Plenary Talks

10/10/08 10:16

NOLTA 20092009 International Symposium on Nonlinear Theory and its Applications
Chateraise Gateaux Kingdom Sapporo Hotel & Spa Resort, Sapporo, Japan,
October 18-21, 2009.

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Plenary Talks
Prof. Toshiyuki NAKAGAKI
Professor Nakagaki was awarded the 2008 Ig Nobel Prize for discovering that slime
molds can solve puzzles. (Winners of the Ig Nobel Prize)[Affiliation]
Research Institute for Electronic Science, Hokkaido University, Sapporo, Japan[Title]Smart behaviors of a living cell based on nonlinear dynamics[Abstract]
We will present that ability of information processing in an amoeboid organism ishigher than we had thought. The model organism is the plasmodium of Physarum
polycephalum (true slime mold), which is a large aggregate of protoplasm with a large
number of nuclei. The organism found the optimal path when it obtained the multiple
locations of food. A simple mathematical model for the path finding was proposed interms of differential equations. As well as the path-finding ability, the organism wasable to anticipate the next timing of periodic climate change after experienced someperiodic changes of climate. We indicated that a simple dynamics was enough toreproduce the observed anticipatory behavior. We may learn a new method for
information processing from the studies of amoeba.

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Committee

Dr. Ferdinand PEPER[Affiliation]
Kobe Advanced ICT Research Center, National Institute of Information andCommunications Technology (NiCT), Japan[Title]Exploiting Noise in Computation[Abstract]
Noise and Fluctuations are usually considered obstacles in the operation of electronic
and mechanical devices, and most strategies to deal with them are focussed onsupression. This presentation gives a focussed overview of systems that employ
different strategies, i.e., strategies that can exploit the properties of noise to improvethe efficiency of operations. These strategies may be an important ingredient in thedesigns of computers with devices of nanometer-scale feature sizes.

Dr. Andrzej CICHOCKI[Affiliation]
Laboratory for Advanced Brain Signal Processing, BSI, RIKEN, Japan

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Plenary Talks

10/10/08 10:16[Title]Tensor Factorizations and Decompositions for Modern Massive Data Sets and theirPotential Applications[Abstract]
Nonnegative and sparse multi-way array (tensor) factorizations and decompositions
have emerged as new tools with a wide range of important potential applications,
including bioinformatics, neuroinformatics, brain computer interface (BCI), text
mining, image understanding, air pollution research, chemometrics, and spectral data
analysis. Tensor factorizations and decompositions have many other applications, suchas multi-way clustering, image classification, neural learning process, soundrecognition, remote sensing, and object characterization. For example, we applied
tensor decompositions for early diagnosis (detection) of Alzheimer Disease before any
clinical symptom and also for Brain Computer Interface. We believe that a potential
impact of the tensor decompositions on scientific advancements in biomedical signal
processing and machine learning might be greater than Independent Component
Analysis (ICA), or even the Singular Value Decomposition (SVD). In contrast to ICA orSVD/PCA approaches, sparse and nonnegative tensor decomposition techniques if
successively implemented, may improve dramatically physical interpretation and 3D
visualization of large-scale noisy data while maintaining the physical feasibility moreclosely. Multi-modality and high-dimensionality massive datasets are rapidly
becoming more commonplace in many applications because they provide useful
information that cannot be obtained from 2D data sets. Our goal is to explore novel
techniques for modeling and analyzing massive, high-dimensional, and nonlinearlystructured scientific data sets. Analysis of such multidimensional data using traditional2D processing is insufficient because of the interleaving and superimposed structuresthat often occlude the target regions of interest. Visual representation andunderstanding of such data is also an emerging area of research interest.
In this talk main emphasize will be given to multi-linear models, nonlinear iterativealgorithms for large-scale tensor factorizations and decompositions and their various
applications.
References:[1] A. Cichocki, R. Zdunek, A.-H. Phan, and S. Amari: Nonnegative Matrix and Tensor
Factorizations: Applications to Exploratory Multi-way Data Analysis, John Wiley, ISBN:
978-0-470-74666-0, (552 pages), September 2009.
[2] A. Cichocki and A-H. Phan: "Fast local algorithms for large scale Nonnegative
Matrix and Tensor Factorizations", IEICE Transaction on Fundamentals, E92-A(3), 708721 (invited paper) (March 2009).
[3] A-H. Phan and A. Cichocki: "Block Decomposition for Very Large-Scale PARAFAC
and Nonnegative Tensor Factorization", ICONIP 2009 (submitted).
[4] C. Caiafa and A. Cichocki: "Methods for Factorization and Approximation ofTensors by Partial Fiber Sampling", CAMSAP-2009 (invited paper).

Contact address: nolta2009@lalsie.ist.hokudai.ac.jp

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