Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear m...
Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.
The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by le...
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The rapid development of deep learning provides great convenience for production and ***,the massive labels required for training models limits further ***-shot learning which can obtain a high-performance model by learning few samples in new tasks,providing a solution for many scenarios that lack *** paper summarizes few-shot learning algorithms in recent years and proposes a ***,we introduce the few-shot learning task and its ***,according to different implementation strategies,few-shot learning methods in recent years are divided into five categories,including data augmentation-based methods,metric learning-based methods,parameter optimization-based methods,external memory-based methods,and other ***,We investigate the application of few-shot learning methods and summarize them from three directions,including computer vision,human-machine language interaction,and robot ***,we analyze the existing few-shot learning methods by comparing evaluation results on mini Image Net,and summarize the whole paper.
Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is ass...
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Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one groundtruth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning(MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named MIPLGP, i.e., multi-instance partial-label learning with Gaussian processes, is *** first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that MIPLGP is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.
The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel...
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With the development of artificial intelligence,neural network provides unique opportunities for holography,such as high fidelity and dynamic *** to obtain real 3D scene and generate high fidelity hologram in real tim...
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With the development of artificial intelligence,neural network provides unique opportunities for holography,such as high fidelity and dynamic *** to obtain real 3D scene and generate high fidelity hologram in real time is an urgent ***,we propose a liquid lens based holographic camera for real 3D scene hologram acquisition using an end-to-end physical model-driven network(EEPMD-Net).As the core component of the liquid camera,the first 10 mm large aperture electrowetting-based liquid lens is proposed by using specially fabricated *** design of the liquid camera ensures that the multi-layers of the real 3D scene can be obtained quickly and with great imaging *** EEPMD-Net takes the information of real 3D scene as the input,and uses two new structures of encoder and decoder networks to realize low-noise phase *** comparing the intensity information between the reconstructed image after depth fusion and the target scene,the composite loss function is constructed for phase optimization,and the high-fidelity training of hologram with true depth of the 3D scene is realized for the first *** holographic camera achieves the high-fidelity and fast generation of the hologram of the real 3D scene,and the reconstructed experiment proves that the holographic image has the advantage of low *** proposed holographic camera is unique and can be used in 3D display,measurement,encryption and other fields.
With the scaling up of high-performance computing systems in recent years,their reliability has been descending ***,system resilience has been regarded as one of the critical challenges for large-scale HPC *** techniq...
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With the scaling up of high-performance computing systems in recent years,their reliability has been descending ***,system resilience has been regarded as one of the critical challenges for large-scale HPC *** techniques and systems have been proposed to ensure the correct execution and completion of parallel *** paper provides a comprehensive survey of existing software resilience ***,a classification of software resilience approaches is presented;then we introduce major approaches and techniques,including checkpointing,replication,soft error resilience,algorithmbased fault tolerance,fault detection and *** addition,challenges exposed by system-scale and heterogeneous architecture are also discussed.
State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embe...
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State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.
This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication...
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This paper investigates an unmanned aerial vehicle(UAV)-assisted multi-object offloading scheme for blockchain-enabled Vehicle-to-Everything(V2X)*** to the presence of an eavesdropper(Eve),the system’s com-munication links may be *** paper proposes deploying an intelligent reflecting surface(IRS)on the UAV to enhance the communication performance of mobile vehicles,improve system flexibility,and alleviate eavesdropping on communication *** links for uploading task data from vehicles to a base station(BS)are protected by IRS-assisted physical layer security(PLS).Upon receiving task data,the computing resources provided by the edge computing servers(MEC)are allocated to vehicles for task *** blockchain-based computation offloading schemes typically focus on improving network performance,such as minimizing energy consumption or latency,while neglecting the Gas fees for computation offloading and the costs required for MEC computation,leading to an imbalance between service fees and resource *** paper uses a utility-oriented computation offloading scheme to balance costs and *** paper proposes alternating phase optimization and power optimization to optimize the energy consumption,latency,and communication secrecy rate,thereby maximizing the weighted total utility of the *** results demonstrate a notable enhancement in the weighted total system utility and resource utilization,thereby corroborating the viability of our approach for practical applications.
This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was ...
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This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was introduced in this paper, which took the gross domestic product(GDP) of each region as one of the factors that affect the spread speed of COVID-19 and studied the relationship between the GDP and the infection density of each region(China's Mainland, the United States, and EU countries). In addition, the geographic distance between regions was also considered in this method and the effect of geographic distance on the spread speed of COVID-19 was studied. Studies have shown that the probability of mutual infection of these two regions decreases with increasing geographic distance. Therefore, this paper proposed an epidemic disease spread index based on GDP and geographic distance to quantify the spread speed of COVID-19 in a region. The analysis results showed a strong correlation between the epidemic disease spread index in a region and the number of confirmed cases. This finding provides reasonable suggestions for the control of epidemics. Strengthening the control measures in regions with higher epidemic disease spread index can effectively control the spread of epidemics.
Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application t...
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Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge *** MCM-CNN is designed by adopting a memristor crossbar composed of a pair of ***-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and *** of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection *** results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
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