Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Contact and communication with healthcare providers regarding influenza vaccination during the 2009-2010 H1N1 pandemic.

Objective. The existence of two vaccines seasonal and pandemic-created the potential for confusion and misinformation among consumers during the 2009-2010 vaccination season. We measured the frequency and nature of influenza vaccination communication between healthcare providers and adults for both seasonal and 2009 influenza A(H1N1) vaccination and quantified its association with uptake of the two vaccines.Methods. We analyzed data from 4040 U.S. adult members of a nationally representative online panel surveyed between March 4th and March 24th, 2010. We estimated prevalence rates and adjusted associations between vaccine uptake and vaccination-related communication between patients and healthcare providers using bivariate probit models.Results. 64.1% (95%-CI: 61.5%-66.6%) of adults did not receive any provider-issued influenza vaccination recommendation. Adults who received a provider-issued vaccination recommendation were 14.1 (95%-CI: -2.4 to 30.6) to 32.1 (95%-CI: 24.3-39.8) percentage points more likely to be vaccinated for influenza than adults without a provider recommendation, after adjusting for other characteristics associated with vaccination.Conclusions. Influenza vaccination communication between healthcare providers and adults was relatively uncommon during the 2009-2010 pandemic. Increased communication could significantly enhance influenza vaccination rates. (C) 2011 Elsevier Inc. All rights reserved.

Anatomical correlates for category-specific naming of objects and actions: A brain stimulation mapping study.

The production of object and action words can be dissociated in aphasics, yet their anatomical correlates have been difficult to distinguish in functional imaging studies. To investigate the extent to which the cortical neural networks underlying object- and action-naming processing overlap, we performed electrostimulation mapping (ESM), which is a neurosurgical mapping technique routinely used to examine language function during brain-tumor resections. Forty-one right-handed patients who had surgery for a brain tumor were asked to perform overt naming of object and action pictures under stimulation. Overall, 73 out of the 633 stimulated cortical sites (11.5%) were associated with stimulation-induced language interferences. These interference sites were very much localized (<1 cm(2) ), and showed substantial variability across individuals in their exact localization. Stimulation interfered with both object and action naming over 44 sites, whereas it specifically interfered with object naming over 19 sites and with action naming over 10 sites. Specific object-naming sites were mainly identified in Broca's area (Brodmann area 44/45) and the temporal cortex, whereas action-naming specific sites were mainly identified in the posterior midfrontal gyrus (Brodmann area 6/9) and Broca's area (P = 0.003 by the Fisher's exact test). The anatomical loci we emphasized are in line with a cortical distinction between objects and actions based on conceptual/semantic features, so the prefrontal/premotor cortex would preferentially support sensorimotor contingencies associated with actions, whereas the temporal cortex would preferentially underpin (functional) properties of objects. Hum Brain Mapp 35:429-443, 2014. © 2012 Wiley Periodicals, Inc.

On optimal learning schedules and the marginal value of cumulative cultural evolution.

The age-dependent choice between expressing individual learning (IL) or social learning (SL) affects cumulative cultural evolution. A learning schedule in which SL precedes IL is supportive of cumulative culture because the amount of nongenetically encoded adaptive information acquired by previous generations can be absorbed by an individual and augmented. Devoting time and energy to learning, however, reduces the resources available for other life-history components. Learning schedules and life history thus coevolve. Here, we analyze a model where individuals may have up to three distinct life stages: "infants" using IL or oblique SL, "juveniles" implementing IL or horizontal SL, and adults obtaining material resources with learned information. We study the dynamic allocation of IL and SL within life stages and how this coevolves with the length of the learning stages. Although no learning may be evolutionary stable, we find conditions where cumulative cultural evolution can be selected for. In that case, the evolutionary stable learning schedule causes individuals to use oblique SL during infancy and a mixture between IL and horizontal SL when juvenile. We also find that the selected pattern of oblique SL increases the amount of information in the population, but horizontal SL does not do so.

Learning ability and longevity: a symmetrical evolutionary trade-off in Drosophila.

Learning ability can be substantially improved by artificial selection in animals ranging from Drosophila to rats. Thus these species have not used their evolutionary potential with respect to learning ability, despite intuitively expected and experimentally demonstrated adaptive advantages of learning. This suggests that learning is costly, but this notion has rarely been tested. Here we report correlated responses of life-history traits to selection for improved learning in Drosophila melanogaster. Replicate populations selected for improved learning lived on average 15% shorter than the corresponding unselected control populations. They also showed a minor reduction in fecundity late in life and possibly a minor increase in dry adult mass. Selection for improved learning had no effect on egg-to-adult viability, development rate, or desiccation resistance. Because shortened longevity was the strongest correlated response to selection for improved learning, we also measured learning ability in another set of replicate populations that had been selected for extended longevity. In a classical olfactory conditioning assay, these long-lived flies showed an almost 40% reduction in learning ability early in life. This effect disappeared with age. Our results suggest a symmetrical evolutionary trade-off between learning ability and longevity in Drosophila.

Perception et communication du risque: du diabète à la maladie cardiovasculaire [Risk perception and communication: from diabetes to cardiovascular diseases].

Evidence-based medicine has enabled to approach disease in a more rational and scientific way. Clinical research has identified behaviours and risk factors that could cause disease often "silent" at the beginning, such as diabetes. Despite the clear impact of these evidences on public health, it seems that the individual risk perception level remains weak. To mention as well, the health professionals very often have a different views, which makes it difficult to communicate the risk with patients. In this article we describe the principles of risk perception, the diabetes related risk perception concerning cardiovascular complications, and suggest some practical strategies and tools which could improve risk communication in the everyday practice.

Changing healthcare: stakeholder perceptions of the burden of chronic disease and the value of teams, measurements and communication.

Canadian healthcare is changing. Over the course of the past decade, the Health Care in Canada Survey (HCIC) has annually measured the reactions of the public and professional stakeholders to many of these change forces. In HCIC 2008, for the first time, the public's perception of their health status and all stakeholders' views of the burden and effective management of chronic diseases were sought. Overall, Canadians perceive themselves as healthy, with 84% of adults reporting good-to-excellent health. However, good health decreased with age as the occurrence of chronic illness rose, from 12% in the age group 18-24 to 65% for the population =65 years. More than 70% of all stakeholders were strongly or somewhat supportive of the implementation of coordinated care, or disease management programs, to improve the care of patients with chronic illnesses. Concordant support was also expressed for key disease management components, including coordinated interventions to improve home, community and self-care; increased wellness promotion; and increased use of clinical measurements and feedback to all stakeholders. However, there were also important areas of non-concordance. For example, the public and doctors consistently expressed less support than other stakeholders for the value of team care, including the use of non-physician professionals to provide patient care; increased patient involvement in decision-making; and the use of electronic health records to facilitate communication. The actual participation in disease management programs averaged 34% for professionals and 25% for the public. We conclude that chronic diseases are common, age-related and burdensome in Canada. Disease management or coordinated intervention often delivered by teams is also relatively common, despite its less-than-universal acceptance by all stakeholders. Further insights are needed, particularly into the variable perceptions of the value and efficacy of team-delivered healthcare and its important components.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium.

Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.