Technical Writing

1. Technical Writing

2. Publish, Why? • Communicate new knowledge • Receive recognition • Get feedback

3. Publish, Where? • Conferences - Some are more prestigious than conferences • Quick feedback • More relevant material • Posters • More exposure • Less Prestigious • Journals • Longer review cycle • More revisions • Longer papers

4. Publish, Who? • Each author should have contributed • Order only important until you become a professor • It doesn’t cost you anything to put someone else on as an author • Set of authors should stay the same through the review process

5. Publish, For Whom? • Not for you!! • Write so your reader can understand what you are saying • Some technical person in your field • Identify the reader by looking at the CFP • Do not brag about your results

6. Advice for review cycle • Don’t submit reviewers you don’t want • Don’t bad mouth other people’s research

7. Paper format • One result per paper • Point every section back at that result • Abstract - Get to the point early • Introduction - Don’t repeat abstract • Include related work

8. “the objectives are unclear” “too little beef” “the authors seem to ignore ...” “... so what?” “the paper fails to deliver what it promises” “unsubstantiated claims” “opinion paper...” “premature...” “the paper provides little evidence that the results do apply in real settings”, “scaleability is questionable”, etc “evaluation is weak” “rambling discusion...” [to editor/PC:] “boring”, “unexciting”, “substance-free” Typical reviewer comments

9. Reader should always know what’s going on • Tell the reader what conclusions they should draw • Don’t expect them to draw conclusions on their own from figures etc.

10. What do most people read? • Title • Abstract if they found the title interesting • Figures • Make captions meaningful • Figure every page or so • Conclusions - Make it interesting • Body

11. Mistakes • Massively parallel computers (MPCs), characterized by their scalable architectures, are a viable platform on which to solve the so-called grand-challenge problems. These distributed-memory systems are expandable and can achieve a proportional performance increase without changing the basic architecture. In order to take full advantage of scalable hardware, the application software must also be scalable to exploit the increased computing capacity.

12. Mistakes • This paper is organized as follows. In Section 2, we describe local transformations in k dimensions. In Section 3, we describe an incremental approach for constructing k-D Delaunay triangulations using local transformations. In Section 4, we prove that this approach always constructs a Delaunay triangulation. In Section 5, we describe three algorithms and a data structure based on this approach. In Section 6, we discuss the time complexities of the algorithms and present experimental results from our implementation of these algorithms.