Abstract Forward modeling approaches in cosmology have made it possible to reconstruct the initial conditions at beginning of Universe from observed survey data. However high dimensionality parameter space still poses a challenge explore full posterior, with traditional algorithms such as Hamiltonian Monte Carlo (HMC) being computationally inefficient due generating correlated samples and perfo...

We demonstrated previously the establishment of a human cytotoxic T-cell clone, 1,u\u i. under culturing with recombinant interleukin that showed the specific cytotoxicity against an autologous breast tumor cell line, HMC-1-8. In the present study, the autologous tumor specific antigens that could be involved in this cytotoxicity were extracted by using n-butyl alcohol and were analyzed for the...

Among Markov chain Monte Carlo algorithms, Hamiltonian (HMC) is often the algorithm of choice for complex, high-dimensional target distributions; however, its efficiency notoriously sensitive to integration-time tuning parameter. When integrating both forward and backward in time using same leapfrog integration step as HMC, set apogees, local maxima potential along a path, whatever point (posit...

Asthma is a chronic inflammatory disease of the airways. Mast cell derived cytokines may mediate both airway inflammation as well as remodeling. It has also been shown that fibroblasts can be the source of proinflammatory cytokines. In the human airways, mast cellfibroblast interactions may have pivotal effects on modulating inflammation. To study this further, we cocultured normal human lung f...

In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore, neighborhood information is included ...

During the last years, immense progress has been achieved in the stochastic treatment of quantum chromodynamics (QCD) on the lattice. Algorithms based on a molecular dynamics like treatment of gauge and quark elds have opened the door to exact ab-initio simulations. The Hybrid Monte Carlo (HMC) algorithm, the most prominent representative of full QCD algorithms, is the favorite scheme to includ...

Prediction of gene function is a so-called hierarchical multilabel classification (HMC) task: a single instance can be labelled with multiple classes rather than just one (i.e., a gene can have multiple functions), and these classes are organized in a hierarchy. Many machine learning methods focus on learning predictive models with a single target variable. One can then learn to predict all cla...

This paper develops Bayesian track extraction algorithms for targets modelled as hidden reciprocal chains (HRC). HRC are a class of finite-state random process models that generalise the familiar hidden Markov chains (HMC). HRC are able to model the “intention” of a target to proceed from a given origin to a destination, behaviour which cannot be properly captured by a HMC. While Bayesian estim...

Hamiltonian Monte Carlo (HMC) is a powerful Markov chain Monte Carlo (MCMC) method for performing approximate inference in complex probabilistic models of continuous variables. In common with many MCMC methods, however, the standard HMC approach performs poorly in distributions with multiple isolated modes. We present a method for augmenting the Hamiltonian system with an extra continuous tempe...

We examined the effect of intact human mast cells (HMC-1 5C6) and their selected mediators on interleukin-6 (IL-6) production and bone morphogenetic protein-2 (BMP-2) expression in human coronary artery endothelial cells (HCAEC) in the presence and absence of lipopolysaccharide (LPS). Scanning electron microscopy showed that HMC-1 5C6 cells adhere to HCAEC in cocultures. Addition of HMC-1 5C6 c...