Expanding the Frontiers of Visual Analytics and Visualization	3
	Foreword	6
	Preface	9
	Contents	11
	List of Contributors	14
	Editors	14
	Invited Authors (in alphabetical order)	16
	Co-authors (in alphabetical order)	30
	Chapter 1: Introduction-The Best Is Yet to Come	45
	1.1 A Tribute	45
	1.2 Background to Visual Analytics and Visualization	46
	1.3 Resources for Visual Analytics and Visualization	47
	1.4 International Conferences	48
	1.5 This Volume	48
	References	49
	Part I: Evolving a Vision	50
	Chapter 2: An Illuminated Path: The Impact of the Work of Jim Thomas	51
	2.1 Introduction	51
	2.2 Three Datasets	53
	2.3 Illuminating the Path	55
	2.3.1 The Spread of the Impact	55
	2.3.2 The Inspired Community	57
	2.3.3 A Document Co-citation Analysis	58
	2.3.4 Major Co-citation Clusters	59
	2.3.5 Landmark Papers	61
	2.3.5.1 Citation Counts	61
	2.3.5.2 Betweenness Centrality	61
	2.3.5.3 Burst and Sigma	62
	2.3.6 Timeline View	62
	2.4 A Broader Context	63
	2.4.1 The Trend of Growth	63
	2.4.2 Major Source Journals and Hot Topics	64
	2.4.3 Highly Cited Documents and Authors	65
	2.4.4 Mapping the Visual Analytics Domain	65
	2.4.5 An Overlay of Network D2 in Network D3	71
	2.5 Conclusion	72
	References	72
	Chapter 3: The Evolving Leadership Path of Visual Analytics	73
	3.1 Leadership Lifecycle	73
	3.2 Mind the Gap	74
	3.3 Bold Vision	76
	3.4 Champions on Board	77
	3.5 Structures and Collaborations	79
	3.6 Technology Deployment	80
	3.7 Strategies for Future Growth	81
	3.7.1 Increase Domains and Applications	81
	3.7.2 Better Integrate the Communities Within Visual Analytics	82
	3.7.3 Broaden the Base of Support	82
	3.8 The Path Ahead	83
	References	84
	Part II: Visual Analytics and Visualization	85
	Chapter 4: Visual Search and Analysis in Complex Information Spaces-Approaches and Research Challenges	86
	4.1 Introduction	87
	4.2 De?nition of Complex Data Sets	88
	4.3 Tasks and Problems of Visual Search and Analysis in Complex Data	90
	4.3.1 Visual Search and Analysis	90
	4.3.2 Problems in Presence of Complex Data	91
	4.3.2.1 Visual Search	92
	4.3.2.2 Visual Analysis	93
	4.4 Approaches	94
	4.4.1 Generic Examples for Visual Search and Analysis Systems	94
	4.4.2 Example Approaches to Visual Search and Analysis of Type-Complex Data	95
	4.4.2.1 Visual Search in 3D Object Data	95
	4.4.2.2 Visual Search in Graphs-Visual Query De?nition	96
	4.4.2.3 Visual Search and Analysis of Biochemical Data-Similarity Function De?nition Using Visual Comparison of Descriptors	98
	4.4.3 Example Approaches to Visual Search and Analysis of Compound-Complex Data	99
	4.4.3.1 Visual Search in Research Data-Visual Query De?nition and Visualization of Search Results	99
	4.4.3.2 Visual Search and Analysis of Spatio-temporal Data-Identi?cation of Interesting Events	100
	4.4.3.3 Visual Analytics for Security	101
	4.5 Research Challenges	103
	4.5.1 Infrastructures	104
	4.5.2 New Data Types	104
	4.5.3 Search Problem and Comparative Visualization	104
	4.5.4 User Guidance in the Visual Analysis Process	105
	4.5.5 Benchmarking	105
	4.6 Conclusions	106
	References	106
	Chapter 5: Dynamic Visual Analytics-Facing the Real-Time Challenge	109
	5.1 Introduction	109
	5.2 Background	111
	5.2.1 Visual Analytics	111
	5.2.2 Data Streams: Management and Automated Analysis	111
	5.2.3 Time Series Visualization	112
	5.3 Dynamic Visual Analytics	113
	5.3.1 Requirements for Dynamic Visual Analytics Methods	113
	5.3.2 The Role of the User in Dynamic Visual Analytics	115
	5.4 Server Log Monitoring Application Example	116
	Processing:	118
	Update Models & Visualizations:	118
	Display & Highlight:	118
	Interact & Explore:	118
	Notify & Adapt:	118
	Feedback Loop:	119
	5.5 Conclusions	119
	References	119
	Chapter 6: A Review of Uncertainty in Data Visualization	121
	6.1 Introduction	121
	6.2 Uncertainty Reference Model	123
	6.3 Why Is Uncertainty so Hard?	124
	6.4 Notation	128
	6.5 Visualization of Uncertainty	128
	6.5.1 Introduction	128
	6.5.2 Point Data UP	130
	6.5.3 Scalar Data US	130
	6.5.3.1 Zero Dimensional Data US0	130
	6.5.3.2 One Dimensional Data US1	131
	6.5.3.3 Two Dimensional Data US2	131
	6.5.3.4 Three Dimensional Data US3	135
	6.5.4 Multi?eld Scalar Data kUS	137
	6.5.4.1 Zero Dimensional Data UkS0	137
	6.5.4.2 Higher Dimensional Data UkS>0	137
	6.5.5 Vector Data UV	137
	6.5.5.1 Two Dimensional Data UV2	137
	6.5.5.2 Three Dimensional Data UV3	139
	6.6 Uncertainty of Visualization	139
	6.6.1 Scalar Data ES	140
	6.6.1.1 One Dimensional Data ES1	140
	6.6.1.2 Two Dimensional Data ES2	140
	6.6.1.3 Three Dimensional Data ES3	141
	6.6.2 Multi?eld Scalar Data kES	143
	6.6.3 Vector Data EV	143
	6.7 Conclusions	144
	References	145
	Chapter 7: How to Draw a Graph, Revisited	150
	7.1 Introduction	150
	7.2 The Barycenter Algorithm	151
	7.2.1 Tutte's General Approach	151
	7.2.2 The Energy Model in Tutte's Algorithm	153
	7.2.3 Tutte's Algorithm for Planar Graphs	153
	7.2.4 Tutte's Algorithm as a Visualization Method	154
	7.3 The Force Directed Approach	156
	7.4 The Planarity Approach	157
	7.4.1 Linear Time Algorithms for Planar Graphs	158
	7.4.2 Planar Drawings with Good Vertex Resolution	159
	7.4.3 Drawing Planar Graphs with Star-Shaped Faces	160
	7.4.4 Drawing Nonplanar Graphs Using Planarity Based Methods	160
	7.5 Remarks	162
	References	163
	Chapter 8: Using Extruded Volumes to Visualize Time-Series Datasets	166
	8.1 Introduction	166
	8.2 Project Description	167
	8.2.1 Envision	167
	8.2.2 Tools Used	168
	8.2.3 Methodology	168
	8.2.4 Data Extraction and Preparation	168
	8.2.5 Rendering Techniques	169
	8.2.6 Visualization User Interface	170
	8.2.6.1 Slicing Tool	170
	8.2.6.2 Alpha Control Tools	171
	8.2.6.3 Highlighter Tool	172
	8.2.6.4 Transitioning Tool	172
	8.2.6.5 Orienteer Tool	173
	8.3 Test Setup	174
	8.4 Results and Discussion	174
	8.4.1 Skagit Study Area: LULC_A	174
	8.4.2 Apache-Sitgreaves National Forest Study Area: Vegetation Type	180
	8.5 Future Work	184
	8.6 Conclusion	185
	References	187
	Chapter 9: Event Structuring as a General Approach to Building Knowledge in Time-Based Collections	188
	9.1 Introduction	188
	9.2 De?ning Events, Creating Event Structures, Organizing the Time Dimension	189
	9.3 Events in Space: 4D GIS	190
	9.4 Events in a Narrative Structure	191
	9.4.1 Human-Computer Generated Linear Narrative	192
	9.5 Events in Non-geographic Information Spaces	193
	9.6 Event Description Language for Linear Narrative	197
	9.7 Towards a GTIS and TIS	198
	References	200
	Chapter 10: A Visual Analytics Approach for Protein Disorder Prediction	202
	10.1 Introduction	203
	10.2 Protein Disorder Prediction	204
	10.3 Discriminant Analysis for Visualization	205
	10.4 Visualization of Protein Disorder Data	207
	10.4.1 Knowledge Discovery from Visualization	207
	10.4.2 Visualizing the Discriminants	209
	10.5 Classi?cation Evaluation and Discussion	209
	10.6 Conclusion	212
	References	212
	Chapter 11: Visual Storytelling in Education Applied to Spatial-Temporal Multivariate Statistics Data	214
	11.1 Introduction	215
	11.2 Related Work	217
	11.3 System Implementation	219
	11.3.1 GAV Flash Framework	219
	11.3.2 Integrated Snapshot Mechanism	222
	11.4 Storytelling	223
	11.4.1 Publisher and Vislets	224
	11.5 Interactive Documents	226
	11.6 Visual Storytelling in Education	229
	11.7 Conclusions and Future Development	230
	References	231
	Part III: Interaction and User Interfaces	233
	Chapter 12: Top Ten Interaction Challenges in Extreme-Scale Visual Analytics	234
	12.1 Introduction	234
	12.2 Related Work	235
	12.2.1 Some Well-Known Extreme-Scale Data Problems Today	236
	12.2.2 Extreme-Scale Data Visualization and Management	236
	12.2.3 Top-Ten Visualization and Visual Interface Challenges in Literature	236
	12.3 Three Fundamental Elements of Extreme-Scale Visual Analytics	237
	12.4 Imminent Challenges of Interface and Interaction Design	237
	12.4.1 In Situ Interactive Analysis	237
	12.4.2 User-Driven Data Reduction	238
	12.4.3 Scalability and Multi-level Hierarchy	238
	12.4.4 Representation of Evidence and Uncertainty	239
	12.4.5 Heterogeneous Data Fusion	239
	12.4.6 Data Summarization and Triage for Interactive Query	240
	12.4.7 Analytics of Temporally Evolving Features	240
	12.4.8 The Human Bottleneck	241
	12.4.9 Design and Engineering Development	241
	12.4.10 The Renaissance of Conventional Wisdom	242
	12.5 Evaluation and Likelihood of Success	242
	12.6 Conclusions	243
	References	243
	Chapter 13: GUI 4D-The Role and the Impact of Visual, Multimedia and Multilingual User Interfaces in ICT Applications and Services for Users Coming from the Bottom of the Pyramid-First Concepts, Prototypes and Experiences	245
	13.1 Introduction	246
	13.2 Scope, De?nitions and Classi?cation	246
	13.3 Design and Implementation	250
	13.4 Requirements and Constraints-Implementation Framework	252
	13.5 The SAP Strategy and Vision on GUI 4D's	255
	13.5.1 Target Groups	255
	13.5.2 Motivation and Mission of SAP Research Internet Applications and Services Africa (Pretoria, South Africa)	257
	13.5.3 Examples and Case Studies from SAP Research Internet Applications and Services Africa (Pretoria)	258
	13.5.3.1 Rustica	259
	13.5.3.2 Smart Energy	259
	13.5.3.3 Siyakhula Living Lab	260
	13.6 Ongoing Projects and R&D Activities in GUI 4D's in Africa	260
	13.6.1 Case Study-The African Cashew Initiative	260
	13.6.1.1 Objectives	260
	13.6.1.2 Use Cases	261
	13.6.1.3 Piloting-Real Life Usage	262
	13.6.1.4 Results	263
	13.6.2 Other Interesting GUI 4D Research and Development Activities in Africa	265
	13.6.3 Conclusions for GUI 4D	265
	13.7 Target Applications and Markets	266
	13.7.1 The Informal Sector in the "Bottom of the Pyramid"	266
	13.7.1.1 Dependencies and Needs Between the Established Economy and Informal Economy	266
	13.7.2 Global Agricultural Supply Chains-The Cashew Market as an Example	268
	13.7.3 Market Potential	269
	13.8 Future Research and Work to Be Done	269
	13.9 Conclusions and Summary	270
	References	271
	Chapter 14: Emotion in Human-Computer Interaction	274
	14.1 Introduction	274
	14.2 Emotion Recognition	276
	14.2.1 Physiological Background	276
	14.2.2 Measuring Emotional Signs	278
	14.2.2.1 Challenges	280
	14.2.2.2 Requirements	280
	14.2.3 The Emotion Recognition Pipeline	281
	14.2.3.1 Data Pre-processing	282
	14.2.3.2 Feature Extraction	282
	14.2.3.3 Classi?cation	283
	14.3 The EREC Emotion Recognition System	283
	14.3.1 The EREC Sensor System	283
	14.3.2 Data Interpretation	287
	14.3.2.1 Data Pre-processing	287
	14.3.2.2 Feature Extraction and Classi?cation	287
	14.4 Applications	287
	14.4.1 Affective Usability Evaluation Tool	288
	14.4.1.1 The RealEYES Framework	288
	14.4.1.2 Affective Extension to the RealEYES Framework	290
	14.4.1.3 Visualizing Classi?cation Results	291
	14.4.2 Affective E-Learning Environment	292
	14.5 Conclusion and Further Prospects	294
	References	294
	Chapter 15: Applying Artistic Color Theories to Visualization	298
	15.1 Introduction	298
	15.2 Some Background on Color Theory	299
	15.3 The Color Wheel for the RYB Painterly Set of Primary Colors	301
	15.4 The Color Wheel for the RGB Model	303
	15.5 Hue, Saturation and Brightness (HSL) & Hue, Saturation and Value (HSV) Models	303
	15.6 Color Schemes	306
	15.7 Color Wheel and Color Scheme Software Tools	308
	15.8 Analyzing Digital Images with the Color Wheel and Color Schemes	309
	15.9 Applying Color Scheme Concepts to Creating Visualizations	311
	15.10 Conclusion	316
	References	316
	Chapter 16: e-Culture and m-Culture: The Way that Electronic, Computing and Mobile Devices are Changing the Nature of Art, Design and Culture	319
	16.1 The Development of Esteem for Cultural Product Creators	320
	16.2 Evolving Culture, with a Capital `C'	321
	16.3 Technological In?uences	322
	16.4 Connecting with the User-The CU in Culture	324
	16.5 Mobile Paradigms Transforming Journalism	325
	16.6 Narrative	327
	16.7 Growing Pains in Mobile Technology	328
	16.8 Technology, Communities and `Culture'	329
	16.9 Where Next?	330
	16.10 Wearable Computing and Communications	331
	16.11 Some Conclusions	334
	References	335
	Part IV: Modeling and Geometry	337
	Chapter 17: Shape Identi?cation in Temporal Data Sets	338
	17.1 What Are Shapes?	339
	17.2 Background	340
	17.2.1 Shape De?nition	340
	17.2.2 Shape Evaluation	341
	17.3 Shape De?nitions	342
	17.3.1 Line Shapes	343
	17.3.2 Spike and Sink Shapes	344
	17.3.3 Rise and Drop Shapes	345
	17.3.4 Plateaus, Valleys and Gaps	346
	17.4 TimeSearcher: Shape Search Edition	347
	17.4.1 Interface	348
	17.4.2 Spike and Sink Shape Identi?cation	349
	17.4.3 Line Shape Identi?cation	351
	17.4.4 Rise and Drop Shape Identi?cation	351
	17.5 Conclusion	353
	References	353
	Chapter 18: SSD-C: Smooth Signed Distance Colored Surface Reconstruction	355
	18.1 Introduction	355
	18.2 Continuous Formulation	357
	18.2.1 Surface Reconstruction	357
	18.2.2 Color Map Reconstruction	359
	18.3 Linearly Parameterized Families	360
	18.4 Discretization with Discontinuous Function	361
	18.5 Octree-Based Implementation	362
	18.6 Evaluation of Surface Reconstruction Methods	363
	18.7 Results	365
	18.8 Conclusion	369
	References	369
	Chapter 19: Geometric Issues of Object Manipulation in Task Animation and Virtual Reality	371
	19.1 Introduction	371
	19.2 The Smart Object Approach	372
	19.3 The Grasping Problem	374
	19.3.1 Introduction	374
	19.3.2 Heuristic Approach for Grasping	374
	19.3.3 Large Objects and Multiple Agents	375
	19.3.4 The Tubular Approach	378
	19.3.5 Combining Smart Objects and the Tubular Grasp	380
	19.3.6 Collision Detection	381
	19.4 The Reaching Problem	383
	19.5 Grasping in VR	385
	19.5.1 Introduction	385
	19.5.2 Haptic Feedback	386
	19.5.2.1 Direct Mapping	386
	19.5.2.2 Proxy Approach	388
	19.5.3 Creating Geometric and Dynamic Environments	389
	19.6 Conclusion	391
	References	392
	Chapter 20: An Analytical Approach to Dynamic Skin Deformation of Character Animation	395
	20.1 Introduction	395
	20.2 Mathematical Model and Analytical Solution	397
	20.3 Relationships Between Curves and Skin Surfaces	402
	20.3.1 Curve-Based Representation of Skin Surfaces	402
	20.3.2 Curve-Based Deformation Control of Skin Surfaces	402
	20.4 Skin Deformation Examples	404
	20.5 Conclusions	405
	References	405
	Part V: Architecture and Displays	407
	Chapter 21: The New Visualization Engine- The Heterogeneous Processor Unit	408
	21.1 Introduction	408
	21.2 Historical Overview	409
	21.3 Moore's Law and Transistor Feature Size	412
	21.4 Evolution of GPU Development	413
	21.5 PC-Based GPUs	413
	21.6 Mobile Devices GPUs	414
	21.7 Introduction of the HPU	414
	21.8 Evolution of Operating System Development	415
	21.9 HPUs in Various Platforms	416
	21.10 PCs	417
	21.11 Game Consoles	417
	21.12 Mobile Devices	419
	21.13 Power Consumption	420
	21.14 Evolution of GPU-Compute Development Environments	421
	21.15 Examples of Multicore Processors	421
	21.16 Programming GPU-SIMDs Represents a Challenge	422
	21.17 HPU Programming Environments	422
	21.18 The Programming Environment	424
	21.19 When Is Parallel Processing Useful?	424
	21.20 Visualization Systems and HPUs	425
	21.21 Summary	426
	References	426
	Chapter 22: Smart Cloud Computing	427
	22.1 Introduction	427
	22.2 Cyberworlds	428
	22.2.1 Set Theoretical Design	428
	22.2.2 Topological Design	428
	22.2.3 Functions	429
	22.2.4 Equivalence Relations	429
	22.2.5 A Quotient Space (an Identi?cation Space)	430
	22.2.6 An Attaching Space (an Adjunction Space, or an Adjoining Space)	431
	22.2.7 Restriction and Inclusion	431
	22.2.8 Extensions and Retractions of Continuous Maps	431
	22.2.9 Homotopy	432
	22.2.10 Cellular Structured Spaces (Cellular Spaces)	433
	22.2.11 An Incrementally Modular Abstraction Hierarchy	435
	22.2.12 Fiber Bundles, Homotopy Lifting Property, and Homotopy Extension Property	436
	22.3 Modeling of E-Business and E-Manufacturing	439
	22.3.1 The Adjunction Space Level	440
	22.3.1.1 A Case of Online Book Shopping in E-Commerce	440
	22.3.1.2 A Case of Assembling for E-Manufacturing	442
	22.3.2 Cellular Space Level	442
	22.3.3 Seat Assembling	444
	22.4 Conclusions	444
	References	444
	Chapter 23: Visualization Surfaces	446
	23.1 The Value of Scale and Detail	446
	23.2 Large Display Mechanisms: Projection	448
	23.3 Large Display Mechanisms: Modular Flat Panels	450
	23.4 Display System Architecture	451
	23.5 Interaction	453
	23.6 Future	454
	References	455
	Part VI: Virtual Reality and Augmented Reality	457
	Chapter 24: The Development of Mobile Augmented Reality	458
	24.1 Introduction	458
	24.2 Program Development	460
	24.2.1 Research Issues	460
	24.2.2 Information Management	461
	24.2.3 Development Iterations	463
	24.3 Program Expansion	465
	24.3.1 Further Research Issues	465
	24.3.2 ONR Program Expansion	465
	24.3.3 The "X-Ray Vision" Problem and the Perception of Depth	468
	24.3.4 Integration of a Component-Based System	468
	24.4 Ongoing Research	469
	24.5 Predictions for the Future	470
	24.5.1 Consumer Use	470
	24.5.2 Tracking	471
	24.5.3 Form Factor	472
	24.6 Summary	473
	References	473
	Chapter 25: Multimodal Interfaces for Augmented Reality	476
	25.1 Introduction	476
	25.2 Related Work	477
	25.3 Speech and Paddle Gesture	479
	25.3.1 Multimodal System	479
	25.3.2 Evaluation	481
	25.4 Speech and Free-Hand Input	483
	25.4.1 Evaluation	486
	25.5 Lessons Learned	489
	25.6 Conclusions and Future Work	490
	References	491
	Part VII: Technology Transfer	493
	Chapter 26: Knowledge Exchange, Technology Transfer and the Academy	494
	26.1 Introduction	494
	26.2 The Bayh-Dole Act	495
	26.3 Technology Transfer Systems in the USA	495
	26.4 Technology Transfer in Germany-The Fraunhofer Model	496
	26.5 Lambert Review	497
	26.6 Case Studies	498
	26.6.1 MIT, Cambridge and Tokyo	498
	26.6.2 Johns Hopkins University	498
	26.6.3 University of Utah	499
	26.6.4 National Visualization and Analytics Centers	499
	26.7 Challenges, Cultural and Social Issues	500
	26.7.1 Time scale	500
	26.7.2 Reward Models	500
	26.7.3 Value of Applied Research	500
	26.7.4 Technology Transfer Culture	501
	26.7.5 Communication and Values	501
	26.7.6 Differences Across Discipline Areas	501
	26.7.7 Performance Metrics	502
	26.7.8 Diversi?cation of Academic Mission	503
	26.8 Conclusions	503
	References	504
	Chapter 27: Discovering and Transitioning Technology	505
	27.1 Introduction	505
	27.2 Projects	506
	27.2.1 General Motors	506
	27.2.2 The Boeing Company	506
	27.2.3 Computer Graphics	507
	27.2.3.1 Evolution	508
	27.2.4 Human Model	508
	27.2.4.1 Evolution	508
	27.2.5 B-Spline Surface Rendering	509
	27.2.5.1 Evolution	510
	27.2.6 Solid Modeling	510
	27.2.7 Fractals	510
	27.2.7.1 Evolution	510
	27.2.8 User Interface Management Systems	511
	27.2.8.1 Evolution	512
	27.2.9 Augmented Reality	512
	27.2.9.1 Evolution	512
	27.2.10 FlyThru/IVT	513
	27.2.10.1 Evolution	514
	27.2.11 Voxmap PointShell	514
	27.2.11.1 Evolution	514
	27.2.12 Massive Model Visualization	515
	27.2.12.1 Evolution	515
	27.2.13 Visual Analytics	516
	27.2.13.1 Evolution	516
	27.3 Observations	516
	27.4 Implications	518
	27.4.1 Sources of New Technology	519
	27.4.2 Fragmented Technical Community	519
	27.4.3 Business Climate	520
	27.4.4 Immediate Return on Investment	520
	27.5 One Successful Approach	521
	27.6 Conclusion	521
	References	522
	Chapter 28: Technology Transfer at IBBT-EDM: Case Study in the Computer Graphics Domain	523
	28.1 Interdisciplinary Institute for BroadBand Technology (IBBT)	524
	28.1.1 Strategic Research	524
	28.1.2 Cooperative Research	525
	28.1.3 Living Labs	526
	28.1.4 Venture	526
	28.2 Expertise Centre for Digital Media (EDM)	527
	28.3 ANDROME	528
	28.4 Case Study: Ultra Pictura	528
	28.4.1 Company Summary	528
	28.4.2 From Idea to Business	529
	28.4.3 Company Management	531
	28.5 Conclusions	532
	References	532
	Chapter 29: Building Adoption of Visual Analytics Software	533
	29.1 Introduction	533
	29.2 The Technology Adoption Life Cycle	535
	29.3 Adoption Challenges for Visual Analytics	537
	29.4 Case Study: Moore's Life Cycle Applied to an Organization	541
	29.5 Cultural Implications of Adoption	543
	29.6 Recommendations for Building Visual Analytics Technology Adoption	544
	29.6.1 Initiating the Adoption Process	545
	29.6.2 Building Interest Among Innovators	547
	29.6.3 Technology Adoption by Early Adopters	548
	29.6.4 Adoption by the Early Majority	550
	29.6.5 Adoption by the Late Majority and Laggards	551
	29.6.6 Adaptive Approaches for Technology Adoption	552
	29.7 Conclusion	552
	References	553
	Author Index	555