Trends In The Utilization of Antiglaucoma Medication: An Analysis of Canadian Drug Insurance Claims

Objectives: To assess: i) utilization of generic anti-glaucoma drugs in Canada; ii) the impact of the 2010 Ontario Drug System Reform on generic anti-glaucoma drug usage in Ontario. Methods: Monthly drug insurance cost and claims from January 2001 to January 2013 were used as proxies for drug utilization. Evaluation of the impact of the 2010 reform was conducted using interrupted time series analysis with ARIMA models. Results: Generic antiglaucoma medication utilization increased in Ontario during Quarter 3, 2006. Increases in utilization across study provinces were observed in Quarter 4, 2011. The 2010 reform was not associated with changes in generic drug utilization. Conclusion: The results of the study demonstrated that introduction of new generic equivalents increases in the utilization of generics drugs. Lowering the price of generic medications did not lead to a change in the utilization. Alternative strategies should be implemented to increase generic drug use in glaucoma treatment

Striped spin liquid crystal ground state instability of kagome antiferromagnets

The Dirac spin liquid ground state of the spin 1/2 Heisenberg kagome antiferromagnet has potential instabilities[1-4]. This has been suggested as the reason why it is not strongly supported in large-scale numerical calculations[5]. However, previous attempts to observe these instabilities have failed. We report on the discovery of a projected BCS state with lower energy than the projected Dirac spin liquid state which provides new insight into the stability of the ground state of the kagome antiferromagnet. The new state has three remarkable features. First, it breaks both spatial symmetry in an unusual way that may leave spinons deconfined along one direction. Second, it breaks the U(1) gauge symmetry down to $Z_2$. Third, it has the spatial symmetry of a previously proposed "monopole" suggesting that it is an instability of the Dirac spin liquid. The state described herein also shares a remarkable similarity to the distortion of the kagome lattice observed at low Zn concentrations in Zn-Paratacamite suggesting it may already be realized in these materials.Comment: 4+ pages, 3 figure

Learning from Guided Play: Improving Exploration for Adversarial Imitation Learning with Simple Auxiliary Tasks

Adversarial imitation learning (AIL) has become a popular alternative to supervised imitation learning that reduces the distribution shift suffered by the latter. However, AIL requires effective exploration during an online reinforcement learning phase. In this work, we show that the standard, naive approach to exploration can manifest as a suboptimal local maximum if a policy learned with AIL sufficiently matches the expert distribution without fully learning the desired task. This can be particularly catastrophic for manipulation tasks, where the difference between an expert and a non-expert state-action pair is often subtle. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of multiple exploratory, auxiliary tasks in addition to a main task. The addition of these auxiliary tasks forces the agent to explore states and actions that standard AIL may learn to ignore. Additionally, this particular formulation allows for the reusability of expert data between main tasks. Our experimental results in a challenging multitask robotic manipulation domain indicate that LfGP significantly outperforms both AIL and behaviour cloning, while also being more expert sample efficient than these baselines. To explain this performance gap, we provide further analysis of a toy problem that highlights the coupling between a local maximum and poor exploration, and also visualize the differences between the learned models from AIL and LfGP.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'23), Detroit, MI, USA, Oct. 1-5, 2023. arXiv admin note: substantial text overlap with arXiv:2112.0893

Softening the Impact of Adjustment to Reform: The China Experience

This paper examines the structural adjustments induced as China moved from a planned economy that subsidized capital-intensive industry at the expense of agriculture to a nationally integrated market economy more fitting with China's underlying resource endowments. We argue that there were few losers in the process because of 1) a gradual implementation process that maintained transfers to the favored groups under the planned economy, such as urban industrial workers, while the market economy developed benefiting the non-favored groups, such as farmers; 2) high growth rates allowed a large portion of the economy to benefit from the overall reform process and bolstered the government's commitment to further reform; and 3) labor, the most important resource that farm households hold in China, was much less institutionally constrained than land and capital during the reform period, allowing rural workers to participate in the fast growing nonagricultural sector.Agricultural and Food Policy, Political Economy,

Molar Pregnancy in the Emergency Department

A 15-year-old female presented to the emergency department with complaints of vaginal bleeding. She was pale, anxious, cool and clammy with tachycardic, thready peripheral pulses and hemoglobin of 2.4g/dL. Her abdomen was gravid appearing, approximately early to mid-second trimester in size. Pelvic examination revealed 2 cm open cervical os with spontaneous discharge of blood, clots and a copious amount of champagne-colored grapelike spongy material. After 2L boluses of normal saline and two units of crossmatched blood, patient was transported to the operating room. Surgical pathology confirmed a complete hydatidiform mole

Something Old, Something New, Something Borrowed, Something Blue: A Marriage of Innovation in Nursing EBP and Digital Literacy Education

Background: Evidence-based practice (EBP) is the foundation of modern health services. It improves patient outcomes and quality of care by combining clinical expertise, patient values, and the best research evidence to guide health care decisions. The ability to find, evaluate and apply evidence is essential for EBP. However, preparing the future nursing workforce with the required knowledge and skills to do so can be a challenge. Objectives: At Murdoch University, we have integrated various digital tools with our personal learning platform to develop an interactive tutorial for final-year nursing students. The tutorial aims to improve skills in the areas of research, critical appraisal and digital literacy. Methods: Using the analogy of a marriage, this paper will present the case study of a collaborative project between the University Library and the College of Science, Health, Engineering & Education (the ‘wedding party’) to develop a self-paced, interactive online tutorial on database searching and systematic reviews, as applied in nursing practice. Four key elements went into planning this marriage: Something old: Camtasia (familiar to both Library and College) Something new: LibWizard (a new Library software acquisition) Something borrowed: PebblePad (managed by the College) Something blue: Digital badging (micro-credentialing) These elements were integrated into a single digital learning object, which was launched in late January 2019 (the ‘wedding’). Results: This innovative online tutorial was successful in engaging students and developing their digital and information literacy skills for evidence-based practice, and future improvements were also identified

Static Analysis for Efficient Affine Arithmetic on GPUs

Range arithmetic is a way of calculating with variables that hold ranges of real values. This ability to manage uncertainty during computation has many applications. Examples in graphics include rendering and surface modeling, and there are more general applications like global optimization and solving systems of nonlinear equations. This thesis focuses on affine arithmetic, one kind of range arithmetic. The main drawbacks of affine arithmetic are that it taxes processors with heavy use of floating point arithmetic and uses expensive sparse vectors to represent noise symbols. Stream processors like graphics processing units (GPUs) excel at intense computation, since they were originally designed for high throughput media applications. Heavy control flow and irregular data structures pose problems though, so the conventional implementation of affine arithmetic with dynamically managed sparse vectors runs slowly at best. The goal of this thesis is to map affine arithmetic efficiently onto GPUs by turning sparse vectors into shorter dense vectors at compile time using static analysis. In addition, we look at how to improve efficiency further during the static analysis using unique symbol condensation. We demonstrate our implementation and performance of the condensation on several graphics applications

Satellite Navigation for the Age of Autonomy

Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires a solution that supports safety-critical operation, centimeter positioning, and cyber-security for millions of users. To meet these demands, we propose a navigation service from Low Earth Orbiting (LEO) satellites which deliver precision in-part through faster motion, higher power signals for added robustness to interference, constellation autonomous integrity monitoring for integrity, and encryption / authentication for resistance to spoofing attacks. This paradigm is enabled by the 'New Space' movement, where highly capable satellites and components are now built on assembly lines and launch costs have decreased by more than tenfold. Such a ubiquitous positioning service enables a consistent and secure standard where trustworthy information can be validated and shared, extending the electronic horizon from sensor line of sight to an entire city. This enables the situational awareness needed for true safe operation to support autonomy at scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS

Heteroscedastic Uncertainty for Robust Generative Latent Dynamics

Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control. The problem has recently been studied from a generative perspective through latent dynamics: high-dimensional observations are embedded into a lower-dimensional space in which the dynamics can be learned. Despite some successes, latent dynamics models have not yet been applied to real-world robotic systems where learned representations must be robust to a variety of perceptual confounds and noise sources not seen during training. In this paper, we present a method to jointly learn a latent state representation and the associated dynamics that is amenable for long-term planning and closed-loop control under perceptually difficult conditions. As our main contribution, we describe how our representation is able to capture a notion of heteroscedastic or input-specific uncertainty at test time by detecting novel or out-of-distribution (OOD) inputs. We present results from prediction and control experiments on two image-based tasks: a simulated pendulum balancing task and a real-world robotic manipulator reaching task. We demonstrate that our model produces significantly more accurate predictions and exhibits improved control performance, compared to a model that assumes homoscedastic uncertainty only, in the presence of varying degrees of input degradation.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Intelligent Robots and Systems (IROS'20), Las Vegas, USA, October 25-29, 202