Modelling and Simulation in Scienc: 6th International Workshop on Data Analysis in Astronomy, Erice, Italy 15-22 April 2007

Workshop photographs......Page 6
Organizing Committees......Page 7
Preface......Page 8
CONTENTS......Page 12
Memory of Livio Scarsi......Page 10
Part A — Astrophysics, Cosmology and Earth Physics......Page 16
1. Cosmic Rays and Air Showers......Page 18
2. The Pierre Auger Observatory......Page 19
4. Air Shower Simulations and the CORSIKA Program......Page 21
5. Some Selected Details......Page 23
6. Outlook......Page 25
References......Page 26
1. Introduction......Page 27
2.1. Atmospheric neutrinos and their oscillation......Page 28
2.2. Physics and detector simulation......Page 30
3. Cosmic Rays studies with the Pierre Auger experiment......Page 31
3.1. The detector simulation......Page 32
3.2. The Surface Detector......Page 33
3.3. The Fluorescence Detector......Page 34
3.4. Hybrid events......Page 35
References......Page 37
1. Introduction......Page 38
2. The Pierre Auger Observatory......Page 39
3. Software Framework......Page 40
4. Detector Simulation......Page 41
4.1.1. Water tank simulation......Page 42
4.2. Fluorescence telescopes......Page 43
5. Conclusions......Page 44
References......Page 45
1. Introduction......Page 47
2. Comparison of Models......Page 48
3. Conclusions......Page 51
References......Page 53
1. Introduction......Page 54
2. Atmosphere-Ionosphere-Magnetosphere System and its Solar Drivers......Page 55
3.1. Observations of plasma waves......Page 56
3.2. Generation and propagation of plasma waves......Page 57
3.3. Modeling and simulations of plasma waves......Page 59
4. Concluding Remarks......Page 60
References......Page 61
Electron Flux Maps of Solar Flares: A Regularization Approach to Rhessi Imaging Spectroscopy......Page 63
1. Introduction......Page 64
3. Electron Flux Spectrum Images......Page 65
4. Application to RHESSI Data......Page 67
5. Conclusions......Page 68
References......Page 69
1. Introduction......Page 70
2. The Equations of Motion......Page 71
3. Parameter Settings and Numerical Solutions......Page 74
4. An Heuristic Model......Page 77
5. Conclusions......Page 79
References......Page 80
1. Introduction......Page 81
2. Supernovae Remnants......Page 82
3. Protostellar Jets......Page 84
4. Conclusions......Page 86
5. Acknowledgements......Page 87
References......Page 88
1. Introduction......Page 89
2. Astrophysical Model......Page 90
4. Governing Equations......Page 91
5. Modeling Approaches......Page 92
6. Numerical Methods......Page 93
7. Three-dimensional Type Ia Supernova Simulations......Page 95
References......Page 96
1.1. Observations, physical model and numerical simulations......Page 98
1.2. Results and comments......Page 99
2. Numerical Simulations of pre-runaway CO White Dwarfs......Page 100
2.1. Physical models, numerical tools and FAQ......Page 101
2.3. The small Rayleigh number case......Page 102
2.4. The large Rayleigh number case......Page 103
References......Page 104
1. Introduction......Page 105
2. Statistical Analysis of Data from the V-C(2006) and SDSS Quasar Catalogues......Page 107
2.1. Truncation relationship......Page 108
2.2. Permutation tests of independence......Page 109
3. Possible Implications and Discussion......Page 110
References......Page 112
1. Introduction......Page 114
2.1. Truncation Relationship......Page 115
2.2. Permutation Tests of Independence......Page 117
3. Regression analysis of Data and Hubble Law......Page 119
4. Possible Cosmological Implications......Page 120
References......Page 121
1. Introduction......Page 122
3. Tests......Page 123
4. Applications......Page 124
5. 3-D Morphology of Galaxy Clusters......Page 125
6. Summary......Page 127
References......Page 128
1. Introduction......Page 129
2. Properties of Light in Astronomical Observations......Page 131
3. The Intensity Interferometer......Page 133
4. Intensity-Correlation Spectroscopy......Page 134
5. Approaching the Heisenberg Limit: High Time Resolution Astrophysics......Page 135
6. Ultimate Data Rates......Page 136
7. Conclusions......Page 137
References......Page 138
1. Introduction......Page 140
2. Supervised and Unsupervised Methods......Page 141
3.1. Dataset and base of knowledge......Page 142
3.2. Features selection......Page 143
4. Steps Towards a Photometric Classification of Galaxies......Page 145
References......Page 148
1. Introduction......Page 149
2. Simulation of the Atmospheric Seeing......Page 151
3. Simulation of the Observed Point Spread Function......Page 152
5. Results......Page 153
References......Page 155
Part B — Biology, Biochemistry and Bioinformatics......Page 156
1.1. The ENCODE project......Page 158
1.3. Protein structure prediction......Page 159
2. Results......Page 161
3. Discussion......Page 162
References......Page 164
Exploring Biomolecular Recognition by Modeling and Simulation......Page 165
References......Page 167
1. Introduction......Page 169
References......Page 171
1. Introduction......Page 173
2. Materials and Methods......Page 174
3. Results......Page 176
4. Discussion......Page 179
References......Page 181
1. Introduction......Page 184
2. Multi-Layers Model......Page 186
3.1. Synthetic data generation......Page 189
3.2. Experimental results......Page 190
4. Final remarks......Page 191
References......Page 192
1.1. Introduction......Page 193
1.2. BioinfoGRID project......Page 194
2.1. Technology description......Page 196
2.2. GRID database applications example......Page 197
2.3. GRID large scale in silico docking application on avian flu......Page 198
2.4. GRID based microarray expression pro.ling analysis......Page 199
3. Conclusion......Page 200
References......Page 201
1. The topological compaction of the double-helix molecule into the chromatin and the role of supercoiling......Page 202
2. A mathematical model for explaining the interphase folding of chromatin .ber......Page 205
3. Biological justi.cations for the model above......Page 208
4. Conclusions and prospects......Page 211
References......Page 213
Part C — Methods and Techniques......Page 216
1. Exploring Parameter Spaces using Evolutionary Programming......Page 218
2. Retrieving Patterns: following the Hough Transform......Page 220
3. From Phenomena to Processes: Identifying Internal Parameters......Page 222
4. Parisian Evolution or How To Split Optimisation: The Fly Algorithm......Page 224
5. Conclusion......Page 225
References......Page 226
1. Introduction......Page 228
3. Hierarchical Multi-agent Systems......Page 229
4.2.2. level 2......Page 230
4.3. Experimental results......Page 231
5.2.1. Level 1......Page 233
5.2.2. Level 2......Page 234
5.3. Experimental results......Page 235
6. Discussion......Page 236
References......Page 237
1. Introduction......Page 239
2. Quantifying Degree of Ultrametricity......Page 240
3.1. Distance properties in very sparse spaces......Page 241
3.2. No “curse of dimensionality” in very high dimensions......Page 242
4. Increasing Ultrametricity Through Data Recoding: Ultrametricity of Time Series......Page 243
5. Fast Clustering through Baire Space Embedding......Page 245
6. Conclusions......Page 246
References......Page 247
1. Introduction......Page 249
2. Computational Theory of Perceptions and F-granulation......Page 252
3. Granular Computation and Rough-Fuzzy Approach......Page 253
4. Rough-Fuzzy Granulation and Case Based Reasoning......Page 254
5. Rough-Fuzzy Clustering and Segmentation of Brain MR Images......Page 256
References......Page 259
1. Introduction......Page 261
2. Biclustering Problem......Page 262
3. Possibilistic Approach to Biclustering......Page 263
4.1. Experimental validation......Page 266
4.2. Comparative study......Page 267
Acknowledgments......Page 268
References......Page 269
1. Introduction......Page 270
2. The supervised learning problem from the statistical point of view......Page 271
2.1.1. Finite size of the set of examples......Page 273
3. Supervised Learning Models......Page 274
3.1.1. Partitioning the input space......Page 275
3.1.2. Projecting......Page 276
3.1.4. Underlying idea to norms and projections......Page 277
References......Page 278
1. Introduction......Page 279
2.1. Parameter estimation......Page 280
3. Cluster Reliability......Page 281
5. Interactive Agglomerative Clustering......Page 282
6. Clustering Maps......Page 284
7. Conclusions......Page 285
References......Page 286
1. Introduction......Page 287
3. Clustering and Assessment......Page 288
5. Results Analysis......Page 290
6. Conclusions......Page 291
References......Page 292
1. Introduction......Page 294
3. Bayesian Modeling......Page 295
3.1. Global and local image modeling......Page 296
4. Bayesian Inference and Variational Approximation of the Posterior Distribution for Super-Resolution Reconstruction of Multispectral Images......Page 297
5. Experimental Results......Page 299
References......Page 300
1. Introduction......Page 302
2. The Qubit......Page 303
2.1. Qubit applications......Page 304
2.2. Quantum algorithms......Page 305
2.3. Quantum search on unstructured databases......Page 306
3.1. Coherent states applications......Page 307
4. Conclusions......Page 308
References......Page 309
1. Introduction......Page 310
2. VisIVO: A Tool for 3D Visualization......Page 311
5. Conclusion......Page 313
References......Page 314
1. Introduction......Page 315
2. The Stored Simulations......Page 316
3. The ITVO Database and Archive......Page 317
4. The ITVO Web Interface and Scienti.c Use......Page 318
6. The SNAP and Theory Data Model......Page 319
References......Page 320
1. Introduction......Page 322
3.2. Inter-levels exchange......Page 323
4.1. Model validation......Page 324
4.3. Results......Page 325
5. Discussion......Page 326
References......Page 327
1. Introduction......Page 328
2.1. PSF Modeling......Page 329
3.1. Low redshift quasars observed by HST-WFPC2......Page 331
3.2. NIR images of quasar at 1 < z < 2 observed with VLT+ISAAC......Page 332
References......Page 333
1. Introduction......Page 334
2. Disparity Map Computation......Page 335
4. Experiments and Results......Page 337
References......Page 339
Author Index......Page 342
Participants......Page 344