Automated Parallelization of Discrete State-Space Generation

We consider the problem of generating a large state-space in a distributed fashion. Unlike previously proposed solutions that partition the set of reachable states according to a hashing function provided by the user, we explore heuristic methods that completely automate the process. The first step is an initial random walk through the state space to initialize a search tree, duplicated in each processor. Then, the reachability graph is built in a distributed way, using the search tree to assign each newly found state to classes assigned to the available processors. Furthermore, we explore two remapping criteria that attempt to balance memory usage or future workload, respectively. We show how the cost of computing the global snapshot required for remapping will scale up for system sizes in the foreseeable future. An extensive set of results is presented to support our conclusions that remapping is extremely beneficial.     

The role of examples and rules in the acquisition of a cognitive skill

In 3 experiments, participants memorized 8 examples, each exemplifying a different rule. Participants were asked to extend these rules to new examples. They practiced applications of the rules to examples over a period of 4 days (Experiment 1) or 5 days (Experiments 2 and 3). Although these rules were bidirectional, an asymmetry gradually built up such that participants became more facile in using the rules in the practiced direction. Participants also showed an advantage when the initial study example was repeated or when test examples were repeated. It is argued that skill acquisition involves development of a complex set of strategies based on use of rules and retrieval of examples. Four overlapping stages of skill acquisition are described.     

Impact and penetration by L/D ≤ 1 projectiles

Calculations of steel target penetration by L/D ≤ 1 tungsten and tungsten alloy projectiles have been extended to L/D = 1/32 over the velocity range 1.5 to 5 km/s. The ratio of crater to projectile diameter tends to 1 as L/D decreases over this entire velocity range. For impact velocities of 1.5 and 3 km/s, penetration depth normalized by projectile length, P/L, increases with decreasing projectile L/D up to a maximum value and then decreases for still lower L/D. Experiments at impact velocities of 2 and 3 km/s confirm these results. For 5 km/s impact velocity, the calculations show P/L increasing with decreasing projectile L/D over the entire range 1/32 ≤ L/D ≤ 1. The projectile L/D for which the maximum P/L occurs appears to depend on the impact velocity. P/L generally scales with impact velocity as P/L v^f(L/D) where f(L/D) ranges from 0 for a long rod to, we believe, 2 in the limit as projectile L/D approaches zero. The calculations show for 1/8 ≤ L/D ≤ 1/2, P/L v^0.9; for L/D = 1/16, P/L v^1.5; and for L/D = 1/32, the new results give P/L v^1.9.     

Path integral Monte Carlo simulations of the melting of molecular hydrogen surfaces

We have used Path Integral Monte Carlo to study the surface melting of molecular hydrogen. Density profiles perpendicular and parallel to the bare H₂ surface are computed showing the formation of a liquid adlayer at 6 K, less than half the bulk melting temperature of para-hydrogen, 13.8 K. To estimate the onset temperature and depth of H₂ surface melting we determine the static structure factor within the individual H₂-layers for wave vectors in the plane and find no crystalline order down to 3 K in a partially filled H₂ adlayer at the free surface. We find quantum effects amplify the melting point depression at the free H₂ surface compared to bulk by a factor of five over classical Lennard-Jones solids and find that the zero-point fluctuations of molecules at the surface are much enhanced over their bulk values. We see vacancy formation in the solid before melting.     

Genetic control of immunity to Heligmosomoides polygyrus: presentation of promiscuous antigens to parasite-specific T cell hybridomas

The role of MHC class II in the presentation of Heligmosomoides polygyrus antigens has been investigated, using a number of T cell hybridomas produced in A and E positive and negative mice. By using fixed and irradiated antigen presenting cells (APC), further evidence has emerged, to support earlier data, that there can be differential processing requirements during the presentation of H. polygyrus antigens by A and E molecules. In concordance with these earlier observations, this work provides further evidence than individual T cells can respond to antigen when presented by more than one MHC molecule. Previously, this evidence has been restricted to individual MHC molecules of the same haplotype, but these data show that H. polygyrus produces antigens which can be presented by both syngeneic and allogeneic MHC molecules. These antigens do not appear to be synonymous with the previously described H. polygyrus superantigen, as presentation is restricted to specific MHC haplotypes. It is proposed that H. polygyrus may produce these antigenic molecules as part of its strategy to manipulate the host immune system.     

Advancing controlled release packaging through smart blending

Researchers from Rutgers University and Clemson University have collaborated to develop a concept of using smart blending to generate functional packaging films for the controlled release of active compounds such as antimicrobials, antioxidants and flavour compounds to extend the shelf‐life of food. In this paper, literature results are reviewed to justify the significance of controlled release packaging (CRP) and the research gaps for further development are identified. A major research gap is the lack of packaging materials that can provide the release of active compounds at rates suitable for a wide range of food packaging applications. Smart blending is a promising technology for bridging this research gap. To fully realize the potentials of smart blending, a systematic approach for developing CRP using smart blending is also presented. Copyright © 2005 John Wiley & Sons, Ltd.     

Guest Editors' Introduction: Machine Ethics

Machine ethics is concerned with how machines behave toward human users and other machines. It aims to create a machine that's guided by an acceptable ethical principle or set of principles in the decisions it makes about possible courses of action it could take. As ethics experts continue to progress toward consensus concerning the right way to behave in ethical dilemmas, the task for those working in machine ethics is to codify these insights. Eight articles in this special issue address the issues.This article is part of a special issue on Machine Ethics.     

PI control of discrete linear repetitive processes

Repetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or 2D systems theory. In this paper, we exploit their unique physical structure to show how two term, i.e. proportional plus integral (or PI) action, can be used to control these processes to produce desired behavior (as opposed to just stability).     

Non-redundant data clustering

Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice, this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and non-numeric attributes. We discuss extensions of the technique to the tasks of semi-supervised classification and enumeration of successive non-redundant clusterings. We present experimental results for applications in text mining and computer vision.