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I don't get it. Where is the spec? What's the aperture size, focal length of this beast? What about abberations? How does it work in extreme conditions that a normal lens can't take a good image?
So many questions, but the author decided to use it to take portraits in day light ...
I guess different people have different definition for "significant circumstantial evidence". We have two hypotheses: 1. virus come from seafood market, 2. virus come from lab. Let's see the likelihood for each with each piece of data.
Data 1: Index case. There is no evidence that the "index case" is ever found. The earliest patient backtracked to Nov 2019 [1], but there is no report that patient is related to the lab / market.
Data 2: close lab location to seafood market. This alone does not favor one hypothesis to other, since distance is communicative. The virus can start from either location to the other.
Data 3: Bat species. Not sure what aspect of the bat species is related, but one thing to note is the virus sample found in bat is only 96% similar to the virus sample. At 30k genome and an mutation rate of 1bp per two weeks, these samples have at least 20 years worth of evolution time. Unlikely to relate to either lab / seafood market. Some researchers believe there might be intermediate hosts, but there doesn't seem to be evidence what that intermediate host might be.
Data 4. Earlier lab accident. I think the lab accident data is actually not favoring the lab hypothesis. If you think in a Bayesian way, earlier lab leaks are quickly identified and controlled. Given that this time it is not, your belief for lab leak should decrease a bit. Anyway, for the lab data what's important is the likelihood of (the lab, without genetic engineering, get a hold of this virus, and leaked) vs. (an intermediate host). Having worked in Biohazard level 2/3 labs, I think a leak from level 4 lab will be more unlikely compared to intermediate host, but we don't have any good estimate of the two likelihood yet.
So I think people can have strong beliefs about where the virus come from (since everyone's prior is different), but from the data the likelihood really doesn't strongly favor any of the two hypotheses.
[1] https://www.scmp.com/news/china/society/article/3074991/coro...
Being someone who is still struggling with these concepts, I like this tutorial because at least it doesn't use the common list/maybe/state to illustrate the concepts. Somehow I feel these concepts are so abstract - unless one is well versed in category theory, maybe only a data approach can prevent people from overfitting these concepts to specific examples.
I would really hope to see a tutorial that have a diverse set of examples and just fmap each example with a light explanation of say, what is a monad in this code and what is not, and because it's a monad we can do this.
Essentially the tutorial can just train a classifier in one's head, and with a nice set of examples maybe the brain can learn a general representation of concepts for the classifier ...
Digging down a bit this method is published in Nature! https://www.nature.com/articles/nature21054
I am still pretty new learning haskell, and find this very helpful. Not necessarily agree whether monadic solution is prefered, but I like how it shows how general monad is: it's hard to grasp the generalization from only a couple of simple examples normally found in any haskell book. I hope there are more articles like this showing how these abstract concepts can be applied in somewhat unexpected places.
Just to clarify, these MRI images looks at white matter, which are mostly bundles of long-range myelinated axons. This is more likely a map of major highways in the brain (Or really the map of bundles of major highways in the brain, as MRI only has millimeter resolution).
The connectome at the cellular level is massive. I think part of the mouse visual cortical connectome has been mapped out, and the data is on the order of tens of terabytes.
I think it really depends on the task. We are just hijacking the brain machinery to do jobs it is not evolved to deal with. If we can find some highly evolved / optimized brain function which reflects the structure of the new job, the brain can process it much more efficiently.
Text might be good for programming because, most of the programming are sequential, text is sequential, and text processing is highly optimized in the brain because language. But in fact, we also use a bit of visual programming (indentation, paragraphs) in text to reflect part of the program structure that are not sequential.
If the program is completely non-sequential, visual tools which reflects the structure of the program are going to be much better than text. For example, if you are designing a electronic circuit, you draw a circuit diagram. Describing a electronic circuit purely in text is not going to be very helpful.
I have thought about the same thing when playing with deep learning at home, until I put electricity bills into the equation. A beast like what the author has built, with full power, will cost $0.1/hour in electricity bill alone at where I live ... which almost equal to the cost of a p2.xlarge spot instance on Amazon.
So if someone else is paying for the electricity, build your own gig, otherwise, Amazon is pretty hard to beat.
I spent almost equal amount of my life in China / North America now, it's interesting to see and compare what one side think about the other.
In north America, what I get is China is controlled by dictators. Life sucks over there because there is no freedom, a lot of corruption, no respect to rules, and is generally a chaotic mess. While in north America, democracy and freedom is wonderful, and everyone has the power to make changes of society, and in the end the government has to care about people to stay in power.
In China, what I get is the US (Canada is usually less discussed) is controlled by a corrupt and impotent government. Life sucks over there because the majority of the people, who actually has the power, believes the doublespeak of the government and are easily manipulated by the wealth. While in China, people make jokes about the official media, and if the government decides to do something people don't like, people simply ignore it and with enough people (there is always enough people in China) doing this, the government has to care about people's opinion to do anything.
I'm a little worried about the situation of both sides now. They seem to go towards what the opposite believes: on one hand in China, the government starts to get better at propaganda and manipulating public opinion; on the other hand in the States ... there is enough happening in the past year.
One question to ask is then: what method is useful to figure out the function of an unknown microprocessor? The engineers sometimes reverse engineer chips with electron microscope, with a perfect understanding of electronic principle, a rudimentary idea of how the microprocessor works, what the high-level function of the chip is, etc. And even equipped with this knowledge cracking a chip still takes a lot of effort, and may not succeed at all.
The tools of a neuroscientist would be laughable in comparison, yet neuroscientists have cracked the auditory code (and you can have an artificial cochlea now), the lower visual system is close to be cracked, we have a pretty good understanding of hippocampus and how space is represented in the brain, and many other accomplishment. These are all results from the laughable tools we have for investigating brain.
Having a full connectome of the brain, and running the brain in a simulator will be a huge step of understanding it. We don't know how to use the data now simply because we don't have the data yet, and therefore no effort is putting on interpreting the data. However the usefulness can be glimpsed from neural structures where the connections are clear: the peripheral system, spinal cord, midbrain, and lower sensory and motor regions in the brain. We understand them far better than regions we don't even know where it connects: claustrum comes into mind.
A simulator of the brain, I imagine, will be similar to the human genome project: nobody will understand the whole thing quickly, but it will hugely prop neuroscience forward, sometimes in ways we cannot imagine now.
I still fail to see why having a single neuron that circumnavigates the brain should lead to consciousness.
The number of neurons you can trace is limited by the methods: traditionally you can only stain a couple of neurons at most, otherwise the mangling axons and dendrites just make it impossible to differentiate between the neurons. Although now there are better methods (e.g. "brainbow" for barcoding neurons, "CLARITY" for whole-brain imaging).
In this case although it's just a couple sample neurons from claustrum, but given that same type of neurons in the brain tend to cluster together (which is how we differentiate brain regions anyway), it is likely that there are many, many more neurons like this in claustrum.
Claustrum has long been hypothesized to play a role in conciousness, however we know so little about this region (it was only a couple years ago people first recorded neural activity from that region!). This provides support about it because consciousness contains information about just all sensor/motor/cognition, therefore a neuron that computes consciousness should have access to a large amount of brain regions. So finding this neuron which basically connects the whole brain is supportive to this hypothesis. What's more supportive is the fact we have never seen neurons like this anywhere else: so even if it's just a couple of neurons in the claustrum, the potential bayes factor favouring their role in consciousness can be quite high.
"all you have to do is read the methods section in the paper and follow the instructions."
I wish science was that simple. The methods section only contains variables the authors think worth controlling, and in reality you never know, and the authors never know.
Secondly, I wish people say: "I replicated the methods and got a solid negative result" instead of "I can't replicate this experiment". Because most of the time, when you are doing an experiment you never done, you just fuck it up.
Here is an example: we are studying memory using mice. Mice don't remember that well if they are anxious. Here are variables we have to take care of to keep the mice happy, but they are never going to go to the methods section:
Make sure the animal facility haven't cleaned their cages.
But make sure the cage is otherwise relative clean.
Make sure they don't fight each other.
Make sure the (usually false) fire alarm hasn't sound for 24 hours.
Make sure the guy who was installing microscope upstairs has finished producing noise.
Make sure there is no irrelevant people talking/laughing loudly outside the behaviour space.
Make sure the finicky equipment works.
Make sure the animals love you.
The list can go on.
Because if one of this happens, the animals are anxious, then they don't remember, and you got a negative result which have nothing to do with your experiment (although you may not notice this). That's why if your lab just start to do something you haven't done for years, you fail. And replicating other people's experiment is hard.
A positive control would help to make sure your negative result is real, but for some experiments a good positive control can be a luxury.
I have a wild theory about longevity. For all the mammals, increased heart rate correlates with lower life span [0]. But heart rate itself may just be a measurement, and what's important is rate of metabolism - higher metabolism, higher heart rate, lower life span. And vice versa.
If we very boldly assume these relationships are causal and predictive to individuals, it follows lower energy consumption -> longer life. Caloric restriction might be one way to do this.
However, with lower metabolism, stuff in the body tend to get older. This will make them fail more easily, then you are likely to get sick, which will shorten your life. Therefore the optimal strategy to maximum life expectancy seems to be control metabolism (by exercise / calory intake etc.) to a certain point where you are just unlikely to get sick, then stop.
[0] biology.stackexchange.com/questions/20489/is-there-any-relationship-between-heartbeat-rate-and-life-span-of-an-animalH
For many assertions, my quick filter is: what the _direct_ experimental design would look like to draw such conclusions? Is it possible that it is done somewhere?
Biology is so complex and we know so little about it, so that the normal logic often fails on it: if A -> B, B -> C, you need to do experiments to show A -> C. Error accumulates, other unknowns come in.
If you have to draw conclusion by logic in biology, look at the errorbars and sample size. Then stretch the errorbars by the square root of the sample size. Then stretch it again 3 times. If the conclusion still look obvious to you, proceed with caution.
Maybe I should explain better. I think with a project receiving so much opposition both from the government and citizens, I imagine Liu would have to use extraordinary methods to push it forward, which will hurt the power players hard (especially those who created the opposing voice).
The embezzlement and favours are usually just an excuse. Most people with power (at least in China) get away with it even if everyone knows about it. It only become important when someone who don't like you gets more power.
It's a very fine line to walk though: too much profit, people hate it (it's taking money from them), run at a loss, people hate it (it's a waste of tax money).
But since it is built and owned by the government, the economical benefit is also in the equation other than operation profit. A province would welcome the bullet train network even if it has to pay, if it attracts a capital influx to the province. This is usually true: given the convenience of taking the train, a city get connected means you are almost merged into a mega city, economically.
Right now the bullet train is for passenger, but cargo trains are on the timeline now. Think of goods shipped by train but arrive almost as fast as by air: this alone would make the investment a bit worthier than it looks.
The ticket price of the bullet train is usually cheaper than by air (and the train is much, much more convenient than flights), and affortable by average citizens. This has created a huge headache to the airlines, which leads to some healthy competition.
As someone who grew in China, I just want to point out that besides the engineering, it is a political wonder as well.
Before the bullet train network was built, China already has an extensive railway network. The first step is to run the bullet train on traditional railways, with a peak speed of 180km/h. People think it's great. No objections.
But to raise the speed further, a new network has to be built, almost side-by-side to traditional railways. This idea horrored almost everybody: new railways for 10s of thousands kms, new train stations (yes for most of the place it needs a separate station), just to be a bit faster? Of course it got a constant concensual blaze from both the citizens and all the official and unofficial media. This state maintains from the beginning of the project all the way a couple years into it operates (which spans many years).
The head of the project, Liu Zhijun had to be in jail of course. Then everything changes, 180 degree. People took the trains and realize it's great. What I (and probably many people too) didn't know before was with the speed increase, the perception of distance changes. The trains run like buses: between major cities, the train runs across in every several minutes. You can just get a ticket, go to the station, an hour later you are on the subway of another city. Many times when we were holding a meeting, people from other cities actually took less time to arrive than someone driving from suburbs.
Then the new railway network gets praise from both national and overseas. The official media shut up about Liu. People start to think Liu as a fanatically great engineer.
I cannot imagine how this project can even get started, and what Liu had to do to get this to work. There must have been a great story, but we may never know.
I think you nailed an important point here: text is great in representing code because text is sequential, and so does a lot of the code we write, and the computer it runs on. Maybe if the code have different structure, some 'visual' code would serve better. Idk, maybe coding for an FPGA/GPU would benefit from a visual representation?
If anyone like me thinks all these visualizations just make the code more confusing, I want to point out it might be a personal (although it can be very strong) preference.
I always firmly believe programs are universally clearer represented as line of texts with indentations, and any attempt to visualize it doesn't help except for simple toy programs. Then I met some architects who design buildings with code using Grasshopper 3D (which is a graphical functional language for 3D modelling) [0]. Those people can easily navigate a messy web of connected lines for their hugely complex models, yet finding a block of text confusing and unintuitive. I am sure some of the more visual-inclined haskellers will find Glance very useful.
Just to be pedantic:
Each of the data points is an average of many users (and potentially have huge errorbars they do not show). Therefore for single individuals, this result may not be very predictive.
In fact, given that the distribution of number of posts likely follows power law, the average is hugely influenced by a small number of heavy facebook users. I would say this figure summaries more about top facebook users rather than the majority of users. So when "you" (an average facebook user) fall in love, this is probably not what facebook sees.
There are a number of window managers for wayland already. The ArchWiki has a list of them [0].
[0] https://wiki.archlinux.org/index.php/Wayland#Window_managers...
I thought about remapping at the beginning. But with vim you almost need to remap the whole keyboard to get hjkl in the usual place ... so I didn't. The hjkl on dvorak is at jcvp on qwerty, they are not too uncomfortable.
but now I realize it might be a benefit: I don't use hjkl as much as before, but mostly rely on other ways to jump, so this disadvantage actually makes using vim a bit more efficient.
I have switched to programmer's dvorak (dvp) for a year now. I have recorded the amount of typing vs speed. Right after the switch, the speed increase is logarithmic to amount of typing: I will need to practice twice the amount of typing to get a constant speed increase.
But now, I definitely feels much better than qwerty if typing English / code regularly. I no longer feel the strain on my fingers after long periods of typing. I also got a ~15% speed improvement, but that might just because I can touch type better. However there are several things that come up surprising me after the switch:
1. I forget all about qwerty. Every time I type on someone else's computer, I will have to look at the keyboard and picking letters one by one. It doesn't seem to get better over time.
2. Passwords are annoying to type even after I am generally comfortable with dvp after 3 months.
3. Shortcut keys are even more resilient to change. It takes very long to get comfortable with vim again (about 6 months), but now after a year of dvp I still have trouble Ctrl+c and Ctrl+v.
4. I spent a lot of time changing keyboard settings in games.
So for most of people who don't specifically focus on typing long paragraphs of english texts but press keys mostly as short cuts, I can see there is not sufficient reason to switch, if everything is designed around qwerty.
I always wonder why some disciplines are more open than others [0]. As someone in biology: the state of publication is very sad. We have to pay thousands to _submit_ a manuscript, and then:
- get rejected right away, or
- the manuscript gets distributed to fellow scientists (who reviews for free). The reviews get collected and manuscript rejected, or
- we get a chance to address the reviewer's concern, resubmit and gets rejected, or
- The editor does some proof-reading and publish the paper behide a paywall. I lose all the rights and I may need to ask the journal for permission to use part of it in my thesis, otherwise I risk plagirizing my own writing.
Sometimes when I read preprints in computer science/physics/bioinformatics etc. I feel in those disciplines researchers are a big happy family, and we biologists are locked in a prisoner's dilemma because we can't communicate. Then we fight each other and the publication companies are selling tickets for others to watch.
[0] http://www.idea.org/blog/2011/04/04/fees-and-other-business-...
Most of the neurogenesis studies are done in rodents. So the conclusion should be carefully taken, especially for something like neurogenesis which varies wildly across species.
Running promotes neurogenesis, but also enriched environment, many drugs, genetics etc. Its effect in mice is also somewhat elusive at this point: we aren't sure whether it promotes learning or forgetting.
That being said, all the nice cognitive effects from exercise probably all due to endorphins (literally endogenous morphine) released in the brain afterwards.
There is also the roboroach if you want to have a taste of this on your hand, with a cockroach. https://backyardbrains.com/products/roboroach
In the neuroscience conference they also showed a DIY optogenetic fruitfly kit, but it's a pity the channelrhodopsin transgenic fly is not available outside a neuroscience lab.
I might have missed something, but if they only have 2 features, simply plotting the data out will make any trend very clear.
It is great to see Unicode being able to encode almost every symbol people can think of, however I am still struggling to make them appear on my screen - is there a good font that has great coverage for unicode? Many times there are clever use of unicode yet I can only see empty rectangles.
Just to clarify, optically recording neural activity from ensamble of cells ("thoughts") has been done in neuroscience for many years using voltage / glutamate / calcium indicators + imaging. CNiFER is a breakthrough is because it provide ways to measure the "hormone" of brain, these are often molecules in tiny amount that modulate how cells in an area fire.
A not-so-good analogy is before we can measure electrical current in wires, now CNiFER allows us to measure electromagnetic field. This will help to explain why some headphones nearby will beep when your cellphone received a message.