Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time.

Classifying human actions using an incomplete real-time pose skeleton

Currently, most human action recognition systems are trained with feature sets that have no missing data. Unfortunately, the use of human pose estimation models to provide more descriptive features also entails an increased sensitivity to occlusions, meaning that incomplete feature information will be unavoidable for realistic scenarios. To address this, our approach is to shift the responsibility for dealing with occluded pose data away from the pose estimator and onto the action classifier. This allows the use of a simple, real-time pose estimation (stick-figure) that does not estimate the positions of limbs it cannot find quickly. The system tracks people via background subtraction and extracts the (possibly incomplete) pose skeleton from their silhouette. Hidden Markov Models modified to handle missing data are then used to successfully classify several human actions using the incomplete pose features.

Fire management on private conservation lands : knowledge, perceptions and actions of landholders in eastern Australia

Fire is an important natural disturbance process within the Australian landscape, but the complex and hazardous nature of fire creates a conservation management dilemma. For landholders of private conservation lands, management for conservation of biodiversity and risk reduction is complicated. Private conservation landholders in eastern Australia directed far less effort towards fire management than other conservation management actions, despite clearly acknowledging the risk and associated responsibilities of fire management on their lands. Nonetheless, landholders did undertake actions to reduce fuel hazards and prepare for wildfire events on their land. Despite the established role and benefits of fire to many ecosystems in the region, landholder understanding of the ecological role of fire was generally poor. Few landholders were aware of ecologically appropriate fire regimes for the vegetation types on their property, and few undertook fire management actions to achieve ecological outcomes. Site-specific obstacles, lack of fire management knowledge and experience, and legal and containment concerns contributed to the low level of fire management observed. There is a need for property-specific fire management planning across all private conservation lands, to further integrate ecological fire requirements into biodiversity management, and prioritise actions that aim to improve conservation outcomes while safeguarding life and property.

cGMP phosphodiesterase inhibition improves the vascular and metabolic actions of insulin in skeletal muscle

There is considerable support for the concept that insulin-mediated increases in microvascular blood flow to muscle impact significantly on muscle glucose uptake. Since the microvascular blood flow increases with insulin have been shown to be nitric oxide-dependent inhibition of cGMP-degrading phosphodiesterases (cGMP PDEs) is predicted to enhance insulin-mediated increases in microvascular perfusion and muscle glucose uptake. Therefore, we studied the effects of the pan-cGMP PDE inhibitor zaprinast on the metabolic and vascular actions of insulin in muscle. Hyperinsulinemic euglycemic clamps (3 mU·min−1·kg−1) were performed in anesthetized rats and changes in microvascular blood flow assessed from rates of 1-methylxanthine metabolism across the muscle bed by capillary xanthine oxidase in response to insulin and zaprinast. We also characterized cGMP PDE isoform expression in muscle by real-time PCR and immunostaining of frozen muscle sections. Zaprinast enhanced insulin-mediated microvascular perfusion by 29% and muscle glucose uptake by 89%, while whole body glucose infusion rate during insulin infusion was increased by 33% at 2 h. PDE2, -9, and -10 were the major isoforms expressed at the mRNA level in muscle, while PDE1B, -9A, -10A, and -11A proteins were expressed in blood vessels. Acute administration of the cGMP PDE inhibitor zaprinast enhances muscle microvascular blood flow and glucose uptake response to insulin. The expression of a number of cGMP PDE isoforms in skeletal muscle suggests that targeting specific cGMP PDE isoforms may provide a promising avenue for development of a novel class of therapeutics for enhancing muscle insulin sensitivity.

Representative actions, proof of fact and author identification in copyright law: a history and a concern

Recent judgments in Australia have called for author identification in order that copyright subsistence may be established. There is a risk that such calls will be taken too literally, to the detriment of author privacy. This article considers the legal mechanisms by which author identity has historically been shielded from disclosure, without the operation of the copyright system being impaired. It expresses the hope that those who are responsible for developing copyright law will be mindful of the concern for author privacy which has long been part of copyright discourse.

Monitoring policy and actions on food environments: rationale and outline of the INFORMAS policy engagement and communication strategies

 The International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support (INFORMAS) proposes to collect performance indicators on food policies, actions and environments related to obesity and non-communicable diseases. This paper reviews existing communications strategies used for performance indicators and proposes the approach to be taken for INFORMAS. Twenty-seven scoring and rating tools were identified in various fields of public health including alcohol, tobacco, physical activity, infant feeding and food environments. These were compared based on the types of indicators used and how they were quantified, scoring methods, presentation and the communication and reporting strategies used. There are several implications of these analyses for INFORMAS: the ratings/benchmarking approach is very commonly used, presumably because it is an effective way to communicate progress and stimulate action, although this has not been formally evaluated; the tools used must be trustworthy, pragmatic and policy-relevant; multiple channels of communication will be needed; communications need to be tailored and targeted to decision-makers; data and methods should be freely accessible. The proposed communications strategy for INFORMAS has been built around these lessons to ensure that INFORMAS's outputs have the greatest chance of being used to improve food environments.