Modeling, simulating, and simplifying links between stress, attachment, and reproduction

John Bowlby's use of evolutionary theory as a cornerstone of his attachment theory was innovative in its day and remains useful. Del Giudice's target article extends Belsky et al.'s and Chisholm's efforts to integrate attachment theory with more current thinking about evolution, ecology, and neuroscience. His analysis would be strengthened by (1) using computer simulation to clarify and simulate the effects of early environmental stress, (2) incorporating information about non-stress related sources of individual differences, (3) considering the possibility of adaptive behavior without specific evolutionary adaptations, and (4) considering whether the attachment construct is critical to his analysis.

RELAX:a language to address uncertainty in self-adaptive systems requirement

Self-adaptive systems have the capability to autonomously modify their behavior at run-time in response to changes in their environment. Self-adaptation is particularly necessary for applications that must run continuously, even under adverse conditions and changing requirements; sample domains include automotive systems, telecommunications, and environmental monitoring systems. While a few techniques have been developed to support the monitoring and analysis of requirements for adaptive systems, limited attention has been paid to the actual creation and specification of requirements of self-adaptive systems. As a result, self-adaptivity is often constructed in an ad-hoc manner. In order to support the rigorous specification of adaptive systems requirements, this paper introduces RELAX, a new requirements language for self-adaptive systems that explicitly addresses uncertainty inherent in adaptive systems. We present the formal semantics for RELAX in terms of fuzzy logic, thus enabling a rigorous treatment of requirements that include uncertainty. RELAX enables developers to identify uncertainty in the requirements, thereby facilitating the design of systems that are, by definition, more flexible and amenable to adaptation in a systematic fashion. We illustrate the use of RELAX on smart home applications, including an adaptive assisted living system.

Goal-based modeling of dynamically adaptive system requirements

Self-adaptation is emerging as an increasingly important capability for many applications, particularly those deployed in dynamically changing environments, such as ecosystem monitoring and disaster management. One key challenge posed by Dynamically Adaptive Systems (DASs) is the need to handle changes to the requirements and corresponding behavior of a DAS in response to varying environmental conditions. Berry et al. previously identified four levels of RE that should be performed for a DAS. In this paper, we propose the Levels of RE for Modeling that reify the original levels to describe RE modeling work done by DAS developers. Specifically, we identify four types of developers: the system developer, the adaptation scenario developer, the adaptation infrastructure developer, and the DAS research community. Each level corresponds to the work of a different type of developer to construct goal model(s) specifying their requirements. We then leverage the Levels of RE for Modeling to propose two complementary processes for performing RE for a DAS. We describe our experiences with applying this approach to GridStix, an adaptive flood warning system, deployed to monitor the River Ribble in Yorkshire, England.

A goal-based modeling approach to develop requirements of an adaptive system with environmental uncertainty

Dynamically adaptive systems (DASs) are intended to monitor the execution environment and then dynamically adapt their behavior in response to changing environmental conditions. The uncertainty of the execution environment is a major motivation for dynamic adaptation; it is impossible to know at development time all of the possible combinations of environmental conditions that will be encountered. To date, the work performed in requirements engineering for a DAS includes requirements monitoring and reasoning about the correctness of adaptations, where the DAS requirements are assumed to exist. This paper introduces a goal-based modeling approach to develop the requirements for a DAS, while explicitly factoring uncertainty into the process and resulting requirements. We introduce a variation of threat modeling to identify sources of uncertainty and demonstrate how the RELAX specification language can be used to specify more flexible requirements within a goal model to handle the uncertainty. © 2009 Springer Berlin Heidelberg.

Maternal mind-mindedness during infancy, general parenting sensitivity and observed child feeding behavior:a longitudinal study

Maternal mind-mindedness, or the tendency to view the child as a mental agent, has been shown to predict sensitive and responsive parenting behavior. As yet the role of mind-mindedness has not been explored in the context of feeding interactions. This study evaluates the relations between maternal mind-mindedness at 6 months of infant age and subsequently observed maternal sensitivity and feeding behaviors with children at age 1 year. Maternal mind-mindedness was greater in mothers who had breast-fed compared to formula-fed. Controlling for breast-feeding, mind-mindedness at 6 months was correlated with observations of more sensitive and positive feeding behaviors at 1 year of age. Mind-mindedness was also associated with greater general maternal sensitivity in play and this general parenting sensitivity mediated the effect of mind-mindedness on more sensitive and positive feeding behaviors. Interventions to promote maternal tendency to consider their child's mental states may encourage more adaptive parental feeding behaviors. © 2014 Taylor & Francis.

Adaptive responses by mouse early embryos to maternal diet protect fetal growth but predispose to adult onset disease

Poor maternal nutrition during pregnancy can alter postnatal phenotype and increase susceptibility to adult cardiovascular and metabolic diseases. However, underlying mechanisms are largely unknown. Here, we show that maternal low protein diet (LPD), fed exclusively during mouse preimplantation development, leads to offspring with increased weight from birth, sustained hypertension, and abnormal anxiety-related behavior, especially in females. These adverse outcomes were interrelated with increased perinatal weight being predictive of later adult overweight and hypertension. Embryo transfer experiments revealed that the increase in perinatal weight was induced within blastocysts responding to preimplantation LPD, independent of subsequent maternal environment during later pregnancy. We further identified the embryo-derived visceral yolk sac endoderm (VYSE) as one mediator of this response. VYSE contributes to fetal growth through endocytosis of maternal proteins, mainly via the multiligand megalin (LRP2) receptor and supply of liberated amino acids. Thus, LPD maintained throughout gestation stimulated VYSE nutrient transport capacity and megalin expression in late pregnancy, with enhanced megalin expression evident even when LPD was limited to the preimplantation period. Our results demonstrate that in a nutrient-restricted environment, the preimplantation embryo activates physiological mechanisms of developmental plasticity to stablize conceptus growth and enhance postnatal fitness. However, activation of such responses may also lead to adult excess growth and cardiovascular and behavioral diseases. © 2008 by the Society for the Study of Reproduction, Inc.

Fully probabilistic control design in an adaptive critic framework

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem. In particular very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic contro algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this short paper.

Adaptive agents in changing environments, the role of modularity

We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network's synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities. © 2014 Springer Science+Business Media New York.