This document contains lecture slides about comparison and math instructions used in programmable logic controllers (PLCs). It discusses various comparison instructions like equal, not equal, less than, greater than, and masked comparisons. It also covers math instructions for addition, subtraction, multiplication, and division. Finally, it describes how the arithmetic status bits in the PLC are updated after instructions are executed to indicate conditions like carry, overflow, zero, and sign.
Molecular Descriptors: Comparing Structural Complexity and Software Svetlana Gelpi
What are descriptors? And how are they used? In a large effort to predict the compound activity of over 1.7 million compounds in various in-vitro assays, the time it takes to extract molecules from a database and process them for virtual screening is crucial. Applications need to take advantage of LLNL’s high performance computing.
presentation from my thesis defense on text summarization, discusses already existing state of art models along with efficiency of AMR or Abstract Meaning Representation for text summarization, we see how we can use AMRs with seq2seq models. We also discuss other techniques such as BPE or Byte Pair Encoding and its effectiveness for the task. Also we see how data augmentation with POS tags and AMRs effect the summarization with s2s learning.
Molecular Descriptors: Comparing Structural Complexity and Software Svetlana Gelpi
What are descriptors? And how are they used? In a large effort to predict the compound activity of over 1.7 million compounds in various in-vitro assays, the time it takes to extract molecules from a database and process them for virtual screening is crucial. Applications need to take advantage of LLNL’s high performance computing.
presentation from my thesis defense on text summarization, discusses already existing state of art models along with efficiency of AMR or Abstract Meaning Representation for text summarization, we see how we can use AMRs with seq2seq models. We also discuss other techniques such as BPE or Byte Pair Encoding and its effectiveness for the task. Also we see how data augmentation with POS tags and AMRs effect the summarization with s2s learning.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
ACL-WMT2013.A Description of Tunable Machine Translation Evaluation Systems i...Lifeng (Aaron) Han
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In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching
(PPM) compression algorithm. This achieves significantly better compression for different natural
language texts compared to other well-known compression methods. Our method first generates a grammar
based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in
the text being compressed and then substitutes these sequences using the respective non-terminal symbols
defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly
improved results in compression for various natural languages (a 5% improvement for American English,
10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We
describe further improvements using a two pass scheme where the grammar-based pre-processing is
applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary
Corpus and also achieve significantly improved results in compression, between 11% and 20%, when
compared with other compression algorithms, including a grammar-based approach, the Sequitur
algorithm.
Adversarial and reinforcement learning-based approaches to information retrievalBhaskar Mitra
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
ACL-WMT2013.A Description of Tunable Machine Translation Evaluation Systems i...Lifeng (Aaron) Han
Proceedings of the ACL 2013 EIGHTH WORKSHOP ON STATISTICAL MACHINE TRANSLATION (ACL-WMT 2013), 8-9 August 2013. Sofia, Bulgaria. Open tool https://github.com/aaronlifenghan/aaron-project-lepor & https://github.com/aaronlifenghan/aaron-project-hlepor(ACM digital library, ACL anthology)
In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching
(PPM) compression algorithm. This achieves significantly better compression for different natural
language texts compared to other well-known compression methods. Our method first generates a grammar
based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in
the text being compressed and then substitutes these sequences using the respective non-terminal symbols
defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly
improved results in compression for various natural languages (a 5% improvement for American English,
10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We
describe further improvements using a two pass scheme where the grammar-based pre-processing is
applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary
Corpus and also achieve significantly improved results in compression, between 11% and 20%, when
compared with other compression algorithms, including a grammar-based approach, the Sequitur
algorithm.
Adversarial and reinforcement learning-based approaches to information retrievalBhaskar Mitra
Traditionally, machine learning based approaches to information retrieval have taken the form of supervised learning-to-rank models. Recently, other machine learning approaches—such as adversarial learning and reinforcement learning—have started to find interesting applications in retrieval systems. At Bing, we have been exploring some of these methods in the context of web search. In this talk, I will share couple of our recent work in this area that we presented at SIGIR 2018.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
Using an SQL coverage measurement for testing database applicationKhalidKhan412
To define a measurement of coverage for SQL SELECT queries in database loaded with test data and to present an algorithm that automates the calculation of coverage.
Additional Descriptive Statistics methods for Data Analysis / Maxim Neronov (...DevGAMM Conference
This topic is about different approach to standard data analysis questions. It's more about mathematics & statistics rather than general talk-about. My idea is to talk about some new instruments we can use to "see through data" from a different angle. There will be some real problems we solved with a statistical methods that are less known in game-development segment of IT
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Presentation on the operation of the AB ControlLogix comparison instructions. Included is the basics of the Siemens S7-1200 comparison instructions and the AB Creative Components Workbench (CCW) comparison instdructions.
QUANTIFYING THE THEORY VS. PROGRAMMING DISPARITY USING SPECTRAL BIPARTIVITY A...ijcsit
Some students in the Computer Science and related majors excel very well in programming-related
assignments, but not equally well in the theoretical assignments (that are not programming-based) and
vice-versa. We refer to this as the "Theory vs. Programming Disparity (TPD)". In this paper, we propose a
spectral bipartivity analysis-based approach to quantify the TPD metric for any student in a course based
on the percentage scores (considered as decimal values in the range of 0 to 1) of the student in the course
assignments (that involves both theoretical and programming-based assignments). We also propose a
principal component analysis (PCA)-based approach to quantify the TPD metric for the entire class based
on the percentage scores (in a scale of 0 to 100) of the students in the theoretical and programming
assignments. The spectral analysis approach partitions the set of theoretical and programming
assignments to two disjoint sets whose constituents are closer to each other within each set and relatively
more different from each across the two sets. The TPD metric for a student is computed on the basis of the
Euclidean distance between the tuples representing the actual numbers of theoretical and programming
assignments vis-a-vis the number of theoretical and programming assignments in each of the two disjoint
sets. The PCA-based analysis identifies the dominating principal components within the sets of theoretical
and programming assignments and computes the TPD metric for the entire class as a weighted average of
the correlation coefficients between the dominating principal components representing these two sets.
Some students in the Computer Science and related majors excel very well in programming-related
assignments, but not equally well in the theoretical assignments (that are not programming-based) and
vice-versa. We refer to this as the "Theory vs. Programming Disparity (TPD)". In this paper, we propose a
spectral bipartivity analysis-based approach to quantify the TPD metric for any student in a course based
on the percentage scores (considered as decimal values in the range of 0 to 1) of the student in the course
assignments (that involves both theoretical and programming-based assignments). We also propose a
principal component analysis (PCA)-based approach to quantify the TPD metric for the entire class based
on the percentage scores (in a scale of 0 to 100) of the students in the theoretical and programming
assignments. The spectral analysis approach partitions the set of theoretical and programming
assignments to two disjoint sets whose constituents are closer to each other within each set and relatively
more different from each across the two sets
BACKPROPAGATION LEARNING ALGORITHM BASED ON LEVENBERG MARQUARDT ALGORITHMcscpconf
Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily
compressible to humans. Data Mining represents a process developed to examine large amounts
of data routinely collected. The term also refers to a collection of tools used to perform the
process. One of the useful applications in the field of medicine is the incurable chronic disease
diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status.
Fuzzy Systems are been used for solving a wide range of problems in different application
domain and Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning
and adaptation capabilities. Neural Networks are efficiently used for learning membership
functions. Diabetes occurs throughout the world, but Type 2 is more common in the most
developed countries. The greater increase in prevalence is however expected in Asia and Africa
where most patients will likely be found by 2030. This paper is proposed on the Levenberg –
Marquardt algorithm which is specifically designed to minimize sum-of-square error functions.
Levernberg-Marquardt algorithm gives the best performance in the prediction of diabetes compared to any other backpropogation algorithm
This is the third of a series of powerpoints presented at a CAT/IRT workshop at the University of Brasilia in 2012. It provides an introduction to item response theory (IRT), discussing advanced topics like linking & equating, scaling, differential item functioning, polytomous models, and dimensionality. Learn more at www.assess.com.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
Embracing GenAI - A Strategic ImperativePeter 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.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Model Attribute Check Company Auto PropertyCeline George
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
Lect07
1. 7-1
Dr. D. J. Jackson Lecture 7-1Electrical & Computer Engineering
Programmable Logic
Controllers
Comparison and Math Operations
Dr. D. J. Jackson Lecture 7-2Electrical & Computer Engineering
Comparison Instructions
Instruction Purpose
Mnemonic Name
EQU Equal Test whether two values are equal
NEQ Not Equal Test whether one value is not equal to a second
value
LES Less Than Test whether one value is less than a second
value
LEQ Less Than
or Equal
Test whether one value is less than or equal to a
second value
GRT Greater Than Test whether one value is greater than another
GEQ Greater Than or
Equal
Test whether one value is greater
than or equal to a second value
MEQ Masked Comparison
for Equal
Test portions of two values to see whether they
are equal. Compares data through a mask
LIM Limit Test Test whether one value is within the limit range
of two other values
2. 7-2
Dr. D. J. Jackson Lecture 7-3Electrical & Computer Engineering
About the Comparison Instructions
• Comparison instructions are used to test
pairs of values to condition the logical
continuity of a rung
– Thus, comparison instructions would seldom, if
ever, be the last instruction on a rung
• As an example, suppose a LES instruction is
presented with two values
– If the first value is less than the second, then the
comparison instruction is true
Dr. D. J. Jackson Lecture 7-4Electrical & Computer Engineering
Equal (EQU) & Not Equal (NEQ)
• Use the EQU instruction to test
whether two values are equal.
– If source A and source B are
equal, the instruction is logically
true. If these values are not
equal, the instruction is logically
false.
• Use the NEQ instruction to test
whether two values are not
equal.
– If source A and source B are not
equal, the instruction is logically
true.
• Source A must be an address.
Source B can be either a
program constant or an address.
• Values are stored in two’s
complementary form.
3. 7-3
Dr. D. J. Jackson Lecture 7-5Electrical & Computer Engineering
Less Than (LES) & Less Than or
Equal (LEQ)
• Use the LES instruction to test
whether one value (source A) is
less than another (source B).
– If source A is less than the value
at source B, the instruction is
logically true.
• Use the LEQ instruction to test
whether one value (source A) is
less than or equal to another
(source B).
– If the value at source A is less
than or equal to the value at
source B, the instruction is
logically true.
• Source A must be an address.
Source B can be either a
program constant or an address.
• Values are stored in two’s
complementary form.
Dr. D. J. Jackson Lecture 7-6Electrical & Computer Engineering
Greater Than (GRT) & Greater Than
Or Equal (GEQ)
• Use the GRT instruction to
test whether one value
(source A) is greater than
another (source B).
– If the value at source A is
greater than the value at
source B, the instruction is
logically true.
• Use the GEQ instruction to
test whether one value
(source A) is greater than or
equal to another (source B).
– If the value at source A is
greater than or equal to the
value at source B, the
instruction is logically true.
4. 7-4
Dr. D. J. Jackson Lecture 7-7Electrical & Computer Engineering
Masked Comparison for Equal (MEQ)
• Source is the address of the value you want to compare.
• Mask is the address of the mask through which the instruction moves
data
– The mask can be a hexadecimal value.
• Compare is an integer value or the address of the reference.
– If the 16 bits of data at the source address are equal to the 16 bits
of data at the compare address (less masked bits), the instruction
is true.
– The instruction becomes false as soon as it detects a mismatch.
• Use the MEQ instruction to
compare data at a source address
with data at a compare address.
• Use of this instruction allows
portions of the data to be masked
by a separate word.
Dr. D. J. Jackson Lecture 7-8Electrical & Computer Engineering
Limit Test (LIM)
• Use the LIM instruction to
test for values within or
outside a specified range,
depending on how you set
the limits.
• The Low Limit, Test, and
High Limit values can be
word addresses or constants,
restricted to the following
combinations:
– If the Test parameter is a
program constant, both the
Low Limit and High Limit
parameters must be word
addresses.
– If the Test parameter is a
word address, the Low Limit
and High Limit parameters
can be either a program
constant or a word address.
5. 7-5
Dr. D. J. Jackson Lecture 7-9Electrical & Computer Engineering
True/False Status of the LIM
Instruction
• If the Low Limit has a value equal to or less than the
High Limit, the instruction is true when the Test
value is between the limits or is equal to either limit.
Example, low limit less than high limit:
Low Limit High Limit Instruction is True when
Test value is
Instruction is False
when Test value is
5 8 5 through 8 -32768 through 4 and 9
through 32767
TrueFalse False
High LimitLow Limit
-32768 +32767
Dr. D. J. Jackson Lecture 7-10Electrical & Computer Engineering
True/False Status of the LIM
Instruction (continued)
• If the Low Limit has a value greater than the High
Limit, the instruction is false when the Test value is
between the limits.
Example, high limit less than low limit:
Low Limit High Limit Instruction is True when
Test value is
Instruction is False
when Test value is
8 5 -32768 through 5 and 8
through 32767
6 through 7
FalseTrue True
Low LimitHigh Limit
-32768 +32767
6. 7-6
Dr. D. J. Jackson Lecture 7-11Electrical & Computer Engineering
Math Instructions
• Math Instructions Overview
• Source is the address(es) of the value(s) on which the
mathematical, logical, or move operation is to be performed.
– This can be word addresses or program constants.
– An instruction that has two source operands does not accept
program constants in both operands.
• Destination is the address of the result of the operation.
Signed integers are stored in two’s complementary form and
apply to both source and destination parameters.
• Location of math instructions in ladder logic determine to
operation performed (i.e. if the operation A(B+C) is desired, the
ADD operation must appear before the MUL).
• There are notable differences between the simulator
implementation of these instructions and the actual PLC
hardware.
Dr. D. J. Jackson Lecture 7-12Electrical & Computer Engineering
Available Math Instructions
• ADD:
– Add two values Dest=(A+B)
• SUB:
– Subtract one value from another
Dest=(A-B)
• MUL:
– Multiply two values Dest=(A*B)
• DIV:
– Divide one value by another
Dest=(A/B)
• SQR:
– Take the square root of a value
• NEG:
– Negate a value (2’s complement)
• TOD:
– Convert from integer to BCD
• FRD:
– Convert from BCD to integer
• All integer operations only
• *Other operations available with the
Micrologix PLC
7. 7-7
Dr. D. J. Jackson Lecture 7-13Electrical & Computer Engineering
Updates to Arithmetic Status Bits
• The arithmetic status bits are found in Word 0, bits
0–3 in the controller status file.
• After an instruction is executed, the arithmetic status
bits in the status file are updated:
With this bit: The Controller:
S:0/0 Carry (C) sets if carry is generated; otherwise cleared
S:0/1 Overflow (V) indicates that the actual result of a math
instruction does not fit in the designated
destination
S:0/2 Zero (Z) indicates a 0 value after a math, move, or logic
instruction
S:0/3 Sign (S) indicates a negative (less than 0) value after a
math, move, or logic instruction