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2025 International Conference on Intelligent Control, computing and Communications, IC3 2025

作者： Jain, Alok Al-Rubaye, Taqi Sengar, R.S. Balakrishna, Bhukya Kumar, Narendra Soujanya, Rajireddy Gopal, K. Lovely Professional University Phagwara India College of Medical Technologies The Islamic University Department of Medical Laboratory Technology Najaf Iraq Gl Bajaj Group of Institutions Department of Electrical & Electronics Engineering Mathura India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Griet Department of Cse Bachupally Telangana Hyderabad India New Horizon College of Engineering Bangalore India

ISBN: (纸本)9798331527495

One big change that has happened in differential equations is the use of new computer methods to solve hard problems. The growth of differential equations has been helped by this effect. If these strategies hadn't been used during the process, there would have been no change at all. In this abstract, neural networks and adaptive differential equation solvers are used together. This is one of the new ideas being looked into within the limits of this abstract. You can't use any other way to solve this type of dynamic net ODE. There isn't any other good answer. For people who want to understand how the process works, this outline also includes a description of the method. The goal of this study is to show that the suggested plan is better than other methods by looking at six different ways. The point of this study is to show why the suggested approach is better. The similarity that was just shown is one example of how speed has gotten better. © 2025 IEEE.

关键词： Differential equations

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Causally-Aware Unsupervised Feature Selection Learning

arXiv

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arXiv 2024年

作者： Shen, Zongxin Huang, Yanyong Wang, Dongjie Ma, Minbo Lv, Fengmao Li, Tianrui Joint Laboratory of Data Science and Business Intelligence School of Statistics Southwestern University of Finance and Economics Chengdu611130 China Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intrinsic causal mechanisms within the data, resulting in the selection of irrelevant features and poor interpretability. Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph. To address these issues, a novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed. CAUSE-FS introduces a novel causal regularizer that reweights samples to balance the confounding distribution of each treatment feature. This regularizer is subsequently integrated into a generalized unsupervised spectral regression model to mitigate spurious associations between features and clustering labels, thus achieving causal feature selection. Furthermore, CAUSE-FS employs causality-guided hierarchical clustering to partition features with varying causal contributions into multiple granularities. By integrating similarity graphs learned adaptively at different granularities, CAUSE-FS increases the importance of causal features when constructing the fused similarity graph to capture the reliable local structure of data. Extensive experimental results demonstrate the superiority of CAUSE-FS over state-of-the-art methods, with its interpretability further validated through feature visualization. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking

arXiv

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arXiv 2024年

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Hu, Ye Liu, Yuchen Yin, Changchuan The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and System Engineering University of Miami Coral GablesFL33146 United States The Department of Computer Science North Carolina State University RaleighNC27695 United States

In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflection signals allow each passive UAV to estimate the signal transmission distance which will be transmitted to a base station (BS) for the estimation of the position of the target UAV. Due to the movement of the target UAV, each active/passive UAV must optimize its trajectory to continuously localize the target UAV. Meanwhile, since the accuracy of the distance estimation depends on the signal-to-noise ratio of the transmission signals, the active UAV must optimize its transmit power. This problem is formulated as an optimization problem whose goal is to jointly optimize the transmit power of the active UAV and trajectories of both active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards thus finding better transmit power of the active UAV and trajectories for both active and passive UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 39.4% and 64.6%, compared to VD-RL and independent deep RL methods, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Energy Efficient Probabilistic Semantic Communication over SAGIN

Energy Efficient Probabilistic Semantic Communication over S...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhouxiang Zhao Zhaohui Yang Mingzhe Chen Xu Gan Chongwen Huang Wei Xu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm.

关键词： Energy consumption Satellites Autonomous aerial vehicles Space-air-ground integrated networks Semantic communication Probabilistic logic Energy efficiency Transceivers Relays Optimization

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Control of an Environmental Spin Defect beyond the Coherence Limit of a Central Spin

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PRX Quantum 2024年第1期5卷 010321-010321页

作者： Alexander Ungar Paola Cappellaro Alexandre Cooper Won Kyu Calvin Sun Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute for Quantum Computing University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Physics University of Illinois at Urbana-Champaign Illinois 61801 USA

Electronic spin defects in the environment of an optically active spin can be used to increase the size and hence the performance of solid-state quantum registers, especially for applications in quantum metrology and quantum communication. Previous works on multiqubit electronic spin registers in the environment of a nitrogen-vacancy (NV) center in diamond have only included spins directly coupled to the NV. As this direct coupling is limited by the central spin coherence time, it significantly restricts the maximum attainable size of the register. To address this problem, we present a scalable approach to increase the size of electronic spin registers. Our approach exploits a weakly coupled probe spin together with double-resonance control sequences to mediate the transfer of spin polarization between the central NV spin and an environmental spin that is not directly coupled to it. We experimentally realize this approach to demonstrate the detection and coherent control of an unknown electronic spin outside the coherence limit of a central NV. Our work paves the way for engineering larger quantum spin registers with the potential to advance nanoscale sensing, enable correlated noise spectroscopy for error correction, and facilitate the realization of spin-chain quantum wires for quantum communication.

关键词： Coherent control Crystal defects Hybrid quantum systems Quantum sensing Nitrogen vacancy centers in diamond Electron spin resonance Optically detected magnetic resonance

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Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving 39

Dual-View Interaction-Aware Lane Change Prediction for Auton...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yuhuan Zhang, Zhen Bai, Rufan Liu, Han Wang, Wei Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering Southeast University China School of Software Dalian University of Technology China School of Medical Technology Beijing Institute of Technology China

ISBN: (纸本)157735897X

As artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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KANsformer for Scalable Beamforming

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IEEE Transactions on Vehicular Technology 2025年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms the existing benchmark DL approaches, and is adaptable to the variation in the number of mobile users with real-time and near-optimal inference. © 1967-2012 IEEE.

关键词： Budget control

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Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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AgInS_(2)/ZnS quantum dots for noninvasive cervical cancer screening with intracellular pH sensing using fluorescence lifetime imaging microscopy

引用

Nano Research 2022年第6期15卷 5193-5204页

作者： Wenhua Su Dan Yang Yulan Wang Yawei Kong Wanlu Zhang Jing Wang Yiyan Fei Ruiqian Guo Jiong Ma Lan Mi Department of Optical Science and Engineering Shanghai Engineering Research Center of Ultra-precision Optical ManufacturingKey Laboratory of Micro and Nano Photonic Structures(Ministry of Education)School of Information Science and TechnologyFudan UniversityShanghai 200433China Institute for Electric Light Sources School of Information Science and TechnologyFudan UniversityShanghai 200433China Department of Gynecology and Obstetrics The Central Hospital of WuhanTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430014China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence NanophotonicsSchool of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Institute of Biomedical Engineering and Technology Academy for Engineer and TechnologyFudan University220 Handan RoadShanghai 200433China Shanghai Engineering Research Center of Industrial Microorganisms the Multiscale Research Institute of Complex Systems(MRICS)School of Life SciencesFudan University220 Handan RoadShanghai 200433China

Intracellular pH plays a critical role in biological functions,and abnormal pH values are related to various ***,we report on an intracellular pH sensor AgInS_(2)(AIS)/ZnS quantum dots(QDs)that show long fluorescence lifetimes of hundreds of nanoseconds and low *** lifetime imaging microscopy(FLIM)combined with AIS/ZnS QDs is used for the imaging of live cells in different pH buffers and different cell *** FLIM images of AIS/ZnS QDs in live cells demonstrate different intracellular pH values in different regions,such as in lysosomes or *** method can also distinguish cancer cells from normal cells,and the fluorescence lifetime difference of the AIS/ZnS QDs between the two types of cells is 100±7 *** importantly,the exfoliated cervical cells from 20 patients are investigated using FLIM combined with AIS/ZnS *** lifetime difference value between the normal and cervical cancer(CC)groups is 115±9 ns,and the difference between the normal and the precancerous lesion group is 64±9 *** the first time,the noninvasive method has been used for cervical cancer screening,and it has shown great improvement in sensitivity compared with a clinical conventional cytology examination.

关键词： AIS/ZnS quantum dots fluorescence lifetime imaging microscopy intracellular pH sensing cervical cancer screening noninvasive

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