El Niño-related summer precipitation anomalies in Southeast Asia modulated by the Atlantic multidecadal oscillation

AbstractHow the Atlantic Multidecadal Oscillation (AMO) affects El Niño-related signals in Southeast Asia is investigated in this study on a subseasonal scale. Based on observational and reanalysis data, as well as numerical model simulations, El Niño-related precipitation anomalies are analyzed for AMO positive and negative phases, which reveals a time-dependent modulation of the AMO: (i) In May?June, the AMO influences the precipitation in Southern China (SC) and the Indochina peninsula (ICP) by modulating the El Niño-related air-sea interaction over the western North Pacific (WNP). During negative AMO phases, cold sea surface temperature anomalies (SSTAs) over the WNP favor the maintaining of the WNP anomalous anticyclone (WNPAC). The associated southerly (westerly) anomalies on the northwest (southwest) flank of the WNPAC enhance (reduce) the climatological moisture transport to SC (the ICP) and result in wetter (drier) than normal conditions. In contrast, during positive AMO phases, weak SSTAs over the WNP lead to limited influence of El Niño on precipitation in Southeast Asia. (ii) In July?August, the teleconnection impact from the North Atlantic is more manifest than that in May?June. During positive AMO phases, the warmer than normal North Atlantic favors anomalous wave trains, which propagate along the ?great circle route? and result in positive pressure anomalies over SC, consequently suppressing precipitation in SC and the ICP. During negative AMO phases, the anomalous wave trains tend to propagate eastward from Europe to Northeast Asia along the summer Asian jet, exerting limited influence on Southeast Asia

Entangling Power in the Deterministic Quantum Computation with One Qubit

The deterministic quantum computing with one qubit (DQC1) is a mixed-state quantum computation algorithm that evaluates the normalized trace of a unitary matrix and is more powerful than the classical counterpart. We find that the normalized trace of the unitary matrix can be directly described by the entangling power of the quantum circuit of the DQC1, so the nontrivial DQC1 is always accompanied with the non-vanishing entangling power. In addition, it is shown that the entangling power also determines the intrinsic complexity of this quantum computation algorithm, i.e., the larger entangling power corresponds to higher complexity. Besides, it is also shown that the non-vanishing entangling power does always exist in other similar tasks of DQC1.Comment: 6 pages and 1 figur

On computing explanations in argumentation

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Argumentation can be viewed as a process of generating explanations. However, existing argumentation semantics are developed for identifying acceptable arguments within a set, rather than giving concrete justifications for them. In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation. We identify different types of explanations defined in terms of the new semantics. We also give a correct computational counterpart for explanations using dispute forests

New transformation of Wigner operator in phase space quantum mechanics for the two-mode entangled case

As a natural extension of Fan's paper (arXiv: 0903.1769vl [quant-ph]) by employing the formula of operators' Weyl ordering expansion and the bipartite entangled state representation we find new two-fold complex integration transformation about the Wigner operator (in its entangled form) in phase space quantum mechanics and its inverse transformation. In this way, some operator ordering problems can be solved and the contents of phase space quantum mechanics can be enriched.Comment: 8 pages, 0 figure

Nonparametric Bayesian models for learning network coupling relationships

University of Technology, Sydney. Faculty of Engineering and Information Technology.As the traditional machine learning setting assumes that the data are identically and independently distributed (i.i.d), this is quite like a perfect conditioned vacuum and seldom a real case in practical applications. Thus, the non-i.i.d learning (Cao, Ou, Yu & Wei 2010)(Cao, Ou & Yu 2012)(Cao 2014) has emerged as a powerful tool in describing the fundamental phenomena in the real world, as more factors to be well catered in this modelling. One critical factor in the non-i.i.d. learning is the relations among the data, ranging from the feature information, node partitioning to the correlation of the outcome, which is referred to as the coupling relation in the non-i.i.d. learning. In our work, we aim at uncovering this coupling relation with the nonparametric Bayesian relational models, that is, the data points in our work are supposed to be coupled with each other, and it is this coupling relation we are interested in for further investigation. The coupling relation is widely seen and motivated in real world applications, for example, the hidden structure learning in social networks for link prediction and structure understanding, the fraud detection in the transactional stock market, the protein interaction modelling in biology. In this thesis, we are particularly interested in the learning and inferencing on the relational data, which is to further discover the coupling relation between the corresponding points. For the detail modelling perspective, we have focused on the framework of mixed-membership stochastic blockmodel, in which membership indicator and mixed-membership distribution are noted to represent the nodes’ belonging community for one relation and the histogram of all the belonging communities for one node. More specifically, we are trying to model the coupling relation through three different aspects: 1) the mixed-membership distributions’ coupling relation across the time. In this work, the coupling relation is reflected in the sticky phenomenon between the mixed-membership distributions in two consecutive time; 2) the membership indicators’ coupling relation, in which the Copula function is utilized to depict the coupling relation; 3) the node information and mixed-membership distribution’s coupling relation. This is achieved by the new proposal transform for the node information’s integration. As these three aspects describe the critical parts of the nodes’ interaction with the communities, we are hoping the complex hidden structures can thus be well studied. In all of the above extensions, we set the number of the communities in a nonparametric Bayesian prior (mainly Hierarchical Dirichlet Process), instead of fixing it as in the previous classical models. In such a way, the complexity of our model can grow along with the data size. That is to say, while we have more data, our model can have a larger amount of communities to account for them. This appealing property enables our models to fit the data better. Moreover, the nice formalization of the Hierarchical Dirichlet Process facilitates us to some benefits, such as the conjugate prior. Thus, this nonparametric Bayesian prior has introduced new elements to the coupling relations’ learning. Under this varying backgrounds and scenarios, we have shown our proposed models and frameworks for learning the coupling relations are evidenced to outperform the state-of-the-art methods via literature explanation and empirical results. The outcomes are sequentially accepted by top journals. Therefore, the nonparametric Bayesian models in learning the coupling relations presents high research value and would still be attractive opportunities for further exploration and exploit