Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...
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Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy ...
Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.
Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mob...
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Working as aerial base stations,mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target ***,a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users,while considering the mobility of on-ground *** this paper,to solve this issue,we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage ***,we propose a hybrid deployment algorithm based on the improved quick artificial bee *** algorithm is composed of a centralized deployment algorithm and a distributed *** proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically *** results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.
Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal ...
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Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a *** and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle *** this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly *** is an encoder-decoder-based framework with four main ***-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal ***-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context *** module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical ***,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly ***,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids *** on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.
The application of the electronic control unit (ECU) motivates dynamic models with high precision to simulate mechatronic systems for various analysis and design tasks like hardware-in-the-loop (HiL) simulation. Unlik...
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Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...
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Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
Tomato(Solanum lycopersicum), an economically important vegetable crop cultivated worldwide, often suffers massive financial losses due to Phytophthora infestans(P. infestans) spread and breakouts. Arbuscular mycorrhi...
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Tomato(Solanum lycopersicum), an economically important vegetable crop cultivated worldwide, often suffers massive financial losses due to Phytophthora infestans(P. infestans) spread and breakouts. Arbuscular mycorrhiza(AM) fungi mediated biocontrol has demonstrated great potential in plant resistance. However, little information is available on the regulation of mycorrhizal tomato resistance against P. ***, microRNAs(miRNAs) sequencing technology was used to analyse miRNA and their targets in the mycorrhizal tomato after *** infection. Our study showed a lower severity of necrotic lesions in mycorrhizal tomato than in nonmycorrhizal controls. We investigated 35 miRNAs that showed the opposite expression tendency in mycorrhizal and nonmycorrhizal tomato after P. infestans infection when compared with uninfected P. infestans. Among them, miR319c was upregulated in mycorrhizal tomato leaves after pathogen infection. Overexpression of miR319c or silencing of its target gene(TCP1) increased tomato resistance to P. infestans, implying that miR319c acts as a positive regulator in tomato after pathogen infection. Additionally, we examined the induced expression patterns of miR319c and TCP1 in tomato plants exposed to salicylic acid(SA) treatment, and SA content and the expression levels of SA-related genes were also measured in overexpressing transgenic plants. The result revealed that miR319c can not only participates in tomato resistance to P. infestans by regulating SA content, but also indirectly regulates the expression levels of key genes in the SA pathway by regulating TCP1. In this study, we propose a novel mechanism in which the miR319c in mycorrhizal tomato increases resistance to P. infestans.
The flexible job shop scheduling problem (FJSP) is a classic NP-hard problem, and the quality of its scheduling solution directly affects the operational efficiency of the manufacturing system. However, the traditiona...
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The accurate characterization of the spatial electric field generated by electrodes in a surface electrode trap is of paramount *** this pursuit,we have identified a simple yet highly precise parametric expression to ...
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The accurate characterization of the spatial electric field generated by electrodes in a surface electrode trap is of paramount *** this pursuit,we have identified a simple yet highly precise parametric expression to describe the spatial field of a rectangularshaped *** this expression,we introduced an optimization method designed to accurately characterize the axial electric field intensity produced by the powered electrode and the stray *** from the existing methods,our approach integrates a diverse array of experimental data,including the equilibrium positions of ions in a linear string,the equilibrium positions of single trapped ions,and trap frequencies,to effectively reduce the systematic *** approach provides considerable flexibility in voltage settings for data acquisition,making it especially advantageous for surface electrode traps where the trapping height of ion probes may vary with casual voltage *** our experimental demonstration,we successfully minimized the discrepancy between observations and model predictions to a remarkable *** relative errors of secular frequencies were contained within±0.5%,and the positional error of ions was constrained to less than 1.2μm,which surpasses the performance of current methodologies.
With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, i...
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With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.
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