An evaluation of space time cube representation of spatiotemporal patterns.

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

The Trade-offs with Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Fast and correct analysis of such information is important in for instance geospatial and social visualization applications. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a dataset to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap we report on a between-subjects experiment comparing novice users error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the dataset, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users when analyzing complex spatiotemporal patterns.

The influence of stacks on flow patterns and stratification associated with natural ventilation

We investigate the steady state natural ventilation of an enclosed space in which vent A, located at height hA above the floor, is connected to a vertical stack with a termination at height H, while the second vent, B, at height hB above the floor, connects directly to the exterior. We first examine the flow regimes which develop with a distributed source of heating at the base of the space. If hBhB>hA, then two different flow regimes may develop. Either (i) there is inflow through vent B and outflow through vent A, or (ii) the flow reverses, with inflow down the stack into vent A and outflow through vent B. With inflow through vent A, the internal temperature and ventilation rate depend on the relative height of the two vents, A and B, while with inflow through vent B, they depend on the height of vent B relative to the height of the termination of the stack H. With a point source of heating, a similar transition occurs, with a unique flow regime when vent B is lower than vent A, and two possible regimes with vent B higher than vent A. In general, with a point source of buoyancy, each steady state is characterised by a two-layer density stratification. Depending on the relative heights of the two vents, in the case of outflow through vent A connected to the stack, the interface between these layers may lie above, at the same level as or below vent A, leading to discharge of either pure upper layer, a mixture of upper and lower layer, or pure lower layer fluid. In the case of inflow through vent A connected to the stack, the interface always lies below the outflow vent B. Also, in this case, if the inflow vent A lies above the interface, then the lower layer becomes of intermediate density between the upper layer and the external fluid, whereas if the interface lies above the inflow vent A, then the lower layer is composed purely of external fluid. We develop expressions to predict the transitions between these flow regimes, in terms of the heights and areas of the two vents and the stack, and we successfully test these with new laboratory experiments. We conclude with a discussion of the implications of our results for real buildings.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Forward Smoothing Using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Essentially, it is an on-line or "forward only" implementation of a forward filtering backward smoothing SMC algorithm proposed by Doucet, Godsill and Andrieu (2000). Compared to the standard \emph{path space} SMC estimator whose asymptotic variance increases quadratically with time even under favorable mixing assumptions, the non asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using an SMC implementation of an on-line version of the Expectation-Maximization algorithm which does not suffer from the particle path degeneracy problem.