Laboratory of Computational Engineeringand
Research Centre for Computational Science andEngineering
Annual Report1.1.2002 - 31.12.2002
Eeva LampinenEditor
Internal Reports A5July 2003
Laboratory of Computational EngineeringHelsinki University of Technology
Tekniikantie 14P.O. Box 9203
FIN-02015 HUTFINLAND
ISSN 1455-0466ISBN 951-22-6523-0
Picaset Oy
Contents
1 Introduction 1
2 Personnel 2
3 List of Courses 5
4 Theses 6
5 Research Projects 75.1 Computational Information Technology . . . . . . . . . . . . . . . . . . . . 7
5.1.1 Modelling and Data-Analysis . . . . . . . . . . . . . . . . . . . . . 75.1.2 Complex Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5.2 Computational Materials Research . . . . . . . . . . . . . . . . . . . . . . . 195.2.1 Atomic Level Modelling of Structure and Growth of Materials . . . . 195.2.2 Research in Biophysics, Soft Materials and Pattern Formation . . . . 275.2.3 Research on Semiconductor Quantum Structures and Physics of New
Information Technologies . . . . . . . . . . . . . . . . . . . . . . . 315.2.4 Medical physics: Radiation Dosimetry at Cellular Level . . . . . . . 39
5.3 Cognitive Science and Technology . . . . . . . . . . . . . . . . . . . . . . . 405.3.1 Psychophysical Research . . . . . . . . . . . . . . . . . . . . . . . . 405.3.2 Cognitive Neuroscience . . . . . . . . . . . . . . . . . . . . . . . . 41
5.4 Intel computing cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6 Research Activities 436.1 Visits to the Laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436.2 Visits by Laboratory Personnel . . . . . . . . . . . . . . . . . . . . . . . . . 436.3 Participation in Conferences and Seminars . . . . . . . . . . . . . . . . . . . 446.4 Other Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
7 Publications 50
1 Introduction
In 2002 the Laboratory of Computational Engineering (LCE) & Research Centre for Com-putational Science and Engineering (CCSE) steadied its staff to about 70 members. Thisyear marks the third year of our Centre as the national Centre of Excellence with truely in-ternational team of researchers of 10 different nationalities. As an extension to the Centreof Excellence activities a joint affiliate centre between CCSE and Wolfson College of OxfordUniversity was set up in Oxford with the state of the art cluster-computing facilities and startedits activities with two full time researcher concentrating on Advanced Computational Scienceand Engineering.
LCE’s and CCSE’s research is multidiciplinary in nature and has till recently been carriedout in three mutually supportive fields: Computational Information Technology, Computa-tional materials research, and Cognitive science and technology. In Computational informa-tion technology the areas of interest are Modelling and Data-Analysis, Machine Vision, Com-plex Systems, and Cognitive and Interactive Robot. In Computational materials research theareas of interest are Biophysics and Statistical Mechanics, Modelling of Structure and Growthof Materials, and Nanotechnology and Coherent Quantum Systems. In Cognitive science andtechnology the areas of interest are Psychophysical Research, Neurophysiological Research,Artificial Person, and Brain Computer Interface. In 2002 the fourth field of research wasadded to our repertoire, namely Computational Systems Biology which combines appropriateparts of Computational Information Technology research like Modelling and Data-Analysis,and Complex Systems, and of Computational Materials Research like Biophysics and Statis-tical Mechanics. This new addition marks also an educational extension to our curriculumsuch that we now offer three majors: Computational Engineering, Cognitive Technology andComputational Systems Biology. All of them are part of the new Bioinformation Technologystudy programme, which LCE has played a key role in developing and establishing.
Year 2002 was in many ways extremely successful year for us: 1) The joint appointmentof Dr. Adrian Sutton, FRS and Professor of Materials Science at Oxford University to a pro-fessorship of Computational Engineering in LCE at Helsinki University of Technology, and2) The appointment of Dr. Christina Krause as a Professor of Cognitive Science at Universityof Helsinki. It should also be mentioned that in 2002 Sami Brandt, Markus Varsta and Ju-uso Töyli were granted Ph.D.’s, and eight M.Sc. degrees were finished by research associatesworking in the laboratory. It is worth mentioning that Juuso Töyli became the youngest (28years) double doctor in Finland, with degrees in Economy and in Technology.
Kimmo KaskiAcademy professor
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2 Personnel
All the laboratory personnel can be reached by e-mail with [email protected]. More complete contact information can be foundfrom the laboratory web page http://www.lce.hut.fi/.List of the personnel in the laboratory:
ProfessorsKaski Kimmo Academy ProfessorLampinen Jouko ProfessorSams Mikko Academy ProfessorTiippana Kaisa Professor (1.8.-31.12.2002)Tulkki Jukka Professor
Adjunct Professors (Docents/Visiting Professors)Alexandrov Yurii Prof. (Russian Academy on Sciences, Russia)Barrio Rafael Prof. (Universidad Nacional Autonoma de Mexico, Mexico)Kertész Janós Prof. (Technical University of Budapest, Hungary)Landau David Prof. (University of Georgia, USA)Lehtokangas Mikko Docent(Tampere University of Technology)Mouritsen Ole Prof. (Technical University of Denmark, Denmark)Parkkinen Jussi Prof. (University of Joensuu)Rissanen Jorma Prof. (IBM Research Center, Almadena, USA)Räihä Kari-Jouko Prof. (University of Tampere)Sutton Adrian Prof. (University of Oxford, UK)Tirri Henry Prof. (University of Helsinki)
SecretariesJärvenpää AinoLampinen Eeva
Senior ResearchersChakraborti Anirban Ph.D.Fingelkurts Alexander Ph.D.Fingelkurts Andrey Ph.D.Frydrych Michael Ph.D.Heikkonen Jukka Academy Fellow, Dr.Tech.Jääskeläinen Iiro Ph.D.Karttunen Mikko Ph.D.Klucharev Vasily Ph.D.Krause Christina Ph.D.Kuronen Antti Academy Fellow, Ph.D.Patra Michael Marie Curie Fellow.Patriarca Marco Ph.D.Tiippana Kaisa Ph.D.Tuomainen Jyrki Ph.D.Töyli Juuso Ph.D.Varsta Markus Dr. TechVehtari Aki Dr. Tech.
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Researchersvon Alfthan Sebastian M.Sc.Andersen Tobias M.Sc.Auranen Toni M.Sc.Boxberg Fredrik M.Sc.Brandt Sami M.Sc.Dobsik Martin M.Sc.Huhtala Maria M.Sc.Huttunen Anu M.Sc.Junttila Virpi M.Sc.Juujärvi Jouni M.Sc.Kalliomäki Ilkka M.Sc.Koskela Timo M.Sc.Kostiainen Timo M.Sc.Krylov Andrey M.Sc.Kätsyri Jari M.Sc.Lahtinen Jani M.Sc.Lehtonen Janne M.Sc.Leppänen Teemu M.Sc.Litkey Paula M.Sc.Möttönen Riikka M.Sc.Nikunen Petri M.Sc.Nummenmaa Aapo M.Sc.Nurminen Laura M.Sc.Nykopp, Tommi M.Sc.Ojanen Ville M.Sc.Oksanen Jani M.Sc.Onnela Jukka-Pekka M.Sc.Riederer Klaus M.Sc.Rodriquez Mirta M.Sc.Selonen Arto M.Sc.Sysi-Aho Marko M.Sc.Szelestey Peter M.Sc.Särkkä Simo M.Sc.Tamminen Toni M.Sc.Terechonkov Roman M.Sc.Zhao Wei M.Sc.
Researchers in Oxford UniversitySchenk Veit Ph.D.Mustonen Ville M.Sc.
Research StudentsHyttinen JuhaHäyrynen TeppoJuvonen JuhaKettunen JuhoLaitinen Laura
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Meriläinen VeeraMiettinen MarkusMogami KotaMäkinen JariMäkinen Ville-PetteriOjanen JannePalo PerttiRitaluoto PasiRuokola TomiSaari JukkaSalminen Mikko
TraineesKaapro AatuKannala JuhoKorhonen ReettaLamminen AnttiLi JingyuanNieminen TapioParviainen MikkoRiihimäki JaakkoViinikainen Mikko
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3 List of Courses
Courses in the Laboratory of Computational Engineering� S-114.100 Computational Science (3 cr)� S-114.200 Special Course on Computational Engineering (4 cr) P� S-114.202 Special Course on Computational Engineering II (2-5 cr) P
Topic: On-line and sequential learning� S-114.204 Modelling of Perception (3 cr) P� S-114.210 Individual Project in Computational Engineering (2 cr)� S-114.215 Special Project in Computational Engineering (2-5 cr)� S-114.220 Research Seminar on Computational Science (2-4 cr) P� S-114.230 Individual Studies on Computational Engineering (1-6 cr) P� S-114.240 Seminar on Computational Engineering (2 cr)
Topic: Mathematical modeling and methods in natural sciences & engineering� S-114.245 Laboratory Seminar on Computational Engineering (2 cr) P� S-114.250 Special Course in Computational Science (4 cr)� S-114.300 Topical Lectures on Information Theory and Modeling (2 cr) P� S-114-310 Basic Course on Modeling and Information Theory (2 cr)� S-114.325 Physics III (S) (2 cr)� S-114.326 Physics IV (S) (3 cr)� S-114.401 Semiconductor Quantum Structures (4 cr)� S-114.425 Physics III (Sf) (4 cr)� S-114.426 Physics IV (Sf) (4 cr)� S-114.500 Basic Course for Biosystems on the Cell (3 cr)� S-114.520 Introduction to Bioinformatics (2 cr)� S-114.600 Introduction to Bayesian Modelling (2 cr)� S-114.710 Perception and Production of Speech (4 cr)� Research Seminar on Communication and Cogntion:- The Philosophy of Mind (2 cr)- Audiovisual Speech Perception (2 cr)� S-114.740 Special course on Communication and Cognition (2 credits)
Topic: Biological Human-Machine Interfaces� S-114.750 Individual studies in Communication and Cognition (1 - 6 credits)� S-114.780 Perception and action (4 cr)� S-114.790 Communication and cognition (4 credits)
For more information see publication: Study Programme, Helsinki University of Technology,or the www-page http://www.lce.hut.fi/teaching/.
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4 Theses
Doctor of Technology� Sami Brandt Theorems and Algorithms for Multiple view Geometry with Applications to
Electron Tomography� Juuso Töyli Essays on Asset Return Distributions� Markus Varsta Self-Organizing Maps in Sequence Processing
M.Sc. - Diplomas� Sebastian von Alfthan, Computational Study of Amorphous Silicon and Silica� Toni Auranen, Nonparametric Statistical Analysis of Time-frequenccy Representations of
Magnetoencephalographic Data� Ilkka Kalliomäki Inference on Three-Dimensional Shape in a Scene Analysis System� Janne Lehtonen EEG-based Brain Computer Interfaces� Veera Meriläinen Magnetic Resonance Imaging and Simultaneous Electroencephalogra-
phy Recording: Safery Issues� Jukka-Pekka Onnela Taxonomy of Financial Assets� Marko Sysi-Aho The Modelling of Stock Markets and Stock Market Indices� Toni Tamminen Bayesian Object Matching with Gabor Filters and Markov Chain Monte
Carlo
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5 Research Projects
5.1 Computational Information Technology
Computational information technology deals with problems where efficient numerical meth-ods of computational engineering are combined with the problems of information processing.During recent years, computational approach in information processing has gained increasingimportance, with progress in fields like computational intelligence and data mining. Compu-tational intelligence comprises methods such as neural networks, probabilistic methods, fuzzysystems and evolutionary computing, where iterative numerical methods are used for solv-ing complex propblems, involving large amounts of measured data and only uncertain priorknowledge about the solutions or modeled phenomena. A central element is inference in novelsituations, based on observed data, that is, inductive learning systems. Similar capabilities forknowledge discovery are needed in data mining, where useful pieces of information are soughtfrom large, possibly unorganized, databases.
In the laboratory the research in computational information technology is focused in neuralcomputing, Bayesian methods, and pattern recognition and modeling applications. A goodexample of fields in information processing, in which computational methods have large im-pact, is the Bayesian approach, where the estimation of a specific set of model parameters isreplaced by constructing the propbability distribution of the model parameters and integratingover it during the use of the model. This integration requires efficient numerical methods,such as Markov chain Monte Carlo methods. The numerical solution also makes it posibleto avoid some standard assumptions (such as linearity and/or gaussianity), that may be bad inpractice.
In the following chapters we give short descriptions of the research projects in the compu-tational information technology group.
5.1.1 Modelling and Data-Analysis
Bayesian Methods for Neural Networks
Researchers: Jouko Lampinen, Aki Vehtari, Paula Litkey, Ilkka Kalliomäki, SimoSärkkä, and Jani Lahtinen
Neural networks are popular tools in classification and non-linear function approximation.The main difficulty with neural networks is in controlling the complexity of the model. Itis well known that the optimal number of degrees of freedom in the model depends on thenumber of training samples, amount of noise in the samples and the complexity of the under-lying function being estimated. With standard neural networks techniques the means for bothdetermining the correct model complexity and setting up a network with the desired complex-ity are rather crude and often computationally very expensive. Also, tools for analyzing themodels (such as confidence intervals) are rather primitive. The Bayesian approach is basedon a consistent way to do inference by combining the evidence from data to prior knowledgefrom the problem, and it provides efficient tools for model selection and analysis.
In the laboratory of computational engineering we are studying full Bayesian approch forneural networks, where the high-dimensional integrals required in computation of variousmarginal distributions are approximated by Markov Chain Monte Carlo methods. We are de-veloping methods for using more general prior distributions for model parameters and noisemodels than currently available. Examples of recent results are non-Gaussian noise models
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with arbitrary correlations between model outputs, outlier tolerant noise models with adapt-able tailness, and Bayesian bootstrap methods for analyzing the performance of the models.
We have applied Bayesian neural networks in a number of modelling tasks. In practicalapplications the Bayesian approach usually requires more expert work than the standard errorminimization approach, to build the probability models and priors, and to integrate out all thehyperparameters. The obtained results in our experience have been consistently better thanwith other statistical estimation methods, and the possibility of compute reliable confidenceintervals of the results is necessary in real world applications. Figures 1 and 2 show twoexamples of Bayesian neural networks in function approximation and classifcation tasks.
Figure 1: Example of Bayesian neural network for image reconstruction in ElectricalImpedance Tomography (EIT). The left figure shows a cross section of a pipe filled with liquidand some gas bubbles (marked by dark green contours). The color shade shows the poten-tial field due to injection of electric current from the redmost electrode, with the bluemostelectrode gounded. The right figure shows the reconstruction of the conductivity image fromthe potential measurements of the 16 electrodes, using Bayesian neural network. The colorindicates the bubble probability and blue contour the detected bubble boundary.
KNN CART MLP ESC Bayes−MLP Bayes−MLP +ARDForest scene
Figure 2: Example of a classifying forest scene to tree trunks and background. The figuresfrom left are: the forest image; CART (Classification and Regression Tree); k-Nearest Neigh-bor classifier with k chosen by leave-one-out cross-validaton; Committee of early-stoppedMLP neural networks; Bayesian MLP; Bayesian MLP with ARD prior.
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Bayesian Model Assessment and Selection Using Expected Utilities
Researchers: Aki Vehtari, Jouko Lampinen
The goal of the project is to study theoretically justified and computationally practical meth-ods for the Bayesian model assessment, comparison, and selection. A natural way to assessthe goodness of the model is to estimate its future predictive capability by estimating expectedutilities, that is, the relative values of consequences. We synthesize and extend the previouswork in several ways. We give a unified presentation from the Bayesian viewpoint empha-sizing the assumptions made and propose practical methods to obtain the distributions of theexpected utility estimates.
The reliability of the estimated expected utility can be assessed by estimating its distri-bution. The distributions of the expected utilities can also be used to compare models, forexample, by computing the probability of one model having a better expected utility thansome other model. The expected utilities take into account how the model predictions aregoing to be used and thus may reveal that even the best model selected may be inadequate ornot practically better than the previously used models.
The developed methods have already been applied with great success in model assessmentand selection in real world concrete quality modeling case in cooperation with Lohja Rudus.By using the models and conclusions based on them made by the concrete expert it is, e.g.,possible to achieve 5-15% savings in material costs in concrete factory.
Below is another example from a case project where one subgoal was a classification ofthe forest scene image pixels to tree and non-tree classes. The main problem in the task wasthe large variance in the classes. The appearance of the tree trunks varies in color and texturedue to varying lighting conditions, epiphytes, and species dependent variations.In the non-tree class the diversity is much larger, containing,for example, terrain, tree branches, and sky.This diversity makes it difficult to choose the optimal features for the classification. Figure 3shows comparison of the expected classification accuracies for two models. Model 1 uses 84texture and statistical features extracted from images. Model 2 uses only 18 features, selectedusing the methods developed in the project, from the set of all 84 features used by Model 1.Although Model 2 is simpler, it has practically same expected accuracy as the Model 1.
Figure 3: An example of Bayesian modelassessment and selection using expectedutilities in forest scene classificationproblem. The figure shows the distribu-tions of the expected classification accu-racies for two different models classify-ing image pixels to tree trunks and back-ground. Distribution describes howlikely different values for the expectedutility are. 84 86 88 90 92
Expected classification accuracy−%
Pro
babi
lity
dens
ity
Model 1Model 2
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Probability Density Model for the Self-Organizing Map
Researchers: Jouko Lampinen, Timo Kostiainen
The Self-Organizing Map, SOM, is a very popular tool in exploratory data analysis. It is oftenused for the visualization of high-dimensional data. A theoretical and practical challenge inthe SOM has been the difficulty to treat the method as a statistical model fitting procedure.This has greatly undermined the reliability of the results of data analysis and thus lead to a lotof time-consuming work in validating the results by other means.
In earlier attempts to associate a probability density model with the SOM, the SOM modelhas been modified. In this work we have derived the probability density model for whichthe unchanged SOM training algorithm gives the maximum likelihood estimate. The densitymodel allows the application of model selection techniques to choose the parameters of theSOM to ensure as good generalization to the data as possible. Quantitative analysis of depen-dencies between data variables can also be carried out by calculating conditional distributionsfrom the density model.
Figure 4: Left: Training data points and self-organizing map which consists of � � � units.Right: Probability density model associated with the SOM. The density model attempts todescribe the distribution of the training data. The density function is not continuous due tothe winner-take-all training rule of the SOM.
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Learning Scene and Object Analysis
Researchers: Jouko Lampinen, Timo Kostiainen, Ilkka Kalliomäki,Toni Tamminen, and Aki Vehtari
The project is funded by TEKES and participating enterprises in the the USIX technologyprogramme. The project started in June 2000 and is scheduled for three years.
The goal of the project is to develop an object recognition and scene analysis system thatcan locate and recognize the objects in the scene and analyze the 3D structure of the objectsand the scene. The approach we study is based on combining elements from view-based andmodel based methods using full Bayesian inference. The objects are defined by prior modelsthat are learned from example images. The 3D representation is based on eigen shapes, so thata linear combination of the base shapes produces the perceived shape. View based approachis used in matching the perceived image and the image due to the assumed 3D structure.
We have developed a distortion tolerant feature matching method based on subspace ofGabor filter responses. The object is defined as a set of locations, with associated Gabor-features, and a prior model that defines the variations of the feature locations. The eigen-shapemodel corresponds to determining the covariance matrix for the feature locations, which islearned in bootstrap fashion by matchihg a large number of images by simpler prior models.
We have constructed efficient MCMC samplers for drawing samples from the posteriordistributions of the matching locations, usig mainly Gibbs and Metropolis sampling.
Figure 5 shows exmaple of feature location matching. Figure 6 shows example of inferringthe 3D shape of a human head from one image, without any manual assistance in the process.
Figure 5: Example of detail matching. Local features in the grid points in the left figure arematched to the image in the middle figure, using Gibbs sampling. In the nodes with yellowring the probability of finding a matching detail is low. The right figure shows samples fromthe posterior distribution of the grid node locations, giving an idea of the accuracy of thedetail matching.
The goal is to recognize 3-D objects in a 2-D scene by inferring the 3-D shape and thetexture of the object. There exists no unambiguos solution to the inference problem withoutadditional constraints on the solution space. The external or prior information is representedusing models for known objects or classes of objects, represented in terms of prior probabil-ities for different configurations of the models, and learnt from example images. Figure 7shows an example of shape model for human faces. The shape is represented as linear com-bination of eigen shapes, learnt from a set of manually cropped training images of humanfaces.
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Figure 6: Example of inferring the 3D shape of a human head. The right figure shows theperceived image. The middle figure shows the posterior median of the locations of the featuregrid nodes over the image. The right image shows a standard head shape morphed accordingto the distortion of the feature grid to match the estimted shape of the perceived face.
Figure 7: Leading eigen-shapes of faces, learnt from a set of training images. The face on theleft has been morphed according to the eigen shapes, into positive direction (upper row) andnegative direction (lower row). It can be seen that components 2 and 3 are related to rotationsof the head, while components 1, 4, and 5 are shape-related.
a) b) c) d) e)
Figure 8: Example of estimation of shape, texture and illumination, and synthesis of a novelview of the object. a) Target image. b) Estimated object shape. c) Illumination-correctedestimate of the texture of the object. d) Estimate of object shape and the texture. e) Image withnovel view and illumination.
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Prediction of Steel Jominy Curves
Researchers: Jouko Lampinen, Paula Litkey, Laboratory of Computational Engineer-ing, HUTJukka Laine, Laboratory of Metallurgy, HUT
The project is done in co-operation with the Laboratory of Metallurgy, within a TEKES projectthat studies the possibilities of using neural networks in steel manufacturing and casting. Theproject is an example of application oriented modelling task in industrial environment, but italso requires methodological research related to assessment of the reliability of the modelsand scaling of the modelling methods in real world problems.
The steel jominy-curve represents the hardeneability of steel. It is important that a steelplant can provide a reliable measure of the steel hardeneability to a client who is manufactur-ing demanding products, e.g. safety critical parts for automobiles.
Normally, the jominy curve is defined by quenching a steel sample and by measuring thehardness from several points along the specimen, which is an expensive test. With a reliablemodel for jominy curves the physical tests, c.a. 1500 pc/a, can be avoided and substantialsavings can be achieved. The model can also be used to control the alloying of the steelduring manufacturing and in the development of new steel grades. An ideal model shouldbe applicable to a wide range of steel grades with different analysis and warn the user if thechemical composition is out of range of the model.
The goal of the project is to develop usable neural network models for the steel industryusing large sets of real life process data. In addition to getting fine models we are examiningmeans to assure the easy updating of the model with new data.
There are interactions of different chemical elements that are nonlinear and this makes itimpossible to determine the complexity of the data only from the variation of the input values.The use of neural networks makes it relatively easy to model the relationship of the chemicalcomposition and the shape of the measured jominy curve, which is quite difficult task for e.g.a regression model.
rmsserr = 0.64, grade: 9
no: 190590
rmsserr = 0.61, grade: 10
no: 235470
rmsserr = 0.46, grade: 5
no: 280590
rmsserr = 1.05, grade: 9
no: 310570
rmsserr = 1.18, grade: 8
no: 340640
Exaples of measured jominy curves and neural network model predictions with 5% and 95% confidence intervalsrmsserr = 1.21, grade: 4
no: 372580
NN 5% CI 95% CItarget
Figure 9: Example of jominy curve estimates from a neural network model. The shown curvesare selected randomly from test set used to evaluate the model performance.
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Optimizing the Web Cache
Researchers: Timo Koskela, Jukka Heikkonen, and Kimmo Kaski
Recently we studied the quality of service (QoS) of Internet services from the user’s pointof view. Currently the WWW is the most important service for the end users, generating mostof the traffic volume in the Internet. Web caching is a technique where Web objects requestedby clients are stored in a cache which is located near the clients. Subsequent requests for thesame object are then served from the cache, improving the response time for the end users,reducing the overall network traffic, and reducing the load on the server.
Figure 10: Proxy cache fetches and stores the objects requested by the clients.
Figure 10. shows how the requests from the clients are routed through the proxy, whichfetches the objects and stores them to cache. Since the cache’s storage is limited, an importantproblem in optimizing cache’s operation is to decide which strategy to use in replacing ofcache objects. Commonly heuristic rules are used to decide which objects to replace. Ourproposed model predicts the popularity of each object by using syntactic features collectedfrom the HTTP responses and from the HTML structure of the document. Cache’s operationcan then be optimized by using the predicted object popularities.
In a case study, about 50000 HTML documents were classified according to their popularityby using linear and nonlinear models. Results showed that linear model could not find cor-relation between the features and document popularity. Nonlinear model gave better results,yielding mean classification percentages of 64 and 74 for the documents to be stored or to beremoved from the Web cache, respectively.
Replacement strategy and also prefetch- and refresh strategies of the cache can be devisedto use the predicted object popularities. For instance, a replacement strategy can replace firstthe largest objects which have the lowest popularity. This will free space for several smallerobjects, increase the hit rate of the cache and decrease the outbound traffic volume. Similarly,a refresh strategy can check the validity of only the most popular objects, and fetch in advancethose objects that were changed. This will minimize the traffic generated by the validationrequests and improve the response time for the end users.
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The On-line Adaptive Brain-Computer Interface
Researchers: Mikko Sams, Jukka Heikkonen, Tommi Nykopp, Janne Lehtonen, LauraLaitinen and Mikko Viinikainen
Brain Computer Interfaces (BCIs) are intended for enabling both the severely motor dis-abled as well as the healthy people to operate electrical devices and applications through con-scious mental activity. Our approach bases on an artificial neural network that recognizes andclassifies different brain activation patterns associated with carefully selected mental tasks.By this means we pursuit to develop a robust classifier with short classification time and, mostimportantly, a low rate of false positives (i.e. wrong classifications. Figure 1 demonstrates aBCI in use.
Our group concentrates on the neurophysiological basis of BCIs. We believe that beforethe signals can be classified they need to be fully understood. We study the signals, e.g., usingtime frequency representations (TFRs, see Fig. 2) and pick out important features from them.We are especially interested in the activation of the motor cortex when the user controls theBCI by either moving hands or by imagining doing so.
Currently we are developing a BCI that measures the signals produced in the brain withmagnetoencephalography (MEG). Most BCI groups measure the electric activity of the brainusing electroencephalography (EEG). MEG signals are more localised than EEG signals andthus easier to classify. We are extending our research on BCIs to simultaneous EEG and MEGrecordings.
TV apperances We appeared twice on television: 5th May in YLE Teema’s news and 3rdDecember in FST’s current affairs program OBS.
Funding The project is funded by the Academy of Finland (Research Programme on Proac-tive Computing, 2002-2005).
Figure 11: The user wears an EEGcap. The user controls the virtual key-board with her brain activity, thinkingto move left or right hand.
Figure 12: TFR on one MEG sensorover the motor cortex. The activation ofthe brain is plotted with the time on thex-axis and the frequency on the y-axis.The colour indicates the power of theactivation. Subject began to move hisright finger at zero. Strong activationin the 10-30 Hz range can be detectedafter the movement has ended.
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5.1.2 Complex Systems
Taxonomy of Financial Assets
Researchers: Jukka-Pekka Onnela, Anirban Chakraborti, Kimmo Kaski, JanosKertesz�� Dept. of Theoretical Physics, Budapest University of Technology andEconomics
Our research concentrates on two connected fields, both of which are closely related to theportfolio optimization problem. First, we have analyzed temporal properties of asset corre-lations. For example, in a rising or falling market, financial assets display greater correlatedmovement than during “business as usual” periods. We have investigated some statisticalproperties of correlation distributions, and this is one of the findings our study has confirmed.Second, we have investigated asset taxonomy. Building upon the first research field, distancesbetween stocks are defined from asset correlations, and an asset tree is constructed by deter-mining the minimum spanning tree of the distances. Thus, all stocks are connected, but eachis linked only to its nearest neighbor. This produces a unique market taxonomy, in whichstocks are divided into economically meaningful clusters (tree branches). Further, minimumrisk Markowitz portfolio stocks are practically always located on the outskirts of the branches.We have also studied the dynamics of this system and found it to reflect upon the state of themarket. Due to these and some additional properties, we believe dynamic asset trees can pro-vide an intuition friendly approach to and facilitate incorporation of subjective judgement tothe portfolio optimization problem. Overall, they can further our understanding of the stockmarket as an evolving complex system.
Figure 13: Snapshots of asset taxonomy. Left: Normal topology. Right: Crash topology dueto Black Monday, October 19, 1987.
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Multiagent models for complex adaptive systems
Researchers: Marko Sysi-Aho, Anirban Chakraborti, Kimmo Kaski, Janos Kertesz �� Dept. of Theoretical Physics, Budapest University of Technology andEconomics
Agent based models, which try to describe features in real-world complex systems, havebecome more popular during the last years. Thanks to the increasing computing power, it ispossible to excecute simulations for ever complicating models, although the complexity is nota favourable quality as regards to the understanding of the hided, fundamental factors in aphenomenon under interest.
An interesting, simple and powerful model including many features present in real worldcomplex systems is a minority game. At each time step, the players of the game have to de-cide between two alternatives, say whether to choose side A or B, and those who, after thedecisions, happen to belong to the minority win. This simple game exposes many interestingfeatures that have been extensively studied in recent years. Our contribution to the develop-ment of this game, is the introduction of learning, or intelligent agents, who can adapt to thechanging environment and try to improve their competence, if they find it too low. We allowagents to use genetic manipulations to cross their strategies in order to find good ones. Thisadded feature describes better many real-world situations, where one is required to fight forones survival. It is not enough to be good or best at one time, but one has to improve and fightall the time. Examples that proof the need of this continuous developing can be found frombusiness, academy, sports, biology, evolution, ... The intelligence leads to interesting changescompared to the basic minority game. Especially, the system as a whole strives towards astate that maximizes the utility of the whole community. This is quite an interesting feature,if taking into consideration the fact, that individual agents are only interested in their ownperformance.
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Figure 14: (a) Comparison of the total utilities of the system, when agents use different kind of rules tomodify their strategies. (b) The same plot reversed and in log-log scale.
17
Statistical Analysis of Small World Networks
Researchers: Jani Lahtinen, János Kertész� and Kimmo Kaski� Budapest University of Technology and Economics
The small world networks are graphs, which albeit having a large amount of vertices still onaverage retain small distance between individual vertices relative to traversal of links. Suchnetworks are for example the internet, WWW, stock market trading interlinking, biochemistrysignaling and metabolism in protein systems, epidemics, formation of polymers, tranportationsystems or interlinked systems of social interactions. These real world examples can - to cer-tain extent - be modelled either as random graphs of Erdös–Rényi, Watts–Strogatz, Barabasietc. types, or as regular graphs like lattices and cages. Regular graphs are of great interestfrom the point of view of manageable analytical theory approach but because they offer meansto algorithmically generate structures of smallest possible diameter (i.e. the longest distancein the graph) for a given amount of vertices. An example of such a regular graph is cage and itcould perhaps be applied in developing efficient telecommunication network topologies. Ourresearch consentrates on analysis of statistical properties of random graphs, and constructionof deterministic graphs as approximations of cages and investigation of their properties.
Figure 15: An Erdös–Rényi (left) and a Watts–Strogatz (right) graphs with 100 vertices.
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5.2 Computational Materials Research
5.2.1 Atomic Level Modelling of Structure and Growth of Materials
The research of our group is concerned with the study of structural properties of and growthphenomena in solid materials. All studies rely on microscopic modeling, in which the inter-atomic interactions are described through pairwise and many-body model potentials. Ingeneric studies we have used Lennard-Jones potentials, while in more specific cases the Ter-soff, Stillinger-Weber and Valence Force Field potentials as regards to the study of semicon-ductors and Effective Medium and the Embedded Atom model potentials as regards to studiesof metals have been used. Because these semiempirical potential models have their drawbacksand limitations when attempting to describe bonding in certain materials (especially carbonin tubular form) the more accurate tight-binding approach has been used as a complementarymethod to describe inter-atomic interactions.
Large scale Molecular Dynamics (MD) and Monte Carlo (MC) simulations have been thestandard tools employed in all these studies. Cluster computer CLUX – installed at the HUTComputing Centre and partly owned by the laboratory – has been used to perform MC andMD simulations of systems containing up to ����� atoms. Execution of smaller scale paralleljobs and program development for parallel machines has been done using our in-house clustercomputers.
The main topics of investigation in the case of semiconductor materials have been structureand mechanical properties of surfaces and various nanostructures – e.g. thin films, quantumdots and carbon nanotubes. In addition to crystalline materials properties of silicon nanocrys-tals in amorphous silicon dioxide have been studied by computational methods.
As regards to metals, the focus has been on detailed microscopic structure and dynamics ofdislocations.
In connection with the MD simulations, development of the graphical user interface for thesimulation programs has been continued. The interactive simulation programs have been usedto study dislocation dynamics and strain relaxation in two and three-dimensional heteroepi-taxial systems and mechanical and structural properties of carbon nanotubes. Development ofscientific visualization has also been done using the open-source program package OpenDX.
In relation to all these simulation studies, parallel computation algorithms and methods arebeing developed, specifically targeted to computer cluster environments.
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Dislocation nucleation in mismatched heterostructures
Researchers: Marco Patriarca, Antti Kuronen, Kimmo Kaski
We study the conditions for nucleation of dislocations in lattice-mismatched heterostruc-tures, which have recently risen a great interest due to their technological importance. To thisaim we use programs BOUNDARY3D and BOUNDARY2D developed in our laboratory andconsisting of a graphical user interface coupled to a molecular dynamics code. We considerdifferent values of the misfit parameter, temperature, layer thickness, and boundary condi-tions. Examples of simulation results are shown in Figs. 16 and 17.
Figure 16: Left: Typical geometry used for the numerical simulations. Colours signify atomtype. Right: The same system with colours signifying potential energy. Steps on the top andirregularities on the lateral surface are due to stacking faults formed beyond the critical misfit.
Reaction coordinate
-3.0
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V)
-4.5%-4.2%-3.0%-2.5%Misfit
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(c)
(d)
Figure 17: Results from dislocation nucleation barrier calculation of a 2D Lennard-Jones sys-tem. Left: Atomic configurations of a nucleation event in the case of -2.5% misfit. Right: Thesystem potential energy along the reaction path for different values of the misfit. Letters a-don the left correspond to positions marked a-d on the reaction path on the right hand side ofthe picture.
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Minimum energy paths in 2D systems
Researchers: Antti Kuronen, Tapio Nieminen
The 2D simulation code BOUNDARY has been further developed by implementing thenudged elastic band (NEB) method to calculate the minimum energy path between two atomicconfiguration.
Figure 18: The main window of BOUNDARY simulation program.
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Phase separation in amorphous semiconductors
Researchers: Sebastian von Alfthan, Antti Kuronen and Kimmo Kaski
We have developed computational methods to study phase separation in amorphous solidson an atomistic level. We are using these methods to study the formation of silicon nanocrys-tals in SiO. This problem has practical applications since one possibility for creating activelight emitting devices made of silicon is based on small clusters of crystalline silicon em-bedded in amorphous SiO�. Recently, a promising method for creating these nanocrystalswith greater control of their density and size distribution has been presented. This methodconsists of annealing SiO/SiO� superlattices which results in phase separation and thermalcrystallization of small silicon nanocrystals in the SiO layer.
The computational method we are using is a variant of a Monte Carlo (MC) method calledthe Wooten Wiener and Weaire (WWW) method. In the WWW method the structure of thematerial is described by the topology of the bonds connecting the atoms. The system is al-lowed to evolve by doing trial moves that change the bonding topology. Using this methodwe are able to directly model the development of the structure of the material. This enables usto study the phase separation and the crystallization. This would be impossible with normalmolecular dynamics or Monte Carlo methods because of the timescales involved.
We have also using openDx (www.opendx.org) developed a visualization programcalled atomicDx that can be used to visualize atomic systems. The program has been de-veloped for analyzing the structural properties of semiconductors but it can also be used forother cases.
Figure 19: Pictures created using atomicDx of a phase separated SiO system. left: Red atomsdepict silicon atoms while blue atoms depict oxygen atoms. middle: The color of the bondscorresponds to their length. right: Blue corresponds to atoms in pure silicon while red corre-sponds to atoms in silica.
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Static and Dynamic Properties of Dislocations in FCC Metals
Researchers: Péter Szelestey, Marco Patriarca, and Kimmo Kaski
Dislocations are topological defects that have major influence on the plastic nature of crys-tals. We are using Molecular Dynamics simulations to determine properties of the most com-mon �
������ type of dislocations in face-centered-cubic metals. These dislocation generally
split into partials and may extend over several lattice constants laterally. Primarily we concen-trate on nickel as a model material. Most of our simulation tools have already been developedin the previous years including the inter-atomic potential.
In our recent work we calculated the properties of both edge and screw type of dislocationsand carried out a detailed comparison of both type of dislocations with theoretical results andeach other. The separation distance of partials have been calculated and we found that thereexists several meta-stable separation distances. The profile of dislocation distribution functionwas compared with the Peierls-Nabarro model and possible reasons of the discrepancies wereexplained.
Dislocation motion is basic mechanism responsible for plastic behaviour of materials. Dis-location motion is affected by the external stress and the underlying lattice, which results ina small periodic potential for the dislocation. We measured the minimum stress required tomove a dislocation, the so-called Peierls barrier and found reasonable agreement with thePeierls-Nabarro model. In addition our results show that dislocations may change their struc-ture during the motion reflected from the change in the separation distance. Although the-oretically this has been prediced for a long time, we could observe this effect by means ofsimulations.
Figure 20: Snaspot of an extendedscrew dislocation looking from the di-rection of the dislocation line. Parti-cles are denoted with different colorsrepresenting the magnitude of poten-tial energy.
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Modeling of thin semiconductor films
Researchers: Laura Nurminen, Francesca Tavazza*, David P. Landau*,Antti Kuronen, and Kimmo Kaski
*Center for Simulational Physics, The University of Georgia, Athens, GA, USA
Understanding the growth of thin semiconductor films is crucial for developing new typesof nanoelectronic devices. We are studying the structure and properties of heteroepitaxialGe/Si(001) systems which are estimated to be one of the most promising materials for novelelectronic and optoelectronic components.
The Ge/Si(001) system consists of a thin germanium layer on a silicon (001) surface. De-spite its relatively simple composition, the system displays a wide variety of interesting phe-nomena related to relaxation of lattice-mismatch induced strain. Studying strain-related ef-fects requires using large-scale simulation methods because these systems are strongly influ-enced by long-range elastic interactions. Accurate quantum-mechanical first-principles meth-ods are currently limited to systems composed of a few hundred atoms at best. Tight-bindingtechniques can extend to a few thousand atoms, but in many cases empirical potentials are stillthe only practical choice.
We use empirical potentials in connection with advanced Monte Carlo techniques. Thisapproach can be used to study phenomena where considerable atomic rearrangement takesplace as well as temperature-dependent phenomena. Figure 21 shows the surface structureof a system which consists of two Ge layers deposited on a Si(001) substrate. Compressivestrain in the Ge overlayer is relieved in part by formation of dimer vacancy lines and in partby Si/Ge intermixing.
Figure 21: Surface structure of 2ML of Ge on Si(001). Relaxation of strain is achieved bytwo mechanisms: (i) dimer vacancy lines allow for inward relaxation of the Ge overlayer and(ii) Si/Ge intermixing provides additional strain relief. The atomic interactions were modeledby the Stillinger-Weber potential. Si atoms are depicted as dark blue and Ge atoms are colorcoded according to their energies [from red (high energy), to green and light blue (lowerenergy)].
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Structural Properties of Carbon Nanotubes and Nanotori
Researchers: Maria Huhtala, Kota Mogami, Antti Kuronen, and Kimmo Kaski
Carbon nanotubes are tubular all-carbon molecules with fascinating properties. Singlewalled nanotubes can be visualized as a graphite layer rolled seamlessly into a tubular form.Of the properties, the large aspect ratio and richness of achievable electronic properties havemade carbon nanotubes the proposed material for diverse nanoelectronic and nanoelectro-mechanical devices whereas proposed mechanical applications rely on high elasticity com-bined with high yielding strength of the tubes.
The properties of a carbon nanotube depend on the local atomic configuration and in manyof the proposed electromechanical applications this changes from one mode of function toanother. The tube can be manipulated to bend, or buckle, or defects can be induced. Fordevice development it is essential to understand these structural changes and our work strivesafter shedding some more light on the occurring phenomena. The tools employed are bothclassical molecular dynamics and dynamical tight binding methods. Fig. 22 shows nanotubebuckling and bond reconstruction in the buckled region.
Figure 22: A (14,0)-nanotube first bent to an angle of 36 degrees and then annealed in atemperature of 3300 K for three periods of 130 ps. After each of the annealing sessions, thestructure has been cooled down to 0 K. The enlarged protions at left and right show bondreconstructrion which relaxes the strain.
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Studies on Microelectromechanical Systems
Researchers: Virpi Junttila, Jarmo Hietanen, Kimmo Kaski
This research is a project of "MIKSU technology program", which is partially funded byTEKES (National Technology Agency).
Studies on different problems arising in fabrication of MEM-systems were performed usingdifferent numerical methods. The possibility of contact of the membrane and the perforatedSi-plate of a microphone fabricted on a SOI were studied. The affect of the pretension in theSiO� layer between the plate and membrane was studied using Ansys. It revealed, that the dis-placement of the solid plates of varying thicknesses is small, 10-20� of the air layer thicknessand 1.5-5.6� of the plate thickness. The homogenization of the holes would in stationary caseaffect only the Young’s modulus; but even with half of the Young’s modulus the displacementwould be only 13-23� of the air layer thickness and 1.9-6.9� of the plate thickness. Thus itis very unlikely that the contact between the perforated plate and the membrane occur onlydue to the pretension.
The membrane displacement due to pressure difference was modelled using Elmer soft-ware. The modelling was performed in two levels: First the affect of an individual hole wasmodelled to an air layer thickness dependent acoustic impedance using linearized Navier-Stokes model. Second the membrane displacement was solved using time-dependent coupledhomogenized Reynold’s equation and Kirchhoff equation and linearly vanishing pressure dif-ference. The membrane would contact the plate if the pressure difference vanishes slowly,in microseconds, even if the difference is small. The Acoustic impedance and the membranedisplacement are shown in figure 23.
Also the breaking of the contact of the attachment lines of a wind sensor due to wind arisenpressure difference have been modelled. The force of the condensators of the sensor weresolved using Femlab software and homogenized. The plate displacement was then solvedusing Kirchhoff equation. The membrane would unstick from the attachment area if the capa-sitive force was less than about twice the force arising from the wind. Two cases of a memranedisplacement and plots of the maximum displacament as functions of force ratio are shown infigure 24.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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p1=1e4−1e4*t/(0.2e−6s)
Figure 23: Left: Acoustic impedance of a hexagonal hole. Results for compressible andincompressible fluid and curves fitted for data.Right: Minimum gas layer thickness as a function of time at pressure difference of 10�.
26
7.3306e−07
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Figure 24: Left: Membrane displacement in a case where Capasitive force is 1.32 times theforce of pressure difference. On the left the free membrane, on the right a membrane glued tothe sensor are in the middle. Right:Maximum and the corner displacement of the membraneof the wind sensor as a function of the ratio of the force of the condensator and force of thepressure difference. On top results for a free membrane, on the bottom results for a membraneglued to the condensator in the middle.
5.2.2 Research in Biophysics, Soft Materials and Pattern Formation
The soft matter and biophysics group started at LCE in September 2000. In general, theresearch is geared towards the interface between condensed matter physics, biology and ma-terial science. The great diversity of these systems, ranging, for instance, from complexesof DNA and cationic liposomes used in gene transfer to unexpected morphological evolutionof polymers under shear flow and to pattern formation in biological processes, provides newchallenges in both fundamental and applied research.
Typically, biological processes take place under non-equilibrium conditions. Modelingthese processes provides many theoretical challenges since eventually the validity of equi-librium concepts, such as universality and scaling laws, breaks down. It is important to studytheir range of validity, and how the emergence of new time and length scales, and possibly asteady state, is manifested in dynamical systems. A good example of that is the shear flow be-havior of complex fluids where the dynamics of order-disorder transition depends intimatelyon the application of shear. As vast number of industrial processes involve complex fluids andpolymer mixtures under shear flow conditions, it is clear that a better theoretical understandingof these processes has immediate practical applications.
Another challenge arises from the interdisciplinary nature of these problems. A strong in-teraction between theory, computation, and experiments is essential in order to get insight ofinto the physical mechanisms producing these complex, often collective, phenomena. A clearexample of this is the study of lipoplexes, i.e., the formation and behavior of DNA-cationicliposome complexes. There exists a large amount of experimental data, and in vivo experi-ments have shown that clinical application of lipoplexes is effective and safe. However, theprocesses and physical mechanisms, e.g., those involving interactions of electrostatic origin,that control the formation of these complex structures are not well established. Theoreticalstudies and, in particular, simulational studies, have the potential of helping to characterizebetter these complex processes.
The studies introduced below briefly describe our efforts in soft matter and biophysics. Fordetails and up-to-date information, please see the corresponding project home page as givenin connection of each project.
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Computer Simulations of a Polymer Chain under Shear Flow
Researchers: Markus Miettinen, Mikko Karttunen, Michael Patra and IlpoVattulainen�
�Laboratory of Physics and Helsinki Institute of Physics, HUT
Project home page: http://www.lce.hut.fi/research/polymer/
The effect of shear flow on rhelogical properties of polymer mixtures is of great interestbecause the nonequilibrium nature of the problem makes it theoretically and computationallydifficult. On the other hand, looking from the practical point of view, industrial processes ofteninvolve polymer mixtures under shear flow. A better theoretical knowledge of how to, e.g.,control viscosity and phase separation would have immediate consequences in developingmore efficient processes.
Dissolved polymer chains are known to undergo a globule to open coil transition as thesolvent quality changes from poor to good. Likewise, it has been found out that an individualpolymer chain undergoes a collapsing – stretching behaviour when the solute is exposed toshear. In this study, we look into the combined effect of shear flow and solvent properties tothe conformational changes of the polymer chain.
The first part of the study has concentrated on the effect of solvent quality, i.e., studying theproperties of a freely floating chain as a function of solubility. This offers a sturdy referencefor the second part, which will introduce applying shear to the system. The chain propertieswill be measured as a function of both the shear strength and the solvent quality.
The solvent is modeled explicitly by monomers interacting with each other through aLennard-Jones -type (LJ) potential. The polymer model is made up of a few dozen LJ-monomers freely jointed together by nonlinear FENE-springs. The first part of the study willbe performed by carrying out Molecular Dynamics (MD) simulations in three dimensions,changing the solvent properties by modifying the interaction coefficients of the LJ-potential.The second part shall consist of Nonequilibrium Molecular Dynamics (NEMD) simulationsusing the SLLOD algorithm with Lees-Edwards boundary conditions for implementing theshear.
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Pattern Formation in Turing Systems
Researchers: T. Leppänen, M. Karttunen, K. Kaski, and R.A. Barrio�
�Instituto de Fisica, Universidad Nacional Autonoma de Mexico
In 1952 one of the greatest mathematicians of the 20th century, Alan Turing proposed a sys-tem of reaction-diffusion equations describing chemical reactions and diffusion to account formorphogenesis, i.e., the development of patterns, shapes and structures found in nature. Therecent growth in computing resources has enabled numerical simulations of Turing systems,which has brought a great deal of knowledge concerning their properties. These complex sys-tems have been used in explaining, e.g. patterns on animal coatings and the segmentation inembryos.
We study numerically structures generated by the Turing mechanism in two and three di-mensions. We investigate the dependence of the resulting structures on the system parameters,transitions between these structures, growth from two to three dimensions and percolation ofchemicals in the system. In addition, we are interested in the effect of random noise on devel-oping structures, since it is very important from the point of view of biological applications.
The goal of these studies is to develop biological growth models based on Turing systems.We are developing a model for neural patterning, i.e., the signaling mechanism that neuronsuse to form connections to other neurons in a developing nervous system. Figure 25 showshow connections between certain points can be grown by using a Turing system with sourcesof chemicals. For applications, it is very important to have a better insight of the morphologi-cal characteristics of Turing systems in order to be able to implement the required qualitativefeatures to the model in a biologically plausible manner.
Figure 25: Fully connected networks generated by Turing system in two and three dimensions.The sources are seen in red and the connections are yellow in the two-dimensional case (left).
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Dissipative Particle Dynamics Studies of Coarse-grained Polymer Systems
Researchers: Petri Nikunen, Mikko Karttunen, and Ilpo Vattulainen ��Laboratory of Physics and Helsinki Institute of Physics, HUT
Project home page: http://www.softsimu.org/
The physics of polymeric liquids has been a problem of considerable interest in recent years.From a modeling point of view, these systems are problematic due to the fact that numerousphenomena take place at mesoscopic time and length scales, which are not accessible bydetailed simulation techniques such as molecular dynamics. To overcome this problem, anumber of “coarse-grained” approaches have been suggested and developed to simplify theunderlying microscopic model without changing the essential physics.
One candidate to work with is the dissipative particle dynamics method. It is a particle-based simulation technique which suits particularly well for studies of soft condensed mattersystems. Due to this, it has been applied to various systems, including the structure of lipidbilayers, self-assembly, and the formation of polymer-surfactant complexes. In our project, weconcentrate on methodological aspects of this method, and apply it e.g. to vesicle formation(figure below).
Figure 26: Formation of a vesicle. Time goes from left to right, top row illustrating the vesiclefrom outside and bottom row from inside.
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5.2.3 Research on Semiconductor Quantum Structures and Physics of New Informa-tion Technologies
Optically active quantum dots (QD) and quantum wires (QWR) are compound semiconductorstructures that confine both electrons and holes in a potential box having a dimension of fewtens of nanometers. These semiconductor structures have exceptionally high optical qualityon special transport properties, which makes them ideal for both fundamental research andtechnological applications. In the enclosed project descriptions we describe few topics thathave been in focus during 2002.
This work includes extensive domestic and international collaboration with the followinglaboratories: Optoelectronics Laboratory, HUT, VTT Electronics, Instituto Nazionale di Fisicadella Materia, University of Lecce, MegaGauss Laboratory, University. of Tokyo, Inst. ofIndustrial Science, University of Tokyo. Center for Teraherz Science, USCB.
The ever-decreasing size of the basic components of information processing will give quan-tum effects an important role in future technologies. Recent developments such as quantumcryptography and the idea of a quantum computer have shown that, rather than being onlyharmful, these effects can probably be utilized to a great extent. In communications technolo-gies, optical transmission is setting the trend in the development of the networks. The fullharnessing of the huge bandwidth provided by light still requires for replacing the switching,routing and processing electronics by all-optical components. Research on nonlinear opti-cal materials and light-induced quantum effects will be crusial in the development of futureall-optical processing technologies.
We have investigated cold atomic Fermi-gases which can be used for studing importantquantum many-body effects such as superconductivity. Cold atomic gases may also serve asa source of atoms in quantum information processing applications. This research is done incollaboration with the University of Innsbruck, Austria, with Nordita, Denmark, and with theLoomis Laboratory, University of Illinois, USA. We have also participated in the Universityof Jyväskylä -based experimental research on the idea of using superconducting Josephsonjunctions as the basic processing element of a quantum computer. Related to optical com-munications technology, we have started a project investigating the possibilities of all-opticalswithing and processing using nonlinear materials, combined with novel material structuressuch as photonic crystals.
Modelling of nanoscale semiconductor devices
Researchers: Fredrik Boxberg, Roman Terechonkov and Jukka Tulkki
The semiconductor technology of today is mainly based on silicon. However, it has beenshown very difficult to obtain long phase coherence lengths in silicon structures. For quantummechanical operation the phase coherence is crucial. We are studying the possibilities forquantum mechanical electronics in both silicon and III-V compound semiconductor (eg GaAs)devices. We have modelled the growth process of silicon dioxide on silicon quantum wiresand its effect on the electronic properties of fabricated devices. We have observed great strain-induced effects on the electronic properties of the device.
Another topic of research is the electronic and optical properties of complex III-V com-pound semiconductor devices. We are studying the possibility to fabricate both single photondevices (eg needed for possible quantum information processes) and laser devices. For thispurpose we are developing very general tools for strain analysis and band structure calcula-tions.
31
The strain in the modelled structures is due to different lattice constants of the epitaxiallycombined materials. The strain is of the order ����. Hence, the devices can be built withhardly any dislocations and we now that the strain is completely elastic. However, the strainaffects though remarkably the electronic structure and it cannot be omitted from the electronicand optic model. The assumption of a dislocation-free structure is one of the corner-stones ofour model. The strain and the piezoelectric field in these structures affects the position andthe interband coupling of the energy bands.
The strain calculations are based on the elastic continuum theory. The full treatment of thestrain in compound semiconductors leads to a electro-elastic coupled problem where the strainis coupled to the piezoelectric field. This coupling is due to the ionic atomic structure of III-Vcompound materials. The effect is absent in purely one material semiconductors like siliconor germanium.
The optical properties rely completely on the underlying electronic bands. From the elec-tronic structure we can model properties like photon recombination rates, polarization of theemitted light and light amplification. We intend to extend our modeling to the time domainbut so far we have only considered time independent and long term limiting properties.
We have been working on quantum wells, quantum wires and quantum dots. Figure 27shows an 8 nm thick corrugated quantum well with a corrugation period of 13 nm. Figure 27A and B show the electron and hole probability densities respectively, close to the fundamentalenergy band gap. The electronic band structure of the structure is shown in Fig. 27 C and Fig.27 D shows schematically the geometry of the structure. The shaded layer is the InGaAsquantum well inside AlGaAs barriers. The periodic corrugation and strain induce alignedquantum wires as seen in Fig 27.
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Energy band structure calculations.
Conductance and optical properties of electronic and photonic devices are governed bythe electronic structure of the active materials of the components. Due to the fast electronrelaxation, we pay attention only to the electronic states near the band edge. This motivatesthe use of the k�p-theory.
In this project we have developed numerical methods for calculation of electronic structurein semiconductor quantum wells, wires and dots. Considering the basis composed of thehigh s- and p-orbitals and taking into account the electron spin we derived the 8x8 Luttinger-Kohn hamiltonian to be discretised. The strain effects calculated separately using ANSYSlibrary are included to this hamiltonian. Finite differences method is used to discretise thehamiltonian in such a way that the final matrices are symmetrical [1]. Closed, open or cycleboundary conditions are included to take into account the finite geometry of the semiconductorstructures.
Special libraries are required to diagonalise the obtained matrices which size may be of theorder up to 1000000 by 1000000. We are using PARPACK library to parallize the eigenvalueproblem to run it on the cluster systems.
References
[1] O. Stier, Electronic and Optical Properties of Quantum Dots and Wires (Wissenschaftund Technik Verlag Berlin, 2001).
Modelling optical components for access networks
Researchers: Jani Oksanen and Jukka Tulkki
With the long haul network backbone transformed into an optical information highway, thetransfer capacity is mostly limited by the electronic bottlenecks in the access and metropoli-tan area networks. The undisputed success of the optical fibres in the network backboneencourages to resolve the bottlenecks by passing to optical solutions in the other parts of thenetworks as well. This solution, however, requires all-optical components which do not existcommercially (or at all) at present.
Metropolitan area networks include a large number of separate connections, which makesfast switching devices and inexpensive laser transceivers essential. The goal of this project isto create models and new ideas for the components needed in expanding the optical network.
We have investigated the differences of the quantum well and dot lasers with respect totheir chirp under direct current modulation. This is done by evaluating the changes caused bythe current modulation to the refractive index of the laser waveguide. The basic mechanismcausing the chirp is the relation between the refractive index and the absorption of the medium.When a laser is modulated with current, the carrier concentration in the lasers changes in time.The changes in carrier density alter the gain, which further induces changes in the refractiveindex. And as the refractive index changes the wavelength of the optical field that ’fits’ in thelaser cavity changes.
The refractive index changes corresponding to a change in the carrier density can be trackedusing the Kramers-Kronig relation stating that the the refractive index at a given wavelengthdepends on an integral of the absorption spectrum of the material. It has been shown that thequasi-equilibrium is a good approximation of the carrier distribution under lasing conditions,and if the carrier distribution in the material is known, also the absorption spectrum can becalculated. This enables to evaluate the linewidth enhancement factor at a given carrier density
33
1000 1200 1400 1600 1800 200010
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Figure 28: The calculated absorption spectrum of a quandum dot laser
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Figure 29: Schematic representation of the linear optical amplifier including a waveguide anda vertical microcavity with highly reflecting distributed Bragg reflectors (DBR).
and wavelength even without a dynamical model.To evaluate the actual chirp, it is necessary to use dynamical equations to find out just how
much the current modulation changes the carrier density of the laser. The simplest model isthe two-level rate equations describing the relation between carrier density and the laser field.After the amplitude of the carrier density fluctuation is known it becomes a simple task toevaluate the chirp, provided the refractive index spectrum is known.
It turns out that, in addition of having a low threshold current, the quantum dot lasers can
34
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Figure 30: The coupling between the signals, vertical cavity laser (VCL) and carriers of thesystem. a) The signal of Ch. 1 as a function of time at different positions along the amplifier.The weak self modulation of the signal is seen at the output. b) The constant signal of Ch. 2at different positions. The weak cross modulation of Ch. 1 is seen at the amplifier output. c)The relaxation oscillation of the carrier density caused by changes in the signal powers. d)The photon density fluctuations in the VCL mode captures most of the disturbances.
add very low chirp – an order of magnitude lower than corresponding quantum well lasers –to the signal, even when modulated directly with current. In practice this might remove the
35
need of costly external modulators in the laser transmitters.We have also studied a new kind of semiconductor amplifiers that have a stabilizing laser
field perpendicular to the amplified signal. The analysis is based on a stochastic rate equationmodel coupling the signal and perpendicular laser field photon densities to the carrier densityof the laser. These differential equations are solved numerically to see the effects of thestabilizing field to the amplification. Due to the compact size, customizable amplificationspectrum, integrability and current injection, these amplifiers are ideal for signal amplification,because their signal degradation is small.
Future topics include switching, adjustable wavelength lasers and other related components.Also the electronic structure calculations of the material research group are to be used in thefuture simulations.
36
Photonic Crystals
Researchers: Anu Huttunen and Päivi Törmä�� Department of Physics, University of Jyväskylä.
Photonic crystals are periodic dielectric structures. The periodicity causes bandgaps forlight to appear, i.e., light with a certain wavelength cannot travel in the crystal. Photonic crys-tals are a very attractive solution to various problems in telecommunications. The periodicity,and thus the bandgap, can be in either one, two or three dimensions. A widely used exam-ple of a one-dimensional photonic crystal is the Bragg grating. Two-dimensional photoniccrystals embedded with defects could be used, e.g., as a waveguide for integrated optics andthree-dimensional photonic crystals as a microcavity.
Photonic crystals may become the key material for integrated optics. Then they are de-manded to have as small size as possible. Thus we study thin slabs of one- and two-dimensional photonic crystals. Band structures and eigenmodes, and thus the functioningof the photonic crystal change markedly, when the finite height and the surrounding mate-rial is taken into account. We also study photonic crystals made of Kerr-nonlinear materials,which means that the material properties are dependent on the local light intensity. Thusthe bandstructure of the nonlinear photonic crystal, i.e., the transmission as a function of thefrequency, can be changed dynamically by applying a high-intensity control pulse. This phe-nomenon could be used for fast all-optical switching in optical telecommunications. We havedeveloped an iterative Fourier-method to calculate the bandstructures. To study light propaga-tion in the structures we use a finite difference time domain (FDTD) method in collaborationwith VTT Electronics.
Geometry
100 200 300 400 500
20
40
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Figure 31: On the left cross section of a one-dimensional photonic crystal slab and a gaussianpulse propagating along it. On the right the shape of one component of the eigenmode in theregion marked with a square.
37
Cold Degenerate Atomic Fermi Gases
Researchers: Mirta Rodriguez, Gheorghe-Sorin Paraoanu � and Päivi Törmä��Department of Physics, University of Jyväskylä
The remarkable achievement of Bose-Einstein condensation (BEC) in alkali gases has stim-ulated the trapping and cooling of also the Fermionic isotopes. The regime of quamtum de-generacy has been achieved for ��
� and ��� by several experimental groups.
Atomic gases can be efficiently and accurately manipulated. They are dilute and weaklyinteracting thus offering the ideal tool for studying fundamental quantum statistical and many-body physics.
The most prominent phenomena for the fermionic samples would be the superfluid BCStransition. When fermionic atoms in two different hyperfine states are trapped they may havean attractive interaction between them caused by s-wave scattering. According to theoreticalpredictions, the system then lowers its energy by the formation of atomic Cooper-pairs andbecomes a superfluid.
We are studying different manisfestations of superfluidity in these novel systems:1) The vortex core size reflects the typical coherence length of the system. We have ana-
lyzed the single vortex solution for the order parameter very close to the transition temper-ature, and studied how the trapping potential affects the healing length of the system. Wefound that the healing length differs essentially from that of metallic superconductors due tothe trapping effects.
2) We have proposed the use of on-resonant or near-resonant light to probe the order param-eter in order to detect the superfluid transition and the Cooper-pair coherence across differentregions of the superfluid. The possibility of exciting collective modes by the probing laserlight has also been considered.
3) We have reformulated the josephson effect between two superfluids when it is possibleto couple in a different way the two atoms forming the pair.
A purely quantum transport phenomena such as Bloch oscillations can also be observed inthese Fermi gases when they are loaded in a periodic optical potential.
g
e
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−δ/2 −δ /2
δ δ ∼ ∼
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Figure 32: Proposed laser scheme to observe the josephson current between two superfluidatomic Fermi gases. In these systems one more degree of freedom will be experimentallyavailable and one can couple with different “voltages” atoms in “spin up” and “spin down”states.
38
5.2.4 Medical physics: Radiation Dosimetry at Cellular Level
Researchers: P. Välimäki�, A. Kuronen, S. Savolainen�
and J. Stepanek��Helsinki University Central Hospital, Finland�University of Zurich and Paul Scherrer Institute, Switzerland
New promising methods in cancer treatment like radioimmuno and neutron capture thera-pies provide new challenges to dosimetry. Contrary to the therapies exploiting external beam,radiation distributions in these new methods are generally highly non-uniform. Since thereexists at the moment no means to measure the doses absorbed by the patients from internallydeposited radionuclides, the only way to proceed is to develop theoretical methods to estimatethese doses.
One remarkable, but mostly disregarded factor in analytical microdosimetry is the cell clus-ter model to which the dosimetric calculation itself is applied. Three dimensional cell clustermodels have so far been quite simple consisting mainly of clusters of spheres largely due toinsufficient computing capacity. On the other hand the cell-level dosimetry field has lackedtotally the verification of the cluster models against biological reality. We are currently devel-oping a new method to overcome both of these deficiencies. As the basis for the modellingthe method uses realistic cell related microscopy data of the tissues. Dose calculations in therealistic cell cluster makes it possible to verify the usefulness of the currently used cell clustermodels and to further estimate the effects of the cell shape, the cell size variation and thespatial distribution of the cells in the cell-level dosimetry.
39
5.3 Cognitive Science and Technology
5.3.1 Psychophysical Research
Researchers: Tobias Andersen, Riikka Möttönen, Ville Ojanen, Mikko Sams,Kaisa Tiippana, Jyrki Tuomainen.
Our main interest is to study the mechanisms of audiovisual speech perception, i.e. to de-termine how seeing the talking face affects the speech percept, by conducting psychophysicalexperiments. Computational models are also being tested and developed to account for theresults. The research will enhance understanding of the basic mechanisms of human speechperception, and will have applications in communication technology, for example in the de-velopment of automatic audiovisual speech recognizers.
In a typical experiment, an observer is presented with audiovisual speech, i.e. speech soundsthrough headphones or loudspeakers together with a talking face on a computer monitor, andthe observer reports what he or she heard. One of the main research tools is the McGurkeffect. For example, an auditory syllable /pa/ is dubbed onto a visual syllable /ka/. This kindof combination is typically perceived as /ta/. The McGurk effect is used to study audiovisualintegration since its strength is related to the extent of visual influence in speech perception.
Main research areas are:Attentional influences on audiovisual speech perception: We have shown in two different
experimental set-ups that distraction of visual attention weakens the McGurk effect, implyingthat audiovisual integration is not an entirely automatic process. See Figure for experimentalresults.
Audiovisual speech perception in children: Young children show less visual influence inspeech perception than adults, and we have shown that children reach adult performance be-tween 8-14 years of age. We have also investigated audiovisual speech perception in childrenwith learning problems who have deficits in categorization of auditory speech and phono-logical awareness. These children seem to be relatively more influenced by visual speechin audiovisual conditions, even though their lipreading skills may be worse than in normal-learning children. This research is conducted in collaboration with the Auditory NeuroscienceLaboratory at Northwestern University, IL, U.S.
Modelling of audiovisual speech perception: The above results as well as others havebeen modelled with the Fuzzy Logical Model of Perception developed by D. Massaro andco-workers, and it accounts for the results well. The model has also been extended to accountfor the effect of audio signal-to-noise ratio.
Articulatory influences on speech perception. We have found that silent articulation ofa vowel influences the subsequent classification of a target vowel continuum by assimilatingthe ambiguous vowels at the phoneme boundary to the category of the articulated vowel.However, if the context vowel is presented auditorily, a contrast effect is obtained yielding thetwo vowels more separate in the vowel space. Current focus of investigation is on whetherboth effects can be explained with reference to the individual vowel prototypes in the auditoryspace.
Audio-visual speech perception and speech mode. By using so-called sine wave replicasof speech we have shown that audio-visual integration of speech only occurs when the subjectsperceive these stimuli as speech. This suggests that speech-specific mechanisms are essentialfor the integration of heard and seen speech. We are currently investigating by the fMRI andMEG techniques the neural correlates of speech specificity of audiovisual speech perception.
40
Figure 33: The McGurk effect was stronger when the face was attended (fewer auditory /p/responses, purple bars). When attention was directed at a leaf floating across the face, theMcGurk effect was weaker (more auditory /p/ responses, green bars).
5.3.2 Cognitive Neuroscience
Researchers: Mikko Sams, Christina Krause �, Kaisa Tiippana, Iiro Jääskeläinen, Toni Au-ranen, Vasily Klucharev, Laura Laitinen, Veera Meriläien, Riikka Möttönen,Ville Ojanen, Aapo Nummenamaa�Cognitive Science, University of Helsinki
Our cognitive neuroscience studies focus mainly on disclosing the neural basis of auditoryand visual speech perception. In particular, we aim at elucidating the spatiotemporal orches-tration of acoustic and visual speech feature integration in the human brain. During 2002, wehave observed that the superior temporal sulcus plays an integral role in audio-visual speechperception, and obtained empirical support for the so-called mirror-neuron theory of speechperception in our multi-modal experiments. We found preliminary evidence about specificaudio-visual selective attention mechanism, which could not be explained by separate audi-tory and visual attention systems. We evaluated various statistical methods in studying MEGtime-frequency representations (TFRs) during real and imagined hand movements. In ourEEG study, we found evidence of an early audiovisual interaction effect, probably related togeneral interaction mechanisms (time synchrony, spatial location). Interaction at phonemiclevel started clearly later.
Experiments have been conducted using the 306-channel magnetoencephalography (MEG)in the Low Temperature Laboratory of the Helsinki University of Technology, and in theBiomag Laboratory of the Helsinki University Central Hospital. We have also recorded high-resolution electroencephalography (EEG) in our own EEG-laboratory (and simultaneouslywith MEG at the Biomag Laboratory). Further, we extended our research methodology to in-clude high-field (3 tesla) functional magnetic resonance imaging (fMRI), and also conductedsafety studies related to simultaneous recording of EEG and fMRI. During year 2002, we havebegun to develop and apply signal analysis algorithms whereby the complementary informa-tion provided by fMRI, MEG, and EEG are optimally combined to enhance the spatiotempo-ral accuracy with which task or stimulus related neural activity can be tracked across corticallocations. For an example of this type of combination approach, see the figure below. Toaccomplish this goal we collaborate internationally with investigators from the MassachusettsGeneral Hospital / Harvard Medical School - Nuclear Magnetic Resonance Center.
41
Figure 34: Neural activity disclosed using a combination of fMRI and MEG/EEG.
5.4 Intel computing cluster
The last year has seen a further shift of computing capacity available within the LCE (i. e., notincluding the LCE’s share of the Clux cluster) from the original Alpha processor based archi-tecture towards off-the-shelf Intel based computers. By now, ��� of the total installed capity,as measured by the LINPACK benchmark [1], is installed using Intel based architecture. Halfof this is on a computing cluster that was installed during the first half of 2002.
This cluster, internally named acis, consists of 22 nodes (with a total of 27 cpu’s) and150 GByte of available harddisc capacity. The nodes can communicate (only) via inexpensive100 MBit/s Ethernet network cards, resulting in a relatively large communication penalty tobe paid. This makes the other clusters available at the LCE more suitable for heavily parallelapplications [2].
Still, within the LCE there is a significant demand for serial and modestly parallel applica-tions. The Intel cluster, in contrast to the other clusters available at the LCE, shares the sameuser base, directory structure etc. as the regular workstation computers, thereby introducingno obstacles to using it. Over the past year, the computing capacity offered by the aciscluster was utilised for approximately ��, making it the most used computer system at theLCE.
[1] J. Dongarra, J. Bunch, C. Moler, and G. W. Stewart, LINPACK User’s Guide, SIAM(Philadelphia), 1979.
[2] A. Lukkarinen and A. Selonen, in E. Lampinen (ed.), Annual report of the Laboratory ofComputational Engineering and Research Centre for Computational Science and Engineer-ing, Helsinki, 2001.
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6 Research Activities
6.1 Visits to the Laboratory
� Yurii Alexander, Prof., Russian Academy of Science, Russia.� Janos Kertesz,Prof., Budapest Technical University, Hungary.� Rafael Barrio,Prof., Oxford University, UK and Autonomous University of Mexico,
Mexico.� Adrian Sutton, Prof., Oxford University,UK.� David Landau, Prof., University of Georgia, USA.� Aatto Laaksonen,Prof., Stockholm University, Sweden.� Alexander Lyubartsev, Dr., Stockholm University, Sweden.� Alex Bunker, Unilever Research, Port Sunlight (UK)� Ji-Ping Huang, Dept. of Physics, Chinese University of Hong Kong� Nick Braun, Dept. of Chemistry, Heriot-Watt University, UK� Li Jingynan, Zhejiang University, China� Iaroslav Ispolatov, Departamento de Fisica, Universidad de Santiago de Chile, Santiago,
Chile� Mogami Kota, University of Tokyo, Japan
6.2 Visits by Laboratory Personnel
Sebastian von Alfthan� University of Oxford, Department of Materials, July-August,2002. Visiting Fellow,
Wolfson College, Oxford.Maria Huhtala
� University of Oxford, Department of Materials, 17.7.-17.9.2002. Visiting Fellow, Wolf-son College, Oxford.
Mikko Karttunen� Dept. of Physics, McGill University, Montreal (Canada), Jan. 1 – Jan 15. 2002.� Visiting professor, Department of Physics, University of Santiago de Chile (Chile) Sep.
9 – Oct. 7, 2002Kimmo Kaski
� Institute Nacional de Pesquisas Espaciais, INPE, Sâo José dos Campos, Brazil, Febru-ary 3-10, 2002.
� Department of Theoretical Physics, Institute of Physics, Budapest University of Tech-nology and Economics, Budapest, Hungary, November 1-7, 2002.
Riikka Möttönen� Brain Research Unit, Low Temperature Laboratory, HUT, 1.3.-15.7.2002.
Laura Nurminen� 3.1.-29.3.2002. Center for Simulational Physics, The University of Georgia, Athens,
USA.Klaus Riederer
� Center for Speech Technology (CTT), Royal Institute of Technology, Stockholm,Sweden, 17.1.2002. Main organizer for the GETA (Graduate School in Electronics,Telecommunications and Automation) visit to the CTT laboratory / Björn Granström.
43
Mirta Rodriguez� Research visit to the experimental group led by Prof. T. Esslinger’s at ETH Zurich
(22-30 June 2002) funded by ESF.� Quantum Optics Group leg by Professor M. Lewensteind in Hannover University, Ger-
many, October 27-December 1, 2002. Visit funded by Hannover University and ESF.
6.3 Participation in Conferences and Seminars
Sebastian von Alfthan� SoftSimu2002 - Novel Methods in Soft Matter Simulations. Helsinki University of
Technology, Espoo, Finland. 31.5.-6.6.2002.� 36th Annual Meeting of the Finnish Physical Society, Joensuu, Finland, 14.-16.3.2002.
Toni Auranen� Darmstadt University of Technology, Graduate College 340: Summer Academy. 15-
22.9.2002, Riezlern/Kleinwalsertal, Austria, 2002.- Presentation: Time-frequency representations of magnetoencephalographic data and
their statistical analysis.Sami Brandt
� Statistical Methods for Video Processing Workshop, June 2002,Copenhagen, Denmark.� 53rd Annual Meeting of the Scnadinavian Society for Electron Microspcopy (SCAN-
DEM 2002), June 2002, Tampere, Finland.Alexander and Andrey Fingelkurts
� International Conference on Cognitive Neuroscience (ICON8), France, September 9-15, 2002.
- Poster: Fingelkurts, Al.A., Fingelkurts, An.A., Krause, C.M., Möttönen, R., Sams, M.The role of brain oscillations in audiovisual speech integration: The McGurk study.
- Fingelkurts, An.A., Fingelkurts, Al.A., Krause, C.M., Möttönen, R., Sams, M. Audio-visual speech integration and operational synchrony of cortical networks (a McGurkstudy).
� 11th World Congress on Psychophysiology, Montreal, Canada, July 29 - August 3,2002.
- Poster: Fingelkurts, Al.A., Fingelkurts, An.A., Krause, C.M., Sams, M. Oscillatorybrain dynamics in audiovisual speech integration.
- Poster: Fingelkurts, An.A., Fingelkurts, Al.A., Krause, C.M., Sams, M. The durationof cortical operations during audiovisual speech integration.
Maria Huhtala� 36th Annual Meeting of the Finnish Physical Society, Joensuu, Finland, 14.-16.3.2002.- Poster: Carbon Nanotubes under Bending� Nanotube 2002, Boston, USA, 6.-11.7.2002.- Poster: Influence of Bending on Local Structure of Carbon Nanotubes.
Anu Huttunen� 36th Annual Meeting of the Finnish Physical Society, March 14-16,2002, Joensuu, Fin-
land.- Talk: Field distributions of nonlinear photonic crystals.� Finnish Optics Days 2002, April 24-26, 2002, Kajaani, Finland.- Poster: Effects of boundary materials in linear and nonlinear photonic crystals.
44
� International Quantum Electronics Conference (IQEC/LAT) 2002, June 22-27,2002,Moscow, Russia.
� 3rd International WE Heraeus Summerschool on Photonic Crystals, July 15-25, 2002,Wittenberg, Germany.
- Poster: All-optical switching with nonlinear photonic crystal slabs.� Jyväskylä summer school, August 12-30, 2002, Jyväskyla, Finland. Participation in
courses:- Electronic properties of nanostructure quantum systems.- Mesoscopic physics and quantum measurement problem.
Mikko Karttunen� ’Liposomes: From Models to Application’, International Conference. Wro-
claw[/]Szklarska Poreba, Poland, May 26-29, 2002. Invited talk.� ’Modeling Soft Materials and Biological Systems’ intensive course. University of San-
tiago de Chile, October 2002. Guest lecturer.Kimmo Kaski
� Conference on Computational Physics, CCP 2002, San Diego, USA, August 24-27,2002.
� The International Econophysics Conference, Bali, Indonesia, August 29-31.� Academia Europaea Annual Meeting, Lisbon, Portugal, October 9-11, 2002.
Christina M. Krause� Internatioanl Multisensory Research Forum, 3rd Annual Meeting. Geneva, Switzerland,
May 24-26,2002.- Poster: Krause, C.M., Möttönen, R., Auranen, T., Tiippana, K., Lampinen, J., Sams, M.
Brain oscillatory responses during an audiovisual memory task a MEG study.� Bioelectromagnetics Society Annual Meeting, Quebec City, Canada, June 23-27, 2002.- Invited talk: EMF Effects on Human Cognitive Processes and the EEG.
Antti Kuronen� The XXXVI Annual Conference of the Finnish Physical Society, 14.-16.3.2002, Joen-
suu, Finland.- Poster: Computer simulation of Si(001) surface and Ge on Si(001) thin films.� Materials Research Society Fall Meeting 2002, Boston, MA, USA, 2.-6.12.2002.- Poster: Adatom Diffusion on Strained Si(001)-(2x1) Surface.
Laura Laitinen� fMRI-based Experiments course by Dr. Robert Savoys. AMI Center, HUT, Finlands,
March 2002.Jouko Lampinen
� RSS’2002 International Conference of the Royal Statistical Society, 3-6 September,Plymouth, UK, 2002
� Wallenberg Foundation invited symposium Learning and Memory - from Brains toRobots, 25-26. Oct., Stanford University, Palo Alto, CA, USA, 2002
Teemu Leppänen� Advanced Course Molecular Simulation, University of Amsterdam, the Netherlands,
7.-19.4.2002.� SoftSimu2002 - Novel Methods in Soft Matter Simulations. Helsinki University of
Technology, Espoo, Finland. 31.5.-6.6.2002.� Let’s Face Chaos Through Nonlinear Dynamics, University of Maribor, Maribor, Slove-
nia.
45
Markus Miettinen� 36th Annual Meeting of the Finnish Physical Society, March 14-16,2002, Joensuu, Fin-
land.- Poster: Computer simulations of a polymer chain under shear flow.� Second Annual Stockholm-Helsinki Miniconference in Micro-Meso Multi-Scale Sim-
ulations. 4.4.2002, Stockholm, Sweden.- Talk: Computer simulations of a polymer chain under shear flow.� Modern Methods in Molecular and Cellular Biophysics winter school. 18.-20.3.2002,
Turku, Finland.� SoftSimu2002 - Novel methods in soft matter simulations. 31.5.-6.6.2002, Espoo, Fin-
land.- Poster: Computer simulations of a polymer chain under shear flow.� Dynamics of Biological Systems From Molecules to Networks. International workshop
in biophysics. 10.-17.8.2002, Krogerup Hojeskole, Denmark.- Poster: Computer simulations of a polymer chain under shear flow.� NATO-ASI: Computer simulations of a surfaces and interfaces. International summer
school. 9.-20.9.2002, Albena, Bulgaria.- Poster: Computer simulations of a polymer chain under shear flow.
Kota Mogami� 36th Annual Meeting of the Finnish Physical Society, Joensuu, Finland, 14.-16.3.2002.- Talk: Thermostat Dependency of the Simulation of Carbon Nanotube.
Riikka Möttönen� fMRI-based Experiments, March 18-21, 2002, AMI Center, HUT, Espoo, Finland.� fMRI & EEG Data Analysis Training Seminar, May 30 - June 1, 2002, Sendai, Japan.� 8th International Conference on Functional Mapping of the Human Brain, June 2 - 6,
2002, Sendai, Japan.- Abstract: Möttönen. R amd Sams,M.: Cortical Processing of Audiovisual Speech.
Petri Nikunen� 36th Annual Meeting of the Finnish Physical Society, Joensuu, Finland.- Poster: Effects of quenched impurities on surface diffusion, spreading and ordering of
O/W(110),- Poster: A testbench for integration schemes used in dissipative particle dynamics.
Laura Nurminen� 15th Annual Workshop, Recent Developments in Computer Simulation Studies in Con-
densed Matter Physics, March 18-22, Athens, GA, USA� NATO Advanced Study Institute, Computer Simulations of Surfaces and Interfaces,
September 9-20, Varna, BulgariaKlaus Riederer
� GETA (Graduate School in Electronics, Telecommunications and Automation) BoatSeminar, Jan 16-18, 2001, Silja Line, Finland and Sweden.
� Audio Engineering Society (AES) 21st International Conference on ArchitecturalAcoustics & Sound Reinforcement, June 1-3, 2002, St. Petersburg, Russia.
- Presentation of paper and sound demonstration: Riederer and R. Niska: Sophisticatedtube headphones for spatial sound reproduction.
Mirta Rodriguez� Finnish Physical Society Annual Conference, March 2002, Joensuu, Finland.- Talk: Supersfluid phenomena in trapped Fermi gases
46
- Poster: A loading system for integrated atom optics� Physics of Ultracold Dilute Atomic Gases workshop, June 9-20, 2002, Benasque, Spain.� International Quantum Electronic Conference IQEC/LAT 2002, June 22-27, 2002,
Moscow, Russia.� Euroconference Quantum Optics, September, 21-26, 2002, San Feliu de Guixols, Spain.- Poster: Bloch oscillations in Fermi gases.
Mikko Sams.� Abstracts- Krause, C., Möttönen, R., Auranen, T., Tiippana, K., Lampinen, J. and Sams, M. Brain
Oscillatory responses During an Audiovisual Memory Task - a MEG study. Interna-tional Multisensory Research Forum, 3rd Annual Meeting, Geneva, Switzerland 24th -26th May, 2002.
- Tiippana, K., Andersen, T. and Sams, M. Illusory flashes are induced by beeps, butare illusory beeps induced by flashes. International Multisensory Research Forum, 3rdAnnual Meeting, Geneva, Switzerland 24th - 26th May, 2002.
- Andersen, T., Tiippana, K. and Sams, M. Endogenous visual spatial attention affectsaudiovisual speech perception. International Multisensory Research Forum, 3rd AnnualMeeting, Geneva, Switzerland 24th - 26th May, 2002.
- Krause, K., Möttönen, R. Jensen, O. Auranen, T., Tiippana, K., Lampinen, J. and Sams,M. Brain oscillatory responses during an audiovisual memory task - an MEG study.International Multisensory Research Forum, 3rd Annual Meeting, Geneva, Switzerland24th - 26th May, 2002.
- Möttönen, R. and Sams, M. Cortical Processing of Audiovisual Speech. 8th Interna-tional Conference on Functional Mapping of the Human Brain, June 2 - 6, 2002, Sendai,Japan.
- Tiippana K., Hayes E., Kraus N. and Sams M. (2002). Maturation of audiovisual speechperception. Cognitive Neuroscience Society Annual Meeting Abstract Book (San Fran-cisco, U.S.A.), 42.
- Tuomainen J., Andersen T.,Tiippana K. and Sams M. (2002). Audio-visual integra-tion of speech with time-varying sine wave speech replicas. Journal of the AcousticalSociety of America, 112(2), 2358.
� Invited talks- Sams, M. Audiovisual speech perception. Seminar in the Department of Neurobiol-
ogy and Anatomy, Wake Forest University School of Medicine, Winston-Salem, USA,March 19, 2002.
- Sams, M. Audiovisual speech perception. In "Speech in Context", Houston, Texas,USA, March 13-16, 2001.
- Sams, M. Ihmisen audiovisuaalinen havaitseminen. Esitelmä kokouksessa "Aivojenkuvantamis- ja mittausmenetelmät kognitiivisessa neurotieteessä, Oulu, 17.5.2002.
- Sams, M. Audiovisual speech perception. Seminar in Northwestern University, De-partment of Communication Sciences Neurobiology and Physiology, Otolaryngology,Evanston, USA, April 18, 2002.
- Sams, M. Neurocognitive mechanisms of audiovisual speech. 6th Finnish-Russian Win-ter School, January 5.-11., 2002, Tvärminne, Finland
Toni Tamminen� Seventh Valencia International Meeting on Bayesian Statistics, Tenerife, 31.5.-
8.6.2002.
47
� STeP 2002, The 10th Finnish Artificial Intelligence Conference, Oulu, Finland, 16.-17.12.2002.
Aki Vehtari:� Ordinary meeting of the Royal Statistical Society, London, UK, March 2002� The Seventh Valencia International Meeting on Bayesian Statistics, Tenerifa, Spain,
June 2002� The 2002 International Conference of the Royal Statistical Society, Plymouth, UK,
September 2002
6.4 Other Activities
Mikko Karttunen has organized� International Summer School: SoftSimu2002 - Novel Methods in Soft Matter Simula-
tions, Espoo, Finland, May 31-June 6, 2002.Timo Koskela has acted as
� Review in journal- Information and Software Technology
Laura Laitinen has acted as� person in charge of Connet activity in LCE. Connet is a virtual university project be-
tween 7 Finnish universities in the area of Cognitive science and technology.Jouko Lampinen has acted as
� Reviewer in Ph.D. dissertations:- Ville Kyrki, Dr.Tech, Lappeenranta University of Technology- Jouni Raitamäki, Ph.D, University of Jyväskylä (in progress)- Juha Vesanto, Dr. Tech, Helsinki University of Technology� Member of organizing committee- Esann 2002� Member of the board in Finnish Brain Research Society� Reviewer in journals- Neurocomputing- Neural Networks- IEEE Systems, Man and Cybernetics- Journal of Machine Learning Research
Mikko Sams has acted as� Reviewer in journals- Journal of Cognitive Neuroscience- Human Brain Mapping- Psychophysiology- Brain Research� Member of the editorial board in Tiede (Finnish popular science magaznie)� Memmer of the editorial board in Polysteekki (Journal of the Helsinki University of
Technology)Aki Vehtari has acted as
� Member of program committee:- STeP 2002 - The 10th Finnish Artifial Conference� Member of the board in Pattern Recognition Society of Finland, Hatutus� Reviewer in journals
48
- Journal of Computational and Graphical Statistics- Journal of Machine Learning Research� Received The best doctoral dissertation award in the field of pattern recognition in
2000–2001 in Finland. Award was issued by the Pattern Recognition Society of Finlandon April 25, 2002.
49
7 Publications
[1] Karsten Albe, Kai Nordlund, Janne Nord, and Antti Kuronen. Modeling of compoundsemiconductors: Analytical bond-order potential for ga, as, and gaas. Phys. Rev. B,66:035205, 2002.
[2] I-C Amari, F. Beltrame, J.G. Bjaalie, T. Dalkara, E. DeSchutter, G. F. Egan, N. H.Goddard, C. Gonzalez, S. Grillner, A. Herz, K. P. Hoffmann, I. P. Jääskeläinen, S. H.Koslow, S. Y. Lee, L. Matthiesses, P L. Miller, F. Mira Da Silva, M. Novak,V. Ravindranath, R. Ritz, U. Ruotsalainen, V. Sebestra, S. Subramaniam, Y. Tang, A. W.Toga, S. Usui, J. Van Pelt, P. Verschure, D. Willshaw, and A. Wrobel.Neuroinformatics: the integration of shared databases and tools towards integrativeneuroscience. Journal of Integrative Neuroscience, 1:117128, 2002.
[3] Tobias Andersen, Kaisa Tiippana, and Mikko Sams. Using the fuzzy logical model ofperception in measuring integration of audiovisual speech in humans. In Proceedings ofthe First International NAISO Congress on Neuro Fuzzy Technologies, Havana, Cuba,January 16-19. 2002.
[4] Sami Brandt. Closed-form solutions for affine reconstruction under missing data. InStatistical Methods for Video Processing Workshop, pages 109–114, Copenhange,Denmark, June 2002.
[5] Sami Brandt. Maximum likelihood robust regression with known and unknown residualmodels. In Statistical Methods for Video Processing Workshop, pages 97–102,Copenhange, Denmark, June 2002.
[6] Sami Brandt, Jukka Heikkonen, and Peter Engelhardt. Automatic alignment of temtilt-series with and eithout fiducial markers. In 53rd Annual Meeting of theScandinavian Society for Electron Microscopy, pages 66–67, Tampere, Finland, June2002.
[7] Sami Brandt, Jukka Heikkonen, and Peter Engelhardt. Automatic alignment oftransmission electron tilt-series without fiducial markers. Journal of Structural Biology,136:201–213, 2002.
[8] Sami Brandt, Jukka Heikkonen, and Peter Engelhardt. On the alignment of transmissionelectron microscope images without fiducial markers. In 16th International Conferenceon Pattern Recognition (ICPR 2002), pages 278–281, Quebec City, Canada, 2002.
[9] A. Fingelkurts, A. Fingelkurts, C. Krause, and M. Sams. Probability interrelationsbetween pre[/]post-stimulus intervals and erd/ers during a memory task. ClinicalNeurophysiology, (113):826–843, 2002.
[10] N. Fujiki, K. A. J. Riederer, V. Jousimäki, J. Mäkelä, and R. Hari. Human corticalrepresentation of virtual auditory space: Differences between sound azimuth andelevation. European Journal of Neuroscience, 16:2207–2213, 2002.
[11] J. P. Huang, Mikko Karttunen, K. W. Yu, and L. Dong. Dielectrophoresis of chargedcolloidial suspensions. Phys. Rev. E., 2002. [Accpeted for publication].
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[12] Maria Huhtala, Antti Kuronen, and Kimmo Kaski. Carbon nanotube structures:molecular dynamics simulations at realistic limit. Computer Physics Communications,146 (1):30–37, 2002.
[13] Maria Huhtala, Antti Kuronen, and Kimmo Kaski. Carbon nanotubes under bendingstrain. In Making Functional Materials with Nanotubes, volume 706 of MaterialsResearch Society Symposium Proceedings, pages Z9.8.1–Z9.8.6, Pittsburg, 2002.Materials Research Society.
[14] Maria Huhtala, Antti Kuronen, and Kimmo Kaski. Carbon nanotubes under bendingstrain. In Materials Research Society Symposium Proceedings, volume 706, page Z9.8,2002.
[15] Maria Huhtala, Antti Kuronen, and Kimmo Kaski. Computational studies of carbonnanotube structures. Computer Physics Communications, 147 (1-2):91–96, 2002.
[16] A. Huttunen and P. Törmä. Band structures for nonlinear photonic crystals. Journal ofApplied Physics, 91:3988, 2002.
[17] Anu Huttunen and Päivi Törmä. Fotonikide on kova sana tulevaisuuden tietokoneissa.Tiede, (8):24–27, 2002.
[18] M. Karttunen and I. Vattulainen. Softsimu2002 - meeting report. Applied Rheology,(12):200–201, 2002.
[19] Mikko Karttunen, K.R. Elder, Martin B. Tarlie, and Martin Grant. Instabilites andresistance fluctuations in thin accelerated superconducting rings. Phys. Rev. E,(66):026115, 2002.
[20] Mikko Karttunen, A. P. Lyuvartsev, Ilpo Vattulainen, and Aatto Laaksonen. Oncoarse-graining by the inverse monce carlo method: Dissipative particle dynamicssimulations made toa precise tool in soft matter modeling. Soft Materials, 1:121, 2002.
[21] Mikko Karttunen, Antti L. Pakkanen, Paavo K.J. Kinnunen, and Kimmo Kaski.Computational modeling of dna-cationic lipid complexation. Cell. Mol. Biol. Lett.,(7):238–239, 2002.
[22] Timo Kostiainen and Jouko Lampinen. On the generative probability density model inthe Self-Organizing Map. Neurocomputing, 48:217–228, October 2002.
[23] C. M. Krause, A. Aromäki, L. Sillanmäki, T. Åström, K. Alanko, E. Salonen, andO. Peltola. Alcohol-induced alterations in erd/ers during an auditory memeory task.Alcohol, 26:145–153, 2002.
[24] Jani Lahtinen, Janos Kertesz, and Kimmo Kaski. Random spreading phenomena inannealed small world networks. Physica A, 311/3-4:571–580, 2002.
[25] Jani Lahtinen and Jouko Lampinen. Reversible jump mcmc for two–state multivariatepoisson mixtures. Kybernetika, (Accepted).
[26] Jouko Lampinen and Timo Kostiainen. Generative probability density model in theSelf-Organizing Map. In U. Seiffert and L. Jain, editors, Self-organizing neuralnetworks: Recent advances and applications, pages 75–94. Physica Verlag, 2002.
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[27] Jouko Lampinen and Aki Vehtari. Neljännesvuosisata Hatutusta:Hahmontunnistustutkimus Suomessa 1977-2002, chapter Bayesilaiset menetelmäthahmontunnistuksessa (in Finnish), pages 86–96. Suomenhahmontunnistustutkimuksen seura ry, 2002.
[28] Teemu Leppänen, Mikko Karttunen, Kimmo Kaski, Rafael A. Barrio, and Limei Zhang.A new dimension to Turing patterns. Physica D, 168-169:35–44, 2002.
[29] Alexander P. Lyubartsev, Mikko Karttunen, Ilpo Vattulainen, and Aatto Laaksonen. Oncoarse-graining by the inverse monte carlo method: Dissipative particle dynamicssimulations made to a precise tool in soft matter modeling. Soft Materials,(1):121–137, 2002.
[30] Jose Millan, Josep Mourino, Marco France, Fabio Cincotti, Markus Varsta, JukkaHeikkonen, and Fabio Babiloni. A local neural classifier for the recognition of eegpatterns associated to mental tasks. IEEE transactions on neural networks,13(3):678–686, 2002.
[31] Riikka Möttönen, Christina Krause, Kaisa Tiippana, and Mikko Sams. Processing ofchanges in visual speech in the human auditory cortex. Cognitive Brain Research,13:417–425, 2002.
[32] M. Niemi, J-P. Laaksonen, J. Vähätalo, J. Tuomainen, O. Aaltonen, and R.-P.Happonen. Effects of transitory lingual nerve impairment on speech: An acoustic studyof vowel sounds. Journal of Oral and Maxillofacial Surgery, 60:647–652, 2002.
[33] Petri Nikunen, Ilpo Vattulainen, and Tapio Ala-Nissila. Effects of quenched impuritieson surface diffusion, spreading, and ordering of o/w(110). Journal of ChemicalPhysics, 117(14):6757–6765, October 2002.
[34] L. Nurminen, F. Tavazza, D. P. Landau, A. Kuronen, and K. Kaski. Monte carlosimulation of the surface structure of ge on si(001). In D. P. Landau, S. P. Lewis, andH. B. Shuttler, editors, Computer Simulation Studies in Condensed Matter Physics XV,Heidelberg, Berlin, 2002. Springer-Verlag. to be published.
[35] Laura Nurminen, Francesca Tavazza, David P. Landau, Antti Kuronen, and KimmoKaski. Comparative study of si(001) surface structure and interatomic potentials infinite-temperature simulations. Phys. Rev. B, 2002. accepted for publication.
[36] J.-P. Onnela, A. Chakraborti, K. Kaski, and J. Kertesz. Dynamic asset trees andportfolio analysis. The European Physical Journal B, 30:285–288, 2002.
[37] Gheorghe-Sorin Paraoanu, Mirta Rodriguez, and Päivi Törmä. Josephson effect insuperfluid atomic fermi gases. Physical Review A, (66):041603, 2002. [RapidCommunication].
[38] Michael Patra. Theory for the photon statistics of random lasers. Phys. Rev. A,65:043809, 2002.
[39] Marco Patriarca, Antti Kuronen, and Kimmo Kaski. Nucleation and dynamics ofdislocations in mismatched heterostructures. In Current Issues in Heteroepitaxial
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Growth – Stress Relaxation and Self-Assembly, volume 696 of Materials ResearchSociety Symposium Proceedings, pages N4.4.1–N4.4.6, Pittsburg, 2002. MaterialsResearch Society.
[40] M.S. Peltola, M. Ek, T. Kujala, J. Tuomainen, O. Aaltonen, and R. Näätänen. Theperception of native and foreign vowels. In Temporal Integration in the Perception ofSpeech, page 88, Aix-en-Provence, France, April 8-10 2002. Cambridge UniversityPress.
[41] Klaus A. J. Riederer and Risto Niska. Sophisticated tube headphones for spatial soundreproduction. In AES 21st International Conference on Architectural Acoustics &Sound Reinforcement, pages 268–276, St. Petersburg, Russia, June 1-3. 2002.
[42] Mirta Rodriguez and Päivi Törmä. Laser-induced collective escitations in atwo-component fermi gas. Physical Review A, 66:033601, 2002.
[43] R. Salmelin and M. Sams. Motor cortex involvement during verbal vs. nonverbal lipand tongue movements. Human Brain Mapping, 16:81–91, 2002.
[44] A. Shestakova, E. Brattico, M. Huotilainen, V. Galunov, A. Soloviev, M. Sams,R. Ilmoniemi, and R. Näätänen. Abstract phoneme representations in the left temporalcortex: Meg study. NeuroReport, 13:1813–1816, 2002.
[45] P. Szelestey, M. Patriarca, L.F. Perondi, and K. Kaski. Modified eam potentials formodelling stacking-fault behavior in cu,al,au and ni. Int. Journ. Mod. Physics,16(2823), 2002.
[46] T. Tamminen and J. Lampinen. Face matching with learned object priors. InP. Ala-Siuru and S. Kaski, editors, STeP 2002 - Intelligence, The Art of Natural andArtificial. Proc. 10th Finnish Artificial Intelligence Conference, 2002.
[47] J. Töyli, K. Kaski, and A. Kanto. On the shape of asset return distribution.Communications in Statistics-Simulation and Computation, 31(4):489–521, 2002.
[48] J. Töyli, K. Kaski, and A. Kanto. Variability of hex return distribution. The FinnishJournal of Business Econimics, 51(1):64–89, 2002.
[49] Juuso Töyli. Essays on asset return distributions. Technical Report B33, HelsinkiUniversity of Technology, Laboratory of Computational Engineering, 2002.
[50] Ilpo Vattulainen, Mikko Karttunen, Gerhard Besold, and James Polson. Integrationschemes for dissipative particle dynamics simulations: From softly interacting systemstowards hybrid models. J. Chem. Phys., 116:3967–3979, 2002.
[51] Aki Vehtari. Discussion of “Bayesian measures of model complexity and fit” bySpiegelhalter et al. Journal of the Royal Statistical Society. Series B (StatisticalMethodology), 64(3):620, 2002.
[52] Aki Vehtari and Jouko Lampinen. Bayesian input variable selection using posteriorprobabilities and expected utilities. Technical Report B31, Helsinki University ofTechnology, Laboratory of Computational Engineering, 2002.
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