Supervised learning for kinetic consensus control

In this paper, how to successfully and efficiently condition a target population of agents towards consensus is discussed. To overcome the curse of dimensionality, the mean field formulation of the consensus control problem is considered. Although such formulation is designed to be independent of the number of agents, it is feasible to solve only for moderate intrinsic dimensions of the agents space. For this reason, the solution is approached by means of a Boltzmann procedure, i.e. quasi-invariant limit of controlled binary interactions as approximation of the mean field PDE. The need for an efficient solver for the binary interaction control problem motivates the use of a supervised learning approach to encode a binary feedback map to be sampled at a very high rate. A gradient augmented feedforward neural network for the Value function of the binary control problem is considered and compared with direct approximation of the feedback law

Gradient-augmented supervised learning of optimal feedback laws using state-dependent Riccati equations

A supervised learning approach for the solution of large-scale nonlinear stabilization problems is presented. A stabilizing feedback law is trained from a dataset generated from State-dependent Riccati Equation solvers. The training phase is enriched by the use of gradient information in the loss function, which is weighted through the use of hyperparameters. High-dimensional nonlinear stabilization tests demonstrate that real-time sequential large-scale Algebraic Riccati Equation solvers can be substituted by a suitably trained feedforward neural network

Aerial tele-manipulation with passive tool via parallel position/force control

This paper addresses the problem of unilateral contact interaction by an under-actuated quadrotor UAV equipped with a passive tool in a bilateral teleoperation scheme. To solve the challenging control problem of force regulation in contact interaction while maintaining flight stability and keeping the contact, we use a parallel position/force control method, commensurate to the system dynamics and constraints in which using the compliant structure of the end-effector the rotational degrees of freedom are also utilized to attain a broader range of feasible forces. In a bilateral teleoperation framework, the proposed control method regulates the aerial manipulator position in free flight and the applied force in contact interaction. On the master side, the human operator is provided with force haptic feedback to enhance his/her situational awareness. The validity of the theory and efficacy of the solution are shown by experimental results. This control architecture, integrated with a suitable perception/localization pipeline, could be used to perform outdoor aerial teleoperation tasks in hazardous and/or remote sites of interest

Acute anterior myocardial infarction: Streptokinase prevents ventricular thrombosis independently of its effect on infarct size

Left ventricular thrombosis (LVT) is a frequent complication after acute anterior myocardial infarction (AMI). The purpose of this study is to evaluate whether streptokinase (SK) therapy prevents LVT, and whether this effect is due to the preservation of left ventricular function or to the fibrinolytic action of the drug. Sixty-five patients who underwent a left ventricular angiography within 2 months after a first AMI were studied. Twenty-eight patients (SK group) received SK 1,500,000 U i.v. administered over 60 min within 6 h from the onset of symptoms. A lower incidence of LVT was found in the SK group (p = 0.0003). We divided patients into two classes according to the value of akinetic-dyskinetic area (AD): the first group with a lower value of AD, the second group with a higher value of AD. In both groups, a reduced incidence of LVT was associated with SK therapy (p = 0.014, p = 0.015, respectively). Early infusion of SK during AMI seems to prevent the development of LVT, with an effect partly independent from its action on infarct size for small to large myocardial infarction

Measuring health inequality among children in developing countries: does the choice of the indicator of economic status matter?

Background Currently, poor-rich inequalities in health in developing countries receive a lot of attention from both researchers and policy makers. Since measuring economic status in developing countries is often problematic, different indicators of wealth are used in different studies. Until now, there is a lack of evidence on the extent to which the use of different measures of economic status affects the observed magnitude of health inequalities. Methods This paper provides this empirical evidence for 10 developing countries, using the Demographic and Health Surveys data-set. We compared the World Bank asset index to three alternative wealth indices, all based on household assets. Under-5 mortality and measles immunisation coverage were the health outcomes studied. Poor-rich inequalities in under-5 mortality and measles immunisation coverage were measured using the Relative Index of Inequality. Results Comparing the World Bank index to the alternative indices, we found that (1) the relative position of households in the national wealth hierarchy varied to an important extent with the asset index used, (2) observed poor-rich inequalities in under-5 mortality and immunisation coverage often changed, in some cases to an important extent, and that (3) the size and direction of this change varied per country, index, and health indicator. Conclusion Researchers and policy makers should be aware that the choice of the measure of economic status influences the observed magnitude of health inequalities, and that differences in health inequalities between countries or time periods, may be an artefact of different wealth measures used

Risk scores for predicting early antiretroviral therapy mortality in sub-Saharan Africa to inform who needs intensification of care: a derivation and external validation cohort study.

BACKGROUND: Clinical scores to determine early (6-month) antiretroviral therapy (ART) mortality risk have not been developed for sub-Saharan Africa (SSA), home to 70% of people living with HIV. In the absence of validated scores, WHO eligibility criteria (EC) for ART care intensification are CD4  37.5 °C (2 points). The same variables plus CD4 < 200/μL (1 point) were included in the CD4-dependent score. Among XPRES enrollees, a CD4-independent score of ≥ 4 would provide 86% sensitivity and 66% specificity, whereas WHO EC would provide 83% sensitivity and 58% specificity. If WHO stage alone was used, sensitivity was 48% and specificity 89%. Among TBFT enrollees, the CD4-independent score of ≥ 4 would provide 95% sensitivity and 27% specificity, whereas WHO EC would provide 100% sensitivity but 0% specificity. Accuracy was similar between CD4-independent and CD4-dependent scores. Categorizing CD4-independent scores into low (< 4), moderate (4-6), and high risk (≥ 7) gave 6-month mortality of 1%, 4%, and 17% for XPRES and 1%, 5%, and 30% for TBFT enrollees. CONCLUSIONS: Sensitivity of the CD4-independent score was nearly twice that of WHO stage in predicting 6-month mortality and could be used in settings lacking CD4 testing to inform ART care intensification. The CD4-dependent score improved specificity versus WHO EC. Both scores should be considered for scale-up in SSA

Generative Embedding for Model-Based Classification of fMRI Data

Decoding models, such as those underlying multivariate classification algorithms, have been increasingly used to infer cognitive or clinical brain states from measures of brain activity obtained by functional magnetic resonance imaging (fMRI). The practicality of current classifiers, however, is restricted by two major challenges. First, due to the high data dimensionality and low sample size, algorithms struggle to separate informative from uninformative features, resulting in poor generalization performance. Second, popular discriminative methods such as support vector machines (SVMs) rarely afford mechanistic interpretability. In this paper, we address these issues by proposing a novel generative-embedding approach that incorporates neurobiologically interpretable generative models into discriminative classifiers. Our approach extends previous work on trial-by-trial classification for electrophysiological recordings to subject-by-subject classification for fMRI and offers two key advantages over conventional methods: it may provide more accurate predictions by exploiting discriminative information encoded in ‘hidden’ physiological quantities such as synaptic connection strengths; and it affords mechanistic interpretability of clinical classifications. Here, we introduce generative embedding for fMRI using a combination of dynamic causal models (DCMs) and SVMs. We propose a general procedure of DCM-based generative embedding for subject-wise classification, provide a concrete implementation, and suggest good-practice guidelines for unbiased application of generative embedding in the context of fMRI. We illustrate the utility of our approach by a clinical example in which we classify moderately aphasic patients and healthy controls using a DCM of thalamo-temporal regions during speech processing. Generative embedding achieves a near-perfect balanced classification accuracy of 98% and significantly outperforms conventional activation-based and correlation-based methods. This example demonstrates how disease states can be detected with very high accuracy and, at the same time, be interpreted mechanistically in terms of abnormalities in connectivity. We envisage that future applications of generative embedding may provide crucial advances in dissecting spectrum disorders into physiologically more well-defined subgroups

Model based dynamics analysis in live cell microtubule images

Background: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. Results: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. Conclusion: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior

Behavioural Thermoregulatory Tactics in Lacustrine Brook Charr, Salvelinus fontinalis

The need to vary body temperature to optimize physiological processes can lead to thermoregulatory behaviours, particularly in ectotherms. Despite some evidence of within-population phenotypic variation in thermal behaviour, the occurrence of alternative tactics of this behaviour is rarely explicitly considered when studying natural populations. The main objective of this study was to determine whether different thermal tactics exist among individuals of the same population. We studied the behavioural thermoregulation of 33 adult brook charr in a stratified lake using thermo-sensitive radio transmitters that measured hourly individual temperature over one month. The observed behavioural thermoregulatory patterns were consistent between years and suggest the existence of four tactics: two “warm” tactics with both crepuscular and finer periodicities, with or without a diel periodicity, and two “cool” tactics, with or without a diel periodicity. Telemetry data support the above findings by showing that the different tactics are associated with different patterns of diel horizontal movements. Taken together, our results show a clear spatio-temporal segregation of individuals displaying different tactics, suggesting a reduction of niche overlap. To our knowledge, this is the first study showing the presence of behavioural thermoregulatory tactics in a vertebrate