科研成果

The perceived position of a moving object in vision entails an accumulation of neural signals over space and time. Due to neural signal transmission delays, the visual system cannot acquire immediate information about the moving object's position. Although physiological and psychophysical studies on the flash-lag effect (FLE), a moving object is perceived ahead of a flash even when they are aligned at the same location, have shown that the visual system develops the mechanisms of predicting the object's location to compensate for the neural delays, the neural mechanisms of motion-induced location prediction are not still understood well. Here, we investigated the role of neural activity changes in areas MT+ (specialized for motion processing) and the potential contralateral processing preference of MT+ in modulating the FLE. Using transcranial direct current stimulations (tDCS) over the left and right MT+ between pre- and posttests of the FLE in different motion directions, we measured the effects of tDCS on the FLE. The results found that anodal and cathodal tDCS enhanced and reduced the FLE with the moving object heading to but not deviating from the side of the brain stimulated, respectively, compared with sham tDCS. These findings suggest a causal role of area MT+ in motion-induced location prediction, which may involve the integration of position information.NEW & NOTEWORTHY Perceived positions of moving objects are related to neural activities in areas MT+. We demonstrate that tDCS over areas MT+ can modulate the FLE, and further anodal and cathodal tDCS facilitated and inhibited the FLE with a moving object heading to but not deviating from the side of the brain stimulated, respectively. These findings suggest a causal role of area MT+ in motion-induced location prediction and contribute to understanding the neural mechanism of the FLE.

Fe2O3, as an earth-abundant photocatalyst for water purification, has attracted great attention. However, the high-spin FeIII in traditional Fe2O3 restricts its catalytic performance. In this work, based on the nanocrystal size alteration strategy, cubic Fe2O3 nanoclusters (3–4 nm) with low-spin FeIII were successfully anchored on six-fold cavities of the supramolecular condensed g-C3N4 rod (FCN) through the impregnation-coprecipitation method. FCN showed high photocatalytic activity, as the d band center of Fe 3d orbital (−1.79 eV) in low-spin FeIII shifted closer to Femi level, generating a weaker antibonding state. Then, the enhanced bonding state strengthened the interaction between Fe and O, further accelerating the charge carrier separation and enhancing its ability to capture OH−. Thus, low-spin FeIII enhanced the production of dominant reactive oxygen species (•OH/•O2−), promoting diclofenac photocatalytic degradation under solar light, with a kinetic rate constant (0.206 min−1) of  5 times compared with that of pristine g-C3N4.

In face of the critical endurance issue, for the first time we take a holistic perspective to co-optimize the ferroelectric materials and interlayer in FeFET. Compared to the common HZO based gate stack, the novel combination of Hf0.95 Al0.05 O2+Al2 O3 enhances the endurance to $\gt 5 \times 10 ^9$ cycles while maintaining a retention > 10 years. In-depth analysis based on DFT and DQSCV reveal the reduction of interlayer electric field and interface charge trapping as the mechanism of optimization. We also develop a distributed interface trap model to correlate different trapping dynamics with the interlayer property in each device. This work pushes forward the understanding and development of high endurance strategy for FeFET.

This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, to generate a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides comparable performance to other model selection and combination approaches but at a lower computational cost and a higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, the intrinsic mechanisms of the meta-learners, and how the forecasting performance is affected by various factors.

This study uses the data of a nationally representative survey in China to investigate the role of financial literacy overconfidence in investment fraud victimization. The study finds that male, wealthy, and educated respondents tend to be more confident about their financial knowledge. Moreover, overconfident respondents are more likely to believe that the abnormally high returns claimed in two hypothetical investment opportunities are attainable.

Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy task. In recent years, the idea of using time series features for forecast combinations has flourished. Although this idea has been proved to be beneficial in several forecasting competitions, it may not be practical in many situations. For example, the task of selecting appropriate features to build forecasting models is often challenging. Even if there was an acceptable way to define the features, existing features are estimated based on the historical patterns, which are likely to change in the future. Other times, the estimation of the features is infeasible due to limited historical data. In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features. We use out-of-sample forecasts to obtain weights for forecast combinations by amplifying the diversity of the pool of methods being combined. A rich set of time series is used to evaluate the performance of the proposed method. Experimental results show that our diversity-based forecast combination framework not only simplifies the modeling process but also achieves superior forecasting performance in terms of both point forecasts and prediction intervals. The value of our proposition lies on its simplicity, transparency, and computational efficiency, elements that are important from both an optimization and a decision analysis perspective.

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.