dataesr/scientific-paragraphs-categorization huggingface dataset:
Model generation Annotate ART-Plan (V1. 8. 3, TheraPanacea), a commercially available auto-segmentation software that harnesses ensemble learning like anatomy preserving deep neural networks, was retrained using the data described in Section 2. 1. Anatomically preserving data augmentation was performed (mapping the data to 4 reference anatomies), generating 928 training samples. The 4 reference anatomies were full body CT scans, selected by clinicians to be representative of the target patients, including male and female with different body shapes. They were used as preprocessing to ease the learning task by normalizing the MRIs onto fixed references as well to determine the structures of interest being present and approximate location. A full end-to-end ensemble neural networks approach involving three steps was deployed: (i) a localization algorithm using affine registration to reference anatomies (ii) combination of the predictions from the neural networks with hard clinical constraints to ensure anatomical consistency (iii) standard ensemble learning approach by averaging the predictions of the 4 networks.
Quantitative evaluation For the quantitative evaluation, one experienced boardcertified radiation oncologist annotated the MRI data of the test cohort manually, without prior structural information. These gold standard contours (GSC) were compared against the AI-based automatic contours (AIC) for anal canal, bladder, rectum, seminal vesicles, prostate, sacrum, pelvis left and right as well as femur left and right in a caudal-cranial window of AE 4 cm with respect to the GSC prostate borders. To assess differences between GSC and AIC, the dice similarity coefficient (DSC) and the 95% Hausdorff-Distance (95% HD) were evaluated. In addition, to assess quality and the potential of the obtained contours to decrease MRgRT treatment time, surface DSC (sDSC) and added path length (APL) were calculated with tolerances varying between 1 and 5 mm in steps of 1 mm as defined by Vaassen et al.
3 and 1. 1 for OARs and bony structures, respectively. The qualitative evaluation using a subjective assessment by Likert scale showed a high overall acceptability of structures for the usage in an online MRgRT workflow. However, one individual patient, as visualised in Fig. 3 , showed a strong decline in structure quality. For this patient case, a high signal was visible outside the bladder due to accumulation of fluid within the intestine. As this scenario is uncommon and was not part of the training dataset the automatically generated structures, especially near the liquid, could not be generated correctly. Therefore, such uncommon scenarios should be identified on bigger datasets either on visual inspection or based on the prediction uncertainty to include them within the model retraining.
The deep learning literature on calibration proposes various methods to measure this calibration (Brier score, expected calibration error among others) and to calibrate networks, usually through temperature scaling, ensemble learning or Bayesian neural networks  [18] [19] [20]. The generated training dataset is with 232 MRIs comparable in size to imaging datasets used within segmentation challenges  [21] , but due to multiple MRIs per patient, not all data samples can be considered independent. Therefore for further improvement, retraining of the model with more annotated data must be investigated. However, due to this uncertainty towards unseen patient anatomical configurations, an autonom patient treatment as proposed by Künzel et al. [15, 22]  still needs the supervision of a physican. This could possibly be supplemented by extensive automatic quality assurances of structures similar to sanity checks in current synthetic CT software tools  [23].
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [The authors report institutional collaborations with TheraPanacea, Elekta, Philips, Kaiku and PTW Freiburg which provided technical and/or financial support. TheraPanacea developed the proposed model, but were not involved in the analysis and scoring of the testing dataset. ] Appendix A Supplementary material Supplementary data associated with this article can be found, in the online version, at https://doi. org/10. 1016/j. zemedi. 2023. 05. 001.
, 2007; Medjoubi et al. , 2010; Le Bourlot et al. , 2012). The distance between the specimen surface and the detector is about 710 mm. A scheme of the experimental configuration is given in Fig. 2. The analysis of diffraction patterns is caried out with a method similar to that presented by  Ors et al. (2019). The different steps, illustrated in Fig. 3 , are (i) a transformation of the image obtained with the X-ray detector in the scattering angle and azimuth angle coordinate system, (ii) an azimuthal integration using the AzimuthalIntegrator class of the pyFAI package  (Ashiotis et al. , 2015)  and (iii) a fit with an asymmetric Pearson VII function, Ið2Þ ¼ I max 1 þ 2 1=m l À 1 ð Þ 2 À 2 0 w l À Á 2 h i m l if 2 2 0 I max 1 þ 2 1=m r À 1 ð Þ 2 À 2 0 w r À Á 2 h i m r if 2 ! 2 0 8 > > > < > > > : ð4Þ where I max , 2 0 , w l , w r , m l and m r are the fit parameters representing, respectively, the height of the peak, the peak position, and parameters related to the widths (w) and shapes (m) of the two branches.
2. 4. 2. Error in estimating the phase shift of the strain evolution. The measured jitter associated with the gauge signal (2S = 394 ns) remains too high compared with the expected time shift induced by the material and especially for the low stress amplitude related to the VHCF domain. In order to reduce these jitters, an accumulation of 1500 independent measurements per delay in a loading cycle are performed using the stroboscopic method. The jitter of the mean value is therefore reduced by a factor of about 40. To validate this reduction, an artificial delay is applied by the delay generator board on the triggering of the Redpitaya data acquisition board and the evolution of the gauge signal is obtained for one reconstructed cycle (42 points spaced by 1. 1812 ms). The time shift of the strain signal is estimated by a sinusoidal fit on the 42 points.
Acknowledgements Synchrotron SOLEIL  is acknowledged for granting beam time for X-ray diffraction experiments and development of timeresolved X-ray techniques using single-bunch mode on the DiffAbs beamline. P. Joly  is acknowledged for providing technical support. J. -P. Ricaud  is acknowledged for help with TimBeL installation and characterization as well as  G. Renaud  and  F. Ben Chabane  for the installation of the Redpitaya board and the development of the code to drive it.
The curve shows a plateau at the maximum load; this is a depth-time relaxation experiment at a constant load, used for evaluation of the activation volume of the deformation process (published elsewhere  [10] ). The nano-indentation test is ending by the unloading part. Serrations size and waiting times were automatically detected and measured using a tailor-made automated procedure based on the detection of slope discontinuities along the loading part of the indentation curves. The resolution in depth displacement is lower than 1 nm. 7980 serrations were measured from 320 indentation curves. The large dataset enables a relevant statistical analysis, containing in particular large size events having very low probability of occurrence. To connect the serrations to the PEL structure, the glass is discretized in non-periodic regions of size l , called "clusters", having various potential energy in the PEL.
The literature also refers to methods based on the results high-fidelity simulations such as the Proper Orthogonal Decomposition (POD) method  [15] [16] [17]. The major drawback is that a preliminary database of representative solutions is required to determine the projection basis, the former being computationally expensive and directly dependent on the set of parameters of the test case. The ROM used in this paper is built by projection on a reduction basis containing both the linear normal modes of the structure and additional dual modes identified from nonlinear static computations with external loads defined by combinations of linear modes shapes. In the frame of finite deformations with large displacements and small strains, the nonlinear internal geometric forces are approximated by a third-order polynomial of generalized coordinates.
When the distribution of the external forces applied to the structure is fixed and known, nonlinear static solutions (also known as static modes) can be included in the reduction basis. Likewise, if high-fidelity nonlinear dynamic computations have already been obtained from previous calculations, the POD modes can be added to the reduction basis. They are determined by Singular Value Decomposition (SVD) from samples of the high-fidelity computations and embrace relevant information characterizing the nonlinear deformation of the structure. Nevertheless, static modes and the POD modes are particularly dependent on the load case, making them inadequate to the various load cases such as those encountered in aerodynamics. Besides, POD modes require a database of high-fidelity nonlinear solutions, whose generation involves expensive calculations.
The loads are imposed positively and negatively so that no side is favored in the construction of the database of nonlinear static solutions for the n L = 26 different load cases. A singular value decomposition of the static resolution residuals is then performed according to the process of selection of the dual modes detailed in section 3. 1. The singular values and the linearized strain energies of the dual modes candidates are plotted in Figure  3. In the present case, the SVD modes with the highest singular values are also those with the highest linearized strain energy contribution. The dual modes appended to the initial basis of linear modes are thus the vectors d {1,2,3,4,5}. The new reduction basis considered is therefore the concatenation of the first 3 linear normal modes and the 5 dual modes: V = [Φ, d {1,2,3,4,5} ].
The Strouhal number associated to this vortex shedding will be compared to validate the test case. The structured fluid mesh is refined around the structure and in the near wake  (Figures 13, 14) , and is then progressively coarsened from the structure to the external domain boundaries. The fluid is solved with the Finite Volume CFD software elsA  [35]  (ONERA-Safran property). The fluid is a diatomic gas, following the perfect gas equations whose properties are detailed in Table  8.
Applied to a Euler-Bernoulli/von Kármán beam, both under local and distributed loads, as well as during a fluid-structure coupling, such a ROM captured very precisely the static and the dynamic responses of the structure. Future work will deal with application of such coupling procedures for more complex three-dimensional flexible structures coupled with high-speed flows. 8 Declarations H 3 (x) = x 2 L e 3 - 2x L e (A8) H 4 (x) = x 2 L e x L e -1 (A9) Besides, the rotation of the section is given by: The mass matrix of one element is computed under the assumption that the local rotational inertia of the section is neglected: The nonlinear internal forces are computed with a single Gauss point integration to prevent membrane locking, see  [37]  for the detailed expressions. Their computation with the symbolic Python library Sympy leads to the following expressions: M e = ρA 2 f nl = f quad nl + f cub nl.
The optical resolution of the system is ~5 μm. The sample is imaged through a flat glass window of 1mm of thickness. The whole optical system is placed at 38 cm from the sample surface. The lighting of the surface is ensured by a light source (LLS3 LED). The exposure time is fixed to 4 ms per image. The image acquisition by the camera is triggered using the same Dspace module that acquires the electric field E. The images record is then synchronized with the application of the loading. A MATLAB program allows to calibrate the positioning of the sample based on an image gradient analysis and is used to Fig. 3. schematic drawing and general view of the measurement bench. process the images. The TIFF format of the recorded images are correlated using CorelliRT3  (Tomičevć et al. , 2013)  which is a 2D-DIC MATLAB program.
* Florent Foucaud was financed by the  French government IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ) and by the  ANR  project  GRALMECO  ( ANR-21-CE48-0004 ). Tuomo Lehtilä 's research was supported by the  Finnish Cultural Foundation  and by the  Academy of Finland  grant  338797.
For each receiver line investigated, direct and reverse shots with sources in line with the receiver line were recorded. Cross-line shots fired at distances of 40, 50, and 60 m perpendicular to the receiver line were also recorded (example of cross-line shot in Figure  2b ). Processing the data from in-line direct and reverse shots gathers the results in a vertical section of 240 m in-line extension (the blue arrow, in Figure  2a ), while a cross-line shot gathers results in a vertical section of only 120 m extension along the in-line direction (the red arrow on the location map of seismic lines, in Figure  2a ). The whole set of single-fold vertical sections of arrival times were assembled to build a 3-D block of 240 and 300 m extension along the in-line and crossline directions, respectively.
For the in-line direction profile, the reference zero in the horizontal direction is taken as the location of the sources points, in the middle of the receiver lines. This renders reflecting point locations varying between -120 and +120 m and a lag distance between two neighbor reflecting points of 2. 5 m (half the distance between two adjacent receivers). For the cross-line shots, the reference zero is still taken at the middle of the receiver lines, but with a lag distance between neighbor reflecting points of 5 m. Seismic data processing was carried out to transform raw arrival times into a 3-D block of wave amplitudes reflected at diverse depth and horizontal locations of the aquifer system. A VSP recorded via geophones placed in well C1 (Figure  3a ), was also processed to obtain a law (a conversion) of reflected time versus depth and a velocity model (V rms ) of reflected wave propagation.
A 3-D refraction seismic tomography  [Mari and Mendes, 2012]  was finally carried out, with the idea of mapping the irregular shape of the clay cover at the surface (up to 25-30 m depth) overlying the limestone aquifer. This experiment is assumed to render static corrections for the seismic reflected block and a propagation velocity model in the clay cover of the aquifer (also for corrections of the seismic 3-D block). The processing sequence  [Mari and Delay, 2011, Mari and Porel, 2008]  includes: amplitude recovery, deconvolution, wave separation, static corrections, and normal move-out corrections (using the VSP velocity model, Figure  3b ). Times of the reflected sections were also converted into depths via the VSP time versus depth law inferred in well C1 (Figure  3b ). Acoustic data were also recorded in the boreholes of the HES and that of the well C1 is exemplified hereafter.
The acoustic probe is a flexible monopole tool holding a source as a magnetostrictive transducer and two pairs of receivers beneath: a pair of near receivers (1 and 1. 25 m lag-distances (offsets) beneath the source) and a pair of far receivers (3 and 3. 25 m offsets). The acoustic data were recorded in the 1-20 kHz frequency band. It is worth noting that compared with seismic data, the vertical resolution of the acoustic logging is very high, on the order of 10 cm, but the lateral investigation is limited to a few tens of centimeters around the borehole. Figure  3c  reports on the 3 m constant offset section (receiver R1) with the vertical axis as the depth of receiver R1 from ground level, and the horizontal axis the recorded travel times (ms) of acoustic waves from source to receiver. In the acoustic section, the refracted P -waves appear in the 0.
Acknowledgments This work was supported by the  National Natural Science Foundation of China  (Grant Nos. 62104156 ,  62074102 ), the  Guangdong Basic and Applied Basic Research Foundation  (Grant Nos. 2023A1515011256 ,  2022A1515010979 )  China ,  Science and Technology plan project of Shenzhen  (Grant Nos. 20220808165025003 ,  20200812000347001 ) China. This project was also supported by the open foundation of  Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, Guangxi University  (Grant No. 2022GXYSOF13 ). Appendix A. Supplementary data.
A. Databases In our experiments, five benchmark LFIQA databases are used, including Win5-LID  [75] , NBU-LF1. 0  [76] , LFDD  [77] , VALID10bit  [78] , and SHU  [79]. Based on which, 240 distorted LFIs are generated with 5 distortion types and 6 distortion levels, including JPEG, JPEG2k, Gaussian blur, white noise, and motion blur. The MOS label ranged from 0 to 5 is presented. For all databases, the distorted LFIs generated from realworld and synthetic reference scenes are of spatial resolutions 434×625 and 512×512, respectively. The distorted LFIs from different databases have different original angular resolutions (ranging from 9×9 to 15×15). However, due to the hardware limitations of light field cameras, the quality of the edge views of LFIs is impacted to some extent. Therefore, we only use the central 9×9 angular views of LFIs for all databases in our metric.
B. Experimental Settings and Evaluation Criteria K-fold cross-validation is adopted as the train-test split strategy to conduct our experiments. For each database, all distorted LFIs are divided into K folds according to their reference scenes, which guarantees that the scenes in the training and test sets are completely independent. Here, K is set to half of the reference scene number in each database. For example, Win5-LID database has 10 reference scenes, so K is set to 5. As a result, each fold contains the distorted LFIs from two reference scenes. In the experiments, we train the model on K-1 randomly-selected folds and test the performance on the remaining fold. Consequently, we loop through all train-test splits and take the average result as the reported performance. To evaluate the performance, four standard criteria are adopted, including Pearson Linear Correlation Coefficient (PLCC), Spearman Rank Order Correlation Coefficient (SROCC), Kendall Rank Order Correlation Coefficient (KROCC), and Root Mean Square Error (RMSE).
In addition, due to the diversity of reference scenes, distortion types and levels of the adopted LFIQA databases, existing FR/blind LFIQA metrics generally struggle to perform well on all This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. databases. However, the proposed ASEM-BLiF metric consistently achieves SOTA results on most databases. Especially on the two most complex databases Win5-LID and NBU-LF1. 0, which contain both real-world and synthetic LFIs, our metric outperforms all IQA metrics by a significant margin. In most cases, the learning-based blind LFIQA metrics can achieve competitive or even superior performance than the FR LFIQA metrics. However, on the VALID10bit database, the FR LFIQA metrics generally obtain better results than the blind LFIQA metrics.
One possible reason is that the VALID10bit database contains only 100 distorted LFIs, which may be insufficient to train a well-performing blind LFIQA model. Nevertheless, our metric still yields competitive performance on the VALID10bit database compared with the FR LFIQA metrics, while outperforming other blind LFIQA metrics significantly. The overall performance further demonstrates the superiority of our metric over other state-of-the-arts. Moreover, TABLE  I  shows that compared with our proposed ASEM-BLiF metric, the performance of three existing deep learning-based metrics (i. e. , DeLFIQE  [36] , DNNF-LFIQA  [37] , and DeeBLiF  [64] ) is quite limited, and the possible reasons behind this deserve further investigation. First, DeLFIQE executes quality evaluation using a small number of EPI patches, thus its effectiveness is significantly constrained when dealing with LFIs of low angular resolution.
In DNNF-LFIQA, each LFI is treated as an individual sample, and the limited size of the LFIQA database inevitably leads to overfitting during the training process. In contrast, DeeBLiF obtains better results by addressing the aforementioned two issues. However, DeeBLiF uses symmetric branches for angular and spatial information respectively, which leads to insufficient measurement of angular distortions. The proposed ASEM-BLiF metric strives to address the imbalance problem between angular and spatial information, thereby achieving superior quality evaluation performance.
Accepted manuscript Since K-fold cross-validation is adopted to conduct the experiments, each split has different training and test sets, resulting in different results. The average result reported in TABLE I is sensitive to outliers as it is a parametric measure of central tendency. In contrast, the median is a non-parametric measure that is robust to outliers. Therefore, we present the median PLCC/SROCC performance of blind LFIQA metrics for a more comprehensive performance comparison, as shown in Fig. 2. Here, due to the poor performance of DeLFIQE  [36] , we have removed it for better visualization. It can be seen that our metric still achieves outstanding performance on all databases compared to other metrics when considering the median result. To further demonstrate the effectiveness of our metric in quality evaluation, we provide the scatter plots of the predicted quality score versus the MOS label for the best-performing handcrafted feature-based metric (4D-DCT-LFIQA  [35] ) and four deep learning-based metrics (DeLFIQE  [36] , DNNF-LFIQA  [37] , DeeBLiF  [64] , and our proposed ASEM-BLiF).
As shown in Fig. 3 , three databases with different numbers of predictions are used. The scatter plots of the Win5-LID, NBU-LF1. 0, and LFDD databases are exhibited in the top, middle, and bottom rows, with 44, 30, and 120 This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. predictions each, respectively. The red line represents the perfect prediction. From the figure we can see that the predictions of our metric are more consistent with the subjective scores than other state-of-the-art metrics, demonstrating its superior ability to simulate the human visual perception.
D. Performance Comparison of Individual Distortion Types An excellent IQA metric should show efficacy in various distortion types. Therefore, the performance of different IQA metrics for different distortion types is investigated and compared in this subsection. The experiments are performed on the Win5-LID and NBU-LF1. 0 databases based on K-fold train-test strategy. Here we do not consider two CNN-based distortions in the Win5-LID database because both of them have only one distortion level. Due to the space constrains, we only provide the SROCC performance in  distortions. A possible explanation is that the availability of reference information can greatly help to distinguish the same distortion type but different distortion levels. In addition, we can also observe that the performance of most blind LFIQA metrics exhibits significant variations as the type of distortion changes.