1) Mathematics plays a key role in modern technologies like Google, iPods, wireless communication, and medical imaging.
2) Error correcting codes invented in the 1940s are widely used to detect and correct mistakes in digital communication and storage. They allow reliable transmission of information using techniques like Hamming distance.
3) Google's PageRank algorithm uses the idea that more important websites are those linked to by many other important websites to rank websites and provide the most relevant search results. It involves calculating the principal eigenvector of a huge matrix representing the web link structure.
Computer data representation (integers, floating-point numbers, text, images,...ArtemKovera
How computers represent different types of data.
1) Why learning how computers represent data is important
2) Binary, Octal, and Hexadecimal number systems.
3) A few words about computer memory organization
4) Representing integer numbers in computers
(two's-complement and other encodings)
5) Representing floating-point numbers
(single-precision, double-precision, quadruple-precision)
6) Binary-Coded Decimal (BCD) Representation
7) Introduction to representing text in computers (ASCII, Unicode encodings: UTF-8, UTF-16, etc)
8) Introduction to representing images in computers
9) Introduction to representing sound in computers
10) Books on Artificial Intelligence
Computer data representation (integers, floating-point numbers, text, images,...ArtemKovera
How computers represent different types of data.
1) Why learning how computers represent data is important
2) Binary, Octal, and Hexadecimal number systems.
3) A few words about computer memory organization
4) Representing integer numbers in computers
(two's-complement and other encodings)
5) Representing floating-point numbers
(single-precision, double-precision, quadruple-precision)
6) Binary-Coded Decimal (BCD) Representation
7) Introduction to representing text in computers (ASCII, Unicode encodings: UTF-8, UTF-16, etc)
8) Introduction to representing images in computers
9) Introduction to representing sound in computers
10) Books on Artificial Intelligence
LESSON 1: INTRODUCTION TO PYTHON, VARIABLES, DNA CODING, AI
Introduction to Python, how to download (Python 3), Create your own Chat Bot. Introducing variables, sequence, programs, Alan Turing and Artificial Intelligence. Big ideas to discuss: DNA Coding and Intelligent design. Create apps which include the use of random number and item generation. Suggested videos on ‘Introducing Python’ and History of Computing. Learn about Mathematical and comparison operators and the importance of indentation in Python. Includes a suggested videos, ‘Big ideas’ discussion, and HW/research projects section.
A short presentation I made for work trying to teach myself the absolute most basic aspects of networking. Covers bits, encoding, packets, and protocols.
Chatbots are growing in popularity as developers face the
limitations of the mobile app. User interfaces that simulate a human
conversation, the history of chatbots goes back to the late 18th
century. I'll take you on a tour of that history with an eye on finding
insights on what is possible today and in the near future with chatbots.
Issues Covered: Amazon Alexa, Facebook Messenger Chatbots, Alan
Turing, and much more.
Given at the BugCrowd conference in January 2019, this was the first time for doing this deck.:
For 25 years or more we have fought the battle of passwords and patches while all around us, the world has developed, data has exponentially increased, attack surfaces are everywhere and technology had quite simply forced the human race to consider the evolution cycle in single lifespans as opposed to millennia. During the last 25 years we have done little to protect the charges we are responsible for, we have failed to secure systems, allowed financial attacks, infrastructure attacks, and now attacks directly against humans. At what point will we be able to stem the bleeding and actually take charge of our realm? Have we left it too late, or are we still able to claw back out of the abyss and face our adversary in a more asymmetrical defensive manner? Can we actually provide safety and security to our charges or will we continue to fail? And, critically, how do we communicate this, and educate a population that is content to watch from the sidelines, while they are being digitally eviscerated.
What exactly is machine learning? Moreover, what is the machine learning? This desk was first presented in 2020 at the Thadomal Shahani College of Engineering
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
LESSON 1: INTRODUCTION TO PYTHON, VARIABLES, DNA CODING, AI
Introduction to Python, how to download (Python 3), Create your own Chat Bot. Introducing variables, sequence, programs, Alan Turing and Artificial Intelligence. Big ideas to discuss: DNA Coding and Intelligent design. Create apps which include the use of random number and item generation. Suggested videos on ‘Introducing Python’ and History of Computing. Learn about Mathematical and comparison operators and the importance of indentation in Python. Includes a suggested videos, ‘Big ideas’ discussion, and HW/research projects section.
A short presentation I made for work trying to teach myself the absolute most basic aspects of networking. Covers bits, encoding, packets, and protocols.
Chatbots are growing in popularity as developers face the
limitations of the mobile app. User interfaces that simulate a human
conversation, the history of chatbots goes back to the late 18th
century. I'll take you on a tour of that history with an eye on finding
insights on what is possible today and in the near future with chatbots.
Issues Covered: Amazon Alexa, Facebook Messenger Chatbots, Alan
Turing, and much more.
Given at the BugCrowd conference in January 2019, this was the first time for doing this deck.:
For 25 years or more we have fought the battle of passwords and patches while all around us, the world has developed, data has exponentially increased, attack surfaces are everywhere and technology had quite simply forced the human race to consider the evolution cycle in single lifespans as opposed to millennia. During the last 25 years we have done little to protect the charges we are responsible for, we have failed to secure systems, allowed financial attacks, infrastructure attacks, and now attacks directly against humans. At what point will we be able to stem the bleeding and actually take charge of our realm? Have we left it too late, or are we still able to claw back out of the abyss and face our adversary in a more asymmetrical defensive manner? Can we actually provide safety and security to our charges or will we continue to fail? And, critically, how do we communicate this, and educate a population that is content to watch from the sidelines, while they are being digitally eviscerated.
What exactly is machine learning? Moreover, what is the machine learning? This desk was first presented in 2020 at the Thadomal Shahani College of Engineering
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
1. Maths and the making of the modern world
The maths behind Google and the Ipod
Chris Budd
2. Mathematics is completely useless
Mathematicians are evil souless geeks
All Mathematicians are mad!
Some common views on maths and mathematicians
3. The modern world would not exist without maths
With maths you can tell the future and save lives
Maths lies at the heart of art and music
The truth is rather different!
4. Spot the mathematician, and why are they important?
Maxwell and the discovery of electromagnetic waves
Electromagnetism, radio, WiFi,TV, radar, mobile phones,
microwaves all come from the work of Maxwell!
5. Linear algebra, graph theory, SVD
Google:
Error correcting codes: Galois theory
Internet: Network theory
Security: Fermat, RSA
Mathematicians really have made the modern world possible
Medical imaging: Radon Transform
Communications: FFT, Shannon
Medical Statistics: Nightingale
A few other examples ….
7. It is important that we store, transmit and search this
information carefully and without making mistakes
Maths helps us to do this…
8. Pick a number 0,1,2,3,…,7
Q1. Is your number 4,5,6,7?
Q2. Is your number 2,3,6,7?
Q3. Is your number 1,3,5,7?
Answer the following questions truthfully
Storing information by telling the truth
10. 3 Bit Binary Number: x
x represented by three digits a b c eg. 101
a,b,c are 0 or 1
x = 4*a + 2*b + c
eg. 101 = 4+0+1 = 5
011 = 0+2+1 = 3
11. 1, 0 are called bits of information
All information in a computer is made up of bits
Simplest information has ONE BIT
Are you OK?
12. Usually binary numbers have more than 3 bits
eg. 10011011 has 8 bits
A symbol of 8 bits is called a byte.
You can have 256 such symbols
Letters A,B,C, … are converted into 8 bit ASCII
Other languages eg. Japanese use 32 bit Unicode
Binary numbers 0..7 have 3 bits
13. Camera takes picture made up of PIXELS
8 BITS per pixel ….. 256 range of intensity = 1 byte
1 000 000 Pixels per Picture
3 colours
Total 3 M Byte per picture
One bite memory
14. Using binary you can count from 0 to 31 on one hand with
5 bit binary numbers
How does a monster count to 25?
On his fingers!
eg. 10110 = 16 + 4 + 2 = 22
11001 = 16 + 8 + 1 = 25
15. Sometimes we make mistakes
How to avoid errors.
Mean to send 11100011
Make a mistake on one bit and send
11101011
Can we tell if we have made a mistake?
16. Answer the following questions.
Either tell the truth or lie at most once
Pick a number between 0 and 7
Q1 Is it 4,5,6,7?
Q2 Is it 2,3,6,7?
Q3 Is it 1,3,5,7?
Q4 Is it 1,2,4,7?
Can we find the liar?
17. 0 0 0 0 0
1 0 0 1 1
2 0 1 0 1
3 0 1 1 0 answer to last question
4 1 0 0 1
5 1 0 1 0
6 1 1 0 0
7 1 1 1 1
If all true there are an: even number of 1s
If one lie there is an: odd number of 1s
Last digit/question is called a parity bit and tells us if we
have made a mistake
19. Once we spot an error we can either
Discard the whole message
…. OR ….
Ask for the information to be sent again
…. OR ….
We can try to correct it
20. Error correcting codes.
Used to store the numbers 0,1,2,3,4,5,6,7 and other data
in such a way that any errors can not only be detected but
corrected.
21. Answer the following questions .. You can
either tell the truth or lie at most once
Choose a number 0,1,2,3,4,5,6,7
Q1 Is the number 4,5,6,7?
Q2 Is the number 2,3,6,7?
Q3 Is the number 1,3,5,7?
Q4 Is the number 1,3,4,6?
Q5 Is the number 1,2,5,6?
Q6 Is the number 2,3,4,5?
23. Start with a binary number 110110
Telling the truth doesn’t change the number 110110
Lying once changes the number by one digit 100110
Hamming Distance:
Take two binary numbers. How many digits
do we have to change to turn one into the
other?
24. 0 000 000
1 001 110
2 010 011
3 011 101
4 100 101
5 101 011
6 110 110
7 111 000
All are a Hamming distance of
3 apart
Choose the closest number to
the one you are sent. This
must be correct.
Binary number Correcting number
25. Error correcting codes.
Extend these ideas using Finite Field Theory (Galois)
These are widely used in
• CDs
• Digital TV and Radio
• Mobile phones
• Satellites
Invented in the 1940s by
Hamming in the Bell Labs
26. Different types of code are used depending upon
• The Level of noise
• Whether the noise is random or comes in bursts
27. These are used in IPODs
IPOD also compress the information.
28. Instead of sending this message
which has lots of vowels in it which
we don’t really need
W cn snd ths mssg nstd whch ds nt
hv ny vwls t ll
Nw try ths fr yrslf
For example
29. MPEG file also compresses sound waveforms
Decompose into a sum of harmonics. Only store the first few of these
30. The Maths Behind Google
Google searches for information stored on many web-sites.
Web-sites are linked together by a network showing which web-
site points to which other web-site
31. It RANKS web-sites in order of the importance of the information
that they contain.
IDEA. A website is important if lots of other websites link to it.
A website is even more important if it is linked to by lots of
important web-sites.
32. PAGE RANK
Each Web-site has a rank R
Divide R by the number N of web-sites that this web-site links, to get S=R/N
For each web-site, calculate R by adding up the values of S for every web-site that
connects to it.
12
4
9
6
4
4
4
2
2
3
3
9
33. Now do the same for 1 000 000 0000 more
computers
Need to calculate the unit eigenvector of an extremely
large matrix!
And make many $$$$$$$$$$$$$$$$$