Real-time filtering and detection of dynamics for compression of HDTV

The preprocessing of video sequences for data compressing is discussed. The end goal associated with this is a compression system for HDTV capable of transmitting perceptually lossless sequences at under one bit per pixel. Two subtopics were emphasized to prepare the video signal for more efficient coding: (1) nonlinear filtering to remove noise and shape the signal spectrum to take advantage of insensitivities of human viewers; and (2) segmentation of each frame into temporally dynamic/static regions for conditional frame replenishment. The latter technique operates best under the assumption that the sequence can be modelled as a superposition of active foreground and static background. The considerations were restricted to monochrome data, since it was expected to use the standard luminance/chrominance decomposition, which concentrates most of the bandwidth requirements in the luminance. Similar methods may be applied to the two chrominance signals

Local Causal States and Discrete Coherent Structures

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht

A Developmental Organization for Robot Behavior

This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions of dynamic pattern theory in which behavior is an artifact of coupled dynamical systems with a number of controllable degrees of freedom. In our model, the events that delineate control decisions are derived from the pattern of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential knowledge gathering and representation tasks and provide examples of the kind of developmental milestones that this approach has already produced in our lab

The M81 Group Dwarf Irregular Galaxy DDO 165. II. Connecting Recent Star Formation with ISM Structures and Kinematics

We compare the stellar populations and complex neutral gas dynamics of the M81 group dIrr galaxy DDO 165 using data from the HST and the VLA. Paper I identified two kinematically distinct HI components, multiple localized high velocity gas features, and eight HI holes and shells (the largest of which spans ~2.2x1.1 kpc). Using the spatial and temporal information from the stellar populations in DDO 165, we compare the patterns of star formation over the past 500 Myr with the HI dynamics. We extract localized star formation histories within 6 of the 8 HI holes identified in Paper I, as well as 23 other regions that sample a range of stellar densities and neutral gas properties. From population synthesis modeling, we derive the energy outputs (from stellar winds and supernovae) of the stellar populations within these regions over the last 100 Myr, and compare with refined estimates of the energies required to create the HI holes. In all cases, we find that "feedback" is energetically capable of creating the observed structures in the ISM. Numerous regions with significant energy inputs from feedback lack coherent HI structures but show prominent localized high velocity gas features; this feedback signature is a natural product of temporally and spatially distributed star formation. In DDO 165, the extended period of heightened star formation activity (lasting more than 1 Gyr) is energetically capable of creating the observed holes and high velocity gas features in the neutral ISM.Comment: The Astrophysical Journal, in press. Full-resolution version available on request from the first autho

Slow and steady feature analysis: higher order temporal coherence in video

How can unlabeled video augment visual learning? Existing methods perform "slow" feature analysis, encouraging the representations of temporally close frames to exhibit only small differences. While this standard approach captures the fact that high-level visual signals change slowly over time, it fails to capture *how* the visual content changes. We propose to generalize slow feature analysis to "steady" feature analysis. The key idea is to impose a prior that higher order derivatives in the learned feature space must be small. To this end, we train a convolutional neural network with a regularizer on tuples of sequential frames from unlabeled video. It encourages feature changes over time to be smooth, i.e., similar to the most recent changes. Using five diverse datasets, including unlabeled YouTube and KITTI videos, we demonstrate our method's impact on object, scene, and action recognition tasks. We further show that our features learned from unlabeled video can even surpass a standard heavily supervised pretraining approach.Comment: in Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas, NV, June 201

A Method for Data-Driven Simulations of Evolving Solar Active Regions

We present a method for performing data-driven simulations of solar active region formation and evolution. The approach is based on magnetofriction, which evolves the induction equation assuming the plasma velocity is proportional to the Lorentz force. The simulations of active region coronal field are driven by temporal sequences of photospheric magnetograms from the Helioseismic Magnetic Imager (HMI) instrument onboard the Solar Dynamics Observatory (SDO). Under certain conditions, the data-driven simulations produce flux ropes that are ejected from the modeled active region due to loss of equilibrium. Following the ejection of flux ropes, we find an enhancement of the photospheric horizontal field near the polarity inversion line. We also present a method for the synthesis of mock coronal images based on a proxy emissivity calculated from the current density distribution in the model. This method yields mock coronal images that are somewhat reminiscent of images of active regions taken by instruments such as SDO's Atmospheric Imaging Assembly (AIA) at extreme ultraviolet wavelengths.Comment: Accepted to ApJ; comments/questions related to this article are welcome via e-mail, even after publicatio

Tree Memory Networks for Modelling Long-term Temporal Dependencies

In the domain of sequence modelling, Recurrent Neural Networks (RNN) have been capable of achieving impressive results in a variety of application areas including visual question answering, part-of-speech tagging and machine translation. However this success in modelling short term dependencies has not successfully transitioned to application areas such as trajectory prediction, which require capturing both short term and long term relationships. In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems. The proposed network architecture is composed of an input module, controller and a memory module. In contrast to related literature, which models the memory as a sequence of historical states, we model the memory as a recursive tree structure. This structure more effectively captures temporal dependencies across both short term and long term sequences using its hierarchical structure. We demonstrate the effectiveness and flexibility of the proposed TMN in two practical problems, aircraft trajectory modelling and pedestrian trajectory modelling in a surveillance setting, and in both cases we outperform the current state-of-the-art. Furthermore, we perform an in depth analysis on the evolution of the memory module content over time and provide visual evidence on how the proposed TMN is able to map both long term and short term relationships efficiently via a hierarchical structure