Survey of control performance in quantum information processing

There is a rich variety of physics underlying the fundamental gating operations for quantum information processing (QIP). A key aspect of a QIP system is how noise may enter during quantum operations and how suppressing or correcting its effects can best be addressed. Quantum control techniques have been developed to specifically address this effort, although a detailed classification of the compatibility of controls schemes with noise sources found in common quantum systems has not yet been performed. This work numerically examines the performance of modern control methods for suppressing decoherence in the presence of noise forms found in viable quantum systems. The noise-averaged process matrix for controlled one-qubit and two-qubit operations are calculated across noise found in systems driven by Markovian open quantum dynamics. Rather than aiming to describe the absolute best control scheme for a given physical circumstance, this work serves instead to classify quantum control behavior across a large class of noise forms so that opportunities for improving QIP performance may be identified.     

A new framework for understanding educational digital library use: re-examining digital divides in U.S. schools

Despite their decade of deployment, educational digital libraries have not achieved sustained use in elementary and secondary schools in the United States. Barriers to accessing the Internet and computers have been widely targeted by myriad initiatives, but efforts aimed at bridging this first-level “digital divide” have not led to increased use of the Internet and digital library resources in U.S. classrooms. In fact, such programs have revealed additional divides that affect educators’ use. This paper examines the additional digital divide levels and proposes a new framework for understanding technology innovation in schools that can improve development and outreach approaches by digital library developers.     

Numerical Modeling of Grain Boundary Effects in the Diffusional Creep of Copper Interconnect Lines

We propose a numerical simulation technique to model the process of diffusional creep and stress relaxation that occurs in Cu-damascene interconnects of integrated circuit devices in processing stage. The mass flow problem is coupled to the stress analysis through vacancy flux and equilibrium vacancy concentration. The technique is implemented in a software package that seamlessly integrates the problem-oriented code with commercially available finite element program MSC.Marc. It is utilized to model the Coble creep phenomenon by introducing the nanoscale grain boundary region having the thickness on the order of several layers of atoms. As an illustration, the two-dimensional problem of stress relaxation in a single grain subjected to prescribed displacements and tractions is examined.     

The international risk governance council framework and its application to Listeria monocytogenes in soft cheese made from unpasteurised milk

The International Risk Governance Council (IRGC) developed a Risk Governance Framework whose purpose is to help policy makers, regulators and risk managers both understand the concept of risk governance and apply it to their handling of risks. The Framework allows input from the scientific and public health perspective as well as the less-easily measurable side of freedom of choice. For food, the public wants many choices and less regulation, but at the same time safe products to consume. These ideals are at times in conflict. One example of this is the demand for soft cheese made from unpasteurised milk which may be contaminated with L. monocytogenes or other pathogens. Distinguishing between simple, complex, uncertain and ambiguous risks can help to design a risk management strategy. Public health advocates typically see management of soft cheese made from unpasteurised milk as a simple risk (protection of health), whereas for a small but vociferous proportion of the public sees it more of an uncertain or ambiguous one (choice trumps the slight risk of illness). The implications of five management options for the cheese are discussed. The new Codex Guidelines tend to put these cheeses into Option 1, a strict control on the presence of the pathogen, which will be ignored by the cheese aficionados. Other options are worth exploring to give some more choice but at low risk.     

A computationally efficient superresolution image reconstructionalgorithm

Superresolution reconstruction produces a high-resolution image from a set of low-resolution images. Previous iterative methods for superresolution had not adequately addressed the computational and numerical issues for this ill-conditioned and typically underdetermined large scale problem. We propose efficient block circulant preconditioners for solving the Tikhonov-regularized superresolution problem by the conjugate gradient method. We also extend to underdetermined systems the derivation of the generalized cross-validation method for automatic calculation of regularization parameters. The effectiveness of our preconditioners and regularization techniques is demonstrated with superresolution results for a simulated sequence and a forward looking infrared (FLIR) camera image sequence.     

A multiple-layer finite-element model of the surface EMG signal

The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.     

A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge

Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.