Likelihood of death among hospital inpatients in New Zealand: prevalent cohort study

Objectives: (1) To establish the likelihood of dying within 12 months for a cohort of hospital inpatients in New Zealand (NZ) on a fixed census date; (2) to identify associations between likelihood of death and key sociodemographic, diagnostic and service-related factors and (3) to compare results with, and extend findings of, a Scottish study undertaken for the same time period and census date. National databases of hospitalisations and death registrations were used, linked by unique health identifier. Participants: 6074 patients stayed overnight in NZ hospitals on the census date (10 April 2013), 40.8% of whom were aged ≥65 years; 54.4% were women; 69.1% of patients were NZ European; 15.3% were Maori; 7.6% were Pacific; 6.1% were Asian and 1.9% were ‘other’. Setting: All NZ hospitals. Results: 14.5% patients (n=878) had died within 12 months: 1.6% by 7 days; 4.5% by 30 days; 8.0% by 3 months and 10.9% by 6 months. In logistic regression models, the strongest predictors of death within 12 months were: age ≥80 years (OR=5.52(95% CI 4.31 to 7.07)); a history of cancer (OR=4.20(3.53 to 4.98)); being Māori (OR=1.62(1.25 to 2.10)) and being admitted to a medical specialty, compared with a surgical specialty (OR=3.16(2.66 to 3.76)). Conclusion: While hospitals are an important site of end of life care in NZ, their role is less significant than in Scotland, where 30% of an inpatient cohort recruited using similar methods and undertaken on the same census date had died within 12 months. One reason for this finding may be the extended role of residential long-term care facilities in end of life care provision in NZ

Reflectionless evanescent-wave amplification by two dielectric planar waveguides

Utilizing the underlying physics of evanescent wave amplification by a negative-refractive-index slab, it is shown that evanescent waves with specific spatial frequencies can also be amplified without any reflection simply by two dielectric planar waveguides. The simple configuration allows one to take advantage of the high resolution limit of a high-refractive-index material without contact with the object.Comment: 4 pages, 3 figures, v2: accepted by Optics Letters, v3: included the Erratum submitted to Optics Letter

End-to-End Navigation in Unknown Environments using Neural Networks

We investigate how a neural network can learn perception actions loops for navigation in unknown environments. Specifically, we consider how to learn to navigate in environments populated with cul-de-sacs that represent convex local minima that the robot could fall into instead of finding a set of feasible actions that take it to the goal. Traditional methods rely on maintaining a global map to solve the problem of over coming a long cul-de-sac. However, due to errors induced from local and global drift, it is highly challenging to maintain such a map for long periods of time. One way to mitigate this problem is by using learning techniques that do not rely on hand engineered map representations and instead output appropriate control policies directly from their sensory input. We first demonstrate that such a problem cannot be solved directly by deep reinforcement learning due to the sparse reward structure of the environment. Further, we demonstrate that deep supervised learning also cannot be used directly to solve this problem. We then investigate network models that offer a combination of reinforcement learning and supervised learning and highlight the significance of adding fully differentiable memory units to such networks. We evaluate our networks on their ability to generalize to new environments and show that adding memory to such networks offers huge jumps in performanceComment: Workshop on Learning Perception and Control for Autonomous Flight: Safety, Memory and Efficiency, Robotics Science and Systems 201