Colloquium/Seminar

YearMonth
2017 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Oct   Nov   Dec  
2016 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Oct   Nov   Dec  
2015 Jan   Feb   Mar   Apr   May   Jun   Aug   Sep   Oct   Nov   Dec  
2014 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2013 Jan   Feb   Mar   Apr   May   Jun   Aug   Sep   Nov   Dec  
2012 Jan   Feb   Apr   May   Jun   Jul   Aug   Sep   Nov   Dec  
2011 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2010 Jan   Feb   Mar   Apr   May   Jun   Sep   Oct   Nov   Dec  
2009 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2008 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2007 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2006 Jan   Feb   Mar   Apr   May   Jun   Jul   Sep   Oct   Nov   Dec  
2005 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2004 Jan   Feb   Mar   Apr   May   Aug   Sep   Oct   Nov   Dec  

Event(s) on September 2006


  • Monday, 4th September, 2006

    Title: Protein Function Prediction by Information Fusion
    Speaker: Prof. Limsoon Wong, School of Computing and School of Medicine, National University of Singapore, Singapore
    Time/Place: 10:30  -  11:30
    FSC 1217
    Abstract: Sequence homology has been widely used to shed light on the functions of proteins. However, statistical constraints impose limits on the sensitivity of sequence homology. Recent works introduce other sources of biological data such as protein interaction and gene expression profiles to reinforce protein function inference. In our current work, we infer protein function from multiple heterogeneous data sources by reformulating these data sources into functional linkage graphs. Each data source is modeled into a graph representing a network of inter-protein similarity observed from that particular source of evidence. These graphs can be combined to form a more complete graph in a probabilistic manner. The edges in such functional linkage graphs, which correspond to binary protein relationship, can be further weighted by topology using established techniques in graph theory. Functions are inferred for a protein from its known neighbours in the graph using a simple but effective weighted averaging technique. Using this approach, we combined sequence homology from BLAST searches, protein interactions from GRID (General Repository of Interaction Data) and co-occurence of protein names in Pubmed abstracts. Our experiments show that the data sources complement one another and combine to infer protein function with greater sensitivity and precision. [This talk is based on joint work in progress with H.N.Chua & W.K.Sung.]


  • Tuesday, 12th September, 2006

    Title: Introduction to Biometrics and a Case Study of palmprint Identification
    Speaker: Mr. Adams Kong, Department of Electrical and Computer Engineering, Waterloo University, USA
    Time/Place: 11:30  -  12:30
    FSC 1217
    Abstract: Biometrics based on our physiological and/or behavioral characteristics such as fingerprints, faces and irises for automatically recognizing individuals has advantages over traditional authentication approaches. In the first part of this talk, a brief overview of the field of biometrics covering markets, applications, genetic issues and privacy concerns is given. In the second part of this talk, a palmprint identification system based on Competitive Code is presented. This system employs orientation fields of palmprints as features and bitwise angular distance for matching. Experimental results demonstrate that this system can support high speed identification. Using Competitive Code, the genetically related features in palms are identified. Finally, some security measures for this system are also mentioned.


  • Tuesday, 26th September, 2006

    Title: Interactively Solving Imaging and Vision Problems Using Optimization
    Speaker: Prof. Guoping Qiu, School of Computer Science and Information Technology, The University of Nottingham, UK
    Time/Place: 11:30  -  12:30
    FSC 1217
    Abstract: In many imaging and vision applications, it is often very difficult or maybe even impossible to develop fully automatic solutions. For example, despite much research effort, a fully automatic solution to the longstanding image segmentation problem is still an unattainable goal. Other examples where a fully automatic solution is difficult include content-based image retrieval (CBIR). Humans have remarkable abilities in distinguishing different image regions or separating different classes of objects. Furthermore, users may have different intentions in different application scenarios. Therefore, it is both necessary and helpful to incorporate high-level knowledge and human intentions into the computational algorithms. Interactive approaches, which provide semi-automatic solutions, put the users in the computational loop and allow users to supply constraints to the computational algorithms interactively, may offer a more realistic solution paradigm for many imaging and vision problems. One of the important challenges to developing successful imaging and vision algorithms is to effectively model high level knowledge and to incorporate the users intentions in the computational algorithms. Traditionally, this is often achieved by integrating statistically learned prior knowledge into numerous computational algorithms through techniques such as Bayesian Inference. Although the Bayesian approach has been successfully used in the literature, the resulting combinatorial optimization problems have been often solved by inexact and inefficient computational methods. In this talk, I will present a computationally simple optimization-based framework for directly incorporating priors (user inputs which provide both high level knowledge and user intentions) into the computational algorithms for solving problems such as interactive content-based image retrieval and interactive foreground background segmentation.