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Go read Edward Tufte’s books

These are not books you read in the normal way. They don’t offer lists of rules for visually displaying evidence. Instead, these books  beautifully reproduce and evaluate examples of graphs, tables, charts, diagrams, maps, “sparklines” (You’ll have to go find out what that means!), and more. They show examples from centuries of people–such as Galileo, Leonardo da Vinci, Henri Matisse, Richard Feynman, etc– trying to communicate their ideas clearly and beautifully.

Tufte also argues that producing visual representations of our ideas makes those ideas clearer to us, too. In other words–just as writing is thinking–sketching and drafting and moving visual elements around on the page are analytical tools. I can vouch for this myself. I went to Tufte’s day-long seminar last Friday, and I’ve been able to reconceive and clarify for myself the ideas in an article I finished writing two years ago but about which I am just now creating a poster. I thought I was pretty familiar with the ideas in my own completed article, and yet the sketches (and even watercolors!) have made me see the ideas more clearly and in ways that will make them much easier to talk about.

 

(The image shows Galileo’s sketch of the changing positions of Jupiter’s moons from one night to the next.)

Thomas Hager’s The Alchemy of Air

The subtitle of this book is “A Jewish Genius, a Doomed Tycoon, and the Scientific Discovery that Fed the World but Fueled the Rise of Hitler.” But if that’s not a broad enough net to grab you, let me tell you a bit more. This book describes a scientific and engineering feat on the scale of the Manhattan Project (p. 122). In achieving nitrogen fixation on a grand scale, engineer Carl Bosch turns chemist Fritz Haber’s table-top process into a huge city-size factory, and then a bigger one. In doing so, he and his many colleagues invent a whole new type of business model, solve a global crisis (starvation, by creating fertilizer to counteract inevitable soil depletion and thus a decrease in worldwide crop production), and address a national crisis (Germany’s lack of explosives with which to fight WWI).

The book is not only fascinating on the history of chemistry, especially the history of dyes, nitrogen fixation, and the development of synthetic fuels, but also incredibly moving on the human side. The two main scientists are fascinatingly contradictory characters, revealing just how inconsistent and flawed even very brilliant and hard-working humans are. The story of these two men is moving and thought-provoking. Shakespeare could have based a tragedy on these linked stories. The book reiterates the power of science to work for good and evil, but it does so in detailed and original ways, so that the lessons seem newly learned and spelled out more completely. For one, science is not just a double-edged sword: even the good edge has two edges.

These men’s ambitions, ideals, personal strengths and weaknesses, affinities, competition on a corporate or personal scale–and just their intense interests and special abilities–all combine as in a complex chemical reaction with the historical moment’s needs and the political atmosphere to create a unique result. We see how and when they control and lose control of what they create, and how they very differently react to their somewhat self-determined and somewhat uncontrollable fates.

I think an engineer would not only enjoy the book but also benefit from reading it. The story itself is interesting—the important scientific and industrial history—and it’s also  very well written. Hager demonstrates how to define terms in simple, subtle way; how to use a single sentence to clarify and re-emphasize a point that he’s discussed for pages; how to describe methods by describing the materials used; how to give scientific information in detail when needed and just sketchily when not so important; and how to link one boring-sounding topic (“nitrogen fixation” has to be one of the most off-putting nouns I can think of) to far-reaching consequences, important historical moments, philosophical questions, and global influence. The power to link your topic, whatever it is, to many other fascinating ones is worth learning!

 

Long names/nouns are easy to write but difficult to understand

First, a definition. A “noun phrase” is not the entire subject of a sentence. Your sentence might be

The brave, warmly dressed woman holding a saw and the large hawk with a rat in its mouth perch in the tree staring at each other.”

Then the subject has two noun phrases in it. It’s a collection of nouns and adjectives (or even phrases) that have been stuck together to form one long noun. Some other examples are:

power-controlled rate-adaptation interference graph and

wideband, high-resolution analog-to-digital converter.

Here are two places to find more examples: http://www.chompchomp.com/terms/nounphrase.htm and http://www.grammar-monster.com/glossary/noun_phrases.htm.

The common technique in engineering is to stick everything together in one set of adjectives and nouns, and then skip the (helpful, sometimes more explanatory) prepositional phrases. I often want you to unpack the set of adjectives and nouns and use phrases to clarify what you mean. Also, you might discover that you don’t need all that information about the noun; you might already have established this information earlier in the article, and you can just use a shorter name for this thing.

 Here’s example that a student brought in last week:

Understanding spin transport via collective magnetic excitations is currently gaining attention.

This is a refreshingly short sentence, but it’s a bit difficult to unpack (by which I mean, “interpret”). In other words, the reader has to turn the words around in his or her head in order to understand what it means. Here are some possible revisions:

Researchers are now trying to use collective magnetic excitations to understand spin transport.

Researchers are trying to understand spin transport by looking at collective magnetic excitations.

Researchers are trying to understand spin transport by looking at the way that collective magnetic excitations influence them.

But maybe none of these is  accurate. They might not be what the sentence means at all. I had to make up some possible relationships between the two topics, which would not have been necessary if the writer had clarified that relationship. Often, when I quiz students on what they mean by a sentence, we go though many revisions together before I suddenly realize what they meant, and how far that was from my guess!

Take-away message: beware the long noun phrase. If you find yourself writing one, determine if there’s some information in it that has already been clearly established; then take that part out. If it’s still ambiguous or just hard to figure out, explain the relationship between the various parts of the noun phrase.

Anything you can do to make your reader’s job easier will help assure that they are getting the message you intend to send. It will also get you more readers!

 

Short Notes Help

by Yikun Chang

According to my experience, writing is an effective way to help collect ideas, categorize them, and find logical relationships among them. Nowadays, Electronic Design Automation (EDA) tools are highly convenient. However, this fact is a double-edged sword. We become more and more dependent on simulation, and even overwhelmed by it. We sit in front of computers, set up all conditions, and then click “run.” After a while, we collect data and find something not that good. Then we adjust parameters slightly and re-run the simulation. This cycle repeats and repeats until we get lost in simulation and restart the whole flow. Fast simulation makes us little cherish the chances of running simulation, lazy to write down the simulation results, and barely spend time on carefully thinking about our design. Due to this kind of sad experience, I have learned to keep notes about research no matter how meaningless an idea or the data looks. Every time I feel lost in research, I look back at my notebook to re-organize my thoughts with some symbols like arrows or brackets. In this way, writing as well as thinking at the same time helps me figure out where the current problem comes from, and what I should focus on next. The record of the data that you previously think not important may help save a lot of time when you someday find it actually means something or need to compare it with new data.

Drama in an EE paper

I will first not-completely-but-somewhat-jokingly say that this article’s first author is a past student of EE 295, so of course he’d be doing lovely things with his writing! (I will add that he was a good writer when he started in EE 295, and that his advisor’s students are often excellent writers. The advisor is the third author on this paper. A culture of good writing, of valuing writing, seems to develop in some labs.)

This July 2015 article is “Variable-Length Convolutional Coding for Short Blocklengths With Decision Feedback” by Adam R. Williamson, Tsung-Yi Chen, and Richard D. Wesel. Since it is so recent, I will only photograph one short excerpt from the text, although somewhat more will be cited and described.

Drama is developed from the first sentence, when the authors write something along the lines, of “Although the founding father of our field found that feedback was not useful for x, feedback can be used for other purposes”:

Screen Shot 2015-10-27 at 2.10.30 PM

If that’s not dramatic, then what is? It’s dramatic when an important figure in the field is right, or wrong, or just missed noticing something important.

The way that Einstein’s thoughts on the cosmological constant have been cited and argued for and against is perhaps similar. He was right. He was wrong. (Maybe there we even some other back-and-forths, and certainly there were many amendments.) Then, a January 2013 Scientific American called “Right Again, Einstein!” starts, “A new study of one of the universe’s fundamental constants casts doubt on a popular theory of dark energy” (Moskowitz). Almost three years later (Sept 2015), “What Einstein Got Wrong” in that same magazine begins: “Like all people, Albert Einstein made mistakes, and like many physicists he sometimes published them” (Krauss). There’s something exciting and important about great thinkers having limitations, even if they might just be limitations caused by the moment of time in which they lived, and the development of their fields at that moment.

The extensive literature review of the Williamson et al. paper tells a story, too. It’s chronological: this idea was developed, and then that, and then this other one went further. It’s also got characters; the researchers are listed by their names, rather than the papers being listed by reference numbers. Once there are names, some intellectual drama can be introduced: all these authors did stuff in reaction to the work that came before: one name does “pioneering work,” another “formalizes it” or “demonstrates” something else, or “furthers” or “extends” the work. Others “study” or “show” or “provide an overview.” It’s complicated, but there are a lot of people doing stuff, cooperating even, and that’s interesting and appealing.

Storytelling Elements in EE Writing: Personification

My students give me at least as much homework as I give them.

In yesterday’s class, the elements of storytelling were doubted to be of importance in technical writing, or even academic engineering writing. So I’m looking for examples.

Here’s one, in the first article I brought up on my screen, an award-winning June 2000 article by Vítor H. Nascimento and Ali H. Sayed, “On the Learning Mechanisms of Adaptive Filters.”

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Notice the way that adaptive filters are personified in their introduction. They “adjust themselves to an ever-changing environment,” they have a “learning curve,” a “learning process,” and “learning capabilities.” An adaptive filter “reacts.” They are like humans or other species adapting to a habitat.

After seeming to personify adaptive filters, Nascimento and Sayed develop a nurturing relationship with them. The next paragraph of the introduction reads:

Screen Shot 2015-10-27 at 12.59.09 PM

Nurturing in their readers a warm feeling for adaptive filters, the authors say that “special care” must be taken with them (as with human children). “Interpreting learning curves” might be much more mathematical when it comes to adaptive filters, but these authors “care” for the slow and fast learners both, and, like a supportive and patient kindergarten teacher, they believe that slow learners end up “’smarter’” than they appear at first.

Researching without writing is like chewing without swallowing (or swallowing without chewing)

by SV

As scientists, we love to get caught up in our work. We spend countless hours in our own heads, sporadically jotting down notes on the back of an envelope or a nearby white board only to throw the envelope away or erase the board the next day. The most common justification for this sort of behavior is that ”science takes time” and ”research should never be publication focused.”

The truth of the matter is: We are lazy. It is very easy to justify a day spent doing nothing if we can blame it on this one problem we have been stuck on for months. Rather than focus our thoughts and put them on paper, we prefer to leave them as evasive thoughts in our head. Every paper I have ever written began a couple of weeks before the deadline. There was always some problem that I had been struggling with for months, but given that the due date was mere days away, I had no choice but to start writing, in the hopes that the issue would resolve itself through some sort of miracle.

And it always did. Only it wasn’t a miracle. After a couple of these kinds of occurrences, I realized that my ability to perfectly align solutions with deadlines had nothing to do with luck, faith, or countless hours spent in the middle of the night. It was the very motion of writing. See, by typing out my thoughts, pouring my heart and frustration onto a piece of paper, I suddenly had a new perspective.

So my advice to every one who is struggling with that one detail in their proof is: Write it out. Not on the back on envelope. Not on the white board. Type it up like you’re about to submit it to your dream journal. Get the template. Make nice figures. You will soon feel like a reviewer rather than an author. And that perspective might just be all it takes.

Close Reading Gabor

If you bought the EE 295 Sketchbook, then you can (if you choose) review topics such as the types of sentences (simple, compound, complex, and compound complex), the ways good paragraphs are put together (with thesis or topic sentences, moving from simpler sentences to more complex ones and then back, and with coherence), and even the ways that sentences can be written to connect to one another helpfully within paragraphs (“connectivity”). Reading for the authors’ rhetorical choices is a new way of seeing, but I think it’s one of the most useful skills to improve your writing over your whole career.

One other thing to take into account, since we’ll be looking at introductory paragraphs, is that the beginning of a journal article tends to establish common ground, state a problem, and explain the writer’s response.

With all that in mind, let’s look at some examples from journal articles in the field.

Gabor’s classic article, “The Theory of Communication” (1946), starts like this:

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Notice, of course, how the very first paragraph is straightforward: “the purpose of this study” and the organization of the article are immediately specified. The first sentence is long, but it is just a simple sentence: the purpose is to present a method. All the rest are added phrases. The second sentence is complex, but it’s not a difficult one to figure out. There’s a dependent clause (starting with “while”) and then the main, independent clause.

The second paragraph then moves to one of the main goals of any introduction: establishing common ground. But what else does Gabor achieve here? The paragraph reads as as a short, short review article. This works to establish the writer’s authority. And it’s orderly; it’s a chronological story of how one idea develops off of another. After the first sentence or two, every other one begins with a time period. Usually the main subject is the researcher. Then there are specific and varied verbs: “disproved,” “discovered,” generalized.”

And let me emphasize that it’s a story, a narrative, which is an easy way for readers to process information. The engineers become characters in this story, which make the story easier to understand than if the subjects of the sentences were the inanimate “principles,” “claims,” and “convictions.”

Notice that the first two sentences do start with these inanimate subjects, and this is probably to establish the subject of the whole paragraph: principles of transmission. Notice, too, that the paragraph ends with these words, too; the final sentence contains both “transmitted” and “transmission.” It’s hard to get too far off track when reading this paragraph.

I’ll look at other paragraphs from other articles in future posts.

Mixed Messages on Plagiarism

By Dr. Sarah Gibson

I don’t want to be a downer…but the thing that has been on my mind lately regarding academic writing is plagiarism. I found a paper back in 2012 that plagiarized one of my papers. I guess it was not particularly egregious–they didn’t steal my data and try to pass it off as their own, but they did paraphrase (very poorly, like replacing a few words with synonyms while keeping the sentence structure) several sentences and paragraphs of mine without citing me. In doing so they essentially stole my ideas. I followed the IEEE guidelines for reporting plagiarism, but to my disappointment the editor of the journal that published the offending paper “disagreed” with me that this case constituted plagiarism, and went so far as to say that, if anything, I should be happy that the authors agreed with my arguments! I thought, am I taking crazy pills? So I showed it to the director of the UCLA Graduate Writing Center who also showed it to the director of UCLA Writing Programs,  both of whom agreed that this was textbook plagiarism. At the time, I was wrapping up my PhD, getting ready for my defense, and looking for a job, so I didn’t have time to pursue it further. But it has always been in the back of my mind, so last November I decided to bring up the issue again with the new editor of the same journal. He sent the case to the journal’s Plagiarism Committee, but they refused to hear my case because they said that my complaint had not changed and had already been addressed by the previous editor.

Needless to say, I’m furious. All my life (well, since middle school, probably), all my teachers have put the Fear of God in me about plagiarism, warning me that it is a crime so serious that it could get me kicked out of school, ruin my career, or worse. As such, I’ve taken great pains in my own writing to give proper credit where credit is due, and to be very careful when paraphrasing (even when I am giving proper credit!). So when I am wronged, at the very least I would expect IEEE to have my back. It is so unbelievable to me that they won’t stand up for me–won’t defend the very idea of intellectual property (wrong term?) at all.

One thing that both Directors did point out was how every country/culture (I know you hate slashes! but I used one anyway!) has a different idea about what constitutes plagiarism vs. “common knowledge”, and that this makes the issue much more complicated, since the papers are being written by authors from all across the world, and even the journal editors and IEEE committee members are from all different cultures.

The main purpose of this post is to express my shock and disappointment at this event. But if you have any suggestions for how to proceed, I would love to hear them. I do have a couple more ideas: (1) contact the editor of the plagiarized journal (rather than the plagiarizing journal). This is not IEEE protocol, but maybe they would have my back? (2) Find an IEEE fellow to write a letter on my behalf. (3) Go to the press with a scathing expose.

 

 

Your writing process

What is your writing process?

When and where do you like to write, and why?

What sort of on-going writing to do you, as you think and do research?

How do you know that it’s time to start writing a more formal article (a conference paper or journal article)?

How do you prepare to write?

What do you work on first?

As you’ve gotten more experience, what are the most important things you’ve learned about writing or your particular writing process?