Data partitioning for parallel spatial join processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. We show in this paper that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.

Extended GCD and Hermite normal form algorithms via lattice basis reduction

Extended gcd calculation has a long history and plays an important role in computational number theory and linear algebra. Recent results have shown that finding optimal multipliers in extended gcd calculations is difficult. We present an algorithm which uses lattice basis reduction to produce small integer multipliers x(1), ..., x(m) for the equation s = gcd (s(1), ..., s(m)) = x(1)s(1) + ... + x(m)s(m), where s1, ... , s(m) are given integers. The method generalises to produce small unimodular transformation matrices for computing the Hermite normal form of an integer matrix.

Some new examples for nonuniqueness of the evolution problem of harmonic maps

We find some new examples to show nonuniquence for the heat flow of harmonic maps where weak solutions satisfy the same monotonicity property.

Effects of data characteristics on the results of reserve selection algorithms

We tested the effects of four data characteristics on the results of reserve selection algorithms. The data characteristics were nestedness of features (land types in this case), rarity of features, size variation of sites (potential reserves) and size of data sets (numbers of sites and features). We manipulated data sets to produce three levels, with replication, of each of these data characteristics while holding the other three characteristics constant. We then used an optimizing algorithm and three heuristic algorithms to select sites to solve several reservation problems. We measured efficiency as the number or total area of selected sites, indicating the relative cost of a reserve system. Higher nestedness increased the efficiency of all algorithms (reduced the total cost of new reserves). Higher rarity reduced the efficiency of all algorithms (increased the total cost of new reserves). More variation in site size increased the efficiency of all algorithms expressed in terms of total area of selected sites. We measured the suboptimality of heuristic algorithms as the percentage increase of their results over optimal (minimum possible) results. Suboptimality is a measure of the reliability of heuristics as indicative costing analyses. Higher rarity reduced the suboptimality of heuristics (increased their reliability) and there is some evidence that more size variation did the same for the total area of selected sites. We discuss the implications of these results for the use of reserve selection algorithms as indicative and real-world planning tools.

Asymmetric MRI magnet design using a hybrid numerical method

This paper describes a hybrid numerical method for the design of asymmetric magnetic resonance imaging magnet systems. The problem is formulated as a field synthesis and the desired current density on the surface of a cylinder is first calculated by solving a Fredholm equation of the first kind. Nonlinear optimization methods are then invoked to fit practical magnet coils to the desired current density. The field calculations are performed using a semi-analytical method. A new type of asymmetric magnet is proposed in this work. The asymmetric MRI magnet allows the diameter spherical imaging volume to be positioned close to one end of the magnet. The main advantages of making the magnet asymmetric include the potential to reduce the perception of claustrophobia for the patient, better access to the patient by attending physicians, and the potential for reduced peripheral nerve stimulation due to the gradient coil configuration. The results highlight that the method can be used to obtain an asymmetric MRI magnet structure and a very homogeneous magnetic field over the central imaging volume in clinical systems of approximately 1.2 m in length. Unshielded designs are the focus of this work. This method is flexible and may be applied to magnets of other geometries. (C) 1999 Academic Press.

A hybrid, inverse approach to the design of magnetic resonance imaging magnets

This paper describes a hybrid numerical method of an inverse approach to the design of compact magnetic resonance imaging magnets. The problem is formulated as a field synthesis and the desired current density on the surface of a cylinder is first calculated by solving a Fredholm equation of the first, kind. Nonlinear optimization methods are then invoked to fit practical magnet coils to the desired current density. The field calculations are performed using a semi-analytical method. The emphasis of this work is on the optimal design of short MRI magnets. Details of the hybrid numerical model are presented, and the model is used to investigate compact, symmetric MRI magnets as well as asymmetric magnets. The results highlight that the method can be used to obtain a compact MRI magnet structure and a very homogeneous magnetic field over the central imaging volume in clinical systems of approximately 1 m in length, significantly shorter than current designs. Viable asymmetric magnet designs, in which the edge of the homogeneous region is very close to one end of the magnet system are also presented. Unshielded designs are the focus of this work. This method is flexible and may be applied to magnets of other geometries. (C) 2000 American Association of Physicists in Medicine. [S0094-2405(00)00303-5].

A twin study of psychometric intelligence and efficiency of information processing

Intelligence (IQ) can be seen as the efficiency of mental processes or cognition, as can basic information processing (IP) tasks like those used in our ongoing Memory, Attention and Problem Solving (MAPS) study. Measures of IQ and IP are correlated and both have a genetic component, so we are studying how the genetic variance in IQ is related to the genetic variance in IP. We measured intelligence with five subscales of the Multidimensional Aptitude Battery (MAB). The IP tasks included four variants of choice reaction time (CRT) and a visual inspection time (IT). The influence of genetic factors on the variances in each of the IQ, IP, and IT tasks was investigated in 250 identical and nonidentical twin pairs aged 16 years. For a subset of 50 pairs we have test–retest data that allow us to estimate the stability of the measures. MX was used for a multivariate genetic analysis that addresses whether the variance in IQ and IP measures is possibly mediated by common genetic factors. Analyses that show the modeled genetic and environmental influences on these measures of cognitive efficiency will be presented and their relevance to ideas on intelligence will be discussed.

Psychophysiological phenotypes of cognition: The P3 and SW components

As part of a large ongoing project, the Memory, Attention and Problem Solving (MAPS) study, we investigated whether genetic variability explains some of the variance in psychophysiological correlates of brain function, namely, the P3 and SW components of event-related potentials (ERPs). These ERP measures are minute time recordings of brain processes and, because they reflect fundamental cognitive processing, provide a unique window on the millisecondto- millisecond transactions that occur at the cognitive level and taking place in the human brain. The extent to which the variance in P3 and SW components is influenced by genetic factors was examined in 350 identical and nonidentical twin pairs aged 16 years. ERPs were recorded from 15 scalp electrodes during the performance of a visuospatial delayed response task that engages working memory. Multivariate genetic analyses using MX were used to estimate genetic and environmental influences on individual differences in brain functioning and to identify putative genetic factors common to the ERP measures and psychometric IQ. For each of the ERP measures, correlation among electrode sites was high, a spatial pattern was evident, and a large part of the genetic variation in the ERPs appeared to be mediated by a common genetic factor. Moderate within-pair concordance in MZ pairs was found for all ERP measures, with higher correlations found for P3 than SW, and the MZ twin pair correlations were approximately twice the DZ correlations, suggesting a genetic influence. Correlations between ERP measures and psychometric IQ were found and, although moderately low, were evident across electrode site. The analyses show that the ERP components, P3 and SW, are promising phenotypes of the neuroelectrical activity of the brain and have the potential to be used in linkage and association analysis in the search for QTLs influencing cognitive function.

Minimum-order stable recursive filter design via the genetic algorithm approach

In this paper, the minimum-order stable recursive filter design problem is proposed and investigated. This problem is playing an important role in pipeline implementation sin signal processing. Here, the existence of a high-order stable recursive filter is proved theoretically, in which the upper bound for the highest order of stable filters is given. Then the minimum-order stable linear predictor is obtained via solving an optimization problem. In this paper, the popular genetic algorithm approach is adopted since it is a heuristic probabilistic optimization technique and has been widely used in engineering designs. Finally, an illustrative example is sued to show the effectiveness of the proposed algorithm.

A randomized trial of a problem-based learning approach for teaching epidemiology

Purpose. To conduct a controlled trial of traditional and problem-based learning (PBL) methods of teaching epidemiology. Method. All second-year medical students (n = 136) at The University of Western Australia Medical School were offered the chance to participate in a randomized controlled trial of teaching methods fur an epidemiology course. Students who consented to participate (n = 80) were randomly assigned to either a PBL or a traditional course. Students who did not consent or did not return the consent form (n = 56) were assigned to the traditional course, Students in both streams took identical quizzes and exams. These scores, a collection of semi-quantitative feedback from all students, and a qualitative analysis of interviews with a convenience sample of six students from each stream were compared. Results. There was no significant difference in performances on quizzes or exams between PBL and traditional students. Students using PBL reported a stronger grasp of epidemiologic principles, enjoyed working with a group, and, at the end of the course, were more enthusiastic about epidemiology and its professional relevance to them than were students in the traditional course. PBL students worked more steadily during the semester but spent only marginally more time on the epidemiology course overall. Interviews corroborated these findings. Non-consenting students were older (p < 0.02) and more likely to come from non-English-speaking backgrounds (p < 0.005). Conclusions. PBL provides an academically equivalent but personally far richer learning experience. The adoption of PBL approaches to medical education makes it important to study whether PBL presents particular challenges for students whose first language is not the language of instruction.

Analytical solution to the zero-inertia problem for surge flow phenomena in nonprismatic channels

Surge flow phenomena. e.g.. as a consequence of a dam failure or a flash flood, represent free boundary problems. ne extending computational domain together with the discontinuities involved renders their numerical solution a cumbersome procedure. This contribution proposes an analytical solution to the problem, It is based on the slightly modified zero-inertia (ZI) differential equations for nonprismatic channels and uses exclusively physical parameters. Employing the concept of a momentum-representative cross section of the moving water body together with a specific relationship for describing the cross sectional geometry leads, after considerable mathematical calculus. to the analytical solution. The hydrodynamic analytical model is free of numerical troubles, easy to run, computationally efficient. and fully satisfies the law of volume conservation. In a first test series, the hydrodynamic analytical ZI model compares very favorably with a full hydrodynamic numerical model in respect to published results of surge flow simulations in different types of prismatic channels. In order to extend these considerations to natural rivers, the accuracy of the analytical model in describing an irregular cross section is investigated and tested successfully. A sensitivity and error analysis reveals the important impact of the hydraulic radius on the velocity of the surge, and this underlines the importance of an adequate description of the topography, The new approach is finally applied to simulate a surge propagating down the irregularly shaped Isar Valley in the Bavarian Alps after a hypothetical dam failure. The straightforward and fully stable computation of the flood hydrograph along the Isar Valley clearly reflects the impact of the strongly varying topographic characteristics on the How phenomenon. Apart from treating surge flow phenomena as a whole, the analytical solution also offers a rigorous alternative to both (a) the approximate Whitham solution, for generating initial values, and (b) the rough volume balance techniques used to model the wave tip in numerical surge flow computations.

Heroin-assisted treatment as a response to the public health problem of opiate dependence

Injection drug use (involving the injection of illicit opiates) poses serious public health problems in many countries. Research has indicated that injection drug users are at higher risk for morbidity in the form of HIV/AIDS and Hepatitis B and C, and drug-related mortality, as well as increased criminal activity. Methadone maintenance treatment is the most prominent form of pharmacotherapy treatment for illicit opiate dependence in several countries, and its application varies internationally with respect to treatment regulations and delivery modes. In order to effectively treat those patients who have previously been resistant to methadone maintenance treatment, several countries have been studying and/or considering heroin-assisted treatment as a complementary form of opiate pharmacotherapy treatment. This paper provides an overview of the prevalence of injection drug use and the opiate dependence problem internationally, the current opiate dependence treatment landscape in several countries, and the status of ongoing or planned heroin-assisted treatment trials in Australia, Canada and certain European countries.