Retrosynthetic planning plays an important role in the field of organic chemistry, which could generate a synthetic route for the target product. The synthetic route is a series of reactions which are started from the available molecules. The most challenging problem in the generation of the synthetic route is the large search space of the candidate reactions. Estimating the cost of candidate reactions has been proved effectively to prune the search space, which could achieve a higher accuracy with the same search iteration. And the estimation of one reaction is comprised of the estimations of all its reactants. So, how to estimate the cost of these reactants will directly influence the quality of results. To get a better performance, we propose a new framework, named GNN-Retro, for retrosynthetic planning problem by combining graph neural networks(GNN) and the latest search algorithm. The structure of GNN in our framework could incorporate the information of neighboring molecules, which will improve the estimation accuracy of our framework. The experiments on the USPTO dataset show that our framework could outperform the state-of-the-art methods with a large margin under the same settings.
Infectious diseases have been recognized as major public health concerns for decades. Close contact discovery is playing an indispensable role in preventing epidemic transmission. In this light, we study the continuous exposure search problem: Given a collection of moving objects and a collection of moving queries, we continuously discover all objects that have been directly and indirectly exposed to at least one query over a period of time. Our problem targets a variety of applications, including but not limited to disease control, epidemic pre-warning, information spreading, and co-movement mining. To answer this problem, we develop an exact group processing algorithm with optimization strategies. Further, we propose an approximate algorithm that substantially improves the efficiency without false dismissal. Extensive experiments offer insight into effectiveness and efficiency of our proposed algorithms.
The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data. The collected raw data usually contain errors and anomalies information caused by device failure, sensor error, and environment influence. Low-quality data fails to support application requirements and therefore raw data will be comprehensively cleaned before usage. Existing methods are suboptimal to detect GPS data errors and do the repairing. To solve the problem, we propose a framework called GPSClean to analyze the anomalies data and develop effective methods to repair the data. There are primarily four modules in GPSClean : (i) data preprocessing, (ii) data filling, (iii) data repairing, and (iv) data conversion. For (i), we propose an approach named MDSort (Maximum Disorder Sorting) to efficiently solve the issue of data disorder. For (ii), we propose a method named NNF (Nearest Neighbor Filling) to fill missing data. For (iii), we design an approach named RCSWS (Range Constraints and Sliding Window Statistics) to repair anomalies and also improve the accuracy of data repairing by mak7ing use of driving direction. We use 45 million real trajectory data to evaluate our proposal in a prototype database system SECONDO. Experimental results show that the accuracy of RCSWS is three times higher than an alternative method SCREEN and nearly an order of magnitude higher than an alternative method EWMA.
Blockchain technology in recent years has become potentially pervasive in the cryptocurrency market, thus providing tamper-proof security to decentralized transaction management systems. Structurally, the design foundation is an ideal advancement of the distributed ledger technology that maintains a set of global states across nodes. As technology expands with a higher trend towards mobile computing, the development of new applications demands understanding the current progression, especially concerning performance, data management, and storage prospects. Here, we report the principle design structure of the blockchain technology combined with the state of the arts, thus characterizing their original topological contexts. We depart from the fundamental concepts of the technology and analyze performance of the Ethereum blockchain on two devices having different computing power. Our presentation is tailored to provide a systematic review of the technology, thus facilitating their possible adoption into the new application domains like the Internet of Things (IoT). Further, we developed Debug-Bench, the first VSCode (Visual Studio Code) extension that enables benchmarking and profiling of the blockchain applications. Finally, we demonstrate several critical challenges concerning the design space of the current blockchain platforms for their implementation over resource-constrained devices.INDEX TERMS Blockchain, blockchain data management, distributed processing, blockchain for IoT, resource-constrained devices, blockchain debugging.
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