dataesr/scientific-paragraphs-categorization huggingface dataset:
Two metrics, classification accuracy and intersection over union (IoU), are used to evaluate the performance of deep-learning models in pavement defect detection. Classification accuracy is the percent of defects in a GPR dataset that are correctly classified, while IoU is to take the ratio of the intersection between predicted results and ground truth labels over the union between these two sets. Thus, classification accuracy and IoU are used to evaluate the performance of the deep-learning models in defect recognition and location, respectively. From Table  1 , we can find: Compared with the architectures exploiting B-and C-scan, the ones exploiting A-scan have a slight advantage in defect class recognition and location computation. It is because some necessary pre-processing (e. g. , filtering and information compress) is conducted on GPR data for the utilization of B-and C-scan as the input.
Compared with A-and B-scan data, C-scan data contains complete space information of concealed defects in the pavements. It means more representations and features can be extracted from C-scan data than A-and Bdata, which are essential to further improve the performance of DL architectures exploiting GPR data. Unfortunately, to our best knowledge, now no DL architectures use C-scan data without pre-processing procedures. The state-of-the-art DL architectures  [80]  should be considered to exploit C-scan data directly in the future. RNNs outperform CNNs and autoencoders in the use of A-scan data because of their temporal dynamic behavior. This indicates that RNNs can take the input data sequence into account, while CNNs and autoencoders are not. The sequence of input data, especially A-scan data, is an essential feature of GPR data.
Some techniques are useful and essential for improving the precision and generalization of DL architectures exploiting GPR data. The first is data augmentation, including cropping, rotating, and flipping input images. It can reduce overfitting and improve the generalization of DL architectures because it can be considered as noises in the training. A gradient descent algorithm tends to balance the negative effects of the noise to minimize the overall error. In practice, this type of noise is common, such as object incline, rotation, and angulation. Another is prior knowledge, such as hand-crafted features and transfer learning. It can increase the training effectiveness because the pretraining phase is compressed. We also find that data-driven features from transfer learning work better than hand-crafted features because the prior knowledge learning from a desirable data set is better than the one provided by humans.
Dependence on big data It has been widely known that the performance of a DL model heavily depends on the quality of its learning dataset. Insufficient sample number, sample unbalance among the class membership, and label corrosion always lead a poor capacity of a DL model, such as overfitting, low generality, and unacceptable robustness. Unfortunately, GPR datasets for training a DL model are not as ample as the benchmark datasets for developing a DL model to solve classic issues, such as the CIFAR-10 [109], ImageNet  [110] , and ''For Music Analysis"  [111]  datasets. Many previous studies mentioned in Section 4. 1-4. 3 reported that their DL models were trained by a small number of GPR-data samples, less than 10 4. To make matters worse, the unbalance in these GPR datasets is inevitable because some data are not easy to collect in practice.
Now, there are main three solutions to the issue. The first solution is the use of transfer learning in the pre-training phase. Transfer learning is a technique applying the knowledge acquired while solving one issue to a different but related problem. In the work of Bralich et al. [112]  and Reichman et al. [113] , the prior knowledge learning from the CIFAR-10 dataset was transferred to the CNN model for buried target detection in the pre-training phase. Then the pre-trained CNN model is fine-tuned by a small GPR dataset. In the study of Enver and Yüksel  [114] , the learned weights in the imagenet-matconvnet-vgg-f model trained on the Imagnet Large Scale Visual Recognition Challenge (ILSVRC 2012) data  [115]  was transformed to a CNN for buried wire detection. Unfortunately, a transfer learning strategy can only help a DL model learn some low-level representations from these benchmark datasets, such as lines and gray scales.
This is because the latent middle-and high-level representations related to the class membership in these benchmark datasets are very different from the targets in GPR data, such as outlines and waveforms. In addition, there are commonly two different and distinct phases in the training of DL: pre-training and fine-tuning. Schwartz-Ziv and Tishby  [116]  indicated that the fine-tuning phrase could be considered as compressing the internal representations under the training error constraint, which is mainly responsible for the absence of overfitting in DL. Thus, we can conclude that transfer learning in the pre-training phase has limited help for the problem because the procedure of compressing low-level representations to middle-and high-level representations in the fine-tuning phase still raises overfitting owing to the lack of training samples.
The second solution is semi-supervised learning. It means that humans provide some hand-crafted representations before training a DL-model. For example, Malof et al. [117]  proposed to construct a CNN architecture that closely emulates successful handcrafted feature designs for GPR buried object detection. The experiment results indicated the feasibility and effectiveness of this approach for training a DL-model. However, the problem is that it is not easy for humans to summary all useful representations related to the buried object detection formally, especially highlevel representations. The third approach is to enlarge the dataset using simulation data or data augmentation. In the works of Pham et al. [102]  and Sonoda and Kimoto  [118] , thousands of GPR images were generated using finite-difference-time-domain simulation.
Veal et al. [119]  proposed a generative adversarial network-based method to impute new data based on limited and class imbalance GPR data. These works reported improvement of accuracy and robustness because of the reduction of class and condition unbalance in the training datasets. Unfortunately, a problem still exists that the developed DL model has undesirable stability on noises and backgrounds. This is because the simulation conditions are simpler than the real-world conditions, especially noise patterns and electromagnetic properties and distributions of the mediums. In addition, data augmentation, as a widely-used technique to avoid overfitting  [120] , is also used to reduce the dependence of big data in the GPR DL architectures, such as the study of Reichman et al. [113] , though it has limited help to solve the problem of the class imbalance.
In summary, from the findings of this section, we can conclude that the dependence of big data in the training of DL architectures exploiting GPR data is still not solved well because of the limitations of the three solutions. As a large number of the publications reported their well-developed CNN for exploiting GPR data, we think the optimal solution for the problem is to share the data from the GPR researchers in the world to build a benchmark GPR dataset. The similar works are standard in the field of deep learning  [109] [110] [111] , even computer science, but cannot be found in the field of NDT. As a pioneer, we provide our GPR dataset of pavement defect inspection used in Section 4. 4. It is the first step for our proposed solution.
For example, x provides confusing information indicating the DL model should classify the sample to y 1 or y 2 but cannot make a precision decision between the two classes. However, no existing DL can perform it. Additionally, conflict information exists if the sample includes two or more classes, such as a B-scan GPR image with two types of pavement distresses. However, traditional DL models ignore conflict information and make a arbitrary decision. This problem should not be neglectful in the applications of DL in GPR for civil engineering. As discussed in Section 4. 5. 1, the observations in a GPR dataset usually are concentrated on a small volume. Still, a DL architecture is expected to provide definite predictions for the entire space. For instance, some buried objects are made of different materials but have the same shape, which are difficult to distinguish from GPR images as their signatures look very similar.
The same object buried in different soils shows different signatures in a GPR image. In addition, some detection objects usually exist in the same area, which means an abnormal signal in A-scan data or a hyperbolic signature from a B-scan image may contain information representing more than one object. Therefore, the hubristic bias raises a problem of arbitrary decision-making. One approach to solve this problem is to fuse the data from different sources to make a decision. In the work of Sakaguchi et al. [122] , three strategies for fusing data from L-band and LIDAR GPR were proposed. The first strategy is data-level fusion, where the two types of data are stacked and used as input data, while the second one is also data-level fusion, which is realized by concatenating the side of the images by the side.
However, in the GPR detection for civil engineering, the frequencies of different buried objects are obviously different. For example, the number of cracks is much more than the number of uneven settlements in a pavement. Thus, p y a ð Þ are different from p y b ð Þ for y a and y b 2 y 1 ;. ; y k f g. Therefore, there are significant advantages to use p x j y j À Á to build a DL classifier for exploiting GPR data by taking p y j À Á into account and tieing the prediction of an observation x with the likelihood of observing that class. Tong et al. [126]  proposed a distance-based DL allowing for ambiguous rejection, called ConvNet-BF classifier or evidential DL. In the method, the distances between a pattern x and some prototypes are computed and used to build mass functions based on Dempster-Shafer theory.
Multi-class prediction is a assignment to a non-empty subset whose cardinality is larger than one. ConvNet-BF classifier can also indicate the uncertainty from GPR data (e. g. , the same object buried in different areas showing different signatures in GPR data) using its additional output mass functions m X ð Þ. The conflicts in GPR data can be characterized by two near values of output mass functions (e. g. , two different types of the buried objects with similar signatures from GPR data). The maximal conflict corresponds to m y i f g ð Þ¼m y j È É À Á ¼ 0:5. For complete introduction, readers are invited to refer to Denoeux's original work  [129]  and its extension to DL  [126]. However, little has been done to combine recent techniques of cautious and evidential DL with GPR data. It will be an important issue for the combination of GPR and DL for civil engineering.
Conclusions The progressive evolution of GPR techniques with desirable capabilities poses unique chances, as well as new challenges, to NDT for civil engineering. DL managed to revolutionize many classification and regression tasks achieving or even exceeding the human-level precision, and it currently began to be employed in the field of GPR. Even though GPR devices provide precise and stable representations of buried objects and backgrounds, its intricate data structure leads the exploitation using DL architectures not easy. In this survey, we divided DL architectures exploiting GPR data into three groups from the view of the scanning types of GPR. In general, the experiment results indicated a slight advantage of DL architectures exploiting A-scan data for the GPR detection in comparison to those using B-scan images.
The recent works managed to achieve promising performance utilizing Cscan data; however, more complex architectures or preprocessing procedures were required. The dependence of big data, a current research issue of combining DL and GPR for civil engineering detection, is currently attracting a lot of interest. There are three directions to reduce the dependence, transfer learning, semi-supervised learning based on hand-crafted representations, and enlargement a dataset using simulation data or data augmentation. However, the possibility of overfitting and low generalization are still not solved well owing to a small volume of the real observations in a GPR dataset. The optimal solution for the problem is to share the data from the GPR researchers in the world to build a benchmark GPR dataset.
Another current research problem is the arbitrary decisionmaking of DL models raised by its overconfidence in assigning a GPR sample to a definite class. Fusing data from different types of GPR devices, even other NDT techniques, is an effective solution. In addition, novel evidential DL has the generalized potential to solve the problem. From the view of the GPR detection for civil engineering, an evidential DL architecture can provide a multiclass and imprecise prediction when conflict and uncertainty exist in GPR data. However, little has been done to combine evidential DL techniques with GPR data. It will be an essential issue for the application of DL on GPR detection for civil engineering.
[96] CNN Normalized cross correlation Thresholding 75. 69 0. 6420 Besaw and Stimac [97] CNN Hand-crafted feature 76. 42 0. 6682 Gao et al. [107] Faster R-CNN Data augmentation 82. 57 0. 8491 Pham and Sébastien [102] Faster R-CNN Transfer learning GPR dataset from FDTD 80. 43 0. 8002 Xu et al. [100] Faster R-CNN Data augmentation Feature cascade 81. 62 0. 8134 Pau et al. [108] RNN Long short-term memory 81. 62 0. 8332 C-scan Tong et al. [37] CNN Feature point extraction Cascade connection 78. 43 - Kim et al.
The annual dissolved solid budget of the Ogooué basin is ~2. 52 t. yr -1 for a rate of 11. 7 t. km -2. yr -1. According to the source discrimination method performed based on the geochemical analysis, the atmospheric inputs contributes to around 20% to the TDS, the silicate weathering contribution dominates the dissolved exports throughout 70% of its production while the carbonates weathering lowly contributes to the TDS production. By comparison to the other large shields rivers, this basin exhibit a lower range of chemical silicate weathering rate than most of the world's large rivers, with values similar to those of the Congo River. This new dataset provides a key information to complete the World River chemistry database, which is limited for inter-tropical regions, especially in tectonically quiescent environments.
Acknowledgements The authors gratefully thank all following members of the GIHP and the GFHT for their help in defining these proposals. Members of the " Groupe d'intérêt en hémostase périopératoire " (GIHP):  P. Albaladejo  ( Anesthesia and Critical Care, Grenoble France ),  N. Blais  ( Hematology-Hemostasis, Montréal, Canada ),  F. Bonhomme  ( Anesthesia and Critical Care, Geneva, Switzerland ),  A. Borel-Derlon  ( Hematology-Hemostasis, Caen, France ),  A. Cohen  ( Cardiology, Paris, France ),  J. -P. Collet  ( Cardiology, Paris, France ),  E. de Maistre  ( Hematology-Hemostasis, Dijon, France ),  P. Fontana  ( Hematology-Hemostasis, Geneva, Switzerland ),  D. Garrigue Huet  ( Anesthesia and Critical Care, Lille, France ),  A. Godier  ( Anesthesia and Critical Care, Paris, France ),  Y.
Restricting Expression by Muscle and CNS-Specific Promoters Apart from capsid choice, a careful selection of the transgene regulatory elements, especially the promoter, is essential for specific expression. Viral promoters, such as the cytomegalovirus (CMV) or the Rous sarcoma virus (RSV), have generally been used for proofs of concept in early muscular gene therapy development, as they allow broad and powerful transgene expression  [139] [140] [141] [142]. The CMV promoter is currently used in several clinical trials for Duchenne and Becker muscular dystrophies, sporadic inclusion body myositis and Pompe disease (NCT02354781, NCT01519349, NCT00428935 and NCT00976352)  [143] [144] [145] [146] [147]. However, it is now known that eukaryotic cells progressively silence transgene expression driven by viral promoters as a result of an immune mechanism to shut off viral expression, limiting their use for gene therapy applications where the long-lasting expression of the transgene is crucial  [148] [149] [150] [151].
Transgene expression efficacy driven by the Des promoter was successfully demonstrated in 2014 in preclinical studies performed in murine and canine models of X-linked myotubular myopathy (XLMTM)  [163]  and has recently shown promising results in a clinical trial with meaningful improvements in neuromuscular and respiratory functions (NCT03199469)  [164]. Despite specific gene expression, muscle-specific promoters usually do not allow a high level of transgene expression in muscle cells and have a large size, limiting the packaging capacity for the transgene. Therefore, different laboratories have developed truncated muscle-specific promoters by selecting specific regulatory sequences to optimise both promoter strength and muscle-specific expression  [165] [166] [167] [168]. In 2008, Wang et al.
designed compact muscle-specific promoters by combining an 87 bp proximal basal MCK promoter with a double (dMCK) or a triple (tMCK) tandem of the modified MCK enhancer [169], leading to highly efficient shorter promoters of 509 bp and 720 bp, respectively  [170]. These two hybrid promoters demonstrate high transgene expression in skeletal muscles (except for the diaphragm), with no expression in the brain or liver. Interestingly, the dMCK and tMCK promoters are not active in the heart, which could be an advantage for the gene therapy of muscular diseases without cardiomyopathies. Following a successful proof of principle of gene transfer efficacy using tMCK in mouse models of Charcot-Marie-Tooth neuropathy type 1A  [171]  and limb girdle muscular dystrophy (LGMD) type 2D  [172] , this promoter has moved forward to clinical trials for these diseases (NCT03520751, NCT01976091 and NCT00494195)  [173, 174].
9-to 7. 9-month-old patients were treated intravenously with an rAAV9-hSMN product controlled by the hybrid CMV enhancer/chicken-β actin promoter (product referred to as AVXS-101), either with 6. 7 × 10 13 vg/kg (three patients) or 2 × 10 14 vg/kg (twelve patients) (AveXis/Novartis, NCT02122952)  [44]. To this day, all the patients are alive and show significant amelioration of motor, respiratory and nutritional functions  [44, 153]. The improvements are substantial as seen from a comparison with a natural history cohort  [154]. The effect is dose-dependent and related to the time of initiation: the earlier the injection, the more efficient the treatment  [156]. This treatment proved more efficient than Spinraza ®  [155]. It is also longer-lived and safer, as Spinraza ® necessitates constant re-administration by the risky intrathecal route  [199].
Based on these preclinical proofs of concept, three clinical trials using micro-dystrophin gene transfer have been in progress since 2017 (Pfizer [New York, NY, USA], NCT03362502; Sarepta Therapeutics: NCT03375164; Solid Biosciences [Cambridge, MA, USA], NCT03368742). The three trials use muscle-specific promoters, rAAV9 or rAAVrh74 serotypes, and high and comparable doses of vector (1 × 10 14 to 3 × 10 14 vg/kg) (see Table  3 ). AAVrh74 was chosen by one group (Sarepta's trial) because of its simian origin, which should decrease the likelihood of pre-existing immunity. Indeed, in a population of DMD patients, AAVrh74 sero-prevalence was shown to be low (measured in fewer than 20% of the patients tested)  [223] , and the average titres were also amongst the lowest  [224]. Quite surprisingly, this seems to be a specific feature of DMD, as higher titres of antibodies are measured in non-DMD children, possibly owing to the small size of the population or to a disease-specific effect on AAV biology  [224].
These encouraging results preclude dose escalation, and a new randomized, placebo-controlled clinical trial with a much larger sample size is under way (NCT03769116). However, at very close doses, two other products composed of an rAAV9, a muscle-specific promoter and a micro-dystrophin transgene led to product-related SAEs. In one trial (product PF-06930026, Pfizer), the six participants included to date have shown a mean of ≈40% dystrophin-positive fibres at 1 × 10 14 vg/kg and ≈70% at 3 × 10 14 vk/kg in a bicep biopsy taken two months after injection, corresponding altogether to ≈24 to 30% of normal dystrophin expression  [187]. The NSAA score increased by 4. 5 points after one year in two participants treated with the lowest dose. However, one child treated with 3 × 10 14 vg/kg developed a rapid antibody response with complement activation, acute kidney injury, haemolysis and thrombocytopenia.
The ASPIRO phase 1/2 trial aims at treating ventilatory-assisted patients aged less than 5 years with ascending doses (1 × 10 14 vg/kg or 3 × 10 14 vg/kg) of an rAAV2/8-Des-hMTM1 vector (AT132 product, Audentes Therapeutics [San Francisco, USA], NCT03199469). Until very recently, the results were strikingly positive. To date, twenty-three patients have been treated, six at 1 × 10 14 vg/kg and 17 at 3 × 10 14 vg/kg: the CHOP-INTEND (Children's Hospital Of Philadelphia INfant Test of Neuromuscular Disorders) has improved by various levels, the limb and trunk strength have increased, and new developmental skills have been achieved, such as controlling head movement, rolling over or sitting unassisted  [247] [248] [249]. Respiratory function has improved significantly resulting in patients being weaned off ventilators completely.
Detargeting with miRNA-Based Elements While regulatory elements such as introns, polyA signals or the Woodchuck hepatitis virus post-transcriptional regulatory element (WPRE) can be added to improve global transgene expression, miRNA-based sequences can mitigate tissue-specific transgene expression  [273]. MiRNAs are small (approximately 22-nucleotide-long) non-coding RNAs post-transcriptionally silencing gene expression in plants and animals. Once bound to complementary target sites (TS) in mRNA, they either reduce its stability or inhibit its translation, which results altogether in the reduction of protein expression  [274]. While the number of identified miRNAs has constantly increased since their discovery in Caenorhabditis elegans in 1993  [275] , the miRBase database reports 1917 annotated hairpin precursors and 2654 mature sequences in the human genome  [276].
Indeed, AAV therapy remains the main source of hope for patients affected by neuromuscular disorders, and there are many more diseases to treat. Importantly, our laboratory is planning a new rAAV-based clinical trial using micro-dystrophin transfer in DMD patients, in partnership with Sarepta  [314]. The baseline study, aiming at collecting data on the natural disease course in DMD male subjects aged from 5 to 9 years of age, is currently ongoing (GNT-014-MDYF, NCT03882827), and the interventional gene therapy trial should start in early 2021.
For clouds with α p > 10 -3 , the interactions among particles such as collisions or droplets coalescences become an important factor in the turbulence modulation. This is known as four-way coupling regime. Other researches  [24] [25] [26]  argued that even with a very small volume fraction, the dispersed phase can still influence the turbulence in the carrier phase. However, the volume fraction solely cannot clearly discern the attenuation or the enhancement of turbulence. Tanaka and Eaton  [21]  mapped out 30 experimental databases, from the open literature with different values of Re and S t (see Fig. 4 ). The particle momentum number, Pa St , was proposed in Ref. [21] , by solving dimensionless particle-laden Navier-Stokes equation  [27, 28] P a St = S t Re 2 ( η l t ) 3 = 1 54 R 2 e 2S t ( ρ p ρ f ) 3/2 ( d p l t ) 3.