15,209 research outputs found
Fuel Efficient Computation in Passive Self-Assembly
In this paper we show that passive self-assembly in the context of the tile
self-assembly model is capable of performing fuel efficient, universal
computation. The tile self-assembly model is a premiere model of self-assembly
in which particles are modeled by four-sided squares with glue types assigned
to each tile edge. The assembly process is driven by positive and negative
force interactions between glue types, allowing for tile assemblies floating in
the plane to combine and break apart over time. We refer to this type of
assembly model as passive in that the constituent parts remain unchanged
throughout the assembly process regardless of their interactions. A
computationally universal system is said to be fuel efficient if the number of
tiles used up per computation step is bounded by a constant. Work within this
model has shown how fuel guzzling tile systems can perform universal
computation with only positive strength glue interactions. Recent work has
introduced space-efficient, fuel-guzzling universal computation with the
addition of negative glue interactions and the use of a powerful non-diagonal
class of glue interactions. Other recent work has shown how to achieve fuel
efficient computation within active tile self-assembly. In this paper we
utilize negative interactions in the tile self-assembly model to achieve the
first computationally universal passive tile self-assembly system that is both
space and fuel-efficient. In addition, we achieve this result using a limited
diagonal class of glue interactions
Fast matrix multiplication techniques based on the Adleman-Lipton model
On distributed memory electronic computers, the implementation and
association of fast parallel matrix multiplication algorithms has yielded
astounding results and insights. In this discourse, we use the tools of
molecular biology to demonstrate the theoretical encoding of Strassen's fast
matrix multiplication algorithm with DNA based on an -moduli set in the
residue number system, thereby demonstrating the viability of computational
mathematics with DNA. As a result, a general scalable implementation of this
model in the DNA computing paradigm is presented and can be generalized to the
application of \emph{all} fast matrix multiplication algorithms on a DNA
computer. We also discuss the practical capabilities and issues of this
scalable implementation. Fast methods of matrix computations with DNA are
important because they also allow for the efficient implementation of other
algorithms (i.e. inversion, computing determinants, and graph theory) with DNA.Comment: To appear in the International Journal of Computer Engineering
Research. Minor changes made to make the preprint as similar as possible to
the published versio
Improved Lower Bounds for Constant GC-Content DNA Codes
The design of large libraries of oligonucleotides having constant GC-content
and satisfying Hamming distance constraints between oligonucleotides and their
Watson-Crick complements is important in reducing hybridization errors in DNA
computing, DNA microarray technologies, and molecular bar coding. Various
techniques have been studied for the construction of such oligonucleotide
libraries, ranging from algorithmic constructions via stochastic local search
to theoretical constructions via coding theory. We introduce a new stochastic
local search method which yields improvements up to more than one third of the
benchmark lower bounds of Gaborit and King (2005) for n-mer oligonucleotide
libraries when n <= 14. We also found several optimal libraries by computing
maximum cliques on certain graphs.Comment: 4 page
Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective
Rapid advances in human genomics are enabling researchers to gain a better
understanding of the role of the genome in our health and well-being,
stimulating hope for more effective and cost efficient healthcare. However,
this also prompts a number of security and privacy concerns stemming from the
distinctive characteristics of genomic data. To address them, a new research
community has emerged and produced a large number of publications and
initiatives.
In this paper, we rely on a structured methodology to contextualize and
provide a critical analysis of the current knowledge on privacy-enhancing
technologies used for testing, storing, and sharing genomic data, using a
representative sample of the work published in the past decade. We identify and
discuss limitations, technical challenges, and issues faced by the community,
focusing in particular on those that are inherently tied to the nature of the
problem and are harder for the community alone to address. Finally, we report
on the importance and difficulty of the identified challenges based on an
online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies
(PoPETs), Vol. 2019, Issue
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