The significance of higher-order ... procedures is that they enable us to represent procedural abstractions explicitly as elements in our programming language, so that they can be handled just like other computational elements.
This document discusses using MATLAB to calculate Laplace transforms and inverse Laplace transforms. It provides examples of:
1) Using the laplace command to calculate the Laplace transform of a function and simplify the result.
2) Using the ilaplace command to calculate the inverse Laplace transform and express the result as a time-domain function.
3) How to rewrite inverse Laplace transform results involving hyperbolic sine and cosine functions in terms of exponentials.
This document discusses composite functions. It defines a composite function as when a number or letter is affected by more than one function. It provides examples of combining different functions, such as f(x) = 3x and g(x) = x^2, and explains that the order matters as f(g(x)) is not the same as g(f(x)). The document then shows how to write composite functions using formulas, such as if f(x)= 2x + 1 and g(x) = 4x, then f(g(x)) = 2(4x) + 1 = 8x + 1 and g(f(x)) = 4(2x + 1) = 8x
The document provides examples of calculating slopes using the difference quotient formula for various functions including:
1) A quadratic function to find the slope of the cord between two points.
2) Simplifying the difference quotient for a quadratic, rational, and root function.
3) Using the difference quotient formula to find the slope of a cord between two points for a given quadratic function.
Equation of a straight line y b = m(x a)Shaun Wilson
1. The document discusses finding the equation of a straight line given two points or the gradient and a point.
2. It provides the formula for finding the equation of a line as y - b = m(x - a), where m is the gradient, (a, b) is a point on the line.
3. An example finds the equation of the line connecting points A(2, 6) and B(5, 8) using the formula, getting the equation y = 2/3x + 14/3.
The document contains a mathematical expression involving square roots, division, addition, subtraction and exponents. Variables a and b appear under square roots and in denominators, along with exponents on constants 3 and x+1. The expression combines multiple operations in a single line of notation.
The document discusses key concepts in coordinate geometry, including:
1) How to calculate the distance between two points using their coordinates. The distance formula is given as the square root of the sum of the squared differences between the x- and y-coordinates.
2) How to find the midpoint between two points by taking the average of their x- and y-coordinates. The midpoint formula is given.
3) How to find a point that divides a line segment between two end points in a given ratio of distances, using a formula that involves the x- and y-coordinates and the ratio. Examples of each concept are worked out.
This document defines mathematical concepts related to sets including:
1. Types of sets such as finite, infinite, empty sets.
2. Set operations including union, intersection, complement, difference.
3. Notations for representing sets in tubular and set builder forms.
4. Relationships between sets such as equal sets, equivalent sets, and the power set of a set.
5. Common sets of numbers such as natural numbers, integers, rational numbers.
To find the sum of two functions f and g, add the corresponding terms of f(x) and g(x). To find the difference, subtract the terms of g(x) from f(x), distributing the negative sign. To find the product, multiply corresponding terms using FOIL. To find the quotient, divide the functions. The composition f o g means to substitute g(x) for each x in f(x). The domain of a composition is numbers where g(x) is in the domain of f.
This document defines common symbols used in set theory. It lists symbols such as curly brackets { } to denote a set, union (∪), intersection (∩), subset (⊆), proper subset (⊂), not a subset (⊄), superset (⊇), proper superset (⊃), not a superset (⊅), complement (c), difference (-), element of (∈), not element of (∉), empty set (Ø), power set (P(A)), equality (=), cardinality (|A|), such that (|), for all (∀), there exists (∃), therefore (∴) and defines types of numbers like natural
This document contains terminology and examples related to functions. It defines f(x) as "the value of y in function f, where the independent variable is x". It provides examples of evaluating functions like f(x) = -7x + 100 for different x values. It also gives examples of composite functions like f(f(x)) and using different functions like f(x) and g(x) together. The examples relate to word problems about distances over time on a boat trip.
The document discusses the Pythagorean theorem and distance formula. The Pythagorean theorem states that for any right triangle, the sum of the squares of the two sides equals the square of the hypotenuse. The distance formula allows you to calculate the distance between two points in the xy-plane by taking the square root of the sum of the squared differences between the x-coordinates and y-coordinates. An example using points (2,4) and (7,13) demonstrates applying the distance formula to get a distance of 10.3 units between the two points.
The document provides information about the height of a ball thrown upwards over time. It can be summarized as:
1) The height of a ball t seconds after being thrown is given by an equation involving t and the ball's initial upward velocity.
2) The document asks questions about determining the ball's starting height, initial velocity, whether its height was increasing or decreasing at a given time, and the time and height at which it reached its maximum.
3) It also provides information about the derivative of a function and asks questions relating the derivatives of related functions.
This document discusses evaluating expressions involving functions using addition, subtraction, multiplication, division, and composition. It provides examples of defining functions f(x) and g(x) and evaluating expressions like f(3) - g(-1) and 2f(1). Tables are given showing the evaluation of functions like f(x), g(x) and others based on their definitions. Composition is described as taking the output of one function as the input of another, written as f(g(x)).
This document contains Python code to analyze text and find word frequencies. It cleans the input text, splits it into individual words, sorts the words, counts the frequency of each word, and organizes the words and counts into lists grouped by starting letter. The code then prints out the lists grouped by each letter with the words and their associated counts.
This document summarizes the steps to approximate the value of an integral using the trapezoid method with n=4. Specifically:
- The integral is from -3 to 5 of (4x+5)^3.
- The trapezoid method formula is used with h=(b-a)/n, where h=2.
- Plugging the function into the trapezoid method formula results in an approximation of 26568.
- Expanding the function as a cubic binomial results in an approximation of 27515.
1. The document contains 30 multiple choice questions about limits, functions, and graphs. Questions cover topics like domains, ranges, limits, periodicity, symmetry, and functional equations.
2. Several questions involve greatest integer functions, fractional parts of numbers, trigonometric functions like sine, cosine, and tangent, and inverse trigonometric functions.
3. Correct answers are requested for questions involving evaluating limits, determining properties of functions, finding function values, and identifying function domains and ranges.
The document contains information about functions, including:
1) Two tables that represent different functions - y as a function of x, and distance as a function of time.
2) Examples of evaluating functions f(x) at different values of x.
3) Definitions and examples of addition, subtraction, multiplication, and division of functions.
4) Examples of combining two functions using addition, subtraction, and multiplication.
Variables, Expressions, and the Distributive Propertyleilanidediosboon
1. The document shows 5 math problems involving distributing terms in expressions.
2. It then shows the step-by-step work and solutions for distributing and simplifying the expressions.
3. The rest of the document explains and provides examples of using the area model and distributive property to multiply expressions like 7(98) and 2(5x + 3). Rectangles are drawn to represent multiplying the first term over the grouped terms in parentheses.
The document provides a step-by-step guide for finding the vertex of parabolic functions by completing the square. It gives two examples, finding that the vertex of f(x)=x^2 -4x+3 is (2,-1) and the vertex of f(x)=-2x^2 -2x+1 is (-1/2,-1). Completing the square involves factoring the quadratic term and rearranging constants to put the function in vertex form f(x)=a(x-h)^2 + k, where (h,k) gives the vertex coordinates.
All isotopes of an element have the same number of protons. Isotopes are forms of an element that have different numbers of neutrons. Isotopes are named by writing the element name followed by the mass number, which is the number of protons plus neutrons. The average atomic mass shown for an element on the periodic table takes into account the relative abundance of all its isotopes.
constant strain triangular which is used in analysis of triangular in finite element method with the help of shape function and natural coordinate system.
The purchase of assets from distressed entities typically begins with the submission of a term sheet to the debtor, followed by due diligence on both the asset and the distressed entity itself.
Cascading - A Java Developer’s Companion to the Hadoop WorldCascading
Presentation by Dhruv Kumar, Sr. Field Engineer at Concurrent.
Amid all the hype and investment around Big Data technologies, many Java software engineers are asking what it takes to become big data engineers. As Java professionals, towards which path shall I steer my career?
Join Dhruv Kumar as he introduces Cascading, an open source application development framework that allows Java developers to build applications on top of Hadoop through its Java API. We’ll provide an overview of the application development landscape for developing applications on Hadoop and explain why Cascading has become so popular, comparing it to other abstractions such as Pig and Hive. Dhruv will also show you how Java developers can easily get started building applications on Hadoop with live examples of good ‘ole Java code.
This document provides an overview of the finite element method (FEM). It discusses the potential energy approach, discretization, boundary conditions, strain-displacement relationships, stress-strain behaviors, element and global stiffness matrices, and solution schemes for structural analysis problems. It also covers FEM terminology and concepts such as nodes, elements, and iterative methods for solving systems of linear equations. Finally, it notes some limitations of the FEM.
This document outlines standard operating procedures for regulatory affairs in the pharmaceutical industry in India. It discusses maintaining proper documentation for clinical trials, including investigator files, informed consent forms, and regulatory forms like the FDA Form 1572. It provides the order that documents should be filed and stored. It also describes the regulatory approval process and requirements for medical devices in India through the Central Drugs Standard Control Organisation.
This document discusses seven steps for achieving customer success at scale for software as a service (SaaS) companies. It begins by reviewing the current state of the SaaS business model and profitability challenges. It then outlines four cost buckets that impact profitability: cost of goods sold, customer acquisition costs, customer expansion costs, and customer retention costs. The document proposes that customer success is critical to long-term profitability. It provides seven steps for customer success at scale, including establishing a charter, financial model, critical practices, success metrics, skills, offers, and technology to support the customer lifecycle from adoption to renewal to expansion.
A critical guide to selecting metrics to define a data-driven customer success strategy. Here is the table of contents:
- Metrics are for Decisions
- The Nature of Metrics
- Metrics Can Be Difficult
- Customer Success Metrics
Customer Lifetime Value (CLV)
Customer Churn Rate
Net Promoter Score (NPS)
Customer Health Score
Support Ticket Volume
Customer Log-in Counts Customer Acquisition Cost (CAC)
Product Activity Score
CSM Subjective Score
Customer Newsletter CTR
Background Signals
This standard operating procedure establishes a uniform process for collecting wastewater samples for compliance with permit requirements. It outlines safety gear for samplers, proper handling of preservatives, and ensuring representative samples by avoiding contamination. The SOP specifies collecting the required type of sample, parameters to analyze based on permit limits, preparing samples for shipping to the laboratory, and documentation and data reporting procedures.
The document outlines Rincon Company's customer success strategy. It will focus on later stages of the customer lifecycle like engagement, adoption, and renewal. Key aspects include implementing a health score to measure adoption effectiveness, standardizing CLV and churn calculations, allocating 12% of ACV to customer retention costs, and developing tailored playbooks for different customer segments. Barriers customers may face and ways to address them at each stage are identified. The customer success team structure with roles for client services, support, and success managers is also presented.
Standard Operating Procedure (SOP) for Information Technology (IT) OperationsRonald Bartels
Title of SOP
Dates
Issue date
Effective date
Document history
Approvals
Description
Purpose and background
Scope
Definitions
Operations
Maintenance
Projects
Business justification and project request form
Project Lite methodology (mini projects)
Large projects
Fulfilment
Example - Video conferencing
Quality and targets
Vital functions affected by this SOP
Lessons learned
Record and Document Management
References
Standards
Images
Diagrams
Equipment, hardware and software lists
Labelling and naming standards
Checklists
Installation
Configuration
Testing
Financial
Budget exception / deviation
Risk
The CRAMM Risk management methodology
Meerkat Risk Methodology
Information Security
Physical security
Service Continuity
Risk evaluation and control
Business impact analysis
Develop continuity strategies
Emergency response and operations
Developing and implementing the BCP
Awareness and training program
Maintaining and exercising the BCP
Standards and guidelines
Escalations
Roles and responsibilities
The Uberfingers team leaders dashboard
Shifts
Training
Monitoring requirements
Change
Stakeholders
Request for change
Apply for testing
Configuration management database
Impact and risk assessment
Change Advisory Board (CAB)
Installation in testing
Test installation review
Testing in progress
Operational acceptance phase
Ready for live
Implementation in live
Go Live acceptance
Live
Integration with Service Desk
Change types
Vendors
Review and evaluation of vendors
Maintenance
Warranty
Handling Incidents and Troubleshooting procedures
The Expanded Incident Lifecycle
Service review
Meetings
Previous period
Performance review
Current issues
Peripheral issues
Grading of service desk interaction
Grading of service desk escalation
Checklist for SOP
Addendum
Service catalogue
This document provides guidance on developing standard operating procedures (SOPs). It defines an SOP as written instructions that document a routine activity. The development and use of SOPs promotes consistency and minimizes variation. SOPs should describe technical and administrative procedures in a clear, step-by-step format. They must be reviewed periodically to ensure the procedures are current. Analytical SOPs specifically describe laboratory methods and require additional elements like applicable analytes and quality control measures.
(i) A contract of sale is an agreement where the seller transfers ownership of movable goods to the buyer for a price.
(ii) It requires two parties, goods as the subject matter, transfer of property, and a price to be valid.
(iii) The contract can be for existing goods or future goods, and includes conditions, warranties, and the doctrine of caveat emptor (let the buyer beware), with exceptions for misrepresentation, unmerchantability, or unfitness of goods.
This topic is about the management of human resource in a efficient way for the betterment of an organization and how it can be used to stabilize and economically power the employee as well as the organization.
Pycon 2011 talk (may not be final, note)c.titus.brown
This document discusses handling large amounts of genomic data using probabilistic data structures like Bloom filters. Bloom filters allow storing and querying large amounts of genomic sequence data in a memory-efficient way. They can be used to assemble short DNA sequences, reduce graph complexity, and trim errors from assemblies. The approach works well for pre-filtering large metagenomic datasets, enabling assembly of 200GB datasets using a single machine.
PyCon 2011 talk - ngram assembly with Bloom filtersc.titus.brown
This document summarizes a talk about using probabilistic data structures like Bloom filters to handle large genomic and sequencing datasets. Bloom filters allow storing and querying enormous numbers of DNA fragments and sequences in a way that is memory efficient and scales to very large datasets. The talk describes how Bloom filters can be used to assemble genomes and reduce complexity in assembly graphs. While not perfect representations, Bloom filters enable genomic assembly and analysis that would otherwise not be possible given the volume of data.
GE8151 Problem Solving and Python ProgrammingMuthu Vinayagam
The document provides information about various Python concepts like print statement, variables, data types, operators, conditional statements, loops, functions, modules, exceptions, files and packages. It explains print statement syntax, how variables work in Python, built-in data types like numbers, strings, lists, dictionaries and tuples. It also discusses conditional statements like if-else, loops like while and for, functions, modules, exceptions, file handling operations and packages in Python.
MATLAB DOCUMENTATION ON SOME OF THE MODULES
A.Generate videos in which a skeleton of a person doing the following Gestures.
1.Tilting his head to right and left
2.Tilting his hand to right and left
3.Walking
in matlab.
B. Write a MATLAB program that converts a decimal number to Roman number and vice versa.
C.Using EZ plot & anonymous functions plot the following:
· Y=Sqrt(X)
· Y= X^2
· Y=e^(-XY)
D.Take your picture and
· Show R, G, B channels along with RGB Image in same figure using sub figure.
· Convert into HSV( Hue, saturation and value) and show the H,S,V channels along with HSV image
E.Record your name pronounced by yourself. Try to display the signal(name) in a plot vs Time, using matlab.
F.Write a script to open a new figure and plot five circles, all centered at the origin and with increasing radii. Set the line width for each circle to something thick (at least 2 points), and use the colors from a 5-color jet colormap (jet).
G. NEWTON RAPHSON AND SECANT METHOD
H.Write any one of the program to do following things using file concept.
1.Create or Open a file
2. Read data from the file and write data to another file
3. Append some text to already existed file
4. Close the file
I.Write a function to perform following set operations
1.Union of A and B
2. Intersection of A and B
3. Complement of A and B
(Assume A= {1, 2, 3, 4, 5, 6}, B= {2, 4, 6})
This document provides an overview of essential data wrangling tasks in R, including importing, exploring, indexing/subsetting, reshaping, merging, aggregating, and repeating/looping data. It discusses functions for reading different file types like CSV, Excel, and plain text. It also covers exploring data structure and summary statistics, subsetting vectors, data frames and matrices, reshaping between wide and long format, performing different types of joins to merge data, and using loops and sequences to repeat operations.
Arrays are data structures that store a collection of data values of the same type in consecutive memory locations that can be individually referenced by their numeric index. Different representations of arrays are possible including using a single block of memory or a collection of elements. Common operations on arrays include retrieving or storing elements by their index and traversing the entire array sequentially.
This document provides an introduction to MATLAB. It discusses that MATLAB is a high-performance language for technical computing that integrates computation, visualization, and programming. It can be used for tasks like math and computation, algorithm development, modeling, simulation, prototyping, data analysis, and scientific graphics. MATLAB uses arrays as its basic data type and allows matrix and vector problems to be solved more quickly than with other languages. The document then provides examples of entering matrices, using basic MATLAB commands and functions, plotting graphs, and writing MATLAB code in M-files.
The document provides information about error handling in Python programming. It discusses different types of exceptions that may occur during program execution and how to handle them using try, except, else and finally blocks. It gives examples of programs that handle errors from inputting non-integer values or dividing by zero. The document also covers other Python programming concepts like lists, random numbers, and comparing Python to C/C++.
The document discusses the benefits of declarative programming using Scala. It provides examples of implementing algorithms and data structures declaratively in Scala. It also discusses the history and future of Scala, as well as how Scala encourages thinking about programs as transformations rather than changes to memory.
1. MATLAB is a software package for mathematical computation, numerical computation, algorithm development, data analysis, and more. It allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs in other languages.
2. The document introduces basic MATLAB operations like arithmetic operations, variables, matrices, plotting, scripts and functions. It also discusses flow control and logical operations like if/else statements and loops.
3. MATLAB can be used for scientific and engineering applications like modeling, simulation, and prototyping through its implementation of algorithms, data analysis tools, and graphical capabilities for visualizing data.
This document summarizes a presentation about monads in Scala. It discusses how monads allow structuring computations and combining them. Some key monads described include Option for handling failures, State for managing state, and Identity. For comprehensions in Scala emulate do notation in Haskell. Monads are demonstrated through an evaluator for arithmetic expressions that uses different monadic types like Identity, Option and State.
This document provides a cheat sheet overview of key concepts in the IRODS rule language, including numeric and string literals, arithmetic and comparison operators, functions for strings, lists, tuples, if/else statements, foreach loops, defining functions and rules, handling errors, and inductive data types. It describes syntax for defining data types using constructors, and using pattern matching to define functions over data types.
The document discusses using Ruby to count URL access frequencies from a log file. It explains that the map function processes the log by outputting pairs of URLs and a count of 1. The reduce function then combines these pairs by adding the counts for the same URL, outputting pairs of URLs and total access counts. This is a common pattern in MapReduce programming where map generates key-value pairs that reduce then combines.
Following a game show format made popular by Joshua Bloch and Neal Gafter's Java Puzzlers this presentation intends to both entertain and inform. Snippets of Python code the whose behaviour is not entirely obvious are shown, the audience will then be asked to pick from a number of options what the behaviour of the program is. The correct and sometimes non-intuitive answer will then be given along with a brief explanation of the idea the puzzle exposes. Only a modest working knowledge of the Python language is required to understand the puzzles, but the puzzles may also entertain the more experienced Python programmer.
- Templates in C++ allow functions and classes to be reused for different data types through generic programming. This avoids defining multiple functions to handle the same task on different types.
- A template can be viewed as a variable that can be instantiated to any data type. Two functions min() are shown, one for ints and one for doubles, demonstrating the need for templates.
- A template solution defines a single min() function that accepts any type through a template parameter <Type>. This provides a more flexible solution than separate overloaded functions.
- Templates in C++ allow functions and classes to be reused for different data types through generic programming. This avoids defining multiple functions to handle the same task on different types.
- A template can be viewed as a variable that can be instantiated for any data type. Two min() functions showed how templates provide a cleaner solution than separate functions.
- A stack is an abstract data type that follows LIFO (last-in, first-out) behavior. Elements are added and removed only from one end, called the top. Common stack operations are push, pop, empty, and top.
This document provides an introduction to MATLAB. It covers MATLAB basics like arithmetic, variables, vectors, matrices and built-in functions. It also discusses plotting graphs, programming in MATLAB by creating functions and scripts, and solving systems of linear equations. The document is compiled by Endalkachew Teshome from the Department of Mathematics at AMU for teaching MATLAB.
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
The document shows examples of using lambda functions and functional programming techniques in various languages like JavaScript, Python, C#, and Java. It demonstrates how to define anonymous functions, map, filter and reduce collections, use closures, and more. Key examples include summing a list of integers with reduce, filtering even numbers from a list, mapping string transformations, and converting a list of strings to uppercase.
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
AC Atlassian Coimbatore Session Slides( 22/06/2024)apoorva2579
This is the combined Sessions of ACE Atlassian Coimbatore event happened on 22nd June 2024
The session order is as follows:
1.AI and future of help desk by Rajesh Shanmugam
2. Harnessing the power of GenAI for your business by Siddharth
3. Fallacies of GenAI by Raju Kandaswamy
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
this resume for sadika shaikh bca studentSadikaShaikh7
I am a dedicated BCA student with a strong foundation in web technologies, including PHP and MySQL. I have hands-on experience in Java and Python, and a solid understanding of data structures. My technical skills are complemented by my ability to learn quickly and adapt to new challenges in the ever-evolving field of computer science.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
Navigating Post-Quantum Blockchain: Resilient Cryptography in Quantum Threatsanupriti
In the rapidly evolving landscape of blockchain technology, the advent of quantum computing poses unprecedented challenges to traditional cryptographic methods. As quantum computing capabilities advance, the vulnerabilities of current cryptographic standards become increasingly apparent.
This presentation, "Navigating Post-Quantum Blockchain: Resilient Cryptography in Quantum Threats," explores the intersection of blockchain technology and quantum computing. It delves into the urgent need for resilient cryptographic solutions that can withstand the computational power of quantum adversaries.
Key topics covered include:
An overview of quantum computing and its implications for blockchain security.
Current cryptographic standards and their vulnerabilities in the face of quantum threats.
Emerging post-quantum cryptographic algorithms and their applicability to blockchain systems.
Case studies and real-world implications of quantum-resistant blockchain implementations.
Strategies for integrating post-quantum cryptography into existing blockchain frameworks.
Join us as we navigate the complexities of securing blockchain networks in a quantum-enabled future. Gain insights into the latest advancements and best practices for safeguarding data integrity and privacy in the era of quantum threats.
What Not to Document and Why_ (North Bay Python 2024)Margaret Fero
We’re hopefully all on board with writing documentation for our projects. However, especially with the rise of supply-chain attacks, there are some aspects of our projects that we really shouldn’t document, and should instead remediate as vulnerabilities. If we do document these aspects of a project, it may help someone compromise the project itself or our users. In this talk, you will learn why some aspects of documentation may help attackers more than users, how to recognize those aspects in your own projects, and what to do when you encounter such an issue.
These are slides as presented at North Bay Python 2024, with one minor modification to add the URL of a tweet screenshotted in the presentation.
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
How to Avoid Learning the Linux-Kernel Memory ModelScyllaDB
The Linux-kernel memory model (LKMM) is a powerful tool for developing highly concurrent Linux-kernel code, but it also has a steep learning curve. Wouldn't it be great to get most of LKMM's benefits without the learning curve?
This talk will describe how to do exactly that by using the standard Linux-kernel APIs (locking, reference counting, RCU) along with a simple rules of thumb, thus gaining most of LKMM's power with less learning. And the full LKMM is always there when you need it!
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
1. Higher-Order Procedures (in Ruby) based on ‘Structure and Interpretation of Computer Programs’ (1985 MIT Press) by Hal Abelson and Gerald Jay Sussman. http://swiss.csail.mit.edu/classes/6.001/abelson-sussman-lectures/ Nathan Murray < [email_address] > v1.0 12/13/06 http://www.natemurray.com
2. legal The copy in this presentation is taken directly from Structure and Interpretation of Computer Programs by Hal Abelson and Gerald Jay Sussman (MIT Press, 1984; ISBN 0-262-01077-1). Specifically section 1.3 Formulating Abstractions with Higher-Order Procedures. There are a few paraphrases and additional examples added. The main difference is that the code has been converted from Lisp to Ruby. The full text of this book and accompanying video lectures can be found at: http://swiss.csail.mit.edu/classes/6.001/abelson-sussman-lectures/ The video lectures are copyright by Hal Abelson and Gerald Jay Sussman. The video lectures, and in turn this document, are licensed under a Creative Commons License. http://creativecommons.org/licenses/by-sa/2.0/
3. • Procedures are abstractions def cube (a) a * a * a end
7. The first computes the sum of the integers from a through b: def sum_integers (a, b) return 0 if a > b a + sum_integers((a + 1 ), b) end sum_integers( 1 , 10 ) #=> 55
8. The second computes the sum of the cubes of the integers in the given range: def sum_cubes (a, b) return 0 if a > b cube(a) + sum_cubes((a + 1 ), b) end sum_cubes( 1 , 3 ) #=> 36
9. The third computes the sum of a sequence of terms in the series: def pi_sum (a, b) return 0 if a > b ( 1.0 / ((a + 2 ) * a)) + (pi_sum((a + 4 ), b)) end pi_sum( 1 , 1000 ) * 8 #=> 3.13959265558978 which converges to π/8 (very slowly)
10. a pattern... def sum_integers (a, b) return 0 if a > b a + sum_integers((a + 1 ), b) end def sum_cubes (a, b) return 0 if a > b cube(a) + sum_cubes((a + 1 ), b) end def pi_sum (a, b) return 0 if a > b ( 1.0 / ((a + 2 ) * a)) + (pi_sum((a + 4 ), b)) end
11. template def <name> (a, b) return 0 if a > b <term>(a) + <name>(< next >(a), b) end
13. def <name> (a, b) return 0 if a > b <term>(a) + <name>(< next >(a), b) end def sum (term, a, the_next, b) return 0 if a > b term.call(a) + sum(term, the_next.call(a), the_next, b) end
14. sum cubes def inc (n) n + 1 end def sum_cubes (a, b) cube = self .method( :cube ).to_proc inc = self .method( :inc ).to_proc sum(cube, a, inc, b) end sum_cubes( 1 , 3 ) #=> 36
15. sum integers def identity (x) x end def sum_integers (a, b) id = self .method( :identity ).to_proc inc = self .method( :inc ).to_proc sum(id, a, inc, b) end sum_integers( 1 , 10 ) #=> 55
16. π sum def pi_term (x) ( 1.0 / (x * (x+ 2 ))) end def pi_next (x) (x + 4 ) end def pi_sum (a, b) term = self .method( :pi_term ).to_proc nex = self .method( :pi_next ).to_proc sum(term, a, nex, b) end pi_sum( 1 , 1000 ) * 8 #=> 3.13959265558978 λ
17. λ def pi_sum (a, b) sum( , a, , b ) end lambda { | x | ( 1.0 / (x * (x+ 2 ))) } lambda { | x | (x + 4 ) }
18. another example def even? (i) i % 2 == 0 end def filter_evens (list) new_list = [] list.each do | element | new_list << element if even?(element) end new_list end filter_evens( [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] ) #=> [2, 4, 6, 8]
19. returning procedures def make_filter (predicate) lambda do | list | new_list = [] list.each do | element | new_list << element if predicate.call(element) end new_list end end filter_odds = make_filter( lambda {| i | i % 2 != 0 } ) filter_odds.call(list) #=> [1, 3, 5, 7, 9]