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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Xiaze Ding, Ziheng Jing, Qi Cheng, Ying Ding, Wenchao Feng, Rui Fudan University Shanghai China The School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China The Academy for Engineering and Technology Fudan University China The Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (纸本)9798400706868

Simultaneous Localization and Mapping (SLAM) plays a pivotal role in autonomous driving and robotics. Existing methods often rely on hand-craft feature extraction and cross-modal fusion techniques, resulting in limited feature representation capability and reduced robustness. To address this challenge, we introduce DeepPointMap2, a novel learning-based LiDAR-Visual SLAM architecture that leverages neural descriptors to tackle multiple SLAM sub-tasks in a unified manner. Our approach employs neural networks to extract multi-modal tokens, which are then adaptively fused by the Visual-Point Fusion Module to generate sparse 3D neural descriptors, ensuring precise and robust performance. As a pioneering work, our method achieves state-of-the-art localization performance among various Visual-, LiDAR-, and Visual-LiDAR-based methods in widely-used benchmarks, as shown in the experiment results. Furthermore, the approach proves to be robust in scenarios involving camera failure and LiDAR obstruction. © 2024 ACM.

关键词： SLAM robotics

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Dielectric Resonator Magnetoelectric Dipole Arrays with Low Cross Polarization,Backward Radiation,and Mutual Coupling for MIMO Base Station Applications

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China Communications 2024年第6期21卷 290-304页

作者： Song Simin Da Yiran Qian Bingyi Huang Xiaoyu Chen Xiaoming Li Yingsong Ahmed A.Kishk School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui UniversityHefei 230039China Department of Electrical and Computer Engineering Concordia UniversityMontrealQC H3G 1M8Canada

Dielectric resonator magnetoelectric dipole(DRMED)arrays with enhanced isolation,reduced cross-polarization,and backward radiation are proposed for base station(BS)*** proposed antenna comprises an elevated dielectric resonator antenna(DRA)on a small metal plate above a sizeable common ground *** DRA is designed in its T Eδ11 mode,acting like a magnetic *** surface current excited by the differential probes flowing on the small ground plane is equivalent to an electric *** these two equivalent dipoles are orthogonal,they have the magnetoelectric dipole characteristics with reduced backward ***,the small ground planes can be treated as decoupling structures to provide a neutralization path to cancel the original coupling path.A linearly-polarized 4-element prototype array was verified experimentally in previous ***,a dual-polarized DRMED antenna is presented to construct a 2-element and 4×4 array for BS *** investigate its MIMO performance,sophisticated multi-cell scenario simulations are carried *** using the proposed dualpolarized DRMED array,the cellular system capacityis improved by 118.6%compared to a conventional DRA *** significant MIMO system improvement is mainly due to the reduced backward radiation and,therefore,reduced inter-cell *** align well with the simulations.

关键词： backward radiation dielectric resonator antennas(DRAs) isolation magnetoelectric-dipole(ME-dipole)antennas

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Multimodal emotion recognition based on multilevel acoustic and textual information 5

Multimodal emotion recognition based on multilevel acoustic ...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Zhou, Ya Xing, Yuyi Huang, Guimin Guo, Qingkai Deng, Nanxiao School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

The study and application of multimodal emotion recognition have gained significant popularity in recent years, representing one of the challenging tasks in the field of affective computing. We propose a multimodal speech emotion recognition model that utilizes multiple acoustic and textual information layers. This model incorporates transcribed textual data to complement speech data and enable accurate emotion recognition. In the unimodal model, we employ AlexNet, BiGRU, and HuBERT to extract multi-layer acoustic feature information from speech, and the RoBERTa encoder to extract text features. Additionally, we perform fusion between speech and text by utilizing the co-attentive mechanism to extract complementary information across modalities and eliminate inter-modality noise. This process ultimately enhances the emotional representation of the target modality. Finally, the fused features are utilized to predict the emotion category. Our model achieved a weighted sentiment recognition accuracy of 77.41% and an unweighted accuracy of 78.66%. © 2023 SPIE.

关键词： Emotion Recognition

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GDTNet: A Synergistic Dilated Transformer and CNN by Gate Attention for Abdominal Multi-organ Segmentation 30th

GDTNet: A Synergistic Dilated Transformer and CNN by Gate ...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Can Wang, Zhiqiang Zhang, Yuan Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9783031533013

As one of the key problems in computer-aided medical image analysis, learning how to model global relationships and extract local details is crucial to improve the performance of abdominal multi-organ segmentation. While current techniques for Convolutional Neural Networks (CNNs) are quite mature, their limited receptive field makes it difficult to balance the ability to capture global relationships with local details, especially when stacked onto deeper networks. Thus, several recent works have proposed Vision Transformer based on a self-attentive mechanism and used it for abdominal multi-organ segmentation. However, Vision Transformer is computationally expensive by modeling long-range relationships on pairs of patches. To address these issues, we propose a novel multi-organ segmentation framework, named GDTNet, based on the synergy of CNN and Transformer for mining global relationships and local details. To achieve this goal, we innovatively design a Dilated Attention Module (DAM) that can efficiently capture global contextual features and construct global semantic information. Specifically, we employ a three-parallel branching structure to model the global semantic information of multiscale encoded features by Dilated Transformer, combined with global average pooling under the supervision of Gate Attention. In addition, we fuse each DAM with DAMs from all previous layers to further encode features between scales. Extensive experiments on the Synapse dataset show that our method outperforms ten other state-of-the-art segmentation methods, achieving accurate segmentation of multiple organs in the abdomen. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer aided analysis

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Generating Molecules Conditional on 3D Protein Pockets with HGAF

Generating Molecules Conditional on 3D Protein Pockets with ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhu, Qinglian Lin, Xiaoli Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350337488

Most current representations of protein pockets are the atom-pair graph, which ignore the global structural information of amino acids. Therefore, we propose a new molecular generation model, which uses the hypergraph to represent protein pocket structure, and combines the structural features obtained by atom-pair graph representation. These two levels of graphs are more capable of representing the complex structural information of protein pockets. Then, the graphs of the two levels of the protein pockets are input into the improved network model, which is named Hypergraph Graph Attention Fusion (HGAF), to obtain the embedding representation of the protein pockets, which is used as a condition to constrain the molecule generation. The molecules sampled by HGAF are subjected to quality assessment and docking targeting validation. Experimental results show that the molecules generated by proposed method can achieve better results in both of these assessment approaches. © 2023 IEEE.

关键词： 3D Protein Pockets Drug Design Graph Attention Networks

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SepMLP: An All-MLP Architecture for Music Source Separation 9th

SepMLP: An All-MLP Architecture for Music Source Separation

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9th Conference on Sound and Music Technology, CSMT 2021

作者： Qian, Jiale Gao, Yongwei Wei, Weixing Zhao, Jiahao Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Most previous deep learning based methods use convolutional neural networks (CNNs) or Recurrent neural networks (RNNs) to model the separation process in music signals. In this paper, we propose a MLP-like encoder-decoder architecture, in which per-location features and spatial information in music signals are exclusively handled by multi-layer perceptrons (MLPs). Additionally, We introduce a novel fully-connected decoder for feature aggregation without using skip-connection. The experimental results on the benchmark dataset MUSDB18 show the proposed model achieves comparable performance to previous CNN and RNN based methods, which demonstrates that MLP serves as a promising backbone for the data-driven methods in music source separation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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A personalized test paper generation model for English examination based on knowledge tracking 5

A personalized test paper generation model for English exami...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Zhou, Ya Shao, Xiuli Huang, Guimin Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

In recent years, personalized paper grouping is a hot research topic in the field of intelligence education. In this paper, we propose an optimized deep knowledge tracking model Mul-MAKT combined with a genetic algorithm for personalized paper grouping with English practice as the target. Firstly, we use students’ records (exercise labels, response results, learning behaviors, exercise attributes) as input to the knowledge tracking model to predict students’ performance at the next moment and also to obtain students’ mastery level of knowledge points. Based on the students’ mastery levels, we adjust the students’ ability in each type of English exercise and calculate the students’ weaknesses in each type of question, so as to extract the questions from the test bank with the difficulty matching the students’ ability and containing the students’ weaknesses. © 2023 SPIE.

关键词： Students

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A RIS-Aided MIMO Secure Communication Scheme Based on Subarray Partition 9

A RIS-Aided MIMO Secure Communication Scheme Based on Subarr...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Wang, Suzhen Zhi, Hui Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Anhui University School of Electronic and Information Engineering Hefei China

ISBN: (纸本)9798350317251

Subarray partition of reconfigurable intelligent surface (RIS) can significantly reduce the computational complexity of solving optimal reflection coefficients. However, there is no research about the RIS subarray partition of RIS-aided multiple-input multiple-output (MIMO) communication for the eavesdropper scenario. In this paper, we intend to solve the optimization subarray partition problem for the eavesdropper scenario, design a RIS-aided MIMO secure communication scheme based on subarray partition (RSC-SP). We consider minimizing the number of subarrays while satisfying secrecy rate requirements. First, this problem is described as a nonconvex combinatorial optimization problem, then we solve it by combining alternating optimization and bisection. In this scheme, we derive the closed expressions of the optimal transmit covariance matrix and the optimal reflection coefficients of RIS. The alternating optimization algorithm is used to jointly optimize the transmit covariance matrix and the reflection coefficients of RIS, the bisection method is used to calculate the minimum number of subarrays. Simulation results show that compared with the traditional scheme without subarray partition, RSC-SP can significantly reduce the computational complexity while meeting secrecy rate requirements. © 2023 IEEE.

关键词： Covariance matrix

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Dilation-Supervised Learning: A Novel Strategy for Scale Difference in Retinal Vessel Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1693-1707页

作者： Wang, Huadeng Zuo, Wenbin Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China

Retinal fundus image segmentation based on deep learning is an important method for auxiliary diagnosis of ophthalmic diseases. Due to the scale difference of the blood vessels and the imbalance between foreground and background pixels in the fundus image, the deep learning network will inevitably ignore thin vessels when downsampling and feature learning. For the scale difference problem, this article aims to tackle its limitation from two perspectives: changing the supervised approach and adapting the feature learning. Correspondingly, a dilation-supervised learning method and an adaptive scale dimensional attention mechanism which are used to construct a two-stage segmentation model is proposed. Moreover, we introduce a quantitative approach to evaluate the scale difference of the blood vessels. With the help of the proposed weighted loss function, the segmentation results are refined, and the class imbalance problem between foreground and background pixels is resolved. Finally, the proposed adaptive threshold selection method is used in the postprocessing of segmentation results. The experiments on DRIVE, STARE, CHASE_DB1, and HRF datasets show that the proposed method achieves better segmentation performance compared with other state-of-the-art methods, and has good generalization ability and robustness. © 2020 IEEE.

关键词： Semantics

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