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At least 397 records · Page 22Linked to original sources
PubMed2006

Large-scale data exploration with the hierarchically growing hyperbolic SOM.

We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that allows for incremental training with an automated adaptation of lattice size to achieve a prescribed quantization error and (ii) an approximate best match search that utilizes the special structure of the hyperbolic lattice to achieve a tremendous speed-up for large map sizes. Using the MNIST and the Reuters-21578 database as benchmark datasets, we show that the H2SOM yields a highly efficient visualization algorithm that combines the virtues of the SOM with extremely rapid training and low quantization and classification errors.

Algorithms↗
PubMed2004

A new approach to data storage using localized structures.

In this paper we describe how to use the bifurcation structure of static localized solutions in one dimension to store information on a medium in such a way that no extrinsic grid is needed to locate the information. We demonstrate that these principles, deduced from the mathematics adapted to describe one-dimensional media, also allow one to store information on two-dimensional media.

Computers, Molecular↗
PubMed2002

TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing.

SUMMARY: TREE-PUZZLE is a program package for quartet-based maximum-likelihood phylogenetic analysis (formerly PUZZLE, Strimmer and von Haeseler, Mol. Biol. Evol., 13, 964-969, 1996) that provides methods for reconstruction, comparison, and testing of trees and models on DNA as well as protein sequences. To reduce waiting time for larger datasets the tree reconstruction part of the software has been parallelized using message passing that runs on clusters of workstations as well as parallel computers. AVAILABILITY: http://www.tree-puzzle.de. The program is written in ANSI C. TREE-PUZZLE can be run on UNIX, Windows and Mac systems, including Mac OS X. To run the parallel version of PUZZLE, a Message Passing Interface (MPI) library has to be installed on the system. Free MPI implementations are available on the Web (cf. http://www.lam-mpi.org/mpi/implementations/).

Algorithms↗
PubMed2006

Three-dimensional cerebral contrast-enhanced magnetic resonance venography at 3.0 Tesla: initial results using highly accelerated parallel acquisition.

OBJECTIVE: The objective of this study was to evaluate a high spatial resolution 3-dimensional (3D) contrast-enhanced magnetic resonance (CE-MR) venography protocol for evaluation of intracranial venous system using highly accelerated parallel imaging at 3.0 T. MATERIALS AND METHODS: Ten patients (4 male, 6 female; age, 38-76 years) with suspected cerebrovascular disease were prospectively studied on a 32-channel 3.0 T MR system. After a single intravenous contrast injection, high spatial resolution 3D CE-MR angiography of the entire supraaortic arteries was performed followed immediately by 3D cerebral CE-MR venography. By using a fast 3D gradient-recalled-echo sequence with elliptic centric k-space ordering and highly accelerated parallel acquisition (acceleration factor 3 and 2 in phase and slice encoding direction, respectively), 3D cerebral CE-MR venography was acquired with voxel dimensions of 0.7 x 0.7 x 0.8 mm in 24 seconds. Image evaluation was performed independently by 2 neuroradiologists for overall image quality, presence of noise, and artifacts. The image quality of 30 venous segments was evaluated in each subject using a 1 to 4 scoring scale. In 2 patients, catheter angiography was available for correlation. Statistical analysis of data was performed by using Wilcoxon rank sum test and kappa coefficient. RESULTS: All studies were determined to be of diagnostic image quality by both observers. The majority (90%) of cerebral venous segments were evaluated to be of diagnostic image quality (median, 3; range, 3-4) by both readers and with excellent interobserver agreement (kappa = 0.86; 95% confidence interval, 0.79-0.93). One meningioma invading the superior sagittal sinus and one superior sagittal sinus fistula were detected subsequently confirmed by conventional angiography. CONCLUSION: High spatial resolution 3D cerebral CE-MR venography is feasible and promising. Using a 32-channel 3.0 T system combined with multichannel array coils effectively supports highly accelerated parallel imaging, enabling subsequent acquisition of both high spatial resolution CE-MR angiography and CE-MR venography after a single contrast injection without impairing the image quality. More extensive clinical studies are warranted to establish the range of applications and confirm the accuracy of this technique.

Adult↗
PubMed2005

Aligning biological sequences on distributed bus networks: a divisible load scheduling approach.

In this paper, we design a multiprocessor strategy that exploits the computational characteristics of the algorithms used for biological sequence comparison proposed in the literature. We employ divisible load theory (DLT) that is suitable for handling large scale processing on network based systems. For the first time in the domain of DLT, the problem of aligning biological sequences is attempted. The objective is to minimize the total processing time of the alignment process. In designing our strategy, DLT facilitates a clever partitioning of the entire computation process involved in such a way that the overall time consumed for aligning the sequences is a minimum. The partitioning takes into account the computation speeds of the nodes and the underlying communication network. Since this is a real-life application, the post-processing phase becomes important, and hence we consider propagating the results back in order to generate an exact alignment. We consider several cases in our analysis such as deriving closed-form solutions for the processing time for heterogeneous, homogeneous, and networks with slow links. Further, we attempt to employ a multiinstallment strategy to distribute the tasks such that a higher degree of parallelism can be achieved. For slow networks, our strategy recommends near-optimal solutions. We derive an important condition to identify such cases and propose two heuristic strategies. Also, our strategy can be extended for multisequence alignment by utilizing a clustering strategy such as the Berger-Munson algorithm proposed in the literature. Finally, we use real-life DNA samples of house mouse mitochondrion (Mus Musculus Mitochondrion, NC_001569) consisting of 16,295 residues and the DNA of human mitochondrion (Homo Sapiens Mitochondrion, NC_001807) consisting of 16,571 residues, obtainable from the GenBank, in our rigorous simulation experiments to illustrate all the theoretical findings.

Algorithms↗
PubMed2005

Partially connected feedforward neural networks structured by input types.

This paper proposes a new method to model partially connected feedforward neural networks (PCFNNs) from the identified input type (IT) which refers to whether each input is coupled with or uncoupled from other inputs in generating output. The identification is done by analyzing input sensitivity changes as amplifying the magnitude of inputs. The sensitivity changes of the uncoupled inputs are not correlated with the variation on any other input, while those of the coupled inputs are correlated with the variation on any one of the coupled inputs. According to the identified ITs, a PCFNN can be structured. Each uncoupled input does not share the neurons in the hidden layer with other inputs in order to contribute to output in an independent manner, while the coupled inputs share the neurons with one another. After deriving the mathematical input sensitivity analysis for each IT, several experiments, as well as a real example (blood pressure (BP) estimation), are described to demonstrate how well our method works.

Algorithms↗
PubMed2005

Mixtures-of-experts of autoregressive time series: asymptotic normality and model specification.

We consider a class of nonlinear models based on mixtures of local autoregressive time series. At any given time point, we have a certain number of linear models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, we assume the existence of a latent multinomial variable, whose distribution depends on the same covariates as the experts, that determines which linear process is observed. This structure, denoted as mixture-of-experts (ME), is considerably flexible in modeling the conditional mean function, as shown by Jiang and Tanner. In this paper, we present a formal treatment of conditions to guarantee the asymptotic normality of the maximum likelihood estimator (MLE), under stationarity and nonstationarity, and under correct model specification and model misspecification. The performance of common model selection criteria in selecting the number of experts is explored via Monte Carlo simulations. Finally, we present applications to simulated and real data sets, to illustrate the ability of the proposed structure to model not only the conditional mean, but also the whole conditional density.

Algorithms↗
PubMed2005

Gabor wavelet associative memory for face recognition.

This letter describes a high-performance face recognition system by combining two recently proposed neural network models, namely Gabor wavelet network (GWN) and kernel associative memory (KAM), into a unified structure called Gabor wavelet associative memory (GWAM). GWAM has superior representation capability inherited from GWN and consequently demonstrates a much better recognition performance than KAM. Extensive experiments have been conducted to evaluate a GWAM-based recognition scheme using three popular face databases, i.e., FERET database, Olivetti-Oracle Research Lab (ORL) database and AR face database. The experimental results consistently show our scheme's superiority and demonstrate its very high-performance comparing favorably to some recent face recognition methods, achieving 99.3% and 100% accuracy, respectively, on the former two databases, exhibiting very robust performance on the last database against varying illumination conditions.

Algorithms↗
PubMed2005

A universal learning rule that minimizes well-formed cost functions.

In this paper, we analyze stochastic gradient learning rules for posterior probability estimation using networks with a single layer of weights and a general nonlinear activation function. We provide necessary and sufficient conditions on the learning rules and the activation function to obtain probability estimates. Also, we extend the concept of well-formed cost function, proposed by Wittner and Denker, to multiclass problems, and we provide theoretical results showing the advantages of this kind of objective functions.

Algorithms↗
PubMed2005

Inland and coastal flooding: developments in prediction and prevention.

We review the scientific and engineering understanding of various types of inland and coastal flooding by considering the different causes and dynamic processes involved, especially in extreme events. Clear progress has been made in the accuracy of numerical modelling of meteorological causes of floods, hydraulics of flood water movement and coastal wind-wave-surge. Probabilistic estimates from ensemble predictions and the simultaneous use of several models are recent techniques in meteorological prediction that could be considered for hydraulic and oceanographic modelling. The contribution of remotely sensed data from aircraft and satellites is also considered. The need to compare and combine statistical and computational modelling methodologies for long range forecasts and extreme events is emphasized, because this has become possible with the aid of kilometre scale computations and network grid facilities to simulate and analyse time-series and extreme events. It is noted that despite the adverse effects of climatic trends on flooding, appropriate planning of rapidly growing urban areas could mitigate some of the worst effects. However, resources for flood prevention, including research, have to be considered in relation to those for other natural disasters. Policies have to be relevant to the differing geology, meteorology and cultures of the countries affected.

Computer Simulation↗
PubMed2006

Breast tumor malignancy modelling using evolutionary neural logic networks.

The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.

Artificial Intelligence↗
PubMed2006

An introduction to coil array design for parallel MRI.

The basic principles of radiofrequency coil array design for parallel MRI are described from both theoretical and practical perspectives. Because parallel MRI techniques rely on coil array sensitivities to provide spatial information about the sample, a careful choice of array design is essential. The concepts of coil array spatial encoding are first discussed from four qualitative perspectives. These qualitative descriptions include using coil arrays to emulate spatial harmonics, choosing coils with selective sensitivities to aliased pixels, using coil sensitivities with broad k-space reception profiles, and relying on detector coils to provide a set of generalized projections of the sample. This qualitative discussion is followed by a quantitative analysis of coil arrays, which is discussed in terms of the baseline SNR of the received images as well as the noise amplifications (g-factor) in the reconstructed data. The complications encountered during the experimental evaluation of coil array SNR are discussed, and solutions are proposed. A series of specific array designs are reviewed, with an emphasis on the general design considerations that motivate each approach. Finally, a set of special topics is discussed, which reflect issues that have become important, especially as arrays are being designed for more high-performance applications of parallel MRI. These topics include concerns about the depth penetration of arrays composed of small elements, the use of adaptive arrays for systems with limited receiver channels, the management of inductive coupling between array elements, and special considerations required at high field strengths. The fundamental limits of spatial encoding using coil arrays are discussed, with a primary emphasis on how the determination of these limits impacts the design of optimized arrays. This review is intended to provide insight into how arrays are currently used for parallel MRI and to place into context the new innovations that are to come.

Algorithms↗
PubMed2006

A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising.

In this paper, a simple moving average-based computing method for real-time QRS detection is proposed. In addition, for signal preprocessing our detection algorithm also incorporates a wavelet-based denoising procedure to effectively reduce the noise level for electrocardiogram (ECG) data. The overall computational structure of the proposed algorithm allows the QRS detection to be performed and implemented in real-time with high time- and memory-efficiency. Algorithm performance was evaluated against the MIT-BIH Arrhythmia Database. The numerical results indicated that the novel algorithm finally achieved about 99.5% of the detection rate for the standard database, and also, it could function reliably even under the condition of poor signal quality in the measured ECG data.

Algorithms↗
PubMed2003

Beyond input-output computings: error-driven emergence with parallel non-distributed slime mold computer.

The emergence derived from errors is the key importance for both novel computing and novel usage of the computer. In this paper, we propose an implementable experimental plan for the biological computing so as to elicit the emergent property of complex systems. An individual plasmodium of the true slime mold Physarum polycephalum acts in the slime mold computer. Modifying the Elementary Cellular Automaton as it entails the global synchronization problem upon the parallel computing provides the NP-complete problem solved by the slime mold computer. The possibility to solve the problem by giving neither all possible results nor explicit prescription of solution-seeking is discussed. In slime mold computing, the distributivity in the local computing logic can change dynamically, and its parallel non-distributed computing cannot be reduced into the spatial addition of multiple serial computings. The computing system based on exhaustive absence of the super-system may produce, something more than filling the vacancy.

Animals↗
PubMed2004

Evaluation of parallel decomposition methods for biomechanical optimizations.

As the complexity of musculoskeletal models continues to increase, so will the computational demands of biomechanical optimizations. For this reason, parallel biomechanical optimizations are becoming more common. Most implementations parallelize the optimizer. In this study, an alternate approach is investigated that parallelizes the analysis function (i.e., a kinematic or dynamic simulation) called repeatedly by the optimizer to calculate the cost function and constraints. To evaluate this approach, a system identification problem involving a kinematic ankle joint model was solved using a gradient-based optimizer and three parallel decomposition methods: gradient calculation decomposition, analysis function decomposition, or both methods combined. For a given number of processors, analysis function decomposition exhibited the best performance despite the highest communication and synchronization overhead, while gradient calculation decomposition demonstrated the worst performance due to the fact that the necessary line searches were not performed in parallel. These findings suggest that the method of parallelization most commonly used for biomechanical optimizations may not be the most efficient, depending on the optimization algorithm used. In many applications, the best computational strategy may be to focus on parallelizing the analysis function.

Algorithms↗
PubMed2005

IMRT planning on adaptive volume structures--a decisive reduction in computational complexity.

The objective of radiotherapy planning is to find a compromise between the contradictive goals of delivering a sufficiently high dose to the target volume while widely sparing critical structures. The search for such a compromise requires the computation of several plans, which mathematically means solving several optimization problems. In the case of intensity modulated radiotherapy (IMRT) these problems are large-scale, hence the accumulated computational expense is very high. The adaptive clustering method presented in this paper overcomes this difficulty. The main idea is to use a preprocessed hierarchy of aggregated dose-volume information as a basis for individually adapted approximations of the original optimization problems. This leads to a decisively reduced computational expense: numerical experiments on several sets of real clinical data typically show computation times decreased by a factor of about 10. In contrast to earlier work in this field, this reduction in computational complexity will not lead to a loss in accuracy: the adaptive clustering method produces the optimum of the original optimization problem.

Algorithms↗