Given a pattern of length m and a text of length n, the goal in k-mismatch pattern matching is to compute, for every m-substring of the text, the exact Hamming distance to the pattern or report that it exceeds k. This can be solved in either Õ(n √ k) time as shown by Amir et al. [J. Algorithms 2004] or Õ((m + k) · n/m) time due to a result of Clifford et al. [SODA 2016]. We provide a smooth tim...

We introduce a measure for energetic efficiency of biological cilia acting individually or collectively and numerically determine the optimal beating patterns according to this criterion. Maximizing the efficiency of a single cilium leads to curly, often symmetric, and somewhat counterintuitive patterns. However, when looking at a densely ciliated surface, the optimal patterns become remarkably...

A new algorithm for learning one-variable pattern languages from positive data is proposed and analyzed with respect to its average-case behavior. We consider the total learning time that takes into account all operations till convergence to a correct hypothesis is achieved. For almost all meaningful distributions deening how the pattern variable is replaced by a string to generate random examp...

We demonstrate that the pattern forming partial differential equation derived from the auxin distribution model proposed by Meyerowitz, Traas, and others gives rise to all spiral phyllotaxis properties observed on plants. We show how the advancing pushed pattern front chooses spiral families enumerated by Fibonacci sequences with all attendant self-similar properties, a new amplitude invariant ...

Multiple instance data are sets or multi-sets of unordered elements. Using metrics or distances for sets, we propose an approach to several multiple instance learning tasks, such as clustering (unsupervised learning), classification (supervised learning), and novelty detection (semi-supervised learning). In particular, we introduce the Optimal Sub-Pattern Assignment metric to multiple instance ...

In this work we present the parameter optimization of a distributed model for the classification of temporal sequences. The model we present codes information with a population of units, and it can be applied to discriminate between several spatiotemporal sequences. The implementation of this classification technique strongly depends on how the the connection weights are set according to the se...

As the need for strategic information is escalating at a tremendous rate worldwide, it has become a challenging task to manage the growing data and efficiently mine useful knowledge out of this raw data. Pattern Mining is one such technique to mine useful patterns out of the pattern warehouse. Patterns are the one way of knowledge representations. Few pattern generation and mining approaches ar...