Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data

Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains. The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects. However, the existing functional alignment methods cannot handle well various kinds of fMRI datasets today, especially when they are not temporally-aligned, i.e., some of the subjects probably lack the responses to some stimuli, or different subjects might follow different sequences of stimuli. In this paper, a cross-subject graph that depicts the (dis)similarities between samples across subjects is used as a priori for developing a more flexible framework that suits an assortment of fMRI datasets. However, the high dimension of fMRI data and the use of multiple subjects makes the crude framework time-consuming or unpractical. To address this issue, we further regularize the framework, so that a novel feasible kernel-based optimization, which permits nonlinear feature extraction, could be theoretically developed. Specifically, a low-dimension assumption is imposed on each new feature space to avoid overfitting caused by the highspatial-low-temporal resolution of fMRI data. Experimental results on five datasets suggest that the proposed method is not only superior to several state-of-the-art methods on temporally-aligned fMRI data, but also suitable for dealing `with temporally-unaligned fMRI data.Comment: 17 pages, 10 figures, Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI-20

Summer maize grain yield and water use efficiency response to straw mulching and plant density

The demand for food security and fresh water due to global warming causes an elevated requirement for food production and water efficiency in the North China Plain (NCP). To establish the optimal summer maize (Zea mays L.) planting schedule, a study was conducted to understand the effects of different straw mulching conditions and plant density on grain yield (GY) and water use efficiency (WUE). During 2012 and 2013 summer maize growing seasons, experiments were conducted with two different mulching treatments, i.e., 0.6 kg m-2 straw mulching (M)and non-mulching (N), and three plant density conditions, i.e., 10.0 plants m-2 (1, high plant density), 7.5 plants m-2 (2, medium plant density), and 5.5 plants m-2 (3, low plant density). The six treatment combinations were: 10.0 plants m-2 density without straw mulching (N1), 10.0 plants m-2 density with 0.6 kg m-2 straw mulching (M1), 7.5plants m-2 density without straw mulching (N2), 7.5 plants m-2 density with 0.6 kg m-2 straw mulching (M2), 5.5 plants m-2 density without straw mulching (N3), and 5.5 plants m-2 density with 0.6 kg m-2 straw mulching (M3). The results showed medium and high plant density treatments had a significant increase in spike number compared tothe low plant density treatment. Straw mulching significantly improved both the GY and WUE of summer maize under low and medium plant density treatments in both dry and normal rainfall years. M2 treatment achieved the highest GY and showed the greatest improvement in WUE of 35.4% over the non-mulching treatment across the three plant densities, and so it will be promoted as an agricultural practice in the NC