Alzheimer's disease (AD) is a long-term neurodegenerative disease. The research in the field of AD is rapidly devel-oping, but most research methods are based on unimodal without considering the complementarity be...
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The location and scale of distributed generation (DG) units are critical for minimizing losses and enhancing the performance of distribution networks. This research establishes the optimization approach for establishi...
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This review article introduces the concepts, server architecture and application scenarios of Mobile Edge computing (MEC) and Wireless sensor Network (WSN). By differentiating between rechargeable and non-rechargeable...
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The rapid proliferation of some real-time applications (e.g., video surveillance) has driven enormous interest in maximizing information freshness, quantified by the age of information (AoI). For some computation-inte...
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distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum com...
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ISBN:
(纸本)9798350364613;9798350364606
distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations via classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of circuit executions required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in circuit executions. Nevertheless, each teleportation requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. Addressing this, our work explores utilizing non maximally entangled qubit pairs in wire cutting, a specific form of circuit cutting, to mitigate the associated costs. The cost of this cutting procedure reduces with the increasing degree of entanglement in the pre-shared qubit pairs. We derive the optimal sampling overhead in this context and present a wire cutting technique employing pure non-maximally entangled slates that achieves this optimal sampling overhead. Hence, this offers a continuum between existing wire cutting and quantum teleportation.
Recently, user privacy in distributedcomputing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, lin...
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Gradient descent (GD) is an iterative optimization method to minimize a differentiable cost function. A main drawback of GD is its use of the entire dataset to perform a parameter update per learning step, which is co...
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ISBN:
(纸本)9798350336672
Gradient descent (GD) is an iterative optimization method to minimize a differentiable cost function. A main drawback of GD is its use of the entire dataset to perform a parameter update per learning step, which is cost-prohibitive for large-scale problems. Variants of GD such as mini-batch GD and stochastic GD compute the update direction with respect to a randomly sampled selection of the training dataset per iteration. This reduces the computational burden at the cost of adding jitters to the learning direction. Alternatively, we propose an iterative optimization algorithm that performs cheap parameter updates at minimal perturbation of the GD direction. The new method computes a deterministic summary of the training dataset, and then computes the learning direction per iteration with respect to the summary. We provide a convergence analysis for the proposed method and show that the thoroughness of the summary can be tweaked to optimize the trade-off between computational complexity and convergence rate. Simulation results are illustrated numerically for single and multi-agent systems.
Cooperative adaptive cruise control (CACC) using vehicle-to-vehicle(V2V) communication is attracting considerable attention for reducing energy consumption. The operator (driver) cost and traffic congestion to improve...
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Wireless sensor Networks are deployed to sense data from the physical world and transmit these data to the server(s). To reduce the maintenance cost of replacing batteries, Energy-Harvesting nodes are developed which ...
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Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, s...
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