Optimization on Riemannian manifolds is an intuitive generalization of the traditional optimization algorithms in Euclidean spaces. In these algorithms, minimizing along a search direction becomes minimizing along a s...
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ISBN:
(纸本)9781467377058
Optimization on Riemannian manifolds is an intuitive generalization of the traditional optimization algorithms in Euclidean spaces. In these algorithms, minimizing along a search direction becomes minimizing along a search curve lying on a manifold. Computing such a curve to be subsequently searched upon is itself computational intensive. We propose a new minimization scheme aiming to find a better step size utilizing the first order information of the search curve. We prove that this scheme can provide further reduction for the cost function when the retraction and the vector transport are collinear. Then we adapt this scheme to propose a heuristic strategy for line search. In numerical experiments, we apply this heuristic strategy to one of the geometric algorithms for matrix completion and show its feasibility and the potential in accelerating computation.
This paper considers high reliability transmission for anchored indoor New Radio Unlicensed (NR-U) systems, in which unlicensed spectrum with wideband operation is combined with other NR licensed spectrum as anchor. T...
This paper considers high reliability transmission for anchored indoor New Radio Unlicensed (NR-U) systems, in which unlicensed spectrum with wideband operation is combined with other NR licensed spectrum as anchor. To achieve high reliable transmission using unlicensed spectrum, success transmission ensuring scheme is investigated for indoor NR-U deployment. By modeling of Success Transmission Probability (STP) with nonlinear Levenberg-Marquardt method, STP-based packet request algorithm and STP-based channel allocation algorithm are proposed for initial transmission and Automatic Repeat reQuest (ARQ) retransmission, respectively. Simulation results show that the proposed success transmission ensuring scheme significantly decreases retransmission ratio and further provides less transmission latency and higher throughput. In a word, this paper provides an effective solution which satisfies high reliable transmission requirement for NR-U.
Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning. For incorporating structured data information into column sampling, research effort...
Selecting a small informative subset from a given dataset, also called column sampling, has drawn much attention in machine learning. For incorporating structured data information into column sampling, research efforts were devoted to the cases where data points are fitted with clusters, simplices, or general convex hulls. This paper aims to study nonconvex hull learning which has rarely been investigated in the literature. In order to learn data-adaptive nonconvex hulls, a novel approach is proposed based on a graph-theoretic measure that leverages graph cycles to characterize the structural complexities of input data points. Employing this measure, we present a greedy algorithmic framework, dubbed Zeta Hulls, to perform structured column sampling. The process of pursuing a Zeta hull involves the computation of matrix inverse. To accelerate the matrix inversion computation and reduce its space complexity as well, we exploit a low-rank approximation to the graph adjacency matrix by using an efficient anchor graph technique. Extensive experimental results show that data representation learned by Zeta Hulls can achieve state-of-the-art accuracy in text and image classification tasks.
Alternative Mobility Services (AMS) could change travellers' mobility and possibly fleets of pooled autonomous vehicles will replace the need for current personal vehicles. Contrary to the previous studies, the go...
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To understand the function of networks we have to identify the structure of their interactions, but also interaction timing, as compromised timing of interactions may disrupt network function. We demonstrate how both ...
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ISBN:
(纸本)9781457717871
To understand the function of networks we have to identify the structure of their interactions, but also interaction timing, as compromised timing of interactions may disrupt network function. We demonstrate how both questions can be addressed using a modified estimator of transfer entropy. Transfer entropy is an implementation of Wiener’s principle of observational causality based on information theory, and detects arbitrary linear and non-linear interactions. Using a modified estimator that uses delayed states of the driving system and independently optimized delayed states of the receiving system, we show that transfer entropy values peak if the delay of the state of the driving system equals the true interaction delay. In addition, we show how reconstructed delays from a bivariate transfer entropy analysis of a network can be used to label spurious interactions arising from cascade effects and apply this approach to local field potential (LFP) and magnetoencephalography (MEG) data.
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