dataesr/scientific-paragraphs-categorization huggingface dataset:
Data for these indicators can be provided through global, national and regional data providers. Given that part of such data is provided by Eurostat, we would like to have a view of the relationship between the selected UN SDG indicators with the scientific community, policy makers and educational organizations. Following, we refer to a set of limitations that exist in the release of the SustainGraph, as it is detailed in this manuscript, along with suggested ways to overcome such limitations in the future. The existing user interface for submitting queries to the SustainGraph is mostly targeted to data scientists and application developers that have some experience on preparing questions in the form of descriptive queries (e. g. , by using the Cypher language supported by Neo4j). Upon submission of a query, access to the results is provided in a visual way as well as in formats that can be easily used as input for further analysis (e.
g. , tabular data). There is a need to develop user-friendly interfaces to make the SustainGraph easily adoptable by end users without expertise in computer science. To achieve this, a set of visualization tools are considered that simplify the interaction with the SustainGraph, while work is in progress towards the development of user-friendly querying interfaces for submission of questions by end users. Another crucial need has to do with the development of mature solutions to easily populate the SustainGraph with data. As already mentioned, data quality issues along with the need for harmonization of the provided datasets by different data providers hinder the ease adoption and usage of such data. A set of data population mechanisms are already into place to support the population of the SustainGraph with qualitative data, however, further mechanisms have to be developed in the future, considering the volatility of the structure of the produced data and the inclusion of further data sources (e.
g. , data coming from initiatives related to the development of sustainable cities, data associated with the classification of activities according to the EU Taxonomy). Periodical review of the applicable data protection regulation has to be considered to ensure data management in accordance with the legislative framework. Changes in the definition of indicators per SDG have to be considered, as it may happen in the case of the EU SDG indicator set that is under an annual review process. The flexibility provided in the SustainGraph to appropriately extend the data representation schema can help us to incorporate such changes. Semantic alignment of the introduced data is also crucial to support interoperability with existing semantically aware data as well as enable reasoning functionalities over the populated KG.
However, this specialization should not be considered restrictive for the tracking of the status of the SDG indicators and targets and the support of socio-environmental analyses for other countries or regions across the globe. Data series for the UN SDG and third-party indicators are collected for all the countries, while part of the considered policy frameworks have also a global range (e. g. , Nationally Determined Contributions based on the Paris Agreement, six SDG Transformations). Furthermore, openness and extensibility are two of the core principles considered by design for the SustainGraph. Further policy frameworks that are applicable in different areas can be introduced and interlinked with the existing concepts in the SustainGraph. Importance has also to be given on the effortless support of participatory modeling approaches throughout the SustainGraph, taking advantage of the harmonization of the represented concepts and the provision of access to data that are accompanied by their meaning and can be understood by end users.
Instead, the inferred NICER phase-averaged source spectrum was compared a posteriori to a phase-averaged source spectrum derived independently from XMM-Newton observations (for which the background is empirically determined and better constrained from the observations, because XMM-Newton focuses photons and images the region of interest). These results indicated that the NICER PPM analysis might be attributing slightly too much of the unpulsed component to the non-source background (see Figure  15  of  Riley et al. 2019; and Figure 12 of Miller et al. 2021a ). In the 2021 analysis of PSR J0740+6620, a different approach was taken: we performed simultaneous inference of both the phase-resolved NICER data set and a smaller phaseaveraged XMM-Newton data set, using a blank-sky estimate of the astrophysical background as an additional constraint on the XMM-Newton fit.
Modeling Procedure The modeling procedure is largely shared with that of  Riley et al. (2019 ), Bogdanov et al. (2019 ), R21, and Bogdanov et al. (2021). In this section, we summarize that methodology and report the new model components in more detail. For all posterior computations, we use the X-ray Pulse Simulation and Inference (X-PSI) code, version v0. 7. 10 (Riley et al. 2022), which belongs to the same v0. 7 framework as used by R21. 19 he analysis files may be found in the persistent repository on Zenodo 20  including the data products, the numeric model files, scripts, and Jupyter notebooks in the Python language to produce the results and figures of this article using the X-PSI framework, and posterior sample files.
Each line is an averaged shifted histogram (e. g. ,  Scott 1985)  of the posterior samples from the converged portion of an MCMC analysis. Blue solid lines illustrate the results from  Miller et al. (2021a) , which analyzed the W21 data set. Green dashed lines illustrate the results from analyses that used the 3C50-selected NICER data, but did not incorporate the 3C50 background estimate as a lower limit; and orange dashed-dotted lines illustrate results from analyses, which used the 3C50-selected NICER data set and incorporated the 3C50 background estimate, with uncertainties, as a lower limit on the NICER background. The top panel includes results from analyses of NICER data alone, while the bottom panel includes results derived using both NICER and XMM data. Notes. We also show the log-evidences   ln a to compare the model performance between ST-U, ST-Ua, and ST-S.
Arithmetical Voting In automatic as well as hand-made segmentation, one often creates not just one, but many segmentors S 1 ,. , S n for the same problem, using different CNNs, or different training datasets, or different data augmentation methods, etc. , and then ensembles them by a voting method to hopefully obtain a segmentor which is more accurate on the average than each one of them. The most obvious voting method is the majority vote: for each pixel, each segmentor has one vote, and the candidate value (0 or 1) that has the most votes wins. This majority voting method is also called the hard arithmetical voting, and there is another variant of arithmetical voting called the soft voting, see, e. g. ,  [4, 5]. In soft voting, one uses continuously-valued segmentors S1 ,. , Sn instead of discrete-valued segmentors S 1 ,.
Salt Segmentation in Seismic Images The automatic segmentation of salt deposits in seismic images is an important problem for geology companies in search of hydrocarbons. In 2018, Kaggle organized the "TGS Salt Identification Challenge" on this problem, and provided a training dataset of 4000 annotated grayscale images of size 101 × 101  [13]. Our aim here is not to build a "state-of-the-art" automatic segmentor for the Kaggle challenge (in real life, one will not cut the seismic images into very small pieces and compute the accuracy scores the way Kaggle did anyway), but just to do experiments on the topological voting method. For that purpose, we will build our AI models based on a popular light-weight convolutional neural network architecture called MobileNet  [14] , which is very handy in the sense that one can train it very fast, and it can run on small devices, such as mobile phones.
Some concrete examples of the masks given by our five individual segmentors, and the results of two different voting methods (arithmetical and topological) and shown in Figures  3 4 5 6 , together with the original images and the true masks (the ground truth given in the dataset). These four figures illustrate how the topological voting method works differently from the arithmetical one. We can see in these figures that the arithmetical chooses the one that equals to average of all mask pixel-wise regardless the structure of each individual mask, consequently, it causes a final voting result which is unstructured regarding that the region of salt is often smooth and continuous. Meanwhile, the topological chooses the mask which is the most common to the others in the meaning that it has the smallest "distance" to the other masks and it is thus reasonable close to the true mask.
Each input is an image taken randomly from the training set and then undergoing random transformations (augmentations in data pre-processing). We do not take the AI model after exact 500 epochs of training, but rather the AI model after the epoch that offers the highest Jaccard scores on validation among all the 500 epochs. After the above training process, we obtain 5 AI segmentors, corresponding to our 5 folds. Then, for each image in the test set, we vote on the 5 masks given by these 5 segmentors, using arithmetical voting and the (soft and hard) topological voting methods. The results are shown in Table  1. Here, the Jaccard score is the means Jaccard score over the test set. Some concrete examples of the masks given by our five individual segmentors, and the results of two different voting methods (arithmetical and topological) and shown in Figures  3 4 5 6 , together with the original images and the true masks (the ground truth given in the dataset).
Human Face Segmentation in Photos The Face and Skin Detection Database which contains 4000 images is created by S. L. Phung, T. Y. Ke, and F. H. C. Tivive and used in  [17]  to support research on skin segmentation and face recognition. The dataset provides many types of ground-truth, such as human face segmentation and skin segmentation. Here, we will use the human face segmentation of this dataset. Actually, the masks that we will use are not exactly the human faces, but the smallest rectangles containing them. To make the problem more interesting, we will partially hide the human faces on the photos by random rectangles, and let the machine learn to segment the full human faces despite those hidden parts. Figure  7e  shows the original image in which the faces are covered by a random rectangle and Figure  7f  (the rightmost image) shows the mask of that image.
The dataset of 4000 photos is divided into 2 subsets: 1000 images for testing, and 3000 images for training. The 3000 training images are divided into 10 folds, each fold contains 600 images, so the folds overlap: the first 5 folds are a partition of the training data, and the second 5 folds are another partition as well. The original photos in the dataset are of different sizes, but we will augment and resize them into images of size 256 × 256 before feeding them into our CNN models. The transformations that we use to augment the images are the standard ones: random rotation, flipping, brightness modification, cropping, resizing, padding, and noise adding. As mentioned above, we also add a random back rectangle to each image to partially cover the faces (without changing the masks). We use two different CNN architectures for our experiment: the light-weight Mo-bileNet  [14] , and the more heavy-weight EfficientNet B4  [18] , so in total, we obtain 20 different AI segmentors for our human face segmentation problem.
Blood Vessel Segmentation The problem of segmentation of tree-like structures, such as microglia extensions, neurovascular structures, blood vessels, and pulmonary trees, are of great interest in medical AI, see, e. g. ,  [19]  and references therein. Rouchdy and Cohen  [19]  studied the problem of segmentation of blood vessels in retinal images using a method called geodesic voting with radius (no deep learning), and showed the superiority of their method to older approaches such as the edge-based level set method  [20] , the Chan and Vese method  [21] , and the fuzzy connectedness method  [22]. The database that they used is the digital retinal images for vessel extraction (DRIVE) data  [23]. In this section, we propose to use deep learning to solve the retinal blood vessel segmentation problem using this same DRIVE dataset.
Again, we will use the light-weight MobileNet  [14]. Not surprisingly, the deep neural networks can give better segmentation results than the previous image processing methods, including the geodesic voting method  [19]. The DRIVE dataset contains 20 images of size 565 × 584. We first divide it into two sets: 15 images for training and 5 images for testing. We create 15 folds for the training and the cross-validation of our AI using the 15 training images. Each fold leads to one segmentor, so in total we have 15 individual segmentors to vote on. Each fold uses 13 images for training and 2 images for validation. Each image is then transformed (augmented) using a combination of operations, including random rotating, flipping, padding, brightness modification, noise adding, cropping, and padding, into square images of size 256 × 256, before being fed into our CNN based on MobileNet for training and validation.
Methionine (Met)   is the first limiting amino acid in corn and soybean meal-based diets (containing L-Met) in broiler chickens, which are often supplemented with synthetic DL-Met or DL-Hydroxy Met (OH-Met). Our objective was to quantitatively assess the efficacy of synthetic Met sources and determine differences in growth rate of broilers fed at or below requirements in response to Met intake. A systematic literature search resulted in building a database containing 480 treatment means from 39 articles published between 1985 and 2019 globally. The database was divided into starter, grower, and finisher subsets based on the age of the broilers. For each subset, linear-plateau and quadratic-plateau models were fitted to determine Met or sulfur amino acid (SAA; Met + Cysteine) requirements using average daily gain as a response variable.
, 2006; Sauer et al. , 2008). A meta-analysis that compared Met sources (DL-Met vs. Met-hydroxyanalogue-free-acid) was conducted by  Sauer et al. (2008)  but only included studies published until 2006. A new meta-analysis based on recent empirical studies in the literature comparing DL-Met and OH-Met is lacking. Furthermore, development in statistical methods and software (packages such as WinBUGS and Stan) has enabled fitting of complex models using Bayesian approach, which can take into account the multiple variances in a single model simultaneously  (Sorensen et al. , 2016). Therefore, fitting model using Bayesian approach accounting for multiple variances instead of using frequentist approach such as nonlinear regression  (Sauer et al. , 2008)  or multi-exponential regression  (Hoehler et al.
Search Strategy and Inclusion Criteria A literature search was performed on pre-defined scientific databases including Web of Knowledge, Scopus, and PubMed. The search was performed in each database, using the keywords "methionine OR hydroxymethionine AND broiler". To maximize the search completeness, a new search was performed with a larger number of keywords. The new search was performed in each base plus SciELO, using the keywords: "hydroxy analog OR OH-Met OR methionine hydroxy analog OR 2-hydroxy-4-methylthio-butanoic acid AND broiler OR chickens* OR poultry". A total of 3,279 publications were identified through the literature search. In the next step, after removing duplicate papers, the results found in each database were exported to an excel file and the title and abstract of the studies were examined to exclude irrelevant articles (Figure  1 ).
Database The database included 480 records of treatments means from 39 studies conducted from 1985 to 2019 by research entities from Asia: China (n = 132 from 6 studies), South Korea (n = 14 from 1 study), Thailand (n = 15 from 2 studies), Iran (n = 8 from 1 study), India (n = 6 from 1 study) and Turkey (n = 5 from 1 study); Australia (n = 5 from 1 study); Europe: Germany (n = 58 from 5 studies) the Netherlands (n = 45 from 2 studies), Poland (n = 9 from 1 study), Spain (n = 9 from 1 study), France (n = 4 from 1 study); North America: United States (n = 88 from 6 studies), Canada (n = 7 from 1 study), and Mexico (n = 8 from 1 study); and South America: Brazil (n = 55 from 8 studies). The list of articles and reports included in the database are given in the supplementary material. Studies included in this database used broilers from different strains such as Cobb, Ross, Arbor Acres, Indian River, and Shaver Starbro.
The database also contained information about the trial (authors, journal, year), experimental design (number of repetitions per treatment, number of animals per repetition, weight range of the broilers), ingredient composition, and nutritional values of the experimental diets and performance information such as ADG, ADFI, and FCR. Some studies did not report dietary nutrient composition. Thus, the nutrient composition of all diets was recalculated using the Practical Program for Formulation of Rations  (PPFR, Garcia-Neto, 2008)  to get a complete and consistent (i. e. , calculated with similar method) dietary nutrient composition. All energy and nutrients available in the software were considered including AME, CP, amino acids, and some minerals. Additionally, the daily nutrient intake was estimated by multiplying the daily feed intake with respective dietary nutrient concentration.
Based on the age of the birds, the broiler database was subsetted into starter (≤21 d of age at the end of the experiment and ≤16 d as average experimental age), grower (<21 and >21 days of age at the start and end of the experiment respectively, and >16 d of average age) and finisher (≥ 21 d of age at the start of the experiment) phases. The relationships between predetermined variables (e. g. , ADG vs. animal age, ADG vs. feed intake, ADG vs. Met dose, ADG vs. Met intake) were visually assessed using scatter plots for each growth phase. This visual exploration resulted in the identification of outliers and next in the removal of the records that belonged to 4 different studies.
In other words, basal digestible Met intake was subtracted from treatment digestible Met intake within study, which then resulted in zero synthetic Met intake for all basal treatments in the database. Furthermore, the growth functions had no plateaus and represented a linear model (Equation  6. 1): f u i ; x ij À Á ¼ a i þ b i ¢ x ij ; ð6: 1Þ where, the intercept parameter a i represents the average daily gain at zero supplementation of synthetic Met source, and b i is the slope of the function. The k parameter of the quadratic function was assigned the maximum values of x ij of the specific subset of a subset used for model fitting, which resulted in the top of the parabolic function at the maximum value of x ij. Priors used for the linear model was as described for the linear-plateau model (Equation  5 ).
Dietary Information and Performance Data of the Broiler Database Growth performance and nutrient composition of broiler diets grouped into starter, grower, and finisher are shown in Table  1. As birds grew older, ADG, ADFI and FCR of birds also increased. With increasing age of the birds, dietary energy concentrations increased as typically observed for broiler diets. As expected, the starter phase had greater CP concentrations compared to grower and finisher phases. Similarly, total and digestible Lys and concentrations of other amino acids in the diets decreased with increasing age of the birds. Digestible Met or digestible SAA intake was approximately 3 times the starting value for grower compared to starter and 4 times the starting value for finisher compared to starter phase. Average synthetic Met dose in the diet was similar between grower and finisher phases but slightly greater for starter phase compared to the other two phases.
Assessing Effect of Synthetic Methionine Sources The models for predicting ADG against synthetic Met intake (excluding Met from basal diets) for different phases are shown in Table  4. As stated earlier, linear models were fitted for 4 different datasets of each broiler Table  2. Linear-and quadratic-plateau model parameters 1  and leave-one-out information criterion (LOOIC) that were fitted to the three subsets of data using digestible methionine intake as independent variable. Note that the models that were fitted did not account for the type of Met. 4  and 5 ), the 2 synthetic Met sources did not differ in their performance to fit the broilers' growth response data. Across broiler phases, no Met effect difference was observed between DL-Met and OH-Met.
DISCUSSION Relative efficacy of 2 most common forms of Met (DL-Met vs. OH-Met) has been well-studied and reported performance differences under certain environment and management conditions. Individual studies demonstrated that one form is favored over another under certain conditions, which has created controversy and confusion. Thus, the main goal of this study was to compile up-to-date data from the literature and compare the efficacy of 2 sources of Met using a robust Bayesian meta-analysis approach. We first divided the dataset into starter, grower and finisher age group and then determined Met requirement using either linear or quadratic-plateau models. Most importantly, our final model included the observations below the Met requirements determined using either linear or quadratic-plateau models where previous meta-analysis included all observations within certain dose limits of Met  (Sauer et al.
Bayesian Modeling Approach Recent improvement in computational statistics such as development of programming languages (e. g. , WinBUGS and Stan) enables fitting complex models in Bayesian setting  (Stan Development Team, 2016). In the current meta-analysis, Bayesian modeling approach was selected because: 1) pre-existing knowledge could be incorporated as prior information in Bayesian model, which helps in estimating the requirement parameter k by restricted it to the range of the independent variable, viz. digestible Met and SSA intake and 2) the Bayesian approach facilitates fitting of complex models with large number of random variances  (Sorensen et al. , 2016). Additionally, Bayesian approach Table  3. Linear-and quadratic-plateau model parameters 1 and leave-one-out information criterion (LOOIC) that were fitted to the three subsets of data using digestible sulfur amino acid intake as independent variable.
5 k LP,SAA : Sub setting was done using linear-plateau model using sulfur amino acid as independent variable. 6 Cells are left blank when parameters were not estimable. provides reliable estimate of the variance components with relatively small dataset which is often not possible using non-Bayesian approach  (Bates et al. , 2015). Meta-analysis often warrants powerful statistical approach because meta-analysis using frequentist approach for non-linear regression  (Sauer et al. , 2008)  or multi-exponential regression  (Hoehler et al. , 2005)  models might face difficulty to converge due to lack of suitable starting values for the parameters. In that sense, Bayesian approach does not require any starting values for model convergence. In Bayesian modeling, posterior distribution is influenced by the prior information and data.