The Hessian Estimation Evolution Strategy

We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism

Associations between preschool attendance and developmental impairments in pre-school children in a six-year retrospective survey

BACKGROUND: Many school-aged children suffer physical and mental impairments which can adversely affect their development and result in significant morbidity. A high proportion of children in western countries attend pre-school, and it is likely that the preschool environment influences the prevalence and severity of these impairments. Currently there is insufficient data available on the prevalence of these impairments and their causal associations. The influence that location of a pre-school and the duration of preschool attendance have on the prevalence of these impairments is not known. METHODS: In a retrospective survey spanning six years (1997–2002) we reviewed the records of 6,230 preschool children who had undergone routine school entry assessments. These children had been assessed utilising a modified manual of the "Bavarian Model" for school entry examinations. This model outlines specific criteria for impairments of motor, cognitive, behavioural and psychosocial functioning. Prevalence rates for physical and behavioural impairments were based on the results of these assessments. The relationship between the prevalence of impairments and the duration of preschool attendance and the location of the preschool attended was estimated utilizing logistic regression models. RESULTS: We found that 20.7% of children met the criteria for at least one type of impairment. Highest prevalence rates (11.5%) were seen for speech impairments and lowest (3.5%) for arithmetic impairments. Boys were disproportionately over represented, with 25.5% meeting the criteria for impairment, compared to 13.0% for girls. Children who had attended preschool for less than one year demonstrated higher rates of impairment (up to 19.1% for difficulties with memory, concentration or perseverance) compared to those who had attended for a longer duration (up to 11.6% for difficulties with pronouncation). Children attending preschool in an urban location had slightly elevated rates of impairment (up to 12.7%), compared to their rural counterparts (up to 11.1%). CONCLUSION: Our results demonstrate that there are high prevalence rates for physical and mental impairments among preschool children. Furthermore, children without preschool experience are a risk group for struggling with educational successes. The associations between the duration of preschool attendance and location of preschool attended and rates of impairment need replication and further exploration. Larger prospective studies are needed to examine if these relationships are causal and may therefore lend themselves to specific intervention strategies

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process

Civil conflict and sleeping sickness in Africa in general and Uganda in particular

Conflict and war have long been recognized as determinants of infectious disease risk. Re-emergence of epidemic sleeping sickness in sub-Saharan Africa since the 1970s has coincided with extensive civil conflict in affected regions. Sleeping sickness incidence has placed increasing pressure on the health resources of countries already burdened by malaria, HIV/AIDS, and tuberculosis. In areas of Sudan, the Democratic Republic of the Congo, and Angola, sleeping sickness occurs in epidemic proportions, and is the first or second greatest cause of mortality in some areas, ahead of HIV/AIDS. In Uganda, there is evidence of increasing spread and establishment of new foci in central districts. Conflict is an important determinant of sleeping sickness outbreaks, and has contributed to disease resurgence. This paper presents a review and characterization of the processes by which conflict has contributed to the occurrence of sleeping sickness in Africa. Conflict contributes to disease risk by affecting the transmission potential of sleeping sickness via economic impacts, degradation of health systems and services, internal displacement of populations, regional insecurity, and reduced access for humanitarian support. Particular focus is given to the case of sleeping sickness in south-eastern Uganda, where incidence increase is expected to continue. Disease intervention is constrained in regions with high insecurity; in these areas, political stabilization, localized deployment of health resources, increased administrative integration and national capacity are required to mitigate incidence. Conflict-related variables should be explicitly integrated into risk mapping and prioritization of targeted sleeping sickness research and mitigation initiatives