This paper settles the sample complexity of single-parameter revenue maximization by showing matching upper and lower bounds, up to a poly-logarithmic factor, for all families of value distributions that have been considered in the literature. The upper bounds are unified under a novel framework, which builds on the strong revenue monotonicity by Devanur, Huang, and Psomas (STOC 2016), and an information theoretic argument. This is fundamentally different from the previous approaches that rely on either constructing an -net of the mechanism space, explicitly or implicitly via statistical learning theory, or learning an approximately accurate version of the virtual values. To our knowledge, it is the first time information theoretical arguments are used to show sample complexity upper bounds, instead of lower bounds. Our lower bounds are also unified under a meta construction of hard instances.

Production and verification of multipartite quantum state are an essential step in quantum information processing. In this work, we propose an efficient method to decompose symmetric multipartite observables, which are invariant under permutations between parties, with only (N +1)(N +2)/2 local measurement settings, where N is the number of qubits. We apply the decomposition technique to evaluate the fidelity between an unknown prepared state and any target permutation invariant state. In addition, for some typical permutation invariant states, such as the Dicke state with a constant number of excitations, m, we derive a tight linear bound on the number of local measurement settings, m(2m + 3)N + 1. Meanwhile, for the GHZ state, the W state, and the Dicke state, we prove a linear lower bound, Θ(N ). Hence, for these particular states, our decomposition technique is optimal. * xma@tsinghua.edu.cn 2

Fog computing has changed the distributed computing rapidly by including the smart devices widely distributed at the network edges. It is able to provide less latency and is more capable of decreasing traffic jam in the network. However, it will bring more difficulties for resource managing and task scheduling especially in a decentralized ad hoc network. In this paper, we propose a method that takes advantages of the immune mechanism to schedule tasks in a decentralized way for fog computing. By using forward propagation and backward propagation in the ad hoc network, the power of distributed schedulers is used to generate the optimized scheduler strategies to deal with computing nodes overloaded and achieve the optimal task finishing time reducing. The experiment results show that our approach can beat similar methods.

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Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to further improve the prediction accuracy, the empirical modal decomposition algorithm is used to decompose the input data into intrinsic mode function (IMF) components of different frequencies. Each intrinsic mode function (IMF) and residual is predicted using a separate long-term and short-term memory neural network, and the predicted values are reconstructed from each long-term and short-term memory model. Finally, experimental verification was carried out on Amazon’s public data set and compared with autoregressive integrated moving average and Prophet models. The experimental results show the superior performance of the proposed IF-EMD-LSTM prediction model in information system load trend prediction.