36,359 research outputs found
Self-Replication and Self-Assembly for Manufacturing
It has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach
to very low-cost manufacturing. The research presented here is intended to
be a step towards this vision. We describe a computational simulation of
nanoscale machines floating in a virtual liquid. The machines can bond
together to form strands (chains) that self-replicate and self-assemble
into user-specified meshes. There are four types of machines and the
sequence of machine types in a strand determines the shape of the mesh
they will build. A strand may be in an unfolded state, in which the bonds
are straight, or in a folded state, in which the bond angles depend on the
types of machines. By choosing the sequence of machine types in a strand,
the user can specify a variety of polygonal shapes. A simulation typically
begins with an initial unfolded seed strand in a soup of unbonded machines.
The seed strand replicates by bonding with free machines in the soup. The
child strands fold into the encoded polygonal shape, and then the polygons
drift together and bond to form a mesh. We demonstrate that a variety of
polygonal meshes can be manufactured in the simulation, by simply changing
the sequence of machine types in the seed
Self-Replicating Strands that Self-Assemble into User-Specified Meshes
It has been argued that a central objective of nanotechnology is to make
products inexpensively, and that self-replication is an effective approach to
very low-cost manufacturing. The research presented here is intended to be a
step towards this vision. In previous work (JohnnyVon 1.0), we simulated
machines that bonded together to form self-replicating strands. There were two
types of machines (called types 0 and 1), which enabled strands to encode
arbitrary bit strings. However, the information encoded in the strands had no
functional role in the simulation. The information was replicated without being
interpreted, which was a significant limitation for potential manufacturing
applications. In the current work (JohnnyVon 2.0), the information in a strand
is interpreted as instructions for assembling a polygonal mesh. There are now
four types of machines and the information encoded in a strand determines how
it folds. A strand may be in an unfolded state, in which the bonds are straight
(although they flex slightly due to virtual forces acting on the machines), or
in a folded state, in which the bond angles depend on the types of machines. By
choosing the sequence of machine types in a strand, the user can specify a
variety of polygonal shapes. A simulation typically begins with an initial
unfolded seed strand in a soup of unbonded machines. The seed strand replicates
by bonding with free machines in the soup. The child strands fold into the
encoded polygonal shape, and then the polygons drift together and bond to form
a mesh. We demonstrate that a variety of polygonal meshes can be manufactured
in the simulation, by simply changing the sequence of machine types in the
seed
Broken scaling in the Forest Fire Model
We investigate the scaling behavior of the cluster size distribution in the
Drossel-Schwabl Forest Fire model (DS-FFM) by means of large scale numerical
simulations, partly on (massively) parallel machines. It turns out that simple
scaling is clearly violated, as already pointed out by Grassberger [P.
Grassberger, J. Phys. A: Math. Gen. 26, 2081 (1993)], but largely ignored in
the literature. Most surprisingly the statistics not seems to be described by a
universal scaling function, and the scale of the physically relevant region
seems to be a constant. Our results strongly suggest that the DS-FFM is not
critical in the sense of being free of characteristic scales.Comment: 9 pages in RevTEX4 format (9 figures), submitted to PR
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM concentration
A typical problem in air pollution epidemiology is exposure assessment for
individuals for which health data are available. Due to the sparsity of
monitoring sites and the limited temporal frequency with which measurements of
air pollutants concentrations are collected (for most pollutants, once every 3
or 6 days), epidemiologists have been moving away from characterizing ambient
air pollution exposure solely using measurements. In the last few years,
substantial research efforts have been placed in developing statistical methods
or machine learning techniques to generate estimates of air pollution at finer
spatial and temporal scales (daily, usually) with complete coverage. Some of
these methods include: geostatistical techniques, such as kriging; spatial
statistical models that use the information contained in air quality model
outputs (statistical downscaling models); linear regression modeling approaches
that leverage the information in GIS covariates (land use regression); or
machine learning methods that mine the information contained in relevant
variables (neural network and deep learning approaches). Although some of these
exposure modeling approaches have been used in several air pollution
epidemiological studies, it is not clear how much the predicted exposures
generated by these methods differ, and which method generates more reliable
estimates. In this paper, we aim to address this gap by evaluating a variety of
exposure modeling approaches, comparing their predictive performance and
computational difficulty. Using PM in year 2011 over the continental
U.S. as case study, we examine the methods' performances across seasons, rural
vs urban settings, and levels of PM concentrations (low, medium, high)
Universal bound on the efficiency of molecular motors
The thermodynamic uncertainty relation provides an inequality relating any
mean current, the associated dispersion and the entropy production rate for
arbitrary non-equilibrium steady states. Applying it here to a general model of
a molecular motor running against an external force or torque, we show that the
thermodynamic efficiency of such motors is universally bounded by an expression
involving only experimentally accessible quantities. For motors pulling cargo
through a viscous fluid, a universal bound for the corresponding Stokes
efficiency follows as a variant. A similar result holds if mechanical force is
used to synthesize molecules of high chemical potential. Crucially, no
knowledge of the detailed underlying mechano-chemical mechanism is required for
applying these bounds.Comment: Invited contribution to proceedings of STATPHYS26, Lyo
Primordial Evolution in the Finitary Process Soup
A general and basic model of primordial evolution--a soup of reacting
finitary and discrete processes--is employed to identify and analyze
fundamental mechanisms that generate and maintain complex structures in
prebiotic systems. The processes---machines as defined in
computational mechanics--and their interaction networks both provide well
defined notions of structure. This enables us to quantitatively demonstrate
hierarchical self-organization in the soup in terms of complexity. We found
that replicating processes evolve the strategy of successively building higher
levels of organization by autocatalysis. Moreover, this is facilitated by local
components that have low structural complexity, but high generality. In effect,
the finitary process soup spontaneously evolves a selection pressure that
favors such components. In light of the finitary process soup's generality,
these results suggest a fundamental law of hierarchical systems: global
complexity requires local simplicity.Comment: 7 pages, 10 figures;
http://cse.ucdavis.edu/~cmg/compmech/pubs/pefps.ht
Large-scale photonic Ising machine by spatial light modulation
Quantum and classical physics can be used for mathematical computations that
are hard to tackle by conventional electronics. Very recently, optical Ising
machines have been demonstrated for computing the minima of spin Hamiltonians,
paving the way to new ultra-fast hardware for machine learning. However, the
proposed systems are either tricky to scale or involve a limited number of
spins. We design and experimentally demonstrate a large-scale optical Ising
machine based on a simple setup with a spatial light modulator. By encoding the
spin variables in a binary phase modulation of the field, we show that light
propagation can be tailored to minimize an Ising Hamiltonian with spin
couplings set by input amplitude modulation and a feedback scheme. We realize
configurations with thousands of spins that settle in the ground state in a
low-temperature ferromagnetic-like phase with all-to-all and tunable pairwise
interactions. Our results open the route to classical and quantum photonic
Ising machines that exploit light spatial degrees of freedom for parallel
processing of a vast number of spins with programmable couplings.Comment: https://journals.aps.org/prl/accepted/7007eYb7N091546c41ad4108828a97d5f92006df
Spinodal decomposition of off-critical quenches with a viscous phase using dissipative particle dynamics in two and three spatial dimensions
We investigate the domain growth and phase separation of
hydrodynamically-correct binary immiscible fluids of differing viscosity as a
function of minority phase concentration in both two and three spatial
dimensions using dissipative particle dynamics. We also examine the behavior of
equal-viscosity fluids and compare our results to similar lattice-gas
simulations in two dimensions.Comment: 34 pages (11 figures); accepted for publication in Phys. Rev.
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