Make Your Writing Readablefor scientists and engineers: Part-2

Y N Mohapatra

IIT Kanpur

‘How to ’

IDC 603A MAR 08, 2018 IIT Kanpur

Part II

Clarity, Continuity & Cohesion

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A search for…..

Clarity

while being Concise

Continuity

or Flow while retaining emphasis

Cohesion

with Unity of purpose and style

These do not come naturally to most of us.

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Wait for Part - 2

More Examples on Clarity

Continuity or Flow

Editing For Emphasis

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Cohesion

How to ensure that the readers will

Grasp the TOPIC

Get the POINT

Keep track of PLAYERS, &

See how one IDEA follows from another

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Show rather tan Tell.

Cohesion

The Topic

is the comforting Umbrella.

The Point

the local focus : the Handle.

Linkages

connection to the topic: Spokes

Held together tightly to protect against rain of distractions.ynm iit kanpur

What is it about ?

A newspaper is better than a magazine.

A seashore is a better place than the street.

At first it is better to run than to walk.

You may have to try several times.

It takes some skill but it’s easy to learn.

Even young children can enjoy it.

Once successful, complications are minimal.

Birds seldom get too close.

Rain, however, soaks in very fast.

Too many people doing the same thing can also cause problems.

One needs lots of room.

If there are no complications, it can be very peaceful.

A rock will serve as an anchor.

If things break loose from it, however, you will not get a second chance.

ynm iit kanpur Pinker: p 148

Organization Patterns: Arcs of Coherence

Cause & Effect : First cause, then effect vice versa

Chronology : Implied & Explicit Sequencing

Resemblance: Comparison & Contrast, Analogy

Listing : Must be parallel

Elaboration : Exemplification, Extended Definition

Classification Division, Attribution,

General to Particular, or Particular to General

A clear and deliberately chosen pattern. Can be nested or linked.ynm iit kanpur

Example: Surface Tension

The energy E required to remove all surface moleculesout of the energy of the forces of the remaining liquid isproportional to the surface area; therefore E=σA whereσ, the proportionality factor, is called the surfacetension, σ=E/A and is measured in joules/m2. One ofthe most important properties of the liquid is that itssurface behaves like an elastic covering that is alwaystrying to decrease its area. The surface moleculesexperience a net inward force; and consequently,moving a surface molecule out of the surface requiresenergy. A result of this tendency for the surface tocontract is the formation of liquids into droplets asspherical as possible considering the constraint of theever present gravity force. Surface tension arisesbecause the elastic attractive forces between moleculesinside a liquid are symmetrical; molecules attracted nearthe surface are attracted from the inside but not theoutside.

Example: Cause-EffectOne of the most important properties of the liquid is that its

surface behaves like an elastic covering that is always trying todecrease its area. A result of this tendency for the surface tocontract is the formation of liquids into droplets as spherical aspossible considering the constraint of the ever present gravityforce. Surface tension arises because the elastic attractive forcesbetween molecules inside a liquid are symmetrical; moleculesattracted near the surface are attracted from the inside but notthe outside. The surface molecules experience a net inwardforce; and consequently, moving a surface molecule out of thesurface requires energy. Surface tension arises because theelastic attractive forces between molecules inside a liquid aresymmetrical; molecules attracted near the surface are attractedfrom the inside but not the outside. The energy E required toremove all surface molecules out of the energy of the forces ofthe remaining liquid is proportional to the surface area; therefore

E=σAwhere σ, the proportionality factor, is called the surface tension,σ=E/Aand is measured in joules/m2.

Step-by-step linking :Appropriate connectives

Surface tension..............molecules situated near the surface

The surface molecules............requires energy.

The energy E required..................is proportional to......

The proportionality factor ......is measured in joules/sq.m.

Intensifiers & Signal Words or Phrases

Cause & Effect : therefore, Thus consequently, accordingly,

asa aresult, leads to, affects, requires, produces etc.

Chronology : In 1857, last week, sequence of process

Resemblance: however, on the other hand, conversely,

similarly, likewise, in contrast to; More than less than, X will beeasy but Y would entail complications; while, where as, but

Listing : given blow, as follows etc.

Elaboration : specifically, in general, is attributed to

A clear and deliberately chosen pattern. Can be nested or linked.ynm iit kanpur

Listing: frequently used in S&T

Formatted : Grammatically parallelEx.1 In this particular case the most important variables are the following:

1. Pressure and temperature of the boiler

2. what type of boiler is required

3. The amount of oxygen

4. Fuel temperature

Ex.2. The present duties include repairing computers, printing devices and scanning instruments.

Ex.3. Mr. B. Prasad, the senior systems manager, asked me to develop a hard disk management system, to reside in the server, to give better control, and to co-ordinate numerous disk drives.

Ex.4. We learn many reasons why a concept fails to be appreciated by reading, observing, talking an listening to our students.

Unformatted: Indication that a list follows.

Order of importance: Descending or Ascending

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Negative Example :PNAS on ‘Cockroach Locomotion’

Stuffy Writing

Confusion regarding Tone & Register

Bad listing

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Formal or Informal

Audience : Err on the conservative side

Register: A continuum for formal to informal

Tone:Dismissive ……. Arrogant

Timid………………confident

Dull…………………Energetic

Cynical…………….Optimistic

Condescending ……..Caring

In scientific communication, most desired tone: Neutral, Confident (persuasive), Open, emotionally enthusiastic, and a sense of self-assurance.

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Informal Register of BlogsSo why did we discuss Königsberg bridges problem and Euler graphs in the first place? Well, it’s not so boring and by investigating the problem and foregoing solution we touched the elements behind graphs (vertex, edge, directed, undirected) avoiding a dry theoretical approach. And no, we are not done with Euler graphs and the problem above, yet. 😶

We should now move on to the computer representation of graphs as that is the topic of interest for us programmers. By representing a graph in a computer program, we will be able to devise an algorithm for tracing graph path(s), and therefore find out if it is an Euler path. Before that, try to think of a good application for an Euler graph (besides fiddling around with bridges).

How to think in graphs:An illustrative introduction to Graph Theory and it’s applications, Vardan Grigaroyan, Medium, 21 February, 2018

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Negative Example: Readability in“Cockroach Locomotion”

Finally, we hypothesized that compression of the bodyand legs would demonstrate nonlinear, viscoelasticbehavior (41), suggesting crevice crossing might beaffected by rate and that the magnitude of peakcompression forces would reveal the extent of exoskeletalrobustness. As a first step toward quantifying theexoskeletal material properties (41) and shape changesthat enable cockroaches to traverse crevices and crawl inconfined spaces, we measured the compression ofselected head and body segments by adding loads toanesthetized animals and performed a series of dynamiccompressive cycle tests on living animals.

Expanding Scope: Discussion in“Cockroach Locomotion” & Lists

Inspired by the data on cockroach segment and bodycompression, postural change, and kinematics, wedesigned a legged robot using the smart compositemicrostructure (SCM) manufacturing (44⇓–46) approachinvolving laser-cutting, laminating, and the folding ofexoskeleton-like plates. We see this robot useful both as aphysical model to test future hypotheses of themechanisms permitting confined-space locomotion, aswell as a first step toward the development of a softsearch-and-rescue robot that can penetrate the rubble leftby tornados, earthquakes, or explosions.

NYT Popular version of “Cockroach Locomotion”

How Cockroaches Crash Into Walls and Keep GoingDOUGLAS QUENQUA FEB. 13, 2018 NYT

Anyone who’s tried to kill a cockroach knows that the ancient pests have some world-class evasive maneuvers. Or at least they appear to.

The agility of cockroaches may owe less to lightning-fast reflexes and fancy footwork than their tough, shock-absorbent bodies. According to a new study, American cockroaches can run full-speed into walls and other obstacles because their exoskeletons allow them to recover quickly with hardly any loss in momentum.

“Their bodies are doing the computing, not their brains or complex sensors,” said Kaushik Jayaram, a biologist at Harvard University and lead author of the study, which was published in the Journal of the Royal Society Interface.

The findings — which were further validated by a tiny, cockroach-sized robot — could influence the design of the next generation of robots that run, jump and fly.

The escape methods of the American cockroach are legendary among scientists. Studies have shown them to be among the world’s fastest insects, reaching speeds of up to 3.4 miles per hour (or about 50 body lengths per second). They can pivot quickly, scamper across ceilings and disappear into tiny crevices.

NYT continuation on Cockroach Locomotion

But they are also known to frequently collide with obstacles. Dr. Jayaram wanted to know whetherthose collisions counted as missteps or were part of a strategy that favored speed over accuracy. Tofind out, he and a team of researchers focused on one of the insect’s signature moves: the blink-of-an-eye transition from running along a floor to scaling a vertical wall.

Using high-speed videography, the researchers recorded 18 male American cockroaches repeatedlyrunning across an acrylic track with only one climbable wall (other walls were coated with petroleumjelly). When they viewed the tapes in slow motion, the researchers were surprised to discover that 80percent of the time, the insects were simply crashing headfirst into the wall at top speed before makingthe transition. Other times, they were angling their head upward and using their legs to slow downbefore reaching the wall.

It turns out the cautious approach wasn’t necessary. The roaches that ran headlong into the wall couldmake the upward shift just as quickly — in about 75 milliseconds — the researchers found. Apparently,the roaches preferred to run full speed knowing their exoskeleton could take the hit.

To be sure the roaches were relying on their bodies and not their wits, the researchers ran similar testswith a tiny, cockroach-inspired robot that had no sensors or feedback mechanisms. The results weresimilar, appearing to confirm Dr. Jayaram’s conclusions.

The findings could prove helpful to engineers as robots become smaller, softer and lighter, Dr. Jayarambelieves. “There is an increasing trend to make robots smarter and more capable, so that they canoperate effectively indoors or within cluttered natural environments,” he said.

If the current study is right, “small robots can be built with simple, robust, smart bodies to safely bump into obstacles instead of using complex and expensive sensing and control systems,” he said.

His message to engineers? Just follow the cockroach.

Artwork Personalization at NetflixBy Ashok Chandrashekar, Fernando Amat, Justin Basilico and Tony Jebara

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For many years, the main goal of the Netflix personalized recommendation system has been

to get the right titles in front each of our members at the right time. With a catalog spanning

thousands of titles and a diverse member base spanning over a hundred million accounts,

recommending the titles that are just right for each member is crucial. But the job of

recommendation does not end there. Why should you care about any particular title we

recommend? What can we say about a new and unfamiliar title that will pique your interest?

How do we convince you that a title is worth watching? Answering these questions is critical

in helping our members discover great content, especially for unfamiliar titles. One avenue to

address this challenge is to consider the artwork or imagery we use to portray the titles. If the

artwork representing a title captures something compelling to you, then it acts as a gateway

into that title and gives you some visual “evidence” for why the title might be good for you.

The artwork may highlight an actor that you recognize, capture an exciting moment like a car

chase, or contain a dramatic scene that conveys the essence of a movie or TV show. If we

present that perfect image on your homepage (and as they say: an image is worth a thousand

words), then maybe, just maybe, you will give it a try. This is yet another way Netflix differs

from traditional media offerings: we don’t have one product but over a 100 million different

products with one for each of our members with personalized recommendations and

personalized visuals.

Skillful movement in arcs of cohesion:

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Much of the Netflix recommendation engine is powered by machinelearning algorithms. Traditionally, we collect a batch of data on how ourmembers use the service. Then we run a new machine learningalgorithm on this batch of data. Next we test this new algorithm againstthe current production system through an A/B test. An A/B test helps ussee if the new algorithm is better than our current production system bytrying it out on a random subset of members. Members in group A getthe current production experience while members in group B get thenew algorithm. If members in group B have higher engagement withNetflix, then we roll-out the new algorithm to the entire memberpopulation. Unfortunately, this batch approach incurs regret: manymembers over a long period of time did not benefit from the betterexperience. This is illustrated in the figure below.

Netflix : Bandit algorithm for personalization

To reduce this regret, we move away from batch machine learning and consider online machine learning. For artwork personalization, the specific online learning framework we use is contextual bandits. Rather than waiting to collect a full batch of data, waiting to learn a model, and then waiting for an A/B test to conclude, contextual bandits rapidly figure out the optimal personalized artwork selection for a title for each member and context. Briefly, contextual bandits are a class of online learning algorithms that trade off the cost of gathering training data required for learning an unbiased model on an ongoing basis with the benefits of applying the learned model to each member context. In our previous unpersonalized image selection work, we used non-contextual bandits where we found the winning image regardless of the context. For personalization, the member is the context as we expect different members to respond differently to the images.

A key property of contextual bandits is that they are designed to minimize regret. At a high level, the training data for a contextual bandit is obtained through the injection of controlled randomization in the learned model’s predictions. The randomization schemes can vary in complexity from simple epsilon-greedy formulations with uniform randomness to closed loop schemes that adaptively vary the degree of randomization as a function of model uncertainty. We broadly refer to this process as data exploration. The number of candidate artworks that are available for a title along with the size of the overall population for which the system will be deployed informs the choice of the data exploration strategy. With such exploration, we need to log information about the randomization for each artwork selection. This logging allows us to correct for skewed selection propensities and thereby perform offline model evaluation in an unbiased fashion, as described later.

Exploration in contextual bandits typically has a cost (or regret) due to the fact that our artwork selection in a member session may notuse the predicted best image for that session. What impact does this randomization have on the member experience (and consequently on our metrics)? With over a hundred millions members, the regret incurred by exploration is typically very small and is amortized across our large member base with each member implicitly helping provide feedback on artwork for a small portion of the catalog. This makes the cost of exploration per member negligible, which is an important consideration when choosing contextual bandits to drive a key aspect of our member experience. Randomization and exploration with contextual bandits would be less suitable if the cost ofexploration were high.

Under our online exploration scheme, we obtain a training dataset that records, for each (member, title, image) tuple, whether thatselection resulted in a play of the title or not. Furthermore, we can control the exploration such that artwork selections do not change too often. This gives a cleaner attribution of the member’s engagement to specific artwork. We also carefully determine the label for each observation by looking at the quality of engagement to avoid learning a model that recommends “clickbait” images: ones thatentice a member to start playing but ultimately result in low-quality engagement.

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Editing for Emphasis

Handling repetitionsFull Form

Short Form

Pronoun

Relative Clauses

Compound Nouns

➢Combining Closely related Sentences

➢Being Concise

➢Using Intensifiers & Connectives

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‘No Unnecessary Words’

Every time a writer adds a word to a sentence, he is imposing not one but two cognitive demands on the reader:

➢ Understanding the word, and

➢ Fitting it into the tree (of ideas).

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Ex.

The increase in crystallinity of the sample has positive

correlation with increase in thermal energy provides, and

hence the temperature.

Ex. The sample becomes more crystalline at higher temperature.

What is unnecessary?

➢Hedge Terms : Redundancies in uncertainty

Ex. The new experiment may potentially revolutionize the filed.

➢Obvious Expressions: obvious or excessive details

Ex. I received your letter that you wrote yesterday, and read it thoroughly.

➢Unnecessary determiners and modifiers:Ex. Any particular type of hard surface will be adequate.

➢Repetitive Terms:The Laboratory considered the procedure an uneeded extra step.

➢Redundant Pairs: various differences, past history, future plans, past

memories, final outcome, unexpected surprise, free gift, sudden crisis

➢Redundant Criteria: large in size, period in time, round in shape, of

cheap quality, in a confused state. The precipitate was red in colour and shiny in appearance.

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