dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion In order to test the models discussed above, the experimental data of Wu et al. [32]  have been selected. The authors provided experimental results of pure hydrogen, nitrogen, and methane gas adsorption on JX101 activated carbons for temperatures ranging from 283 to 313 K. The adsorption equilibrium data of the ternary mixture (CH 4 , N 2 , H 2 ) have been driven up to moderate pressure. Because the models used are purely predictive, the previously obtained results for the adsorption equilibrium of pure gases are needed. The numerical experiments have been carried out in the programming environment MATLAB, which develops the relevant file functions and software communication links for the mathematical models used. The accurate calculation of the thermodynamic properties of gases (compressibility factor, density, and fugacity) determines the accuracy of further calculations.
In a previous work  [33] , different equations of state (EOS) have been tested for their ability to describe the real behaviour of hydrogen gas; the results of these equations were compared to the experimental results obtained from the Universal Gas Encyclopaedia and to those obtained from the NIST database  [34]. From this previous study, it was demonstrated that the SBWR EOS model can be chosen for calculating the gas compressibility factor, the density, and the fugacity. In the last study  [11] , different models for the calculation of pseudovapour pressures and molar volumes of the adsorbed phase have been compared. A critical discussion of their accuracy and precision has been made; as a result, the use of the models of Do  [28]  and Dubinin  [35]  have been recommended. In the same study, eight models for the single-component adsorption have been fitted to the experimental isotherms of pure CH 4 , N 2 , and H 2 by nonlinear regression; in the case of pure-gas adsorption, the adsorbateadsorbent interactions are more significant than the adsorbate-adsorbate interactions.
Methods The larger qualitative study from which the data for this article were drawn aims to understand the HIV risk faced by immigrants from the PRC to Canada in the contexts of international migration and transnationalism (see  Zhou et al. , 2017  for more details). The data reported here were collected through individual, semi-structured, in-depth interviews with eight Chinese immigrant women who self-identified their sexual orientation as "heterosexual," their relationship status as "(legally) separated or divorced", and who were willing to talk about the sexual health (including HIV) risk faced by themselves and/or by Chinese immigrants to Canada. Using purposive sampling, these women were recruited through the Internet (e. g. recruitment notices posted on Chinese-language websites popular among immigrants from the PRC), Chinese community networks (including settlement services), and personal referrals in  Toronto (4/8)  and Vancouver (4/8 ).
To ensure the data's accessibility to all researchers in the project, interviews were transcribed into English by an experienced translator who is highly proficient in spoken and written Chinese and English. All transcripts were imported into NVivo (9. 2), a computer software program for qualitative data analysis. Initially, a tentative coding scheme, consisting of a set of major coding categories (e. g. , "post-immigration lives") and subcategories (e. g. , "employment challenges" and "relationship changes") and their defining criteria, was developed by the research team, consisting of eight researchers from both Canada and China, based on their reading of selected transcripts. At the stage of writing this article, we developed a comprehensive synthesis of the themes relating to its focus through intensive engagement with the transcripts of interviews with the eight women, while attending to the larger study in which this subset of data is situated.
Acknowledgments The authors wish to express their appreciation to research participants of this study in both Canada and China. We also thank this project's other research investigators ( Drs. William D. Coleman ,  Yingying Huang ,  Wei Wei , and  Roy Cain ), research associates and assistants ( Helen Hong Su ,  Liping Peng ,  Emmy Arnold ,  Nancy Johnson ,  Xiaoqing Gao ,  Xiaoxin Ji , and  Jane Ma ), and collaborators ( Chi Heng Foundation, Shanghai, China; Institute of Sexuality and Gender, Renmin University of China; Asian Community AIDS Services, Toronto, Canada; St. Stephen's House, Toronto, Canada ) for their contributions at different stages of this study. This work was carried out with the aid of the  Canadian Institutes of Health Research  ( CIHR Grant  # 111081 ).
Study Design This is a prospective, observational blinded study with repeated measures to determine the intra-and inter-rater reliability of the measurements obtained by ultrasound images from the quadriceps femoris components, triceps surae, TA, and PT. We analyzed the muscle architecture (Lf, θp, thickness), the CSA of the PT, and the echogenicity of all the muscles and the PT, in post-COVID-19 participants. The research was conducted at the Laboratory of Clinical Physiology of the University of Brasilia, following the guidelines for reporting reliability and agreement studies (GRRAS)  [20]. All the protocols were approved by the Human Research Ethics Board at the Faculty of Ceilândia (CAAE: 45043821. 0. 0000. 8093) in accordance with the Declaration of Helsinki 1975. All the participants signed a consent form before the data collection.
Muscle and Tendon Ultrasound Imaging The θp, Lf, thickness, CSA, and echogenicity were assessed using ultrasound (M-Turbo ® , Sonosite, Bothwell, WA, USA) as described by Blazevich et al. [21]. The data were collected from the right limb (dominant limb) of all the participants. For the visualization and to obtain images of the RF, VL, VM, and TA, the participants were positioned in a supine position, with the lower limbs supported using a knee-bend to keep the muscle relaxed and reduce the fascicle curvature, in order to maximize the assessment reliability  [21]. GL, GM, and SO images were obtained with patients in a prone position with the lower limbs fully extended and the ankle suspended off the stretcher. A water-soluble gel was applied to the probe to supply acoustic contact. The sequence of the muscle images obtained was randomized.
5 Mhz linear transducer. Two raters performed the ultrasound measurem physical therapists, and one had experience with ultrasound imaging and the experienced rater (T1-ISA). The other, considered the novice rater, six months to perform the measurements (T2-LGJF). During the data c one of the raters was collecting the images, the other was out of the la auxiliary, both the raters and the third physical therapist (T3) handled the the images were analyzed with ImageJ software (National Institute of H MD, USA).
Statistical Analysis and Sample Size The data were analyzed using SPSS 22. 0 software (IBM Corporation USA). The statistical level adopted was p < 0. 05. The ultrasound data for were obtained at the same time of day by the raters, who did not have other's measurements. A third researcher randomized the examiners' e using the brown envelope method. Double and triple measures, calculated of the records obtained in the two first and the three total records, were a the five conditions of analysis: first (A1), second (A2), and third (A3) single as well as double (A1 + A2/2) and triple (A1 + A2 + A3/3) calculated mea the triple measure for the inter-rater reliability analysis because it represe of the three measurements made by each rater. For the intra-rater reliabil chose the three single trials recorded.
Statistical Analysis and Sample Size The data were analyzed using SPSS 22. 0 software (IBM Corporation, Armonk, NY, USA). The statistical level adopted was p < 0. 05. The ultrasound data for each participant were obtained at the same time of day by the raters, who did not have access to each other's measurements. A third researcher randomized the examiners' evaluation order using the brown envelope method. Double and triple measures, calculated by the average of the records obtained in the two first and the three total records, were also used to form the five conditions of analysis: first (A1), second (A2), and third (A3) single trials recorded, as well as double (A1 + A2/2) and triple (A1 + A2 + A3/3) calculated measures. We chose the triple measure for the inter-rater reliability analysis because it represents the average of the three measurements made by each rater.
Micro-CT Scanning In order to eliminate metal artifacts, the wear particles were removed from the fixed calvarial samples. An Inveon micro-CT scanner (Siemens Inveon ™ , Munich, Germany) was utilized to scan calvarial samples in moderate resolution. The scanning protocol was set at an isometric resolution of 33 μm, with X-ray energy settings of 80 kV and 500 μA. For threedimensional (3D) image reconstruction with Inveon Research Workplace 3. 0 software, the same region of interest (ROI) around the midline suture was selected for further quantitative analysis. The bone volume-to-tissue volume ratio (BV/TV) was measured using the MicroView software (GE Medical Systems, Waukesha, WI, United States), and the number of pores of each sample was measured using Image-Pro Plus 6. 0 software.
Immunohistochemistry Analysis After the micro-CT analysis was completed, decalcification was carried out in 10% ethylenediaminetetraacetic acid solution at 4 °C for 4 days, followed by paraffin embedding. Histological sections (5 mm thick) were cut and prepared in the coronal plane for tartrate-resistant acid phosphatase (TRAP) staining, hematoxylin and eosin (H&E) staining, and detection of TNF-α (GB11188, Servicebio, China) and IL-6 (GB11117, Servicebio, China). Detection of SPHKs, BECN1, and TRAF2 was performed on the specimens. The number of TRAP-positive multinucleated OCs was assessed in each calvaria and specimens. Stained sections were observed and photographed under an optical microscope (Leica Aperio CS2, Germany). The Image-Pro Plus 6. 0 software was used to analyze the ROI of each section.
Statistical Analysis Data of quantitative experiments were expressed as the mean ± standard error of the mean (SEM). A comparison of paired groups was performed using Student's t-test or Kruskal-Wallis test. Continuous variables of normal distribution were analyzed by one-way analysis of variance (ANOVA). Statistical analysis was performed using SPSS 19. 0 software (SPSS, Chicago, United States) and GraphPad Prism version 8. 0 (GraphPad software, San Diego, CA). The difference was regarded as statistically significant (*/#) when the p-value was <0. 05 (*/#, p < 0. 05; **/##, p < 0. 01; ***/###, p < 0. 001).
The input to these classifiers is a dataset whose columns are the seven moments of Hu h 0 ; h 1 ; h 2 ; h 3 ; h 4 ; h 5 ; h 6 along with the classes ''healthy" and ''un- healthy"; nevertheless, these approaches are considered to have the following deficiencies: 1. When images are transformed from polar to cartesian, an interpolation occurs thereby losing information and adding uncertainty to the measurements. 2. It is considered the imbalance between the number of class elements in the training dataset given the low prevalence of the disease, which increases the generalized error. 3. The previous models only employ the seven Hu moments for training, which is suitable as the moments are powerful descriptors; however, other features may influence the detection of this disease. For the aforementioned reasons, it was decided to implement a new configuration of models which addresses these problems using CNNs.
Meanwhile, in  [34]  for an application in voice command recognition, the error rate reduced when the original dataset was combined with augmentation methods involving speed perturbation and room impulse response reverberation, improving the generalization capability of convolutional neural network when used for voice command recognition. Room impulse response reverberation produces voice command variations caused by reflected sound paths while speed perturbation generates voice command variations caused by shorter or longer time duration. In  [35] , it is designed a 13-layer CNN, using three types of data augmentation methods: Gamma correction, noise injection, and image rotation, these approaches are used for image-based fruit classification; the results show that data augmentation can increase the overall accuracy.
Data augmentation and image processing For the present study case, the iris is the region of interest (ROI) which will be segmented to perform the classification. Segmentation in CAD is critical as incorrect segmentation can lead to false positives (FP) and false negatives (FN), therefore, the database is critical, particularly, when the samples are unbalanced. This section first presents the background on data augmentation and the Gabor filter application since the proposed approach employs these techniques. Subsequently, the algorithm to perform the balance of the dataset is also shown.
Data augmentation Data augmentation techniques (AT) generate input data for machine learning models to increase performance, reduce both the reliance on training data and overfitting  [33, 39]. Thus, as the occurrence of UM is rare, it is then suggested to use AT. In particular, generic input transformations are useful for this task as CNNs can robustly classify objects, even if they are positioned in different orientations, as they are invariant to translation, viewpoint, size, and illumination (or a combination of these), Moreover, rotation AT is potentially useful for the iris dataset as the iris can be seen as circle. According to  [40] , data augmentation techniques are proposed being the most popular: Flip: The image can be flipped horizontally and vertically depending on the object.
For example, it is not possible to flip an image of a car vertically, as it becomes a different object (e. g. , a horizontal flipped car is still car but a flipped ''3" will not be a number any more). Zoom: This technique creates new versions of an image with different zooming views. The resulting images are zoomed in or out according to a predefined range. In  [41] , the range can be specified by a value or a list of two threshold values (e. g, objects in a scene can be zoomed into obtain greater detail while losing contextual details). Rotation: This technique rotates the image with a predefined or random angle. Each rotated image is unique in the dataset. Typically, there are 359 degrees of rotation (e. g, it is still possible to read the characters in the book by turning the book 2 degrees, but it will be almost impossible to do so by turning it 180 degrees).
Noise addition: Applying noise involves altering some pixels of the image, while keeping the actual meaning of the image as a whole (e. g, adding white noise evenly to an image or a song in a light way will not affect the meaning). Channel shift: For each channel R; G; B of an image, channel shift randomly move the pixel values in each channel by a predefined range. As a result, multiple color versions were produced by this technique. This augmentation technique increases the model robustness to color variation. As the color is influenced by light, it is particularly useful to apply it when the detection system is used in a different light context (e. g, selfdriving cars). The correct zoom ranges are defined for each dataset since given that the location and zoom of the region of interest vary in each set of images.
In particular, very large zoom values can cut off the region of interest and very small values can blur the region due to loss of information. For this case, the values were calculated experimentally; a similar procedure can be employed to find the ranges of rotation and noise, since for each case study the valid zoom, rotation ranges, and noise values are different. In a formal way, given a dataset X 2 R nÂm where n is the number of rows and m the number of features and Y the classes (with/without UM), so that the classifier f : X ! Y with performance Pðf Þ which is not accurate enough, thus the n can be extended by means of using AT, so that Z ¼ ½X0; X where X0 is the augmented data and f 0 : Z ! Y resulting in Pðf 0Þ P Pðf Þ. The mutual information inequality states that IðZ; YÞ 6 IðX; YÞ, however, as this study helps to generate new data with an external method like the Gabor kernels, it is therefore possible to say that IðZ; YÞ P IðX; YÞ which provides theoretical bases to the employed method.
Light reflect compensation In the dataset is observed some spot lights in the pupil, potentially caused by the device that captured the photo. Especially in healthy class dataset, as this may be a distinguishing mark, it is therefore necessary to remove them in order to minimize the bias. In this way, it is proposed a configuration III (see 6. 5) with an algorithm to compensate the light reflection in the eyes, as described in 4. This process is divided in the following steps: Step (a): Build a mask using the threshold and the circular Hough transform. As the light reflection has a strong grey value, it is fairly simple to use a threshold to obtain the desired regions. However, in some images of the healthy class, the parts outside the iris may contain high grey level pixels. As it is only needed to cover the light pattern in the pupil for the healthy class, it is therefore necessary to locate the region where the pupil is located.
Transfer learning Transfer learning is a technique that uses a model that was trained on one task and is transferred to a new related task. For the case considered in this work, an image classification model Fig. 2. Contour for a Gabor filter given by Equation (  5 ). trained on ImageNet (a huge dataset containing 1. 2 million labelled training images) can be reused for classification and detection of ocular anomalies. For this reason, transfer learning is useful when the size of the dataset does not allow deep neural networks to be trained from scratch. Using transfer learning, neural networks can have adequate initial parameters and require only minor modifications to adapt to new tasks  [53]. When using transfer learning, there are two main approaches. In the first one, the pre-trained model weights are considered as the initial weights for the model in a new job, and these weights are trained and updated during the training process.
To solve this problem, ResNet proposed the novel idea of using the bypass pathway concept as a way to skip the backward connections between layers and create identity shortcuts in the gradients' path, which allows the gradients flow faster to the initial layer  [65]. ResNet also has been used in medical image classification and diagnosis. In  [53] , S. Minaee et al. used ResNet-18 and ResNet-50 with transfer learning for predicting COVID-19 from chest X-ray images. These two models were pre-trained on ImageNet dataset. Their classification results were 89:58% with ResNet-18 and 92:29% with ResNet-50. In another research, S. Ayyachamy et al. [66]  applied ResNet-18 into their content-based image retrieval framework to retrieve the images which have similar pathology of the one under examination.
Preliminary data analysis For classification process, it is especially important to check the differentiation between classes in the dataset, which allows the classifiers to learn and distinguish between different types of images to save time and resources in the design and formation of configurations. Clearly from the figures it could be seen that there is a difference between the feature groups, this is an evidence of such differentiation in the sample, but is not enough in a statistical way. Therefore, it is necessary to establish a statistical test that provides stronger evidence of whether the two samples come from two different populations. For this procedure, it is used the moments of an image, which are measures that provide a generic representation of an object with simple or complex figures  [68].
Experimental results For generating the results it was used Colab of Google that provides GPU computing and enough memory to train the classifiers. Regarding the dataset, there are 150 healthy and 33 unhealthy images, some of them are shown in 7. Two experimental configurations were designed, Configuration I and Configuration II to train VGG-16 and ResNet-18, to reduce bias and to increment the robustness of the proposed method. Additionally, Configuration III is proposed to compensate the light reflect in the eyes. Also to be considered in these results in certain images within the healthy class, the parts outside the iris may contain high grey level pixels, this is due to the conditions in which the pictures were taken; nevertheless, as the conditions were in a dark environment, this caused parts such as the sclera to have a grey tone; fortunately, this also increased the tone of the iris.
Configuration I For this dataset configuration, ResNet-18 and VGG achieve 98% and 97:6% testing accuracy. However, it is considered that this high accuracy is not due to the tumour, but to other differentiating features, such as the light pattern in the pupil and the scale of the iris. Precisely, almost all images of the healthy class contain a pattern of reflected light in their pupils, hence a biased dataset, which makes these networks primarily trained based on this noise. The confusion matrixes are shown in 8 and 9. Additionally, the metrics are shown in 2. Even though the performance of CNN is heavily impacted by the number of images, it was created a novel way to create abnormalities as described above. On the other hand, this can cause the neural network to be focused on learning on this artificial-created tumors whose pattern is intended to be as similar as the one presented in the unhealthy images.
Configuration II This section displays the results using the augmented dataset with the artificial texture-like abnormalities, some of them can be seen in 10. The classifiers were again trained and tested with 1024 and 250 images respectively, this time, half of them were with images generated simulating abnormalities to reduce the bias caused by illumination and brightness; a sample of this dataset can be seen in 11. After training and testing, both models VGG-16 and ResNet-18 provide suitable results with 96% testing accuracy. In 12 and 13 it  is possible to see the results of configuration II, the metrics are shown in 2.
Configuration III In this part, the model is trained and tested using the dataset, in which the light reflection is compensated in healthy and unhealthy class. The classifiers were still trained and tested with 1024 and 250 images for each class. The arrangement of dataset was the same as configuration I (which means there is no mixture in unhealthy class like configuration II). After training and testing, both models VGG-16 and ResNet-18, provide remarkable results with 99% and 97% and testing accuracy respectively, in 14 and 15. As the results shown in the confusion matrices, after light reflect compensation, the dataset turned to be less bias. Especially with VGG-16, the classifier provided an excellent result.
Uveal melanoma detection After producing the classifiers for uveal melanoma diagnosis, an object detection system was trained to identify the region where this abnormality is located. Then it is reused the pre-trained ResNet-18 model and put on top of it a fully connected layer that has 4 neurons, whose outputs represent 2-dimensional coordinates of two points that define a rectangle box: the top left point and the right bottom. The network is trained with a dataset of 900 images labeled with bounding boxes, the training and testing ratios are 0:8 and 0:2 respectively. During the training process, it is applied L 1 loss function to measure the improvement of the model and Adam optimizer to update parameters. The L 1 loss function is defined as in Eq. (  21 ), where Y True and Y Predict are the ground-truth coordinates and predicted coordinates respectively.