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.
The present study monitored bacterial succession,physicochemical properties,and volatile organic compounds(VOCs)changes in smoked chicken legs with modified atmosphere packaging(MAP,60% CO_(2) and 40%N_(2))during a 25...
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The present study monitored bacterial succession,physicochemical properties,and volatile organic compounds(VOCs)changes in smoked chicken legs with modified atmosphere packaging(MAP,60% CO_(2) and 40%N_(2))during a 25-day storage period at 4℃.After 15 days of storage,S erratia proteamaculans and Pseudomonas fragi became the predominant ***,physicochemical properties changed significantly,as evidenced by an increase in thiobarbituric acid reactive substances and b*(yellowness)value,and a decrease in hardness.A total of 65 VOCs were identified during *** between bacterial succession and quality indicators(including VOCs and physicochemical properties)allowed the identification of 26 core dominant bacteria,including ***,Psychrobacter alimentarius,Pseudomonas putida,and Pseudomonas poae,which were positively related to spoilage VOCs(e.g.,1-octen-3-ol,1-pentanol,and 3-methyl-1-butanol)and could be defined as specific spoilage organisms(SSOs).The results of this study provide a systematic approach to predict SSOs in smoked chicken legs during storage,which can also provide a basis for product safety.
Ramsey oscillations typically exhibit an exponential decay envelope due to environmental noise. However,recent experiments have observed nonmonotonic Ramsey fringes characterized by beating patterns, which deviate fro...
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Ramsey oscillations typically exhibit an exponential decay envelope due to environmental noise. However,recent experiments have observed nonmonotonic Ramsey fringes characterized by beating patterns, which deviate from the standard behavior. These beating patterns have primarily been attributed to charge-noise *** this paper, we have experimentally observed Ramsey fringe with beating pattern for transmon qubits, and traced the origin to electric instruments induced flux noise.
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.
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.
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.
Drug addiction is a complex brain disease closely related to the expression and methylation of many *** genes can influence addiction,and some key differential genes may have a significant impact on the occurrence and...
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Drug addiction is a complex brain disease closely related to the expression and methylation of many *** genes can influence addiction,and some key differential genes may have a significant impact on the occurrence and development of ***,data on addictive drugs is widely *** our knowledge,there are very few databases on genes related to addictive drugs,and existing addiction related databases are not available.
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.
Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar...
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Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.
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