<issue_start>username_0: I am a first-year Ph.D. student in an engineering field in my home country, and my Ph.D. supervisor is my master's supervisor.

The central problem is that he is very busy with administrative work, thus he has no time to guide his students. I must emphasize that this is very common, at least in my school (though I don't consider this to be right), so other students in the research group seem to accept the fact.

Also, there are other problems:

1. I don't like the environment. I used to be a master's student for the same supervisor, but I was basically guided by another supervisor in another institution since I was working on a cooperative project. Thus I was not familiar with the current research group when I applied for the Ph.D. candidate. The supervisor has more than 30 students with around 10 Ph.D. students, thus the resources are very limited, and everything can be competitive (this is quite common in my country). The master's students seem to be very antiforeign for some reason. Though I don't really have to be close to them, it kind of irritates me.
2. No money, the dormitory sucks, and the administrative department is not helpful.

Actually, I have reached out to the supervisor that I decided to quit after two months since I got in. But he persuaded me not to do that and promised me he would make some changes.

I have talked to one of the Ph.D. students in this group, and she also persuaded me not to do that because there are plenty of ways to get instructions. So anyway I decided to stay at that time.

But the central problem cannot be solved, and I have to be on my own to start my project with very limited help.

All I want is to get basic and systematic scientific training from a supervisor who is prominent in my field. The current situation is quite the opposite.

If I quit, I would apply for another Ph.D. project in another country (I am so sick of the environment in this field of this country). I want to know the situations in countries other than China: the majority of supervisors would give basic instructions or not, and how they treat students.

I'm almost 24 years old, and I think I can afford to quit and wait for another year. Anyway, there are plenty of struggles for me, as no one would support me to quit, and the language barrier (I have never been abroad), and whether I am qualified to get into a project.<issue_comment>username_1: When I was first looking for graduate programs, I met a post doc and asked him what he advise someone starting out in grad school. He simply said "pick the right advisor". It sounds like you picked the right advisor for your masters program, but not for PhD.

>
> The central problem is that he is very busy with administrative work, thus he has no time to guide his students. I must emphasize that this is very common, at least in my school (though I don't consider this to be right), so other students in the research group seem to accept the fact.
>
>
>

I know in some countries, as it seems in yours, this is normal; in others, its highly variable and really depends on the professor. This same post doc I met advised that in America, "star students go with professors who are writing their last book and are nearing retirement; doctoral students who would prefer a more active advisor should go for the recent hire, who will often involve them more and also have more time for them." It's not a hard and fast rule but by and large I've seen this to be the case, and might apply to you.

>
> All I want is to get basic and systematic scientific training from a supervisor who is prominent in my field.
>
>
>

Given what I just said above, this might not be possible with your current advisor. Also, cconsta1 is right when they said that supervisors typically lack the time, and as you've already noticed,

>
> The supervisor has more than 30 students with around 10 Ph.D. students, thus the resources are very limited, and everything can be competitive (this is quite common in my country).
>
>
>

That is a crazy busy life, not to mention the administrative work.

>
> I'm almost 24 years old, and I think I can afford to quit and wait for another year. Anyway, there are plenty of struggles for me, as no one would support me to quit, and the language barrier (I have never been abroad), and whether I am qualified to get into a project.
>
>
>

Plenty of time. The real question is whether the best use of your time and energy is to find a different program/advisor, or whether you should bare knuckle it and learn on your own, and pretend as if you don't even have an advisor, because as you've noticed even when asking for more involvement, you still got none. If you still aren't sure where to start, maybe ask your advisor what textbooks he would recommend.

I wish you all the best.
Upvotes: 2 <issue_comment>username_2: Hope you're doing well.

*(Sorry for the length of my intervention)*

I won't advise you to quit or stay, but I can just tell you that you're the only one who knows what is good or bad for you.

* First thing first: **As a PhD student you must have or build the ability to work independently (for most of the time without supervision). Since the moment you start your master, you must start build this ability or improve it**.
Here in my country and particularly in my University, a master student who doesn't know how to work independently will probably never defend his end of study dissertation because about 90% of supervisors will never ask you what level you are with your dissertation or how you're progressing. The student is the one who must contact him to show the progression of work and even suggest improvements .
* **If you aren't comfortable with the environment and your security and/or health can be in danger, then you know what you have to do**.
* Don't already stuck on the idea that language is a barrier. As far as you can learn new concepts and notions in your field of specialization, why not do the same for a foreign language?!

**In all cases, take the right decision (the one you think is right) for your good**.
Upvotes: 2


<issue_start>username_0: I am in contact with another researcher and want to share the direction of my current research for a possible opportunity. Would it be okay to share just the abstract and author list of my article with them. The article is still under review and not accepted/published yet.<issue_comment>username_1: Yes, until you sign away your copyright after acceptance for publication, the article (in full) is yours and your co-authors.

And, even after you sign away the copyright, you normally get back a license that would permit such local sharing in any case - especially for collaboration purposes.

As user Snijderfrey notes in a comment it would be proper to get agreement from co-authors.
Upvotes: 2 <issue_comment>username_2: What is copyrighted is the concrete work. Ideas as such aren't copyrightable. You are free to share your insights with others.

In any case, publication is precisely to disseminate ideas, I'd be very surprised if the journal objects to sharing e.g. preliminary versions or preprints (but look up their policies or ask if in any doubt).
Upvotes: 0


<issue_start>username_0: Is an author (coauthor or corresponding author) of an already published paper in a journal (IOP and Elsevier) allowed to use figures of the (already-) published paper in their poster or oral presentation at a conference by clearly citing the source of figures once the figure appears? Indeed, the presentation is a review of what we have done in different papers.<issue_comment>username_1: There are a number of reasons, but the most important one is that the German constitution, the *Basic Law*, guarantees the freedom to chose one's profession as a fundamental right in Art. 12 (1.1).

Based on this provision, Germany's Federal Constitutional Court strictly limited the legal room for maneuver for restrictions on access to public universities in the famous *Numerus Clausus Judgment* of 1972 (BVerfGE [33, 303](https://de.wikipedia.org/wiki/Numerus-clausus-Urteil)). Since then, the jurisprudence has evolved, but generally speaking, limitations such as minimum grades (sometimes not quite correctly referred to a *Numerus Clausus*) are only permitted when there is a demonstrable shortage of positions that cannot be fixed in the short term. In general, everybody with *Abitur* must be given the same access to a place to study.

---

**Edit by ccprog**, as invited in the comments: I'd like to add a few details about the actual procedures.

1. There are two seperate processes for admission to programs with restricted access: a central admission for all German universities by [contract](https://revosax.sachsen.de/vorschrift_gesamt/10738.html) between the state governments, and local admission on proposal by individual universities and governed by state regulations.
2. Both types are under the restriction to actually *prove* that the number of admissions are really exhausting the university's capacity to teach students. From my own time involved in these matters, I know that universities each year had to admit 10% or more above the initial numbers on court orders. (There are actually law firms that make a living on asserting admittance for refused applicants on these sort of grounds.)
3. The *Numerus Clausus* might not be the sole basis of admittance. Most states have laws that allow additional criteria besides the *Abitur* grade. As an example, in [Berlin](https://gesetze.berlin.de/bsbe/document/jlr-HSchulZulGBE2019pG1) universities first fill 20%–30% of the places with reseved quota like foreign students and applicants with a completed vocational training. Then, at most 60% of the places are filled by admittance criteria determined by the university and approved by the state administration. All other places are filled according to the Numerus Clausus and the number of semesters the applicant had to wait since his first petition ("waiting time").

Mind you, most universities don't bother to design special admittance criteria and just use the *Abitur* grade for everyone. First, short of interviewing everyone individually the results probably won't differ, and second, it is just too costly to do all this testing.
Upvotes: 5 <issue_comment>username_2: First: Most people don't fail university in Germany, it is very much dependent on the topic.

Concerning your suggestion of an entrance exam: education is a state-level competence in the federal system of Germany, that is each of the 16 Bundesländer can decide on how primary and secondary education is organized in their state (of course not completely but to a certain extent). As a result the levels of knowledge in certain subjects at Abitur level varies **considerably** between the Bundesländer. As an example, when I started studying, my Abitur math level from southern Germany was such that almost all of the first semester Analysis 1 course I already knew from school while some of my co-students from other Bundesländer hadn't heard any of it before.

Due to this federal education system, implementing generalized entrance exams would be very unfair and would skew the admittance in favor of those that were more "lucky" in where they grew up.
Upvotes: 4 <issue_comment>username_3: The other answers do not explicitely mention this, but in some fields it has actually been common for decades to have some rather strict secondary school grade requirements. E.g. in medicine, veterinary medicine, psychology, architecture etc. That is the *numerus clausus* mentioned in username_1's answer.

For several years it has also been common to supplement this requirement for good Abitur grades with entrance tests, at least in medicine ([source in German](https://www.tagesschau.de/inland/hochschulzugang-eignungspruefung-101.html), though arguably in the arts some kind of entrance testing has been common for decades).

Most STEM fields except biology do not have such requirements because demand for student positions is not so high most of the time. A high rate of failure then usually means that the field is inherently very difficult. However, it is sometimes alleged that exams are made more difficult if the number of first-term students is much higher then expected, in order to have fewer students in the later terms.
Upvotes: 3 <issue_comment>username_4: There are some subjects that have restrictions like this, mostly those with limited capacity like medicine. For those your final high school grade (Abitur) are typically used, which likely results in a selection similar to the one some aptitude test would create.

My impression is that this is simply not seen as a problem. It is arguably a more fair system than the alternatives. Your ability to succeed in the subject is measured by the actual courses and not by a proxy like some general aptitude test or by some subjective and potentially biased manual selection of candidates.

Difficult subjects also tend to put some of the difficult parts into the first semester, so you will usually know pretty quickly whether you're doing well or not. It does waste some time for students that pick a subject they don't succeed in, but usually not too much of it. And university is free in Germany, so the financial consequences are limited.
Upvotes: 3 <issue_comment>username_5: username_1 has already given the most important answer why nobody bothers to take the legal hurdles.

A few other practical or political hurdles include:

* The top-tier high school diploma (Abitur) is generally regarded as a sufficient selection criterion to avoid an overflow of students. That being said, you can also enroll at a university if you complete a vocational training, with details left to the states and universities.
* Implementing another countrywide admission test would meet political opposition: it costs money, it would be considered too bureaucratic, and meet opposition by angry middle class parents. And if any politician dares messing with their kids' Abitur, [they get very, very angry](https://www.dw.com/en/referendum-quashes-hamburg-school-reform-cripples-coalition/a-5814250).
* That being said, the Abitur isn't a perfect selection criterion either. It's considerably easier to get in some parts of the country due to uneven educational policies in each state. A national college entry exam (similar to SAT in the US) would meet with opposition by the lower-ranking states. The pressure isn't that high since most students don't move too far away from their home area, typically staying within their state.
* In that sense, the college entrance exam is basically deferred to the individual university. It's not uncommon to switch programs after a semester or two, and I know people who enrolled in several programs for the purpose of checking them out.
* Another important difference to the US, India, or China: the quality of the universities is much more even. There are a few top-tier universities but generally speaking the variability in quality of the programs is not nearly as huge as in other countries. The reasons are probably political/historical. Generally speaking, a 'university' in Germany compares to an R1 college/university in the US.
Upvotes: 4 <issue_comment>username_6: **Why should they?**

Your base assumption in that question is that a) many people failing early on is bad and b) a universal entry exam would somehow change this.

Regarding a) : A lot of courses (usually those with ease of providing high capacity because no practical stuff is necessary) intentionally have a low barrier of admittance (because they have capacity this is constitutionally required), but make up for that by challenging the students intensely in the first couple of semesters (causing many to fail). This is not a bad thing. It doesn't judge people on the performance on a singular day & test but instead on their ability to (and implied likelihood of) finish the course. This also allows for students with bad school performance to get their act together and ignores failure in fields irrelevant to the actual studies. If you CAN admit a lot of people (because the first few semesters allow for mass processing of students), why wouldn't you?

b) a universal entrance exam (completely universal like the Chinese one or per-subject?) **wouldn't change this without exorbitant cost in terms of students that would have made the course but failed the exam.** If the bar for this test is set too high, it is unconstitutional may fail people because of irrelevant subjects (a math-focused student might fail the German part for example) or for poor performance years past. It also prevents people from checking out a subject and deciding for themselves that they either dislike it, or have to work harder for it than previously. Especially the latter is impossible with a test you likely can take once as a teenager when you didn't really care for education (or for a specific field that in the end motivates them to try hard enough).
Upvotes: 4 <issue_comment>username_7: Western Public Education as a Whole
-----------------------------------

What you are describing is a very common phenomenon in Western style public education as a whole. Not just Germany. Despite socialized college education, Germany is still by-in-large a capitalist country. In most Western public school systems, the government pays for tuition and grants, not actual operating costs. When the government only pays for tuition, it means that the schools still need to compete for funding, and the most important metric for determining funding is how many students you serve.

For example, if you have the facilities and staff for 5000 students, but only 3000 pass the entrance exams because they are so difficult, then the school is still stuck paying for thier larger facility but with less income; so, it is in the best interest of a public school to always fill every available seat. So, to balance the budget, public universities set thier entrance requirement to the minimum it takes to guarantee 100% enrollment, not based on what it takes to complete the coursework.

Germany Specifically
--------------------

This said, there is also a cultural element to German education that they try to make it as non-competitive as possible. Competitive learning environments are experimentally proven to reduce the effectiveness of education, and Germany is among the top nations when it comes to applying this principle. In Germany, there is a much greater understanding that a person can fail one test, and still move on to achieve great success down the road. So, while East Asian countries have built thier public education around minimizing wasted investments, Germany has built thier system around maximizing the possible return on thier investment.
Upvotes: 3


<issue_start>username_0: Last year I came across a student in our department (we are both PhD students) who copied a journal article in her final paper. To be sure, she didn’t copy the article word from word but did use the exact same research design (variables, data, methodology, mechanism part). The two abstracts therefore look extremely similar, as the student replicated the journal article without citation. She almost got away with this since the journal article is in another language that the instructor doesn't know. My advisor considered this as borderline plagiarism and supported me in reporting. Eventually the faculty regraded the paper but didn’t term it as plagiarism.

Several months later the student and her friend posted this incident online and attacked me personally. I heard mixed opinions from fellow students. Many people say that even though the student is guilty, I should not report her in the first place.

I am confused. Was I wrong in reporting academic misconduct?<issue_comment>username_1: It's common that people caught cheating are upset they are caught.

It's unfortunately also common that people who act unethically feel like consequences for their unethical behavior are the fault of someone besides them, the person who decided to behave unethically.

There is sometimes an "us versus them" mentality, where it may seem to other students that you've somehow "betrayed" them by revealing evidence of cheating to the authorities. However, if they simply had not cheated in the first place, they would have no consequences to bear because you'd have nothing to report. The social stigma behind this is designed to protect cheaters.

I don't think you did anything wrong here. It seems you were especially careful in that you consulted with your advisor, and the paper was reevaluated with your information taken into consideration. Once you report, it's not really your responsibility anymore. There's nothing much you can do if people want to blame you for their own misconduct.

---

Here's a story of someone who has decided to become basically a professional detector of cheating in academic publishing: [Meet this super-spotter of duplicated images in science papers](https://www.nature.com/articles/d41586-020-01363-z). Specifically, she finds cases where people have manipulated and duplicated images to falsify data, or to represent old work (sometimes by completely different authors) as new work.

>
> “At some point, I am afraid people will sue me,” she says. She tries to keep her critiques to research papers, rather than accusing their authors. Bik has not faced a lawsuit, but has been harassed and has sometimes taken time off Twitter. One person e-mailed her former colleagues at Stanford arguing that she had abused her research grant funding by pursuing image integrity investigations during work hours. (Bik says this was untrue.) Another posted personal information on PubPeer (now removed). “I’ve been called a bitch a couple of times,” she says. “It comes with the work I do.”
>
>
>
Upvotes: 6 <issue_comment>username_2: >
> Was I wrong in reporting borderline plagiarism?
>
>
>

No. You are never wrong to *report* (*suspected* or *actual*) plagiarism to a professor or department for review. You should not feel guilty about bringing this case to someone's attention. In short, you did the right thing!

Even if you turned out to be wrong, it doesn't matter: an honest, good-faith report cannot alone be used as evidence against the student, but will instead be a pointer to look into the case further and determine if there is sufficient evidence for plagiarism.

>
> My advisor considered this as borderline plagiarism and supported me in reporting.
>
>
>

Talking to your advisor (or another trusted faculty member) is exactly the right thing to do here. The fact they supported it is further evidence you were not completely misguided in your suspicions.

In general: simply turning the case over to the department or a trusted faculty member is the right thing to do, rather than trying to figure out yourself whether it counts as plagiarism. They will decide how to proceed (as they did in your case), whether that be to pursue academic misconduct proceedings, or just to give a warning, or to ignore it.

>
> Several months later the student and her friend posted this incident online and attacked my personality.
>
>
>

Many plagiarism reports are anonymous. I would encourage you in the future to ensure your name can't necessarily be tied to your report. On the other hand, being attacked online for this could be considered bullying or harassment. As the student and their friend are only posting one side of the story, try not to worry about what people are saying about it online.
Upvotes: 5 <issue_comment>username_3: To answer your question, no, reporting plagiarism is not the wrong thing to do, ever. Even if it had been a misunderstanding, checking for plagiarism is part of the system, it's nothing personal. But since the student was reprimanded, it further shows you did exactly the right thing.

I have to admit I am mainly commenting to add a strong suggestion. I am not sure how long ago those public comments were made by the student. However, such comments don't just put you in a bad light, they also inadvertently imply that the faculty reprimanding the person was in the wrong too. Students are welcome to challenge any and all judgements, but doing so publicly without the possibility for the institution to explain their reasoning is unfair and puts the whole faculty in a bad light.

If I was the head/member of said faculty, I would want to know this is happening, have a discussion with this student and unless they can prove their stance, handle this case according to the institution's guidelines.

But, of course, it is not your duty to do this. I would fully understand why you would want to avoid anything to do with this student for the rest of your career.
Upvotes: 3 <issue_comment>username_4: I'm not going to tell you whether it's right or wrong.

I believe you should think consciously about your own ethics. What is the basis for you thinking that something is wrong and something else is right? I think people should not rely on other people's opinions regarding what's wrong and what's right, they should have their own. (This doesn't mean you shouldn't listen to other people and potentially learn from what they have to say. Fair enough you ask this question and get some views, still try to get yourself up to the point where you have a well founded view on this yourself.)

The bigger picture here is how you think society as a whole should work and what role science should play in it. What are the implications of plagiarism, and of somebody getting a degree based on plagiarised work? Could it be a problem for society? Or at least for some people (such as those who later employ the plagiator based on their degree, or those who don't win a position because a competitor has plagiarised and looks better on paper than they actually are)? How important is this to you?

More generally you may ask yourself whether and why you think that we should generally "play by the rules" (or not)? To what extent does it depend on the specific rule, or on who enforces them, or who benefits from it, and who is maybe disadvantaged by it?

You may also ask yourself about your ideas on solidarity and relations between students. Do you see the students as a group of people that should generally help each other, also against authorities who by marking them may determine their future fate to some extent? On the other hand, you may rather have the view that ultimately students compete against each other, and the best ones should "win", so solidarity should definitely not go as far as helping others to achieve better marks than they deserve (you may also think about who, in your view, really "deserves" what for what reason, and whether you think the the marking at universities is fair in this respect, or what would make it better, or worse).

And then you may ask yourself about whether generally standing in for your ethical principles is valuable also in the face of potential backlash. You can expect that people won't be happy if you do something that damages their position, so if you so something like this there is always the chance that you get a hostile reaction. Do you think it would be worthwhile avoiding it? Why or why not?

There are probably even more aspects of this. In any case I'd like to encourage you to train your own judgement and to learn how to use it confidently.
Upvotes: 1


<issue_start>username_0: I've read statements with meaning similar to this: "We received an extremely strong pool of applications this year, therefore..." in several of my decision letters from US grad schools in math this year. Is this a generic statement that is used while declining admission to soften the blow or is there something specific about this year that makes this statement true for many of the US grad schools? If there is, then what is the reason for that(covid?) and is there a reason to believe that admissions will get more or less competitive for the next academic year?<issue_comment>username_1: Unfortunately this is most probably a generic statement, which does not make it less true. Funding at almost all grad schools is usually short and not all candidates who would deserve funding very well receive it. I would doubt that covid positively influenced the quality of applicants.

Assuming you received a negative response, I would strongly advise to take the reviewers' comments seriously and then try again next year. Sometimes things work out even with only minimally edited applications.
Upvotes: 2 <issue_comment>username_2: It is a generic statement used when declining admission to soften the blow.

That said, admissions tends to be fairly stochastic, so yes, admissions next year will be both more and less competitive. You can't know which will apply to each particular program, so the best way to optimize your odds of acceptance if you didn't make it in this application cycle are to 1) Apply broadly to programs that have a range of acceptance thresholds, and 2) Do whatever you can in the coming months to improve your application. Research opportunities and training relevant to your field, but also improving the content of your application itself by having people you trust who are familiar with graduate admissions (professors, ideally) review your application and suggest ways that you might present yourself optimally.
Upvotes: 4 [selected_answer]<issue_comment>username_3: #### This is a risk-management response used by all major corporate institutions

To augment the other answers here, let me give the realist (cynic?) view:

Because of the existence of various employment laws and anti-discrimination laws, modern HR departments are trained/conditioned to avoid giving applicants any substantive feedback on the strengths, weaknesses or quality of their application. They know that any information they give can be used against them in complaints and litigation, so they avoid giving any response of substance. This is essentially a risk-management method, which is frustrating for unsuccessful applicants but protective for the institution.

"We received an extremely strong pool of applications this year..." is a stock-response that has developed over the last several decades in virtually all major corporate firms, and modern corporatised universities are no different. This response allows the institution to give a reason for non-success without making any substantive statement in relation to the quality of your application or the relative merits of particular parts of different applications. The advantage from the institution's point of view (and disadvantage from the applicant's point of view) is that this gives you no information and your non-success remains a mystery. In particular, it allows the institution to retain complete latitude in its explanation for your non-success in the event of a subsequent complaint or legal case.

I have personally attempted to break through this wall on one occasion where I applied for an academic position and was unsuccessful. I usually wouldn't bother because I know the risk-management method at issue here, but in this particular case it was a position I was particularly well-aligned to and I really wanted to know what aspects of my application were letting me down that I could improve for the future. I went so far as to arrange a meeting with the contact person to get feedback on my application and it was like talking to a robot --- no matter how much I pressed for specifics or advice for future improvements, back came the stock non-answers that HR staff and managers are conditioned to give. Welcome to the corporatised future.
Upvotes: 2


<issue_start>username_0: I am currently a second year masters student in Math in India and I wish to go to the US or Europe for pursuing my PhD.

But due to a low GPA in my first year, I plan to apply next year after completing my masters with a decent aggregate GPA. This would also aid my application because I will have taken more advanced courses related to my research interest. Moreover, I need to save some money for an year so that I can pay the hefty TOEFL and GRE fees, not to mention the insane application fee per university.

Now since the grad school admission process is quite erratic (so have I heard), I also wish to join some grad school here in India just after my masters. That means I plan to be in the coursework phase of my PhD in India while applying for a PhD abroad.

Simple, straight query : Would it negatively affect my application?
Any advice/suggestion shall be highly appreciated.<issue_comment>username_1: I think I found it! [<NAME>](https://en.wikipedia.org/wiki/Richard_Hamming), mathematician and Turing Award recipient said it in a 1986 seminar at Bell Labs. The talk was titled "You and Your Research", and a transcript is available [here](https://www.cs.virginia.edu/%7Erobins/YouAndYourResearch.pdf). The most relevant part is in the Q&A session after the main talk:

>
> *Question:* You mentioned the problem of the Nobel Prize and the subsequent notoriety of what was done to
> some of the careers. Isn't that kind of a much more broad problem of fame? What can one do?
>
>
> *Hamming:* Some things you could do are the following. Somewhere around every seven years make a significant, if not complete, shift in your field. Thus, I shifted from numerical analysis, to hardware, to software, and so on, periodically, because you tend to use up your ideas. When you go to a new field, you have to start over as a baby. You are no longer the big mukity muk and you can start back there and you can start planting those acorns which will become the giant oaks. Shannon, I believe, ruined himself. In fact when he left Bell Labs, I said, "That's the end of Shannon's scientific career." I received a lot of flak from my friends who said that Shannon was just as smart as ever. I said, "Yes, he'll be just as smart, but that's the end of his scientific career," and I truly believe it was.
>
>
> You have to change. You get tired after a while; you use up your originality in one field. You need to get
> something nearby. I'm not saying that you shift from music to theoretical physics to English literature; I mean within your field you should shift areas so that you don't go stale. You couldn't get away with forcing a change every seven years, but if you could, I would require a condition for doing research, being that you *will* change your field of research every seven years with a reasonable definition of what it means, or at the end of 10 years, management has the right to compel you to change. I would insist on a change because I'm serious. What happens to the old fellows is that they get a technique going; they keep on using it. They were marching in that direction which was right then, but the world changes. There's the new direction; but the old fellows are still marching in their former direction.
>
>
> You need to get into a new field to get new viewpoints, and *before* you use up all the old ones. You can do
> something about this, but it takes effort and energy. It takes courage to say, "Yes, I will give up my great reputation." For example, when error correcting codes were well launched, having these theories, I said,
> "Hamming, you are going to quit reading papers in the field; you are going to ignore it completely; you are
> going to try and do something else other than coast on that." I deliberately refused to go on in that field. I wouldn't even read papers to try to force myself to have a chance to do something else. I managed myself, which is what I'm preaching in this whole talk. Knowing many of my own faults, I manage myself. I have a lot of faults, so I've got a lot of problems, i.e. a lot of possibilities of management.
>
>
>

The Shannon referred to in the quote would be [<NAME>](https://en.wikipedia.org/wiki/Claude_Shannon), who is often called "the father of information theory". Shannon laid the foundations for this discipline while at Bell Labs, and in his talk Hamming suggests that Shannon would not continue to "plant acorns" afterwards.

---

While it isn't really phrased as a recommendation for others, it is perhaps also worth noting that
[<NAME>](https://en.wikipedia.org/wiki/Donald_J._Cram) (1987 Nobel laureate in chemistry) has said

>
> To retain my fascination with chemistry, I have had to change my research fields about every 10 years.
>
>
>

This quote can be found in I. Hargittai (2003) *Candid Science III: More Conversations with Famous Chemists*. Imperial College Press, p. 192.
Upvotes: 7 [selected_answer]<issue_comment>username_2: I am sure many scholars came up with this piece of advice. This is an excerpt from [an interview](http://faddeev.com/%D0%BB%D1%8E%D0%B4%D0%B2%D0%B8%D0%B3-%D1%84%D0%B0%D0%B4%D0%B4%D0%B5%D0%B5%D0%B2-%D0%BE%D1%81%D0%BD%D0%BE%D0%B2%D0%BD%D0%BE%D0%B5-%D0%B1%D0%BE%D0%B3%D0%B0%D1%82%D1%81%D1%82%D0%B2%D0%BE-%D1%80/) with [<NAME>](https://en.wikipedia.org/wiki/Ludvig_Faddeev) published in the newspaper of Saint Petersburg State University, № 5 (3791), 30.03.2009:

>
> I recall, many told me: "Why did you abandon this golden mine? You have just started it, and it's high time to develop it. Keep on digging..." I am, however, dead set against digging gold mines. And this is what I have preached to my disciples: if you have written five works on a certain topic -- leave it and seek another topic. It is important to switch topics... As a result of this, my disciples and I have a broad variety of directions of research.
>
>
>

However, in academic folklore this piece of advice of his was known long before that. I learned about this rule of Faddeev, through word of mouth, back in my undergraduate years in Saint Petersburg, which was in the early 1980s.
Upvotes: 4 <issue_comment>username_3: [<NAME>](https://en.wikipedia.org/wiki/Paul_Halmos) in his biography *I Want to Be a Mathematician* (1985) raised that point several times, however he gave himself only five years. Here are some excerpts:

>
> I had a theory, first subconscious and later deliberate, that the way to stay young is to change fields often. I never did become an algebraist, but I did change from measure theory (under which I mean to include probability and ergodic theory) to Hilbert space, then to algebraic logic, and then back to Hilbert space, with some dabbling with topological groups and statistics sprinkled in. After about thirty years, around 1968, I realized that I wasn't likely to be able to change again, and, at about the same time, I started writing more expository papers (such as "Ten problems in Hilbert space") and sermons (such as "How to write mathematics").
>
>
>

>
> In the late 1940's I began to act on one of my beliefs: to stay young, you have to change fields every five years. Looking back on it I can now see a couple of aspects of that glib commandment that weren't always obvious. One; I didn't first discover it and then act on it, but, instead, noting that I did in fact seem to change directions every so often, I made a virtue out of a fact and formulated it as a piece of wisdom. Two: it works. A creative thinker is alive only so long as he grows; you have to keep learning new things to understand the old. You don't really have to change fields—but you must stoke the furnace, branch out, make a strenuous effort to keep from being locked in.
>
>
>
Upvotes: 4 <issue_comment>username_4: The physicist [<NAME>](https://en.wikipedia.org/wiki/George_Uhlenbeck) was another famous scientist known for repeating a maxim like this, although I don't know whether he every specified how long one should pursue an area of research before changing to something else. Uhlenbeck's student <NAME> described his advisor's view in the introduction to Putterman's book, [*Superfluid Hydrodynamics*](https://archive.org/details/superfluidhydrod0003putt/):

>
> In April 1968 during my second year as a graduate student at the Rockefeller University I was one afternoon surprised to see upon entering my thesis adviser’s office, a large pile of books and papers dealing with superfluidity. My adviser <NAME> proceeded to ask me if I knew of any other works on this subject. I replied by asking what had brought about this shift of emphasis away from relativistic thermodynamics. **His answer was that changing fields was a time tested means of renewing himself** and to bring it about he had committed himself to give a course on superfluidity (along with <NAME>, Jr.) in the 1968 summer school at the University of Colorado. In this way he assured me that his course would also fulfill the first requirement of Ehrenfest: that the teacher learn something.
>
>
>

He first became famous for co-devising (with <NAME>, when they were both graduate students of Paul Ehrenfest) the idea of electron spin, but he changed topics frequently. Over time, he worked a wide range of topics related to statistical, thermal, and fluid mechanics: the quantum Boltzmann equation, biased and damped Brownian motion, the relativistic thermodynamics that Putterman had been expecting to work on, and superfluidity.
Upvotes: 3


<issue_start>username_0: Let me ask an academician who knows lifehacks well. I'm thinking of ways to avoid the following vicious cycle related to writing manuscripts.

Go straight to one's keyboard or terminal
→ people can't come up with anything
→ People can't write manuscripts
→ Procrastination related to writing them

How can we avoid this vicious circle?

How about starting from the handwriting or brain-storming (e.g., KJ method) stage? Does this method put people off in the aspect of transcribing of handwritten manuscripts? How can we avoid this transcription procrastination problem?

For the time being, is it recommended to input anything what I can come up with and repeat the revision (brush up) with the text-to-speech system?

Dr. SJP of Microsoft Research advocates writing the paper first, but is there possibility
that one may postpone this step. Please also advise how to avoid this procrastination.<issue_comment>username_1: *Full disclaimer that this is a subjective answer which originates from my personal experience from both academic and creative writing. It may not apply to everyone reading this, but I sure hope it will help!*

The way I do it is somewhat similar to the KJ method, except I skip the handwriting part altogether because transcription, in my opinion, sucks. What I do is:

1. I open up a new word document (dubbed lovingly the word-vomit document),
create a few headings like "personal ideas" (sometimes also adding
rough section subheadings), "literature excerpts + sources"
(classified by topic/section/however I think is suitable), and other
relevant headings I might need.
2. I do what needs to be done in the day, making food, doing chores,
regular human stuff etc., and whenever I have a particular idea on
how to start a paragraph in my academic work, or a random sentence I
want to use, I quickly type it down into the document, and go about
my day. Reading some papers and found an excerpt to paraphrase/use?
Copy and paste that bad boy into the word document. Do this for a
few days, and I usually end up with a substantial amount of material
to work with.
3. Once I believe I have enough information to work with, I start to
visualize how my current points in the word-vomit document will fit
in the big-picture. At this point, it becomes more clear to me what
is lacking, what is redundant, and I go from there. Sometimes, if
things are too messy, I'd open up another new word document and do
one more pass of reorganizing. Once I'm done with this, I'm usually
ready to start working on my main document, and everything in a word
file means easy copy-and-pasting/editing my own ideas! I've noticed
that my brain is especially intimidated when staring at a blank
document, but once I have something to work with, either to add to,
edit, or correct, it's substantially easier to manage.
4. When writing the main document, I try not to stress myself too much
on going section by section. Sometimes my brain itches and really
wants to work on a particular section, so I take that free
motivation and do that instead!

Again, I believe there is no "one true answer" when it comes to motivation. Some people like to plan things out and do things methodically, some prefer to let the last-minute adrenaline take over before pumping out words, and others may rely on something in-between. What I'm about to say might sound like (and probably is) a non-answer, but I think it's important to find what's right for you. Everyone is built different mentally, and sometimes it just comes down to experimenting with what works and what doesn't. Whatever you do, just try to do ***something***, any progress in the right step is good progress, however small. :)
Upvotes: 2 <issue_comment>username_2: For me, I usually draft a plan of analysis before I begin my work. The dataset I'll use, the method, background, simple hypotheses tests.

From then on, if I'm honest, you just gotta be hungry enough! Life for me will get in the way (between school, internship, other projects), but very generally, for the papers I care about, I'm self motivated to move them forward.

And that's what it takes. You have to want to move it forward, as nobody else will do it for you. I don't have a method, so much as I have a mindset.
Upvotes: 1


<issue_start>username_0: I just finished my first year studying physics but honestly I didn't like it at all. I started studying a day before every exam and skipped almost every class. However, I love physics, I read books at home, watch documentaries and research on my own. I think it is the most beautiful field of human knowledge, but I hate the way it is teached at university and I really prefer experimental physics over theoretical physics.

I'm also very interested on AI and data analysis, so I'm thinking of switching careers to data science.

Would it be possible to work in physics research with a degree on data science? (With physics reasearch I mean doing data analysis of experiments and observations in a research group, for example to map galaxies, to classify particle crashes from a particle accelerator, etc.)

I think studying data science may be the best option for me because with the marks I have on physics I won't be able to do research. But I think I will be able to excel at data science, I'm studying every day on my own and taking online course after online course.

So what do you think? I don't want to continue with the physics degree, but I don't want to “say goodbye” to physics forever. Any advice is appreciated.<issue_comment>username_1: >
> However, I love physics, I read books at home, watch documentaries and
> research on my own.
>
>
>

Engaging with popular books and documentaries on physics can be exciting and inspiring, but it's almost completely unlike actually performing scientific research. Actually doing the research involves a lot of inescapable drudgery: obsessively going over complex chains of mathematical reasoning, debugging huge and perhaps obscurely written computer programs, hours spent aligning components on an optics table, etc. All stuff that has to be done, but may not provide the easy thrill that comes from reading about 'big ideas'.

A friend studying geology went on two research cruises in the south Pacific in their first two years of graduate school. Exciting! Exotic ports! Big picture conversations about geology in the mess! The next three years were spent in a sub-basement, looking through a microscope, picking out little bits of volcanic glass from crushed rock samples using tiny watercolor paintbrushes. Nature's secrets are frequently hidden behind curtains of unbearable tedium. I don't intend to be discouraging, but you can't just blow off the hard, boring bits of a subject.

>
> Would it be possible to work in physics research with a degree on data
> science?
>
>
>

Maybe? It wouldn't be an easy path unless you turn out to be an absolute star in data science. Most physics laboratories just use their grad students and post-docs do all the programming and data science work. As @anyon mentions in a comment, some of the very biggest centers like CERN and NASA *do* hire data science specialists. However, counting on that means narrowing your choices to a *very* few employers and geographic locations, and they still might expect you to have a significant formal background in physics or astronomy. It's my experience that the life sciences labs are bigger employers of data science and software staff than physics labs.

For the moment at least, data science does provide a lot of opportunities. If you find you like data science work, it might be a good choice. With some luck and flexibility you might eventually be able to land a spot in a physics research team. Just be aware that you are aiming at a tiny niche, and be prepared to pivot in other directions.
Upvotes: 5 <issue_comment>username_2: My close friend, a professor at a highly respected university, is in charge of a physics lab doing nano- and biotechnologies. Having a wealth of experimental results to process, he is now seeking to hire a postdoc or a highly advanced PhD student proficient in big data. However, the professor has firmly stated that a candidate should have taken the intermediate-level courses in physics back in their undergraduate years. It doesn't matter for this professor if a candidate has a BS degree in physics or chemistry or engineering -- but (s)he absolutely must be educated in physics at the intermediate college level, at least.

This story is, of course, just one datapoint -- but, trust me, a representative one.
Upvotes: 3 <issue_comment>username_3: No
==

I've spent around 12 years in university studying/working in physics and if you didn't study it, there's no way you'll ever work in it professionally. It is an insanely hard subject. Physics requires advanced mathematical skills and builds strongly on top of its own concepts, like Maths. You will have no success for example in electrodynamics, if you didn't already study classical mechanics for basic concepts like Lagrangians and Hamiltonians, let alone all the math required for doing calculus on vector fields, green functions, etc.

Frontline physics is HARD, for the very sole reason that if it were easy, it would have been done already. For example, data science would come in very handy in particle accelerators where you need to correlate datapoints quickly. But to get a basic understanding of relativistic scattering physics reallistically takes a masters degree in physics (5 years studying) and that's the "easy" textbook examples. If you already knew you'd want to focus on that, it could probably be done in 3, if you gave it all.

Physics is a really competetive field, with lots of PhD students and less post doc let alone permanent positions. Not to take away from data science, but at the end of the day, it's math, and physicists are good at applied math. I don't see any reason why a PI would choose a non-physicist over a physicist.
Upvotes: 2 <issue_comment>username_4: Yes (with reservations)
=======================

I would refer you primarily to this great answer on the topic of the "research computing" career path

<https://academia.stackexchange.com/a/178616/173832>

Many national labs and universities have dedicated groups of programmers that play a critical role in scientific research. This is more like being a techinician/lab worker than a post-doc. You write code for researchers, or help them improve their existing codebases and workflow.

This is becoming an increasingly common and recognized career path, see e.g. the The [United States Research Software Engineer Association](https://us-rse.org/)

Source: I work as a data scientist/scientific programmer in a small earth science research group
Upvotes: 3


<issue_start>username_0: I have contacted a professor (in Germany) to ask if he would take me as a PhD student.

I have written to him a follow up email 7 days after the first contact, and now it's two weeks with no answer.

Is it appropriate or advised to make a phone call to the administrative office of the chair, and understand whether the professor is out of office due to e.g. holiday?

This way I would know if and when to try the third email.<issue_comment>username_1: Yes, it is fine to contact the office, such as the department secretary. You may or may not get the information you need, of course.

However, if you were responding to a call and it had a date for submission, then you might not get any communication from the professor until that date passes. They have other things to do, including, as you note, vacations. A few weeks at this season is probably quite short.

I wouldn't, however, contact the professor again for a while, unless you have some actual need to know, such as another pending deadline.
Upvotes: 1 <issue_comment>username_2: I live in Germany half the year, and I can attest that these guys take their holiday time seriously. It's common for people to take a full month, even two months of vacation, and to them no email during holidays means no email. And we are straight in the middle of summer holiday season. So by all means contact the secretary, but be aware that he/she might be taking their own holiday.
Upvotes: 2 <issue_comment>username_3: In general, I would not expect answers in July and August and only follow up after. Part of the summer people can be expected to be out on holidays, for the other half they are away at conferences, wrapping up the prior academic year or preparing for the next, or finally focusing on some core tasks like writing grants and papers after the teaching is done.

In either case, cold e-mails are likely to be less of a priority to respond with during that time.
Upvotes: 1


<issue_start>username_0: I realize there are many ways to structure an introduction. This research paper's introduction has 3 sections: Background and Significance, Relevant Literature, and a third section where I actually propose the method to be explored later in the paper. What is the proper name for that last section of the introduction? Please note, there will be a Methods section next but I still need to introduce an overview of the method in the introduction.<issue_comment>username_1: Perhaps "Contributions": at least this term, as a paragraph title, is common in my area for what you seem to be referring to.
Upvotes: 3 <issue_comment>username_2: Another option I have seen would be for that section to simply carry the name of your method, or a short description of it.

For instance:

***Section 3: The Direct-Section-Name Method.***

***Section 3: A Methodology for Naming Sections Containing the Main Contribution.***
Upvotes: 2 <issue_comment>username_3: If you use structured titles and well labeled references there is no room for confusing the reader.

**Section 1: Introduction**

Blahing a blah...

**1.4 Proposed method**

Introductory summary of the method you propose.

**Section X: Methods**

**X.1 Method #1**

Description of Method #1...

Then you can use references like "...as mentioned in 1.4...", "...because of limitations in Method #1, following conditions must be met, see X.1..."

If the core purpose of the article is the Method proposal, then the Method should be described appropriately in all following sections:

* Abstract - Very brief summary of why you have developped it and what it returns. (Motivate the plausible readers to read introduction and conclusions)
* Introduction - Not that brief summary of why and what from you have developped it, and briefly how it works. (Assure possible readers they want to read the Conclusions)
* Method proposal - Describe in detail how the method works, what are the inputs and what it returns.
* Discussion - Dicsuss all the upsids and downsides of the method.
* Conclusion - describe briefly how it works and sum up the dicsussion.
Upvotes: 1


<issue_start>username_0: When is a diploma thesis in mathematics (bachelor or master thesis) actually considered good enough for a supervisor to propose publishing it in a journal? In some universities (e.g., ETH), this appears to be a prerequisite to obtain an excellent grade. What does one have to do to achieve this? Is proving a new theorem or anything equivalent necessary?

Most math theses at the bachelor or master level contain results that are already known. Therefore, there might be no point in publishing them in an academic journal I think.<issue_comment>username_1: Some theses, especially at the undergraduate level, are expository on existing theory rather than new contributions. There are some venues that would publish such things but they need to be at a high standard.

It is difficult for students at both the undergraduate and masters level to do research resulting in true advances, partly, at least, due to the constrained timelines for such things. It is even difficult for most students to gain sufficient insight into a small area of a field to be able to begin such explorations. Some achieve it, with the help of a good advisor and a lot of work. And some advisors are better at suggesting appropriate problems than others are.

But for research in math, as in other fields to be publishable it has to be both novel and correct. "Novel" means that it makes a valuable contribution to the literature. This is judged by reviewers and editors, however, and is known only after review. Your advisor can likely judge if what you produce is something ready for submission.

But a literal answer to your headline question is that it is "publishable" when the editor says it is publishable.
Upvotes: 4 [selected_answer]<issue_comment>username_2: Essentially seconding @username_1's answer: it's not easy, and not typical, for undergrad (or Master's) theses (at least in the U.S., where I've observed) to be publishable in serious journals.

Why? Well, for most even-high-end students, they've still not had enough time to really catch up to the state-of-the-art, to begin with. Further, surely it's not reasonable for absolute novices to somehow depend upon succeeding at things that much more senior people had *failed* to do (and would have wanted). Yes, an expert advisor can help in subtle choices of projects... and in doing the non-obvious things that might make them work ... but, still, this can't be cranked out as though on an assembly line.

(... let's not talk about increased commodification by "higher" administration...)
Upvotes: 2


<issue_start>username_0: I've just asked [Can Chinese "scientists who publish in the top Western journals... earn in excess of $100,000 per paper" via cash-per-publication incentives?](https://academia.stackexchange.com/q/200467/69206) in which I reference the recent Politics SE question [Did China scientific incentives address the fact that the system can be gamed?](https://politics.stackexchange.com/q/80736/16047) which includes the following:

>
>
> >
> > These guys have surveyed the financial incentives offered by the top 100 universities in China and mined that data for interesting trends. They say that cash-per-publication incentives are common and that **scientists who publish in the top Western journals can earn in excess of $100,000 per paper.** What’s more, there are already worrying signs that these financial rewards are skewing the process of science in China.
> >
> >
> > China has well over 1,000 universities. But in the 1990s it began a program called Project 211 to turn 100 of them into world-class institutions. “Eventually, 116 universities were admitted to Project 211, forming an elite group of universities occupying 70% of national research funding and supervising 80% of doctoral students,” say Wei and co.
> >
> >
> >
>
>
> https://www.technologyreview.com/2017/07/12/150506/the-truth-about-chinas-cash-for-publication-policy/
>
>
>

**Question:** How common are cash-per-publication incentives in different countries?

Are these pretty common or rare? Of course there are benefits; monetary and otherwise, an excellent publication track record can impact salary, tenure, side gigs (contracts, consulting) but here *I'm only asking about explicit "cash-per-publication incentives".*

Until there's some verification of that substantially large upper limit, I can't really ask "Is China exceptional in the size of its monetary incentives?" but I am curious about that.<issue_comment>username_1: Normally a researcher does not make any money directly from the publications. It is possible to make money indirectly, like building up a reputation that could make getting a research grant later easier, or a better scientific position in the future (or any). Publications are the requirement for retaining the status of the researcher, problems are expected if the researcher does not have them, or they are too old. China is doing a very unusual thing.

No the question is, how easy is to get this money, maybe this is reserved for a few exceptional cases. But if a publication in reputable, international journal is enough, then it is probably a good time to start learning Chinese (be sure you pick a simplified one).
Upvotes: 2 <issue_comment>username_2: In Denmark, part of the department's core funding is settled in proportion to its publication rate (per scientist) adjusted by journal scores (higher impact journals scoring more). The total sum handed out across all university departments in the country is constant however, so in effect there is competition among the departments. For me as a scientist in Denmark, personally, I couldn't care less. This is just yet another futile exercise of New Public Management.
Upvotes: 2


<issue_start>username_0: My Ph.D. supervisor assigned me a literature review paper to complete on a topic that I choose. I know very well the field and I did the necessary research, downloading every publication that I found on the topic. Nonetheless, I am feeling a bit lost since I don't know if there are guidelines for conducting a review paper.

Should I have a target journal for the submission before completing the review or should I choose one after the completion of the paper? I ask since maybe every journal may have different rules on review papers.

Are there rules that I can follow so I can fulfill the review's goals and not veer off into irrelevant details?

Thanks to everyone who may help me see light at the end of this tunnel.<issue_comment>username_1: If you have a (potential) journal in mind. Read some of their review articles and try to abstract from that what works there. Perhaps do the same for a different journal in the field. Ask yourself why certain elements were included.

The details depend on the field, of course, but you probably want to cover major results, trends, typical methodology (and exceptions). The outlines of the various papers might be enough to get you started.
Upvotes: 2 <issue_comment>username_2: In my experience, is a good idea to know some examples from related reviews and articles publish in specific journals. Some journals have a guide to admitting proposals. Without knowing what is the specific topic that you are working is a little difficult to do precise guidance, nevertheless [here](https://State-of-the-art%20literature%20review%20methodology:%20A%20six-step%20approach%20for%20knowledge%20synthesis) you have an open-access article related to a methodology to make a literature review.
Upvotes: 1 <issue_comment>username_3: I think you are trying to get the work effectively done for you by asking a pile of questions on it.

Look, a review paper can be requested by a supervisor for many reasons, e.g.

* To make the student develop an overview of a new topic
* To provide a base paper on which the supervisor him/herself may write a better review paper for presentation to a regional or national conference
* To provide a base paper for a published review of the topic in question
* To test the PhD student's capacity for information extraction, absorbtion, synthesis and written communication
* To do some or all of the aforementioned purposes

For your own working convenience I'd advise you to pretend you are drafting this review for presentation to a group of Y1 PhD students so the others can understand in outline the topic, its challenges and current lines of research into it.

So write it in simple English, small words, minimal jargon, short sentences and a natural flow - beginning, middle, end.

Before you begin your draft, look at other review papers (preferably on this topic but otherwise on closely related topics) and think about how the better ones are (1) structured and (2) presented, i.e. the writing and the diagrams.

Just go ahead and give it a lash. The only bad review paper is the one that's not attempted.

Your supervisor will critique your finished paper but you should still give it a good effort.
Upvotes: 2 <issue_comment>username_4: If possible, try to check whether you local university library offers courses / classes for that. In addition, there are some good Youtube tutorials out there for beginners. Take hand written notes of every article you read, summarizing key points and publication details. This will help you to detect patterns (e.g. outcomes) that are worth reporting / considering. Good luck!
Upvotes: 2 [selected_answer]


<issue_start>username_0: I am a PhD student at a University that isn't top rated in my field, and have only published once before and wasn't even cited. I want to submit what I believe is a really great paper to the top conference in my field. Will my paper be judged only on its merits? Or will my lack of credentials prevent reviewers taking it as seriously as someone with a high h-index at a prestigious University?<issue_comment>username_1: I don't know your field, but in mine (CS) the paper will get a fair reading and accepted or not on its merits.

There is a lot of competition, of course, and a limited number of slots for presentation so nothing is automatic. But my experience is one of acceptance and a desire to push the boundaries of knowledge and practice, not to assume that everything good comes from Harvard.

And use the time at the conference to build yourself a circle of contacts who can eventually become collaborators.

There are lots of great people working at universities that don't have great names. Don't be intimidated.
Upvotes: 3 <issue_comment>username_2: First, in my field, some top conferences have a double blind reviewing process. Second, the large number of competent researchers and limited positions at top 100 institutions mean many of these researchers are now located beyond the top 100 universities. Third, it is easier than before to access information and become a competent researcher. For these reasons, a high quality paper with a good idea can come from almost anywhere.

Having said that, the key problem with lower rank institutions is that they may not have the people or/and resources to carry out high quality research. I have reviewed many papers from these institutions, and many authors simply do not have the processes to carry out high quality research nor know what is high quality research.

Whether your paper has a chance will very much depend on your supervisor(s). Does he/she have a high track record of publishing high quality work? If not, even if your idea is great, it may lack many key ingredients expected by your community. This means it is not the 'brand' of your institution that matters, but the quality of your work.
Upvotes: 0 <issue_comment>username_3: It depends solely on the conference. In machine learning conferences, papers are typically reviewed in a double-blind fashion. While the existence of a *preprint* might pose an unfavourable advantage for 'big names,' your paper will still be reviewed based on its merits, and **no one will take your previous publication record into account,** at least not in a negative fashion.

Good luck with your submission!
Upvotes: 0


<issue_start>username_0: I recently defended my master's thesis in artificial intelligence. Among other things, I invented a new algorithm that I would like to publish. The domain is natural language processing, with papers viewable for free on [ACL Anthology](https://aclanthology.org/).

For backstory: I developed my thesis almost entirely by myself. I only saw the professor who commissioned the proposal 4 times (which includes the final defence) as she resides in a different country, and although I had weekly meetings with my mentor (one of her PhD students), he was always behind on my research and hence he mainly helped with e.g. stylistic choices. I don't blame him for not having made deep contributions, since (1) it wasn't his thesis and (2) he wasn't as deep into the literature as I was throughout the year.

Now, although I included numerical experiments in my thesis, he correctly pointed out that my algorithm should be tested as part of a computationally heavy model using industry-standard metrics if I were to publish it in a paper. Being a PhD student, he has access to computational resources I don't as a mere alumnus. So, it made sense to let him collaborate on this paper.

We started a shared LaTeX document a while back, and unsurprisingly, the vast majority of all the text (intro, related work, theory, algorithm, discussion ...) will have been written by me, since I developed my thesis and hence I know both the algorithm and the literature thread that led up to it best. I also wrote all the code for my algorithm, because I needed it in my thesis. His contribution will be general remarks about the text, and providing me with the computational resources and interfaces to run large-scale experiments, so I expect that he will likely be writing that section.

I don't blame him at all for this distribution of the work; it is natural given the backstory. He's not freeloading, because his access to more, bigger experiments is a valuable addition.

---

My issue is that I want to ensure that I, a fresh graduate with zero publications, get maximal public recognition for the year-long independent work I did to reach this literary thread and this novel algorithm. *If* this paper is ever cited, it will be cited as **<NAME> (2023)**, and to be honest, it breaks my heart just seeing it that way. It is *my* brainchild. I rarely see a paper cited for its numerical experiments rather than the theoretical idea presented by it, and hence I lament that such a citation only seems to give me half the credit. That *does* feel like freeloading, even though it isn't his intention nor his fault that citations work that way. It feels like I'm selling half my credit for a single (useful and hopefully interesting) section.

He and I are are amicable, and I'd like to keep it that way. However, since my thesis was quite large (170+ pages), more than one paper can be distilled from it, and he has already made it clear that he would want co-authorship on all such papers since it is supposedly customary to give one's thesis advisor co-authorship, even though him and I both know that the content of the thesis was entirely my own doing. Whilst I respect this -- and furthermore, he did have to sit through weekly meetings with me mostly failing to articulate mathematical ideas orally -- I feel like I'm being partially robbed of making a name for myself for a unique contribution to the field. For him it seems like a closed case, and I'm not sure how to handle peddling it back. He finds my ideas valuable and has interest in them, but being interested and receiving recognition (as one of the 2 authors) are quite opposed. *What should I do?*

If he becomes co-author of these papers, then *at the very least*, I would like to somehow indicate to the reader that I was responsible for the idea and he was responsible for the experiments. Otherwise, since he already has publications, it might even seem to the reader like I am the novice freeloading off the master, and that the order of authors should secretly be reversed. *How can I handle this as neutrally as possible to the reader, and communicate my desire about this to my mentor?*

---

Perhaps I'm just insecure about lack of recognition from past experiences, or perhaps I'm insecure about possibly being a one-trick pony... but that pony still deserves proper credit for its one trick.

There is a similar thread on the site [here](https://academia.stackexchange.com/questions/121789/can-i-add-an-author-contributions-section-to-make-clear-that-i-did-all-the-wor), but quite a large emphasis is placed there in the answers on the fact that the paper has already been submitted, and also, the author is a PhD student publishing with higher-ups. In my case, the first paper has largely been written -- except for the experiments -- but has *not* been submitted for review, *and* I'm no longer a student (but might start a PhD in the near future), making my mentor moreso my colleague than my boss -- but obviously, he did have power over me when he was in charge of overseeing my thesis backed by the professor.<issue_comment>username_1: There is such a thing as the contributions section where the contribution of each other is specified. In some journals such a section is already mandatory, exactly for the purpose to provide the reader with the information which author was responsible for what.

So asking for such a section to be added to your paper is not only reasonable, but also not that unusual.
Upvotes: 3 <issue_comment>username_2: ---

tl;dr:

Remove all contributions of your co-author from your paper. What is left of your paper? no correct mathematical formalization of your idea, no testing of the algorithm, just an idea and an algorithm applying it. Treat your algorithm as an experimental apparatus. Without a rigorous formal mathematical description, you are bound to err (see [this dramatic example of how dangerous is an idea alone](https://kottke.org/19/02/flat-earther-proves-in-simple-experiment-that-the-earth-is-round)).

---

>
> My issue is that I want to ensure that I, a fresh graduate with zero
> publications, get maximal public recognition for the year-long
> independent work I did to reach this literary thread and this novel
> algorithm.
>
>
>

You can show it at due time: conference and when you give a talk. But please say explicitly "I had this idea and my co-author did have to sit through weekly meetings with me mostly failing to articulate mathematical ideas orally".
It will be then clear who had the idea and who formalized it (please note: I am being a bit tongue-in-cheek).

>
> If this paper is ever cited, it will be cited as Mew & Him
> (2023), and to be honest, it breaks my heart just seeing it that way.
> It is my brainchild. I rarely see a paper cited for its numerical
> experiments rather than the theoretical idea presented by it, and
> hence I lament that such a citation only seems to give me half the
> credit.
>
>
>

Without the numerical experiment, your paper will be perceived like "yes, crazy idea, how did this **unknown crank** published it on a peer-reviewed journal without presenting serious testing, let's wait for someone to perform proper tests on it" which means no one will test it, no one will prove it, no one will cite it, maybe 2-3 years down the line some master thesis will prove it and publish and cite your precious idea.

With the numerical experiment it will be perceived like "yes, crazy good idea, this **unknown person** published it on a peer-reviewed journal with a good set tests, relevant, I may build on it with citation". The work can then enter in the flow of citations of your field.

The numerical large testing experiment is then necessary. Would you have done without your advisor? no. Therefore it was *their* idea to perform them and they deserve recognition for that, so a co-authorship seems to me relevant.

Having an idea is a necessary, but not sufficient, requirement to publish a paper.
Upvotes: 3 <issue_comment>username_3: I also did research in NLP for quite a number of years, and at least in NLP specifically, single author paper is rare, and, to cater your worry, in a two-author paper people *never* think "oh, the first author must be freeloading off the more capable second author".

In NLP, so far I've seen only long-term professors who have published as solo author. It's completely normal and extremely common to have two-author paper in NLP. And when that paper is good, so far I've seen that the NLP community is giving credits to the first author.

tl;dr It's fine to publish a two-author paper in NLP, first author still gets the recognition.
Upvotes: 2 <issue_comment>username_4: If you want a career in academia, I think you are making a serious mistake by focusing too much on a single paper, especially if you reject the contributions of a collaborator.

Long term, you are probably better off being generous than selfish about such things as they can lead to the next paper and the one after that and additional people to share ideas with, extending your field and boosting your career.

How often does a paper come along that a career is "made" based on a single paper by a single author? Even Einstein had a lot of collaborators and a difficult time starting his career. It is easy to look back on AE and forget how difficult it was for him.

Also, note that "ideas" aren't enough. It is in the verification of those ideas that value is assigned. Often said that "ideas are free", though some would disagree.
Upvotes: 3


<issue_start>username_0: My personal position is we should indeed take basic questions on any aspect of AI, so I'm not suggesting we end our experiment accepting implementation questions, but, where these questions haven't attracted an answer on AI, should we migrate to SE:Data Science or SE:Cross Validated?<issue_comment>username_1: No.
===

If a question is on topic, then it should stay here. Migrating is for *high-quality, but off topic* questions. This is why migrating a question involves closing as "off-topic". In this case, they're *not* off topic - they just haven't gotten an answer.

Now, *why* don't they have an answer? Probably because there's nobody on the site who knows how to answer it... or the right person just hasn't seen it. If nobody on the site knows how to answer a question, then the best thing to do would be to **attract users who *do* know how to answer the questions**, namely, "experts".

How do these "experts" find the site, though? Usually through *content already on the site* - it'll come up in a Google search or something. So to attract the experts, you need content, and if you send all the content away, then AI.SE won't get new users and the site will stagnate.

There's nothing wrong with having some unanswered questions around, as long as not *all* questions are unanswered. And if that happens, the site's got a big problem.

See also [Meta.SE guidance on migrations](https://meta.stackexchange.com/a/212271/294691).
Upvotes: 2 <issue_comment>username_2: [Questions older than 60 days cannot be migrated](https://meta.stackexchange.com/q/151890/295684), even by moderators. I don't think we can expect questions to always be answered in two months — from my experience on Super User, it's not at all unusual for months to pass before the right expert stumbles upon the question and solves it. We therefore have a bit of a catch-22 here. Even if we could ship out old unanswered questions, that's probably less than ideal for site growth; see [Mithrandir's answer](https://ai.meta.stackexchange.com/a/1327/75) for more on that.
Upvotes: 1


<issue_start>username_0: *I know that every program has some positive and negative points, and I know maybe .net programming languages are not the best for AI programming.*

**But I prefer .net programming languages because of my experiences and would like to know for an AI program which one is better, C or C++ or C# and or VB ?**

*Which one of this languages is faster and more stable when running different queries and for self learning ?*

To make a summary, i think C++ is the best for AI programming in .net and also C# can be used in some projects, Python as recommended by others is not an option on my view !

because :

1. It's not a complex language itself and for every single move you need to find a library and import it to your project (most of the library are out of date and or not working with new released Python versions) and that's why people say it is an easy language to learn and use ! (If you start to create library yourself, this language could be the hardest language in the world !)
2. You do not create a program yourself by using those library for every single option on your project (it's just like a Lego game)
3. I'm not so sure in this, but i think it's a cheap programming language because i couldn't find any good program created by this language !<issue_comment>username_1: If clarity is your goal, you should attempt to avoid anthropomorphic language - doing so runs a danger of even misleading *yourself* about the capabilities of the program.

This is a pernicious trap in AI research, with numerous cases where even experienced researchers have ascribed a greater degree of understanding to a program than is actually merited.

<NAME> describes the issue at some length in a chapter entitled ["The Ineradicable Eliza Effect and Its Dangers"](https://en.wikipedia.org/wiki/ELIZA_effect) and there is also a famous paper by <NAME>, entitled ["Artifical Intelligence meets natural stupidity"](https://www.inf.ed.ac.uk/teaching/courses/irm/mcdermott.pdf).

Hence, in general one should make particular effort to avoid anthropomorphism in AI. However, when speaking to a non-technical audience, 'soundbite' descriptions are (as in any complex discipline) acceptable *provided you let the audience know that they are getting the simplified version*.
Upvotes: 4 [selected_answer]<issue_comment>username_2: I think the correct answer is the easy but unhelpful, "It depends."

Even when I'm talking to other technical people, I often use anthropomorphic language and metaphors. Especially at the start of the conversation. "The computer has to figure out .." "How can we prevent the computer from getting confused about ..." etc. Sure, we could state that in a more technically correct way. "We need to modify the algorithm to reduce the number and variety of instances of inadequate data that result in inaccurate setting of ..." or some such. But among technical people, we know what we mean, and it's just easier to use metaphorical language.

When trying to solve technical computer problems, I often start with a vague, anthropomorphic concept. "We should make a list of all the words in the text, and assign each word a weight based on how frequently it occurs. Oh, but we should ignore short, common words like 'the' and 'it'. Then let's pick some number of words, maybe ten or so, that have the greatest weight ..." All that is a long way from how the computer actually manipulates data. But it's often a lot easier to think about it in "human" terms first, and then figure out how to make the computer do it.

When talking to a non-technical audience, I think the issue is, Anthropomorphic language makes it easier to understand, but also often gives the impression that the computer is much more human-like than it really is. You only need to watch science fiction movies to see that apparently a lot of people think that a computer or a robot thinks just like a person except that it's very precise and has no emotions.
Upvotes: 2 <issue_comment>username_3: The problem you're referencing is not just an AI problem but a problem for highly technical fields in general. When in doubt, I would always recommend using [plain language](http://plainlanguagenetwork.org/plain-language/what-is-plain-language/).

However, there is another reason the AI community will often eschew anthropomorphic connotations for AI. Some AI luminaries often like warning us that an artificial *general* intelligence may behave in alien ways that defy our human expectations, potentially leading to a robot apocalypse.

This idea about evil alien-like AGIs, however, derives from a widespread misunderstanding in the AI community that conflates two different notions of generality:

* Turing machine generality, and
* human domain generality

What regular people mean when they say generality is the later. Even the [official definition](https://en.wikipedia.org/wiki/Artificial_general_intelligence) of AGI hinges off of that human-contingent context:

>
> ...perform any intellectual task that a human being can.
>
>
>

But by that definition, generalizing behavior does not make it more alien. To generalize is to anthropomorphize. As Nietzche said,

>
> "Where you see ideal things, I see— human, alas! All too human things.”
>
>
>
Upvotes: 2


<issue_start>username_0: If said AI can assess scenarios and decide what AI is best suited and construct new AI for new tasks. In sufficient time would the AI not have developed a suite of AIs powerful/specialized for their tasks, but versatile as a whole, much like our own brain’s architecture? What’s the constraint ?<issue_comment>username_1: The approach you listed here is not really an approach, this is very very vague idea of how someone can achieve some task. You basically told we have an algorithm `f(image) = result` and there can be infinite amount of real approaches to solve this.

In majority of CNN approaches the image travels through a convolution/pooling layers which reduces the dimensions of each current layer. In the end you end up with a significantly smaller layer which goes through the softmax and gets probabilities of different classes. This type of networks does not tell you where something was found, it just tells you that something was found somewhere in your original image.
Upvotes: 2 <issue_comment>username_2: This problem is called [object detection](https://en.wikipedia.org/wiki/Object_detection).

If you have a trainings set of images with boxed objects you can just train a neural network to directly predict the box. I.e. the output has the same dimension as the input and the NN learns to assign each pixel the probability of belonging to a certain object.

If you don't have such a convenient dataset you could just recursively narrow the location down by feeding parts of the image to the network until you find the smallest part that still fully activates a certain classification.

[In this paper](https://pdfs.semanticscholar.org/713f/73ce5c3013d9fb796c21b981dc6629af0bd5.pdf) they try a mixture of these two approaches.
Upvotes: 4 [selected_answer]<issue_comment>username_3: To add to @username_2's answer, the object detection problem in NN context isn't necessarily solved by using a network that produces output with the same size as the input. Actually this approach - assigning each pixel the object probability - better fits the [Instance/ Semantic Segmentation](https://stackoverflow.com/questions/33947823/what-is-semantic-segmentation-compared-to-segmentation-and-scene-labeling) problem.

To briefly review the common state of the art object detectors, there are mainly two families:

1. The two-stage approach (one stage find the bounding boxes by a variant of sliding window, another stage finds the object probabilities, some of the layers might be common to both stages) - from [RCNN](https://arxiv.org/pdf/1311.2524.pdf) (propose boxes by the Selective Search algorithm, check if they contain an object by running CNN - AlexNet in this case - on each box, if a box contains an object run linear regression to tighten the box), through [Fast-RCNN](https://arxiv.org/pdf/1504.08083.pdf) (Share the computations for all the boxes, use ROI pooling to select the region that corresponds to each box), [Faster-RCNN](https://arxiv.org/pdf/1506.01497.pdf) (Speed up the region proposal stage by using a fully convolutional Region Proposal Network instead of the heavy Selective Search), [R-FCN](https://arxiv.org/pdf/1605.06409.pdf) (Share all computations for both objects and box proposals), and [Mask-RCNN](https://arxiv.org/pdf/1703.06870.pdf) that extends the faster-RCNN solution to semantic segmentation, but also solves better the detection problem by fitting the box coordinates+object features correspondence by using bilinear interpolation, replacing ROI pooling with ROI align.
2. The single-stage approach, such as [SSD](https://arxiv.org/pdf/1512.02325.pdf), [DSSD](https://arxiv.org/pdf/1701.06659.pdf), [YOLO](https://pjreddie.com/darknet/yolo/), [YOLO2](https://arxiv.org/abs/1612.08242) and lastly [RetinaNet with the focal loss](https://arxiv.org/pdf/1708.02002.pdf), that in a single shot produce both boxes and objects scores, and work much faster that the two-stage approaches.

A great summary for object recognition, detection and segmentation is published [here](https://medium.com/@nikasa1889/the-modern-history-of-object-recognition-infographic-aea18517c318) - highly recommended.
Upvotes: 2


<issue_start>username_0: Something I like about neural network AI is that we already have a blueprint for it - we know in great detail how different types of neurons work, and we don't have to invent the AI, we can just replicate what we already know works (neural networks) in a simplified way and train it.

So what's confusing me right now is why many popular neural models I've seen when studying neural networks include both inhibitory and stimulating connections. In real neural networks, from my understanding, there is no negative signal being transferred, rather the signals sent between neurons are comparable to values between 0.1 and 1. There's no mechanic for an inverse (inhibiting) signal to be sent.

For example, in this network (seen overlaying a snake being simulated), the red lines represent inhibiting connections, and the blue lines represent stimulating neurons:

[![enter image description here](https://i.stack.imgur.com/nLT4I.png)](https://i.stack.imgur.com/nLT4I.png)

Is this just an inconsequential detail of neural network design, where there's really no significant difference between a range of 0 to 1 and a range of -1 to 1? Or is there a reason that connections in our simulated neural networks benefit from being able to a express a range from -1 to 1 rather than just 0 to 1?<issue_comment>username_1: Neural networks learn. That's what they are for. For your task there are two sensible scenarios:

1. You have a fixed reaction for danger and a fixed reaction for food and you only have to learn how to distinguish between them. In that case you basically try to classify the situation to trigger the right fixed response and this classification would be learned by [backpropagation](http://neuralnetworksanddeeplearning.com).
2. You directly learn to act for a given situation. In that case you can either use a genetic algorithm or you use reinforcement learning with backpropagation.

I would recommend using a genetic algorithm, because it is significantly easier and also makes sense in this situation. You would randomly initialise your network, let it run around in the environment and remember how much food it ate and how often or how quickly it died. Then you would randomly change the weights of your network and do the same thing again. If it did better this time around you would proceed to use the new weights otherwise you go back to the old weights and try a different random change.
By selecting successful random changes it would over time learn to avoid danger and seek out food.

Edit: To my mind you have a fundamental misunderstanding how perception works. If you see a lion and a cake, do those trigger different kinds of cells on your retina? No! All nerve cells are used to detect all kinds of objects! The classification, i.e. whether you are seeing a lion or a cake happens in the neural network i.e. in the higher regions of your visual cortex, far removed from the initial nerve activation. Your lion might be yellow and your cake might be yellow, only if you analyse the high level structure of your nerve inputs can you decide what you are seeing. That is the task of a neural network. And that high level structure analysis is what a neural network learns.

What seems to confuse you is the example you linked. In that example this very sparse distance measuring is enough to differentiate between walls and boosters in your high level structure analysis, because the different points of the walls that you sample have a certain relative position that you can analyse and conclude that they constitute the wall.

In your scenario very sparse distance measuring will not help you obviously. The distance of an object doesn't tell you whether it's a lion or a cake. Distance and color would be a solution to that. Or, more realistically, you have different shapes and much tighter distance sampling, and high level analysis can work out the shape from a couple of closely measured distances.
Upvotes: 2 <issue_comment>username_2: @username_1 answered my question in comments, but his answer doesn't reflect that information. If he changes his answer to contain the actual answer, I'll remove mine.

The answer to the question "How can object types be differentiated in the input of a neural network?" is:

**Use different sensors (nerves) to detect different types of input.**

So for example:

* If you want the network to be capable of differentiating two different types of objects in a simple 2D environment, use two different sets of nerves, one to detect one object type, one to detect the other. So if you want to sense 10 points around your "organism", have 20 nerves, 10 neurons in your input layer dealing with one object type, 10 dealing with the other.
* That's the most simple example, dealing with binary differences (object type 1 or object type 2), but it could be less binary, like this: Let's say there are three object types and think of each object types as 1/3 of a color value. So you have three sets of 10 nerves in total transmitting to your input layer (30 neurons) and each nerve set senses either R, G, or B. If you set up your system so that there are three "objects" to collide with, stacked on top of each other (like a single object), your neural network will be capable of handling objects differently based on their RGB color value, meaning it can now handle nearly infinite "types" of objects.
Upvotes: 1 [selected_answer]


<issue_start>username_0: I am starting to learn machine learning (ML). I'm thinking about starting to take part in some projects in ML.

Is [Kaggle](https://www.kaggle.com/competitions) is a good place to find projects in ML to work on?<issue_comment>username_1: Yes, Kaggle is a great place to start working on ML projects.
You will start using ML algorithms (at first, using scikit-learn or any other ML library of your choice), learn the concepts of cross-validation (very important concept). You will cope with imbalanced classes, various size of datasets. Moreover, you will be able to ask questions and receive feedback from the community.

It's a great place to start and keep learning, but do not limit yourself at using ML algorithms blindly. Try to understand them, even re-write them if you want.

But it's definitely a lot of fun and there are always several competitions running so you can choose whatever dataset you prefer.
Upvotes: 3 [selected_answer]<issue_comment>username_2: Yes, definitely it is.

It puts you in front of real-world problems, theoretical questions and practical issues that are not necessarily what you deal with in college/university tasks and projects.
Upvotes: 2 <issue_comment>username_3: Kaggle is a great website. The website provides challenges that allow you to see how various components of ML interplay with each other. For example, large data sets, standard debugging of ML code (much like a "standard" computer programmer), and seeing the breadth of applications of ML to everyday problems are just a few of these components.

Coupling working on Kaggle projects with theoretical study of AI/ML is an effective way to learn ML quickly.
Upvotes: 2


<issue_start>username_0: In this [note](https://people.cs.umass.edu/~domke/courses/sml2011/08autodiff_nnets.pdf) <NAME> says that

>
> In practice, neural networks seem to usually find a reasonable solution when the number of layers is not too large, but find poor solutions when using more than, say, 2 hidden layers.
>
>
>

But in [Bengio's remark](https://qr.ae/pNrt0p), he says

>
> Very simple. Just keep adding layers until the test error does not improve anymore.
>
>
>

There seems to be a conflict. Can anyone explain why they suggest differently? Or am I missing something?<issue_comment>username_1: There are **many** problems requiring more than two hidden layers. Randomly select a recent Google journal paper on deep learning, you'll see their network could have something like 5 (or more) hidden layers.

<NAME> wrote his notes for students, so he probably tried to make his points as simple as possible. For a "typical" machine learning problem that students would most likely work on, two hidden layers should be sufficient. But that doesn't add up for a real practical problem. "Deep" learning usually mean more than two hidden layers.

Number of hidden layers is network design that nobody knows for sure. Yosh<NAME>'s suggestion is common and simple. It's not a mathmatic proof, but simply a guideline if you don't know what to do. You just repeat and repeat, until you see the test error no longer improve.
Upvotes: 2 <issue_comment>username_2: In fact they are telling the same thing: Plot the x-axis as the number of hidden layers, and the y-axis as the performance (e.g. classification accuracy), then this curve will have an upside-down U shape.

Justin's note is clearly saying the same thing as what I wrote above, with added note that the maximum of the curve will happen when x = 2, and Bengio's note is saying the same thing without telling you where the peak could be.
Upvotes: -1 <issue_comment>username_3: Your first link is from 2011, which essentially predates the current deep learning explosion. In the many years that have since passed (AlexNet 2012, ResNet 2015) we have since found that if you keep adding layers, we generally do see improved performance.

[![enter image description here](https://i.stack.imgur.com/chMZQ.png)](https://i.stack.imgur.com/chMZQ.png)

This is due to improved training techniques and optimization breakthroughs (residual connections, ReLU, dropout etc.). But do note that the result can be diminishing. In particular, take a look at [Deep Equillibrium Models](https://arxiv.org/pdf/1909.01377.pdf), which essentially allow us to train (in the limit equivalence) *infinite depth* neural networks.
Upvotes: 0