9,584 research outputs found
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
This paper takes a step towards temporal reasoning in a dynamically changing
video, not in the pixel space that constitutes its frames, but in a latent
space that describes the non-linear dynamics of the objects in its world. We
introduce the Kalman variational auto-encoder, a framework for unsupervised
learning of sequential data that disentangles two latent representations: an
object's representation, coming from a recognition model, and a latent state
describing its dynamics. As a result, the evolution of the world can be
imagined and missing data imputed, both without the need to generate high
dimensional frames at each time step. The model is trained end-to-end on videos
of a variety of simulated physical systems, and outperforms competing methods
in generative and missing data imputation tasks.Comment: NIPS 201
Protosymbols that integrate recognition and response
We explore two controversial hypotheses through robotic implementation: (1) Processes involved in recognition and response are tightly coupled both in their operation and epigenesis; and (2) processes involved in symbol emergence should respect the integrity of recognition and response while exploiting the periodicity of biological motion. To that end, this paper proposes a method of recognizing and generating motion patterns based on nonlinear principal component neural networks that are constrained to model both periodic and transitional movements. The method is evaluated by an examination of its ability to segment and generalize different kinds of soccer playing activity during a RoboCup match
Odor-driven attractor dynamics in the antennal lobe allow for simple and rapid olfactory pattern classification
The antennal lobe plays a central role for odor processing in insects, as demonstrated by electrophysiological and imaging experiments. Here we analyze the detailed temporal evolution of glomerular activity patterns in the antennal lobe of honeybees. We represent these spatiotemporal patterns as trajectories in a multidimensional space, where each dimension accounts for the activity of one glomerulus. Our data show that the trajectories reach odor-specific steady states (attractors) that correspond to stable activity patterns at about 1 second after stimulus onset. As revealed by a detailed mathematical investigation, the trajectories are characterized by different phases: response onset, steady-state plateau, response offset, and periods of spontaneous activity. An analysis based on support-vector machines quantifies the odor specificity of the attractors and the optimal time needed for odor discrimination. The results support the hypothesis of a spatial olfactory code in the antennal lobe and suggest a perceptron-like readout mechanism that is biologically implemented in a downstream network, such as the mushroom body
Deterministic polarization chaos from a laser diode
Fifty years after the invention of the laser diode and fourty years after the
report of the butterfly effect - i.e. the unpredictability of deterministic
chaos, it is said that a laser diode behaves like a damped nonlinear
oscillator. Hence no chaos can be generated unless with additional forcing or
parameter modulation. Here we report the first counter-example of a
free-running laser diode generating chaos. The underlying physics is a
nonlinear coupling between two elliptically polarized modes in a
vertical-cavity surface-emitting laser. We identify chaos in experimental
time-series and show theoretically the bifurcations leading to single- and
double-scroll attractors with characteristics similar to Lorenz chaos. The
reported polarization chaos resembles at first sight a noise-driven mode
hopping but shows opposite statistical properties. Our findings open up new
research areas that combine the high speed performances of microcavity lasers
with controllable and integrated sources of optical chaos.Comment: 13 pages, 5 figure
Time without time: a stochastic clock model
We study a classical reparametrization-invariant system, in which ``time'' is
not a priori defined. It consists of a nonrelativistic particle moving in five
dimensions, two of which are compactified to form a torus. There, assuming a
suitable potential, the internal motion is ergodic or more strongly irregular.
We consider quasi-local observables which measure the system's ``change'' in a
coarse-grained way. Based on this, we construct a statistical timelike
parameter, particularly with the help of maximum entropy method and Fisher-Rao
information metric. The emergent reparametrization-invariant ``time'' does not
run smoothly but is simply related to the proper time on the average. For
sufficiently low energy, the external motion is then described by a unitary
quantum mechanical evolution in accordance with the Schr\"odinger equation.Comment: 18 pages; LaTeX. 4 (.ps) plus 2 (.gif) figure file
Scientific possibilities of a solar electric powered rendezvous with comet Encke
The minimum scientific spacecraft instrumentation is considered that is likely to result in as complete an understanding of the composition, structure, and activity of a cometary nucleus as is possible without landing on it. The payload will also give useful results on secondary goals of a better understanding of physical processes in the inner and outer coma. Studies of composition, by means of an actual landing on the surface, details of the internal structure of the nucleus, and sample return were considered beyond the scope of this mission
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