We consider pure quantum states of N qubits and study the genuine N −qubit entanglement that is shared among all the N qubits. We introduce an information-theoretic measure of genuine N -qubit entanglement based on bipartite partitions. When N is an even number, this measure is presented in a simple formula, which depends only on the purities of the partially reduced density matrices. It can be easily computed theoretically and measured experimentally. When N is an odd number, the measure can also be obtained in principle.PACS numbers: 03.67.-a, 03.65. Ud, 73.43.Nq, 89.70.+c The nature of quantum entanglement is a fascinating topic in quantum mechanics since the famous EinsteinPodolsky-Rosen paper [1] in 1935. Recently, much interest has been focused on entanglement in quantum systems containing a large number of particles. On one hand, multipartite entanglement is valuable physical resource in large-scale quantum information processing [2,3]. On the other hand, multipartite entanglement seems to play an important role in condensed matter physics [4], such as quantum phase transitions (QPT) [5,6] and high temperature superconductivity [7]. Therefore, how to characterize and quantify multipartite entanglement remains one of the central issues in quantum information theory.In the present literature, there exist very few measures of multipartite entanglement with a clear physical meaning [8,9,10,11,12,13,14,15]. Because of this, most research in quantum entanglement and QPT focused on bipartite entanglement, for which there have been several well defined measures [16,17,18,19,20,21,22]. However, bipartite entanglement can not characterize the global quantum correlations among all parties in a multiparticle system. Since the correlation length diverges at the critical points, multipartite entanglement plays an essential role in QPT. Though localizable entanglement (LE) [23] can be used to describe long-range quantum correlations, its determination is a formidable task for generic pure states. Therefore, computable measure of multipartite entanglement with clear physical meanings are highly desired [24,25].In this paper, we define a new measure of genuine N −qubit entanglement based on different bipartite partitions of the qubits and existing measures for mutual information. The central idea is that, through bipartite partitions, we can get information about the genuine multi-qubit entanglement. Our measure is a polynomial SLOCC (stochastic local operations and classical communication) invariant [26] and is unchanged under permutations of qubits. When N is an even number, we derive * Electronic address: zwzhou@ustc.edu.cn a simple formula for this measure, which is determined by the purity of partially reduced density matrices only. It can be computed through experimentally observable quantities [10,27]. Therefore, it is easy to obtain not only theoretically but also experimentally. For N = 4, we show that this measure satisfies all the necessary conditions required for a natural entanglement measure [20] exactly....

This paper presents a new monitoring method for multimode processes based on subspace decomposition. In the proposed method, the influence of quality variables and multimode information are considered in multimode processes modeling, which is crucially important to ensure industrial production safety and quality stabilization. Process data are decomposed into the global common subspace and the local specific subspace and monitoring is performed in each subspace to simplify the model structure. Two experiments: penicillin fermentation processes and practical foods industrial production processes, have been used to demonstrate the excellent performance of the proposed method.

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

This paper describes the models designated for the MEDIQA 2019 shared tasks by the team PANLP. We take advantages of the recent advances in pre-trained bidirectional transformer language models such as BERT (Devlin et al., 2018) and MT-DNN (Liu et al., 2019b). We find that pre-trained language models can significantly outperform traditional deep learning models. Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large. A knowledge distillation process is implemented, to distill the knowledge contained in a set of models and transfer it into an single model, whose performance turns out to be comparable with that obtained by the ensemble of that set of models. Finally, for test submissions, model ensemble and a re-ranking process are implemented to boost the performances. Our models participated in all three tasks and ranked the 1st place for the RQE task, and the 2nd place for the NLI task, and also the 2nd place for the QA task.

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