Comparison of the Forest Tenure in Brazil and China

Brazil and China both have extensive forest areas in the world, making important contribution to reversal of the worldwide decline in forest. And as the world’s leading importers and exporters of timber and timber-based products, sustainable forest management for both countries are crucial for global economy and environment, so there is an intense international interest in their sustainability and well-being. Tenure arrangements functioned as powerful tools of forest policy, is not only important for economic growth, social cohesion, poverty reduction and environmental protection - it is also essential for climate change mitigation. This paper is to present and analyze the state of forest tenure in Brazil and China; then followed by a brief comparison of these two countries in terms of changing trends and reform impacts; Furthermore, it identifies some of the main challenges to the reform and points our several opportunities for extending the future forest tenure reform especially for mitigating climate change, and finally making a conclusion to widen the reach of local community tenure and to deepen the exercise of tenure rights

Mining of nutritional ingredients in food for disease analysis

Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. In this paper, we profoundly analyzed the relationship between nutritional ingredients and diseases by using data mining methods. First, more than 7,000 diseases were obtained and we collected the recommended food and taboo food for each disease. Then, referring to the China Food Nutrition, we used noise-intensity and information entropy to find out which nutritional ingredients can exert positive effects on diseases. Finally, we proposed an improved algorithm named CVNDA_Red based on rough sets to select the corresponding core ingredients from the positive nutritional ingredients. To the best of our knowledge, this is the first study to discuss the relationship between nutritional ingredients in food and diseases through data mining based on rough set theory in China. The experiments on real-life data show that our method based on data mining improves the performance compared with the traditional statistical approach, with the precision of 1.682. Additionally, for some common diseases such as Diabetes, Hypertension and Heart disease, our work is able to identify correctly the first two or three nutritional ingredients in food that can benefit the rehabilitation of those diseases. These experimental results demonstrate the effectiveness of applying data mining in selecting of nutritional ingredients in food for disease analysis

Aggregation signature for small object tracking

Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.Comment: IEEE Transactions on Image Processing, 201

Audio Imputation Using the Non-negative Hidden Markov Model

Abstract. Missing data in corrupted audio recordings poses a challeng-ing problem for audio signal processing. In this paper we present an approach that allows us to estimate missing values in the time-frequency domain of audio signals. The proposed approach, based on the Non-negative Hidden Markov Model, enables more temporally coherent es-timation for the missing data by taking into account both the spectral and temporal information of the audio signal. This approach is able to reconstruct highly corrupted audio signals with large parts of the spectro-gram missing. We demonstrate this approach on real-world polyphonic music signals. The initial experimental results show that our approach has advantages over a previous missing data imputation method.

Research on far-field spot search and centre location algorithms

The energy distribution of the far-field spot is uneven, and the background is complex. Therefore, the identification of far-field points and the positioning of the centre are difficult. This article proposes an algorithm for multi-scale Gaussian cyclic convolution for locating the centre of far-field spots. First, wavelet denoising is performed on the image of multiple far-field spots, and the images of adjacent frames are subtracted. Then, the absolute values of the differences are added. Due to the large size of the far-field spot, the Gaussian distribution of the laser energy is not obvious. Therefore, a multi-scale Gaussian convolution kernel is used to perform circular convolution on these images. To preserve low-scale Gaussian features, features are spliced between two convolutions. In this article, we also design multi-angle combination filters to filter the enhanced Gaussian distribution features. Finally, curved polynomials are fitted to the reconstructed Gaussian energy distribution to obtain the maximum value; at this point, the position where the maximum value lies is the centre of the far-field spot. The experiments showed that this method has better robustness