Australian new graduate experiences during their transition program in a rural/regional acute care setting

The transition process from student to Registered Nurse has been recognised as an important yet challenging time for newly graduated nurses. Knowledge about this experience from the nurse’s perspective, particularly in a rural setting, is limited. This paper reports the findings of a qualitative study of the experiences of newly graduated nurses working in a rural acute care facility in New South Wales. The study examined, from the perspective of the new nurse, the orientation and support which can help to facilitate the transition from student to registered nurse. Four themes emerged which were being supported, being challenged, reflections on being a new graduate, and reflections on a rural new graduate program. These findings contribute to what is know about the transition of new graduates in a rural facility and have implications for program improvements, specifically within the rural acute care environment. The findings are also relevant to students considering rural employment on graduation and for the recruitment and retention of New Graduate Registered Nurses in rural areas

Entropy on Spin Factors

Recently it has been demonstrated that the Shannon entropy or the von Neuman entropy are the only entropy functions that generate a local Bregman divergences as long as the state space has rank 3 or higher. In this paper we will study the properties of Bregman divergences for convex bodies of rank 2. The two most important convex bodies of rank 2 can be identified with the bit and the qubit. We demonstrate that if a convex body of rank 2 has a Bregman divergence that satisfies sufficiency then the convex body is spectral and if the Bregman divergence is monotone then the convex body has the shape of a ball. A ball can be represented as the state space of a spin factor, which is the most simple type of Jordan algebra. We also study the existence of recovery maps for Bregman divergences on spin factors. In general the convex bodies of rank 2 appear as faces of state spaces of higher rank. Therefore our results give strong restrictions on which convex bodies could be the state space of a physical system with a well-behaved entropy function.Comment: 30 pages, 6 figure

Experiences of mistreatment among medical students in a University in south west Nigeria

Objective: This study was conducted to assess the experiences of mistreatment and harassment among final-year clinical students in a Nigerian medical school.Materials and Methods: A self-administered questionnaire was used to obtain information on the various forms of mistreatment experienced by 269 students in the 2007 and 2008 graduating classes of a medical school in Nigeria.Results: Almost all the respondents (98.5%) had experienced one or more forms of mistreatment during their training. The commonest forms experienced by the students were being shouted at (92.6%), public humiliation or belittlement (87.4%), negative or disparaging remarks about their academic performance (71.4%), being assigned tasks as punishment (67.7%), and someone else taking credit for work done by the student (49.4%). Religious or age discrimination was reported by 34.2%, sexual harassment and other forms of gender-based mistreatment by 33.8%, and threats of harm by 26.4%. These incidents were mainly perpetrated by physicians and occurred mostly during surgical rotations. The effects included strained relationships with the perpetrators, reduced self-confidence and depression.Conclusion: Most medical students experienced verbal forms of mistreatment and abuse during their training. Appropriate strategies for the prevention and reduction of medical student mistreatment should be developed

Differentiable Game Mechanics

Deep learning is built on the foundational guarantee that gradient descent on an objective function converges to local minima. Unfortunately, this guarantee fails in settings, such as generative adversarial nets, that exhibit multiple interacting losses. The behavior of gradient-based methods in games is not well understood -- and is becoming increasingly important as adversarial and multi-objective architectures proliferate. In this paper, we develop new tools to understand and control the dynamics in n-player differentiable games. The key result is to decompose the game Jacobian into two components. The first, symmetric component, is related to potential games, which reduce to gradient descent on an implicit function. The second, antisymmetric component, relates to Hamiltonian games, a new class of games that obey a conservation law akin to conservation laws in classical mechanical systems. The decomposition motivates Symplectic Gradient Adjustment (SGA), a new algorithm for finding stable fixed points in differentiable games. Basic experiments show SGA is competitive with recently proposed algorithms for finding stable fixed points in GANs -- while at the same time being applicable to, and having guarantees in, much more general cases.Comment: JMLR 2019, journal version of arXiv:1802.0564

Quantum algorithm for robust optimization via stochastic-gradient online learning

Optimization theory has been widely studied in academia and finds a large variety of applications in industry. The different optimization models in their discrete and/or continuous settings has catered to a rich source of research problems. Robust convex optimization is a branch of optimization theory in which the variables or parameters involved have a certain level of uncertainty. In this work, we consider the online robust optimization meta-algorithm by Ben-Tal et al. and show that for a large range of stochastic subgradients, this algorithm has the same guarantee as the original non-stochastic version. We develop a quantum version of this algorithm and show that an at most quadratic improvement in terms of the dimension can be achieved. The speedup is due to the use of quantum state preparation, quantum norm estimation, and quantum multi-sampling. We apply our quantum meta-algorithm to examples such as robust linear programs and robust semidefinite programs and give applications of these robust optimization problems in finance and engineering.Comment: 21 page