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Computing the similarity between time series data is one of the core challenges frequently occurring in time series analysis across various disciplines. Particularly, in building energy management, there are many applications that need to cluster the time series with simultaneous rise, peak, similar patterns, pattern-average allowing small amplitude shift and small phase shift as one group. Commonly used distance measures like Euclidean and DTW being sensitive to phase shift or amplitude shift, are unable to identify similarity between two time series with simultaneous rise, peak and similar patterns. To address this problem, we propose a novel time series similarity measure, sub-sequence based DTW, SeqDTW based on time series segmentation approach. SeqDTW attempts to find best match of each subsequence in the segmented time series. Overall distance is the weighted aggregate of distance between the matching subsequences. In addition to existing monotonicity in DTW warping path, SeqDTW reduces the number of computations across the distance matrix and avoids singularity. An exhaustive experiment shows the superiority of the proposed method in finding time series having simultaneous rise, peak and similar patterns.

Systematic delineation of complex biological systems is an ever-challenging and resource-intensive process. Single-cell transcriptomics allows us to study cell-to-cell variability in complex tissues at an unprecedented resolution. Accurate modeling of gene expression plays a critical role in the statistical determination of tissue-specific gene expression patterns. In the past few years, considerable efforts have been made to identify appropriate parametric models for single-cell expression data. The zero-inflated version of Poisson/negative binomial and log-normal distributions have emerged as the most popular alternatives owing to their ability to accommodate high dropout rates, as commonly observed in single-cell data. Although the majority of the parametric approaches directly model expression estimates, we explore the potential of modeling expression ranks, as robust surrogates for transcript abundance. Here we examined the performance of the discrete generalized beta distribution (DGBD) on real data and devised a Wald-type test for comparing gene expression across two phenotypically divergent groups of single cells. We performed a comprehensive assessment of the proposed method to understand its advantages compared with some of the existing best-practice approaches. We concluded that besides striking a reasonable balance between Type I and Type II errors, ROSeq, the proposed differential expression test, is exceptionally robust to expression noise and scales rapidly with increasing sample size. For wider dissemination and adoption of the method, we created an R package called ROSeq and made it available on the Bioconductor platform.

The purpose of influence maximization in a cascade model is to extricate a group of hubs from underlying network that can maximize propagation. Greedy methods may be used to perform influence maximization. However, as it has ahigh computational cost, it becomes inefficient. Centrality-based heuristic methods can be considered as a replacement. But it gets trapped in local optima. To address these issues, a framework has been introduced which uses community detection, tries to find the most significant nodes from each community using closeness centrality. Then, it employs Coral Reefs Optimization to determine the best solutions, i.e., the most important nodes that maximize propagation. Adaptive β-Hill Climbing has been used. It acts as a local search to avoid getting caught in local optima. A few datasets were used to evaluate the proposed framework’ scalabilities. Moreover, the outcomes were compared to several state-of-the-art techniques based on 2-hop influence spread and Monte-Carlo simulations.