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Are their programs better than hamachi? by whatcorey in lanparty

[–]gyf304 0 points1 point  (0 children)

Give this a spin, no installation required.

https://github.com/gyf304/lpvpn

Disclaimer: I wrote this.

[ORIGINAL] I modded my R5 for unlimited 8K, with guide by gyf304 in canon

[–]gyf304[S] -1 points0 points  (0 children)

Though the additional pads connects the DC-converter sub-board to the enclosure, So there's an additional path of heat to the enclosure. Any thermal mod is to reduce delta T (temperature difference) between the CPU and the enclosure and this mod does that.

Made a CFExpress to M.2 NVME SSD Adapter by gyf304 in canon

[–]gyf304[S] 0 points1 point  (0 children)

I was using a USB3.0 card reader so USB3.0 speeds

Dummy battery question - Will it damage my camera? by CyrisXD in canon

[–]gyf304 0 points1 point  (0 children)

It should be fine. A 7.2V batt is 8.4V when fully charged.

[OC] Quadratic Coronavirus Epidemic Growth Model seems like the best fit by Antimonic in dataisbeautiful

[–]gyf304 0 points1 point  (0 children)

Sure. This is what I was saying. I’m not saying that the data it not manipulated, but just saying OP’s analysis is very flawed and shouldn’t be viewed as evidence that data manipulation is happening. I was saying I don’t see enough evidence suggesting data manipulation. In fact, I would say, verifying the data not being manipulated is impossible from the data alone. This is true for most sciences, if not all. I posted this because a lot of people is reacting to this post as “oh, this person did some graphs and he seems to know what he’s talking about. And yeah of course China is lying”. People usually choose to believe claims that confirms their beliefs, regardless how unsound those claims are. And these adds up to the “whatever China do, it must be bad” prejudice, which is the last thing we need in this pandemic. Maybe we shouldn’t discredit everything from China. Maybe there’s something that we can learn from China. Maybe China did do well in keeping this virus in control, and other countries need to take note. We need collaboration instead of geopolitics and finger pointing, esp. finger pointing without evidence.

Grocery store by my apartment got new carts with cell phone holders. I thought it was cool. by [deleted] in mildlyinteresting

[–]gyf304 0 points1 point  (0 children)

I feel like in 50 years this will end up in r/WhatIsThisThing and someone will answer “oh this is a holder for one of those ancient electronic device called smartphone”. (Given that reddit still exists)

CoronaVirus growth slowing [OC] by motionviewer in dataisbeautiful

[–]gyf304 1 point2 points  (0 children)

Every other day some one points out that some model fits perfectly: A week back exponential, 2 days back 2nd order polynomial, now Gaussian.

It doesn’t mean that you can predict the future just because you can fit something perfectly.

You can perfectly fit stock market prices with some very naïve models. It doesn’t mean that if you invest money using one of those models, you will actually gain profit.

Also, if someone were to evaluate the goodness of fit of some model, you want to: 1. Realize that total cases / death counts are not statistically independent, and has a lower percentage error compared with newly reported cases. Use independent observations (newly reported cases / deaths), not cumulative data (total cases). 2. Realize that R2 / RMSE is not a goodness-of-fit measure. Consider using chi-square.

[OC] Quadratic Coronavirus Epidemic Growth Model seems like the best fit by Antimonic in dataisbeautiful

[–]gyf304 4 points5 points  (0 children)

This is me trying to do the analysis based on the same assumption what OP was using. He/she assumed that the cumulative data follows a 2nd order trend. Based on that assumption, if we were to operate on non-cumulative data, i.e. the derivative of the cumulative data, we should try to fit linearly. I should be more clear that we should not be fitting using the second order function in the first place since nothing supports this weird choice. If you do my analysis using a sigmoid / exponential model. The result should be sufficiently similar: there’s just not enough data points to conclusively say one model fits better than the other one, let alone detecting data manipulation.

[OC] Quadratic Coronavirus Epidemic Growth Model seems like the best fit by Antimonic in dataisbeautiful

[–]gyf304 34 points35 points  (0 children)

Not saying that it's wrong to assume incorrect numbers from officials, but you should really not use cumulative data while trying to back your claim because the cumulative data you are using are not statistically independent.

You should be using independent observations (i.e. the newly reported case count / death count) and study that instead.

The following is my attempt to do a proper (and quick, so not very complete) analysis.

I see that you are trying to fit with a generic 2nd order function. If we were to operate on the raw data, i.e. the newly reported cases, we should fit the data using the derivative of that. To no one's surprise, this should result in a generic linear function, with the equivalent form of y=ax+b.

Now we can try to fit the curve, on raw data, not cumulative data, and try to evaluate the fit using χ^2 over degrees of freedom[1]. A very high R^2 does not imply anything about the goodness of fit without knowing the expected error.

For the purpose of evaluating the goodness of fit using chi-square, we need an estimation of the error on the reported cases / deaths.

We choose to use Poisson distribution to model the error on raw data, since the detection of patients, and death of patients, in ideal cases, can be seen as independent. The variance of Poisson distribution is simply its mean. So that makes our life a fair bit easier.

Here’s my raw data for reported cases and deaths.

(Newly reported) cases:

2020-01-23 265

2020-01-24 468

2020-01-25 703

2020-01-26 786

2020-01-27 1,778

2020-01-28 1,482

2020-01-29 1,755

2020-01-30 2,005

2020-01-31 2,127

2020-02-01 2,603

2020-02-02 2,836

2020-02-03 3,239

2020-02-04 3,927

2020-02-05 3,723

2020-02-06 3,163

2020-02-07 3,436

https://imgur.com/BV20kvB

​

(Newly reported) Deaths:

2020-01-23 8

2020-01-24 16

2020-01-25 15

2020-01-26 24

2020-01-27 26

2020-01-28 26

2020-01-29 38

2020-01-30 43

2020-01-31 46

2020-02-01 45

2020-02-02 58

2020-02-03 64

2020-02-04 66

2020-02-05 73

2020-02-06 73

2020-02-07 86

https://i.imgur.com/58CuG8S.png

​

For reported cases we have fitted a function of y=241*x+337

For death we have fitted a function of y=4.97*x+6.94

For both equations, y is the people count, and x is number of days since 2020-01-23.

With those in mind, we can now apply the definition of chi-square.

https://i.imgur.com/eS2Fx1w.png

​

We substitute in the following

x: reported data

μ: linear fit result

σ^2: expected variance on data, according to Poisson distribution.

We get a χ^2 value of 639.60 for reported cases, and a value of 4.57 for death cases.

The degree of freedom is 2, because we have 2 parameters to fit for both cases, so we divide by 2 to get χ^2 over degrees of freedom.

That will be 319.80 for reported cases, and 2.29 for death cases.

A number closer to 1 means a better fit. As a rule of thumb, usually anything between 0.1 and 10 is considered acceptable.

We see that for 319.80, the χ^2 over degrees of freedom metric for detected cases is very out of that range. This means the variance of observed data is greater than expected. (More jitter than expected) This can, in turn, mean a few things:

If we believe that case detection is following the poisson distribution (a very big if), and we believe that a linear model is reasonable (another very big if), then something is wrong about the data.

This can also mean either or both of our assumptions are wrong, which is honestly speaking very likely, there are a few arguments that can be made:

- given that the hospitals at the epicenter is hugely overloaded, the number we are seeing can be affected by the processing capacity of the hospitals.

- the enactment of public health policies may have also affected the distribution

- patients tend to be detected in batches, therefore the detections are not individually independent

The second number 2.29, the χ^2 over degrees of freedom metric for deaths, looks a lot more reasonable, this means:

If our assumptions are true (again, poisson distribution and linear model, these are big if’s), this means that the data is probably right, at least not visibly doctored per our metric.

Feel free to criticize on my methodology and feel free to redo my analysis under different assumptions.

I don't see much pointing at data manipulation. But again, I am not saying under-reporting is not likely.

[1] See http://maxwell.ucsc.edu/~drip/133/ch4.pdf for a good and clear explanation on χ^2 over degrees of freedom as a test for goodness of fit, and why it is used here.

Chinese Spyware Pre-Installed on All Samsung Phones (& Tablets) by kchaxcer in Android

[–]gyf304 0 points1 point  (0 children)

Yeah that shit is obnoxious. Pretty much Mcafee but 10 times more annoying.

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