My research group at Stanford has been alpha testing Tinker, it's both very useful and also really technically impressive in my opinion. It's a unified framework for post-training models and it abstracts almost all of the complexity of managing these jobs across resources. That it manages to do this while also allowing a lot of algorithmic flexibility is pretty unique.
In fact, the shoe was not banned and it is one of the most widely used marathon racing shoes out there. Nearly all other running shoe companies followed suit and made more efficient racing flats with carbon plates and high energy return foams. Some restrictions did come out, including limits on the "stack height" racing shoes and more stringent restrictions on the track. The current generation of these, the alphafly 3, were just used to break a world record in the Chicago Marathon by Kelvin Kiptum. Those are unreleased as of yet.
Statistical physicist here. Negative temperatures occur when a system has a finite number of high energy states. On average, when temperature increases, both the energy and the "randomness" or entropy of a configuration increase as well. Of course, if there are only a few high energy states available, then the randomness will not increase, it will decrease. That's negative temperature! Because we define temperature as the rate of change of the energy with respect to the energy, in systems like the one I described, this rate of change becomes negative.
Convolution is in fact multiplication in Fourier space (this is the convolution theorem [1]) which says that Fourier transforms convert convolutions to products.
I follow court news pretty closely and was genuinely curious about this decision. Here is a brief description of what I understand the decision to mean (I am not an expert by any means). Under the federal "Major Crimes Act" (MCA) members of various Native American tribes are not subject to state prosecution for crimes committed in "Indian territory". This case was brought by McGirt, a member of the Creek Nation, who challenged his conviction based on the original treaty establishing the Creek reservation in eastern Oklahoma. The headline is a bit misleading: for purposes of the MCA, eastern OK is now considered a part of the Creek Nation. This means McGirt must be convicted with the reservation's justice system or in federal court. It also has the consequence (discussed extensively in Roberts' dissent) that many existing convictions could potentially be vacated. It will be interesting to see how the Creek nation works with Oklahoma to address these changes.
I follow SCOTUS news pretty closely; the discussions below are a bit misguided. Audio transcripts of oral arguments are already widely available---you can even subscribe to the Oyez podcast feed and find them in your podcast queue a few days after the court hears the case. The new thing here is "live", so as a practical matter, it probably doesn't constitute a huge change. If there were an incentive for the justices to produce "sound bites", it would already exist. C-SPAN coverage will probably increase the visibility a little, but, having listened to many of the arguments this term, I'd say that most cases are too technical to be of much general interest.
A second point: oral arguments are performative. The cases are argued via written briefs and oral arguments provide a venue for the justices to question the petitioners about their arguments and air their responses to what the believe the other justices are thinking. Streaming the arguments, as opposed to making available courtroom audio after the fact, doesn't seem to change the dynamic much.
Many court watchers have taken this as an optimistic sign that perhaps the court will allow video. However, this is one thing that the court has strongly resisted. Some of the justices are known to prize their relatively low public profile and there's been speculation that maintaining that pseudo-anonymity is perhaps a reason for the hesitance.
As many have pointed out, this rediscovers Newton's method. The reason that this type of approach with a "hessian pre-conditioning" is not widely used in practice is that computing the hessian is costly. Avoiding that additional computation is the idea underlying "quasi-Newton" methods like BFGS and (more loosely) popular methods like Adagrad.
In the chemistry / molecular biology community, that is definitely part of the role played by a journal like Cell. It is considered a very prestigious journal and tends to publish only what the editors and reviewers perceive to be very important findings. Of course, we (scientists) are imperfect reviewers and sometimes don't see the value in great work and likewise sometimes imagine greatness in mediocre work. Editors also feel pressure to select work that they believe will be rapidly and frequently cited---as the number of citations per paper in the first two years after publication affects the impact factor of the journal.
Unlike math and physics, many disciplines have only recently adopted preprint servers (like bioRxiv) and there's still some bias against posting preprints within the community.
This is a fairly bizarre way to rank academic institutions. First of all, the methodology almost entirely neglects computer science because its metric of success is papers published in top tier journals; computer scientists tend to submit to conferences (e.g., NeurIPS) and hence get no credit for their work in this count. However, other fields seem over-represented from the list of journals---this is likely why Cold Spring Harbor, a very good biological lab where probably the vast majority of the papers are published in Nature-approved venues seems to be so elite.
The "normalization" they use divides the proportional count of authors that have contributed to an article in the "Nature Index" to the total output of the institution in the sciences, measured by a company called Dimensions. This has the odd effect of penalizing institutions for publishing outside their listed journals.
Finally, as an academic, there are some journals on the index that I have published in, but many venues I have published in did not make their cut. Sometimes more specialized journals are necessary---one cannot easily publish, for example, a detailed proof of a theorem in Nature, even if the result is very important.
As a statistical physicist, the title of this article looks the way "Two new papers explore the complexity of neural networks" might to a working ML researcher. Bubbles and foams are, and have been, a topic of intense research in soft matter physics for decades. Foams, in particular, have myriad uses in industry and have many well-established models.
It's a good question: you can see a fairly granular breakdown of the workforce by industry [here](https://www.bls.gov/cps/cpsaat11b.htm). There are about 5.1 million people working in "Computer and mathematical occupations", the majority of whom are developers.
It's probably worthwhile to think about scale in this context: There are only 85000 H-1B visas issued each year by the US; 20k of those are reserved for people with advanced degrees [1]. Given that there are an estimated 3.87 million software developers working in the USA [2], this seems unlikely to be the root cause of the inability of some graduates to find entry level software jobs.
In NYC, I would say the average at an upscale place is about $3 for a cup of coffee, more like $5 for a cappuccino or similar espresso drink. With tip, I often end up spending $4-$5 for an espresso, though it pains me to admit that.
I have been using Ubuntu on and off since about 2005. Last year I switched from OSX on a dated macbook to Ubuntu on a newer Dell XPS. On the whole I am happy with the switch: with many of the apps that I use based on electron (slack, atom, mailspring) or directly in the browser (google docs), it is hard to notice the difference. I do most of my work related writing in latex, so this has also not been very noticeable.
I don't like latex slides and I give presentations frequently for work. The only thing that has proved consistently annoying is that LibreOffice Impress is nowhere near as good as keynote.
It's an interesting and complicated question, but it seems like the coinbase business model should be pretty dramatically affected by the massive sell-off. First, as prices go lower, the average value of a transaction is (probably) declining, so the money they make on fees is (probably) going down as well. Second, as the market looks worse and worse, the number of people casually entering or trading is likely declining too. The have a lot of correlated risk with the decline.
The article concludes that it is acceptable to stay on Facebook, but I take issue with its approach.
Underlying the logic is a premise: that because Facebook is used by some to spread propaganda and promote hate, it is (at least in part) to blame for the illiberal trajectory of some Western democracies. For many, it is more simplistic: there is a belief that Facebook is to blame for our election outcome. Of course, it is extremely difficult to know how much "to blame" Facebook as opposed to traditional media, and conclusive facts will be impossible to obtain. Both lines of reasoning, it should be noted, neglect the possibility that the election was determined by the will of people under a non-democratic system via the electoral college.
To me, the question is whether or not Facebook is a unique actor in this capacity. Does Facebook do something substantially different from other social media platforms or content providers that particularly undermines democratic values? I don't believe it does: Twitter spreads conspiracy theories, Snapchat is used for bullying and harassment, Google has comparable troves of data for sale to advertisers. While it has certainly had some public relations snafus, on the whole, as a platform it isn't that unique.
Currently, for "hybrid" journals, the researcher must pay a publishing fee, typically on the order of $2000-$3000. I have an independent postdoctoral fellowship, which means I have some funding, but not enough for it to make sense to pay. These fees essentially lock me out of open access publishing.
Of course, I could simply choose to publish only in open access journals (running the risk of career consequences). I hope changes like this will lead to increasingly prestigious fully open access journals. However, I have found the most practical option to be posting my papers on arXiv.
I have found that the machine learning research community uses twitter pretty effectively. I don't tweet myself, but I do follow a good number of other researchers and often find interesting papers and results through the venue. It's much more effective than scouring the arXiv.
One peculiarity of the academic system (true to some extent in start-ups) is that people transition from researchers to managers without any real training or evaluation of their proclivity for management. While you may be a spectacular researcher, that does not mean that you have the disposition, skill, or compassion to be an effective leader. Of course, some are naturals and many others get ample experience in labs where there's a hierarchical structure. Still, the utter lack of evaluation and reflection on group dynamics, productivity, etc., strikes me as a real weakness for the way that we currently organize academic research.