Recent research has demonstrated that affective states elicited by viewing pictures varying in valence and arousal are identifiable from whole brain activation patterns observed with functional magnetic resonance imaging (fMRI). Identification of affective states from more naturalistic stimuli has clinical relevance, but the feasibility of identifying these states on an individual trial basis from fMRI data elicited by dynamic multimodal stimuli is unclear. The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing. These findings extend previous results on the distributed representation of affect to multimodal dynamic stimuli.

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With the rapid development of urban rail transit, more and more people choose to travel by subway. Therefore, accurate passenger flow forecasting is of great significance for passengers and municipal construction and contributes to smart city services. In this paper, we propose a multi-type attention-based network to forecast the subway passenger flow with multi-station and external factors. The proposed network has different types of attention mechanisms to adaptively extract relevant features, including multi-station, external factors, and historical data. In addition, the hierarchical attention mechanism is used to model the hierarchical relationship between subway lines and stations. In addition, the embedding method is applied to better combine the different kinds of data. The experiments on real subway passenger flow data in a city in China demonstrate that our method outperforms five baseline methods. Moreover, our method can visualize the impact of different stations and other factors on traffic, which plays an important role in passenger travel and subway dispatch. INDEX TERMS Passenger flow forecasting, attention mechanism, recurrent neural networks.

Through the implementation of ecological compensation policy, it is of great significance to protect ecosystems, coordinate regional development, and achieve sustainable development goals. This study selected the carbon sequestration service in Yantai as an example and carried out a study on the measurement of ecological compensation based on the ecosystem services supply and demand. Moreover, this study clarified the whole process of the generation, circulation and social demand docking of ecological benefits from the perspective of “nature-society”, proposed a spatial flow characterization method for carbon sequestration services, and described the “externality” spillover of ecosystem services. The results showed that most areas of Yantai belonged to the ecological surplus area, which were important sources of carbon sequestration services. Ecological compensation was needed, with a total amount of about 2.2 billion yuan. Qixia, Muping and Penglai had greater comparative ecological radiation force (CERF), and the total amount of carbon sequestration services transferred to the external areas was large. Although the carbon sequestration flows of Yantai showed a spatial decay law, there were significant differences in the direction of different districts and cities. The study can provide a reference for achieving sustainable development of Yantai and formulating ecological compensation policy.

Native speakers can judge whether a sentence is an acceptable instance of their language. Acceptability provides a means of evaluating whether computational language models are processing language in a human-like manner. We test the ability of language models, simple language features, and word embeddings to predict native speakers’ judgments of acceptability on English essays written by non-native speakers. We find that much sentence acceptability variance can be captured by a combination of misspellings, word order, and word similarity (r = 0.494). While predictive neural models fit acceptability judgments well (r = 0.527), we find that a 4-gram model is just as good (r = 0.528). Thanks to incorporating misspellings, our 4-gram model surpasses both the previous unsupervised state-of-the art (r = 0.472), and the average native speaker (r = 0.46), demonstrating that acceptability is well captured by n-gram statistics and simple language features.