Given a real dataset and a computation family, we wish to encode and store the dataset in a distributed system so that any computation from the family can be performed by accessing a small number of nodes. In this wor...
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Plug-and-Play Alternating Direction Method of Multipliers (PnP-ADMM) is a widely-used algorithm for solving inverse problems by integrating physical measurement models and convolutional neural network (CNN) priors. Pn...
Plug-and-Play Alternating Direction Method of Multipliers (PnP-ADMM) is a widely-used algorithm for solving inverse problems by integrating physical measurement models and convolutional neural network (CNN) priors. PnP-ADMM has been theoretically proven to converge for convex data-fidelity terms and nonexpansive CNNs. It has however been observed that PnP-ADMM often empirically converges even for expansive CNNs. This paper presents a theoretical explanation for the observed stability of PnP-ADMM based on the interpretation of the CNN prior as a minimum mean-squared error (MMSE) denoiser. Our explanation parallels a similar argument recently made for the iterative shrinkage/thresholding algorithm variant of PnP (PnP-ISTA) and relies on the connection between MMSE denoisers and proximal operators. We also numerically evaluate the performance gap between PnP-ADMM using a nonexpan-sive DnCNN denoiser and expansive DRUNet denoiser, thus motivating the use of expansive CNNs.
Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. As intelligent transportation systems evolve, accurate and real-time identification of driver distract...
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Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning...
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ISBN:
(数字)9798350308365
ISBN:
(纸本)9798350308372
Performing feature selection on a small number of instances with high-dimensional datasets poses a needed challenge in preventing over-fitting. To address this issue, this paper proposes a sequential transfer-learning approach combined with a multi-objective genetic algorithm (STMO-GA) for feature selection. Firstly, for the multi-objective component of our method, we employ a Non-dominated Sorting Genetic Algorithm (NSGA-II) to generate a Pareto front. Then, features are ranked based on their number of appearances in the same Pareto front. Next, during the sequential knowledge transfer process, the ranked features are iteratively selected until a predetermined
$n$
number of features remains. This feature subspace is further refined by a k-fold cross-validation operation, starting from the rank-one feature, to determine the cut-off of the
$n$
features that will remain. Comparative evaluations against both GA-based as well as traditional feature selection methods demonstrate that the proposed method achieves superior classification accuracy, while retaining the smallest number or a comparable number of features.
A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd mana...
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A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd management, conventional solutions, such as Upwork and Amazon Mechanical Turk, generally depend on a trusted third-party platform. There exist several potential security concerns(e.g., sensitive leakage, single point of failure and unfair judgment) in such a centralized paradigm. Hence, a recent trend in crowdsourcing is to leverage blockchain(a decentralized ledger technology) to address some of the existing limitations. A small number of blockchain-based crowdsourcing systems(BCSs) with incentive mechanisms have been proposed in the literature, but they are generally not designed with security in mind. Thus, we study the security and privacy requirements of a secure BCS and propose a concrete solution(i.e., SecBCS)with a prototype implementation based on JUICE.
The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". ...
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Multi-z confocal microscopy provides simultaneously optically-sectioned multi-plane imaging but has limited resolution. Here, we describe a novel multi-z microscope by introducing a diffractive optical element that re...
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Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers a...
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The overall inertia of modern power systems is drastically impacted by the penetration level of converter-based resources (CBRs). The intermittent output of CBRs and their dependence on the weather conditions affirm t...
The overall inertia of modern power systems is drastically impacted by the penetration level of converter-based resources (CBRs). The intermittent output of CBRs and their dependence on the weather conditions affirm the need for a realtime inertia estimation approach. This can help the transmission system operators(TSOs) to be continuously aware of the changes in the system inertia and to take suitable control actions. This paper is a step forward to a totally data-driven framework for power system inertia estimation using the available measurements from the phasor measurement units (PMUs). This inertia estimation is done based on the spectral analysis of the so-called Koopman linear operator. Koopman operator considers the relationship between the system’s inertia and its dynamical response to slight disturbances in the load. Simulation results show that Koopman operator can approximate the system behavior in a reconstructed linear space hence inertia constant is estimated accurately.
Here, we demonstrate local writing and erasing of selected light-emitting defects using fs laser pulses in combination with hydrogen-based defect activation and passivation which also lead to rediscovering a potential...
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