1,666 research outputs found
Evolutionary Dynamics in a Simple Model of Self-Assembly
We investigate the evolutionary dynamics of an idealised model for the robust
self-assembly of two-dimensional structures called polyominoes. The model
includes rules that encode interactions between sets of square tiles that drive
the self-assembly process. The relationship between the model's rule set and
its resulting self-assembled structure can be viewed as a genotype-phenotype
map and incorporated into a genetic algorithm. The rule sets evolve under
selection for specified target structures. The corresponding, complex fitness
landscape generates rich evolutionary dynamics as a function of parameters such
as the population size, search space size, mutation rate, and method of
recombination. Furthermore, these systems are simple enough that in some cases
the associated model genome space can be completely characterised, shedding
light on how the evolutionary dynamics depends on the detailed structure of the
fitness landscape. Finally, we apply the model to study the emergence of the
preference for dihedral over cyclic symmetry observed for homomeric protein
tetramers
S-RASTER: Contraction Clustering for Evolving Data Streams
Contraction Clustering (RASTER) is a single-pass algorithm for density-based
clustering of 2D data. It can process arbitrary amounts of data in linear time
and in constant memory, quickly identifying approximate clusters. It also
exhibits good scalability in the presence of multiple CPU cores. RASTER
exhibits very competitive performance compared to standard clustering
algorithms, but at the cost of decreased precision. Yet, RASTER is limited to
batch processing and unable to identify clusters that only exist temporarily.
In contrast, S-RASTER is an adaptation of RASTER to the stream processing
paradigm that is able to identify clusters in evolving data streams. This
algorithm retains the main benefits of its parent algorithm, i.e. single-pass
linear time cost and constant memory requirements for each discrete time step
within a sliding window. The sliding window is efficiently pruned, and
clustering is still performed in linear time. Like RASTER, S-RASTER trades off
an often negligible amount of precision for speed. Our evaluation shows that
competing algorithms are at least 50% slower. Furthermore, S-RASTER shows good
qualitative results, based on standard metrics. It is very well suited to
real-world scenarios where clustering does not happen continually but only
periodically.Comment: 24 pages, 5 figures, 2 table
On Fugledeās conjecture and the existence of universal spectra
Recent methods developed by, Too [18], Kolountzakis and Matolcsi [7] have led to counterexamples to Fugelde's Spectral Set Conjecture in both directions. Namely, in R(5) Tao produced a spectral set which is not a tile, while Kolountzakis and Matolcsi showed all example of a nonspectral tile. In search of lower dimensional nonspectral tiles we were led to investigate the Universal Spectrum Conjecture (USC) of Lagarias and Wang [14]. In particular, we prove here that the USC and the "tile --> spectral " direction of Fuglede's conjecture are equivalent in any dimensions. Also, we show by an example that the sufficient condition of Lagarias and Szabo [13] for the existence of universal spectra is not necessary. This fact causes considerable difficulties in producing lower dimensional examples of tiles which have no spectra. We overcome these difficulties by invoking some ideas of Revesz and Farkas [2], and obtain nonspectral tiles in R(3)
Simulating Radiating and Magnetized Flows in Multi-Dimensions with ZEUS-MP
This paper describes ZEUS-MP, a multi-physics, massively parallel, message-
passing implementation of the ZEUS code. ZEUS-MP differs significantly from the
ZEUS-2D code, the ZEUS-3D code, and an early "version 1" of ZEUS-MP distributed
publicly in 1999. ZEUS-MP offers an MHD algorithm better suited for
multidimensional flows than the ZEUS-2D module by virtue of modifications to
the Method of Characteristics scheme first suggested by Hawley and Stone
(1995), and is shown to compare quite favorably to the TVD scheme described by
Ryu et. al (1998). ZEUS-MP is the first publicly-available ZEUS code to allow
the advection of multiple chemical (or nuclear) species. Radiation hydrodynamic
simulations are enabled via an implicit flux-limited radiation diffusion (FLD)
module. The hydrodynamic, MHD, and FLD modules may be used in one, two, or
three space dimensions. Self gravity may be included either through the
assumption of a GM/r potential or a solution of Poisson's equation using one of
three linear solver packages (conjugate-gradient, multigrid, and FFT) provided
for that purpose. Point-mass potentials are also supported. Because ZEUS-MP is
designed for simulations on parallel computing platforms, considerable
attention is paid to the parallel performance characteristics of each module.
Strong-scaling tests involving pure hydrodynamics (with and without
self-gravity), MHD, and RHD are performed in which large problems (256^3 zones)
are distributed among as many as 1024 processors of an IBM SP3. Parallel
efficiency is a strong function of the amount of communication required between
processors in a given algorithm, but all modules are shown to scale well on up
to 1024 processors for the chosen fixed problem size.Comment: Accepted for publication in the ApJ Supplement. 42 pages with 29
inlined figures; uses emulateapj.sty. Discussions in sections 2 - 4 improved
per referee comments; several figures modified to illustrate grid resolution.
ZEUS-MP source code and documentation available from the Laboratory for
Computational Astrophysics at http://lca.ucsd.edu/codes/currentcodes/zeusmp2
Best-first heuristic search for multicore machines
To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PBNF yield faster search than improved versions of previous parallel search proposals
- ā¦