Narrow Results

The heterogeneous distribution of immunization probability has been proven to suppress the recurrent epidemic outbreaks effectively, but the reason for this interesting phenomenon is still unclear. To solve this problem, the effects of immunization probability on the maximal infected density and the average infected density are investigated, and it’s found that large immunization probability will depress the maximal infected density sharply. Moreover, it’s proved that small immunization probability is of great importance, which could not only reduce the cost of immunization at low risk but also decrease the outbreak risk of the next year by making the average infected density larger in the current year. Besides, a generalized immunization strategy is presented, which makes the immunization strategy easy to carry out and meanwhile keeps the maximal and the average infected density almost as good as those in the original dynamic immunization strategy. Moreover, the control effects in eight real networks are also investigated, which proves the usefulness of the modified dynamic immunization strategy. Our work proves the usefulness of the heterogeneous distribution of immunization probability and may present theoretical guidance for the government and the public to control the recurrent epidemic outbreaks.

Music emotion recognition plays an important role in many applications such as music material library construction and music recommendation system. The current music emotion recognition mainly focuses on discrete emotions or continuous emotions under single scene. However, on the one hand, intensity is one of important aspects of emotion, which can be represented as emotion rank or ordinal class. On the other hand, there may be not enough music emotion data in training set, which needs to transfer music emotion recognition model learnt from the music data in a source domain. The distribution of existing music data in target domain may differ from target music dataset. In order to overcome these two issues, this paper proposes to utilize transfer ordinal label learning (TOLL) to estimate music emotion. Compared with the previous works, TOLL-based music emotion intensity estimation implements music intensity estimation through transferring the knowledge in the existing source domain to unknown target domain. The experiments on several datasets show that TOLL can achieve promising results for emotion intensity estimation in single scene or across different scenes.

Near-infrared spectroscopy (NIRS) can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical, which can be deployed for the cerebral function study. However, NIRS-based cerebral function detection accuracy can be significantly influenced by the physiological activities such as cardic cycle, respiration, spontaneous low-frequency oscillation and ultra-low frequency oscillation. The distribution difference of the capillary, artery and vein leads to the heterogeneity feature of the cerebral tissues. In the case that the heterogeneity is not serious, good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach. The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious. In this study, the cerebral function activity signal is extracted using recursive least square (RLS) method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition (EEMD) algorithm. The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal. Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology. The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.