3,862 research outputs found
Convex Graph Invariant Relaxations For Graph Edit Distance
The edit distance between two graphs is a widely used measure of similarity
that evaluates the smallest number of vertex and edge deletions/insertions
required to transform one graph to another. It is NP-hard to compute in
general, and a large number of heuristics have been proposed for approximating
this quantity. With few exceptions, these methods generally provide upper
bounds on the edit distance between two graphs. In this paper, we propose a new
family of computationally tractable convex relaxations for obtaining lower
bounds on graph edit distance. These relaxations can be tailored to the
structural properties of the particular graphs via convex graph invariants.
Specific examples that we highlight in this paper include constraints on the
graph spectrum as well as (tractable approximations of) the stability number
and the maximum-cut values of graphs. We prove under suitable conditions that
our relaxations are tight (i.e., exactly compute the graph edit distance) when
one of the graphs consists of few eigenvalues. We also validate the utility of
our framework on synthetic problems as well as real applications involving
molecular structure comparison problems in chemistry.Comment: 27 pages, 7 figure
Consciousness as a State of Matter
We examine the hypothesis that consciousness can be understood as a state of
matter, "perceptronium", with distinctive information processing abilities. We
explore five basic principles that may distinguish conscious matter from other
physical systems such as solids, liquids and gases: the information,
integration, independence, dynamics and utility principles. If such principles
can identify conscious entities, then they can help solve the quantum
factorization problem: why do conscious observers like us perceive the
particular Hilbert space factorization corresponding to classical space (rather
than Fourier space, say), and more generally, why do we perceive the world
around us as a dynamic hierarchy of objects that are strongly integrated and
relatively independent? Tensor factorization of matrices is found to play a
central role, and our technical results include a theorem about Hamiltonian
separability (defined using Hilbert-Schmidt superoperators) being maximized in
the energy eigenbasis. Our approach generalizes Giulio Tononi's integrated
information framework for neural-network-based consciousness to arbitrary
quantum systems, and we find interesting links to error-correcting codes,
condensed matter criticality, and the Quantum Darwinism program, as well as an
interesting connection between the emergence of consciousness and the emergence
of time.Comment: Replaced to match accepted CSF version; discussion improved, typos
corrected. 36 pages, 15 fig
Chance and Necessity in Evolution: Lessons from RNA
The relationship between sequences and secondary structures or shapes in RNA
exhibits robust statistical properties summarized by three notions: (1) the
notion of a typical shape (that among all sequences of fixed length certain
shapes are realized much more frequently than others), (2) the notion of shape
space covering (that all typical shapes are realized in a small neighborhood of
any random sequence), and (3) the notion of a neutral network (that sequences
folding into the same typical shape form networks that percolate through
sequence space). Neutral networks loosen the requirements on the mutation rate
for selection to remain effective. The original (genotypic) error threshold has
to be reformulated in terms of a phenotypic error threshold. With regard to
adaptation, neutrality has two seemingly contradictory effects: It acts as a
buffer against mutations ensuring that a phenotype is preserved. Yet it is
deeply enabling, because it permits evolutionary change to occur by allowing
the sequence context to vary silently until a single point mutation can become
phenotypically consequential. Neutrality also influences predictability of
adaptive trajectories in seemingly contradictory ways. On the one hand it
increases the uncertainty of their genotypic trace. At the same time neutrality
structures the access from one shape to another, thereby inducing a topology
among RNA shapes which permits a distinction between continuous and
discontinuous shape transformations. To the extent that adaptive trajectories
must undergo such transformations, their phenotypic trace becomes more
predictable.Comment: 37 pages, 14 figures; 1998 CNLS conference; high quality figures at
http://www.santafe.edu/~walte
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