Time-space trade-offs in population protocols for the majority problem.

Population protocols are a model for distributed computing that is focused on simplicity and robustness. A system of n identical agents (finite state machines) performs a global task like electing a unique leader or determining the majority opinion when each agent has one of two opinions. Agents communicate in pairwise interactions with randomly assigned communication partners. Quality is measured in two ways: the number of interactions to complete the task and the number of states per agent. We present protocols for the majority problem that allow for a trade-off between these two measures. Compared to the only other trade-off result (Alistarh et al. in Proceedings of the 2015 ACM symposium on principles of distributed computing, Donostia-San Sebastián, 2015), we improve the number of interactions by almost a linear factor. Furthermore, our protocols can be made uniform (working correctly without any information on the population size n), yielding the first uniform majority protocols that stabilize in a subquadratic number of interactions.

Not all scale-free networks are born equal: the role of the seed graph in PPI network evolution.

The (asymptotic) degree distributions of the best-known "scale-free" network models are all similar and are independent of the seed graph used; hence, it has been tempting to assume that networks generated by these models are generally similar. In this paper, we observe that several key topological features of such networks depend heavily on the specific model and the seed graph used. Furthermore, we show that starting with the "right" seed graph (typically a dense subgraph of the protein-protein interaction network analyzed), the duplication model captures many topological features of publicly available protein-protein interaction networks very well.

Identifying uniformly mutated segments within repeats.

Given a long string of characters from a constant size alphabet we present an algorithm to determine whether its characters have been generated by a single i.i.d. random source. More specifically, consider all possible n-coin models for generating a binary string S, where each bit of S is generated via an independent toss of one of the n coins in the model. The choice of which coin to toss is decided by a random walk on the set of coins where the probability of a coin change is much lower than the probability of using the same coin repeatedly. We present a procedure to evaluate the likelihood of a n-coin model for given S, subject a uniform prior distribution over the parameters of the model (that represent mutation rates and probabilities of copying events). In the absence of detailed prior knowledge of these parameters, the algorithm can be used to determine whether the a posteriori probability for n=1 is higher than for any other n>1. Our algorithm runs in time O(l4logl), where l is the length of S, through a dynamic programming approach which exploits the assumed convexity of the a posteriori probability for n. Our test can be used in the analysis of long alignments between pairs of genomic sequences in a number of ways. For example, functional regions in genome sequences exhibit much lower mutation rates than non-functional regions. Because our test provides means for determining variations in the mutation rate, it may be used to distinguish functional regions from non-functional ones. Another application is in determining whether two highly similar, thus evolutionarily related, genome segments are the result of a single copy event or of a complex series of copy events. This is particularly an issue in evolutionary studies of genome regions rich with repeat segments (especially tandemly repeated segments).