A Study on China’s Reform of Teachers’ Education: A Narrative Research About the Substituted Post Exercitation of a Junior Student

Substituted post exercitation is a new teachers’ education model. To prove the operation effect of the new model through the practical situations is of vital significance to the curriculum reform of teachers’ education in normal universities and colleges and the cultivation of teaching skills of normal university and college students. This paper adopts the narrative research method by narrating the substituted post exercitation experiences of a junior student, C, and analyzes the details of her substituted post exercitation experiences with problems pointed out. Based on the case study of Student C, this paper discusses the status quo of teachers’ education in China, and comes to some referential and thought-provoking conclusions

Slim Embedding Layers for Recurrent Neural Language Models

Recurrent neural language models are the state-of-the-art models for language modeling. When the vocabulary size is large, the space taken to store the model parameters becomes the bottleneck for the use of recurrent neural language models. In this paper, we introduce a simple space compression method that randomly shares the structured parameters at both the input and output embedding layers of the recurrent neural language models to significantly reduce the size of model parameters, but still compactly represent the original input and output embedding layers. The method is easy to implement and tune. Experiments on several data sets show that the new method can get similar perplexity and BLEU score results while only using a very tiny fraction of parameters.Comment: To appear at AAAI 201

Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models

In this article we develop a tractable procedure for testing strict stationarity in a double autoregressive model and formulate the problem as testing if the top Lyapunov exponent is negative. Without strict stationarity assumption, we construct a consistent estimator of the associated top Lyapunov exponent and employ a random weighting approach for its variance estimation, which in turn are used in a t-type test. We also propose a GLAD estimation for parameters of interest, relaxing key assumptions on the commonly used QMLE. All estimators, except for the intercept, are shown to be consistent and asymptotically normal in both stationary and explosive situations. The finite-sample performance of the proposed procedures is evaluated via Monte Carlo simulation studies and a real dataset of interest rates is analyzed.Comment: 33 pages, 6 figure

An Online Discriminative Approach to Background Subtraction

We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that is able to cope with illumination changes due to discontinuous switching, or illumination drifts caused by slower processes such as varying time of the day. Starting from a discriminative learning principle, we derive a training algorithm that, for each pixel, computes a weighted linear combination of selected past observations with time-decay. We present experimental results that show the proposed approach outperforms existing methods on both synthetic sequences and real video data

From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective

Nonparametric estimation of the mean and covariance functions is ubiquitous in functional data analysis and local linear smoothing techniques are most frequently used. Zhang and Wang (2016) explored different types of asymptotic properties of the estimation, which reveal interesting phase transition phenomena based on the relative order of the average sampling frequency per subject $T$ to the number of subjects $n$, partitioning the data into three categories: ``sparse'', ``semi-dense'' and ``ultra-dense''. In an increasingly available high-dimensional scenario, where the number of functional variables $p$ is large in relation to $n$, we revisit this open problem from a non-asymptotic perspective by deriving comprehensive concentration inequalities for the local linear smoothers. Besides being of interest by themselves, our non-asymptotic results lead to elementwise maximum rates of $L_2$ convergence and uniform convergence serving as a fundamentally important tool for further convergence analysis when $p$ grows exponentially with $n$ and possibly $T$. With the presence of extra $\log p$ terms to account for the high-dimensional effect, we then investigate the scaled phase transitions and the corresponding elementwise maximum rates from sparse to semi-dense to ultra-dense functional data in high dimensions. Finally, numerical studies are carried out to confirm our established theoretical properties

Shared memory parallel computing procedures for nonlinear dynamic analysis of super high rise buildings

The proposed parallel state transformation procedures (PSTP) of fiber beam-column elements and multi-layered shell elements, combined with the parallel factorization of Jacobian (PF), are incorporated into a finite element program. In PSTP, elements are classified into different levels of workload prior to state determination in order to balance workload among different threads. In PF, the multi-threaded version of OpenBLAS is adopted to compute super-nodes. A case study on four super high-rise buildings, i.e. S1~S4, has demonstrated that the combination of PSTP and PF does not have any observable influence on computational accuracy. As number of elements and DOFs increases, the ratio of time consumed in the formation of the Jacobian to that consumed in the solution of algebraic equations tends to decrease. The introduction of parallel solver can yield a substantial reduction in computational cost. Combination of PSTP and PF can give rise to a further significant reduction. The acceleration ratios associated with PSTP do not exhibit a significant decrease as PGA level increases. Even PGA level is equal to 2.0g, PSTP still can result in acceleration ratios of 2.56 and 1.92 for S1 and S4, respectively. It is verified that it is more effective to accelerate analysis by reducing the time spent in solving algebraic equations rather than reducing that spent in the formation of the Jacobian for super high-rise buildings