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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Ying Cheng Yang Li Junjie He Rui Feng School of Computer Science Fudan University and Shanghai Key Laboratory of Intelligent Information Processing and Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University and Shanghai Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

With the rapid development of multimedia technology, audio-visual learning has emerged as a promising research topic within the field of multimodal analysis. In this paper, we explore parameter-efficient transfer learning for audio-visual learning and propose the Audio-Visual Mixture of Experts (AVMoE) to inject adapters into pre-trained models flexibly. Specifically, we introduce unimodal and cross-modal adapters as multiple experts to specialize in intra-modal and intermodal information, respectively, and employ a lightweight router to dynamically allocate the weights of each expert according to the specific demands of each task. Extensive experiments demonstrate that our proposed approach AVMoE achieves superior performance across multiple audio-visual tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, visual-only experimental results also indicate that our approach can tackle challenging scenes where modality information is missing. The source code is available at https://***/yingchengy/AVMOE.

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Development of a Universal RNA Dual-Terminal Labeling Method for Sensing RNA-Ligand Interactions

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CCS Chemistry 2023年第1期5卷 221-233页

作者： Longhuai Cheng Dejun Ma Jie Zhang Xueying Kang Yang Wu Yi Zhao Long Yi Zhen Xi State Key Laboratory of Elemento-Organic Chemistry Department of Chemical BiologyCollege of ChemistryNational Pesticide Engineering Research CenterCollaborative Innovation Center of Chemical Science and EngineeringNankai UniversityTianjin 300071 State Key Laboratory of Organic-Inorganic Composites and Beijing Key Laboratory of Bioprocess Beijing University of Chemical Technology(BUCT)Beijing 100029 Key Laboratory of Intelligent Information Processing Research Center for Ubiquitous Computing SystemsInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190

Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct dual-terminal labeling of any RNA of *** explored new Michael cycloaddition for facile labeling of 5′-terminal RNA with improved *** chemical tetrazinylation of RNA at the 3′-terminus was achieved with the highly efficient and catalysis-free tetrazine-cycloalkyne *** single-terminal labeling methods were combined for dual-terminal labeling of an RNA including short hairpin RNA,pre-miRNA,riboswitch,and noncoding ***,these dual-labeled RNA-based FRET sensors were used to monitor RNA-ligand interactions in vitro and in live *** is anticipated that these universal RNA labeling strategies will be useful to study RNA structures and functions.

关键词： dual labeling Förster resonance energy transfer sensor Michael cycloaddition tetrazinecycloalkyne ligation RNA-ligand interaction

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Linear Precoding for Faster-Than-Nyquist signaling with Imperfect Channel State Information

Linear Precoding for Faster-Than-Nyquist Signaling with Impe...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Yan Wang Qiang Li Yingsong Li Liping Li Xingwang Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China School of Physics and Electronic Information Engineering Henan Polytechnic University China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In faster-than-Nyquist (FTN) transmission over frequency-selective fading channels, it is essential to eliminate both FTN-induced intersymbol-interference (ISI) (FTN-ISI) and channel-induced ISI (channel-ISI). Although frequency domain equalization (FDE) techniques can mitigate channel-ISI at the receiver, their performance is often inadequate. In contrast, cyclic prefix/suffix (CPS)-G-to-minus-half (GTMH) precoding shows superior effectiveness in mitigating channel-ISI. However, existing CPS-GTMH precoding techniques assume the transmitter obtains perfect channel state information (CSI) estimated at the receiver. This is impractical due to the high latency in satellite communication links. Therefore, this paper explores precoding for FTN signaling under imperfect CSI (ICSI) and proposes CPS-minimum mean squared error (MMSE) and linear pre-equalization (LPE)-MMSE precoding schemes based on the MMSE criterion. Both schemes employ a cyclic prefix to transform the channel-ISI matrix into the circular matrix, thereby avoiding inter-block interference. The primary distinction is that CPS-MMSE precoding handles FTN-ISI and channel-ISI together using MMSE precoding. In contrast, LPE-MMSE precoding addresses FTN-ISI with LPE and channel-ISI with MMSE precoding separately. Simulation results indicate that CPS-MMSE and LPE-MMSE precodings outperform CPS-FDE and LPE-FDE in mitigating channel-ISI when the estimation error is 0.005 and 0.01. For instance, with a packing ratio of 0.9, a roll-off factor of 0.25, and an estimation error of 0.01, CPS-MMSE and LPE-MMSE precodings achieve bit error rate (BER) gains of approximately 0.8 dB and 1 dB at a BER of 1e4, respectively. However, when the estimation error increases to 0.1, the BER performance of CPS-FDE and LPE-FDE surpasses that of the CPS-MMSE and LPE-MMSE precodings, indicating the proposed precoding schemes' unsuitability for larger channel estimation errors.

关键词： Estimation error Precoding Simulation Bit error rate Channel estimation Interference Receivers Matrix converters Satellite communications Channel state information

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Conformal Geometric Algebraic Description of Flight Collision Detection Algorithm Based on Cylinder Protection Zone

Conformal Geometric Algebraic Description of Flight Collisio...

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Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Haobo Wu Weidong Jiao Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

In order to provide sufficient reaction time for air traffic controllers and pilots, a Flight Conflict Detection Algorithm (FCDA) based on Cylindrical Protection Zone (CPZ) in the Conformal Geometric Algebra (CGA) is proposed. Initially, the CGA description of the CPZ is represented by using the CGA forms of the central circle and central axis segments of the cylinder. Then based on the inner and outer product and geometric product operations of CGA, the positional relationship of the two CPZs is judged to determine whether a flight conflict occurs or not. Subsequently, we compared the performance of flight conflict detection methods based on rectangular protection zones, spherical protection zones, and cylindrical protection zones through experiments in CGA and Euclidean space, respectively, the experimental results show that in CGA, the proposed method has a faster speed than the flight conflict method based on cuboids, and is basically equivalent to the method based on spherical protection zones. Significantly, all CGA-based methodologies demonstrated superior speed over their Euclidean space counterparts. Considering the propensity of rectangular and spherical zones to elicit false alarms due to their expansive coverage of airspace, the CGA-CPZ-FCDA method emerges as the superior choice.

关键词： Algebra Computational efficiency Trajectory Complexity theory Logic Protection Spatial resolution Detection algorithms Collision avoidance Aerospace engineering

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Leveraging catastrophic forgetting to develop safe diffusion models against malicious finetuning 24

Leveraging catastrophic forgetting to develop safe diffusion...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Jiadong Pan Hongcheng Gao Zongyu Wu Taihang Hu Li Su Qingming Huang Liang Li Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS and University of Chinese Academy of Sciences University of Chinese Academy of Sciences The Pennsylvania State University Nankai University Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS

ISBN: (纸本)9798331314385

Diffusion models (DMs) have demonstrated remarkable proficiency in producing images based on textual prompts. Numerous methods have been proposed to ensure these models generate safe images. Early methods attempt to incorporate safety filters into models to mitigate the risk of generating harmful images but such external filters do not inherently detoxify the model and can be easily bypassed. Hence, model unlearning and data cleaning are the most essential methods for maintaining the safety of models, given their impact on model parameters. However, malicious fine-tuning can still make models prone to generating harmful or undesirable images even with these methods. Inspired by the phenomenon of catastrophic forgetting, we propose a training policy using contrastive learning to increase the latent space distance between clean and harmful data distribution, thereby protecting models from being fine-tuned to generate harmful images due to forgetting. The experimental results demonstrate that our methods not only maintain clean image generation capabilities before malicious fine-tuning but also effectively prevent DMs from producing harmful images after malicious fine-tuning. Our method can also be combined with other safety methods to maintain their safety against malicious fine-tuning further.

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Economic Scheduling of Multi-microgrid Systems Based on Distributed Event-triggered Consensus Strategy

Economic Scheduling of Multi-microgrid Systems Based on Dist...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Tingting Xu Xiaohan Fang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

Multi-microgrid (MMG) systems offer notable advantages in terms of power sharing and mutual support when compared to individual microgrids (MGs). This paper presents a based on distributed event-triggered consensus strategy for optimizing the economic dispatch of multiple microgrids. The incorporation of the event-triggered mechanism into the multi-microgrid framework significantly reduces unnecessary communication among microgrids in contrast to the conventional time-triggered approach. For the multi-microgrid setup, a two-layer control strategy consisting of inter-microgrid and intra-microgrid layers is employed to achieve coherence by regulating the micro-incremental rates of distributed generation (DG) units. Leaderless event-triggered consistency is utilized for inter-microgrid coordination, while leader event-triggered consistency is implemented for intra-microgrid coordination, ultimately realizing the economic objective of minimizing the operational costs of the multi-microgrid system. Simulation results validate the efficacy of introducing the event-triggered mechanism and its impact on the economic dispatch of the multi-microgrid system.

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Geometric Algebra Based Kalman Filter Trajectory Prediction

Geometric Algebra Based Kalman Filter Trajectory Prediction

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Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Taiyu Li Weidong Jiao Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

To improve the safety and efficiency of aviation operation, a Kalman filtered trajectory prediction algorithm based on geometric algebra is proposed in this space. Firstly, the Kalman filter equations for trajectory prediction in geometric algebraic space are given by taking position, velocity and acceleration as state variables and position, velocity and acceleration in a typical trajectory as observations. Secondly, the matrix operation in the geometric algebraic space is simplified to the matrix operation by establishing a composite geometric algebraic space. Then, the versor operator in the conformal geometric algebraic space is introduced into the Kalman filter, and the steps of geometric algebra-based Kalman filter trajectory prediction are given to improve the prediction rate and accuracy. The experimental results show that the proposed trajectory prediction algorithm has the highest accuracy and the operation efficiency is improved by 27.66 % compared with the Kalman filter algorithm in Euclidean space and the DBSCAN clustering algorithm.

关键词： Accuracy Algebra Clustering algorithms Filtering algorithms Predictive models Prediction algorithms Mathematical models Trajectory Safety Kalman filters

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Video Rumor Classification Based on Multi-modal Theme and keyframe Fusion 17th

Video Rumor Classification Based on Multi-modal Theme and ...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： You, Jinpeng Lin, Yanghao Lin, Dazhen Cao, Donglin Artificial Intelligence Department Xiamen University Xiamen361005 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China

ISBN: (纸本)9789819923557

In recent years, short video platforms have become the main source of online rumors. According to the statistics of Shanghai online rumor refutation platform in 2021, the number of short video rumors was about five times that of short video rumors in 2020, which makes it necessary to detect rumors of short videos. At present, short video rumor detection has the problem of multi-modal information fusion, the traditional multi-modal fusion uses deep learning to obtain the underlying features of multi-modality and then aggregate them into cross-modal features. However, there are distortions of theme and tampering with key-frame in rumor videos. Therefore, short video rumors need to learn features from the perspective of theme and key-frame. Aiming at the problem of multi-modal information fusion of short video rumors, this paper proposes a short video rumor detection model (TKCM) based on theme and key-frame. It uses aggregation network to obtain the theme feature of video, attention network to obtain the key-frame feature, and fuses multi-modality by modal adjustment mechanism for short video rumor detection. Experimental results show that the F1 score of the proposed method on the short video rumor dataset is improved by 2%–5% compared with some state-of-the-art video classification models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Broadband Circularly Polarized Hybrid Metasurface Antenna for Wearable Devices

Broadband Circularly Polarized Hybrid Metasurface Antenna fo...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Zhiyi Liu Yufa Sun Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University HeFei China

A 38×38×4mm3 wideband circularly polarized (CP) antenna with Hybrid Metasurface (MTS) for wearable devices is proposed. The proposed antenna achieves CP radiation by cutting corners on a square microstrip patch. A hybrid MTS is placed above a square patch with cut corners, achieving broadband radiation performance. The working mechanism of hybrid MTS is analyzed by characteristic mode analysis (CMA). The simulated -10dB bandwidth is 40.1% (4.61-6.82 GHz) and the 3dB axial ratio bandwidth is 25.2% (5.16-6.55 GHz) with a peak gain of 8.1 dBi. In addition, the antenna has a front-to-back ratio of approximately 20 dB, which makes it suitable for wearable applications.

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Real-time pricing method through market auction based on multi-agent reinforcement learning

Real-time pricing method through market auction based on mul...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Peng Hong Shuping He Xiaohan Fang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

With the development of distributed energy systems, pricing different energy sources in microgrids has become a significant challenge. To solve this problem, this paper proposes an real-time pricing method through market auction based on multi-agent reinforcement learning. The energy supply side provides different energy production and quotations, while consumers on the energy demand side freely quote and purchase the required energy in the energy market based on their own needs. The final transaction price of different energy sources is determined by the quotes of the buyer and seller who have reached the transaction. The simulation results indicate that the proposed pricing method have a good performance.

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