Dynamic systems approach to the life sciences

Journal ArticleEach of the chapters in this book points to expanding our understanding of the multiple and complex relationships that surround development through the lifespan. In this chapter, we as the organizing committee of the Council for Human Development give a brief description and overview of the science of dynamic systems that is exemplified in the other chapters in this book. The goal of this chapter is to help people see how dynamic systems research helps us to understand human development and how it can assist in creating relevant policies and funding priorities. The dynamic systems approach is fundamentally different from existing ideas about simple cause and effect. It begins with the realization that the living world is too complex for any one factor to have a significant effect on an outcome in the absence of many other competing and cooperating factors, all of which change over time. Dynamic systems scientists, such as the authors of the chapters in this book, seek to understand certain aspects of this constantly changing network of mutual influences according to their focus of study

Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices

This paper addresses the problem of accelerating large artificial neural networks (ANN), whose topology and weights can evolve via the use of a genetic algorithm. The proposed digital hardware architecture is capable of processing any evolved network topology, whilst at the same time providing a good trade off between throughput, area and power consumption. The latter is vital for a longer battery life on mobile devices. The architecture uses multiple parallel arithmetic units in each processing element (PE). Memory partitioning and data caching are used to minimise the effects of PE pipeline stalling. A first order minimax polynomial approximation scheme, tuned via a genetic algorithm, is used for the activation function generator. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design

Changes in American and British Stature Since the Mid-Eighteenth Century: A Prelimanary Report on the Usefulness of Data on Height...

This paper is a progress report on the usefulness of data on physical height for the analysis of long-ten changes in the level of nutrition and health on economic, social, and demographic behavior. It is based on a set of samples covering the U.S. and several other nations over the years from 1750 to the present. The preliminary results indicate that native-born. American Revolution, but there were long periods of declining nutrition and height during the 19th century. Similar cycling has been established for England. A variety of factors, including crop mix, urbanization, occupation, intensity of labor, and immigration affected the level of height and nutrition, although the relative importance of these factors has changed over time. There is evidence that nutrition affected labor productivity. In one of the samples individuals who were one standard deviation above the mean height (holding weight per inch of height constant) were about 8% more productive than individuals one standard deviation below the mean height. Another finding is that death did not choose people at random. Analysis of data for Trinidad indicates that the annual death rate for the shortest quintile of males was more than twice as great as for the tallest quintile of males.

Detecting change and dealing with uncertainty in imperfect evolutionary environments

Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Post‐translationally‐modified structures in the autophagy machinery: an integrative perspective

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113777/1/febs13356_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/113777/2/febs13356.pd

Freedom, Servitude and Voluntary Labor

We present an economic framework to revisit and reframe some important debates over the nature of free versus unfree labor and the economic consequences of emancipation. We use a simple general equilibrium model in which labor can be either free or coerced and where land and labor will be exchanged on markets that can be competitive or manipulated or via other non-market collusive arrangements. By working with variants of the same basic model under different assumptions about initial economy-wide factor endowments and asset ownership we can compare equilibrium distributional outcomes under different institutional and contractual arrangements including markets with free labor and free tenancy, slavery, and tenancy arrangements with tied labor-service obligations. Analysis of these different contractual and organizational forms yields insights that accord with common sense, but that are often overlooked or downplayed in academic debates, particularly amongst economists

From evolutionary computation to the evolution of things

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems

Coevolutionary systems and PageRank

Coevolutionary systems have been used successfully in various problem domains involving situations of strategic decision-making. Central to these systems is a mechanism whereby finite populations of agents compete for reproduction and adapt in response to their interaction outcomes. In competitive settings, agents choose which solutions to implement and outcomes from their behavioral interactions express preferences between the solutions. Recently, we have introduced a framework that provides both qualitative and quantitative characterizations of competitive coevolutionary systems. Its two main features are: (1) A directed graph (digraph) representation that fully captures the underlying structure arising from pairwise preferences over solutions. (2) Coevolutionary processes are modeled as random walks on the digraph. However, one needs to obtain prior, qualitative knowledge of the underlying structures of these coevolutionary digraphs to perform quantitative characterizations on coevolutionary systems and interpret the results. Here, we study a deep connection between coevolutionary systems and PageRank to address this issue. We develop a principled approach to measure and rank the performance (importance) of solutions (vertices) in a given coevolutionary digraph. In PageRank formalism, B transfers part of its authority to A if A dominates B (there is an arc from B to A in the digraph). In this manner, PageRank authority indicates the importance of a vertex. PageRank authorities with suitable normalization have a natural interpretation of long-term visitation probabilities over the digraph by the coevolutionary random walk. We derive closed-form expressions to calculate PageRank authorities for any coevolutionary digraph. We can precisely quantify changes to the authorities due to modifications in restart probability for any coevolutionary system. Our empirical studies demonstrate how PageRank authorities characterize coevolutionary digraphs with different underlying structures