7 research outputs found
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Complex Systems Engineering: Designing Advanced Functions In Dynamical And Mechanical Systems
From computation in neural networks to allostery in proteins, numerous natural and artificial systems are comprised of many interacting parts that give rise to advanced functions. To study such complex systems, a diverse array of interdisciplinary tools have been developed that relate the interactions of existing systems to their functions. However, engineering the interactions to perform designed functions in novel systems remains a significant challenge due to the nonlinearities in the interactions and the vast dimensionality of the design space. Here we develop design principles for complex dynamical and mechanical systems at the lowest level of their microstate interactions. In dynamical neural systems, we use methods from control theory and dynamical systems theory to mathematically map precise patterns of neural connectivity to the control of neural states in human and non-human brains (Chapter 2) and to the learning of computations on internal representations in artificial recurrent neural networks (Chapter 4). In mechanical systems, we use methods from algebraic geometry and dynamical systems to mathematically map precise patterns of mechanical constraints to design shape changes as a minimal model of protein allostery and cooperativity (Chapter 6) and to engineer mechanical metamaterials that possess arbitrarily complex shape changes (Chapter 8). These intuitive maps allow us to navigate previously unexplored design spaces in nonlinear and high-dimensional regimes, enabling us to reverse engineer form from function in novel complex systems that have yet to exist
Unsupervised inference methods for protein sequence data
L'abstract è presente nell'allegato / the abstract is in the attachmen
Power Converters in Power Electronics
In recent years, power converters have played an important role in power electronics technology for different applications, such as renewable energy systems, electric vehicles, pulsed power generation, and biomedical sciences. Power converters, in the realm of power electronics, are becoming essential for generating electrical power energy in various ways. This Special Issue focuses on the development of novel power converter topologies in power electronics. The topics of interest include, but are not limited to: Z-source converters; multilevel power converter topologies; switched-capacitor-based power converters; power converters for battery management systems; power converters in wireless power transfer techniques; the reliability of power conversion systems; and modulation techniques for advanced power converters