20 research outputs found
Mechanical Entanglement via Detuned Parametric Amplification
We propose two schemes to generate entanglement between a pair of mechanical
oscillators using parametric amplification. In contrast to existing parametric
drive-based protocols, both schemes operate in the steady-state. Using a
detuned parametric drive to maintain equilibrium and to couple orthogonal
quadratures, our approach can be viewed as a two-mode extension of previous
proposals for parametric squeezing. We find that robust steady-state
entanglement is possible for matched oscillators with well-controlled coupling.
In addition, one of the proposed schemes is robust to differences in the
damping rates of the two oscillators.Comment: 13 pages, 2 figure
Detuned Mechanical Parametric Amplification as a Quantum Non-Demolition Measurement
Recently it has been demonstrated that the combination of weak-continuous
position detection with detuned parametric driving can lead to significant
steady-state mechanical squeezing, far beyond the 3 dB limit normally
associated with parametric driving. In this work, we show the close connection
between this detuned scheme and quantum non-demolition (QND) measurement of a
single mechanical quadrature. In particular, we show that applying an
experimentally realistic detuned parametric drive to a cavity optomechanical
system allows one to effectively realize a QND measurement despite being in the
bad-cavity limit. In the limit of strong squeezing, we show that this scheme
offers significant advantages over standard backaction evasion, not only by
allowing operation in the weak measurement and low efficiency regimes, but also
in terms of the purity of the mechanical state.Comment: 17 pages, 2 figure
From real-time adaptation to social learning in robot ecosystems
While evolutionary robotics can create novel morphologies and controllers that are well-adapted to their environments, learning is still the most efficient way to adapt to changes that occur on shorter time scales. Learning proposals for evolving robots to date have focused on new individuals either learning a controller from scratch, or building on the experience of direct ancestors and/or robots with similar configurations. Here we propose and demonstrate a novel means for social learning of gait patterns, based on sensorimotor synchronization. Using movement patterns of other robots as input can drive nonlinear decentralized controllers such as CPGs into new limit cycles, hence encouraging diversity of movement patterns. Stable autonomous controllers can then be locked in, which we demonstrate using a quasi-Hebbian feedback scheme. We propose that in an ecosystem of robots evolving in a heterogeneous environment, such a scheme may allow for the emergence of generalist task-solvers from a population of specialists
Cavity optoelectromechanical regenerative amplification
Cavity optoelectromechanical regenerative amplification is demonstrated. An
optical cavity enhances mechanical transduction, allowing sensitive measurement
even for heavy oscillators. A 27.3 MHz mechanical mode of a microtoroid was
linewidth narrowed to 6.6\pm1.4 mHz, 30 times smaller than previously achieved
with radiation pressure driving in such a system. These results may have
applications in areas such as ultrasensitive optomechanical mass spectroscopy
Quantifying the structure and dynamics of fish shoals under predation threat in three dimensions
Detailed quantifications of how predators and their grouping prey interact in three dimensions (3D) remain rare. Here we record the structure and dynamics of fish shoals (Pseudomugil signifer) in 3D both with and without live predators (Philypnodon grandiceps) under controlled laboratory conditions. Shoals adopted two distinct types of shoal structure; 'sphere-like' geometries at depth, and flat 'carpet-like' structures at the water's surface, with shoals becoming more compact in both horizontal and vertical planes in the presence of a predator. The predators actively stalked and at- tacked the prey, with attacks being initiated when the shoals were not in their usual configurations. These attacks caused the shoals to break apart, but shoal reformation was rapid, and involved individuals adjusting their positions in both horizontal and vertical dimensions. Our analyses revealed that targeted prey were more isolated from other conspecifics, and were closer in terms of distance and direction to the predator compared to non-targeted prey. Moreover, which prey were targeted could largely be identified based on individuals' positions from a single plane. This highlights that previously proposed 2D theoretical models and their assumptions appear valid when considering how predators target groups in 3D. Our work provides experimental, and not just anecdotal, sup- port for classic theoretical predictions, and also lends new insights into predatory-prey interactions in three-dimensional environments
Rapid rhythmic entrainment in bio-inspired central pattern generators
Entrainment of movement to a periodic stimulus is a characteristic
intelligent behaviour in humans and an important goal for adaptive robotics. We
demonstrate a quadruped central pattern generator (CPG), consisting of modified
Matsuoka neurons, that spontaneously adjusts its period of oscillation to that
of a periodic input signal. This is done by simple forcing, with the aid of a
filtering network as well as a neural model with tonic input-dependent
oscillation period. We first use the NSGA3 algorithm to evolve the CPG
parameters, using separate fitness functions for period tunability, limb
homogeneity and gait stability. Four CPGs, maximizing different weighted
averages of the fitness functions, are then selected from the Pareto front and
each is used as a basis for optimizing a filter network. Different numbers of
neurons are tested for each filter network. We find that period tunability in
particular facilitates robust entrainment, that bounding gaits entrain more
easily than walking gaits, and that more neurons in the filter network are
beneficial for pre-processing input signals. The system that we present can be
used in conjunction with sensory feedback to allow low-level adaptive and
robust behaviour in walking robots.Comment: 7 pages, 6 figures. To appear in Proceedings of the IEEE
International Joint Conference on Neural Networks 202
Using neuronal models to capture burst-and-glide motion and leadership in fish
While mathematical models, in particular self-propelled particle models, capture many properties of large fish schools, they do not always capture the interactions of smaller shoals. Nor do these models tend to account for the use of intermittent locomotion, often referred to as burst-and-glide, by many species. In this paper, we propose a model of social burst-and-glide motion by combining a well-studied model of neuronal dynamics, the FitzHugh-Nagumo model, with a model of fish motion. We first show that our model can capture the motion of a single fish swimming down a channel. Extending to a two-fish model, where visual stimulus of a neighbour affects the internal burst or glide state of the fish, we observe a rich set of dynamics found in many species. These include: leader-follower behaviour; periodic changes in leadership; apparently random (i.e. chaotic) leadership change; and tit-for-tat turn taking. Moreover, unlike previous studies where a randomness is required for leadership switching to occur, we show that this can instead be the result of deterministic interactions. We give several empirically testable predictions for how bursting fish interact and discuss our results in light of recently established correlations between fish locomotion and brain activity
Friend of a friend models of network growth
One of the best-known models in network science is preferential attachment. In this model, the probability of attaching to a node depends on the degree of all nodes in the population, and thus depends on global information. In many biological, physical and social systems, however, interactions between individuals depend only on local information. Here, we investigate a truly local model of network formation-based on the idea of a friend of a friend-with the following rule: individuals choose one node at random and link to it with probability p, then they choose a neighbour of that node and link with probability q. Our model produces power-laws with empirical exponents ranging from 1.5 upwards and clustering coefficients ranging from 0 up to 0.5 (consistent with many real networks). For small p and q = 1, the model produces super-hub networks, and we prove that for p = 0 and q = 1, the proportion of non-hubs tends to 1 as the network grows. We show that power-law degree distributions, small world clustering and super-hub networks are all outcomes of this, more general, yet conceptually simple model