The generalized Robinson-Foulds metric

The Robinson-Foulds (RF) metric is arguably the most widely used measure of phylogenetic tree similarity, despite its well-known shortcomings: For example, moving a single taxon in a tree can result in a tree that has maximum distance to the original one; but the two trees are identical if we remove the single taxon. To this end, we propose a natural extension of the RF metric that does not simply count identical clades but instead, also takes similar clades into consideration. In contrast to previous approaches, our model requires the matching between clades to respect the structure of the two trees, a property that the classical RF metric exhibits, too. We show that computing this generalized RF metric is, unfortunately, NP-hard. We then present a simple Integer Linear Program for its computation, and evaluate it by an all-against-all comparison of 100 trees from a benchmark data set. We find that matchings that respect the tree structure differ significantly from those that do not, underlining the importance of this natural condition.Comment: Peer-reviewed and presented as part of the 13th Workshop on Algorithms in Bioinformatics (WABI2013

Obtaining common pruned trees

Common pruned trees, Consensus trees, Hierarchical classification, Regrafting,

Experimental validation of the performance improvement obtained by using two similar passive bistatic radar systems

This paper tests and demonstrates the ability of a twin set of passive radar systems to monitor real time an area of interest and track medium size targets. The results are based on a real data acquisition campaign performed using two eleven element array antenna based passive radars in the Oslo fjord area in September 2016

Where is Palestine in Critical Terrorism Studies? A roundtable conversation

Our ethical responsibilities as researchers within or related to the study of “terrorism” could not be clearer than in moments when the “terrorism” label is used to justify mass killing and destruction. The state of Israel has relentlessly continued to bombard Gaza since 7 October 2023 and, with the support of Western nations, built consensus around framing all Palestinians as (potential) terrorists. In light of the horrors that the world is witnessing today, and the lack of engagement with Palestine in Critical Terrorism Studies research, we ask, how does – and should - Palestine feature in Critical Terrorism Studies scholarship? How can we, as researchers and educators, facilitate deeper conversations that challenge hegemonic and racist framings of “terrorism”? This roundtable discussion brings critical, anti-colonial, anti-racist, feminist, and queer scholars together to discuss 1) the exclusion of Palestine from the critical study of “terrorism” and 2) the significance of the Palestinian struggle for liberation for all researchers invested in the critical study of “terrorism”. We call for more serious engagement with Palestine and acknowledge that such a call is inseparable from a commitment to anti-colonial scholarship and activism

Boolean property encoding for local set pattern discovery: an application to gene expression data analysis

International audienceIn the domain of gene expression data analysis, several researchers have recently emphasized the promising application of local pattern (e.g., association rules, closed sets) discovery techniques from boolean matrices that encode gene properties. Detecting local patterns by means of complete constraint-based mining techniques turns to be an important complementary approach or invaluable counterpart to heuristic global model mining. To take the most from local set pattern mining approaches, a needed step concerns gene expression property encoding (e.g., over-expression). The impact of this preprocessing phase on both the quantity and the quality of the extracted patterns is crucial. In this paper, we study the impact of discretization techniques by a sound comparison between the dendrograms, i.e., trees that are generated by a hierarchical clustering algorithm on raw numerical expression data and its various derived boolean matrices. Thanks to a new similarity measure, we can select the boolean property encoding technique which preserves similarity structures holding in the raw data. The discussion relies on several experimental results for three gene expression data sets. We believe our framework is an interesting direction of work for the many application domains in which (a) local set patterns have been proved useful, and (b) Boolean properties have to be derived from raw numerical data