The Double-ITCZ Bias in CMIP3, CMIP5 and CMIP6 Models Based on Annual Mean Precipitation

The double‐intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual double‐ITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multi‐model ensemble mean precipitation errors in the tropics. The notorious double‐ITCZ bias and its big inter‐model spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the double‐ITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its inter‐model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6

The Double-ITCZ Bias in CMIP3, CMIP5 and CMIP6 Models Based on Annual Mean Precipitation

The double‐intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual double‐ITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multi‐model ensemble mean precipitation errors in the tropics. The notorious double‐ITCZ bias and its big inter‐model spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the double‐ITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its inter‐model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6

Oxidation tuning of ferroic transitions in Gd2_2C monolayer

Tuning of ferroic phases provides great opportunities for material functionalities, especially in two-dimensional materials. Here, a $4f$ rare-earth carbide Gd$_2$C monolayer is predicted to be ferromagnetic metal with large magnetization, inherited from its bulk property. Based on first-principles calculations, we propose a strategy that the surface passivation can effectively tune its ferroicity, namely switching among ferromagnetic, antiferromagnetic, and ferroelectric phases. Metal-insulator transition also occurs accompanying these ferroic transitions. Our calculation also suggests that the magneto-optic Kerr effect and second harmonic generation are effective methods to monitor these phase transitions.Comment: 5 pages, 4 figure

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.Comment: Accepted by ICSE 201

Localization for general Helmholtz

In \cite{gmw2022}, Guan, Murugan and Wei established the equivalence of the classical Helmholtz equation with a ``fractional Helmholtz" equation in which the Laplacian operator is replaced by the nonlocal fractional Laplacian operator. More general equivalence results are obtained for symbols which are complete Bernstein and satisfy additional regularity conditions. In this work we introduce a novel and general set-up for this Helmholtz equivalence problem. We show that under very mild and easy-to-check conditions on the Fourier multiplier, the general Helmholtz equation can be effectively reduced to a localization statement on the support of the symbol.Comment: 10 page

Negative brand engagement in the online context

This thesis advances the understanding of negative online brand engagement. Previous studies mostly presume consumer brand engagement to be positive. However, many studies highlight the importance of negative online brand engagement and appreciate that it can be more common and potentially more impactful or detrimental to both brands and consumers, particularly in the online context, than positive online brand engagement. Negative online brand engagement is relatively new in the field of marketing and branding research, with no agreement on its conceptualisation and robustly developed measurement. The current thesis aims to address the gap in the conceptualisation and operationalisation and identify and test prominent drivers and outcomes of negative online brand engagement. The theoretical development involves a systematic literature review of positive consumer engagement, reviews existing articles on negative consumer engagement and builds the foundation for conceptual model development. The empirical analysis adopts a sequential mixed-methods research design. The qualitative study (online observation, semi-structured interviews) was firstly conducted to identify dimensionality, antecedents and outcomes of negative online brand engagement, and develop the conceptual model. Survey data (N=431) were then used in the measurement development and hypotheses testing. The findings show the multi-dimensional nature of negative online brand engagement, consisting of cognition, affection, online constructive and destructive behaviour. The quantitative results identify six drivers of the phenomenon, namely perceived brand quality, brand failure severity, unacceptable brand behaviour, anti-consumption in general, consumer brand disidentification and oppositional attitudinal loyalty. Finally, the same evidence supports five outcomes including consumers’ intention to participate in anti-brand communities, brand disloyalty, happiness, offline destructive and constructive behaviour. The thesis offers theoretical and managerial implications. It provides an improved, innovative conceptualisation and a valid measurement of negative online brand engagement and identifies its key drivers and outcomes, none of which have been clearly identified in previous studies. These findings also provide strategic implications for managers to develop the appropriate marketing and branding strategies and avoid or manage the effects of negative online brand engagement

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure