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Accurate recognition of rock fractures is an important problem in rock engineering because fractures greatly influence the mechanical and hydraulic properties of rock structures. However, existing image segmentation methods for identifying rock fractures tend to be limited to handling only very simple fracture images, despite many real cases containing interfering objects or features such as dark surfaces, stripes (e.g., from foliation), infilling materials, scratches, shadows, and vegetation. Here, we propose a novel deep convolutional neural network to construct the first model that is applicable in the field. After selecting U-Net, a simple and powerful network for semantic segmentation, as a baseline network, we tested network architectures by applying atrous convolutions and extra skip connections to develop an optimal network specialized for rock fracture segmentation. The rate of erroneously detecting non-fracture objects or features was reduced by using the atrous convolution module, and more skip connections were appropriately added to increase the detection rate of the actual fractures. The model's performance gradually improved as these new techniques were added to the original model. Contrast-limited adaptive histogram equalization and a fully connected conditional random field were applied before and after the network, respectively, to enhance the model’s performance. Evaluation of the proposed model using raw images of diverse site conditions shows that it can effectively distinguish rock fractures from various interfering objects and features. The source code and pre-trained model can be freely download from GitHub repository ( https://github.com/Montherapy/Rock-fracture-segmentation-with-Tensorflow ).

Finding an optimal learning trajectory is an important question in educational systems. Existing Artificial Intelligence in Education (AiEd) technologies mostly used indirect methods to make the learning process efficient such as recommending contents based on difficulty adjustment, weakness analysis, learning theory, psychometric analysis, or domain specific rules. In this study, we propose a recommender system that optimizes the learning trajectory of a student preparing for a standardized exam by recommending the learning content(question) which directly maximizes the expected score after the consumption of the content. In particular, the proposed RCES model computes the expected score of a user by effectively capturing educational effects. To validate the proposed model in an end-to-end system, we conduct an A/B test on 1713 real students by deploying 4 recommenders to a real mobile application. Result shows that RCES has better educational efficiencies than traditional methods such as expert designed models and item response theory based models.

This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a fidelity-embedded generative adversarial network (GAN) to learn a denoising function from unpaired training data of low-dose CT (LDCT) and standard-dose CT (SDCT) images, where the denoising function is the optimal generator in the GAN framework. This paper analyzes the f-GAN objective to derive a suitable generator that is optimized by minimizing a weighted sum of two losses: the Kullback-Leibler divergence between an SDCT data distribution and a generated distribution, and the $\ell _{2}$ loss between the LDCT image and the corresponding generated images (or denoised image). The computed generator reflects the prior belief about SDCT data distribution through training. We observed that the proposed method allows the preservation of fine anomalous features while eliminating noise. The experimental results show that the proposed deep-learning method with unpaired datasets performs comparably to a method using paired datasets. A clinical experiment was also performed to show the validity of the proposed method for noise arising in the low-dose X-ray CT.

The inevitable shift towards online learning due to the emergence of the COVID-19 pandemic triggered a strong need to assess students using shorter exams whilst ensuring reliability. This study explores a data-centric approach that utilizes feature importance to select a discriminative subset of questions from the original exam. Furthermore, the discriminative question subset’s ability to approximate the students exam scores is evaluated by measuring the prediction accuracy and by quantifying the error interval of the prediction. The approach was evaluated using two real-world exam datasets of the Scholastic Aptitude Test (SAT) and Exame Nacional do Ensino Medio (ENEM) exams, which consist of student response data and the corresponding the exam scores. The evaluation was conducted against randomized question subsets of sizes 10, 20, 30 and 50. The results show that our method estimates the full scores more accurately than a baseline model in most question sizes while maintaining a reasonable error interval. The encouraging evidence found in this paper provides support for the strong potential of the on-going study to provide a data-centric approach for exam size reduction.

A new semi-automated method is proposed in this study that detects joint traces from digital images and calculates the length distribution through three-dimensional data structuring. The method comprises detecting the pixels corresponding to the joint trace in digital images and calculating the length distribution through three-dimensional data structuring of the pixels detected as the joint trace. Semantic segmentation based on deep learning techniques is applied to the detection of joint trace pixels to overcome the limitations of rule-based image processing techniques. For training a deep learning network, various rock joint images were sampled and labeled, following which the data sets for training, validation, and testing were prepared through classification and augmentation. Using the prepared datasets, the performance of the classifiers based on widely used semantic segmentation networks, namely U-Net (University of Freiburg, Germany) network and DeepLabV3+ (Google, USA) network, was compared. The classifier based on the DeepLabV3+ network was ultimately selected and applied. The trained classifier successfully detected pixels corresponding to the joint trace in images of both flat and uneven rock surfaces, indicating that the deep learning technique could be applied effectively to joint trace detection from digital images. Further, a data structuring technique is proposed to calculate the joint trace length using pixelwise data detected through the trained classifier. This technique uses point cloud data obtained by photogrammetry, and comprises two-dimensional thinning and segmentation, three-dimensional projection, segmentation, and segment linking. The entire proposed method, from detecting the joint trace pixels to three-dimensional data structuring, was verified by applying it to both simple flat and uneven rock surface models.

Knowledge tracing, the act of modeling a student's knowledge through learning activities, is an extensively studied problem in the field of computer-aided education. Although models with attention mechanism have outperformed traditional approaches such as Bayesian knowledge tracing and collaborative filtering, they share two limitations. Firstly, the models rely on shallow attention layers and fail to capture complex relations among exercises and responses over time. Secondly, different combinations of queries, keys and values for the self-attention layer for knowledge tracing were not extensively explored. Usual practice of using exercises and interactions (exercise-response pairs) as queries and keys/values respectively lacks empirical support. In this paper, we propose a novel Transformer based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder structure where exercise and response embedding sequence separately enter the encoder and the decoder respectively, which allows to stack attention layers multiple times. To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately. The empirical evaluations on a large-scale knowledge tracing dataset show that SAINT achieves the state-of-the-art performance in knowledge tracing with the improvement of AUC by 1.8% compared to the current state-of-the-art models., Comment: L@S 2020

Etanercept is a dimeric genetic recombinant glycoprotein consisting of Fc domain of human Immunoglobulin G1 and the extracellular domain of human tumor necrosis factor (TNF) receptor type II. Etanercept exerts therapeutic effects on inflammatory diseases such as rheumatoid arthritis and juvenile idiopathic arthritis by neutralizing biological activities of TNFα/Lymphotoxin (LT) α. Mochida Pharmaceutical and LG Chem have developed syringe, pen, and vial products of Etanercept BS (biosimilar) as the first biosimilar of Enbrel in Japan. The active ingredient of those products "Etanercept biosimilar 1" has the identical primary structure to that of Enbrel. The development of the Etanercept BS, including evaluations of quality attributes, nonclinical and clinical studies was performed in accordance with "Policies on Assurance of Quality, Safety and Efficacy of Biosimilars". The quality attributes of Etanercept BS were similar to those of Enbrel, and the binding affinities to TNFα/LTα, TNFα neutralizing activity, nonclinical pharmacokinetics and toxicological profiles of Etanercept BS were comparable to Enbrel. Additionally, the pharmacokinetic profile and efficacy of Etanercept BS were equivalent to those of Enbrel and there was no clinically significant difference in safety profiles between them in Phase I and Phase III clinical studies. The marketing approval application of the Etanercept BS with the same indications as Enbrel filed by Mochida Pharmaceutical was approved in January 2018 and the products will be launched by Ayumi Pharmaceutical in the near future. The Etanercept BS, which is as highly effective as Enbrel is expected to make beneficial therapies more easily accessible to patients.

Biopharmaceuticals, with their ability to treat many unmet needs, are seen as promising medications in diabetes mellitus, growth hormone deficiency, chronic renal failure, cancer, and rheumatoid arthritis. However, almost all biopharmaceuticals should be administrated by injection; IV, IM, and SC. In addition, these treatments are long term, and patients should receive frequent injections for many years. Patient compliance is therefore of critical importance to ensure treatment benefits. Therefore, the goal of drug product development should be focused on improving patient compliance by reducing injection-associated pain as well as stable formulation development. This review will suggest the kinds of factors that should be considered to minimize injection pain with regard to formulation, device, and injection procedures focused on SC injections.