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Invited Talks

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12701)

Abstract

This document contains the summaries of the invited talks that have been delivered at CIAC 2021. A detailed description of the lecture by Henning Fernau (titled Abundant Extensions) is on pages 1–15, while the abstracts of the lectures by Katharina T. Huber (Phylogenetic networks, a way to cope with complex evolutionary processes) and Joseph (Seffi) Naor (Recent Advances in Competitive Analysis of Online Algorithms) are on pages 16 and 17, respectively.

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Notes

  1. 1.

    Katrin Casel is with Hasso Plattner Institute, University of Potsdam and Universität Trier; Henning Fernau is with Universität Trier, Mehdi Khosravian Ghadikolaei, Jérôme Monnot, Florian Sikora are with Université Paris-Dauphine, PSL University.

  2. 2.

    ETH is a conjecture asserting that there is no \(2^{o(n)}\) (i.e., no sub-exponential) algorithms for solving 3-SAT, where n is the number of variables; the number of clauses is somehow subsumed into this expression, as this number can be assumed to be sub-exponential in n (after applying the famous sparsification procedure); cf. [22].

  3. 3.

    Presumably, this is the origin of the naming of “flashlight” for an algorithm that tries to prune branches of a search tree when enumerating minimal sets with a certain property, e.g., minimal vertex covers.

  4. 4.

    The reader might have expected us talking on Precoloring Extension and similar problems known from the literature (see, e.g., [4, 32]); however, these types of extension problems do not really fit into our framework due to the lack of a suitable notion of a partial order.

  5. 5.

    Katharina T. Huber is with School of Computing Sciences, University of East Anglia, UK.

  6. 6.

    Joseph (Seffi) Naor is with Computer Science Dept., Technion, Haifa 32000, Israel.

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Correspondence to Henning Fernau .

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Appendices

Phylogenetic Networks, A Way to Cope with Complex Evolutionary Processes

by Katharina T. Huber Footnote 5

Understanding how pathogens such as Covid-19, birdflu, or ash dieback might have arisen is among some of the most challenging scientific questions of today. Phylogenetics is a burgeoning area at the interface of Computer Science, Mathematics, Statistics, Evolutionary Biology and also Medicine concerned with developing powerful algorithms and mathematical methodology to help with this. Going back to at least the beginning of the 19th century, treelike structures (now formalized as phylogenetic trees) have been used to visualize and model the evolution of a set of organisms of interest. Similar to a genealogy, such a tree is a certain rooted or unrooted graph-theoretical tree whose leaf set is the set of organisms of interest. In the rooted case, the unique root represents the last common ancestor of the organisms under consideration and the interior vertices correspond to hypothetical speciation events.

Growing evidence from the tsunami-like amounts of data generated by modern sequencing technologies however suggests that for certain organisms the model of a phylogenetic tree might be too simplistic to explain their complex evolutionary past (e.g. recombination in viruses or hybridization in plants). This has led to the introduction of phylogenetic networks as a tool to model and visualise evolutionary relationships between organisms. Introduced in rooted and unrooted form, these graphs naturally generalize phylogenetic trees in terms of a rooted directed acyclic graph (rooted case) or as a splits graph (unrooted case).

Although deep algorithmic and mathematical results concerning phylogenetic networks have been established over the years, numerous questions (including some very fundamental ones) have remained open so far. These include

  1. (i)

    What kind of data do we require to be able to uniquely reconstruct the evolutionary scenario that gave rise to it?

  2. (ii)

    How can we combine potentially conflicting gene trees (i.e. phylogenetic trees supported by a gene or a genomic region) into an overall evolutionary scenario for a set of organisms of interest?

  3. (iii)

    How many potential phylogenetic networks can a set of organisms of interest support and what can we say about their space of phylogenetic networks?

  4. (iv)

    How are rooted and unrooted phylogenetic networks related?

In this talk, we first give a brief introduction to phylogenetics in general and phylogenetic networks in particular and then discuss recent developments regarding some of the questions above. This will also include pointing out potential further directions of research.

Recent Advances in Competitive Analysis of Online Algorithms

by Joseph (Seffi) Naor Footnote 6

This talk will survey recent advances in competitive analysis of online algorithms. I will discuss recent work on deriving online algorithms for several problems from Bregman projections and its connections to previous work on online primal-dual algorithms. A primal-dual approach to the k-taxi problem, a generalization of the k-server problem, will be discussed, as well as non-standard caching models such as writeback-aware caching.

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Fernau, H., Huber, K.T., Naor, J.(. (2021). Invited Talks. In: Calamoneri, T., Corò, F. (eds) Algorithms and Complexity. CIAC 2021. Lecture Notes in Computer Science(), vol 12701. Springer, Cham. https://doi.org/10.1007/978-3-030-75242-2_1

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