Language Model Co-occurrence Linking for Interleaved Activity Discovery

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from previous work in that it explicitly aims to deal with interleaving (switching back and forth between between activities) in a principled manner, by utilising the long-term memory capabilities of a recurrent neural network cell. We present our approach and test it on a realistic dataset to evaluate its performance. Our results show the viability of the approach and that it shows promise for further investigation. We believe this is a useful direction to consider in accounting for the continually changing nature of behaviours

Incidence of anogenital warts in Germany: a population-based cohort study

<p>Abstract</p> <p>Background</p> <p>Human papilloma virus (HPV) types 6 and 11 account for 90 percent of anogenital warts (AGW). Assessment of a potential reduction of the incidence of AGW following introduction of HPV vaccines requires population-based incidence rates. The aim of this study was to estimate incidence rates of AGW in Germany, stratified by age, sex, and region. Additionally, the medical practitioner (gynaecologist, dermatologist, urologist etc.) who made the initial diagnosis of AGW was assessed.</p> <p>Methods</p> <p>Retrospective cohort study in a population aged 10 to 79 years in a population-based healthcare insurance database. The database included more than 14 million insurance members from all over Germany during the years 2004-2006. A case of AGW was considered incident if a disease-free period of twelve months preceded the diagnosis. To assess regional variation, analyses were performed by federal state.</p> <p>Results</p> <p>The estimated incidence rate was 169.5/100,000 person-years for the German population aged 10 to 79 years. Most cases occurred in the 15 to 40 years age group. The incidence rate was higher and showed a peak at younger ages in females than in males. The highest incidence rates for both sexes were observed in the city-states Berlin, Hamburg and Bremen. In females, initial diagnosis of AGW was most frequently made by a gynaecologist (71.7%), whereas in males, AGW were most frequently diagnosed by a dermatologist (44.8%) or urologist (25.1%).</p> <p>Conclusions</p> <p>Incidence of AGW in Germany is comparable with findings for other countries. As expected, most cases occurred in the younger age groups. The frequency of diagnoses of AGW differs between sexes and women and men receive treatment by doctors of different specialties.</p

Global distribution of the sickle cell gene and geographical confirmation of the malaria hypothesis

It has been 100 years since the first report of sickle haemoglobin (HbS). More than 50 years ago, it was suggested that the gene responsible for this disorder could reach high frequencies because of resistance conferred against malaria by the heterozygous carrier state. This traditional example of balancing selection is known as the 'malaria hypothesis'. However, the geographical relationship between the transmission intensity of malaria and associated HbS burden has never been formally investigated on a global scale. Here, we use a comprehensive data assembly of HbS allele frequencies to generate the first evidence-based map of the worldwide distribution of the gene in a Bayesian geostatistical framework. We compare this map with the pre-intervention distribution of malaria endemicity, using a novel geostatistical area-mean comparison. We find geographical support for the malaria hypothesis globally; the relationship is relatively strong in Africa but cannot be resolved in the Americas or in Asia

A ROC analysis-based classification method for landslide susceptibility maps

[EN] A landslide susceptibility map is a crucial tool for landuse spatial planning and management in mountainous areas. An essential issue in such maps is the determination of susceptibility thresholds. To this end, the map is zoned into a limited number of classes. Adopting one classification system or another will not only affect the map's readability and final appearance, but most importantly, it may affect the decision-making tasks required for effective land management. The present study compares and evaluates the reliability of some of the most commonly used classification methods, applied to a susceptibility map produced for the area of La Marina (Alicante, Spain). A new classification method based on ROC analysis is proposed, which extracts all the useful information from the initial dataset (terrain characteristics and landslide inventory) and includes, for the first time, the concept of misclassification costs. This process yields a more objective differentiation of susceptibility levels that relies less on the intrinsic structure of the terrain characteristics. The results reveal a considerable difference between the classification methods used to define the most susceptible zones (in over 20% of the surface) and highlight the need to establish a standard method for producing classified susceptibility maps. The method proposed in the study is particularly notable for its consistency, stability and homogeneity, and may mark the starting point for consensus on a generalisable classification method.Cantarino-Martí, I.; Carrión Carmona, MÁ.; Goerlich-Gisbert, F.; Martínez Ibáñez, V. (2018). A ROC analysis-based classification method for landslide susceptibility maps. Landslides. 1-18. doi:10.1007/s10346-018-1063-4S118Armstrong MP, Xiao N, Bennett DA (2003) Using genetic algorithms to create multicriteria class intervals for choropleth maps. 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The index of rural access: an innovative integrated approach for measuring primary care access

<p>Abstract</p> <p>Background</p> <p>The problem of access to health care is of growing concern for rural and remote populations. Many Australian rural health funding programs currently use simplistic rurality or remoteness classifications as proxy measures of access. This paper outlines the development of an alternative method for the measurement of access to primary care, based on combining the three key access elements of spatial accessibility (availability and proximity), population health needs and mobility.</p> <p>Methods</p> <p>The recently developed two-step floating catchment area (2SFCA) method provides a basis for measuring primary care access in rural populations. In this paper, a number of improvements are added to the 2SFCA method in order to overcome limitations associated with its current restriction to a single catchment size and the omission of any distance decay function. Additionally, small-area measures for the two additional elements, health needs and mobility are developed. By utilising this improved 2SFCA method, the three access elements are integrated into a single measure of access. This index has been developed within the state of Victoria, Australia.</p> <p>Results</p> <p>The resultant index, the Index of Rural Access, provides a more sensitive and appropriate measure of access compared to existing classifications which currently underpin policy measures designed to overcome problems of limited access to health services. The most powerful aspect of this new index is its ability to identify access differences within rural populations at a much finer geographical scale. This index highlights that many rural areas of Victoria have been incorrectly classified by existing measures as homogenous in regards to their access.</p> <p>Conclusion</p> <p>The Index of Rural Access provides the first truly integrated index of access to primary care. This new index can be used to better target the distribution of limited government health care funding allocated to address problems of poor access to primary health care services in rural areas.</p

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value