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arXiv 2021年

作者： Ye, Jiacheng Cai, Ruijian Gui, Tao Zhang, Qi School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University

The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source document can result in generating uncontrollable and inaccurate absent keyphrases. To address these problems, we propose a novel graph-based method that can capture explicit knowledge from related references. Our model first retrieves some document-keyphrases pairs similar to the source document from a pre-defined index as references. Then a heterogeneous graph is constructed to capture relationships of different granularities between the source document and its references. To guide the decoding process, a hierarchical attention and copy mechanism is introduced, which directly copies appropriate words from both the source document and its references based on their relevance and significance. The experimental results on multiple KG benchmarks show that the proposed model achieves significant improvements against other baseline models, especially with regard to the absent keyphrase prediction. © 2021, CC BY-NC-ND.

关键词： Graph neural networks

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Performance Optimization for Multicast Mm Wave MIMO Networks with Mobile Users

Performance Optimization for Multicast Mm Wave MIMO Networks...

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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： Multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences Multicast communication Real-time systems

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Bi-Contrastive Alignment for Incomplete Multi-view Clustering

Bi-Contrastive Alignment for Incomplete Multi-view Clusterin...

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Chinese Control and Decision Conference, CCDC

作者： Shengxia Gao Yaoying Wang Yuqing Shi Kaiwu Zhang Shiqiang Du Chinese National Information Technology Research Institute Northwest Minzu University Lanzhou China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Province Northwest Minzu University Lanzhou China College of Electrical Engineering Northwest Minzu University Lanzhou China Key Laboratory of Linguistic and Cultural Computing of Ministry of Education Northwest Minzu University Lanzhou China College of Mathematics and Computer Science Northwest Minzu University Lanzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Multi-view clustering (MVC) represents a significant challenge in the realms of machine learning and data mining. Recently, the advent of contrastive learning (CL) has catalyzed the development of numerous innovative methods within this field. The efficacy of these methods typically hinges on the availability of consistent and comprehensive data. Nevertheless, real-world scenarios of data collection and transmission frequently encounter issues like partial unaligned-views (PU), which result in incomplete datasets and pose formidable challenges for effective MVC. To address this prevalent issue, our paper introduces an innovative framework, termed bi-contrastive alignment for incomplete multi-view clustering (BCA). BCA specifically targets the challenge of incomplete data stemming from PU. It encompasses a unique module, semantic guided bi-contrastive alignment (SGBA), designed to counteract the PU by harnessing semantic similarities. Moreover, the framework augments data alignment by integrating a contrastive learning-based corrective component into the loss function. The comparative experiments demonstrate that the ARI metric of our method surpasses the second-best contrasting method on all four datasets.

关键词： Measurement Semantics Contrastive learning Machine learning Fasteners Data collection Benchmark testing

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A relation-oriented clustering method for open relation extraction

arXiv

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arXiv 2021年

作者： Zhao, Jun Gui, Tao Zhang, Qi Zhou, Yaqian School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes. In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relation-oriented representation. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly. To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods. © 2021, CC BY.

关键词： Cluster analysis

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Xdn: towards efficient inference of residual neural networks on cambricon chips 2nd

Xdn: towards efficient inference of residual neural networks...

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2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Li, Guangli Wang, Xueying Ma, Xiu Liu, Lei Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology Jilin University Changchun China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

ISBN: (纸本)9783030495558

In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine. © Springer Nature Switzerland AG 2020.

关键词： Engines

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Mcc: a predictable and scalable massive client load generator 2nd

Mcc: a predictable and scalable massive client load generato...

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2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Wu, Wenqing Feng, Xiao Zhang, Wenli Chen, Mingyu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783030495558

The network load generators are widely used by network researchers to analyze link bandwidth, evaluate network performance and test device capabilities. Data center and IoT networks are quickly evolving and we desire to get a load generator that can precisely generate flow-level workload with high-throughput. Often researchers choose software-based generators because of their flexibility and open-source nature. However, despite the emerging of different solutions, existing software-based flow-level generators have difficulty in generating millions of concurrent TCP connections or achieving one-microsecond precision of packet inter departure time (IDT) which can undermine the correctness of experiments. In this paper, we present a new network load generator, called Massive Client Connections (MCC). MCC is a client load generator which means it performs flow-level load simulation. We separate the control plane from the data plane and design a two-stage timer mechanism to get higher precision. To take full advantage of multicore processors, we utilize the shared-nothing multi-threaded model. Our evaluation demonstrates that MCC generates network load conforming to expected distribution with one-microsecond precision. Moreover, MCC shows definite scalability of throughput in multicore systems. And it is capable of generating more than three million concurrent TCP connections with ten CPU cores. © Springer Nature Switzerland AG 2020.

关键词： Scalability

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Low-resource dialogue summarization with domain-agnostic multi-source pretraining

arXiv

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arXiv 2021年

作者： Zou, Yicheng Zhu, Bolin Hu, Xingwu Gui, Tao Zhang, Qi Institute of Modern Languages and Linguistics Fudan University Shanghai Key Laboratory of Intelligent Information Processing Fudan University School of Computer Science Fudan UniversityShanghai China

With the rapid increase in the volume of dialogue data from daily life, there is a growing demand for dialogue summarization. Unfortunately, training a large summarization model is generally infeasible due to the inadequacy of dialogue data with annotated summaries. Most existing works for low-resource dialogue summarization directly pretrain models in other domains, e.g., the news domain, but they generally neglect the huge difference between dialogues and conventional articles. To bridge the gap between out-of-domain pretraining and in-domain fine-tuning, in this work, we propose a multi-source pretraining paradigm to better leverage the external summary data. Specifically, we exploit large-scale in-domain non-summary data to separately pretrain the dialogue encoder and the summary decoder. The combined encoder-decoder model is then pretrained on the out-of-domain summary data using adversarial critics, aiming to facilitate domain-agnostic summarization. The experimental results on two public datasets show that with only limited training data, our approach achieves competitive performance and generalizes well in different dialogue scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Divide-and-conquer variational quantum algorithms for large-scale electronic structure simulations

arXiv

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arXiv 2022年

作者： Ma, Huan Fan, Yi Liu, Jie Shang, Honghui Li, Zhenyu Yang, Jinlong Hefei National Laboratory University of Science and Technology of China Hefei230088 China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei230026 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Exploring the potential application of quantum computers in material design and drug discovery has attracted a lot of interest in the age of quantum computing. However, the quantum resource requirement for solving practical electronic structure problems are far beyond the capacity of near-term quantum devices. In this work, we integrate the divide-and-conquer (DC) approaches into the variational quantum eigensolver (VQE) for large-scale quantum computational chemistry simulations. Two popular divide-and-conquer schemes, including many-body expansion (MBE) fragmentation theory and density matrix embedding theory (DMET), are employed to divide complicated problems into many small parts that are easy to implement on near-term quantum computers. Pilot applications of these methods to systems consisting of tens of atoms are performed with adaptive VQE algorithms. This work should encourage further studies of using the philosophy of DC to solve electronic structure problems on quantum computers. © 2022, CC BY.

关键词： Electronic structure

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DC-store: eliminating noisy neighbor containers using deterministic I/O performance and resource isolation 20

DC-store: eliminating noisy neighbor containers using determ...

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Proceedings of the 18th USENIX Conference on File and Storage Technologies

作者： Miryeong Kwon Donghyun Gouk Changrim Lee Byounggeun Kim Jooyoung Hwang Myoungsoo Jung Computer Architecture and Memory Systems Laboratory Korea Advanced Institute of Science and Technology Computer Architecture and Memory Systems Laboratory Samsung

ISBN: (纸本)9781939133120

We propose DC-store, a storage framework that offers deterministic I/O performance for a multi-container execution environment. DC-store's hardware-level design implements multiple NVM sets on a shared storage pool, each providing a deterministic SSD access time by removing internal resource conflicts. In parallel, software support of DC-Store is aware of the NVM sets and enlightens Linux kernel to isolate noisy neighbor containers, performing page frame reclaiming, from peers. We prototype both hardware and software counterparts of DC-Store and evaluate them in a real system. The evaluation results demonstrate that containerized data-intensive applications on DC-Store exhibit 31% shorter average execution time, on average, compared to those on a baseline system.

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Investigation of Circular Patch Antenna for Wi-Fi and Bluetooth Applications

Investigation of Circular Patch Antenna for Wi-Fi and Blueto...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Maryam Ghassan Majeed Haitham Bashar Qasim Shakhlokhon Kosimova Hussein Alaa Diame Alaa M. Lafta Haider Alchilibi Computer Engineer Department Al-Kunooze University College Basra Iraq Forensic sciences department Al-Esraa University College Baghdad Iraq Department of Architecture Fergana Polytechnic Institute Fergana Uzbekistan National University of Science and Technology Dhi Qar Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghdad Iraq

The purpose of this paper is to examine and contrast the Novel Circular Patch Antenna employing FR4 and RT Droid (CPA). Resonant frequency of 2.5 GHz in a static medium. To create the antenna, we use the HFSS and two different sample sets. Ten samples are taken from each antenna at 80% G-Power. Assume that the members of Group 1 are all CPAs that use the FR4 substrate. The Novel circular patch antenna (CPA) on RT roger's substrate is in the second group under consideration. This antenna operates at 2.5 GHz to measure reflectance at both ends. A VSWR of 2.1786 dB was measured at 2.4 GHz for a circular CPA designed for RT rogers, while the measured value for a CPA with FR4 was 0.7088 dB. The significance level in SPSS was 0.038 (p 0.05). The findings of this study show that RT rogers is approximately as effective as the FR4 substrate.

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