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Zhang Qi
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publications
Ethylenediaminetetraacetic acid-assisted synthesis of Bi2Se3 nanostructures with unique edge sites
Published in Nano Research, 2016
Nanomaterials with unique edge sites have received increasing attention due to their superior performance in various applications. Herein, we employed an effective ethylenediaminetetraacetic acid (EDTA)-assisted method to synthesize a series of exotic Bi2Se3 nanostructures with distinct edge sites. It was found that the products changed from smooth nanoplates to half-plate-containing and crown-like nanoplates upon increasing the molar ratio of EDTA to Bi3+. Mechanistic studies indicated that, when a dislocation source and relatively high supersaturation exist, the step edges in the initially formed seeds can serve as supporting sites for the growth of epilayers, leading to the formation of half-plate-containing nanoplates. In contrast, when the dislocation source and a suitably low supersaturation are simultaneously present in the system, the dislocation-driven growth mode dominates the process, in which the step edges form at the later stage of the growth responsible for the formation of crown-like nanoplates.
Recommended citation: Liu, Xianli, Zhicheng Fang, Qi Zhang, Ruijie Huang, Lin Lin, Chunmiao Ye, Chao Ma, and Jie Zeng. "Ethylenediaminetetraacetic acid-assisted synthesis of Bi2Se3 nanostructures with unique edge sites." Nano Research 9, no. 9 (2016): 2707-2714
Aftershock Detection with Multi-scale Description Based Neural Network
Published in 2019 IEEE International Conference on Data Mining (ICDM), 2019
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.
Recommended citation: Zhang, Qi, Tong Xu, Hengshu Zhu, Lifu Zhang, Hui Xiong, Enhong Chen, and Qi Liu. "Aftershock detection with multi-scale description based neural network." In 2019 IEEE International Conference on Data Mining (ICDM), pp. 886-895. IEEE, 2019.
基于知识图谱的推荐系统研究综述
Published in 《中国科学：信息科学》, 2020
推荐系统旨在为用户推荐个性化的在线商品或信息,其广泛应用于众多Web场景之中,来处理海量信息数据所导致的信息过载问题,以此提升用户体验.鉴于推荐系统强大的实用性,自20世纪90年代中期以来,研究者针对其方法与应用两方面,进行了大量广泛的研究.近年来,很多工作发现知识图谱中所蕴含的丰富信息可以有效地解决推荐系统中存在的一系列关键问题,例如数据稀疏、冷启动、推荐多样性等.因此,本文针对基于知识图谱的推荐系统这一领域进行了全面的综述.具体地,首先简单介绍推荐系统与知识图谱中的一些基本概念.随后,详细介绍现有方法如何挖掘知识图谱不同种类的信息并应用于推荐系统.此外,总结了相关的一系列推荐应用场景.最后,提出了对基于知识图谱的推荐系统前景的看法,并展望了该领域未来的研究方向.
Recommended citation: 秦川, 祝恒书, 庄福振, 郭庆宇, 张琦, 张乐, 王超, 陈恩红, 熊辉. "基于知识图谱的推荐系统研究综述." 中国科学: 信息科学 50, no. 7 (2020): 937-956.
Market-oriented job skill valuation with cooperative composition neural network
Published in Nature Communications, 2021
The value assessment of job skills is important for companies to select and retain the right talent. However, there are few quantitative ways available for this assessment. Therefore, we propose a data-driven solution to assess skill value from a market-oriented perspective. Specifically, we formulate the task of job skill value assessment as a Salary-Skill Value Composition Problem, where each job position is regarded as the composition of a set of required skills attached with the contextual information of jobs, and the job salary is assumed to be jointly influenced by the context-aware value of these skills. Then, we propose an enhanced neural network with cooperative structure, namely Salary-Skill Composition Network (SSCN), to separate the job skills and measure their value based on the massive job postings. Experiments show that SSCN can not only assign meaningful value to job skills, but also outperforms benchmark models for job salary prediction.
Recommended citation: Sun, Ying, Fuzhen Zhuang, Hengshu Zhu, Qi Zhang, Qing He, and Hui Xiong. "Market-oriented job skill valuation with cooperative composition neural network." Nature communications 12, no. 1 (2021): 1-12.
TransQuake: A transformer-based deep learning approach for seismic P-wave detection
Published in Earthquake Research Advances, 2021
Recent years, we have witnessed the increasing research interest in developing machine learning, especially deep learning which provides approaches for enhancing the performance of microearthquake detection. While considerable research efforts have been made in this direction, most of the state-of-the-art solutions are based on Convolutional Neural Network (CNN) structure, due to its remarkable capability of modeling local and static features. Indeed, the globally dynamic characteristics contained within time series data (i.e., seismic waves), which cannot be fully captured by CNN-based models, have been largely ignored in previous studies. In this paper, we propose a novel deep learning approach, TransQuake, for seismic P-wave detection. The approach is based on the most advanced sequential model, namely Transformer. To be specific, TransQuake can exploit the STA/LTA algorithm for adapting the three-component structure of seismic waves as input, and take advantage of the multi-head attention mechanism for conducting explainable model learning. Extensive evaluations of the aftershocks following the 2008 Wenchuan MW 7.9 earthquake clearly demonstrates that TransQuake is able to achieve the best detection performance which excels the results obtained using other baselines. Meanwhile, experimental results also validate the interpretability of the results obtained by TransQuake, such as the attention distribution of seismic waves in different positions, and the analysis of the optimal relationship between coda wave and P-wave for noise identification.
Recommended citation: Hu, Yumeng, Qi Zhang, Wenjia Zhao, and Haitao Wang. "TransQuake: A transformer-based deep learning approach for seismic P-wave detection." Earthquake Research Advances 1, no. 2 (2021): 100004.
EL-Picker: 基于集成学习的余震P波初动实时拾取方法
Published in 《中国科学：信息科学》, 2021
在实时地震监测中, 地震 P 波 (primary wave) 的初动拾取任务具有至关重要的作用, 其有助于 地震应急响应的及时实施. 虽然此前在该领域已开展了大量的研究, 但是如何从地震分布密集并且充 满噪声的监测波形中有效地识别出 P 波仍然是一个具有挑战性的任务. 例如对于大地震的余震监测, 实践中使用的普遍方法仍依赖于专家辅助标注. 本文针对地震实时监测任务, 基于集成学习策略, 提 出一个全新的技术框架 EL-Picker, 实现从连续地震波形中自主拾取 P 波的初动到时. 具体而言, EL-Picker 包含 3 个模块, 即触发器、分类器和精化器. 其中, 分类器模块借鉴集成学习策略, 实现对多 个个体学习器的整合, 提升整体模型性能. 基于汶川 Ms8.0 地震的余震数据集进行的大量实验, 我们 发现 EL-Picker 不仅较好地实现 P 波初动拾取效果, 并且多诊断出 120% 被人工遗漏的地震 P 波. 同 时, 实验结果也启发我们探索如何针对不同的地震站台选取个性化的个体学习器构建分类器模块. 此 外, 我们进一步地讨论了被人工遗漏的地震波形的规律特点, 用于指导人工地震标注. 这些发现清晰 地验证了 EL-Picker 框架的鲁棒性、时效性、灵活性以及稳定性.
Recommended citation: Shen, Dazhong, Qi Zhang, Tong Xu, Hengshu Zhu, Wenjia Zhao, Zikai Yin, Peilun Zhou, Lihua Fang, Enhong Chen, and Hui Xiong. "EL-Picker: 基于集成学习的余震 P 波初动实时拾取方法." Scientia Sinica Informationis 51, no. 6 (2021): 912-926.
Exploiting Real-time Search Engine Queries for Earthquake Detection: A Summary of Results
Published in ACM Transactions on Information Systems (TOIS), 2021
Online search engine has been widely regarded as the most convenient approach for information acquisition. Indeed, the intensive information-seeking behaviors of search engine users make it possible to exploit search engine queries as effective “crowd sensors” for event monitoring. While some researchers have investigated the feasibility of using search engine queries for coarse-grained event analysis, the capability of search engine queries for real-time event detection has been largely neglected. To this end, in this article, we introduce a large-scale and systematic study on exploiting real-time search engine queries for outbreak event detection, with a focus on earthquake rapid reporting. In particular, we propose a realistic system of real-time earthquake detection through monitoring millions of queries related to earthquakes from a dominant online search engine in China. Specifically, we first investigate a large set of queries for selecting the representative queries that are highly correlated with the outbreak of earthquakes. Then, based on the real-time streams of selected queries, we design a novel machine learning–enhanced two-stage burst detection approach for detecting earthquake events. Meanwhile, the location of an earthquake epicenter can be accurately estimated based on the spatial-temporal distribution of search engine queries. Finally, through the extensive comparison with earthquake catalogs from China Earthquake Networks Center, 2015, the detection precision of our system can achieve 87.9%, and the accuracy of location estimation (province level) is 95.7%. In particular, 50% of successfully detected results can be found within 62 s after earthquake, and 50% of successful locations can be found within 25.5 km of seismic epicenter. Our system also found more than 23.3% extra earthquakes that were felt by people but not publicly released, 12.1% earthquake-like special outbreaks, and meanwhile, revealed many interesting findings, such as the typical query patterns of earthquake rumor and regular memorial events. Based on these results, our system can timely feed back information to the search engine users according to various cases and accelerate the information release of felt earthquakes.
Recommended citation: Zhang, Qi, Hengshu Zhu, Qi Liu, Enhong Chen, and Hui Xiong. "Exploiting Real-time Search Engine Queries for Earthquake Detection: A Summary of Results." ACM Transactions on Information Systems (TOIS) 39, no. 3 (2021): 1-32.
Talent Demand Forecasting with Attentive Neural Sequential Model
Published in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 2021
To cope with the fast-evolving business trend, it becomes critical for companies to continuously review their talent recruitment strategies by the timely forecast of talent demand in recruitment market. While many efforts have been made on recruitment market analysis, due to the sparsity of fine-grained talent demand time series and the complex temporal correlation of the recruitment market, there is still no effective approach for fine-grained talent demand forecast, which can quantitatively model the dynamics of the recruitment market. To this end, in this paper, we propose a data-driven neural sequential approach, namely Talent Demand Attention Network (TDAN), for forecasting fine-grained talent demand in the recruitment market. Specifically, we first propose to augment the univariate time series of talent demand at multiple grained levels and extract intrinsic attributes of both companies and job positions with matrix factorization techniques. Then, we design a Mixed Input Attention module to capture company trends and industry trends to alleviate the sparsity of fine-grained talent demand. Meanwhile, we design a Relation Temporal Attention module for modeling the complex temporal correlation that changes with the company and position. Finally, extensive experiments on a real-world recruitment dataset clearly validate the effectiveness of our approach for fine-grained talent demand forecast, as well as its interpretability for modeling recruitment trends. In particular, TDAN has been deployed as an important functional component of intelligent recruitment system of cooperative partner.
Recommended citation: Zhang, Qi, Hengshu Zhu, Ying Sun, Hao Liu, Fuzhen Zhuang, and Hui Xiong. "Talent demand forecasting with attentive neural sequential model." In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3906-3916. 2021.
Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning
Published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD), 2021
Talent demand and supply forecasting aims to model the variation of the labor market, which is crucial to companies for recruitment strategy adjustment and to job seekers for proactive career path planning. However, existing approaches either focus on talent demand or supply forecasting, but overlook the interconnection between demand-supply sequences among different companies and positions. To this end, in this paper, we propose a Dynamic Heterogeneous Graph Enhanced Meta-learning (DH-GEM) framework for fine-grained talent demand-supply joint prediction. Specifically, we first propose a Demand-Supply Joint Encoder-Decoder (DSJED) and a Dynamic Company-Position Heterogeneous Graph Convolutional Network (DyCP-HGCN) to respectively capture the intrinsic correlation between demand and supply sequences and company-position pairs. Moreover, a Loss-Driven Sampling based Meta-learner (LDSM) is proposed to optimize long-tail forecasting tasks with a few training data. Extensive experiments have been conducted on three real-world datasets to demonstrate the effectiveness of our approach compared with five baselines. DH-GEM has been deployed as a core component of the intelligent human resource system of a cooperative partner.
Recommended citation: Zhuoning Guo, Hao Liu, Le Zhang, Qi Zhang, Hengshu Zhu, and Hui Xiong. "Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning." In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2022, Accepted.
talks
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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