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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tianjiao Wan Yutao Dou Kele Xu Zijian Gao Bo Ding Dawei Feng Huaimin Wang National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

Deep supervised learning has demonstrated strong capabilities; however, such progress relies on massive and expensive data annotation. Active Learning (AL) has been introduced to selectively annotate samples, thus reducing the human labeling effort. Previous AL research has focused on employing recently trained models to design sampling strategies, based on uncertainty or representativeness. Drawing inspiration from the issue of model forgetting, we propose a novel AL framework called Temporal Inconsistency-Based Active Learning (TIR-AL). In this framework, multiple snapshots of the models across consecutive cycles are jointly utilized to select samples with higher temporal inconsistency, by computing the proposed self-weighted nuclear norm metric. Furthermore, we introduce a consistency regularization term to mitigate the issue of forgetting. Together, these components make full use of the potential of data and facilitate effective interaction within the AL loop. To demonstrate the efficacy of TIR-AL, we conducted a set of experiments illustrating how our approach outperforms state-of-the-art methods without incurring any additional training costs.

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Transformer-based Joint Source Channel Coding for Textual Semantic Communication

Transformer-based Joint Source Channel Coding for Textual Se...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Liu, Shicong Gao, Zhen Chen, Gaojie Su, Yu Peng, Lu Beiing Institute of Technology Miit Key Laboratory of Complex-Field Intelligent Sensing Beijing100081 China Beijing Institute of Technology Advanced Research Institute of Multidisciplinary Science Jinan250307 China Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Jiaxing314000 China Institute for Communication Systems University of Surrey Guildford United Kingdom Institute of Research and Development Chengdu610000 China

ISBN: (纸本)9798350345384

The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming. In this paper, we propose a textual semantic transmission framework for robust transmission, which utilizes the advanced natural language processing techniques to model and encode sentences. Specifically, the textual sentences are firstly split into tokens using wordpiece algorithm, and are embedded to token vectors for semantic extraction by Transformer-based encoder. The encoded data are quantized to a fixed length binary sequence for transmission, where binary erasure, symmetric, and deletion channels are considered for transmission. The received binary sequences are further decoded by the transformer decoders into tokens used for sentence reconstruction. Our proposed approach leverages the power of neural networks and attention mechanism to provide reliable and efficient communication of textual data in challenging wireless environments, and simulation results on semantic similarity and bilingual evaluation understudy prove the superiority of the proposed model in semantic transmission. © 2023 IEEE.

关键词： Semantics

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Multi-Home Energy Coordination Scheduling Based on Adaptive Dynamic Programming

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The International Journal of Intelligent Control and systems 2024年第1期29卷 30-36页

作者： Kang Xiong Qinglai Wei Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China.

Implementing cooperative scheduling of multi-home microgrid energy and reducing the dependence on the main grid have become the focus of microgrid energy management *** paper proposes a new multi-agent adaptive dynamic programming(MAADP)method for the cooperative control of distributed home *** home is defined as a learning agent that needs to reasonably schedule the energy storage system to meet the respective load demand while accomplishing cooperative scheduling among the individual *** addition,an energy clearing center(ECC)is introduced to complete the energy exchange between each microgrid to protect the benefits of all *** proposed method adopts the learning strategy of“centralized learning and decentralized execution”to avoid the leakage of private *** experimental comparison with the benchmark method verifies that the method can realize the cooperative scheduling of each home and reduce the dependence on the main grid.

关键词： Multi-home scenarios adaptive dynamic programming(ADP) optimal home energy management smart grid

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A Solution Analysis of U.S. Weapon Equipment systems-of-systems Development and Application 7

A Solution Analysis of U.S. Weapon Equipment Systems-of-Syst...

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7th International Conference on Information science and Control Engineering, ICISCE 2020

作者： Lin, Menglong Yi, Shuanghui Zhang, Mengmeng Shu, Zhen National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China National Key Laboratory for Complex System Simulation Beijing China

ISBN: (纸本)9781728164069

In recent years, the development and deployment of the U.S. weapon equipment systems-of-systems (SoS) have yielded rich research results. However, there are still large gaps in the SoS development, joint testing, and design and acquisition of our own equipment SoS. The barriers of these steps have not yet been opened up. This article mainly analyzes the solutions of the U.S. weapon equipment SoS from three aspects, such as capability-based requirement generation and architecture design technology, interconnected-oriented integration test technology, and weapon equipment design and acquisition technology based on complex adaptive systems. We explore key technologies involved in order to form an overall understanding and explore related research approaches. © 2020 IEEE.

关键词： System of systems

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Optimizing Learning Performance through AI-Enhanced Discussion Forums 14

Optimizing Learning Performance through AI-Enhanced Discussi...

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14th International Conference on Intelligent systems: Theories and Applications, SITA 2023

作者： Rafiq, Jamal Eddine Amraouy, Mohammed Namir, Abdelwahed Bennane, Abdellah Zakrani, Abdelali Hassan Ii University Laboratory of Information Technology and Modeling Ben m'Sik Faculty of Science Casablanca Morocco National Institute of Posts and Telecommunications Rabat Morocco Mohammed v University Inspectors Training Center for Education Rabat Morocco Hassan Ii University Laboratory of Artificial Intelligence & Complex Systems Engineering National School of Arts and Crafts of Casablanca Casablanca Morocco

ISBN: (纸本)9798350308211

As online learning evolves rapidly and discussion forums become increasingly important in educational environments, this study analyzes the impact of participation in discussion forums on the learning performance of future computer science teachers. Data were collected to assess their level of engagement and academic outcomes. The methodology included the use of natural language processing techniques, such as the Bidirectional Encoder Representations from Transformers (BERT) model, and Latent Semantic Analysis (LSA) to analyze the emotional state and relevance of their contributions. Preliminary results indicate a positive correlation between active participation and their learning performance. Additionally, this study makes a significant contribution by analyzing the correlation between various participation factors and learner performance. These findings highlight the importance of discussion forums as interactive educational tools to enhance learning and promote learner engagement. Important implications are also presented in relation to the obtained results. © 2023 IEEE.

关键词： Semantics

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Harvesting random embedding for high-frequency change-point detection in temporal complex systems

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National science Review 2022年第4期9卷 92-104页

作者： Jia-Wen Hou Huan-Fei Ma Dake He Jie Sun Qing Nie Wei Lin Research Institute of Intelligent Complex Systems Fudan University Centre for Computational Systems Biology Institute of Science and Technology for Brain-Inspired IntelligenceFudan University School of Mathematical Sciences Soochow University Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine School of Mathematical Sciences and Shanghai Center for Mathematical Sciences Fudan University Department of Mathematics Department of Developmental and Cell Biologyand NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of California Shanghai Key Laboratory for Contemporary Applied Mathematics LNMS (Fudan University)and LCNBI (Fudan University) State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceFudan University

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection(TCD), and integrate both dynamical and statistical methods to address this important challenge in a novel way. The proposed TCD approach, basing on exploitation of spatial information of the observed time series of high dimensions, is able not only to detect the separate change points of the concerned systems without knowing, a priori, any information of the equations of the systems, but also to harvest all the change points emergent in a relatively high-frequency manner, which cannot be directly achieved by using the existing methods and *** effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.

关键词： temporal systems time series change-point detection complex dynamical systems

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UR4NNV: Neural Network Verification, Under-approximation Reachability Works!

arXiv

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arXiv 2024年

作者： Liang, Zhen Wu, Taoran Zhao, Ran Xue, Bai Wang, Ji Yang, Wenjing Deng, Shaojun Liu, Wanwei College of Computer Science and Technology National University of Defense Technology China Institute of Software Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

Recently, formal verification of deep neural networks (DNNs) has garnered considerable attention, and over-approximation based methods have become popular due to their effectiveness and efficiency. However, these strategies face challenges in addressing the "unknown dilemma" concerning whether the exact output region or the introduced approximation error violates the property in question. To address this, this paper introduces the UR4NNV verification framework, which utilizes under-approximation reachability analysis for DNN verification for the first time. UR4NNV focuses on DNNs with Rectified Linear Unit (ReLU) activations and employs a binary tree branch-based under-approximation algorithm. In each epoch, UR4NNV under-approximates a sub-polytope of the reachable set and verifies this polytope against the given property. Through a trial-and-error approach, UR4NNV effectively falsifies DNN properties while providing confidence levels when reaching verification epoch bounds and failing falsifying properties. Experimental comparisons with existing verification methods demonstrate the effectiveness and efficiency of UR4NNV, significantly reducing the impact of the "unknown dilemma".MSC Codes 68Q60, 68T07 Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep Learning Enhanced Fluorescence Fluctuations Super-Resolution Microscopy 3

Deep Learning Enhanced Fluorescence Fluctuations Super-Resol...

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3rd Conference on Biomedical Photonics and Cross-Fusion, BPC 2024

作者： Li, Yanru Liu, Lixin Wang, Lin School of Optoelectronic Engineering Xidian University Shaanxi Xi'an710071 China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Guangdong Shenzhen518060 China Central Laser Facility Research Complex at Harwell Science and Technology Facilities Council Rutherford Appleton Laboratory Harwell Didcot OxfordOX11 0QX United Kingdom

ISBN: (纸本)9781510682849

Fluorescence fluctuations super-resolution microscopy (FF-SRM) is a powerful tool in imaging and monitoring of biological subcellular structures and dynamics in cells. A variety of image reconstruction algorithms have been developed for FF-SRM. In order to obtain high spatiotemporal resolution, a U-Net-based deep learning method for super-resolution imaging of fluorescence fluctuations was developed. With just 20 frames, super-resolution images reconstructed using U-Net model could be comparable to those reconstructed using VeSRRF algorithm with several hundred frames, demonstrating its capability of advancing imaging capabilities. © 2024 SPIE

关键词： Fluorescence imaging

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Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

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自动化学报（英文版） 2021年第10期8卷 1657-1666页

作者： Ting Zhang Wenjie Song Mengyin Fu Yi Yang Meiling Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of TechnologyBeijing 100081 Nanjing University of Science and Technology Nanjing 210014China

Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20％. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.

关键词： Autonomous vehicle intersection motion prediction prior trajectories model turning intention

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High-order rogue wave and mixed interaction patterns for the three-component Gross-Pitaevskii equations in F=1 spinor Bose-Einstein condensates

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Physical Review E 2024年第4期109卷 044215-044215页

作者： Xiao-Yong Wen Zhe Lin Deng-Shan Wang School of Applied Science Beijing Information Science and Technology University Beijing 100192 China. School of Mathematical Sciences Shanghai Key Laboratory of Pure Mathematics and Mathematical Practice East China Normal University Shanghai 200241 China and School of Applied Science Beijing Information Science and Technology University Beijing 100192 China. Laboratory of Mathematics and Complex Systems (Ministry of Education) School of Mathematical Sciences Beijing Normal University Beijing 100875 China.

Under investigation are the three-component Gross-Pitaevskii equations in F=1 spinor Bose-Einstein condensates. Various localized waves' generation mechanisms have been derived from plane wave solutions using modulation instability. The perturbed continuous waves produce a large number of rogue wave structures through the split-step Fourier numerical method. Based on the known Lax pair, we construct the generalized iterative (n,N−n)-fold Darboux transformation to generate various high-order solutions, including the bright-dark-bright structure of rogue waves, periodic waves, and their mixed interaction structures. Numerical simulations show that rogue waves with a two-peaked structure have robust noise resistance and stable dynamical behavior. The asymptotic states of high-order rogue waves as the parameter approaches infinity are also predicted using the large parameter asymptotic technique. In addition, the position of these localized wave patterns can be controlled by some special parameters. These results may help us understand the dynamic behavior of spinor condensates for the mean-field approximation.

关键词： Continuum mechanics Nonlinear waves

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