Deep learning has brought a dramatic development in molecular property prediction that is crucial in the field of drug discovery using various representations such as fingerprints, SMILES, and graphs. In particular, SMILES is used in various deep learning models via character-based approaches. However, SMILES has a limitation in that it is hard to reflect chemical properties. In this paper, we propose a new self-supervised method to learn SMILES and chemical contexts of molecules simultaneously in pre-training the Transformer. The key of our model is learning structures with adjacency matrix embedding and learning logics that can infer descriptors via Quantitative Estimation of Drug-likeness prediction in pre-training. As a result, our method improves the generalization of the data and achieves the best average performance by benchmarking downstream tasks. Moreover, we develop a web-based fine-tuning service to utilize our model on various tasks.
rough wireless sensor networks (WSN), we can acquire the various interesting event information around sensor nodes through multihop communications. In WSN, there are two types of applications, that is, event or query based. Commonly, in these application, the value on each sensor node is very sensitive to delay or latency. So, it is strongly required to deliver data to sink node within the deadline since data received a er the deadline is not acceptable at all in WSN. e good example of application demanding real-time communication in WSN includes tracking of moving object and intrusion detection.However, compared to typical networks, it is very difficult to achieve real-time communication in WSN. Severe constraints such as limited computing power and narrow bandwidth are not suitable to provide real-time communication accordingly. So, a number of important issues and research challenges have to be addressed to provide realtime communication in WSN. Based on this demand, this special issue is planned to contribute to advances in real-time communications in WSN.While considering our objective, editors believe that this special issue provides collection of articles on networking technique in real-time communications. We have selected valuable papers by evaluating several aspects such as relevance to special issue and novelty of solution. e topic of these papers is roughly categorized into the following areas: multichannel transmission, MAC protocol, testbed for routing protocol, and comprehensive survey for real-time in WSN.In the paper entitled "Priority-Based Dynamic Multichannel Transmission Scheme for Industrial Wireless Networks," Y. Igarashi et al. proposed priority-based dynamic multichannel transmission scheme for industrial wireless sensor networks (IWSN) where applications are required to provide precise measurement functions as feedback for controlling devices. In order to guarantee latency for unpredictable on-demand communications, a root node controls the transmission timing of high-priority packets, while other nodes autonomously decide what channel to use and when to transmit packets to a neighbor. In the proposed scheme, packet priority is determined in accordance with application requirements. e proposed scheme operates over a MAC layer and does not rely on any specific MAC protocol. Since there are some standard protocols for real-time communications in IWSN such as ISA . a and wirelessHART, the authors discuss compatibility with ISA . a in this paper.Another paper is related to MAC protocol. However, instead of general MAC protocol, T. Kim et al. proposed an efficient MAC protocol for radio frequency (RF) energy harvesting in WSN, called REACH. Unlike conventional RF energy harvesting methods, an Energy Transmitter (ET) in the proposed scheme can actively send RF energy signals without Request-for-Energy (RFE) messages. An ET determines the active energy signal transmission according to the consequence of the passive energy harvesting procedures. e other feature of the proposed scheme is that an...
With the recent emergence of new paradigm, ie, open science and big data, the need for data sharing and collaboration is becoming important in the computational science field as well. The EDISON-DATA platform aims to provide services that computational simulation data can easily published, preserved, shared, reused, discovered, and analyzed. First, this paper analyzed computational science platform-related issues, obtained during the development of the EDISON-DATA platform, regarding the sharing and reusing of the computational science data. These issues include data complexity, diversity, reliability, heterogeneity, etc. To solve the above issues and support data analysis in an efficient and integrated manner, this study proposes various ideas used in the EDISON-DATA platform. First, we suggested an automated preprocessing framework to handle the complexity of computational science data. Second, to solve the diversity issue, we presented ways to develop preprocessing logic and data presentation logic customized for each data type. Third, to improve the reliability of computational science data, some quality control and provenance management techniques were presented. Fourth, we proposed a way to manage related data in groups. Fifth, to solve data heterogeneity problem and to analyze data in an integrated way, we let the preprocessing framework to use controlled vocabularies to express descriptive metadata. Lastly, we demonstrated feasibility and usability of the proposed ideas in this paper by presenting a case study of building a research portal service in the materials field based on the EDISON-DATA platform.
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