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Objective Neurophysiological Indices for the Assessment of Chronic Tinnitus Based on EEG Microstate Parameters

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 983-993页

作者： Wang, Yingying Zeng, Peiying Gu, Zhixiang Liu, Hongyu Han, Shuqing Liu, Xinran Huang, Xin Shao, Liyang Tao, Yuan Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Peking Univ Shenzhen Hosp Dept Otolaryngol Shenzhen 518036 Peoples R China Peking Univ ShenZhen Hosp Dept Rheumatol & Immunol Shenzhen 518036 Peoples R China Peking Univ Shenzhen Hosp Dept Otolaryngol Shenzhen 518036 Peoples R China

Chronic tinnitus is highly prevalent but lacks precise diagnostic or effective therapeutic standards. Its onset and treatment mechanisms remain unclear, and there is a shortage of objective assessment methods. We aim to identify abnormal neural activity and reorganization in tinnitus patients and reveal potential neurophysiological markers for objectively evaluating tinnitus. By way of analyzing EEG microstates, comparing metrics under three resting states (OE, CE, and OECEm) between tinnitus sufferers and controls, and correlating them with tinnitus symptoms. This study reflected specific changes in the EEG microstates of tinnitus patients across multiple resting states, as well as inconsistent correlations with tinnitus symptoms. Microstate parameters were significantly different when patients were in OE and CE states. Specifically, the occurrence of Microstate A and the transition probabilities (TP) from other Microstates to A increased significantly, particularly in the CE state (32-37%, ${p}\le 0.05$ );and both correlated positively with the tinnitus intensity. Nevertheless, under the OECEm state, increases were mainly observed in the duration, coverage, and occurrence of Microstate B (15-47%, p < 0.05), which negatively correlated with intensity (R < -0.513, p < 0.05). Additionally, TPx between Microstates C and D were significantly reduced and positively correlated with HDAS levels (R > 0.548, p < 0.05). Furthermore, parameters of Microstate D also correlated with THI grades (R} < -0.576, p < 0.05). The findings of this study could offer compelling evidence for central neural reorganization associated with chronic tinnitus. EEG microstate parameters that correlate with tinnitus symptoms could serve as neurophysiological markers, contributing to future research on the objective assessment of tinnitus.

关键词： Electroencephalography Auditory system Neural activity Neural networks Ear Visualization Physiology Chronic tinnitus EEG microstates objective assessment various resting states abnormal central neural activity

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Graph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-View Clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO technology 2024年第5期34卷 3728-3741页

作者： Wen, Jie Xu, Gehui Tang, Zhanyan Wang, Wei Fei, Lunke Xu, Yong Harbin Inst Technol Shenzhen Key Lab Visual Object Detect & Recognit Shenzhen 518055 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

In recent years, many incomplete multi-view clustering methods have been proposed to address the challenging and new clustering task on incomplete multi-view data whose part of view representations are not fully collected for some samples. Although extensive experiments have validated the effectiveness of these methods for handling the incomplete learning issue, a common issue exists, i.e., these methods all ignore the discriminative/important difference of discriminative features and noisy features. In this paper, to address the above issue, a new incomplete multi-view clustering model, called Graph Regularized and fEature Aware maTrix Factorization (GreatF), is proposed. Different from the existing methods, we introduce an adaptive feature weighting constraint to the matrix factorization-based multi-view representation learning model. With this weighting constraint, the effect of the discriminative features can be enhanced while the negative effect caused by the redundant and noisy features can be eliminated for the model optimization;thus, the robustness of the model can be enhanced. In addition, in this work, we designed a new graph-embedded consensus representation learning term in which consensus representation learning and structure information preservation are integrated into a joint model with one term. In particular, this term provides a more concise approach to obtain the structured consensus representation from incomplete multi-view data. Experimental results on four well-known datasets demonstrate that GreatF performs better than the state-of-the-art incomplete multi-view clustering methods.

关键词： Incomplete multi-view clustering graph regularization structured consensus representation multi-view learning

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Boosted Multi-Task Learning for Inter-District Collaborative Load Forecasting

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IEEE TRANSACTIONS ON SMART GRID 2024年第1期15卷 973-986页

作者： Liu, Haizhou Zhang, Xuan Sun, Hongbin Shahidehpour, Mohammad Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China IIT Elect & Comp Engn Dept Chicago IL 60616 USA

This paper proposes a boosted multi-task learning framework for inter-district collaborative load forecasting. The proposed framework involves two subsequent stages: in the first stage, districts would collaborate under a seamlessly-integrated federated learning scheme to capture the global load pattern;in the second stage, districts would withdraw and perform local training to capture the local load patterns. The probabilistic Gradient-Boosted Regression Tree (GBRT) is applied as the bottom-level machine learning algorithm, which would allow for an easy and intuitive embodiment of the generalized multi-task learning framework. We further propose two candidate district withdrawal mechanisms to connect the two stages: the simultaneous withdrawal, which prioritizes prediction accuracy, and the dynamic withdrawal, which prioritizes training efficiency and district incentivization. The follow-up performance analyses and the case study on 11 districts of the Zhuhai city confirm the superiority of the proposed framework and district withdrawal mechanisms.

关键词： Multi-task learning gradient-boosted regression tree probabilistic load forecasting federated learning

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Identify priority protected areas for biodiversity conservation adapting to future climate and land cover changes

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ECOLOGICAL INDICATORS 2025年 170卷

作者： Liu, Ruixue Kong, Huilin Wang, Qixuan Li, Yun Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China Shenzhen Univ State Key Lab Subtrop Bldg & Urban Sci Shenzhen 518060 Peoples R China

Biodiversity, essential for sustainable development, faces threats from climate and land cover changes, which are the primary drivers of biodiversity loss. While previous studies have proposed approaches to identify priority protected areas (PPAs) for biodiversity conservation and simulated regional biodiversity responses to climate change or land cover change, however, they often fail to accurately identify PPAs and neglect the combined effects of climate and land cover changes on biodiversity. This study fills these gaps by proposing an integrated modeling approach with MaxEnt, InVEST, and Zonation to identify PPAs by prioritizing rare and endangered species, and exploring the spatial-temporal dynamics of these PPAs under the combined effects of climate and land cover changes from 2020 to 2050 by predicting land cover dynamics under the shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245 and SSP585). Conservation gaps are revealed by spatial overlay analyses between these PPAs and existing protected areas. The spatial pattern of PPAs in shenzhen is concentrated in the southeast with poor connectivity in the west. Primary PPAs are predominantly located in the southeast, secondary PPAs are mainly found on the periphery of the primary ones, and tertiary PPAs are scattered with limited connectivity across central, west, and northeast regions. From 2020 to 2050, biodiversity faces significant threats due to climate and land cover changes, resulting in a notable reduction in primary PPAs and increased habitat fragmentation within PPAs. In 2020, 70.19 % of the PPAs in shenzhen were within existing PAs, with 89.66 % of primary PPAs covered. Conservation gaps are mainly in secondary and tertiary PPAs. Climate and land cover changes will diminish the effectiveness of existing PAs from 2020 to 2050. Spatial management suggestions for biodiversity conservation are proposed to mitigate challenges posed by climate and land cover changes.

关键词： Biodiversity Rare and endangered species Climate change Land cover change Scenario simulation

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3-Acyl-4-Pyranone as a Lysine Residue-Selective Bioconjugation Reagent for Peptide and Protein Modification

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BIOCONJUGATE CHEMISTRY 2024年第3期35卷 286-299页

作者： Nong, Keyi Zhao, Yi-Lu Yi, Shandong Zhang, Xuchun Wei, Siyuan Yao, Zhu-Jun Nanjing Univ Sch Chem & Chem Engn State Key Lab Coordinat Chem Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Sch Chem & Chem Engn Jiangsu Key Lab Adv Organ Mat Nanjing 210023 Jiangsu Peoples R China

Chemoselective protein modification plays extremely important roles in various biological, medical, and pharmaceutical investigations. Mimicking the mechanism of the chemoselective reaction between natural azaphilones and primary amines, this work successfully simplified the azaphilone scaffold into much simpler 3-acyl-4-pyranones. Examinations confirmed that these slim-size mimics perfectly kept the unique reactivity for selective conjugation with the primary amines including lysine residues of peptides and proteins. The newly developed pyranone tool presents remarkably increased aqueous solubility and compatible second-order rate constant by comparison with the original azaphilone. Additional advantages also include the ease of biorthogonal combinative use with a copper-catalyzed azide-alkyne Click reaction, which was conveniently applied to decorate lysozyme with neutral-, positive- and negative-charged functionalities in parallel. Moderate-degree modification of lysozyme with positively charged quaternary ammoniums was revealed to increase the enzymatic activities.

关键词： Amines Column chromatography Modification Monomers Peptides and proteins

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Three-Port Reflectionless Down-Converter for Wideband and Multichannel Receiver With Flat Gain, Enhanced EVM, and High Stability

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2024年第7期72卷 4337-4349页

作者： Li, Qingxian Luo, Xun Univ Elect Sci & Technol China Ctr Adv Semicond & Integrated Microsyst Chengdu 611731 Peoples R China Univ Elect Sci & Technol China UESTC Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China UESTC Ctr Adv Semicond & Integrated Microsyst Chengdu 611731 Peoples R China

This article presents a three-port reflectionless down-converter to achieve flat gain, enhanced EVM, and high stability for wideband and multichannel receivers. The down-converter consists of a mixer with three reflectionless filters at IF, RF, and LO ports. The analysis of the proposed reflection model demonstrates that the elimination of reflected signals alleviates the problems of gain fluctuation and harmonics generation. Meanwhile, reflectionless operation effectively improves the dynamic range of the receiving system. To verify the mechanism and operation mentioned above, a series of reflectionless down-converters is designed and fabricated. The measurement results fully confirm the theoretical analysis, in which the three-port reflectionless converter supports a 300 MHz 256-QAM signal with an error vector magnitude (EVM) of 3.02% . The EVM of 400 MHz 64-QAM signal is below 4.50 % when overlap bandwidth varies from 0 to 400 MHz. For an eight-carrier signal of 40 MHz 256-QAM, the proposed three-port reflectionless converter maintains the EVM difference among each channel lower than 0.15 %.

关键词： Down-converter multichannel receiver reflectionless three-port wideband

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SDSR: Optimizing Metaverse Video Streaming via Saliency-Driven Dynamic Super-Resolution

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第4期42卷 978-989页

作者： Chai, Baili Chen, Jinyu Luo, Zhenxiao Wang, Zelong Hu, Miao Zhou, Yipeng Wu, Di Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou 510006 Peoples R China Macquarie Univ Fac Sci & Engn Sch Comp Macquarie Pk NSW 2119 Australia

Metaverse (especially 360-degree) video streaming allows broadcasting virtual events in the metaverse to a broad audience. To reduce the huge bandwidth consumption, quite a few super-resolution (SR)-enhanced 360-degree video streaming systems have been proposed. However, there is very limited work to investigate how the granularity of SR model affects the system performance, and how to choose a proper SR model for different video contents under diverse environmental conditions. In this paper, we first conduct a dedicated measurement study to unveil the impact of different granularities of SR models. It is found that the scene of a video largely determines the effectiveness of SR models in different granularities. Based on our observations, we propose a novel 360-degree video streaming framework with saliency-driven dynamic super-resolution, called SDSR. To maximize user QoE, we formally formulate an optimization problem and adopt the model predictive control (MPC) theory for bitrate adaptation and SR model selection. To improve the effectiveness of SR model, we leverage the saliency information, which well reflects users' view interests, for model training. In addition, we reuse an SR model for similar chunks based on temporal redundancy of a video. Finally, we conduct extensive experiments on real traces and the results show that SDSR outperforms the state-of-the-art algorithms with an improvement up to 32.78% in terms of the average QoE.

关键词： Metaverse 360-degree video streaming quality of experience super-resolution bitrate adaptation

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BadCM: Invisible Backdoor Attack Against Cross-Modal Learning

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 2558-2571页

作者： Zhang, Zheng Yuan, Xu Zhu, Lei Song, Jingkuan Nie, Liqiang Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

Despite remarkable successes in unimodal learning tasks, backdoor attacks against cross-modal learning are still underexplored due to the limited generalization and inferior stealthiness when involving multiple modalities. Notably, since works in this area mainly inherit ideas from unimodal visual attacks, they struggle with dealing with diverse cross-modal attack circumstances and manipulating imperceptible trigger samples, which hinders their practicability in real-world applications. In this paper, we introduce a novel bilateral backdoor to fill in the missing pieces of the puzzle in the cross-modal backdoor and propose a generalized invisible backdoor framework against cross-modal learning (BadCM). Specifically, a cross-modal mining scheme is developed to capture the modality-invariant components as target poisoning areas, where well-designed trigger patterns injected into these regions can be efficiently recognized by the victim models. This strategy is adapted to different image-text cross-modal models, making our framework available to various attack scenarios. Furthermore, for generating poisoned samples of high stealthiness, we conceive modality-specific generators for visual and linguistic modalities that facilitate hiding explicit trigger patterns in modality-invariant regions. To the best of our knowledge, BadCM is the first invisible backdoor method deliberately designed for diverse cross-modal attacks within one unified framework. Comprehensive experimental evaluations on two typical applications, i.e., cross-modal retrieval and VQA, demonstrate the effectiveness and generalization of our method under multiple kinds of attack scenarios. Moreover, we show that BadCM can robustly evade existing backdoor defenses. Our code is available at https://***/xandery-geek/BadCM.

关键词： Visualization Training Flowering plants Perturbation methods Dogs Generators Computational modeling Backdoor attacks cross-modal learning dataset security imperceptibility

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Cascade Graph Neural Networks for Few-Shot Learning on Point Clouds

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8788-8798页

作者： Li, Yangfan Chen, Cen Yan, Weiquan Cheng, Zhongyao Tan, Hui Li Zhang, Wenjie Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Infocomm Res Inst Singapore 138632 Singapore Peng Cheng Lab Shenzhen 511464 Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China

cloud data, a flexible 3D object representation, is critical for various applications such as autonomous driving, robotics and remote sensing. Despite the recent success of deep neural networks (DNNs) on supervised point cloud analysis tasks, they still rely on tedious manual annotation of point clouds and cannot make predictions for new classes. Unlike few-shot learning for 2D images with the advantages of large-scale datasets and high-quality deep pre-trained models like ResNet, for 3D few shot learning, obtaining discriminative representations of unseen classes with high intra-class similarity and inter-class difference is very challenging. To address this issue, this work proposes a novel cascade graph neural network for few-shot learning on point clouds, termed as CGNN, in which two cascade GNNs are adopted to extract the intra-object topological information and learn the inter-object relations respectively. To further increase the discriminability of point cloud features, we first design a novel discriminative edge label to model the intra-class similarity and inter-class dissimilarity based on channel-wise feature variance and class consistency. Second, we propose a novel few-shot circle loss which classifies the nodes into two subsets, i.e., support to support pairs and support to query pairs, and optimizes the pair-wise similarity on two subsets independently. Extensive experiments on benchmark CAD and real LiDAR point cloud datasets have demonstrated that CGNN improves accuracy by 5.98% over the state-of-the-art GNN-based few-shot classification methods.

关键词： Point cloud compression Three-dimensional displays Training Task analysis Feature extraction Graph neural networks Deep learning Few-shot learning graph neural networks point clouds

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Divergence-free finite volume WENO scheme for relativistic magnetohydrodynamics preserving positivity and subluminal velocity

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2025年第2期538卷 1167-1190页

作者： Ding, Shengrong Wu, Kailiang Yuan, Caiyou Southern Univ Sci & Technol Shenzhen Int Ctr Math Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Math Shenzhen 518055 Peoples R China

This paper presents a high-order, locally divergence-free finite volume weighted essentially non-oscillatory (WENO) scheme for special relativistic magnetohydrodynamics (RMHD) with a general equation of state. The scheme rigorously enforces essential physical bounds, including positivity of density and pressure and the subluminal constraint on fluid velocity, achieving bound-preserving (BP) properties. Maintaining BP properties in RMHD simulations requires a discrete divergence-free magnetic field. To address this, we introduce a local projection technique tailored for WENO schemes, without reconstructing approximation polynomials and solving additional equations, thus enhancing computational efficiency and ease of implementation. We also develop a simplified BP limiter for the WENO reconstructed point values and a precondition for BP properties of updated cell averages, maintaining the discretely divergence-free property enforced by the projection. We provide a theoretical proof of BP properties for the updated cell averages in the proposed scheme, overcoming challenges posed by RMHD's non-linear couplings, complex variable transformations, and non-linear physical constraints. Our approach uses geometric quasilinearization to reformulate these non-linear constraints into equivalent linear forms relative to the conserved variables. Additionally, a provably convergent Newton-Raphson method robustly recovers primitive variables from conserved quantities, ensuring physical validity. Extensive numerical experiments validate the robustness of our method across challenging RMHD scenarios, including those with strong shocks, high Lorentz factors, and conditions of low density, low pressure, and low plasma beta.

关键词： MHD relativistic processes shock waves methods: numerical galaxies: jets

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