https://www.youtube.com/watch?v=zOY8jXnwzNw

This is an update to a previously pinned meta post on this subject. For those who saw the previous post, the two changes from before are:

• Financial compensation is now offered as an extra incentive for participation.

• You can now sign up directly via a form, rather than having to contact the researchers.

A group of researchers from Cornell University are partnering with r/changemyview to understand how the quality of online discussions can be improved. They have a track record of successful collaborations with r/changemyview 1, 2 and for their new project they would need the help of r/changemyview users.

They developed a tool that promotes healthier discussions by helping users foresee whether a r/changemyview discussion they are taking part in is at risk of derailing into personal attacks and incivility (i.e. Rule 2 violations). As part of their research, they are seeking volunteers to try out the tool and provide feedback.

As an added incentive, users who try out the tool for one month and complete a brief exit survey at the end will receive a $20 Amazon gift card as compensation.

If you are interested in trying this tool out, the researchers have set up a self-service sign up via this form. The form will ask a series of basic eligibility questions and a consent statement; if you are eligible and consent to the study it will then link you directly to instructions to get started with the tool right away.

We are posting this message on behalf of the The Cornell ConvoWizard study team, lead by Cristian Danescu-Niculescu-Mizil. Any questions may be directed to jpc362@cornell.edu or /u/redditoooooo.

P.S.: If you had previously signed up for this study via the previous pinned post, you should have already received a DM from the researchers via /u/redditoooooo regarding the added financial incentive. If you did not receive this DM, contact the researchers.

I feel like the meme of Abe Simpson shaking his fist at the clouds.

There was a very recent thread regarding the situation with China's uncontrolled deorbit and the comments inside are absolutely shocking. The bottom half of the thread is mostly either jokes that China's actually going to bomb the United States or straight up unmoderated racist commentary about China's citizens and government.

The top half of the thread has some high quality discussion about what and why it's happening but even two children into a comment tree it's straight up yellow panic rhetoric.

I've been constantly dissapointed with the quality of discussion in this subreddit and I'm hoping there's a smaller subreddit where we can actually have unbiased discussions about the global space effort.

Doing literature reviews in unfamilar areas can be like being an explorer wading in a deep dark jungle full of plants and animal life and it is very unclear at first what specimans you should pay attention to, let alone collect. Are you collecting the right specimens (papers), or even looking at the right places in the jungle (using the right keywords, in the right database)?

Wouldn't it be great if you had the benefit of notes, maps from past "explorers" as a guide?

This is where you might want to start your research from things like

• Review article
• Systematic reviews
• Meta-analysis
• Phd and Masters Theses
• Bibliographies & more

These types of content, not only point you to literature you may have missed because you were using the wrong keyword or database but they provide the author's overview of how the literature is connected or grouped which is very valuable.

But how do you find them? The simplest method is to go to Google Scholar and type something like

<topic> review

or variants of this. But if you want to be more comprehensive, look at my article

4 Ways to find review papers, systematic reviews, meta-analysis, and other rich sources of references https://medium.com/a-academic-librarians-thoughts-on-open-access/4-ways-to-find-review-papers-systematic-reviews-meta-analysis-and-other-rich-sources-of-82898aebb6e7

In particular one of the techniques I mention was devised by me using a combination of 2 relatively new tools - 2Dsearch and Lens.org and in my testing provides a pretty good blend of recall and precision in pulling out review papers etc . I worked with phds who were surprised at how I managed to find review papers they missed because I used this technique.

Finding reviews on any topic using Lens.org and 2d search — a new efficient method. I don't focus so much on the STEM area or systematic reviews so I didnt really do a formal test in that respect but this should be helpful for the rest of you doing narrative (normal) literature reviews.

And of course this technique works well when combined with the citation based tools I already mentioned in [https://www.reddit.com/r/PhD/comments

So, at the moment, I am looking at the CST portfolio, it says for max points:

"I am first author, or joint-first author, of two or more PubMed-cited original research publications (or in press)"

If the answer is no to the question above, how do I start an original research publication as an FY1/2 working in a district hospital which still relies on paper notes? Please kindly advise. Thank you.

Optimizing Returns of a Meta Liquidity Pool

– Research Grant Proposal for Popsicle Finance –

Tl;dr We propose academic research on DeFi markets with the goal to optimize the capital allocation strategy of Popsicle Finance.

Who we are

The non-profit organization Blockchain Research Lab (BRL) is dedicated to independent, interdisciplinary research on blockchain technology and to publishing the results for the benefit of society.

We conduct peer-reviewed academic research in the field of blockchain, with an emphasis on cryptocurrency markets. We pursue a data-driven empirical methodology and sound economic models for tackling the research questions. For example, past investigations focused on market (in-)efficiencies and covered topics like cryptocurrency adoption [1], efficiency of crypto and stablecoin markets [2], market reactions to large Bitcoin transfers [3], exclusive mining of Bitcoin transactions [4], security token offerings (STOs) [5] or initial coin offerings (ICOs) [6]. All of our publications can be found on our publication site.

Our research is regularly referenced by media outlets, e.g. Forbes [7], and our papers have been published in prestigious journals like Finance Research Letters, Telematics & Informatics, Journal of Grid Computing, Decisions in Economics and Finance, Digital Finance, Scientometrics or Journal of Risk and Financial Management. Besides our academic dissemination, we publish research results in scientific reports and spread the word through social media.

Motivation

At the latest since the launch and rapid growth of the Compound token (COMP) in June 2020, DeFi has developed into a highly significant phenomenon that is characterized by ever new innovations. The use of decentralized networks and open source software in the context of financial processes can sustainably transform the financial industry and form the basis for efficient and trustworthy systems and processes. Exciting times lie ahead.

However, it must be noted that a market as young as DeFi cannot yet function efficiently. Challenges exist in making the underlying infrastructure more efficient [8], designing decentralized markets and order books [9] or finding a solution of the impermanent loss problem [10]. This requires scientific approaches that contribute to solving these challenges.

One benefit of unsolved

Without calling anyone out specifically, I’ve seen a few posts this week that ask questions about readily-available information on lost media. Between the Lost Media Wiki, a treasure trove of YouTube videos, and even posts previously made here, there are plenty of resources available to learn more about lost media.

I think that in the interest of having productive discussions, we should limit or ban posts here that ask very basic questions with easy-to-find answers. For example, instead of asking “Has anyone found ‘A Day with SpongeBob SquarePants’ yet?”, posts here should ask more specific questions that could generate better discussions.

TL;DR : Do your research before posting! Any thoughts from others? Maybe mods could make this a new rule?

so what is the most optimal research path you typically go down if it was available.

bare essentials?

anything an idiot would forget to grab before meeting an empire or expanding beyond a few planets?

Do I only build academies to get the maximum amount of nobles, or do I also build three courts of law to get more citizens and less freemen, but also less nobles? Has anyone done the math on this?

Also, are libraries worth it? How many of them should I build

Is there a build that tries to optimize their ability to research econ upgrades early and turn that into something useful?

Alchemy, a novel open-source benchmark for meta Reinforcement learning (RL) in the recent decade, has garnered much attention in the ML field. The RL approach not only cuts down the requirement of labeled data but has also yielded incredible successes on a wide variety of specific tasks. But issues such as generalization, sample efficiency, and transfer learning are still hurdles for RL. Researchers have been exploring meta-RL to overcome these hurdles.

In Meta-RL, learning strategies can quickly adapt to novel tasks. The above is done using experience gained on a large set of functions that have a shared structure. Even after the innovation of many exciting meta-RL techniques, no ideal task benchmark exists for testing new algorithms.

Paper summary: https://www.marktechpost.com/2021/02/21/deepmind-and-university-college-london-introduce-alchemy-a-novel-open-source-benchmark-for-meta-reinforcement-learning-rl-research/

Paper: https://arxiv.org/pdf/2102.02926.pdf

Github: https://github.com/deepmind/dm_alchemy

Hey guys, I wanted to give a shout out to a group that has let me learn more about gear in the past six months that I learned the last 29 before that. Blister Gear Review puts out a free Buyer’s Guide and does awesome reviews on about as many skis as you can think of. They also have an awesome podcast called Gear30 that has a lot of creators of your favorite equipment. It’s a great starting spot for the beginning of a search. For another 50 bucks, they’ll even send you a personalized recommendation (in addition to a bunch of other perks). I highly recommend that you all check them out!

The fandom I write for (a game series) has TONS of lore. There isn't only the main game though, there are also a crapton of officially released side novels, comics, and data books that expand on the main story. Not to mention the hordes of meta-analyses that surround various aspects of the game and the different takes on what its countless in-game murals, musical cues, references, etc. might truly mean.

I keep having writer's block because I find myself agonizing about details of the lore and trying to make sure it remains true to everything pre-established. The series is also still ongoing so everything that I already know is subject to change from future revelations, so I just find myself stagnating and left in a perpetual daydream/research mode for months or even years.

Anyone else struggle with this obsession with accuracy and incessant "analysis paralysis"? How do you snap out of this so you can just write?

Recently, there was a discussion about having a subreddit strictly for academic journal articles. Such a subreddit was even founded during the discussion.

Some users, such as /u/kzhou7 and /u/sigmoid10 , pointed out that such content doesn't really survive on this subreddit, because few people here have enough expertise to be able to read research papers in any given field. On the other hand, popular articles which are written about recent research tend to be extremely light on details, to the point where some of the posts on the sub are more or less a lengthy string of buzzwords. It's not the first time we've had this discussion on this sub. If we want more good content on research articles, we need a strategy.

One way that I can think of is articles in Reviews of Modern Physics. They are written in such a way that even if you're not an expert in, say, climate science, you can still go read their recent article on progress in climate science as long as you have a good general university-level physics education. Not only are they usually possible to read by a person who is not an expert in that particular field, but Rev. Mod. Phys is also highly reputable and often has articles from the very best experts of their field.

Now, since reading Rev.Mod.Phys doesn't necessarily require deep expertise, a person who has a degree in physics could read it and write a popular summary that still goes in to enough details to explain the fundamentals of the field. Perhaps doing so would generate fruitful discussion between enthusiasts and experts. It seems to me that the subreddit doesn't shy away from all technicalities and math, since my own post a few days ago, which did include basic linear algebra, calculus and programming, seemed fairly popular: thus one could summarize in a way that includes details without going in to the most difficult technicalities, at least for some fields.

So, does anyone have any other ideas for bringing more research in to the subreddit? Do you think what I outlined here is still too casual? Also would like to hear from people who are not necessarily physics experts but are undergraduates or otherwise read some of the semi-technical material in this sub -- does this sort of thing seem interesting to you?

I'm willing to take a stab at this sort of effort myself, but only if there's some sort of discussion of the idea first. After all,

Hi, I am a PhD statistics student and need to assemble a new meta-analysis database so that I can demonstrate the possibility of meta-analyzing logistic regression slopes. As a statistics student, I can write the methods and results part (I’ve written two meta-analyses that are currently in the process of being published), but I need help doing two things: (1) the literature search and (2) coding articles that meet the inclusion criteria into a meta-analytic database. I am looking for research partners who have studied at the graduate level (MA, MS or MD students) and have some research experience. I’ll add my partners as second, third authors etc. based on your contribution.

The research question is set in the medical sciences: “Is hypoalbuminemia casually predictive of mortality among hospital patients.” The dependent variable is mortality (a binary outcome, survive or not) and the independent variable is hypoalbuminemia. I’m no doctor, but basically hypoalbuminemia is a medical condition where total protein production in the blood plasma decreases to dangerously low levels (read about it here on Wikipedia). There is much research showing that hypoalbuminemia is associated with poor outcomes. There is even one meta-analysis on this topic (Hypoalbuminemia in Acute Illness: Is There a Rationale for Intervention? A Meta-Analysis of Cohort Studies and Controlled Trials), but it was poorly done and is 20 years old now. So we can easily improve upon it, and find new research that has been published since 2000. I have already found about 20 studies which can be included in the meta-analysis and coded them in a meta-analytic database. I mainly need help doing a literature search from 2000 onward (this meta-analysis was published in 2002).

If you're interested in joining the research project, just send me a message or reply to this threat and I'll get in touch!

I cant find any information about this, what is the meta about this? Only noble buildings, only citizen buildings, a mix or both?

Alchemy, a novel open-source benchmark for meta Reinforcement learning (RL) in the recent decade, has garnered much attention in the ML field. The RL approach not only cuts down the requirement of labeled data but has also yielded incredible successes on a wide variety of specific tasks. But issues such as generalization, sample efficiency, and transfer learning are still hurdles for RL. Researchers have been exploring meta-RL to overcome these hurdles.

In Meta-RL, learning strategies can quickly adapt to novel tasks. The above is done using experience gained on a large set of functions that have a shared structure. Even after the innovation of many exciting meta-RL techniques, no ideal task benchmark exists for testing new algorithms.

Paper summary: https://www.marktechpost.com/2021/02/21/deepmind-and-university-college-london-introduce-alchemy-a-novel-open-source-benchmark-for-meta-reinforcement-learning-rl-research/

Paper: https://arxiv.org/pdf/2102.02926.pdf

Github: https://github.com/deepmind/dm_alchemy

Hi, I am a PhD statistics student and need to assemble a new meta-analysis database so that I can demonstrate the possibility of meta-analyzing logistic regression slopes. As a statistics student, I can write the methods and results part (I’ve written two meta-analyses that are currently in the process of being published), but I need help doing two things: (1) the literature search and (2) coding articles that meet the inclusion criteria into a meta-analytic database. I am looking for research partners who have studied at the graduate level (MA, MS or MD students) and have some research experience. I’ll add my partners as second, third authors etc. based on your contribution.

The research question is set in the medical sciences: “Is hypoalbuminemia casually predictive of mortality among hospital patients.” The dependent variable is mortality (a binary outcome, survive or not) and the independent variable is hypoalbuminemia. I’m no doctor, but basically hypoalbuminemia is a medical condition where total protein production in the blood plasma decreases to dangerously low levels (read about it here on Wikipedia). There is much research showing that hypoalbuminemia is associated with poor outcomes. There is even one meta-analysis on this topic (Hypoalbuminemia in Acute Illness: Is There a Rationale for Intervention? A Meta-Analysis of Cohort Studies and Controlled Trials), but it was poorly done and is 20 years old now. So we can easily improve upon it, and find new research that has been published since 2000. I have already found about 20 studies which can be included in the meta-analysis and coded them in a meta-analytic database. I mainly need help doing a literature search from 2000 onward (this meta-analysis was published in 2002).

If you're interested in joining the research project, just send me a message or reply to this threat and I'll get in touch!