Unsupervised domain adaptation aims to transfer the classifier learned from the source domain to the target domain in an unsupervised manner. With the help of target pseudo-labels, aligning class-level distributions and learning the classifier in the target domain are two widely used objectives. Existing methods often separately optimize these two individual objectives, which makes them suffer from the neglect of the other. However, optimizing these two aspects together is not trivial. To alleviate the above issues, we propose a novel method that jointly optimizes semantic domain alignment and target classifier learning in a holistic way. The joint optimization mechanism can not only eliminate their weaknesses but also complement their strengths. The theoretical analysis also verifies the favor of the joint optimization mechanism. Extensive experiments on benchmark datasets show that the proposed method yields the best performance in comparison with the state-of-the-art unsupervised domain adaptation methods.

Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of moment matching, most existing discrepancy-based methods are designed to match the second-order or lower moments, which however, have limited expression of statistical characteristic for non-Gaussian distributions. In this work, we propose a Higher-order Moment Matching (HoMM) method, and further extend the HoMM into reproducing kernel Hilbert spaces (RKHS). In particular, our proposed HoMM can perform arbitrary-order moment matching, we show that the first-order HoMM is equivalent to Maximum Mean Discrepancy (MMD) and the second-order HoMM is equivalent to Correlation Alignment (CORAL). Moreover, HoMM (order≥ 3) is expected to perform fine-grained domain alignment as higher-order statistics can approximate more complex, non-Gaussian distributions. Besides, we also exploit the pseudo-labeled target samples to learn discriminative representations in the target domain, which further improves the transfer performance. Extensive experiments are conducted, showing that our proposed HoMM consistently outperforms the existing moment matching methods by a large margin. Codes are available at https://github.com/chenchao666/HoMM-Master

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Currently, low-rank tensor completion has gained cumulative attention in recovering incomplete visual data whose partial elements are missing. By taking a color image or video as a three-dimensional (3D) tensor, previous studies have suggested several definitions of tensor nuclear norm. However, they have limitations and may not properly approximate the real rank of a tensor. Besides, they do not explicitly use the low-rank property in optimization. It is proved that the recently proposed truncated nuclear norm (TNN) can replace the traditional nuclear norm, as a better estimation to the rank of a matrix. Thus, this paper presents a new method called the tensor truncated nuclear norm (T-TNN), which proposes a new definition of tensor nuclear norm and extends the truncated nuclear norm from the matrix case to the tensor case. Beneficial from the low rankness of TNN, our approach improves the efficacy of tensor completion. We exploit the previously proposed tensor singular value decomposition and the alternating direction method of multipliers in optimization. Extensive experiments on real-world videos and images demonstrate that the performance of our approach is superior to those of existing methods.

Detoxification plays an important role in herbicide action. Herbicide safeners selectively protect crops from herbicide injury without reducing the herbicidal efficiency against the target weeds. With the large-scale use of herbicides, herbicide safeners have been widely used in sorghum, wheat, rice, corn, and other crops. In recent years, an increasing number of unexpected new herbicide safeners have been designed. The varieties, structural characteristics, uses, and synthetic routes of commercial herbicide safeners are reviewed in this paper. The design ideas and structural characteristics of novel herbicide safeners are summarized, which provide a basis for the design of bioactive molecules as new herbicide safeners in the future.

Relaxor-lead titanate (PbTiO 3 ) crystals, which exhibit extremely high piezoelectricity, are believed to possess high electro-optic (EO) coefficients. However, the optical transparency of relaxor-PbTiO 3 crystals is severely reduced as a result of light scattering and reflection by domain walls, limiting electro-optic applications. Through synergistic design of a ferroelectric phase, crystal orientation, and poling technique, we successfully removed all light-scattering domain walls and achieved an extremely high transmittance of 99.6% in antireflection film–coated crystals, with an ultrahigh EO coefficient r 33 of 900 picometers per volt (pm V −1 ), >30 times as high as that of conventionally used EO crystals. Using these crystals, we fabricated ultracompact EO Q-switches that require very low driving voltages, with superior performance to that of commercial Q-switches. Development of these materials is important for the portability and low driving voltage of EO devices.

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Partial domain adaptation (PDA) extends standard domain adaptation to a more realistic scenario where the target domain only has a subset of classes from the source domain. The key challenge of PDA is how to select the relevant samples in the shared classes for knowledge transfer. Previous PDA methods tackle this problem by re-weighting the source samples based on the prediction of classifier or discriminator, thus discarding the pixel-level information. In this paper, to utilize both high-level and pixel-level information, we propose a reinforced transfer network (RTNet), which is the first work to apply reinforcement learning to address the PDA problem. The RTNet simultaneously mitigates the negative transfer by adopting a reinforced data selector to filter out outlier source classes, and promotes the positive transfer by employing a domain adaptation model to minimize the distribution discrepancy in the shared label space. Extensive experiments indicate that RTNet can achieve state-of-the-art performance for partial domain adaptation tasks on several benchmark datasets. Codes and datasets will be available online.