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International Conference on data Engineering

作者： Xu Xie Jin Niu Lifang Deng Dan Wang Jiandong Zhang Zhihua Wu Kaigui Bian Gang Cao Bin Cui School of CS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Alibaba Group China Lazada National Engineering Lab for Big Data Analysis and Applications Beijing Academy of Artificial Intelligence (BAAI) Institute of Computational Social Science Peking University Qingdao China

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users’ accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key *** this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users’ limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://***/xiaojin-nj/HIM.

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Causality Network of Infectious Disease Revealed With Causal Decomposition

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IEEE Journal of Biomedical and Health Informatics 2023年第7期27卷 3657-3665页

作者： Sun, Jingpeng Yuan, Kai Chen, Chen Xu, Heng Wang, Hesong Zhi, Yuxing Peng, Silong Peng, Chung-Kang Huang, Norden Huang, Guangrui Yang, Albert Chinese Academy of Sciences Institute of Automation Beijing100040 China Beijing University of Chinese Medicine School of Life Sciences Beijing065001 China Harvard Medical School Division of Interdisciplinary Medicine and Biotechnology Beth Israel Deaconess Medical Center BostonMA02115 United States State Oceanic Administration Key Laboratory of Data Analysis and Applications First Institute of Oceanography Qingdao266061 China National Yang Ming Chiao Tung University Institute of Brain Science Digital Medicine and Smart Healthcare Promotion Center Hsinchu30010 Taiwan Taipei Veterans General Hospital Department of Medical Research Taipei11217 Taiwan

Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions. © 2013 IEEE.

关键词： COVID-19

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Evaluating deep graph neural networks

arXiv

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arXiv 2021年

作者： Zhang, Wentao Sheng, Zeang Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

Graph Neural Networks (GNNs) have already been widely applied in various graph mining tasks. However, they suffer from the shallow architecture issue, which is the key impediment that hinders the model performance improvement. Although several relevant approaches have been proposed, none of the existing studies provides an in-depth understanding of the root causes of performance degradation in deep GNNs. In this paper, we conduct the first systematic experimental evaluation to present the fundamental limitations of shallow architectures. Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs;(2) when we need and how to build deep GNNs. The answers to the above questions provide empirical insights and guidelines for researchers to design deep and well-performed GNNs. To show the effectiveness of our proposed guidelines, we present Deep Graph Multi-Layer Perceptron (DGMLP), a powerful approach (a paradigm in its own right) that helps guide deep GNN designs. Experimental results demonstrate three advantages of DGMLP: 1) high accuracy - it achieves state-of-the-art node classification performance on various datasets;2) high flexibility - it can flexibly choose different propagation and transformation depths according to graph size and sparsity;3) high scalability and efficiency - it supports fast training on large-scale graphs. Our code is available in https://***/zwt233/DGMLP. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Fe2alb2@Feal-Ldhs Core-Shell Heterostructures for Electromagnetic Wave Absorption

SSRN

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SSRN 2024年

作者： Hou, Peng Chen, Ke Wang, Xudong Hu, Xiaofei Li, Bing Liu, Ming Hu, Xiao Song, Yujie Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Qianwan Institute of CNiTECH Ningbo315336 China Center of Test and Analysis Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Harbin Institute of Technology Harbin150001 China China China Engineering Laboratory of Advanced Energy Materials China

The rapid development of high-performance materials has garnered considerable attention in addressing electromagnetic pollution. The structural design of core-shell heterostructures is an effective approach for regulating the electromagnetic parameters of the absorbers. However, the adhesive shell phase and core phase usually lacks a direct channel for charge transport. Herein, Fe2AlB2@FeAl-LDHs core-shell heterostructures were prepared by a top-down approach. The FeAl-LDHs nanosheets tightly connected to the Fe2AlB2 parent phase improved the poor electronic transport. Compared to raw Fe2AlB2, the spherical absorber with an embroidered structure demonstrated stronger reflection loss and a broader effective absorption bandwidth for the electromagnetic wave (2-18 GHz) due to the enhanced dielectric and magnetic losses. This study presents a practical method for designing new controllable core-shell heterostructures for electromagnetic wave-absorbing materials. © 2024, The Authors. All rights reserved.

关键词： Shells (structures)

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Computility-Power Coupled Scheduling of data Centers Toward Grid Resilience Enhancement

Computility-Power Coupled Scheduling of Data Centers Toward ...

引用

Power Science and Technology (ICPST), IEEE International Conference on

作者： Youzhen Wu Jianxiao Wang Qi An Zhaohao Ding Jing Dai College of Engineering Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University School of Electrical and Electronic Engineering North China Electric Power University Beijing China Energy Internet Research Institute Tsinghua University Beijing China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

In light of escalating natural disasters, the resilience of power systems, particularly concerning data centers, has become an increasingly important area of study. This paper addresses a notable gap in the existing literature by examining the impact of these disasters on data centers and their responses. We introduce innovative workload management strategies for data centers aimed at bolstering grid stability during such crises. Our approach features a two-stage resilience model coupled with a workload scheduling framework, specifically designed to fortify the resilience of crucial data centers against N-K failures. A case study is presented to illustrate the effectiveness of these strategies. The results show a noteworthy enhancement in both resilience and economic efficiency. Notably, our flexible workload scheduling approach led to a reduction in load-shedding by 7517.66 kW and generated an estimated cost saving of $225,528.

关键词： data centers Costs Processor scheduling Disasters Computational modeling Power system stability data models

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What have I discovered from ocean phenomena

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Journal of Hydrodynamics 2019年第6期31卷 1089-1098页

作者： Norden E.Huang Key Laboratory of Data Analysis and Applications(LDAA) State Oceanic AdministrationQingdao 266061China Pilot National Laboratory for Marine Science and Technology Qingdao 266237China

Almost everything I have done in the last twenty years is based on what I have discovered from studying ocean ***,the waves on the surface of the ocean and their *** items stand out:The first one is from studying the evolution of the waves from ripples to fully developed giant ocean *** quantify the changes,I found that frequency is very different from what had been defined traditionally through Fourier *** frequency should be determined by difterentiation of the phase function defined by an adaptive method rather than by any form of integral ***,it should have ever changing instantaneous *** second discovery is from studying wave turbulence *** reveal the detailed dynamics,I found that spectral representation should not be a single line on the frequency and energy *** spectrum should be a high dimensional manifold to cover all the possible dynamic interactions:additive and multiplicative *** discoveries have enabled me to develop a set of nonlinear and nonstationary data analysis tools to study many other phenomena,from turbulence to brain *** have led me to new wonders I have never even contemplated before.

关键词： data analysis empirical mode decomposition(EMD) HHT Holo-Hilbert spectral analysis wave evolution

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Predicting Response to Patients with Gastric Cancer Via a Dynamic-Aware Model with Longitudinal Liquid Biopsy data

SSRN

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SSRN 2024年

作者： Chen, Zifan Zhao, Jie Li, Yanyan Li, Yilin Liu, Huimin Feng, Xujiao Nan, Xinyu Dong, Bin Shen, Lin Chen, Yang Zhang, Li Center for Data Science Peking University Beijing China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Guangzhou Medical University Guangzhou China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Changsha Institute for Computing and Digital Economy Changsha China

Gastric cancer (GC) presents challenges in predicting treatment responses due to its patient-specific heterogeneity. Recently, liquid biopsies have become recognized as a valuable data modality, offering essential cellular and molecular insights and enabling the capture of time-sensitive information. This study aimed to harness artificial intelligence (AI) technology to analyze longitudinal liquid biopsy data. We collected a dataset from longitudinal liquid biopsies of 91 patients at Peking Cancer Hospital, spanning from July 2019 to April 2022, which included 1,895 tumor-related cellular images and 1,698 tumor marker indices. Subsequently, we introduced the Dynamic-Aware Model (DAM) to predict GC treatment responses. DAM incorporates the dynamic data through AI-engineered components, enabling an in-depth longitudinal analysis. Utilizing three-fold cross-validation, DAM exhibited superior performance over traditional cell-counting-based methods, achieving an AUC of 0.807 in predicting GC treatment responses. In the test set, DAM maintained stable efficacy with an AUC of 0.802. Besides, DAM showed the capability to accurately predict treatment responses from early treatment data. Moreover, DAM's visual analysis of attention mechanisms identified six dynamic focus-area-related visual features and their strong association with treatment response. These findings represent a pioneering effort in applying AI technology for interpreting longitudinal liquid biopsy data and employ visual analytics in GC, offering a promising avenue toward precise response prediction and tailored treatment strategies for patients with ***: This work was supported by the National Natural Science Foundation of China (81801778 to Li Zhang., 82203881 to Yang Chen, U22A20327 to Lin Shen, 12090022 to Bin Dong), Beijing Natural Science Foundation (7222021 to Yang Chen), Beijing Hospitals Authority Youth Programme (QML20231115 to Yang Chen), Clinical Medicine Plus X-Young Scholars Project of P

关键词： Diseases

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Grain: Improving data efficiency of graph neural networks via diversified influence maximization

arXiv

引用

arXiv 2021年

作者： Zhang, Wentao Yang, Zhi Wang, Yexin Shen, Yu Li, Yang Wang, Liang Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Tencent Inc Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications

data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets. However, recent deep learning models have moved forward from independent and identically distributed data to graph-structured data, such as social networks, e-commerce user-item graphs, and knowledge graphs. This evolution has led to the emergence of Graph Neural Networks (GNNs) that go beyond the models existing data selection methods are designed for. Therefore, we present Grain, an efficient framework that opens up a new perspective through connecting data selection in GNNs with social influence maximization. By exploiting the common patterns of GNNs, Grain introduces a novel feature propagation concept, a diversified influence maximization objective with novel influence and diversity functions, and a greedy algorithm with an approximation guarantee into a unified framework. Empirical studies on public datasets demonstrate that Grain significantly improves both the performance and efficiency of data selection (including active learning and core-set selection) for GNNs. To the best of our knowledge, this is the first attempt to bridge two largely parallel threads of research, data selection, and social influence maximization, in the setting of GNNs, paving new ways for improving data efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： data reduction

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ROD: Reception-aware online distillation for sparse graphs

arXiv

引用

arXiv 2021年

作者： Zhang, Wentao Jiang, Yuezihan Li, Yang Sheng, Zeang Shen, Yu Miao, Xupeng Wang, Liang Yang, Zhi Cui, Bin School of EECS Key Laboratory of High Confidence Software Technologies Peking University Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Alibaba Group

Graph neural networks (GNNs) have been widely used in many graph-based tasks such as node classification, link prediction, and node clustering. However, GNNs gain their performance benefits mainly from performing the feature propagation and smoothing across the edges of the graph, thus requiring sufficient connectivity and label information for effective propagation. Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs. Recent interest in this sparse problem has focused on the self-training approach, which expands supervised signals with pseudo labels. Nevertheless, the self-training approach inherently cannot realize the full potential of refining the learning performance on sparse graphs due to the unsatisfactory quality and quantity of pseudo labels. In this paper, we propose ROD, a novel reception-aware online knowledge distillation approach for sparse graph learning. We design three supervision signals for ROD: multi-scale reception-aware graph knowledge, task-based supervision, and rich distilled knowledge, allowing online knowledge transfer in a peer-teaching manner. To extract knowledge concealed in the multi-scale reception fields, ROD explicitly requires individual student models to preserve different levels of locality information. For a given task, each student would predict based on its reception-scale knowledge, while simultaneously a strong teacher is established on-the-fly by combining multi-scale knowledge. Our approach has been extensively evaluated on 9 datasets and a variety of graph-based tasks, including node classification, link prediction, and node clustering. The result demonstrates that ROD achieves state-of-art performance and is more robust for the graph sparsity. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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On The Decomposition of Locational Marginal Hydrogen Pricing

On The Decomposition of Locational Marginal Hydrogen Pricing

引用

IEEE General Meeting Power& Energy Society

作者： Qi An Gengyin Li Jianxiao Wang Yiyang Song Xuanyuan Wang Zhen Liu Guannan He State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China State Grid Jibei Electric Power Co. Ltd. Beijing China College of Engineering Peking University Beijing China

Electrolytic hydrogen is going to be dominant in hydrogen production industry, which calls for a novel hydrogen pricing mechanism suitable for high proportion of electricity-hydrogen interaction. Compared with traditional hydrogen pricing methods such as cost plus and pay-as-bids, marginal-pricing (MP) mechanism could characterize the correlation between hydrogen price (locational marginal hydrogen price, LMHP) and electricity price (locational marginal price, LMP). However, the decomposition of LMHP has not been investigated, which is crucial for the operation of a future hydrogen market. Due to physical characteristics of hydrogen system, the decomposition of LMHP is quite different from that of LMP. In view of the above knowledge gap, we put forward the concept and decomposition method of LMHP for the first time in this paper. Based on the cross-period dynamic characteristics of hydrogen system, the difference between the composition of LMHP and LMP is explored. The hydrogen market clearing model is proposed and the derivation method of composition of LMHP is proved. On top of the conventional three LMP components, we theoretically derive four components of LMHP: energy, loss, congestion, and time-dependent components. Case studies based on Belgium 20-node system demonstrate the effectiveness of the proposed concept and decomposition method of LMHP.

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