dataesr/scientific-paragraphs-categorization huggingface dataset:
Different saliency thresholds and predictors analysis In this section, many tests have been conducted to define the best network architecture and to identify suitable saliency threshold. Saliency-based patches are extracted with regard to threshold value. The five pre-trained models are adjusted and tested using the same train configurations as discussed in Section 3. 4. Starting with value of 0. 1, we update the threshold and notice the performance using the LIVE P-2 database in Table  3. The Table also includes the number of patches extracted at each threshold. Obtained PLCC results show that the VGG-16 and VGG-19 architectures are better for mapping the extracted patches to quality scores. From plots in Fig. 4 , we notice that using different saliency-based cropping thresholds influence the quality prediction with best threshold value of 0.
3. As we increase the starting value, we get better results for all models. After threshold of 0. 3, the coefficient correlations decrease while saliency thresholds cropping increase. The fact that higher saliency threshold gives smaller datasets, it may limit the model to learn best quality prediction from the salient regions. For instance, 0. 3 gives 46 300 patches for training, while only 12 276 patches for 0. 5. This is a trade-off between the saliency threshold and the training dataset size that need to be balanced. For example, although using a threshold of 0. 1 that yields more training sets (i. e. 82277 patches), the better precision results are still obtained with a threshold equal to 0. 3. Based on these results, the saliency-guided cropping step allows to considerably improve the performance.
Notice that the performance drops for thresholds which offer small train datasets, such as the 0. 6 threshold. Moreover, AlexNet gives the lowest correlation performance among all models. VGG-16 and VGG-19 yield similar correlation performance with little differences since they have nearly the same architecture. These models contain more series of convolutional layers and thus extract higher and better quality indicators for prediction. In the meantime, going deeper than VGG-16 model, ResNet18 and ResNet50 regressors appear to slightly diverge from the path toward the best quality predictions. For instance, using the best saliency threshold of value 0. 3, AlexNet model with performance of RMSE = 2. 491 comes in the last place compared to the other four networks. VGG-16 and ResNet18 behave slightly better compared to deeper ones; VGG-19 and ResNet50, respectively.
This could explain why increas- ing the depth of ResNet and VGG architectures has a negative impact on performance. After the selection of the best pre-trained model and saliency threshold, we evaluated the impact of the saliency-based patch selection and the RGB cyclopean image. For the no saliency test, all possible patches of the cyclopean image were sequentially extracted by sliding over the whole scene from left to right with a stride of 32 pixel (i. e. without overlap). This creates 220 patches for every scene in the LIVE P-2 database, while 128 patches are approximately cropped for the saliency-guided extraction. Table  4  shows PLCC, SROCC and RSME results over LIVE P-2 database with and without saliency-guided patches as well as the grayscale cyclopean versus RGB cyclopean as inputs.
Comparison with the state-of-the-art Obtained results have been compared with several FR, RR and NR SIQA. Among them, there are recent blind metrics based on the use of CNN models, namely PAD-Net  [35] , Chen  [62] , Zhou  [39]  and Sun  [40]. Table  6  shows the results of these methods on both LIVE-P1 and P2 datasets. Best metric of each category (FR, RR and NR) is represented on bold and the best one whatever the category is with a gray background. As can be seen, our metric outperforms all the compared NR metrics on both databases. The top best FR metrics   are the ones proposed in et al  [22, 23] , while the method proposed by Ma et al. [26]  achieved the best performance among all the compared RR methods. On LIVE-P1, compared to the best metrics in each category (i. e. Chen for FR and Ma for RR) the improvements in term of PLCC are 7% for FR and 5.
6% for RR. While on LIVE-P2, the improvements are 8. 8% for FR and 6. 3% for RR. Overall, the performance of our method from both LIVE datasets is somewhat equivalent with slight advantage for LIVE-P2. Indeed, the PLCC and SROCC values obtained for LIVE-P1 are respectively 0. 982 and 0. 981, while those obtained for LIVE-P2 are 0. 984 and 0. 985, respectively. Furthermore, we report the performance of our method according to the size of the training set. Table  8  shows the correlations achieved for a training set of size 50%, 70% and 80% using LIVE databases. The partition ratio has a slight impact on the performance. And it does not suffer from an over-fitting problem. The diminution is similar for both datasets. Meanwhile, performance evaluation on Waterloo datasets are not reported in several metrics papers.
Performance on individual distortions The overall performance on the four databases has shown good performance and remarkable consistency. Furthermore, the proposed scheme has been examined on individual distortion types. The performance indexes are computed for each distortion individually. Performance in  indicates that the proposed metric predicts perceptual quality well regardless of types of distortion. Overall, the proposed metric delivers stable performance. On FF subsets, the best accuracy in term of PLCC is achieved by PAD-net metric. In term of SROCC on LIVE-P2, the performance of our metric has achieved the state-of-the-art on all distortion subsets. For Waterloo databases, both the PLCC and SROCC indexes are observed to be above 0. 9 on the three distortions JPEG, WN, and BLUR.
The highest score has been reached on BLUR distortion. From the used Waterloo and LIVE databases, the metric has reached it highest performance on BLUR. This is also observed in other metrics scores. Usually, the BLUR distortions are easy to detect and they are compared to other forms of distortion such as JPEG one. In the proposed model, the well tuned convolutional layers have given a step further to capture this distortion. On BLUR's distortion over the four datasets, the accuracy of quality assessment was found to be 98% in terms of PLCC. Table  12  shows the performance of our metric on symmetric and asymmetric distorted stimuli. As can be seen, some metrics totally fail to predict the quality for asymmetric distorted images. They give high correlations for symmetric distorted images  (Benoit, You and Bensalma).
Cross database performance Cross-database experiments have been conducted in order to verify the generalization ability of the proposed approach. The implemented tests are shown in Table  13. Metrics shown are all NR methods. They have been trained in the former database and tested on the latter. Comparing with the NR metrics, our method has competitive prediction about the quality of stereo pairs despite cross-  database tests. DECOSINE, Sun and PAD-net algorithms deliver decent performance in the four cross-database tests, but Sun is the only algorithm which gives performance over 0. 9 in term of PLCC in the L1/L2 test. From LIVE datasets, the performance of the other NR algorithms is not as good as the performance of the individual database tests. For instance, Chen and DBN metrics showed good results on the individual database tests where Pearson correlations (PLCCs) of 0.
959 and 0. 956 have been achieved on LIVE P-1 for Chen and DBN, respectively. They gave low perfor-mance scores in the L1/L2 test. PLCC of 0. 869 and 0. 827 are reported for Chen and DBN respectively. Waterloo datasets have shown lower correlations than LIVE datasets. It is important to notice that which makes Waterloo databases more challenging than LIVE is that they not only include both symmetric and asymmetric distorted pairs like LIVE phase-II. Also, the left and right views of a stereo pair may be distorted by different distortion types. The cross-database tests revealed that the proposed approach ranks third after the two metrics DECOSINE and PADnet. However on LIVE datasets, the correlation gaps are not profound, 0. 003 and 0. 005 are the difference values of our metric with PAD-net and DECOSINE respectively (see Fig.
Computational complexity We compare here computational time with the most recent NR-SIQA metrics that incorporate deep learning into their designs. The working platform uses the MATLAB2020a on a computer equipped with Intel(R) Xeon(R) CPU E5-2620 v4 processor at 2. 10 GHz, 64 GB Fig. 6. Asymmetric and symmetric distortion plots from the four databases using the proposed method. Shen  [38]  PAD-Net  [37]  of memory and a NVIDIA Quadro P5000 GPU -16 GB of memory. It should be noted that the other approaches have been tested on various hardware. The test was performed on a stereo image from the LIVE phase II database with a resolution of 640 Â 360 pixels (see Table  14 ). The run time (in seconds) tests are listed in Table  15. It is worth noting that for our model we record the time around 17 s for predicting quality score.
Conclusion In this paper, a no-reference stereoscopic IQA based on the use of cyclopean image and saliency map has been proposed. The simplicity of the proposed scheme is a benefit for an easy implementation in the multimedia software. Cyclopean image has been introduced to consider asymmetrical distortion, while the saliency aims to focus on the most perceptual relevant regions by selecting relevant patches from the cyclopean image. These patches are then fed as input to a modified version of a pre-trained CNN model to estimate the quality. We compared five pre-trained models (i. e. AlexNet, VGG16, VGG19, ResNet18 and resent50) and we also show the impact of the saliency selection. The best performance has been obtained with VGG16 for a saliency threshold equals to 0. 3. Experimental results have demonstrate the efficiency of the proposed metric since it outperforms all the compared FR and NR SIQA of the state-of-the-art on LIVE and Waterloo databases.
IHC and image analysis CCA_PDX and patient tumors were fixed immediately after excision in 4% buffered formalin solution for a maximum of 24 hours at room temperature before being dehydrated and paraffin embedded. The following primary mAbs were used for IHC staining: anti-KRT19 (Atlas Antibodies, catalog no. HPA002465, RRID: AB_1079179), anti-HepPar1 (Novus, catalog no. NBP3-08970, RRID:AB_2909615), anti-Ki67 (Roche, catalog no. 05278384001, RRID:AB_2631262), anti-cleaved caspase 3 (Asp175; Cell Signaling Technology, catalog no. 9661, RRID:AB_2341188) and secondary anti-rabbit (Jackson ImmunoResearch Labs, catalog no. 711-035-152, RRID:AB_10015282) and anti-mouse (Thermo Fisher Scientific, catalog no. G-21040, RRID:AB_2536527), and UltraMap anti-Rabbit antibody (horseradish peroxidase; Roche, catalog no.
anti-KRT19 (Atlas Antibodies, catalog no. HPA002465, RRID: AB_1079179) diluted 1:200 or with HepPar1 (Novus, catalog no. NBP3-08970, RRID:AB_2909615) diluted 1:500, in blocking buffer and then incubated at room temperature for 1 hour with anti-rabbit or anti-mouse secondary antibody (Thermo Fisher Scientific, catalog no. G-21040, RRID:AB_2536527) at a 1:250 dilution. Samples were stained with DAB substrate chromogen (#K3468, Agilent) for 1-10 minutes and counterstained with Harris hematoxylin (#109254; Sigma) for 2 minutes, followed by dehydration with ethanol and xylene, and finally mounted in DPX (06522, Sigma). Positive and negative controls were run along the tested slides for each marker. For Ki67 and cleaved caspase 3 staining, slides were heated in the instrument at 75 C for 8 minutes and deparaffinized with EZ prep solution (Ventana Medical System, catalog no.
Copy-number alterations were calculated with CNVkit (v0. 9. 6. dev0) using an in-house 2N pool as the standard sample. For somatic variant analysis, frequent SNPs in the population were filtered on the basis of the gnomAD database (allele frequency ≤ 0. 0001). Variants were manually curated, and identified variants were classified using publicly available databases: COSMIC (RRID: SCR_002260), cBio-Portal (RRID: SCR_014555), ClinVar (RRID: SCR_006169), Var-Some, and OncoKB (RRID: SCR_014782). CCA tumoroid ex vivo three-dimensional cultures CCA patient-derived tumor cells were isolated from CCA_PDX through a combination of mechanic disruption and enzymatic disaggregation following a described protocol  (4). Briefly, PDX tumors with a volume of less than 500 mm 3 were freshly collected in DMEM/F12/ HEPES (L0093-500, Biowest) after surgical resection, minced using sterile scalpels, and dissociated for a maximum of 90 minutes in DMEM/F12/HEPES supplemented with 0.
Immunofluorescence staining For immunofluorescence staining, cells were seeded on a Matrigel precoated 8-well Nunc Lab-Tek Chamber Slide system (C7182, Thermo Fisher Scientific) at 4 Â 10 4 cells/well. After a 72-hour to 144-hour incubation, cells were fixed with 4% paraformaldehyde (30525-89-4, Santa Cruz Biotechnology) for 15 minutes, washed with 1% BSA in PBS for 5 minutes, and permeabilized with 1% Triton X-100 for 20 minutes at room temperature. After 1 hour of blocking in 5% BSA-PBS, cells were incubated at room temperature for 2 hours with the primary antibody anti-KRT19 (Atlas Antibodies, catalog no. HPA002465, RRID:AB_1079179) or anti-HepPar1 (Novus, catalog no. NBP3-08970, RRID:AB_2909615) diluted 1:200 in blocking buffer. Cells were then incubated for 1 hour with a secondary anti-rabbit antibody (Jackson ImmunoResearch Labs, catalog no.
Notably, the oncometabolite 2-hydroxyglutarate produced by mutant IDH1/2 enzymes, and loss of function of ARID1A, BAP1, or BRCA2 can impair the HR pathway, rendering cancer cells sensitive to PARPi treatment  (18-21, 47, 48). However, preclinical evidence supporting the employment of PARP inhibition in advanced CCA with these mutations is still missing. Among our 19 established CCA_PDX   PARPi inhibits the growth of CCA_PDXs with a BRCA2 mutation. A, Dose-response curves of olaparib (left) or pamiparib (right) for a panel of tumoroids derived from CCA_PDXs. Cell viability was determined using a Cell Titer-Glo assay 7 days after the treatment initiation. Data are mean AE SD from independent biological replicates. For olaparib treatment: PDX41, n ¼ 8; PDX68. 2, n ¼ 2; PDX78, n ¼ 6; PDX85, n ¼ 11; PDX75, n ¼ 2; PDX75.
Moreover, a 73-year-old male patient with metastatic CCA with a pathogenic BRCA2 mut (D2723H) was identified by NGS (Foundatio-nOne Panel). This patient was initially diagnosed with localized extrahepatic CCA (T2N2M0), and had received surgical resection (R0) and adjuvant chemotherapy with capecitabine. Unfortunately, multiple metastatic lesions were found after the adjuvant chemotherapy, mainly localized in the celio-mesenteric lymph nodes. He then received cisplatin/gemcitabine, carboplatin/gemcitabine, and durvalumab/tremelimumab (as part of the IMMUNO-BIL clinical trial, NCT03704480) treatments. Because pathogenic BRCA2 mut (D2723D) was identified in the tumor samples, he was compassionately treated with olaparib at a dose of 300 mg orally, twice a day continuously. After 10 weeks of treatment, CT scans showed regressions of several target lesions (Fig.
Acknowledgments The authors would like to thank the patients and their families for their support. This work was supported by grants from the  Fundaci o Marat  o  TV3  awarded to  T. Macarulla ,  M. Mel  e, and  S. Peir  o; BeiGene research grant awarded to  T. Macarulla  and  S. Peir  o; AECC (INVES20036TIAN),  Ram on y Cajal investigator program  ( RYC2020-029098-I ),  Proyecto de IþDþi  ( PID2019-108008RJ-I00 ), and  FERO Foundation  grant awarded to T. V. Tian;  Proyecto de Investigaci on en Salud  from the  Instituto de Salud Carlos III (ISCIII)  ( PI20/00898 ) awarded to  T. Macarulla ;  FIS/FEDER  from the  Instituto de Salud Carlos III (ISCIII)  ( PI12/01250 ;  CP08/00223 ;  PI16/00253  and  CB16/12/00449 ) awarded to  S. Peir  o; and  Ram on y Cajal investigator program  ( RYC-2017-22249 ) awarded to  M.
Mel e. Q. Serra-Camprubí  is a recipient of the Ph. D. fellowship from  La Caixa Foundation  ( LCF/PR/PR12/51070001 ). A. Llop-Guevara was supported by the  AECC  ( INVES20095LLOP ) and V. Serra by the  ISCIII  ( CPII19/00033 ). E. J. Arenas  was funded by the  AECC  ( POSTD211413AREN ). J. Arribas  is funded by the  Instituto de Salud Carlos III  ( AC15/00062 ,  CB16/12/ 00449 , and  PI22/00001 ). This publication is based upon the work of  COST Action   CA18122 ,  European Cholangiocarcinoma Network , supported by the  COST (European Cooperation in Science and Technology , www. cost. eu), a funding agency for research and innovation networks. The authors would like to thank  Dr. V. A. Raker  for manuscript editing and  Drs. N. Herranz  and  J. Mateo  for scientific discussions. The authors acknowledge the infrastructure and support of the  FERO Foundation ,  La Caixa Foundation , and the  Cellex Foundation.
ACKNOWLEDGMENTS All authors are partially supported by the  German Research Foundation (DFG)  in project  389792660 ,  TRR 248  (Center for Perspicuous Systems). M. Krötzsch is additionally supported by the  Bundesministerium für Bildung und Forschung (BMBF)  in project  ScaDS. AI (Center for Scalable Data Analytics and Artificial Intelligence ), and by the  Center for Advancing Electronics Dresden  (cfaed). R. Beutner  and  J. Hofmann  carried out this work as members of the  Saarbrücken Graduate School of Computer Science.
This study aimed at applying machine learning (ML) methods to analyze dynamic strength of 3D-printed polypropylene (PP)-based composites. The dynamic strength of additive manufactured PP-based composites with different fillers and printing parameters was investigated by split Hopkinson pressure bars. Based on experimental results, six machine learning approaches were applied to express the relationships between the dynamic strength and materials as well as printing parameters. The performance of the six machine learning algorithms with relatively small training datasets was evaluated. The comparison results showed that artificial neural network could achieve the highest prediction accuracy but with relatively low computational efficiency, whereas the support vector regression could provide satisfactory prediction with both good accuracy and efficiency.
The results indicate that the error of the ANN prediction is smaller. Mohamed et al. [26]  investigated the influence of individual and interactive ME-AM process parameters on the performance of printed polycarbonate (PC)-ABS products under dynamic and cyclic conditions. Experiments were conducted and fractional factorial models and ANN were applied to predict the dynamic modulus of elasticity of printed parts. The results showed that a properly trained ANN presented a significant advantage in recognizing the nonlinear relationship of the system for both data fitting and extrapolation capabilities. Bayraktar et al. [27]  developed a mathematical model using ANN to capture the tensile strength of Polylactic acid (PLA) specimens fabricated by ME-AM in different printing strategies. The results show that the ANN model presents great predictive capability for the tensile strength of the printed components.
In addition, these algorithms have certain advantages that ANN does not, such as good interpretability and faster response speed  [23]. Moreover, the datasets from experiments are often small and sparse in ME-AM fields. It is essential to investigate the performance of various ML methods when experimental datasets are relatively small for boosting the application of ML in ME-AM fields. The purpose of this study is to apply ML methods on dynamic strength analysis for 3D-printed polypropylene (PP)-based composites. PP, as a new 3D printing material, has many advantages compared to commonly used ABS and PLA  [39]. Therefore, 3D-printed PP-based composites attracted wide attention  [40] [41] [42] [43] [44]. The existing studies mainly focused on the quasi-static mechanical properties of 3D-printed PP, and less work investigated their mechanical properties at high strain rate.
However, 3D-printed composites have a huge potential in structural applications under impact loading, and the impact resistance of materials could affect human safety when applied in aviation and transportation applications  [2, 3, 5, 39, [45] [46] [47]. Thus, it is necessary to study the mechanical properties of the 3D-printed materials at high strain rate. In this study, the dynamic strength of 3D-printed specimens was tested by split Hopkinson pressure bars. The experimental dataset was relatively small and sparse in this work as the experiments were timeconsuming and costly. Based on a relatively small experimental dataset, six ML methods were applied to predict the dynamic mechanical properties of printed specimens with different materials and under different process parameters. In addition, the performance of these ML methods was evaluated and compared.
According to the full factorial experimental design method  [30, 56] , the printing temperatures in this work were set at 190 • C, 220 • C and 250 • C, and the layer thicknesses were set at 0. 1 mm, 0. 25 mm, and 0. 4 mm. The detailed printing parameters selection reasons were presented in Supplementary material. In addition, to prepare the test dataset, four intersection test points were evenly selected within the printing temperature and layer thickness ranges, which are: (0. 2 mm, 205 • C), (0. 2 mm, 235 • C), (0. 3 mm, 205 • C) and (0. 3 mm, 235 • C). Fig. 1  shows the distribution of the experimental tests of PP/EPDM/talc in the parameters space. Points with number in the figure represent the experimental tests number. Tests 1 to 9 were the experimental points selected according to the full factor design method, and they were set as training set.
Experimental results and discussion Table  1  showed the dynamic compressive strength of the two materials under different printing parameters. The test numbers in Table  1  correspond to the numbers in Fig. 1. These results will be used as training and testing datasets for ML models. The experimental results for two materials according to the full factor design (FFD) method are presented in Fig. 3. In order to show clearly the variation trends of the strength of printed parts with the change of printing parameters, only the experimental points of FFD are shown. All the experimental results are shown in Table  1. It can be observed in Table  1  and Fig. 3  that the dynamic compressive strengths of PP/EPDM/talc were higher than those of PP/EPR at a given printing parameters combination. Similar results also reported in literature  [18, 50, 52].
Machine learning models used in this study To determine the most suitable models for predicting the dynamic mechanical properties of ME-AM fabricated PP-based composites when experimental dataset are small, six machine learning algorithms commonly used for regression problems were applied. These six ML algorithms are Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), K Nearest Neighbors (KNN) and Artificial Neural Network (ANN) (also known as Multilayer Perceptron (MLP)). The ML models in this study were developed using Python (Python Software Foundation). Scikit-learn (scikit-learn package, v0. 24. 2)  [66] , an open-source machine learning library for Python, was used to execute all ML algorithms except for XGB, which was implemented using xgboost library (xgboost package,v1.