No abstract

Software-defined network (SDN) is widely used in smart grid for monitoring and managing the communication network. Big data analytics for SDN-based smart grid has got increasing attention. It is a promising approach to use machine learning technologies to analyze a large amount of data generated in SDN-based smart grid. However, the disclosure of personal privacy information must receive considerable attention. For instance, data clustering in user electricity behavior analysis may lead to the disclosure of personal privacy information. In this paper, an optimizing and differentially private clustering algorithm named ODPCA is proposed. In the ODPCA, the differentially private K-means algorithm and K-modes algorithm are combined to cluster mixed data in a privacy-preserving manner. The allocation of privacy budgets is optimized to improve the accuracy of clustering results. Specifically, the loss function that considers both the numerical and categorical attributes between true centroids and noisy centroids is analyzed to optimize the allocation the privacy budget; the number of iterations of clustering is set to a fixed value based on the total privacy budget and the minimal privacy budget allocated to each iteration. It is proved that the ODPCA can meet the differential privacy requirements and has better performance by comparing with other popular algorithms.INDEX TERMS Differential privacy, clustering, machine learning, SDN-based smart grid, big data.

In this paper, we first give a method to construct large sets of resolvable Mendelsohn triple systems of order q +2, where q =6t +1 is a prime power. Then, using a computer, we find solutions for t ∈ T ={35, 38, 46, 47, 48, 51, 56, 60}. Furthermore, by a method we introduced, large sets of resolvable directed triple systems with the same orders are obtained too. Finally, by the tripling construction and product construction for LRMTSs and LRDTSs, and by new results for LR-designs, we obtain the existence of an LRMTS(v) and an LRDTS(v) for all v of the formwhere t ∈ T and M and N are finite multisets of non-negative integers. This provides more infinite classes for LRMTSs and LRDTSs with odd orders.

SUMMARYSwitch defective/sucrose non‐fermentable (SWI/SNF) chromatin remodeling complexes are evolutionarily conserved, multi‐subunit machinery that play vital roles in the regulation of gene expression by controlling nucleosome positioning and occupancy. However, little is known about the subunit composition of SPLAYED (SYD)‐containing SWI/SNF complexes in plants. Here, we show that the Arabidopsis thaliana Leaf and Flower Related (LFR) is a subunit of SYD‐containing SWI/SNF complexes. LFR interacts directly with multiple SWI/SNF subunits, including the catalytic ATPase subunit SYD, in vitro and in vivo. Phenotypic analyses of lfr‐2 mutant flowers revealed that LFR is important for proper filament and pistil development, resembling the function of SYD. Transcriptome profiling revealed that LFR and SYD shared a subset of co‐regulated genes. We further demonstrate that the LFR and SYD interdependently activate the transcription of AGAMOUS (AG), a C‐class floral organ identity gene, by regulating the occupation of nucleosome, chromatin loop, histone modification, and Pol II enrichment on the AG locus. Furthermore, the chromosome conformation capture (3C) assay revealed that the gene loop at AG locus is negatively correlated with the AG expression level, and LFR‐SYD was functional to demolish the AG chromatin loop to promote its transcription. Collectively, these results provide insight into the molecular mechanism of the Arabidopsis SYD‐SWI/SNF complex in the control of higher chromatin conformation of the floral identity gene essential to plant reproductive organ development.

Using the Katona–Kierstead (K–K) definition of a Hamilton cycle in a uniform hypergraph, we investigate the existence of wrapped K–K Hamilton cycle decompositions of the complete bipartite 3‐uniform hypergraph Kn,n(3) and their large sets, settling their existence whenever n is prime.