This paper studies a Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) system operating in the millimeter-wave (mmWave) band to address the energy efficiency demands of urban computation-intensive and delay-sensitive tasks. By exploiting mmWave’s large bandwidth and UAVs’ mobility, we propose a joint optimization framework for UAV deployment, task offloading, and resource allocation to minimize the system energy cost. Since the proposed problem is a mixed-integer nonlinear programming (MINLP) problem with high computational complexity, conventional solvers may fail to find efficient solutions. To address this, we propose an alternating optimization framework. Specifically, the original problem is decomposed into two subproblems: (1) UAV deployment combined with user offloading, and (2) resource allocation, which are solved iteratively. For the former, we employ an obstacle-aware K-means method integrated with coordinate search to optimize UAV positions and user offloading decisions. For the latter, we combine the Lambert W function and the Lagrangian dual method to efficiently allocate communication and computational resources. Simulation results demonstrate that the proposed method substantially lowers the system energy cost compared to baseline methods, presenting a practical solution for urban UAV-MEC systems.

In an era of increasingly complex biological datasets, one of the key steps in gene functional analysis comes from clustering genes based on co-expression. Biclustering algorithms can identify gene clusters with local co-expressed patterns, which are more likely to define genes functioning together than global clustering methods. However, these algorithms are not effective in uncovering gene regulatory networks because the mined biclusters lack genes that may be critical in the function but may not be co-expressed with the clustered genes. In this paper, we introduce a biclustering method called skeleton biclustering (SKB), which builds high quality biclusters from microarray data, creates relationships among the biclustered genes based on gene ontology annotations, and identifies genes that are missing in the biclusters. SKB thus defines inter-bicluster and intra-bicluster functional relationships. The delineation of functional relationships and incorporation of such missing genes may help biologists to discover biological processes that are important in a given study and provides clues for how the processes may be functioning together. Experimental results show that, with SKB, the biological significance of the biclusters is considerably improved.

Robust hashing for multimedia authentication is an emerging research area. A novel key-dependent robust speech hashing based on speech production model is proposed in this letter. Robust hash is calculated based on linear spectrum frequencies (LSFs) which model the vocal tract. The correlation between LSFs is decoupled by discrete cosine transformation (DCT). A randomization scheme controlled by a secret key is applied in hash generation for random feature selection. The hash function is key-dependent and collision resistant. Meanwhile, it is highly robust to content preserving operations as well as having high accuracy of tampering localization.

This paper addresses the problem of finding frequent closed patterns (FCPs) from very dense data sets. We introduce two compressed hierarchical FCP mining algorithms: C-Miner and B-Miner. The two algorithms compress the original mining space, hierarchically partition the whole mining task into independent subtasks, and mine each subtask progressively. The two algorithms adopt different task partitioning strategies: C-Miner partitions the mining task based on Compact Matrix Division, whereas B-Miner partitions the task based on Base Rows Projection. The compressed hierarchical mining algorithms enhance the mining efficiency and facilitate a progressive refinement of results. Moreover, because the subtasks can be mined independently, C-Miner and B-Miner can be readily paralleled without incurring significant communication overhead. We have implemented C-Miner and B-Miner, and our performance study on synthetic data sets and real dense microarray data sets shows their effectiveness over existing schemes. We also report experimental results on parallel versions of these two methods.

Mining biclusters that exhibit both consistent trends and trends with similar degrees of fluctuations is vital to bioinformatics research. However; existing biclustering methods are not very efficient and effective at mining such biclusters. Moreover, few inter-bicluster relationships are delivered to biologists. In this paper, we introduce a quick hierarchical biclustering algorithm (QHB) to efficiently mine biclusters with both consistent trends and trends with similar degrees of fluctuations. Our QHB produces not only biclusters but also a hierarchical graph of inter-bicluster relationships. We experimented with the Yeast dataset and compared QHB against an existing biclustering scheme, DBF Our results show that QHB identifies biclusters with better quality. In addition, QHB shows the relationships among biclusters. Moreover compared with DBF, QHB is much more efficient and offers users a progressive way of bicluster exploration

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.

MOTIVATION: Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs. RESULTS: We experimented with the Yeast dataset and compared our resulting PNCGCs with the association rules generated by the Apriori mining algorithm. Our results show that our PNCGCs identify some missing co-regulations of association rules, and our algorithm greatly reduces the large number of rules involving uncorrelated genes generated by the Apriori scheme. AVAILABILITY: The software is available upon request.