In this paper, the neural network based optimal control synthesis is presented for solving free and fixed final time optimal control problems with the control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem, which is implemented with the adaptive critic neural network. The proposed simulation methods are illustrated by the optimal control problem of photosynthetic production and the nitrogen transformation cycle model. The results show that the adaptive critic based systematic approach is promising in obtaining free and fixed final time optimal control with the control and state constraints.

We present a simple, work-optimal and synchronization-free solution to the problem of stably merging in parallel two given, ordered arrays of m and n elements into an ordered array of m+n elements. The main contribution is a new, simple, fast and direct algorithm that determines, for any prefix of the stably merged output array, the exact prefixes of each of the two input arrays needed to produce this output prefix. More precisely, for any given index in the resulting, but not yet constructed output array, representing the desired output prefix, the algorithm computes the indices (called co-ranks) in each of the two input arrays representing the required input prefixes without having to merge the input arrays. The co-ranking algorithm takes [Formula: see text] time steps, and uses O(1) space. Co-ranking is used in parallel to partition the input arrays into a collection of as many pairs as desired, each pair with exactly the same number of elements. Any stable, sequential merge algorithm can be used to merge pairs independently. The result is a perfectly load-balanced, stable, parallel merge algorithm. Co-ranking and sequential merging of pairs can be done without synchronization. Compared to other, linear speed-up approaches to the parallel merge problem, the algorithm is considerably simpler and can be up to a factor of two faster. Compared to previous algorithms for solving the co-ranking problem, the new algorithm works for arbitrary output array indices and maintains stability in the presence of repeated elements at no extra space or time cost. When the number of processing elements p does not exceed (m + n)/ log min(m, n), the parallel merge algorithm has perfect, linear speedup p. Furthermore, it is easy to implement on both shared and distributed memory systems.

The star graph and the alternating group graph were introduced as competitive alternatives to the hypercube, and they are indeed superior over the hypercube under many measures. However, they do suffer from scaling issues. To address this, different generalizations, namely, the (n,k)-star graph and the arrangement graph were introduced to address this shortcoming. From another direction, the star graph was recognized as a special case of Cayley graphs whose generators can be associated with a tree. Nevertheless, all these networks appear to be very different and yet share many properties. In this paper, we will solve this mystery by providing a common generalization of all these networks. Moreover, we will show that these networks have strong connectivity properties.

We aim at mapping streaming applications that can be modeled by a series-parallel graph onto a 2-dimensional tiled chip multiprocessor (CMP) architecture. The objective of the mapping is to minimize the energy consumption, using dynamic voltage and frequency scaling (DVFS) techniques, while maintaining a given level of performance, reflected by the rate of processing the data streams. This mapping problem turns out to be NP-hard, and several heuristics are proposed. We assess their performance through comprehensive simulations using the StreamIt workflow suite and randomly generated series-parallel graphs, and various CMP grid sizes.

We describe an approach to data analytics on large systems using a suite of robust parallel algorithms running on both clouds and HPC systems. We apply this to cases where the data is defined in a vector space and when only pairwise distances between points are defined. We introduce improvements to known algorithms for functionality, features and performance but review state of the art as this is not broadly familiar. Visualization is valuable for steering complex analytics and we discuss it for both the non vector semi-metric case and for clustering high dimension vector spaces. We exploit deterministic annealing which is heuristic but has clear general principles that can give reasonably fast robust algorithms. We apply methods to several life sciences applications.

Hypercomputation is about the feasibility of machines and systems that are either more expressive or computationally more powerful than the Turing machine. A number of researchers and thinkers have put forth a number of supposedly knock-out arguments against hypercomputation. Nevertheless, these arguments are not unwavering as they seem to be and here I explain why.

We discuss the problem of assembling complex physical systems that are structured from the nanoscale up through the macroscale, and argue that embryological morphogenesis provides a good model of how this can be accomplished. Morphogenesis (whether natural or artificial) is an example of embodied computation, which exploits physical processes for computational ends, or performs computations for their physical effects. Examples of embodied computation in natural morphogenesis can be found at many levels, from allosteric proteins, which perform simple embodied computations, up through cells, which act to create tissues with specific patterns, compositions, and forms. We outline a notation for describing morphogenetic programs and illustrate its use with two examples: simple diffusion and the assembly of a simple spine with attachment points for legs. While much research remains to be done — at the simulation level before we attempt physical implementations — our results to date show how we may implement the fundamental processes of morphogenesis as a practical application of embodied computation at the nano- and microscale.

Sensor networks that can support time-critical operations pose challenging problems for tracking events of interest. We propose an architecture for a sensor network that autonomously adapts in real-time to data fusion requirements so as not to miss events of interest and provides accurate real-time mobile target tracking. In the proposed architecture, the sensed data is processed in an abstract space called Information Space and the communication between nodes is modeled as an abstract space called Network Design Space. The two abstract spaces are connected through an interaction interface called InfoNet, that seamlessly translates the messages between the two. The proposed architecture is validated experimentally on a laboratory testbed for multiple scenarios.