Discriminative measures for comparison of phylogenetic trees

Arslan, Omur; Guralnik, Dan P.; Koditschek, Daniel E.

doi:10.1016/j.dam.2016.10.003

Cited by 5 publications

(12 citation statements)

References 30 publications

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“…On the other hand, if agents have perfect sensing and actuation modelled as in the present paper, the hybrid navigation planner automatically deploys at most 1 2 (n − 1) (n − 2) local controllers [85], illustrating the computational efficiency of our construction.…”

Section: Numerical Simulationsmentioning

confidence: 84%

“…Alternatively, we have recently developed in [85] an efficient recursive procedure for navigating in the NNI graph N J = (BT J , E N ) towards any given binary tree τ ∈ BT J , taking the form of an abstract discrete dynamical system as follows:…”

Section: Navigation In the Space Of Binary Treesmentioning

confidence: 99%

“…Given such a goal we show in [85] that the cost of computing an appropriate NNI move from any other σ ∈ BT J toward an adjacent tree at a lower value of a "discrete Lyapunov function" relative to that destination is O (|J|). We summarize such important properties of our NNI navigation algorithm as:…”

Section: Navigation In the Space Of Binary Treesmentioning

confidence: 99%

“…Since the trees σ, τ are NNI-adjacent, we may apply Lemma 1 from [85] to find common disjoint clusters A, B, C such that…”

Section: Portal Transformationsmentioning

confidence: 99%

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Coordinated Robot Navigation via Hierarchical Clustering

2016

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Abstract-We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of n perfectly sensed and actuated Euclidean spheres in a d-dimensional ambient space (for arbitrary n and d). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller which is quadratic in n and algebraic in d and prove that its execution brings all but a measure zero set of initial configurations to the desired goal with the guarantee of no collisions along the way.

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Section: Numerical Simulationsmentioning

confidence: 84%

Section: Navigation In the Space Of Binary Treesmentioning

confidence: 99%

Section: Navigation In the Space Of Binary Treesmentioning

confidence: 99%

“…Since the trees σ, τ are NNI-adjacent, we may apply Lemma 1 from [85] to find common disjoint clusters A, B, C such that…”

Section: Portal Transformationsmentioning

confidence: 99%

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Coordinated Robot Navigation via Hierarchical Clustering

2016

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“…22. Those measures were designed for quantifying such discrepancies on a global scale (distances between arbitrary hierarchies are considered) rather than for the (at least) seemingly small-scale transitions such as those exhibited by a meticulously crafted poisoning attack.…”

Section: Resultsmentioning

confidence: 99%

Detecting poisoning attacks on hierarchical malware classification systems

et al. 2017

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Anti-virus software based on unsupervised hierarchical clustering (HC) of malware samples has been shown to be vulnerable to poisoning attacks. In this kind of attack, a malicious player degrades anti-virus performance by submitting to the database samples specifically designed to collapse the classification hierarchy utilized by the anti-virus (and constructed through HC) or otherwise deform it in a way that would render it useless. Though each poisoning attack needs to be tailored to the particular HC scheme deployed, existing research seems to indicate that no particular HC method by itself is immune. We present results on applying a new notion of entropy for combinatorial dendrograms to the problem of controlling the influx of samples into the data base and deflecting poisoning attacks. In a nutshell, effective and tractable measures of change in hierarchy complexity are derived from the above, enabling on-the-fly flagging and rejection of potentially damaging samples. The information-theoretic underpinnings of these measures ensure their indifference to which particular poisoning algorithm is being used by the attacker, rendering them particularly attractive in this setting. ABSTRACT Anti-virus software based on unsupervised hierarchical clustering (HC) of malware samples has been shown to be vulnerable to poisoning attacks. In this kind of attack, a malicious player degrades anti-virus performance by submitting to the database samples specifically designed to collapse the classification hierarchy utilized by the anti-virus (and constructed through HC) or otherwise deform it in a way that would render it useless. Though each poisoning attack needs to be tailored to the particular HC scheme deployed, existing research seems to indicate that no particular HC method by itself is immune. We present results on applying a new notion of entropy for combinatorial dendrograms to the problem of controlling the influx of samples into the data base and deflecting poisoning attacks. In a nutshell, effective and tractable measures of change in hierarchy complexity are derived from the above, enabling on-the-fly flagging and rejection of potentially damaging samples. The information-theoretic underpinnings of these measures ensure their indifference to which particular poisoning algorithm is being used by the attacker, rendering them particularly attractive in this setting.

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Navigation of Distinct Euclidean Particles via Hierarchical Clustering

Arslan

Guralnik

Koditschek

2015

Springer Tracts in Advanced Robotics

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We present a centralized online (completely reactive) hybrid navigation algorithm for bringing a swarm of n perfectly sensed and actuated point particles in Euclidean d space (for arbitrary n and d) to an arbitrary goal configuration with the guarantee of no collisions along the way. Our construction entails a discrete abstraction of configurations using cluster hierarchies, and relies upon two prior recent constructions: (i) a family of hierarchy-preserving control policies and (ii) an abstract discrete dynamical system for navigating through the space of cluster hierarchies. Here, we relate the (combinatorial) topology of hierarchical clusters to the (continuous) topology of configurations by constructing "portals"-open sets of configurations supporting two adjacent hierarchies. The resulting online sequential composition of hierarchy-invariant swarming followed by discrete selection of a hierarchy "closer" to that of the destination along with its continuous instantiation via an appropriate portal configuration yields a computationally effective construction for the desired navigation policy.

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Discriminative measures for comparison of phylogenetic trees

Cited by 5 publications

References 30 publications

Coordinated Robot Navigation via Hierarchical Clustering

Coordinated Robot Navigation via Hierarchical Clustering

Detecting poisoning attacks on hierarchical malware classification systems

Navigation of Distinct Euclidean Particles via Hierarchical Clustering

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