The real prerequisite for machine learning isn't math, it's data analysis (2016) - <AUTHOR> https://www.r-bloggers.com/the-real-prerequisite-for-machine-learning-isnt-math-its-data-analysis/ <comment><USER2> As a mathematician, people routinely tell me that mathematics isn't applicable in a particular situation. Upon deeper investigation, I usually find that they simply have a very limited understanding of what math is. Most people associate it with numbers and equations -- but those are just a few tools that are taught early on. Actual math is about analysis and some of these tools MAY be used when preforming it. Even some of the deeper topics, such as calculus and algebra are simply tools in a greater framework of understanding.

Even if you're using very little of what you think of as "math" to interact with your machine learning framework, to dismiss math as not being necessary is ignoring all of the underlying mathematics that went into creating it. You may not need calculus, or the like, but the math is there -- even if you don't recognize it as such.

<comment><USER3> "Even if you're using very little of what you think of as "math" to interact with your machine learning framework, to dismiss math as not being necessary is ignoring all of the underlying mathematics that went into creating it."

This seems like a straw man to me. The article never claimed that math is not necessary for the development of machine learning frameworks. In fact, it explicitly states the opposite:

"First of all, math is particularly important if you're doing machine learning research in an academic setting."

"In particular, there are people at companies like Google and Facebook who are pushing the boundaries of machine learning – people working on bleeding edge tools. These people almost certainly employ calculus, linear algebra, and more advanced math routinely in their work."

<comment><USER2> Perhaps adding that last part without acknowledging these claims from the article wasn't fair to the author. I meant it more of a rebuttal to the subject line. I probably should have ended with something along the lines of "data analysis is a different type of math" rather than trying to promote the idea that "there's still math in machine learning".

</comment></comment></comment><comment><USER4> I'm mixed on this article. While I strongly agree that data analysis trumps math for applying machine learning, I think there's a middle ground the article is missing.

Like programming, machine learning has its 10xers, and I've worked with several. There's one thing they all have in common (beyond experience): they can think like the model. They've read a bunch of ML papers, get the intuition behind them, and envision from start to finish how data can be captured by various models. Beyond a small amount of hyperparameter optimization they often build a great model on the first or second take, and can put out production models in days (which would normally take novices weeks or even months). This requires math knowledge, because ML models are built on top of math. Even in higher level ML packages that do a lot of the work for you, novices get stuck in dead ends where their models don't work and they don't know why. The 10xers drive right through it because they know why their model isn't working.

When I interview for my team, I usually have non-junior candidates describe a ML algorithm they would use for a specific problem, then ask why certain things could go wrong - e.g. if they say they would use a logistic regression model to predict an action, I ask why the model may be returning the same score for every test case. A good candidate would understand that this means the coefficients are getting pushed to zero, and would list off reasons why this could be occurring (too much regularization, underpowered data, constant target variable, etc). You learn these things from experience, but you understand them because you know the math behind the models.

</comment><comment><USER5> Yeah, I'm going to politely disagree that one can do multidimensional statistics and _understand what one is doing_ without rudimentary understanding of linear algebra and probability and basic concepts of calculus (derivatives, integrals). The branch of science specialized in analyzing data is known as "statistics": it's a mature field that's been around for some time, and the things a working statistician should know are quite well established.

Similar attitudes in the past have lead into misconceptions like if you are only applying statistics in your research, you don't really have to understand what _p_ -value is, just run the magical tests and report if it's "significant" or not.

</comment><comment><USER6> It really depends upon the organization — both the specific role of the data scientist and the way in which machine learning is applied.

In some organizations, "data scientist" is the modern name for a business analyst and the responsibilities largely involve basic data sanitization and analysis, streamlining processes that were manual and tedious in previous eras. In other organizations, people with advanced mathematics skills find ways to apply mathematics and machine learning to gain a marginal advantage over competitors or to deliver something novel to the market. This latter case does indeed require a deep understanding of mathematics.

Using off-the-shelf implementations of ML algos might seem like it obviates the math skills, but problem definition, algorithm selection and feature engineering are difficult for laymen to do well, and can insidiously poison the validity or efficacy of the results if done poorly.

</comment><comment><USER7> Good ML people I know or have observed through interviews are good at a bunch of things. The ones that come to mind are: Linear Algebra, Statistics, Python, ETL operations with databases, Application Performance Analysis and testing, and the crucially overlooked and slightly more fuzzy what I call 'problem space analysis' (ie, looking at the problem space, whether it's a game or a process, and discovering which human learned activities can be effectively replaced or reimagined by training a model of some kind).

</comment><comment><USER8> I would say that what they're calling "data analysis" here is really maths.

Knowing how to manipulate tables is relational algebra, even if you never learn the formalisms.

Representing the parts of a problem as symbols is just algebra.

</comment><comment><USER9> I am curious why there is so much talk about deep learning and math. In my country calculus was part of high school curriculum as well as a hint of diff eqs. DL theory is very undemanding in terms of math (cf quantum algorithms) and if you know basic differentiation its easy to have a very good grasp of the theory. I find it intellectually lazy to say than one should skip the math. I mean, learning programming is an equally difficult, if not more difficult task. Why is discussion so often about the math ?

<comment><USER10> Same here. Even to understand the theory, the only maths tools you really need are differentiation of composite functions (derivatives of f(g(x)), taught in high school ), what a gradient is (taught in 5 minutes if not taught in high school), basic matrix operations (and I mean really basic ones like transposition, nothing fancy like finding kernels) and then you can undertand stochastic gradient descent.

The hardest may be thinking in terms of higher dimension tensors (matrices that have 3 or more dimensions) but that is not even mathematics, just an intellectual effort.

</comment></comment><comment><USER11> Doing machine learning without math is like programming without knowing about unit testing or data structures. Sure, you can get by. Good luck to anyone who has to live with what you've made.

<comment><USER12> But unit testing and data structures naturally follow as you progress as a developer, even if your early work is poor.

Which supports the headline. The math will naturally follow as you gain experience with ML, but not having good data will make even just starting your journey difficult.

<comment><USER11> In an academic/learning environment I agree. But, for professional purposes I wouldn't hire even an entry level person who didn't have at least enough of a basis to know when those concepts apply. I also wouldn't hire a data scientist who can't at least tell me the high level workings of an algorithm they might choose. I interview for both types of positions and know what it's like to have to compensate for an amateur.

<comment><USER12> _> In an academic/learning environment I agree. _

That is where everyone starts though. With time, they will become the professionals that you want to hire.

</comment></comment></comment></comment><comment><USER13> Not all the good data scientists I used to work with/for are mathematicians but all of them have a deep understanding of mathematics. Knowing math upfront is not a must but if you are not willing to learn it along the way than you shouldn't choose the path of ML.

</comment><comment><USER14> The real prerequisite for machine learning is a domain expertise.

Otherwise algorithms will learn nonsense and random correlations made from datasets of random noise.

Look at finance to see how it "works".

<comment><USER15> Yup, the ability to understand what's going on and how it fits together is critical.

But also : can work in a team and leverage relationships for insight.

</comment></comment><comment><USER16> The importance of math for machine learning didn't fully sink-in for me until I developed a deeper understanding of the back-propogation algorithm that makes training deep neural networks computationally feasible. If it weren't for the introduction of a continuous activation function combined with the nice properties of the multivariate chain rule, we probably wouldn't be having this discussion at all. Another example that comes to mind is kernel methods used for Support Vector Machines.

No matter where the inspiration for a model comes from, the final formulation is always mathematical, and I think that without an appreciation for the mathematics, it's hard to get a true feeling of a model's effectiveness and limitations.

</comment><comment><USER17> The most important thing is not mentioned by the author or by the deep maths thinking he critiques. It is understanding when and how to apply a model, how to evaluate your results for robustness, how to interpret what it's saying. These things can be taught/learned by solving problems along with a master, even if that master is represented by a really good book, without a deep understanding of the math.

In short, to _apply_ machine learning tools, you need a good understanding of _applied_ machine learning, which is not a small subject area, but is very approachable for most.

</comment><comment><USER18> How can you run a good analysis without math?

<comment><USER19> By doing good statistics. No-one cares whether a model's application is well founded. What matters is if it's predictions hold up statistically. This means lots of validation runs and defense against overfitting. That can all be done while viewing the model as a black-box.

Model knowledge certainly helps, but it isn't neccesary.

<comment><USER13> "Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data." /wikipedia/

<comment><USER20> The relationship between math and statistics is an interesting one. In my experience, "Statistics is a form of Math" is true when the math folks want it to be, and is a totally separate thing that doesn't belong in the club at all when they don't.

<comment><USER21> I've never seen a mathematician claim that statistics is not a branch of mathematics. Nor would that really make sense.

You can certainly _practice_ statistics in a way that mathematicians would take issue with, but it's intrinsically a branch of math.

<comment><USER20> I've seen a number of mathematicians take umbrage at the idea of describing statisticians as mathematicians, or even applied mathematicians.

<comment><USER13> Thinking that we are somehow special makes us feel better. The truth is that we are not.

</comment></comment></comment></comment></comment></comment></comment><comment><USER22> Sure, that's the way you'd think you have to do it if your resources about this primarily come from HN. But there's whole different bag of tricks from the purer side of math which those who've decided to follow the data analysis path long-term use very fruitfully.

</comment><comment><USER23> (I'm assuming that we are talking about the original post [http://sharpsightlabs.com/blog/machine-learning- prerequisite...](http://sharpsightlabs.com/blog/machine-learning-prerequisite- isnt-math/) reposted on r-bloggers, focusing on business/company use of data science, not necessarily on the many other uses).

Lots of folks like to slag these articles and talk about how "If it's not real Maths, it's crap" ([http://www.dailymotion.com/video/xgzfxs](http://www.dailymotion.com/video/xgzfxs)), but if you've worked in larger orgs, you recognize that there is a need for more than just advanced math. There really are myriad needs, and the failure of AI/ML (yes, I'm combining them for simplicity here) in an org is usually the lack of understanding these needs. Here are some I've seen:

1) What is the problem that AI/ML is being applied to? What is it optimizing, deciding, predicting, forecasting, categorizing? How will this decision be used in a process or flow? This requires a business analytic approach, understanding available data, _what it means_, how it's generated, and how the business might evaluate the impact of the ML/AI. These folks need to interact with the business folks as well, so some communication skills are helpful... though that's true for every role these days.

2) How will said decision be implemented both in tech build, test/QA/FUT/UAT/etc, and prod? This technical architecture and approach is data engineering, but some folks in the data science world are amazing at this. BTW, a model that works in dev may not scale in prod. The fact that so many folks keep "re-discovering" this is scary to me. There is a whole class of amazing folks who can re-implement models to scale them, and if you know them, reward them well.

3) How will models/algos/systems be built? This workflow is often pretty sloppy, just a bunch of jupyter notebooks or a tonload scripts (but they're in Git, so it's ok), and so replication and scaling becomes painful... esp. in regulated industries. Again, data engineering approach, but needs a more nuanced understanding of the vagaries of ML/AI. I find that solving business problems may not always fit a traditional software workflow (call it "agile" all you want, it doesn't always fit) but ymmv. So, you may need to create new workflows for your org's needs, and this tooling may not be off the shelf, but like automated testing, this tech debt will need to be paid sooner or later.

4) What ML/AI approach should I use, esp. if I'm using pre-existing approaches? This becomes more of the data science analytic approach, mixing the understanding of how ML/AI works with the data landscape (how was my internal data generated? What does it mean? Is it stable? What's available at score time, and what's it's latency?) and how to build various working predictive models. Note that this is traditionally where data scientists/model builders/analysts spend their time, from data cleansing to preliminary analysis to generating/training various models to crying when none of them predict well to stumbling onto a fascinating and amazing combination of models and approaches at 3am. Yes, math is defn helpful here, but _understanding_ the underlying math is often helpful enough, vs. _mastery_. A good understanding of the levers affecting each model/algo/DL architecture/etc. and how to diagnose them can get you pretty far, though you may violate assumptions or overfit if you aren't careful.

5) Real Algo design: Using all that math that underlie the models to not just diagnosing a pre-designed package but making your own optimizer, or your minimizers, or your own way of computing the Hessian, or a new weighting approaches, or whatever new approach your expertise is in. From writing your own primitives to making your own deep learning architecture, this is often the real wizardry, but it's not needed in EVERY case. But the effort can really pay off, from optimized prediction, improved use of resources, and unique IP which can be a competitive advantage. Remember, custom work is great, but maintenance of the resulting product can be painful, esp. if done in a language few folks in the org understand and if few resources are available outside.

Finding skills to meet all of these needs in one person is certainly possible, but somewhat unicornish. And you may not need all of these either. But the best orgs that have to scale have at least a "Data Prep/Data Engineering" role, a "Data Science" role, and if needed, a "Data Analyst" role who can help translate specific business needs into analytic problems, and also carve off the easy stuff (ad-hoc pulls and simpler analyses).

So, to original post: yeah, sort of. You need to be able to analyze, but you also need to be able to do some of the math, and understand the impact of the rest. If you are awesome at the math, go up to making your own magic. But if you aren't, at least try to understand as much as you can, while trying to also be familiar with the other areas. I do think that the emphasis on visualization is somewhat glossy-ily ignoring real analysis skills, but it's one of many helpful approaches to understanding the problem. (Pet peeve: Just drawing graphs/charts is usually not analysis. Sorry, but true.)

If you are hiring for a data scientist, at least be clear with yourself on what of these needs you are looking for, and if you need them all. Also be clear about where you hope to grow, and if you are hiring for now, near future, or the year 3000.

(Ok, one more: do yourself a favor and learn experimental design. I am sort of shocked at how many data scientists I chat with who haven't really tested champion/challenger models, or compared 2 or more processes or approaches in a randomly assigned test, or gotten as close as they can with quasi-experimental or matched-groups approaches. The simple ones are indeed really Excel simple, but if it's so easy, why haven't you at least tried it? And the causal- modeling stuff is a bayesian's dream come true, so it's worth learning.)

</comment><comment><USER24> In universities, it usually devolves into a land-grab around

1\. who teaches it

2\. what gets taught first

3\. what forms and notations get used

</comment><comment><USER25> what is a good source to learn advanced data analysis for someone with mathematical background?

<comment><USER15> Judea Pearl & Andrew Gelman are great people. Trevor Hastie's books are good too.

[https://web.stanford.edu/~hastie/pub.htm](https://web.stanford.edu/~hastie/pub.htm)

[http://bayes.cs.ucla.edu/jp_home.html](http://bayes.cs.ucla.edu/jp_home.html)

[http://andrewgelman.com/books/](http://andrewgelman.com/books/)

I guess it all depends on what is a mathematical background and what is advanced!

<comment><USER25> Great. Thanks for the recommendation. What I mean by mathematical background is at or above undergraduate level (so definitely covers calculus, linear algebra, intermediate statistics). A background that can read Elements of Statistical Learning (ESL) comfortably.

What I found is that many "data science" books cover how to use R or Pandas at a very introductory level. Books like ESL focus on core theories (which is great) but do not focus on how to tackle a tough real-world data.

I suppose much of data insight come from experience, but I was wondering whether there are sources to help me jump start.

<comment><USER15> The thing I struggle with is recognising what the problems in front of me are. It's usually bloody obvious, no problem, but then the work is quick and straightforward. The things that fill my calendar are the ones where we have problems which cost massive money which no one has found a treatment for.

Sometime it suddenly becomes apparent and we suddenly see how to do it and everyone feels pretty sheepish!</comment></comment></comment></comment>


Ask HN: What service/ framework should I learn about next? - <AUTHOR> Below is what I have become knowledgeable around. What should I learn next as a cloud architect/ app developer?<p>* Spark<p>* Hadoop<p>* Kafka<p>* MongoDB<p>* Graph databases (Neo4j)<p>* Kubernetes<p>* Docker<p>* REST APIs & OpenAPI/ Swagger<p>* Web App frameworks (Django, Laravel, Shiny, Rails)<p>* Machine learning, predictive analytics<p>* Traditional networking, HW, virtualization<p>* Jupyter Notebooks, RStudio<p>* R (tidy), Python (pandas) <comment><USER2> You need some Elixir.

</comment><comment><USER3> service mesh / istio / knative

apollokit.org

Golang

DevOps

<comment><AUTHOR> Ah, yeah. Big fan of GraphQL.</comment></comment>


The Idea That Women Are Cyclical Cuckolders Bites the Dust - <AUTHOR> https://www.economist.com/news/science-and-technology/21741964-attractiveness-mens-faces-not-related-ovulation-idea-women <comment><USER2> "Dear Editor: Once again this Journal has published research supposedly supporting a null hypothesis on the basis of null-results. Even a large-scale study, laid out carefully, cannot support the notion that a phenomenon or relationship does not exist. A null result is no result at all. Please preserve the integrity and reputation of Psychological Science. Erhard Eimer PhD Professor Emeritus of Psychology APS Member"

[https://www.psychologicalscience.org/news/releases/womens- pr...](https://www.psychologicalscience.org/news/releases/womens-preference- for-masculine-faces-not-linked-with-hormones.html#comment-8358848)

<comment><USER3> It might be more accurate to say that previous studies showing a relationship between sexual preference and menstrual cycles are not reproducible. I think they just put it in more layperson's terms.

The economist headline is naturally more sensationalized being a popular press article, but the physiological science headline starting with "no evidenced that. . . " is okay enough to pass imo.</comment></comment>


Don't drink and bike - <AUTHOR> http://legaltimes.typepad.com/blt/2010/04/dc-court-dont-drink-and-bike.html

<comment><USER2> Biking drunk should be legal, or at least a far less serious offense than driving drunk.

If you're looking to minimize injuries or deaths (which is the ostensible reason for drunk driving laws) it makes sense to provide an incentive for people to not drive drunk. A cyclist is _extremely_ unlikely to kill or injure a pedestrian or another cyclist--and probably incapable of injuring a driver-- while drivers kill tens of thousands of other drivers and thousands of pedestrians every year. Cars are incredibly dangerous.

If someone wants to ride a bicycle drunk, they are realistically only endangering themselves. If people are going to get drunk anyway--and they will --allowing them an additional relatively safe, legal method to get home would make the streets much safer for everyone.

In Portland, people bike drunk all the time. There are extremely few arrests. Cops drove by my obviously drunk friend the other night and chose to do nothing. I guess it's not an enforcement priority here.

In this case, it sounds like the guy probably deserved it. Pedestrian-bicycle collisions do occur and people are occasionally injured or killed. But it's not common.

I got these stats from <http://bicycleuniverse.info/transpo/almanac- safety.html>. Not the most unbiased, I know, so if someone wants to take issue with them let me know.

</comment><comment><USER3> Biking drunk should be an offense. Looks like the guy who got the BUI almost hit a small child. That could be serious.

I hope that the courts find a way to treat it as a lesser offense than driving drunk, though. I'm not saying it's good, I'm, just saying that if a drunk guy wants to get home and is looking at his bike and his six ton SUV, I would hope that the law would say "both are illegal, so call a cab or use your feet - that said, we'll punish you much more severely if you drive the six ton SUV."

<comment><USER4> Washington state has a law specifically addressing this. If you're not a threat to public safety, a police officer can give you a verbal warning and offer you a ride home. He may not arrest or otherwise compel you. If he deems you to be a threat to public safety, he can compel you to accept a ride home, and he can confiscate your bicycle -- but you must be allowed to pick it up, without financial penalty, within 30 days.

</comment></comment><comment><USER5> Oregon has super clear laws on this.

ORS814.400 states "bicycle is a vehicle for purposes of the vehicle code"

I've known plenty of people who have gotten BUI's. On the up side of that it also means bikes are allowed the full lane! which as a biker, is really great and necessary sometimes.

here is the full law: <http://www.leg.state.or.us/ors/814.html></comment>


Google Search Volume by Language - <AUTHOR> http://workshop.chromeexperiments.com/globe-search/

<comment><AUTHOR> Here's the code on GH: [https://github.com/dataarts/webgl- globe](https://github.com/dataarts/webgl-globe)

And here's some info about the project: [http://www.chromeexperiments.com/globe](http://www.chromeexperiments.com/globe)

You can also see a population graph: [http://workshop.chromeexperiments.com/globe/](http://workshop.chromeexperiments.com/globe/)</comment>


Can you crack it? - <AUTHOR> http://canyoucrackit.co.uk

<comment><USER2> Duplicate : <http://news.ycombinator.com/item?id=3301056></comment>


An Incremental Approach to Compiler Construction (2006) [pdf] - <AUTHOR> http://scheme2006.cs.uchicago.edu/11-ghuloum.pdf <comment><USER2> Thread from 2016: [https://news.ycombinator.com/item?id=13207441](https://news.ycombinator.com/item?id=13207441)

2015: [https://news.ycombinator.com/item?id=10785164](https://news.ycombinator.com/item?id=10785164)

2010: [https://news.ycombinator.com/item?id=1408241](https://news.ycombinator.com/item?id=1408241)

Lots of other mentions: [https://hn.algolia.com/?query=Ghuloum&sort=byDate&dateRange=...](https://hn.algolia.com/?query=Ghuloum&sort=byDate&dateRange=all&type=comment&storyText=false&prefix=false&page=0)

</comment><comment><USER3> Recent discussion that is also relevant, "My First Fifteen Compilers": [https://news.ycombinator.com/item?id=20408011](https://news.ycombinator.com/item?id=20408011)

</comment><comment><USER4> Now with WASM, a good way to go through this paper is to generate WASM instead of the suggested Assembly and use WASM constructs as macros.

<comment><USER5> Can you clarify "use WASM constructs as macros"?

Here's a repo with working test scripts and the tutorial pdf: [https://github.com/namin/inc](https://github.com/namin/inc)

<comment><USER4> WASM text variant is Lisp based, so one could build on top of it, just like many Lisp constructs happen to be macros on top of a very tiny set of primitives.</comment></comment></comment>


Show HN: Pay-per-minute video streaming - <AUTHOR> https://www.browzable.com <comment><AUTHOR> I'm developing this microtransaction alternative to ads for video streaming. Users prepay for minutes (1 cent each) which transfer to content owners as they watch. Eager for feedback!

There are some test accounts and test videos, and feel free to upload your own test videos.</comment>


Array Languages Rock - <AUTHOR> http://williamtpayne.blogspot.com/2012/09/array-languages-rock.html

<comment><USER2> The idea appears to be that array languages have builtin functions for operations that would require map/reduce/etc in the applicative programming paradigm. I agree that is convenient, but the downside is that only array operations that are supported this way are the ones that are anticipated by the language designer. If you want to split up an array in some way other than intended, you're back to applicative programming, so your language needs good support for that too.

I would argue that the more important thing that makes something an "array language" is that scatter/gather operations on arrays have syntactic support, as described here:

<http://prog21.dadgum.com/141.html>

As a personal anecdote: In my thesis work I switched most of my data analysis workflow from MATLAB to R. In terms of paradigms I'd say R is a slightly worse array-language (although it does have builtin syntax for things that require messing with sub2ind() in MATLAB) but a much better functional language than MATLAB. The empirical result is that I finished my first analysis project -- including the time it took to learn R -- faster than it had taken me to do a similar task in MATLAB, using 1/3 the code. Add to that that there is presently more active development of open graphics and statistical analysis libraries in R, and I haven't really looked back, though I occasionally think of picking up an APL-derivative like J to play with.

<comment><USER3> _the downside is that only array operations that are supported this way are the ones that are anticipated by the language designer. … I occasionally think of picking up an APL-derivative like J to play with._

You really should -- it provides a nice counterexample to the "downside" you worry about. Having mapping over arrays (even with mismatched dimensionality) built into the mechanics of function application means that any function the programmer writes is automatically supported by the array mapping.

<comment><USER2> I can see that from this distance -- I should have been more specific about is being a downside of the way MATLAB is only half-assedly array-oriented, not necessarily a feature of all array oriented languages.

</comment></comment></comment><comment><USER4> > Having done a bit of Prolog programming in the dim and distant past, my > intuition is that trying to make everything declarative is a mistake...

Calling Prolog "declarative" or "logical" is just marketing. Prolog is really built around two concepts: unification and backtracking. This produces a system that can be used as a general-purpose language, but you will probably get more mileage thinking about it as a database engine, capable of expressing non-finite relations. Compare it to SQL, which only supports finite relations (finite number of rows in each table). Indeed, optimizing Prolog compilers will remind you more of SQL engines -- optimizing Prolog compilers produce indexes, does that sound familiar?

<comment><USER5> Yeah, I've always wished that I could query my SQL databases with Prolog. It's really so much more powerful than SQL, and allows you to actually create abstractions, rather than repeating yourself endlessly in joining tables together or using clunky views.

<comment><USER6> You might be interested in Datomic.

"Datomic embeds Datalog, a subset of Prolog, to move queries into the application." (<http://www.infoq.com/news/2012/03/clojure-west>)

</comment></comment></comment><comment><USER7> You missed significant language here -- R (<http://www.r-project.org/>). Its array model is way better than Matlab, has all functional goodies on board, excellent expressiveness, can flawlessly represent real data (there are built- in missing values, ordered and unordered categorical variables, data commenting, arbitrary metadata, even a "spreadsheet" type) and absolutely huge library (biased into data science, but it features linear algebra, optimization and DEs). It is also pretty fast, especially because it is easy to write C/Fortran accelerators so they are present behind many library functions. And in contrast to Julia/Cobra it exists for a while, so way more bugs/flaws is already fixed/understood.

</comment><comment><USER8> if you haven't seen it yet, the concept of OOPAL[1] as seen in fscript[2] is rather interesting.

Basically it boils down to integration of array programming and objects through the concept of "messaging patterns", which change the behaviour of a message/method call so that it multiplexes over self/arguments via syntactic magic

So you have stuff like

        # at aggregate level
        [[0,1],["a"]] count #=> 2
        # content level, looping left
        [[0,1],["a"]] @count #=> [2, 1]
        # content level, looping right
        2 greatherThan@ [1,3] #=> [true, false]

you can combine these things for multiple levels of nesting, and there are other messaging pattern for indexing, reduction etc.

This feels more declarative than performing explicit loops or using map/fold&co for some things, but also a fair bit mor obscure

(Perl6 also has something similar via hyperoperators and metaoperators, I think)

[1] <http://www.fscript.org/documentation/OOPAL.pdf> [2] <http://www.fscript.org>

</comment><comment><USER9> Some computer scientists (e.g. Martin Odersky, creator of Scala) don't see object oriented programming as a true paradigm, for it can be quite easily combined with them (hence Scala and its oop + functional programming approach).

I think this sounds reasonable, especially considering the pseudo-oop of todays languages.

<comment><AUTHOR> Yeah, but OOP is not just a class of languages, it is a whole different way of thinking about problems and their solutions.

</comment></comment><comment><USER10> Where's the love for languages like APL and J?

<comment><AUTHOR> If I had any experience with them, I am sure that I would love them. :-)

<comment><USER3> They highlight what I would call a limitation in other array languages. Octave (and I would expect MATLAB as well) allows scalar operators to be lifted to multidimensional arrays only if one operand is scalar or both have the same dimensionality, which looks to a J programmer like it's just incomplete special-casing.

By comparison, J's lifting permits mismatched dimensionality ("rank"), e.g. using a scalar operator on a list and a matrix, though the dimensions of the particular axes ("shapes" of the arrays) must still match (one must be a prefix of the other). This generalizes to operators written for higher-rank arrays too.

<comment><AUTHOR> This is making me seriously consider investing some time learning J.

</comment></comment></comment></comment><comment><USER11> Totally agree with the author. For a Matlab-flavored large scale processing DSL, check out the matrix library in scalding: [https://github.com/twitter/scalding/wiki/Introduction-to- Mat...](https://github.com/twitter/scalding/wiki/Introduction-to-Matrix- Library)

</comment><comment><USER12> I could be misunderstanding here, but isnt this what monads are for?

<comment><AUTHOR> I thought that monads were something to do with I/O? (I never really grokked Haskell, so please forgive my ignorance).

<comment><USER12> Im not a Haskell guy either, but monads are a concept not restricted to Haskell.

A trivial example is if you know jQuery - $(".myclass").addClass("myclass2") ...the selector returns zero or more objects. You dont need to iterate over each and manually add myclass2.

<comment><USER13> Note that jQuery isn't necessarily a monad in the strictest sense (i.e. it's unclear whether it satisfies the laws), but it certainly has many attributes that are similar, and so is a good example nonetheless.

Reference: [http://stackoverflow.com/questions/10496932/is-jquery-a- mona...](http://stackoverflow.com/questions/10496932/is-jquery-a-monad)

</comment></comment></comment></comment><comment><USER14> Cobra seems to have the most potential of those bantered about, but it also looks like the most conventional language of the three.

</comment><comment><USER15> Did he misspell declarative as array?

<comment><AUTHOR> Yes. Yes I did. :-)

</comment></comment><comment><USER16> Intriguing, but a small code example illustrating it would have been nice.

<comment><AUTHOR> I will try to dig one up...</comment></comment>


How Undocumented Youth Resistance Manifests Itself Through Social Media - <AUTHOR> http://www.citizenorange.com/orange/2011/04/right-to-stay-how-migrant-yout.html

<comment><AUTHOR> Been reading HN for a while. This is my first submit and also the first post I've written that I think would interest folks here. Please give me any suggestions if you got any.</comment>


Marium, the Dugong Who Charmed Thailand, Dies After Ingesting Plastic - <AUTHOR> https://www.npr.org/2019/08/17/752042032/marium-the-dugong-that-charmed-thailand-dies-after-ingesting-plastic <comment><USER2> >But last week, her caregivers found her listless and bruised, reports the AP.

>In addition to Marium eating plastic, biologists believe she was pursued by an overly aggressive male during the mating season.

>"We assume she wandered off too far from her natural habitat and was chased and eventually attacked by another male dugong or dugongs," Jatuporn Buruspat, director-general of the Department of Marine and Coastal Resources, told the AP.

It seems that Marium died after being attacked by one or more males. Although the article mentions plastic in her intestines, nowhere does it say that the plastic was the cause of death. There is a good chance that a lot of the marine life invests plastic and we just notice it when we perform a necropsy after a death.

<comment><USER3> The more I hear about plastic pollution the more worried I am that it's working as a distraction for air pollution, global warming and problems of the global economy:

[https://www.sciencedirect.com/science/article/pii/S0308597X1...](https://www.sciencedirect.com/science/article/pii/S0308597X1830681X)

<comment><USER4> I sort of wonder this too. The logic seems simple. Companies are attacked for being bad for the environment. In ways such as fossil fuel consumption it is impossible for them to change. However, in ways such as making packaging slightly lighter or of different materials it is easy to change. Companies support existing environmental groups fixated on fringe issues as a way of appearing progressive while the main issue (which would be costly) goes on touch.

As a resident of California who has seen plastic straws be banned (which are highly visible but small) while millions of other types of waste are allowed I suspect it is the case.

<comment><USER5> That's more an issue of PR departments pushing out platitudes whilst not actually doing anything.

The solution to plastic pollution is not 'make the container thinner' because the amount of material used is not really relevant. Two thin plastic bags are likely worse than one 3x thickness bag a pollution perspective.

It would be equally silly for companies to talk about "hey we now use 3% less petrol" or whatever. They need to use 90%+ less.

<comment><USER6> It's not about platitudes. It's about raising awareness with a small gesture in hopes it will lead to a bigger change. It already is a pretty needless thing (especially as compostable straws exist) that accumulates in large numbers, and is purely single use. We already have had single use plastic bags banned for a long time in SF, and I cannot imagine going backwards. We need to continue moving onward... not bringing back straws but further reducing this single use plastic lifestyle.

<comment><USER5> I think you're misreading my comment. I agree with you.

I'm saying that making plastic containers a bit thinner is a platitude (greenwashing), not that reducing plastic use is a platitude.

The meaningful move would be to stop producing single-use plastics entirely. A jar of jam or something that lasts a few weeks is low priority at the moment. A microwave meal is lazy. A plastic fork is just a piss take.

</comment></comment></comment></comment></comment></comment><comment><USER7> I consider all these articles about plastic pollution to be a kind of "fake news" because they are completely misleading people as to the nature of the problem.

The plastic waste that is entering the oceans is because people in developing countries that lack waste management systems, dump their trash in rivers and it floats out to sea.

The root cause is poverty and corrupt, ineffective governments. It's not a problem that can be resolved by western consumer action or even western legislation.

<comment><USER8> Western governments absolutely can and do apply geopolitical pressure via measures like sanctions, tariffs and trade agreements.

Now whether they have the will to do it over plastic waste (or carbon emissions or democratic governance or human rights) is another matter, because they have only a few tools and there are thousands of priorities competing for those tools.

It also doesn't help with leverage when there is a powerful economic actor which doesn't require any of these terms as conditions of doing business: China.

<comment><USER9> Alternatively, instead of starving countries to death[0], maybe Western countries should stop sending their trash to third world countries[1][2].

Perhaps it's not the fault of those poorer countries, but instead the fault of the Enlightened West (those that care about carbon emissions, democratic governance and human rights) illegally dumping their garbage onto other countries[3]?

There's something funny about Western governments illegally dumping thousands of tonnes of trash on the shores of poor countries[4] and then people having the audacity to support _sanctions_ \- literally punishing the civilians there by starving them through siege - for not having the ability to properly manage the waste.

[0] [https://en.wikipedia.org/wiki/Sanctions_against_Iraq#Effects...](https://en.wikipedia.org/wiki/Sanctions_against_Iraq#Effects_on_the_Iraqi_people_during_sanctions)

[1] [https://www.dw.com/en/tired-of-being-trashed-philippines- rea...](https://www.dw.com/en/tired-of-being-trashed-philippines-ready-to- dump-garbage-in-canada/a-48829282)

[2] [http://www.atchuup.com/countries-used-as-dumping-grounds- of-...](http://www.atchuup.com/countries-used-as-dumping-grounds-of-worlds- trash/)

[3] [https://www.abc.net.au/news/2019-05-29/malaysia-to-send- tonn...](https://www.abc.net.au/news/2019-05-29/malaysia-to-send-tonnes-of- plastic-waste-back-to-foreign-nations/11159208)

[4] [http://collections.unu.edu/eserv/UNU:6349/PiP_Report.pdf](http://collections.unu.edu/eserv/UNU:6349/PiP_Report.pdf)

<comment><USER8> You're aware that Iraq and the Malaysia are different countries, right? Just checking.

<comment><USER9> I didn't realise sieges have different effects depending on which third world country they're applied to.

</comment></comment></comment></comment></comment><comment><USER10> I'm doubtful that humanity will be able to resolve its plastics crisis without a radical, 'immediate' shift in how nations manufacture and consume product. No matter what great research or innovations exist or are on the horizon, we've long passed the point of no return and are entrenched in our habits. There's been a profusion of news illustrating how egregious our situation is and I'm always left wondering how the future is going to look like and in what new ways people are going to adapt.

</comment><comment><AUTHOR> When I was in Vietnam earlier this year, I finally understood how much of a problem we have. The ocean water was totally clogged with plastic and trash. In North America, we are not seeing what a lot of the rest of the world is dealing with in terms of persistent waste, presumably because we have more land for landfills and money to move trash into them?

<comment><USER11> You can put a LOT of trash in a small space. The cost of landfills is usually measured in the cost to collect and transport garbage to the landfills. Also, once the landfill is full it is often still usable after the fact. Especially if it is managed properly. Add this to the fact that quarries are creating excellent places to consign garbage.

The big problem is the cost of energy to move and bury the garbage. A lot of third world countries don't have extensive waste management networks like the USA.

</comment></comment><comment><USER12> I think it's a decent bet that future generations will see our usage of plastic in the past few decades pretty similarly to the way we see the historical usage of asbestos during the middle part of the 20th century.

<comment><USER13> I really don't think so. Plastics are used because they solve a lot of very real problems for the amount they cost to produce. That's a good thing. The vast majority of the plastic waste in the environment comes from rivers that flow through countries that lack even basic sanitation/garbage collection facilities. A plastic bottle floating down a river has a real potential environmental impact. A plastic bottle sitting in a landfill isn't hurting anything, and isn't going to.

Investing in waste management infrastructure in these communities would essentially solve the plastics problem, even if nothing else was changed. It is, unfortunately, not very exciting to talk about civil waste management infrastructure as the way forward, but it doesn't stop it from being the correct answer.

<comment><USER6> They'll still exist for a long time, and will be eaten by birds, etc, even in a landfill.

Managing AND reducing waste needs to happen simultaneously.

<comment><USER13> Plastics that sit undisturbed in the earth are a form of carbon sequestration. That is not a bad thing, as long as they are concentrated in a known place.

Landfills are lot more complex than just dumping trash in a pit. [https://www.calrecycle.ca.gov/swfacilities/landfills/needfor...](https://www.calrecycle.ca.gov/swfacilities/landfills/needfor#EnsureSafety) may be of interest. The tl;dr is that properly-managed ones genuinely are a safe form of waste disposal.

<comment><USER14> Well-designed landfills are probably better than loading plastic into confetti cannons, but calling anything in a man-engineered landfill "undisturbed in the earth" is a stretch.

Plastics sitting in our landfills are, at human timescales, sitting relatively undisturbed on the outer skin of this world, where they are probably a teensy bit less "sequestered" than they were several thousand feet into the crust.

At anything approaching geologic time, some fraction of these feats of engineering will be graciously buried by layer upon layer of new sediments; others will be sundered by the same forces that flatten mountain-ranges and carve canyons, their contents re-circulated through nature's supply chains.

</comment></comment></comment></comment></comment><comment><USER15> We need biodegradable plastic bottles now, and an outright ban on styrofoam....

<comment><USER16> Biodegradability means nothing in the context of plastic that ends up in rivers, seas and oceans. Nothing. That is not a solution.

We need waste control, enforced harsh laws on producers (industries and tourists and waste manager companies), and we needed it a decade ago.

This is what happens when capitalism relies on an externality and that externality is fed up with its bullcrap.

</comment></comment><comment><USER17> Moral of the history: Nothing can substitute mother's lessons about what to eat and what not. Orphans of species that need a long period of weaning and teaching have more probabilities of die young.

<comment><USER18> You think the moral has nothing to do with our deliberate industries?

</comment></comment><comment><USER19> Natural selection. The ecosystem is evolving; if you can't stop yourself from eating plastic you'll be wiped.

<comment><USER20> Some species cannot stop eating plastic, other populations can but will still be seriously harmed.

Keep in mind that humans introduced plastic and have control over its presence.

Perhaps the term "ecosystem evolution" is too general in this case; ecosystem destruction might be more accurate.

It's up to us what is the next step. We can leave things up to natural selection, but do we want to?</comment></comment>


Query Combinators (2017) - <AUTHOR> https://arxiv.org/abs/1702.08409 <comment><AUTHOR> There is also a prototypical work-in-progress Julia implementation at [https://github.com/rbt-lang/DataKnots.jl](https://github.com/rbt- lang/DataKnots.jl) and more references at [https://querycombinators.org/](https://querycombinators.org/)</comment>


Raytracer on a business card in Rust, C++ and Go - <AUTHOR> https://mail.mozilla.org/pipermail/rust-dev/2013-September/005735.html

<comment><USER2> Not seeing the "business card" formatted versions for Rust or Go. Nicely minimized, but with generous whitespace across several pages.

<comment><AUTHOR> Rust one is here: [https://github.com/huonw/card- trace/blob/master/mini.rs](https://github.com/huonw/card- trace/blob/master/mini.rs)

The Go one is indeed not minified.</comment></comment>


SpaceX faces high bar at FCC in bid for rural-broadband funding - <AUTHOR> https://arstechnica.com/tech-policy/2020/06/fcc-has-serious-doubts-that-spacex-can-deliver-latencies-under-100ms/ <comment><USER2> FYI: it takes 1.9ms for light to travel 570km. Given a situation where people are just streaming Netflix, then the latency should not be an issue, but if you are browsing a webpage that hits 50 domains downloading random javascript stuff, it will result in a summation of a bunch of 1.9ms*2 events occurring. Plus, if people are at an angle to the satellite, or your data hops between satellites, then you will be seeing additional latency. It would seem like the optimal approach to web browsing for example would be similar to the Amazon Silk browser, where you communicate your "intent" of what you want to do like which webpage you want. Then it renders that webpage in a local instance on the cloud, then transmits the fully rendered webpage to you in a streaming sense. That process would likely cause additional latency before you start to see something show up, but the result is actually a shorter total time to get your page in front of you.

<comment><USER3> This is literally what happens all the time for all Internet users though wires and fiber on ground-based internet at similar or worse single-hop latencies.

The number of domains is irrelevant since most of those requests would be asynchronous. What matters is the number of hops that the traffic needs to take and the latency for each hop.

1.9ms times any reasonable number of hops would still produce a sub-100ms latency which is the FCC sites as their requirement, and would be a perfectly serviceable latency for nearly all Internet use (and is around what most users experience now).

It is unclear why the FCC would reference "the laws of physics" as a barrier for StarLink achieving this, as it is unclear how they would be causing any problems.

<comment><USER4> SpaceX does not have cross links. They will need to have several hops up and back to different satellites before the signal arrives in many cases. It's not unlikely that when processing time is included, it's well over 100ms.

<comment><USER3> If conventional geosynchronous satellite internet can achieve real world latencies between 600ms and 1200ms, can you describe a scenario where having a hop time that is 60x faster could not manage to improve this by a factor of 12x?

<comment><USER4> Think of it this way: traditional geo satellite has a theoretical latency of 430-500ms in most cases. That means there's AT LEAST another 100ms purely due to processing delay and other hops, but typically much more.

</comment></comment></comment></comment></comment><comment><USER5> Naked preference for big telecos on display.

<comment><USER6> I don't think the 100ms limit is to prevent competition, it's near the max recommended latency for VOIP and video conferencing. Both of these are important for rural Americans to have access to.</comment></comment>


Ask HN: How are you funding your open-source work? - <AUTHOR>

Is anyone here working on open-source full-time? How are you funding your work and what do you think a solution should be? We're trying to solve this problem and we would like to hear your thoughts. <comment><USER2> Well, I'm working on a startup that's based on Open Source software, but right now everything is funded out of my pocket, essentially. We plan to sell subscriptions to the official, certified, released version(s), ala Red Hat, as well as offering a SaaS based on our offerings, but we don't have paying customers just yet.

<comment><AUTHOR> Any particular services that you will be using for setting up subscription and distributing versions or are you rolling your own?

<comment><USER2> There's at least one open source project[1] out there, something I think Red Hat may actually be involved with (or behind) for managing subscriptions and what-not. We haven't deployed it yet, as we aren't yet at the point of having paying customers. And if we signed a deal tomorrow we'd just keep track of things manually in the interim.

As for distribution... we're working on our own homegrown "customer portal" which will deal with downloads and what-not, as well as other "customer stuff".

[1]: [http://www.candlepinproject.org/](http://www.candlepinproject.org/)</comment></comment></comment>


IOC Chief, Japanese Prime Minister agree to postpone Tokyo Olympics until 2021 - <AUTHOR> https://www.ktvb.com/article/news/health/coronavirus/ioc-chief-japan-prime-minister-agree-to-postpone-tokyo-olympics-until-2021/277-0ab982b1-cbaf-41fe-8774-ffb168d1566b <comment><USER2> I was a bit worried what decisions were going to be made, but Abe and the IOC Chief have made the right decision.

The games are about celebrating the health of every country - winning has a tremendous amount of pride to go with it for an entire nation and its prosperity.

Celebrating health during corona would cause the virus to spread faster and decrease the health of all participants and viewers.

Looking forward to an exciting 2021 games!</comment>


Crossrail: The End of the Beginning - <AUTHOR> http://www.londonreconnections.com/2015/crossrail-end-beginning/ <comment><USER2> As someone who has an amateur interest in how modern British railways operate I found this a thoroughly enjoyable read.

If you like this kinda thing then I can recommend RAIL Magazine [0], comes out fortnightly (I have a regular order at my local paper shop) and is packed with all sorts of technical goodies about rail ops in the UK and has nice pictures as well :)

[http://www.railmagazine.com/](http://www.railmagazine.com/)

<comment><USER3> Thanks for the tip, I love rail magazines. In Switzerland, I subscribe to Railway Update [0] which is produced every two months and covers Swiss rails news as well as Europe and Asia (as related to European trains). They have great analysis of new planning, recent accidents, and deep tech discussions.

[0] [http://www.minirex.ch/railway-update.aspx](http://www.minirex.ch/railway- update.aspx)

</comment></comment><comment><USER4> "...the problem of no toilet on board..."

That really surprised me. They're running a service from Reading out to Shenfield with no loo. I'd have thought things could get quite urgent under those circumstances.

<comment><USER5> London Overground doesn't have toilets on train. Better this than just dumping it straight onto the track like a lot of the existing stock still does. A vacuum system would be better but then they have to empty them. Less space to fit people in too.

<comment><USER6> The trains on South Eastern in from Kent all have vacuum toilets. It's only a few train lines that dump waste on to the tracks.

</comment></comment></comment><comment><USER7> Educational video of what the Crossrail project is with a lot of details on the Tunnel Boring Machines.

[http://www.crossrail.co.uk/construction/tunnelling/meet- our-...](http://www.crossrail.co.uk/construction/tunnelling/meet-our-giant- tunnelling-machines/)

</comment><comment><USER5> Nice article. I thought they missed a trick by not putting a Crossrail station at London City Airport. See how it goes in 3 years when it opens.

<comment><USER8> ...Or a spur to the West Anglia mainline at Liverpool St/Thameslink at Farringdon. With those in place there'd be the possibility of direct rail connectivity between all of Heathrow, Gatwick, Stansted & Luton airports. The link should already be possible to Southend via Stratford.

</comment></comment><comment><USER9> Thanks, that was a rather detailed read.</comment>


MRI Scans of a person's brain - <AUTHOR> https://github.com/dcunited001/mri-scans <comment><USER2> Just as a general note, these images contain personally identifying information about the patient. This includes not just the patient's name, but also his age, address, phone number, and an extremely limited health history.

<comment><USER3> For the record, i wanted to post my brain online for many reasons. First of all, I did it for science of course. It'd be great for more of this data to be available online for people to study and work with.

There are many anonymized datasets available on Radiopaedia. [http://radiopaedia.org/](http://radiopaedia.org/) I've spent quite a bit of time studying neurological conditions on that site and on wikipedia. Those are more useful, but anonymized. I'm not sure that there are many healthy MRI datasets available there, which makes it more difficult to statistically model MRI data.

I'm hoping to use Nipy and Dipy to create visualizations of my brain. In particular, i'm hoping to recreate some of the Diffusion Tensor Imaging work in this talk by Ariel Rokem: [https://www.youtube.com/watch?v=HdrSZtB0uX0](https://www.youtube.com/watch?v=HdrSZtB0uX0). However, i'm not sure if my DT images are high enough resolution.

I also did it for philosophical reasons. I believe that all digitized information past and present will be inevitably become known & public post facto. It's the nature of information & knowledge in our technological world to distribute itself until the world converges on total information omnipresence. So if, in the end, all [digitized] information is known, then there is no point in hiding information. This is more of a philosophical viewpoint though. obviously it's impossible for all information to converge in this way.

But this is true even of our own minds. And if our mind can be digitized from our brain, this also has interesting security implications. If you've seen the show stitchers, then you know what i mean.

Under no circumstances was this done in the name of art.

<comment><USER4> Thanks for posting your scans. As noted in the issue I just made (I am @maedoc on Github), if you add the diffusion imaging scans we can virtualize your brain [1] and simulate it [2].

What strength MRI was used for the scans? 1.5T, 3T, 7T?

[1] github.com/timpx/scripts [2] github.com/the-virtual-brain/tvb-library

<comment> david-given I doubt the scans really high enough resolution for a meaningful simulation --- I don't believe MRI scans can capture the complete connectome of a single human brain, let alone any subcellular data.

e.g. the Human Connectome Project is still working on capturing the macroscale connectome, that is, high level structures only. Recording the microscale connectome, that is, the neuronal structure, currently requires destroying the brain you're analysing.

<comment><USER5> You need at least 10 picometer percision to go down to the cell level (1.5L / 86 billion neurons), MRIs are 1mm according to the talk dcunit3d posted [0].

[0] [https://www.youtube.com/watch?v=HdrSZtB0uX0](https://www.youtube.com/watch?v=HdrSZtB0uX0)

<comment><USER6> 10 picoliter, perhaps? That's much less crazy than 10 picometer.

But you want the dendrites, too, not just cells.

</comment></comment></comment></comment></comment></comment><comment><USER7> Last year an MRI scan was made of my brain. This was part of a promotion research at the university. All I got back was a lousy screenshot of my brain. ;-)

No but seriously - it was a big shock to see my brain in a picture. It is a really weird experience, similar when I saw myself for the first time on film. Maybe this is a normal experience for the current generation, but I hadn't seen myself on film before age 15. You have an image how you move, what you look like, but then you see this on film, and it's a total shocker. All these small movements you make, typical for you, and everybody around you knows them, except for you. So everybody else sees nothing strange when viewing that movie, except you.

I've seen many MRI scans in films and tv series, although I can't remember one since then. From these I have a general picture of what a brain looks like. I've seen plastic 3D brain models. And then I see my brain and it's so different. It's clearly me, no doubt, but still... Totally weird!!!

<comment><USER8> How do you feel like a low resolution 3D scan of what someone told you was the inside of your head is clearly you? I'd feel like they could've shown me anyone's MRI and I'd have no way of telling them apart.

<comment><USER7> I got one screenshot with three images in it. The (vertical) profile image is clearly me. The other two (vertical frontal and horizontal at eye level) that could be anyone.

<comment><USER8> Ahh, that's super neat. I didn't realize MRIs included vertical profiles!

</comment></comment></comment></comment><comment><USER9> Back in December of 2007 a nodule was detected on my lung. Not knowing what to think of it, I posted the images on my blog and recruited other med students to comment on the images. Here's the Way Back Machine link to the blog post.

[https://web.archive.org/web/20081205032710/http://www.runfat...](https://web.archive.org/web/20081205032710/http://www.runfatboy.net/blog/2007/12/28/diagnose- me/)

<comment><USER10> So... what was it?

<comment><USER9> Ended up being benign. I probably went a bit overboard, but did a pet scan, nothing lit up. Continued to do CT scans to monitor for growth, and it stayed the same. I was just so scared..

</comment></comment></comment><comment><USER11> I've submitted a pull request: [https://github.com/dcunited001/mri- scans/pulls](https://github.com/dcunited001/mri-scans/pulls)

<comment><USER12> you're funny

</comment></comment><comment><USER13> Was wondering, in the general, is the patient always the copyright owner of this kind of data? Can they relicense it, publish it, do anything they want with it?

It seems like that would be the case, but there has been talk about patenting parts of the DNA in the past, and legally sometimes things work in un-expected ways.

<comment><USER14> You can't copyright such material at all.

[https://meta.wikimedia.org/wiki/Wikilegal/Copyright_of_X- ray...](https://meta.wikimedia.org/wiki/Wikilegal/Copyright_of_X-ray_Images)

<comment><USER15> I'm not sure how definitive that guide is. For what it's worth, all of my medical textbooks are very careful to include licensing and attribution information for all of their medical images, including X-rays. So even if there's not much case law on it, publishers seem to be erring on the side of caution.

</comment></comment></comment><comment><USER16> Anything cool one can do with this? I have my MRI + DTI and wonder what to do with it.

Mine looks like any brain to me (no tumors, yay!).

From the DTI I was able to calculate the white matter trajectory with slicer: [http://screencast.com/t/yDYFJdL7D](http://screencast.com/t/yDYFJdL7D)

(I was a little let down I couldn't follow the visual nerves, but as they cross there is no clear direction of diffusion that could be imaged I guess...)

<comment><USER17> Yes. For my girlfriend's birthday I grabbed her MRI scans, extracted the pial surface as a mesh, 3D printed the mesh, made a silicone mold from the print, and cast her brain in chocolate so she could eat her own brain, thus becoming an auto-zombie.

<comment><USER18> I did this too, but it took much longer than the simple steps you've outlined would seemingly take. I spent at least an hour on the whole process, which is more than I'd prefer for something so straightforward. I blame the early tech of 3d printing for not being as swift as I'd hoped.

<comment><USER19> Can I email you?

<comment><USER18> Yes but at your own risk.

<comment><USER19> Ok, but I would need your email :-)

I added my email to my profile.

</comment></comment></comment></comment></comment></comment><comment><USER20> Does he accept pull requests?

</comment><comment><USER21> if you are interested in viewing patient scans, with the PHI anonymized but the rest of the metadata in tact, check out:

[https://www.mypacs.net/mpv4/hss/casemanager](https://www.mypacs.net/mpv4/hss/casemanager)

(30,380 cases. 187,945 images.) its a teaching/sharing site for radiologists where they can send the data from their PACS. this is the free public site which includes a basic web based dicom viewer to zoom/pan/window level etc

<comment><USER22> Or The Cancer Imaging Archive: [http://www.cancerimagingarchive.net/](http://www.cancerimagingarchive.net/)

Though I'm not sure how much of it can be accessed without an account.

</comment></comment><comment><USER23> Here's the "Colin 27" brain atlas, derived from 27 scans of Colin J. Holmes's brain: [http://www.bic.mni.mcgill.ca/ServicesAtlases/Colin27](http://www.bic.mni.mcgill.ca/ServicesAtlases/Colin27)

Here's a lot of data from Russ Poldrack, who scanned his brain and collected behavioral and metabolic data from himself very regularly over the course of a year: [http://myconnectome.org/wp/data- sharing/](http://myconnectome.org/wp/data-sharing/)

</comment><comment><USER19> With a usual CT scan, you can do a volume rendering. Play around with the opacity to show the skin - this will give you a 3D image of your face.

Example on [http://1.bp.blogspot.com/-LuLV6F-Fp0o/TbcY7BUvpjI/AAAAAAAAAM...](http://1.bp.blogspot.com/-LuLV6F-Fp0o/TbcY7BUvpjI/AAAAAAAAAMo/4AEDNzhHqko/s320/Martin%2BFace5.jpg)

With a usual MRI, unfortunately slices are too far apart to be able to stitch them together like this.

<comment><USER24> All the MRI scans of my brain I've gotten ("structural" scans taken as part of fMRI studies) have been sufficient to get a pretty decent volume rendering of my head. Some of them cut off about half way through the jaw, but that's another issue entirely.

<comment><USER25> The research scans use 3D T1 (and sometimes T2) scans for mapping the functional data onto a brain region. They have to put the low res functional scans onto something that has some degree of resolution. Functional scans are acquired with temporal resolution in mind - the change in signal after activation is something like 6 seconds, and to accurately sample this you want a whole brain acquisition in much less time that that. We typically acquire whole brain scans in something like 3 seconds. New techniques such as multiband imaging are acquiring whole brain scans in 600-700ms or so. This is pretty fast. Clinical scans do use 3D techniques but tend to go for 2D scans (which are a slice, then a gap of 10-30% usually of slice thickness) then a slice. 2D scans are faster, have much better in plane resolution (lower through plane as slices are fatter and have gaps between them). Clinical scans are also one hell of a lot better for identifying things as numerous image weighting a are obtained. MR tech here.

<comment><USER19> What's the resolution of the whole brain scan you deal with in research these day? (ie the cubic size of the voxel when acquired in 3s? What about in 600ms)

I have to keep myself up-to-date, it's been a while since I played with that!

<comment><USER25> I'll check - I'd guess 3mm slices but with 600ms acquisitions I haven't looked recently enough to remember.

</comment></comment></comment></comment></comment><comment><USER26> Can I just get an MRI or CT scan without a medical reason and at a reasonable cost? Perhaps that is an absurd question in the United States.

<comment><USER27> Find a study and sign up.

Look for research groups in your local institutions and send an email to a couple grad students asking about studies they or their colleagues are running in the near future. A researcher may give you the scan if you ask nicely and say it's for personal interest, plus you'll be compensated for your time /and/ simultaneously help with the advancement science :)

</comment></comment><comment><USER28> I 3D printed mine (skull is from another project) [http://i.imgur.com/BelYUSih.jpg](http://i.imgur.com/BelYUSih.jpg) and a Blender render: [http://i.imgur.com/oPCSxbo.png](http://i.imgur.com/oPCSxbo.png)

<comment><USER29> Beautiful! What did you use to turn the DICOM/NIFTI files in to a printable format?

</comment></comment><comment><USER30> Hey David, I didn't expect to see your brain on the front page of HN. Can you confirm for us that everything is OK? :-)

<comment><USER3> hey! yeh, everything's going well. the scans were done to help diagnose some things about 2 years ago. nothing turned up in the scans though.

thanks for tweeting me earlier. i had no idea this was on Hacker News! lol

</comment></comment><comment><USER31> Here's a normal brain MRI, compared to photographed slices of a brain:

[https://www.claripacs.com/a.php?a=vw](https://www.claripacs.com/a.php?a=vw) (Click and drag to scroll. "Share > Download" to download the full DICOM data)

This site has thousands of anonymized MRI and CT images of normal and abnormal scans, with clinical history in many cases. For example, here are annotated scans of normal anatomy:

[https://www.claripacs.com/folder/Anatomy&view_type=-1](https://www.claripacs.com/folder/Anatomy&view_type=-1)

One challenge with writing web DICOM viewers is the lack of full 16 bit image support in browsers. This requires writing custom code to properly handle window / level on 16-bit medical images.

Disclosure: I'm a co-founder of Claripacs.

</comment><comment><USER32> Wow. This goes to show that people in field X often have no idea what's interesting to people outside their own field. My PhD is in cognitive neuroscience, and so I have many dozens of MRIs of myself done for test or calibration purposes, done using all sorts of different machines, sequences, etc. It's never occurred to me that these images would be of interest to anyone but me.

I suspect this is true for pretty much anyone in any field.

</comment><comment><USER33> If we get better at this and are able to create detailed enough scans of a brain maybe we can recreate that person some day, including memories and everything.

<comment><USER34> As kendallpark pointed out this is highly unlikely. I upvoted you because it's an interesting "hmmm... what would it take?" The spatial resolution of an fMRI is high compared to something like EEG, but the time resolution is much slower. Even the spatial resolution is on the order of cubic mm at the absolute best. So, we are no where close to being able to scan the activity of every one of the ~100 billion neurons in a human brain and it would 7+ orders of magnitude better spatial recognition and associated processing capabilities. So, the magnitude of your "if" is large enough that it's on the level of Kurzweil Crazy, but cool to think about regardless.

<comment><USER35> I know nothing about neurology, but my money would be on using a different mechanism to extract all the data, maybe something like fake neurons that dump all the information via WiFi, a MITM attack for the brain itself if you like.

<comment><USER36> With our current understanding, such tasks are comparable to trying to break the speed of light. However, we could probably get incredibly close without _any_ internal data extraction. Our personalities, while highly complex, can be approximated into some simple traits (e.g. 74% 'passive-aggressive', 47% empathy) and replication of our knowledge isn't strictly necessary. Find 10,000 interests and create a int_8 each of how much someone is into it. Et cetra.

</comment></comment></comment></comment><comment><USER37> Anyone recommend a viewer?

<comment><USER38> OsiriX [1] is a popular OS X/iOS viewer.

3DSlicer [2] is an excellent alternative that runs on Win/OS X/Linux

[1] [http://www.osirix-viewer.com](http://www.osirix-viewer.com)

[2] [http://www.slicer.org](http://www.slicer.org)

<comment><USER39> On OS X, Horos is a free and open derivative of OsiriX.

[http://www.horosproject.org](http://www.horosproject.org)

</comment></comment></comment><comment><USER40> Not quiet the same, of course, but this reminds me of the game Soma, where [SPOILERS!] the protagonist has his brain fully mapped and "uploaded" as part of a treatment, and his scans are used as a template for A.I. research in the future, leading him to be "reborn" a century later in a different body.

</comment><comment><USER41> The McGill university also offers these MRI simulations, if you need brain scans for your visualisation/processing needs. [http://brainweb.bic.mni.mcgill.ca/brainweb/](http://brainweb.bic.mni.mcgill.ca/brainweb/)

Very useful when I did my undergraduate thesis.

</comment><comment><USER42> This couldn't come at a more opportune time. I'm studying neuro this block and need me some brains to look at.

<comment><USER43> I have a set of slices which weren't so healthy. Let me know if sharing them would be of value to you.

[https://d0921abcf0d58ba49c3d-6ecbc7565855acc8ab3c742259f09fe...](https://d0921abcf0d58ba49c3d-6ecbc7565855acc8ab3c742259f09feb.ssl.cf1.rackcdn.com/slice.jpg)

</comment></comment><comment><USER44> For those who are interested, the human connectome project is in the process of acquiring and publicly releasing a database of 1000+ MRIs collected from normal subjects:

[http://www.humanconnectome.org/data/](http://www.humanconnectome.org/data/)

<comment><USER45> For those who are interested, this Nature table summarizes the MRI datasets that are out there:

[http://www.nature.com/neuro/journal/v17/n11/fig_tab/nn.3818_...](http://www.nature.com/neuro/journal/v17/n11/fig_tab/nn.3818_T1.html)

</comment></comment><comment><USER46> This is nice. It would be even nicer if this was complemented by medical records, and genotyping data (e.g. from 23andme).

</comment><comment><USER47> I'm starting to be afraid that one day my brain, memories will be pushed on git :<

</comment><comment><USER48> Now to apply compressive sensing!</comment>


Show HN: I have created an open-source version of YouTube Red - <AUTHOR> http://imshyam.github.io/mintube/ <comment><USER2> Note that the title refers to YouTube PIP on Android. (YouTube Red encompasses a large number of features and the HN title is ambiguous)

<comment><AUTHOR> The main feature is that you can play videos in parallel with other applications. YouTube Red facilitates it.

One addition thing is that you can see the video in minimized window, not in YouTube Red.

</comment></comment><comment><USER3> Have you considered submitting it to F-Droid? ([https://f-droid.org/](https://f-droid.org/)) Could be a nice addition to the FOSS listings there.

<comment><AUTHOR> Thanks for the suggestion, I have submitted now.

</comment></comment><comment><USER4> I'm guessing the APK on Google Drive is because this does not comply with Youtube terms ?

<comment><AUTHOR> Yo! You guessed right man.

On a serious note: My app was rejected because it plays YouTube videos in the background, but there are others apps like ListenOnRepeat([https://play.google.com/store/apps/details?id=com.youtuberep...](https://play.google.com/store/apps/details?id=com.youtuberepeatfree)) which also do the same.

I did not get any satisfactory reply from google about what in particular I was doing wrong, even after asking multiple times.

<comment><USER5> this is not the first time when I read this... what's happening with google lately?

</comment></comment></comment><comment><USER6> I love your product page!</comment>


Amazon to open brick-and-mortar retail shops in Britain? - <AUTHOR> http://www.techflash.com/seattle/2009/12/amazon_to_open_brick_and_mortar_shops_in_britain.html

<comment><USER2> I'm curious as to why the might be testing this new channel in the UK rather than the US. Does anyone have any sense of the market share of Amazon in Britain?

<comment><USER3> Amazon.co.uk has for some time offered _same-day_ evening delivery in urban areas of Greater London (within the M25) and Birmingham--check details on their website--for a fairly-stiff minimum charge. Local Amazon.shops could share whatever infrastructure supports same-day delivery. FWIW, it seems that in the UK local courier service (using motorcycles) is comparatively cheaper than in most major US cities, and much more commmonly used even outside the legal and financial businesses, so this idea could be expanded.

Other possibly-relevant factors:

Logistics of delivery in the US are, on average, much simpler; both UPS and FedEx are superlative (and Amazon uses such private carriers exclusively in much of the US), and there are good contract-delivery options for heavier and bulkier objects. Delivery locations (both residences and offices) are mostly accessible, being on average much newer. It's common for customers to take delivery at workplaces, and use their cars to take objects home.

By contrast, in the UK all the logistics companies are marginal performers, partly because the government's subsidized Royal Mail continues to command some usage. Much of the housing stock is vastly older and less easily accessible than in the US; heavy and oversize deliveries are often very much a matter of engineering custom solutions. Homes and offices can be far apart, and the customers often commute between by train, so there's no convenient auto to carry things home that were delivered to the office.

All these considerations may have contributed to the model of ordering over the web and picking up from a physical location. This model has been very rare in the U.S., but is not so uncommon in the U.K.

<comment><USER2> Thanks rg. That's very insightful info.

</comment></comment></comment><comment><USER4> Yes, Amazon is behaving surprisingly. First surprise was Kindle, second - vegetables. Now - this.</comment>


In the last 12 years I have never got a job thanks to my CV - <AUTHOR> https://medium.com/@kemyd/in-the-last-12-years-i-have-never-got-a-job-thanks-to-my-cv-255213bbbf0e <comment><USER2> Ok, so this guy created something, which was interesting enough for his first employer. After that, he bootstrapped himself to the next jobs. This has nothing to do with "start creating", but rather with "having luck". It is the same as having luck to have the right relationships. Or having the luck to have a CV interesting enough for an employer. And it that regard, it makes more sense to concentrate on a strong CV, starting with a useful degree.

<comment><AUTHOR> As stated in the first paragraph, the article is for the beginners. From my observation, they focus too much on adding next courses from Udemy, or making a clone of app X, instead of trying to build something more significant that they will want to use. I recruited dozens of novice developers and after seeing the same cv, with the same examples, I know that they have little or no chance to stand out.

<comment><USER3> You still don't get it. The guy was lucky enough to build an app for his country that got extreme attention back in 2007 and sold it for 154 millions. Trust me, from that day there's not a single company that would be skeptical about him cause everyone's think he's sone kind of a Rock Star Dev for what he did.

<comment><AUTHOR> I wrote this article :) I sold to Gadu-Gadu my bot for pennies. Owners of Gadu-Gadu sold the network to the Naspers. It did not have anything common with my small project (my bot had ~2k users when I sold it. Gadu-Gadu network had 11 million). They did not even buy it from me to work on it further. I edited the original paragraph because it could be misunderstood.

</comment></comment></comment></comment><comment><AUTHOR> very intense discussion on reddit: [https://www.reddit.com/r/webdev/comments/aujsme/in_the_last_...](https://www.reddit.com/r/webdev/comments/aujsme/in_the_last_12_years_i_have_never_got_a_job/)</comment>


Live Demonstration of Mind Control of a Computer using Non-Invasive Technology - <AUTHOR> http://fora.tv/2008/12/12/Tan_Le_Brings_the_Force_to_Life_with_Mind_Control_Device

<comment><AUTHOR> The video is highly impressive. Within a few minutes the test subject learns to rotate a cube on screen and also make it disappear.

More info is available on the official website:

<http://emotiv.com/></comment>


How I got to #7 in the App Store (Including Analytics) - <AUTHOR> http://www.andrewgarrison.com/Blog/tabid/62/EntryId/19/How-I-got-to-7-in-the-App-Store.aspx

<comment><USER2> In your case, you had a user base you could pry at to push the next app up the chart. Knowing the method by which you did this is valuable for developers in a similar position, for sure. However, what advice might you give to developers that haven't yet reached that point? How did you establish the base that allowed you to do this?

</comment><comment><AUTHOR> I posted this story earlier, but I decided to make it into a blog post since I reached #7, and I also included some analytics on the bulk e-mail that I sent out.

</comment><comment><USER3> Would be helpful to know how you got BridgeBasher up in numbers as well, as this success seems predicated on the prior success.</comment>


Ask HN: How to recover disabled Facebook account? - <AUTHOR> My Facebook account, about 15 years old (yes, going back to the days when it was only open to those at certain colleges), was disabled without warning:

  Your Account Has Been Disabled
  For more information, or if you think your account was disabled by mistake, please visit the Help Center.
 The Help Center link says:

  Why was my account disabled?
  Your account was disabled for violating Facebook's Terms. Please review our Community Standards to learn more about what's allowed on Facebook. If you think your account was disabled by mistake, please let us know.
 The "let us know" link gives me a form which I've filled out with a copy of my driver's license (and heard nothing back), but based on what it says I don't think it's the right outlet. When searching for "disabled" in Help Center, I see

  Why is my account disabled?
  We disable Facebook accounts that don't follow the Facebook Terms.
  Some examples include:
  Posting content that doesn't follow the Facebook Terms.
  Using a fake name.
  Impersonating someone.
  Continuing behavior that's not allowed on Facebook by violating our Community Standards.
  Contacting other people for the purpose of harassment, advertising, promoting, or other conduct that's not allowed.
 I do not believe any of the above applies to me:
* I almost never posted anything to Facebook, much less anything political/controversial. Mostly I used the account to sometimes "Like" companies when required to enter giveaways.
* I used my real name.
* I never contacted anyone other than a couple of times to request customer support for non Facebook-related products.
I do not know anyone who works at Facebook. Suggestions? <comment> n-gauge if it's just been used for give aways create a new one?

<comment><AUTHOR> Two reasons:

* My account is very old and I'd like to have it back for that reason alone.

* I don't know what would happen if I created another one without first finding out why my previous one was disabled.</comment></comment>


The Federal Reserve is usurping authority and here's why you should care - <AUTHOR> https://www.dallasnews.com/opinion/commentary/2019/06/08/the-federal-reserve-is-usurping-authority-and-heres-why-you-should-care <comment><USER2> Strange how someone in the field doesn't seem to know how the Fed works. It's a consortium of 12 PRIVATE banks, given limited oversight in exchange for the power to "print" money out of thin air.

This "warning" about usurping authority comes about 80 years too late, and is far too vague, though Henry Ford wasn't much more explicit, "It is well that the people of the nation do not understand our banking and monetary system, for if they did, I believe there would be a revolution before tomorrow morning."</comment>


Ask HN: Does a IRC or equivalent chat exist for HNers? - <AUTHOR> <comment><AUTHOR> Holy smokes, I found [https://www.hnchat.com/](https://www.hnchat.com/)

check it out.

<comment><USER2> Just did. I think OP wants a civilized discussion channel :)

</comment></comment><comment><USER3> Yeah, great community for HN folks is ##passiveincome on Freenode

[http://webchat.freenode.net/?channels=%23%23passiveincome&ui...](http://webchat.freenode.net/?channels=%23%23passiveincome&uio=MTY9dHJ1ZSYyPXRydWUac)

</comment><comment><USER4> #startups on Freenode IRC. There's also a #hn channel, but there's never anybody in there.

</comment><comment><USER5> Are you referring to a channel? Or the client itself? I believe the channel startups on freenode has catered to the HN community (and was where I heard about it initially), however I'm not sure how many users it has online typically.

<comment><AUTHOR> channel or area where people chat in a chatroom type deal. I'll take a look at startup, thanks!</comment></comment>


Apple fanatic returns Surface,blogs about it,gets burnt in comments. As usual. - <AUTHOR> http://www.zdnet.com/reboxing-why-im-returning-my-microsoft-surface-7000007937/

<comment><USER2> Lol the comments are full of the worst fanboys I think , anyone denying that surface is a turd hasn't actually used one.</comment>


Apple WWDC keynote event, What did you like the most? - <AUTHOR> <comment><USER2> I liked the watchOS 3 improvements the best. It was clear that Apple had listened to the complaints from Apple Watch owners and took meaningful measures to attempt to rectify them. For example, entirely changing the function of the side button.

<comment><AUTHOR> Oh great</comment></comment>


Viral load&contact network predict SARS-CoV-2 transmission&superspreading events [pdf] - <AUTHOR> https://www.medrxiv.org/content/10.1101/2020.08.07.20169920v1.full.pdf <comment><AUTHOR> full title: Wrong person, place and time: viral load and contact network structure predict 5 SARS-CoV-2 transmission and super-spreading events</comment>