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Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of constructing an orbital computed tomography (CT) segmentation model based on U-Net. For supervised learning, a labor-intensive and time-consuming process is required. The method of labeling with super-resolution to efficiently mask the ground truth on orbital CT images is introduced. Also, the volume of interest is cropped as part of the pre-processing of the dataset. Then, after extracting the volumes of interest of the orbital structures, the model for segmenting the key structures of the orbital CT is constructed using U-Net, with sequential 2D slices that are used as inputs and two bi-directional convolutional long-term short memories for conserving the inter-slice correlations. This study primarily focuses on the segmentation of the eyeball, optic nerve, and extraocular muscles. The evaluation of the segmentation reveals the potential application of segmentation to orbital CT images using deep learning methods.

Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein.

Nanomaterials are widely believed to induce toxic effects on organisms by evoking oxidative stress. In this study, we evaluated the toxic effects of nanomaterials on cardiac and behavioral changes in Daphnia magna under varying exposure conditions. Titanium dioxide nanoparticles (TiO2 NPs), silver nanoparticles (AgNPs), and silver nitrate (AgNO3) were selected for the acute toxicity tests. The adverse effects of the substances on the neonates including heart rate, swimming speed, and oxidative stress, were measured. The heart rate level decreased as the concentration of NPs and silver ions (Ag+) increased. The average swimming speed was measured to be approximately 15 mm/min for the control group. The swimming speed generally increased for longer exposure to both NPs, although it reached a plateau at the lowest concentration of AgNPs. A similar but less clear trend was observed for Ag+. For all substances, the overall swimming speed exhibited no correlation or weak negative correlations with the exposure concentration. The oxidative stress levels increased after exposure compared to the control group. We conclude that aquatic nanotoxicity tests should consider multilevel physicochemical, physiological, and behavioral parameters for the official guidelines to quantify more robust adverse outcomes.

The demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object detection and scale-invariant feature transform in different feature spaces are developed to extract global and local features from several independent convolutional neural networks. The conditions of shipping label images are classified by fully connected fusion layers with concatenated global and local features. The experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods. These results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions., Accepted at ICPR 2020

Increasing global concern over COVID-19 has recently brought greater attention to studies due to the ease of person-to-person transmission and the current lack of effective antiviral therapy. Here, we proposed the application of the adverse outcome pathway (AOP) framework to support re-search on the pathogenesis of viral disease. We first constructed adverse outcome pathways (AOPs) applicable to COVID-19 management to understand whether the infection causes severe acute respiratory distress. Based on the AOP framework where mechanistic elucidation of the pathway from the interaction of chemicals (or viruses) to apical endpoints is represented, our COVID-19 AOP indicated that the molecular initiating event (MIE) was angiotensin-converting enzyme 2 (ACE2) interaction, and the key events (KEs) were the increased pro-inflammatory cytokines in immune cells, with increased mortality as an apical adverse outcome (AO). However, there is still limited information on the toxicity mechanisms of AOPs in COVID-19; therefore, detailed KEs and AOs on toxicity mechanisms will be required to fill these gaps in the data. This study demonstrated that the COVID-19 AOP framework is a suitable tool to design new drugs and to integrate crowded-sourced information for the battle against the COVID-19 pandemic.

In the computational prediction of chemical compound properties, molecular descriptors and fingerprints encoded to low dimensional vectors are used. The selection of proper molecular descriptors and fingerprints is both important and challenging as the performance of such models is highly dependent on descriptors. To overcome this challenge, natural language processing models that utilize simplified molecular input line-entry system as input were studied, and several transformer-variant models achieved superior results when compared with conventional methods. In this study, we explored the structural differences of the transformer-variant model and proposed a new self-attention based model. The representation learning performance of the self-attention module was evaluated in a multi-task learning environment using imbalanced chemical datasets. The experiment results showed that our model achieved competitive outcomes on several benchmark datasets. The source code of our experiment is available at https://github.com/arwhirang/sa-mtl and the dataset is available from the same URL., Accepted at ICPR2020

International audience; Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal false alarm rates, and the need for expert domain knowledge, which is costly. In this paper, we propose a novel data-driven health division method based on convolutional neural networks using a graphical representation of time series data, called a nested scatter plot. The proposed method trains the model with a small amount of labeled data and does not require a threshold value to predict the health state of rotary machines. Notwithstanding the lack of datasets that show the ground truth of health stages, our experiments with two open datasets of run-to-failure bearing demonstrated that our method is able to detect the early symptoms of bearing wear earlier and more efficiently than other threshold-based health indicator methods.

The data imbalance problem in classification is a frequent but challenging task. In real-world datasets, numerous class distributions are imbalanced and the classification result under such condition reveals extreme bias in the majority data class. Recently, the potential of GAN as a data augmentation method on minority data has been studied. In this paper, we propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. In addition, we propose an ambiguity reduction method using the generated synthetic minority data for the case of multiple similar classes that are degenerating the classification accuracy. The proposed method is demonstrated with five benchmark datasets. The results indicate that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.

In toxicity evaluation based on the nuclear receptor signalling pathway, in silico prediction tools are used for the detection of the early stages of long-term toxicities, the prioritization of newly synthesized chemicals and the acquisition of the selectivity and sensitivity. Computational prediction model is one of the promising tools for the toxicity screening of the chemical-protein interaction as deep learning has been improved the prediction accuracies. However, the challenge is that data-imbalanced conditions, where the volume of toxic chemical compound dataset is much smaller than the nontoxic dataset, result in low prediction accuracy of the toxic dataset providing valid information to toxicity hazard. In this paper, we have examined the effect of data imbalance in the toxicity assessment data of AR (LBD), ER (LBD), AhR, and PPAR as nuclear receptors, and identified the severe imbalance between the prediction of the toxic and nontoxic datasets. As the acquisition of the balanced selectivity and sensitivity is required for the assessment of toxicity hazards, data resampling methods have been investigated in order to improve the bias problem in binary classification for toxicity hazard profiling of nuclear receptor. The experimental results achieved a sensitivity of 0.714 and a specificity of 0.787, with an overall accuracy of 0.829 and a ROC-AUC of 0.822 by the simple resampling methods.

Shipping labels are widely used in logistics. It is important to ensure the quality of printing label and to verify contents of the shipping label on the package. We developed a verification and recognition method for various types of shipping labels by using deep neural networks. The experimental results showed 96% recognition accuracy in rotation-invariant conditions. Also, we introduce Google Maps API for validating the address which can reduce the cost of returning packages due to the invalid address. To train and evaluate the method, we have generated and collected 25 different types of shipping label dataset. We plan to release the dataset on our website1.

Graphical representation of correlated multi-channel time series signal is studied in this paper. With the proposed multi-layer nested scatter plot (NSP), we can compress multichannel time series signals into static sized data and can obtain advantages inherent in scatter plots.

This study was conducted to develop an artificial neural network (ANN)-based prediction model that can calculate the amount of cooling energy during the setback period of accommodation buildings. By comparing the amount of energy needed for diverse setback temperatures, the most energy-efficient optimal setback temperature could be found and applied in the thermal control logic. Three major processes that used the numerical simulation method were conducted for the development and optimization of an ANN model and for the testing of its prediction performance, respectively. First, the structure and learning method of the initial ANN model was determined to predict the amount of cooling energy consumption during the setback period. Then, the initial structure and learning methods of the ANN model were optimized using parametrical analysis to compare its prediction accuracy levels. Finally, the performance tests of the optimized model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results and the predicted results under generally accepted levels. In conclusion, the proposed ANN model proved its potential to be applied to the thermal control logic for setting up the most energy-efficient setback temperature.

Applications such as Voice over IP and video streaming require continuous network service, requiring fast failure recovery mechanisms. Proactive failure recovery schemes have been recently proposed to improve network performance during the failure transients. These proactive failure recovery schemes need extra infrastructural support in the form of routing table entries, extra addresses etc. In this paper, we study if the extra infrastructure support can be exploited to build disjoint paths in those frameworks, while keeping the lengths of the recovery paths close to those of the primary paths. Our evaluations show that it is possible to extend the proactive failure recovery schemes to provide support for nearly-disjoint paths which can be employed in multipath routing for load balancing and QoS.

Situational awareness in a disaster is critical to effective response. Disaster responders require timely delivery of high volumes of accurate data to make correct decisions. To meet these needs, we present DistressNet, an ad hoc wireless architecture that supports disaster response with distributed collaborative sensing, topology-aware routing using a multichannel protocol, and accurate resource localization. Sensing suites use collaborative and distributed mechanisms to optimize data collection and minimize total energy use. Message delivery is aided by novel topology management, while congestion is minimized through the use of mediated multichannel radio protocols. Estimation techniques improve localization accuracy in difficult environments.