The detection of multiple targets in a single sample is important for many applications, including medical diagnostics, genotyping, and drug discovery. The current approaches to multiplexing, such as planar arrays (such as DNA microarrays) and suspension (particle-based) arrays, require expensive or cumbersome means of encoding, decoding, or functionalizing substrates. Currently, commercially a...

MOTIVATION The programs currently available for the analysis of nucleic acid and protein sequences suffer from a variety of problems: Web-based programs often require inconvenient reformatting of sequences when proceeding from one analysis to the next, and commercial-console-based programs are cost prohibitive. Here, we report the development of DNASSIST:, an inexpensive, multiple-document, int...

We evaluate the use of Deep Belief Networks as classifiers in a text categorisation task (assigning category labels to documents) in the biomedical domain. Our preliminary results indicate that compared to Support Vector Machines, Deep Belief Networks are superior when a large set of training examples is available, showing an F-score increase of up to 5%. In addition, the training times for DBN...

Standard Monte Carlo simulation needs prohibitive time to achieve reasonable estimations. for untractable integrals (i.e. multidimensional integrals and/or intergals with complex integrand forms). Several statistical technique, called variance reduction methods, are used to reduce the simulation time. In this note, we propose a generalization of the well known antithetic variate method. Princip...

Clinical diagnosis environments often require the availability of processed data in real-time, unfortunately, reconstruction times are prohibitive on conventional computers, neither the adoption of expensive parallel computers seems to be a viable solution. Here, we focus on development of mathematical software on high performance architectures for Total Variation based regularization reconstru...

Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical pr...

Macro economic studies of the costs of reducing CO2 emissions generally estimate the global cost of stabilising the atmospheric concentrations of CO2 in the range 350–550 ppm in trillions of USD. This creates the impression that the cost of CO2 reductions is so large that it threatens economic development. But, presented in another way, a completely different picture emerges. There is widesprea...

thyroid nodules are common, occurring in almost two-thirds of some populations; among these only about 7% are malignant. the most important question with any new discovered thyroid nodule is, “is this malignant?” the main arbiter of malignancy or benignity remains fine needle aspiration and the mainstay of treatment surgery. but given the resources involved, doing an fnac or surgery in every di...

this research presents a new application of the cloud theory-based simulated annealing algorithm to solve mixed model assembly line sequencing problems where line stoppage cost is expected to be optimized. this objective is highly significant in mixed model assembly line sequencing problems based on just-in-time production system. moreover, this type of problem is np-hard and solving this probl...

An optimal solution to the problem of scheduling real-time tasks on a set of identical processors is derived. The described approach is based on solving an equivalent uniprocessor real-time scheduling problem. Although there are other scheduling algorithms that achieve optimality, they usually impose prohibitive preemption costs. Unlike these algorithms, it is observed through simulation that t...