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When should you stop asking your PhD advisor to do advisor like things?
After you have finished a PhD when does it become inappropriate to ask your advisor for advice, read over a manuscript, or do any other "advisor things"? Presumably if every student an advisor ever had continued to act as if the former advisor was still his/her advisor, the professor wouldn't get anything else done. Basically how should one interact with the former advisor once the PhD is finished?

I recently wrote a draft of a manuscript, and send it to several co-authors for comments. I incorporated them, and then sent it to my advisor. Without knowing it, in several places he ended up changing the text to match what I had initially written (before the comments from the other co-authors). To me this signalizes that I learned to think like him sufficiently that it's time to go learn from someone new.

@Ana It also signalizes that not all suggestions from co-authors must always be uncritically followed.

My advisor, at two different points in my academic career, said:

"I am your advisor for life. You can always count on me."
"I think it's time for my old students to start finding new people to comment on their work."

I took away from this that your advisor should always be available at some level, it's part of the point of taking on students. You are part of their scientific (or otherwise scholarly) contribution. At the same time, post-PhD you have to develop your own network of support, which is part of your growth as a scholar.
But how do you know when to do this? I think the best answer is simply to ask your advisor (see, they're always there to answer the hard questions). Hopefully they'll give you an honest opinion - either to keep asking them for help or that they think you're ready to fly on your own. Either, if you don't feel confident that you have enough peers to count on, it's probably a sign that you both can still ask your advisor for help and that it's time to put some more energy into expanding your professional network.

I guess you had a nice advisor.

All the answers here are very good. I'd just like to add one extra point.
There are often two kinds of advice one looks for in one's career:

What should someone do in this situation ?
what should I do in this situation ?

The first class of questions are things that often get asked on this forum, and are relatively easy for someone more experienced whose advice you respect to answer. And in fact it's useful to cultivate a few such people whose opinions you respect. When you're a student, most of your questions are of this kind.
The second class of questions are much harder: they're not really about the situation but about you. So the person answering has to know both the situation and understand some things about you. Sometimes (and not always) an advisor can provide that dual insight since they are both experienced, as well as experienced in understanding you. Again, this is not true for many advisors, but it can be true.
Of course, the best person to answer the second type of question is yourself :), but sometimes the perspective from outside helps.
What typically happens is that as you "leave the nest", you stop asking your advisor for answers to questions of the first kind, and you might occasionally still ask them for advice on the second kind of question. There are no hard and fast rules here.

Honestly, I'm not sure if it ever becomes "inappropriate" to have your advisor continue to give you advice, to read manuscripts, or even to help write them (as long as that person gets authorship credits), unless of course, your advisor says, "No".
Many universities however evaluate a tenure-track faculty member based on his or her ability to do independent research, which means that if your entire CV is filled with publications co-authored with your advisor, they will not consider you as doing independent research.

A few years after finishing my PhD I emailed my advisors (I had two) asking for advice about something.  I prefaced the email with something like, "How long after finishing a degree is one entitled to ask one's advisors for advice?"  One of them responded, "When they start asking you for advice, you might want to reconsider."

Note that something industry has been trying to do is encourage more employees to seek out mentors and/or become mentors -- either in working effectively with the company, or in specific skills (which may result in the new kid teaching the oldtimers).
Try not to waste their time with trivia you could teach yourself, and have the grace to be embarassed if you have to ask for advice on something you really should already know... and accept that sometimes the right answer is going to be "go read X and come back to me if you have specific questions" -- but if someone has the answer you need, there's nothing wrong with asking them how to approach the problem.


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Can a paper directly be written on a new application developed rather then an algorithm from existing research results or algorithms?
Suppose we design a prototype for a new product that is indeed novel.
Would writing a paper on such a thing be useful as mostly the papers talk about new algorithms or their upgrades or some new theory ?
I am talking about just a new product that does something better than the existing ones but uses old algorithms and research.

Useful to whom?

@JeffE Depends upon the application like it could be for business analytics, advertising etc.

So basically you are finding a new (and maybe promising) application to an existing technique X. If that's the case then yes. You are showing a new perspective of X applicability to real domains.
This is specially important when the applicability of X is questionable (i.e. X literature lacks real applications for it), then definitely this seems strong findings.

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Do we mention the work that we did ourselves while giving a research proposal?
A university has asked for a research proposal of around 200-250 words in length.
I have done some work on it myself and hence I want to propose that same project for research. Should I mention about the work that I did or should I just present an abstract?

Including preliminary results that indicates the strength of the proposed research is never wrong. If your work has just started and your results are just inconclusive then you may consider not including them, simply because they do not contribute much to indicate the potential success of the proposed project. Mentioning that you have started work could still be useful to mention since it indicates you have thought through the process very carefully but then you should probably also consider adding something that makes it clear you cannot complete the research without additional resources. Otherwise, someone might get the idea you do not need any support and that you will complete the work anyway.

I have a pre-print of all my work done yet published on arxiv.org. And yeah I will mention that I cannot complete the research without additional resources.

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Importance of "independent" under-graduate research in graduate admissions
By independent research I mean, the research was not done with some organization or under a professor. I and my roommate tried out some new things that we thought to get published. My ultimate aim would be getting into research.
Though, I also have a paper published where the I did my work under the supervision of a professor.
But I was wondering if sharing the experience of independent research in a Statement of Purpose really matters while applying for masters ?

What's important is the quality of the peer-reviewed published paper.

In an application, list all material that shows you in a positive light. Being able to perform successful research, both on your own and under supervision, is good. The fact that you engaged, on your own (or with friends) on research and were able to publish it (I assume in a peer-reviewed journal) is a plus for your application… unless the quality of the paper is really (visibly) bad.


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Acknowledgement: non-academic persons
In my industry-sponsored dissertation, I want to thank my industry contact and some non-academic persons in the acknowledgement section of my dissertation. How should I refer to them:

John Smith
J.   Smith
Mr. John Smith

Considering the slightly informal nature of the acknowledgement section, yet keeping in mind that these are industry folks who wouldn't mind a bit of respect.

With their full industry title: Mr. John Smith, the chief engineer of the Qwert corporation.

Also, if they hold a formal academic degree as well, Mr. becomes Dr.

Ah, I knew I had seen a similar question before… and now I know why! “Should honorifics be included in acknowledgments?” was actually my first ever contribution to this site, almost a year ago!

I'd first look for guidance in a style guide somewhere. Assuming there is none to be found, though, I'd also consider asking Mr. Smith himself: "I plan to mention you in my acknowledgements section – how would you like me to write your name?"

Acknowledgments usually skip formal titles, i.e. no Prof., Dr., Mr. or Ms. Some journals even insist on it in their authors guidelines (here for J. Chem. Ed.):

Acknowledgment
  Include acknowledgment of grant and other financial support, technical assistance, colleagues’ advice, and so on. Do not use professional titles or honorifics in this section.

If your journal has guidelines on the topic, follow them.
Otherwise, just refer to the person as you would if you were giving his name to a colleague, i.e. omit the titles and honorifics unless you barely know them. But if you think they will like it, use their titles, it doesn't hurt!

To give a few examples from Nobel-prize winning papers:

True, but in journals it is understood that, by default, the title is something like Dr. or Prof. When you want to clarify, you can always do it though the exact style may have to be agreed upon with the editors: in your first example, the words "members of the ... groups in Harvard" together with the exact description of who did what give you a very clear idea of the exact position held by each individual mentioned. The last example makes the reader wonder who that P. Ingrassia might be: janitor, secretary, technical assistant, student... I would be more explicit here.

I don't think people wonder or infer anything, except that these people are thanked for their help. I think you're overthinking this :)

I don't think that most of them even read the "thanks" sections, unless it is they who are mentioned there and I think you will agree that in this case you may expect any level of "overthinking". No real argument though: your point of view is as legitimate and sensible as mine :-).

in journals it is understood that, by default, the title is something like Dr. or Prof. — No, it isn't.  It's understood that the authors did the work; that's all.  So what if you thank the janitor?  If the janitor helped, he helped.

@JeffE: My father was a custodian, and he had a policy of never hurrying anyone out, even after closing time, but always greeting them with friendly words instead. For that, he got mentioned in Acknowledgements once. :^)

@JeffE I am not against thanking the janitor if he helped: that would be both impolite and ridiculous. I'm merely in favor of being more explicit about who is who and what exactly you thank every person for. Part of it is a matter of personal style, and here no argument will lead us anywhere: I will still do it my way and you will do it your way. I see your point and I believe you see mine as well :-).

You should refer to him in the precise manner in which he wishes to be referred to, which you can only determine by asking him directly.
In other words: The same way you would refer to an academic person.

Yeah, but if he declares himself "the emperor of Earth", you are in trouble. :) My point was that a typical full title of an academic person is "Professor James, Lancroft University" and I wouldn't hesitate to refer to him this way without asking him any questions. Neither would I hesitate to refer to an industry person telling his exact position and affiliation as well (every reporter of every newspaper does exactly that). I do not mean that you are wrong, I just mean that for me the first sentence in your answer contradicts the second one (with which I agree). :)

@fedja I had been in industry for many years. Please allow me to clarify this answer. An academic person can hold the same title for many years while an industry person changes his title more often. Asking the referred industry person directly about his official title would be better. A dissertation is a serious official matter after all.

Thanks! Let me clarify too. I have never said that you shouldn't learn the exact title before using it or that asking a person for his title is a bad way to find it out when it is not written in big bold letters on his business card or in the company directory :). I just meant that my question would be structured somehow as "I am mentioning you in my paper and I would prefer to make it an official reference, so what exactly is your title at this moment?", not "How do you want to be called?". That's all :)

@fedja I agree. Why not make your comment above as a supplemental answer? It sounds a good one!

As you're talking acknowledgements rather than co-authorships, you need to take into account journal style and be sensible and polite.  If someone in industry has helped you, in company time or with company resources, you should acknowledge that.  So "... would like to thank J Smith, XYZ corporation for ...", but if that doesn't fit with journal style guides, you could try "...would like to thank XYZ corporation for support, especially J Smith for valuable discussions".
A company may not get anything for helping you, except that a mention in the literature is good for their profile. Of course if you paid them for work, they could end up anywhere from not mentioned to co-authors depending on the contribution)


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Paper on project without any result - how to conclude
I'm reporting on an engineering project that is in its early phases. Therefore, there are no results, but there is already a quite detailed plan on how the problems in the project can be conquered. Now I was asked to present the project at a conference. I want to present the current state, but I'm not sure how to structure a conclusion as there is no result, and I can't evaulate, if the planned methods will be successful. Still I believe presenting the intended methods will bring value to the community.
How do you conclude a project presentation that doesn't have any results but already has a quite concrete and detailed plan? What content could I provide? Or can one just not provide a conclusion in a paper? That seems a bit unbalanced...

Is it a requirement that you must have Conclusion in the paper? Can you have "Plan" or "Future Outlook" section or something like that?

I'm free to do what I want, but the paper should have a round finish. If that thing is coined conclusion or anything else, doesn't really matter.

I think the conclusion of a paper and the conclusion of a project are two different things.
In my view, the conclusion of a paper is the summary of the paper while the conclusion of a project is the end results of the project. Your paper has the current state and the future plan of the project. These can be the conclusion of the paper. They are not necessarily the conclusion of the project.
If you don't feel comfortable with having "Conclusion" section in your paper without stating the true conclusion of the project, my suggestion is to write a ""Summary" or "Future Outlook" section at the end of the paper. This is a personal taste, in my opinion.
There are many papers without "Conclusion" section either by authors' choice or the final conclusive results for the problem/conjecture were not obtained yet at the time the paper was written.

What goes into the conclusions section in a paper is a bit field-specific. I am doing research in computer science (services / software engineering), and oftentimes, the conclusions are basically the place where you summarize the main points of your paper. It is not required in my field that the conclusions contain hard data / findings. If the paper is of more positional nature (a roadmap paper, a "Towards ..." paper, etc.), the conclusions will be more of an outlook on the challenges ahead.

You could include information about metrics for success, what a solution might look like, future directions, etc. And then the conclusions for the paper would summarize what you presented, rather than what you're going to do.

The paper is about an analysis that has been performed to address some challenges and fulfill some objectives. The result of the analysis is a plan.
The plan should fulfill the objectives, it should be fault tolerant, it should consider the possible risks that may materialize during the project or it should do whatever it should do and it should be like whatever it should be (I don't know the specific details about the objectives and the analysis).
In any case, the conclusion should be that the plan fulfills the objectives that were set for the design of such a plan. If it doesn't then you should try to do the plan again, because that would mean it's wrong. I guess it's right, that's the main conclusion and that's the reason to publish a paper.
If after the project you find out it was wrong, then that could be a good reason to write another paper. With lessons learned, mistakes, etc. so that nobody else makes the same mistakes (we should make some progress in the state of the art of mistakes as well ;-) )
PD: Future lines are not exactly conclusions, a conclusion is something that ends. Every end implies a new beginning, and thus future lines (imminent beginnings) are included in this section, but let me note they are two different things (and opposite to a great extent).


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Program/Process for updating calculation results in manuscripts
When writing a manuscript, my current practice is to conduct whichever calculations needed in one specific program (e.g. Python or Matlab) then copy and paste the result of the calculation into the manuscript.
This process is prone to errors, and requires maintenance with every update of the calculation results. What process is recommended to (if needed, semi-) automatically update a manuscript with new calculation results?
One solution could be to maintain the manuscript with constants with specific names (CALCULATION_RESULT_1) and write some dedicated scripts that map these names to a respective calculation , and "render" a manuscript replacing the name with the result every time it is updated. But this seems like reinventing the wheel.
I'm guessing some tools exist for achieving this with  LaTeX, but if there are also solution to (us mortals using) MS-word, that would be appreciated.

I'd suggest that writing a manuscript should come quite late in the research process. In which case results typically should not change anymore. Before then it's useful to have a "lab notebook" type of document to keep track of all your calculations.

@user2705196 I disagree and my workflow includes using a Quarto file to update changes as I go. Especially helpful if things change during the peer review process.

@RichardErickson Thanks for bringing a different perspective and your answer! I am happy to see that others have a workflow different from mine that works for them.

See similar question: https://academia.stackexchange.com/questions/46228/im-looking-for-an-application-or-workflow-which-integrates-data-values-into-the?rq=1

@user2705196 you're welcome. Also, people write books they update on a regular basis using Quarto. The book https://r4ds.had.co.nz/ is an R example, and the Pandas book (by the package's author) was also written in Quarto https://wesmckinney.com/book/

Quarto uses an easier to use Markdown Language (that can render through LaTeX) and includes an option to output as a Word Document.
Quarto also can use Word-based templates (as well as LaTeX and similar type templates).
Quarto also has a visual edit mode that makes it more accessible to people who do not know Markdown well.
Jupyter Notebooks also have similar rendering abilities that I am not familiar with.

I support Richard Erickson's answer to use Quarto. In addition to what he's already said, I use Quarto in VSCode as something close to an IDE for paper-writing: I write Gnuplot scripts to generate my plots from data, use RunOnSave to automatically regenerate output PNGs as I adjust presentation settings in Gnuplot, and then use Quarto in another tab to incorporate the figures into publications (or presentations!!).
But for something less tech-y, you can still use Word and Excel. Word has "dynamic fields" that you can link to cells in an Excel file using this method; just have your Python script regenerate the target Excel file every time it reruns (for example with Pandas and openpyxl) and you should be good to go.

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When has an unemployed post-doc refereed enough papers to decline another?
I am a post-doc and I get regular requests to referee papers, largely because of a review paper I published as a grad student.
I am yet to turn down a review request--It is rewarding, an opportunity to immerse oneself in another topic, and it feels good to give back to the community.
Unfortunately, I am peri-employed, and the rational decision maker with non-infinite time resources is regrettably obliged to consider her CV.
At what point will listing another journal (or another instance of refereeing for a given journal) on your CV generate negligible returns for an early-career researcher?
At what point does the rational actor say no?

What does peri-employed mean? My dictionary only has “around, about” for the peri- prefix. Does it mean you're about to get a job? Or you've just left one?

By peri-employed I do mean around and about employed; moving from one short-term contract to another.  Indeed, I am now in such a contract that will soon end, and I have so far nothing to replace it.

It is probably peri-employment in another sense too. About to achieve something better. We hope.

when you get a job....

One simple criterion is that if you have refereed more papers than the number of referee reports you have received for your papers, perhaps even divided by the number of coauthors you have for each paper, then you are ahead of the game and shouldn't feel guilty about turning requests down.  Ultimately, if your career goes really well you should expect to do substantially more than your share of reviewing, but you aren't obligated to do this while still a postdoc.
Even if you haven't reached this point, it's reasonable to put your career development first for now.  Strategically, you should referee a paper if reading it will be valuable for your research, if you can impress an influential editor, or if it's for a particularly prestigious journal (so it will stand out on your CV in the list of journals you have reviewed for).  Beyond that, you can do what you reasonably have time for, but you should feel free to decline review requests when you are busy with other things.  If you feel guilty about this, you can make up for it by reviewing more papers than you would otherwise like someday when you have a stable job.

At what point will listing another journal (or another instance of refereeing for a given journal) on your CV generate negligible returns for an early-career researcher?

I'd say the returns become negligible pretty quickly.  Adding a really fancy journal can look impressive, but even doubling the number of run of the mill journals will make only a small difference.  (People want to see that your reputation leads to review requests, but don't care much beyond that.)
To the extent refereeing helps your career, it's more likely to be because you impressed the editor than because a hiring committee liked the line on your CV.  However, writing papers and giving talks will reach a much broader audience than refereeing can, so extra refereeing is not an efficient career boost.

In my field (mathematics) it is not usual for CVs to mention referee assignments, and I don't believe they are considered at all in hiring decisions, so the direct returns are negligible at any level (not counting the indirect benefit that you may learn something from reading the paper carefully).
Keep in mind that every referee is busy and has other things to do that would be more directly rewarding, but they do it anyway.  However, if your situation means it would be a drastically larger burden for you than for an average researcher, then by all means decline with a clear conscience.

Apart from the plus minus statistics of "you do for other what they do for you" I think there are two points yu could ask yourself:

Reviewing keeps you in touch with the field. Do you think it helps you stay in touch with developments until you find a position (assuming that is what you want?
Reviewing keeps you on the radar within the field. By reviewing you will "advertise" your expertise which may not hurt in the future. So do you wish to be seen as an active player aiming for a position?

Clearly none of these are make-or-break for the future but it involves assessing your own interests for the future. If you clearly think you will look for employment outside of academia and association with research, then you simply pick and chose your reviews based on interest.

Assuming unemployed means you are looking for an academic job and not moving to a new field, the answer is NEVER. Since you are unemployed, your chances to network are greatly reduced. I would suggest taking every review possible and consider adding my name to reviews.
I would actually suggest contacting editors to request that they consider you to review articles in the future.

Strongly disagree.  Refereeing is not the most significant bit to focus on.  Publish, publish, publish.

@JeffE But in the state of unemployment, publishing in certain fields is very challenging (like synthetic chemistry or molecular biology).

@JeffE it is not the refereeing that I am focusing on, it is the networking. Getting a job (especially a post doc) is not just about publications and grants, it is about networking.

Fair point, but refereeing seems like an awfully indirect way to network.  Why not just, you know, talk to people?

Writing reviews is anonymous. How does it help with networking? Very few people will know that you wrote the review!

@DaveClarke I suggested adding your name to the review, but even getting your name out to a handful of editors is beneficial since they tend to be influential/prominent people.


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What's arxiv “endorsement” policy?
arxiv has an endorsement system, where

during the submission process, we may require authors who are submitting papers to an archive or subject class for the first time to get an endorsement from another arXiv author

However, I submitted a paper to arxiv for the first time, and I was not asked for an endorsement. The relevant help page says:

During the initial deployment of the system, we may also give automatic endorsements to submitters from known academic institutions.

So this may be what happened to me (I have a verified email address from an academic institution), but this seems weird, because it says “during the initial deployment of the system”, and this is dated 2004.
So: does someone know what is the current endorsement policy for arxiv?

You are quoting a current page and ask what the current policy is?

We may give some people automatic endorsements based on topic, previous submissions, and academic affiliation.

@NobleP.Abraham I am asking if someone who has experience with arxiv knows in more detail how endorsements work. The page is thoroughly unclear: 1. it says they may require endorsement (but do not say how the decision is made); 2. academic affiliation is cited twice, once saying “during initial deployment” (making it clear the page wasn't updated since 2004), once without mention of temporal restriction (but again with the may, and no stated criterion).

They are being deliberately vague, to give themselves flexibility.  For example, some departments are very lax about making people unpaid visiting scholars.  That's justifiable: it often offers nothing but library privileges, and one can make a case that it's good to extend library access to just about anyone, even crackpots.  However, it's not clear that arXiv posting should be treated similarly.  If the arXiv promised that academic affiliation always guaranteed an endorsement, then they would have to explain what counts as a "real" academic affiliation and why.  It's easiest to be vague.

@AnonymousMathematician to be fair, there's plenty of crackpot content on arxiv anyway, even with these rules in place :)

Incidentally, I view the vagueness as a feature, rather than a bug.  The downside is that it allows a small potential for abuse of the endorsement system, but I've never heard of it causing problems for anything I'd consider to be legitimate research.  The upside is that the vagueness protects the arXiv from an endless stream of legalistic complaints from people outside of mainstream research.

Regarding the crackpot content, that's true.  There's a trade-off here, where you want to minimize the junk without actually causing a burden for anything serious.  I imagine that completely eliminating all crackpot papers would require a slower, more intrusive, or more error-prone system for everyone else.  :-/

I believe the help page describes the current endorsement policy.  You may also have fallen into the category described by "We may give some people automatic endorsements based on topic, previous submissions, and academic affiliation."
As I understand it, most people are in that category and never see the endorsement system in action.  In practice, it seems to get applied to cranks and to people who look like they might possibly be cranks (e.g., people with no academic affiliation, or who are writing on controversial topics).

This is also my experience.

Some people (having affiliation) submit using their webmail id, endorsement is required in that case. Some universities do not yet provide official email ids.


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How much time before (possible) effect of US sequester is felt on grant funding?
When a US institution that manages grant programs (such as NSF) suddenly sees its funding cut, as will be the case after the US sequester takes effect in March, how long does it take for that cut to trickle down onto grant programs and grants?
Namely: Can grants already started get cut (like, they tell you you'll lose 20% funding for the last year)? What about grant programs where the selection was already announced, can they make changes to that? Or will it “merely” impact the number of grants they can fund from now on?
(I know that the government will move, and though the situation is stupid, it's not as much of a dead-end as it is pictured… this question assumes that the sequester goes in effect, and not deal is made to lessen its impact.)

This question was spurred by today's PhD Comics:

See the NIH answer below on what's happening to grant budgets, at least for their agencies. It sounds like there is some budget trimming for the amount awarded for new grants, but they're trying to mitigate that somewhat. Where I think there's going to be a much greater impact is with new awards - with less money, and not wanting to hamstring grants with further cuts, they're simply likely to make less awards.
As for how much time before the possible effect of the sequester hits? It already has. Several people I've spoken to who do program planning, grants work, etc. for the Federal government have expressed the feeling that, because of the level of uncertainty about what money they'll have in the future, funding agencies are being very conservative about what they commit to spending. We could see this in the last budget cycle and the near shut-down - funding slowed to a crawl for a bit, and then when the continuing resolution got passed, there was a small "bump" as agencies spent out money they hadn't yet promised "just in case".
So if the sequester goes through, what that will really do is make those conservative, "We better not spend $$$ until we know we'll have it" plans a reality, followed by more severe paylines etc. in the next grant cycle.
So worst case: It's already here, we're just not committed to it yet.
Best case:  The next funding cycle.
Edit: The actual answer has come from the NIH:
http://grants.nih.gov/grants/guide/notice-files/NOT-OD-13-043.html

The NIH continues to operate under a Continuing Resolution as
  described in NOT-OD-13-002, and therefore all non-competing
  continuation awards are currently being funded at a level below that
  indicated on the most recent Notice of Award (generally up to 90% of
  the previously committed level).  Final levels of FY 2013 funding may
  be reduced by a sequestration.  Despite the potential for reduced
  funding, the NIH remains committed to our mission to seek fundamental
  knowledge about the nature and behavior of living systems and the
  application of that knowledge to enhance health, lengthen life, and
  reduce the burdens of illness and disability.
Should a sequestration occur, NIH likely will reduce the final FY 2013
  funding levels of non-competing continuation grants and expects to
  make fewer competing awards to  allow the agency to meet the available
  budget allocation. Although each NIH Institute and Center (IC) will
  assess allocations within their portfolio to maximize the scientific
  impact, non-competing continuation awards that have already been made
  may be restored above the current level as described in NOT-OD-13-002
  but likely will not reach the full FY 2013 commitment level described
  in the Notice of Award. Finally, in the event of a sequestration, NIH
  ICs will announce their respective approaches to meeting the new
  budget level.

Okay, I read today in Chemical & Engineering News an article that gives good, fact-based information on the issue. It seems to indicate that already accepted grants would be cut down, not only current and future proposals:

At the National Institutes of Health, for example, sequestration is expected to cut $1.5 billion of its funding for 2013. The cuts would amount to a 5.1% reduction for each of the agency’s 27 institutes and centers. “That translates into hundreds of grants that would have been funded in this fiscal year that simply won’t get paid,” says NIH Director Francis S. Collins. As a result, several thousand research positions could be eliminated.


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Why is it customary to call people with doctoral degrees doctors but not people with masters degrees masters?
Why is it customary to call people with doctoral degrees doctors but not people with masters degrees masters? They are both graduate degrees that supersede the undergraduate degree.

Speculation: "Master" is sometimes used as a formal title of address for boys or young men, akin to "Mistress/Miss" for women or girls in that age range.. There are also bad associations with slavery. Either of those would make many of us uncomfortable with being addressed as Master in anything but a teaching or guild-like context.

And why aren't people with bachelor's degrees called bachelors? ;)

@keshlam "There are also bad associations with slavery." I think that's very location-dependent. I wouldn't say that the word evokes any strong connotations in England, for example (where slavery has been effectively illegal since the 1770s).

What do you mean with call people? In written form/titles I've always seen: Dr. John Doe or John Doe MBM (Master of Business Marketing)

And why aren't people wish an elementar degree called elementaries?

@PieterB - it's not strange to be introduced to someone as "This is Dr. John Doe". But I've never heard someone say "This is Master/Batchelor John".

@Davor with the master titels they go often behind the name: This is John Doe Master of Business Marketing.

They are both graduate degrees that supersede the undergraduate degree. You can get both postgraduate and undergraduate master's degrees, at least in Europe.

@PieterB I think OP's point is that you can say "This is John Doe, MSc", but you can say both "This is John Doe, PhD" and "This is Dr John Doe".

I don't know about you guys, but when I got my master's degree I asked everybody to call me master.

Once you get your PhD every appointment you make is a doctor's appointment.

@keshlam: But people don't seem to care when people are called master in Taekwondo, Chess, etc.

@mehrdad: that falls in the teacher/guild category.

@JimConant I have a bachelor's degree and people call me a bachelor. Maybe it's because my degree is in computer science.

"Good evening, Dr. Jones."

why where and when? it is very customary in some countries still and no doubt used to be even more so. If you read latin American novels of the boom (Garcia Marquez, Vargas Llosa, etc.) you'll see licenciado to be a common form of address, e.g. "El licenciado López, etc." Or in Italy, "maestro" is still common, but less so than "avvocato" or "ingegnere". There is apparently a whole wikipedia page on this per country, e.g. http://en.wikipedia.org/wiki/Italian_honorifics

There's a funny scene in K-Pax when the main protagonist repeats the title "doctor" several times in a row.

The difference between a Doctorate and a PhD is purely philosophical.

Comments are not for extended discussion; this conversation has been moved to chat.

If you're talking about the use of doctor as a title, as in "Dr. Smith", I doubt there's any compelling explanation.  Most degrees don't come with titles: nobody say Master Smith or Bachelor Smith or Associate Smith.  Historically, magister (corresponding to the master's degree) was just as appropriate a Latin title as doctor was, but it simply isn't used in modern English.  These titles are nearly gone, with just one remaining.  It's probably no coincidence that the last remaining title is also the fanciest, but that's just speculation.
Even for the doctorate, the use of the term "doctor" has degenerated to the point where in English it can only be used as a title, and not as a general noun.  If you say "my friend John Smith is a doctor", practically everyone will assume he's a medical doctor.  You could only get away with the more general usage in the narrowest academic context, and even there it would be considered pretentious and archaic.

And at least amongst Americans pretty much the only people who I ever hear call me Dr. are headhunters and nervous students...

Though we do say, "my friend John has a doctorate", I presume to distinguish a PhD from an MD degree.

And to add to the confusion, the chaps we tend to call "Dons" at Oxford were once called "Students".

The academics of Christ Church Oxford are still known as "Official Students". Historically Oxford undergraduates would take umbrage at being referred to as "students"...they were "undergraduate members".

Somehow the practice of almost exclusively calling physicians "doctor" and attributing significantly more respect to the learning involved than that required for a PhD strikes me as funny; in Germany, people get their MD in passing for a thesis that is sometimes restricted in exam regulations to about the effort you have to put in for a bachelor's degree in any other subject, and where they don't, it is only rarely significantly more involved than that.

See http://academia.stackexchange.com/a/9822/612 for discussion of how the term "doctor" came to mean "medical doctor".

In some countries the custom is different.
In the Czech Republic, Europe, where I come from, it still is generally customary to call masters masters. The title is different (magister, ingenieur*, or doctor**) but is more or less equivalent to the American master. And yes, it makes it easier to study for the sole purpose of being called names titles.
In neigbouring Germany, however, only doctors with degree equivalent to PhD. are titled by degree.

*) Not to be confused with engineer, the ingenieur degree means roughly master of engineering.
**) To add to the confusion, doctor degree can mean various degrees of a degree, not all of them being equal to PhD.
Update for clarification
Magister, ingenieur, and doctor are called magistr, inženýr and doktor, respectively, in Czech. The names come from Latin which is still used widely in Czech academia (where applicable). The respective abbreviations are Mgr, Ing and Dr. Thank you, Emil Jeřábek, for bringing this up.

... and to make it even more complicated, the Austrians, although they speak German as well, actually sometimes still do call people "Herr Magister", which would indeed - as you say - be a ridiculous form of addressing someone in Germany.

Umm, why are the purportedly Czech titles translated to half-German, half-Latin in the answer?

@EmilJeřábek: Good point! They are Latin and French (ingenieur), because that are languages these degreees originally come from. Latin is still used widely in Czech academia, where applicable. The respective abbreviations are Mgr, Ing and Dr. I'll update the answer to be clear in this aspect.

In Germany, there is the title of "Meister" (literal translation of "Master"). However, this is a title awarded to craftspeople, not academics. For example, someone might be awarded the title "Tischlermeister" (Master of Carpentry). There used to be a time where people with a craft-related Meister title were called "Meister Surname", but nowadays this is quite archaic.

and in Germany you can be called Doctor Doctor, if you are twice a doctor naturally.

The situation in Austria is similar to what Pavel Petrman describes. We do like our titles a lot.
Although nowadays almost all studies follow the Bachelor/Master system, as an engineering/science graduate one is still allowed to use the traditional title "Diplomingineur", usually abbreviated as "Dipl-Ing", instead of a title indicating the Master's degree. This is done mostly because it has a very high reputation in Austria and other German speaking countries. So once I graduate, I will be allowed to either call myself "mort, MSc" or "Dipl-Ing mort". However, calling myself "Dipl-Ing mort, MSc", which is sort of a wet dream for every title lover, is not allowed (but you do see it sometimes).
The equivalent for non-engineering/science studies was the title "Magister". However, graduates of those studies are only allowed to use their Master's degree (typically a Master of Arts degree), as was the original intention when switching to the Bachelor/Master system.
In regard to titles, Austria has a lot more anachronisms. For example, the title "Hofrat" is still in use, it usually comes with a high-ranking government job (it's not an academic degree). The title comes from the good old times when Austria was an Empire: "Rat"¹ means advisor, "Hof" designates the imperial court, so Hofrat literally means "advisor of the imperial court". Although Austria is a Republic for 70 years now, the title is still in use.
So think that's a bit crazy? Well. You can also combine academic and other titles. So a high-ranking government official might feel that he should only be addressed as "Herr Hofrat Dr. Huber". His wife, although not having earned any titles herself, might call herself "Frau Hofrat Huber".
Like I said, we do like our titles.
¹ Pronounced on the "a", not like the English "rat".

Historically, in the US, titles are not emphasized.  Part of this has to do with the history of the US rejecting royal authority (ie, knighthood and family/land titles).  Another aspect, though, is that academia is already considered pretentious to some degree, and requesting others address you according to your educational title in all social situations won't endear you to others, instead it sets up an unequal relationship.
Today, however, so many people have bachelors (30% of the US) and even masters degrees that it makes little sense to call out your achievement, when so many others around you have attained the same degree. PhDs are still relatively rare.
Lastly, note that it's largely within academia that the title is used on a regular basis - where even more people have bachelors and masters degrees.  Usually one addresses their teacher with a title in many cultures (先生 in Japan, for instance).  In the US, it's "professor" or "doctor", and usually professor is preferred.
The reality, though, is that other than medical doctors and outside academia, few PhDs that I'm aware of want or expect others to use their title. Demanding someone use a title when they address you is often seen as arrogance.

+1 for the rejecting royal authority. In medieval Europe, universities were often run by church and one had to be christened, for example, to be accepted for university in such case. I find the connection between the two rejections (church and nobility) interesting - in many cases can the pronounced titles in some European countries be as empty as the In God We Trust on every single Dollar bill...

teachers in Japan are addressed with sensei 先生, not senpai.

This question sounds like you can go around calling people doctor [name] outside an academic environment. I don't think that's true in English-speaking countries. When I got engaged to my wife (US born and raised), her mother was so excited that she was going to marry someone with a PhD that she started introducing me to the extended family as my future son-in law, Dr. Koldito. Literally everybody who heard her introducing me like that thought she meant I was a medical doctor.
So, I don't think there is anything weird in not being able to call people master [name]. You just can't use academic titles outside academia.

Hmm, I've worked with many people in a business setting who were addressed or referred to as Doctor so-and-so because they had a PhD. Maybe it just depends on the environment.

Technically the proper term of respect for an individual with a master's degree is "Mister".  I think this usage is similar to that in the Royal Navy, where a ship's master would normally be referred to as mister, as in "Mr. Brown". Common usage has long ago eroded any significance that the title may once have had.


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European BS - American PhD (Graduate Admission)
I posted something similar a while back and it was closed, but I believe this post will be more suited for discussion.
I'm heading to Europe from the U.S. to complete my BS in physics. I intend on returning to the U.S. to complete a PhD (ie. no masters degree in-between). I'm aware that the European system has a three year BS program, as compared to the four years that are done in the U.S.
The central question is: Does having a three year BS affect admission into a competitive American graduate program?
From where I sit, I can't help but feel PhD programs will raise an eyebrow at applying with only two years of experience. All of the graduate application deadlines available online (Princeton, Cornell, Columbia) have a deadline of the 15th of December, which correlates to a little over two years of undergraduate education.
I've been lucky enough to have a huge head-start on the material (few formal credits, however), which means I could start research within my first year of study.

My friend did that, and get to a top tier place.

Some US universities are unwilling to consider a 3 year BS as equivalent to a 4 year US BS and insist that students have at least another year (e.g. an honors year or a master's degree) before admission into their graduate programs.  At other institutions it may be up to the admissions committee in the individual department or their may be some universal policy that 3 year BS degrees are acceptable.  It all depends on the particular institution and department, so I've voted to close this question as being dependent on institutional rules.

Possible duplicate of How does the admissions process work for Ph.D. programs in the US, particularly for weak or borderline students?

I don't think there will be a huge problem in taking a European bachelor's degree to a US institution, so long as you do a little explanatory work in getting ready to apply. The reason for this is that in those first two years, you will often have more major-specific work than most US students will have completed in three years!
So, I would recommend contacting the graduate admissions of programs you're interested in applying to, and explaining your situation to them. Take a look at the undergraduate programs they offer—and in particular, the list of required courses, and show that you have a similar amount of preparation (or more!). Then ask if there's anything else that you need to do.

I've done a lot of research into physics programs from top tier schools here in the U.S., the university I'm looking at(Universität Wien) has a solid physics program, and I shouldn't have any problem when it comes to knowledge, I'm a bit concerned about "how it looks". I suppose that the worst case situation is finishing an MSc before applying to the U.S.
Will European institutions accept students for REUs after only a year of study?


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What to do with PhD advisor who does not revise journal manuscript
Say that someone has already spent three years in PhD with an advisor who never revises their drafts for journal submission for a period longer than 8 months given imaginary excuses. However, when it comes to conference papers the advisor has time since conference drafts are short and there is a deadline. I am wondering what should the PhD student do in this situation.

Have you tried splitting the journal draft revision task into more manageable slices, and asking the advisor to commit to a schedule for each slice?

@PatriciaShanahan I have tried everything with him. It is not just one draft they are many. Plus he always keeps promising to have time by giving exact dates then it never happens. Right now I just break down some of them into multiple conference papers to not loose them because they are getting very outdated.

You may have already tried this, but how about asking him, in advance, to commit to submitting on a specific date? If he does have time to revise before then, great. If not, he agrees to submit your best effort. He may be quite likely to agree because he expects to have time before then.

I've heard this one before.

Give the advisor a deadline and polish the paper yourself. After the deadline passes, submit the paper yourself.

You may have tried the following approach already. If not, here it goes:
You could approach your advisor by asking whether he thought the draft was missing any results or needed additional work. If he says no, then, ask whether you could set a target date for submission and to have weekly meetings to discuss various parts of your paper. Also, express your concerns and explain how important it is for your career to get credit/recognition for your work. All of this can be done politely and face-to-face.
This approach worked for me when I was a graduate student.


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Are PhD students allowed to collaborate with other faculty without their advisors being involved
Is it possible/ethical for PhD students to collaborate with a professor from different institution on a topic different from their dissertation research without telling their advisors.
Are PhD students limited to exclusively work for their advisors or it is legitimate for them to do research during their spare time to publish papers with other collaborators.

What 'spare' time? PhDs are a lot of work. If not focused on the main problem area. If the other problem area is so attractive that you would lie to your current advisor (and it will come to that, no doubt), then you should leave for the other group.

The answer to both the title and the second paragraph of the question is the commonest answer to questions about PhD studies: "Discuss it with your advisor.". The advisor knows about relationships between your department and other institutions, and may know more than you do about the history of collaborations with them. There is a serious issue of whether you can make fast enough progress on your dissertation research if also pursuing other research. The advisor is supposed to give advice about how to proceed with your research career.
The answer to the first paragraph of the question body, about not even telling one's advisors, is that it is just not going to work. As soon as you co-author anything with the other institution, the collaboration is public. Even if your advisor does not come across it, someone will mention it to them. At the best it will be something like "I see your student user2987 is doing interesting work with Professor X."
Maybe the advisor would have liked the collaboration if it had been discussed in advance. If they find out about it indirectly, they will know you have gone behind their back. That is likely to damage your relationship with your advisor.

"likely" to damage that relationship is a good candidate for the understatement of the year.

If they're doing interesting work with Professor X, they might be a mutant. Just saying.

Is it possible/ethical for PhD students to collaborate with a professor from different institution on a topic different from their dissertation research

Of course it is!  The goal of any PhD program is to develop strong independent researchers.  Limiting any PhD student's collaborations to one faculty member or group would be directly opposed to that goal.  I consider publication of at least one paper without the advisor to be an iron-clad graduation requirement in my field, and in particular for my own PhD students.
However, if your advisor is paying you as a research assistant, then you do have an obligation to work on the specific project for which you are being paid.  You can't ethically work on another project—research or otherwise—at the expense of the work you are being paid to do.  But as long as you are fulfilling the terms of your funding, what you do with the rest of your time is up to you.
Note: Your advisor may disagree with my opinion about independent work.  Working on research without their explicit approval may violate their expectations for you as their PhD student, possibly to the point of them refusing to work with you further.  While I believe such a response is both deeply unethical and counterproductive, that doesn't mean it can't happen.

without telling their advisors.

No, no, no.  Don't even think about it.
If you feel the need to hide your research activities (the entire point of getting a PhD) from your advisor (the one person whose job is to help you succeed), then your relationship with your advisor is deeply broken.  If you lie to your advisor, they will almost certainly find out later.  If you can't trust your advisor, find a new advisor.  If you think your advisor would disapprove of your independent work, either live with the disappointment or find a new advisor.


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Strategies for avoiding "You forgot to cite paper XYZ" criticism despite having an extensive related work section
Discussing the related work is expected in many (if not all?) types of research papers. In the related work section, the author explains the relationship of the current work to previous work and provides an overview of the relevant literature to readers who are not so familiar already with the research direction to which the paper belongs. As a secondary purpose, with the discussion of the related work, the author also shows that she/he knows about the field.
There are however soft limits to the length of such a section, stemming from the expectation that a substantial part of the paper's pages are used for the actual contribution. This means that only the most relevant related work can be discussed. And what is most relevant is decided by the author.
Now a common criticism in paper reviews, often combined with a rejection or request for major changes in the paper, is that seemingly relevant related work is missing. Typically, the papers mentioned by the review are related, but not infrequently at a level for which 100s of other similarly related papers can be found.
Are there strategies to defend against such criticism, provided that the related work section is quite polished?
The question is particularly relevant for work that uses concepts or builds on approaches from multiple streams of research, which all need to be discussed.
Just resubmitting with some discussed papers replaced by the reviewers' requests until the paper is accepted probably wastes reviewer and author time. Extending the related work section to cover >20% of the paper seems excessive (at least in CS in papers presenting new approaches to solve some problem) and invites criticism about a low density of novel content. Adding additional related work to a not-published appendix may look odd and invites the criticism that it should be part of the main part of the paper.

@Roland While I am particularly interested in the CS case, this question is not actually specific to CS.
I deliberately did not write that this is about papers primarily rejected for missing citations. I've seen the missing citation argument both on the program committee member side of CS conferences as well as from the author's side to round off lists of points against accepting a paper. More than once, the missing citations were the only seemingly objective point against a paper (others being contribution being too small, problem solved not interesting enough, etc.)

@Roland That probably means you're already implicitly using the sort of strategies that the question is asking about...

@Roland I think of that less as a property of experimental work than of study of existing systems vs. construction of novel systems.

Personally, if I truly felt my coverage of related work is adequate, then I would assume the reviewers are citation fishing.  In such a scenario I would make the editor (or whoever else is in charge) aware that their reviewers are citation fishing and publish elsewhere. Reputable venues should not partake in citation fishing and we should make sure editors feel the negative impact of allowing the practice. On the other hand, softer versions of it where they merely suggest reading something can slide if they don't use it to condition publication. I.e., you need to judge if it is truly fishing.

Personally, I approach this question by explicitly framing related work in terms of categories and examples. In this manner, as long as the related work is within one of my categories, it is covered by the description even if the specific work is not cited.
For example, in a paper about an improved algorithm for widget sorting, I might write something like this:

Most prior approaches to widget sorting fall into two categories: weight-based and optical. Weight-based sorting (e.g., [1],[2],[3]) works well for widgets of the same size but different materials. Optical sorting (e.g., [4],[5],[6]) works well for widgets with different sizes but the same material. Neither performs well, however, at distinguishing both characteristics at once. In recent years, several alternatives have been proposed that handle both, including Gruber and McClane's approach based on high-powered lasers [7] and Voldemort's alchemical method [8], but to the best of our knowledge none of these alternative methods can be applied at low cost for high-throughput manufacturing.

It's even better if you can find a recent review paper that provides these categories for you, so that you don't have to defend them, e.g.,:

As reviewed in Jones et al. [9], most prior approaches to widget sorting ...

This sort of structure provides a good defense against "you didn't cite my favorite / my work" complaints from reviewers because you aren't claiming to need to cite everything.
From this point, reviewer requests for citation generally fall into three categories for response:

Identification of a missing key work or category that you really did need to discuss:  Thank the reviewer and add it!
Related work already covered by your categories: If it's reasonable, add another citation to your "e.g." lists; if it's pushing it, explain why it's already covered.  If the reviewer asks for four citations in the same category, the "e.g." structure gives you a good reason to select only the ones you want as being representative of their list.
Dubiously related material: If your categories are broad and comprehensive, you can explain why you believe the proposed material is either covered by one or out of scope entirely. Invite the reviewer to correct you if you are missing something. This puts it into minor revision space at best and is unlikely to be an issue for rejection.

Thanks for your answer. It tells me that I should probably restrict my question to the case in which there are no revisions and no reponses, as in scientific conferences (common in computer science). Would you mind if I change my question to its case? The second part of your answer looks odd then, however.

@DCTLib What I've written here is my strategy for conferences too. The only difference is that you don't have to actually write the response to the reviewers (for some conferences: some CS conferences have now started including a response to reviewers phase!). The actions that I take on the manuscript are generally the same, however, since a reviewer is often representative of many readers.

The 2nd part of the question makes perfect sense even for conferences: a) some conferences now allow a single rebuttal phase before the final decision -- in case your comments will be read by the people making the decision and b) even if your paper is accepted already, this is good advice. Super-related work you missed? Include and be glad the reviewers pointed it out. Unknown example? Include if relevant/representative/space allows. Tangentially related? Out of scope.

Gruber and McClane were coauthors?! Brings a whole new perspective on the events that Christmas....

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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How to teach "puzzling skills" (for coming up with examples) to PhD students?
Background: Some sub-areas of computer science are concerned with developing new methods to solve practically motivated problems of certain shapes (determined by the sub-area). While obviously using real practical problem instances would be best, few papers in these areas actually use them, because (1) it is hard to obtain these problem instances (from industry) without first giving them a method to solve them (chicken-egg problem) and (2) many of these problem instances have too many intricacies that need to be handled at the same time, so that to make progress in the area, most papers pick one particular challenge, show the challenge by means of a convincing but synthetic example and propose a novel method that gives some insight into how to solve the challenge.
Doing research in this area requires to identify the shortcomings of current techniques and to derive convincing examples that actually demonstrate the shortcomings of previous approaches. Coming up with such examples requires to marry (1) the understanding of previous techniques, (2) the basic idea of what the practical problems are about, and (3) conciseness of the example, so that it can be used as running examples for thinking session at the whiteboard and as running examples in papers.
Question: Coming up with such examples requires creativity and can be seen as a puzzling problem, as the requirements are somewhat contradictory. Now there are some PhD students who will excel at such tasks and solve them without any guidance, and others will not know how to start, despite having a working knowledge of all the involved concepts. So what are suitable strategies to teach such puzzling skills to PhD students? I find it highly difficult to give help in such situations, as vague hints such as "think about what happens in this situation in that application" are often insufficient for a PhD student who is not good at puzzling, but hints such as "start by doing this and that" yield no learning effect since "this and that" will typically contain all ideas for one particular solution. The strategies should help students who are already familiar with all formal definitions and basic concepts, but never looked at them in combination yet.
Example: To clarify the question, let me give a (made up) example that is at least understandable to computer scientists. Assume that there is some reason to believe that when translating a deterministic automaton over finite words (DFA) to a digital circuit, we can make the circuit smaller whenever there are two states s1 and s2 in the automaton such that the language accepted when starting the automaton from s1 is the complement of the language accepted from s2. We also have a reason to believe that this is relevant for some application that involves all possible kinds of computations with binary-encoded strings. The tasks is to find a DFA that has two such states s1 and s2, whose implemented language can be easily explained in a sentence or two, for which the DFA is non-trivial (i.e., no state accepts no words or all words), and that makes at least a little bit of sense from the application point of view (here: arbitrary operations over binary strings)
Example solution to the example: One solution is to take a binary alphabet and to encode the language containing all strings that have an even number of 1s. The minimal automaton will have two states that naturally have this property. Many other solutions are possible, though.
Notes: I'm posting this here as the problem may not be exclusive to computer science. The example description above can very likely be improved. But let's assume that the task is already crystal clear to the PhD student, including the motivation.

It's not exactly the same skill, but it's closely related:  I suspect that, if there was a reliable way to teach this skill, the median score in the Putnam Competition would not be 0.

@AlexanderWoo Maybe. I wouldn't rule out that there may have been some studies that contained some ideas that are applicable to some students, though. And that would already be a helpful answer eve though that may be unrealiable. Note that I've modified the question and asked it again at https://academia.stackexchange.com/questions/129801/helping-tas-creativity-when-writing-exercise-sheets - perhaps the added generality helps to get some (partial) answers)


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Helping TA's creativity when writing exercise sheets
Teaching assistants (TAs) often have the task to compile exercise sheets for the students and need to come up with new problems for such sheets.
Let's assume that in the lecture, two methods for a certain use case were presented, and a TA is supposed to write a problem that should make the students use the second method. The first one should be cumbersome to use, so that the students can figure out themselves that the second method is the one to use in this case. Also, the problem should be somewhat motivated by an arbitrary problem from practice, so that solving the exercise sheet problem also teaches the students that the second method has its use in practice.
Now some TAs will be naturally good at such tasks, and others do not know where to start, despite knowing all of the relevant scientific results. How to teach TAs the creative skills to performing such tasks?

Note: This is a follow-up question to an earlier question on a related topic, which went without answer ideas. This new question may be of more universal interest, and I suspect that if good answers can be given, they are the same in both cases anyway.

Is the student eager to learn? If so, you could try giving examples or detailed instructions; after iterating with you a couple of times, they'll start to internalize the goal. But in my experience, most people who don't "naturally" have this skill have very little interest in learning it, they just do things as quickly as possible and never improve -- in this case, I would just do it myself and not waste time on them.

I'm not sure why you find this to be a special problem. We basically know how to teach people things. Just do it in the ordinary way.
If no framework for understanding how to do a task exists then you need to develop one. If you know how to do it, but are unique, then you need to capture what you do in some way that can be transmitted.
Then you "teach" it by presenting the framework and giving some exercises so that it can be practiced and giving some feedback on how people do.
It is a standard teaching problem.
If no one knows how to do it, then you need to do something like research to discover how it can be done and then operationalize that research. Again, a standard problem, with a standard solution.
But, one thing I would suggest is that you use some sort of team learning so that those who do it naturally can pass skills to those who struggle with it. Pairing is a good approach and one I often recommend for such tasks.
Beyond that, it will likely take the examination of good and bad examples, using discussion (by the TAs) of why some things work and others don't.
But it is practice and feedback that are the key to learning almost anything, hence key to teaching it.

Don't take the word "framework" too literally.
Also, the task itself is ill defined here. Expect that no one will be perfect at it and that the "examples" created will sometimes fail. But there are ways, such as pre-testing or other evaluative methods, to deal with that as well.

Ok, thanks - the team-based approach is definitely a good idea. About the suggestion to capture in a framework what I am doing myself: I wish I knew how to do that. Puzzling together examples until they work is to me like walking - there is no known answer how exactly it works (witnessed by the fact there is a whole research community in robotics about this) even though many people are doing it. The practice and feedback approach can of course be applied again, as you wrote. I wish there was teaching material on this.

Creating an exercise is a harder task than solving it. Creating an exercise with restrictions on how a solution should go is even harder. The skill comes only after
you get really proficient in solving problems and that requires a few years, IMHO, so it is hopeless to try to teach it in one semester or so. When you look at a typical medium level exercise, you see in a split second what techniques should be applied and in another split second which approach is most promising (though it may take you some noticeable time to actually carry it out). Some graduate students are like that too and then they can play the problem design meta-game of going from an approach or a combination of approaches to some particular setup, but some others are still at the level of "I can figure out how to use this method of solution on this problem, but my only way to figure out which method to use is to randomly try all methods I remember until I either get through with one of them, or get stuck on everything". Those are just not ready for problem composition and, IMHO, won't be ready for it until their problem solving skills improve dramatically. So for them I would assign a simpler task of choosing an appropriate problem from a set of textbook exercises (which requires just solving the problems and seeing whether the method you used is the one that was specified). In the worst cases, just compose the exercises yourself and give up on the hope to teach in a few weeks the skill that takes a few years to develop.

A bit of background, so you can see where I am coming from:
I recently created exercises for two courses on applied physics:

one about a subject I was mostly very familiar, where I had to create exercises from scratch,
one about a subject that was mostly new to me, but for which I had with an existing collection of exercises targeted at a different audience for inspiration.

Almost all the exercises I created came into being via one of the following:

I simplified the hell out of a prominent scientific application of the topic.
I took an existing exercise and modified it for the target audience.
Through my knowledge of the first subject, I could almost immediately recall a bunch of examples for a given topic, and could choose something fitting for an exercise.
Note that I would consider my knowledge here to go beyond “knowing all of the relevant scientific results”, since it also involved being familiar with applications, not so prominent results, etc.
Not seldom these were cases where I had already thought that the case would make for a good exercise years before.
Also, some of these examples directly came from my own research.
Fun fact: Some of these cases were problems I had first encountered in SE questions and in one case debunking a wrong SE answer.
Somebody experienced with the subject suggested a topic to me.
Discussing the topic in general, rejected exercise ideas, etc. with somebody knowledgeable about the topic.

Note that thinking very hard about the topic is not on this list. If it were in the case you are outlining (relevant applications), one could easily come up with a new research topics just like that. Instead, my standard workflow for the second course would be to delve into the topic at hand, reading the lecture notes, textbooks, online material, papers, etc. – for the first course I had already done this and at worst had to jog my memory. In particular, creating exercises for the second course was considerably more difficult, even though I had more material to work from.
Once I had recognised a good topic for an exercise as such, fleshing it out to an actual exercise was mostly legwork.
So, in your case, the following may help:

Consider if it is subject knowledge that your TA is lacking. If yes, encourage them to spend more time on studying the subject.
Suggest topics to the TA and task them with finding out whether the topic is suitable for an exercise and, if yes, fleshing it out. This relieves your TA of the problem of finding suitable topics and trains their skills in recognising good topics as such and doing the legwork.
Discuss the topic with your TA. If there are no exercises to discuss about, talk about what skills you want the students to learn, etc.

Finally beware that creating exercises mostly from scratch is a considerable time sink and may cost more time than your advisees can reasonably spend on teaching.


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Reading (& understanding) a lot of papers is hard. How to deal with it?
I am a student doing my Masters (in Computer Science). I am much interested in Ph.D and I know to do Ph.D I really need to read and understand a lot of papers. (I also know that its not all about reading papers, but doing something new and useful) but I find difficult to understand papers, especially mathematical. There are lots of things (like bloom filters, k-means algorithms, and a lot lot much more terminologies) which we, normally, don't encounter (except if you are a Ph.D or doing one) but are really crucial to understand papers.
I am not very good at understanding such stuff (atleast quickly, if given time I can but it takes time). Are there people like me who have done/are doing a Ph.D and had the same problem? If yes, how do you overcome it?

There are great books around which will bring you up to speed on a lot of general techniques e.g Pattern Recognition and Machine Learning by Bishop, Machine Learning: a Probabilistic Perspective by Murphy. There are great written tutorials online made by people to explain specific techniques such as k-means). Also as you are in CS papers can be accompanied by code which often assists in deciphering technical sections of the paper.

It took me a month to read and understand the first paper I needed in my research. Now, 5 years later, I can skim through 4-5 papers in a day, and follow the ideas. The key is practice, practice, practice, and read papers that are less technical first, that gives the background.

The good news is that you have a good 4-6 years of time to learn how to process that sort of material, and you aren't expected to be able to do it immediately.  A professor once told me (about physics papers, but it translates into mathematical and CS papers fairly well) that eventually you start to read the math-heavy parts a bit like music -- you see familiar patterns, and you get the general idea without having to dig deep into the details.
Additionally, you'll find that the more classes you take, and the more reading you do really does prepare you for that next paper and that next step.  You'll find that as you start specializing on a particular topic, most of the papers in your topic will become easier to read, simply because you have the experience and have gained the knowledge about the particulars over time.  Furthermore, you'll start coming up with your own ideas in the subject, and this just happens to be the goal of graduate school!

There are lots of things (like bloom filters, k-means algorithms, and a lot lot much more terminologies) which we, normally, don't encounter...but are really crucial to understand papers.

The beauty is that you will start encountering them more, and you'll start understanding them more and more as you continue your education.
To answer your particular question,

Are there people like me who have done/are doing a Ph.D and had the same problem?

I still have difficulty getting through math-heavy papers (in CS), but I either re-read sections closely until I figure it out, or I ask someone to help, or I simply move on and hope that the rest of the paper fills in the details.  I don't expect to understand everything technical that I read, but it becomes easier over time to get what you need out of a particular paper even if you don't get each and every detail.

+1 for "eventually you start to read the math-heavy parts a bit like music" :)

Agreed, what you get better at really is figuring out what you should spend the time to truly understand.

I don't think very many people come into academia with the ability to parse tons of math-heavy articles quickly. It's a skill that develops over time from reading lots of papers, asking someone in the know to help you understand confusing parts, and getting through relevant coursework that will build up your skills and intuition. If it's a matter of speed of getting through things, that will undoubtedly improve in time. If it's a matter of you simply not understanding them at all, then you may want to reconsider your options before diving into a PhD.

Start by not reading papers straight through. Most research papers are difficult to follow if you read through start to finish without a loooot of context.
Normally, I read them in approximately this order to begin with:

Abstract/introduction
Conclusion/summary
Future Work
Results
Methods

You want to understand what the paper is about first. Generally speaking, unless I know a topic well, the beginning section doesn't do a good job answering the, "what is the point of the next X pages" but a conclusion and summary do. Future work helps me understand, "why is this research even relevant" and then going backwards results helps me understand what they did, and then, once I have context, I read through methods.
Honestly, too, I don't care about understanding detailed methods unless I see a value in understanding them. Reading for the sake of reading is hard, so I "incentivize" myself by showing "oh, there is value in understanding the details of what they did!" and basically trick myself by how I read through things.
Depending on how well I must understand a paper sometimes I stop early in that process or reread sections, etc.

In addition to this, make sure you don't read every paper word for word. You don't have to.
Also determine if you are able to read on your computer or if you need to print them off. Some people cannot effectively read on a computer. Some can. Find out your personal abilities here.

OP specifically mentions mathematical papers.  Mathematics papers almost never have "methods" sections,  and rarely have separate "conclusion" or "results" sections (because the entire paper is results, and the results are the conclusion).

If it helps, I was two years' into a full time research degree in psychology (no courses) in which I read all day long and one day I suddenly realised that I was reading an economics book with very little effort.  I could read!  I still feel the excitement.  You do just get better and better but it is like going to the gym - constant application is needed.
Writing took much longer to get easier and I strongly recommend that you make a habit of writing daily.  Make sure you always have a writing project on the go and add something every day, no matter how bad.  Get a calendar; cross off the days; and don't break the chain. You needn't spend long - under an hour will do. But constant application is the key.
If you are lucky, you will have mentors who demand papers from you.  It is a lot easier to write if you have deadlines. But if not, start a blog and start building up a portfolio.  The feeling of pressing the publishing button and seeing your blog grow makes up for disinterested supervisors.
Stick with it - in few years you will marvel at the ease with which you find your way around 'heavy' material.


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Should you request graduate school recommendation letters only from those that know you as a researcher?
In a discussion with a friend of mine who is applying to graduate school in physics, she was worried that she wouldn't be able to get a good recommendation from one of her advisors because she hadn't done much creditable research under that advisor. I suggested that since she has two other recommendations from other professors who she had done research under, she could ask for a recommendation from a professor who thought highly of her academic (i.e. non-research) performance.
I was surprised by how negatively she responded to the idea: she would much prefer a mediocre letter from a research advisor than a good letter from someone who does not know her as a researcher, but does as a student. I reasoned that her research credentials would probably be spoken for with her other letters, but she didn't seem to accept this reasoning.
Is it generally true that a PhD program in the sciences looks more favorably on less-than-glowing recommendation letters about an applicant's research experience than on positive letters about his/her academic performance? Even when his/her research is attested to by other letters?
If it is not true in general, does it hold for programs in the experimental sciences? Does it vary between fields? (I am a mathematics student, and to my knowledge this question doesn't apply as well to mathematics programs.)

Graduate schools admitting students are looking for evidence of ability as (or at least strong potential to become) a good researcher. Therefore, letters of recommendation that do not discuss research at all are simply not nearly as helpful to us than letters that do.
However, a mediocre letter of recommendation can be damaging to a student's chances of admission. Someone who isn't enthusiastic and immediately willing to write a letter should not be asked to write one; the "hit" to the student's admission chances simply aren't worth it.
So, under the circumstances, someone who is willing to write a very good letter that can testify to academic talent is certainly better than a weak to mediocre research-based letter. But a letter that only talks about a student taking tests well and "acing" a class is not really helpful. A letter that talks about, for instance, an in-depth class assignment, or can include details and observations from outside of class, would make for a much stronger and more useful letter.


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Ph.D Transfer from other country to US
In this application cycle, I got a PhD offer from Nanyang Technology University at Singapore and will study in the field of programming language. However, I recently received a mail from my supervisor which is said that he will soon leave NTU for some reasons. After searching the faculty list of NTU I found there is no other professor who works in programming language.
I don't want to compromise on the topic I will do during my PhD. Maybe it is a good choice for me to find a matching supervisor in some other universities.
But I have to go to NTU to do my PhD because I have no alternative choice now since this application cycle is ended.
What I want to know is that is it possible to transfer my PhD from singapore to US if I re-apply for some American universities after my current supervisor's leave?
Any advise will be so much appreciate for that I somehow don't know what to do.

Have you asked the supervisor for advice?  If he is moving to another university, and you are particularly intent on studying with him, you might be able to go with him.

In general, it is difficult to transfer between universities in different countries unless one is moving with the thesis advisor. This is in part because of funding rules: usually, money in one country cannot be used to fund graduate students working or studying in another country. (In the US, for instance, graduate fellowships are normally valid only at US universities.)
If you were to attempt to transfer on your own, the most likely scenario is that you would be expected to start the PhD over; depending on the department, they may not recognize coursework completed at your old school, or at best may choose to give you placement out of the equivalent courses, but still expect you to complete additional electives. It would be even more challenging to move your project over, if funding isn't available to work on the same project.
Since you are just starting, perhaps it would be possible to complete a master's degree in Singapore, and then try to transfer to another university for the doctoral studies. (I'm not sure how doctoral programs in CS handle an international master's. In my department, though, students with master's from abroad were still expected to complete the "core" coursework requirements.)

It certainly is possible, even across countries. Some of my students are following me on my move from Belgium to Sweden. To ensure that this was possible, I needed to enquire with the administration on both the source and target of the move. There were some restrictions on both sides – for instance, if a student is too close to finishing, both sides are reluctant to let the move happen.


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Gaps between PhD and employment?
What is the longest gap of unemployment that will likely not affect your chance of getting an academic position (post-doc or faculty)?
For example if you graduate from your PhD in December or January but most post-doctoral/faculty positions in your field don't start until May-Sept, is a partial year's worth of unemployment looked down upon in future job applications? If not, what is the longest gap that won't look so negative while applying to future jobs?

This isn't an answer to your question, but you might want to try avoiding the situation entirely by talking to members of the department to get temporary research assistant positions or instructor positions to fill the gap until the positions start up.

In a situation like this, you'd need to find ways to extend your Ph.D. -- e.g., if you can defend in January, teach in the spring semester and defend in May.

It depends on a number of factors. Did you need to relocate? Plus, what were you doing during the down time? Actively looking for employment? Or sitting on the dock of the bay wasting time? I don't think less than a year would be much of a problem, particularly if the school won't hire until then. After all, what else were you supposed to be doing – working in fast food? I don't see how that would look any better on a future CV than a 6- or 9-month gap. If a gap is unavoidable, try doing a few things during that time that could boost the overall quality of a future CV, like volunteer work.

@StasK, that's not an option, the department and my advisor have run out of money, and tuition is 15k a semester.

Hmmm, I just realized, If I were to graduate in January instead of December, I could just put the year of graduation on my CV and then the gap would be relatively unnoticeable

I'm eagerly awaiting the community to respond to this question as I am in a similar situation.  Rather than posting a new question which is directly related to this one, I want to expand upon it and ask "Is it possible to continue to work for your mentor upon graduating during the 'gap' until the next round of post-doc job offers roll around?"  Do people do this and if so, whats the best way to ask?

Many job applications want your specific date of graduation, so I'm not sure graduating in January would save you from this concern, to whatever extent you should have it. Anyway, bear in mind that it's not a case of employable vs. unemployable based on this factor; it's more a matter of how competitive you can be (I think your question's wording acknowledges this well enough BTW). Also, you can catch up later if you don't feel the need to snag the dream job straight out the gate.

I am not sure how you are using the term unemployed. Clearly someone with a paid academic position (e.g., adjunct teaching or lab tech) would not be considered unemployed. I am not sure if you consider an unpaid lab tech or a paid burger flipper as being employed. To me the real issue is being out of the field.
If you cannot get a relevant paid position and but can afford to be without income for a few months, then a year gap, and probably longer, isn't problematic. In fact many labs will hire unpaid researchers. In this case you could continue to conduct new research, publish, apply for grants, and gain new contacts. You could likely stay in an unpaid position as long as you could afford it without any affects on future job prospects. If you cannot get an unpaid research position then it really depends on how long you can milk publications from your past research and produce new research without any affiliation to a research group.
If you cannot get a relevant paid position and cannot afford to be without income for a few months you can sometimes find paid work in a related field. Working as a paid lab tech (e.g., washing test tubes) or adjunct teaching. These types of jobs won't help you publish more or get grants and in fact take time away from publishing, research, and getting grants. That said they can provide a limited set of new skills and cotnacts so are probably sustainable for a year or so.
In the absence of getting even a peripherally related job taking an unrelated job (e.g., burger flipper) even for a short period (i.e., months) gap can be problematic. Not only will it slow down publishing, research, and grants it may make you less flexible about being able to take up a new related position (e.g., how much notice would you have to give). You are also not building new skills or contacts.
A lot of the impact will depend on how important publication speed is in your field. If a few month delay in publishing will result in you being scooped, any gap is probably bad. Similarly the ability to do research without any resources will help you weather a gap. Similarly, if your field has new instigator grants with a clock that starts ticking upon graduation then gaps are bad.


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Is it appropriate to compare students to other successful students in a letter of recomendation?
Say you are writing a letter of recommendation for a freshman student of yours who is applying for an undergraduate research position in a field very different from yours. You can of course say some things about how well she did in your class. However, you had a student that this person reminds you of who went on to do great undergraduate research in the exact specialized field that she is applying for, getting two first author publications. Would it be inappropriate to spend a paragraph comparing the two students and how that in your class they shared all of these great qualities that you think were the driving force for the previous student doing well as a researcher. This seems like it may be a bit unorthodox, is it advantageous or perhaps utterly inappropriate to compare students like this? I'm split, ideally I should just be focused on her but the fact that she reminds me of this student is really the most compelling argument I think I can make.

Definitely do it -- this is one of the things that makes a reference letter truly useful.

This makes the letter of recommendation more personal and shows, you really want to support her. It makes a difference to all the other "this student has good grades and is always on time"-letters. So go ahead!