Single spin detection by qubit SWAP to a molecular nanomagnet

Spin state detection is a key but very challenging step for any spin-based solid-state quantum computing technology. In fullerene based quantum computer technologies, we here propose to detect the single spin inside a fullerene by transferring the quantum information from the endohedral spin to the ground states of a molecular nanomagnet Fe$_{8}$, with large spin S=10. We show how to perform the required SWAP operation and how to read out the information through state-of-the-art techniques such as micro-SQUID.Comment: Europhysics Letters 69,699 (2005

Efficient Contact State Graph Generation for Assembly Applications

An important aspect in the design of many automated assembly strategies is the ability to automatically generate the set of contact states that may occur during an assembly task. In this paper, we present an efficient means of constructing the set of all geometrically feasible contact states that may occur within a bounded set of misalignments (bounds determined by robot inaccuracy). This set is stored as a graph, referred to as an Assembly Contact State Graph (ACSG), which indicates neighbor relationships between feasible states. An ACSG is constructed without user intervention in two stages. In the first stage, all hypothetical primitive principle contacts (PPCs; all contact states allowing 5 degrees of freedom) are evaluated for geometric feasibility with respect to part-imposed and robot-imposed restrictions on relative positioning (evaluated using optimization). In the second stage, the feasibility of each of the various combinations of PPCs is efficiently evaluated, first using topological existence and uniqueness criteria, then using part-imposed and robot-imposed geometric criteria

Robust Procedures for Obtaining Assembly Contact State Extremal Configurations

Two important components in the selection of an admittance that facilitates force-guided assembly are the identification of: 1) the set of feasible contact states, and 2) the set of configurations that span each contact state, i.e., the extremal configurations. We present a procedure to automatically generate both sets from CAD models of the assembly parts. In the procedure, all possible combinations of principle contacts are considered when generating hypothesized contact states. The feasibility of each is then evaluated in a genetic algorithm based optimization procedure. The maximum and minimum value of each of the 6 configuration variables spanning each contact state are obtained by again using genetic algorithms. Together, the genetic algorithm approach, the hierarchical data structure containing the states, the relationships among the states, and the extremals within each state are used to provide a reliable means of identifying all feasible contact states and their associated extremal configurations

A novel approach to detect hot-spots in large-scale multivariate data

Background: Progressive advances in the measurement of complex multifactorial components of biological processes involving both spatial and temporal domains have made it difficult to identify the variables (genes, proteins, neurons etc.) significantly changed activities in response to a stimulus within large data sets using conventional statistical approaches. The set of all changed variables is termed hot-spots. The detection of such hot spots is considered to be an NP hard problem, but by first establishing its theoretical foundation we have been able to develop an algorithm that provides a solution. Results: Our results show that a first-order phase transition is observable whose critical point separates the hot-spot set from the remaining variables. Its application is also found to be more successful than existing approaches in identifying statistically significant hot-spots both with simulated data sets and in real large-scale multivariate data sets from gene arrays, electrophysiological recording and functional magnetic resonance imaging experiments. Conclusion: In summary, this new statistical algorithm should provide a powerful new analytical tool to extract the maximum information from complex biological multivariate data

Iron(III)-chelating resins X. Iron detoxification of human plasma with iron(III)-chelating resins

Iron detoxification of human blood plasma was studied with resins containing desferrioxamine B (DFO) or 3-hydroxy-2-methyl-4(1H)-pyridinone (HMP) as iron(III)-chelating groups. The behaviour of four resins was investigated: DFO-Sepharose, HMP-Sepharose and crosslinked copolymers of 1-(ß-acrylamidoethyl)-3-hydroxy-2-methyl-4(1H)-pyridinone (AHMP) with 2-hydroxyethyl methacrylate (HEMA) and of AHMP with N,N-dimethylacrylamide (DMAA). The efficiency of iron detoxification of plasma of the resins was mainly dependent on the affinity of the ligands and the hydrophilicity of the resins. The results of a stability study in phosphate-buffered saline at a physiological pH indicated that AHMP-DMAA was the most stable resin, whereas the Sepharose gels had a relatively lower stability. Experiments with the AHMP-DMAA resin showed that the resin was able to remove iron from plasma with different iron contents, and from plasma poisoned with FeCl3, iron(III) citrate or transferrin. A rapid removal from free serum iron was observed, whereas iron from transferrin was removed slowly afterwards. Only the overload iron was removed since in all cases the normal serum iron level of ca. 1 ppm was obtained