科研成果

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.

Bacteria present in natural environment especially those in cold regions would experience freeze-thaw (FT) process during day-night and season turns. However, knowledge about the influence of FT on bacteria release behaviors in porous media was limited. In present study, the bacteria release behaviors from quartz sand columns without and with 1 and 3 FT treatment cycles under three water saturations (θ=100%, 90%, and 60%) were investigated. We found that for all three water saturated columns without FT treatment, negligible bacteria released from columns via background salt solution elution, while the subsequent release of bacteria from sand columns via low ionic strength (IS) solution elution decreased with decreasing column water saturations. More importantly, we found unlike the negligible bacteria release in columns without FT treatment, for columns with high saturations (θ=100% and 90%), FT treatment could promote bacteria release with background salt solution elution. Moreover, for high saturated columns, FT treatment would decrease subsequent bacteria release with low IS solution elution. This phenomenon was more obvious with increasing FT treatment cycles. In contrast, FT treatment had negligible influence on bacteria release from columns with lower saturation (θ=60%). The decreased bacterial sizes, the loss of bacterial flagella, as well as the change of local configuration of porous media (via changing water into ice and ice back into water) during the FT processes contributed to increased bacteria release via background salt solution elution from high saturated sand columns. While, the reduced amount of bacteria being retained at secondary energy minima drove to the subsequently decreased bacteria release via low IS solution elution. The results of this study clearly showed that for porous media with high saturations, FT cycles would increase the risk of bacteria detaching from porous media with flushing by the background solution.