Trying to learn some non-native languages. (Yes, all these three are not my mother tone).

• Die Bilanz bildet den Vermögens- sowie Schuldenstatus eines Unternehmens zu einem bestimmten Stichtag ab.
• The balance sheet shows the asset and liability status of a company on a specific reporting date.
• 资产负债表显示公司在特定报告日期的资产和负债状况。

https://github.com/gaulinmp/latexdiff

This guy has many interesting things. A cool github collection, and many good working papers.

Nowadays, we have a lot of cores in the CPU. But we cannot make any good use of it unless we program in multicore.

Say we have thousands of SEC filings to process every year; we write a function to collect data from it. For most filings, it works just fine. But for a small group of filings (8 out of 8000+ in 2005, for my data set, as an example), it runs with no end. 10 hours later, the process for this filing is still stuck somewhere. As Obama once famously (and maybe unfairly) said, “Eight is enough.” (It was unfair, according to McCain, because Obama made Bush a pet name for him.) We can put a timeout exception to end it.

Let’s first make a troublesome function, one that sometimes runs overtime.

from time import sleep

def f(x):
    """ x can be any integer. 
        But when x is a even number, it takes x seconds to finish; 
        when x is an odd number, it takes one second. 
        When x=2, it causes an error/exception.
    """
    sl = x if (-1)**x >0 else 1
    print("Start running with x={} and sleep={}".format(x,sl))
    sleep(sl)
    try:
        print("  finished with x={} and sleep={}, result={}".format(x, sl, 1 / (x-2)))
        return x
    except Exception as e:
        print('\n\nCaught exception {} in worker thread (x = {:d}):'.format(e, x))
        return None

And, let’s make use of a 2-core CPU by using Python’s multiprocessing. Run f(x) parallelly, and collect results as a list. And every child-process is allowed to run for at most 5 seconds. In the end, we collect the result and do some analysis (that’s not the job of this post, though).

import multiprocessing
if __name__ == '__main__':
    with multiprocessing.Pool(2) as pool:
        async_results = [pool.apply_async(f, (i,)) for i in range(20)]
        results_collection=[]
        for async_res in async_results:
            try:
                this_res = async_res.get(timeout=5)
                results_collection.append(this_res)
            except Exception as e:
                print("Exception: {}".format(e))
                results_collection.append(None)
                pass
        print(results_collection)
        #Removing unsuccessful ones
        results_collection = [r for r in results_collection if r !=None]
        print(results_collection)

The outputs:

[0, 1, None, 3, 4, 5, 6, 7, None, 9, None, 11, None, 13, None, 15, None, 17, None, 19]
[0, 1, 3, 4, 5, 6, 7, 9, 11, 13, 15, 17, 19]

The first line of output has 20 elements. The return value of f(x) for x in range(20). Some are None (x=2, 8, 10, 12, 14, 16, 18) because it either had an error or was timed out.

And the final result is ready for analysis next step: [0, 1, 3, 4, 5, 6, 7, 9, 11, 13, 15, 17, 19]

How it runs live:

The code in GitHub:

The code looks too simple. Why would I bother writing a post in my blog? Well, to me, yesterday it was not this simple. I tried a few other ways. They didn’t work. The worst case was, my workstation was even frozen, and I cannot run any command even in bash. There was an error message from the OS: “-bash: fork: retry: Resource temporarily unavailable.” And I googled a way out… “exec killall python3”

Machine Learning sounds like a cool thing. I have wanted to learn it for some while, but with little progress. Perhaps a lack of motivation is a problem.

Today I have a task at hand that may need to use it. I guess learning by doing could solve my lack of motivation issue.

Here are a few things I would like to check:

• A paper about using Supervised learning to identify paragraph titles:

https://www.groundai.com/project/a-supervised-learning-approach-for-heading-detection/1

This paper is so cool. It compares several classifiers, their timing, and precision in doing the classification. And found that Decision Tree is among the best in precision.

Another way this paper is very cool with is, its raw input is PDF, and it converts it to HTML with formats, and then uses HTML format tag as input for classifying!

• What is Decision Tree?

https://www.datacamp.com/community/tutorials/decision-tree-classification-python

https://scikit-learn.org/stable/modules/tree.html

A minimal example

from sklearn import tree
clf = tree.DecisionTreeClassifier()


X = [[0, 0], [1, 1]]
Y = [0, 1]

clf = clf.fit(X, Y)

clf.predict([ [2., 2.], [-0.1, -0.1], [1,2]])

It runs. The result makes sense too: array([1, 0, 1])

# cython: language_level=3
# The above comment is for CPython to read. DO NOT REMOVE.

from distutils.core import setup
from Cython.Build import cythonize

import os, shutil

#Provide the file name of your original Python code:
python_file = "Parse.py"
#Automatically copy it to make a .pyx file, and set up if os.path.isfile(python_file+"x"): os.remove(python_file+"x") shutil.copy(python_file, python_file+"x") setup( ext_modules = cythonize(python_file+"x") )

python Parse_Setup.py build_ext --inplace

from Parse import parse
parse()

python Parse_Run.py

Further thoughts…

Here (URL) someone compares the speed of pure python with numpy and with Cython. It seems that Numpy are x31 as fast, Native Cython is x24 as fast… Noting that Numpy is also running C in the backend, this is reasonable.

I tried a minimal example of using the Monte Carlo method to estimate \PI, which uses numpy.random, and the result is…. disappointing. CPython only makes it slowlier…

I may need to build a minimal example to compare the performance with and without CPython for RegEx, the only thing whose speed bothers me.

Dear Professor Dr. ▮▮▮▮▮▮▮▮▮▮▮,

I’m a student from your class TAX 101 and I’m preparing for the second exam. I wish to ask a few questions about the case Apple’s Tax Expenses.
From the income statement of Apple’s annual report 2017, we know that Apple has Income before provision for income taxes 64,089 and provision for income taxes 15,738, from which we get the tax rate of 24.6%. Is this the “Book ETR” you mentioned in the lecture?And, from the notes we see Apple defer 5,966 of the 15,738 provision for income taxes, leaving the current component only 9,772. Is this the actual tax payable? And is the corresponding tax rate 15.2% the so called “Cash ETR”? 

I was trying to figure out what information we can extract out of this reconciliation table. Also, I come up with a paper Henlon (2003) who criticized a lot about this calculation and implied this is wrong and that is also wrong… She was our ZEW guest keynote speaker… I’m so confused and worried about the exam. Could you please help me, professor?

Hanlon, Michelle. “What can we infer about a firm’s taxable income from its financial statements?.” National Tax Journal(2003): 831-863.

Best wishes,

Reeyarn

Reeyarn Zhiyang Li

University of Mannheim | Business School
Schloss | 68131 Mannheim | Germany | Phone +49 (0) 621 ▮▮▮ ▮▮▮▮
Email: ▮▮▮▮@uni-mannheim.de

Dear Reeyarn,

Look at the Statement of Cash Flows and you find a line which reads

Cash paid for income taxes, net: 11,591,

…from which you get the cash ETR of 18.1%.

Best regards,

▮▮▮▮▮▮▮▮▮▮▮

Legal Theory Blog mentioned our paper. Fascinating. Thanks a lot.

https://lsolum.typepad.com/legaltheory/2018/05/huang-hui-li-on-federal-judge-ideology-litigation-risk.html

Welcome to https://reeyarn.li.

Yes. This is a classic Hello world! page generated by WordPress. And I am a big fan of Hello world! Anyone who took a course in any programming language must have learned writing a Hello World! code.

Yes. I think of myself as an amateur programmer. And I am also trying to become a data scientist. The world may be heading toward a future where human beings are no longer needed, and Artificial Intelligence could one day become the dominating intelligent beings. We are in a serious competition, fighting for the survival of human beings. Learn maths, learn programming, learn machine learning, and learn to survive.

Reeyarn Zhiyang Li