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LGM is an algorithm that efficiently mines frequent subgraphs from a set of linear graphs. It uses a reverse search approach to enumerate all subgraphs without duplication, defining a search tree with a reduction map. By inverting the reduction map, it can extend patterns from parent to children nodes. Experiments apply LGM to mine motifs from protein structures, finding statistically significant patterns associated with thermophilic or mesophilic functions.

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Lgm saarbrucken

The document summarizes a method for mining frequent subgraphs from linear graphs. It describes:
1) Representing data like proteins, RNA and texts as linear graphs and the need for algorithms to mine frequent patterns from such graphs.
2) A method called LGM that can efficiently enumerate and mine both connected and disconnected subgraphs from linear graphs using reverse search techniques.
3) Experiments applying LGM to mine motifs from protein structures and phrases from texts, achieving better performance than existing methods.

gSpan algorithm

Gspan is an algorithm for frequent subgraph mining that avoids two major costs of previous approaches. It represents graphs as depth-first search (DFS) codes to compare graphs for isomorphism testing. The algorithm grows patterns by extending edges in lexicographic order, checking the anti-monotonic property to prune infrequent subgraphs. Gspan compares the minimum DFS codes of two graphs to determine isomorphism, allowing simple string comparison of graphs. This helps reduce the problem size versus subgraph isomorphism testing.

Data Mining Seminar - Graph Mining and Social Network Analysis

Delivered a formal presentation on course material for the Data Mining (EECS 4412) course at York University, Canada, about graph mining. Graphs have become increasingly important in modeling sophisticated structures and their interactions, with broad applications including chemical informatics, bioinformatics, computer vision, video indexing, text retrieval, and Web analysis. The formal seminar was 50 to 60 minutes followed by 10 to 20 minutes for questions.
https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412
https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/lectures

Close Graph

The document discusses improvements made in the CloseGraph algorithm over the previous gSpan algorithm for mining frequent graph patterns. CloseGraph mines only closed frequent graphs, which significantly reduces the number of generated patterns compared to gSpan. This is done by introducing the concepts of equivalent occurrence and extended frequency counting to allow for early termination of the pattern growth. Experimental results show CloseGraph outperforms gSpan by a factor of 4 to 10 in both runtime and number of patterns generated.

AI Lesson 06

Iterative deepening A* (IDA*) is an informed search algorithm similar to iterative deepening depth-first search but uses an f-limit instead of depth limit. It expands nodes in best-first order up to the f-limit. The f-limit is increased each iteration by the minimum f-value of any node pruned in the previous iteration. IDA* is complete, optimal, and requires less space than A* but can expand more nodes on problems where heuristic values are unique. Local search methods like hill climbing iteratively improve the current state by moving to a neighboring state with better value until no improvement is possible.

09 heuristic search

This document discusses various heuristic search algorithms including A*, iterative-deepening A*, and recursive best-first search. It begins by introducing the concept of using evaluation functions to guide best-first search and preferentially expand nodes with lower heuristic values. It then presents the general graph search algorithm and describes how A* specifically reorders nodes using an evaluation function that considers path cost and estimated cost to the goal. Consistency conditions for the heuristic function are discussed which guarantee A* finds optimal solutions.

AI Lesson 05

The document summarizes informed search strategies, including best-first search algorithms like greedy search, uniform-cost search (UCS), and A* search. It provides an overview of how heuristics can be used to guide search toward more promising solutions. A* search is described as using both path cost g(n) and heuristic estimate h(n) to determine the best order of node expansion. The properties of A*, including admissibility, completeness, and optimality, are proven assuming h(n) underestimates cost to the goal. Performance depends on heuristic accuracy, with exponential growth possible if errors are large.

Example of iterative deepening search & bidirectional search

There are the some examples of Iterative deepening search & Bidirectional Search with some definitions and some theory related to the both searches. If you have any query please ask in comment or mail i will be happy to help you

Lgm saarbrucken

The document summarizes a method for mining frequent subgraphs from linear graphs. It describes:
1) Representing data like proteins, RNA and texts as linear graphs and the need for algorithms to mine frequent patterns from such graphs.
2) A method called LGM that can efficiently enumerate and mine both connected and disconnected subgraphs from linear graphs using reverse search techniques.
3) Experiments applying LGM to mine motifs from protein structures and phrases from texts, achieving better performance than existing methods.

gSpan algorithm

Gspan is an algorithm for frequent subgraph mining that avoids two major costs of previous approaches. It represents graphs as depth-first search (DFS) codes to compare graphs for isomorphism testing. The algorithm grows patterns by extending edges in lexicographic order, checking the anti-monotonic property to prune infrequent subgraphs. Gspan compares the minimum DFS codes of two graphs to determine isomorphism, allowing simple string comparison of graphs. This helps reduce the problem size versus subgraph isomorphism testing.

Data Mining Seminar - Graph Mining and Social Network Analysis

Delivered a formal presentation on course material for the Data Mining (EECS 4412) course at York University, Canada, about graph mining. Graphs have become increasingly important in modeling sophisticated structures and their interactions, with broad applications including chemical informatics, bioinformatics, computer vision, video indexing, text retrieval, and Web analysis. The formal seminar was 50 to 60 minutes followed by 10 to 20 minutes for questions.
https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412
https://wiki.eecs.yorku.ca/course_archive/2014-15/F/4412/lectures

Close Graph

The document discusses improvements made in the CloseGraph algorithm over the previous gSpan algorithm for mining frequent graph patterns. CloseGraph mines only closed frequent graphs, which significantly reduces the number of generated patterns compared to gSpan. This is done by introducing the concepts of equivalent occurrence and extended frequency counting to allow for early termination of the pattern growth. Experimental results show CloseGraph outperforms gSpan by a factor of 4 to 10 in both runtime and number of patterns generated.

AI Lesson 06

Iterative deepening A* (IDA*) is an informed search algorithm similar to iterative deepening depth-first search but uses an f-limit instead of depth limit. It expands nodes in best-first order up to the f-limit. The f-limit is increased each iteration by the minimum f-value of any node pruned in the previous iteration. IDA* is complete, optimal, and requires less space than A* but can expand more nodes on problems where heuristic values are unique. Local search methods like hill climbing iteratively improve the current state by moving to a neighboring state with better value until no improvement is possible.

09 heuristic search

This document discusses various heuristic search algorithms including A*, iterative-deepening A*, and recursive best-first search. It begins by introducing the concept of using evaluation functions to guide best-first search and preferentially expand nodes with lower heuristic values. It then presents the general graph search algorithm and describes how A* specifically reorders nodes using an evaluation function that considers path cost and estimated cost to the goal. Consistency conditions for the heuristic function are discussed which guarantee A* finds optimal solutions.

AI Lesson 05

The document summarizes informed search strategies, including best-first search algorithms like greedy search, uniform-cost search (UCS), and A* search. It provides an overview of how heuristics can be used to guide search toward more promising solutions. A* search is described as using both path cost g(n) and heuristic estimate h(n) to determine the best order of node expansion. The properties of A*, including admissibility, completeness, and optimality, are proven assuming h(n) underestimates cost to the goal. Performance depends on heuristic accuracy, with exponential growth possible if errors are large.

Example of iterative deepening search & bidirectional search

There are the some examples of Iterative deepening search & Bidirectional Search with some definitions and some theory related to the both searches. If you have any query please ask in comment or mail i will be happy to help you

20110319 parameterized algorithms_fomin_lecture03-04

The document discusses graph minors and fixed parameter algorithms. It introduces several important concepts in fixed parameter algorithm design like treewidth, kernelization, color coding, and iterative compression. It also discusses applications of the Graph Minors Theorem to showing that certain problems are fixed-parameter tractable.

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Pa...

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Pa...Mumbai B.Sc.IT Study

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Paper]
april - 2017, april - 2016, april - 2015, april - 2014, april - 2013, october - 2017, october - 2016, october - 2015, october - 2014, may - 2016, may - 2017, december - 2017, 75:25 pattern, 60:40 pattern, revised course, old course, mumbai bscit study, mumbai university, bscit semester vi, bscit question paper, old question paper, previous year question paper, semester vi question paper, question paper, CBSGS, IDOL, kamal t, internet technology, digital signals and systems, data warehousing, ipr and cyber laws, project management, geographic information systemDigital Signals and System (April – 2015) [Revised Syllabus | Question Paper]

mumbai bscit study, kamal t, mumbai university, old question paper, previous year question paper, bscit question paper, bscit semester vi, internet technology, april - 2015, 75:25 Pattern, 60:40 Pattern, revised syllabus, old syllabus, cbsgc, question paper, may - 2016, april - 2017, april - 2014, april - 2013, may – 2016, october – 2016, digital signals and system

Data structure

This document contains a 50 question quiz on data structures. The questions cover topics like linked lists, stacks, queues, trees, sorting algorithms, hashing and more. For each question there are 4 multiple choice answers and the correct answer is indicated. The quiz is assessing understanding of fundamental data structure concepts and their applications.

Dfs presentation

The document describes depth-first search (DFS), an algorithm for traversing or searching trees or graphs. It defines DFS, explains the process as visiting nodes by going deeper until reaching the end and then backtracking, provides pseudocode for the algorithm, gives an example on a directed graph, and discusses time complexity (O(V+E)), advantages like linear memory usage, and disadvantages like possible infinite traversal without a cutoff depth.

Functions

The document defines and explains key concepts related to functions:
- A function relates an input to an output and maps elements from its domain to its codomain.
- For a function to be valid, each input can only map to one output.
- Functions have properties like being one-to-one (injective), onto (surjective), or both (bijective).
- Other topics covered include the domain, codomain, range, and examples of floor, ceiling, integer and absolute value functions.

Jarrar: Informed Search

Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123

C applications

The document discusses several applications of stacks, including evaluating arithmetic expressions in Polish notation without needing operator precedence or parentheses, converting expressions between infix and postfix notation, matching parentheses in expressions, and other applications like reversing strings and generating code from expressions. Polish notation places operators after operands to simplify evaluation using a stack. Converting expressions to postfix form uses a stack to remove parentheses and preserve operator order.

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]Mumbai B.Sc.IT Study

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]
april - 2017, april - 2016, april - 2015, april - 2014, april - 2013, october - 2017, october - 2016, october - 2015, october - 2014, may - 2016, may - 2017, december - 2017, 75:25 pattern, 60:40 pattern, revised course, old course, mumbai bscit study, mumbai university, bscit semester vi, bscit question paper, old question paper, previous year question paper, semester vi question paper, question paper, CBSGS, IDOL, kamal t, internet technology, digital signals and systems, data warehousing, ipr and cyber laws, project management, geographic information systemSolving problems by searching Informed (heuristics) Search

This document discusses various informed (heuristic) search strategies for solving problems, including greedy best-first search, A* search, and memory-bounded variations. Greedy best-first search uses the heuristic function h(n) alone to select nodes for expansion. A* search combines the path cost g(n) and heuristic estimate h(n) to select nodes, guaranteeing an optimal solution if h is admissible. The document provides examples of applying these searches to route finding between cities in Romania. A* search is identified as finding the optimal solution for this problem if using an admissible heuristic like straight-line distance.

Digital Signals and Systems (October – 2016) [Question Paper | IDOL: Revised ...

Digital Signals and Systems (October – 2016) [Question Paper | IDOL: Revised ...Mumbai B.Sc.IT Study

Data Warehousing (April – 2016) [Question Paper | CBSGS: 75:25 Pattern]
april - 2017, april - 2016, april - 2015, april - 2014, april - 2013, october - 2017, october - 2016, october - 2015, october - 2014, may - 2016, may - 2017, december - 2017, 75:25 pattern, 60:40 pattern, revised course, old course, mumbai bscit study, mumbai university, bscit semester vi, bscit question paper, old question paper, previous year question paper, semester vi question paper, question paper, CBSGS, IDOL, kamal t, internet technology, digital signals and systems, data warehousing, ipr and cyber laws, project management, geographic information systemGraphical Models In Python | Edureka

(** Graphical Models Certification Training: https://www.edureka.co/graphical-modelling-course **)
This Edureka "Graphical Models" PPT answers the question "Why do we need Probabilistic Graphical Models?" and how are they compare to Neural Networks. It takes you through the basics of PGMs and gives real-world examples of its applications.
Why do you need PGMs?
What is a PGM?
Bayesian Networks
Markov Random Fields
Use Cases
Bayesian Networks & Markov Random Fields
PGMs & Neural Networks
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Formal semantics for Cypher queries and updates

Presented at the Fourth openCypher Implementers Meeting in Copenhagen, Denmark, May 2018 @ http://www.opencypher.org/event/2018/05/22/ocim4/

Informed search (heuristics)

1) The document discusses various search algorithms including uninformed searches like breadth-first search as well as informed searches using heuristics.
2) It describes greedy best-first search which uses a heuristic function to select the node closest to the goal at each step, and A* search which uses both path cost and heuristic cost to guide the search.
3) Genetic algorithms are introduced as a search technique that generates successors by combining two parent states through crossover and mutation rather than expanding single nodes.

Lecture 12 Heuristic Searches

The document describes heuristic search algorithms including best first search, branch and bound search. Best first search maintains a priority queue of nodes and expands the node with the lowest cost function first. Branch and bound finds the optimal solution by keeping track of the best solution found so far and abandoning partial solutions that cannot improve on the best. It uses pruning to reduce the number of explored nodes. Both algorithms use concepts like traversing the root node and its neighbors in ascending order of distance from the root until reaching the goal node.

Lecture 08 uninformed search techniques

This document discusses various uninformed search techniques including breadth-first search (BFS), depth-first search (DFS), uniform cost search, and others. It provides descriptions of each technique including concepts, properties, advantages, and disadvantages. Uniform cost search is described as expanding nodes in order of cost from the source to ensure the lowest cost node is selected, making it complete and optimal/admissible.

[Question Paper] Data Communication and Network Standards (Revised Course) [J...

[Question Paper] Data Communication and Network Standards (Revised Course) [J...Mumbai B.Sc.IT Study

This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Data Communication and Network Standards] (Revised Course). [Year - June / 2014] . . .Solution Set of this Paper is Coming soon.. 8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...

Lijun Chang, DECRA Fellow at the University of New South Wales talked about how to make subgraph matching more efficient thanks to postponing Cartesian products.

A star algorithms

The document describes best first search algorithms. It discusses how best first search algorithms work by always selecting the most promising path based on a heuristic function. The algorithm expands the node closest to the goal at each step. The document provides pseudocode for the best first search algorithm and discusses its advantages of being more efficient than breadth-first and depth-first search, but that it can also get stuck in loops like depth-first search. An example of applying best first search to a problem is given.

AI Lesson 04

1) The document describes uninformed search algorithms, including breadth-first search and depth-first search.
2) Breadth-first search explores all neighbors of the initial node before moving to the next level, finding the shortest path.
3) Depth-first search explores deep paths first, expanding the deepest node at each step and implementing the fringe as a stack.

Mlab2012 tabei 20120806

The document describes a workshop on machine learning and applications to biology held in Sapporo, Japan in August 2012. It focuses on presenting space-efficient data structures for large-scale chemical fingerprint searches, including multibit trees and succinct representations of trees and tries. The goal is fast similarity searches of chemical fingerprints while using less memory than pointer-based representations.

SPIRE2013-tabei20131009

FOLCA is a fully-online grammar compression method that builds a partial parse tree in an online manner and directly encodes it into a succinct representation using just nlgn+2n+o(n) bits of space. This is asymptotically optimal. It achieves small working space of (1+α)nlgn+n(3+lg(αn)) bits using a compressed hash table. It can extract substrings in O(l+h) time using extra space of nlg(N/n)+3n+o(n) bits. Experiments show it compresses and extracts faster than LZend while using less space.

20110319 parameterized algorithms_fomin_lecture03-04

The document discusses graph minors and fixed parameter algorithms. It introduces several important concepts in fixed parameter algorithm design like treewidth, kernelization, color coding, and iterative compression. It also discusses applications of the Graph Minors Theorem to showing that certain problems are fixed-parameter tractable.

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Pa...

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Pa...Mumbai B.Sc.IT Study

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Paper]
april - 2017, april - 2016, april - 2015, april - 2014, april - 2013, october - 2017, october - 2016, october - 2015, october - 2014, may - 2016, may - 2017, december - 2017, 75:25 pattern, 60:40 pattern, revised course, old course, mumbai bscit study, mumbai university, bscit semester vi, bscit question paper, old question paper, previous year question paper, semester vi question paper, question paper, CBSGS, IDOL, kamal t, internet technology, digital signals and systems, data warehousing, ipr and cyber laws, project management, geographic information systemDigital Signals and System (April – 2015) [Revised Syllabus | Question Paper]

mumbai bscit study, kamal t, mumbai university, old question paper, previous year question paper, bscit question paper, bscit semester vi, internet technology, april - 2015, 75:25 Pattern, 60:40 Pattern, revised syllabus, old syllabus, cbsgc, question paper, may - 2016, april - 2017, april - 2014, april - 2013, may – 2016, october – 2016, digital signals and system

Data structure

This document contains a 50 question quiz on data structures. The questions cover topics like linked lists, stacks, queues, trees, sorting algorithms, hashing and more. For each question there are 4 multiple choice answers and the correct answer is indicated. The quiz is assessing understanding of fundamental data structure concepts and their applications.

Dfs presentation

The document describes depth-first search (DFS), an algorithm for traversing or searching trees or graphs. It defines DFS, explains the process as visiting nodes by going deeper until reaching the end and then backtracking, provides pseudocode for the algorithm, gives an example on a directed graph, and discusses time complexity (O(V+E)), advantages like linear memory usage, and disadvantages like possible infinite traversal without a cutoff depth.

Functions

The document defines and explains key concepts related to functions:
- A function relates an input to an output and maps elements from its domain to its codomain.
- For a function to be valid, each input can only map to one output.
- Functions have properties like being one-to-one (injective), onto (surjective), or both (bijective).
- Other topics covered include the domain, codomain, range, and examples of floor, ceiling, integer and absolute value functions.

Jarrar: Informed Search

Lecture slides by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2012/04/aai-spring-jan-may-2012.html
and http://www.jarrar.info
and on Youtube:
http://www.youtube.com/watch?v=aNpLekq6-oA&list=PL44443F36733EF123

C applications

The document discusses several applications of stacks, including evaluating arithmetic expressions in Polish notation without needing operator precedence or parentheses, converting expressions between infix and postfix notation, matching parentheses in expressions, and other applications like reversing strings and generating code from expressions. Polish notation places operators after operands to simplify evaluation using a stack. Converting expressions to postfix form uses a stack to remove parentheses and preserve operator order.

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]Mumbai B.Sc.IT Study

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]
april - 2017, april - 2016, april - 2015, april - 2014, april - 2013, october - 2017, october - 2016, october - 2015, october - 2014, may - 2016, may - 2017, december - 2017, 75:25 pattern, 60:40 pattern, revised course, old course, mumbai bscit study, mumbai university, bscit semester vi, bscit question paper, old question paper, previous year question paper, semester vi question paper, question paper, CBSGS, IDOL, kamal t, internet technology, digital signals and systems, data warehousing, ipr and cyber laws, project management, geographic information systemSolving problems by searching Informed (heuristics) Search

This document discusses various informed (heuristic) search strategies for solving problems, including greedy best-first search, A* search, and memory-bounded variations. Greedy best-first search uses the heuristic function h(n) alone to select nodes for expansion. A* search combines the path cost g(n) and heuristic estimate h(n) to select nodes, guaranteeing an optimal solution if h is admissible. The document provides examples of applying these searches to route finding between cities in Romania. A* search is identified as finding the optimal solution for this problem if using an admissible heuristic like straight-line distance.

Digital Signals and Systems (October – 2016) [Question Paper | IDOL: Revised ...

Digital Signals and Systems (October – 2016) [Question Paper | IDOL: Revised ...Mumbai B.Sc.IT Study

Data Warehousing (April – 2016) [Question Paper | CBSGS: 75:25 Pattern]
april - 2017, april - 2016, april - 2015, april - 2014, april - 2013, october - 2017, october - 2016, october - 2015, october - 2014, may - 2016, may - 2017, december - 2017, 75:25 pattern, 60:40 pattern, revised course, old course, mumbai bscit study, mumbai university, bscit semester vi, bscit question paper, old question paper, previous year question paper, semester vi question paper, question paper, CBSGS, IDOL, kamal t, internet technology, digital signals and systems, data warehousing, ipr and cyber laws, project management, geographic information systemGraphical Models In Python | Edureka

(** Graphical Models Certification Training: https://www.edureka.co/graphical-modelling-course **)
This Edureka "Graphical Models" PPT answers the question "Why do we need Probabilistic Graphical Models?" and how are they compare to Neural Networks. It takes you through the basics of PGMs and gives real-world examples of its applications.
Why do you need PGMs?
What is a PGM?
Bayesian Networks
Markov Random Fields
Use Cases
Bayesian Networks & Markov Random Fields
PGMs & Neural Networks
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Formal semantics for Cypher queries and updates

Presented at the Fourth openCypher Implementers Meeting in Copenhagen, Denmark, May 2018 @ http://www.opencypher.org/event/2018/05/22/ocim4/

Informed search (heuristics)

1) The document discusses various search algorithms including uninformed searches like breadth-first search as well as informed searches using heuristics.
2) It describes greedy best-first search which uses a heuristic function to select the node closest to the goal at each step, and A* search which uses both path cost and heuristic cost to guide the search.
3) Genetic algorithms are introduced as a search technique that generates successors by combining two parent states through crossover and mutation rather than expanding single nodes.

Lecture 12 Heuristic Searches

The document describes heuristic search algorithms including best first search, branch and bound search. Best first search maintains a priority queue of nodes and expands the node with the lowest cost function first. Branch and bound finds the optimal solution by keeping track of the best solution found so far and abandoning partial solutions that cannot improve on the best. It uses pruning to reduce the number of explored nodes. Both algorithms use concepts like traversing the root node and its neighbors in ascending order of distance from the root until reaching the goal node.

Lecture 08 uninformed search techniques

This document discusses various uninformed search techniques including breadth-first search (BFS), depth-first search (DFS), uniform cost search, and others. It provides descriptions of each technique including concepts, properties, advantages, and disadvantages. Uniform cost search is described as expanding nodes in order of cost from the source to ensure the lowest cost node is selected, making it complete and optimal/admissible.

[Question Paper] Data Communication and Network Standards (Revised Course) [J...

[Question Paper] Data Communication and Network Standards (Revised Course) [J...Mumbai B.Sc.IT Study

This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Data Communication and Network Standards] (Revised Course). [Year - June / 2014] . . .Solution Set of this Paper is Coming soon.. 8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...

Lijun Chang, DECRA Fellow at the University of New South Wales talked about how to make subgraph matching more efficient thanks to postponing Cartesian products.

A star algorithms

The document describes best first search algorithms. It discusses how best first search algorithms work by always selecting the most promising path based on a heuristic function. The algorithm expands the node closest to the goal at each step. The document provides pseudocode for the best first search algorithm and discusses its advantages of being more efficient than breadth-first and depth-first search, but that it can also get stuck in loops like depth-first search. An example of applying best first search to a problem is given.

AI Lesson 04

1) The document describes uninformed search algorithms, including breadth-first search and depth-first search.
2) Breadth-first search explores all neighbors of the initial node before moving to the next level, finding the shortest path.
3) Depth-first search explores deep paths first, expanding the deepest node at each step and implementing the fringe as a stack.

20110319 parameterized algorithms_fomin_lecture03-04

20110319 parameterized algorithms_fomin_lecture03-04

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Pa...

B.Sc.IT: Semester - VI (December - 2017) [IDOL - Revised Course | Question Pa...

Digital Signals and System (April – 2015) [Revised Syllabus | Question Paper]

Digital Signals and System (April – 2015) [Revised Syllabus | Question Paper]

Data structure

Data structure

Dfs presentation

Dfs presentation

Functions

Functions

Jarrar: Informed Search

Jarrar: Informed Search

C applications

C applications

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]

B.Sc.IT: Semester - VI (October - 2016) [IDOL - Revised Course | Question Paper]

Solving problems by searching Informed (heuristics) Search

Solving problems by searching Informed (heuristics) Search

Digital Signals and Systems (October – 2016) [Question Paper | IDOL: Revised ...

Digital Signals and Systems (October – 2016) [Question Paper | IDOL: Revised ...

Graphical Models In Python | Edureka

Graphical Models In Python | Edureka

Formal semantics for Cypher queries and updates

Formal semantics for Cypher queries and updates

Informed search (heuristics)

Informed search (heuristics)

Lecture 12 Heuristic Searches

Lecture 12 Heuristic Searches

Lecture 08 uninformed search techniques

Lecture 08 uninformed search techniques

[Question Paper] Data Communication and Network Standards (Revised Course) [J...

[Question Paper] Data Communication and Network Standards (Revised Course) [J...

8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...

8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...

A star algorithms

A star algorithms

AI Lesson 04

AI Lesson 04

Mlab2012 tabei 20120806

The document describes a workshop on machine learning and applications to biology held in Sapporo, Japan in August 2012. It focuses on presenting space-efficient data structures for large-scale chemical fingerprint searches, including multibit trees and succinct representations of trees and tries. The goal is fast similarity searches of chemical fingerprints while using less memory than pointer-based representations.

SPIRE2013-tabei20131009

FOLCA is a fully-online grammar compression method that builds a partial parse tree in an online manner and directly encodes it into a succinct representation using just nlgn+2n+o(n) bits of space. This is asymptotically optimal. It achieves small working space of (1+α)nlgn+n(3+lg(αn)) bits using a compressed hash table. It can extract substrings in O(l+h) time using extra space of nlg(N/n)+3n+o(n) bits. Experiments show it compresses and extracts faster than LZend while using less space.

CPM2013-tabei201306

This document summarizes research presented at the 24th Annual Symposium on Combinatorial Pattern Matching. It discusses three open problems in optimally encoding Straight Line Programs (SLPs), which are compressed representations of strings. The document presents information theoretic lower bounds on SLP size and describes novel techniques for building optimal encodings of SLPs in close to minimal space. It also proposes a space-efficient data structure for the reverse dictionary of an SLP.

WABI2012-SuccinctMultibitTree

This document summarizes a presentation on succinct representations of multibit trees for efficient chemical fingerprint searches. It describes:
1) Using succinct data structures like rank/select dictionaries and LOUDS representations to compactly encode multibit trees and fingerprint databases in memory.
2) Two approaches for compactly representing fingerprint databases - a variable-length array and succinct trie.
3) How the succinct representations allow fast similarity searches on large chemical fingerprint datasets while using less memory than pointer-based representations.

Gwt sdm public

(1) The document describes a method for efficient similarity search in massive graph databases using wavelet trees. (2) It converts graphs into bags-of-words representations using the Weisfeiler-Lehman procedure and indexes the words with a wavelet tree to enable fast semi-conjunctive queries. (3) Experiments on 25 million chemical compounds showed the method was significantly faster than alternative approaches while using less memory.

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

河原林ERATO感謝祭IIIの発表資料です。

Gwt presen alsip-20111201

The document describes using a wavelet tree data structure to enable fast similarity searches of massive graph databases. A Weisfeiler-Lehman procedure is used to represent graphs as bags-of-words. The wavelet tree indexes these bags-of-words and allows semi-conjunctive queries to find graphs sharing a minimum number of words with a query graph in sublinear time. Experiments on 25 million molecular graphs showed the approach significantly outperformed inverted indexes in search time and memory usage.

Dmss2011 public

This document summarizes a method for performing kernel-based similarity search in massive graph databases using wavelet trees. It introduces the need for efficient graph similarity search as graph databases grow large. It describes representing graphs as bags-of-words and using a semi-conjunctive query to relax cosine similarity searches. The method replaces inverted indexes with a wavelet tree to enable fast top-down search while using less memory than traditional inverted indexes. Experiments on a dataset of 25 million chemical compounds demonstrate the method's ability to perform similarity search efficiently in large graph databases.

Sketch sort sugiyamalab-20101026 - public

- The document describes a multiple sorting method called SketchSort for efficiently finding all pairs of similar items in large-scale datasets.
- SketchSort maps high-dimensional vector data to binary sketches while preserving distances. It then performs multiple sorting on the sketches to enumerate similar item pairs.
- Experiments show SketchSort can efficiently find neighbor pairs in large image and genetic datasets, outperforming other state-of-the-art methods. It enables applications like clustering and information retrieval in big data domains.

Sketch sort ochadai20101015-public

The document summarizes a multiple sorting method called SketchSort for performing all pairs similarity search on large-scale datasets. It maps vector data to binary sketches to reduce memory usage, then applies locality sensitive hashing and multiple sorting to efficiently find all pairs of data points within a given distance threshold. The method is evaluated on large image, chemical compound, and genome sequence datasets and is shown to outperform other state-of-the-art similarity search methods.

GIW2013

The document summarizes research on developing a scalable method for predicting compound-protein interactions using minwise hashing. Key points:
- Minwise hashing is used to build compact fingerprints from high-dimensional fingerprints of compound-protein pairs, reducing memory and training time compared to previous methods.
- Linear support vector machines trained on the compact fingerprints achieve similar prediction accuracy as previous nonlinear methods, while requiring less memory and training faster, especially on large datasets of 216 million compound-protein pairs.
- Experiments show the proposed method, MH-L1SVM and MH-L2SVM, outperform baselines in training time while maintaining predictive performance, and it can extract important predictive features.

Kdd2015reading-tabei

KDD2015読み会発表資料

DCC2014 - Fully Online Grammar Compression in Constant Space

FREQ_FOLCA and LOSSY_FOLCA are variants of FOLCA that work in constant space by removing infrequent production rules from the hash table. FREQ_FOLCA divides text into blocks and removes the lowest frequency rules each time the hash table reaches a size limit. LOSSY_FOLCA divides text into blocks and keeps rules for successive blocks based on frequency. Experiments show they can compress 100 human genomes totaling 306GB in about one day while using only a few dozen megabytes of working space.

CSMR11b.ppt

This document summarizes a research paper on identifying micro-architectures in evolving object-oriented software systems. The paper presents an approach called SGFinder that models class diagrams as labeled graphs and defines micro-architectures as connected induced subgraphs. SGFinder efficiently enumerates all micro-architectures up to a given size. The paper applies SGFinder to two open-source systems and analyzes the identified micro-architectures to find those that are particularly fault-prone, fault-free, stable or change-prone. The results provide insights into common micro-architecture patterns and their relationships to quality attributes.

Jayant lrs

The document introduces lexicographic reverse search (LRS), an algorithm for enumerating the vertices of a polyhedron given its H-representation. LRS uses a lexicographic pivoting rule within the simplex method and traces all possible paths in reverse to enumerate all vertices. It was developed by Avis and Fukuda to improve upon the original reverse search algorithm. The document outlines the key concepts behind LRS, including lexicographic ordering of vectors, the lexicographic simplex method, and its applications in areas like convex hull problems and linear programming.

LEXBFS on Chordal Graphs

Cordal graphs are graphs where every cycle of 4 or more vertices has an edge connecting two non-adjacent vertices (a chord). There are three equivalent properties of cordal graphs: 1) they are chordal, 2) they have a perfect elimination ordering, and 3) minimal vertex separators induce complete subgraphs. The LEX BFS algorithm uses a lexicographic breadth-first search to find a perfect elimination ordering in polynomial time, identifying if a graph is cordal. It partitions the vertices into adjacent and non-adjacent sets at each step until all vertices are visited.

Mlab2012 tabei 20120806

Mlab2012 tabei 20120806

SPIRE2013-tabei20131009

SPIRE2013-tabei20131009

CPM2013-tabei201306

CPM2013-tabei201306

WABI2012-SuccinctMultibitTree

WABI2012-SuccinctMultibitTree

Gwt sdm public

Gwt sdm public

NIPS2013読み会: Scalable kernels for graphs with continuous attributes

NIPS2013読み会: Scalable kernels for graphs with continuous attributes

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices

Gwt presen alsip-20111201

Gwt presen alsip-20111201

Dmss2011 public

Dmss2011 public

Sketch sort sugiyamalab-20101026 - public

Sketch sort sugiyamalab-20101026 - public

Sketch sort ochadai20101015-public

Sketch sort ochadai20101015-public

Ibisml2011 06-20

Ibisml2011 06-20

GIW2013

GIW2013

Kdd2015reading-tabei

Kdd2015reading-tabei

DCC2014 - Fully Online Grammar Compression in Constant Space

DCC2014 - Fully Online Grammar Compression in Constant Space

Lp Boost

Lp Boost

CSMR11b.ppt

CSMR11b.ppt

20110501 csseminar rybalkin_substructure_search

20110501 csseminar rybalkin_substructure_search

Jayant lrs

Jayant lrs

LEXBFS on Chordal Graphs

LEXBFS on Chordal Graphs

gSpan algorithm

Gspan is an algorithm for frequent subgraph mining that avoids two major costs of previous approaches. It represents graphs as depth-first search (DFS) codes and builds a DFS code tree to systematically explore the search space. Each node in the tree represents a unique graph. Gspan tests for graph isomorphism by comparing minimum DFS codes, allowing it to prune redundant portions of the search space. An experimental evaluation showed it has good performance and scales well compared to previous methods.

Graphs In Data Structure

A graph G is composed of vertices V connected by edges E. It can be represented using an adjacency matrix or adjacency lists. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices. DFS recursively explores edges until reaching the end of a branch before backtracking, while BFS explores all neighbors at each level before moving to the next.

Graphs In Data Structure

A graph G is composed of vertices V connected by edges E. It can be represented using an adjacency matrix or adjacency lists. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices. DFS recursively explores edges until reaching the end of a branch before backtracking, while BFS explores edges in levels starting from the starting vertex.

Graph data structure

The document discusses various topics related to graphs:
- It defines directed and undirected graphs, paths, connected graphs, trees, degree, isomorphic graphs, cut sets, and labeled graphs.
- Key aspects include paths being sequences of vertices with edges connecting them, connected graphs having paths between all vertex pairs, trees being connected and acyclic graphs, and isomorphic graphs having the same structure.
- It also covers graph concepts such as degrees measuring incident edges, cut sets separating graphs, and labeling providing additional data to graphs' vertices or edges.

Skiena algorithm 2007 lecture12 topological sort connectivity

1. The document discusses depth-first search (DFS), including its recursive implementation which eliminates the need for an explicit stack. 2. DFS classifies the edges of a graph as tree edges, back edges, forward edges, or cross edges. Tree edges and back edges are the only possible edge types in an undirected graph searched with DFS. 3. Applications of DFS include finding cycles, articulation vertices, topological sorting of directed acyclic graphs, and finding strongly connected components.

Attributed Graph Matching of Planar Graphs

Many fields such as computer vision, scene analysis, chemistry and molecular biology have
applications in which images have to be processed and some regions have to be searched for
and identified. When this processing is to be performed by a computer automatically without
the assistance of a human expert, a useful way of representing the knowledge is by using
attributed graphs. Attributed graphs have been proved as an effective way of representing
objects. When using graphs to represent objects or images, vertices usually represent regions
(or features) of the object or images, and edges between them represent the relations
between regions. Nonetheless planar graphs are graphs which can be drawn in the plane
without intersecting any edge between them. Most applications use planar graphs to
represent an image.
Graph matching (with attributes or not) represents an NP-complete problem, nevertheless
when we use planar graphs without attributes we can solve this problem in polynomial time
[1]. No algorithms have been presented that solve the attributed graph-matching problem and
use the planar-graphs properties. In this master thesis, we research about Attributed-Planar-
Graph matching. The aim is to find a fast algorithm through studying in depth the properties
and restrictions imposed by planar graphs.

Lecture 8

This document discusses edge detection techniques in digital image processing. It begins by defining edges as areas of abrupt intensity change and explains that differentiation is used to detect edges locally. It then discusses the importance of edge detection for image segmentation and defect detection. The document goes on to explain that edge detection involves high-pass spatial domain filtering to eliminate low and medium frequencies while passing or enhancing high frequencies. It covers various first and second order derivative approaches to edge detection, including Roberts, Prewitt, Sobel, and Laplacian methods. Thresholding and boolean filtering techniques are also summarized for precise edge detection.

Data Structures - Lecture 10 [Graphs]

Graphs are data structures consisting of nodes and edges connecting nodes. They can be directed or undirected. Trees are special types of graphs. Common graph algorithms include depth-first search (DFS) and breadth-first search (BFS). DFS prioritizes exploring nodes along each branch as deeply as possible before backtracking, using a stack. BFS explores all nodes at the current depth before moving to the next depth, using a queue.

Graph theory

Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. Key concepts include:
- A graph contains vertices connected by edges or arcs. It can be simple (no loops or multiple edges) or directed (edges have orientations).
- Adjacency matrices and lists are common ways to represent a graph structure and connections between vertices.
- Examples demonstrate simple graphs, directed graphs, complete graphs, and representing graphs using matrices and lists.

Propertiesofexponents

The document discusses scientific notation and operations with exponents:
1) Addition and subtraction require that bases and exponents match, then coefficients are added or subtracted.
2) Multiplication is done by adding the exponents of the same bases.
3) Division is done by subtracting the exponents of the same bases.

Graph

A graph G consists of a set of vertices V connected by edges E. An edge e is represented as an ordered pair of vertices (u,v). Graphs can be directed or undirected. The degree of a vertex is the number of edges incident to it. A path is a sequence of adjacent vertices, while a cycle is a path where the first and last vertices are the same. Graphs can be represented using an adjacency matrix where a 1 indicates an edge and 0 no edge between two vertices.

Double Patterning (4/2 update)

This document discusses double patterning lithography techniques. It introduces how optical lithography is approaching its limits and double patterning is needed for smaller feature sizes. It describes the double patterning process and challenges including feature distortion and decreased yield. The document outlines techniques for polygon cutting, priority search trees, and decomposing conflict graphs into tri-connected components to solve the layout splitting problem. Experimental results on test cases including a 320k polygon design show the method achieves 3-10x speedup.

6.2 Notes

1. The document provides information about properties of parallelograms and examples to practice identifying and using those properties. It defines a parallelogram, lists four key properties, and provides two practice examples asking students to use the properties.
2. Students are assigned homework problems from their textbook on identifying and applying properties of parallelograms. The problems cover identifying parallelograms based on given information, finding missing measures, and proving statements about parallelograms.

Object Recognition with Deformable Models

This document summarizes research on using deformable models for object recognition. It discusses using deformable part models to detect objects by optimizing part locations. Efficient algorithms like dynamic programming and min-convolutions are used for matching. Non-rigid objects are modeled using triangulated polygons that can deform individual triangles. Hierarchical shape models capture shape variations. The document applies these techniques to the PASCAL visual object recognition challenge, achieving state-of-the-art results on 10 of 20 object categories through discriminatively trained, multiscale deformable part models.

RTree Spatial Indexing with MongoDB - MongoDC

Thermopylae Sciences & Technology chose to customize MongoDB's spatial indexing capabilities to better support their needs for indexing multi-dimensional and geospatial data. They developed a custom R-tree spatial index that leverages existing MongoDB data structures and provides improved performance over MongoDB's existing geohash-based approach. Their custom index supports complex queries on multidimensional geometric shapes and scales to large geospatial datasets through potential sharding and distribution techniques. They have contributed their work back to the MongoDB open source project and collaborate with MongoDB to further integrate their contributions.

Surveys

The document discusses cosmological surveys and their history. It provides an overview of several major galaxy surveys from the 1970s-present, including their sky coverage and number of galaxies observed. It also describes techniques for measuring galaxy clustering statistics like the two-point correlation function ξ(r) and power spectrum P(k) from survey data, and methods for estimating errors. Finally, it summarizes the current BOSS survey, which is aiming to constrain dark energy by measuring the baryon acoustic oscillation scale to 1% in distance and 2% in Hubble parameter in two redshift bins.

MinFill_Presentation

This document discusses the minimum fill-in problem for sparse matrices. It begins with an introduction to fill-in that occurs during Gaussian elimination due to the introduction of new non-zero elements. It describes how the minimum fill-in problem is NP-hard and discusses various heuristics to minimize fill-in, including minimum degree ordering and nested dissection. The minimum degree algorithm works by repeatedly eliminating the vertex with minimum degree but does not always produce optimal orderings. The document provides examples to illustrate minimum degree and discusses enhancements like mass elimination to improve its performance.

Lec28

This document discusses graph operations and representations. It provides examples of graph problems including path finding, connectedness problems, and spanning tree problems. It also discusses different representations of graphs like adjacency matrices and adjacency lists. It notes that 12 Java classes would be needed to fully represent directed/undirected weighted/unweighted graphs using adjacency matrices, linked lists, and array-based adjacency lists. It provides an example of an abstract Graph class that can be extended to implement different graph representations and types.

Unit 2: All

Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph consists of vertices and edges connecting pairs of vertices. There are many types of graphs including trees, which are connected acyclic graphs. Spanning trees are subgraphs of a graph that connect all vertices using the minimum number of edges. Key concepts in graph theory include paths, connectedness, cycles, and isomorphism between graphs.

141205 graphulo ingraphblas

This document discusses implementing various graph algorithms using GraphBLAS kernels. It describes how degree filtered breadth-first search, k-truss detection, calculating the Jaccard index, and non-negative matrix factorization can be expressed using operations like sparse matrix multiplication, element-wise multiplication, scaling and reduction. The goal is to demonstrate how fundamental graph problems can be solved within the GraphBLAS framework using linear algebraic formulations of graph computations.

gSpan algorithm

gSpan algorithm

Graphs In Data Structure

Graphs In Data Structure

Graphs In Data Structure

Graphs In Data Structure

Graph data structure

Graph data structure

Skiena algorithm 2007 lecture12 topological sort connectivity

Skiena algorithm 2007 lecture12 topological sort connectivity

Attributed Graph Matching of Planar Graphs

Attributed Graph Matching of Planar Graphs

Lecture 8

Lecture 8

Data Structures - Lecture 10 [Graphs]

Data Structures - Lecture 10 [Graphs]

Graph theory

Graph theory

Propertiesofexponents

Propertiesofexponents

Graph

Graph

Double Patterning (4/2 update)

Double Patterning (4/2 update)

6.2 Notes

6.2 Notes

Object Recognition with Deformable Models

Object Recognition with Deformable Models

RTree Spatial Indexing with MongoDB - MongoDC

RTree Spatial Indexing with MongoDB - MongoDC

Surveys

Surveys

MinFill_Presentation

MinFill_Presentation

Lec28

Lec28

Unit 2: All

Unit 2: All

141205 graphulo ingraphblas

141205 graphulo ingraphblas

“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...

“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Dmitrii Kamaev, PhD
Senior Product Owner - QIAGEN

Generating privacy-protected synthetic data using Secludy and Milvus

During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.

Serial Arm Control in Real Time Presentation

Serial Arm Control in Real Time

Building Production Ready Search Pipelines with Spark and Milvus

Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.

June Patch Tuesday

Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j

Nordic Marketo Engage User Group_June 13_ 2024.pptx

Slides from event

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host

WeTestAthens: Postman's AI & Automation Techniques

Postman's AI and Automation Techniques

GraphRAG for Life Science to increase LLM accuracy

GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Presentation of the OECD Artificial Intelligence Review of Germany

Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm

Energy Efficient Video Encoding for Cloud and Edge Computing Instances

Energy Efficient Video Encoding for Cloud and Edge Computing Instances

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!

TrustArc Webinar - 2024 Global Privacy Survey

How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program

The Microsoft 365 Migration Tutorial For Beginner.pptx

This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/

“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...

“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians

Generating privacy-protected synthetic data using Secludy and Milvus

Generating privacy-protected synthetic data using Secludy and Milvus

Serial Arm Control in Real Time Presentation

Serial Arm Control in Real Time Presentation

Building Production Ready Search Pipelines with Spark and Milvus

Building Production Ready Search Pipelines with Spark and Milvus

June Patch Tuesday

June Patch Tuesday

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Nordic Marketo Engage User Group_June 13_ 2024.pptx

Nordic Marketo Engage User Group_June 13_ 2024.pptx

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays - June 2024

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

WeTestAthens: Postman's AI & Automation Techniques

WeTestAthens: Postman's AI & Automation Techniques

GraphRAG for Life Science to increase LLM accuracy

GraphRAG for Life Science to increase LLM accuracy

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

Presentation of the OECD Artificial Intelligence Review of Germany

Presentation of the OECD Artificial Intelligence Review of Germany

Energy Efficient Video Encoding for Cloud and Edge Computing Instances

Energy Efficient Video Encoding for Cloud and Edge Computing Instances

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Driving Business Innovation: Latest Generative AI Advancements & Success Story

TrustArc Webinar - 2024 Global Privacy Survey

TrustArc Webinar - 2024 Global Privacy Survey

The Microsoft 365 Migration Tutorial For Beginner.pptx

The Microsoft 365 Migration Tutorial For Beginner.pptx

- 1. The 15th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011) 25 May 2011 LGM: Mining Frequent Subgraphs from Linear Graphs Yasuo Tabei ERATO Minato Project Japan Science and Technology Agency joint work with Daisuke Okanohara (Preferred Infrastructure), Shuichi Hirose (AIST), Koji Tsuda (AIST) 1 1
- 2. Outline • Introduction to linear graph ★ Linear subgraph relation ★ Total order among edges • Frequent subgraph mining from a set of linear graphs • Experiments ★ Motif extraction from protein 3D structures 2 2
- 3. Linear graph (Davydov et al., 2004) • Labeled graph whose vertices are totally ordered • Linear graph g = (V, E, L , L ) V E ‣ V ⊂ N : ordered vertex set ‣ E ⊆ V × V : edge set ‣ LV → ΣV : vertex labels ‣L →Σ E E : edge labels Example: c b a a 1 2 3 4 5 6 A B A B C A 3 3
- 4. Linear subgraph relation • g1 is a linear subgraph of g2 i) Conventional subgraph condition ★ Vertex labels are matched ★ All edges of g1 exist in g2 with the correct labels ii) Order of vertices are conserved Example: b b c 1 a 2 3 ⊂ a a 1 2 3 4 5 6 A B A A A B B C A g1 g2 4 4
- 5. Subgraph but not linear subgraph • g1 is a subgraph of g2 ★ vertex labels are matched ★ all edges in g1also exist in g2 with correct labels • g1 is not a linear subgraph of g2 ★ the order of vertices is not conserved b b c a c 1 2 3 1 2 3 4 A A B A A B A g1 g2 5 5
- 6. Total order among edges in a linear graph • Compare the left vertices ﬁrst. If they are identical, look at the right vertices • ∀e1 = (i, j) , e2 = (k, l) ∈ Eg , e1 <e e2 if and only if (i) i < k or (ii) i = k, j < l Example: e1 e2 2 3 1 i j k l 1 2 3 4 6 6
- 7. Outline • Introduction to linear graph ★ linear subgraph relation ★ Total order among edges • Frequent subgraph mining from a set of linear graphs • Experiments ★ Motif extraction from protein 3D structures 7 7
- 8. Frequent subgraph mining from linear graphs • Enumerate all frequent subgraphs from a set of linear graphs ★ Subgraphs included in a set of linear graphs at least τ times (minimum support threshold) ★ Enumerate connected and disconnected subgraphs with a uniﬁed framework ★ Use reverse search for an efﬁcient enumeration (Avis and Fukuda, 1993) • Polynomial delay ★ gSpan = exponential delay 8 8
- 9. Enumeration of all linear subgraph of a linear graph • Before considering a mining algorithm, we have to solve the problem of subgraph enumeration ﬁrst • How to enumerate graph withoutof the following linear all subgraphs duplication 9 9
- 10. Search lattice of all subgraphs !"#$% *+,-+!./!0+12!3!24 & ' ( ) 10 10
- 11. Reverse search (Avis and Fukuda, 1993) • To enumerate all subgraphs without duplication, we need to deﬁne a search tree in the search lattice • Reduction map f ★ Mapping from a child to its parent ★ Remove the largest edge 2 3 f 2 1 1 1 2 3 4 1 2 3 11 11
- 12. Search tree induced by the reduction map • By applying the reduction map to each element, search tree can be induced !"#$% 12 12
- 13. Inverting the reduction map f −1 • When traversing the tree from the root, children nodes are created on demand • In most cases, the inversion of reduction map takes the following two steps: ★ Consider all children candidates ★ Take the ones that qualify the reduction map • However, in this particular case, the reduction map can be inverted explicitly ★ Can derive the pattern extension rule (parent to children) 13 13
- 14. Pattern extension rule 14 14
- 15. Traversing search tree from root • Depth ﬁrst traversal for its memory efﬁciency $&!'()*+!,$'!-+! .!/')--!-'!-+! !"#$% 15 15
- 16. Frequent subgraph mining • Basic idea: ﬁnd all possible extensions of a current pattern in the graph database, and extend the pattern • Occurrence list L G (g) ★ Record every occurrence of a pattern g in the graph database G ★ Calculate the support of a pattern g by the occurrence list !"#$%&'($"" • Usesupport for pruningof the anti-monotonicity )$*+,+- 16 16
- 17. Outline • Introduction to linear graph ★ linear subgraph relation ★ Total order among edges • Frequent subgraph mining from a set of linear graphs • Experiments ★ Motif extraction from protein 3D structures 17 17
- 18. Motif extraction from protein 3D structures • Pairs of homologous proteins in thermophilic organism and mesophilic organism • Construct a linear graph from a protein ★ Use vertex order from N- to C- terminal ★ Assign vertex labels from {1,...,6} ★ Draw an edge between pairs of amino acid residues whose distance is 5Å • # of data:742, avg. # of vertices:371, avg. # of edges: 496 • Rank the enumerated patterns by statistical signiﬁcance (p-value) ★ Association to thermophilic/methophilic labels ★ Fisher exact test 18 18
- 19. Runtime comparison • Compared to gSpan • Made gapped linear graphs and run gSpan • LGM is faster than gSpan 19 19
- 20. • Minimum support = 10 • 103 patterns whose p-value < 0.001 •★Thermophilic (TATA), Mesophilic (pol II) Share the function as DNA binding protein, but the thermostatility is different 20 20
- 21. Mapping motifs in 3D structure • Thermophilic (TATA), Mesophilic (pol II) 21 21
- 22. Summary • Efﬁcient subgraph mining algorithm from linear graphs • Search tree is deﬁned by reverse search principle • Patterns include disconnected subgraphs • Computational time is polynomial-delay • Interesting patterns from proteins 22 22