We propose a method for computational ghost imaging (CGI) with an optical phased array (OPA), wherein phase biases on the phase shifters are intrinsically unknown. In our method, an object is illuminated with a number...
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In sixth-generation networks, Terahertz technology enables high-speed, low-latency communication capabilities, while Mobile Edge computing (MEC) enhances remote computation, leveraging Autonomous Vehicles capable of p...
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Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell sur...
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Lattice-based post-quantum cryptography (PQC) has attracted significant attention as a promising solution to the security challenges posed by quantum computing. Unlike traditional cryptographic algorithms, lattice-bas...
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The quantum alternating operator ansatz (QAOA) represents a branch of quantum algorithms for solving combinatorial optimization problems. A specific variant, the Grover-mixer (GM) QAOA, ensures uniform amplitude acros...
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The quantum alternating operator ansatz (QAOA) represents a branch of quantum algorithms for solving combinatorial optimization problems. A specific variant, the Grover-mixer (GM) QAOA, ensures uniform amplitude across states that share equivalent objective values. This property makes the algorithm independent of the problem structure, focusing instead on the distribution of objective values within the problem. In this work we provide an alternative proof for the upper bound on the probability of measuring a computational basis state from a GM QAOA circuit with a given depth, which is a critical factor in QAOA cost. Using this, we derive the upper bounds for the probability of sampling an optimal solution and for the approximation ratio of maximum optimization problems, both dependent on the objective value distribution. Through numerical analysis, we link the distribution to the problem size and build the regression models that relate the problem size, QAOA depth, and performance upper bound. Our results suggest that the GM QAOA provides a quadratic enhancement in sampling probability and requires circuit depth that scales exponentially with problem size to maintain consistent performance.
Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by i...
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Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is e...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.
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