Femtosecond pulse laser-induced self-organized nanostructures on the surface of ZnO crystal

This paper reports self-organized nanostructures observed on the surface of ZnO crystal after irradiation by a focused beam of a femtosecond Ti:sapphire laser with a repetition rate of 250 kHz. For a linearly polarized femtosecond laser, the periodic nanograting structure on the ablation crater surface was promoted. The period of self-organization structures is about 180 nm. The grating orientation is adjusted by the laser polarization direction. A long range Bragg-like grating is formed by moving the sample at a speed of 10 mu m/s. For a circularly polarized laser beam, uniform spherical nanoparticles were formed as a result of Coulomb explosion during the interaction of near-infrared laser with ZnO crystal.

Modeling habits as self-sustaining patterns of sensorimotor behavior

In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and self-organizing concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel "iterant deformable sensorimotor medium (IDSM)," designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated robot, and show that under certain conditions, the IDSM conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the robot's body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor "meso-scale" between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks.

Influence of indium composition on the surface morphology of self-organized InxGa1-xAs quantum dots on GaAs substrates

InxGa1-xAs self-organized quantum dots with x=1.0, 0.5, and 0.35 have been grown by molecular beam epitaxy. The areal density, distribution, and shapes have been found to be dependent on x. The dot shape changes from a round shape for x=1.0 to an elliptical shape for x less than or equal to 0.5. The major axis and minor axis of the elliptical InxGa1-xAs dots are along the [(1) over bar 10] and [110] directions, respectively. The ordering phenomenon is also discussed. It is suggested that the dot-dot interaction may play important roles in the self-organization process. (C) 2000 American Institute of Physics. [S0021-8979(00)10701-7].

Structural and infrared absorption properties of self-organized InGaAs GaAs quantum dots multilayers

Self-organized InGaAs/GaAs quantum dots (QDs) stacked multilayers have been prepared by solid source molecular beam epitaxy. Cross-sectional transmission electron microscopy shows that the InGaAs QDs are nearly perfectly vertically aligned in the growth direction [100]. The filtering effect on the QDs distribution is found to be the dominant mechanism leading to vertical alignment and a highly uniform size distribution. Moreover, we observe a distinct infrared absorption from the sample in the range of 8.6-10.7 mu m. This indicates the potential of QDs multilayer structure for use as infrared photodetector.

Structural and infrared absorption properties of self-organized InGaAs GaAs quantum dots multilayers

Self-organized InGaAs/GaAs quantum dots (QDs) stacked multilayers have been prepared by solid source molecular beam epitaxy. Cross-sectional transmission electron microscopy shows that the InGaAs QDs are nearly perfectly vertically aligned in the growth direction [100]. The filtering effect on the QDs distribution is found to be the dominant mechanism leading to vertical alignment and a highly uniform size distribution. Moreover, we observe a distinct infrared absorption from the sample in the range of 8.6-10.7 mu m. This indicates the potential of QDs multilayer structure for use as infrared photodetector.

Self-assembly of ionic liquids-stabilized Pt nanoparticles into two-dimensional patterned nanostructures at the air-water interface

In this paper, we demonstrate the self-assembly of ionic liquids (ILs)-stabilized Pt nanoparticles into two-dimensional (2D) patterned nanostructures at the air-water interface under ambient conditions. Here, ILs are not used as solvents but as mediators by virtue of their pronounced self-organization ability in synthesis of self-assembled, highly organized hybrid Pt nanostructures. It is also found that the morphologies of the 2D patterned nanostructures are directly connected with the quantities of ILs. Due to the special structures of ILs-stabilized Pt nanoparticles, 2D patterned Pt nanostructures can be formed through the pi-pi stack interactions and hydrogen bonds. The resulting 2D patterned Pt nanostructures exhibit good electrocatalytic activity toward oxygen reduction.

Influence of capping ligands on the self-organizations of gold nanoparticles into superlattice from CTAB reverse micelles

Gold nanoparticles with size 3-10 nm (diameter) were prepared by the reduction of HAuCl4 in a CTAB/octane + 1-butanol/H2O reverse micelle system using NaBH4 as the reducing agent. The as-formed gold nanoparticle colloid was characterized by UV/vis absorption spectrum and transmission electron microscopy(TEM). Various capping ligands, such as alkylthiols with different chain length and shape, trioctylphosphine (TOP), and pyridine are used to passivate the gold nanoparticles for the purpose of self-organization into superstructures. It is shown that the ligands have a great influence on the self-organization of gold nanoparticles into superlattices, and dodecanethiol C12H25SH is confirmed to be the best ligand for the self-organization. Self-organization of C12H25SH-capped gold nanoparticles into 1D, 2D and 3D superlattices has been observed on the carbon-coated copper grid by TEM without using any selective precipitation process.

A Self-Organizing Neural Model for Motor Equivalent Phoneme Production

This paper describes a model of speech production called DIVA that highlights issues of self-organization and motor equivalent production of phonological units. The model uses a circular reaction strategy to learn two mappings between three levels of representation. Data on the plasticity of phonemic perceptual boundaries motivates a learned mapping between phoneme representations and vocal tract variables. A second mapping between vocal tract variables and articulator movements is also learned. To achieve the flexible control made possible by the redundancy of this mapping, desired directions in vocal tract configuration space are mapped into articulator velocity commands. Because each vocal tract direction cell learns to activate several articulator velocities during babbling, the model provides a natural account of the formation of coordinative structures. Model simulations show automatic compensation for unexpected constraints despite no previous experience or learning under these constraints.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

A Self-Organizing Neural Model of Motor Equivalent Reaching and Tool Use by a Multijoint Arm

This paper describes a self-organizing neural model for eye-hand coordination. Called the DIRECT model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and other animals to flexibly employ an arm with more degrees of freedom than the space in which it moves to carry out spatially defined tasks under conditions that may require novel joint configurations. During a motor babbling phase, the model endogenously generates movement commands that activate the correlated visual, spatial, and motor information that are used to learn its internal coordinate transformations. After learning occurs, the model is capable of controlling reaching movements of the arm to prescribed spatial targets using many different combinations of joints. When allowed visual feedback, the model can automatically perform, without additional learning, reaches with tools of variable lengths, with clamped joints, with distortions of visual input by a prism, and with unexpected perturbations. These compensatory computations occur within a single accurate reaching movement. No corrective movements are needed. Blind reaches using internal feedback have also been simulated. The model achieves its competence by transforming visual information about target position and end effector position in 3-D space into a body-centered spatial representation of the direction in 3-D space that the end effector must move to contact the target. The spatial direction vector is adaptively transformed into a motor direction vector, which represents the joint rotations that move the end effector in the desired spatial direction from the present arm configuration. Properties of the model are compared with psychophysical data on human reaching movements, neurophysiological data on the tuning curves of neurons in the monkey motor cortex, and alternative models of movement control.

Self-organized vegetation patterning as a fingerprint of climate and human impact on semi-arid ecosystems

Spatially periodic vegetation patterns are well known in arid and semi-arid regions around the world. Mathematical models have been developed that attribute this phenomenon to a symmetry-breaking instability. Such models are based on the interplay between competitive and facilitative influences that the vegetation exerts on its own dynamics when it is constrained by arid conditions, but evidence for these predictions is still lacking. Moreover, not all models can account for the development of regularly spaced spots of bare ground in the absence of a soil prepattern. We applied Fourier analysis to high-resolution, remotely sensed data taken at either end of a 40-year interval in southern Niger. Statistical comparisons based on this textural characterization gave us broad-scale evidence that the decrease in rainfall over recent decades in the sub-Saharan Sahel has been accompanied by a detectable shift from homogeneous vegetation cover to spotted patterns marked by a spatial frequency of about 20 cycles km-1. Wood cutting and grazing by domestic animals have led to a much more marked transition in unprotected areas than in a protected reserve. Field measurements demonstrated that the dominant spatial frequency was endogenous rather than reflecting the spatial variation of any pre-existing heterogeneity in soil properties. All these results support the use of models that can account for periodic vegetation patterns without invoking substrate heterogeneity or anisotropy, and provide new elements for further developments, refinements and tests. This study underlines the potential of studying vegetation pattern properties for monitoring climatic and human impacts on the extensive fragile areas bordering hot deserts. Explicit consideration of vegetation self-patterning may also improve our understanding of vegetation and climate interactions in arid areas. © 2006 The Authors.

Quantum ground state of self-organized atomic crystals in optical resonators

Cold atoms, driven by a laser and simultaneously coupled to the quantum field of an optical resonator, may self-organize in periodic structures. These structures are supported by the optical lattice, which emerges from the laser light they scatter into the cavity mode and form when the laser intensity exceeds a threshold value. We study theoretically the quantum ground state of these structures above the pump threshold of self-organization by mapping the atomic dynamics of the self-organized crystal to a Bose-Hubbard model. We find that the quantum ground state of the self-organized structure can be the one of a Mott insulator, depending on the pump strength of the driving laser. For very large pump strengths, where the intracavity-field intensity is maximum and one would expect a Mott-insulator state, we find intervals of parameters where the phase is compressible. These states could be realized in existing experimental setups.

Self-organized electromagnetic field structures in laser-produced counter-streaming plasmas

Self-organization(1,2) occurs in plasmas when energy progressively transfers from smaller to larger scales in an inverse cascade(3). Global structures that emerge from turbulent plasmas can be found in the laboratory(4) and in astrophysical settings; for example, the cosmic magnetic field(5,6,) collisionless shocks in supernova remnants(7) and the internal structures of newly formed stars known as Herbig-Haro objects(8). Here we show that large, stable electromagnetic field structures can also arise within counter-streaming supersonic plasmas in the laboratory. These surprising structures, formed by a yet unexplained mechanism, are predominantly oriented transverse to the primary flow direction, extend for much larger distances than the intrinsic plasma spatial scales and persist for much longer than the plasma kinetic timescales. Our results challenge existing models of counter-streaming plasmas and can be used to better understand large-scale and long-time plasma self-organization.

Self-organized sociopolitical interactions as the best way to achieve organized patterns in human social systems: going beyond the top-down control of classical political regimes

La monografía presenta la auto-organización sociopolítica como la mejor manera de lograr patrones organizados en los sistemas sociales humanos, dada su naturaleza compleja y la imposibilidad de las tareas computacionales de los regímenes políticos clásico, debido a que operan con control jerárquico, el cual ha demostrado no ser óptimo en la producción de orden en los sistemas sociales humanos. En la monografía se extrapola la teoría de la auto-organización en los sistemas biológicos a las dinámicas sociopolíticas humanas, buscando maneras óptimas de organizarlas, y se afirma que redes complejas anárquicas son la estructura emergente de la auto-organización sociopolítica.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.