dataesr/scientific-paragraphs-categorization huggingface dataset:
The proposed framework is able to learn discriminative features from RGB and DEM pairs. Despite the differences, our contrastive learning framework has demonstrated the feasibility of mapping positive data pairs into close feature embeddings for retrieval tasks. In general, the trained encoder learns to map the features correctly, and the similarity score at the target patch is notably higher than all other patches. Our experimental result indicates that (with other factors fixed) higher image resolution gives better accuracy. However, one thing we noticed during our experiments is that with limited computation resources, the lower resolution allows a larger dataset and batch size. To sum up, resolution and computation power is compensated when applying this method for image retrieval tasks.
We explore different data pre-processing methodologies and also fine-tuning of the hyperparameters. In our best scenario, we achieved 0. 71 for the Top-1 score and 0. 81 for the Top-5 score on the test dataset. With the heat map to visualize the similarity score between the target patch and the entire dataset, we see that the feature descriptors are discriminative and can efficiently retrieve the target patch. The proposed framework can take an arbitrary base encoder. Some future directions would be to change the backbone architecture, like a different version of ResNet. In our work, we tried only one strategy to concatenate the digital terrain models. Further analysis can be done to compare with other choices of the input data. The input data is not restricted to 2D imagery, as contrastive learning simply learns the embedding based on user-defined positive data pairs.
Data Fusion Contest (DFC) dataset. The DFC dataset (Xu et al. , 2019)  dataset by the IEEE Geoscience and Remote Sensing Society consists of multi-sensor data covering 4 km 2 of the University of Houston campus, which is divided into 14 image tiles, each accounting for 601 × 596 m 2. The predefined tiles are split into a training set (8 tiles), validation set (2 tiles), and testing set (4 tiles) as shown in Fig. 3. Modality creation. The original dataset is designed for multi-sensor fusion and land cover classification task. We make use of the paired RGB imagery and DSM data to train a crossmodal localization model. In the original dataset, four digital surface model products derived from LiDAR data are provided, namely (1) first surface model (DSM) generated from first returns, (2) bare-earth digital elevation model (DEM) from returns classified as ground, (3) bare-earth DEM with void filling for man-made structures, and (4) a hybrid ground and building DEM.
2018 Data Fusion Contest (DFC) dataset. The DFC dataset (Xu et al. , 2019)  dataset by the IEEE Geoscience and Remote Sensing Society consists of multi-sensor data covering 4 km 2 of the University of Houston campus, which is divided into 14 image tiles, each accounting for 601 × 596 m 2. The predefined tiles are split into a training set (8 tiles), validation set (2 tiles), and testing set (4 tiles) as shown in Fig. 3. Modality creation. The original dataset is designed for multi-sensor fusion and land cover classification task. We make use of the paired RGB imagery and DSM data to train a crossmodal localization model. In the original dataset, four digital surface model products derived from LiDAR data are provided, namely (1) first surface model (DSM) generated from first returns, (2) bare-earth digital elevation model (DEM) from returns classified as ground, (3) bare-earth DEM with void filling for man-made structures, and (4) a hybrid ground and building DEM.
Since the early 2000s, digital soil maps have been successfully used for various applications, including precision agriculture, environmental assessments and land use management. Globally, however, there are large disparities in the availability of soil data on which digital soil mapping (DSM) models can be fitted. Several studies attempted to transfer a DSM model fitted from an area with a well-developed soil database to map the soil in areas with low sampling density. This usually is a challenging task because two areas have hardly ever the same soil-forming factors in two different regions of the world. In this study, we aim to determine whether finding homosoils (i. e. , locations sharing similar soil-forming factors) can help transferring soil information by means of a DSM model extrapolation.
We hypothesize that within areas in the world considered as homosoils, one can leverage on areas with high sampling density and fit a DSM model, which can then be extrapolated geographically to an area with little or no data. We collected publicly available soil data for clay, silt, sand, organic carbon (OC), pH and total nitrogen (N) within our study area in Mali, West Africa and its homosoils. We fitted a regression tree model between the soil properties and environmental covariates of the homosoils, and applied this model to our study area in Mali. Several calibration and validation strategies were explored. We also compared our approach with existing maps made at a global and a continental scale. We concluded that geographic model extrapolation within homosoils was possible, but that model accuracy dramatically improved when local data were included in the calibration dataset.
This may be very useful for DSM purposes, because this information can be used to delineate areas from which DSM models can be fitted or areas within which they can be transferred geographically, under the assumption that the soilcovariate relationship is similar. In this study, we aim to determine whether finding homosoils can help the geographic extrapolation of a DSM model. We hypothesize that within areas in the world considered as homosoils, we can leverage on areas with high sampling density and fit DSM models, which can then be applied in an area with little or no data. The paper is organized as follows. First, we find homosoils of a study area and collect global soil data available within homosoils. Next, we fit a DSM model using these data, and transfer it to an area with little/no data.
Next, we applied principal components analysis to reduce the set of climate and biovegetal covariates and selected a number of components that explained at least 97% of the original dataset variance. The parent material covariate (lithology) was converted from categorical to continuous using non-metric multidimensional-scaling (nMDS). More information on nMDS and how it was used can be found in the Supplementary Material. Table  1  shows the final list of 18 covariates after dimension reduction.
| Collecting soil data Within homosoils, we collected soil data from the World Soil Information Services (WoSIS,  Batjes et al. , 2020). The soil depth intervals were harmonized using a mass preserving soil-depth function spline  (Bishop et al. , 1999) , to the standard GlobalSoilMap specification depth intervals 0-5, 5-15, 15-30, 30-60, 60-100 and 100-200 cm. The soil properties collected are clay, silt and sand (%), organic carbon (OC, g/kg), pH (H 2 0) and total nitrogen (Total N, g/kg). The clay, silt and sand values were harmonized so that their sum equals 100%. These soil properties were selected primarily for their importance for crop growth and availability in the WoSIS dataset.
The estimates from these individual models are then averaged to generate the final prediction. On the other hand, neighbour defines the number of neighbouring points from the training set and is used to adjust predictions when predicting new samples. Model trees like cubist are advantageous in that the logical construct of the model rules require little data pre-processing and can handle nonlinear relationships between the explanatory and the response variables  (Kuhn and Johnson, 2013). Cubist models were calibrated for each soil property and horizon depth interval using two calibration strategies. In the first calibration strategy, models were calibrated using WoSIS soil data within homosoils excluding those of the study area. Models were then geographically extrapolated to predict the soil property within the study area.
In the second calibration strategy, models were calibrated using the WoSIS soil data collected for both within homosoils and within the study area, and then used to predict the soil property in the study area. The objective of the first strategy is to evaluate the prediction accuracy when the model (built within homosoils only) is extrapolated geographically, while the second strategy evaluates the prediction accuracy of the model when the training dataset include both the data collected within homosoils and within the study area. Maps of soil properties at 1 km Â 1 km resolution were generated for each depth interval. The map predictions of clay, silt and sand at any prediction location were normalized to satisfy the condition that their sum should be equal to 100%. The cubist models were calibrated with 10 committees for the two calibration strategies, using 0 and 9 neighbours for the prediction of the first and second calibration strategies, respectively.
| Map validation The maps generated from the two models were validated using the following three approaches: the first approach was used to validate the maps from the first calibration strategy. This approach is the most common method for map validation. The second and third approaches were used to validate maps from the second calibration strategy. When we used the first calibration strategy, we validated the predictions using the WoSIS data for the study area (within Mali). Recall that in this calibration strategy, the WoSIS data within the study area were not used for model calibration. Predictions made at validation locations were compared to the measured values with statistical indices. The second approach for validation made use of models calibrated with the second calibration strategy.
Predictions were obtained through a 10-fold crossvalidation of the WoSIS data within the study area, and compared to the measured values of the soil property using statistical indices. Finally, maps, from models calibrated with the second strategy, are validated using an independent dataset obtained from multiple sources (i. e. ,  Doumbia et al. , 2009; Benjaminsen et al. , 2010; Verbree et al. , 2015; Degerickx et al. , 2016; Falconnier et al. , 2016; Bayala et al. , 2020; Birhanu et al. , 2020; Huet et al. , 2020). The units of the soil properties in this dataset were harmonized to that of the WoSIS dataset. Their depth intervals were also standardized. However, because most of the sources measured soil properties at 0-15 cm depth interval, the values of the 0-5 and 5-15 cm samples were combined to 0-15 cm where appropriate, and only this dataset was used for validation.
| Map comparison with existing products The maps generated using the second calibration strategy were further compared against existing digital soil maps produced at continental (i. e. , iSDAsoil,  Hengl et al. , 2021)  and at global (i. e. , SoilGrids,  Hengl et al. , 2017a)  extents. These maps were also generated using the WoSIS database. We compared them using two approaches: first, we carried a visual comparison to evaluate the predicted spatial pattern of the soil property at 0-15 cm soil depth interval. Second, we validated the maps using the third validation approach on an independent dataset and compared the resulting validation statistics. Because of the computational demand to carry the third validation strategy, the global and continental maps were brought to a common resolution of 1 km Â 1 km.
The Pearson's r correlation values of the second validation approach were on average 190% higher than those of the first validation approach, indicating a better linear relationship between the measured and predicted values by the model. Finally, the MEC of the first validation strategy was nearly always negative for all soil properties and all horizon depth intervals. This suggests that the models from the first calibration strategy performed poorly, and that using the mean of the measured values as an estimate would be a better predictor than the predictions from the models from the first calibration strategy. Models from the second calibration strategy, conversely, had positive MEC values (on average 160% larger than that from the first calibration strategy). Thus, Figure  5  shows that the performance of an extrapolated model calibrated on homosoils values of soil properties dramatically changed whether or not it included training dataset from the area of study within Mali.
, 2010). For instance, the WoSIS soil data gathered for Burkina Faso were collected between 1966 and 2000. The low sampling density, as found in India, reflects the difficulty in accessing country data  (Reddy et al. , 2021). Overall, it was possible to find some areas in the world that share similar soil-forming factors as our area of study, which suggest they might also have similar soils, and these areas also have a relatively high sampling density. The soil data collected within the homosoils and the study area showed a similar vertical pattern with different variability (Figure  4 ) for most soil properties. The average pH content, for example, was generally constant across soil depth intervals, but higher in homosoils compared to our study area. This is expected because of the dominating climate regime (semi-arid to arid) covering the homosoils.
| Validation with clustered data We acknowledge that the comparison of the maps (homosoils, SoilGrids, iSDAsoils) was made using an independent and spatially clustered sample, which may result in biased estimates of the map accuracy. Clustered data in the geographic space often lead to a clustering in the covariate space  (Elliott and Valliant, 2017)  and failure in assessing map accuracy in areas with zero sampling density, thus leading to over-optimistic map accuracy estimates. However, unbiased estimates of digital soil maps accuracy can only be obtained through the collection of a post-mapping independent soil dataset with probability sampling and design-based statistical inference methods  (Brus et al. , 2011). Due to the pandemic, we could not collect an additional probability sampling for map validation and therefore used a modelbased validation approach using a geostatistical model, where the map residuals were kriged with ordinary kriging and the sampling distribution of the map accuracy indices was computed using 500 simulations of the residuals.
| Limitations One major limitation of this study is the inaccuracy of the covariate that describes the state factor lithology. The global lithology dataset  (Hartmann and Moosdorf, 2012)  is described with multiple orders: first, second and third lithology orders. The highest order, wherever present, provides further granular differentiation within a given first-order lithological unit. This surely has had an effect on the soil classes and properties due to the different mineral content and morphology (particularly texture) that it represents. In our study, we only used lithological information of the first order because higher orders were not publicly available. The effect of lithology is reflected in the statistical indices (RMSE, r and MEC) presented in Figure  5 , where those of the texture maps (i.
ACKNOWLEDGEMENTS This research and salary support of the ICRISAT authors were in part funded by the  CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)  carried out with support from the  CGIAR Trust Fund and  through bilateral funding agreements. For details, visit https://ccafs. cgiar. org/donors. The  World Bankfunded AICCRA project (Accelerating Impacts of CGIAR Climate Research for Africa)  Project  ID 173398  is acknowledged for funding the involvement of Anthony Whitbread in this study. Open access publishing facilitated by  The University of Sydney , as part of the  Wiley -The University of Sydney agreement via the Council of Australian University Librarians. DATA AVAILABILITY STATEMENT Public data are available on request from the authors.
Data analysis: In this study, 9 substrates and control were studied. Each treatment was repeated 4 times. The recording of the results and the graphs were made with Excel software (version 2016). The statistical analysis of the results of this study was carried out using MINITAB software (version 2016) for the analysis of variance (ANOVA), using the general linear model procedure (α =0. 05). The Tuckey test was used to compare the means between different treatments (significance level P ≤ 0. 05). 3 Results.
Before the deeplearning era, hand-crafted features such as DCT, steerable pyramids, etc. , were carried out to then build a perceptual model. With the advances of deep learning, automated features extraction has been considered, obtaining reliable semantic features from deep architectures (e. g. , CNNs) achieving remarkable efficiency at capturing perceptual image artifacts  [153] [154] [155] [156]. This however requires a large-sized labeled dataset, which is expensive (and not always feasible) to collect. Transfer learning has been adopted to learn feature extraction models from well-known image datasets (such as Imagenet) and then fine-tune the current model on subjectively tested dataset  [157]. As an alternative, the recent ML advances on contrastive learning  [158]  and self-supervised learning  [159]  might have opened the gate to self-supervised quality assessment.
) and inter-session relationships (e. g. , variation in the inter-session times of the viewer and their hourly/daily/weekly viewing times). Crosscorrelating actions and change of habits among different viewers is also effective in gaining significant insight into human satisfaction of the streaming service, which is still a measure of QoE. Machine learning can indeed help toward this goal. Another important challenge to mention is the bias that comes in training ML model (to extract and fuse features) via subjective tests. As pointed out in recent and very interesting panels  [165]  and works  [166] , the impact of bias and diversity in the training dataset is a key challenge in machine learning and it can therefore have an impact on QoE-leading to a model that fits mostly part of the population but ignores minorities.
The latter ones are limited to historical data trajectory for the viewport prediction. Conversely, multi-modal input prediction can be considered in the case in which the input of the neural network is not only the user head position but also video content features, motion information, object tracking  [198]  as well as attention models  [199]. Interestingly, recent works  [195, 200, 201]  have shown that multi-modal learning systems, which are usually composed of neural networks and, therefore, require heavy computation and large datasets, are not necessarily achieving the best performance. Transformers have been used in  [195]  in which the deep learning framework is trained with the past viewport scanpath only, and yet it achieves a state-of-the-art performance.
Feed values of individual ingredients were calculated according to INRA (2018) using the Prevalim Ò module of the Inration V5 Ò software. Nutritive values of experimental diets were estimated using the Inration V5 Ò software (https://app. inration-ruminal. fr/) from the ingredient and chemical compositions of diets, and the digestive interactions which depend on the feeding level (DMI in % of BW), the concentrate dietary proportion and the estimated rumen protein balance  (INRA, 2018).
2 N retained was calculated as the estimated protein gain divided by the length from the start to the end of the experimental period (196d). Archibeque, 1999) or both. Improved N gain at the expense of urinary N loss was also measured in efficient RFI bulls by  Guarnido-Lopez et al. , (2021)  using the natural 15 N abundance as an NUE biomarker and a larger experimental set-up (n = 600, including the present bulls). By contrast, NUE calculated from the N balance measurements did not support a greater NUE in efficient RFI animals despite a measurement period longer than the minimum recommended duration (10 d vs 7 d;  Firkins and Reynolds, 2005)  and a moderate to high repeatability (r = 0. 77; data not shown) of intake between the first RFI test (Test A; 0-84 d) and N balance trial  (Test B; (99) (100) (101) (102) (103) (104) (105) (106) (107) (108) (109) (110) (111) (112) (113) (114).
Our results agree with previous studies using large datasets already showing that dressing percentage is phenotypically and genetically higher in efficient vs non-efficient RFI Charolais young bulls  (Taussat et al. , 2019). In addition, differences in the non-carcass part were only due to smaller rumen-reticulum and smaller heads of efficient RFI individuals because of similar amounts of digestive contents between RFI groups (Supplementary Table  S3 ), confirming results from  Kenny et al. (2018)  on visceral organs (digestive tract mainly). This point is important because rumen size is known to vary with the feeding level  (Basarab et al. , 2003). Therefore, current results suggest that the higher intake of inefficient RFI animals may result in bigger rumens and thus in lower dressing percentage.
Acknowledgements We wish to thank  APISGENE  for their financial support of this project, which forms part of the larger national program BEEFALIM 2020. Thanks to  Marine Gauthier  (UMRH),  Jeremie David  (UNH) and  Pascal Faure ,  Patrice Mandon ,  Sébastien Alcouffe  and  Dominique Chassaignes  ( Herbipôle ) for their great technical support. This study was part of the PhD thesis of the first author.
Study selection and search strategy This meta-analysis was implemented by the PRISMA guidelines (the preferred reporting item for systematic reviews and metaanalysis)(8). the ID is CRD42021248704. As of March 1, 2021, we searched various electronic databases, including the Cochrane Library, PubMed, and Embase. Each database was independently searched by two reviewers (Qifan. Y and Jing. L) using the ). The senior author will resolve any disagreements (Lei. T). We looked at the title and abstract of each article we found and then read the complete text of the studies that ful lled the criteria. Also, I looked through the reference list of the included literature to see whether there was any research that met the criteria. There are no limitations on the location of study, the type of research, or the status of publications.
Studies characterization Table  1  illustrates the general information about the studies that were included. All of the randomized controlled trials were published between 1994 and 2021, with a one minimum follow-up time. A total of 426 patients with CLAI were included in the study, with 222 receiving Other operations treatment and 204 receiving MBG treatment. The average age of the two groups was not signi cantly different (P > 0. 05). Figure  2  summarizes the ndings of the quality assessment. The FAOS pain score with SD was studied in four trials  (4, 10, 13, 14) , totaling 204 participants. The results showed that there was no considerable disparity (P= 0. 23, I 2 = 23%). Hence the data was analyzed using the random-effects model. There was a signi cant difference in pain ratings between the Other operations and MBG groups (MD= 4, 95% CI: 1.
Sport Data from four studies(4, 10, 13, 14) are provided for the FAOS sports scores. Because of the considerable heterogeneity (P 0. 00001, I 2 = 92 %), we applied a random-effects model to examine the data and discovered that there was a statistical difference between the Other operations and MBG groups (MD= 10. 80, 95%CI: 6. 40-15. 20, P 0. 00001). For various periods, a subgroup assessment was conducted. A total of 88 patients in two investigations(4, 14) reported FAOS exercise scores after a year of followup, with no signi cant heterogeneity in the results (P=1. 00, I 2 =0%). The data were analyzed using a random-effects model, which indicated a signi cant variation in Sports ratings between the Other operations and MBG groups (MD= -12. 10, 95% CI: 10. 00-14. 20, P 0. 00001). The follow-up period was two years, with three items.