dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Many examples of rockfall simulation software provide great flexibility to the user at the expense of a hardly achievable parameter unification, as highlighted by  Berger and Dorren (2006) ,  Berger et al. (2011) ,  Volkwein et al. (2011) , Jarsve (2018),  Garcia (2019) ,  Bourrier et al. (2021) ,  and Noël et al. (2021). Even when using the same rebound model, though it may be implemented in a different piece of software, the results using the same parameters may vary, as shown previously in  Noël et al. (2021)  when comparing CRSP 4  (Pfeiffer and Bowen, 1989; Jones et al. , 2000)  with RocFall 8  (Stevens, 1998; Rocscience Inc. , 2022). The settings of the rebound model parameters are often specific to the model, rockfall software, and version used. Thus, it is difficult to transpose them from experimental results, such as the apparent coefficient of restitution from impact experiments.
Vegetation is often not a problem for freshly affected sites, since large rockfall events usually remove part of it. Artifacts and bushes might be present, however, and should be avoided when evaluating the impact position and the local terrain orientation. They can be highlighted by artificial shading methods, such as the eye dome lighting method (EDL)  (Boucheny, 2009)  or the ambient occlusion method (PCV)  (Duguet and Girardeau-Montaut, 2004; Tarini et al. ,   2006). Local geomorphological features and impact marks are also highlighted with these shading methods (Fig. 8 ). These methods can also be combined with coloring methods based on the local terrain orientation (e. g. , the Coltop method by  Jaboyedoff et al. , 2007 , which is also implemented with a slightly different color distribution in the CloudCompare open-source software;  Girardeau-Montaut, 2006).
All methods can be improved, and the CAVR method is no exception. Therefore, opening access to the valuable input data as previously done by the SLF allows for the independent review of the data, the combination of different approaches, and the development of innovative solutions. The contribution enables transparent and open rockfall science to be very helpful for the geohazard community when assessing the sensitivity of current rockfall simulation software and for finding the right simulation parameters to be used for similar sites. This will hopefully also facilitate the development of more objective rockfall simulation models that are less dependent on inconvenient and expensive back analyses.
Author contributions. FN, MJ, AC, CH, FB, and JPM conceptualized the research. FN and AC oversaw the data curation. FN and MJ did the formal analysis. FN and AC contributed to the investigation. FN, MJ, and AC developed the methodology. FN, MJ, and AC oversaw the project administration. FN, MJ, and AC provided the resources. FN developed the software. MJ and AC supervised the experiment and the research. FN, MJ, AC, CH, FB, and JPM validated the reconstruction approach. FN designed and produced the figures. FN wrote the original draft. FN, MJ, AC, CH, FB, and JPM reviewed and edited the original draft. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. The authors acknowledge American Journal Experts (AJE) for editing the English of the manuscript. A special thanks goes to the SLF and Geobrugg for the collaboration and access to the Chant Sura test site during the rockfall experiment on 13 September 2019. We thank Synnøve Flugekvam Nordang for her help with the reconstruction of the trajectories with the presented CAVR method and with the design of the figures. Finally, the authors would like to acknowledge the reviewers of the present paper. Review statement. This paper was edited by Wolfgang Schwanghart and reviewed by two anonymous referees.
NOEMA 1. 8 mm observations and data reduction We observed 3C 186 with the NOrthern Extended Millimeter Array (NOEMA) on 1, 5, and 10 March 2021, in A configuration, as part of the program W20CO (PI: Castignani). We used 10 antennas on 5 March and 11 antennas on the other two days of observations. With the 10-and 11-antenna configurations, the time spent on-source was 3. 4 h and 8. 2 h, respectively. The total observing time was 16. 2 h (i. e. , 11. 6 h on source). We set the phase center of the observations equal to the 3C 186 quasi-stellar object (QSO) coordinates, RA = 7 h :44 m :17. 5 s and Dec = 37 • :53 :17. 2. We used the Poly-Fix correlator, which covers a total bandwidth of 15. 5 GHz, in each linear polarization, split between the lower sideband (LSB) and the upper sideband (USB). We adopted a tuning frequency in the upper inner baseband of 223.
7 GHz, which is the mean between the redshifted CO(4→3) observed frequencies, ν obs , for the NLR (z = 1. 068, ν obs = 222. 940 GHz) and the BLR (z = 1. 054, ν obs = 224. 459 GHz). With this tuning, the CO(4→3) lines of both the BLR and the NLR thus fall within the 3. 9 GHz wide upper inner baseband. The baseline range was 32-760 m. The program was executed in average, albeit unstable, winter weather conditions, with a system temperature T sys = 120-250 K and a precipitable water vapor column of ∼1 -3 mm. The sources 0805+410 and 0738+313 were used as phase and amplitude calibrators, while 3C 84 and 3C 279 served as bandwidth and flux calibrators. We reduced the data using the clic package of the GILDAS software to obtain the final (u,v) tables. We then imaged the visibilities using the MAPPING software of GILDAS.
Acknowledgements. We thank the anonymous referee for helpful comments which contributed to improve the paper. This work is based on observations carried out under project number  W20CO  with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). GC thanks  Melanie Krips  and all IRAM staff for the observations and help concerning the data reduction. GC acknowledges the support from the grant ASI n. 2018-23-HH. 0. The authors thank  Bryan Hilbert  and  Erini Lambrides  for helpful discussion. The imaging data of HST/ACS F606W in Fig. 1 was taken in the  HST GO-15254 program  and processed in Morishita et al. (2022).
Multiple effective representations are learned for kinship verification statements (children and parents) using a margin maximization learning scheme based on Tensor Cross-view Quadratic Exponential Discriminant Analysis. Through the exponential learning process, the large gap between distributions of the same family can be reduced to the maximum, while the small gap between distributions of different families is simultaneously increased. The WCCN metric successfully reduces the intra-class variability problem caused by deep features. The explanation of black-box models and the problems of ubiquitous face recognition are considered in our system. The extensive experiments on four challenging datasets show that our system performs very well compared to state-of-the-art approaches.
Moreover, the VGG-Face model achieved the best performance in face recognition with accuracy of 98. 95% and 97. 3% on LFW  (Huang, Mattar, Berg, & Learned-Miller, 2008)  and YTF  (Wolf, Hassner, & Maoz, 2011)  databases, respectively. Our knowledge-based tensor subspace model also addresses the problem of ubiquitous face recognition by fusing features extracted from the VGG-Face model with those from the VGG-F, VGG-M, and VGG-S models via a tensor design. Moreover, we succeed in transferring the one entry problem to the model (i. e. , face recognition/object detection) to deal with the binary and ternary entries model (bi-subject and tri-subject kinship verification). Moreover, our TXQEDA+WCCN approach addresses the problems of black-box models and considers the following properties: accuracy, fidelity, consistency, stability, representativeness, certainty, novelty, importance, and comprehensibility.
The combination of TXQEDA and WCCN would minimize the expected classification error. 4. Many experiments were conducted with four public databases (TSKinFace, Cornell, KinFaceW-I, and KinFaceW-II), with favorable results compared to state-of-the-art approaches The remainder of this paper is organized as follows. We begin with an overview of our system methodology in Section 2. In Section 3, we give an overview of our knowledge-based multilinear subspace for face kinship verification. The TXQEDA+WCCN approach is presented in Section 4. The experimental data, setup and results are presented in Section 5. Finally, conclusions are drawn in Section 6.
Fig. 3  illustrates the proposed knowledge-based multi-linear subspace, each face image is represented using four deep feature descriptors (i. e. , VGG-F, VGG-M, VGG-S, and VGG-Face) extracted in two fully connected layers FC6 and FC7, each with 4096 neurons. Thus, the feature vectors are considered as 2D arrays for each face. The features of the face images in each database are arranged to form a third-order tensor. Furthermore, feature fusion has already been brought to unfold in each tensor mode. Our method concerns the feature fusion process without using the usual feature fusion techniques. In other words, using a multi-dimensional tensor-based metric learning method simplifies the feature fusion task. Fig. 4  illustrates how unfolding is used to perform deep feature fusion. The process of unfolding is performed mathematically as follows: For a tensor B ∈ ℜ I 1 ×I 2 ×•••×I N with size (I 1 × I 2 × • • • × I N ) tensor, then element (i 1 × i 2 × • • • × i N ) of B maps to element (i k , j) of matrix B (k) ∈ ℜ I k × ∏ i̸ =k I i , the k-mode unfolding of B, with: B k ⇐ k B (1) where B (k) i k ,j = B i 1.
Performance evaluation The proposed kin verification approach has been experimentally verified by applying it to publicly available kin verification datasets(KinFaceW-I, KinFaceW-II  (Lu et al. , 2014) , TSKin-Face  (Qin, Tan, & Chen, 2015) , and Cornell KinFace database  (Fang, Tang, Snavely, & Chen, 2010) ). Images in these databases were captured and recorded under uncontrolled conditions for gestures, demographic attributes, background, lighting, partial occlusion, and facial expressions.
Parameter settings We followed the framework and testing protocol in  Lu et al. (2014)  and used a five-fold cross-validation testing scheme to evaluate the performance of our approach. For the four kinship databases KinFaceW-I, KinFaceW-II  (Lu et al. , 2014) , TSKin-Face  (Qin et al. , 2015) , and the Cornell KinFace database  (Fang et al. , 2010) , we cropped the images to face size 64 × 64 pixels, then we resized the input images to the required 224 × 224 dimensions to use as input for deep feature extraction. The TXQEDA+WCCN subspaces are set to 96% energy of the eigenvalues, preserving the eigenvalues that provide up to 96% of the information. For example, in KinFaceW-II, and for the F-S relation, the A, B are ∈ ℜ 4096×8×200 and their projection 1000 × 8 × 200, and also provide the dimensions of the two projections W 1 with dimensions 8 × 8 and W 2 with dimensions 4096 × 1000.
Compute the final lower dimensions i k by: Energy k   KinFace). Experiments were also performed on the baseline approaches with the same settings, which are included in Tables  9  and 10. We note that: = ∑i k j=1 λ k j ∑I k j=1 λ k j × 100, where ( λ k 1 > λ k 2 >. > λ I k k ). 4. Sort the i N eigenvectors W iter k ∈ ℜ I k ×i k according to Λ k in decreasing order, k = 1, • • • , N. • From the analysis of the results associated with the four databases Table  1  (KinFaceW-I), Table  2  (KinFaceW-II), Table 3 (TSKinFace) and, Table  4  (Cornell KinFace) using the Mv-VGG description, our proposed TXQEDA+WCCN method outperforms the TXQDA schemes for most of the image features. The results reported in Tables  1, 2, 3,  and 4  show   (Yan et al. , 2015)  71. 90 SIEDA  (Laiadi, Ouamane, Boutellaa et al.
, 2019)  81. 50 KVRL+fcDBN  (Kohli et al. , 2017)  89. 75 AdvKin  (Zhang et al. , 2020)  81. 40 KML  (Zhou et al. , 2019b)  81. 40 TXQDA  (Laiadi et al. , 2020b)  93. 04 Method in  Kohli et al. (2018)  94. 40 SSADL-HF  (Chen et al. , 2020)  70 that using Mv-VGG with our method improves the accuracy of 14%, 3. 5% and 1. 07% for bi-subject kinship verification on KinFaceW-I, KinFaceW-II and TSKinFace databases and.
00 77, 30 76, 31 70, 29 74, 66 76, 31 74, 39   Performance comparison (%) of baseline methods with the same settings as our method using Mv-VGG on KinFaceW-I, KinFaceW-II, TSKinFace and Cornell datasets. Cornell KinFace database, respectively. For a general observation, TXQEDA+WCCN was trained with a single feature representation to learn kinship verification, with the goal of finding the image representation that leads to the highest accuracy. Tables  1, 2 , 3 and 4 show the results of these tests. Accordingly, the TXQEDA+WCCN approach performs better than the TXQDA approach on all features, both in verifying single or multi-view kin verification. Features KinFaceW-I KinFaceW-II TSKinFace Cornell F-S F-D M-S M-D Mean F-S F-D M-S M-D Mean F-S F-D M-S M-D • The proposed TXQEDA+WCCN method outperforms other shallow approaches  (Laiadi et al.
(2018)  outperforms all approaches to kinship verification approaches in KinFace-I, KinFace-II, and the Cornell KinFace database. Also, the KVRL+fcDBN  (Kohli et al. , 2017)  approach outperforms ours and other CNN-based methods except for the method in  Kohli et al. (2018)  in KinFace-I, KinFace-II database. The reason is that the work in  Kohli et al. (2017)  is based on a deep learning approach (KVRL-fcDBN), the fcDBN algorithm was to learn a large number of external data (more than 600,000 external face images) and achieved 96. 20% on KinFaceW-II databases, 96. 10% on KinFaceW-I databases, and 89. 50% on Cornell KinFace. The authors in  Kohli et al. (2018)  used an external dataset in a new kinship video database (KIVI) that contains over 250. 000 still images extracted from 355 real kinship video pairs, and achieved 97.
10% on KinFaceW-II, 96. 90% on KinFaceW-I, and 94. 40% on Cornell KinFace. However, in our work, we used tensor approach TXQEDA+WCCN to learn only the available data (i. e. , no external data used in the training phase) and we achieved performances 90. 30% on the KinFaceW-II database, 91. 16% on the KinFaceW-I database, 90. 68% on the TSKinFace database, and 93. 77% on the Cornell KinFace database. In general, CNN-based methods provide low performance when using small datasets. The 4 VGG models (i. e. , VGG-M, VGG-F, VGG-Face, and VGG-S) also used external images like the Imagenet and VGGFace datasets, but only for object features and face features, respectively. In fact, training these CNN architectures did not use external kinship (i. e. , mother-son, Father-Son, Mother-Daughter, and father-daughter, etc.
, extracted from the face parts (i. e. , eyes, eyebrows, nose, mouth, etc. ). • In contrast to the TXQEDA+WCCN approach, most existing kinship verification methods use hand-crafted low-level descriptors (Shallow features), which are unlikely to be powerful enough to characterize kinship based on facial images. Also, the other kinship verification methods that use deep features simply do not take into account the problem of cross-view matching and the curse of intra-class variability. • For the databases provided, the competing methods reached an accuracy of 66% to 93. 77%, without counting the results of KVRL+fcDBN  (Kohli et al. , 2017)  and the method in  Kohli et al. (2018) , the proposed TXQEDA+WCCN obtained the best results (90. 30% for the KinFaceW-II database, 91. 16% for the KinFaceW-I database, 90.
68% for the TSKinFace database, and 93. 77% for the Cornell KinFace database). To make a fair comparison, we need to take into account the external data used to train kinship verification methods. The methods that used a large external face database to train the algorithm (i. e. KVRL+fcDBN Kohli et al. , 2017 , method in Kohli et al. , 2018)  were able to increase the average verification accuracy of kinship verification. We believe that the results can be higher and exceed the results of KVRL+fcDBN  (Kohli et al. , 2017)  and the method in  Kohli et al. (2018)  when external data are used to train our TXQEDA+WCCN. • All parent-child pairs in the KinFaceW-II dataset are cropped from the same source. This fact of cropping seems to simplify the classification considerably. The KinFaceW-II dataset contains a larger number of photos cropped from the same original image compared to KinFaceW-I.
The authors in  López, Boutellaa, and Hadid (2016)  concluded that the KinFaceW-II dataset is less challenging than the KinFaceW-I dataset. Therefore, we have greater confidence results for KinFaceW-I dataset. • To gain more confidence in the efficacy of our system in face pruning, we also evaluated our approach using another database. In the Cornell KinFace database, most pairs were obtained from different images from different cameras and different times under uncontrolled environment. From Table 8, our approach is clearly outperforming recent methods for kinship verification. To better illustrate the high performance and effectiveness of our TXQEDA+WCCN and its TXQDA counterpart, the receiver operating characteristic (ROC) curves of the different deep  descriptors are shown in Figs. 7, 8, 9, and 10, respectively.
, 2019b ) was 11%, 18%, 19% and 18% on KinFaceW-I, KinFaceW-II, TSKinFace and Cornell database, respectively. The improvement over TXQDA multi-linear subspace methods was 14. 75%, 14. 35%, 3. 57% and 1. 07%, while the improvement over MSIDA  (Laiadi et al. , 2020a)  was 12. 57%, 16. 15%, 15. 97% and 12. 61% on KinFaceW-I, KinFaceW-II, TSKinFace and Cornell databases, respectively. Otherwise, our work is better than the MSIDA+WCCN (Laiadi et al. , 2020a) approach with a performance of 5. 38%, 3. 25%, 7. 51% and 9. 90% for the KinFaceW-I, KinFaceW-II, TSKinFace and Cornell database, respectively. Since the proposed framework is based on several components that have higher performance, we consider the three components: the tensor mode, the WCCN technique, and the exponential criterion. The performance of the TXQEDA+WCCN approach using different component combinations compared to some baseline methods is shown in Table  10.
Computational cost The experiments were performed on a Windows 10 computer (Intel(R) Xeon(R) CPU E5-2620 v2 @ 2. 10 GHz 2. 10 GHz and 48 GB RAM) using MATLAB 2018b. Table  11  shows the CPU time taken to test the kin relation between a pair of face samples using our TXQEDA+WCCN approach and the baseline TXQDA approach. The CPU time of feature extraction and total time are given in seconds (s), while the projection and matching time is given in milliseconds (ms). From Table  11 , it can be seen that the time cost for projection and matching is negligible compared to the time for feature extraction. The total time cost for testing kinship faces with Mv-VGG features and TXQEDA+WCCN method is 1. 08 (s). The test time is very small (not more than 0. 0798 ms), so the proposed kinship verification method performs in real time.
The proposed knowledge-based tensor approach achieved successful results in kinship verification on four public databases KinFaceW-I,  KinFaceW-II, TSKinFace and Cornell KinFace. In terms of perspectives, future work mainly involves two aspects. First, we envision extracting more discriminative features using less pre-trained networks. Our goal is to reduce the size of the training data while maintaining the peak performance already achieved. Second, we are interested in increasing the dissimilarity between negative pairs (the problem of classifying negative pairs as positive pairs) for the kinship verification task, since we have handled intra-class variability well in our work.
The KinFaceW-I dataset contains 4 kinship relations. There are 156 (F-S), 134 (F-D), 116 (M-S) and 127 (M-D) kinship pairs. The KinFaceW-II dataset contains 4 kinship relations (F-S), (F-D), (M-S) and (M-D), 250 pairs of kinship images for each kinship relation. We also used the TSKinFace database, which contains two types of kinship relations between three individuals: Father-Mother-Daughter (FM-D) with 502 relationships and Father-Mother-Son (FM-S) with 513 relationships. To make a fair comparison, we reorganized the TSKinFace database by splitting the Father-mother-daughter group into two groups father-daughter (F-D) with 502 relationships, Mother-Daughter (M-D) with 502 kinship relations, and the Father-Mother-Son group into two groups Father-Son (F-S) with 513 kinship relations, Mother-Son (M-S) with 513 kinship relations, in total the TSKinFace database contains 2052 face images.
(2018) - - - - 97. 10 TXQDA (Laiadi et al. , 2020b) 90. 20 86. 40 85. 60 86. 40 87. 15 AdvKin (Zhang et al. , 2020) 88. 40 85. 80 88. 00 89. 80 88. 00 Method in Dibeklioglu (2017) - - - - 82. 30 WGEML (Liang et al. , 2019) 88. 60 77. 40 83. 40 81. 60 82. 80 NESN-KVN (Wang & Yan, 2020) 86. 70 88. 70 91. 60 89. 10 89. 00 Method in Yan and Song 90. 40 86. 60 91. 00 87. 20 88. 80 (2020) MSIDA+WCCN (Laiadi et al. , 89. 40 82. 80 87. 80 88. 00 87. 00 2020a) SSADL-HF (Chen et al. , 2020) 88. 60 80. 00 81. 00 80. 00 82. 40 TXQDA (Mv-VGG) 78. 40 72. 20 76. 80 76. 40 75. 95 TXQEDA+WCCN (Mv-VGG) 89. 80 90. 60 87. 60 93. 20 90. 30 Table 7 Comparison of the proposed approach with other kinship verification approaches on TSKinFace database. Method F-S F-D M-S M-D Mean FM-S FM-D RSBM (Qin et al. , 83. 00 80.
Materialized View Selection is one of the most studied problems in the database field, covering SQL and NoSQL technologies as well as different deployment infrastructures (centralized, parallel, cloud). This problem has become more complex with the arrival of data warehouses, being coupled with the physical design phase that aims at optimizing query performance. Selecting the best set of materialized views to optimize query performance is a challenging task. Given their importance and the complexity of their selection, several research efforts both from academia and industry have been conducted. Results are promising -some solutions are being implemented by commercial and open-source DBMSs -, but they do not factor in the following properties of nowadays analytical queries: (i) largescale queries, (ii) their dynamicity, and (iii) their high interaction.
Introduction Selection of materialized views is a challenging task for designing advanced database applications such as Analytical Databases [23], Autonomous Databases  [2] , Semantic Databases  [21] , Cloud Databases  [9]  and NoSQL Databases  [56]. The idea of using materialized views to satisfy the quality-of-service of databases does not date from today, but since forty years  [33]. Their importance has been amplified since data warehouse physical design has become more sophisticated to cope with complex decision support queries  [13, 4]. Selecting a set of materialized views that would satisfy functional and non-functional requirements is complex  [23]. This gave rise to the materialized view selection problem (VSP). Due to the importance and complexity of this selection, several research efforts from academia and industry have been conducted.
and respects C. This situation is inadequate with the nowadays requirements of analytical applications, where high number, dynamic, and high interacted queries are simultaneously considered. Due to the importance of the above three properties of nowadays workloads containing a high number of dynamic, and highly interacted queries, their clarification is necessary. With regard to the high number of queries, let us consider the following real examples covering analytical and semantic databases: (i) the Periscope application manages twentysomething million queries per day 3  ; (ii) the snowflake platform deals with more than 300 million queries per day from its customer base  4  , and (iii) the obtained results of a recent paper published in VLDB'2020, dealing with the problem of selecting materialized views in Oracle DBMS, are obtained based on 650 queries running on a star schema  [2] , and (iv) the SPARQL query logs executed at scholarly data of DBpedia contains 43 284 queries  [32].