Multiple Mobile Robots Controlled by Artificial Neural Networks

Multiple small mobile robots have been created that were controlled by individual artificial neural networks. Each mobile robot was self-contained and capable of independent actions, as determined by the on-board artificial neural network. Information about the environment was collected from sensors mounted on each individual mobile robot chassis. Different sensors were available that were capable of providing information about different aspects of the environment. Currently there were sensors for detecting and following a black line as well as short range distance sensors for detecting and interacting with objects and other mobile robots. The artificial neural networks on the individual mobile robots were all provided with the same training data and a standard back-propagation training algorithm was used. However the randomised component of training the artificial neural networks did mean that there could have been subtle differences in the responses of individual mobile robots to the same sensor data. This effect was eliminated when needed by using an off-line training process and programming all the mobile robots with the same trained ANN. The small group of mobile robots was used to investigate two simple aspects of swarm behaviour; that of flocking and also of follow-my-leader, which are examples where the swarm appeared to operate with more intelligence than the individual members

Study of instabilities in linear Hall current accelerators

Ion and electron conservation equations used in analysis of instability of linear Hall current accelerato

Analysis of MPD arcs with nonequilibrium ionization

One dimensional analysis of magnetoplasm dynamic arcs with nonequilibrium ionizatio

Supporting Experimentation via an Evaluation Infrastructure for Semantic Technologies

One of the challenges of the Future Internet is to manage and combine information about dierent digital and real-world entities and the characteristics of these entities, covering related issues such as the trust or provenance of this information. One way to allow an eective representation and integration of this information is to use semantic technologies to correctly manage not just these heterogeneous content and data but also their associated metadata

Non-flow correlations and elliptic flow fluctuations in gold-gold collisions at sqrt(s_NN)= 200 GeV

This paper presents results on event-by-event elliptic flow fluctuations in Au+Au collisions at sqrt(s_NN)=200Gev, where the contribution from non-flow correlations has been subtracted. An analysis method is introduced to measure non-flow correlations, relying on the assumption that non-flow correlations are most prominent at short ranges (Delta eta < 2). Assuming that non-flow correlations are of the order that is observed in p+p collisions for long range correlations (Delta eta > 2), relative elliptic flow fluctuations of approximately 30-40% are observed. These results are consistent with predictions based on spatial fluctuations of the participating nucleons in the initial nuclear overlap region. It is found that the long range non-flow correlations in Au+Au collisions would have to be more than an order of magnitude stronger compared to the p+p data to lead to the observed azimuthal anisotropy fluctuations with no intrinsic elliptic flow fluctuations.Comment: 9 pages, 7 figures, Published in Phys. Rev.

Forward-Backward Multiplicity Correlations in sqrt(s_NN) = 200 GeV Gold-Gold Collisions

Forward-backward correlations of charged-particle multiplicities in symmetric bins in pseudorapidity are studied in order to gain insight into the underlying correlation structure of particle production in Au+Au collisions. The PHOBOS detector is used to measure integrated multiplicities in bins centered at eta, defined within |eta|<3, and covering intervals Delta-eta. The variance sigma^2_C of a suitably defined forward-backward asymmetry variable C is calculated as a function of eta, Delta-eta, and centrality. It is found to be sensitive to short range correlations, and the concept of "clustering'' is used to interpret comparisons to phenomenological models.Comment: 5 Pages, 5 Figures, submitted to Physical Review C -- Rapid Communication

Latest Results from PHOBOS

This manuscript contains a summary of the latest physics results from PHOBOS, as reported at Quark Matter 2006. Highlights include the first measurement from PHOBOS of dynamical elliptic flow fluctuations as well as an explanation of their possible origin, two-particle correlations, identified particle ratios, identified particle spectra and the latest results in global charged particle production.Comment: 9 pages, 7 figures, PHOBOS plenary proceedings for Quark Matter 200

System size, energy, centrality and pseudorapidity dependence of charged-particle density in Au+Au and Cu+Cu collisions at RHIC

Charged particle pseudorapidity distributions are presented from the PHOBOS experiment at RHIC, measured in Au+Au and Cu+Cu collisions at sqrt{s_NN}=19.6, 22.4, 62.4, 130 and 200 GeV, as a function of collision centrality. The presentation includes the recently analyzed Cu+Cu data at 22.4 GeV. The measurements were made by the same detector setup over a broad range in pseudorapidity, |eta|<5.4, allowing for a reliable systematic study of particle production as a function of energy, centrality and system size. Comparing Cu+Cu and Au+Au results, we find that the total number of produced charged particles and the overall shape (height and width) of the pseudorapidity distributions are determined by the number of nucleon participants, N_part. Detailed comparisons reveal that the matching of the shape of the Cu+Cu and Au+Au pseudorapidity distributions over the full range of eta is better for the same N_part/2A value than for the same N_part value, where A denotes the mass number. In other words, it is the geometry of the nuclear overlap zone, rather than just the number of nucleon participants that drives the detailed shape of the pseudorapidity distribution and its centrality dependence.Comment: 5 pages, 4 figures. Presented at the 20th International Conference on Nucleus-Nucleus Collisions (Quark Matter 2008), Jaipur, Rajasthan, India, 4-10 February 200

System Size, Energy, Pseudorapidity, and Centrality Dependence of Elliptic Flow

This paper presents measurements of the elliptic flow of charged particles as a function of pseudorapidity and centrality from Cu-Cu collisions at 62.4 and 200 GeV using the PHOBOS detector at the Relativistic Heavy Ion Collider (RHIC). The elliptic flow in Cu-Cu collisions is found to be significant even for the most central events. For comparison with the Au-Au results, it is found that the detailed way in which the collision geometry (eccentricity) is estimated is of critical importance when scaling out system-size effects. A new form of eccentricity, called the participant eccentricity, is introduced which yields a scaled elliptic flow in the Cu-Cu system that has the same relative magnitude and qualitative features as that in the Au-Au system

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe