Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-...
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Numerous reports have elucidated the importance of mechanical resonators comprising quantum-dot-embedded carbon nanotubes(CNTs)for studying the effects of single-electron ***,there is a need to investigate the single-electron transport that drives a large amplitude into a nonlinear ***,a CNT hybrid device has been investigated,which comprises a gate-defined quantum dot that is embedded into a mechanical resonator under strong actuation *** Coulomb peak positions synchronously oscillate with the mechanical vibrations,enabling a single-electron Chopper*1 ***,the vibration amplitude of the CNT versus its frequency can be directly visualized via detecting the time-averaged single-electron tunneling *** understand this phenomenon,a general formula is derived for this time-averaged single-electron tunneling current,which agrees well with the experimental *** using this visualization method,a variety of nonlinear motions of a CNT mechanical oscillator have been directly recorded,such as Duffing nonlinearity,parametric resonance,and double-,fractional-,mixed-frequency *** approach opens up burgeoning opportunities for investigating and understanding the nonlinear motion of a nanomechanical system and its interactions with electron transport in quantum regimes.
To solve the emerging complex optimization problems,multi objective optimization algorithms are *** introducing the surrogate model for approximate fitness calculation,the multi objective firefly algorithm with surrog...
To solve the emerging complex optimization problems,multi objective optimization algorithms are *** introducing the surrogate model for approximate fitness calculation,the multi objective firefly algorithm with surrogate model(MOFA-SM) is proposed in this ***,the population was initialized according to the chaotic ***,the external archive was constructed based on the preference sorting,with the lightweight clustering pruning *** the process of evolution,the elite solutions selected from archive were used to guide the movement to search optimal *** results show that the proposed algorithm can achieve better performance in terms of convergence iteration and stability.
Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform to assist machine learning in multiple-data-owners scenarios. However, the issue of dat...
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Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training da...
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ISBN:
(纸本)9781665479691
Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training data and test data come from the same label space. However, the real network environment is open, and it is necessary for traffic identification systems to deal with previously unseen categories. To classify known categories at a fine granularity and identify unknown ones, we propose a novel open-set recognition method for encrypted traffic. The decision boundary sample generation method based on constrained Generative Adversarial Networks is presented to generate samples from low-density regions in the known data space. Furthermore, an end-to-end open-set classifier is built using the accurate and diverse simulative samples of unknown classes through intra-class partition and boundary sample generation. Finally, the effectiveness of the proposed method is validated through numerical experiments.
Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for substantial enhancement in both the spe...
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Recently, the need for real-time deep learning applications, such as face recognition system, implement on the embedded devices is increasing. At the system level, caching system is one of the most effective ways to r...
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Malicious intelligent algorithms greatly threaten the security of social users’ privacy by detecting and analyzing the uploaded photos to social network platforms. The destruction to DNNs brought by the adversarial a...
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As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we pr...
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As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.
The task of next POI recommendations has been studied extensively in recent ***,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,bec...
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The task of next POI recommendations has been studied extensively in recent ***,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,because of the heterogeneity nature of these ***,effective mechanisms to smoothly handle cold-start cases are also a difficult *** by the recent success of neural networks in many areas,in this paper,we propose a simple yet effective neural network framework,named NEXT,for next POI *** is a unified framework to learn the hidden intent regarding user's next move,by incorporating different factors in a unified ***,in NEXT,we incorporate meta-data information,e.g.,user friendship and textual descriptions of POIs,and two kinds of temporal contexts(i.e.,time interval and visit time).To leverage sequential relations and geographical influence,we propose to adopt DeepWalk,a network representation learning technique,to encode such *** evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based *** results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI *** experiments show inherent ability of NEXT in handling cold-start.
Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilit...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.
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