Using agent-based models and machine learning to enhance spatial decision support systems

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Using agent-based models and machine learning to enhance spatial decision support systems

  1. 1. PhD thesis of University Pierre and Marie Curie, Paris, FranceUsing agent-based models and machine learning to enhance spatial decision support systemsApplication to resource allocation in situations of urban catastrophes Defended by CHU Thanh-Quang, the 1st July 2011 Supervisor: M. Alexis DROGOUL, DR, MSI/IRD, UMI 209 UMMISCO Co-supervisor: M. Alain BOUCHER, Prof. AUF, MSI/IRD UMI 209 UMMISCO Reviewers: M. Nicolas BREDECHE, MdC HDR, LRI, Université Paris-Sud M. Bernard PAVARD, Prof., IRIT, Université Paul Sabatier, Toulouse Examinators: M. NGUYEN Hong Phuong, Prof., VAST, Hanoi, Vietnam Mme. Julie DUGDALE, MdC, LIG, Université Pierre Mendès-France M. Christophe GONZALES, Prof., LIP6, UPMC, Paris, France 1
  2. 2. Outline• Context: Spatial Decision Support System (SDSS) for resource allocation in emergency response• Proposal: • ABM&GIS: Agent-Based Modeling and Geographic Information System to build the underlying models of SDSS, • PD: Participatory Design to involve users in the design process and to enhance the realism of the models, • ML: Machine Learning algorithms to automate the extraction of knowledge from stakeholders• Experiments and results• Conclusion and prospects 2
  3. 3. Disasters• Natural disasters • Earthquake • Tsunami • Flooding, etc. Natural disasters in Asia (1980 - 2010)• No of events: 3,341 Causing huge loss of human life and No of people killed: 1,144,006 property Average killed per year: 39,448• Cities are especially vulnerable to No of people affected: 4,742,092,443 disasters: Average affected per year: 163,520,429 • Ecomomic Damage 673,457,207 Density of population, buildings and (US$ X 1,000): Ecomomic Damage per infrastructure year (US$ X 1,000): 23,222,662 http://www.preventionweb.net/ 3
  4. 4. Emergency response & resource allocation Loss• Emergency response [CPC, 07]: • Reducing life-threatening conditions Response effectiveness • Providing life-sustaining aid • Stopping additional damage to property• Resource allocation (particularly important in urban areas): • Where and when do rescue resources need to be allocated? • How to organize and coordinate these allocations? 4
  5. 5. Spatial decision support systems (SDSSs) pointing operations, a wireless connection is immediately in real space.• Decision support systems aim at: • A multiagent-based simulation with a large number of supporting decisions of stakeholdersin was performed parallel with the experiment in real space. See-through GPS • head-mounted displays are not suitablesolve problems training stakeholders to for presenting the simulation of augmented experiments, since it is unsafe to• mask the views of passengers. As described above, since we Spatial DSSs involve location in used mobile phones, small and low-resolution images of three dimensional virtual spaces are difficult to understand. decisions [CPC, 07], e.g.: Instead of displaying visual simulations, the mobile phones • design evacuation and rescue routes in this • allocate evacuees to shelters 2D Virtual Space Outdoor Real Space Figure 4. Outdoor Experiment • select optimal locations for rescue teams Digital City, from [Ishida et al., 07] 5
  6. 6. Literature of SDSSs for emergency response• DrillSim [Balasubramanian et al., 06], [Massaguer et al., 06],• ALADDIN [Adams et al., 08], [Gianni et al., 08],• DEFACTO [Marecki et al., 05], [Schurr et al., 05],• Plan-C [Narzisi et al., 07],• Digital City (JST CREST) [Ishida et al., 07], etc.• Modeling and Simulation with ABM & GIS are core techniques to: • Camera model emergency situations • design response solutions In summary, an augmented experiment consists of 1) to represent human 3D Virtual Space Indoor Real Space Figure 3. Indoor Experiment Digital City, from [Ishida et al., 07] 6
  7. 7. ALADDIN (Autonomous Learning Agents for Decentralized Data and Information Networks) [Adams et al., 08], [Gianni et al., 08]• Evacuating a building on fire• Improve situational awareness • data collection • data fusion• Improve path planing and coordination strategy • auction methods • coalition methods • learning in games 7
  8. 8. DEFACTO (Demonstrating Effective Flexible Agent Coordination of Teams through Omnipresence) [Marecki et al., 05], [Schurr et al., 05]• Fire evacuation• Improve situational awareness (a) (b) • 3D visualization • human-interaction• Focus on modeling (c) (d) • detailed-level of situations (e) (f) 8
  9. 9. Plan-C (Planning with Large Agent- Networks against Catastrophes) [Narzisi et al., 07]• Emergency planning, • Response planning as a medical relief operations problem of multi-objectives• use evolutionary algorithms optimization 9
  10. 10. Lack of flexibility and realism Realism of Project Application Main limitation Lack of (behavioral realism) situations DrillSim Fire evacuation Difficultly generalized Small scale DEFACTO Fire evacuation Manual modeling 3D with OpenGL Learning from users’ solution ALADDIN Fire evacuation Poor user-interface Simple GISResQ Freiburg Search&Rescue Lack of reusability Simple GIS Interest on domain knowledge Medical relief Limited configurability of PLAN C GIS Interest on domain knowledge operations agent behaviorDamas Rescue Search&Rescue Lack of flexibility Simple GIS Interest on domain knowledge Digital City Large-scale evacuation Lack of solution support GIS Learning from users’ solution • Lack of realism of emergency situations • Environments are simply represented in small scale • Lack of realism of rescue activities (i.e. agent behaviors) • Small interest on domain knowledge to improve response effectiveness 10
  11. 11. Proposal• Problem: Lack of realism of emergency situations • Step 1: Using ABM&GIS (geospatial data of Hanoi and earthquake loss estimation of IG-VAST) to build a realistic rescue model• Problem: Lack of realism of rescue activities • Step 2: Using Participatory Design to involve practitioners, experts of emergency to improve agent behaviors• Problem: The improvement of agent behaviors has to be made manually and offline by modelers • Step 3: Using Machine Learning to automate the acquisition of experts’ knowledge 11
  12. 12. Step 1: Building a realistic rescue model !"#$%&%()*!!(++),"-.+)"-)/#(-0(+)1023)*-!4567)83(),"%%"109:)+!-((9+3"2)+3"1+)1 $"++0%()1023);<)*9&%=+2)*-!456)(>2(9+0"9?)+")23(-()&-()!(-2&09%=)(&#20,#%)2309:+)%( @"7)• Collect from Earthquake Loss Estimation System of IG-VAST [Nguyen-Hong, 03]: • Real GIS data of Hanoi • Disaster impact data: building damage and casualties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
  13. 13. Step 1: Building a realistic rescue model !"#$%&%()*!!(++),"-.+)"-)/#(-0(+)1023)*-!4567)83(),"%%"109:)+!-((9+3"2)+3"1+)1 $"++0%()1023);<)*9&%=+2)*-!456)(>2(9+0"9?)+")23(-()&-()!(-2&09%=)(&#20,#%)2309:+)%( @"7)• Collect from Earthquake Loss Estimation System of IG-VAST [Nguyen-Hong, 03]: • Real GIS data of Hanoi • Disaster impact data: building damage and casualties• Rescue agents: inspired from the agents found in RobocupRescue simulations !"#$$%&()*+,-*./01*234*(5*63738$6*9:;<6;%8&*;%*=36;%*1;&)#;")*(5*>3%(;*?;)@ [www.robocuprescue.org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
  14. 14. Step 1: Building a realistic rescue model !"#$%&%()*!!(++),"-.+)"-)/#(-0(+)1023)*-!4567)83(),"%%"109:)+!-((9+3"2)+3"1+)1 $"++0%()1023);<)*9&%=+2)*-!456)(>2(9+0"9?)+")23(-()&-()!(-2&09%=)(&#20,#%)2309:+)%( @"7)• Collect from Earthquake Loss Estimation System of IG-VAST [Nguyen-Hong, 03]: • Real GIS data of Hanoi • Disaster impact data: building damage and casualties• Rescue agents: inspired from the agents found in RobocupRescue simulations !"#$$%&()*+,-*./01*234*(5*63738$6*9:;<6;%8&*;%*=36;%*1;&)#;")*(5*>3%(;*?;)@ [www.robocuprescue.org] ) *%(>0+)&9@)A3#"9:)$-"$"+(@).()2")@")&%%)230+)1"-B),-".)C-&9!()@#-09:)23()=(&-7)5)1 !"9209#().=)+2#@0(+)09):("%":=)230+)=(&-?)&9@)02)10%%)@($(9@)"9)23()D"%#.()",).=)!%&+ +!3(@#%(7)E#2)02)0+)+".(2309:)23&2)-(&%%=)092(-(+2+).(?)+")13=)9"2F)• GAMA (GIS and agent-based modeling ) ) platform [Amouroux et al., 07]) is used$%&&""(")*!%+!+,),-.!"/).-/&0"1! """# to build model *9"23(-)$"++00%02=)1()(9D0+&:(@)0+)2")+(2)#$)&9"23(-)092(-9+30$),"-)9(>2)=(&-?)."+2) 09)23()59+202#2()",)4("$3=+0!+G) A$9B(#;$%)$6) H!"..#90!&20"9?) $("$%() &1&-(9(++IG) 5) 3&@) +".() !&$&0%020( 12 1(.&$$09:)2(!39"%":0(+)+")5)!"#%@)#+()23(.)2")@")+".()1"-B)09)23()!"9209#0
  15. 15. Organization of rescue agents• Rescue agents are organized in multiple levels• Agent decision models are represented as sets of rules• Agents coordinate by exchanging messages 13
  16. 16. Behaviors of agents dedicated to resource allocation• Agent “center” assigns rescue agencies to damaged districts• Agencies allocate rescuers to damaged wards Hanoi City Ba-Dinh District 14
  17. 17. This model is a foundationto build the targeted SDSS 15
  18. 18. Restrictions of the current model and proposal• Restrictions: • The agent behaviors are not realistic enough • The simulated rescue activities are not performant 16
  19. 19. Restrictions of the current model and proposal• Restrictions: • The agent behaviors are not realistic enough • The simulated rescue activities are not performant• Next step of the proposal: • Make stakeholders (experts) play the role of agents to control the rescue activities • Acquire the knowledge of stakeholders to improve the behavior of agents 16
  20. 20. Step 2: improving agents’ behavior by Participatory Design ) MADFAM, from [Nguyen-Duc & Drogoul, 07]Design process of agent-based participatorysimulations, from [Guyot & Honiden, 06] Digital City, from [Ishida et al., 07] 17
  21. 21. Applying participatory design to the rescue model 18
  22. 22. A first experiment• Involving 27 master students of the IFI in a half-day Number of• They play simulations to improve the behaviors of ambulances (i.e. reducing the “number of deaths”) Improvement students• Students: 0 16/27 • execute separately from 5 to 8 playing sessions 2 4/27 • follow the same progression of 4 scenarios • take 5 minutes of discussion between two playing sessions 3 1/27 • attend a final 30 minutes of debriefing session 4 2/27• Results: 5 2/27 • 11 students showed real improvements • they reached the maximal improvement in the first scenario 6 1/27 • No student reached the optimal result (=8) for all four 7 1/27 scenarios 19
  23. 23. Requirements• User-interface must be friendly and interactive• Scenarios • must be understandable, realistic, rich, varied • sound progression from simple to complex ones• Experimental protocol with well-design questionnaires (for debriefing sessions) 20
  24. 24. Limitations of the current participatory design process• A effective model requires: • a large number of playing sessions • the analysis of a large base of user trace• Limitations: • Manual analysis of modelers takes a lot of time • Offline change of model lacks an from [Nguyen-Duc & Drogoul, 07] immediate feedback 21
  25. 25. Step 3: automating the acquisition of experts’ knowledge by ML I will save victimX, he’s very close.• Machine learning • Automatically extract the behaviors of users• Online and interactive learning No, I prefer victimY he’s in a more critical state • Immediately improve the behaviors of agents • Let agents intelligently negotiate with users • Help agents learn more quickly the users’ Ok, so the gravity is more decision-making important than the distance 22
  26. 26. Requirements of an online and interactive learning• Being effective under constraints of time and resources• Being supervised (by the user)• Being incremental• Providing visualizable and understandable "outcomes" • SVM, KNN, Neural Network, HMM are not suitable • Decision Tree, Bayesian Network are more suitable• Supported by an interactive interface and a language • to allow negotiations between users and agents 23
  27. 27. Learning the behavior of agents• Layered learning of Robocup-Soccer [Stone, 98]• Real-time Belief Space Search (RTBSS) of Damas-Rescue [Paquet, 06] Visualizable & Method Effective Supervised Incremental Interactive Understandable RTBSS v v x v x Layered v v x v x• Limitations of these methods: • Outcomes are difficultly visualized in a understandable way • Lack of interaction with stakeholders (i.e. learning without human supervisors) • Need of large training sets of examples 24
  28. 28. My choice: combining decision tree and utility function• Binary decision tree [Payne & Meisel, 77], [Cerny et al., 79] • Additive utility function [Keeney & Raiffa, 76] • to treat categorial data • to treat numerical data • to solve classification problems • to solve regression problems • to filter alternatives • to represent preferences Decision model of agents An utility function to choose a target district for hospitals An utility function to choose a target district for police offices An utility function to choose a target district for fire-stations Hospital has an UF to choose a target ward for ambulances Police office has an UF to choose a target ward for police forces Fire-station has an UF to choose a target ward for firefighters Each ambulance, firefighter, police force has: - A decision tree to choose target type - For each type, an utility function to choose a precise target 25
  29. 29. Behavior of an ambulance Can carry more• No Yes Ambulance have two questions: • Serious victim Go to an onsite victim for first-aid or Hospital carried take the carried victims to hospital? No Yes • If the type is determined, which Victim Hospital precise target will be chosen? Criteria to choose a victim Min/ Name• Decision model of ambulance Distance (from ambulance to victim) Max (-) C1 contains: Gravity (of victim) (+) C2 • Distance (from victim) to closest other victim (-) C3 One decision tree to choose a target Number of victims nearby (+) C4 type (victim or hospital) Max gravity of victims nearby (+) C5 • Two utility functions to choose a F(Vk)  =  ∑  wi  *  Cki target of a specific type The  vic(m  Vmax  will  be  selected  if:  Vmax  =  ArgMax{F(Vk)} 26
  30. 30. Learning decision tree Can carry I will go to V1 because: more No Yes I can carry more victim and V1 is close to meHospital Victim 27
  31. 31. Learning decision tree Can carry I will go to V1 because: more No Yes I can carry more victim and V1 is close to meHospital Victim User change decision You must go to H1 because Alternatives: {V1, V2, V3, V4, H1, H2} Decision: H1 you carry a victim in critical state and H1 has free beds Reasoning for change Boolean function: SeriousVictimCarried Numerical criteria: High(freeBedNumber) 27
  32. 32. Learning decision tree Can carry I will go to V1 because: more No Yes I can carry more victim and V1 is close to me Hospital Serious victim carried No Yes Victim Hospital User change decision You must go to H1 because• find the leaf-node corresponding Alternatives: {V1, V2, V3, V4, H1, H2} Decision: H1 you carry a victim in critical state to current context and H1 has free beds Reasoning for change• replace the leaf-node by a Boolean subtree function: SeriousVictimCarried Numerical• boolean condition of sub-tree is criteria: High(freeBedNumber) defined by users 27
  33. 33. Learning utility function Ambulance1 choose a target I will go to V1 because: Alternatives: {V1, V2, V3, V4, H1, H2} s/he is close to meF(Vk)= -1* distance Decision: {V1} Reasoning for decision Boolean CanCarryMore function: Numerical criteria: Low(distance) 28
  34. 34. Learning utility function Ambulance1 choose a target I will go to V1 because: Alternatives: {V1, V2, V3, V4, H1, H2} s/he is close to meF(Vk)= -1* distance Decision: {V1} Reasoning for decision Boolean CanCarryMore function: Numerical criteria: Low(distance) You must go to V2 because: s/he’s in a more critical state 28
  35. 35. Learning utility function Ambulance1 choose a target I will go to V1 because: Alternatives: {V1, V2, V3, V4, H1, H2} s/he is close to meF(Vk)= -1* distance Decision: {V1} Reasoning for decision gravity Boolean function: CanCarryMore Numerical criteria: Low(distance) • Add new numerical criteria (identified by user) to the function You must go to V2 because: s/he’s in a more critical state 28
  36. 36. Learning utility function Ambulance1 choose a target I will go to V1 because: Alternatives: {V1, V2, V3, V4, H1, H2} s/he is close to meF(Vk)= -0.4* -1* distance Decision: {V1} Reasoning for decision +0.6* gravity Boolean function: CanCarryMore Numerical criteria: Low(distance) • Add new numerical criteria (identified by user) to the function You must go to V2 because: s/he’s in a more critical state • Update criteria’ weights by solving “inequalities system” (Simplex method for linear programming [Vanderbei, 08]) 28
  37. 37. Experiments• Test with an "Oracle" to validate: • Learning decision tree • Learning utility function• Real-life test involves PhD students of MSI • Ten scenarios to improve the behaviors of ambulances • Improvement means the reduction in “number of deaths” • Evaluation by the best result with all participants 29
  38. 38. Validation of learning decision tree Have onsite victim Victim carried No Yes No Yes Victim Hospital Victim carried Victim carried No Yes No YesWait Hospital Victim Can not carry more No Yes Tree of the Oracle Tree learnt by ambulance Serious victim Hospital carried No Yes Victim Hospital 30
  39. 39. Validation of learning decision tree Situation1 Have onsite victim Victim carried No Yes No Yes Have not onsite Hospital Victim carried Victim carried victim No Yes No Yes No YesWait Hospital Victim Can not Victim Wait carry more No Yes Tree of the Oracle Tree learnt by ambulance Serious victim Hospital carried No Yes Victim Hospital 30
  40. 40. Validation of learning decision tree Have onsite victim Victim carried No Yes Situation 2 No Yes Have not onsite Have onsite victim Victim carried Victim carried victim No Yes No Yes No Yes No Yes VictimWait Hospital Victim Can not Victim Wait Hospital carry more No Yes Tree of the Oracle Tree learnt by ambulance Serious victim Hospital carried No Yes Victim Hospital 30
  41. 41. Validation of learning decision tree Have onsite victim Victim carried No Yes No Yes Have not onsite Have onsite victim Victim carried Victim carried victim No Yes No Yes No Yes No Yes Can not Serious victimWait Hospital Victim Victim Wait Hospital carry more carried Tree of the Oracle No Yes Situation 3 Tree learnt by ambulance No Yes Serious victim Victim Hospital Hospital carried No Yes Victim Hospital 30
  42. 42. Validation of learning decision tree Have onsite victim Victim carried No Yes No Yes Have not onsite Have onsite victim Victim carried Victim carried victim No Yes No Yes No Yes No Yes Can not Serious victimWait Hospital Victim Victim Wait Hospital carry more carried No Yes No Yes Tree of the Oracle Tree learnt by ambulance Serious victim Can not Hospital Hospital carry more carried No Yes No Yes Victim Hospital Victim Hospital• The same set of rules generated from the two trees 30
  43. 43. Validation of learning utility function Difference Error in the utility function of agents aiDifference(kmin) = ∑| – kmin* | wiwith kmin= ArgMin{Difference(k)} First ambulance Second ambulanceWhere: ai are coefficients of thefunction of Oracle: Fo(Vk) = ∑ ai * Ckiwi are coefficients of the function ofagent: Fa(Vk) = ∑ wi * Cki Time (in simulation steps) • The function of agent converges towards UF of the Oracle 31
  44. 44. Real-life test with users Victim carried No Yes Victim HospitalF(Vk)= -1* distanceF(Hk)= -1* distance 32
  45. 45. Real-life test with users Victim carriedScenario1 No YesReduce 2 deaths Have onsite victim Hospital No YesF(Vk)= -1* -0.4* distance Wait Victim 0.6* gravityF(Hk)= -1* distance 32
  46. 46. Real-life test with users Victim carried Scenario 2 No Yes Reduce 1 death Have onsite victim Have onsite victim No Yes No YesF(Vk)= -1* -0.2* distance -0.4* Victim Can not Wait Hospital carry more 0.7* 0.6* gravity No Yes -0.1* distance to closest other victim Victim HospitalF(Hk)= -1* distance 32
  47. 47. Real-life test with users Victim carried Scenario 3 No Yes Reduce 3 deaths Have onsite victim Have onsite victim No Yes No YesF(Vk)= -0.1* distance -1* -0.2* -0.4* Victim Can not Wait Hospital carry more 0.7* 0.5* 0.6* gravity No Yes -0.1* distance to closest other victim Have Hospital reachable 0.3* number of victims nearby No victims Yes Hospital VictimF(Hk)= -1* distance 32
  48. 48. Real-life test with users Victim carried Scenario 4 No Yes Reduce 2 deaths Have onsite victim Have onsite victim No Yes No YesF(Vk)= -0.1* distance -1* -0.2* -0.4* Victim Can not Wait Hospital carry more 0.67* 0.7* 0.5* 0.6* gravity No Yes -0.1* -0.03* distance to closest other victim Have Hospital reachable 0.13* number of victims nearby 0.3* victims No Yes 0.07* distance to closest ambulance Hospital Have reachable savable victims No YesF(Hk)= -0.9* distance -1* 0.1* number of free beds Victim Hospital 32
  49. 49. Victim carried The final decision No Yes model of ambulances Have onsite victim Have onsite victim No Yes No Yes Can not Wait Victim Hospital carry more No Yes Criteria to choose a victim Min/ Weight Max Have Hospital reachableGravity (of victim) (+) 0.5459 No victims YesNumber of victims nearby (+) 0.1345 HospitalDistance (from ambulance to victim) (-) 0.1034 Have reachable savable victimsDistance (from victim) to closest other ambulance (+) 0.0725 No YesMax gravity of victims nearby (+) 0.0665 Have reachableDistance (from victim) to closest other victim (-) 0.0635 Hospital savable victims with safe pathDistance (from victim) to closest hospital (-) 0.0137 No Yes Criteria to choose a hospital Min/ Weight Max Hospital Serious victim carriedDistance (from ambulance to hospital) (-) 0.4106 No YesNumber of free beds (of hospital) (+) 0.2477Number of victims nearby (+) 0.1267 Have serious Victim (reachable, reachable savableDistance (from hospital) to closest ambulance (+) 0.0975 savable, safe path) victims with safe pathMax gravity of victims nearby (+) 0.0674 No YesDistance (from hospital) to closest other victim (-) 0.0365 Victim (serious, Hospital reachable, savable,Distance (from hospital) to closest other hospital (-) 0.0136 safe path) 33
  50. 50. Results for all ten scenarios Parameters ImprovementScenario Hospital Ambulance Victim Ambulance (in reducing the number number number capacity number of deaths) 1 1 1 6 1 2 2 1 1 8 2 1 3 1 1 18 3 3 4 2 2 33 3 2 5 2 4 42 4 4 6 2 4 54 5 3 7 5 15 67 6 6 8 5 15 86 8 8 9 6 24 128 10 7 10 6 24 242 10 12 34
  51. 51. Conclusions• Concerning the design of a SDSS, my proposal: • automatically acquire part of the stakeholders’ knowledge • enhance the realism and the effectiveness of system • reduce the number of tests and focus on a few prototypes• The outcomes of this PhD thesis can be easily generalized to support the modeling of different socio-environmental systems: • My proposal of PD augmented with ML can be used in any applicative context • I designed the interactive interface, such that it can be reused in any context of decision-making • I designed the combination of DT and UF in order to be adaptable to model any agent behaviors 35
  52. 52. Prospects• Improving user/agent interaction with a more friendly interface and a more natural language • Currently, learning process requires a lot of efforts from the users when playing with the agents• Improving learning algorithm to support fault-tolerance • Currently, learning algorithm requires a high-level consistency in decisions of users• Designing experiments with real practitioners and experts of emergency • 2006: meeting with the Population Committee of Vietnam • 2007: meeting with the Vietnam Search and Rescue Committee (VINASARCOM) • ... 36
  53. 53. Thanks and Questions?• Step 1: Using ABM&GIS (geospatial data of Badinh and earthquake loss estimation of IG-VAST) to build a realistic rescue model • to solve the lack of realism of emergency situations• Step 2: Using Participatory Design to improve agent behaviors • to solve the lack of realism of rescue activities• Step 3: Using online interactive learning (DT and UF) to automate the acquisition of experts’ knowledge • to tackle the manual, offline improvement of agent behaviors, which is done by modelers 37

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