When do you switch a student membership to a non-student membership for a professional society Students enjoy discounted memberships to many professional/academic societies. However, membership renewals rarely align with one's degree date. So when do you switch your membership from a student member to non-student membership? I see two obvious possibilities

When your degree is conferred, and you are no longer officially a student

or

The first renewal notice you receive while not being a student

Surprisingly, it seems like very few societies address this issue in their guidelines.

The IEEE (Institute of Electrical and Electronics Engineers) mentions this in the Qualifications for IEEE Membership:

Graduate Student members, upon graduation or upon reaching the total cumulative eight-year limit as a Graduate Student member (whichever occurs first), will be transferred automatically to member grade.

And the change of membership, i.e. Member Grade Elevation:

Student elevation is handled twice a year - in June and August. Once an IEEE Student member or Graduate Student member reaches his or her graduation date (based on information provided by the student), IEEE elevates them to the next grade of IEEE membership for which they qualify.
The first elevation is done in June (Student members or Graduate Student members with graduation dates between 1 January and 30 June).


How to negotiate multiple tenure-track offers I'm in the lucky situation that I received 2 offers for tenure-track positions within days of each other from institutions A and B. Either one of the institutions would be a good fit. So far, neither A nor B know about my application at the respective other institution.

How do I go about negotiating these offers? Should I mention the details of the offer of A to institution B and vice versa? If no, what if they explicitly ask?

Related: How do people get simultaneous offers?

First and foremost, you need to become aware of your own preferences and requirements. Knowing that "both would be a good fit" is nice, but it isn't enough. You'll need to know what your preferred option is, and how much better the offer from the other institution needs to be to change your mind. As long as you don't have a pretty good idea about this, I am unsure what you are even negotiating for. This should also be informed by your initial offers. Don't forget that offers are not just salary, but also (and maybe more importantly) also include a startup package.

From then on, I suggest playing with open cards. Go to your second option and tell them that, while their place is great, you have an offer that you would prefer more. It probably does not hurt to tell them specifically which other offer you have, and why you prefer it (especially if the reason is something that can potentially be fixed, e.g., a better package). See if they make any motion to improve their offer. If they don't, go to your preferred option. If they improve their offer and it subsequently becomes preferable, you can also talk to the other offer to check whether they also agree to an upgrade of their offer.

After that, I would probably stop. One round of negotiation should not annoy anybody, but going back and forth between offers too much may leave a sour taste in the mouth of your future employer, and it seems unlikely that you will be able to increase your offer much more after the first round of negotiation anyway (at least that's what I have been told).

Some things to do:

Keep it professional. Negotiating your salary / package isn't dirty, but make sure that you are not getting greedy and/or ridiculous in your demands.
Don't let yourself be fooled by non-arguments. If, for instance, one institution justifies their weaker offer with internal arguments ("You would be making more than some Associate Professors here!"), you should be aware that this may or may not be true, but it is entirely irrelevant for your decision process. A weak offer does not magically get better for you because an institution cannot offer more for whatever reason.
For similar reasons, try to keep discussions on an objective level as far as possible. As soon as somebody starts to argue based on fuzzy factors ("Ok, we don't pay as much, but the research environment here is much better than over there."), negotiations tend to take a turn towards the personal. That is not to say that you should not take these factors into account (you most definitely should), but your actual negotiation arguments should be based on objective data (salary, package, travel grants, tenure conditions, etc.).
Never try to use an objectively worse offer to improve the already superior offer. That is, just having another offer is not enough to negotiate, you also need to be able to tell the people you are negotiating with (with a straight face) that the other offer is actually better for you. If you have one offer for 80.000 USD and one for 75.000 USD, the fact that you have another weaker offer helps you not at all to negotiate the 80.000 USD.


Is one spelling mistake in the academic CV a big deal? Just after submitting my CV to graduate school for masters admission, I realized that there was one spelling mistake on the first page of my CV. I misspelled the word 'merchandising'(missed the h). Is it going to be a major issue for my application?

I'll give the same answer as Allure, but for a very different reason. Not only is it common, but most people won't notice it. And of the few that do, fewer yet would think it an important enough issue to bother with.

"Egad, this person misspelled a word. Horrors."

Nope, it ain't gonna happen.

But, you also need to be assured that no single thing, whatever it is, would likely derail an application (yes, a few obvious exceptions). Your acceptance is based on a judgement and the judges try to look at a complete picture of an applicant; both their past accomplishments and the likelihood of success in the future. The CV (as a whole), your statement of purpose, your grades and test scores, your letters of recommendation. Each of those contribute to a (fairly) complete picture. Flaws or issues in one part can be balanced and overcome by the positive and supporting parts elsewhere.

Relax. The judges are no more perfect than you are.


Good approaches to integrate research and teaching? There is an effort at my university to promote "research integrated teaching" [Barnett, 2005; Robertson, 2006] which got me thinking about the topic. I generally make an effort to discuss research problems and recent results when they fit with the material we're covering in a course, but it occurs to me that this is a very passive approach.

I'm interested in developing more active or creative ways that research work and teaching efforts can be combined to benefit the quality of teaching without negatively impacting research. Are there any approaches that have shown to be particularly effective?

An experience that worked out well in my past: in grad school as a TA, I helped to create an "intensive" section of the main introductory artificial intelligence course.  Students who opted into the intensive section did two projects and got 25% more units of course credit.

For each project, the students had two options, an "applied" option and a "research" option, each on something highly topical.  The research options were always restricted-scope versions of academic research problems that the professor and/or TAs were actually involved in.  This was a nice motivator for students, since they could see how what they were doing related to a real scientific problem.
The applied option was similarly relevant to industrial R&D. For example, the machine learning project's applied option was typically to build a spam filter against whatever turned up in that year's "wild-harvested" corpus.  Students met with their TAs multiple times got guidance while working on the project, and were coached on how to produce a good technical report at the end.

These projects also gave a nice path to getting talented and interested students involved in research as undergraduates.  We made a point of reaching out to students whose projects were particularly good and offering to help get them connected with undergraduate research opportunities.  Many ended up working joining the professor's research group, and many others joined research groups of other professors where we helped make introductions.  A large fraction of those went on to grad school, and at least some to faculty positions (though I don't think anybody ever did a proper quantitative assessment). In effect, doing a "trial run" of working on research-style projects in class both helped students discover interest in research that they might not have realized, and also helped reduce the risk for professors to take them on afterwards.

The main challenges in doing this were:

selecting appropriately scoped projects, complex and interesting enough to give the students a taste of research, but not requiring deep background or more than a few dozen hours of work, and
making sure TAs were sufficiently advanced as grad students to be able to coach the students well.


Peer review - discussing paper with the author I am currently attending a week-long conference. A few weeks ago, a quite respected professor in my field posted a paper on an online preprint server, making a series of claims, which are quite linked to my own research. In the beginning of the conference I approached him, asking if we could discuss his latest paper. He said yes, but asked if we could maybe wait a couple of days, since he was swamped with tasks during the beginning of the conference.

In the evening, I am discussing this paper with a couple of colleagues over beer. I am quite critical about some of the claims, and I voice my criticism. After the discussion, one of my colleagues pulls me aside. He says that he is currently reviewing the paper for a journal, and since I obviously have strong criticisms, he would like to suggest to the editor that I join the review panel, if I have no objections. I agree, he contacts the editor, and I am now reviewing the paper.

My question is now: Can I still discuss the claims of the paper with the author as we initially agreed upon?

(By the way: I know that my colleague overstepped the line by revealing himself as a reviewer. This is not part of my question.)

The benefits of peer review are first to detect and prevent fraud and ridiculous claims, and second to improve the clarity of the published record. So, in general terms, if your conversation furthers these objectives, it's a beautiful thing.

On the other hand, if formally reviewing the paper comes with a commitment to maintain confidentiality, you will want to reconsider, and think carefully about how you back out of either the conversation or the formal review.

Confidentiality has benefits, like allowing reviewers to be critically honest without fear of retribution. It also has problems, like the inability to have an efficient conversation about honest disagreements and misunderstandings. This handicap is why some journals (along with the preprint system) are starting to allow for conversation without the blinds of confidentiality. If your journal-specific reviewer expectations do not prevent an open conversation, it may be the best approach.


Before proposing reviewers, should I notify them? Some journals require the author to propose potential reviewers. Should I just propose reviewers and let the editor contact them? Or should I first contact the potential reviewers myself and ask if they agree?

I never asked them and never heard of anybody doing so. It’s the journal who is responsible to organise peer review, not you, and they have to and will humbly ask¹ potential reviewers anyway. By asking potential reviewers yourself, you cause the following problems:

Not every reviewer you suggest will be chosen by the editor. For a reviewer who will not be chosen to review, deciding upon your request will be a waste of time.
Even a reviewer who is eventually chosen will waste a little time by responding to the same request twice.
If a potential reviewer declines your request and you do not suggest them, the editor may still choose them on their own (most editors to whom I have talked make a point of choosing at least one reviewer who was not suggested). In that case, the potential reviewer may think that you ignored their wish.
Asking reviewers directly is a first step to breaching the independence of peer review as it opens up the path for manipulating peer review: “Sure I will review your paper [favourably] – provided that you will do the same thing for my next paper.” Some oppose even the mere option to suggest peer reviewers for this reason.
If you know only a limited list of potential reviewers for your paper, removing those who deny your request may make the list smaller than what is requested by the journal. If those reviewers only refuse when asked by the journal, you still did your duty of suggesting sufficiently many potential reviewers (and this may not even be a problem, if the journal requests a list that is longer than the number of actual reviewers).
You waste some of your time by asking the potential reviewers and waiting for their response may delay the actual submission.
If you show your paper to your potential reviewers privately, they have an advantage in the rare case that they try to steal your ideas or commit other dishonesties. By contrast, peer reviewers are often bound to terms of non-disclosure and similar and also the journal serves as an independent register of who had knowledge of the manuscript. This point does of course not apply, if you trust all potential reviewers or published a preprint of your paper anyway.

¹ Except perhaps for some journals with a questionably demanding demeanor.


How to handle capitalized abbreviations in Book, Anthology, Journal, etc. titles in APA? I am using APA and need to cite an article from a conference which has a capitalized abbreviation in its name:

Proceedings of the 1992 ACM/IEEE Conference on Supercomputing

https://dl.acm.org/citation.cfm?id=147877

I can't find information on how to handle abbreviations in the reference entry. My Biblatex code is this:

@inproceedings{schutze_1992,
    author = {Schütze, H},
    year = {1992},
    pages = {787 - 796},
    title = {Dimensions of Meaning},
    booktitle = {Proceedings of the 1992 {ACM/IEEE} Conference on Supercomputing},
    editor = {Robert Werner},
    location = {Los Alamitos, CA, USA},
    publisher = {IEEE Computer Society Press}
}

On suspicion, I manually kept the abbreviations capitalized, producing this output

enter image description here

But per default ACM/IEEE is not capitalized by Biblatex.

What is correct?

First of all, do not trust Bibtex/Biblatex on capitalization issues. Every Bibtex/Biblatex style file I've looked at handles capitalization using quite simple rules - either it's "Title Case" (capitalizing every "major" word) or "Sentence case" (capitalizing first letter). There's no extensive hidden database used to tell if a given word is a name, proper noun, abbreviation, chemical element, etc. That is, Bibtex/Biblatex would happily output "On gustafson–barsis's law", for example.

Now, APA generally recommends abbreviations to be capitalized (there are some exceptions like 'laser' where an acronym has become overly familiar), which presumably also applies in the reference list. The clearest statement to this effect I've found is from this library guide:

  Capitalize proper nouns, initials, and acronyms in a title.


Which is more important to get into top CS PhD program: High GPA or industry R&D project experience? As a computer science undergraduate I'll only be allowed to start a research project/ publication in the 3rd and 4th year at our university. I am in my 1st year 1st semester and got the chance to join R&D project firm. But I noticed that it is slightly hindering my GPA (assignments etc.).

When applying to a CS PhD program is it more beneficial to maintain a high GPA or do R&D industrial projects for 1st and 2nd year(mostly image processing based) until I get to start research publications?

Edit - I am talking about a GPA difference of around 3.6/3.7 to at least 3.8.
I forgot to mention that I may be able to join few research publications (probably nothing that can be regarded as high quality research though) if I continue to work at the R&D firm.

Neither.

Top CS PhD progams are looking for strong evidence of future potential as an independent researcher.  Everything else is secondary. Recommendation letters that speak directly to your research potential, in personal, technical, and credible detail, with direct comparisons with other students they have mentored who have gone on to strong PhD programs, will serve you far better than either a perfect GPA or industry internships or even both.  Strong research results are even better.

A better way to way to ask this question is...

  Which is more important for finding a strong, supportive mentor and a fruitful topic for undergraduate research: high GPA or industry internships?

But even this is missing the mark. It's not your GPA that you want to maximize, but rather (1) your mastery of the material, and (2) your interactions with faculty.  A recommendation letter that says nothing but "My class was hard; Oshan got an A." is utterly useless. (We call them DWIC letters, for "Did Well In Class".)

Similarly, industry interships are only useful if they provide you with opportunities for intellectual creativity, or leverage for getting into a specific research project that you care about.  In particular, unless you actually care enough about image processing to want to do research on it, an internship doing image processing is just spinning your wheels.

The most important advice I can give is to talk to your potential research mentors as soon as possible. Ask them what they look for in potential undergraduate researchers. (They might say "a high GPA" or "industry internships", and then you have your answer.)  Ask them about their track record in placing students in top PhD programs.  Ask them what they would expect you to justify a strong recommendation letter. Ask them what sort of research they do, and how to get involved.  If they don't have time to work with you ask for pointers to interesting papers to read, problems to think about, projects to work on on your own.  Do not wait for permission; just start knocking on doors. Today.


What to do when you discover a mistake in the archived copy of your PhD thesis? I've just defended my PhD in mathematics and started a postdoc. While working  on an improvement of one of the results of my thesis, I realized that there are several minor mistakes and a big bug in a proof that invalids a minor result in the thesis (about 3-4 pages out of 110). Unfortunately neither I nor my advisor or referees figured it out the mistake. Though the result is minor, it is announced in the introduction and the manuscript is on-line on an ArXiv-like server, so that I could publish a new version but not cancel the one on-line.

What is the best thing to do? Upload an errata? Upload a "revised" version of the thesis?  Publish a "revised version" of the thesis on my web-page?

Can this damage my future career, making me look not "reliable" as a researcher?

Though I am not in mathematics (I'm a philosopher), I would only add that generally speaking, the PhD dissertation should be one of the worst papers you ever publish. That doesn't mean it should be terrible...it only means that it's the start of your career, and your writing--and your research--will get better with experience.

You could still go ahead and submit a correction, but at the same time, don't stress yourself out over it. So many doctorandi today think the dissertation has to be flawless, and that they have to make some ground-breaking, Nobel-prize-worthy advancement in their field for the thesis to be any good. This is simply not true. Look--you have a postdoc, the results of which will no doubt be more influential on your future than your dissertation.

Again, I'm not saying to not submit the errata. Rather, I just don't want you to lose sleep over the mistake. Don't be too hard on yourself.


Conducting independent research while in a university This could possibly be very specific to each university, but I was wondering if there is a rule prohibiting researchers who are working within a university from conducting independent research — perhaps external funding.

This isn't a question that can be answered in general. It varies by place and by university. Some universities, like commercial organizations, will want to claim that all of your IP developed as an employee belongs to them. Others will pose no obstacle. Your best answer will come from the university you are interested in. There may be a legal affairs office that can answer such questions.

For an individual, having the university claim ownership can, possibly, have an advantage as they may support commercialization of your ideas and share the profits with you. But you can't assume that. So, it is best to ask before you begin so that you know of any limitations.


How to handle salary negotiations while negotiating a postdoc position? I am a couple of months off my defence and started thinking about postdoc possibilities. I seem to have an opportunity at a very prestigious lab in the U.S. (I'm currently located in Europe).

After a brief chat with the PI at a conference, and some e-mails back and forth I was invited to come for a visit, meet the people and present myself and my work so far. Considering that we are still in contact and started discussing more regarding the projects and funding possibilities, I feel like they genuinely do want me to work there.

Putting aside all the parameters regarding the scientific matters, I am a bit concerned about the economic aspects. The lab is located at a city which is among the most expensive in the country, based on what I read/hear. I have been in contact with the postdoc-community (turns out there is one) at this university and asked them about how salaries are decided and how it compares to the cost of living in that city. The answer wasn't very simple, but my understanding is that it's not regulated at uni or faculty level but instead decided on a case-by-case basis. I hear figures varying from $30K to $75K, which is a pretty huge span.

So, coming back to my contact with the PI, at no point during our communication the subject of salary came up, and while initially I was cool with that, I am starting to feel like it would be good to know what I am getting myself into before we start putting serious effort into writing grant applications and designing projects.

I realize money shouldn't be the first concern, while negotiating an academic position; but if I'm going to relocate to the other side of the planet without any security of a future there, or back here, I'd like to know that I'm taking a step up from my current life rather than a step down. The issue is that as a grad student in Sweden, you have a pretty decent life and you get a respectable salary (despite being 20-35% down from a comparable position at industry).

Q1: How can I inquire about the salary offer/negotiation without appearing greedy or money-oriented?

Q2: As a follow up, is it reasonable to expect/ask them to match my current living standard?

I think all your concerns are very reasonable. Honestly, I am not really buying into the "money is no concern in academic job hunts" story. A postdoc is a job. Part of a job is a salary. I never understood why you are allowed to ask about housing, students, packages, whatever, but not what money you are going to make.

  Q1: how can I inquire about the salary offer/negotiation without appearing greedy or money-oriented?

In the past, I have brought up this topic during my faculty visits (i.e., when they invited me over). At this point, nothing is promised yet, but it should be reasonably clear that both sides are interested and will not be scared away by trivialities. That is, if the PI went through the pain and costs to fly you over, (s)he isn't going to drop you just because you said the nasty M-word. Also, a slightly uncomfortable topic is better discussed face-to-face, over coffee.

Usually, there is a point in the conversation where you can drop it in without being overly heavy-handed. Don't make it the first thing you ask, but some time during the day quite naturally the formalities of your employment will come up. At this time you just ask directly for your yearly salary.

"You say that my employment would be full-time for 2 years? By the way, how much in yearly salary would this roughly amount to?"

If this is above what you expect, nod sagely and let the topic go. If this is slightly below your expectations, ask if there is possibility to increase (e.g., with additional teaching, by bringing in grants). I wouldn't really consider this negotiating (as they like to say over at Workplace.SE, one cannot negotiate without being prepared to walk away), but rather an open discussion what you would need to do to get to your expected salary. If the offer is way below your expectations, to the extent that your standard-of-living would actually unreasonably sink from your current position, explicitly say so.

However, keep in mind that there is a good chance that in this third case, you may well have an unfixable problem - likely, the PI will be unable and unwilling to go up very far from whatever (s)he currently has in mind, so there is a good chance to you will either have to take the lower salary for the experience, or be prepared to walk away. However, at least then you know - this is still much better than finding out when you are already in the US that your salary isn't close to what you are used to.

Edit:
There is, unfortunately, also the very real chance that the answer will be something evasive, for instance:

"We'll tackle this when you are here, ok?"
"Salary will follow the university standards / NSF standards / whatever."
"I don't know from the top of my head, but I am sure we will be able to figure it out."
...

If this is the annoying case, I would press the topic. Really. No matter what anybody says, it is not unreasonable to expect to have more or less exact knowledge about salary before agreeing to changing jobs, especially but not only if that "changing jobs" will mean that you move to a different continent. Also, it is a super-bad sign if the PI won't even give you a ballpark number. That alone would make me very anxious about the entire salary topic.

  Q2: As a follow up, is it reasonable to expect/ask them to match my current living standard?

Partially. Your current standard of living isn't really of much concern to your new employer, but of course you can use this argument to plausibly explain why you care about having a specific minimal salary goal. Of course for the PI it's not an ethical problem at all to respond with "I don't have this money", or "For this money I can get two qualified people who don't have your expectations regarding standard-of-living". So I certainly wouldn't expect them to match your current salary, but it is not unreasonable to ask for this.


Using movie clips in a conference presentation I'm a drafting a presentation for an upcoming conference. To illustrate some points I'd like to include some clips from well-known scifi movies about VR. Would this be considered "fair use"?

Could said powerpoint file be made available for download (which would include the movie clips I guess) afterwards?

Let's just preface this with 'I am not a Lawyer' (I am a Sociologist interested in copyright and piracy).

Fair-Use in the U.S. supports educators using small amounts of copyrighted material for educational purposes, and this includes to peers for conferences and workshops, so long as that material was legally obtained.

Copyright (in the majority of cases) gets invoked at the point of distribution so putting those clips into a .ppt and then sticking it online is not protected as you're now distributing copyrighted material without permission.

I would use the clips live, and then put placeholder images in the slides for the upload. If clips are available from reputable hosting sites like YouTube or Vimeo you can always include a link in the slide. Technically in the U.S. this can be framed as second order copyright infringement under 'inducement' (i.e. you are inducing, or encouraging someone to access copyrighted material illicitly by providing a route to it).

However with all copyright it's a matter of enforcement, and more likely if the material is unlawfully available on a streaming site, the rights-holder will pursue the site, not the linker.


How to confront new office mate regarding personal hygiene? I am a senior graduate student and share an office with 3 other post-docs and graduate students.  We recently had a first year graduate student join our lab for a rotation and he has been placed in our office.  This new student has intense body odor, to the point where you can smell it in the hallway outside our office when the door is open.  I have asked my other office mates what to do about it and their responses have ranged from "I don't notice it" to "Yeah it's bad but there's nothing we can do about it/I'm not saying anything".

To me, there is no way one can't notice  I have found it distracts me from my work.  I imagine my advisor has noticed, but so far no one has mentioned anything and I have been the only one to bring it up with anyone else.  I've asked friends who work in industry what they'd do, and they say "don't say anything but immediately report it to HR", which doesn't particularly help in my situation.

As the semester is coming to a close, it's only getting hotter outside and the rotating student will be present more often in the lab.  I'm afraid to say anything because

I don't want to make things uncomfortable, especially since we need to train him;
I'm afraid I'm being culturally insensitive as he is a foreign student.
He may have a medical, religious, or other reason that leads to the smell, and I don't want to offend.

Because this is an academic lab, I have no access to an HR department.  Any advice?

A related question (which was not closed) can be found here.  In this question, and those found on The Workplace SE, most answers are "tell him privately and nicely", but I'm worried about being insensitive because he is new, I haven't gotten to know him personally, and he may have cultural, religious, or other reasons for not wearing deodorant.

After some clarifications via comments above:

There is surely an office with a name something like "Student Services" that would be the front end for many issues involving conflicts/issues between students or conflicts/issues between students and faculty. It is not possible to guess the name of the corresponding office at your university, so some experimentation or asking around is necessary. You should definitely consult with them about what actions on your part would be approved or disapproved.

There surely is an HR office at your university (assuming it's in the U.S.), which has some oversight powers/responsibilities for any paid personnel. Probably most grad students have not had any interactions with that office, but it surely exists, and does some back-end checking of hiring and such.

Many years ago, as director of graduate studies in mathematics at a U.S. "R1" university, I found myself the recipient of complaints from office-mates about the hygiene of a grad student. Having been already sensitized to the volatility of such issues, I wrote a somewhat vague, but pointed, message to all grad students in the math dept about "expectations" for hygiene, given their TA duties. Naturally, most were confused as to why I was writing this at all, but the "broadcast" aspect did avoid confrontation with individuals. Also, there was certainly the aspect that there was a job getting done for pay, and interference, accidental or not, was not ok. If the person had not been paid by our department, I would have been much more inhibited about strongly-recommending hygiene modifications, just given the way things seem to work here.


What are the differences between a research paper and a patent? I would like to know particularly how is regarded in a scientific community since it is not peer reviewed.

  It should be noted patents do not go through similar peer review as journal publications. Of course, this is slightly depending on whatever requirements patent laws in a specific country put on the claims put forward bu the patentee. But no country really requires another researcher to scrutinize the claims made by a patent at the point of filing it, like a publication does.
  There are patents of cars that run on water, fusion-powered space craft etc.etc, ie. things that do not work. So as a reference a patent is definitely a weaker source, especially if it is not something someone is using/selling yet.

Is it allowed to publish patent and then paper?

You're asking multiple questions at once, so let's start with:

  What are the differences between a research paper and a patent

There are many, but briefly a research paper has the purpose of communicating research findings to the relevant scientific community and the general public. Patents are legal documents used to prevent other people from commercializing the process or devices they describe. Research papers typically report results of a scientific process, whether experimental or not, while patents describe processes and devices along with their respective intended applications.

Patents are typically very open-ended, not unlike a very long "future works"-section of an academic paper. The strategy is to cover a maximum of possible usages for the technology.

  it is not peer reviewed

That is correct, peer review is a strategy used by researchers to filter, improve, and curate scientific literature. For patents, the review is driven by the legal requirements only. A major point is absence of overlap with existing patents and other publicly available material*. In some regions, a minimal demonstration of how the process/device works is expected by patent offices, but it's not the same as peer review.

  Is it allowed to publish patent and then paper?

Yes, it's the other way that's problematic because as stated above, if you (or someone else) published a paper about your process or device you cannot patent it anymore.

Most journals are interested in novel results and all (legitimate) journals don't want to re-publish existing literature but the fact that the processes are patented is not an issue. As stated in my first paragraph, both documents should have very little overlap.

*On that subject, see the entertaining if not entirely historically accurate
Donald Duck as prior art case


Can i post my article in arxiv for comments from other mathematicians? I have recently written a paper on Quantum Polynomial Algebra. But I am not studying anywhere, so I havenot got anyone to comment. Even I have mailed a few math professors but they may be to busy to answer. That is why I was asking whether can I post my paper in arxiv to know whether the result that I have obtained is good enough to publish or not? Idonot want to send my paper in reputed journals and get rejected after 2 or 3 months.

The rules for submitting an article to Arxiv are fairly clear and do not exclude you on the basis of not being affiliated to a research institution. You have to be registered, but that's about it.

I feel that a change of terminology is in order - you don't "post" to Arxiv - it's a preprint server, not discussion forum or a message board. You "submit" work to Arxiv. Among the functions that a preprint server provides is a record of intellectual ownership. If you submit it to a preprint server, you could claim later on that this work is your own and was published on a certain date. A second function is that of peer review, which you are asking about here. If indeed your work was plagiarized, this would likely be discovered by peer reviewers if you submitted it to the Arxiv (or for that matter, to any journal with a reasonable editorial process).

Note that Arxiv does not have a peer-review process - it's a preprint server that exists specifically to speed up publication of non peer-reviewed research. However, it does have a moderation process. So while submitting your research to a preprint server makes it more likely to have it peer reviewed, it does not guarantee it.

So, to answer your question

  Can i post my article in arxiv for comments from other mathematicians?

the answer is undoubtedly "yes". Nothing prevents you from doing this.

However, the corollary which is implied in your question:

  If I submit my article to Arxiv, will it be peer-reviewed by mathematicians?

is more difficult to answer. Almost all peer review is a volunteer activity, irrespective of where you submit, so someone needs to volunteer their time and intellect to reviewing your contribution.

Ask yourself what your motivations are. Do you want a publication, or do you want your contribution to objectively evaluated? Either way, submit it to the Arxiv to state that the work is yours.


Bad data - how to move on successfully I work in an interdisciplinary field. My input is not generated by myself, but by talented people that I trust and that trust me to analyse their data and generate fascinating insights.

But here I am, once more stuck with a project where the input is bad. There is no use for blame and finding a scape goat, we are in this together. And people learn. But I have been stuck in projects with big promises and bad/insufficient input since the start of my PhD. I moved to another place for a PostDoc now, but this situation seemingly haunts me wherever I go.

There is not much I can achieve when most of my projects stop after the input quality control. But if I want to stay in academia, I desperately need to step up my game in actual output, not just in my ability to troubleshoot, right?

How do I move on from this? What chances do I have if I can just never land a "prestigious project" that results in a valuable publication? Is there a chance to still build up a good scientific reputation without those? Should I try myself at writing a review? Take on projects until one finally works out? (But how long will I succeed in getting another job if they don't?)
I could work with published data for a bit, but it is often not comparable between studies and severely lacking metadata.

The question is, do I have to accept that success (as in, basically being able to stay) in academia is to a large degree based on luck and I am not one of the lucky ones or is there anything major I can do? I really love the work I do, I would like to keep going.

So far I have taken on additional projects, tried to do my own 'side projects' on at least roughly usable parts of the data in the hope I will find a better dataset eventually where this could come in handy and kept in touch with collaborators in an effort to troubleshoot and eventually produce better input.

EDIT:
To address some questions:
I produce analysis pipelines, partially based on my own methods. Without "real data" application it's hard to publish those in my field.
Yes, it is "real world data". I do not expect perfect data at all. But I do expect technically correct, usable data. If the input is random/to few features to be statistically relevant there is nothing I can do, though.
Imagine trying to do a statistical test on the similarity of blog posts based on word usage written by different groups of people but many "groups" only are represented by two authors, the text is sometimes just one sentence long and quite a few of the posts looking like they are produced by a random letter generator, not having any actual words in them. While I was promised at least 5 authors per group, minimum 5000 words per text and of course the post actually written by the author assigned to it.

Summary:

Real research life with real world data is messy*, and there will hardly ever be sufficient samples (my very personal prediction).
There are huge opportunities (and needs) in working about small and messy data. Maybe that could become your area of research?
Good data analysis work requires close collaboration. Actually already when planning the experiments, but for sure during data analysis.
Close collaboration will allow you to make them aware of your needs and that data analysis cannot work miracles. It is also necessary for your because otherwise you may be employing inadequate analysis methods.

* When I say messy I don't mean bad curation (although I do see opportunities here as well - though maybe more business than research) but reality sneaking in with lots of influencing factors creating structure in your data where many (most?) data analysis approaches assume nicely independent data. I think this is a field that not only deserves more research but also that this has high practical importance.

I feel your pain. Been (almost) there as well. Actually, I still am (just my PhD is long done): so far with ≈ 15 years of professional experience in chemometrics, all the real world data I've encountered so far has one thing in common: too small sample size (even if it may look nice at first glance).

One consequence I drew for me is that I started to do research on such less-than-ideal situations I encounter in practice, e.g.

small sample size situations: knowing that I have far too few cases (some orders of magnitude below rule of thumb recommendations), how to diagnose when things break down, how to stabilize models, what will break down, are there hard limits, etc.
in terms of messy data in the sense above (with lots of influencing factors):
I had situations where it turned out that biology doesn't really obey the disease classification medical doctors use (that was developed for totally different purposes, as I learnt later on) how to adapt data analysis methodology to these situations (that were somewhere in between classification and regression)
how to adapt validation/verification procedures in such situations
(I work very much along the lines that you can to whatever you think may work for modeling as long as you do a honest verification and validation of that model)
I see tons of similarly important questions that are unanswered.
To the extent that if you need such research ideas, I'd happily supply you with questions ;-)

In my field, I think, it is going to stay like this: well-characterized samples are expensive.
In some respect you may even say that basic restearch isn't meant to have comfortable sample sizes. It's meant to find basic knowledge and point out promising possibility but the leg work of obtaining (and paying for) large sample sizes to make a method robust for routine use is something applied research/industry is supposed to do (and pay for). That point of view would say that taxpayer money should not wasted on work that industry could and should do.
On the other hand, I often see unnecessarily too small sample sizes in academic research: too small here meaning that given the sample size even without any experimental data it is (or would have been if one had bothered to check) clear that no knowledge is gained because the study is too underpowered. This is clearly just bad science, and a total waste of experimental and data analysis effort.
If that's what you refer to in your question, it's going to be hard work to improve this - but don't give up! Science needs people like you pointing this out.

My experience with that is that as PhD student or fresh postdoc how much you can actually do to improve the data may depend very much on how much weight what you say has with your supervisor (or even top level director).
What you can (and should) always do is to clearly discuss the limitations in terms of possible interpretation of the results of your study - including in the manuscripts you write.
To be fair, there are practical limitations. If we study a rare disease where the big university hospital gets maybe one sample per year, I tend to think that working with very few cases is necessary (but again: spell out the limitations). After all, one has to start somewhere.
Whereas, if we're talking easily accessible measurements of no particular ethical concern of a disease where the hospital sees 10s of cases per week then of course a thesis on 5 cases looks somehow lazy (not necessarily on the PhD student's side, though: The PhD student may not have been able to change pre-existing sample plans)
One consequence for my PhD thesis was: as I did not only data analysis but also sample preparation and measurements for my thesis, I put in substantial effort to have more samples (fortunately I had access to a comparatively large data bank, but in the end also that approach was limited by availability of the more rare conditions).
I'd recommend to at least take a decided interest how the data are generated (get a lab tour, get the collaboration partners explain how things work and what the data mean).

   If the input is random/to few features to be statistically relevant there is nothing I can do, though.

Yes. Again, this needs to be clearly communicated: I do have the experience that applied groups may expect miracles from data analysis (and you may even have a particularly uphill fight here if this group in the past got data analyses that were badly overfit and thus looking overoptimistic and noone realized this).
In addition, you'll have to document that it isn't your "fault" that no nice results come out of this data. It's doable though (and again in my experience something that is needed in everyday data analysis work as well: I'm just having such a situation on my desk right now again).

  [...] "groups" only are represented by two authors, the text is sometimes just one sentence long and quite a  few of the posts looking like they are produced by a random letter generator, not having any actual words in them. While I was promised at least 5 authors per group, minimum 5000 words per text and of course the post actually written by the author assigned to it.

A few thoughts about this. I "smell" some communication/collaboration issues here. Again, they are typical everyday issues in my research and data analysis work:

I've met similar things due to fundamental communication issues (e.g. between statisticians talking about necessary sample sizes in the 1000s to answer a particular question and experimental scientists "translating" this to "many" ≈ 7.
"quite a  few of the posts looking like they are produced by a random letter generator"
Your experimental collaboration partners may have no clue what you don't know about their techiques (again communication issue): unless you have a background in these techniques you have no chance to recognize what is going in those noisy measurements and how to deal with them.
They could be anything from artifacts that should be deleted because the underlying mechanism that causes them is well known and can be disregarded over "the signal is hidden under this noise and you data analyst surely have some magic to get it of there" (not going to work, but typical expectation) to "your outlier is my most interesting case" - without the help of the experimental/data supplying folks you won't be able to adequately deal with this.
Seeing all this makes me think whether you do have sufficient information about the background of the study to even decide which data analysis approach is suitable?


How to motivate students to do readings I am planning on teaching a couple of math-y classes next semester that will involve a large amount of in-class group work and discussions, and relatively little lecturing.  There will be required outside reading for almost every class (3x/week), but as I am at a large public university where student work habits are not generally great, I am worried about making sure students stay up on the reading for each class.  Also, the students will probably not be in the habit of doing reading for math classes.

Currently, to encourage students to stay up-to-date on the readings, this is what I am thinking of doing:

Use books that are relatively easy to read and hopefully interesting for the students (closer to popular books than textbooks when possible), and try to make each individual reading assignment short.
Give simple quizzes on the reading once a week or so
Generally try to make sure the in-class activities are closely related to the assigned readings.

  Are there any other effective techniques that would help encourage students to stay current on the reading?

Note: while I want them to read certain things before each class period, I do not want to give a reading quiz every class period.

I require students to fill out a short summary sheet of the reading prior to the start of class for classes with small group work based on required reading. The summary sheet looks something like a structured abstract of the reading. In total it is less than 200 words of writing. I make my summary sheet available after the class. Based on feedback, the students like the sheets since they help them in reviewing for the exams. It also helps them gauge differences between what I think is important and what they think is important.

I divide the class into groups based on if the students have completed the sheet or not. Generally, I tell students to go to one side of the room if they have completed the sheets and the other side if they have not. I and the students can then quickly visually inspect the sheets to see if they are completed or empty. The groups of students who have completed the sheet get to work on the group activity, for which having the sheet is useful. The groups of students who have not completed the sheet work on the sheet and if time allows work on the activity. I make it a point to reassign students if the sheet is filled out with nonsense.


Is your advisor allowed to read your emails? In a typical workplace (North America), it is accepted that all communications through your work email are not personal property, and can be accessed by managers, IT department etc. Is this also true for graduate students? Assuming the student works in an university or research institute, the IT department obviously will have access to all emails. Does this also hold for advisors/supervisors? Are they allowed to, or typically have access to student emails?

What is the method of the email access?  If it is provided through the university or accessed on a university-provided computer, then it may be the case that they have a legal right to those contents.  This was the case at at least one of my workplaces in the USA.  If you are using your own email address, and your own computer, then I don't see how they could have the rights to access it.

Either way, it is best to have a separate email for all personal/private communication rather than risk it on your school email.


What are the goals and benefits of doing a PhD? I'm a master computer science student in a German university and I will be graduating soon. I'm in a situation between accepting a PhD position or leaving academia to start looking for job. This is a very hard decision to make so I need some advice.

I see most of the people who do a PhD leave academia afterwards and start looking for jobs (is it also the same in the US?). Very few people continue with a postdoc and remain in academia after getting the PhD.

My question is then, what is the point of doing a PhD (especially if one gets a job in industry afterwards)? What are the benefits of spending 3-5 years obtaining a PhD? What kind of goals should a person enrolling in a PhD program have?

From the point of view of industry, I realize that after a PhD they might start with a high position in the company. But, on the other hand, in those 5 years they could have started a small tech company and make it somehow medium or even a bit successful. Instead of doing a PhD, after 5 years of investing in their own company, they could be paid for working for themselves and not for others, having their own companies.

From the point of view of obtaining knowledge, if someone is curious about knowing, they could learn new things by themselves. After graduation, finding a job or starting a company, knowledge could be obtained by buying books and reading during one's free time, or following extra courses. I don't think you don't need a university for this.

Am I right in my analysis? Is it true most people go to the industry after the PhD (I'm especially curious about people in the US from top universities)?

If my analysis is at least partially right, and since there is other viable ways to become successful in industry and to obtain knowledge, what are the benefits of doing a PhD? Is it only a good idea for people with strong plans to continue in academia, or are there other goals one can achieve (better) by obtaining a PhD? I am afraid that doing a PhD might be a waste of time if I plan to continue in industry.

most of the people who do a PhD leave the academia afterwards and start looking for jobs (is it also the same in the US?)

Depends on how you see "most". In my experience, about 50% of the students in top-tier graduate schools leave academia; most students in lower-tier graduate schools leave academia. In Europe, this percentage increases quite a bit, since the Europeans generally enter a PhD program to become a professor (In the US, some people enter the program with the intention of getting a PhD, and nothing more.) You can judge for yourself where you fit, since I don't know which school you are thinking of attending.

  in those 5 years they could have started a small tech company and make it somehow medium or even a bit successful.

Sure. But in those 5 years, your tech company could have gone out of business, too! Perhaps you will say that you learn from your mistakes, so that was a valuable period of time. But you learn from doing a PhD too. I'm not necessarily talking about technical things, but the fact that you are able to learn things faster than the non-PhDs (since that's one of the basic skills that research requires.) If you do a CS PhD, depending on how you choose your research topic, it could be useful in real life too.

  I mean you could definitely graduate, find a job or start a company and buy books and read all night or weekends! You don't need a university for this.

True. But grad students are reading all day, then all night or weekends (in theory; in reality they don't actually do this, but they still would have more time than you). You would learn slower. Not to mention that not having a mentor would make things much harder for you (you could tell your PhD supervisor about the courses that you liked, and particular ideas that you liked; from there, she could tell you about some papers that you might like. As an independent research, that's not an option).

Last bit of advice: my father, who is also an academic, always told me not to go into academia unless I loved research. He told me that there are easier ways to obtain everything else in life; money, fame, etc. can all be obtained without being an academic, and more easily at that. And what he said was true for me. Being in academia extremely strenuous -- you'll deal with competitive peers in graduate school, maybe you won't get along with your supervisor, jealous colleagues, people who try to steal your work, thesis gone wrong, error in your paper, etc. I have encountered some of these, and each of these is enough to make you want to quit. The only reason I was able to hold on was because I found that I genuinely loved research. I knew that I couldn't have a job like this elsewhere, so I had to hold on. Any other reason will eventually drive you out of academia, though.

FYI, I attended a top US institution.


Web Crawl Dataset - Legal & Practical feedback I'm working on a web site classification method. Papers in field (Text Classification / Web Page Classification) are usually using:

a) Ancient, but well known & widely adopted, datasets like WebKB 7-sectors
b) Their own datasets, collected from domains found at a public categorized web directory

What puzzles me:

after review of recent research, I found that for b) cases, authors are not that happy to make dataset (corpus) publicly available.

They clearly state where they harvested web domains and do provide parameters of the crawl - but, I assume it would make scientific sense to publish exact corpus - so, future comparisons have more solid ground. Yet, I rarely find dataset link in a paper (interestingly enough: in few cases, I recall: authors published the corpus but never mentioned it in the article - I found it accidentally while searching GitHub / Google for PDF of the original source)

Now, I'm worried if there are legal concerns here - that I might not be aware of. I would appreciate very much thoughts & feedback from your practice / experience:

Should we allow our self to interpret copyright notice (~100% of pages in the sample) "all rights reserved" less rigidly, for our case, case of a scientific research?

But how to sleep well if I published gigabytes of web pages, having such copyright notice, on a Data Set repository with Creative Commons or GPLv3 licence? I mean, it is hard to play dumb if ever called out for such "disagreement" between the repository content and the licence.

Is it about me, or you agree - that it sounds really insane if we should ask for written permission to each of 1400 legal entities (in my current sample), running business web site - being researched?
how you guys handle this dilemma?
or would you stick only with ancient/mainstream datasets?

My particular research has focus more toward "web site" than "web page / text" classification aspect. Hence, I have strong motive to crawl an additional dataset (from domains harvested from https://dmoztools.net/), beside using the mainstream ones (since these rarely provide web-site context, and mostly contain web pages / single documents). If I take that methodological path, I have to make it available -- but: are there hidden problems in such path?

Thanks for all thoughts in advance & this is not an easy one :)

There seem to me to be two issues here: copyright and reproducibility of research. You pose them as being in conflict, but I don't think that is the case.

First, in most jurisdictions, publishing someone's copyright work is a violation. "Just for research purposes" probably won't save you in these times, though once it might have done so.

However, the reproducibility issue isn't quite as you pose it here. Suppose I publish an algorithm (I'm assuming it is an algorithm, actually - deterministic). Suppose I also publish the data set on which it is run. If someone else runs my algorithm on my data set does anyone expect that it won't produce the same result. Even a randomized "algorithm" should produce the same results within statistical limits when run on the same data set.

But that isn't really the meaning of reproducible. I make an hypothesis about what should occur in a situation. I test it. Others should be able to test it under similar constraints. But I doubt that my hypothesis was "X happens in Y dataset with Z methodology". More likely Y here is treated as a category of datasets, not a specific one.

So, if you publish the mechanism by which you gather the dataset, rather than the dataset itself (along with Z), then others can test your work and the results will be more meaningful than if you publish the dataset itself. Thus you avoid the copyright problem altogether.

One reason that authors don't want to publish some datasets is to avoid stepping in quagmires such as copyright. Others involve human subject privacy, etc. But, that shouldn't matter if the hypothesis isn't overly tied to a specific dataset, but to a category of similarly gathered datasets.


Why is ASU asking for my wife's high school transcripts when she is already in college? I know I graduated from the university many moons ago, but my wife has been attending community college for awhile and wants to transfer to an online program with Arizona State University (ASU).

My understanding is when you transfer from one institution of higher learning to another you transfer your transcripts. I am not sure how did conversation asking my wife for her high school transcripts from her country of origin, where she has to get them notarized and translated and what not ended up as part of the conversation.

It's a given she did this once already as that is how you get into a US accredited local community college already.

So can anyone help me out here because otherwise I am thinking they are hearing an accent and asking her what they shouldn't and just going down a road of treating her like she is some international student who is trying to apply to school here.

The latest degree matters

She's joining a new institution from scratch. If she hasn't graduated anything else, the new college is admitting a high school graduate, and they want to see these transcripts to verify that she is a high school graduate. They have no reason to assume that some other organization did the verification in the way they require. They may have some process to transfer (partial) credits for courses completed in the previous college, but that happens after she's been admitted.


If a professor analyzed my work, does the university own my idea? If I had completed work on a certain idea, and would like to have it analyzed from an expert in the field. And shared it with a few professors, to get their opinions on my work... would that mean the university has contributed to my work? And somewhat has rights to the idea? Knowing that I never used the university's resources or facilities during the development of this concept.

I wont publish anything, just need an opinion on my work, if it has errors and overall worth pursuing.

This is a good question for the legal department at your university. At the university where I teach, there is a standard software license agreement form that I sign with students to basically ensure that software is owned by everyone at the end of the class. But your university will likely have its own rules and policies. If you have your own attorney, you can work with the university to modify such agreements.


What's an efficient way to learn simple formulas by heart without understanding? Is there an unorthodox way to memorize formulas for daily use, similar to the assimilation of multiplication tables in primary school? I included 'without understanding' because I do understand the formulas: given pen and paper and time to think (about the physics involved and/or dimensions) I can figure out the formula. I need to be able to use them instantly, without thinking.
Context
I am a Physics PhD student who has spent about 7 years in the field. I thought that I would by now automatically have assimilated formulas that I use daily, but this hasn't happened. If anything, the situation progressively worsens because I vaguely remember so many formulas that I mix up similar ones. I can never come up with a formula when asked on the spot, which is embarrassing. On my desk I have stacks of formula sheets that I have to use continuously, including highschool-level formulas, which is very time-consuming.
Examples include

λ = c / f;

k = Ω2 m;

trigonometric identities; hamiltonians; derivatives; etc..
A long time ago when I studied Latin I managed to learn meaningless sequences by heart by repeating them hundreds of times (orally an in writing), rhyming, making songs etc.
This is also the only approach I have found thus far for physics formulas, but the problem is that I need them every time in another form (k = Ω2 m,  Ω = (k/m)1/2,  Ω2 = k/m, m = k/Ω, and so on). This means that I would have to separately learn by heart all different permutations of a formula, which is too much.

Note: this answer was migrated from physics.SE, so I've had to re-render the formulas as images. The answer is also specific to learning physics, though it might work for other fields too.

This looks like a classic XY problem. I don't know of any practicing physicists that use a special memorization scheme 'without understanding', and I think such a thing would be counterproductive. Here are a few tips that might help instead.

1. Use conceptual handles

Here are formulas for the speed of sound in a gas, the speed of waves on a string, and the frequencies of oscillation of a mass on a spring and a physical pendulum.

The intuition for all of them is the same: the numerator is some measure of a restoring force, whether that's how hard the gas pushes back, how hard the rope pulls, the strength of the spring, or the torque of gravity. The denominator is always some measure of inertia, proportional to the mass of the system. Thanks to this intuition I don't have to remember anything, except that a square root is involved.

As another example, consider all the annoying conversions between wave quantities,

along with many others. To remember the conversions between (ω,k) and (T,λ), I just use

which is the fundamental definition of the wavenumber four-vector. This fixes the factors of 2π, since that's the change in phase of one cycle. There's no place all these little tricks are written down; everybody has their own and it's best if you make them yourself as you go.

2. Don't worry about judgment

I was just watching a summer school lecture where a renowned theoretical physicist took a good 30 seconds to flip a fraction. This is completely typical and not embarrassing at all. Some people function better rearranging symbols in their heads and some function better using chalk or paper. Personally I can't do anything in my head; I have to use paper or write it out in the air, but I have never felt judged for doing this. If your colleagues are being judgmental, they are being rude and you shouldn’t let them get you down.

When I see somebody able to remember or rederive something much faster than me, I often ask them what their conceptual handles are. Unless the person is exceptionally rude, they’re typically happy to explain. (This is especially true in physics, where mere memorization is uncool.)

3. Chunk concepts, separately from tools

If you're having trouble writing a grant proposal, the solution is not to memorize the exact sequence of muscle activations needed to write every individual letter. Similarly, if physics feels too 'big', the very worst thing you can do is to make it even bigger, by unpacking every equation into eight separate equations. Learning is instead done by chunking things together.

For example, take the derivation of the wave equation, which is a full two pages of math written out. You don't want to store every line in your head. Instead, you just want to store a general intuition that "curvature means a restoring force because strings under tension straighten out", which gives you the ∂²y/∂x² term. To get from that to the final result you need to know Newton's second law (giving the ∂²y/∂t² term), the small angle approximation, and the binomial approximation. But none of these are specific to the wave equation -- they're just general tools.

As another example, I was lost when first exposed to tensor notation. It looked like there was an enormous amount of stuff to memorize! But it faded away once I sat down and wrote out all the allowed manipulations. It turns out there aren't that many, ten common ones at most. All tensor calculations up to graduate level are just using the same ten steps over and over again, so in a technical sense it's actually easier than high school algebra, which has many more allowed manipulations. Once you have this understanding, many derivations get shorter; they get chunked into "use the standard steps, plus this one trick in the middle".

Then all you have to do is remember the trick, ideally with a conceptual handle.

4. Construct your own understanding

As I emphasized above, the best way to get this kind of understanding is to construct it yourself! There's no shame in revisiting a subject that's "basic" and rebuilding it yourself from the ground up; I've done this with calculus and mechanics several times whenever I felt I was getting rusty. Make a formula sheet, or if you think visually, try drawing a mind map or a dependency diagram. Challenge yourself to rederive key equations without a reference. If you do this a lot, you'll naturally construct the necessary conceptual handles and chunks, and get better at recognizing which tool to use.


Are there any students at the United Nations University? The United Nations University (UNU) is an academic arm of the United Nations.

If I understood well, it is doing only research.

Are there any people who are actually enrolled as "students" there?

References appreciated

The United Nations University does not really work as a normal university, but rather as a research center. The difference is that they recruit students from developing countries as for a period that can go from several months to a year, in order to make them work on concrete research projects, so that they can gain experience (which can help them to apply for a Master of PhD program somewhere else).

For instance, this a job position for students at the UNU-IIST (International Institute for Software Technology). Note that the UNU-IIST also plan to offer a joint PhD program with the University of Pisa. Note also that it seems that each institute of the UNU is to some extent independent, so maybe other institutes provide more academic programs.

So, to answer your questions, technically speaking there are students at the UNU (when I was working at the UNU-IIST, there were something between 10 and 20 students), but they are not "enrolled" by the UNU, it's more like some kind of internship.


IEEE Referencing: Same source, different page numbers I'm fairly new to IEEE referencing and have a slight dilemma.
If I'm going to be citing the same paper twice in my writing but want to cite specific pages of it, do I include the source twice in the reference list or is there a different way round this?
For example:

Lorem ipsum dolor sit amet 100 [1], consectetur adipiscing elit. Phasellus vestibulum nunc nec velit iaculis viverra [2].

References:

[1] M. Abramowitz and I. A. Stegun, Eds., Handbook of Mathematical Functions (Applied Mathematics Series 55).
Washington, DC: NBS, 1964, pp. 32-33.

[2] M. Abramowitz and I. A. Stegun, Eds., Handbook of Mathematical Functions (Applied Mathematics Series 55).
Washington, DC: NBS, 1964, pp. 84-86.

No, you include a source only once in the reference list. If you have to refer to a specific part of a source, you do it in the in-text citation, not the reference list, as in one of these examples:

  [3, Th. 1]; [3, Lemma 2]; [3, pp. 5-10]; [3, eq. (2)]; [3, Fig. 1]; [3, Appendix I]; [3, Sec. 4.5]; [3, Ch. 2, pp. 5-10];
  [3, Algorithm 5].

Source: IEEE Editorial Style Manual (Wayback Machine)


What does the status "under editor evaluation" entail after a 4-month review phase? I just found out that the review status changed to "under editor evaluation" after a four-month review. This seems to be a new status information in the Elsevier Review System. What's the difference between "decision-in-progress" and "under editor evaluation"?

According to this answer:

  The status "under editor evaluation" indicates that the paper is now with the Associate Editor (AE) who will evaluate it based on the reviews and decide whether the paper should be accepted, rejected, or revised. However, the AE's decision is not final: he/she can only give a recommendation to the Editor-in-chief (EiC) in this matter. The final decision is taken by the EiC. This is when the status changes to "decision in process." Thus, to put it simply, "under editor evaluation" means that the paper is with the AE, while "decision in process" indicates that it is with the EiC.

Also see:

the Elsevier's peer review process,
and this answer: After with editor the current status is submitted to journal


Should I, as an amateur, get my result published based on a well known professor's opinion? I don't have any formal education in mathematics that the large part of mathematicians have but fortunately I have the ability to use intuition to obtain results in higher mathematics that many professional mathematicians find interesting.

So recently I happened to send this famous professor (whom I've been corresponding with for some time) one of my results and he responded by saying that "indeed he thinks the result is new"

So what's the next step? Should I try to get the result published?

Assuming the following premises are true:

The professor you state is well-known and established in his field
You have produced a tangible result in this field
The professor endorses your result to be new

Then,

  Get the result published!

I do not see any reason for this not to be published. Better yet, contact the professor for further directions (see comments).


Is it legitimate to present self-developed, but commercial, software within my dissertation? The situation goes like this: Within my dissertation (doctoral degree) I present a software library which I wrote by myself for a company I work for. Thus, the software is commercial and the rights are owned by the company. The company is fine that I present the software in the dissertation. The software is presented in the dissertation by an own chapter and used for experimental evaluations. However, is it legitimate to present such a software as part of the dissertation from an academia point of view?

It depends on your actual area. In general, I believe that it is acceptable (I'm in Electrical engineering). However, you need to justify your scientific contribution. How is the work you did in this software a contribution to your thesis?

Also, as a thesis examiner, I would expect a detailed description of the algorithms / methods developed for this software to be able to assess your contributions. Are you willing to do so? Does the company you work for allow you to disclose this information?


How can one best seek out collaborators in obscure topics as a young researcher? Some context: I will be starting a Ph.D. program in mathematics this fall (a 5 year, U.S. program), and I have a wide array of research interests. In the majority of these areas, I have a (relatively) broad conceptual knowledge, and a few ideas for potential research projects, but I don't think that my command of the material is sufficient to allow meaningful contributions on my part (viz. writing publication-worthy papers) without collaboration.

For example, I have developed a type theory I believe to be compelling for qualities both philosophical and technical. This intellectual endeavor would benefit from the experience and specialized reference of a more knowledgeable collaborator who could elevate my general understanding and vision of how to go about proving the relative consistency of this type theory and enrich the discourse in parts where my experience may be more limited. This brings me to my question: How should I go about seeking (and attracting!) potential collaborators for such a project given my lack of experience in the field?

One other issue that I should mention is that most of these projects are relatively far outside the faculty/student's areas of expertise at my university, so I don't think these projects (like the type theory example given above) would be reasonable for my dissertation, or more generally collaboration with my peers/the faculty at the university I will be attending.

Should I put projects like these on hold until I finish graduate school, make more connections, and have more academic freedom, or is it worth it to try to reach out to academics who might be interested through e-mail/conferences and try to convince them to co-author papers/collaborate with me? I'd appreciate any input on how to best approach this dilemma, but some things I've already thought of are:

Type up a short analysis of the literature to ensure potential collaborators that I've done my research, and give an argument of why this research might be significant.
Correspond with potential collaborators via e-mail with more pointed questions relating to the potential research question before formally asking if they would like to co-author a paper/collaborate.

Any other input along those lines I think would be helpful, but I think I'd also like to get some context on how my position might fit into the general academic etiquette of the mathematical community, and how someone in my situation might best approach different researchers as a newcomer to the research community.

Thanks in advance for any responses!

As mentioned by aparente001 in the comments, the best way to start collaborations as a beginning PhD student is through your supervisor.

Your supervisor is hopefully a well-established researcher who's made some decent contributions to the field. People working on close areas of research will likely know their name and what kind of things they work on, and consequently the sort of things their students are likely to be doing.

Your supervisor may bring up the idea of collaboration without you asking, or you could approach them with the idea you have and, if it has merit, they might suggest names of people working on similar areas whose work you could read up on and get in touch with.

In this day and age, it's easy to start a conversation through email, and your second point about corresponding with potential collaborators and asking questions about their work is a good approach, but make it clear who you are,  what you're working on and why you're interested in their work from the outset.

If you're serious about starting collaborations with people completely outside your network (or your supervisor's network), the best way to do it is face to face, at conferences, meetings or summer schools. The latter are particularly useful for PhD students, as the attendees are also going to be PhD students or post-docs, who will definitely have more time and incentive to start new collaborations than senior academics will.

Lastly, don't worry too much! People are aware that new PhDs are still finding their feet in academia, and as long as you are professional in your enquiries, I don't think you'll go far wrong. It's certainly not bed etiquette to want to work with different people and broaden your research horizons.


As a student, should I offer alternative ways of understanding a point in lectures? Often, I’ll be in lectures and the lecturer will explain the point to me, and I make a comparison with something else that helps me understand the point, and it clicks. For example, in Syntax, the lecturer was talking about the difference between a noun and a determiner phrase; how the former describes a set of things, and the latter points to a specific thing. This reminds me of computing, so I think: ‘Oh, right. Nouns are classes, and DPs instantiate those classes.’

Is this type of rephrasing ever a useful thing to vocalise in lectures, for other students or the lecturer? I do sometimes vocalise these thoughts, usually in a smaller class setting though, not lectures. I usually try to keep quiet because I feel like either people won’t understand what I mean, or the teacher doesn’t consider it relevant or useful. So, my questions are: Is this kind of contribution in lectures valuable? Or do lecturers prefer that you ask questions instead of making statements?

If you are in a small lecture that encourages participation, then it is definitely appropriate to participate, and understanding things in more than one way is often a great thing.  I would recommend phrasing your thoughts as a question rather than a statement, however, e.g.:

  Would I be right in understanding this like [comparison]?

After all, you're only just learning the material and don't yet know if your comparison is actually right!  If you can concerned that you might be talking too much then a) a very good lecturer may be able to let you know gently in the midst of class and b) you can ask the lecturer their opinion after class.


Submitting article to journal where I committed misconduct I am interested in submitting an article to a journal that has previously identified misconduct from me. The misconduct occurred several years ago and was caused by a disability.
How should I proceed if I am to submit an article to this journal?

If you have done things to repair your reputation, there might not be an issue. But you can always submit and then see what happens. If you have been banned by the journal you are likely to get a terse reply.
How you would react to a summary rejection is another issue. You might try to assure the editors that things are different now and hope that people will understand, in your case, that people can learn and change. If you have a respected colleague who can serve as your advocate in such an exchange with an editor it might help.
Apologies and assurance that you are producing valid and valuable stuff is more likely to be effective, I think, than excuses. Accept what you did and hope people can accept that you can (and have) changed.
Ideally, the focus will be on the work you produce and not on you. But it is hard to guarantee that, depending on the nature of the past transgression. Expect people to be wary, at least.


How to approach a professor for research? There is a similar question posted here, but the answers don't exactly fulfill my needs, as will become obvious in this post.

I just graduated from an undergraduate program in engineering and in September will be beginning my Masters program at the same university. The Masters program is advertised as coursework-only, with the official website saying that students can "contact professors for research opportunities to augment coursework."

That's all well and good, but I'm not sure how exactly to contact a professor. I had a high undergraduate GPA, completed internships in industry, not academia, and took one course each with the professors I'm interested in - I did well in their courses, but they probably don't remember me since I didn't often attend office hours (other commitments).

There are certain topics I'm interested in researching, and I've casually read some PhD theses produced by students of the labs I'm interested in, but I've clearly never done formal research work.

So, the general question is, how should I contact a professor about joining his/her lab? It's currently the summer, so should I email him/her? Should I wait until September to talk to him/her in-person? Do professors generally just look at resumes, or do they expect prior research experience? Should I indicate the topics I'm interested in, even though my knowledge is gated by the courses I've taken and papers I've read?

I haven't decided about whether I'd like to do a PhD, so does my indecision make it harder for me to get a position at a lab for the 1-2 years it'll take for my Masters program?

How should I contact a professor about joining his/her lab?

Email now. Express interest and see if they're willing to talk via Skype or would be willing to meet in person when you're both in the same city. You want to talk face to face. This is also a good spot to ask if they have any advice on what to read to get up to speed on their work if it's going to be a while until you can meet them.

  Do professors generally just look at resumes, or do they expect prior research experience?

No to both. Research experience is a plus, but you have to start somewhere.

  Should I indicate the topics I'm interested in?

You should be interested in the topics that the professor works on and should identify what those are in your initial email.

  I haven't decided about whether I'd like to do a PhD, so does my
  indecision make it harder for me to get a position at a lab for the
  1-2 years it'll take for my Masters program?

If you end up wanting to do a PhD you should go somewhere other that where you did your Masters and undergrad work. Any decent professor shouldn't expect you to stick around.

There are some professors who won't work with Masters students. There's nothing you can do about that. Others will want you to take their class first. Take it, ace it, and then remind them that you're still interested and now have other ideas from their interesting and wonderfully taught class.

Now for Ric's guide for getting into research

Go to your department's website and find the page listing all professors (hopefully with links to their websites).
Skim each professor's bio and see if their research areas interest you.
For every professor that sounds interesting, read through some of their current papers. Focus on more recent works. Read abstracts and intros. Use this to get a better idea of what they're working on.
For every professor you're still interested in, find a recent paper or two that you like and read it fully. In this step you want to try and figure out what they did, why they did it, and how their work fits into the current work in the field. This is the hard part, but the more effort you put in here, the better. Besides, you want to get into research, right? You need to learn to read papers =)
Send a short email to each professor. Something like:

  Professor Webb,

  My name is Francis Thurston and I am a first year Masters student interested in meta-physical topology. I was reading through your recent paper on non-Euclidean architecture ("His House at R'lyeh: Modern architecture of Greenland cults") and was wondering if you had considered Professor Atwood's claims on the subject ("Mountains of Madness: The innate wrongness of the city") in light of your recent work. I think that combining his claims with some of the results of your recent experiments would be interesting. Would it be possible for me to come by your office next week to discuss your work and any possible research opportunities?

  Thank you,

  Francis Thurston

When you meet with a professor. Ask questions that show you're interested in the subject and have put in some work. Extra points if you have ideas about directions their work might go in (e.g. areas to explore, experiments to run, &c.). Ask if they're taking students, if they would consider you, and what the workload would be like.


Failed entrepreneur applying for master oversea I have BSc in computer science and entrepreneurship background with strong technical skills (software) but low GPA and no publication. I have bean away from university for few years and have not been working as employee so I can't provide any letters of recommendation.

As my previous businesses have not been successful (partly because of country I live in, it is one of the top worst) I'm thinking about starting over by applying for a master program and moving to a better place (preferably US). It can also help me find better ideas and like-minded people for future ventures.

All I can provide are few links to my previous works, some of them shiny.

Do you think I have any chance to get admission and grant from a good university? How should I prepare myself for it?

At least for admission to strong CS departments in the US, I strongly recommend that you take the GRE, especially the subject test in computer science, and do well.  Otherwise, the evidence that you would complete an MS program is rather thin.

Also, you must have recommendation letters.  Otherwise, your application may not even be reviewed by the admissions committee.  (In my department, for example, incomplete applications are automatically flagged by our admissions database system, and applications that are still flagged several weeks after the application deadline are automatically rejected without review.)


junior professor's strategy of postdoc recruitment If the professor has 15 publications and a postdoc applicant has 10 papers, will the professor think the applicant is too strong to colloborate well as to not obey the professor's ideas all the time?

Being a young professor I will not hesitate to hire an applicant with a profile even stronger than mine; especially, if I feel that postdoctoral candidate has good plans which will be beneficial for my research group. My own Postdoctoral experience was very good, my professor never treated me as student, rather a peer colleague.  Also, in my opinion a professor who want an obedient postdoc is not a professor I would ever consider to work with. In academics healthy criticism is the best feedback on your work and the way to improve.


Is it possible to do part of my PhD remotely? I have following situation. No good quality phd is available in my country (field: computer Science). On the other hand leaving my country for too long is also a little bit difficult for me mainly for family situation. My question is: is it not possible that I negotiate somehow with possible supervisors etc. that I do some part of my Phd remotely? Or say I visit the University for 6 month, then next 6 month I will spend in my country, etc. Or similar arrangements. I know sometimes they call such programs also Sandwich phd programs. Field is computer science so I don't need to be there in some "lab" all the time. Do you know if this is possible? And where/how can I find such programs? Who do I have to "negotiate" it with and how?

Let me give you a different perspective to your question.

  No good quality phd is available in my country

It is good that you aim high. But you must see things through the PHD admissions' perspective. Good universities and good PHD programs have a lot more candidates than PHD positions. Why should they choose you over the other candidates? Is your undergraduate research work that good? Are your grades excellent? Is your university reputable (probably not). If the answer to these questions is no, then the main issue is that you would not get accepted to these programs anyway, regardless of your plan to do it remotely or not.

Also do you have a MSc degree? In many countries in Europe you need a MSc degree before doing a PHD and in USA you need to pass some courses before conducting the actual PHD research. This simply (in either case) cannot be done remotely

Also from your comments I understand that you also want to have funding during your PHD (you would also agree to a reduced funding). In other words, you want someone to hire you (and pay you) to do research, coming from an unknown university, remotely, without actually having a first hand experience of how you can perform at this level, coming directly from an undergraduate university and with no industrial real-world work experience. This is too big a bet for any advisor and university to undertake, unless you have already proven yourself as an extremely talented individual. In that case, exceptions may be possible but still a remotely done PHD is still a major gamble for the advisor and the univesity. Without funding the stakes are smaller but as @xLeitix said remote PHDs do not always work that good. But I think expecting funding for a remote PHD is highly unrealistic.

The only solution I can think of, is for you to work a couple of years on site for a PHD (if you get accepted to a good PHD program) and after proving your
worth there (with some good publications) during the second-half / dissertation writing portion of your PHD you can ask / convince your advisor to work part-time / remotely from your home country.


What is a fast way to get the reference in a specific style? For example, if I want to get the reference in APA, what web resources provide this style? I found a way through Refworks, but it requires several steps. Is there a faster way?

Write in LaTeX, and use BibTeX which will automatically format your references in various styles.


Is it acceptable to use a microscope in the office and not in the laboratory? I often see microscopes inside the office rooms of professors as well as other teaching members. They may not use them on daily basis but once in a while, or sometimes heavily used by a few of them. Some of them examine hospital slides received by that department from patients sputa to look for hazardous organisms (e.g. TB bacilli).

My question, according to the quality assurance standards of teaching laboratories, and from the safety point of view, is it acceptable to use microscopes inside offices instead of laboratory rooms? Is it acceptable to use them inside the office while wearing the casual clothes instead of the lab coat, and without gloves?

I see three main potential issues in the use of a microscope informally in an office, rather than in a laboratory environment:

Safety: some samples might be potentially hazardous to a person handling them without laboratory protections.
Sample damage: some samples may be vulnerable to contamination or damage through exposure to an office environment.
Ethics: information on a patient or student subject may be exposed through informal handling of a sample.

If all three of these can be dealt with (e.g., through proper sample preparation and reasonable precautions against exposure of adverse information), then I see no reason that a microscope cannot be used in an informal office environment. I am not familiar with precautions around the particular samples you mention in your post, but many fixatives used with specimens are extremely effective and might well render samples quite inert.


How is cumulative GPA calculated? This might be a dumb question, but since I couldn't find what I am looking for, I decided to ask here. I think that cumulative GPA is the average of all each semesters' GPA, however the part that I don't understand is if I have the below points for three semesters,

1st semester = 3.1
2nd semester = 3.4
3rd semester = 2.9

would the cumulative GPA for the three semesters be calculated as:

((1st + 2nd)/2 +3rd)/2, or
(1st + 2nd + 3rd)/3 ?

The cumulative GPA can't in general be known from just the GPAs of the individual semesters.  The cumulative GPA is your total grade points divided by your total possible grade points, over all semesters completed so far.  If you took the same number of classes each semester, and all had equal numbers of units, and all were included in GPA (i.e., no pass/fail or the like), then it would be (1st+2nd+3rd)/3.

The reason it can't be known just from the GPA of the individual semesters is that it depends on how many units you took each semester.  If you took more classes/units in one semester than in another, then that semester will have greater weight in the cumulative GPA.

This applies to averages in general.  If I tell you I have one bag of stones with an average weight of 1kg, and another bag of stones with an average weight of 2kg, you don't know what the average weight would be if you combined all the stones into one group.  To know that, you have to know how many stones are in each bag.


PhD student with mental health issues and bad performance One of my PhD students told me during a supervisory meeting that they have been struggling with anxiety and depression and self-referred to the university psychological support. They asked me not to share with anyone and I didn’t.

Moving forward eight months and their penultimate year, their performance has declined to a degree that they might fail the PhD altogether. I tried to support with regular meetings, breaking tasks, teaming with other students, nothing seems to help pick up the pace.

Normally, at this point, I would set in place a special process: an independent committee is set to review progress regularly and set objectives and I’m only one of the three members. This is the formal process and usually works very well to address the progress issues but also puts more stress on the student because there is a stick at the end: the committee could decide to remove the student from the PhD program.

So, here is my trilemma:

If I go on with the process, I don’t know what this will mean for the student’s health. There might be a note on their record from the psychological services to instigate special treatment, but I’ll not know beforehand.
If I discuss this with the support team in hypothetical terms, it would be very easy to understand which student it is since my group is small and that student is the only one I have in that particular phase of the PhD. This would violate his explicit request.
If I don’t do anything, the certain outcome is that the student will go unprepared to the viva presentation (if they manage to prepare a thesis) and fail.

Anyone has experience how to deal with a situation like this? Any resources?

Update: After laying out the options to the student, they decided to leave the program. It seems this was something they were planning for even before my talk. I am not sure if the plan to leave the program was also a contributing factor to the bad performance or luck of effort.

Be forthright with the student about what they are facing. Explain the various contingencies. They may be unaware or ill-informed. You could ask them which path they would prefer you to take... True, they may not be the best judges of their own best interests, or may not be able to act, but you can ask.

In particular, probably you indeed should not share information that you'd promised to keep confidential, short of issues like potential self-harm or harm-to-others. At the same time, it may be that your university has fairly strong guidelines about your supposed responsibilities in a situation like this. At least try to read up on them... whether or not you think it is humane or wise to comply. I suggest that you keep in mind that the university will be "covering itself" legally, first of all, with concern for students and faculty somewhat subordinate... so it is non-trivial to see what the truly best action will be.

In any case, I think it would be good to avoid allowing a disastrous thesis defense to occur, especially if there is no room for a second attempt (as at my own university). Being late but doing ok is vastly preferable to being on time and catastrophically failing...

No, I do not mean to suggest that laying this out for the student will magically transform them... but that it is fair to inform them of consequences of this and that, and ask them what route they'd want you to take.

And, by the way, is "removal from the program" merely (!?!) loss of funding? In my university, it is possible to continue to register (after everything but thesis and defense are done) without cost... and, thus, without necessarily having financial support from the program.