dataesr/scientific-paragraphs-categorization huggingface dataset:
85 to 0. 75, which raises the significance of Ω Λ > 0 from 2. 95 to 4. 65. Combining other BAO data to date, including the DR16 QSO and Lyman- measurements, the nonzero Ω Λ is now favoured at a ∼ 11 confidence level, which is consistent with the multi-tracer analysis in the configuration space in a complementary paper  (Wang et al. 2020)  Figure  9. The one dimensional (1D) posterior distribution (panels on the diagonal) and 68 and 95% CL contour plots for BAO and RSD parameters derived from the eBOSS DR16 (panels in the lower triangle) and EZmock catalogues (upper triangle), respectively. For measurements from the DR16 catalogue, results are derived at three (blue) and two (red) effective redshifts, respectively, denoted as DR16 (3) and DR16 (2) in the legend. For results derived from the mocks, measurements are preformed at three effective redshifts, from three different data combinations: QQP3 (gray solid contours); PQP3 (filled) and PPP3 (gray dashed).
Figure13. Constraints on Ω M and Ω Λ using BAO observations alone. The left panel shows the 68 and 95% CL constraints derived from three datesets: (I) the BOSS DR12 BAO consensus result at three effective redshifts ('BOSS gal. '; blue dashed) (Alam et al. 2017 ); (II) BOSS DR12 combined with this work (red dash-dotted; Note that we eliminated the BOSS BAO measurement for  ∈ [0. 6, 0. 8]) in this combination, because BOSS galaxies in this redshift range are included in the DR16 LRG sample); (III) a further combination with BAO measurements using samples of MGS (Ross et al. 2015) , 6dFGS (Beutler et al. 2011) , eBOSS DR16 QSO (Hou et al. 2020 ) and DR16 Lyman- forest (du Mas des Bourboux et al. 2020) ('Full BAO'; gray filled). The right panel shows the 1D posterior distribution of Ω Λ using three datasets.
ACKNOWLEDGEMENTS GBZ is supported by the  National Key Basic Research and Development Program of China  (No. 2018YFA0404503 ), and a grant of  CAS Interdisciplinary Innovation Team. GBZ, YW and WBZ are supported by  NSFC  Grants  11925303 ,  11720101004 ,  11673025  and  11890691. YW is also supported by the  Nebula Talents Program of NAOC  and by the  Youth Innovation Promotion Association CAS. EMM has received funding from the  European Research Council (ERC)  under the European Unions  Horizon 2020 research and innovation programme  (grant agreement No  693024 ). Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah.
Recall at last the classical generating series for the Bernoulli numbers B n z exp(z) -1 = 1 + n>0 B n n! z n as well as its inverse exp(z) - 1 z = 1 + n>0 1 (n + 1)! z n. Acknowledgements: The fourth author would like to thank CONACyT (Mexico) for its support via the scholarship 714236. This work was partially supported by the project "Pure Mathematics in Norway", funded by Trond Mohn Foundation and Tromsø Research Foundation.
The Need for an External Platform to Manage Communication and Coordination between TSOs/DNOs The need for coordination, communication, and data exchange are typically high in a context with high level of congestion and a large number of stakeholders involved with varied sizes and interests. In such context, local flexibility markets which are focusing on enhancing the coordination between stakeholders can play an important role in the electricity system. The alternative solution (bilateral coordination between network operators) could be less efficient given the transaction costs implied by recurrent coordination of actions for congestion management between several stakeholders. Developing a local flexibility market provides a coordination tool between stakeholders, in particular if it is based on an external platform centralizing data and actions for several TSO/DNOs.
In this context the external platform would also allow to share methods and understand potential issues created by some flexibility offers activation on the different networks. This can represent a first step towards an external platform having a potentially more extended technical role. A local flexibility market using an external platform to centralize data and the needs of each network operator can thus bring benefits to coordinate and centralize multiple parallel bilateral processes or of parallel local markets developed by each DNO. The co-construction of a platform between TSOs and DNOs, the communication and centralization of their network constraints, and a joint monitoring of network operators on a local flexibility platform can bring benefits to organize and facilitate cooperation between TSOs and DNOs.
In order to decrease the invasiveness and morbidity of this intervention, laparoscopic  [8]  and robotassisted approaches  [14]  have been proposed, but few data exist on these types of experience. After the first description by Al-Othman et al  [14] , very few cases of robot-assisted supratrigonal cystectomy and augmentation cystoplasty (RASCAC) have been reported so far. However, robotassisted radical cystectomy is an established procedure to treat bladder cancer, having shown similar results to open surgery  [15]. The objective of this study was to report the feasibility and safety of a standardized technique of RASCAC with total intracorporeal reconstruction, and to report the perioperative and 1-yr functional outcomes obtained with this technique.
Study population Overall, 10 consecutive patients with refractory neurogenic detrusor overactivity were prospectively selected after providing written informed consent to undergo RASCAC in a French tertiary referral center from August 2016 to April 2018 (to ensure minimum 1-yr followup). All patients were followed by a multidisciplinary neurourology team comprising urologists, rehabilitation physicians, and dedicated nurses. The motivation and ability to perform ISC were evaluated thoroughly. The dataset included preoperative medical history of the patient, and intraoperative and follow-up data.
In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multiclasses CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures.
In fact, state-of-the-art approaches have investigated the classification of two tissue types  [10, 11]  or multi-class tissue types analysis  [8, 12, 13]. The two tissue types are tumor and stroma tissue categories. Actually, the classification of just two tissue categories is not suitable for more heterogeneous parts of the tumor  [12]. To overcome this limitation, the authors of  [12]  proposed the first multi-class tissue type database, where they considered eight tissue types. In this work, we deal with the classification of multi-class tissue types. In order to classify different CRC tissue types, we proposed two ensemble approaches which are: Mean-Ensemble-CNNs and NN-Ensemble-CNNs. Our proposed approaches are based on combining four trained CNN architectures, which are ResNet-101, ResNeXt-50, Inception-v3 and DenseNet-161.
• We conducted extensive experiments to study the effectiveness of our proposed approaches. To this end, we evaluated two texture descriptors (BSIF and LPQ) and their combination using two classifiers (SVM and NN) in two CRC tissue types databases. • Implicitly, our work contains comparison between CNN architectures and handcrafted feature-based methods for the classification of CRC tissue types using two publicly databases. This paper is organized as follows: In Section 2, we describe the state-of-the-art methods. Section 3 includes description of the used databases, methods and evaluation metrics. In addition, Section 3 contains an illustration of our proposed approach and experimental setup. Section 4 represents the experimental results. In Section 5, we compare our results with the state-of-the-art methods.
Related Works In recent years, CRC tissue phenotyping has been subject to increasing interest in both computer vision and machine learning fields due to the availability of CRC-tissue-type databases such as  [8, 12, 14, 15]. Supervised methods are widely used to classify the tissue types in histological images  [12]. The supervized state-of-the-art methods for phenotyping the CRC tissues can be categorized as texture  [10] [11] [12] 16] , or learned methods  [8, 15, 17, 18]. In addition, there are some works that combined deep and shallow features such as  [19]. The texture methods are hand-crafted algorithms that were designed based on mathematical model to extract specific structures within the image regions  [20]. However, deep learning methods have the ability to learn more relevant and complex features directly from the images across their layers.
In particular, when there is no prior knowledge about the relationship between input data and the outcomes to be predicted. Since the pathology imaging tasks are very complex and little is known about which quantitative image features predict the outcomes, deep learning methods are suitable for these tasks  [21, 22]. In this section, we will describe the state-of-the-art works that have addressed multi-class CRC tissue types and used supervized methods. In  [12] , J. N. Kather et al. were the first who addressed CRC multi-class tissue types, where they created their database from 5000 histological images of human colorectal cancer including eight CRC tissue types. J. N. Kather et al. tested several state-of-the-art texture descriptors and classifiers. Their proposed approach is based on the combination GLCM and LBP local descriptors beside with global lower-order texture measures which achieved promising performance.
They tested five pretrained CNN models: VGG19  [24] , AlexNet  [25] , SqueezeNet version 1. 1  [26] , GoogLeNet  [27] , and ResNet-50  [28]. They concluded that VGG19 was the best model among the five CNN models. Javed et al. [8]  proposed a new CRC-TP database which consists of 280K patches extracted from 20 WSIs of CRC; these patches are classified into seven distinct tissue phenotypes. To classify these tissue types, they used 27 state-of-the-art methods including texture, CNN and Graph CNN-based (GCN) methods. From their experimental results, the GCN outperformed the texture and CNN methods. Despite hand-crafted feature-based and deep learning methods having been used for multi-class CRC tissue type classification, the performance of these methods still needs more improvement. To this end, we proposed two ensemble-CNN approaches that achieved considerable improvement on two popular databases.
Kather-CRC-2016 database  [12]  consists of 5000 CRC tissue type images, where each tissue type has 625 samples. J. N. Kather et al. [12]  used 10 anonymized H&E stained CRC tissue slides from the pathology archive at the University Medical Center Mannheim, Germany. Firstly, they digitized the slides. Then, contiguous tissue areas were manually annotated and tessellated. From each slide, they created 625 non-overlapping tissue tiles of dimension 150 × 150 pixels. The following eight types of tissues were selected in their database:.
CRC-TP Database The CRC-TP database  [8]  consists of 280K CRC tissue type images. These CRC tissue type images are patches that were extracted from 20 WSIs of CRC stained with H&E taken from University Hospitals Coventry and Warwickshire (UHCW). Each slide was taken from a different patient. With the aid of expert pathologists, the WSI slides were manually divided into non-overlapping patches and these patches were annotated into seven distinct tissue phenotypes, where each patch was assigned to a unique label based on the majority of its content. Table  1  contains the CRC tissue types and their corresponding number of samples. The CRC tissue image size is fixed to 150 × 150 pixels. Javed et al. [8]  divided the 280K CRC tissue images into training and testing splits to evaluate the performance of their methods, where 70% of each tissue phenotype patches are randomly selected as the training split and the remaining 30% are used as testing split.
Support Vector Machine (SVM) In machine learning, SVM  [32]  is one of the most powerful supervized learning methods. For D features, the SVM algorithm seeks to define a hyperplane in D-dimensional space that distinctly classifies the data points. To separate two classes of data points, there are many possible hyperplanes that could be chosen. SVM objective is to find the plane that has the maximum margin, i. e. , the maximum distance between data points of all classes. Maximizing the margin distance provides some reinforcement so that future data points can be classified with more confidence. Figure  5  shows an example of possible hyperplanes between two classes and the linear-SVM hyperplane, which separates the two classes of data points with maximum margin. In our experiments, we used linear-SVM as a benchmark classifier for the hand-crafted features.
Neural Network (NN) Classifier In addition to the SVM classifier, we built a seven-layer NN classifier to classify the shallow features that were obtained from LPQ and BSIF descriptors and their combination. Figure  6  illustrates the used architecture. We chose these seven layers to make the classifier simple since the extracted features are already middle-level features. To this end, we tested a different number of layers (3, 5, 7 and 9) on the first fold of the Kather-CRC-2016 database, then we picked out the number of layers corresponding to the best performance which was seven layers. Consequently, the seven-layer NN architecture was used in the other folder of the Kather-CRC-2016 database and CRC-TP database experiments. The seven-layer NN classifier was trained for 20 epochs, initial lr = 10 -6 with decay of 0.
Evaluation Metrics To evaluate the performance of the tested methods, we used three metrics which are: accuracy, F1-score and F1-score. Accuracy is the measurement of all correctly classified samples over the total number of samples. The accuracy is mainly used to evaluate the methods on the Kather-CRC-2016 database because it is a balanced database. Since the CRC-TP database is not a balanced database, we used F1-score and F1-score. F1-score is defined by the formula: F1-score = 2 * Precision * Recall Precision + Recall (2) where: Precision and Sensitivity (also called Recall) are defined by the following formulas: Precision = TP TP + FP (3) Sensitivity = TP TP + FN ( 4 ) where TP is the number of True Positive instances, FP is the number of False Positive instances and FN is the number of the False Negative instances.
Then, these deep feature vectors are concatenated alongside each other to obtain an ensemble deep feature vector. The extracted training features (from the training data) are used to train new NN architecture, which consists of four layers. On the other hand, the extracted testing features (from the testing data) are used to test the four-layer NNs. Figure  13  illustrates our NN-Ensemble-CNN approach. The selection of four layers for our NN-Ensemble-CNNs approach was after testing different small numbers of layers  (3, 4 and 5)  on the first fold of the Kather-CRC-2016 database. This was similar to what we did for the NN classifier of the hand-crafted features in Section 3. 2. 4.
Experimental Setup For hand-crafted feature extraction and SVM classification, we used MATLAB 2019. For deep learning and NN training, we used the Pytorch  [39]  library with NVIDIA GPU Device Geforce TITAN RTX 24 GB. For training the deep learning architectures, we used data pre-processing including normalizing and resizing the input images to have the correct input size for each network. Inception-v3 input size is 299 × 299 pixels, while DenseNet-161, ResNeXt-50 and ResNet-101 need an input size of 224 × 224. Moreover, we used the following active data augmentation techniques:.
Kather-CRC-2016 Database In Kather-CRC-2016 database experiments, we used 5-fold cross-validation evaluation scheme. Table  2  summarizes the obtained accuracy for each fold and the mean of the five folds accuracies. From this table, we notice that the combination of LPQ and BSIF gave better performance for both classifiers (SVM and NN). On the other hand, we observe that combined features achieved similar results with the two classifiers, with slightly better accuracy with the seven-layer NN classifier. In the CRC-TP database, we used the train and validation splits that were provided with the database  [8]. Table  3  summarizes the obtained results of LPQ and BSIF descriptors and their combination using SVM and NN classifiers. Similar to Kather-CRC-2016 experiments, we noticed that the combination of LPQ and BSIF gave better performance for both classifiers (SVM and NN).
Kather-CRC-2016 Database Similar to the hand-crafted feature experiments on the Kather-CRC-2016 database, we used a 5-fold cross-validation evaluation scheme. For training the CNN architectures, we used a batch size of 64. To train the NN network for our NN-Ensemble-CNN approach, we used a batch size of 32. Table  4  summarizes the obtained results using the CNN architectures and the proposed ensemble approaches. By comparing these results with the ones from Table  2 , we notice that the CNN architectures exceed the hand-crafted featurebased methods in the classification of CRC tissue types. We noticed that the performance of the two proposed ensemble approaches outperformed the performance of the four CNN architectures. Figures  14  and 15  show the confusion matrices of our proposed Mean-Ensemble-CNN and NN-Ensemble-CNN approach, respectively.
CRC-TP Database To train the CNN architectures using the training data of CRC-TP database, we used batch size of 128. Similarly, we used batch size of 128 to train the four NN layers of our NN-Ensemble-CNN approach. In CRC-TP database experiments, we selected bigger batch sizes than the experiments of the Kather-CRC-2016 database because the CRC-TP database contains a larger number of samples for each class. Table  5  summarizes the obtained results using the CNN architectures and the proposed ensemble approaches. By comparing these results with the ones from Table  3 , we notice that the CNN architectures exceed the handcrafted feature-based methods in the classification of CRC tissue types. On the other hand, the performance of the two proposed ensemble approaches outperformed the performance of the four CNN architectures.
Figures  16  and 17  show the confusion matrices of our proposed Mean-Ensemble-CNN and NN-Ensemble-CNN approaches. The comparison between the performance of the two proposed ensemble approaches (from Table  5  and Figures  16  and 17 ) show that the NN-Ensemble-CNN approach performs slightly better on the recognition of CRC tissue types. Since our approaches are an ensemble of trained CNN architectures, it is interesting to compare the computational cost of our proposed approaches with these CNN architectures. Table  6  contains the required time to test single CRC tissue type image using the trained CNN architectures and our approaches on Kather-CRC-2016 and CRC-TP databases. From Table  6 , we notice that our approaches' testing time is equal to the sum of single models' testing times. Moreover, we notice that the required time is very trivial for all the evaluated methods in both databases.
Discussion In this section, we will compare our results with state-of-the-art methods. Table  7  contains the comparison between our proposed approaches and the state-of-the-art methods. In  [12] , J. Kather et al. tested different texture descriptors with an SVM classifier. In  [41] , Ł. R ączkowski et al. proposed the Bayesian Convolutional Neural Network approach. In  [19] , L. Nanni et al. proposed an ensemble (FUS_ND+DeepOutput) approach based on combining deep and texture features. The comparison in Table  7  shows that our proposed ensemble approaches outperform the state-of-the-art methods. 8  contains the comparison between our proposed approaches and the state-ofthe-art methods on the CRT-TP database. In  [8] , S. Javed et al. used supervized and semisupervized learning methods. In this comparison, we consider the results of the supervized approaches which are similar to our approaches.
In Table  8 , we compare our approaches with texture and deep learning methods that were tested on  [8]. The comparison shows that our approaches (Mean-Ensemble-CNNs and NN-Ensemble-CNNs) outperform the state-of-the-art methods. The comparison with the hand-crafted feature-based methods, deep learning architectures and the state-of-the-art methods proves the efficiency of our proposed ensemble approaches (Mean-Ensemble-CNNs and NN-Ensemble-CNNs). From the results on the Kather-CRC-2016 database, we notice that our proposed approaches (Mean-Ensemble-CNNs and NN-Ensemble-CNNs approach) achieved similar results (Table  7 ). Meanwhile, in the CRC-TP database, we notice that the NN-Ensemble-CNNs performance is better than Mean-Ensemble-CNNs (Table  8 ). On the other hand, we noticed that the performance of different methods on Kather-CRC-2016 is better than the performance on the CRC-TP database.
This is probably because the CRC-TP database contains more challenging classes than Kather-CRC-2016. In addition, CRC-TP is not a balanced database that can influence the overall performance. Another possible reason can be the splitting and labeling of the tissue types, which were performed by different expert pathologists for each database. Despite our approach outperforming the state-of-the-art methods in both databases, the results in the CRC-TP database need more improvements for real-world applications. One possible solution is to use more data augmentation techniques to increase the training data.
The experimental results showed that deep learning methods (single architecture) surpass the hand-crafted feature-based methods. On the other hand, our proposed approaches outperform both the hand-crafted feature-based methods and the CNN architectures. In addition, our ensemble approaches outperform the state-of-the-art methods in both databases. As for future work, we are planning to use more data augmentation techniques to augment the training data. Moreover, including other powerful CNN architectures to our ensemble approaches will help to improve the performance.
Fluorescence Fluorescence has been recorded in THF to explore more in detail the behaviour of the set of compounds, as it known to be a more sensitive technique. Spectra are displayed on Fig. 7  and data are gathered in Table  3. At the working concentrations (normalized at absorption 0. 2), a significant effect of the sole presence of the peptoid helix is observed when comparing 1 and 2. The fluorescence quantum yield (Φ F ) of 1 is indeed only 2/3 of those of the reference phthalocyanine 4. When comparing the data for 3 and 4, Φ F value is significantly lower than for the monomeric derivatives, and is interestingly slightly higher for 2 than for 3, even though the helix of conjugate 2 is more likely to promote intramolecular interactions as it would promote co-facial geometry. It confirms the fact the phthalocyanines have enough freedom of movement and hence that their relative configuration is not much affected by the helix configuration but rather by being maintained close to each other by the grafting on the same peptoid backbone.