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IEEE Globecom Workshops

作者： Zezhong Zhang Guangxu Zhu Shuguang Cui The Future Network of Intelligent Institute (FNii) The Chinese University of Hong Kong (Shenzhen) Shenzhen China The School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Pengcheng Lab Shenzhen China

ISBN: (纸本)9781665459761

In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency. The unreliability of conventional spectrum sensing methods by using measurements from a single secondary user (SU) has motivated research on cooperative spectrum sensing (CSS). In this work, we propose a vertical federated learning (VFL) framework to exploit the distributed features across multiple SUs without compromising the data privacy. However, the repetitive training process in VFL faces the issue of high communication latency. To accelerate the training process, we propose a truncated vertical federated learning (T-VFL) algorithm, where the training latency is highly reduced by integrating the standard VFL algorithm with a channel-aware user scheduling policy. The convergence performance of T-VFL is provided via mathematical analysis and justified by simulation results. Moreover, to guarantee the convergence performance of the T-VFL algorithm, we conclude three design rules on the neural architectures used under the VFL framework, whose effectiveness is proved through simulations

关键词： Training Wireless communication Wireless sensor networks Federated learning Simulation Scheduling Sensors

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Not all diffusion model activations have been evaluated as discriminative features 24

Not all diffusion model activations have been evaluated as d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at https://***/Darkbblue/generic-diffusion-feature.

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Drug package recommendation via interaction-aware graph induction

arXiv

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arXiv 2021年

作者： Zheng, Zhi Wang, Chao Xu, Tong Shen, Dazhong Qin, Penggang Huai, Baoxing Liu, Tongzhu Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China Huawei Technologies China First Affiliated Hospital of USTC China

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collaborative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collaborative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Multilayer neural networks

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The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm

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The European Physical Journal Special Topics 2024年 1-11页

作者： You, Yujie Zhou, Fei Yue, Yimin Qiu, Yue Wang, Xinhang Yu, Yangchao Li, Bing Li, Ran Zhang, Le College of Computer Science Sichuan University Chengdu China Jinan Institute of Quantum Technology Jinan China Jinan Branch Hefei National Laboratory Jinan China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu People’s Republic of China Med-X Center for Informatics Sichuan University Chengdu People’s Republic of China West China Hospital- SenseTime Joint Lab Chengdu People’s Republic of China Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

The abdominal aortic aneurysm (AAA) rupture is an extremely dangerous surgical acute abdominal disease. An important preventive method for AAA rupture is to simulate the hemodynamic parameters of AAA for medical evaluation through computational fluid dynamics. To fill the gap between the application of quantum computing and the field of biomedical engineering, we report a classical iterative HHL-based quantum algorithm for AAA hemodynamic simulation, and provide a Hamiltonian simulation approach for the coefficient matrix of linearized one-dimensional hemodynamic equations. Simulation experiments on IBM’s Qiskit (virtual machine) show that our proposed algorithm cannot only significantly reduce quantum resource consumption, but also improve simulation accuracy compared with the HHL algorithm. In addition, we have established a search-based circuit mapping which considers the hardware structure of the ‘Xiaohong’ quantum computer and selects mapping circuits with lower noise to improve the accuracy of measured results.

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Hyperspectral Classification of Coastal Wetland Based on Multi-objective CNN and Decision Fusion Model

Hyperspectral Classification of Coastal Wetland Based on Mul...

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Information Systems and Computing Technology (ISCTech), International Conference on

作者： Yabin Hu Junfang Yang Guangbo Ren Yi Ma Jianbu Wang Xiaoheng Zhu Lab of Marine Physics and Remote Sensing First Institute of Oceanography Ministry of Natural Resources Qingdao China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xian China College of Oceanography and Space Informatics China University of Petroleum Qingdao China College of Geodesy and Geomatics ShanDong University of Science and Technology Qingdao China

Coastal wetland play an irreplaceable role in maintaining biodiversity and improving climate. In this paper, we proposed a hyperspectral classification algorithm of coastal wetland based on multi-objective Convolutional Neural Networks (CNN) and decision fusion model. The algorithm is named that the multi-objective spatial-spectral CNN classification with binary morphological strategy decision fusion model algorithm (MB2SCNN-BMSDF). The multi-objective hyperspectral feature band selection model of coastal wetland based on spectral deviation algorithm is developed and constructed to effectively obtain hyperspectral sensitive bands for single target surface object types. On this basis, a CNN hyperspectral classification model that integrates multi-objective spatial spectral information, and a binary morphological decision fusion algorithm that combines spatial position information and fuzzy membership degrees are proposed, which solves the problems of salt and pepper noise and sand eye effect in the classification process, and reduces noise information. The results showed that the CNN classification of multi-objective spatial spectral information and the decision fusion model of binary morphology strategy have better classification effect and performance.

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Hardware-Supported Remote Persistence for Distributed Persistent Memory

Hardware-Supported Remote Persistence for Distributed Persis...

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Supercomputing Conference

作者： Zhuohui Duan Haodi Lu Haikun Liu Xiaofei Liao Hai Jin Yu Zhang Song Wu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

The advent of Persistent Memory (PM) necessitates an evolution of Remote Direct Memory Access (RDMA) technologies for supporting remote data persistence. Previous software-based solutions require remote CPU intervention and postpone the visibility of remote persistence. In this paper, we design several hardware-supported RDMA primitives to flush data from the volatile cache of RDMA Network Interface Cards (RNICs) to the PM. We also propose durable RPCs based on the proposed RDMA Flush primitives to support remote data persistence and fast failure recovery. We emulate the performance of RDMA Flush primitives through other RDMA prim-itives, and compare our proposals with several state-of-the-art RPCs in a real testbed equipped with PM and InfiniBand networks. Experimental results show that our proposals can improve the throughput of RPCs by up to 90%, and reduce the 99th percentile latency by up to 49%. The experimental studies also provide instructive guidelines for designing RDMA-based distributed PM systems.

关键词： High performance computing Throughput Proposals Network interfaces Guidelines

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Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF data

Grace: An Efficient Parallel SPARQL Query System over Large-...

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International Conference on Computer Supported Cooperative Work in Design

作者： Xiang Kang Yuying Zhao Pingpeng Yuan Hai Jin 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 Wuhan China

As a markup language for describing web resources, RDF is often used to represent graph data. SPARQL is a standard query language for RDF data, which is convenient in querying RDF. As RDF data grows rapidly, how to deal with complex queries over large-scale data in a reasonable time still remains many challenges. The existing RDF query systems often fail to respond within a reasonable time when dealing with complex SPARQL queries. Therefore, we propose an efficient parallel SPARQL query system and novel in three aspects. First, the proposed design provides a dynamic selectivity estimation strategy and generates an optimal query plan for parallel query processing. Second, the new design proposes a parallel processing model to maximize the parallelism of the system. Finally, chunk-based task distribution strategy is implemented to assist the parallel processing model. Based on the proposed design, we implement an efficient parallel query system (Grace). Extensive experiments on LUBM and BTC benchmarks show that Grace outperforms RDF-3X, Virtuoso, TripleBit and achieves a good performance of scalability on the number of threads.

关键词： Distribution strategy Scalability Query processing Markup languages Instruction sets Estimation Parallel processing

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Adaptive Context Selection for Polyp Segmentation 23rd

Adaptive Context Selection for Polyp Segmentation

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Ruifei Li, Guanbin Li, Zhen Cui, Shuguang Qian, Dahong Yu, Yizhou School of Data and Computer Science Sun Yat-sen University Guangzhou China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong ShenzhenGuangdong China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Deepwise AI Lab Beijing China

ISBN: (纸本)9783030597245

Accurate polyp segmentation is of great significance for the diagnosis and treatment of colorectal cancer. However, it has always been very challenging due to the diverse shape and size of polyp. In recent years, state-of-the-art methods have achieved significant breakthroughs in this task with the help of deep convolutional neural networks. However, few algorithms explicitly consider the impact of the size and shape of the polyp and the complex spatial context on the segmentation performance, which results in the algorithms still being powerless for complex samples. In fact, segmentation of polyps of different sizes relies on different local and global contextual information for regional contrast reasoning. To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM). Specifically, LCA modules deliver local context features from encoder layers to decoder layers, enhancing the attention to the hard region which is determined by the prediction map of previous layer. GCM aims to further explore the global context features and send to the decoder layers. ASM is used for adaptive selection and aggregation of context features through channel-wise attention. Our proposed approach is evaluated on the EndoScene and Kvasir-SEG datasets, and shows outstanding performance compared with other state-of-the-art methods. The code is available at https://***/ReaFly/ACSNet. © 2020, Springer Nature Switzerland AG.

关键词： Diagnosis

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Multi-task representation learning with multi-view graph convolutional networks

arXiv

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arXiv 2021年

作者： Huang, Hong Song, Yu Wu, Yao Shi, Jia Xie, Xia Jin, Hai National Engineering Research Center for Big Data Technology Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Link prediction and node classification are two important downstream tasks of network representation learning. Existing methods have achieved acceptable results but they perform these two tasks separately, which requires a lot of duplication of work and ignores the correlations between tasks. Besides, conventional models suffer from the identical treatment of information of multiple views, thus they fail to learn robust representation for downstream tasks. To this end, we tackle link prediction and node classification problems simultaneously via multi-task multi-view learning in this paper. We first explain the feasibility and advantages of multi-task multi-view learning for these two tasks. Then we propose a novel model named as MT-MVGCN to perform link prediction and node classification tasks simultaneously. More specifically, we design a multi-view graph convolutional network to extract abundant information of multiple views in a network, which is shared by different tasks. We further apply two attention mechanisms: view attention mechanism and task attention mechanism to make views and tasks adjust the view fusion process. Moreover, view reconstruction can be introduced as an auxiliary task to boost the performance of the proposed model. Experiments on real-world network datasets demonstrate that our model is efficient yet effective, and outperforms advanced baselines in these two tasks. © 2021, CC BY.

关键词： data mining

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Whale: Efficient One-to-Many data Partitioning in RDMA-Assisted Distributed Stream Processing Systems

Whale: Efficient One-to-Many Data Partitioning in RDMA-Assis...

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Supercomputing Conference

作者： Jie Tan Hanhua Chen Yonghui Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

To process large-scale real-time data streams, existing distributed stream processing systems (DSPSs) leverage different stream partitioning strategies. The one-to-many data partitioning strategy plays an important role in various applications. With one-to-many data partitioning, an upstream processing instance sends a generated tuple to a potentially large number of downstream processing instances. Existing DSPSs leverage an instance-oriented communication mechanism, where an upstream instance transmits a tuple to different downstream instances separately. However, in one-to-many data partitioning, multiple downstream instances typically run on the same machine to exploit multi-core resources. Therefore, a DSPS actually sends a data item to a machine multiple times, raising significant unnecessary costs for serialization and communication. We show that such a mechanism can lead to serious performance bottleneck due to CPU overload. To address the problem, we design and implement Whale, an efficient RDMA (Remote Direct Memory Access) assisted distributed stream processing system. Two factors contribute to the efficiency of this design. First, we propose a novel RDMA-assisted stream multicast scheme with a self-adjusting non-blocking tree structure to alleviate the CPU workloads of an upstream instance during one-to-many data partitioning. Second, we re-design the communication mechanism in existing DSPSs by replacing the instance-oriented communication with a new worker-oriented communication scheme, which saves significant costs for redundant serialization and communication. We implement Whale on top of Apache Storm and conduct comprehensive experiments to evaluate its performance with large-scale real world datasets. The results show that Whale achieves 56.6x improvement of system throughput and 97% reduction of processing latency compared to existing designs.

关键词： Costs Storms High performance computing Memory management Whales Distributed databases Throughput

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