The ecology of suffering: developmental disorders of structured stress, emotion, and chronic inflammation

'Punctuated equilibrium' models of cognitive process, adapted from the Large Deviations Program of probability theory, are applied to the interaction between immune function and emotion in the context of culturally structured psychosocial stress. The analysis suggests: (1) Chronic inflammatory diseases should be comorbid and synergistic with characteristic emotional dysfunction, and may form a collection of joint disorders most effectively treated at the individual level using multifactorial 'mind/body' strategies. (2) Culturally constructed psychosocial stress can literally write an image of itself onto the punctuated etiology and progression of such composite disorders, beginning a trajectory to disease in utero or early childhood, and continuing throughout the life course, suggesting that, when moderated by 'social exposures', these are developmental disorders. (3) At the community level of organization, strategies for prevention and control of the spectrum of emotional/inflammatory developmental disorders must include redress of cross-sectional and logitudinal (i.e. historical) patterns of inequality and injustice which generate structured psychosocial stress. Evidence further suggests that within 'Westernized' or 'market economy' societies, such stress will inevitably entrain high as well as lower stutus subopulations into a unified ecology of suffering

Joint Independent Subspace Analysis Using Second-Order Statistics

International audienceThis paper deals with a novel generalization of classical blind source separation (BSS) in two directions. First, relaxing the constraint that the latent sources must be statistically independent. This generalization is well-known and sometimes termed independent subspace analysis (ISA). Second, jointly analyzing several ISA problems, where the link is due to statistical dependence among corresponding sources in different mixtures. When the data are one-dimensional, i.e., multiple classical BSS problems, this model, known as independent vector analysis (IVA), has already been studied. In this paper, we combine IVA with ISA and term this new model joint independent subspace analysis (JISA). We provide full performance analysis of JISA, including closed-form expressions for minimal mean square error (MSE), Fisher information and Cramér-Rao lower bound, in the separation of Gaussian data. The derived MSE applies also for non-Gaussian data, when only second-order statistics are used. We generalize previously known results on IVA, including its ability to uniquely resolve instantaneous mixtures of real Gaussian stationary data, and having the same arbitrary permutation at all mixtures. Numerical experiments validate our theoretical results and show the gain with respect to two competing approaches that either use a finer block partition or a different norm

Simultaneous Source Localization and Polarization Estimation via Non-Orthogonal Joint Diagonalization with Vector-Sensors

Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods