dataesr/scientific-paragraphs-categorization huggingface dataset:
This research takes place within a recent initiative called GATE-RTion,  12  which is aimed at providing a validated long-term application, based on a GATE and Geant4 version, along with associated software tools for clinical dosimetric implementation to facilitate collaborations between hadron therapy institutes. Current collaborators include the Centre Antoine Lacassagne (Nice, France), the Christie NHS Foundation Trust (Manchester, UK) and MedAustron (Wiener Neustadt, Austria). GATE/Geant4 offers a set of prebuilt physics lists which the user may adopt in his/her specific simulation scenario. Physics lists may use different models to describe a specific electromagnetic or hadronic physics interaction and different parameters such as the production cut and maximum step size. In order to support the Geant4 medical physics community, the Geant4 Medical Physics Benchmarking Group  13, 14  has been recently established to benchmark Gean-t4 for medical physics and to provide recommendations in terms of physics lists, production cuts and maximum step sizes.
5. 02, 9. 6. 03, 10. 0. 02, 10. 1, 10. 2. 02), concluding that agreement to measurements substantially depended on the multiple scattering model set in the specific Geant4 version used. 19 Recently, Resch et al. compared simulated lateral dose profiles and field size factors to measurements to investigate the accuracy of electromagnetic and nuclear scattering models (GATE, Geant4 v. 10. 03. 01), showing a high sensitivity of the elastic scattering cross sections. 20 his study extends and complements the work above by comparing a wide range of combinations of physics options, step limiter and production cuts to a comprehensive set of measurements, aimed at clinical applications of Geant4/ Medical Physics, 47 (11), November 2020 GATE for proton therapy dosimetry. It takes the whole (clinical) process of beam characterization into account and all experimental data were acquired as part of clinical commissioning or quality assurance.
Additionally, the influence of different settings is quantified in simulations performed in a realistic CT-based patient model. This is therefore a comprehensive validation of Geant4 v. 10. 3. 3 in combination with GATE v. 8. 1, and therefore also of GATE-RTion v. 1. 0, which is a long-term stable branch of GATE v. 8. 1 intended for clinical purposes. A set of parameter recommendations is presented, taking both dose distributions and calculation efficiency into account. The first part of the paper gives an overview of the simulation setup and the Geant4 settings under investigation. Next, the influence of the different settings on the beam-modeling process and on spot sizes in air is investigated by comparison of simulations of individual pencil beams and measurement data acquired during the commissioning of a proton therapy facility.
2. A. AUTOMC simulations In the following, MC settings are summarized according to Ref. [21]. A set of in-house developed scripts (AUTOMC  22  ) written in GNU Octave  23 (v. 4. 4. 1) , which automatically create GATE macro files, launch simulations and analyse simulation results, have been developed for clinical proton therapy dose calculations at The Christie NHS Foundation Trust. This system performs all its MC calculations using GATE v. 8. 1/ Geant4 v. 10. 3. 3. 2  These are the same versions included in GATE-RTion v. 1. 0,  12 and in keeping with the goals of GATE-RTion, this work aims to provide recommendations in terms of simulation parameters to adopt for proton pencil beam scanning. The MC settings for simulations performed in this work are summarized in Table  I. Simulations and data analysis were performed on a Linux (based on Ubuntu 18.
2. C. Overview of study Simulation results were compared to experimental data for the combinations of production cuts and step limiters summarized in Table  III. From here on, these will be referred to by the scenario numbers shown in the table. Each of these six scenarios was combined with the four prebuilt physics lists (Option0, EMY, EMZ, HP_EMZ) leading to a total of 24 combinations. First, the effect of these settings was evaluated for the energy tuning process (Section 3. A) and for beam size simulations in air (Section 3. B). The effect of each physics list was analyzed while setting both production cuts and step limiter to the smallest values considered, which should provide the most accurate dosimetric results (reference scenario #1). Next, production cuts in phantom and world were varied while leaving the step limiter constant (scenarios 1, 2 and 3).
ACKNOWLEDGMENTS This study was performed within the  GATE-RTion framework , and we acknowledge the support of both the GATE and Geant4 community. We are thankful for the computation time provided on the proton therapy development cluster, and the computation support by  Ian Porter. Furthermore, we thank  Edward Smith  for providing GATE support. This work was funded by the  Science and Technology Facilities Council (STFC) Advanced Radiotherapy Network , grant number  ST/N002423/1  and the  Engineering and Physical Sciences Research Council , grant number  EP/R023220/1  supported by the  NIHR Manchester Biomedical Research Council.
The general workflow is then similar to that of SFDI (illustrated in Fig. 1. (B)). Of importance, one should note that demodulation is the key step with which the resulting accuracies of both OP map retrieval and visual quality depend. Details of the acquisition, demodulation and processing typically used in SFDI and SSOP are provided in  [3, 4] , and details about creating a custom SFDI system and processing code are available on www. openSFDI. org and www. healthphotonics. org [23].
U-NET 2: Profilometry: A second small-size U-NET (less than 2 × 10 4 parameters), identical in structure to the first one, is used to get wrapped phase and obtain the profile, avoiding the use of Fourier domain 3D profilometry that leads to poor accuracy due to its dependency to spatial frequency. In our implementation, the real and imaginary part are extracted from the network to compute the wrapped phase by mean of atan2() function  [35]. Real-time profile-corrected optical properties imaging: To reduce the inference time of both U-NETs, a custom-made GPU implementation of the networks was undertaken using the CUDA deep learning library cuDNN. Following the use of the two computationally inexpensive U-NETs described above, (used for demodulation and profilometry, respectively), the extraction of optical properties can be performed along with profile correction at high speed using a previously developed real-time optical properties GPU processing code  [24].
At the final stage, a 7 × 7 n-1 convolution layer with linear activation is employed to retrieve the output. In total, the twin U-NET architecture is comprised of either 3064 or 18886 weights (according to n equal to 1 or 2, respectively) for 17 convolutional layers. The python code for each architecture is available at www. healthphotonics. org in the "resources" section. The network was implemented with Tensorflow using Keras backend and is compiled with mean squared error (MSE) as loss function and Adam as optimizer  [42]. The training starts with a learning rate of 0. 001 and employs a learning rate reduction (factor of 1. 11) if there is no loss improvement after 15 epochs. The model is trained for 900 epochs using NVIDIA GTX 1080Ti computation power in less than 2 hours. The training dataset consists of a total of 100 images that are 1024 × 1024 pixels in size composed of a combination of 7-phases SFDI acquisitions (for both accuracy and visual quality) of hands (N = 60 from 10 different hands placed in various orientations), silicon-based tissue mimicking phantoms (N = 20 from 18 different phantoms in various orientations) and ex vivo pig organs (N = 20 from 6 different organs).
GPU C CUDA implementation Our custom SSOP processing code (using C CUDA) is made with Microsoft Visual Studio and NVIDIA CUDA toolkit environment. Based on the previous developed code  [24] , our work focused on the GPU implementation of the novel demodulation via the previously described twin U-NET workflow. To perform model inference, there are three options: using the training framework itself (convention), using TensorRT  [39]  or writing a custom code to execute the network using cuDNN low-level libraries and math operations  [40, 41]. We choose the latter for its high efficiency compared to other solutions. Since the network is a succession of many convolution, max pooling, up-sampling and concatenation, layers will be presented only once.
Validation and performance assessment Our validation dataset consisted of the images of 4 different hands and 20 images from 6 different ex vivo pig organs in various configurations and orientations (intestine, colon, stomach, liver, esophagus and pancreas). None of the validation images were used for training. Images were acquired via 7-phase SFDI using 4 spatial frequencies (0. 1 mm -1 , 0. 2 mm -1 , 0. 3 mm -1 , 0. 4 mm -1 ) to extract optical properties. These images were processed using four different processing modalities: SFDI, SSOP Filtering  [12] , SSOP Deep Learning (noted "SSOP DL conv") with convolutions of 7 × 1 and 7 × 7. For the quantitative performance assessment, 3D profile and optical properties maps obtained using SSOP methods at each spatial frequency were compared to that of 7-phase SFDI.
These comparisons were undertaken by measuring the mean absolute percentage error given by the following Eq. (  1 ) where N and M refer to image size (number of pixels in x and y directions): Error(%) = 1 N × M N-1 n=0 M-1 m=0 100 × Result REFERENCE n,m -Result TEST n,m Result REFERENCE n,m (1) For the image visual quality assessment, structural similarity index (SSIM) of 3D profile and optical properties maps obtained using SSOP methods at each spatial frequency were compared to that of 7-phase SFDI. For the GPU implementation performance assessment, NVIDIA Nsight environment was used to trace the C CUDA code processing time. This time does not account for the transfer time between the CPU and the GPU memories (which is 338 µs for a 1024 × 1024 pixels size image) on our system. The evaluation of the quality of the images was performed visually to consider degradations, such as ripples and edge artifacts.
Finally, a movie of a moving hand was acquired at f x = 0. 2 mm -1 with rotation and translation to compare and contrast the three SSOP analytic method's real-time capabilities. Note that the codes, for each demodulation method (SFDI 7 phases, SSOP Filtering, SSOP DL conv 7 × 1 and SSOP DL conv 7 × 7), and all data (calibration phantoms, hands, and ex vivo sample) used in this work are available at www. healthphotonics. org in the "resources" section.
Results The 24 images of the test dataset were processed as described before with the four different modalities. To illustrate these results, we show in Fig. 3. , Fig. 4. and Fig. 5. respectively along with their pixel-wise percentage error map the results of the measured profile, profile-corrected absorption coefficient and profile-corrected reduced scattering coefficient at each spatial frequency obtained on a hand. These images allow one to both qualitatively appreciate and quantitatively assess each SSOP processing method's reconstructive performance and against 7-phase SFDI. We also present in Fig. 6  the results obtained at spatial frequency f x = 0. 2 mm -1 on another hand image and 4 images of ex vivo porcine organs that illustrate how shape variety influence the accuracy of the optical properties extracted via each method.
2mm -1. Although not demonstrated, the use of frequencies that are typically lower than 0. 3mm -1 seems preferable to allow deeper penetration of the photons and better signal to noise ratio (amplitude modulation decreases significantly with spatial frequency). As such, the use of the presented SSOP DL methodology allows for a more reliable use of SFDI methods for healthcare application that necessitate real-time employability. However, this work is not without limitations. The performance of a trained network always depends on the training dataset. In our case, we avoided the 3 phases SFDI acquisition signal to noise ratio limitation when spatial frequency increases by using 7 phases SFDI acquisition. Despite this precaution, SFDI ground truth at spatial frequencies 0. 3 mm -1 and 0. 4 mm -1 are still subject to some imperfections, which likely affect the network training and subsequent reconstruction upon inference (see Fig.
3 4 5 ). These artifacts could explain, in part, why the error values for absorption and reduced scattering are not decreasing when increasing the spatial frequency from 0. 3 mm -1 to 0. 4 mm -1. Another classical limitation regarding the datasets used for training in DL is the variety of configurations offered by the sample. Including more images of organs having different shapes and edge discontinuities would likely improve the network accuracy. Indeed, this was observed in previously published work  [33]  and noted as crucial for model generalizability. This study highlights also the trade-off that exists between the choice of the spatial frequency used for acquisition and the capacity of SSOP to provide both accurate and high visual quality images. One can notice for all the SSOP Filtering images obtained at low frequency that the capability to retrieve the structure of the hand is limited by the half-period of the spatial frequency used.
Finally, all code and data used for this work is available at www. healthphotonics. org under the resources tab. This is an open initiative for transparency and with the objective to share and accelerate developments in the field of SFDI. Sources codes, raw data as well as executable code readily usable can be downloaded. Other resources such as www. openSFDI. org can be used to fabricate a SFDI system that can be used with the codes provided here  [23]. In summary, this work lays the foundation for real-time multispectral quantitative diffuse optical imaging for surgical guidance and healthcare applications.
Funding  H2020 European Research Council (715737 );  Agence Nationale de la Recherche  ( ANR-11-INBS-006 ,  ANR-18-CE19-0026 );  Université de Strasbourg (IdEx) ;  Campus France  ( Eiffel Scholarship );  National Institutes of Health  ( R01 CA237267 ,  R01 CA250636 ).
We present a new galaxy cluster lens modeling approach, hybrid -Lenstool, that is implemented in the publicly available modeling software Lenstool. hybrid -Lenstool combines a parametric approach to model the core of the cluster, and a non-parametric (free-form) approach to model the outskirts. hybrid -Lenstool optimizes both strongand weak-lensing constraints simultaneously (Joint-Fit), providing a self-consistent reconstruction of the cluster mass distribution on all scales. In order to demonstrate the capabilities of the new algorithm, we tested it on a simulated cluster. hybrid-Lenstool yields more accurate reconstructed mass distributions than the former Sequential-Fit approach where the parametric and the non-parametric models are optimized successively. Indeed, we show with the simulated cluster that the mass density profile reconstructed with a Sequential-Fit deviates form the input by 2-3σ at all scales while the Joint-Fit gives a profile that is within 1-1.
In the cluster outskirts, the density of constraints is high, and a more flexible free-form model with many mass "pixels" would allow a better tracing of the potentially irregular matter distribution, as well as the detection of the presence of (lower density than the cluster) infalling substructures (see for example  Meneghetti et al. 2017; Remolina González, Sharon & Mahler 2018 , for a comparison between the different types of models). Combining these approaches is the obvious next step, and with this in mind, we have developed a new version of the Lenstool software, hybrid -Lenstool, that combines the parametric modeling approach in the cluster core, with a free-form grid model in the outskirts. A key challenge when modeling galaxy clusters over an extended spatial scale is the nature of the different lensing constraints depending on the cluster region considered.
Weak lensing component modeling. As a second step, we add a set of 353 RBFs to model the outskirts of the cluster. They are located at the nodes of a multi-scale grid. The resolution of the grid traces the mass distribution of the simulated cluster, with higher resolution in the densest region (for more details on the multiscale grid, see for instance  Jullo & Kneib 2009; Jauzac et al. 2012 ). The resulting grid potentials have core radii varying between s = 11 -87 (i. e. 50-400 kpc). We remove the RBFs covering the cluster core,  and model this central region with the best-fit values of the parametric model described in the previous paragraph (see Table  1 ). The grid of RBFs is created in a Lenstool input format using our set of publicly available scripts 2. We present the projected mass distribution maps resulting from these two successive models in the middle panel of Fig.
1 , and refer to it as the Sequential-Fit. The presented 2 https://github. com/AnnaNiemiec/grid_lenstool mass maps are the mean maps computed as the average of 1000 MCMC realizations. Input Sequential-Fit Joint-Fit C1 C2 N NW S1 S2 S3 S4 C1 C2 C1 C2 x (arcsec) -2.
In addition, the Joint-Fit performs better at predicting the position of multiple images in the cluster core, reducing the rms from 0. 63 to 0. 57. After demonstrating the power of this new algorithm with simulated data in this method paper, we will present the first application of hybrid-Lenstool to real observations in a forthcoming paper  (Niemiec et al. , in prep). As mentioned before, the ongoing BUFFALO survey (GO-15117, PIs:  Steinhardt & Jauzac, Steinhardt et al. 2020 ) is extending the HST coverage of the 6 HFF clusters (PI:  Lotz, Lotz et al. 2018) , and will complement the strong-lensing constraints in the core of these clusters with high resolution weak-lensing data. Combining these datasets, i. e. high resolution constraints both in the strong-and weak-lensing regions of the clusters, with hybrid-Lenstool will produce high-precision models of the mass distribution of the HFF clusters up to ∼ 3/4 Rvir.
ACKNOWLEDGMENTS AN would like to thank  Jonathan Vacher  for the helpful discussions. Support for BUFFALO surveys Program number GO-15117 was provided by  NASA  through a grant from the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. MJ is supported by the  United Kingdom Research and Innovation (UKRI)   Future Leaders Fellowship 'Using Cosmic Beasts  to uncover the  Nature of Dark Matter ' [grant number  MR/S017216/1 ]. This project was also supported by the  Science and Technology Facilities Council  [grant number  ST/L00075X/1 ]. AN, PN, KS acknowledge support from the  BUFFALO program. ML, EJ, AN acknowledges  CNRS  and  CNES  for support.
| Short-read strategy Sequencing data were aligned to the human genome version GRCh37. 1 using the BWA-MEM algorithm of the Burrows-Wheeler Aligner (BWA) v0. 7. 4  (Li & Durbin, 2010). Alignments were analyzed using the BreakDancerMax algorithm  (Chen et al. , 2009). BreakDancer uses discordant read pairs (unexpected relative orientation and/or insert size) to call 4 different types of events: translocations, insertions, deletions, and inversions. Recurrent SVs and sequencing artifacts were filtered out from the events call list provided by Breakdancer, using an internal database containing data from about 80 WGS. Integrative Genome Viewer (IGV, version 2. 3. 4) (Robinson et al. , 2011)  was used to validate events. Variants from BreakDancer were visualized with the "color alignment by insert size and pair orientation" option selected.
| Linked-read strategy Sequencing data were analyzed using LongRanger (v. 2. 1. 4. ). The algorithm performed alignment (to the GRCh37 reference genome), haplotyping, and SV detection. LongRanger first generated the list of putative large events (>30 kb), denoted by "candidates. " Then, the candidate events with a high degree of confidence were denoted by "calls. " Smaller events (<30 kb) were separately listed. 10X Genomics proposes a visualization software called Loupe. It enables visualization of the variants detected by LongRanger in a linear and matrix view. Call and candidate events are listed, for easy visualization. A linked-read view is available to visualize the reconstructed linked-reads. Because LongRanger files contain only events with a length > 30 kb, we decided to filter the variants from short-read strategy (BreakDancer) discarding all the events under 30 kb.
| Sensitivity of the two pipelines In this paragraph, we focus on the performances of the bioinformatic software only (alignment and calling). Sensitivity was computed on lists of events that were filtered to retain events found in only one patient. It was defined as the proportion of correctly detected events among the total expected ones. As breakpoint detection is not at the basepair resolution, intervals (± 250 bp) around breakpoints (BreakDancer) or around intervals defining the breakpoints (LongRanger) were defined. To assess the sensitivity of each strategy, breakpoints were considered similar when their intervals were overlapping and events were considered similar when their two breakpoints were similar.
| Linked-read strategy detected the breakpoints before and after unblinding LongRanger pipeline detected a mean 3,009 "candidates" and 16 "call" events per patient. The first (blinded) analysis only explored the "call" events and the Loupe viewer enabled us to find one complete SV (patient 1) and 5 SVs partially (patients 4, 6, 9, 10, and 11, where only a part of the CCR was detected). After unblinding, the analysis of the targeted "candidate" events enabled us to find 4 more diagnoses (patients 2, 7, 12, and 13) and completed the diagnosis in 5 patients  (patients 4, 6, 9, 10, and 11). We analyzed the "call" events of all the patients. Apart from the events belonging to the SV, we found mostly polymorphisms or recurrent events (already identified in our local database or shared by at least two patients in the present study).
| DISCUSSION Short-read strategy led to the identification of rearrangement in 10/13 patients (patients 1, 2, 4, 5, 6, 9, 10, 11, 12, 13) in blind analysis. Using linked-read strategy, we were able to identify the appropriate SV in 10/13 patients (patients 1, 2, 4, 6, 7, 9, 10, 11, 12, 13), and one of them only partially. However, most of them were found only after unblinding. This could be explained by the use of a database of known events in short-read strategy which aided the analysis of output files that contained many possible events, including small polymorphisms and artifacts. Another reason could be that the 10X Genomics variant caller is less performing in prioritizing the SVs. Another important issue to point out is the quality of data; the mean molecule length for Chromium library preparation was lower than that recommended by the manufacturer (above 40,000 base pairs), and this could explain the lower sensitivity of the LongRanger analysis in SV detection.
, 2011; Gymrek, 2014) , or large SVs  (Spies, Zook, Salit, & Sidow, 2015). Herein, we chose to use IGV for short-read strategy and the 10X Genomics Loupe software for linked-read strategy. IGV enables to navigate from the different breakpoints belonging to the same rearrangement. Thus, we detected some breakpoints not pointed out by BreakDancer, mostly in the complex rearrangements such as chromoanagenesis. The Loupe viewer enables the visualization of the SVs with a linear and matrix view and offers a linked-read view to improve the variant characterization. A structural variant list allowed easy visualization of the variants, despite the inability to filter them. It is of note to future users of this program that there is a steep learning curve for the use of Loupe viewer, but also that substantial improvements could be made by the manufacturer (e.