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Attention-Based Interaction Trajectory Prediction 9th

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Attention-Based Interaction Trajectory Prediction

9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Liu, Zhe Zhang, Lizong Rao, Zhihong Liu, Guisong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528402 China

ISBN: (纸本)9783030596040

Trajectory prediction is a hot topic in the field of computer vision and has a wide range of applications. Trajectory prediction refers to predicting the future trajectory of a target based on its past trajectory. This paper proposes a method based on graph neural network and attention mechanism, in order to update trajectory characteristics by implement global pedestrian interaction. And, a direct relationship between history and future is introduced with the attention module for reducing error propagation. The method was evaluated on several real-world crowd datasets, the results demonstrate the effectiveness of our method. © 2020, Springer Nature Switzerland AG.

关键词： Trajectories

A Survey of Recent Advances in Optimization Methods for Wireless Communications

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arXiv 2024年

作者： Liu, Ya-Feng Chang, Tsung-Hui Hong, Mingyi Wu, Zheyu So, Anthony Man-Cho Jorswieck, Eduard A. Yu, Wei The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China The Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The Institute for Communication Technology Technische Universitat Braunschweig Germany The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

Mathematical optimization is now widely regarded as an indispensable modeling and solution tool for the design of wireless communications systems. While optimization has played a significant role in the revolutionary progress in wireless communication and networking technologies from 1G to 5G and onto the future 6G, the innovations in wireless technologies have also substantially transformed the nature of the underlying mathematical optimization problems upon which the system designs are based and have sparked significant innovations in the development of methodologies to understand, to analyze, and to solve those problems. In this paper, we provide a comprehensive survey of recent advances in mathematical optimization theory and algorithms for wireless communication system design. We begin by illustrating common features of mathematical optimization problems arising in wireless communication system design. We discuss various scenarios and use cases and their associated mathematical structures from an optimization perspective. We then provide an overview of recently developed optimization techniques in areas ranging from nonconvex optimization, global optimization, and integer programming, to distributed optimization and learning-based optimization. The key to successful solution of mathematical optimization problems is in carefully choosing or developing suitable algorithms (or neural network architectures) that can exploit the underlying problem structure. We conclude the paper by identifying several open research challenges and outlining future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

Optimization from Structured Samples for Coverage Functions

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arXiv 2020年

作者： Chen, Wei Sun, Xiaoming Zhang, Jialin Zhang, Zhijie Microsoft Research Asia Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we cannot obtain a constant approximation ratio for the maximum coverage problem using polynomially many independent samples of the form {Si, f (Si)}ti=1 (Balkanski et al., 2017), even if coverage functions are (1 − ǫ)-PMAC learnable using these samples (Badanidiyuru et al., 2012), which means most of the function values can be approximately learned very well with high probability. In this work, to circumvent the impossibility result of OPS, we propose a stronger model called optimization from structured samples (OPSS) for coverage functions, where the data samples encode the structural information of the functions. We show that under three general assumptions on the sample distributions, we can design efficient OPSS algorithms that achieve a constant approximation for the maximum coverage problem. We further prove a constant lower bound under these assumptions, which is tight when not considering computational efficiency. Moreover, we also show that if we remove any one of the three assumptions, OPSS for the maximum coverage problem has no constant approximation. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Random Forest-Based Ensemble Estimator for Concrete Compressive Strength Prediction via AdaBoost Method 15th

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Random Forest-Based Ensemble Estimator for Concrete Compress...

15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Lv, Yuanxin Shi, Xiaoyu Ran, Longyu Shang, Mingsheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030325909

As one of the most important building materials, the quality of concrete directly affects the safety of buildings. Hence, it is an important and hot issue to predict the compressive strength of concrete with highly accuracy. Most of existing methods heavily depend on building a single model to predict the compressive strength of concrete. However, the proposed single models are not one-size-fits-all, different model have different limitations, making them a better or worse fit for different situation. To address this issue, this paper proposes a novel predict model for concrete compressive strength based on ensemble learning method. In detail, we build our ensemble framework via using the AdaBoost method, while the random forests methods as weak classifier are integrated to the AdaBoost framework. For dealing with the noisy and missing value problems, a set of statistical methods are employed. Furthermore, we utilize the Pearson correlation coefficient to analysis the relationship between different input materials, which can effectively drop out the irrelevant and redundant features. Experimental results on two industrial data sets show that proposed ensemble estimator can significantly improve the prediction accuracy in comparing with other five state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

Low Complexity SLM for OFDMA System with Implicit Side Information

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Low Complexity SLM for OFDMA System with Implicit Side Infor...

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shicheng Hu Miao Yang Kai Kang Hua Qian School of Information Science and Technology Shanghai Tech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences (CAS) Shanghai China University of Chinese Academy of Sciences Beijing China Key Laboratory of Wireless Sensor Network Communication CAS China

Selected mapping (SLM) is an efficient peak-to-average-power-ratio (PAPR) reduction algorithm for orthogonal frequency division multiplexing (OFDM) systems. Conventional SLM requires extra resources for side information transmission. If the side information is transmitted implicitly, significantly high computation overhead is imposed to the receiver at the user equipment (UE) side. In the orthogonal frequency division multiple access (OFDMA) system, the SLM can not be directly applied since the UE does not have access to the signal of other UEs. In this paper, we propose a novel SLM algorithm for the OFDMA system that requires no side information transmission. With the proposed SLM algorithm, each UE can receive its own data without the knowledge of other UEs. The SLM demapping at the UE side is much simplified. Besides, detection of the implicit side information with the proposed algorithm is more robust than existing SLM algorithms. Numerical results validate the theoretical performance of the proposed algorithm.

关键词： Signal processing algorithms Peak to average power ratio Information processing Receivers Signal processing Frequency conversion Complexity theory

Structure Embedded Nucleus Classification for Histopathology Images

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arXiv 2023年

作者： Lou, Wei Wan, Xiang Li, Guanbin Lou, Xiaoying Li, Chenghang Gao, Feng Li, Haofeng Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China Pazhou Lab Guangzhou510330 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Pathology Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Artificial Intelligence Thrust The Hong Kong University of Science and Technology at Guangzhou Guangzhou510030 China Department of Colorectal Surgery Department of General Surgery Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital Sun Yat-sen University Guangzhou510655 China Shanghai Artificial Intelligence Laboratory Shanghai China

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

Deep Sentiment Learning network for Temporal-aware Recommendation Based on User Reviews

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Deep Sentiment Learning Network for Temporal-aware Recommend...

IEEE International Conference on Big data Analysis (ICBDA)

作者： Xinxin Li Tianqi Shang Dezhong Peng Xiaoyu Shi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science Sichuan University Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Recently, many websites encourage users to fill in reviews on items and services to improve the quality of personalized recommendation, because of the rich sentiment information hidden in user reviews. However, most existing recommendation methods based on reviews heavily emphasize extracting the rich sentiment information from reviews by using deep learning technologies as far as possible and always ignore the effects of temporal dynamical of user preferences. To address the above issues, we propose DSL- TR, a deep sentiment learning framework for the temporal-aware recommendation, using an intricate combination of bidirectional long short-term memory (BLSTM) and Convolution Neural network (CNN). It consists of four layers: embedding layer, BLSTM layer, CNN layer and feature fusion layer. In the embedding layer, we explicitly combine the time information in the review embedding layer by regarding the review time as an independent factor. Then, we utilize the BLSTM and CNN networks to extract the long and short-terms of latent features of user and item in parallel. Finally, the factorization machine technique, as a rating predictor, is introduced on the last layer to estimate the rating based on the learned user and item latent features. We conduct extensive experiments on Amazon's four data sets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

关键词： Deep learning Convolution Conferences Neural networks Big data Feature extraction data mining

Analysis and Low-power Hardware Implementation of a Noise Reduction Algorithm

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Analysis and Low-power Hardware Implementation of a Noise Re...

High Performance Big data and Intelligent Systems (HPBD&IS)

作者： Ang Wang Lina Yu Yuyan Lan Weixin Zhou Wan’Ang Xiao Chinese Academy of Sciences Institute of Semiconductors Beijing China School of Microelectronics University of Chinese Academy of Science Beijing China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China

ISBN: (纸本)9781665413282

In order to reduce noise and power consumption of audio electronic products, audio noise reduction and its low- power hardware implementation is studied. Considering the noise reduction effect and the amount of calculation, Log-MMSE algorithm and statistical-model-based Voice Activity Detection (VAD) noise estimator is selected in this paper. And the amount of calculation is optimized at the algorithm level. The ability of tracking non-stable noise is simulated and studied, and a possible noise reduction failure in hardware applications is prevented. In the hardware design, the existing memory is reused to save an extra memory. Two special operations are combined with one operation, and approximate calculations are implemented according to the characteristics of the combined operation. The design of this paper has passed the functional verification on FPGA and taped out with a digital hearing aids chip under the SMIC 0.13μm Mixed Signal Process. The area and the post-simulation power consumption under the tt process corner are 0.206mm 2 and 15.3μW respectively, which meet the requirements of low-power audio chip.

关键词： Voice activity detection Power demand Noise reduction Signal processing algorithms Speech enhancement Approximation algorithms Hearing aids

The Quantum Internet (Technical Version)

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arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing