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Neural Temporal Opinion Modelling for Opinion Prediction on Twitter

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arXiv 2020年

作者： Zhu, Lixing He, Yulan Zhou, Deyu Department of Computer Science University of Warwick United Kingdom School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users’ tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user’s historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

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A Clinical Decision Support System of Pressure Ulcers Tissue...

5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

Systematic Control of the Interlayer Exchange Coupling in Perpendicularly Magnetized Synthetic Antiferromagnets

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Physical Review Applied 2022年第5期18卷 054051-054051页

作者： Teng Xu Jiahao Liu Xichao Zhang Qihan Zhang Heng-An Zhou Yiqing Dong Pierluigi Gargiani Manuel Valvidares Yan Zhou Wanjun Jiang State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Frontier Science Center for Quantum Information Tsinghua University Beijing 100084 China Department of Electrical and Computer Engineering Shinshu University 4-17-1 Wakasato Nagano 380-8553 Japan ALBA Synchrotron Light Source Cerdanyola del Vallès 08290 Barcelona Spain School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong 518172 China

The interlayer exchange coupling (IEC) in synthetic antiferromagnets (SAFs) is attracting considerable attention in the spintronics community. While exciting physics is revealed, systematic control of the IEC via tuning the thickness of the magnetic layer is yet to be adequately addressed in SAFs with perpendicular magnetic anisotropy (PMA). In this work, we experimentally investigate this aspect in Pt/Co/Ru/Fe0.55Tb0.45 [ferromagnet (FM)/Ru/ferrimagnet (FIM)] multilayers with PMA, which possess a broader material-parameter space beyond what can be achieved with traditional SAFs. More importantly, the incorporation of the Fe−Tb FIM layer into the SAF multilayers allows us to directly probe the layer-resolved magnetization reversal by utilizing the anomalous Hall effect and standard magnetometry measurements. Our findings are further verified by x-ray magnetic circular dichroism measurements by providing element-resolved magnetic behaviors. Moreover, through systematic control of the IEC, IEC-modulated SAFs with different magnetization reversal behaviors can be acquired, which is further studied by performing magnetic simulations. Our results suggest that systematic control of the IEC through tuning the thickness of FIM layers can be achieved in FM/Ru/FIM multilayers, which could be useful for designing SAF-based magnetic memory and sensing devices.

关键词： Exchange interaction Magnetic anisotropy Spintronics Ferrimagnets Synthetic antiferromagnetic multilayers Transition-metal rare-earth alloys Film deposition Magnetization measurements

Correction: Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2655-2655页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Norof University College Kristiansand Norway Artifcial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Technology Universidad Internacional de La Rioja Logroño Spain

A Survey on Computationally Efficient Neural Architecture Search

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arXiv 2022年

作者： Liu, Shiqing Zhang, Haoyu Jin, Yaochu Nature Inspired Computing and Engineering Faculty of Technology Bielefeld University Bielefeld33619 Germany The Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom The Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai201620 China

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve this major limitation of NAS, improving the computational efficiency is essential in the design of NAS. However, a systematic overview of computationally efficient NAS (CE-NAS) methods still lacks. To fill this gap, we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods, together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities. The remaining challenges and open research questions are also discussed, and promising research topics in this emerging field are suggested. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Comparing Relaxation Mechanisms in Quantum and Classical Transverse-Field Annealing

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Physical Review Applied 2021年第1期15卷 014029-014029页

作者： Tameem Albash Jeffrey Marshall Department of Electrical and Computer Engineering University of New Mexico Albuquerque New Mexico 87131 USA Department of Physics and Astronomy and Center for Quantum Information and Control CQuIC University of New Mexico Albuquerque New Mexico 87131 USA QuAIL NASA Ames Research Center Moffett Field California 94035 USA USRA Research Institute for Advanced Computer Science Mountain View California 94043 USA

Annealing schedule control provides opportunities to better understand the manner and mechanisms by which putative quantum annealers operate. By appropriately modifying the annealing schedule to include a pause (keeping the Hamiltonian fixed) for a period of time, we show that it is possible to more directly probe the dissipative dynamics of the system at intermediate points along the anneal and examine thermal relaxation rates, for example, by observing the repopulation of the ground state after the minimum spectral gap. We provide a detailed comparison of experiments from a D-Wave device, simulations of the quantum adiabatic master equation, and a classical analogue of quantum annealing, spin-vector Monte Carlo, and we observe qualitative agreement, showing that the characteristic increase in success probability when pausing is not a uniquely quantum phenomena. We find that the relaxation in our system is dominated by a single timescale, which allows us to give a simple condition for when we can expect pausing to improve the time to solution, the relevant metric for classical optimization. Finally, we also explore in simulation the role of temperature whilst pausing as a means to better distinguish quantum and classical models of quantum annealers.

关键词： Adiabatic quantum optimization Open quantum systems & decoherence Quantum information processing Quantum master equation Quantum Monte Carlo Simulated annealing

Optically-Sampled Superconducting-Nanostrip Photon-Number Resolving Detector for Non-Classical Quantum State Generation

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arXiv 2024年

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Yabuno, Masahiro Miki, Shigehito Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi Kobe651-2492 Japan NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Photon number-resolving detectors (PNRDs) are the ultimate optical sensors. Superconducting-nanostrip photon detectors (SNSPDs), traditionally known as ON-OFF detectors, have recently been found to have photon number resolving capability without multiplexing. This discovery positions them to become true PNRDs. However, their practical use is limited by the need to precisely detect tiny signal differences with low signal-to-noise ratios within sub-nanosecond time frames. We overcome this challenge using optical sampling with a dual-output Mach Zehnder modulator (DO-MZM) and ultra-short pulsed laser. By adjusting the DO-MZM’s bias voltage to nearly balance the outputs, this method enables sensitive detection of picosecond-order signal differences, achieving a temporal resolution of 1.9 ps and facilitating real-time photon number resolution. We applied this method to produce various non-classical quantum states, enhancing their non-classicality through photon number resolution. This advancement marks a significant shift from principle verification to practical application for SNSPD-type PNRDs in diverse quantum optics fields. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum optics

Cube sampled K-prototype clustering for featured data

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arXiv 2021年

作者： Jain, Seemandhar Shastri, Aditya A. Ahuja, Kapil Busnel, Yann Singh, Navneet Pratap Computer Science and Engineering Indian Institute of Technology Indore Indore India Information Technology NMIMS Shirpur Shirpur India Lab Indian Institute of Technology Indore Indore India Network Systems Cybersecurity and Digital Law Department Institut Mines-Telecom Atlantique Rennes France Computer Science and Engineering NMIMS Indore Indore India

—Clustering large amount of data is becoming increasingly important in the current times. Due to the large sizes of data, clustering algorithm often take too much time. Sampling this data before clustering is commonly used to reduce this time. In this work, we propose a probabilistic sampling technique called cube sampling along with K-Prototype clustering. Cube sampling is used because of its accurate sample selection. KPrototype is most frequently used clustering algorithm when the data is numerical as well as categorical (very common in today’s time). The novelty of this work is in obtaining the crucial inclusion probabilities for cube sampling using Principal Component Analysis (PCA). Experiments on multiple datasets from the UCI repository demonstrate that cube sampled K-Prototype algorithm gives the best clustering accuracy among similarly sampled other popular clustering algorithms (K-Means, Hierarchical Clustering (HC), Spectral Clustering (SC)). When compared with unsampled KPrototype, K-Means, HC and SC, it still has the best accuracy with the added advantage of reduced computational complexity (due to reduced data size).MSC Codes 68T09 © 2021, CC BY.

关键词： Geometry

Pay Less But Get More: A Dual-Attention-based Channel Estimation network for Massive MIMO Systems with Low-Density Pilots

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arXiv 2023年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Department of Automation Tsinghua University Beijing100084 China The School of Intelligent Systems Science and Engineering The GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation