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Electroencephalogram (EEG) signals are physiological indicators of brain activity, offering the advantage of high temporal resolution for capturing subtle emotional changes and providing rich information for emotion recognition. However, extracting effective features from EEG data with a low signal-to-noise ratio poses a significant challenge that hinders progress in this research field. To address this issue, we propose a multi-view time-frequency contrastive learning framework called MV-TFCL to enhance the information representation capability of EEG signals from multiple perspectives. Firstly, we introduce a recursive neural network based on multi-scale time-frequency consistency, which integrates global semantic information across different scales through gated units. To our knowledge, this is the first proposal of the theory of multi-scale time-frequency consistency applied in emotion recognition research. Subsequently, we design a tree-structured time-frequency encoder to capture local semantic information within the time-frequency domain. Finally, we incorporate semantic consistency constraints from both global and local perspectives to learn more generalizable and robust features. Extensive experimental results on two publicly available datasets demonstrate the effectiveness and superiority of our proposed method.

Multivariate time series modeling has been essential in sensor-based data mining tasks. However, capturing complex dynamics caused by intra-variable (temporal) and inter-variable (spatial) relationships while simultaneously taking into account evolving data distributions is a non-trivial task, which faces accumulated computational overhead and multiple temporal patterns or distribution modes. Most existing methods focus on the former direction without adaptive task-specific learning ability. To this end, we developed a holistic spatial-temporal meta-learning probabilistic inference framework, entitled ST-MeLaPI, for the efficient and versatile learning of complex dynamics. Specifically, first, a multivariate relationship recognition module is utilized to learn task-specific inter-variable dependencies. Then, a multiview meta-learning and probabilistic inference strategy was designed to learn shared parameters while enabling the fast and flexible learning of task-specific parameters for different batches. At the core are spatial dependency-oriented and temporal pattern-oriented meta-learning approximate probabilistic inference modules, which can quickly adapt to changing environments via stochastic neurons at each timestamp. Finally, a gated aggregation scheme is leveraged to realize appropriate information selection for the generative style prediction. We benchmarked our approach against state-of-the-art methods with real-world data. The experimental results demonstrate the superiority of our approach over the baselines. [ABSTRACT FROM AUTHOR]

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial–temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial–temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger–Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability. [ABSTRACT FROM AUTHOR]

In conventional p–i–n inverted perovskite solar cells (PSCs), there exists considerable energy loss due to both unsatisfactory light path design and trap‐induced interfacial defects. The sunlight is absorbed competitively by conductive oxide substrates and hole transport material in front of the perovskite layer, while the opaque metal back electrode also prevents light penetration. Worse yet, there is severe nonradiative charge recombination caused by defects between the perovskite layer and the electron transport material. To tackle the above two issues, a new green‐emitting material ((CH3)4 N)2(C2H5)4N·MnBr4 is synthesized and introduced in/on the perovskite layer to achieve both defect passivation and light complementation. The green luminescence of this single crystal at the interface is found to provide secondary light absorption, as evidenced by a remarkable promotion of short‐circuit current density. It is also found that the excess PbI2 on the surface of perovskite can be effectively removed, and as the interfacial additive, ((CH3)4 N)2(C2H5)4 N·MnBr4 inhibits trap‐assisted recombination losses, which provides favorable energy‐level alignment and extends charge carrier lifetime. As a result, the champion PCE (21.23%) of the target‐treated ((CH3)4 N)2(C2H5)4 N·MnBr4 device exceeds that 19.5% of the pristine‐without ((CH3)4 N)2(C2H5)4 N·MnBr4 device. This work provides an effective interfacial strategy for high‐performance and stable inverted PSCs. [ABSTRACT FROM AUTHOR]

In this study, we introduce a newly created sentiment index of individual investors in NZ constructed similar to the well-known sentiment index provided by the American Association of Individual Investors (AAII) in the U.S. This unique setup allows us to compare different aspects of investors' behaviour in both countries. We show that NZ market participants are less confident about the directional movement of the stock market, their expectations are more volatile and their distributions have fatter tails. By contrast, both bullish and bearish sentiment is more persistent among U.S. investors. Furthermore, our analysis of return predictability reveals that both groups of investors behave as noise traders. However, the results for NZ investors are stronger. Overall, our findings call for better financial education, particularly in the area of equity investing. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the extent of cum-ex trading in European markets. We document that the abnormal trading activity around the dividend day of a stock can be attributed to cum-ex trading, in addition to existing tax clientele hypotheses. Cum-ex trading is positively associated with dividend yield, which is consistent with maximizing returns from this strategy. Based on abnormal trading volume, we estimate a substantial loss to treasuries due to illicit tax refunds of withholding tax on dividends. Our results are robust, controlling for confounding effects and investors' tax preference and heterogeneity. [ABSTRACT FROM AUTHOR]