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macroeconomics – How will Bitcoin affect global GDP, employment levels, interest rates and financial stability?

I’m currently in the process of writing an open essay called Money in the Twenty-First Century and am now researching how Bitcoin’s fixed supply of coins could affect GDP, employment levels, interest rates and financial stability globally.

Does anyone here have any views on the matter or know of any studies which maybe able to help me answer my question?

How do bounce rates on different DNS A records affect SEO for a single domain name?

I would like to know what effect DNS A records have on the SEO ranking of a single domain name, when each A record has drastically different bounce rates.

Let’s say a URL has two DNS A records pointing to different webservers.

  1. a.mywebsite.com

  2. b.mywebsite.com

The first website a.mywebsite.com has a 99% bounce rate, but b.mywebsite.com has a 20% bounce rate.

Example:
bit.ly routes users to other websites using a bit.ly link. This means that the bounce rate of bit.ly links will be very high, as no one stays on them for the minimum required time (30 seconds???)

However the bit.ly homepage https://bitly.com/ does not have a high bouncerate. Let’s say hypothetically that instead of using https://bitly.com/ for the homepage, they used home.bit.ly

Would the SEO of home.bit.ly be negatively impacted by the terrible bounce rate of bit.ly shortlinks or will google consider them independant of each other?

Do you know of a cryptocurrency exchange in Turkey where I can buy Bitcoin with low commission rates?

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real analysis – Fast rates in ERM: Extreme case of low-noise assumption implies non-differentiability

Some context: I am going through some literature on empirical risk minimization for bipartite ranking (1) that shows how certain “low-noise” conditions lead to fast rates of convergence of the excess risk to $0$ of the empirical risk minimizer. The low noise condition at hand is:

There exists constants $c>0$ and $alpha in (0, 1)$ such that for all $x in mathcal{X}$:

$$mathbb{E}_X frac{1}{left| eta(x) – eta(X) right|^alpha} leq c$$

where $eta(x) = mathbb{P}(Y = 1 | X=x)$ where $Y$ is supported on ${0, 1}$

The discussion following the statement of this condition in the paper mentions that $alpha = 0$ is the least restrictive and $alpha=1$ is the most restrictive. It is clear that $alpha = 0$ imposes no constraint. They also mention that in the case where $X sim uniform((0,1))$ then $eta$ must be nondifferentiable to satisfy the condition. Why is this the case?

What I’ve tried

The problem seems equivalent to:

Show that if there exists some constant $c>0$ such that the following condition holds for a non-negative $f : (0,1) rightarrow mathbb{R}$ with positive and finite integral then $f$ cannot be differentiable:

$$int_0^1 frac{1}{left| f(x) – f(z) right |}dz leq c$$

This looks like an irregularity condition to me, but I’m having trouble getting a clearer idea on how to show the claim. It’s clear that a constant function does not satisfy this condition since in that case, the integrand is nowhere finite. I can think of examples that do not satisfy this condition, for example: $f(x) = x$ does not satisfy this condition. However I cannot find an example that does satisfy this condition.

Any intuition would also be helpful.

(1) Clémençon, Stéphan; Lugosi, Gábor; Vayatis, Nicolas. Ranking and Empirical Minimization of U -statistics. Ann. Statist. 36 (2008), no. 2, 844–874. doi:10.1214/009052607000000910. https://projecteuclid.org/euclid.aos/1205420521

altcoin – Why does DeFi lending services have so high annual interest rates?

DeFi lending services have very high interest rates, on lending and borrowing too. For example on some platform, for depositing some crypt you could earn over 40% yearly on it, while for borrowing at the same time the cost is over 80%. (for instance aave.com on 0x)

I know that there is a risk in relation to DeFi, and you might not get your funds back, and therefore there is a well needed risk premium on the deposit side.

But I don’t understand that why would it worth for anyone to borrow crypto on such high interest rate. Based on their statistics 60% of the deposited funds in 0x are lent out. Why would somebody choose to borrow in a crypto with such a high interest rate, while in other crypto you could borrow for 3-5%.

how do i get my 2019 macbook pro to support higher refresh rates than 6ohz

if I switch my the external monitor to 85ghz it goes really weird.

What does it need to support an lg uhd with 85hz

macbook pro 2019 i7 16gb & lg gk950-b

python – Class that imports FX Rates from Excel File

I have a class that imports the FX Rates produced by another department.

This works as intended, which is to return a Pandas DataFrame with all FX Rates by month.

The DataFrame it returns is then used by another 5 classes, that basically import other files and do some formatting and calculations with their own columns, always using the FX Rates from the FxRates class.

I run this code in a Jupyter Notebook.

I want to know if:

  1. Code is refactored enough or over-refactored
  2. Is it good practice to update the instance variables as I have done so?
  3. Is there anything else that stands out as being bad practice?
class FxRates:
    def __init__(self, reporting_date):
        self.reporting_date = reporting_date
        self.path = self.get_path()

    def get_path(self):
        """Get path for the Fx Rates."""
        content_list = listdir(os.path.join(os.getcwd(), 'Data'))
        file_path = os.path.join(
            os.getcwd(),
            'Data',
            list(filter(lambda x: 'FX rates' in x, content_list))(0)
        )

        return file_path
    
    def reporting_date_check(self):
        """
        Check if date input follows the criteria below:
        31/12/yyyy, 31/03/yyyy, 30/06/yyyy, 30/09/yyyy
        """

        accepted_dates = (
            '31/12',
            '31/03',
            '30/06',
            '30/09'
        )
        
        # Check if first 5 characters match accepted_dates
        if self.reporting_date(:5) in accepted_dates:
            reporting_date = pd.to_datetime(self.reporting_date,
                                            dayfirst=True)
            self.reporting_date = reporting_date
        else:
            # If not, raise ValueError
            raise ValueError(
        """reporting_date does not match one of the following:
        31/12/yyyy, 31/03/yyyy, 30/06/yyyy, 30/09/yyyy"""
        )


    def import_excel_rates(self):
        """Import FX Rates in Excel file from Group."""
        
        rates = pd.read_excel(self.path,
                              sheet_name='historic rates',
                              skiprows=2,
                              header=None,
                              usecols='B:O',
                              skipfooter=1)
        
        return rates
    

    def EWI_check(self, rates):
        """
        Check if the reporting month already has FX Rates defined.
        If not, copy FX Rates from previous month.
        """

        # For EWI we need to use FX Rates from 1 month before
        if pd.isnull(rates.iloc(0, self.reporting_date.month)):
            print("""
            n########## Warning ##########:
            nThere are no FX Rates for {0}/{1}.
            nFX Rates being copied from {2}/{3}.n""".format(
                rates.columns(self.reporting_date.month),
                self.reporting_date.year,
                rates.columns(self.reporting_date.month - 1),
                self.reporting_date.year
            ))

            # Copy FX Rates from previous month
            rates.iloc(:, self.reporting_date.month) = 
            rates.iloc(:, self.reporting_date.month - 1)

        else:
            pass

        return rates


    def import_rates(self):
        """
        Import Group Fx rates into a Pandas Dataframe
        """

        # Check if reporting date is correct
        self.reporting_date_check()

        # Import FX Rates in Excel file
        rates = self.import_excel_rates()

        # Set column headers manually
        rates.columns = ('ISO Code',
                         'December ' + str(self.reporting_date.year - 1),
                         'January',
                         'February',
                         'March',
                         'April',
                         'May',
                         'June',
                         'July',
                         'August',
                         'September',
                         'October',
                         'November',
                         'December')

        # Set ISO Code as Index
        rates.index = rates('ISO Code').values
        rates.drop('ISO Code', axis=1, inplace=True)

        # Check if we have FX Rates for the reporting month
        # If not, copy from last month
        return self.EWI_check(rates)
 

database – Metrics/Stats updating in MySQL at high rates

Currently have an application that is making changes to records at about 100 records per seconds. Sometimes slower, occasionally even faster. Recently found out it wasn’t hitting that rate due to an update taking place in MySQL with stats of the changes. That update was happening via procedural call like

Update table set values to value + new value.

This call was taking nearly 2 seconds to complete and of course greatly hindering the above desired 100 r/s. What are some better approaches?

  1. Have already updated the power of the DB being used. More memory and CPU’s.
  2. Considering inserting the values into a separate table and having a MySQL scheduled event come through and perform the calculations every minute or similar.
  3. Using a different database altogether like a key/value store (Redis maybe)

get all tax rates in Magento 2 store

I’m trying to get all tax rates I set up in my store my custom plugin, I’m using Magento 2.

I found that in db all tax rates are stored in tax_calculation_rate, but I didn’t find a method to get all data from this.

Thank you for any help