a presentation covering all the topics on Stacks and Queues in Data Structures using C++. Includes self explanatory diagrams, algorithms and program pieces.
a presentation covering all the topics on Stacks and Queues in Data Structures using C++. Includes self explanatory diagrams, algorithms and program pieces.
What is Stack, Its Operations, Queue, Circular Queue, Priority QueueBalwant Gorad
Explain Stack and its Concepts, Its Operations, Queue, Circular Queue, Priority Queue. Explain Queue and It's Operations
Data Structures, Abstract Data Types
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
STACK ( LIFO STRUCTURE) - Data StructureYaksh Jethva
Stack which is known as LIFO structure.Which is type of the Linear data structure and it is Non-Primitive data structure.
Definition:Non primitive data structure are not a basic data structure and depends on other primitive data structure (Integer,float etc).
Non primitive data structure can't be operated by machine level instruction directly.
Data Structure- Stack operations may involve initializing the stack, using it and then de-initializing it. Apart from these basic stuffs, a stack is used for the following two primary operations −
PUSH, POP, PEEP
Please review my code (java)Someone helped me with it but i cannot.pdffathimafancyjeweller
Please make it to clear 12-95. The basketball passed through the hoop even though it barely
cleared the hands of the player B who attempted to block it. Neglecting the size of the ball,
determine the magnitude vn of its initial velocity and the height h of the ball when it passes over
player B 10 ft 5 ft Prob. 12-95
Solution
Consider the motion along horizontal direction
Vox = initial velocity in horizontal direction = Va Cos30
X = horizontal distance = 25 + 3 = 28 ft
t = time taken
t = X / Vox = 28/(Va Cos30) eq-1
consider the motion in vertical direction
Y = vertical displacement = 10 - 7 = 3 ft
a = acceleration = - 32.2
Voy = initial velocity = Va Sin30
t = time taken
using the equation
Y = Voy t + (0.5) a t2
3 = (Va Sin30) (28/(Va Cos30)) + (0.5) (- 32.2) (28/(Va Cos30))2
Va = 35.8 m/s
t\' = time taken to reach B = 25 / (Va Cos30) = 25 / (35.8 Cos30) = 0.81 sec
h = 7 + Voy t\' + (0.5) a t\'2
h = 7 + (35.8 Sin30) (0.81) + (0.5) (- 32.2) (0.81)2
h = 10.94 ft.
What is Stack, Its Operations, Queue, Circular Queue, Priority QueueBalwant Gorad
Explain Stack and its Concepts, Its Operations, Queue, Circular Queue, Priority Queue. Explain Queue and It's Operations
Data Structures, Abstract Data Types
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
STACK ( LIFO STRUCTURE) - Data StructureYaksh Jethva
Stack which is known as LIFO structure.Which is type of the Linear data structure and it is Non-Primitive data structure.
Definition:Non primitive data structure are not a basic data structure and depends on other primitive data structure (Integer,float etc).
Non primitive data structure can't be operated by machine level instruction directly.
Data Structure- Stack operations may involve initializing the stack, using it and then de-initializing it. Apart from these basic stuffs, a stack is used for the following two primary operations −
PUSH, POP, PEEP
Please review my code (java)Someone helped me with it but i cannot.pdffathimafancyjeweller
Please make it to clear 12-95. The basketball passed through the hoop even though it barely
cleared the hands of the player B who attempted to block it. Neglecting the size of the ball,
determine the magnitude vn of its initial velocity and the height h of the ball when it passes over
player B 10 ft 5 ft Prob. 12-95
Solution
Consider the motion along horizontal direction
Vox = initial velocity in horizontal direction = Va Cos30
X = horizontal distance = 25 + 3 = 28 ft
t = time taken
t = X / Vox = 28/(Va Cos30) eq-1
consider the motion in vertical direction
Y = vertical displacement = 10 - 7 = 3 ft
a = acceleration = - 32.2
Voy = initial velocity = Va Sin30
t = time taken
using the equation
Y = Voy t + (0.5) a t2
3 = (Va Sin30) (28/(Va Cos30)) + (0.5) (- 32.2) (28/(Va Cos30))2
Va = 35.8 m/s
t\' = time taken to reach B = 25 / (Va Cos30) = 25 / (35.8 Cos30) = 0.81 sec
h = 7 + Voy t\' + (0.5) a t\'2
h = 7 + (35.8 Sin30) (0.81) + (0.5) (- 32.2) (0.81)2
h = 10.94 ft.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
4. stack
Stack is a data structure that stores data
items. Data items when removed follow a
particular order i.e. the item that was
added to stack most recently will be
removed first (Last In First Out)
5. stack
Stack items cannot be accessed randomly regardless
of underlying data storage e.g. Array, Linked List
A stack can be imagined as a cylinder with one end
closed
All items can be added and/or removed from same
end (that is open)
Adding an item onto stack is called ‘push operation’
Removing an item from stack is called ‘pop operation’
7. Stack Operations …
Operation Description Pseudocode
int inspect_top(); Returns element at ‘top’
with removing it from Stack
If stack is non-empty, return the element immediately
beneath the ‘top’
Return -1 (indicator of Stack being empty) otherwise
void push(int); Adds the new element onto
the Stack
If Stack is full, mention it someway
Otherwise
Add new element to current position of ‘top’
Proceed ‘top’ position by 1
int pop(); Remove and return the
element at ‘top’ from Stack
If Stack is empty, mention it someway
Otherwise
Decrease ‘top’ by 1 (making currently top
element inaccessible for sub-sequent calls)
Return element at ‘top’ position
8. Stack using array … implementation
Implementation details and issues have been discussed during
class
10. Stack Operations …
Operation Description Pseudocode
StackUsingList() A constructor that creates an
underlying empty linked list
Create new instance of underlying linked list as ‘data’
int size(); Returns the number of
elements in the stack
Uses size() function from linked list instance ‘data’ to
return the number of elements in stack
int is_empty(); Returns TRUE if Stack
contains no element. It
returns FALSE otherwise.
Uses is_empty() function from underlying linked
list instance ‘data’ to return whether stack is empty
or not
int inspect_top() Returns the stack’s top
element without removing it
If ‘start’ of ‘data’ points to NULL
It returns -1
Otherwise
It returns value of data item in ‘start’
11. Stack Operations …
Operation Description Pseudocode
void push(int key); Adds the new element
onto the Stack
Creates new node using ‘key’
Adds it to ‘start’ of underlying linked list setting the
next pointer appropriately
int pop(); Remove and return the
element at start of
underlying list
If Stack is empty, return -1
Otherwise
Create a new node pointer ‘temp’ and point it
to start of underlying list
Move ‘start’ of underlying list to one step
forward
Set next of temp to NULL
Return data value of temp;
12. Stack using list … implementation
Implementation details and issues have been discussed during
class
13. Stack Operations(algorithms)
{
Repeat while true
[Menu option]
Write(“1: push”)
Write(“2: pop”)
Write(“3: peep top”)
Write(“4: peep stack”)
Write(“5: exit”)
[selection of option]
Selsct option opt
Option 1:
Write “Enter Value ”
Read value
Push(value)
Option 2:
Pop()
14. Option 3:
Peak top ()
Option 4:
Peak stack()
Option 5:
Exit
[end of selection option ]
[end of loop of step 1]
15. Push(value)
[check for stack overflow]
1. If Top== N
2. Write(“stack is full”)
3. Resturn
4. End if
5. Top =Top+1
6. Stack[Top]=value
7. Return
16. Pop ()
[check for stack underflow]
1. If Top==0
2. Write (“Stack is empty”)
3. Return
4. End if
5. Value = Stack[Top]
6. Delete Stack[Top]
7. Top =Top-1
8. Write (“Deleted value is ”, value)
9. Return
17. Peak Stack/ Stack Traversal
1. [check for underflow ]
if Top==0 then
write (“stack is empty”)
Return
end if
2. A=Top
3. Repeat while A>0
4. Write Stack[A]
5. A=A-1
[End of Loop]
6. Return
24. Pop()
1. [check for underflow]
If( top == -1) then
Write(“stack underflow”)
Return;
[End of if structure]
2. Temp = top ;
3. Top = top -> link
4. Write(“Deleted value is ”);
5. Delete (temp)
6. Return ;
25. Peak stack()
1. [check underflow]
If (top = 0) then
Write(“underflow’’)
Return;
[ End of if structure]
2. A = top
Repeat while (A< top)
Write(top[A])
A = A+1
[End of loop structure]
3. Return;
26. Peak top()
1. [check for underflow]
If(top == -1)
Write(“stack is empty”)
Return ;
[end of if structure]
2. Write (stack[top])
3. Return ;
27. Stack implementations
Array Implementation Linked List Implementation
Stack is empty when ‘top’ is equal to ZERO Stack is empty if underlying list’s ‘start’ points
to NULL
Static array with fixed size i.e. is_full() can be
implemented
Dynamic data structure i.e. elements are
allocated memory on runtime
A variable ‘top’ is required to keep track of
number of elements in stack i.e. constant time
execution
A linear time function size() returns the
number of elements in Stack
‘top’ keeps track of position where elements
are being pushed / popped from Stack
‘start’ of underlying linked list keeps track of
position where elements are pushed / popped
28. Stack uses
Stack is used by operating systems to implement function calls
Recursive functions can be converted into non-recursive functions using
stacks
Stacks can be used to evaluate mathematical expressions in postfix form
Undo mechanism in text editors make use of stack