Teleportation via Multi-Qubit Channels

We investigate the problem of teleporting an unknown qubit state to a recipient via a channel of 2L qubits. In this procedure a protocol is employed whereby L Bell state measurements are made and information based on these measurements is sent via a classical channel to the recipient. Upon receiving this information the recipient determines a local gate which is used to recover the original state. We find that the 2(2L)-dimensional Hilbert space of states available for the channel admits a decomposition into four subspaces. Every state within a given subspace is a perfect channel, and each sequence of Bell measurements projects 2L qubits of the system into one of the four subspaces. As a result, only two bits of classical information need be sent to the recipient for them to determine the gate. We note some connections between these four subspaces and ground states of many-body Hamiltonian systems, and discuss the implications of these results towards understanding entanglement in multi-qubit systems.

Neogenin: A multi-functional receptor regulating diverse developmental processes

Neogenin, a close relative of the axon guidance receptor Deleted in Colorectal Cancer (DCC), has been shown to be a receptor for members of the Netrin and Repulsive Guidance Molecule (RGM) families. While Netrin-l-Neogenin interactions result in a chernoattractive axon guidance response, the interaction between Neogenin and RGMa induces a chemorepulsive response. Evidence is now accumulating that Neogenin is a multi-functional receptor regulating many diverse developmental processes, including neural tube and mammary gland formation, myogenesis and angiogenesis. Little is known of the function of Neogenin in the adult, however, a novel role in the regulation of iron homeostasis is now emerging. While the signal transduction pathways activated by Neogenin are poorly understood, it is clear that the functional outcome of Neogenin activation, at least in the embryo, depends on both the developmental context as well as the nature of the ligand. (c) 2006 Elsevier Ltd. All rights reserved.

Multi-robot control in highly dynamic, competitive environments

The control and coordination of multiple mobile robots is a challenging task; particularly in environments with multiple, rapidly moving obstacles and agents. This paper describes a robust approach to multi-robot control, where robustness is gained from competency at every layer of robot control. The layers are: (i) a central coordination system (MAPS), (ii) an action system (AES), (iii) a navigation module, and (iv) a low level dynamic motion control system. The multi-robot coordination system assigns each robot a role and a sub-goal. Each robot’s action execution system then assumes the assigned role and attempts to achieve the specified sub-goal. The robot’s navigation system directs the robot to specific goal locations while ensuring that the robot avoids any obstacles. The motion system maps the heading and speed information from the navigation system to force-constrained motion. This multi-robot system has been extensively tested and applied in the robot soccer domain using both centralized and distributed coordination.

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

Multi-threaded Communicating Agents in Qu-Prolog

In this tutorial paper we summarise the key features of the multi-threaded Qu-Prolog language for implementing multi-threaded communicating agent applications. Internal threads of an agent communicate using the shared dynamic database used as a generalisation of Linda tuple store. Threads in different agents, perhaps on different hosts, communicate using either a thread-to-thread store and forward communication system, or by a publish and subscribe mechanism in which messages are routed to their destinations based on content test subscriptions. We illustrate the features using an auction house application. This is fully distributed with multiple auctioneers and bidders which participate in simultaneous auctions. The application makes essential use of the three forms of inter-thread communication of Qu-Prolog. The agent bidding behaviour is specified graphically as a finite state automaton and its implementation is essentially the execution of its state transition function. The paper assumes familiarity with Prolog and the basic concepts of multi-agent systems.

A real-time asymmetric multiprocessor-reconfigurable system-on-chip architecture

We propose an asymmetric multi-processor SoC architecture, featuring a master CPU running uClinux, and multiple loosely-coupled slave CPUs running real-time threads assigned by the master CPU. Real-time SoC architectures often demand a compromise between a generic platform for different applications, and application-specific customizations to achieve performance requirements. Our proposed architecture offers a generic platform running a conventional embedded operating system providing a traditional software-oriented development approach, while multiple slave CPUs act as a dedicated independent real-time threads execution unit running in parallel of master CPU to achieve performance requirements. In this paper, the architecture is described, including the application / threading development environment. The performance of the architecture with several standard benchmark routines is also analysed.

Achieving cooperation in a distributed multi-robot team

DMAPS (Distributed Multi-Agent Planning System) is a planning system developed for distributed multi-robot teams based on MAPS(Multi-Agent Planning System). MAPS assumes that each agent has the same global view of the environment in order to determine the most suitable actions. This assumption fails when perception is local to the agents: each agent has only a partial and unique view of the environment. DMAPS addresses this problem by creating a probabilistic global view on each agent by fusing the perceptual information from each robot. The experimental results on consuming tasks show that while the probabilistic global view is not identical on each robot, the shared view is still effective in increasing performance of the team.