Derivative free algorithm for solving nonlinear equations

In this work, we develop a simple yet practical algorithm for constructing derivative free iterative methods of higher convergence orders. The algorithm can be easily implemented in software packages for achieving desired convergence orders. Convergence analysis shows that the algorithm can develop methods of various convergence orders which is also supported through the numerical work. Computational results ascertain that the developed algorithm is efficient and demonstrate equal or better performance as compared with other well known methods.     

Uncountable automatic classes and learning

In this paper we consider uncountable classes recognizable by ω-automata and investigate suitable learning paradigms for them. In particular, the counterparts of explanatory, vacillatory and behaviourally correct learning are introduced for this setting. Here the learner reads in parallel the data of a text for a language L from the class plus an ω-index α and outputs a sequence of ω-automata such that all but finitely many of these ω-automata accept the index α if and only if α is an index for L.It is shown that any class is behaviourally correct learnable if and only if it satisfies Angluin’s tell-tale condition. For explanatory learning, such a result needs that a suitable indexing of the class is chosen. On the one hand, every class satisfying Angluin’s tell-tale condition is vacillatorily learnable in every indexing; on the other hand, there is a fixed class such that the level of the class in the hierarchy of vacillatory learning depends on the indexing of the class chosen.We also consider a notion of blind learning. On the one hand, a class is blind explanatorily (vacillatorily) learnable if and only if it satisfies Angluin’s tell-tale condition and is countable; on the other hand, for behaviourally correct learning, there is no difference between the blind and non-blind version.This work establishes a bridge between the theory of ω-automata and inductive inference (learning theory).     

Generation of Efficient Nested Loops from Polyhedra

Automatic parallelization in the polyhedral model is based on affine transformations from an original computation domain (iteration space) to a target space-time domain, often with a different transformation for each variable. Code generation is an often ignored step in this process that has a significant impact on the quality of the final code. It involves making a trade-off between code size and control code simplification/optimization. Previous methods of doing code generation are based on loop splitting, however they have nonoptimal behavior when working on parameterized programs. We present a general parameterized method for code generation based on dual representation of polyhedra. Our algorithm uses a simple recursion on the dimensions of the domains, and enables fine control over the tradeoff between code size and control overhead.     

Is team training in briefings for surgical teams feasible in simulation?

We developed a module for surgical team training using briefings in simulated crisis scenarios and here we report preliminary findings. Nine surgical teams (34 trainees) participated in a pre-training simulation, followed by an interactive workshop on briefing and checklists, and then a post-training simulation. Both technical and non-technical skills were assessed via observation during simulations by expert trainers who provided feedback on performances at the end of simulation. Trainees also reported their attitudes to briefings and evaluated the training. Pre-training attitudes to briefing were positive, some of which improved post-training and trainees’ evaluation of the training was positive. Surgeons’ technical skill improved significantly post-training, but their decision-making skill was rated lower than other non-technical skills, compared to other trainees. The training did not appear to greatly improve non-technical skill performance. Training surgical teams in simulation is feasible but much more work is needed on measurement development and training strategy to confirm its efficacy and utility.     

Advanced networking services for distributed multimedia streaming applications

Increased network speeds coupled with new services delivered via the Internet have increased the demand for intelligence and flexibility in network systems. This paper argues that both can be provided by new hardware platforms comprised of heterogeneous multi-core systems with specialized communication support. We present and evaluate an experimental network service platform that uses an emergent class of devices—network processors—as its communication support, coupled via a dedicated interconnect to a host processor acting as a computational core. A software infrastructure spanning both enables the dynamic creation of application-specific services on the network processor, mediated by middleware and controlled by kernel-level communication support. Experimental evaluations use a Pentium IV-based computational core coupled with an IXP 2400 network processor. The sample application services run on both include an image manipulation application and application-level multicasting.

Parallel construction of multidimensional binary search trees

Multidimensional binary search tree (abbreviated k-d tree) is a popular data structure for the organization and manipulation of spatial data. The data structure is useful in several applications including graph partitioning, hierarchical applications such as molecular dynamics and n-body simulations, and databases. In this paper, we study efficient parallel construction of k-d trees on coarse-grained distributed memory parallel computers. We consider several algorithms for parallel k-d tree construction and analyze them theoretically and experimentally, with a view towards identifying the algorithms that are practically efficient. We have carried out detailed implementations of all the algorithms discussed on the CM-5 and report on experimental results     

Identification of Pd3+ ions in zeolite L by temperature programmed reduction and electron paramagnetic resonance spectroscopy

Pd supported on KL zeolite has been studied by temperature programmed reduction (TPR) and electron paramagnetic resonance (EPR) spectroscopy. It has been found that upon calcination in pure oxygen Pd³⁺ and Pd²⁺ ions are formed; the Pd³⁺ ions are identified by their EPR signal. As Pd³⁺ ions in the cancrinite cages are difficult to reduce, the H₂ consumption in conventional TPR is below the stoichiometric amount.     

Probabilistic Model for Polycrystalline Microstructures with Application to Intergranular Fracture

A two part probabilistic model for polycrystalline microstructures is described. The model utilizes a Poisson–Voronoi tessellation for the grain geometry and a vector random field model for the crystallographic orientation. The grain geometry model is calibrated to experimental data through the intensity of the Poisson point field underlying the Poisson–Voronoi tessellation and the orientation random field is calibrated to experimental data through its marginal distributions and second moment properties. Realizations of the random microstructure are generated by use of translation methods and are used, with simplified mechanical models, to investigate the problem of intergranular fracture. It is found that intergranular cracks exhibit some statistical properties of a scaled Brownian motion process.