The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease.

In order to improve the quality of a software system, software defect prediction aims to automatically identify defective software modules for efficient software test. To predict software defect, those classification methods with static code attributes have attracted a great deal of attention. In recent years, machine learning techniques have been applied to defect prediction. Due to the fact that there exists the similarity among different software modules, one software module can be approximately represented by a small proportion of other modules. And the representation coefficients over the pre-defined dictionary, which consists of historical software module data, are generally sparse. In this paper, we propose to use the dictionary learning technique to predict software defect. By using the characteristics of the metrics mined from the open source software, we learn multiple dictionaries (including defective module and defective-free module sub-dictionaries and the total dictionary) and sparse representation coefficients. Moreover, we take the misclassification cost issue into account because the misclassification of defective modules generally incurs much higher risk cost than that of defective-free ones. We thus propose a cost-sensitive discriminative dictionary learning (CDDL) approach for software defect classification and prediction. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that CDDL outperforms several representative state-of-the-art defect prediction methods. Categories and Subject DescriptorsD.2.9 [Management]: Software quality assurance (SQA), G.1.3 [Numerical Linear Algebra]: Sparse, structured, and very large systems (direct and iterative methods), I.5.2 [Design Methodology]: Classifier design and evaluation. General Terms Algorithms KeywordsSoftware defect prediction, dictionary learning, sparse representation, cost-sensitive discriminative dictionary learning (CDDL).

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