dataesr/scientific-paragraphs-categorization huggingface dataset:
Model description For this comparison, a 100 m long JT-60SA TF CICC has been modelled with hydraulic characteristics given in Table  1. To ensure the relevance of the comparison, datasets used in each code are equivalent. As we are only interested in hydraulic phenomena, the thermal power is directly applied to the fluid. It is done from 15 m to 25 m of the CICC inlet and its time profile is given in Fig. 8. In both codes, the friction factor is calculated with the Darcy-Forchheimer correlation with a porosity of 0. 32. The CICC is modelled by a 1-D object with 10 000 meshes and a maximum time step of 0. 01 s in order to ensure convergence in mesh and time of both models. Boundary conditions are given in Table  4  and stay constant during all the transient phase.
Indeed numerous effects such as difference in the numerical scheme or in the convergence criteria can influence the results. One reason can be the difference on the helium characteristics databases used in each code. As CATHARE-3 used the REFPROP database which differs from the THEA one, the difference on the constant pressure heat capacity can reach 15% on the temperature and pressure ranges of the simulation. The third step of the transient starts at t = 50 s at the end of the heat   power injection so the flow is again adiabatic. The back flow stops one second earlier in the CATHARE-3 model (Fig. 9 ). When the mass flow through the cable is stabilized, a higher value of 0. 15 g/s is computed by CATHARE-3. Fig. 12  shows the temperature profile along the CICC at different time of the advection phase.
Description of the safety valve modelling CATHARE-3 has a specific sub-module named PIQSOUP that models a branch terminated by a safety valve (see Fig. 13 ). Within this submodule, it calculates the opening and the flow rate through the valve. The inputs are the opening pressure P set , the maximum valve section S max , the differential pressure ΔP necessary to obtain the full opening of the valve and the outside pressure P ext. The valve opening is determined by an interpolation of the pressure gradient between 1 for the full opening and 0 for the closed valve. At each time step, the code computes the real pressure gradient between the upstream valve pressure P up and the downstream pressure P ' set. As the PIQSOUP model exchanges mass with the outside, the downstream pressure P ' set is defined as the maximum between outside pressure P ext and the opening pressure defined by the user P set.
Evaluation of the heat flux during a loss of vacuum Two methods have been proposed by CEA for determining the heat flux received by the fluid  [3, 27]. The first one is based on the first law of thermodynamics and on an internal energy balance before the discharge valve opening (closed system). φ = ρ i × V S × ΔU Δt ΔU = U ( P f , ρ i ) -U(P i , ρ i ) (9) Fig. 13. Description of the PIQSOUP sub-module. ρ i Initial density (kg/m 3 ) V Volume of the tank (m 3 ) S Surface of the tank (m 2 ) ΔU Internal energy variation between the final and the initial time (J) Δt Time variation (s) P f , P i Pressure at final and initial time (Pa) In previous analyses based on experimental campaigns  [3] , the heat flux received by supercritical helium was estimated to be 1. 85 W/m 2. A verification phase was carried out using heating resistors in the tank.
Model description of the Cryostat Soupape It was chosen to only consider the part of the discharge line upstream of the discharge valve. As shown in Fig. 15 , the data set describes the tank with a 0-D module. This volume is linked to a 1-D pipe (160 meshes) which represents the discharge line. According to recommendations made for space discretisation in CATHARE models, the length ratio between two consecutive nodes should be between 0. 8 and 1. 2 and the last node before the component used to model the discharge valve has to be lower than 1 mm. The mesh size is here decreased by a ratio between 0. 85 and 0. 97 near the discharge valve, so that the last mesh is less than 1 mm long. The number of mesh has been chosen to be sufficient to insure the convergence of the calculation. We consider no singular loss along the discharge line or at the junction between the tank and the line.
Only vertical bends are represented to take into account gravity effects. The PIQSOUP sub-module is used to model the discharge valve with the input values given in Table  6. As the line downstream of the discharge valve is not taken into account, the external pressure is fixed to 1 bar. As the major part of the discharge canal upstream of the valve is inside the vacuum chamber, there is no thermal exchange between the line and the liquid nitrogen at 77 K. Moreover, the power deposited on the discharge line is small compared to the power received by helium in the tank, so the line in the CATHARE-3 model is assumed adiabatic. The heat flux is transferred directly to the fluid. The total power injected is 4978 W which corresponds to the value of 1. 85 W/cm 2.
Conclusion on the Cryostat Soupape experiment The modelling of the Cryostat Soupape experiment with CATHARE-3 gives the opportunity to evaluate the abilities of the code to simulate the pressure and temperature increase in a small supercritical helium tank with an input heat flux of 1. 85 W/cm 2. In these conditions, the code gives results close to experimental data with coherent pressure and    temperature evolutions. The assumption made on the choice of initial conditions with supercritical helium does not affect the relevance of the results. The discharge mass flow at the junction between the discharge line and the tank has also been compared with the result of an energy balance on an open system. For this configuration, CATHARE-3 seems to be reliable to model the helium discharge phenomenon.
General conclusion and perspectives This work has shown the abilities of CATHARE-3 to perform calculations of accidental transients on different cryogenic devices used in tokamak reactors. Two safety issues on cryogenic components have been explored, with a first study of thermal-hydraulic calculations in superconducting cables (CICC) and a second study of a supercritical helium discharge. Additional correlations adapted for helium and superconducting cable were implemented and the modular structure of the code allows representing all the thermal and hydraulic components of these cryogenic systems. The code gives consistent results with experimental data and with calculations performed with THEA, the actual reference code for the modelling of superconducting magnets. With these first evaluations and its abilities to model severe transients in various cryogenic set-ups, CATHARE-3 appears to be an efficient and a reliable simulation tool for accidental analysis.
Data collection Clinical and laboratory data Data collection was done on a computer platform at the beginning of first-line chemotherapy (D0). The nutritional data collected on D0 were: body weight at D0 and 6 months before, body mass index (BMI), a potential nutritional intervention (type and calories), blood albumin and PG-SGA. PG-SGA included an overall assessment by the physician classifying the patient as category A (no malnutrition), B (moderate malnutrition) or C (severe malnutrition) and a numerical score, the values of each section (questionnaire completed by the patient and physical examination) being summed. Malnutrition was defined by a grade B-C of PG-SGA and/or a PG-SGA score ≥ 9 and severe malnutrition was defined by a grade C of PG-SGA, as previously described  [13]. Oncological data collected on D0 were: date of diagnosis of CRC and metastatic disease, number of metastatic sites, WHO performance status (PS), chemotherapy protocol, and the plasma levels of lymphocytes, hemoglobin, platelets, carcinoembryonic antigen (CEA), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH).
Measurement of skeletal muscle index (SMI) SMI was measured on D0 and D60 centrally by a radiologist with more than ten years of experience of routine CT scans. Two consecutive cross-sections at the level of the third lumbar vertebra (L3), preferably with a cutting thickness of 2. 5 mm, otherwise 1. 25 mm or 5 mm, were transferred to the DICOM format on the Slice-O-Matic software version 5. 0 (Tomovision, Yves Martel, Magog, Canada). The crosssectional area of skeletal muscle (in cm 2 ) was measured with a window width of -10 to 150 HU. The cross-sectional area of muscle (cm 2 ) at the L3 level computed from the two crosssections was adjusted for height squared (m 2 ) to obtain the SMI (cm 2 /m 2 ). We decided to define sarcopenia as the lowest sex-specific quartile of SMI  [16]. The variation of SMI (in percentage) was evaluated from D0 to D60.
Effect of the Cellular Dielectric Properties on the Clausius-Mossotti Factor Figure  3  illustrates the frequency-dependent cell behavior through the real part of the CM factor (Figure  3a ). The dielectric parameters and cell geometric parameters are reported in Table  1. The real part plot in Figure  3a  is computed thanks to the myDEP software  [11]. nDEP behavior can be observed at very low frequency (lower than 400 kHz) and at high frequency (at least higher than 150 MHz), and pDEP behavior can be seen at medium range frequency (between 500 kHz and 100 MHz). The plot of the CM factor hence presents alternations between a repulsive state (nDEP) and an attractive state (pDEP). Two crossover frequencies fx01 and fx02 appear where the real part of the CM factor becomes null. fx01 occurs at low frequency, whereas fx02 occurs at higher frequency.
Moreover, from Figure  3a , 100 kHz and 1, 20 and 500 MHz frequencies were selected in order to study the dielectric response of the cells. Indeed, these frequencies correspond to the two different DEP behaviors but at low frequency (frequencies n°1 and n°2) and at high-frequency regime (frequencies n°3 and n°4). COMSOL Multiphysics ® computations were performed with the AC/DC electric current module in Figure  3b. The parameters from Table  1  were used for the simulation in such a way that the results correspond to the curve of the real part of the CM factor. As said before, the cell is represented by the singleshell model with the core: its intracellular content, and the shell: its plasma membrane. The cell is here considered to be suspended in a low-conductivity medium. The electric potential is 1 Vpp.
Dielectrophoretic Signatures fx01 and fx02 of U87-MG Cell Line The U87-MG cell line has been characterized using the microfluidic lab-on-a-chip using the method previously described. The first crossover frequency fx01 and the second crossover frequency fx02 of the same trapped cells have been successively measured. The measurement results for both culture conditions summarized in the violin plot thereby illustrate the distribution over frequency of the data (Figure  9 ). Violin plots are very similar to box plots, except that they additionally show the probability density curve of the different data. The small white dot marker labels the median value of the dataset (small white dot). Moreover, as for the box plot, the first and fourth quartiles of the dataset are represented by the thin black line, and 50% of the whole cell population is concentrated in the thick black line.
The extreme peaks correspond to the minimum and maximum values. In Figure  9 , one should notice that the scales for the two crossover frequencies are the same except that the unit are kHz and MHz for fx01 and fx02, respectively. Descriptive statistics including number of cells, median value and standard deviation of crossover frequency for distinct culture conditions are reported in Table  2. One can notice, for both crossover frequencies datasets on Figure  9 , the wide-ranging dispersion of measured values, which is also reflected in the value of the standard deviation. This observation is due to the normal heterogeneity of the cells among the GBM cell population. At low frequency, fx01 is influenced by the small difference of the cell size and morphology within the cell culture, while at high frequency, fx02 might be dependent of intracellular changes or alterations.
For instance, during the cell cycle, the nucleocytoplasmic ratio might differ from a cell to another as they are not synchronized during the culture  [27]. However, the violin plot highlights the fact that cell's crossover frequencies are gathered around their respective median value. A representative number of cells have been individually characterized to statistically consolidate the collected dataset and make the established signatures significant. One can notice that the distribution of the first crossover frequency fx01 is mostly the same for the two culture conditions, NM vs. DM. The median value of fx01 for the undifferentiated enriched population (DM) shows no significant difference with the normal conditions: 74 and 82 kHz, respectively. Indeed, as shown previously in Figure  8 , both U87-MG NM and U87-MG DM present the same round shape morphology.
Image registration is one of the most challenging problems in medical image analysis. In the recent years, deep learning based approaches became quite popular, providing fast and performing registration strategies. In this short paper, we summarise our work presented on Learn2Reg challenge 2020. The main contributions of our work rely on (i) a symmetric formulation, predicting the transformations from source to target and from target to source simultaneously, enforcing the trained representations to be similar and (ii) integration of variety of publicly available datasets used both for pretraining and for augmenting segmentation labels. Our method reports a mean dice of 0. 64 for task 3 and 0. 85 for task 4 on the test sets, taking third place on the challenge. Our code and models are publicly available at.
Introduction In the medical field, the problem of deformable image registration has been heavily studied for many years. The problem relies on establishing the best dense voxel-wise transformation (Φ) to wrap one volume (source or moving, M ) to match another volume (reference or fixed, F ) in the best way. Traditionally, different types of formulations and approaches had been proposed in the last years  [18]  to address the problem. However, with the recent advances of deep learning, a lot of learning based methods became very popular currently, providing very efficient and state-of-the art performances  [10]. Even if there is a lot of work in the field of image registration there are still a lot of challenges to be addressed. In order to address these challenges and provide common datasets for the benchmarking of learning based  [5, 6]  and traditional methods  [11, 1] , the Learn2Reg challenge is organised  [4].
Four tasks were proposed by the organisers with different organs and modalities. In this work, we focused on two tasks: the CT abdominal (task 3) and the MRI hippocampus registration (task 4). In this work, we propose a learning based method that learns how to obtain spatial gradients in a similar way to  [19, 7]. The main contributions of this work rely on (i) enforcing the same network to predict both Φ M →F and Φ F →M deformations using the same encoding and implicitly enforcing it to be symmetric and (ii) integrating noisy labels from different organs during the training, to fully exploit publicly available datasets. In the following sections, we will briefly summarise these two contributions and present our results that gave to our method the third position in the Learn2Reg challenge 2020 (second for task 3 and third for task 4).
In particular, our spatial gradients are obtained by: ∇Φ M →F = D(E(M ) -E(F )) and ∇Φ F →M = D(E(F ) -E(M )). Pretraining and Noisy Labels Supervision has been proved to boost the performance of the learning based registration methods integrating implicit anatomical knowledge during the training procedure. For this reason, in this study, we investigate ways to use publicly available datasets to boost performance. We exploit available information from publicly available datasets namely KITS 19  [12] , Medical Segmentation Decathlon (sub-cohort Liver, Spleen, Pancreas, Hepatic Lesion and Colon)  [17]  and TCIA Pancreas  [16, 8]. In particular, we trained a 3D UNet segmentation network on 11 different organs (spleen, right and left kidney, liver, stomach, pancreas, gallbladder, aorta, inferior vena cava, portal vein and oesophagus).
To harmonise the information that we had at disposal for each dataset, we optimised the dice loss only on the organs that were available per dataset. The network was then used to provide labels for the 11 organs for approximately 600 abdominal scans. These segmentation masks were further used for the pretraining of our registration network for the task 3. After the training the performance of our segmentation network on the validation set in terms of dice is summarised to: 0. 92 (Spl), 0. 90 (RKid), 0. 91 (LKid), 0. 94 (Liv) 0. 83 (Sto), 0. 74 (Pan), 0. 72 (GBla), 0. 89 (Aor), 0. 76 (InfV), 0. 62 (PorV) and 0. 61 (Oes). The validation set was composed of 21 patients of Learn2Reg and TCIA Pancreas dataset. Furthermore, we explored the use of pretraining of registration networks on domain-specific large datasets.
In particular, for task 3 the ensemble of the publicly available datasets together with their noisy segmentation masks were used to pretrain our registration network, after a small preprocessing including an affine registration step using Advanced Normalization Tools (ANTs)  [2]  and isotropic resampling to 2mm voxel spacing. Moreover, for task 4, we performed an unsupervised pretraining using approximately 750 T1 MRI from OASIS 3 dataset  [15]  without segmentations. For both tasks, the pretraining had been performed for 300 epochs.
Experimental Results For each task, we performed an ablation study to evaluate the contribution of each component and task 3, we performed a supplementary experiment integrating the noisy labels during the pretraining. The evaluation was performed in terms of Dice score, 30% of lowest Dice score, Hausdorff distance and standard deviation of the log Jacobian. These metrics evaluated the accuracy and robustness of the method as well as the smoothness of the deformation. Our results are summarised in Table  1 , while some qualitative results are represented in Figure  2. For the inference on the test set, we used our model trained on both training and validation datasets. Concerning the computational time, our approach needs 6. 21 and 1. 43 seconds for the inference respectively for task 3 and 4.
Conclusions In this work, we summarise our method that took the 3rd place in the Learn2Reg challenge, participating on the tasks 3 & 4. Our formulation is based on spatial gradients and explores the impact of symmetry, pretraining and integration of public available datasets. In the future, we aim to further explore symmetry in our method and investigate ways that our formulation could hold diffeomorphic properties. Finally, adversarial training is also something that we want to explore in order to be deal with multimodal registration.
3-c. All cutting experiments were conducted in one way milling. The tests were conducted in dry milling which is recommended by the tool manufacturer. The machine used is a CNC HS Milling HURON KX10, Fig. 3-a. The maximum acceleration is A = 3m/s 2 and a maximum jerk J = 50m/s 3. A KISTLER three components dynamometer model 9257B was used for force data acquisition. The surface topography in 3D image is measured by an in nite focus Alicona machine with an extension multiplied by x20 Fig. 4. and the topography analysis was conducted using the software Mountains Map 7 from Digital Surf. To determinate the length traveled by the tool on the acceleration and the deceleration zones, the velocity pro le is measured using a SINUCOM Software connected to the machine by an acquisition card Simatic Net.
Thirty years after the discovery of the first very-high-energy γ-ray source by the Whipple telescope, the field experienced a revolution mainly driven by the third generation of imaging atmospheric Cherenkov telescopes (IACTs). The combined use of large mirrors and the invention of the imaging technique at the Whipple telescope, stereoscopic observations, developed by the HEGRA array and the fine-grained camera, pioneered by the CAT telescope, led to a jump by a factor of more than ten in sensitivity. The advent of advanced analysis techniques led to a vast improvement in background rejection, as well as in angular and energy resolutions. Recent instruments already have to deal with a very large amount of data (petabytes), containing a large number of sources often very extended (at least within the Galactic plane) and overlapping each other, and the situation will become even more dramatic with future instruments.
Diffuse γ-rays between 100 GeV and 10 TeV (20 • cone opening angle) were simulated on a circle of 700 m (enclosed in the array), using the kaskade/Smash simulation chain developed for H. E. S. S. [6]. Data were analysed using the Model++  [7]  within the H. E. S. S. software framework. Results of this simulation are presented in Figures  4  and 5. Figure  4 , a shows that convergent pointing at high altitude maximises the event multiplicity (number of triggered telescopes). As a consequence, this mode of observation also maximises the precision of the reconstruction (angular and energy resolution in particular), as shown in panel d. In contrast, this corresponds to a rather modest size of the effective field of view, as measured by the squared angular distance of the observed events to the optical axis (panel b).
More elaborate models have been developed to quantify more precisely the level of background systematics, e. g. ,  [23]. State-of-the art analyses of IACT data reach background systematics of the order of 1 -2%. Background systematics can arise in particular from variation of the night sky background across the field of view, the variation of calibration coefficients (high voltage, pixel gains,. ) across the camera, changing atmospheric conditions, but also pointing direction with respect to the earth's magnetic field direction (which affects the lateral development of showers). When using the simulated acceptance (Section 1. 3. 3), the systematics level should not depend much on the FoV, because most of the predictable, field-of-view dependant effects are properly taken into account in the acceptance.
Open-source software such as gammapy  [10]  and ctools  [28]  already propose a template fitting procedure. One very important difference with respect to high-energy γ-ray astronomy lies in the way in which the background model is generated: high-energy γ-ray instruments are signal dominated, and the so called background consists mostly of genuine γ rays, but of diffuse origin. This model can be incorporated directly in the final part of the analysis, using the standard instrument response functions. In contrast, IACTs are background-dominated, and the remaining background consists of mostly hadronic or electronic cosmic rays, which are much more complicated to evaluate. The model used in template fitting analysis must, therefore, incorporate such a background model, or acceptance, produced by the procedure described in Section 1.
Meta-Catalogues and Population of VHE Sources Meta-catalogues are online catalogues collecting the results of several instruments in a unique database. IACTs sometimes publish such catalogues (e. g. ,  [47] ) summarising many years of observations. In the field of VHE astronomy, TeVCat  [48]  is the standard tool ( http://tevcat2. uchicago. edu/). Although the collected data correspond to different thresholds and uneven exposures, these catalogues are useful to perform statistical studies, but do not constitute unbiased and/or complete samples, and need to be filled manually (for the time being). TeVCat only reports positive detections and not upper limits, which could be useful to study source variability and, for instance, examine a transition from an emitting state to a non-emitting state or vice versa.