computing parameters of sequence based dynamic graphs
play

Computing Parameters of Sequence-based Dynamic Graphs Ralf Klasing - PowerPoint PPT Presentation

Sep 08, 2023 •436 likes •1.73k views

Computing Parameters of Sequence-based Dynamic Graphs Ralf Klasing LaBRI, CNRS, University of Bordeaux, France **This is a joint work with Arnaud Casteigts, Yessin M. Neggaz, and Joseph G. Peters. Dynamic Networks Highly dynamic networks? 1

  1. Transitive Closures Computation Minimization version (find the temporal diameter d ) ... G 2 G 1 Temporal Connectivity 9

  2. Transitive Closures Computation Minimization version (find the temporal diameter d ) ... G 2 G 1 Temporal Connectivity 9

  3. Transitive Closures Computation Minimization version (find the temporal diameter d ) ... G 2 × G 1 Temporal Connectivity 9

  4. Transitive Closures Computation Minimization version (find the temporal diameter d ) ... G 2 × × G 1 Temporal Connectivity 9

  5. Transitive Closures Computation Minimization version (find the temporal diameter d ) Strategy: ascending walk ... × G 2 × × G 1 Temporal Connectivity 9

  6. Transitive Closures Computation Minimization version (find the temporal diameter d ) Strategy: ascending walk ... × G 2 × × G 1 Temporal Connectivity 9

  7. Transitive Closures Computation Minimization version (find the temporal diameter d ) Strategy: ascending walk ... × G 2 × × G 1 Temporal Connectivity 9

  8. Transitive Closures Computation Minimization version (find the temporal diameter d ) × Strategy: ascending walk × ×× ... × G 2 × × G 1 Temporal Connectivity 9

  9. Transitive Closures Computation Minimization version (find the temporal diameter d ) × Strategy: ascending walk × The total length of the ×× ladders is O ( δ ) ... × At most O ( δ ) binary G 2 × concatenation and × G 1 completeness tests Temporal Connectivity 9

  10. Transitive Closures Computation Minimization version (find the temporal diameter d ) × Strategy: ascending walk × The total length of the ×× ladders is O ( δ ) ... × At most O ( δ ) binary G 2 × concatenation and × G 1 completeness tests Disjointness property: cat ( G ( i , j ) , G ( i ′ , j ′ ) ) = G ( i , j ′ ) Temporal Connectivity 9

  11. Transitive Closures Computation Minimization version (find the temporal diameter d ) × Strategy: ascending walk × The total length of the ×× ladders is O ( δ ) ... × At most O ( δ ) binary G 2 × concatenation and × G 1 completeness tests Disjointness property: cat ( G ( i , j ) , G ( i ′ , j ′ ) ) = G ( i , j ′ ) Temporal Connectivity 9

  12. Transitive Closures Computation Minimization version (find the temporal diameter d ) × Strategy: ascending walk × The total length of the ×× ladders is O ( δ ) ... × At most O ( δ ) binary G 2 × concatenation and × G 1 completeness tests Disjointness property: cat ( G ( i , j ) , G ( i ′ , j ′ ) ) = G ( i , j ′ ) If G ( i , j ) is complete, then G ( i ′ , j ′ ) is complete, for all i ′ ≤ i and j ′ ≥ j Temporal Connectivity 9

  13. Transitive Closures Computation Minimization version (find the temporal diameter d ) × Strategy: ascending walk × The total length of the ×× ladders is O ( δ ) ... × At most O ( δ ) binary G 2 × concatenation and × G 1 completeness tests Disjointness property: cat ( G ( i , j ) , G ( i ′ , j ′ ) ) = G ( i , j ′ ) If G ( i , j ) is complete, then G ( i ′ , j ′ ) is complete, for all i ′ ≤ i and j ′ ≥ j Temporal-Diameter is solvable with O ( δ ) elementary operations Temporal Connectivity 9

  14. Online Algorithms Temporal Connectivity 10

  15. Online Algorithms The optimal algorithms can be adapted to an online setting The sequence of graphs G 1 , G 2 , G 3 , ... of G is processed in the order of reception Amortized cost of O ( 1 ) elementary operations per graph received Dynamic version: consider only the recent history Temporal Connectivity 11

  16. Generic Framework Temporal Connectivity 12

  17. A Generic Framework Solve other problems using the same framework Temporal Connectivity 13

  18. A Generic Framework Solve other problems using the same framework × × ×× ... × G 2 × × G 1 Framework generalization Transitive closures concatenation Completeness test Transitive closure Temporal Connectivity 13

  19. A Generic Framework Solve other problems using the same framework × × ×× ... × G 2 × × G 1 Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 13

  20. A Generic Framework Solve other problems using the same framework × × ×× ... × G 2 × × G 1 Minimization problems Find the smallest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 13

  21. A Generic Framework Solve other problems using the same framework × × ×× ... × G 2 × × G 1 Minimization problems Maximization problems V.S Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 13

  22. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  23. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  24. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  25. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  26. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  27. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  28. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  29. A Generic Framework Solve other problems using the same framework × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  30. A Generic Framework Solve other problems using the same framework × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  31. A Generic Framework Solve other problems using the same framework × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  32. A Generic Framework Solve other problems using the same framework × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  33. A Generic Framework Solve other problems using the same framework × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  34. A Generic Framework Solve other problems using the same framework × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  35. A Generic Framework Solve other problems using the same framework × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  36. A Generic Framework Solve other problems using the same framework × × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  37. A Generic Framework Solve other problems using the same framework × × × × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  38. A Generic Framework Solve other problems using the same framework × × ×× × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  39. A Generic Framework Solve other problems using the same framework × × ×× × ×× ... × × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  40. A Generic Framework Solve other problems using the same framework × × ×× × × ×× × ... × G T × G 2 × G 1 Minimization problems V.S Maximization problems Find the smallest value Find the largest value Framework generalization Transitive closures concatenation → Composition operation Completeness test → Test operation Transitive closure → Super node Temporal Connectivity 14

  41. A Generic Framework Solve other problems using the same framework × × ×× × × ×× × ... × G T G 2 × × G 1 Minimization problems Maximization problems V.S Find the smallest value Find the largest value Temporal Connectivity 14

  42. A Generic Framework Solve other problems using the same framework × × ×× × × ×× × ... × G T G 2 × × G 1 Minimization problems Maximization problems V.S Find the smallest value Find the largest value Requirements test ( G ( i , j ) ) = true ⇔ { G i , G i + 1 , . . . , G j } satisfies the property P The composition operation is associative Only minimization: If test ( G ( i , j ) ) = true then test ( G ( i ′ , j ′ ) ) = true , ∀ i ′ ≤ i , j ′ ≥ j Only maximization: If test ( G ( i , j ) ) = true then test ( G ( i ′ , j ′ ) ) = true , ∀ i ′ ≥ i , j ′ ≤ j Temporal Connectivity 14

  43. Round-trip Temporal Connectivity Round-trip Temporal Connectivity A dynamic graph G is round-trip temporal connected if and only if a back-and-forth journey exists from any node to all other nodes. Temporal Connectivity 15

  44. Round-trip Temporal Connectivity Round-trip Temporal Connectivity A dynamic graph G is round-trip temporal connected if and only if a back-and-forth journey exists from any node to all other nodes. Round-Trip-Temporal-Diameter (minimization) Finding the smallest duration in which there exists a back-and-forth journey from any node to all other nodes. Temporal Connectivity 15

  45. Round-trip Temporal Connectivity Round-trip Temporal Connectivity A dynamic graph G is round-trip temporal connectivity if and only if a back-and-forth journey exists from any node to all other nodes. Round-Trip-Temporal-Diameter (minimization) Finding the smallest duration in which there exists a back-and-forth journey from any node to all other nodes. Round-trip transitive closure Super node: Round-trip transitive closure concatenation Composition operation: 2 , 4 7 , 6 7 , 6 2 , 4 2 , 4 7 , 6 5 7 6 , 7 2 , 7 7 , 4 7 , 4 2 , 7 7 = 7 = 3 , 3 , rtcat ∪ ⟲ 7 6 7 3 2 6 6 , , 2 , 4 7 , 7 7 , 7 6 , 5 , 6 , 5 7 , 7 , 7 7 , 7 , 6 , 4 4 6 , 6 , 5 6 , 6 6 , 6 6 , 5 6 , 6 G ( 1 , 5 ) ∪ ⟲ G ( 6 , 7 ) G ( 1 , 5 ) G ( 6 , 7 ) G ( 1 , 5 ) → ( 6 , 7 ) G ( 1 , 7 ) Test operation: Round-trip completeness Temporal Connectivity 16

  46. Bounded Realization of the footprint Time-bounded edge reappearance A dynamic graph G has a time-bounded edge reappearance with a bound b if the time between two appearances of the same edge is at most b . Temporal Connectivity 17

  47. Bounded Realization of the footprint Time-bounded edge reappearance A dynamic graph G has a time-bounded edge reappearance with a bound b if the time between two appearances of the same edge is at most b . Bounded-Realization-of-the-Footprint (minimization) Finding the smallest b such that in every subsequence of length b in the sequence G , all the edges of the footprint appear at least once. Temporal Connectivity 17

  48. Bounded Realization of the footprint Time-bounded edge reappearance A dynamic graph G has a time-bounded edge reappearance with a bound b if the time between two appearances of the same edge is at most b . Bounded-Realization-of-the-Footprint (minimization) Finding the smallest b such that in every subsequence of length b in the sequence G , all the edges of the footprint appear at least once. Union graphs Super node: Union Composition operation: Equality to the footprint Test operation: Temporal Connectivity 18

  49. T-interval Connectivity Definition: T -interval connectivity A dynamic graph G is T -interval connected if and only if every T length sequence of graphs has a common connected spanning sub-graph. Temporal Connectivity 19

  50. T-interval Connectivity Definition: T -interval connectivity A dynamic graph G is T -interval connected if and only if every T length sequence of graphs has a common connected spanning sub-graph. Temporal Connectivity 20

  51. T-interval Connectivity Definition: T -interval connectivity A dynamic graph G is T -interval connected if and only if every T length sequence of graphs has a common connected spanning sub-graph. T-Interval-Connectivity (maximization) Finding the largest T for which the graph is T -interval connected. G Temporal Connectivity 20

Recommend

CSE 421 Algorithms Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming

CSE 421 Algorithms Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming

CSE 421 Algorithms Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming Algorithm 2 Sequence Alignment Goal: position characters in two strings to best line up identical/similar ones with one another We can do

473 views • 28 slides
CSE 427 Comp Bio Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming

CSE 427 Comp Bio Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming

CSE 427 Comp Bio Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming Algorithm 2 Sequence Alignment Goal: position characters in two strings to best line up identical/similar ones with one another We can do this

941 views • 56 slides
Dynamic Programming Sequence Of Decisions Sequence of decisions. As in the greedy

Dynamic Programming Sequence Of Decisions Sequence of decisions. As in the greedy

Dynamic Programming Sequence Of Decisions Sequence of decisions. As in the greedy method, the solution to a problem is viewed as the result of a Problem state. sequence of decisions. Principle of optimality. Unlike the

501 views • 5 slides
Dynamic Programming Sequence Of Decisions Sequence of decisions. As in the greedy

Dynamic Programming Sequence Of Decisions Sequence of decisions. As in the greedy

Dynamic Programming Sequence Of Decisions Sequence of decisions. As in the greedy method, the solution to a problem is viewed as the result of a Problem state. sequence of decisions. Principle of optimality. Unlike the

393 views • 7 slides
Mod-L Test: A Brief Report 1. Mod-L Sequence Mod-L sequence can produce full Stokes parameters

Mod-L Test: A Brief Report 1. Mod-L Sequence Mod-L sequence can produce full Stokes parameters

Mod-L Test: A Brief Report 1. Mod-L Sequence Mod-L sequence can produce full Stokes parameters at a cadence of 90 seconds by combining filtergrams from both cameras, the front camera and the side camera. Within the 90-second, the front camera

512 views • 16 slides
2.13 Point Kinematics Having introduced in the previous Sections the different representations of

2.13 Point Kinematics Having introduced in the previous Sections the different representations of

71 Basilio Bona - Dynamic Modelling If we use the quaternions, the Euler parameters, the Cayley-Klein parameters or the Rodrigues vectors, it in unclear what are the parameters that take the place of the i in (2.65). Using these alternative

222 views • 11 slides
Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity Yong Wang 1 , Daniel

Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity Yong Wang 1 , Daniel

Nonuniform Timeslicing of Dynamic Graphs Based on Visual Complexity Yong Wang 1 , Daniel Archambault 2 , Hammad Haleem 1 , Yanhong Wu 3 , Torsten Moeller 4 , Huamin Qu 1 http://yong-wang.org/proj/nu_timeslicing.html 2 3 4 1 Background A

351 views • 14 slides
Two domination parameters in graphs Guangjun Xu Department of Mathematics and Statistics The

Two domination parameters in graphs Guangjun Xu Department of Mathematics and Statistics The

Two domination parameters in graphs Guangjun Xu Department of Mathematics and Statistics The University of Melbourne March 17, 2009 Joint work with Liying Kang, Erfang Shan and Min Zhao Domination in graphs Power domination Rainbow

958 views • 77 slides
Computing Straight Skeletons of Planar Straight-Line Graphs Based on Motorcycle Graphs CCCG2010,

Computing Straight Skeletons of Planar Straight-Line Graphs Based on Motorcycle Graphs CCCG2010,

Computing Straight Skeletons of Planar Straight-Line Graphs Based on Motorcycle Graphs CCCG2010, Winnipeg, Canada Stefan Huber Martin Held Universit at Salzburg, Austria August 11, 2010 Stefan Huber, Martin Held Straight Skeletons of a

494 views • 28 slides
Percolation on sparse random graphs with given degree sequence Nikolaos Fountoulakis School of

Percolation on sparse random graphs with given degree sequence Nikolaos Fountoulakis School of

Motivation Percolation on finite graphs Random graphs with a given degree sequence Why is it true... Questions for further study The End Percolation on sparse random graphs with given degree sequence Nikolaos Fountoulakis School of

934 views • 74 slides
Provenance in Dynamic Linked Data Marcin Wylot Linking Everything: Dynamic Graphs

Provenance in Dynamic Linked Data Marcin Wylot Linking Everything: Dynamic Graphs

Provenance in Dynamic Linked Data Marcin Wylot Linking Everything: Dynamic Graphs Integrated and summarized uncertain graph data Dynamic physical and logical network of things Necessity to established transparency 2 Data

850 views • 17 slides
Mapping CSP Networks to MPI Clusters Using Channel Graphs and Dynamic Instrumentation Gabriella

Mapping CSP Networks to MPI Clusters Using Channel Graphs and Dynamic Instrumentation Gabriella

Mapping CSP Networks to MPI Clusters Using Channel Graphs and Dynamic Instrumentation Gabriella Azzopardi Kevin Vella Adrian Muscat University of Malta Outline Introduction 1. CSP-Library 2. Configuration Language 3. CSP-based

354 views • 20 slides
Sequence comparison: Dynamic programming Genome 559: Introduction to Statistical and

Sequence comparison: Dynamic programming Genome 559: Introduction to Statistical and

Sequence comparison: Dynamic programming Genome 559: Introduction to Statistical and Computational Genomics Prof. James H. Thomas http://faculty.washington.edu/jht/GS559_2013/ Sequence comparison overview Problem: Find the best

647 views • 36 slides
Overview: Kernels for Sequences and Graphs String Kernels 8 Example Sequence Classification

Overview: Kernels for Sequences and Graphs String Kernels 8 Example Sequence Classification

Memorial Sloan-Kettering Cancer Center Overview: Kernels for Sequences and Graphs String Kernels 8 Example Sequence Classification Position-(In)dependent Kernels Advanced Kernels Easysvm Kernels on Graphs 9 Basics Random Walks Subtrees

1.8k views • 148 slides
Dynamic request management algorithms for Web-based services in Cloud computing Riccardo

Dynamic request management algorithms for Web-based services in Cloud computing Riccardo

Dynamic request management algorithms for Web-based services in Cloud computing Riccardo Lancellotti Mauro Andreolini Claudia Canali Michele Colajanni University of Modena and Reggio Emilia COMPSAC 2011 1 Request management for Cloud

370 views • 14 slides
Mining Dynamic and Augmented Graphs A Constraint-Based Pattern Mining View Marc Plantevit MEET

Mining Dynamic and Augmented Graphs A Constraint-Based Pattern Mining View Marc Plantevit MEET

Mining Dynamic and Augmented Graphs A Constraint-Based Pattern Mining View Marc Plantevit MEET THE INDUSTRY DAY, UNIVERSITY-INDUSTRY WORKSHOP ON SYSTEMS BIOLOGY Data Mining and Mining (DM2L) Research Group LIRIS UMR5205 Data: a new

813 views • 42 slides
a project by Approach I: Model-based computing time reduction Solver parameters Solver-based

a project by Approach I: Model-based computing time reduction Solver parameters Solver-based

Methods to reduce computing times of linear energy system optimization models IAEE 2019, Ljubljana Yvonne Scholz , Karl Kin Cao, Manuel Wetzel, Kai von Krbek DLR German Aerospace Center, Department of Energy Systems Analysis Daniel

594 views • 22 slides
Asynchronous sequence circuits An asynchronous sequence machine is a sequence circuit without

Asynchronous sequence circuits An asynchronous sequence machine is a sequence circuit without

Asynchronous sequence circuits An asynchronous sequence machine is a sequence circuit without flip-flops Asynchronous sequence machines are based on combinational gates with feedback Upon analysis it is assumed : Only one signal at a

1.34k views • 66 slides
Hornet: An Efficient Data Structure for Dynamic Sparse Graphs and Matrices Oded Green Hornet

Hornet: An Efficient Data Structure for Dynamic Sparse Graphs and Matrices Oded Green Hornet

Hornet: An Efficient Data Structure for Dynamic Sparse Graphs and Matrices Oded Green Hornet A scalable and dynamic data structure for Sparse data Graph algorithms Linear algebra based problems Formerly known as cuSTINGER

296 views • 27 slides
Genome 559, Winter 2012 Ab initio gene prediction method Define parameters of real genes

Genome 559, Winter 2012 Ab initio gene prediction method Define parameters of real genes

Ab initio gene prediction Genome 559, Winter 2012 Ab initio gene prediction method Define parameters of real genes (based on experimental evidence): 1) Splice donor sequence model 2) Splice acceptor sequence model 3) Intron and exon length

428 views • 25 slides
OPERATIONS ON GRAPHS INCREASING SOME GRAPH PARAMETERS Alexander Kelmans University of Puerto

OPERATIONS ON GRAPHS INCREASING SOME GRAPH PARAMETERS Alexander Kelmans University of Puerto

OPERATIONS ON GRAPHS INCREASING SOME GRAPH PARAMETERS Alexander Kelmans University of Puerto Rico Rutgers University May 25, 2014 1. Let G m n be the set of graphs with n vertices and m edges. Let Q be an operation on a graph such that G G

1.06k views • 82 slides
Detecting Clusters and Nonlinearity in 3D Dynamic Graphs John Fox McMaster University Robert

Detecting Clusters and Nonlinearity in 3D Dynamic Graphs John Fox McMaster University Robert

Detecting Clusters and Nonlinearity in 3D Dynamic Graphs John Fox McMaster University Robert Stine University of Pennsylvania Georges Monette and Nehru Vohra York University Interface 2002 Clusters and Nonlinearity in 3D Dynamic Graphs 1

575 views • 10 slides
15 Tree-based MT In this chapter, we will cover methods for sequence-to-sequence mapping that are

15 Tree-based MT In this chapter, we will cover methods for sequence-to-sequence mapping that are

15 Tree-based MT In this chapter, we will cover methods for sequence-to-sequence mapping that are based on tree structures. 15.1 Motivation for Tree-based Methods Translation Examples A Generalized Rule josei wa ringo wo tabeta NP 1 wa NP 2

648 views • 15 slides
CSE421 Algorithms Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming

CSE421 Algorithms Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming

CSE421 Algorithms Sequence Alignment 1 Sequence Alignment What Why A Dynamic Programming Algorithm 8 Sequence Similarity: What G G A C C A T A C T A A G T C C A A T 9 Sequence Similarity: What G G A C C A T A C T A A G | : | : |

689 views • 26 slides

More recommend