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Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate...
Mimicking the collaborative behavior of biological swarms, such as bird flocks and ant colonies, Swarm Intelligence algorithms provide efficient solutions for various optimization problems. On the other hand, a computational model of the human brain, spiking neural networks, has been showing great promise in recognition, inference, and learning, due to recent emergence of neuromorphic hardware for...
Rebooting computing using in-memory architectures relies on the ability of emerging devices to execute a legacy software stack. In this paper, we present our approach of executing compute kernels written in a subset of the C programming language using flow-based computing on nanoscale memristor crossbars. Our approach also tests the correctness of the design using the parallel Xyces electronic simulation...
Co-evolution exists ubiquitously in biological systems. At the molecular level, interacting proteins, such as ligands and their receptors and components in protein complexes, co-evolve to maintain their structural and functional interactions. Many proteins contain multiple functional domains interacting with different partners, making co-evolution of interacting domains occur more prominently. Multiple...
There are many models for simulating infectious disease outbreaks that assist health policy officials in making better decisions about mitigation strategies in the event of an epidemic. However, none of the existing models address the need to train students and policy makers in the concepts and use of such models. In this paper, we present Flu MODELO 1.0, an implementation of a model that simulates...
The spread of antibiotic resistance is a growing problem known to be caused by antibiotic usage itself. This problem can be analyzed at different levels. Antibiotic administration policies and practices affect the societal system, which is made by human individuals and by their relations. Individuals developing resistance interact with each other and with the environment while receiving antibiotic...
After three decades of research on human immunodeficiency virus (HIV), the causative agent of acquired immunodeficiency syndrome (AIDS), a vaccine has yet to be discovered. Most theoretical and experimental work on HIV vaccines has focused on the relevant molecular interactions at systemic pH levels, but HIV is typically transmitted sexually at mucosal pH levels. We previously developed a computational...
Computer science solutions for molecular biology problems are often presented in the form of workflows. There is a set of activities performed by different processing entities through managed tasks. Knowledge about the data trajectory throughout a given workflow enables reproducibility by data provenance. In order to reproduce an in silico bioinformatics experiment one must consider other aspects...
Due to the rapid increase in biological data dimension and acquisition rate, the traditional analysis methods are unable to achieve acceptable accuracy. Recently, Deep learning technologies have shown outstanding results in many domains; especially in pattern recognition in the field of bioinformatics. In this paper, we provide background of what deep learning and its frameworks. In addition, we review...
The hashtag recommendation problem addresses recommending (suggesting) one or more hashtags to explicitly tag a post made on a given social network platform, based upon the content and context of the post. In this work, we propose a novel methodology for hashtag recommendation for microblog posts, specifically Twitter. The methodology, EmTaggeR, is built upon a training-testing framework that builds...
Vaccines represent nowadays one of the most efficient weapons against foreign pathogens. To be effective, vaccines need a proper administration strategy that requires multiple administrations in order to ensure the acquisition of immunological memory. Vaccination schedules are usually based on past experience, and economical, ethical and time constraints have limited the research for better combinations...
The Human Immune System (HIS) plays a fundamental role in the defense of the body against diseases. However, the multi-scale interactions among several of its components make the complete understanding of the mechanisms involved in the body defense a complex task. Mathematical and computational tools can help towards this goal. Lots of works use differential equations, agent-based models and cellular...
Dense prediction is concerned with predicting a label for each of the input units, such as pixels of an image. Accurate dense prediction for time-varying inputs finds applications in a variety of domains, such as video analysis and medical imaging. Such tasks need to preserve both spatial and temporal structures that are consistent with the inputs. Despite the success of deep learning methods in a...
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as the direction with the lowest Kolmogorov complexity. This notion is very powerful as it can detect any causal dependency that can be explained by a physical process. However, due to the halting problem, it is also not computable. In this paper we propose an computable...
In the recent few years, neural-network-based word embeddings have been widely used in text mining. However, the dense representations of word embeddings act as a black box and lack interpretability. Even though word embeddings are able to capture semantic regularities in free text documents, it is not clear what kinds of semantic relations can be represented by word embeddings and how semantically-related...
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables X and Y from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity...
Understanding newly emerging events or topics associated with a particular region of a given day can provide deep insight on the critical events occurring in highly evolving metropolitan cities. We propose herein a novel topic modeling approach on text documents with spatio-temporal information (e.g., when and where a document was published) such as location-based social media data to discover prevalent...
Learning to optimize AUC performance for classifying label imbalanced data in online scenarios has been extensively studied in recent years. Most of the existing work has attempted to address the problem directly in the original feature space, which may not suitable for non-linearly separable datasets. To solve this issue, some kernel-based learning methods are proposed for non-linearly separable...
The contents generated from different data sources are usually non-uniform, such as long texts produced by news websites and short texts produced by social media. Uncovering topics over large-scale non-uniform texts becomes an important task for analyzing network data. However, the existing methods may fail to recognize the difference between long texts and short texts. To address this problem, we...
We consider the problem of modeling data matrices with locally low rank (LLR) structure, a generalization of the popular low rank structure widely used in a variety of real world application domains ranging from medical imaging to recommendation systems. While LLR modeling has been found to be promising in real world application domains, limited progress has been made on the design of scalable algorithms...
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