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arXiv 2024年

作者： Zhu, Panyu Lv, Jingjie Han, Yunghsiang S. Song, Linqi Hou, Hanxu Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China School of Electrical Engineering & Intelligentization Dongguan University of Technology China Department of Computer Science City University of Hong Kong Hong Kong

EVENODD+ codes are binary maximum distance separable (MDS) array codes for correcting double disk failures in RAID-6 with asymptotically optimal encoding/decoding/update complexities. However, the number of bits stored in each disk of EVENODD+ codes should be an odd number minus one. In this paper, we present a new construction of EVENODD+ codes that have more flexible parameters. The number of bits stored in each disk of our codes is an odd minus one times any positive integer. Moreover, our codes not only have asymptotically optimal encoding/decoding/update complexities but also have lower encoding/decoding/update complexities than the existing EVENODD+ codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

arXiv

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arXiv 2024年

作者： Zhang, Jiasheng Ying, Rex Shao, Jie School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Yale University United States Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Temporal knowledge graphs (TKGs) are valuable resources for capturing evolving relationships among entities, yet they are often plagued by noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches struggle to capture the rich semantics introduced by node and edge categories within TKGs, while TKG embedding methods lack interpretability, undermining the credibility of anomaly detection. Moreover, these methods falter in adapting to pattern changes and semantic drifts resulting from knowledge updates. To tackle these challenges, we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs. When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge. The traversal yields reachable nodes that furnish interpretable evidence for the validity or the anomalous of the new knowledge. Overall, AnoT embodies a detector-updater-monitor architecture, encompassing a detector for offline TKG summarization and online scoring, an updater for real-time rule graph updates based on emerging knowledge, and a monitor for estimating the approximation error of the rule graph. Experimental results on four real-world datasets demonstrate that AnoT surpasses existing methods significantly in terms of accuracy and interoperability. All of the raw datasets and the implementation of AnoT are provided in https://***/zjs123/ANoT. © 2024, CC BY.

关键词： Knowledge graph

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Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System

Real-Time Implementation of a Spiking Neural Network-Based C...

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2024 American Control Conference, ACC 2024

作者： Arana, Diego Chavez Garcia A, Omar A. Scola, Ignacio Rubio Espinoza, Eduardo S. Garcia Carrillo, Luis Rodolfo Sornborger, Andrew Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States National Polytechnic Institute Center for Research and Advanced Studies Mexico City Mexico National Institute of Industrial Technology CONICET National University of Rosario Department of Industrial Products Engineering Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances. © 2024 AACC.

关键词： Neural networks

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Three-Dimensional Shear Wave Elastography Using a 2D Row Column Addressing(RCA)Array

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Biomedical engineering Frontiers 2022年第1期3卷 337-347页

作者： Zhijie Dong Jihun Kim Chengwu Huang Matthew RLowerison U-Wai Lok Shigao Chen Pengfei Song Beckman Institute for Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaILUSA Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaILUSA Division of ICT Convergence Engineering/Major in Electronic Engineering Kangnam UniversityRepublic of Korea Department of Radiology Mayo Clinic College of Medicine and ScienceRochesterMNUSA

*** develop a 3D shear wave elastography(SWE)technique using a 2D row column addressing(RCA)array,with either external vibration or acoustic radiation force(ARF)as the shear wave *** *** proposed method paves the way for clinical translation of 3D SWE based on the 2D RCA,providing a low-cost and high volume rate solution that is compatible with existing clinical *** is an established ultrasound imaging modality that provides a direct and quantitative assessment of tissue stiffness,which is significant for a wide range of clinical applications including cancer and liver *** requires high frame rate imaging for robust shear wave *** to the technical challenges associated with high volume rate imaging in 3D,current SWE techniques are typically confined to *** SWE from 2D to 3D is significant because of the heterogeneous nature of tissue,which demands 3D imaging for accurate and comprehensive ***.A 3D SWE method using a RCA array was developed with a volume rate up to 2000 *** performance of the proposed method was systematically evaluated on tissue-mimicking elasticity phantoms and in an in vivo case ***.3D shear wave motion induced by either external vibration or ARF was successfully detected with the proposed *** 3D shear wave speed maps were reconstructed for phantoms and in *** high volume rate 3D imaging provided by the 2D RCA array provides a robust and practical solution for 3D SWE with a clear pathway for future clinical translation.

关键词： Shear elasticity typically

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3D Object Detection Based on Spatially Adaptive Sparse Convolution

3D Object Detection Based on Spatially Adaptive Sparse Convo...

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2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024

作者： Hu, Jing Jing Fan, Yu Ling Zhang, Ping Duan, Chang Zhang, Chao Chen, Si Yu Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province Luzhou China School of Computer Science And Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350391213

Autonomous driving has gradually gained attention in recent years due to its potential to reduce driver burden and improve safety, in which 3D object detection is the basis of the autonomous driving perception system, aiming at predicting the location, size, and class of 3D objects in the vicinity of an autonomous vehicle, which is an important guide for subsequent path planning, motion prediction, and so on. In particular, 3D object detection has difficulties in input point cloud data processing and multimodal fusion. Unlike the image distribution where pixels are regularly distributed in the image plane, the point cloud is a sparse and irregular 3D representation, so it is difficult to apply the traditional convolution directly on the point cloud for detection. In this paper, we carry out a method for 3D object detection for point clouds, using deformable convolution and sparse convolution as a tool to explore the feature extraction method of sparse convolution for irregular point clouds and propose a spatially adaptive sparse convolution-based method. © 2024 IEEE.

关键词： Motion planning

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Deep Multi-View Clustering via View-Specific Representation and Global Graph

Deep Multi-View Clustering via View-Specific Representation ...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jingyu Pu Chenhang Cui Yazhou Ren Zichen Wen Tianyi Wu Xiaorong Pu Lifang He School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Department of Computer Science and Engineering Lehigh University Bethlehem USA

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method.

关键词： Clustering methods Learning (artificial intelligence) Task analysis Residual neural networks

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Fast Erasure Decoding of Reed-Solomon Codes Based on Decomposition of Vandermonde Matrix

Fast Erasure Decoding of Reed-Solomon Codes Based on Decompo...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Zebing Lin Jingjie Lv Pingping Li Linqi Song Hui Liang Hanxu Hou School of Computer Science and Technology Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan University of Technology Shenzhen Institute for Advanced Study University of Electronic Science & Technology of China Department of Computer Science City University of Hong Kong City University of Hong Kong Shenzhen Research Institute

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Reed-Solomon (RS) codes are widely applied in storage systems. The main bottleneck of RS codes is that the high decoding complexity defined as the number of multiplications and additions. In this paper, we propose a fast decoding algorithm for RS codes that requires fewer number of multiplications. Our fast decoding algorithm is designed by a decomposition of Vandermonde matrices. We show that our decoding algorithm requires fewer number of multiplications than the existing best known algorithm when the number of parities $T$ is large. When the codeword length $N=16$ , our decoding algorithm reduces the number of multiplications by 7% and 29% when $T=4$ and $T=7$ , respectively, compared with the best known decoding algorithm.

关键词： Reed-Solomon codes Wireless communication Codes Upper bound Signal processing algorithms Transforms Signal processing Decoding Matrix decomposition Galois fields

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A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs

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Petroleum Science 2023年第4期20卷 2232-2243页

作者： Gang Hui Zhang-Xin Chen Hai Wang Zhao-Jie Song Shu-Hua Wang Hong-Liang Zhang Dong-Mei Zhang Fei Gu National Key Laboratory of Petroleum Resources and Engineering China University of PetroleumBeijing102249PR China Department of Chemical and Petroleum Engineering University of CalgaryCalgaryABT2N1N4Canada Eastern Institute for Advanced Study Ningbo315200ZhejiangPR China Computer Modeling Group Ltd.Calgary ABT2L2M1Canada CNOOC Research Institute Beijing100028PR China China University of Geoscience(Wuhan) Wuhan430074HubeiPR China Research Institute of Petroleum Exploration and Development Beijing10083PR China

A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicity-frequent regions.

关键词： Induced seismicity Hydraulic fracturing Seismicity susceptibility Risk control Machine learning

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Organic-Perovskite Bilayer Color Conversion Media for Full-Color Displays

Organic-Perovskite Bilayer Color Conversion Media for Full-C...

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International Conference on Display Technology, 2024

作者： Liu, Siyang Ting Tam, Bryan Siu Zhu, Mingchao Ye, Fanghao Yan Yeung, Fion Sze Li, Guijun Dong, Shou-Cheng State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen University Shenzhen China Institute for Advanced Study Hong Kong University of Science and Technology Kowloon Hong Kong Co. Ltd. Suzhou China

Color conversion media (CCM) are crucial for realizing cost-effective, high-resolution micro-LED and OLED displays. In this work, we developed green and red organic-perovskite bilayer CCM that combine the PLQY and color purity of perovskite quantum dots (PeQDs) and the strong blue absorption of perylene-based organic emitters. The bilayer with thickness of 300 nm showed over 99% blue absorption and PLQY of 77% and 61% for green and red CCM, respectively. © 2024 John Wiley and Sons Inc. All rights reserved.

关键词： Perovskite

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Muslim-Violence Bias Persists in Debiased GPT Models

arXiv

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arXiv 2023年

作者： Hemmatian, Babak Baltaji, Razan Varshney, Lav R. Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering

Abid et al. (2021) showed a tendency in GPT-3 to generate mostly violent completions when prompted about Muslims, compared with other religions. Two pre-registered replication attempts found few violent completions and only a weak anti-Muslim bias in the more recent InstructGPT, fine-tuned to eliminate biased and toxic outputs. However, more pre-registered experiments showed that using common names associated with the religions in prompts increases several-fold the rate of violent completions, revealing a significant second-order anti-Muslim bias. ChatGPT showed a bias many times stronger regardless of prompt format, suggesting that the effects of debiasing were reduced with continued model development. Our content analysis revealed religion-specific themes containing offensive stereotypes across all experiments. Our results show the need for continual de-biasing of models in ways that address both explicit and higher-order associations. © 2023, CC BY.

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