensional dissimilarity tensor (three spatial + three displacement dimensions). A smooth displacement field is obtained from the dissimilarities by interleaved (and twice repeated) steps of mean field inference over spatial dimensions and approximated min-convolutions over displacement dimensions. The method is adapted to four of the six challenge tasks (CuRIOUS, Hippocampus MR, Abdomen CT-CT, and Lung CT). https://github.
com/multimodallearning/pdd net PIMed : PIMED use a multi-slice segmentation network and train a two-stage registration network for Abdomen CT-MR and Abdomen CT-CT and a residual VoxelMorph model for OASIS. For lung CT they apply a conventional method, with geodesic density regression and adaptation of intensities to lung tissue density  [46]. Winter : Winter address all three tasks from 2021 by employing a traditional method for lung CT and a attention-based DL registration for Abdomen CT-MR and OASIS brain. Improvements are found by a two-step approach that firstly aligns provided ROI masks. The algorithms achieve intermediate ranks for the considered tasks without using any label supervision. The smoothness complexity is large for abdominal registration. https://github. com/WinterPan2017/ADLReg IV.
Unsupervised Registration: The top-performing methods are all modular in their use of segmentation labels for supervision. As analysed in the label bias experiment, there is a risk of over-fitting registration performance to the chosen subset of manually annotated anatomies. We, hence, compared the unsupervised counterparts of the following methods: LapIRN and ConvexAdam. ConvexAdam already uses an unsupervised method for all three intra-patient tasks, and LapIRN for CuRIOUS and Lung CT. Therefore the additional comparisons are restricted to the abdomen and brain. Transferability: A robust registration method should work well for all scan pairs regardless of acquisition parameters and thus on every comparable dataset. A frequently mentioned limitation of deep-learning-based methods is that they reach higher accuracy on the dataset they are trained on and show a considerable loss of accuracy on other data.
First of all, it is a multimodal registration between MR and US images and the US images are typically noisier than the MR images. Furthermore, the preoperative MR scans show a larger region of the brain whereas the intra-operative US volume was obtained to cover the entire tumor region after craniotomy but before dura opening. Due to these difficulties, only four methods were submitted to this task in addition to the three baseline methods. For two of these methods, some cases caused negative outliers and the average TRE was worse than the initial TRE (c. f. Table  III ). Only the two baseline methods corrField and PDD-Net as well as the ConvexAdam method registered all scan pairs satisfactorily. Hippocampus MR: Due to its small volumetric size and reasonably large training dataset with only two anatomical labels, Hippocampus MR appeared to be a good entry-level task for learning-based registration approaches.
However, the provided initialisation masks can already lead to DSC-4>60% overlap using a similarity transform. The multimodal metrics, MIND  [52]  and NGF  [53]  were used by the majority        of participants, including the two top-performing methods ConvexAdam and corrField. Those methods also capture larger motion robustly using dense discrete correlation. LapIRN and PIMed (3rd and 4th rank) added a Dice loss, where it became obvious that focusing on the supervision with only 4 organs may lead to over-fitting on those structures and a lower accuracy for further anatomies. The provided additional labeled unpaired datasets (30 BCV CT scans and 30 CHAOS MRI scans  [54] ) have not yielded the desired advantages due to subtle differences in appearance. OASIS: The OASIS inter-subject brain task attracted the most learning-based solutions.
72mm), while other teams performed considerably worse (e. g. LapIRN 3. 24mm and PDD-Net 2. 46mm). In 2021, keypoint correspondences were provided for training and the submissions improved with six teams achieving a TRE of less than 2mm. Especially for the deep-learning-based methods LapIRN and Imperial this is a remarkable result, because they were only trained on a small dataset of 20 scan pairs. In contrast to other lung registration challenges like EMPIRE or DIRLab, the TRE is relatively high. This can be explained, in part, by the low resolution of the scans that were chosen in the preprocessing. Fig. 2  visualises the difference images of an example coronal slices for the methods of Driver, convexAdam, and MEVIS overlayed with the landmarks. Overall Ranking: ConvexAdam was among the top 3 on each task (winning Abdomen CT-CT and Abdomen CT-MR) and ranked first overall among the eight methods that were applied to four or more tasks -highlighting the importance of effective optimisation and versatility of using learned semantic or hand-crafted MIND features depending on the application.
e. without anatomical knowledge) improves upon those results and achieves very strong results for OASIS and Abdomen CT-CT (ranks would be third and fourth respectively). This demonstrates that in particular a large training database and an advanced deep learning architecture (LapIRN uses multi-level Res-Nets, multiple warps and multi-resolution loss functions) can narrow the gap between supervised and unsupervised approaches. We evaluated ConvexAdam (that decouples feature extraction from optimisation) for Abdomen CT-CT in three settings: 1) all 13 labels in training with 8 (7 identical) in test (DSC=69%), 2) 4 labels in training with 8 (thereof 3 identical) in test (DSC=55%) and 3) no labels in training (DSC=45%). This shows that partial supervision clearly leads to improvement of those identical anatomies but can also help to align nearby structures: esophagus which was excluded improved by 16% points (likely through the guidance of liver and aorta) and pancreas overlap was increased by 12% points (possibly by including portal vein and adrenal gland).
As mentioned in Sec. V-A training on 4 and evaluating on 9 abdominal organs for MR-CT fusion results in a moderate performance gap between supervised and unsupervised methods (the latter being about 10% better on this metric). Transferability: In this experiment, we were able to show that the three best methods of the lung registration task also perform very well on the DIRLab dataset (MEVIS 1. 22 mm, convexAdam 1. 31 mm, and corrField 1. 34 mm) without any further hyperparameter adaptations. Since the inspiration and expiration images of the DIRLab dataset are extracted from a 4DCT dataset with shallow breathing, the registration task is easier than the Learn2Reg lung CT task. Therefore, the methods reach a lower TRE on the DIRLab dataset compared to the Learn2Reg lung task (improved TRE of 0.
46 mm, 0. 48 mm, and 0. 41 mm for MEVIS, convexAdam, and corrField, respectively). Due to the performed preprocessing and the reduced resolutions, the Learn2Reg methods achieve slightly worse results than state-of-the-art methods evaluated on the DIRLab dataset. For example, method of MEVIS included in a registration pipeline registering the original images reaches a TRE of 0. 94 mm  [55]. LapIRN achieves similar results on both datasets (Learn2Reg lung CT 1. 98 mm and DIRLab 1. 98 mm) showing that the best deeplearning-based methods can also be successfully applied to other datasets without retraining. However, no performance improvement can be observed on the easier dataset mainly due to the limited training data size.
The latter two were only applied to a subset of tasks. NiftyReg achieves reasonable accuracies across all tasks but falls behind supervised methods on interpatient tasks. The original VoxelMorph variant reaches an average Dice overlap of 76. 88%±2. 17 % for OASIS (7th-10th place based on DSC alone) and a TRE of 7. 51±3. 43 mm for lung CT (13th place). When trained on a large additional lung dataset  [49]  a TRE of 1. 71±2. 86 mm was achieved for the additional DIRLAB lung experiment for which the best performing methods in this challenge achieved 1. 3 mm. PDD-Net achieved a second rank for CuRIOUS and fifth place for Lung CT, but lower scores for inter-patient registration. CorrField achieved the best scores overall for CuRIOUS and LungCT and second place for Abdomen MR-CT (each task without supervision), making it stand out as the best performing intra-patient approach.
This demonstrates that conventional methods are still very competitive for datasets without strong label supervision. Reducing Entry Barriers: By pre-processing each dataset to the same dimensions and isotropic resolution and providing anatomical annotations for training data wide participation was achieved from research groups across the world. The OASIS inter-subject brain task attracted the most learningbased solutions, which highlights the importance of large, labeled training datasets for deep-learning-based registration and mirrors the focus of recent research. Lung CT intra-patient registration was addressed by the same number but more diverse set of methods, including conventional, fully deeplearning-based, and hybrid approaches. Therefore, we assert that new application areas have been opened for many participants.
Limitations of the Challenge Design: We have identified a number of limitations that should be addressed in future studies. First, for computational reasons the training of algorithms was performed offline by participants. This could introduce a bias when additional data is used by certain teams that is not accessible to others and prevents the use of larger (central) datasets that cannot be made public due to privacy concerns. Enabling docker-based training or fine-tuning of models directly at grand-challenge. org would be desirable. Second, the amount of available annotated training data varied across tasks and made in particular intra-patient tasks harder for learning-based approaches. Decoupling anatomical feature learning from patient-wise optimisation could be a next step, e. g. by providing training data for airway and fissure segmentation for lung CT.
(2021). The multiband photometry obtained from the crossmatching of these catalogs was later used in the spectral energy distribution (SED) fitting procedure discussed in Sect. 3. The crossmatching was done following  Kim et al. (2021) , where a 3σ positional offset in the RA/Dec coordinates corresponding to each dataset with respect to the HSC coordinates were used as the crossmatching radii. For GALEX, AKARI, WIRCam, FLAMINGOS, IRAC, WISE, PACS, and SPIRE, we used a crossmatching radius of 1. 5 , 1. 5 , 0. 5 , 0. 65 , 0. 58 , 0. 7 , 2. 75 , and 8. 44 , respectively. Figure  3  shows the distribution of galaxies with counterparts in each dataset. About 62% of the galaxies in the sample (1086 out of 1743 sources) have at least one counterpart outside the ugrizy optical range. We also compared our sample with the band-merged catalog of  Kim et al.
Method We used the Code Investigating GAlaxy Emission (CIGALE 5  ;  Noll et al. 2009; Boquien et al. 2019 ) SED fitting tool to estimate the physical parameters of the galaxies in our sample, in particular, the stellar mass, SFR, total infrared luminosity, and dust attenuation. The CIGALE tool uses an energy balance principle where the stellar emission in a galaxy is absorbed and reemitted in the infrared by the dust. This enabled us to simultaneously fit the UV to FIR emission of the galaxies in our sample. The input parameters we used for our SED fitting are given in Table  1. We used the  Bruzual & Charlot (2003)  stellar population synthesis models with a Chabrier (2003) IMF and a fixed subsolar stellar metallicity of 0. 008 (0. 4 Z ) 6. We also adopted a flexible star formation history (SFH) from  Ciesla et al.
(2017) , which includes a combination of delayed SFH with the possibility of an instantaneous; recent burst or quench episode. Such an SFH was successfully used to reproduce a broad range of galaxy properties in the local universe  (Hunt et al. 2019; Ciesla et al. 2021). The range of values adopted for the SFH is given in Table  1. We also included dust attenuation, adopting the dustatt_modified_starburst module of CIGALE, which is a modified version of the well-known  Calzetti et al. (2000)  atten-uation curve, extended with the  Leitherer et al. (2002)  curve between the Lyman break and 150 nm. This module provided a possibility of changing the slope as well as the addition of a UV bump in the attenuation curve. In this work, we fixed these parameters to their standard value to reduce the number of free parameters, as we have only six photometric bands for a large fraction of our sample.
Conclusions We present an optically selected sample of 1631 galaxies at z < 0. 1 from the NEP Wide field. We crossmatched this sample with several multiwavelength sets of available data ranging from UV to FIR and performed an SED fitting procedure to obtain key physical parameters, such as stellar mass, SFR, L IR , and A V. We also extracted radial surface brightness profiles for the sample and estimated their average optical surface brightness and sizes. Our main results can be summarized as follows: -Using the measured average r-band surface brightness ( μe ), our sample consists of 1003 LSBs ( μe > 23 mag arcsec -2 ) and 628 HSBs ( μe ≤ 23 mag arcsec -2 ). -The LSBs have a median stellar mass, surface brightness, and effective radius of 10 8. 3 M , 23. 8 mag arcsec -2 , and 1. 9 kpc, respectively.
A. 2. Comparison of fits with and without FIR data A41, page 14 of 15    (2021). Galaxies with a spectroscopic redshift are marked with the superscript "sp"; (5-6) Average r-band surface brightness within the effective radius (in units of mag arcsec -2 ) and the effective radius obtained from radial profile fitting; (7-10) Stellar mass, star formation rate, total infrared luminosity, and dust attenuation in the V-band, respectively, from CIGALE SED fitting. The error bars are the 1σ uncertainties from the Bayesian analysis of CIGALE. A41, page 15 of 15.
Acknowledgements. J. and K. M. are grateful for support from the  Polish National Science Centre  via grant  UMO-2018/30/E/ST9/00082. W. J. P. has been supported by the  Polish National Science Center  project  UMO-2020/37/B/ST9/00466  and by the  Foundation for Polish Science (FNP). J. K. acknowledges support from  NSF  through grants  AST-1812847  and  AST-2006600. M. R. acknowledges support from the  Narodowe Centrum Nauki  ( UMO-2020/38/E/ST9/00077 ). M. B. gratefully acknowledges support by the  ANID BASAL  project  FB210003  and from the  FONDECYT  regular grant  1211000. D. D. acknowledges support from the  National Science Center (NCN)  grant  SONATA  ( UMO-2020/39/D/ST9/00720 ). D. D. also acknowledges support from the  SISSA visiting research programme. A. P. acknowledges the  Polish National Science Centre  grant  UMO-2018/30/M/ST9/00757  and the  Polish Ministry of Science and Higher Education  grant  DIR/WK/2018/12.
Tracking and Measuring Velocity Fields. We developed an inhouse MATLAB software to segment the animals in each frame, ignoring frames where part of the animal was outside of the camera's field of view or where the stage was moving. We defined the animal size as the area enclosed within the animal's twodimensional footprint, with the animal diameter defined as the diameter of a circle of equivalent area. To minimize measurement error in size caused by out-of-plane buckling of the animal, we considered only frames where the observed area was at least 80% of its maximum observed area. We found no trend of cell size with animal size (SI Appendix), in accordance with histological studies  (41). We computed the optical flow between frames using software described in ref. 32 , keeping vectors within the animal's footprint, while removing velocity vectors within a distance of 10% of the animal diameter from the boundary to eliminate edge effects on the optical flow algorithm.
[8] Here, δ and c o have the same meaning as in Eq. 4. w i = u i u i is the unit vector of the velocity fluctuation of cell cluster i, and η i is its speed fluctuation with respect to the global mean speed, η i = u i -1 N N k=1 u k. We measure the slope of the correlation profile at the zerocrossing using the five-point stencil method, fitting a secondorder polynomial to the points closest to the crossing and then evaluate the slope as the first derivative of the fitted function at the crossing. We measured the susceptibility (integral) of the correlation profile by applying a cubic spline fit of the average correlation profile, interpolating 100 evenly spaced data points across the domain and then integrating using Simpson's rule. These quantities in the directional and speed correlations are largely consistent with the full velocity correlations (SI Appendix).
A : The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions.