The document describes a graph traversal algorithm called breadth-first search (BFS) to find the shortest path between two cities in a graph. It uses a queue data structure to keep track of vertices to visit. The algorithm enqueues the starting city, dequeues vertices to explore their neighbors, and backtracks using an origin array when the destination city is found. This allows finding the minimum number of stops between Denver and Washington in the given link representation.
This document discusses skip lists, including:
- What a skip list is and how it is structured as multiple linked lists of increasing sparsity.
- The basic operations of searching, inserting, and deleting in skip lists and how they are performed by traversing the lists.
- Variables needed to implement a skip list class in Java.
- Examples showing the steps to insert and delete elements from a skip list through modifying pointers between nodes.
- Comparisons of the time complexities of basic operations between linked lists and skip lists.
Pistache: A π-Calculus Internal Domain Specific Language for Scalapmatiello
Pistache is an implementation of the pi-calculus as an internal domain specific language for Scala. It provides pi-calculus abstractions for concurrent computation within Scala. In Pistache, agents communicate by exchanging names through channels and connections between agents can change dynamically. It defines agents, names, channels, actions, and composition of agents. Pistache executes agents concurrently using a cached thread pool for efficient execution of large numbers of agents.
I gave this presentation to my local Scala Meetup on Feb 12th 2014. It presents an aspect of functional programming by implementing a lazy data structure for storing an infinite collection of data. The act of building the stream is the point - you wouldn't use this in real life. The design for the stream is largely taken from Manning's "Functional Programming in Scala" by Paul Chiusano and Rúnar Bjarnason.
Have a look at the book at http://www.manning.com/bjarnason/.
The document discusses different types of queues and their properties and operations. It begins with defining a queue as a first-in, first-out (FIFO) linear structure. Real-world examples of queues mentioned include lines at ticket windows and bus stops. Basic queue operations of enqueue and dequeue are described. The document then discusses implementation of queues using arrays, with front and rear pointers. Circular queues and priority queues are also covered.
1. The document discusses stacks and queues as linear data structures. It describes stack operations like push and pop and provides algorithms for implementing these operations.
2. An example program for implementing a stack using an array is presented, with functions defined for push, pop, and display operations on the stack.
3. Applications of stacks discussed include reversing a list or string by pushing characters onto a stack and popping them off in reverse order, and converting infix expressions to postfix using a stack.
08_Queues.pptx showing how que works given vertexSadiaSharmin40
A queue is a waiting line where items are added to the rear and removed from the front. There are different types of queues including linear, circular, double-ended, and priority queues. A double-ended queue allows items to be added or removed from either end, while a priority queue processes elements based on priority, with higher priority elements before lower priority ones.
This document discusses queues and priority queues. It defines a queue as a first-in first-out (FIFO) linear data structure with elements added to the rear and removed from the front. Circular queues are introduced to address the limitation of linear queues filling up. Priority queues are also covered, with elements ordered by priority and the highest or lowest priority element always at the front. Implementation of priority queues using heaps is explained, with insertion and deletion operations having time complexity of O(log n).
This document discusses skip lists, including:
- What a skip list is and how it is structured as multiple linked lists of increasing sparsity.
- The basic operations of searching, inserting, and deleting in skip lists and how they are performed by traversing the lists.
- Variables needed to implement a skip list class in Java.
- Examples showing the steps to insert and delete elements from a skip list through modifying pointers between nodes.
- Comparisons of the time complexities of basic operations between linked lists and skip lists.
Pistache: A π-Calculus Internal Domain Specific Language for Scalapmatiello
Pistache is an implementation of the pi-calculus as an internal domain specific language for Scala. It provides pi-calculus abstractions for concurrent computation within Scala. In Pistache, agents communicate by exchanging names through channels and connections between agents can change dynamically. It defines agents, names, channels, actions, and composition of agents. Pistache executes agents concurrently using a cached thread pool for efficient execution of large numbers of agents.
I gave this presentation to my local Scala Meetup on Feb 12th 2014. It presents an aspect of functional programming by implementing a lazy data structure for storing an infinite collection of data. The act of building the stream is the point - you wouldn't use this in real life. The design for the stream is largely taken from Manning's "Functional Programming in Scala" by Paul Chiusano and Rúnar Bjarnason.
Have a look at the book at http://www.manning.com/bjarnason/.
The document discusses different types of queues and their properties and operations. It begins with defining a queue as a first-in, first-out (FIFO) linear structure. Real-world examples of queues mentioned include lines at ticket windows and bus stops. Basic queue operations of enqueue and dequeue are described. The document then discusses implementation of queues using arrays, with front and rear pointers. Circular queues and priority queues are also covered.
1. The document discusses stacks and queues as linear data structures. It describes stack operations like push and pop and provides algorithms for implementing these operations.
2. An example program for implementing a stack using an array is presented, with functions defined for push, pop, and display operations on the stack.
3. Applications of stacks discussed include reversing a list or string by pushing characters onto a stack and popping them off in reverse order, and converting infix expressions to postfix using a stack.
08_Queues.pptx showing how que works given vertexSadiaSharmin40
A queue is a waiting line where items are added to the rear and removed from the front. There are different types of queues including linear, circular, double-ended, and priority queues. A double-ended queue allows items to be added or removed from either end, while a priority queue processes elements based on priority, with higher priority elements before lower priority ones.
This document discusses queues and priority queues. It defines a queue as a first-in first-out (FIFO) linear data structure with elements added to the rear and removed from the front. Circular queues are introduced to address the limitation of linear queues filling up. Priority queues are also covered, with elements ordered by priority and the highest or lowest priority element always at the front. Implementation of priority queues using heaps is explained, with insertion and deletion operations having time complexity of O(log n).
The document discusses data structures and abstract data types (ADTs). It provides examples of linear lists and linked lists as implementations of the ADT linear list. Key points made include:
- An ADT defines operations on a data structure without specifying its implementation.
- Linked lists provide flexibility for insertion and deletion compared to arrays.
- The main linked list operations are insertion, deletion, finding an element, and traversal.
- Queues and stacks are examples of linear lists with restricted insertion/deletion points.
The document discusses stacks and queues as data structures. It defines a stack as a linear data structure that follows the LIFO principle with insertion and deletion occurring at one end. Key stack operations are described as push, pop, peek, isEmpty and isFull. Queue is defined as a linear structure that follows the FIFO principle with insertion at the rear and deletion at the front. Common queue operations are enqueue, dequeue, peek, isEmpty and isFull. Array and linked list implementations of stacks and queues are also covered.
The document discusses the Breadth-First Search (BFS) algorithm. It begins with an introduction to graph traversal and explains that BFS is a graph traversal technique that explores all nodes layer-by-layer from a starting node. It then provides an example of applying BFS to a binary tree, showing the steps of selecting a starting node, inserting its children into a queue, extracting nodes from the queue and inserting their children, and repeating until the queue is empty. The document concludes by listing some applications of BFS such as for web crawlers, GPS navigation, and finding the minimum spanning tree of an unweighted graph.
A stack is a linear data structure that follows the LIFO (Last In First Out) principle. Elements are added and removed from the top of the stack. Common operations on a stack include push to add an element and pop to remove an element. A stack can be implemented using arrays or pointers in static or dynamic ways respectively. Stacks have applications in reversing words, undo operations, and backtracking in algorithms.
Data Structures and Algorithm AnalysisSpring 2020 Post Midterm Ejeniihykdevara
Data Structures and Algorithm Analysis
Spring 2020 Post Midterm Exam – 100 points + 10 bonus
NAME:
1. How much memory will the following structures take up? Hint: int takes 4 bytes, float takes 4 bytes, char takes 1 byte. – 9 points
a. struct Structure1 {int a,b,c,d,e[10]; float f;};
b. struct Structure2 {int a[12]; float b[5];};
c. struct Structure3 {char a[10][12][4], b[5][5], c[10];};
2. Show the steps when sorting (smallest to largest) the following arrays of numbers using selection sort i.e. every time a swap occurs, show what the current state of the array looks like, including the final sorted array. – 12 points
a. [10, 2, 5, 8, 9, 1, 4, 7]
b. [7, 1, 3, 2, 5, 4, 8, 12, 9]
c. [8, 7, 6, 5, 4, 3, 2, 1]
d. [5, 3, 8, 1, 9, 4, 2, 6]
3. Big-O: What is meant by f(n) = O(g(n))? Give the definition and then explain what it means in your own terms. – 5 points
4. Big-Omega: What is meant by f(n) = Ω(g(n))? Give the definition and then explain what it means in your own terms. – 5 points
5. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
6. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
7. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
8. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
9. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
10. What principle governs how you add/remove elements in a stack? Spell it out and briefly explain. - 4 points
11. Briefly describe an application of a stack. - 4 points
12. What principle governs how you add/remove elements in a queue? Spell it out and briefly explain. - 4 points
13. Briefly describe an application of a queue. - 4 points
Consider the following graph (pseudocode for BFS and DFS given on page 9):
14. Write the order in which the nodes would be visited in when doing a breadth first search (BFS) traversal starting at node 4. Also, write the distances from 4 to every other node. - 6 points
15. Write the order in which the nodes would be visited in when doing a breadth first search (BFS) traversal starting at node 5. Also, write the distances from 5 to every other node. - 6 points
Same graph (for your convenience):
16. Write the order in which the nodes would be visited in when doing a depth first search (DFS) traversal starting at node 4 (order discovered or order off the stack). - 6 points
17. Write the order in which the nodes would be visited in when doing a depth first search (DFS) traversal starting at node 5 (order discovered or order off the stack). - 6 points
18. Give the definition of a graph. - 5 points
19. Give the definition of a tree (from graph theory). - 4 ...
The document discusses different data structures for representing queues and linked lists, including their implementations and operations. Queues follow FIFO ordering and can be implemented using arrays or linked lists. Linked lists allow efficient insertion/removal at both ends and can be used to implement double-ended queues (deques). Deques support efficient insertion/removal from both ends and can implement stacks and queues. Sequences generalize vectors and linked lists, introducing the concept of positions to provide implementation independence.
The document discusses different types of queues including linear queue, circular queue, deque (double ended queue), and priority queue. It defines a queue as a list where items are inserted at the rear and deleted at the front (FIFO). Circular queues overcome limitations of linear queues by making the array circular. A deque supports insertion/deletion from both front and rear. Priority queues process elements based on priority, with higher priority elements processed before lower priority ones. Heaps provide an efficient implementation for priority queues.
The document discusses different types of queues including linear queue, circular queue, and double ended queue (deque). It provides algorithms for common queue operations like insertion and deletion. For linear and circular queues, insertion adds to the rear and deletion removes from the front. Deque allows insertion and deletion from both front and rear, and there are two types - input restricted allows insertion only at rear, output restricted allows deletion only at front.
This document discusses queues as an abstract data structure. It explains that queues are opened at both ends, with one end for insertion (enqueue) and the other for removal (dequeue), following a First-In-First-Out (FIFO) approach. Real-world examples of queues include lines at ticket windows and bus stops. The document then covers queue representation using arrays or linked lists, basic queue operations like enqueue, dequeue, peek, isfull and isempty, and provides pseudocode algorithms for implementing these operations.
The document discusses different types of queues and their implementations. It begins by defining a queue as a first-in first-out (FIFO) data structure where elements are inserted at the rear and deleted from the front. It then covers linear and circular queue implementations using arrays, including operations like insertion, deletion, checking for empty/full, and traversal. Priority queues are also introduced, which process elements based on assigned priorities. The key types and operations of queues as an abstract data type (ADT) are summarized.
Stack and Queue.pptx university exam preparationRAtna29
Queues and stacks are dynamic while arrays are static. So when we require dynamic memory we use queue or stack over arrays. Stacks and queues are used over arrays when sequential access is required. To efficiently remove any data from the start (queue) or the end (stack) of a data structure
A circular queue is a fixed size data structure that follows FIFO (first in, first out) principles. Elements are added to the rear of the queue and removed from the front. When the rear reaches the end, it wraps around to the beginning so the queue space is used efficiently. Common circular queue operations include enqueue to add an element, dequeue to remove an element, and display to output all elements.
Circular Queue is a linear data structure in which the operations are performed based on FIFO (First In First Out) principle and the last position is connected back to the first position to make a circle. It is also called ‘Ring Buffer’.
The document discusses double-ended queues (deques), which are linear data structures that allow efficient insertion and removal of elements from both ends. A deque provides the capabilities of both stacks and queues in a single structure by allowing new elements to be added and existing elements to be removed from either the front or rear. The key operations for a deque are described, including adding and removing elements from the front/rear. Example applications like browser history and undo operations in applications are given to illustrate the use of deques.
The document discusses stacks as a data structure. It defines stacks as last-in, first-out structures where only the top element can be accessed. Common stack operations like push and pop are introduced. Three common implementations of stacks are described - using arrays, vectors, and linked lists. The advantages and disadvantages of each implementation are provided. Examples of stack applications in areas like compilers, operating systems, and artificial intelligence are given.
This document summarizes a non-predictive parser. It is a top-down parser that builds the parse tree from top to bottom starting with non-terminals. It uses recursive procedures to scan the input and may involve backtracking by repeatedly scanning the input. An example grammar and input string are provided to demonstrate the step-by-step process of parsing using a non-predictive parser.
The document discusses stacks and their implementation and applications. Stacks are LIFO data structures that can be implemented using arrays. Key operations on stacks include push, pop, peek, isEmpty and isFull. Stacks have various applications including expression evaluation, recursion, function calls and memory management. Stacks can grow either upwards or downwards in the array.
Data Structures and Algorithm Analysis1. How much memory w.docxrandyburney60861
This document contains questions about data structures and algorithms analysis including:
1) Memory requirements for different C structs
2) Steps for selection sort on sample arrays
3) Definitions of Big-O and Big-Omega notation
4) Proofs analyzing time complexities of algorithms
5) Operations of stacks and queues and examples of applications
6) Breadth-first search and depth-first search on a sample graph
7) Definitions of graphs and trees
8) Pseudocode for breadth-first search and depth-first search algorithms
9) Bonus questions including steps for mergesort and implementing a queue with stacks
The document discusses the stack data structure. A stack is a collection of elements that follow the LIFO (last-in, first-out) principle. Elements can be inserted and removed from the top of the stack only. Stacks have common applications like undo functions in text editors and web browser history. Formally, a stack is an abstract data type that supports push, pop, top, is_empty and length operations. The document provides examples and explanations of stack operations and applications like infix to postfix conversion, expression evaluation, balancing symbols, function calls and reversing a string.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The document discusses data structures and abstract data types (ADTs). It provides examples of linear lists and linked lists as implementations of the ADT linear list. Key points made include:
- An ADT defines operations on a data structure without specifying its implementation.
- Linked lists provide flexibility for insertion and deletion compared to arrays.
- The main linked list operations are insertion, deletion, finding an element, and traversal.
- Queues and stacks are examples of linear lists with restricted insertion/deletion points.
The document discusses stacks and queues as data structures. It defines a stack as a linear data structure that follows the LIFO principle with insertion and deletion occurring at one end. Key stack operations are described as push, pop, peek, isEmpty and isFull. Queue is defined as a linear structure that follows the FIFO principle with insertion at the rear and deletion at the front. Common queue operations are enqueue, dequeue, peek, isEmpty and isFull. Array and linked list implementations of stacks and queues are also covered.
The document discusses the Breadth-First Search (BFS) algorithm. It begins with an introduction to graph traversal and explains that BFS is a graph traversal technique that explores all nodes layer-by-layer from a starting node. It then provides an example of applying BFS to a binary tree, showing the steps of selecting a starting node, inserting its children into a queue, extracting nodes from the queue and inserting their children, and repeating until the queue is empty. The document concludes by listing some applications of BFS such as for web crawlers, GPS navigation, and finding the minimum spanning tree of an unweighted graph.
A stack is a linear data structure that follows the LIFO (Last In First Out) principle. Elements are added and removed from the top of the stack. Common operations on a stack include push to add an element and pop to remove an element. A stack can be implemented using arrays or pointers in static or dynamic ways respectively. Stacks have applications in reversing words, undo operations, and backtracking in algorithms.
Data Structures and Algorithm AnalysisSpring 2020 Post Midterm Ejeniihykdevara
Data Structures and Algorithm Analysis
Spring 2020 Post Midterm Exam – 100 points + 10 bonus
NAME:
1. How much memory will the following structures take up? Hint: int takes 4 bytes, float takes 4 bytes, char takes 1 byte. – 9 points
a. struct Structure1 {int a,b,c,d,e[10]; float f;};
b. struct Structure2 {int a[12]; float b[5];};
c. struct Structure3 {char a[10][12][4], b[5][5], c[10];};
2. Show the steps when sorting (smallest to largest) the following arrays of numbers using selection sort i.e. every time a swap occurs, show what the current state of the array looks like, including the final sorted array. – 12 points
a. [10, 2, 5, 8, 9, 1, 4, 7]
b. [7, 1, 3, 2, 5, 4, 8, 12, 9]
c. [8, 7, 6, 5, 4, 3, 2, 1]
d. [5, 3, 8, 1, 9, 4, 2, 6]
3. Big-O: What is meant by f(n) = O(g(n))? Give the definition and then explain what it means in your own terms. – 5 points
4. Big-Omega: What is meant by f(n) = Ω(g(n))? Give the definition and then explain what it means in your own terms. – 5 points
5. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
6. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
7. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
8. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
9. Show that , make sure you use the definition and justify the inequalities and constants used. - 4 points
10. What principle governs how you add/remove elements in a stack? Spell it out and briefly explain. - 4 points
11. Briefly describe an application of a stack. - 4 points
12. What principle governs how you add/remove elements in a queue? Spell it out and briefly explain. - 4 points
13. Briefly describe an application of a queue. - 4 points
Consider the following graph (pseudocode for BFS and DFS given on page 9):
14. Write the order in which the nodes would be visited in when doing a breadth first search (BFS) traversal starting at node 4. Also, write the distances from 4 to every other node. - 6 points
15. Write the order in which the nodes would be visited in when doing a breadth first search (BFS) traversal starting at node 5. Also, write the distances from 5 to every other node. - 6 points
Same graph (for your convenience):
16. Write the order in which the nodes would be visited in when doing a depth first search (DFS) traversal starting at node 4 (order discovered or order off the stack). - 6 points
17. Write the order in which the nodes would be visited in when doing a depth first search (DFS) traversal starting at node 5 (order discovered or order off the stack). - 6 points
18. Give the definition of a graph. - 5 points
19. Give the definition of a tree (from graph theory). - 4 ...
The document discusses different data structures for representing queues and linked lists, including their implementations and operations. Queues follow FIFO ordering and can be implemented using arrays or linked lists. Linked lists allow efficient insertion/removal at both ends and can be used to implement double-ended queues (deques). Deques support efficient insertion/removal from both ends and can implement stacks and queues. Sequences generalize vectors and linked lists, introducing the concept of positions to provide implementation independence.
The document discusses different types of queues including linear queue, circular queue, deque (double ended queue), and priority queue. It defines a queue as a list where items are inserted at the rear and deleted at the front (FIFO). Circular queues overcome limitations of linear queues by making the array circular. A deque supports insertion/deletion from both front and rear. Priority queues process elements based on priority, with higher priority elements processed before lower priority ones. Heaps provide an efficient implementation for priority queues.
The document discusses different types of queues including linear queue, circular queue, and double ended queue (deque). It provides algorithms for common queue operations like insertion and deletion. For linear and circular queues, insertion adds to the rear and deletion removes from the front. Deque allows insertion and deletion from both front and rear, and there are two types - input restricted allows insertion only at rear, output restricted allows deletion only at front.
This document discusses queues as an abstract data structure. It explains that queues are opened at both ends, with one end for insertion (enqueue) and the other for removal (dequeue), following a First-In-First-Out (FIFO) approach. Real-world examples of queues include lines at ticket windows and bus stops. The document then covers queue representation using arrays or linked lists, basic queue operations like enqueue, dequeue, peek, isfull and isempty, and provides pseudocode algorithms for implementing these operations.
The document discusses different types of queues and their implementations. It begins by defining a queue as a first-in first-out (FIFO) data structure where elements are inserted at the rear and deleted from the front. It then covers linear and circular queue implementations using arrays, including operations like insertion, deletion, checking for empty/full, and traversal. Priority queues are also introduced, which process elements based on assigned priorities. The key types and operations of queues as an abstract data type (ADT) are summarized.
Stack and Queue.pptx university exam preparationRAtna29
Queues and stacks are dynamic while arrays are static. So when we require dynamic memory we use queue or stack over arrays. Stacks and queues are used over arrays when sequential access is required. To efficiently remove any data from the start (queue) or the end (stack) of a data structure
A circular queue is a fixed size data structure that follows FIFO (first in, first out) principles. Elements are added to the rear of the queue and removed from the front. When the rear reaches the end, it wraps around to the beginning so the queue space is used efficiently. Common circular queue operations include enqueue to add an element, dequeue to remove an element, and display to output all elements.
Circular Queue is a linear data structure in which the operations are performed based on FIFO (First In First Out) principle and the last position is connected back to the first position to make a circle. It is also called ‘Ring Buffer’.
The document discusses double-ended queues (deques), which are linear data structures that allow efficient insertion and removal of elements from both ends. A deque provides the capabilities of both stacks and queues in a single structure by allowing new elements to be added and existing elements to be removed from either the front or rear. The key operations for a deque are described, including adding and removing elements from the front/rear. Example applications like browser history and undo operations in applications are given to illustrate the use of deques.
The document discusses stacks as a data structure. It defines stacks as last-in, first-out structures where only the top element can be accessed. Common stack operations like push and pop are introduced. Three common implementations of stacks are described - using arrays, vectors, and linked lists. The advantages and disadvantages of each implementation are provided. Examples of stack applications in areas like compilers, operating systems, and artificial intelligence are given.
This document summarizes a non-predictive parser. It is a top-down parser that builds the parse tree from top to bottom starting with non-terminals. It uses recursive procedures to scan the input and may involve backtracking by repeatedly scanning the input. An example grammar and input string are provided to demonstrate the step-by-step process of parsing using a non-predictive parser.
The document discusses stacks and their implementation and applications. Stacks are LIFO data structures that can be implemented using arrays. Key operations on stacks include push, pop, peek, isEmpty and isFull. Stacks have various applications including expression evaluation, recursion, function calls and memory management. Stacks can grow either upwards or downwards in the array.
Data Structures and Algorithm Analysis1. How much memory w.docxrandyburney60861
This document contains questions about data structures and algorithms analysis including:
1) Memory requirements for different C structs
2) Steps for selection sort on sample arrays
3) Definitions of Big-O and Big-Omega notation
4) Proofs analyzing time complexities of algorithms
5) Operations of stacks and queues and examples of applications
6) Breadth-first search and depth-first search on a sample graph
7) Definitions of graphs and trees
8) Pseudocode for breadth-first search and depth-first search algorithms
9) Bonus questions including steps for mergesort and implementing a queue with stacks
The document discusses the stack data structure. A stack is a collection of elements that follow the LIFO (last-in, first-out) principle. Elements can be inserted and removed from the top of the stack only. Stacks have common applications like undo functions in text editors and web browser history. Formally, a stack is an abstract data type that supports push, pop, top, is_empty and length operations. The document provides examples and explanations of stack operations and applications like infix to postfix conversion, expression evaluation, balancing symbols, function calls and reversing a string.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
1. University of Science &
Technology , Chittagong
Submitted to Submitted by
Sukanta Paul Name: PIKU DAS
Lecturer Roll No: 17010110
Department of CSE Batch No:29th
2. Graph Traverse
• Graph traversal means visiting every vertex and edge exactly once in a
well-defined order. While using certain graph algorithms, you must
ensure that each vertex of the graph is visited exactly once.
During a traversal , it is important that you track which vertices
have been Visited . The most common way of tracking vertices is to
mark them
3. Breadth First Search(BFS)
• There are many ways to traverse graphs . BFS is the most commonly
used approach .
BFS is a traversing algorithm where you should start traversing from a
selected node and traverse the graph layer-wise or you should visit
both Children of a node.
4. Task
• Our task is to write a procedure with input City Denver to City
Washington from the given Link Representation which will find the
shortest stops.
The Link Representation is given below
5. Link Representation
CITY NO CITY LEFT RIGHT ADJ
1 Atlanta 0 2 12
2 Boston 0 0 1
3 Houston 0 0 14
4 Ney York 3 8 4
5 6
6 0
7 Washington 0 0 10
8 Philadelphia 0 7 6
9 Denver 10 4 8
10 Chicago 1 0 2
8. Search Process
• Here we want the minimum number of stops between Denver to
Washington .
• This can be found by using BFS process beginning at Denver and
ending when Washington is encountered.
• From the given Link Representation we can draw a graph which will
notify the path of Denver to Washington , and their adjacency.
10. Adjacency
CITY ADJ
Denver New York ,Houston , Atlanta
Chicago Boston
Atlanta Washington , Denver
Boston Chicago , New York
New York Boston , Philadelphia , Denver
Houston Denver
Philadelphia New York ,Washington
Washington Philadelphia ,Atlanta
11. Steps
• Here we will use a data structure called Queue , where insertion
starts in Rear and deletion in Front.
• We will also keep track the origin of each node using an array called
origin.
• Step 1:
Initially add city Denver to Queue and add null to orig
Front =1 Queue : Denver
Rear =1 Origin : 0
12. Steps
• Step 2:
Remove the front element Denver and set
Front = Front + 1,
And add t0 Queue the child of Denver.
Front:2 Queue : Denver, New
York ,Houston,
Atlanta
Rear : 4 origin : 0,Denver,Denver
Denver
CITY ADJ
Denver New –York , Houston ,
Atlanta
Chicago Boston
Atlanta Washington , Denver
Boston Chicago , New York
New York Boston , Philadelphia ,
Denver
Houston Denver
Philadelphia New York ,Washington
Washington Philadelphia ,Atlanta
13. Steps
• Step 3:
Remove the front element New York and set
Front = Front + 2,
And add t0 Queue the child of New
York.
Front: 3 Queue : Denver, New
York ,Houston,
Atlanta , Boston , Philadelphia
Rear : 6 origin : 0,Denver,Denver
Denver , New York ,
New York
CITY ADJ
Denver New –York , Houston ,
Atlanta
Chicago Boston
Atlanta Washington , Denver
Boston Chicago , New York
New York Boston , Philadelphia ,
Denver
Houston Denver
Philadelphia New York ,Washington
Washington Philadelphia ,Atlanta
14. Steps
As child of Houston, Denver is already in
Queue , we need not add in Queue
Front: 4 Queue : Denver, New
York ,Houston,
Atlanta , Boston,
Philadelphia
Rear : 6 origin:0 , Denver
Denver, Denver, New-
York , New York
CITY ADJ
Denver New –York , Houston ,
Atlanta
Chicago Boston
Atlanta Washington , Denver
Boston Chicago , New York
New York Boston , Philadelphia ,
Denver
Houston Denver
Philadelphia New York ,Washington
Washington Philadelphia ,Atlanta
15. Steps
Step 4: Remove front element
Atlanta and add the child of Atlanta
Front: 4 Queue : Denver, New
York ,Houston,
Atlanta , Boston,
Philadelphia, Washington
Rear : 6 origin: 0 , Denver
Denver, Denver, New-
York , New York , Atlanta
CITY ADJ
Denver New –York , Houston ,
Atlanta
Chicago Boston
Atlanta Washington , Denver
Boston Chicago , New York
New York Boston , Philadelphia ,
Denver
Houston Denver
Philadelphia New York ,Washington
Washington Philadelphia ,Atlanta
16. • As Washington is added to the Queue we have to stop.
• We now backtrack from Washington using the array origin to find the
shortest stops
Washington Atlanta Denver
So this is the shortest way to get minimum stops from city Denver
to Washington.
21. Source Code
void Dequeue(Queue *Q)
{
/* If Queue size is zero then it is empty. So we cannot pop */
if(Q->size==0)
{
printf("Queue is Emptyn");
return;
}
23. Source Code
int Front(Queue *Q)
{
if(Q->size==0)
{
printf("Queue is Emptyn");
exit(0);
}
/* Return the element which is at the front*/
return Q->elements[Q->front];
}
24. Source Code
void Enqueue(Queue *Q,int element)
{
/* If the Queue is full, we cannot push an element into it as there
is no space for it.*/
if(Q->size == Q->capacity)
{
printf("Queue is Fulln");
}
25. Source Code
• else
{
Q->size++;
Q->rear = Q->rear + 1;
/* As we fill the queue in circular fashion */
if(Q->rear == Q->capacity)
{
Q->rear = 0;
}
/* Insert the element in its rear side */
Q->elements[Q->rear] = element;
}
return;
}
26. Source Code
void Bfs(int graph[][maxVertices], int *size, int presentVertex,int *visited)
{
visited[presentVertex] = 1;
/* Iterate through all the vertices connected to the presentVertex and perform bfs on those
vertices if they are not visited before */
Queue *Q = CreateQueue(maxVertices);
Enqueue(Q,presentVertex);
while(Q->size)
{
presentVertex = Front(Q);
printf("Now visiting vertex %dn",presentVertex);
Dequeue(Q);
28. Source Code
int main()
{ int
graph[maxVertices][maxVertices],size[maxVertices]={0},visited[maxVert
ices]={0};
int vertices,edges,iter;
/* vertices represent number of vertices and edges represent
number of edges in the graph. */
scanf("%d%d",&vertices,&edges);
int vertex1,vertex2;
31. Rough Notes about the Algorithm
• Input Format: Graph is directed and unweighted. First two integers
must be number of vertices and edges which must be followed by
pairs of vertices which has an edge between them.
• maxVertices represents maximum number of vertices that can be
present in the graph.
• vertices represent number of vertices and edges represent number of
edges in the graph.
32. Rough Notes about the Algorithm
graph[i][j] represent the weight of edge joining i and j.
size[maxVertices] is initialed to{0}, represents the size of every vertex
i.e. the number of
• edges corresponding to the vertex.
• visited[maxVertices]={0} represents the vertex that have been visited.
33. Rough Notes about the Algorithm
• The Queue has five properties -
• 1. createQueue function takes argument the maximum number of
elements the Queue can hold, creates a Queue according to it and
returns a pointer to the Queue. It initializes Q- >size to 0, Q->capacity
to maxElements, Q->front to 0 and Q->rear to -1.
• 2. enqueue function - This function takes the pointer to the top of the
queue Q and the item (element) to be inserted as arguments. Check
for the emptiness of queue
34. Rough Notes about the Algorithm
• a. If Q->size is equal to Q->capacity, we cannot push an element into
Q as there is no space for it.
• b. Else, enqueue an element at the end of Q, increase its size by one.
Increase the value of Q->rear to Q->rear + 1. As we fill the queue in
circular fashion, if Q->rear is equal to Q->capacity make Q->rear = 0.
Now, Insert the element in its rear side Q>elements[Q->rear] =
element.
35. Rough Notes about the Algorithm
• 3. dequeue function - This function takes the pointer to the top of the
stack S as an argument.
If Q->size is equal to zero, then it is empty. So, we cannot dequeue.
Else, remove an element which is equivalent to incrementing index
of front by one. Decrease the size by 1. As we fill elements in circular
fashion, if Q->front is equal to Q->capacity make Q->front=0.
36. Rough Notes about the Algorithm
• 4. front function – This function takes the pointer to the top of the
queue Q as an argument and returns the front element of the queue
Q. It first checks if the queue is empty
• (Q->size is equal to zero). If it’s not it returns the element which is at
the front of the queue.
• Q->elements[Q->front]