报告题目：Network Thermodynamics and Non-Classical Condensation Abstract
报 告 人：Edwin Hancock教授
时 间： 2015年05月18日（周一）上午10：00
地 点： 安徽大学磬苑校区行知B楼负一楼学术报告厅
主办单位：计算机科学与技术学院
欢迎各位老师、同学届时前往！
科学技术处
2015年5月13日
报告内容简介:
This talk describes recent work on network characterisation using ideas
from statistical physics. We commence by showing how a simple
picture based on the thermodynamic variables of energy, entropy
and temperature can be used to analyse sequences of evolving networks.
This picture commences from definitions of the thermodynamic variable
which emerge naturally from simple physically intuitive definitions of
the entropy and energy of a network, computed from network degree statistics. This
analysis proves effective, and points to a more rigourous treatment based
on partition function associated with a network. We therefore explore
how a heat bath analogy with energy level population based on
Boltzmann, Bose-Einstein and Fermi-Dirac statistics leads to
different network partition functions, and hence thermodynamic
characterisations. Moreover, these different population statistics
allow phase transitions in network structure to be studied, and non-classical
effects such as Bose-Einstein condensation to be investigated. We explore the application of the resulting network analysis techniques to financial market data.
报告人简介：
holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a Doctorate Honoris Causa from the University of Alicante (2015). From 1981-1991 he worked as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). During this period, he also held adjunct teaching posts at the University of Surrey and the Open University. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical and pattern recognition. He is currently working on graph matching, shape-from-X, image databases, and statistical learning theory. His work has found applications in areas such as radar terrain analysis, seismic section analysis, remote sensing, and medical imaging. He has published about 160 journal papers and 600 refereed conference publications, and has graduated about 45 successful PhD's some of who now hold faculty positions in the UK, USA, Australia, Brazil, China, France and Italy. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. He has also received best paper prizes at CAIP 2001, ACCV 2002, ICPR 2006 and BMVC 2007. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. In 1998, he became a fellow of the International Association for Pattern Recognition. He is also a fellow of the Institute of Physics, the Institute of Engineering and Technology, and the British Computer Society. He has been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer Vision and Image Understanding, and Image and Vision Computing, and the International Journal of Complex Networks . In 2006, he was appointed as the founding editor-in-chief of the IET Computer Vision Journal. He is or has been conference chair for BMVC 1994/2016, Track Chair for ICPR 2004/2016 and Area Chair at ECCV 2006 and CVPR 2008/2014, and in 1997 established with Marcello Pelillo the EMMCVPR workshop series. In 2014 he was a member of the REF Assessment Panel in Computer Science, and in 2015 is Chairing the Computer Science Evaluation Commission for the Czech Academy of Sciences and Arts.
