Superchemistry: dynamics of coupled atomic and molecular Bose condensates

We analyze the dynamics of a dilute, trapped Bose-condensed atomic gas coupled to a diatomic molecular Bose gas by coherent Raman transitions. This system is shown to result in a new type of “superchemistry,” in which giant collective oscillations between the atomic and the molecular gas can occur. The phenomenon is caused by stimulated emission of bosonic atoms or molecules into their condensate phases.

Dependence of transient dynamics in a class-C laser upon variation of inversion with time

The transient statistics of a gain-switched coherently pumped class-C laser displays a linear correlation between the first passage time and subsequent peak intensity. Measurements are reported showing a positive or negative sign of this linear correlation, controlled through the switching time and the laser detuning. Further measurements of the small-signal laser gain combined with calculations involving a three-level laser model indicate that this sign fundamentally depends upon the way the laser inversion varies during the gain switching, despite the added dynamics of the laser polarization in the class-C laser. [S1050-2947(97)07112-6].

Quantum dynamics of an atomic Bose-Einstein condensate in a double-well potential

We consider the quantum dynamics of a neutral atom Bose-Einstein condensate in a double-well potential, including many-body hard-sphere interactions. Using a mean-field factorization we show that the coherent oscillations due to tunneling are suppressed when the number of atoms exceeds a critical value. An exact quantum solution, in a two-mode approximation, shows that the mean-field solution is modulated by a quantum collapse and revival sequence.

Classical and quantum dynamics of a model for atomic-molecular Bose-Einstein condensates

We study a model for a two-mode atomic-molecular Bose-Einstein condensate. Starting with a classical analysis we determine the phase space fixed points of the system. It is found that bifurcations of the fixed points naturally separate the coupling parameter space into four regions. The different regions give rise to qualitatively different dynamics. We then show that this classification holds true for the quantum dynamics.

Entanglement between a qubit and the environment in the spin-boson model

The quantitative description of the quantum entanglement between a qubit and its environment is considered. Specifically, for the ground state of the spin-boson model, the entropy of entanglement of the spin is calculated as a function of α, the strength of the ohmic coupling to the environment, and ɛ, the level asymmetry. This is done by a numerical renormalization group treatment of the related anisotropic Kondo model. For ɛ=0, the entanglement increases monotonically with α, until it becomes maximal for α→1-. For fixed ɛ>0, the entanglement is a maximum as a function of α for a value, α=αM

Ecosystem dynamics at disturbed and undisturbed sites in North Queensland wet tropical rain forest. III. Nutrient returns to the forest floor through litterfall

The nutrient contents and accessions in litterfall over a period of 3 y are reported for undisturbed areas and at two sites disturbed by selective harvesting in tropical rain forest in North Queensland, Australia. Mean concentrations (mg g(-1) dry weight) of nutrients in litterfall ranged from 10 to 12 for nitrogen; 0.33 to 0.43 for phosphorus; 3.6 to 4.3 for potassium; 6.0 to 10.5 for calcium and 1.7 to 2.6 for magnesium. These concentrations are in the middle to lower part of the spectrum of values recorded for tropical forest. Accessions of nutrients in litterfall (kg ha(-1) y(-1)) ranged from 59 to 64 N; 1.9 to 2.4 P; 20 to 24K; 34 to 63 Ca; and 9 to 16 Mg. These rates, particularly for IN and P, are among the lowest recorded for tropical forests. There were no consistent between-site differences in total nutrient accessions in small litterfall. In terms of the contribution of litterfall to the accessions of nutrients to the forest floor, this suggests that the logged sites have recovered from the effects of selective harvesting within 25 y. Nutrient accessions at each site were distinctly seasonal, with maximum accessions occurring in the late dry season to late in the wet season. Leaf-fall accounted for the largest proportion of nutrient accessions over the study period, although at certain times accessions in both reproductive material and wood were significant. A cyclone which crossed the coast near the study sites resulted in large nutrient accessions over a short period but had little effect on the total annual accession. A comparison with previous studies of litterfall in Australian tropical rainforests indicates that nutrient return in litterfall is directly related to soil fertility.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

Structure optimization combining soft-core interaction functions, the diffusion equation method, and molecular dynamics

Smoothing the potential energy surface for structure optimization is a general and commonly applied strategy. We propose a combination of soft-core potential energy functions and a variation of the diffusion equation method to smooth potential energy surfaces, which is applicable to complex systems such as protein structures; The performance of the method was demonstrated by comparison with simulated annealing using the refinement of the undecapeptide Cyclosporin A as a test case. Simulations were repeated many times using different initial conditions and structures since the methods are heuristic and results are only meaningful in a statistical sense.