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Welcome to Programming Homework Help
Python Question Assistance
Making Python Learning Easier
Introducing Python Question Assistance
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Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
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.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
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.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
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2. Problem 1. Oil Well that Ends Well The oil wells of tycoon Ron Jockefeller will produce m
oil barrels this month. Ron has a list of n orders from potential buyers, where the ith order
states a willingness to buy ai barrels for a total price of pi (not per barrel), which may be
negative.1 Each order must be filled completely or not at all, and can only be filled once.
Ron does not have to sell all of his oil, but he must pay s dollars per unsold barrel in
storage costs. Describe an O(nm)-time algorithm to determine which orders to fill so that
Ron can maximize his profit (which may be negative).
Solution:
1. Subproblems
• x(i, j) : maximum possible profit by selling j barrels to the suffix of buyers i through
n
• for i ∈ {1,...,n + 1},j ∈ {0,...,m}
2. Relate
• Ron can either sell or not sell to buyer i
– If he sells, can’t sell to them again but must sell remaining barrels
– If not sells, no change to barrels to be sold
• x(i, j) = max x
3. Topo. Order
• x(i, j) only depends on subproblems with strictly larger i, so acyclic
programminghomeworkhelp.com
3. 4. Base
• If no more buyers remain, Ron must pay s dollars per unsold barrel
• x(n +1,j) = −sj for all j ∈ {0,...,m}
5. Original
• Solve subproblems via recursive top down or iterative bottom up
• x(1,m) is the maximum profit allowing sales of m barrels to all buyers
• Store parent pointers to reconstruct which sales fulfill an optimal order
6. Time
• # subproblems: (n + 1)(m + 1) = O(nm)
• Work per subproblem: O(1)
• O(nm) running time, which is pseudopolynomial in the size of the input
Rubric: (Same for all problems in PS7)
• S: 5 points for a correct subproblem description
• R: 5 points for a correct recursive relation
• T: 2 points for indication that relation is acyclic
• B: 2 point for correct base cases in terms of subproblems
• O: 2 point for correct original solution in terms of subproblems
• T: 2 points for correct analysis of running time
• 4 points if a correct dynamic program is efficient (meets requested bound)
• 3 points for correctly labeling running time polynomial or pseudopolynomial
• Partial credit may be awarde
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4. Problem 2. Splits Bowling
In Lecture 15, we introduced Bowling: a one-player game played on a sequence of n pins,
where pin i has integer value vi (possibly negative). The player repeatedly knocks down
pins in two ways:
• knock down a single pin, providing vi points; or
• knock down two adjacent pins i and i + 1, providing vi · vi+1 points.
Pins may be knocked down at most once, though the player may choose not to knock down
some pins. A Bowling variant, Split Bowling, adds a third way the player can knock down
two pins forming a split, specifically:
• knock down two pins i and j > i + 1 if all pins in {i + 1,...,j − 1} between them have
already been previously knocked down, providing vi · vj points.
Describe an O(n3)-time algorithm to determine the maximum score possible playing Split
Bowling on a given input sequence of n pins.
Solution:
1. Subproblems
• Assume pins are zero-indexed in array V = (v0,...,vn−1)
• x(i, j, c) : maximum possible score playing Split Bowling on substring of pins V [i :
j], where all pins in range must be knocked down if c = 1, but unconstrained if c =
0
• for i ∈ {0,...,n},j ∈ {i,... , n},c ∈ {0, 1}
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5. 2. Relate
• Guess what to do with pin i
– If c = 0, can choose not to knock down pin i
– Otherwise, can knock down pin i in one of three ways:
∗ knock down by itself getting vi points,
∗ knock down with pin i + 1 for vi · vi+1 points, or
∗ knock down with pin k for vi · vk points for some k ∈ {i + 2,...,j − 1}
– In last case, need max value to knock down all pins between and pins after
3. Topo. Order
• x(i, j) only depends on subproblems with strictly smaller j − i, so acyclic
4. Base
• x(i, i, k) = 0 (no more pins gives zero value)
• for i ∈ {0,...,n},k ∈ {0, 1}
5. Original
• Solve subproblems via recursive top down or iterative bottom up
• x(0, n, 0) : maximum score possible playing on all pins, unconstrained
6. Time
• # subproblems: O(n2)
• Work per subproblem: O(n)
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6. • O(n3) running time, which is polynomial in the size of the input
Problem 3. Quarter Partition
Given a set A = {a0,...,an−1} containing n distinct positive integers where m =
describe an O(m3n)-time algorithm to return a partition of A into four subsets A1,
A2, A3, A4 ( A (where A1 ∪ A2 ∪ A3 ∪ A4 = A) such that the maximum of their
individual sums is as small as ai∈A possible, i.e., such that max { ΣaiεAj ai| j ϵ {
1,2,3,4}}is minimized.
Solution:
1. Subproblems,
• x(k, s1,s2,s3) : True if it is possible to partition suffix of items A[k :] into four
subsets A1,A2,A3,A4, where sj = P ai∈Aj ai for all j ∈ {1, 2, 3}, and false
otherwise
• for k ∈ {0,...,n} and s1,s2,s3 ∈ {0,...,m}
2. Relate
• Integer ak must be placed in some partition. Guess!
• x(k, s1,s2,s3) = OR
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7. 3. Topo. Order
• x(k, s1,s2,s3) only depends on subproblems with strictly larger k, so acyclic
4. Base
• x(n, 0, 0, 0) = True (can partition zero integers into zero sum subsets)
• x(n, s1,s2,s3) = False for any s1,s2,s3 > 0
• (cannot partition zero integers into any subset with positive sum)
• for i ∈ {0,...,n},k ∈ {0, 1}
5. Original
• Solve subproblems via recursive top down or iterative bottom up
• Let m(0,s1,s2,s3) =
• Solution to original problem is then given by:
• min{m(0,s1,s2,s3) | s1,s2,s3 ∈ {0,...,m}}
• Store parent pointers to reconstruct each subset
6. Time
• # subproblems: O(m3n)
• Work per subproblem: O(1) any maximal
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8. • Work to compute original: O(m3)
• O(m3n) running time, which is pseudopolynomial in the size of the input
Problem 4. Corrupt Chronicles
Kimmy Jerk is the captain of the USS Exitcost, a starship charged with exploring new
worlds. Each day, Capt. Jerk uploads a captain’s log to the ship’s computer: a string of at
most m lowercase English letters and spaces, where a word in a log is substring not
containing a space. One day, Capt. Jerk is abducted, and Communications Officer Uhota
Nyura goes to the captain’s logs looking for evidence. Unfortunately, the log upload
system has malfunctioned, and has corrupted each of the last n logs by dropping all
spaces. Officer Nyura wants to restore the spaces based on Capt. Jerk’s speech patterns in
previous logs. Given a list Lc of the n corrupted logs, as well as a list Lu of O(m2n)
uncorrupted logs from before the malfunction, Officer Nyura wants to:
• for each word w appearing in any log in Lu, compute f(w): the positive integer number
of times word w appears in Lu (note, f(w) is zero for any word w not appearing in Lu);
and
• for each log ` i ∈ Lc, return a restoration Ri of ` i (i.e, a sequence of words Ri whose
ordered concatenation equals ` i), such that P w∈Ri f(w) is maximized over all possible
restorations.
Describe an O(m3n)-time algorithm to restore Capt. Jerk’s logs based on the above
protocol.
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9. Solution: Observe that if we can process Lu in O(m2n) time, and then compute the
restoration the n corrupted logs, each in O(m3) time, then we will be within the desired
bound.
First, scan through each of the O(m2n) at most length-m uncorrupted logs, and insert each
word w into a hash table H mapping to frequency f(w). Specifically, if w does not appear
in H, add it to H mapping to 1, and if w does appear in H, increase the mapped value by 1.
This process computes all f(w) for words appearing in any log in Lu directly in expected
O(m3n) time, since the time to hash each word is linear in its length. Second, we compute
a restoration for each log ` i in Lc via dynamic programming in expected O(m3) time,
leading to an expected O(m3n) running time in total, which is polynomial in the size of the
input (there are at least Ω(nm) characters in the input).
1. Subproblems
• x(j) : maximum P w∈Ri,j f(w) for any restoration Ri,j of suffix ` i[j :]
• for j ∈ {0,..., |` i| ≤ m}
2. Relate
• Guess the first word in ` i[j :] and recurse on the remainder
• x(j) = max{f(` i[j : k]) + x(k) | k ∈ {j +1,..., |` i|}} H(w) if w ∈ H
• Where f(w) = 0
3. Topo. Order
• x(j) only depends on strictly larger j, so acyclic
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10. 4. Base
• x(|` i|) = 0 (no log left to restore)
5. Original
• Solve subproblems via recursive top down or iterative bottom up
• Solution to original problem is given by x(0)
• Store parent pointers to reconstruct a restoration achieving value x(0)
6. Time
• # subproblems: O(m)
• Work per subproblem: O(m2)
• (O(m) choices, and each hash table lookup costs expected O(m) time)
• Expected O(m3) running time per restoration
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