National survey and analysis of barriers to the utilisation of the 2005 Mental Capacity Act by people with bipolar disorder in England and Wales

Background: The Mental Capacity Act (2005) (MCA) provides a legal framework for advance planning for both health and welfare in England and Wales for people if they lose mental capacity e.g. through mania or severe depression. Aims: To determine the proportion of people with bipolar disorder (BD) who utilise advance planning, their experience of using it and barriers to its implementation. Methods: National survey of people with clinical diagnosis of BD of their knowledge, use and experience of the MCA. Thematically analysed qualitative interviews with maximum variance sample of people with BD. Results: 544 respondents with BD participated in the survey; 18 in the qualitative study. 403 (74.1%) believed making plans about their personal welfare if they lost capacity to be very important. 199 (36.6%) participants knew about the MCA. 54 (10%), 62 (11%) and 21 (4%) participants made advanced decisions to refuse treatment, advance statements and lasting power of attorney respectively. Barriers included not understanding its different forms, unrealistic expectations and advance plans ignored by services. Conclusion: In BD the demand for advance plans about welfare with loss of capacity was high but utilisation of the MCA was low with barriers at service user, clinician and organisation levels

Skin Cancer:Epidemiology, Disease Burden, Pathophysiology, Diagnosis, and Therapeutic Approaches

Skin cancer, including both melanoma and non-melanoma, is the most common type of malignancy in the Caucasian population. Firstly, we review the evidence for the observed increase in the incidence of skin cancer over recent decades, and investigate whether this is a true increase or an artefact of greater screening and over-diagnosis. Prevention strategies are also discussed. Secondly, we discuss the complexities and challenges encountered when diagnosing and developing treatment strategies for skin cancer. Key case studies are presented that highlight the practic challenges of choosing the most appropriate treatment for patients with skin cancer. Thirdly, we consider the potential risks and benefits of increased sun exposure. However, this is discussed in terms of the possibility that the avoidance of sun exposure in order to reduce the risk of skin cancer may be less important than the reduction in all-cause mortality as a result of the potential benefits of increased exposure to the sun. Finally, we consider common questions on human papillomavirus infection

A statistical approach to assessing neural network robustness

We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification framework in that when the property can be violated, it provides an informative notion of how robust the network is, rather than just the conventional assertion that the network is not verifiable. Furthermore, it provides an ability to scale to larger networks than formal verification approaches. Though the framework still provides a formal guarantee of satisfiability whenever it successfully finds one or more violations, these advantages do come at the cost of only providing a statistical estimate of unsatisfiability whenever no violation is found. Key to the practical success of our approach is an adaptation of multi-level splitting, a Monte Carlo approach for estimating the probability of rare events, to our statistical robustness framework. We demonstrate that our approach is able to emulate formal verification procedures on benchmark problems, while scaling to larger networks and providing reliable additional information in the form of accurate estimates of the violation probability

Efficient linear programming for dense CRFs

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent. We show that each block of variables can be efficiently optimized. Specifically, for one block, the problem decomposes into significantly smaller subproblems, each of which is defined over a single pixel. For the other block, the problem is optimized via conditional gradient descent. This has two advantages: 1) the conditional gradient can be computed in a time linear in the number of pixels and labels; and 2) the optimal step size can be computed analytically. Our experiments on standard datasets provide compelling evidence that our approach outperforms all existing baselines including the previous LP based approach for dense CRFs

Exascale deep learning for climate analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively