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Correction: FragGen: towards 3D geometry reliable fragment-based molecular generation

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Chemical science 2025年第3期16卷 1467页

作者： Odin Zhang Yufei Huang Shicheng Chen Mengyao Yu Xujun Zhang Haitao Lin Yundian Zeng Mingyang Wang Zhenxing Wu Huifeng Zhao Zaixi Zhang Chenqing Hua Yu Kang Sunliang Cui Peichen Pan Chang-Yu Hsieh Tingjun Hou College of Pharmaceutical Sciences Zhejiang University Hangzhou 310058 Zhejiang China slcui@*** panpeichen@*** kimhsieh@*** tingjunhou@***. Zhejiang University Hangzhou 310058 Zhejiang China. Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Hefei Anhui China. Montreal Institute for Learning Algorithms McGill University Montreal QC Canada.

[This corrects the article DOI: 10.1039/D4SC04620J.].

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IARA: An Orchestrating Improved Artificial Raindrop Algorithm for VNFs Deployment in Network Function Virtualization

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IAENG International Journal of Computer Science

IAENG International Journal of Computer Science 2020年第4期47卷 1-9页

作者： Xuan, Hejun Zhao, Xuelin Liu, Zhenghui Li, Yanling school of Computer and Information Technology Xinyang Normal University XinyangHenan464000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University XinyangHenan464000 China

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of 5G communication. Resource scheduling for virtual network function service chain (VNF-SC) is the key issue of the NFV. In this paper, the problem of routing and VNFs deployment for VNF service chain (VNF-SC) in inter-data center elastic optical networks (inter-DC EONs) is investigated. We investigate the inter-DC EONs that each data center only can provide some specific VNFs, and the system resources of all the data centers are limited. In addition, part of requiblack VNFs in each VNF-SC is dependent, i.e., the relative order of requiblack VNFs is fixed. To solve the challenging problem, we first establish a mix-integer programming model. Then, to make the population distributed on the search domain uniformly, uniform design method was developed. In addition, an improved artificial raindrop algorithm (IARA) for the established model, which inspired by particle swarm optimization and differential evolution, is proposed to solve the maximum model efficiently. Finally, to demonstrate high performance of the designed algorithm, a series of experiments are conducted in several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain the better results than compared algorithm. © 2020. All Rights Reserved.

关键词： Network function virtualization

Multi-Interactive Attention Network for fine-grained feature learning in CTR prediction

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arXiv 2020年

作者： Zhang, Kai Qian, Hao Cui, Qing Liu, Qi Li, Longfei Zhou, Jun Ma, Jianhui Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China Ant Group Hangzhou China

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature. However, despite being extensively studied, these sequential methods still suffer from three limitations. First, existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction, because users often click on new products that are irrelevant to any historical behaviors. Second, in the real scenario, there are numerous users that have operations a long time ago, but turn relatively inactive in recent times. Thus, it is hard to precisely capture user's current preferences through early behaviors. Third, multiple representations of user's historical behaviors in different feature subspaces are largely ignored. To remedy these issues, we propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features (e.g., gender, age and occupation in user-profile). Specifically, MIAN contains a Multi-Interactive Layer (MIL) that integrates three local interaction modules to capture multiple representations of user preference through sequential behaviors and simultaneously utilize the fine-grained user-specific as well as context information. In addition, we design a Global Interaction Module (GIM) to learn the high-order interactions and balance the different impacts of multiple features. Finally, Offline experiment results from three datasets, together with an Online A/B test in a large-scale recommendation system, demonstrate the effectiveness of our proposed approach. © 2020, CC BY.

关键词： User profile

Molecular property prediction: A multilevel quantum interactions modeling perspective

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arXiv 2019年

作者： Lu, Chengqiang Liu, Qi Wang, Chao Huang, Zhenya Lin, Peize He, Lixin Anhui Province Key Lab. of Big Data Analysis and Application University of SandT of China Key Laboratory of Quantum Information University of SandT of China

Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Molecules

Maximizing the effect of information adoption: A general framework

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Maximizing the effect of information adoption: A general fra...

2018 SIAM International Conference on data Mining, SDM 2018

作者： Jin, Tianyuan Xu, Tong Zhong, Hui Chen, Enhong Wang, Zhefeng Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China

With the development of social networking services, social influence analyses, as well as the influence maximization tasks, have attracted wide attention in both academia and industry. Traditional studies mainly focus on simulating process of influence spread. However, two basic functions of social spread, i.e., information propagation and information adoption have not been clearly distinguished. Usually, as information adoption could be even more significant for information publishers in application scenarios, more comprehensive analysis for effect of adoption is urgently required. To that end, in this paper, we propose a novel framework to generally describe social spread, in which information adoption process is separately formulated as random events. Along this line, when we apply this framework to the information adoption maximization task, with proving that the adoption maximization problem is NP-hard and submodular, we further design a polling-based algorithm to achieve an effective approximation. Extensive experiments on four real-world data sets demonstrate the effectiveness and efficiency of proposed algorithms, which validates that our approach could better summarize the complete social spread process, and further support the necessity of distinguishing information adoption from information propagation. © 2018 by SIAM.

关键词： data mining

tp-MA: Orchestrating Three Populations Memetic Algorithm for VNF Deployment in 5G Network

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IAENG International Journal of Computer Science

IAENG International Journal of Computer Science 2020年第4期47卷 1-10页

作者： Xuan, Hejun Wei, Shiwei Zhao, Xuelin Xue, Yahui Qiao, Lanlan Li, Yanling school of Computer and Information Technology Xinyang Normal University XinyangHenan464000 China School of Computer and Technology Guilin University of Aerospace Technology Guilin541000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University XinyangHenan464000 China

Virtual network function (VNF) is the key issue and can provide various network services and is widely deployed in 5G communication. Routing and VNF deployment for the VNF service chain (VNF-SC) is a very important and well-known NP-hard problem. For this problem, if determining the number and locations of data centers is additionally considered, it will be more complexity. In this paper, we investigate a network planning problem by determining all these factors, i.e, by determining not only the optimal routing and the optimal VNF deployment for VNF-SCs, but also the optimal number and locations of data centers. To achieve this purpose, a three objectives optimization model, which minimizes capital expenditure, the maximum index of used frequency slots and the number of deployed VNFs on all data centers, is estimated. To solve this model efficiency, we integrate three objectives into one objective by using a weighted sum strategy. Then, a high-performance memetic algorithm with three populations (tp-MA), which includes well-designed crossover, mutation, and local search operators, is proposed. To demonstrate reasonable of the model and high performance of the designed algorithm, a series of experiments are conducted in several different experimental scenes. Experimental results indicate that the effectiveness of the proposed model and the efficiency of the proposed algorithm. © 2020. All Rights Reserved.

关键词： 5G mobile communication systems

Explainable fashion recommendation: A semantic attribute region guided approach

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arXiv 2019年

作者： Hou, Min Wu, Le Chen, Enhong Li, Zhi Zheng, Vincent W. Liu, Qi Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China School of Data Science University of S&T of China Hefei University of Technology WeBank

In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). Nevertheless, most previous fashion recommendation models comprehend the clothing images with a global content representation and lack detailed understanding of users' semantic preferences, which usually leads to inferior recommendation performance. To bridge this gap, we propose a novel Semantic Attribute Explainable Recommender System (SAERS). Specifically, we first introduce a fine-grained interpretable semantic space. We then develop a Semantic Extraction Network (SEN) and Fine-grained Preferences Attention (FPA) module to project users and items into this space, respectively. With SAERS, we are capable of not only providing cloth recommendations for users, but also explaining the reason why we recommend the cloth through intuitive visual attribute semantic highlights in a personalized manner. Extensive experiments conducted on realworld datasets clearly demonstrate the effectiveness of our approach compared with the state-ofthe-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

MCNE: An end-to-end framework for learning multiple conditional network representations of social network

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arXiv 2019年

作者： Wang, Hao Xu, Tong Liu, Qi Lian, Defu Chen, Enhong Du, Dongfang Wu, Han Su, Wen Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Tencent Inc China

Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention. Though large efforts have been made, they may fail to describe the multiple aspects of similarity between social users, as only a single vector for one unique aspect has been represented for each node. To that end, in this paper, we propose a novel end-to-end framework named MCNE to learn multiple conditional network representations, so that various preferences for multiple behaviors could be fully captured. Specifically, we first design a binary mask layer to divide the single vector as conditional embeddings for multiple behaviors. Then, we introduce the attention network to model interaction relationship among multiple preferences, and further utilize the adapted message sending and receiving operation of graph neural network, so that multi-aspect preference information from high-order neighbors will be captured. Finally, we utilize Bayesian Personalized Ranking loss function to learn the preference similarity on each behavior, and jointly learn multiple conditional node embeddings via multi-task learning framework. Extensive experiments on public datasets validate that our MCNE framework could significantly outperform several state-of-the-art baselines, and further support the visualization and transfer learning tasks with excellent interpretability and robustness. Copyright © 2019, The Authors. All rights reserved.

关键词： Network embeddings

Aftershock Detection with Multi-scale Description Based Neural Network

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Aftershock Detection with Multi-scale Description Based Neur...

IEEE International Conference on data Mining (ICDM)

作者： Qi Zhang Tong Xu Hengshu Zhu Lifu Zhang Hui Xiong Enhong Chen Qi Liu Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research

Aftershocks refer to the smaller earthquakes that occur following large earthquakes, in the same area of the main shock. The task of aftershocks detection, as a crucial and challenging issue in disaster monitoring, has attracted wide research attention in relevant fields. Compared with the traditional detection methods like STA/LTA algorithms or heuristic matching, neural network techniques are regarded as an advanced choice with better pattern recognition ability. However, current neural network-based solutions mainly formulate the seismic wave as ordinary time series, where existing techniques are directly deployed without adaption, and thus fail to obtain competitive performance on the intensive and highly-noise waveforms of aftershocks. To that end, in this paper, we propose a novel framework named Multi-Scale Description based Neural Network (MSDNN) for enhancing aftershock detection. Specifically, MSDNN contains a delicately-designed network structure for capturing both short-term scale and long-term scale seismic features. Therefore, the unique characteristics of seismic waveforms can be fully-exploited for aftershock detection. Furthermore, a multi-task learning strategy is introduced to model the seismic waveforms of multiple monitoring stations simultaneously, which can not only refine the detection performance but also provide additionally quantitative clues for discovering homologous earthquakes. Finally, comprehensive experiments on the data set from aftershocks of the Wenchuan M8.0 Earthquake have clearly validated the effectiveness of our framework compared with several state-of-the-art baselines.

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