Until now, liquid crystal display (LCD) monitors have not been used widely for research in vision. Despite their main advantages of continuous illumination and low electromagnetic emission, these monitors had problems with timing and reliability. Here we report that there is at least one new inexpensive 120 Hz model, whose timing and stability is on a par with a benchmark cathode-ray tube monitor, or even better. The onset time was stable across repetitions, 95% confidence interval (the error) of which was <0.01 ms. Brightness was also delivered reliably across repeated presentations (<0.04% error) and across blocks with different durations (<3% error). The LCD monitor seems suitable for many applications in vision research, including the studies that require combined accuracy of timing and intensity of visual stimulation.

Evaluating potentially hazardous effects of chemicals on ecosystems has always been an important research topic traditionally studied using laboratory or field experiments. Experiment-based ecotoxicity test results are only available for a limited number of chemicals due to the extensive experimental effort and cost. Given the ever-increasing number of chemicals involved in the modern production process and products, rapidly characterizing chemical ecotoxicity at lower costs has become critical for guiding technology and policy development for chemical risk management. In this study, artificial neural network models are developed to predict chemical ecotoxicity (HC 50 ) based on experimental data to fill data gaps in a widely used database (USEtox). To reduce the manual tuning effort on optimal network architecture, a genetic algorithm is investigated to automatically search and configure the network architecture. The resulting neural network model reached an average test R 2 of 0.632 and had a trivial difference with the global optimal regarding validation MSE. The findings of this study can rapidly predict the ecotoxicity of chemicals and further help to understand the potential risk of chemicals and develop strategies for risk management.

Most existing Chinese word segmentation (CWS) methods are usually supervised. Hence, large-scale annotated domain-specific datasets are needed for training. In this paper, we seek to address the problem of CWS for the resource-poor domains that lack annotated data. A novel neural network model is proposed to incorporate unlabeled and partiallylabeled data. To make use of unlabeled data, we combine a bidirectional LSTM segmentation model with two character-level language models using a gate mechanism. These language models can capture co-occurrence information. To make use of partially-labeled data, we modify the original cross entropy loss function of RNN. Experimental results demonstrate that the method performs well on CWS tasks in a series of domains.

The coherent point drift (CPD) algorithm is a powerful approach for point set registration. However, it suffers from a serious problem-there is a weight parameter w that reflects the assumption about the amount of noise and number of outliers in the Gaussian mixture model, and its value has an influence on the point set registration performance In the original CPD algorithm, the value of w is set manually, and hence an improper value will lead to poor registration results. To solve this problem, a fully automatic algorithm for the selection of an optimal weight parameter is proposed using a hybrid optimization scheme that combines the genetic algorithm with the Nelder-Mead simplex method. The experiments show that the refined CPD algorithm is more effective and extends the original CPD algorithm in its methodology and applications. Keywordspoint set registration, Gaussian mixture model (GMM), coherent point drift (CPD), genetic algorithm (GA), simplex Citation Wang P, Wang P, Qu Z G, et al. A refined coherent point drift (CPD) algorithm for point set registration.

No abstract