Continuous correlated beta processes

In this paper we consider a (possibly continuous) space of Bernoulli experiments. We assume that the Bernoulli distributions are correlated. All evidence data comes in the form of successful or failed experiments at different points. Current state-ofthe-art methods for expressing a distribution over a continuum of Bernoulli distributions use logistic Gaussian processes or Gaussian copula processes. However, both of these require computationally expensive matrix operations (cubic in the general case). We introduce a more intuitive approach, directly correlating beta distributions by sharing evidence between them according to a kernel function, an approach which has linear time complexity. The approach can easily be extended to multiple outcomes, giving a continuous correlated Dirichlet process, and can be used for both classification and learning the actual probabilities of the Bernoulli distributions. We show results for a number of data sets, as well as a case-study where a mixture of continuous beta processes is used as part of an automated stroke rehabilitation system.

Paleoeskimo Demography on Western Victoria Island, Arctic Canada: Implications for Social Organization and Longhouse Development

Paleoeskimo populations on western Victoria Island reached maximum levels in Early Pre-Dorset time and declined abruptly shortly after 3800 14C years BP. The largest subsequent recovery occurred during Dorset time, particularly during Late Dorset, about 1500 to 600 BP. Early Pre-Dorset settlement patterns were similar to those documented for the same period and culture elsewhere in Arctic Canada, with dispersed nuclear families and small extended families occupying the region for most of the year, but with annual aggregations producing sites with 15 or more dwellings. After 3800 BP, large Pre-Dorset aggregation sites are absent. Dorset settlement patterns are dominated by multi-family longhouse – hearth row aggregation sites and by sites with two to four dwellings. Possibly the Dorset were living mainly in snow dwellings on the sea ice during cold seasons, with longhouses and hearth-row sets representing coming-ashore aggregations. Architectural aspects of longhouses and hearth rows indicate a common purpose behind their construction and use throughout the region and apparently throughout Arctic Canada, but their place and time of origin remain obscure. Radiocarbon dates place most longhouses and hearth rows on western Victoria Island in Late Dorset time, as elsewhere, but some dates indicate that these structures were being used in the western Canadian Arctic by Middle Dorset time. The latest Dorset radiocarbon dates from the region overlap with the earliest of the more reliable dates on Thule houses.Les populations paléoesquimaudes de l’ouest de l’île Victoria ont atteint leur summum à l’époque pré-Dorset puis ont chuté rapidement peu après 3800 années radiocarbones BP. Par la suite, la plus grande reprise s’est produite à l’époque du Dorset, plus particulièrement vers la fin de celui-ci, soit entre 1500 et 600 BP. Les types de peuplements du début du Dorset étaient semblables à ceux documentés pour la même période et pour la même culture ailleurs dans l’Arctique canadien, c’està- dire qu’il y avait des familles nucléaires dispersées et des petites familles étendues qui occupaient la région pendant la plus grande partie de l’année, ainsi que des agrégations annuelles composées de 15 habitations ou plus. Après 3800 BP, il y a absence de grandes agrégations telles qu’au pré-Dorset. Pendant le Dorset, les types de peuplements sont dominés par des maisons longues multifamiliales et des rangées de foyers, ainsi que par des sites de deux à quatre habitations. Il est possible qu’à l’époque du Dorset, les occupants vivaient surtout dans des habitations en neige placées sur la glace de mer en saison froide, tandis que les maisons longues et les rangées de foyers étaient installées sur la terre. Les caractéristiques architecturales des maisons longues et des rangées de foyers indiquent que leur construction et leur utilisation avaient une raison d’être commune au sein de la région et apparemment dans l’Arctique canadien, mais leur emplacement et le moment de leur origine demeurent obscurs. D’après la datation au radiocarbone, la plupart des maisons longues et des rangées de foyers de l’ouest de l’île Victoria remontent au Dorset tardif, comme ailleurs, mais certaines dates indiquent que ces structures étaient utilisées dans l’ouest de l’Arctique canadien vers le Dorset moyen. La datation au radiocarbone du Dorset tardif effectuée dans la région chevauche la plus ancienne des dates les plus fiables établies relativement aux habitations de Thulé

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

Structure Learning in Human Sequential Decision-Making

Studies of sequential decision-making in humans frequently find suboptimal performance relative to an ideal actor that has perfect knowledge of the model of how rewards and events are generated in the environment. Rather than being suboptimal, we argue that the learning problem humans face is more complex, in that it also involves learning the structure of reward generation in the environment. We formulate the problem of structure learning in sequential decision tasks using Bayesian reinforcement learning, and show that learning the generative model for rewards qualitatively changes the behavior of an optimal learning agent. To test whether people exhibit structure learning, we performed experiments involving a mixture of one-armed and two-armed bandit reward models, where structure learning produces many of the qualitative behaviors deemed suboptimal in previous studies. Our results demonstrate humans can perform structure learning in a near-optimal manner

On the Regression and Assimilation for Air Quality Mapping Using Dense Low-Cost WSN

International audienceThe use of low-cost Wireless Sensor Networks (WSNs) for air quality monitoring has recently attracted a great deal of interest. Indeed, the cost-effectiveness of emerging sensors and their small size allow for dense deployments and hence improve the spatial granularity. However, these sensors offer a low accuracy and their measurement errors may be significant due to the underlying sensing technologies. The main aim of this work is to reconsider and compare some regression approaches to assimilation ones while taking into account the intrinsic characteristics of dense deployment of low cost WSN for air quality monitoring (high density, numerical model errors and sensing errors). For that, we propose a general framework that allows the comparison of different strategies based on numerical simulations and an adequate estimation of the simulation error covariances as well as the sensing errors covariances. While considering the case of Lyon city and a widely used numerical model, we characterize the simulation errors, conduct extensive simulations and compare several regression and assimilation approaches. The results show that from a given sensing error threshold, regression methods present an optimal sensor density from which the mapping quality decreases. Results also show that the Random Forest method is often the best regression approach but still less efficient than the BLUE assimilation approach when using adequate correction parameters

The effect of hot days on occupational heat stress in the manufacturing industry: implications for workers' well-being and productivity

Climate change is expected to exacerbate heat stress at the workplace in temperate regions, such as Slovenia. It is therefore of paramount importance to study present and future summer heat conditions and analyze the impact of heat on workers. A set of climate indices based on summer mean (Tmean) and maximum (Tmax) air temperatures, such as the number of hot days (HD: Tmax above 30 °C), and Wet Bulb Globe Temperature (WBGT) were used to account for heat conditions in Slovenia at six locations in the period 1981–2010. Observed trends (1961–2011) of Tmean and Tmax in July were positive, being larger in the eastern part of the country. Climate change projections showed an increase up to 4.5 °C for mean temperature and 35 days for HD by the end of the twenty-first century under the high emission scenario. The increase in WBGT was smaller, although sufficiently high to increase the frequency of days with a high risk of heat stress up to an average of a third of the summer days. A case study performed at a Slovenian automobile parts manufacturing plant revealed non-optimal working conditions during summer 2016 (WBGT mainly between 20 and 25 °C). A survey conducted on 400 workers revealed that 96% perceived the temperature conditions as unsuitable, and 56% experienced headaches and fatigue. Given these conditions and climate change projections, the escalating problem of heat is worrisome. The European Commission initiated a program of research within the Horizon 2020 program to develop a heat warning system for European workers and employers, which will incorporate case-specific solutions to mitigate heat stress.The work was supported by the European Union Horizon 2020 Research and Innovation Action (Project number 668786: HEATSHIELD)

The process of recovery of people with mental illness: The perspectives of patients, family members and care providers: Part 1

<p>Abstract</p> <p>Background</p> <p>It is a qualitative design study that examines points of divergence and convergence in the perspectives on recovery of 36 participants or 12 triads. Each triad comprising a patient, a family member/friend, a care provider and documents the procedural, analytic of triangulating perspectives as a means of understanding the recovery process which is illustrated by four case studies. Variations are considered as they relate to individual characteristics, type of participant (patient, family, member/friend and care provider), and mental illness. This paper which is part of a larger study and is based on a qualitative research design documents the process of recovery of people with mental illness: Developing a Model of Recovery in Mental Health: A middle range theory.</p> <p><b>Methods</b></p> <p>Data were collected in field notes through semi-structured interviews based on three interview guides (one for patients, one for family members/friends, and one for caregivers). Cross analysis and triangulation methods were used to analyse the areas of convergence and divergence on the recovery process of all triads.</p> <p>Results</p> <p>In general, with the 36 participants united in 12 triads, two themes emerge from the cross-analysis process or triangulation of data sources (12 triads analysis in 12 cases studies). Two themes emerge from the analysis process of the content of 36 interviews with participants: (1) <it>Revealing dynamic context</it>, situating patients in their dynamic context; and (2) <it>Relationship issues in a recovery process</it>, furthering our understanding of such issues. We provide four case studies examples (among 12 cases studies) to illustrate the variations in the way recovery is perceived, interpreted and expressed in relation to the different contexts of interaction.</p> <p>Conclusion</p> <p>The perspectives of the three participants (patients, family members/friends and care providers) suggest that recovery depends on constructing meaning around mental illness experiences and that the process is based on each person's dynamic context (e.g., social network, relationship), life experiences and other social determinants (e.g., symptoms, environment). The findings of this study add to existing knowledge about the determinants of the recovery of persons suffering with a mental illness and significant other utilizing public mental health services in Montreal, Canada.</p

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Global assessment of marine plastic exposure risk for oceanic birds

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR&nbsp;=&nbsp;2.05, 95%CI&nbsp;=&nbsp;1.39–3.02, p&nbsp;&lt;&nbsp;0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR&nbsp;=&nbsp;0.42, 95%CI&nbsp;=&nbsp;0.18–0.99, p&nbsp;=&nbsp;0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon