We present a novel approach for automatically inferring models of multiobject events. Objects are first detected and tracked, their motion is then segmented into a set of primitive events. These primitive events then form the nodes in a Markov network that encodes the entire event space. A bottomup/top-down search algorithm is developed to detect typical event structures that are used for class...

2 CNV from a single probe 2 2.1 The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Inferring copy number status from signal data . . . . . . . . . 8 2.2.1 From univariate signal intensity . . . . . . . . . . . . . 8 2.2.2 From other algorithms . . . . . . . . . . . . . . . . . . 9 2.2.3 From predetermined thresholds . . . . . . . . . . . . . 9 2.3 Summarizing information ....

We consider the problem of automatically inferring properties of programs. Our approach is to explore the application of familiar type inference principles to a \type system" suuciently expressive that the typing problem is eeectively the checking of program speciications. We use familiar syntax-directed type inference rules to give a polynomial-time procedure for inferring type theorems in thi...

Inferring the direction of causal dependence from observational data is a fundamental problem in many scientific fields. Significant progress has been made in inferring causal direction from data that are independent and identically distributed (i.i.d.), but little is understood about this problem in the more general relational setting with multiple types of interacting entities. This work exam...

A distributed network intrusion detection system (IDS) called SA-NIDS is proposed based on the network-based intrusion detection architecture. It includes three basic components, Local Intrusion Detection Monitor (LIDM), Global Intrusion Detection Controller (GIDC), and Surveillance Agent (SA). Basically, the LIDM is used to do packets capturing, packets de-multiplexing, local intrusion detecti...

Inferring the network structure of gene regulatory networks is one of the most important problems in contemporary bioinformatics. We analyze different methodologies for inferring small to very large sized gene networks. We use the datasets of DREAM 3 in-silico network challenge that is provided online [1]. The challenge involves inferring primarily the network structure from steady state gene e...

Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. I...

Cells must respond to environmental changes to remain viable, yet the information they receive is often noisy. Through a biochemical implementation of Bayes's rule, we show that genetic networks can act as inference modules, inferring from intracellular conditions the likely state of the extracellular environment and regulating gene expression appropriately. By considering a two-state environme...

Haplotype phase information in diploid organisms provides valuable information on human evolutionary history and may lead to the development of more efficient strategies to identify genetic variants that increase susceptibility to human diseases. Molecular haplotyping methods are labor-intensive, low-throughput, and very costly. Therefore, algorithms based on formal statistical theories were sh...

This PhD project aims to investigate how enough information can be collected from an interactive formal proof to capture an expert’s ideas as a high-level proof process. It would then serve for extracting proof strategies to facilitate proof automation. Ways of inferring this proof process automatically are explored; and a family of tools is developed to capture the different proof processes an...