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14,335 results on '"SONG Yan"'

312. A Framework Study of Bohai Sea Ice Comprehensive Service and Expert Aid Decision-making System

Author

Jin Xifang, Guo Donglin, Wang Shuo, Zhao Xiangyu, Song Yan, Liu Aichao, Jiang Wenfei, Wang Ruifu, and Li Xiaomin

Subjects
sea ice, comprehensive service, aid decision-making, system framework, Engineering (General). Civil engineering (General), TA1-2040
Abstract

In this paper, we propose an overall framework about Bohai sea ice comprehensive service and expert aid decision-making system, among which six subsystems consists of sea ice multi-source information acquisition subsystem, sea ice comprehensive information database, sea ice comprehensive information integration subsystem, sea ice expert aid decision-making subsystem, sea ice products release subsystem, and sea ice disaster loss evaluation subsystem. The proposing system has such features as functional integration, business-oriented product services, visual display and decision making, and so on. This paper can provide reference for the construction of sea ice comprehensive service and expert aid decision-making system in China.

Published
2015
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313. Learning to Cluster via Same-Cluster Queries

Author

Li, Yi, Song, Yan, and Zhang, Qin

Subjects
Computer Science - Machine Learning
Abstract

We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries. Different from previous approaches, we do not assume that the total number of clusters is known at the beginning and do not require that the true clusters are consistent with a predefined objective function such as the K-means. These relaxations are critical from the practical perspective and, meanwhile, make the problem more challenging. We propose two algorithms with provable theoretical guarantees and verify their effectiveness via an extensive set of experiments on both synthetic and real-world data.

Published
2021

314. A Note on General Statistics of Publicly Accessible Knowledge Bases

Author

Wang, Feixiang, Fang, Yixiang, Song, Yan, Li, Shuang, and Chen, Xinyun

Subjects
Computer Science - Databases
Abstract

Knowledge bases are prevalent in various domains and have been widely used in a large number of real applications such as applications in online encyclopedia, social media, biomedical fields, bibliographical networks. Due to their great importance, knowledge bases have received much attention from both the academia and industry community in recent years. In this paper, we provide a summary of the general statistics of several open-source and publicly accessible knowledge bases, ranging from the number of objects, relations to the object types and the relation types. With such statistics, this concise note can not only help researchers form a better and quick understanding of existing open accessible knowledge bases, but can also guide the general audience to use the resource effectively when they conduct research with knowledge bases.

Published
2021

315. Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View

Author

Li, Shuang, Wang, Lu, Chen, Xinyun, Fang, Yixiang, and Song, Yan

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Since the first coronavirus case was identified in the U.S. on Jan. 21, more than 1 million people in the U.S. have confirmed cases of COVID-19. This infectious respiratory disease has spread rapidly across more than 3000 counties and 50 states in the U.S. and have exhibited evolutionary clustering and complex triggering patterns. It is essential to understand the complex spacetime intertwined propagation of this disease so that accurate prediction or smart external intervention can be carried out. In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease. We further adopt a generative adversarial imitation learning framework to learn the model parameters. In comparison with the traditional likelihood-based learning methods, this imitation learning framework does not need to prespecify an intensity function, which alleviates the model-misspecification. Moreover, the adversarial learning procedure bypasses the difficult-to-evaluate integral involved in the likelihood evaluation, which makes the model inference more scalable with the data and variables. We showcase the dynamic learning performance on the COVID-19 confirmed cases in the U.S. and evaluate the social distancing policy based on the learned generative model.

Published
2021

316. RevCore: Review-augmented Conversational Recommendation

Author

Lu, Yu, Bao, Junwei, Song, Yan, Ma, Zichen, Cui, Shuguang, Wu, Youzheng, and He, Xiaodong

Subjects
Computer Science - Computation and Language
Abstract

Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items. Incorporating external information (e.g., reviews) is a potential solution to alleviate this problem. Given that reviews often provide a rich and detailed user experience on different interests, they are potential ideal resources for providing high-quality recommendations within an informative conversation. In this paper, we design a novel end-to-end framework, namely, Review-augmented Conversational Recommender (RevCore), where reviews are seamlessly incorporated to enrich item information and assist in generating both coherent and informative responses. In detail, we extract sentiment-consistent reviews, perform review-enriched and entity-based recommendations for item suggestions, as well as use a review-attentive encoder-decoder for response generation. Experimental results demonstrate the superiority of our approach in yielding better performance on both recommendation and conversation responding., Comment: Accepted by ACL-Findings 2021. 13 pages, 3 figures, and 10 tables

Published
2021

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