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• A novel event-triggered-strategy that can relate the dynamic threshold with the communication rate intuitively is presented. • By adopting the novel event-triggered-strategy, a novel distributed multisensor data fusion algorithm is proposed. • By fusing the measurements in the neighborhood only, the energy consumption is saved in the proposed algorithm. In this paper, the event-triggered distributed multi-sensor data fusion algorithm is presented for wireless sensor networks (WSNs) based on a new event-triggered strategy. The threshold of the event is set according to the chi-square distribution that is constructed by the difference of the measurement of the current time and the measurement of the last sampled moment. When the event-triggered decision variable value is larger than the threshold, the event is triggered and the observation is sampled for state estimation. In designing the dynamic event-triggered strategy, we relate the threshold with the quantity in the chi-square distribution table. Therefore, compared to the existed event-triggered algorithms, this novel event-triggered strategy can give the specific sampling/communication rate directly and intuitively. In addition, for the presented distributed fusion in wireless sensor networks, only the measurements in the neighborhood (i.e., the neighbor nodes and the neighbor's neighbor nodes) of the fusion center are fused so that it can obtain the optimal state estimation under limited energy consumption. A numerical example is used to illustrate the effectiveness of the presented algorithm. [ABSTRACT FROM AUTHOR]

Self-paced learning (SPL) is a recently proposed methodology designed by mimicking through the learning principle of humans/animals. A variety of SPL realization schemes have been designed for different computer vision and pattern recognition tasks, and empirically demonstrated to be effective in these applications. However, the literature is in lack of the theoretical understanding of SPL. Regarding this research gap, this study attempts to provide some new theoretical understanding of the SPL scheme. Specifically, we prove that the solution strategy on SPL accords with a majorization minimization algorithm implemented on an implicit objective function. Furthermore, we found that the loss function contained in this implicit objective has a similar configuration with the non-convex regularized penalty (NCRP) known in statistics and machine learning. Such connection inspires us to discover more intrinsic relationships between the SPL regimes and the NCRP forms, like smoothly clipped absolute deviation (SCAD), logarithmic penalty (LOG) and non-convex exponential penalty (EXP). The insight of the robustness under SPL can then be finely explained. We also analyze the capability of SPL regarding its easy loss-prior-embedding property, and provide an insightful interpretation of the effectiveness mechanism under current SPL variations. Moreover, we design a group-partial-order loss prior, which is especially useful for weakly labeled large-scale data processing tasks. By applying SPL with this loss prior to the FCVID dataset, which is currently one of the largest manually annotated video dataset, our method achieves state-of-the-art performance above existing methods, which further supports the proposed theoretical arguments. [ABSTRACT FROM AUTHOR]

This article focuses on the issue of Internet communication. Groups of managers from around the world come together via the Internet to learn general management principles and to work on projects of relevance to their companies. According to a research, a mix of asynchronous and synchronous tools is vital to team success. Asynchronous tools are more critical for team coordination. E-mail and electronic discussion boards are important tools of communication. Asynchronous communication tools are vital when cultural and geographic distances are great and worker mobility is high. Although simultaneous conferencing and chat technologies are important, authors found the asynchronous nature of group discussion and email technologies are even more important to teams separated by cultural differences and multiple time zones. Technologies and member diversity constitute resources for global learning teams. A team that uses these resources develop group identity and trust.