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Fast CU Partition for VVC Using Texture Complexity Classification Convolutional Neural Network

computers, Materials & Continua 2022年第11期73卷 3545-3556页

作者： Yue Zhang Pengyu Liu Xiaowei Jia Shanji Chen Tianyu Liu Chang Liu The Information Department Beijing University of TechnologyBeijing100124China School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810000China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing Key Laboratory Beijing100124China Department of Computer Science University of PittsburghPittsburgh15260USA

Versatile video coding(H.266/VVC),which was newly released by the Joint Video Exploration Team(JVET),introduces quad-tree plus multitype tree(QTMT)partition structure on the basis of quad-tree(QT)partition structure in High Efficiency Video Coding(H.265/HEVC).More complicated coding unit(CU)partitioning processes in H.266/VVC significantly improve video compression efficiency,but greatly increase the computational complexity *** ultra-high encoding complexity has obstructed its real-time *** order to solve this problem,a CU partition algorithm using convolutional neural network(CNN)is proposed in this paper to speed up the H.266/VVC CU partition ***,64×64 CU is divided into smooth texture CU,mildly complex texture CU and complex texture CU according to the CU texture ***,CU texture complexity classification convolutional neural network(CUTCC-CNN)is proposed to classify ***,according to the classification results,the encoder is guided to skip different RDO search *** optimal CU partition results will be *** results show that the proposed method reduces the average coding time by 32.2%with only 0.55%BD-BR loss compared with VTM 10.2.

关键词： Versatile video coding(VVC) coding unit partition convolutional neural network(CNN)

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Enhancing Medical Language Understanding: Adapting LLMs to the Medical Domain through Hybrid Granularity Mask Learning

Enhancing Medical Language Understanding: Adapting LLMs to t...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Fan, Longjun Liu, Xiaohong Wang, Yuhao Yang, Guoxing Du, Zongxin Wang, Guangyu Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798350337488

Large Language models have made remarkable strides in natural language understanding and generation. However, their performance in specialized fields like medicine often falls short due to the lack of domain-specific knowledge during pre-training. While fine-tuning on labeled medical data is a common approach for task adaptation, it may not capture the comprehensive medical knowledge required. In this paper, we proposed a Hybrid Granularity Mask Learning (HGM) method for domain adaptation in the medical field. Our method incorporates multi-level linguistic characteries including token, entity, and subsentence to enable the model to acquire medical knowledge comprehensively. We fine-tune a medical-specific language model derived from ChatGLM-6B and Bloom-7B on downstream medical tasks and evaluate its performance. The results demonstrate a significant improvement compared to the baseline, thus affirming the effectiveness of our proposed method. © 2023 IEEE.

关键词： domain adaptation Language models mask learning medical knowledge medical question-answering

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Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes

Neural 3D Strokes: Creating Stylized 3D Scenes with Vectoriz...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao-Bin Duan Miao Wang Yan-Xun Li Yong-Liang Yang State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Zhongguanchun Laboratory Department of Computer Science University of Bath

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present Neural 3D Strokes, a novel technique to gen-erate stylized images of a 3D scene at arbitrary novel views from multi-view 2D images. Different from existing methods which apply stylization to trained neural radiance fields at the voxel level, our approach draws inspiration from image-to-painting methods, simulating the progressive painting process of human artwork with vector strokes. We develop a palette of stylized 3D strokes from basic primitives and splines, and consider the 3D scene stylization task as a multi-view reconstruction process based on these 3D stroke primitives. Instead of directly searching for the parame-ters of these 3D strokes, which would be too costly, we introduce a differentiable renderer that allows optimizing stroke parameters using gradient descent, and propose a training scheme to alleviate the vanishing gradient issue. The extensive evaluation demonstrates that our approach effectively synthesizes 3D scenes with significant geomet-ric and aesthetic stylization while maintaining a consis-tent appearance across different views. Our method can be further integrated with style loss and image-text con-trastive models to extend its applications, including color transfer and text-driven 3D scene drawing. Results and code are available at http://***/Neura13DStrokes.

关键词： Training Solid modeling Three-dimensional displays Stroke (medical condition) Neural radiance field Vectors Pattern recognition

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StyleTalk++: A Unified Framework for Controlling the Speaking Styles of Talking Heads

arXiv

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arXiv 2024年

作者： Wang, Suzhen Ma, Yifeng Ding, Yu Hu, Zhipeng Fan, Changjie Lv, Tangjie Deng, Zhidong Yu, Xin Fuxi AI Lab Netease Zhejiang Hangzhou China Department of Computer Science and Technology BNRist THUAI State Key Laboratory of Intelligent Technology and Systems Tsinghua University China The School of Computer Science The University of Queensland Brisbane Australia

Individuals have unique facial expression and head pose styles that reflect their personalized speaking styles. Existing one-shot talking head methods cannot capture such personalized characteristics and therefore fail to produce diverse speaking styles in the final videos. To address this challenge, we propose a one-shot style-controllable talking face generation method that can obtain speaking styles from reference speaking videos and drive the one-shot portrait to speak with the reference speaking styles and another piece of audio. Our method aims to synthesize the style-controllable coefficients of a 3D Morphable Model (3DMM), including facial expressions and head movements, in a unified framework. Specifically, the proposed framework first leverages a style encoder to extract the desired speaking styles from the reference videos and transform them into style codes. Then, the framework uses a style-aware decoder to synthesize the coefficients of 3DMM from the audio input and style codes. During decoding, our framework adopts a two-branch architecture, which generates the stylized facial expression coefficients and stylized head movement coefficients, respectively. After obtaining the coefficients of 3DMM, an image renderer renders the expression coefficients into a specific person's talking-head video. Extensive experiments demonstrate that our method generates visually authentic talking head videos with diverse speaking styles from only one portrait image and an audio clip. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Sustainability Forecasting for Deep Learning Packages 31

Sustainability Forecasting for Deep Learning Packages

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Han, Junxiao Wang, Yunkun Liu, Zhongxin Bao, Lingfeng Liu, Jiakun Lo, David Deng, Shuiguang School of Computer & Computing Science Hangzhou City University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798350330663

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as 'sus-tainable' or 'dormant'. Relatedly, a DL package is considered as 'sustainable' if it has sustained activity in its last 12 months. Otherwise, it is deemed as 'dormant'. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers. ©

关键词： Open source software

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Unsupervised Multimodal Clustering for Semantics Discovery in Multimodal Utterances

arXiv

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arXiv 2024年

作者： Zhang, Hanlei Xu, Hua Long, Fei Wang, Xin Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China School of Information Science and Engineering Hebei University of Science and Technology China Technology Development Co. Ltd Nanchang330036 China

Discovering the semantics of multimodal utterances is essential for understanding human language and enhancing human-machine interactions. Existing methods manifest limitations in leveraging nonverbal information for discerning complex semantics in unsupervised scenarios. This paper introduces a novel unsupervised multimodal clustering method (UMC), making a pioneering contribution to this field. UMC introduces a unique approach to constructing augmentation views for multimodal data, which are then used to perform pre-training to establish well-initialized representations for subsequent clustering. An innovative strategy is proposed to dynamically select high-quality samples as guidance for representation learning, gauged by the density of each sample’s nearest neighbors. Besides, it is equipped to automatically determine the optimal value for the top-K parameter in each cluster to refine sample selection. Finally, both high- and low-quality samples are used to learn representations conducive to effective clustering. We build baselines on benchmark multimodal intent and dialogue act datasets. UMC shows remarkable improvements of 2-6% scores in clustering metrics over state-of-the-art methods, marking the first successful endeavor in this domain. The complete code and data are available at https://***/thuiar/UMC. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Role of Urban Underground-Space Development in Achieving Carbon Neutrality:A National-Level Analysis in China

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Engineering 2025年第2期45卷 212-221页

作者： Jiajia Wang Huabo Duan Kunyang Chen Isabelle Y.S.Chan Fan Xue Ning Zhang Xiangsheng Chen Jian Zuo Department of Real Estate and Construction Faculty of ArchitectureThe University of Hong KongHong Kong 999077China School of Environmental Science and Engineering Huazhong University of Science and TechnologyWuhan 430000China State Key Laboratory of Intelligent Geotechnics and Tunnelling College of Civil and Transportation EngineeringShenzhen UniversityShenzhen 518000China Leibniz Institute of Ecological Urban and Regional Development Dresden 01217Germany School of Architecture and Civil Engineering The University of AdelaideAdelaideSA 5005Australia

Surface space constraints and the associated massive carbon emissions present significant challenges to the sustainable development of *** underground space(UUS)construction is expected to provide a practical approach for alleviating the space constraints of surface ***,indepth examinations of the overall UUS system to reveal carbon emissions in the complex matrix are *** study demonstrates the vital role of UUS development in achieving carbon neutrality using a streamlined life-cycle assessment *** emissions and the mitigation potential of building underground spaces,metro systems,and geothermal energy sources are *** construction of underground spaces in buildings is the largest carbon emitter within the entire UUS system,releasing a considerable 547.2 Mt in ***,geothermal carbon sequestration,a significant element of the UUS system,provided an unexpected and impressive contribution,sequestering 170 Mt of carbon in *** study shows that UUS addresses the lack of space for urban development and is a lowcarbon method of urban ***,developing low-carbon building technologies and improving the UUS development model is imperative to achieving better low-carbon *** helps to promote more coordinated and sustainable urban development.

关键词： Urban underground space Carbon emissions Urban development Construction sector

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AoI-aware Incentive Mechanism for Mobile Crowdsensing using Stackelberg Game 42

AoI-aware Incentive Mechanism for Mobile Crowdsensing using ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xiao, Mingjun Xu, Yin Zhou, Jinrui Wu, Jie Zhang, Sheng Zheng, Jun University of Science and Technology of China School of Computer Science and Technology China Suzhou Institute for Advanced Research University of Science and Technology of China China Temple Universityy Department of Computer and Information Sciences United States Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350334142

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. Information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of Information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI. © 2023 IEEE.

关键词： Optimal systems

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