A state space based approach in non-linear hemodynamic response modeling with fMRI data

In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow. The results obtained through numerical solutions showed that the new state space model realization is in close agreement to the actual modified and integrated version of the balloon model. This new system theoretic formulation is likely to open doors to a novel way of analyzing fMRI data with real time robust estimators. With further development and validation, the new model has the potential to devise a generalized measure to make a significant contribution to improve the diagnosis and treatment of clinical scenarios where the brain functioning get altered. Concepts from system theory can readily be used in the analysis of fMRI data and the subsequent synthesis of filters and estimators.

Solid state NMR analysis of polypyrrole grown in a phosphonium ionic liquid

³¹P, ¹⁹F and ¹³C solid state NMR analysis has been used to investigate the intercalation/de-intercalation of both anions and cations in electrochemically synthesized polypyrrole films. Use of a phosphonium-based ionic liquid, tri(hexyl)(tetradecyl)phosphonium bis(trifluoromethanesulfonyl)amide, allows the separate detection of the cation and anion by analysis of the phosphorous and fluorine resonances, respectively. Initial results indicate the incorporation of both cations and anions during film growth in the ionic liquid. There is a notable change in the ³¹P chemical shift of the cation on incorporation into the film, consistent with a significant change in environment compared to the pure ionic liquid. Despite its large size, the phosphonium cation can be completely expelled from the film by oxidation.

Measurement of stable changes of self-management skills after rehabilitation: a latent state-trait analysis of the Health Education Impact Questionnaire (heiQ™)

To assess stable effects of self-management programs, measurement instruments should primarily capture the attributes of interest, for example, the self-management skills of the measured persons. However, measurements of psychological constructs are always influenced by both aspects of the situation (states) and aspects of the person (traits). This study tests whether the Health Education Impact Questionnaire (heiQ™), an instrument assessing a wide range of proximal outcomes of self-management programs, is primarily influenced by person factors instead of situational factors. Furthermore, measurement invariance over time, changes in traits and predictors of change for each heiQ™ scale were examined.

Model based analysis of fMRI/EEG data

This thesis addresses two major topics in neuroscience literature and drawbacks from existing literature are addressed by utilising state space models and Bayesian estimation techniques. Particle filter-based joint estimation of the physiological model for time-series analysis of fMRI data is demonstrated first in the thesis and secondly the Granger causality-based effective connectivity analysis of EEG data is investigated.

New frontiers in biologging science

The term ‘biologging’ refers to the use of miniaturized animal-attached tags for logging and/or relaying of data about an animal's movements, behaviour, physiology and/or environment. Biologging technology substantially extends our abilities to observe, and take measurements from, free-ranging, undisturbed subjects, providing much scope for advancing both basic and applied biological research. Here, we review highlights from the third international conference on biologging science, which was held in California, USA, from 1 to 5 September 2008. Over the last few years, considerable progress has been made with a range of recording technologies as well as with the management, visualization, integration and analysis of increasingly large and complex biologging datasets. Researchers use these techniques to study animal biology with an unprecedented level of detail and across the full range of ecological scales—from the split-second decision making of individuals to the long-term dynamics of populations, and even entire communities. We conclude our report by suggesting some directions for future research.

Diving deeper into individual foraging specializations of a large marine predator, the southern sea lion

Despite global declines in the abundance of marine predators, knowledge of foraging ecology, necessary to predict the ecological consequences of large changes in marine predator abundance, remains enigmatic for many species. Given that populations suffering severe declines are of conservation concern, we examined the foraging ecology of southern sea lions (SSL) (Otaria flavescens)-one of the least studied otariids (fur seal and sea lions)-which have declined by over 90 % at the Falkland Islands since the 1930s. Using a combination of biologging devices and stable isotope analysis of vibrissae, we redress major gaps in the knowledge of SSL ecology and quantify patterns of individual specialization. Specifically, we revealed two discrete foraging strategies, these being inshore (coastal) and offshore (outer Patagonian Shelf). The majority of adult female SSL (72 % or n = 21 of 29 SSL) foraged offshore. Adult female SSL that foraged offshore travelled further (92 ± 20 vs. 10 ± 4 km) and dived deeper (75 ± 23 vs. 21 ± 8 m) when compared to those that foraged inshore. Stable isotope analysis revealed long-term fidelity (years) to these discrete foraging habitats. In addition, we found further specialization within the offshore group, with adult female SSL separated into two clusters on the basis of benthic or mixed (benthic and pelagic) dive behavior (benthic dive proportion was 76 ± 9 vs. 51 ± 8 %, respectively). We suggest that foraging specialization in depleted populations such as SSL breeding at the Falkland Islands, are influenced by foraging site fidelity, and could be independent of intraspecific competition. Finally, the behavioral differences we describe are crucial to understanding population-level dynamics, impediments to population recovery, and threats to population persistence.

The application of a reinforcement learning agent to a multi-product manufacturing facility

An intelligent agent-based scheduling system, consisting of a reinforcement learning agent and a simulation model has been developed and tested on a classic scheduling problem. The production facility studied is a multiproduct serial line subject to stochastic failure. The agent goal is to minimise total production costs, through selection of job sequence and batch size. To explore state space the agent used reinforcement learning. By applying an independent inventory control policy for each product, the agent successfully identified optimal operating policies for a real production facility.

Supporting adaptive learning in hypertext environment : a high level timed Petri net based approach

One problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduced related concepts of hypertext learning state space and Petri net, then proposed a high level timed Petri Net based approach to provide some kinds of adaptation for learning activities. Examples were given while explaining ways to realizing adaptive instructions. Possible future directions were also discussed at the end of this paper.

Supporting adaptive learning in hypertext environment : an agent based approach

One problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduces related concepts of hypertext learning state space, then proposes an agent based approach used to provide kinds of adaptation for learning activities. Examples are given while explaining ways to realizing adaptive instructions. Possible future directions are also discussed at the end of this paper.

Lower bounds for stability margin of n-dimensional discrete systems

This paper derives lower bounds for the stability margin of n-dimensional discrete systems in the Roesser’s state space setting. The lower bounds for stability margin are derived based on the MacLaurine series expansion. Numerical examples are given to illustrate the results.

Supporting adaptive learning in hypertext environments : a high level timed Petri net-based approach

A problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduces related concepts of hypertext learning state space and high level Petri Nets (PNs), then proposes a high level timed PN based approach used to providing kinds of adaptation for learning activities by adjusting time attributes of targeted learning state space. Examples are given while explaining ways to realising adaptive instructions. Possible future directions are also discussed at the end of this paper.

Human action recognition based on pyramid histogram of oriented gradients

Human action recognition has been attracted lots of interest from computer vision researchers due to its various promising applications. In this paper, we employ Pyramid Histogram of Orientation Gradient (PHOG) to characterize human figures for action recognition. Comparing to silhouette-based features, the PHOG descriptor does not require extraction of human silhouettes or contours. Two state-space models, i.e.; Hidden Markov Model (HMM) and Conditional Random Field (CRF), are adopted to model the dynamic human movement. The proposed PHOG descriptor and the state-space models with respect to different parameters are tested using a standard dataset. We also testify the robustness of the method with respect to various unconstrained conditions and viewpoints. Promising experimental result demonstrates the effectiveness and robustness of our proposed method.

Layered dynamic probabilistic networks for spatio-temporal modelling

In applications such as tracking and surveillance in large spatial environments, there is a need for representing dynamic and noisy data and at the same time dealing with them at different levels of detail. In the spatial domain, there has been work dealing with these two issues separately, however, there is no existing common framework for dealing with both of them. In this paper, we propose a new representation framework called the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN), capable of handling uncertainty and representing spatial data at various levels of detail. The framework is thus particularly suited to applications in wide-area environments which are characterised by large region size, complex spatial layout and multiple sensors/cameras. For example, a building has three levels: entry/exit to the building, entry/exit between rooms and moving within rooms. To avoid the problem of a relatively large state space associated with a large spatial environment, the LDPN explicitly encodes the hierarchy of connected spatial locations, making it scalable to the size of the environment being modelled. There are three main advantages of the LDPN. First, the reduction in state space makes it suitable for dealing with wide area surveillance involving multiple sensors. Second, it offers a hierarchy of intervals for indexing temporal data. Lastly, the explicit representation of intermediate sub-goals allows for the extension of the framework to easily represent group interactions by allowing coupling between sub-goal layers of different individuals or objects. We describe an adaptation of the likelihood sampling inference scheme for the LDPN, and illustrate its use in a hypothetical surveillance scenario.

A probabilistic framework for tracking in wide-area environments

Surveillance in wide-area spatial environments is characterised by complex spatial layouts, large state space, and the use of multiple cameras/sensors. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. This requirement is particularly suited to the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN). In this paper, we propose the use of LDPN as the integrated framework for tracking in wide-area environments. We illustrate, with the help of a synthetic tracking scenario, how the parameters of the LDPN can be estimated from training data, and then used to draw predictions and answer queries about unseen tracks at various levels of detail.