Turing Machines are a model of computation that can simulate any algorithm. A Universal Turing Machine can simulate any other Turing Machine. This means that a Universal Turing Machine can perform any computation. Any programming language that can simulate a Universal Turing Machine is considered a universal programming language, as it can perform any computation. Scheme is an example of a universal programming language since it is possible to write a Scheme program that simulates a Universal Turing Machine.
A simple implementation of Turing Machine in C++ programming language.
for the Theory of Languages and Automata course.
Computer Engineering at Khaje Nasir Toosi University of Technology (KNTU).
the source code is available on my github profile:
https://github.com/sina-rostami/Turing-Machine-Implementation
A "screw rich companies and give the money to poor folk" plan by the federal Environmental Protection Agency (EPA)--an agency drunk on its own lawless power. The plan is to use the excuse that poor folk are getting unfairly polluted by mean, nasty, dirty polluters (i.e. business) and "fix" the problem by strict new regulations and enforcement of those regulations. The EPA gets to make up its own laws and then enforce those laws--bypassing Congress altogether.
A simple implementation of Turing Machine in C++ programming language.
for the Theory of Languages and Automata course.
Computer Engineering at Khaje Nasir Toosi University of Technology (KNTU).
the source code is available on my github profile:
https://github.com/sina-rostami/Turing-Machine-Implementation
A "screw rich companies and give the money to poor folk" plan by the federal Environmental Protection Agency (EPA)--an agency drunk on its own lawless power. The plan is to use the excuse that poor folk are getting unfairly polluted by mean, nasty, dirty polluters (i.e. business) and "fix" the problem by strict new regulations and enforcement of those regulations. The EPA gets to make up its own laws and then enforce those laws--bypassing Congress altogether.
Transform Your Business with Big Data and Hortonworks Pactera_US
Customer insight and marketplace predictions are a few of the profitable benefits found in big data technology. Leading companies are using the advanced analytics solution to find new revenue streams, increase customer satisfaction and optimize the supply chain.
《The willpower instinct》reading notes 《自控力》读书笔记@Pem Zhipeng Xie
《自控力》是很棒的一本书,几年前我花了两个月的时间,读完全书,然后把重要有趣的内容都整理出来,做成这个很棒的PPT。如果你没有时间阅读原书的话,完成可以通过阅读这个PPT来完成对这本书的了解。希望对你有用。
Years ago, I made this powerpoint according to the notes I took while reading <the>. This is a great book about the interest things behind our willpower. This powerpoint is also great and popular in my Weibo. I hope you will enjoy it. If I get time in the future, I will make an English version.
Big Data with KNIME is as easy as 1, 2, 3, ...4!KNIMESlides
This presentation shows how we have re-engineered an old legacy workflow to run partially on Hadoop from within the KNIME Analytics Platform, to speed up dramatically the execution time.
It also shows how easy it has been to move the ETL part of the workflow to Hadoop using the KNIME big data access nodes and in-database processing nodes.
KNIME big data nodes are Cloudera, Hortonworks, and MapR certified, as of today (October 21 2015)
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Computation is a general term for any type of information processing that can be represented as an algorithm precisely (mathematically). Get instant help for Computation Theory Assignment from our expert who are available online 24*7 on support@globalwebtutors.com. For more information you can visit our site https://globalwebtutors.com/
In theoretical computer science and mathematics , the theory of computation is the branch that
deals with how efficiently problems can be solved on a model of computation, using an algorithm. The field is
divided into three major branches: automata theory, computability theory, and computational complexity
theory.
In order to perform a rigorous study of computation, computer scientists work with a mathematical
abstraction of computers called a model of computation. There are several models in use such as Lambda
calculus, Combinatory logic, mu-recursive functions , but the most commonly examined is the Turing machine.
Computer scientists study the Turing machine because it is simple to formulate, can be analyzed and used to
prove results, and because it represents what many consider the most powerful possible "reasonable" model of
computation . It might seem that the potentially infinite memory capacity is an unrealizable attribute, but
any decidable problem solved by a Turing machine will always require only a finite amount of memory. So in
principle, any problem that can be solved (decided) by a Turing machine can be solved by a computer that has a
bounded amount of memory.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
In this video you will learn about
--Introduction to Algorithms
--Characteristics of an Algorithm
--Algorithms Analysis
--Priori Analysis
-- Posterior Analysis
-- Algorithm Efficiency
--Time Complexity
--Space Complexity
--Algorithm Design Tools
-- Pseudocode
--Flowchart
--Asymptotic Analysis/ Notations
--Big-Oh Notation
--Omega Notation
--Theta Notation
This set of slides introduces the reader to a subset of the C++ Standard Library called the Standard Template Library (STL). The STL provides a collection of parameterized containers and algorithms, and it is the most successful example of an approach to programming called generic programming. In this presentation, we aim at studying the ideals and concepts of the STL by re-implementing small parts of the library. Specifically, we first show how we can discover requirements on types in order to devise generic algorithms. Then, we focus on how to make algorithms independent of containers through the pivotal abstraction of iterators. To this end, we replicate the standard algorithm for finding the minimum in a sequence (min_element), which we subsequently match with a custom forward iterator over intrusive linked lists of integers. Finally, we see how function objects can be used to customize containers and algorithms alike. This allows us to deepen our understanding of ordering relations, and, in particular, to introduce the concept of strict weak orderings.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Pride Month Slides 2024 David Douglas School District
universality
1. 12.3 Universality
Recall the Turing Machine model from Chapter 6: a Turing Machine consists of
an infinite tape divided into discrete square into which symbols from a fixed alphabet
can be written, and a tape head that moves along the tape.
On each step, the tape head can read the symbol in the current square, write a symbol in
the current square, and move left or right one square or halt.
The machine can keep track of a finite number of possible states, and determines which
action to take based on a set of transition rules that specify the output symbol and head
action for a given current state and read symbol.
Turing argued that this simple model corresponds to our intuition about what can be
done using mechanical computation. Recall this was 1936, so the model for mechanical
computation was not what a mechanical computer can do, but what a human computer
can do.
Turing argued that his model corresponded to what a human computer could do
by following a systematic procedure: the infinite tape was as powerful as a two-
dimensional sheet of paper or any other recording medium, the set of symbols
must be finite otherwise it would not be possible to correctly distinguish all symbols,
and the number of machine states must be finite because there is a limited amount a
human can keep in mind at one time.
2. We can enumerate all possible Turing Machines. One way to see this is to devise
a notation for writing down any Turing Machine.
A Turing Machine is completely described by its alphabet, states and transition rules.
We could write down any Turing Machine by numbering each state and listing
each transition rule as a tuple of the current state, alphabet symbol, next state,
output symbol, and tape direction.
We can map each state and alphabet symbol to a number, and use this encoding to write
down a unique number for every possible Turing Machine. Hence, we can enumerate all
possible Turing Machines by just enumerating the positive integers.
Most positive integers do not correspond to valid Turing Machines, but if we go through
all the numbers we will eventually reach every possible Turing Machine.
This is step towards proving that some problems cannot be solved by any algorithm. The
number of Turing Machines is less than the number of real numbers.
Both numbers are infinite, but as explained in Section 1.2.2, Cantor's
diagonalization proof showed that the real numbers are not countable.
3. Any attempt to map the real numbers to the integers must fail to include all the
real numbers.
This means there are real numbers that cannot be produced by any Turing
Machine: there are fewer Turing Machines than there are real numbers, so there must
be some real numbers that cannot be produced by any Turing Machine.
The next step is to define the machine depicted in Figure 12.2. A Universal
Turing Machine is a machine that takes as input a number that identifies a Turing
Machine and simulates the specified Turing Machine running on initially empty input
tape.
The Universal Turing Machine can simulate any Turing Machine. In his proof, Turing
describes the transition rules for such a machine.
It simulates the Turing Machine encoded by the input number. One can imagine doing
this by using the tape to keep track of the state of the simulated machine.
For each step, the universal machine searches the description of the input machine to
find the appropriate rule. This is the rule for the current state of the simulated machine
on the current input symbol of the simulated machine.
4. The universal machine keeps track of the machine and tape state of the
simulated machine, and simulates each step. Thus, there is a single Turing
Machine that can simulate every Turing Machine.
Fig
ure 12.2: Universal Turing Machine..
Since a Universal Turing Machine can simulate every Turing Machine, and a
Turing Machine can perform any computation according to our intuitive
notion of computation, this means a Universal Turing Machine can perform all
computations.
Using the universal machine and a diagonalization argument similar to the one above
for the real numbers, Turing reached a similar contradiction for a problem analogous to
the Halting Problem for Python programs but for Turing Machines instead.
If we can simulate a Universal Turing Machine in a programming language, that
language is a universal programming language. There is some program that can
be written in that language to perform every possible computation.
5. To show that a programming language is universal, it is sufficient to show that it can
simulate any Turing Machine, since a Turing Machine can perform every possible
computation.
To simulate a Universal Turing Machine, we need some way to keep track of the state of
the tape (for example, the list datatypes in Scheme or Python would be adequate), a way
to keep track of the internal machine state (a number can do this), and a way to execute
the transition rules (we could define a procedure that does this using an if expression to
make decisions about which transition rule to follow for each step), and a way to keep
going (we can do this in Scheme with recursive applications).
Thus, Scheme is a universal programming language: one can write a Scheme program
to simulate a Universal Turing Machine, and thus, perform any mechanical
computation.
6. To show that a programming language is universal, it is sufficient to show that it can
simulate any Turing Machine, since a Turing Machine can perform every possible
computation.
To simulate a Universal Turing Machine, we need some way to keep track of the state of
the tape (for example, the list datatypes in Scheme or Python would be adequate), a way
to keep track of the internal machine state (a number can do this), and a way to execute
the transition rules (we could define a procedure that does this using an if expression to
make decisions about which transition rule to follow for each step), and a way to keep
going (we can do this in Scheme with recursive applications).
Thus, Scheme is a universal programming language: one can write a Scheme program
to simulate a Universal Turing Machine, and thus, perform any mechanical
computation.