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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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Prospect of 5g application in civil airport 1

Prospect of 5g application in civil airport

引用

1st IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2019

作者： Le, Ningning Zheng, Yinger Huang, Qian Pei, Yanqiu Big Data Analysis and Application Center China Academy of Civil Aviation Science and Technology Beijing China Service Lab Huawei Research Institute Huawei Technology Co. Ltd Beijing China China University of Mining and Technology College of Geoscience and Surveying Engineering Beijing China

ISBN: (纸本)9781728125985

With the rapid growth of China's civil aviation transportation volume, the airport has entered the era of big data in which data is massive, diversified, and converted at high speeds. Achievements for intelligent application of big data based on 5G technology have become an indispensable component of smart airports. Based on the characteristics of 5G technology, this paper combines various state-of-art technologies including Discrete Global Grid Systems, Complex Network, Artificial Intelligence, to prospect the application scenarios of 5G in airport, as efficient and secure wide-area wireless communication system, IoT-based smart monitoring, airport unmanned driving, high-speed entertainment experience, intelligent passenger service. © 2019 IEEE.

关键词： big data

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Context aware 3d unet for brain tumor segmentation

arXiv

引用

arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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Harnessing low-fidelity data to accelerate Bayesian optimization via posterior regularization

arXiv

引用

arXiv 2019年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the required number of function evaluations for BO without compromise in solution quality. We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework. The LF data can arise from previous evaluations of an LF approximation of the BOF or a related optimization task. An extra GP model called LF-GP is trained to fit the LF data. We develop an operator termed dynamic weighted product of experts (DW-POE) fusion. The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism. Extensive experimental results on benchmark BOF optimization tasks demonstrate the superior performance of the proposed algorithm over state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Function evaluation

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Robust Particle Filtering via Bayesian Nonparametric Outlier Modeling (Poster)

Robust Particle Filtering via Bayesian Nonparametric Outlier...

引用

International Conference on Information Fusion

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

This paper is concerned with the online estimation of a nonlinear dynamic system from a series of noisy measurements. The focus is on cases wherein outliers are present in-between normal noises. We assume that the outliers follow an unknown generating mechanism which deviates from that of normal noises, and then model the outliers using a Bayesian nonparametric model called Dirichlet process mixture (DPM). A sequential particle-based algorithm is derived for posterior inference for the outlier model as well as the state of the system to be estimated. The resulting algorithm is termed DPM based robust PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to “speak for itself” to determine the complexity and structure of the outlier model. Simulation results show that it performs remarkably better than two state-of-the-art methods especially when outliers appear frequently along time.

关键词： Noise measurement Inference algorithms Bayes methods Computational modeling Atmospheric measurements Particle measurements Pollution measurement

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Inhibition of colorectal cancer targets IL-6, CTLA-4, & B7-2 by Tislelizumab: molecular docking, dynamics, & STRING protein-protein network analysis

引用

Informatics in Medicine Unlocked 2023年 41卷

作者： Elkazzaz, Mahmoud Ullah, Shahid Gao, Tianshun Shamkh, Israa M. Ahmed, Amr Wu, Ted X. Elsharayidi, Maii S. Khan, Mohammad Shahbaz Rehman, Aziz-ur Lotfy, Marwa M. Haikal, Abdullah Abdalrahman, Mahmoud Department of Chemistry and Biochemistry Faculty of Science Damietta University Damietta 7952567 Egypt S Khan Lab Mardan Khyber Pakhtunkhwa Pakistan Big Data Center The Seventh Affiliated Hospital of Sun Yat-sen University Shenzhen 518107 China Botany and Microbiology Department Faculty of Science Cairo University Cairo 12613 Egypt Chemo and Bioinformatics Lab Bio Search Research Institution BSRI Giza 12613 Egypt Public Health Department First Health Cluster Ministry of Health Riyadh 966-11 Saudi Arabia Science Department Milton Academy Milton 02186 MA United States Central Public Health Laboratories Egyptian Ministry of Health Egypt Children's National Hospital Washington DC United States Keystone Pharmacogenomics LLC Bensalem 19020 PA United States Faculty of Pharmacy Zagazig University Zagazig 44511 Egypt Department of Pharmacognosy Faculty of Pharmacy Mansoura University Mansoura 35516 Egypt Lowell Institute CPS Northeastern University 360 Huntington Ave Boston 02115 MA United States

As a DNA mismatch repair deficit (dMMR) anticancer medication, and a humanized monoclonal antibody (hmAb), Tislelizumab has attracted significant interest in recent years. The programmed cell death protein-1 (PD-1) and its ligands (PD-L1/CD274/B7-H1 and PDL2/CD273) may bind to each other with great affinity. This binding prevents activated T-cells from continuing to proliferate, produce cytokines, and engage in cytotoxic action. The B7:PD-1 inhibitory immunological checkpoint receptor binds to the B7-2(CD86) B-lymphocyte activation antigen. This paper presents Tislelizumab's molecular docking and dynamics simulations with the receptor B7-2(CD86) and its downstream signaling proteins CTLA-4 and IL-6. When docked to B cell stimulatory factor-2 soluble IL-6 receptor alpha (sIL6Ra/CD126), Tislelizumab had a strong binding affinity of 329 kcal/mol and provided an excellent quality model with an LGscore of 4.54. The type I membrane protein B7-2(CD86), a member of the immunoglobulin superfamily, responded to docking with an affinity of −332.35 kcal/mol and LGscore of 2.54. Docking to the immune checkpoint protein receptor cytotoxic T-lymphocyte-associated protein-4 (CTLA-4/CD152) had a binding affinity of −305.91 kcal/mol, facilitating immunological control and cellular dissociation. Tislelizumab and these target proteins exhibited persistent and advantageous interactions in molecular dynamics studies. The STRING functional protein-protein network analysis supported our in silico docking and dynamic simulation results. This analysis also included comparisons of the binding modes of mAbs to protein receptors involved in the cancer hallmark mechanism(s), demonstrating that IL-6, B7-2, and CTLA-4 act and are inhibited in group-mode. Functional enrichment analysis and molecular modelling supported the relationship between the hallmarks of the tumor microenvironment that link inflammation to immunity. Tislelizumab may therefore be able to prevent these three proteins from actin

关键词： B7-2 Colorectal Cancer CTLA-4 Dostarlimab IL-6 Immunotherapy PD-1 Tislelizumab

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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

引用

arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent.

arXiv

引用

arXiv 2019年

作者： Hu, Wenqing Zhu, Zhanxing Xiong, Haoyi Huan, Jun Department of Mathematics and Statistics Missouri University of Science and Technology University of Missouri Rolla Peking University Beijing Institute of Big Data Research Beijing China Big Data Lab Baidu Inc. National Engineering Laboratory of Deep Learning Application and Technology

We interpret the variational inference of the Stochastic Gradient Descent (SGD) as minimizing a new potential function named the quasi-potential. We analytically construct the quasi-potential function in the case when the loss function is convex and admits only one global minimum point. We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type. This relation helps us to show that anisotropic noise leads to faster escape than isotropic noise. We then consider the dynamics of SGD in the case when the loss function is non-convex and admits several different local minima. In this case, we demonstrate an example that shows how the noise covariance structure plays a role in "implicit regularization", a phenomenon in which SGD favors some particular local minimum points. This is done through the relation between the noise covariance structure and the quasi-potential function. Our analysis is based on Large Deviations Theory (LDT), and they are validated by numerical experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Global context-aware progressive aggregation network for salient object detection

arXiv

引用

arXiv 2020年

作者： Chen, Zuyao Xu, Qianqian Cong, Runmin Huang, Qingming University of Chinese Academy of Sciences Beijing China Key Lab. of Intelligent Information Processing ICT CAS Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Key Lab. of Big Data Mining and Knowledge Management CAS Beijing China Peng Cheng Laboratory Shenzhen Guangdong China

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiplelevel feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Explainable fashion recommendation: A semantic attribute region guided approach

arXiv

引用

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

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