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Bidomain or monodomain model has frequently been used to study electrical activities in the cardiac tissue. The finite difference method with second order accuracy is commonly used to approximate the spatial derivatives of the governing equations numerically. In this work, a higher order finite difference scheme has been implemented to solve the nonlinear monodomain equation. The unknown transmembrane...
Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to combine recurrent neural networks which have recently shown effectiveness in item...
We consider the problem of anomaly localization in a sensor network for multivariate time-series data by computing anomaly scores for each variable separately. To estimate the sparse Gaussian graphical models (GGMs) learned from different sliding windows of the dataset, we propose a new model wherein we constrain sparsity directly through L0 constraint and apply an additional L2 regularization in...
This study presents a scalable and robust approach to spatial downscaling in the context of climate downscaling. We explore the ability of four techniques to downscale a climate variable to a given location of interest. As an example, we focus on downscaling daily mean air temperature at twelve stations located across the topographically complex province of British Columbia, Canada. The techniques...
Residential Demand Response has emerged as an instrument of the modern smart grid to alleviate supply and demand imbalances of electricity. Utilizing their flexibility of electricity demand, residential households are offered monetary incentives to temporarily reduce energy consumption during times when the grid is strained due to a supply shortage. In this paper, we estimate the magnitude of reductions...
Of the examples of clonal competition for antigen among lymphocytes, the recently predicted “Memory anti-Naïve” phenomenon occurs when the challenging antigen is not identical to the priming, and will be consequently bound with lower avidity by preexisting memory cells. In this study we use computer modeling and a systematic schedule of viral injections to disentangle the complex relationship between...
A new modeling approach and conceptual framework to the immune system response and its dual role with respect to cancer is proposed based on Applied Category Theory. States of cells and pathogenes are structured as mathematical structures (categories), the interactions, at a given phase, between cells of the immune system and pathogenes, correspond to a pair of adjunctions (adjoint functors), the...
An effective yellow fever vaccine has been available since 1937. However, some issues regarding its use remain open, such as the minimum dose that can provide immunity against the disease. Mathematical-computational tools can be useful to assist the search for answers to some of these open issues. In this context, this study presents a simplified mathematical-computational model of the human immune...
Action Rules are vital data mining method for gaining actionable knowledge from the datasets. Meta actions are the sub-actions to the Action Rules, which intends to change the attribute value of an object, under consideration, to attain the desirable value. The essence of this paper to propose a new optimized and more promising system, in terms of speed and efficiency, for generating meta-actions...
Over the past decades, numerous techniques have been developed to forecast the temporal evolution of epidemic outbreaks. This paper proposes an approach that combines high resolution agent-based models using realistic social contact networks for simulating epidemic evolution with a particle filter based method for assimilation based forecasting. Agent-based modeling using realistic social contact...
We consider the problem of causal structure learning from data with missing values, assumed to be drawn from a Gaussian copula model. First, we extend the 'Rank PC' algorithm, designed for Gaussian copula models with purely continuous data (so-called nonparanormal models), to incomplete data by applying rank correlation to pairwise complete observations and replacing the sample size with an effective...
With the rapid rise of various e-commerce and social network platforms, users are generating large amounts of heterogeneous behavior data, such as purchasehistory, adding-to-favorite, adding-to-cart and click activities, and this kind of user behavior data is usually binary, only reflecting a user's action or inaction (i.e., implicit feedback data). Tensor factorization is a promising means of modeling...
Clinical Decision Support (CDS) can be regarded as an information retrieval (IR) task, where medical records are used to retrieve the full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship...
This paper considers the application of finite-time control to a Cucker-Smale flocking model of autonomous agents with collision avoidance. A mathematical expression for the upper bound on the flocking time is derived. Previous results without considering collision avoidance showed that the flocking time decreases as the number of robots in the flock increases, which is counter-intuitive. We showed...
Meshless methods to simulate fluid flows have been increasingly evolving through the years since they are a great alternative to deal with large deformations, which is where mesh-based methods fail to perform efficiently. A well known meshless method is the Moving Particle Semi-implicit (MPS) method, which was designed to simulate free-surface truly incompressible fluid flows. Many variations and...
Virtual Reality (VR) has as goal the creation of digital three dimensional environments to run in real time interactive and realistic experiences. The experiences can be augmented by the use of haptic systems, able to provide sense of touch in virtual objects. The anesthesia procedure is present in all surgeries to keep stable the patient physiology. The use of VR based simulators for anesthesia training...
The maintenance of complex systems is the most expensive and difficult phase of the software development process life cycle due to the significant amount of time spent by developers trying to comprehend the structure and mainly the system behavior. Therefore, this paper aims to present an approach to support the understanding of the dynamic behavior of complex software systems through Virtual Reality...
In the intelligent transportation system, the geometry for the street is an important factor in vehicle monitoring. It helps to point out areas of interest, reduce computing costs, increased accuracy in detecting and identifying objects and facilitate data collection. In this paper, a new robust method of extracting the geometric model of the road is presented. The method is based on vehicle motion...
Text compression based on graph representation model (referred to as “text graph”) is a fairly common approach. However, one of the difficult issues that this approach poses is how to identify nodes that have co-reference relationships in a text graph? This issue is a question that needs to be answered because previous studies have confirmed that it has a great influence on the quality of the summaries...
Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose...
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