Motif search is a fundamental problem in bioinformatics with an important application in locating transcription factor binding sites (TFBSs) in DNA sequences. The exact algorithms can report all (l, d) motifs and find the best one under a specific objective function. However, it is still a challenging task to identify weak motifs, since either a large amount of memory or execution time is required by current exact algorithms. A new exact algorithm, PairMotif, is proposed for planted (l, d) motif search (PMS) in this paper. To effectively reduce both candidate motifs and scanned l-mers, multiple pairs of l-mers with relatively large distances are selected from input sequences to restrict the search space. Comparisons with several recently proposed algorithms show that PairMotif requires less storage space and runs faster on most PMS instances. Particularly, among the algorithms compared, only PairMotif can solve the weak instance (27, 9) within 10 hours. Moreover, the performance of PairMotif is stable over the sequence length, which allows it to identify motifs in longer sequences. For the real biological data, experimental results demonstrate the validity of the proposed algorithm.
Due to the restricted mathematical description of the uncertainty set, the current two-stage robust optimization is usually over-conservative which has drawn concerns from the power system operators. This paper proposes a novel dataadaptive robust optimization method for the economic dispatch of active distribution network with renewables. The scenariogeneration method and the two-stage robust optimization are combined in the proposed method. To reduce the conservativeness, a few extreme scenarios selected from the historical data are used to replace the conventional uncertainty set. The proposed extremescenario selection algorithm takes advantage of considering the correlations and can be adaptive to different historical data sets. A theoretical proof is given that the constraints will be satisfied under all the possible scenarios if they hold in the selected extreme scenarios, which guarantees the robustness of the decision. Numerical results demonstrate that the proposed data-adaptive robust optimization algorithm with the selected uncertainty set is less conservative but equally as robust as the existing two-stage robust optimization approaches. This leads to the improved economy of the decision with uncompromised security.
Brain functional connectome analysis is commonly based on population‐wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population‐wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject‐specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group‐wise inferences and refined connectomes are indeed desirable for brain mapping.
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