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A deep transfer learning framework adapting mixed subdomains is proposed for cross-species plant disease diagnosis. Most existing deep transfer learning studies focus on knowledge transfer between highly correlated domains. These methods may fail to deal with domains that are poorly correlated. In this study, mixed domain images were generated from source and target image groups for improving the correlation between the mixed domain (training dataset) and the target domain (testing dataset). A subdomain alignment mechanism is employed to transfer knowledge from the mixed domain to the target domain. The proposed framework captures the fine-grained information more effectively. Extensive experiments were conducted and prove that the proposed method produces a more effective result compared with existing deep transfer learning technologies for poorly related subdomains.

Abstract:Comparative studies of photosynthetic physiology were carried out to evaluate high light and temperature stresses in reproductive (receptacles) and vegetative (blades) tissues of fertile thalli of Hizikia fusiformisfrom Nanao Island, China. Receptacles had much higher rates of photosynthesis than blades, and temperature sensitivity was much higher for photosynthesis than for respiration. Both receptacles and blades survived extended exposure to 1500 μmol photons m−2s−1; however, above 30°C, photosynthetic activity declined rapidly. Prolonged exposure to high temperature sharply depressed photosynthesis, and extended exposure to 40°C was more detrimental to photosynthesis than exposure to 30°C. Moreover, photosynthesis of receptacles was more susceptible to heat than photosynthesis in blades. Following 15 min at 40°C, receptacle photosynthesis declined to 28% of initial value and recovered to only 60% of initial value within 1 h in recovery conditions. However, after 30 min at 40°C, receptacle photosynthesis did not recover within 4 h, suggesting that the photosynthetic apparatus had suffered irreversible injury. Respiration was not affected by high temperature. Our study shows that high temperature but not high light stress might hamper sexual reproduction in H. fusiformis.

Abstract Discord search is an important technique for time series analysis, especially for anomaly detection. In recent years, many computational approaches of discord search were studied; however, limitation exists while only the problems with univariate time series data can be well addressed. In this study, we proposed a novel computational framework to identify discords from multivariate time series (MTS) data, namely, LRRDS (Local Recurrence Rate based Discord Search). LRRDS accurately identifies the discords by analyzing a recurrence plot, which is transformed from the original time series data. An innovative strategy was employed to improve the efficiency for pair-wise distance comparison of two subsequences. In the experimental simulations, LRRDS was applied to an extensive number of MTS datasets. Results show that the proposed approach is more efficient than existing methods, such as GDS. In conclusion, the LRRDS approach solves the adaptability problem of discord sequences in multi-dimensional space and guarantees the computational effectiveness and efficiency. [ABSTRACT FROM AUTHOR]