Protein dispensability is fundamental to understanding of gene function and evolution. It is usually studied at the individual gene phenotype level. Recent advances in generating high-throughput data such as genomic sequence data, protein-protein interaction data, gene-expression data, and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome sc...

Standard Remote Sensing analysis uses machine learning methods such as SVMs with HOG or SIFT descriptors, but in recent years neural networks are emerging as a key tool regarding the detection of objects. Due to the heterogeneity of remote sensing information (optical, infrared, DSM) the combination of multi-source data is still an open issue. In this paper, we focused on localization of urban ...

Real-time data packet sources are required to remain robust against different security threats. This study proposes a real-time secure scheduling strategy for data transmission to enhance the communication throughput and reduce the overheads. The proposed system combines real-time scheduling with security service enhancement, error detection and realtime scheduler based on EDF algorithm using u...

In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conduct...

We present and evaluate the performance of a reduced complexity variation to the source encoding assisted multiple access (SEAMA) protocol for integrating voice and data over a wireless network. This protocol, denoted as slow movable-boundary SEAMA (SMB-SEAMA), uses the same embedded and multistate voice encoder used in the original SEAMA protocol. However, in SMB-SEAMA, the movable voice/data ...

Capturing, representing, modelling and visualizing the dynamics of urban mobility have been attracting the interest of the research community recently. One of the drivers for recent work in this area is the availability of large datasets representing many aspects of the urban dynamics. Applications for these studies are diverse and include urban planning, security, intelligent transportation sy...

The inverse source problem of the Helmholtz Equation with multiple frequency data is investigated. Three cases are considered: (1) both the magnitude and phase of measurements on the whole boundary (full aperture data) are available; (2) only limited aperture measurements of the field are available; (3) only the magnitude information of the fields on the boundary is available. A continuation me...

In this paper, we propose nonlinear distance-odds models investigating elevated odds around point sources of exposure, under a matched case-control design where there are subtypes within cases. We consider models analogous to the polychotomous logit models and adjacent-category logit models for categorical outcomes and extend them to the nonlinear distance-odds context. We consider multiple poi...

Structure learning in Bayesian network is a big issue. Many efforts have tried to solve this problem and quite a few algorithms have been proposed. However, when we attempt to apply the existing methods to microarray data, there are three main challenges: 1) there are many variables in the data set, 2) the sample size is small, and 3) microarray data are changing from experiment to experiment a...

In this workshop report, we give a summary of the MultiClust workshop held in Chicago in conjunction with KDD 2013. We provide an overview on the history of this workshop series and the general topics covered. Furthermore, we provide summaries of the invited talks and of the contributed papers.