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PubMed2004

A Boolean Hebb rule for binary associative memory design.

A binary associative memory design procedure that gives a Hopfield network with a symmetric binary weight matrix is introduced in this paper. The proposed method is based on introducing the memory vectors as maximal independent sets to an undirected graph, which is constructed by Boolean operations analogous to the conventional Hebb rule. The parameters of the resulting network is then determined via the adjacency matrix of this graph in order to find a maximal independent set whose characteristic vector is close to the given distorted vector. We show that the method provides attractiveness for each memory vector and avoids spurious memories whenever the set of given memory vectors satisfy certain compatibility conditions, which implicitly imply sparsity. The applicability of the design method is finally investigated by a quantitative analysis of the compatibility conditions.

Memory↗
PubMed2004

Bayesian support vector regression using a unified loss function.

In this paper, we use a unified loss function, called the soft insensitive loss function, for Bayesian support vector regression. We follow standard Gaussian processes for regression to set up the Bayesian framework, in which the unified loss function is used in the likelihood evaluation. Under this framework, the maximum a posteriori estimate of the function values corresponds to the solution of an extended support vector regression problem. The overall approach has the merits of support vector regression such as convex quadratic programming and sparsity in solution representation. It also has the advantages of Bayesian methods for model adaptation and error bars of its predictions. Experimental results on simulated and real-world data sets indicate that the approach works well even on large data sets.

Bayes Theorem↗
PubMed2005

Sparse component analysis and blind source separation of underdetermined mixtures.

In this letter, we solve the problem of identifying matrices S is an element of R(n x N) and A is an element of R(m x n) knowing only their multiplication X = AS, under some conditions, expressed either in terms of A and sparsity of S (identifiability conditions), or in terms of X (sparse component analysis (SCA) conditions). We present algorithms for such identification and illustrate them by examples.

Algorithms↗
PubMed2006

Dynamics analysis and analog associative memory of networks with LT neurons.

The additive recurrent network structure of linear threshold neurons represents a class of biologically-motivated models, where nonsaturating transfer functions are necessary for representing neuronal activities, such as that of cortical neurons. This paper extends the existing results of dynamics analysis of such linear threshold networks by establishing new and milder conditions for boundedness and asymptotical stability, while allowing for multistability. As a condition for asymptotical stability, it is found that boundedness does not require a deterministic matrix to be symmetric or possess positive off-diagonal entries. The conditions put forward an explicit way to design and analyze such networks. Based on the established theory, an alternate approach to study such networks is through permitted and forbidden sets. An application of the linear threshold (LT) network is analog associative memory, for which a simple design method describing the associative memory is suggested in this paper. The proposed design method is similar to a generalized Hebbian approach, but with distinctions of additional network parameters for normalization, excitation and inhibition, both on a global and local scale. The computational abilities of the network are dependent on its nonlinear dynamics, which in turn is reliant upon the sparsity of the memory vectors.

Algorithms↗
PubMed2006

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Algorithms↗
PubMed2004

A Bayesian approach to joint feature selection and classifier design.

This paper adopts a Bayesian approach to simultaneously learn both an optimal nonlinear classifier and a subset of predictor variables (or features) that are most relevant to the classification task. The approach uses heavy-tailed priors to promote sparsity in the utilization of both basis functions and features; these priors act as regularizers for the likelihood function that rewards good classification on the training data. We derive an expectation-maximization (EM) algorithm to efficiently compute a maximum a posteriori (MAP) point estimate of the various parameters. The algorithm is an extension of recent state-of-the-art sparse Bayesian classifiers, which in turn can be seen as Bayesian counterparts of support vector machines. Experimental comparisons using kernel classifiers demonstrate both parsimonious feature selection and excellent classification accuracy on a range of synthetic and benchmark data sets.

Algorithms↗
PubMed2005

Sparse Bayesian learning for efficient visual tracking.

This paper extends the use of statistical learning algorithms for object localization. It has been shown that object recognizers using kernel-SVMs can be elegantly adapted to localization by means of spatial perturbation of the SVM. While this SVM applies to each frame of a video independently of other frames, the benefits of temporal fusion of data are well-known. This is addressed here by using a fully probabilistic Relevance Vector Machine (RVM) to generate observations with Gaussian distributions that can be fused over time. Rather than adapting a recognizer, we build a displacement expert which directly estimates displacement from the target region. An object detector is used in tandem, for object verification, providing the capability for automatic initialization and recovery. This approach is demonstrated in real-time tracking systems where the sparsity of the RVM means that only a fraction of CPU time is required to track at frame rate. An experimental evaluation compares this approach to the state of the art showing it to be a viable method for long-term region tracking.

Algorithms↗
PubMed2004

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality.

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Journal Article↗
PubMed2004

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization.

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

Journal Article↗
PubMedDate not supplied

Visualization of vector fields using seed LIC and volume rendering.

Line Integral Convolution (LIC) is a powerful texture-based technique for visualizing vector fields. Due to the high computational expense of generating the 3D textures and the difficulties of effectively displaying the result, LIC has most commonly been used to depict vector fields in 2D or over a surface in 3D. Here, we propose new methods for more effective volume visualization of three-dimensional vector fields using LIC: 1) We present a fast method for computing volume LIC textures that exploits the sparsity of the input texture. 2) We propose the use of a shading technique, called limb darkening, to reveal the depth relations among the field lines. The shading effect is obtained simply by using appropriate transfer functions and, therefore, avoids using expensive shading techniques. 3) We demonstrate how two-field visualization techniques can be used to enhance the visual information describing a vector field. The volume LIC textures are rendered using texture-based rendering techniques, which allows interactive exploration of a vector field.

Journal Article↗
PubMed2001

Errors-in-variables in joint population pharmacokinetic/pharmacodynamic modeling.

Pharmacokinetic (PK) models describe the relationship between the administered dose and the concentration of drug (and/or metabolite) in the blood as a function of time. Pharmacodynamic (PD) models describe the relationship between the concentration in the blood (or the dose) and the biologic response. Population PK/PD studies aim to determine the sources of variability in the observed concentrations/responses across groups of individuals. In this article, we consider the joint modeling of PK/PD data. The natural approach is to specify a joint model in which the concentration and response data are simultaneously modeled. Unfortunately, this approach may not be optimal if, due to sparsity of concentration data, an overly simple PK model is specified. As an alternative, we propose an errors-in-variables approach in which the observed-concentration data are assumed to be measured with error without reference to a specific PK model. We give an example of an analysis of PK/PD data obtained following administration of an anticoagulant drug. The study was originally carried out in order to make dosage recommendations. The prior for the distribution of the true concentrations, which may incorporate an individual's covariate information, is derived as a predictive distribution from an earlier study. The errors-in-variables approach is compared with the joint modeling approach and more naive methods in which the observed concentrations, or the separately modeled concentrations, are substituted into the response model. Throughout, a Bayesian approach is taken with implementation via Markov chain Monte Carlo methods.

Anticoagulants↗
PubMed1995

Cutaneous ultrastructural features of the flaky skin (fsn) mouse mutation.

An autosomal recessive genetic disease with clinical and histopathological skin features resembling human psoriasis vulgaris occurs naturally in flaky skin mice (fsn/fsn). Affected mice are normal at birth, except for a hypochromic anemia. Subsequently, they develop hyperkeratotic plaques and acanthosis with elongation of rete ridges. Scanning electron microscopic examination revealed a greatly thickened epidermis, a sparsity of hairs and scale accumulations on the epidermal surface. Hair shafts had conspicuous pits, striations, and exophytic protrusions. Nails were bent at a 90 degrees angle with surface irregularities and accumulations of scale at the nail base. Transmission electron microscopic examination showed increased epidermal thickness, mitochondrial aberrations, and intraepidermal invasion by neutrophils. Keratohyalin abnormalities were detected using immunocytochemical staining for profilaggrin. At the dermal-epidermal junction, numerous macrophages and mast cells were seen in close proximity to focal dissolutions of the basement membrane. A high density of collagen fibers and cellular infiltrates were evident in the papillary dermis. This constellation of ultrastructural aberrations is typically found in psoriasis vulgaris and supports the theory that the flaky skin mouse mutation is a naturally occurring analog to one variety of human psoriasis vulgaris.

Anemia, Hypochromic↗
PubMed1987

Sclerosing mucoepidermoid tumour of the parotid gland: report of a case.

A case of a low-grade mucoepidermoid tumour showing an unusual pattern of extensive central keloid-like sclerosis is reported. The sparsity of tumour islands renders recognition of its neoplastic nature difficult. It is postulated that the sclerosis results from exaggerated post-inflammatory scarring due to extravasation of mucin from the tumour into the stroma.

Adult↗
PubMed1982

Treatment of hypopigmented sarcoidosis with 8-methoxypsoralen and long wave ultraviolet light.

A patient with hypopigmented sarcoidosis involving the face was treated with topical 8-methoxypsoralen and long wave ultraviolet light. Satisfactory repigmentation occurred within eight months of tri-weekly therapy. Electron microscopic study of involved, untreated skin showed abnormalities of melanocytes, including cytoplasmic vacuolization and the presence of melanosome complexes. Treated skin showed sparsity of melanosomes within the central portions of some melanocytes and large numbers of melanosomes within melanocytic dendrites. The clinical and ultrastructural findings suggest the possibility of toxic damage to melanocytes, perhaps with abnormalities of melanosome transfer, in lesions of hypopigmented sarcoidosis. These abnormalities appear to be responsive to psoralen-UVA therapy.

Adult↗
PubMed2005

Bipolar II disorder: a review.

OBJECTIVES: To review the current knowledge of bipolar II disorder. METHODS: Literature was reviewed after conducting a Medline search and a hand search of relevant literature. RESULTS: Bipolar II disorder is a common disorder, with a prevalence of approximately 3-5%. Distinct clinical features of bipolar II disorder have been described. The key to diagnosis is the recognition of past hypomania, while depression is the typical presenting feature of the illness. This is responsible for a significant rate of missed diagnosis, and consequent management according to unipolar guidelines. It is unclear if bipolar II disorder is over-represented amongst resistant depression populations and if abrupt offset of antidepressant action is a phenomenon over represented in bipolar II disorder, reflecting induction of predominantly depressive cycling. A few mood-stabilizer studies available provide provisional suggestion of utility. A supportive role for psychosocial therapies is suggested, however, there is a sparsity of published studies specific to bipolar II disorder cohorts. A small number of short-term antidepressant trials have suggested efficacy, however, compelling long-term maintenance data is absent. CONCLUSIONS: An emerging literature on the specific clinical signature and management of the disorder exists, however, this is disproportionately small relative to the epidemiology and clinical significance of the disorder.

Anxiety Disorders↗
PubMed1996

Ambulatory monitoring and conventional cystometry in asymptomatic female volunteers.

OBJECTIVES: Ambulatory monitoring continues to gain acceptance and increasing clinical application. There remains, however, a sparsity of published data asymptomatic volunteers, particulary women. Our aim was, by study of such a group, to establish normal ranges for cystometric variables on ambulatory monitoring. PARTICIPANTS: We recruited 22 women from staff and gynaecological inpatients. A standard questionnaire of urinary complaints was administered and any woman with significant symptoms was excluded. Conventional cystometry and ambulatory monitoring were undertaken in all volunteers in random order. Volunteers were asked to go about everyday activities and to keep a detailed event diary whilst undergoing ambulatory monitoring. RESULTS: Using standard International Continence Society terminology, detrusor instability was found in 18% of volunteers on conventional cystometry and in 68% on ambulatory monitoring. Significant differences were found between ambulatory monitoring and conventional cystometry with respect to the detrusor pressure rise on filling (P < 0.001) and voided volumes (P < 0.001). A significant difference was also present with respect to voiding pressures (P < 0.001), although the absence of simultaneous flow rate measurement makes absolute separation of voiding pressure from post-voiding after contractions difficult. CONCLUSIONS: Ambulatory monitoring detects a greater number of abnormalities based on conventional cystometric criteria of normality. Before we can usefully apply the method to symptomatic patients we must first define normal ranges for filling and voiding cystometry for the technique. This study moves towards this goal. (The addition of flow rate data will permit accurate identification and measurement of voiding pressures.)

Adult↗
PubMed2006

Vero-cytotoxigenic Escherichia coli O157 in pasteurized milk containers at the point of retail: a qualitative approach to exposure assessment.

Dairies within the United Kingdom are classified into two groups, namely, off-farm and on-farm dairies (the latter often being small scale). We propose a model for the probability of milk sold as pasteurized reaching the point of retail contaminated with Vero-cytotoxigenic Escherichia coli (VTEC) O157 from each of these two pathways. We evaluate qualitatively the exposures inherent in each, and compare and contrast the two situations. The model framework is generic, in that it can in principle be used, with the relevant data modifications, to provide a qualitative assessment of the likely exposure from milk sold as pasteurized to any potentially milk-borne pathogenic organism. Furthermore, the methodological approaches presented are widely applicable in the microbial risk assessment field. The specific example presented will be of particular interest to the UK dairy and public health communities: we conclude that the exposure potential per liter consumed from milk processed in off-farm dairies is negligible, whereas the exposure potential per liter consumed from milk processed on-farm is low, but not sufficiently small to be regarded as negligible. We also identify areas of data sparsity, which need to be addressed for quantitative risk assessment to proceed, and highlight the critical points in the pasteurized milk production chain, which, in the event of a breakdown, have the potential to increase the risk to the consumer.

Animals↗
PubMed1993

Morphology of the cardiac conduction system in patients with electrophysiologically proven dual atrioventricular nodal pathways.

INTRODUCTION: Although the electrophysiologic criteria for dual atrioventricular nodal pathways are well established, the anatomical substrate is still unclear. METHODS AND RESULTS: We examined the hearts from 10 patients who had been studied electrophysiologically prior to cardiac transplantation. All 10 patients were male, aged 22 to 60 years. Nine of the 10 patients had dual atrioventricular nodal pathways according to accepted criteria. Histologic studies of the atrioventricular conduction system showed normal structure of the atrioventricular node in all 10 hearts, with minor variations within the node in 3 cases, within the penetrating bundle in 3 cases, and within the nonbranching bundle in 3 cases. The atrial approaches to the atrioventricular node were generally scanty in 6 hearts. The solitary case that was shown electrophysiologically to lack dual pathways had no obvious difference in the structure of the nodal area other than sparsity of transitional cells. We were unable to locate any extranodal atrial tracts as described by other investigators. CONCLUSION: The anatomical substrate for conduction over dual pathways may be too subtle to be detected by gross morphologic studies. Since dual pathways were unmasked in all patients but one during electrophysiologic studies, it may be that the potential for these pathways is ubiquitous.

Adult↗