ABSTRACT
An economical and effective method is still lacking for cadmium (Cd) toxicity reduction and food product safety improvement in soil-vegetable systems. Therefore, this study aimed to reduce the Cd toxicity to pak choi (Brassica campestris L.) by jointly using passivators and organic fertilizer, highlighting food products' safety based on pot experiments. The results showed that compared with the control, organic fertilizer decreased the Cd content in edible parts and the soil's available Cd by 48.4% and 20.9% on average, respectively, due to the 0.15-unit increases in soil pH. Once jointly applied with passivators, the decrements increased by 52.3-72.6% and 32.5-52.6% for the Cd content in edible parts and for the soil's available Cd, respectively, while the pH increment increased by 0.15-0.46 units. Compared with the control, the transport factor of Cd was reduced by 61.9% and 50.9-55.0% when applying organic fertilizer alone and together with the passivators, respectively. The combination treatment of biochar and organic fertilizer performed the best in decreasing the Cd content in the edible parts and the soil's available Cd. The combination treatment of fish bone meal and organic fertilizer induced the greatest increases in soil pH. The grey relational analysis results showed that the combination treatment of biochar and organic fertilizer performed the best in reducing the potential Cd pollution risk, thereby highlighting the vegetable food safety. This study provides a potential economical and effective technology for toxicity reduction and food safety in Cd-polluted soil.
ABSTRACT
Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e., transformation. They are solely defined to test how well machines understand concepts and relations within static settings, like one image. Such state driven visual reasoning has limitations in reflecting the ability to infer the dynamics between different states, which has shown to be equally important for human cognition in Piaget's theory. To tackle this problem, we propose a novel transformation driven visual reasoning (TVR) task. Given both the initial and final states, the target becomes to infer the corresponding intermediate transformation. Following this definition, a new synthetic dataset namely TRANCE is first constructed on the basis of CLEVR, including three levels of settings, i.e., Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Next, we build another real dataset called TRANCO based on COIN, to cover the loss of transformation diversity on TRANCE. Inspired by human reasoning, we propose a three-staged reasoning framework called TranNet, including observing, analyzing, and concluding, to test how recent advanced techniques perform on TVR. Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event, View, and TRANCO. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and new problems need to be investigated in this direction.
ABSTRACT
In this paper, we consider the problem of exploring structural regularities of networks by dividing the nodes of a network into groups such that the members of each group have similar patterns of connections to other groups. Specifically, we propose a general statistical model to describe network structure. In this model, a group is viewed as a hidden or unobserved quantity and it is learned by fitting the observed network data using the expectation-maximization algorithm. Compared with existing models, the most prominent strength of our model is the high flexibility. This strength enables it to possess the advantages of existing models and to overcome their shortcomings in a unified way. As a result, not only can broad types of structure be detected without prior knowledge of the type of intrinsic regularities existing in the target network, but also the type of identified structure can be directly learned from the network. Moreover, by differentiating outgoing edges from incoming edges, our model can detect several types of structural regularities beyond competing models. Tests on a number of real world and artificial networks demonstrate that our model outperforms the state-of-the-art model in shedding light on the structural regularities of networks, including the overlapping community structure, multipartite structure, and several other types of structure, which are beyond the capability of existing models.