47,431 research outputs found
Self-organizing particle systems
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Advances in Complex Systems following peer review. The version of record, Malte Harder and Daniel Polani, ‘Self-organizing particle systems’, Advs. Complex Syst. 16, 1250089, published October 22, 2012, is available online via doi: https://doi.org/10.1142/S0219525912500890 Published by World Scientific Publishing.The self-organization of cells into a living organism is a very intricate process. Under the surface of orchestrating regulatory networks there are physical processes which make the information processing possible, that is required to organize such a multitude of individual entities. We use a quantitative information theoretic approach to assess self-organization of a collective system. In particular, we consider an interacting particle system, that roughly mimics biological cells by exhibiting differential adhesion behavior. Employing techniques related to shape analysis, we show that these systems in most cases exhibit self-organization. Moreover, we consider spatial constraints of interactions, and additionaly show that particle systems can self-organize without the emergence of pattern-like structures. However, we will see that regular pattern-like structures help to overcome limitations of self-organization that are imposed by the spatial structure of interactions.Peer reviewe
Collaborative Computation in Self-Organizing Particle Systems
Many forms of programmable matter have been proposed for various tasks. We
use an abstract model of self-organizing particle systems for programmable
matter which could be used for a variety of applications, including smart paint
and coating materials for engineering or programmable cells for medical uses.
Previous research using this model has focused on shape formation and other
spatial configuration problems (e.g., coating and compression). In this work we
study foundational computational tasks that exceed the capabilities of the
individual constant size memory of a particle, such as implementing a counter
and matrix-vector multiplication. These tasks represent new ways to use these
self-organizing systems, which, in conjunction with previous shape and
configuration work, make the systems useful for a wider variety of tasks. They
can also leverage the distributed and dynamic nature of the self-organizing
system to be more efficient and adaptable than on traditional linear computing
hardware. Finally, we demonstrate applications of similar types of computations
with self-organizing systems to image processing, with implementations of image
color transformation and edge detection algorithms
A Local Stochastic Algorithm for Separation in Heterogeneous Self-Organizing Particle Systems
We present and rigorously analyze the behavior of a distributed, stochastic algorithm for separation and integration in self-organizing particle systems, an abstraction of programmable matter. Such systems are composed of individual computational particles with limited memory, strictly local communication abilities, and modest computational power. We consider heterogeneous particle systems of two different colors and prove that these systems can collectively separate into different color classes or integrate, indifferent to color. We accomplish both behaviors with the same fully distributed, local, stochastic algorithm. Achieving separation or integration depends only on a single global parameter determining whether particles prefer to be next to other particles of the same color or not; this parameter is meant to represent external, environmental influences on the particle system. The algorithm is a generalization of a previous distributed, stochastic algorithm for compression (PODC \u2716) that can be viewed as a special case of separation where all particles have the same color. It is significantly more challenging to prove that the desired behavior is achieved in the heterogeneous setting, however, even in the bichromatic case we focus on. This requires combining several new techniques, including the cluster expansion from statistical physics, a new variant of the bridging argument of Miracle, Pascoe and Randall (RANDOM \u2711), the high-temperature expansion of the Ising model, and careful probabilistic arguments
Local Stochastic Algorithms for Alignment in Self-Organizing Particle Systems
We present local distributed, stochastic algorithms for alignment in self-organizing particle systems (SOPS) on two-dimensional lattices, where particles occupy unique sites on the lattice, and particles can make spatial moves to neighboring sites if they are unoccupied. Such models are abstractions of programmable matter, composed of individual computational particles with limited memory, strictly local communication abilities, and modest computational capabilities. We consider oriented particle systems, where particles are assigned a vector pointing in one of q directions, and each particle can compute the angle between its direction and the direction of any neighboring particle, although without knowledge of global orientation with respect to a fixed underlying coordinate system. Particles move stochastically, with each particle able to either modify its direction or make a local spatial move along a lattice edge during a move. We consider two settings: (a) where particle configurations must remain simply connected at all times and (b) where spatial moves are unconstrained and configurations can disconnect.
Our algorithms are inspired by the Potts model and its planar oriented variant known as the planar Potts model or clock model from statistical physics. We prove that for any q ? 2, by adjusting a single parameter, these self-organizing particle systems can be made to collectively align along a single dominant direction (analogous to a solid or ordered state) or remain non-aligned, in which case the fraction of particles oriented along any direction is nearly equal (analogous to a gaseous or disordered state). In the connected SOPS setting, we allow for two distinct parameters, one controlling the ferromagnetic attraction between neighboring particles (regardless of orientation) and the other controlling the preference of neighboring particles to align. We show that with appropriate settings of the input parameters, we can achieve compression and expansion, controlling how tightly gathered the particles are, as well as alignment or nonalignment, producing a single dominant orientation or not. While alignment is known for the Potts and clock models at sufficiently low temperatures, our proof in the SOPS setting are significantly more challenging because the particles make spatial moves, not all sites are occupied, and the total number of particles is fixed
Complexity, Development, and Evolution in Morphogenetic Collective Systems
Many living and non-living complex systems can be modeled and understood as
collective systems made of heterogeneous components that self-organize and
generate nontrivial morphological structures and behaviors. This chapter
presents a brief overview of our recent effort that investigated various
aspects of such morphogenetic collective systems. We first propose a
theoretical classification scheme that distinguishes four complexity levels of
morphogenetic collective systems based on the nature of their components and
interactions. We conducted a series of computational experiments using a
self-propelled particle swarm model to investigate the effects of (1)
heterogeneity of components, (2) differentiation/re-differentiation of
components, and (3) local information sharing among components, on the
self-organization of a collective system. Results showed that (a) heterogeneity
of components had a strong impact on the system's structure and behavior, (b)
dynamic differentiation/re-differentiation of components and local information
sharing helped the system maintain spatially adjacent, coherent organization,
(c) dynamic differentiation/re-differentiation contributed to the development
of more diverse structures and behaviors, and (d) stochastic re-differentiation
of components naturally realized a self-repair capability of self-organizing
morphologies. We also explored evolutionary methods to design novel
self-organizing patterns, using interactive evolutionary computation and
spontaneous evolution within an artificial ecosystem. These self-organizing
patterns were found to be remarkably robust against dimensional changes from 2D
to 3D, although evolution worked efficiently only in 2D settings.Comment: 13 pages, 8 figures, 1 table; submitted to "Evolution, Development,
and Complexity: Multiscale Models in Complex Adaptive Systems" (Springer
Proceedings in Complexity Series
Nanocellulose Fragmentation Mechanisms and Inversion of Chirality from the Single Particle to the Cholesteric Phase
Understanding how nanostructure and nanomechanics influence physical material
properties on the micro- and macroscale is an essential goal in soft condensed
matter research. Mechanisms governing fragmentation and chirality inversion of
filamentous colloids are of specific interest because of their critical role in
load-bearing and self-organizing functionalities of soft nanomaterials. Here we
provide a fundamental insight into the self-organization across several length
scales of nanocellulose, an important bio-colloid system with wide-ranging
applications as structural, insulating and functional material. Through a
combined microscopic and statistical analysis of nanocellulose fibrils at the
single particle level, we show how mechanically and chemically induced
fragmentation proceed in this system. Moreover, by studying the bottom-up
self-assembly of fragmented carboxylated cellulose nanofibrils into cholesteric
liquid crystals, we show via direct microscopic observations, that the
chirality is inverted from right-handed at the nanofibril level to left-handed
at the level of the liquid crystal phase. These results improve our fundamental
understanding of nanocellulose and provide an important rationale for their
application in colloidal systems, liquid crystals and nanomaterials
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